Install necessary packages and import appropriate data
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 174 × 2
## Plot Veg_shannon_index
## <chr> <dbl>
## 1 BI200 2.75
## 2 BI201 2.70
## 3 BI202 2.59
## 4 BI97 1.61
## 5 BI99 2.97
## 6 BN210 2.97
## 7 BN211 2.43
## 8 BN212 2.22
## 9 BN96 3.05
## 10 BN98 2.79
## # ℹ 164 more rows
if (!is.numeric(fuel_data$Height)) {
fuel_data$Height <- as.numeric(as.character(fuel_data$Height))
}
## Warning: NAs introduced by coercion
# Calculate average vegetation height per plot
veg_height <- fuel_data %>%
group_by(Plot) %>%
summarize(avg_veg_height = mean(Height, na.rm = TRUE), .groups = "drop")
# Tree density from point-centered quarter data
if (!is.numeric(tree_data$Distance)) {
tree_data$Distance <- as.numeric(as.character(tree_data$Distance))
}
tree_density_data <- tree_data %>%
group_by(Plot) %>%
summarize(Average_Distance = mean(Distance) / 100, # Convert to meters
Tree_Density = 10000 / (Average_Distance^2)) # Convert to trees per hectare
# Average canopy cover from vegetation quadrats
tree_canopy_data <- Veg_Cover %>%
distinct(Plot, Quadrat, .keep_all = TRUE) %>% # Ensure each quadrat counts once per plot
group_by(Plot) %>%
summarize(Avg_Canopy_Cover = mean(Canopy_Cover, na.rm = TRUE), .groups = "drop") # Calculate the average canopy cover per plot
cor(tree_density_data$Tree_Density, tree_canopy_data$Avg_Canopy_Cover)
## [1] 0.2836106
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter(Class == "Mammalia") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 2) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 11
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$week)
week_matrix <- matrix(rep(week_vals, each = num_sites), nrow = num_sites, ncol = num_weeks, byrow = FALSE)
# Create detection covariate list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_matrix
)
# Remove dash and convert to numeric
week_numeric <- as.numeric(gsub("-", "", det.covs$week))
## Scale and center week_numeric
week_numeric <- scale(week_numeric)
# Reshape into the original 32x36 matrix
det.covs$week <- matrix(week_numeric, nrow = 32, ncol = 36)
str(det.covs)
## List of 3
## $ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
This requires combining the presence data and the site covariate data into a single list. This also means that the presence data is in a 3-d format.
# Combine into a named list
data_list <- list(
y = detection_array,
occ.covs = covariates_matrix,
det.covs = det.covs
)
str(data_list)
## List of 3
## $ y : num [1:32, 1:36, 1:11] 1 1 0 1 0 0 0 1 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## .. ..$ : chr [1:11] "Odocoileus_virginianus" "Canis_latrans" "Sciurus_niger" "Procyon_lotor" ...
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:11, 1:32, 1:36] 1 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:11] "Odocoileus_virginianus" "Canis_latrans" "Sciurus_niger" "Procyon_lotor" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## ----------------------------------------
## Chain 3
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## Sampling ...
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summary(ms_null_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3098
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1966 0.5515 -1.2364 -0.2235 0.9442 1.001 3157
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2493 2.6472 0.8626 2.5425 9.9971 1.0031 1160
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4654 0.4091 -3.2529 -2.4711 -1.611 1.0015 4161
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.803 1.111 0.6084 1.5126 4.6584 1.0067 2121
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4496 1.2362 1.7513 3.2294 6.3881
## (Intercept)-Canis_latrans 0.3208 0.4146 -0.4322 0.2976 1.2214
## (Intercept)-Sciurus_niger -0.7212 0.9208 -2.1085 -0.8352 1.4916
## (Intercept)-Procyon_lotor 0.7126 0.3979 -0.0330 0.6978 1.5325
## (Intercept)-Dasypus_novemcinctus -0.6398 0.3728 -1.3851 -0.6314 0.0612
## (Intercept)-Lynx_rufus 0.3640 0.9121 -0.8467 0.2103 2.6932
## (Intercept)-Didelphis_virginiana -1.3688 0.4465 -2.2820 -1.3482 -0.5458
## (Intercept)-Sylvilagus_floridanus -0.2677 0.6043 -1.2648 -0.3234 1.0656
## (Intercept)-Sciurus_carolinensis -1.3453 0.4543 -2.2969 -1.3267 -0.5020
## (Intercept)-Vulpes_vulpes -1.0226 1.1811 -2.8700 -1.1889 1.8544
## (Intercept)-Sus_scrofa -1.8585 0.6245 -3.1250 -1.8503 -0.6284
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0161 1161
## (Intercept)-Canis_latrans 1.0012 4363
## (Intercept)-Sciurus_niger 1.0070 621
## (Intercept)-Procyon_lotor 0.9999 5303
## (Intercept)-Dasypus_novemcinctus 1.0001 5250
## (Intercept)-Lynx_rufus 1.0298 597
## (Intercept)-Didelphis_virginiana 1.0004 5078
## (Intercept)-Sylvilagus_floridanus 1.0071 1186
## (Intercept)-Sciurus_carolinensis 1.0024 5250
## (Intercept)-Vulpes_vulpes 1.0181 395
## (Intercept)-Sus_scrofa 1.0052 2419
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0036 0.0603 -0.1149 0.0031 0.1226
## (Intercept)-Canis_latrans -2.6130 0.1727 -2.9594 -2.6067 -2.2890
## (Intercept)-Sciurus_niger -3.7999 0.5440 -4.9234 -3.7624 -2.8381
## (Intercept)-Procyon_lotor -2.2626 0.1311 -2.5302 -2.2593 -2.0117
## (Intercept)-Dasypus_novemcinctus -1.5733 0.1316 -1.8423 -1.5704 -1.3207
## (Intercept)-Lynx_rufus -3.5855 0.3476 -4.2896 -3.5733 -2.9550
## (Intercept)-Didelphis_virginiana -2.3135 0.2524 -2.8385 -2.3017 -1.8482
## (Intercept)-Sylvilagus_floridanus -3.2021 0.3236 -3.8859 -3.1802 -2.6294
## (Intercept)-Sciurus_carolinensis -2.4322 0.2626 -2.9901 -2.4193 -1.9613
## (Intercept)-Vulpes_vulpes -4.0306 0.7759 -5.6357 -3.9756 -2.7054
## (Intercept)-Sus_scrofa -2.9423 0.4916 -4.0505 -2.9003 -2.0936
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0016 2864
## (Intercept)-Sciurus_niger 1.0054 649
## (Intercept)-Procyon_lotor 1.0003 4053
## (Intercept)-Dasypus_novemcinctus 1.0007 5250
## (Intercept)-Lynx_rufus 1.0016 843
## (Intercept)-Didelphis_virginiana 1.0041 3865
## (Intercept)-Sylvilagus_floridanus 1.0025 1194
## (Intercept)-Sciurus_carolinensis 1.0084 3701
## (Intercept)-Vulpes_vulpes 1.0330 290
## (Intercept)-Sus_scrofa 1.0103 1564
# Includes all covariates of detection and occupancy
ms_full_full <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1549 1.0561 -2.1961 -0.1686 1.9549 1.0087 1589
## Cogon_Patch_Size -0.8313 0.6673 -2.2720 -0.8003 0.4131 1.0040 974
## Veg_shannon_index 0.8959 0.4943 -0.0256 0.8813 1.9257 1.0305 794
## total_shrub_cover -0.3065 0.4916 -1.3285 -0.2929 0.6250 1.0027 825
## Avg_Cogongrass_Cover 2.0528 0.7258 0.6472 2.0461 3.5352 1.0084 443
## Tree_Density -1.8024 0.7396 -3.3254 -1.7893 -0.3270 1.0067 748
## Avg_Canopy_Cover 1.8862 0.6279 0.7420 1.8525 3.2247 1.0155 672
## avg_veg_height -0.5136 0.4779 -1.4502 -0.5073 0.4289 1.0031 726
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.8372 19.3774 3.3616 13.3458 66.9685 1.0995 268
## Cogon_Patch_Size 2.9354 5.5929 0.1086 1.4846 15.2523 1.0538 429
## Veg_shannon_index 0.9997 1.5006 0.0505 0.4954 4.7994 1.0185 811
## total_shrub_cover 0.8502 1.4267 0.0534 0.4386 3.9984 1.0784 642
## Avg_Cogongrass_Cover 1.1271 2.0061 0.0512 0.5126 6.0168 1.0845 765
## Tree_Density 3.6154 6.6464 0.0734 1.5240 20.3318 1.0876 389
## Avg_Canopy_Cover 2.3596 3.2696 0.1441 1.4329 10.1923 1.0164 691
## avg_veg_height 0.4127 0.5594 0.0395 0.2434 1.7973 1.0076 1632
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9045 2.6353 0.0659 1.0363 8.8586 1.2191 202
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6604 0.4517 -3.5027 -2.6750 -1.7405 1.0006 4851
## shrub_cover 0.2734 0.2569 -0.2207 0.2655 0.8018 1.0045 1988
## veg_height 0.0070 0.1577 -0.3190 0.0072 0.3231 1.0039 3326
## week -0.0383 0.1209 -0.2918 -0.0352 0.1858 1.0008 2795
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2790 1.3879 0.8153 1.9304 5.8295 1.0067 3430
## shrub_cover 0.4859 0.3781 0.0990 0.3841 1.4714 1.0019 1922
## veg_height 0.1970 0.1375 0.0564 0.1620 0.5620 1.0012 3586
## week 0.1012 0.0840 0.0253 0.0792 0.3039 1.0141 2428
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1455 3.5915 3.2207 7.4987
## (Intercept)-Canis_latrans 0.8532 1.0359 -1.0749 0.7918
## (Intercept)-Sciurus_niger 1.7480 3.3780 -2.6292 1.1856
## (Intercept)-Procyon_lotor 0.9165 1.0320 -1.1866 0.9339
## (Intercept)-Dasypus_novemcinctus -1.4916 1.0783 -3.9726 -1.3772
## (Intercept)-Lynx_rufus 2.1015 2.8621 -2.0252 1.6078
## (Intercept)-Didelphis_virginiana -2.8293 1.2193 -5.4777 -2.7453
## (Intercept)-Sylvilagus_floridanus -1.2511 1.3568 -4.0439 -1.2231
## (Intercept)-Sciurus_carolinensis -3.0516 1.3842 -6.2915 -2.9349
## (Intercept)-Vulpes_vulpes -2.2525 1.9863 -6.0677 -2.3090
## (Intercept)-Sus_scrofa -4.4414 2.0147 -9.0299 -4.2131
## Cogon_Patch_Size-Odocoileus_virginianus -0.6379 1.3041 -3.1029 -0.7080
## Cogon_Patch_Size-Canis_latrans 0.5616 1.2127 -1.1759 0.3575
## Cogon_Patch_Size-Sciurus_niger -1.5318 1.8359 -5.9078 -1.3111
## Cogon_Patch_Size-Procyon_lotor -1.1106 0.7296 -2.6292 -1.0753
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6694 0.7494 -2.1763 -0.6786
## Cogon_Patch_Size-Lynx_rufus -0.9954 1.3367 -3.6023 -1.0225
## Cogon_Patch_Size-Didelphis_virginiana 0.6923 0.9106 -0.8521 0.5913
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0472 1.7089 -6.5160 -1.6987
## Cogon_Patch_Size-Sciurus_carolinensis -1.7499 1.3925 -5.3489 -1.4875
## Cogon_Patch_Size-Vulpes_vulpes -1.4157 1.6063 -5.0647 -1.2267
## Cogon_Patch_Size-Sus_scrofa -1.4074 1.4943 -5.2045 -1.1442
## Veg_shannon_index-Odocoileus_virginianus 0.7415 0.9436 -1.2853 0.7696
## Veg_shannon_index-Canis_latrans 1.3114 0.6901 0.1334 1.2453
## Veg_shannon_index-Sciurus_niger 1.1113 1.1445 -0.9414 1.0260
## Veg_shannon_index-Procyon_lotor 1.2381 0.6354 0.1099 1.1854
## Veg_shannon_index-Dasypus_novemcinctus 0.6266 0.5611 -0.5436 0.6418
## Veg_shannon_index-Lynx_rufus 0.9059 0.9373 -1.0724 0.9058
## Veg_shannon_index-Didelphis_virginiana 1.1189 0.6955 -0.1124 1.0568
## Veg_shannon_index-Sylvilagus_floridanus 1.0522 0.7134 -0.2403 1.0141
## Veg_shannon_index-Sciurus_carolinensis 0.1740 0.8327 -1.7555 0.2668
## Veg_shannon_index-Vulpes_vulpes 0.2939 0.9407 -1.9132 0.4148
## Veg_shannon_index-Sus_scrofa 1.6444 1.0806 0.0997 1.4289
## total_shrub_cover-Odocoileus_virginianus -0.0441 0.8556 -1.6381 -0.0994
## total_shrub_cover-Canis_latrans 0.3839 0.7861 -0.7836 0.2613
## total_shrub_cover-Sciurus_niger -0.4168 0.9565 -2.4435 -0.3788
## total_shrub_cover-Procyon_lotor -0.8215 0.6005 -2.1630 -0.7741
## total_shrub_cover-Dasypus_novemcinctus -0.0991 0.5950 -1.2657 -0.0994
## total_shrub_cover-Lynx_rufus -0.4660 0.9868 -2.6663 -0.4235
## total_shrub_cover-Didelphis_virginiana -0.5945 0.7523 -2.3551 -0.5146
## total_shrub_cover-Sylvilagus_floridanus -0.4172 0.8348 -2.2985 -0.3672
## total_shrub_cover-Sciurus_carolinensis -0.3076 0.7751 -1.9804 -0.2684
## total_shrub_cover-Vulpes_vulpes -0.5752 1.0048 -2.7824 -0.4781
## total_shrub_cover-Sus_scrofa -0.1036 0.8764 -1.8031 -0.1389
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9825 1.1007 -0.2474 1.9665
## Avg_Cogongrass_Cover-Canis_latrans 2.4724 0.9699 0.7897 2.3909
## Avg_Cogongrass_Cover-Sciurus_niger 1.5599 1.3790 -1.7155 1.7246
## Avg_Cogongrass_Cover-Procyon_lotor 2.2540 0.8974 0.6313 2.1993
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6303 1.0111 0.9867 2.5288
## Avg_Cogongrass_Cover-Lynx_rufus 2.3800 1.0023 0.6044 2.2967
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1696 0.9160 0.4880 2.1235
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4950 1.0013 -0.5263 1.5237
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3529 0.9531 0.6420 2.2859
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4999 1.0729 0.6840 2.4038
## Avg_Cogongrass_Cover-Sus_scrofa 1.6495 1.2032 -1.0004 1.7493
## Tree_Density-Odocoileus_virginianus -0.7444 1.3450 -2.8784 -0.9051
## Tree_Density-Canis_latrans -2.7206 1.4551 -6.2028 -2.4473
## Tree_Density-Sciurus_niger -1.9456 1.7098 -5.6349 -1.8873
## Tree_Density-Procyon_lotor -1.4932 0.7744 -2.9886 -1.5065
## Tree_Density-Dasypus_novemcinctus -3.6600 2.0540 -9.1262 -3.1152
## Tree_Density-Lynx_rufus -0.4738 1.6295 -2.7910 -0.7233
## Tree_Density-Didelphis_virginiana -2.2362 1.2471 -5.1522 -2.0847
## Tree_Density-Sylvilagus_floridanus -2.4027 1.4370 -5.8711 -2.1985
## Tree_Density-Sciurus_carolinensis -2.4255 1.4524 -6.0202 -2.1940
## Tree_Density-Vulpes_vulpes -1.6960 1.6741 -4.9344 -1.7538
## Tree_Density-Sus_scrofa -2.3467 1.6835 -6.5243 -2.0765
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3170 1.2873 -1.2974 1.3386
## Avg_Canopy_Cover-Canis_latrans 0.3155 0.6793 -0.9880 0.3087
## Avg_Canopy_Cover-Sciurus_niger 2.1425 1.6578 -0.9847 2.0238
## Avg_Canopy_Cover-Procyon_lotor 1.7803 0.7640 0.4553 1.7300
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1186 0.7708 0.8494 2.0313
## Avg_Canopy_Cover-Lynx_rufus 1.4849 1.3795 -1.0889 1.4395
## Avg_Canopy_Cover-Didelphis_virginiana 2.9129 1.1307 1.2782 2.7017
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3389 1.5195 1.2052 3.0733
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6069 1.0749 1.0240 2.4250
## Avg_Canopy_Cover-Vulpes_vulpes 2.2819 1.1895 0.4036 2.1117
## Avg_Canopy_Cover-Sus_scrofa 2.1634 0.9596 0.5732 2.0583
## avg_veg_height-Odocoileus_virginianus -0.5456 0.7402 -2.0243 -0.5253
## avg_veg_height-Canis_latrans -0.5478 0.6002 -1.7763 -0.5426
## avg_veg_height-Sciurus_niger -0.6611 0.8227 -2.4932 -0.6025
## avg_veg_height-Procyon_lotor -0.4261 0.5803 -1.5827 -0.4184
## avg_veg_height-Dasypus_novemcinctus -0.3044 0.5829 -1.4180 -0.3203
## avg_veg_height-Lynx_rufus -0.6241 0.7506 -2.2286 -0.5830
## avg_veg_height-Didelphis_virginiana -0.6498 0.6492 -2.0169 -0.6355
## avg_veg_height-Sylvilagus_floridanus -0.6945 0.6665 -2.1050 -0.6777
## avg_veg_height-Sciurus_carolinensis -0.1978 0.6473 -1.3541 -0.2455
## avg_veg_height-Vulpes_vulpes -0.5107 0.7254 -1.9281 -0.5195
## avg_veg_height-Sus_scrofa -0.5704 0.6849 -1.9659 -0.5577
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 17.1128 1.0479 309
## (Intercept)-Canis_latrans 3.1168 1.0107 1526
## (Intercept)-Sciurus_niger 9.5392 1.0776 133
## (Intercept)-Procyon_lotor 2.9605 1.0134 1409
## (Intercept)-Dasypus_novemcinctus 0.3665 1.0151 757
## (Intercept)-Lynx_rufus 9.0297 1.0638 246
## (Intercept)-Didelphis_virginiana -0.7062 1.0076 1121
## (Intercept)-Sylvilagus_floridanus 1.4187 1.0024 808
## (Intercept)-Sciurus_carolinensis -0.7646 1.0099 582
## (Intercept)-Vulpes_vulpes 1.9824 1.0238 442
## (Intercept)-Sus_scrofa -1.0248 1.0485 389
## Cogon_Patch_Size-Odocoileus_virginianus 2.3251 1.0093 1960
## Cogon_Patch_Size-Canis_latrans 3.3824 1.0048 907
## Cogon_Patch_Size-Sciurus_niger 1.5831 1.0120 479
## Cogon_Patch_Size-Procyon_lotor 0.2310 1.0234 702
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8151 1.0089 1485
## Cogon_Patch_Size-Lynx_rufus 1.7614 1.0115 985
## Cogon_Patch_Size-Didelphis_virginiana 2.7029 1.0034 978
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1511 1.0011 651
## Cogon_Patch_Size-Sciurus_carolinensis 0.2032 1.0028 685
## Cogon_Patch_Size-Vulpes_vulpes 1.1637 1.0168 593
## Cogon_Patch_Size-Sus_scrofa 0.8062 1.0193 688
## Veg_shannon_index-Odocoileus_virginianus 2.5401 1.0080 1916
## Veg_shannon_index-Canis_latrans 2.8821 1.0423 724
## Veg_shannon_index-Sciurus_niger 3.7892 1.0317 894
## Veg_shannon_index-Procyon_lotor 2.6582 1.0345 611
## Veg_shannon_index-Dasypus_novemcinctus 1.7348 1.0080 1588
## Veg_shannon_index-Lynx_rufus 2.8033 1.0240 1268
## Veg_shannon_index-Didelphis_virginiana 2.7426 1.0320 1695
## Veg_shannon_index-Sylvilagus_floridanus 2.6430 1.0130 1378
## Veg_shannon_index-Sciurus_carolinensis 1.5841 1.0037 1293
## Veg_shannon_index-Vulpes_vulpes 1.8102 1.0046 1057
## Veg_shannon_index-Sus_scrofa 4.3955 1.0367 819
## total_shrub_cover-Odocoileus_virginianus 1.8729 1.0031 1994
## total_shrub_cover-Canis_latrans 2.3999 1.0166 971
## total_shrub_cover-Sciurus_niger 1.4672 1.0016 1026
## total_shrub_cover-Procyon_lotor 0.2245 1.0009 1749
## total_shrub_cover-Dasypus_novemcinctus 1.0326 1.0040 1999
## total_shrub_cover-Lynx_rufus 1.3840 1.0180 906
## total_shrub_cover-Didelphis_virginiana 0.6785 1.0143 1177
## total_shrub_cover-Sylvilagus_floridanus 1.0979 1.0055 1150
## total_shrub_cover-Sciurus_carolinensis 1.1221 1.0012 1155
## total_shrub_cover-Vulpes_vulpes 1.0296 1.0118 794
## total_shrub_cover-Sus_scrofa 1.7707 1.0000 1029
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.2095 1.0036 1104
## Avg_Cogongrass_Cover-Canis_latrans 4.6741 1.0519 576
## Avg_Cogongrass_Cover-Sciurus_niger 3.8478 1.0074 596
## Avg_Cogongrass_Cover-Procyon_lotor 4.1482 1.0197 652
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.9015 1.0281 384
## Avg_Cogongrass_Cover-Lynx_rufus 4.5660 1.0111 760
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.1319 1.0195 857
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3787 1.0042 757
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4137 1.0161 587
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.9713 1.0146 753
## Avg_Cogongrass_Cover-Sus_scrofa 3.7593 1.0174 835
## Tree_Density-Odocoileus_virginianus 2.4339 1.0105 699
## Tree_Density-Canis_latrans -0.6807 1.0628 445
## Tree_Density-Sciurus_niger 1.4318 1.0221 1006
## Tree_Density-Procyon_lotor -0.0048 1.0059 1220
## Tree_Density-Dasypus_novemcinctus -1.2065 1.0492 324
## Tree_Density-Lynx_rufus 3.4698 1.0486 446
## Tree_Density-Didelphis_virginiana -0.1100 1.0082 847
## Tree_Density-Sylvilagus_floridanus -0.0098 1.0108 671
## Tree_Density-Sciurus_carolinensis -0.0644 1.0194 863
## Tree_Density-Vulpes_vulpes 1.8543 1.0170 783
## Tree_Density-Sus_scrofa 0.2245 1.0070 975
## Avg_Canopy_Cover-Odocoileus_virginianus 3.8506 1.0065 1730
## Avg_Canopy_Cover-Canis_latrans 1.6792 1.0071 1672
## Avg_Canopy_Cover-Sciurus_niger 5.8986 1.0061 716
## Avg_Canopy_Cover-Procyon_lotor 3.4177 1.0278 1214
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.8692 1.0442 581
## Avg_Canopy_Cover-Lynx_rufus 4.4994 1.0103 568
## Avg_Canopy_Cover-Didelphis_virginiana 5.7987 1.0406 493
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.2399 1.0068 784
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1903 1.0263 653
## Avg_Canopy_Cover-Vulpes_vulpes 5.2074 1.0197 750
## Avg_Canopy_Cover-Sus_scrofa 4.3778 1.0159 1277
## avg_veg_height-Odocoileus_virginianus 0.8909 1.0021 1489
## avg_veg_height-Canis_latrans 0.6193 1.0012 1095
## avg_veg_height-Sciurus_niger 0.7717 1.0001 1086
## avg_veg_height-Procyon_lotor 0.7083 1.0033 1010
## avg_veg_height-Dasypus_novemcinctus 0.9057 1.0041 1096
## avg_veg_height-Lynx_rufus 0.7729 1.0009 1205
## avg_veg_height-Didelphis_virginiana 0.5751 1.0014 1147
## avg_veg_height-Sylvilagus_floridanus 0.5623 1.0013 1242
## avg_veg_height-Sciurus_carolinensis 1.1866 1.0025 1371
## avg_veg_height-Vulpes_vulpes 0.8890 1.0096 1080
## avg_veg_height-Sus_scrofa 0.7607 1.0017 1463
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0068 0.0607 -0.1148 0.0073 0.1252
## (Intercept)-Canis_latrans -2.7553 0.1848 -3.1321 -2.7504 -2.4060
## (Intercept)-Sciurus_niger -4.7044 0.5136 -5.6724 -4.7239 -3.6359
## (Intercept)-Procyon_lotor -2.3026 0.1438 -2.5999 -2.2970 -2.0352
## (Intercept)-Dasypus_novemcinctus -1.7407 0.1570 -2.0526 -1.7345 -1.4413
## (Intercept)-Lynx_rufus -3.9043 0.3901 -4.6215 -3.9221 -3.1215
## (Intercept)-Didelphis_virginiana -2.5387 0.2833 -3.1358 -2.5295 -2.0155
## (Intercept)-Sylvilagus_floridanus -3.1905 0.2757 -3.7628 -3.1786 -2.6826
## (Intercept)-Sciurus_carolinensis -2.6563 0.3246 -3.3213 -2.6503 -2.0499
## (Intercept)-Vulpes_vulpes -4.2494 0.6674 -5.5952 -4.2337 -3.0119
## (Intercept)-Sus_scrofa -3.2630 0.5915 -4.4525 -3.2489 -2.1653
## shrub_cover-Odocoileus_virginianus -0.0550 0.0641 -0.1795 -0.0554 0.0723
## shrub_cover-Canis_latrans -0.3324 0.2225 -0.7708 -0.3319 0.1061
## shrub_cover-Sciurus_niger -0.3262 0.4233 -1.1810 -0.3209 0.5041
## shrub_cover-Procyon_lotor 0.2664 0.1612 -0.0547 0.2693 0.5782
## shrub_cover-Dasypus_novemcinctus 0.8507 0.3028 0.2843 0.8409 1.4603
## shrub_cover-Lynx_rufus -0.2250 0.3586 -0.9375 -0.2251 0.4987
## shrub_cover-Didelphis_virginiana 0.9026 0.3622 0.2398 0.8867 1.6625
## shrub_cover-Sylvilagus_floridanus 0.4181 0.3915 -0.3285 0.4120 1.1932
## shrub_cover-Sciurus_carolinensis 0.8499 0.4192 0.0740 0.8321 1.7079
## shrub_cover-Vulpes_vulpes 0.1391 0.5434 -0.9567 0.1379 1.2420
## shrub_cover-Sus_scrofa 0.5740 0.7382 -0.8311 0.5288 2.1484
## veg_height-Odocoileus_virginianus -0.2983 0.0647 -0.4232 -0.2981 -0.1716
## veg_height-Canis_latrans -0.5843 0.1797 -0.9447 -0.5829 -0.2446
## veg_height-Sciurus_niger -0.0521 0.3362 -0.7191 -0.0538 0.6191
## veg_height-Procyon_lotor 0.3475 0.1218 0.1072 0.3480 0.5882
## veg_height-Dasypus_novemcinctus 0.2373 0.1337 -0.0164 0.2347 0.5034
## veg_height-Lynx_rufus 0.0931 0.2357 -0.3961 0.0960 0.5477
## veg_height-Didelphis_virginiana 0.4230 0.2334 -0.0173 0.4162 0.9023
## veg_height-Sylvilagus_floridanus 0.1523 0.2393 -0.3191 0.1523 0.6272
## veg_height-Sciurus_carolinensis 0.0865 0.2151 -0.3111 0.0812 0.5326
## veg_height-Vulpes_vulpes -0.1994 0.3199 -0.8547 -0.1839 0.3825
## veg_height-Sus_scrofa -0.1401 0.3207 -0.8247 -0.1310 0.4615
## week-Odocoileus_virginianus 0.2119 0.0607 0.0930 0.2122 0.3339
## week-Canis_latrans 0.0745 0.1290 -0.1835 0.0755 0.3163
## week-Sciurus_niger -0.2972 0.2984 -0.9890 -0.2665 0.1991
## week-Procyon_lotor -0.0431 0.1194 -0.2911 -0.0399 0.1757
## week-Dasypus_novemcinctus -0.1577 0.1368 -0.4456 -0.1532 0.1007
## week-Lynx_rufus -0.0234 0.1866 -0.4038 -0.0178 0.3250
## week-Didelphis_virginiana -0.1985 0.2127 -0.6462 -0.1893 0.1929
## week-Sylvilagus_floridanus -0.1381 0.2044 -0.5867 -0.1258 0.2337
## week-Sciurus_carolinensis 0.1466 0.1752 -0.1988 0.1495 0.4873
## week-Vulpes_vulpes -0.1049 0.2815 -0.7110 -0.0879 0.4014
## week-Sus_scrofa 0.1081 0.2320 -0.3534 0.1106 0.5573
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0065 2242
## (Intercept)-Sciurus_niger 1.0185 426
## (Intercept)-Procyon_lotor 1.0016 3393
## (Intercept)-Dasypus_novemcinctus 1.0149 3886
## (Intercept)-Lynx_rufus 1.0228 359
## (Intercept)-Didelphis_virginiana 1.0051 2058
## (Intercept)-Sylvilagus_floridanus 1.0007 1809
## (Intercept)-Sciurus_carolinensis 1.0021 1840
## (Intercept)-Vulpes_vulpes 1.0185 447
## (Intercept)-Sus_scrofa 1.0017 1143
## shrub_cover-Odocoileus_virginianus 1.0003 5250
## shrub_cover-Canis_latrans 1.0024 1812
## shrub_cover-Sciurus_niger 1.0185 1192
## shrub_cover-Procyon_lotor 1.0005 4364
## shrub_cover-Dasypus_novemcinctus 1.0107 2206
## shrub_cover-Lynx_rufus 1.0255 873
## shrub_cover-Didelphis_virginiana 1.0057 1927
## shrub_cover-Sylvilagus_floridanus 1.0035 1409
## shrub_cover-Sciurus_carolinensis 1.0070 1317
## shrub_cover-Vulpes_vulpes 1.0002 1543
## shrub_cover-Sus_scrofa 1.0017 1162
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0050 2372
## veg_height-Sciurus_niger 1.0068 1249
## veg_height-Procyon_lotor 1.0013 4181
## veg_height-Dasypus_novemcinctus 1.0043 4457
## veg_height-Lynx_rufus 1.0015 1541
## veg_height-Didelphis_virginiana 1.0016 3605
## veg_height-Sylvilagus_floridanus 1.0009 2647
## veg_height-Sciurus_carolinensis 1.0046 3131
## veg_height-Vulpes_vulpes 1.0002 1999
## veg_height-Sus_scrofa 1.0046 2894
## week-Odocoileus_virginianus 1.0081 5250
## week-Canis_latrans 1.0000 4283
## week-Sciurus_niger 1.0000 1442
## week-Procyon_lotor 1.0010 4432
## week-Dasypus_novemcinctus 1.0005 5048
## week-Lynx_rufus 1.0077 2763
## week-Didelphis_virginiana 1.0003 3771
## week-Sylvilagus_floridanus 1.0021 2997
## week-Sciurus_carolinensis 1.0007 4653
## week-Vulpes_vulpes 1.0066 2490
## week-Sus_scrofa 1.0001 4461
#Includes all covariates of detection and only null for occupancy
ms_full_null <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8465
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1115 0.5485 -1.1777 -0.1218 1.0156 1.0034 2238
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1115 2.3427 0.8087 2.4602 9.3754 1.0055 1862
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5943 0.4289 -3.4412 -2.6019 -1.7178 1.0009 4949
## shrub_cover 0.2097 0.2398 -0.2635 0.2046 0.7043 1.0033 3025
## veg_height 0.0015 0.1534 -0.3130 0.0018 0.3128 1.0020 3574
## week -0.0408 0.1214 -0.2963 -0.0372 0.1876 1.0019 3096
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0026 1.2495 0.6834 1.6895 5.1441 1.0070 1645
## shrub_cover 0.4581 0.3713 0.0929 0.3548 1.4166 1.0012 2168
## veg_height 0.1885 0.1320 0.0539 0.1552 0.5149 1.0009 3714
## week 0.0992 0.0841 0.0260 0.0758 0.3082 1.0095 3103
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4098 1.1403 1.7680 3.2114 6.1775
## (Intercept)-Canis_latrans 0.4052 0.4253 -0.3895 0.3951 1.2613
## (Intercept)-Sciurus_niger -0.4477 1.1158 -1.9917 -0.6323 2.4035
## (Intercept)-Procyon_lotor 0.7445 0.4099 -0.0140 0.7251 1.6236
## (Intercept)-Dasypus_novemcinctus -0.5757 0.3753 -1.3313 -0.5649 0.1649
## (Intercept)-Lynx_rufus 0.5966 0.9439 -0.7546 0.4264 2.9473
## (Intercept)-Didelphis_virginiana -1.2279 0.4627 -2.2123 -1.2102 -0.3385
## (Intercept)-Sylvilagus_floridanus -0.3210 0.4954 -1.2081 -0.3511 0.7700
## (Intercept)-Sciurus_carolinensis -1.2202 0.4717 -2.1669 -1.2124 -0.3088
## (Intercept)-Vulpes_vulpes -1.0174 1.1669 -2.8019 -1.2164 2.0936
## (Intercept)-Sus_scrofa -1.6930 0.6706 -3.0457 -1.6801 -0.4186
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0127 1561
## (Intercept)-Canis_latrans 1.0002 4576
## (Intercept)-Sciurus_niger 1.0533 422
## (Intercept)-Procyon_lotor 1.0018 5169
## (Intercept)-Dasypus_novemcinctus 1.0008 5250
## (Intercept)-Lynx_rufus 1.0072 776
## (Intercept)-Didelphis_virginiana 1.0036 5558
## (Intercept)-Sylvilagus_floridanus 1.0039 2817
## (Intercept)-Sciurus_carolinensis 1.0017 4617
## (Intercept)-Vulpes_vulpes 1.0537 413
## (Intercept)-Sus_scrofa 1.0026 2491
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0037 0.0601 -0.1148 0.0042 0.1200
## (Intercept)-Canis_latrans -2.7443 0.1870 -3.1170 -2.7368 -2.3963
## (Intercept)-Sciurus_niger -4.1160 0.6444 -5.4019 -4.1087 -2.8623
## (Intercept)-Procyon_lotor -2.2958 0.1440 -2.5894 -2.2914 -2.0273
## (Intercept)-Dasypus_novemcinctus -1.7244 0.1572 -2.0413 -1.7213 -1.4275
## (Intercept)-Lynx_rufus -3.7851 0.3801 -4.5256 -3.7825 -3.0572
## (Intercept)-Didelphis_virginiana -2.5330 0.2876 -3.1332 -2.5134 -2.0098
## (Intercept)-Sylvilagus_floridanus -3.1852 0.3031 -3.8157 -3.1691 -2.6330
## (Intercept)-Sciurus_carolinensis -2.5871 0.3072 -3.2118 -2.5774 -2.0119
## (Intercept)-Vulpes_vulpes -4.1575 0.7930 -5.7951 -4.0926 -2.7646
## (Intercept)-Sus_scrofa -3.2642 0.5862 -4.4431 -3.2538 -2.1200
## shrub_cover-Odocoileus_virginianus -0.0535 0.0636 -0.1754 -0.0544 0.0738
## shrub_cover-Canis_latrans -0.2771 0.2176 -0.7159 -0.2781 0.1356
## shrub_cover-Sciurus_niger -0.3189 0.4473 -1.2038 -0.3160 0.5600
## shrub_cover-Procyon_lotor 0.2489 0.1635 -0.0840 0.2522 0.5625
## shrub_cover-Dasypus_novemcinctus 0.7798 0.2854 0.2422 0.7752 1.3513
## shrub_cover-Lynx_rufus -0.3077 0.3413 -0.9942 -0.3065 0.3667
## shrub_cover-Didelphis_virginiana 0.8630 0.3567 0.2169 0.8453 1.5969
## shrub_cover-Sylvilagus_floridanus 0.2294 0.3972 -0.5054 0.2130 1.0177
## shrub_cover-Sciurus_carolinensis 0.7363 0.3884 0.0230 0.7150 1.5379
## shrub_cover-Vulpes_vulpes -0.0690 0.5279 -1.1693 -0.0591 0.9749
## shrub_cover-Sus_scrofa 0.4984 0.7072 -0.8827 0.4760 1.9111
## veg_height-Odocoileus_virginianus -0.2978 0.0641 -0.4262 -0.2973 -0.1760
## veg_height-Canis_latrans -0.5745 0.1825 -0.9443 -0.5692 -0.2247
## veg_height-Sciurus_niger -0.0642 0.3842 -0.7967 -0.0766 0.7361
## veg_height-Procyon_lotor 0.3358 0.1214 0.1010 0.3344 0.5785
## veg_height-Dasypus_novemcinctus 0.2224 0.1309 -0.0328 0.2204 0.4849
## veg_height-Lynx_rufus 0.0373 0.2351 -0.4348 0.0417 0.4819
## veg_height-Didelphis_virginiana 0.4038 0.2347 -0.0350 0.3940 0.8894
## veg_height-Sylvilagus_floridanus 0.1154 0.2400 -0.3627 0.1149 0.5803
## veg_height-Sciurus_carolinensis 0.0535 0.2056 -0.3466 0.0490 0.4702
## veg_height-Vulpes_vulpes -0.1137 0.3102 -0.7699 -0.1019 0.4657
## veg_height-Sus_scrofa -0.1288 0.3210 -0.7835 -0.1244 0.4877
## week-Odocoileus_virginianus 0.2119 0.0603 0.0946 0.2113 0.3305
## week-Canis_latrans 0.0750 0.1294 -0.1832 0.0781 0.3176
## week-Sciurus_niger -0.2814 0.2966 -0.9607 -0.2458 0.2206
## week-Procyon_lotor -0.0483 0.1172 -0.2849 -0.0455 0.1769
## week-Dasypus_novemcinctus -0.1603 0.1382 -0.4419 -0.1577 0.1018
## week-Lynx_rufus -0.0279 0.1923 -0.4255 -0.0196 0.3169
## week-Didelphis_virginiana -0.2059 0.2138 -0.6680 -0.1903 0.1678
## week-Sylvilagus_floridanus -0.1419 0.2064 -0.5855 -0.1309 0.2309
## week-Sciurus_carolinensis 0.1420 0.1770 -0.2123 0.1468 0.4891
## week-Vulpes_vulpes -0.0982 0.2655 -0.6566 -0.0807 0.3789
## week-Sus_scrofa 0.1073 0.2345 -0.3611 0.1059 0.5638
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0019 2389
## (Intercept)-Sciurus_niger 1.0130 466
## (Intercept)-Procyon_lotor 1.0081 3778
## (Intercept)-Dasypus_novemcinctus 0.9999 4490
## (Intercept)-Lynx_rufus 1.0078 683
## (Intercept)-Didelphis_virginiana 1.0122 2446
## (Intercept)-Sylvilagus_floridanus 1.0097 1726
## (Intercept)-Sciurus_carolinensis 1.0047 2646
## (Intercept)-Vulpes_vulpes 1.0179 423
## (Intercept)-Sus_scrofa 1.0028 1554
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0004 2870
## shrub_cover-Sciurus_niger 1.0027 1409
## shrub_cover-Procyon_lotor 1.0002 4169
## shrub_cover-Dasypus_novemcinctus 1.0031 3671
## shrub_cover-Lynx_rufus 1.0167 1442
## shrub_cover-Didelphis_virginiana 1.0075 2214
## shrub_cover-Sylvilagus_floridanus 1.0011 1932
## shrub_cover-Sciurus_carolinensis 1.0009 2578
## shrub_cover-Vulpes_vulpes 1.0030 1842
## shrub_cover-Sus_scrofa 1.0019 2027
## veg_height-Odocoileus_virginianus 1.0016 5468
## veg_height-Canis_latrans 1.0041 2417
## veg_height-Sciurus_niger 1.0036 2091
## veg_height-Procyon_lotor 1.0014 4214
## veg_height-Dasypus_novemcinctus 1.0001 4931
## veg_height-Lynx_rufus 1.0030 2492
## veg_height-Didelphis_virginiana 1.0014 3107
## veg_height-Sylvilagus_floridanus 1.0044 2694
## veg_height-Sciurus_carolinensis 1.0018 3497
## veg_height-Vulpes_vulpes 1.0020 2118
## veg_height-Sus_scrofa 1.0041 3852
## week-Odocoileus_virginianus 1.0019 4986
## week-Canis_latrans 1.0044 4271
## week-Sciurus_niger 1.0007 1922
## week-Procyon_lotor 0.9999 4580
## week-Dasypus_novemcinctus 1.0029 4454
## week-Lynx_rufus 1.0009 2774
## week-Didelphis_virginiana 1.0005 3940
## week-Sylvilagus_floridanus 1.0019 2827
## week-Sciurus_carolinensis 1.0005 5675
## week-Vulpes_vulpes 1.0018 3001
## week-Sus_scrofa 1.0017 4451
#Includes all covariates of detection and only cover for occupancy
ms_full_cover <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9983
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0613 0.6432 -1.1679 0.0379 1.4053 1.0159 1007
## Avg_Cogongrass_Cover 0.0121 0.3384 -0.6606 0.0158 0.6497 1.0031 1378
## total_shrub_cover -0.6696 0.4504 -1.7247 -0.6158 0.0888 1.0050 467
## avg_veg_height 0.1700 0.3352 -0.4675 0.1645 0.8502 1.0013 924
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5071 3.0298 0.4553 2.7597 11.7671 1.0003 1189
## Avg_Cogongrass_Cover 0.3844 0.5390 0.0403 0.2247 1.6990 1.0490 1940
## total_shrub_cover 0.8132 1.1190 0.0520 0.4530 3.6932 1.0042 513
## avg_veg_height 0.2677 0.3523 0.0356 0.1673 1.1121 1.0006 2018
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3082 1.4481 0.0819 0.8853 5.1735 1.036 271
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6570 0.4429 -3.5173 -2.6553 -1.7407 1.0011 2437
## shrub_cover 0.4491 0.2907 -0.0961 0.4431 1.0384 1.0008 963
## veg_height -0.0102 0.1639 -0.3399 -0.0097 0.3177 1.0029 2804
## week -0.0375 0.1204 -0.2912 -0.0298 0.1785 1.0026 2909
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0399 1.2727 0.6773 1.7313 5.3180 1.0141 2211
## shrub_cover 0.6081 0.5018 0.1200 0.4704 1.9065 1.0146 1182
## veg_height 0.2016 0.1417 0.0578 0.1623 0.5829 1.0065 3035
## week 0.0950 0.0702 0.0253 0.0753 0.2738 1.0037 2939
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6351 1.5499 0.8790 3.5159
## (Intercept)-Canis_latrans 0.6021 0.7861 -0.8545 0.5639
## (Intercept)-Sciurus_niger -0.3618 1.2914 -2.4445 -0.5066
## (Intercept)-Procyon_lotor 0.8218 0.7604 -0.6542 0.8139
## (Intercept)-Dasypus_novemcinctus -0.4595 0.7613 -1.8620 -0.4830
## (Intercept)-Lynx_rufus 0.1667 1.0861 -1.6793 0.0744
## (Intercept)-Didelphis_virginiana -0.9820 0.8388 -2.5610 -1.0025
## (Intercept)-Sylvilagus_floridanus 0.2268 0.9313 -1.4206 0.1513
## (Intercept)-Sciurus_carolinensis -1.0191 0.8639 -2.6864 -1.0369
## (Intercept)-Vulpes_vulpes -0.6306 1.4223 -2.9866 -0.8102
## (Intercept)-Sus_scrofa -1.3416 1.1075 -3.4705 -1.3712
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0161 0.5765 -1.2127 -0.0155
## Avg_Cogongrass_Cover-Canis_latrans 0.3251 0.5003 -0.5558 0.2924
## Avg_Cogongrass_Cover-Sciurus_niger -0.3558 0.6780 -1.9265 -0.2835
## Avg_Cogongrass_Cover-Procyon_lotor -0.0656 0.4643 -1.0224 -0.0425
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1539 0.4293 -0.6793 0.1529
## Avg_Cogongrass_Cover-Lynx_rufus 0.3263 0.5320 -0.6293 0.2976
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1606 0.4832 -0.7924 0.1564
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3496 0.5575 -1.5398 -0.3072
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0642 0.4703 -0.8937 0.0735
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1420 0.5784 -0.9677 0.1260
## Avg_Cogongrass_Cover-Sus_scrofa -0.2521 0.6564 -1.7235 -0.1900
## total_shrub_cover-Odocoileus_virginianus -0.3668 0.6751 -1.6514 -0.3861
## total_shrub_cover-Canis_latrans 0.1848 0.6693 -0.8816 0.1049
## total_shrub_cover-Sciurus_niger -0.8172 0.8309 -2.6640 -0.7414
## total_shrub_cover-Procyon_lotor -1.1787 0.6307 -2.6337 -1.0892
## total_shrub_cover-Dasypus_novemcinctus -0.3793 0.6237 -1.9717 -0.3025
## total_shrub_cover-Lynx_rufus -1.0680 0.8223 -2.9350 -0.9815
## total_shrub_cover-Didelphis_virginiana -0.6941 0.6446 -2.2314 -0.6167
## total_shrub_cover-Sylvilagus_floridanus -1.1735 0.9008 -3.3279 -1.0289
## total_shrub_cover-Sciurus_carolinensis -0.7347 0.7503 -2.5481 -0.6206
## total_shrub_cover-Vulpes_vulpes -0.8661 0.9786 -3.2377 -0.7372
## total_shrub_cover-Sus_scrofa -0.4949 0.8702 -2.4615 -0.4368
## avg_veg_height-Odocoileus_virginianus 0.1206 0.5310 -0.9737 0.1323
## avg_veg_height-Canis_latrans 0.1872 0.4624 -0.7074 0.1781
## avg_veg_height-Sciurus_niger -0.0734 0.6007 -1.3944 -0.0402
## avg_veg_height-Procyon_lotor 0.1909 0.4465 -0.6929 0.1854
## avg_veg_height-Dasypus_novemcinctus 0.3467 0.4522 -0.4577 0.3176
## avg_veg_height-Lynx_rufus 0.1378 0.5456 -0.9367 0.1436
## avg_veg_height-Didelphis_virginiana 0.0764 0.4622 -0.8608 0.0795
## avg_veg_height-Sylvilagus_floridanus 0.1100 0.5021 -0.8648 0.1106
## avg_veg_height-Sciurus_carolinensis 0.4653 0.4845 -0.3992 0.4356
## avg_veg_height-Vulpes_vulpes 0.1358 0.5250 -0.9165 0.1365
## avg_veg_height-Sus_scrofa 0.1738 0.5188 -0.8287 0.1569
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1456 1.0015 855
## (Intercept)-Canis_latrans 2.2713 1.0103 1756
## (Intercept)-Sciurus_niger 2.6502 1.0326 567
## (Intercept)-Procyon_lotor 2.3495 1.0006 1818
## (Intercept)-Dasypus_novemcinctus 1.1434 1.0045 884
## (Intercept)-Lynx_rufus 2.5749 1.0122 851
## (Intercept)-Didelphis_virginiana 0.7597 1.0112 822
## (Intercept)-Sylvilagus_floridanus 2.2863 1.0074 1008
## (Intercept)-Sciurus_carolinensis 0.7610 1.0047 1000
## (Intercept)-Vulpes_vulpes 2.6283 1.0359 372
## (Intercept)-Sus_scrofa 0.9328 1.0036 474
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1325 1.0011 2279
## Avg_Cogongrass_Cover-Canis_latrans 1.4014 1.0013 2531
## Avg_Cogongrass_Cover-Sciurus_niger 0.7612 1.0072 1571
## Avg_Cogongrass_Cover-Procyon_lotor 0.8050 1.0028 2495
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0264 1.0049 1908
## Avg_Cogongrass_Cover-Lynx_rufus 1.4802 1.0041 2543
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1274 1.0004 2217
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6055 1.0012 2097
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9804 1.0025 1575
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3444 1.0047 1967
## Avg_Cogongrass_Cover-Sus_scrofa 0.8510 1.0008 1474
## total_shrub_cover-Odocoileus_virginianus 1.0752 1.0046 2722
## total_shrub_cover-Canis_latrans 1.7742 1.0032 1288
## total_shrub_cover-Sciurus_niger 0.6852 1.0015 1009
## total_shrub_cover-Procyon_lotor -0.1939 1.0014 710
## total_shrub_cover-Dasypus_novemcinctus 0.5873 1.0030 545
## total_shrub_cover-Lynx_rufus 0.2904 1.0031 818
## total_shrub_cover-Didelphis_virginiana 0.3374 1.0032 780
## total_shrub_cover-Sylvilagus_floridanus 0.1797 1.0150 401
## total_shrub_cover-Sciurus_carolinensis 0.4310 1.0091 613
## total_shrub_cover-Vulpes_vulpes 0.7179 1.0051 436
## total_shrub_cover-Sus_scrofa 1.0799 1.0021 402
## avg_veg_height-Odocoileus_virginianus 1.1635 1.0017 2123
## avg_veg_height-Canis_latrans 1.1273 1.0004 1827
## avg_veg_height-Sciurus_niger 1.0211 1.0023 1466
## avg_veg_height-Procyon_lotor 1.0789 1.0009 2318
## avg_veg_height-Dasypus_novemcinctus 1.3267 1.0011 1469
## avg_veg_height-Lynx_rufus 1.2480 1.0017 1734
## avg_veg_height-Didelphis_virginiana 0.9685 1.0024 1935
## avg_veg_height-Sylvilagus_floridanus 1.0978 1.0014 1200
## avg_veg_height-Sciurus_carolinensis 1.5099 1.0000 1895
## avg_veg_height-Vulpes_vulpes 1.1721 1.0000 1457
## avg_veg_height-Sus_scrofa 1.1952 1.0007 1733
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0039 0.0608 -0.1182 0.0038 0.1242
## (Intercept)-Canis_latrans -2.8040 0.1996 -3.2091 -2.7959 -2.4298
## (Intercept)-Sciurus_niger -4.0803 0.6620 -5.3623 -4.0758 -2.8254
## (Intercept)-Procyon_lotor -2.2980 0.1387 -2.5718 -2.2958 -2.0348
## (Intercept)-Dasypus_novemcinctus -1.7879 0.1761 -2.1488 -1.7779 -1.4682
## (Intercept)-Lynx_rufus -3.6188 0.3622 -4.3557 -3.6016 -2.9542
## (Intercept)-Didelphis_virginiana -2.6796 0.3299 -3.3710 -2.6594 -2.0708
## (Intercept)-Sylvilagus_floridanus -3.3179 0.2921 -3.8882 -3.3088 -2.7677
## (Intercept)-Sciurus_carolinensis -2.7812 0.3526 -3.5130 -2.7653 -2.1379
## (Intercept)-Vulpes_vulpes -4.3369 0.7587 -5.8626 -4.3144 -2.9323
## (Intercept)-Sus_scrofa -3.5449 0.6320 -4.7802 -3.5547 -2.2774
## shrub_cover-Odocoileus_virginianus -0.0531 0.0641 -0.1776 -0.0534 0.0751
## shrub_cover-Canis_latrans -0.2853 0.2472 -0.7659 -0.2863 0.1957
## shrub_cover-Sciurus_niger -0.1052 0.5409 -1.1869 -0.0969 0.9439
## shrub_cover-Procyon_lotor 0.3174 0.1581 -0.0023 0.3206 0.6197
## shrub_cover-Dasypus_novemcinctus 0.9985 0.3621 0.3352 0.9734 1.7416
## shrub_cover-Lynx_rufus 0.0443 0.3832 -0.7255 0.0470 0.7719
## shrub_cover-Didelphis_virginiana 1.1472 0.4235 0.4054 1.1217 2.0364
## shrub_cover-Sylvilagus_floridanus 0.7091 0.4345 -0.1901 0.7335 1.5363
## shrub_cover-Sciurus_carolinensis 1.0903 0.4415 0.2576 1.0817 1.9746
## shrub_cover-Vulpes_vulpes 0.2463 0.6098 -0.9551 0.2496 1.4476
## shrub_cover-Sus_scrofa 0.9508 0.8564 -0.8542 0.9487 2.6075
## veg_height-Odocoileus_virginianus -0.2982 0.0651 -0.4246 -0.2974 -0.1705
## veg_height-Canis_latrans -0.6062 0.1901 -0.9949 -0.5993 -0.2539
## veg_height-Sciurus_niger 0.0205 0.4393 -0.8072 -0.0023 0.9675
## veg_height-Procyon_lotor 0.3345 0.1243 0.0902 0.3348 0.5766
## veg_height-Dasypus_novemcinctus 0.2398 0.1368 -0.0181 0.2357 0.5090
## veg_height-Lynx_rufus 0.0303 0.2404 -0.4540 0.0325 0.4972
## veg_height-Didelphis_virginiana 0.4017 0.2433 -0.0520 0.3958 0.9067
## veg_height-Sylvilagus_floridanus 0.0403 0.2470 -0.4324 0.0398 0.5401
## veg_height-Sciurus_carolinensis 0.0822 0.2220 -0.3346 0.0751 0.5336
## veg_height-Vulpes_vulpes -0.1541 0.3244 -0.8200 -0.1434 0.4503
## veg_height-Sus_scrofa -0.1758 0.3280 -0.8511 -0.1658 0.4386
## week-Odocoileus_virginianus 0.2110 0.0605 0.0935 0.2105 0.3311
## week-Canis_latrans 0.0742 0.1304 -0.1926 0.0775 0.3184
## week-Sciurus_niger -0.2790 0.2903 -0.9218 -0.2421 0.2021
## week-Procyon_lotor -0.0459 0.1186 -0.2888 -0.0429 0.1787
## week-Dasypus_novemcinctus -0.1580 0.1346 -0.4314 -0.1535 0.0906
## week-Lynx_rufus -0.0278 0.1961 -0.4288 -0.0193 0.3415
## week-Didelphis_virginiana -0.1924 0.2128 -0.6579 -0.1771 0.1861
## week-Sylvilagus_floridanus -0.1352 0.2047 -0.5612 -0.1249 0.2329
## week-Sciurus_carolinensis 0.1413 0.1798 -0.2088 0.1413 0.4896
## week-Vulpes_vulpes -0.1033 0.2672 -0.6859 -0.0860 0.3789
## week-Sus_scrofa 0.0993 0.2261 -0.3587 0.1010 0.5462
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0017 5590
## (Intercept)-Canis_latrans 1.0253 1548
## (Intercept)-Sciurus_niger 1.0216 517
## (Intercept)-Procyon_lotor 1.0019 3933
## (Intercept)-Dasypus_novemcinctus 1.0018 1389
## (Intercept)-Lynx_rufus 1.0050 983
## (Intercept)-Didelphis_virginiana 1.0032 1064
## (Intercept)-Sylvilagus_floridanus 1.0046 1343
## (Intercept)-Sciurus_carolinensis 1.0031 1116
## (Intercept)-Vulpes_vulpes 1.0100 412
## (Intercept)-Sus_scrofa 1.0106 549
## shrub_cover-Odocoileus_virginianus 1.0004 5467
## shrub_cover-Canis_latrans 1.0067 1723
## shrub_cover-Sciurus_niger 1.0054 956
## shrub_cover-Procyon_lotor 1.0022 3788
## shrub_cover-Dasypus_novemcinctus 1.0124 715
## shrub_cover-Lynx_rufus 1.0183 1084
## shrub_cover-Didelphis_virginiana 1.0097 707
## shrub_cover-Sylvilagus_floridanus 1.0088 715
## shrub_cover-Sciurus_carolinensis 1.0046 875
## shrub_cover-Vulpes_vulpes 1.0043 817
## shrub_cover-Sus_scrofa 1.0120 587
## veg_height-Odocoileus_virginianus 1.0008 5250
## veg_height-Canis_latrans 1.0121 2001
## veg_height-Sciurus_niger 1.0114 1486
## veg_height-Procyon_lotor 0.9998 4105
## veg_height-Dasypus_novemcinctus 1.0034 4477
## veg_height-Lynx_rufus 1.0038 2089
## veg_height-Didelphis_virginiana 1.0036 2824
## veg_height-Sylvilagus_floridanus 1.0047 1815
## veg_height-Sciurus_carolinensis 1.0020 2494
## veg_height-Vulpes_vulpes 1.0062 1701
## veg_height-Sus_scrofa 1.0009 2467
## week-Odocoileus_virginianus 1.0016 5250
## week-Canis_latrans 1.0008 4584
## week-Sciurus_niger 1.0050 2065
## week-Procyon_lotor 1.0040 4546
## week-Dasypus_novemcinctus 1.0011 4444
## week-Lynx_rufus 1.0043 3023
## week-Didelphis_virginiana 1.0030 3572
## week-Sylvilagus_floridanus 1.0010 2870
## week-Sciurus_carolinensis 1.0005 4322
## week-Vulpes_vulpes 1.0009 2763
## week-Sus_scrofa 1.0036 3905
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.966
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1180 0.7427 -1.5178 -0.1504 1.4643 1.0075 1803
## Tree_Density -0.7585 0.3941 -1.6365 -0.7346 -0.0545 1.0327 1315
## Avg_Canopy_Cover 1.0832 0.3716 0.3977 1.0601 1.8833 1.0070 1516
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.4313 5.8354 1.4030 4.8941 21.7132 1.1174 497
## Tree_Density 0.7227 1.2003 0.0440 0.3433 3.7071 1.0129 1153
## Avg_Canopy_Cover 0.7472 0.7675 0.0777 0.5121 2.8120 1.0173 1549
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.3942 0.4614 0.041 0.2385 1.665 1.0155 466
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6233 0.4396 -3.4580 -2.6385 -1.7138 1.0005 5031
## shrub_cover 0.2348 0.2459 -0.2445 0.2317 0.7506 1.0018 3506
## veg_height 0.0184 0.1526 -0.2814 0.0182 0.3283 1.0035 3779
## week -0.0394 0.1208 -0.2955 -0.0340 0.1808 1.0008 2823
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1701 1.3696 0.7348 1.8268 5.6912 1.0082 2508
## shrub_cover 0.4854 0.3897 0.0998 0.3818 1.4848 1.0116 2242
## veg_height 0.1920 0.1309 0.0546 0.1602 0.5120 1.0063 3226
## week 0.0982 0.0784 0.0256 0.0778 0.2880 1.0010 2576
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6738 1.7287 2.1083 4.3872 8.8914
## (Intercept)-Canis_latrans 0.4263 0.6282 -0.7219 0.3935 1.7686
## (Intercept)-Sciurus_niger 0.2351 1.9051 -2.1728 -0.1241 5.0929
## (Intercept)-Procyon_lotor 0.8350 0.6242 -0.3371 0.8115 2.1052
## (Intercept)-Dasypus_novemcinctus -0.9302 0.6034 -2.1754 -0.9143 0.2117
## (Intercept)-Lynx_rufus 1.5001 1.8264 -0.9625 1.1220 6.3313
## (Intercept)-Didelphis_virginiana -1.7119 0.7107 -3.2079 -1.6785 -0.3957
## (Intercept)-Sylvilagus_floridanus -0.6171 0.7032 -1.9782 -0.6391 0.7774
## (Intercept)-Sciurus_carolinensis -1.7913 0.7197 -3.2746 -1.7702 -0.4807
## (Intercept)-Vulpes_vulpes -1.2746 1.5612 -3.6349 -1.5015 2.2736
## (Intercept)-Sus_scrofa -2.5244 0.9953 -4.5486 -2.4742 -0.6773
## Tree_Density-Odocoileus_virginianus -0.4047 0.6482 -1.5060 -0.4604 1.0625
## Tree_Density-Canis_latrans -0.9114 0.5704 -2.2046 -0.8492 0.0403
## Tree_Density-Sciurus_niger -0.8162 0.8095 -2.6576 -0.7579 0.7010
## Tree_Density-Procyon_lotor -0.4921 0.4202 -1.3344 -0.4973 0.3795
## Tree_Density-Dasypus_novemcinctus -1.3100 0.8407 -3.4301 -1.1373 -0.1598
## Tree_Density-Lynx_rufus -0.0742 0.8012 -1.3785 -0.1643 1.8177
## Tree_Density-Didelphis_virginiana -0.9847 0.7448 -2.7664 -0.8634 0.1354
## Tree_Density-Sylvilagus_floridanus -1.0256 0.7244 -2.8019 -0.9190 0.1017
## Tree_Density-Sciurus_carolinensis -0.8920 0.7094 -2.5280 -0.8043 0.2570
## Tree_Density-Vulpes_vulpes -0.6863 0.8044 -2.3859 -0.6505 0.8628
## Tree_Density-Sus_scrofa -0.9633 0.8078 -2.9762 -0.8271 0.2882
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8085 0.7446 -0.6785 0.8025 2.2792
## Avg_Canopy_Cover-Canis_latrans 0.0318 0.4815 -0.9385 0.0364 0.9700
## Avg_Canopy_Cover-Sciurus_niger 1.0498 0.8656 -0.5124 0.9967 2.9496
## Avg_Canopy_Cover-Procyon_lotor 1.0689 0.4822 0.2043 1.0372 2.1303
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0610 0.4390 0.2664 1.0372 1.9929
## Avg_Canopy_Cover-Lynx_rufus 1.0279 0.8080 -0.4527 0.9739 2.8647
## Avg_Canopy_Cover-Didelphis_virginiana 1.4528 0.6056 0.4954 1.3767 2.8838
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8809 0.8312 0.6741 1.7283 3.8826
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3970 0.5847 0.4486 1.3292 2.7615
## Avg_Canopy_Cover-Vulpes_vulpes 1.1064 0.6658 -0.0510 1.0565 2.5620
## Avg_Canopy_Cover-Sus_scrofa 1.3316 0.5862 0.3519 1.2675 2.6241
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0406 892
## (Intercept)-Canis_latrans 1.0012 2764
## (Intercept)-Sciurus_niger 1.0251 293
## (Intercept)-Procyon_lotor 1.0064 2926
## (Intercept)-Dasypus_novemcinctus 1.0051 2804
## (Intercept)-Lynx_rufus 1.1360 367
## (Intercept)-Didelphis_virginiana 1.0058 2558
## (Intercept)-Sylvilagus_floridanus 0.9999 3036
## (Intercept)-Sciurus_carolinensis 1.0015 2771
## (Intercept)-Vulpes_vulpes 1.0752 250
## (Intercept)-Sus_scrofa 1.0137 1453
## Tree_Density-Odocoileus_virginianus 1.0114 1992
## Tree_Density-Canis_latrans 1.0057 2351
## Tree_Density-Sciurus_niger 1.0173 1840
## Tree_Density-Procyon_lotor 1.0044 4075
## Tree_Density-Dasypus_novemcinctus 1.0102 1421
## Tree_Density-Lynx_rufus 1.0219 945
## Tree_Density-Didelphis_virginiana 1.0098 1842
## Tree_Density-Sylvilagus_floridanus 1.0107 1798
## Tree_Density-Sciurus_carolinensis 1.0084 2297
## Tree_Density-Vulpes_vulpes 1.0156 1938
## Tree_Density-Sus_scrofa 1.0159 1738
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0010 2545
## Avg_Canopy_Cover-Canis_latrans 1.0009 2409
## Avg_Canopy_Cover-Sciurus_niger 1.0073 1217
## Avg_Canopy_Cover-Procyon_lotor 1.0019 3170
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0007 4133
## Avg_Canopy_Cover-Lynx_rufus 1.0176 1386
## Avg_Canopy_Cover-Didelphis_virginiana 1.0033 1943
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0069 1647
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0047 2477
## Avg_Canopy_Cover-Vulpes_vulpes 1.0054 2376
## Avg_Canopy_Cover-Sus_scrofa 1.0041 2533
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0058 0.0595 -0.1104 0.0051 0.1231
## (Intercept)-Canis_latrans -2.7767 0.1951 -3.1857 -2.7680 -2.4235
## (Intercept)-Sciurus_niger -4.4077 0.6363 -5.5994 -4.4311 -3.1397
## (Intercept)-Procyon_lotor -2.3003 0.1456 -2.5969 -2.2945 -2.0261
## (Intercept)-Dasypus_novemcinctus -1.7329 0.1579 -2.0560 -1.7300 -1.4415
## (Intercept)-Lynx_rufus -3.9316 0.3596 -4.6092 -3.9399 -3.1932
## (Intercept)-Didelphis_virginiana -2.5735 0.2900 -3.1684 -2.5605 -2.0225
## (Intercept)-Sylvilagus_floridanus -3.1490 0.2738 -3.7088 -3.1450 -2.6476
## (Intercept)-Sciurus_carolinensis -2.6513 0.3200 -3.3029 -2.6410 -2.0683
## (Intercept)-Vulpes_vulpes -4.2459 0.7717 -5.8165 -4.1930 -2.9028
## (Intercept)-Sus_scrofa -3.2087 0.5885 -4.3655 -3.1931 -2.0961
## shrub_cover-Odocoileus_virginianus -0.0530 0.0652 -0.1798 -0.0520 0.0732
## shrub_cover-Canis_latrans -0.2797 0.2225 -0.6996 -0.2836 0.1625
## shrub_cover-Sciurus_niger -0.3554 0.4387 -1.2750 -0.3428 0.4876
## shrub_cover-Procyon_lotor 0.2466 0.1603 -0.0811 0.2478 0.5564
## shrub_cover-Dasypus_novemcinctus 0.8123 0.2874 0.2677 0.8106 1.3818
## shrub_cover-Lynx_rufus -0.3044 0.3168 -0.9481 -0.2940 0.3089
## shrub_cover-Didelphis_virginiana 0.9132 0.3510 0.2892 0.8921 1.6549
## shrub_cover-Sylvilagus_floridanus 0.3788 0.3749 -0.3608 0.3712 1.1346
## shrub_cover-Sciurus_carolinensis 0.8167 0.4025 0.0749 0.7967 1.6369
## shrub_cover-Vulpes_vulpes -0.0379 0.5341 -1.1412 -0.0262 0.9999
## shrub_cover-Sus_scrofa 0.4906 0.7158 -0.8712 0.4691 1.9855
## veg_height-Odocoileus_virginianus -0.2985 0.0655 -0.4299 -0.2982 -0.1718
## veg_height-Canis_latrans -0.5857 0.1837 -0.9662 -0.5819 -0.2444
## veg_height-Sciurus_niger -0.0404 0.3530 -0.7241 -0.0476 0.6729
## veg_height-Procyon_lotor 0.3395 0.1231 0.0955 0.3398 0.5803
## veg_height-Dasypus_novemcinctus 0.2362 0.1340 -0.0225 0.2351 0.4996
## veg_height-Lynx_rufus 0.0879 0.2315 -0.3730 0.0894 0.5324
## veg_height-Didelphis_virginiana 0.4447 0.2367 0.0091 0.4351 0.9386
## veg_height-Sylvilagus_floridanus 0.1502 0.2332 -0.3074 0.1531 0.6002
## veg_height-Sciurus_carolinensis 0.0848 0.2090 -0.3063 0.0808 0.5115
## veg_height-Vulpes_vulpes -0.1139 0.3120 -0.7742 -0.1012 0.4857
## veg_height-Sus_scrofa -0.0989 0.3172 -0.7385 -0.0995 0.5217
## week-Odocoileus_virginianus 0.2120 0.0599 0.0975 0.2105 0.3328
## week-Canis_latrans 0.0738 0.1299 -0.1947 0.0760 0.3198
## week-Sciurus_niger -0.2881 0.2929 -0.9758 -0.2575 0.1949
## week-Procyon_lotor -0.0435 0.1167 -0.2815 -0.0393 0.1799
## week-Dasypus_novemcinctus -0.1593 0.1374 -0.4497 -0.1544 0.0993
## week-Lynx_rufus -0.0288 0.1932 -0.4417 -0.0194 0.3294
## week-Didelphis_virginiana -0.1964 0.2163 -0.6657 -0.1804 0.1882
## week-Sylvilagus_floridanus -0.1410 0.2033 -0.5732 -0.1290 0.2263
## week-Sciurus_carolinensis 0.1454 0.1796 -0.2208 0.1508 0.4863
## week-Vulpes_vulpes -0.1047 0.2806 -0.7042 -0.0901 0.4009
## week-Sus_scrofa 0.1054 0.2314 -0.3462 0.1045 0.5626
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0032 4821
## (Intercept)-Canis_latrans 1.0010 2273
## (Intercept)-Sciurus_niger 1.0063 399
## (Intercept)-Procyon_lotor 0.9998 3747
## (Intercept)-Dasypus_novemcinctus 1.0024 3897
## (Intercept)-Lynx_rufus 1.0545 504
## (Intercept)-Didelphis_virginiana 1.0002 2315
## (Intercept)-Sylvilagus_floridanus 1.0004 2049
## (Intercept)-Sciurus_carolinensis 1.0002 2226
## (Intercept)-Vulpes_vulpes 1.0204 342
## (Intercept)-Sus_scrofa 1.0070 2019
## shrub_cover-Odocoileus_virginianus 1.0011 5891
## shrub_cover-Canis_latrans 1.0054 2145
## shrub_cover-Sciurus_niger 1.0012 1260
## shrub_cover-Procyon_lotor 1.0017 4128
## shrub_cover-Dasypus_novemcinctus 0.9999 3376
## shrub_cover-Lynx_rufus 1.0063 1413
## shrub_cover-Didelphis_virginiana 1.0121 2380
## shrub_cover-Sylvilagus_floridanus 1.0009 2233
## shrub_cover-Sciurus_carolinensis 1.0005 2110
## shrub_cover-Vulpes_vulpes 1.0004 2053
## shrub_cover-Sus_scrofa 1.0019 2598
## veg_height-Odocoileus_virginianus 1.0016 5250
## veg_height-Canis_latrans 1.0004 2234
## veg_height-Sciurus_niger 1.0027 2193
## veg_height-Procyon_lotor 1.0022 4977
## veg_height-Dasypus_novemcinctus 1.0046 4543
## veg_height-Lynx_rufus 1.0036 2277
## veg_height-Didelphis_virginiana 1.0010 3270
## veg_height-Sylvilagus_floridanus 1.0037 3461
## veg_height-Sciurus_carolinensis 1.0001 3282
## veg_height-Vulpes_vulpes 1.0012 2755
## veg_height-Sus_scrofa 1.0012 3956
## week-Odocoileus_virginianus 1.0026 5503
## week-Canis_latrans 1.0006 4432
## week-Sciurus_niger 1.0007 1704
## week-Procyon_lotor 1.0007 4511
## week-Dasypus_novemcinctus 1.0065 4763
## week-Lynx_rufus 1.0045 2690
## week-Didelphis_virginiana 1.0011 3176
## week-Sylvilagus_floridanus 1.0023 3215
## week-Sciurus_carolinensis 0.9998 4469
## week-Vulpes_vulpes 1.0029 2552
## week-Sus_scrofa 1.0000 4433
#Includes all covariates of detection and only movement for occupancy
ms_full_move <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0327
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1083 0.6667 -1.3683 -0.1232 1.2241 1.0000 1273
## Cogon_Patch_Size -0.2469 0.4088 -1.1021 -0.2262 0.5015 1.0109 1940
## Avg_Cogongrass_Cover 0.2350 0.3111 -0.3788 0.2357 0.8588 1.0009 1340
## total_shrub_cover -0.5253 0.3740 -1.3147 -0.5040 0.1494 1.0248 882
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9176 3.3468 0.6425 3.0372 12.0579 1.0164 1218
## Cogon_Patch_Size 0.8628 1.3375 0.0611 0.4636 4.1578 1.0057 898
## Avg_Cogongrass_Cover 0.3247 0.4037 0.0372 0.1993 1.4602 1.0074 2166
## total_shrub_cover 0.5450 0.7193 0.0475 0.3131 2.4659 1.0099 948
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4085 1.2659 0.0993 1.0562 4.9141 1.0003 466
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6251 0.4392 -3.4610 -2.6378 -1.7368 1.0016 4235
## shrub_cover 0.3975 0.2706 -0.1307 0.3944 0.9436 1.0055 1554
## veg_height -0.0042 0.1561 -0.3103 -0.0061 0.3115 1.0022 2541
## week -0.0404 0.1197 -0.2872 -0.0380 0.1761 1.0001 3279
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0557 1.3575 0.6935 1.7182 5.5762 1.0016 1854
## shrub_cover 0.5072 0.4278 0.1006 0.3908 1.6334 1.0024 1484
## veg_height 0.1904 0.1332 0.0530 0.1554 0.5445 1.0050 3612
## week 0.0992 0.0821 0.0256 0.0761 0.3157 1.0084 2714
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6981 1.5907 0.9728 3.5333
## (Intercept)-Canis_latrans 0.5960 0.8071 -0.9292 0.5743
## (Intercept)-Sciurus_niger -0.4959 1.3864 -2.7464 -0.6629
## (Intercept)-Procyon_lotor 0.7182 0.7882 -0.8566 0.7091
## (Intercept)-Dasypus_novemcinctus -0.5798 0.7097 -1.9782 -0.5819
## (Intercept)-Lynx_rufus -0.0245 1.0641 -1.9421 -0.0993
## (Intercept)-Didelphis_virginiana -1.1486 0.8146 -2.8404 -1.1382
## (Intercept)-Sylvilagus_floridanus -0.1113 0.9308 -1.8986 -0.1354
## (Intercept)-Sciurus_carolinensis -1.3023 0.8643 -3.0857 -1.2974
## (Intercept)-Vulpes_vulpes -0.9627 1.4535 -3.4205 -1.1090
## (Intercept)-Sus_scrofa -1.6351 1.0780 -3.8276 -1.6242
## Cogon_Patch_Size-Odocoileus_virginianus -0.0598 0.7207 -1.3362 -0.1038
## Cogon_Patch_Size-Canis_latrans 0.6061 0.7346 -0.4308 0.4782
## Cogon_Patch_Size-Sciurus_niger -0.6017 0.9320 -2.8064 -0.4784
## Cogon_Patch_Size-Procyon_lotor -0.2756 0.4718 -1.2386 -0.2598
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1171 0.4347 -1.0211 -0.1123
## Cogon_Patch_Size-Lynx_rufus -0.2234 0.7526 -1.5792 -0.2593
## Cogon_Patch_Size-Didelphis_virginiana 0.4965 0.4994 -0.3988 0.4714
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8699 0.8618 -2.9549 -0.7258
## Cogon_Patch_Size-Sciurus_carolinensis -0.7272 0.7355 -2.5474 -0.5982
## Cogon_Patch_Size-Vulpes_vulpes -0.5344 0.8913 -2.5990 -0.4438
## Cogon_Patch_Size-Sus_scrofa -0.5097 0.8083 -2.4327 -0.3948
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2222 0.5448 -0.8542 0.2209
## Avg_Cogongrass_Cover-Canis_latrans 0.3806 0.4431 -0.4081 0.3574
## Avg_Cogongrass_Cover-Sciurus_niger -0.1077 0.6570 -1.6037 -0.0438
## Avg_Cogongrass_Cover-Procyon_lotor 0.1988 0.4440 -0.6638 0.1905
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3995 0.3961 -0.3372 0.3896
## Avg_Cogongrass_Cover-Lynx_rufus 0.5175 0.4991 -0.3606 0.4773
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2238 0.4392 -0.6292 0.2192
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0420 0.5282 -1.1417 -0.0154
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4464 0.4355 -0.3432 0.4304
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3463 0.4965 -0.5959 0.3230
## Avg_Cogongrass_Cover-Sus_scrofa 0.0051 0.5927 -1.3410 0.0533
## total_shrub_cover-Odocoileus_virginianus -0.3154 0.6239 -1.4936 -0.3317
## total_shrub_cover-Canis_latrans 0.0624 0.5795 -0.9444 0.0197
## total_shrub_cover-Sciurus_niger -0.6595 0.6662 -2.0838 -0.6217
## total_shrub_cover-Procyon_lotor -0.9799 0.5736 -2.3268 -0.8976
## total_shrub_cover-Dasypus_novemcinctus -0.2721 0.4650 -1.2424 -0.2468
## total_shrub_cover-Lynx_rufus -0.8501 0.7397 -2.5352 -0.7779
## total_shrub_cover-Didelphis_virginiana -0.6051 0.5288 -1.8137 -0.5600
## total_shrub_cover-Sylvilagus_floridanus -0.8354 0.7529 -2.6337 -0.7242
## total_shrub_cover-Sciurus_carolinensis -0.4722 0.5816 -1.8032 -0.4259
## total_shrub_cover-Vulpes_vulpes -0.6100 0.7521 -2.3237 -0.5475
## total_shrub_cover-Sus_scrofa -0.3218 0.7168 -1.8077 -0.3122
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3555 1.0053 954
## (Intercept)-Canis_latrans 2.3215 1.0033 1581
## (Intercept)-Sciurus_niger 2.7583 1.0192 383
## (Intercept)-Procyon_lotor 2.3324 1.0047 1580
## (Intercept)-Dasypus_novemcinctus 0.8160 1.0023 2347
## (Intercept)-Lynx_rufus 2.2659 1.0018 894
## (Intercept)-Didelphis_virginiana 0.4710 1.0013 1708
## (Intercept)-Sylvilagus_floridanus 1.8023 1.0120 1393
## (Intercept)-Sciurus_carolinensis 0.3602 1.0015 1437
## (Intercept)-Vulpes_vulpes 2.2806 1.0079 369
## (Intercept)-Sus_scrofa 0.5021 1.0123 1055
## Cogon_Patch_Size-Odocoileus_virginianus 1.5465 1.0010 3194
## Cogon_Patch_Size-Canis_latrans 2.4037 1.0056 1720
## Cogon_Patch_Size-Sciurus_niger 0.8522 1.0073 1413
## Cogon_Patch_Size-Procyon_lotor 0.6491 1.0052 2517
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7126 1.0065 3403
## Cogon_Patch_Size-Lynx_rufus 1.3827 1.0053 1863
## Cogon_Patch_Size-Didelphis_virginiana 1.5794 1.0068 2882
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3671 1.0054 1472
## Cogon_Patch_Size-Sciurus_carolinensis 0.3597 1.0091 1626
## Cogon_Patch_Size-Vulpes_vulpes 0.9053 1.0119 1553
## Cogon_Patch_Size-Sus_scrofa 0.7405 1.0194 1843
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3233 1.0011 2608
## Avg_Cogongrass_Cover-Canis_latrans 1.3312 1.0072 2825
## Avg_Cogongrass_Cover-Sciurus_niger 1.0368 1.0008 1400
## Avg_Cogongrass_Cover-Procyon_lotor 1.1007 1.0019 2676
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2323 1.0017 2349
## Avg_Cogongrass_Cover-Lynx_rufus 1.6298 1.0004 2838
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1155 1.0070 2074
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9501 1.0030 2138
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3684 1.0021 2590
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3514 1.0035 2837
## Avg_Cogongrass_Cover-Sus_scrofa 1.0567 1.0023 1875
## total_shrub_cover-Odocoileus_virginianus 0.9906 1.0106 2505
## total_shrub_cover-Canis_latrans 1.3627 1.0096 1507
## total_shrub_cover-Sciurus_niger 0.5583 1.0069 1174
## total_shrub_cover-Procyon_lotor -0.0579 1.0090 1137
## total_shrub_cover-Dasypus_novemcinctus 0.5858 1.0026 2181
## total_shrub_cover-Lynx_rufus 0.4124 1.0162 949
## total_shrub_cover-Didelphis_virginiana 0.3314 1.0057 1325
## total_shrub_cover-Sylvilagus_floridanus 0.3356 1.0171 765
## total_shrub_cover-Sciurus_carolinensis 0.5490 1.0065 1290
## total_shrub_cover-Vulpes_vulpes 0.7263 1.0056 1075
## total_shrub_cover-Sus_scrofa 1.0762 1.0092 996
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0068 0.0604 -0.1096 0.0070 0.1257
## (Intercept)-Canis_latrans -2.7692 0.1967 -3.1773 -2.7613 -2.4071
## (Intercept)-Sciurus_niger -4.0971 0.6763 -5.4457 -4.0905 -2.8155
## (Intercept)-Procyon_lotor -2.2971 0.1399 -2.5859 -2.2940 -2.0286
## (Intercept)-Dasypus_novemcinctus -1.7574 0.1684 -2.1056 -1.7495 -1.4442
## (Intercept)-Lynx_rufus -3.5932 0.3604 -4.3183 -3.5704 -2.9424
## (Intercept)-Didelphis_virginiana -2.6087 0.3039 -3.2484 -2.5892 -2.0638
## (Intercept)-Sylvilagus_floridanus -3.2962 0.2939 -3.8981 -3.2866 -2.7535
## (Intercept)-Sciurus_carolinensis -2.7175 0.3467 -3.4236 -2.7032 -2.0793
## (Intercept)-Vulpes_vulpes -4.2759 0.7668 -5.8096 -4.2364 -2.9156
## (Intercept)-Sus_scrofa -3.4722 0.6104 -4.6589 -3.4688 -2.2907
## shrub_cover-Odocoileus_virginianus -0.0509 0.0633 -0.1763 -0.0511 0.0711
## shrub_cover-Canis_latrans -0.2517 0.2377 -0.7107 -0.2521 0.2211
## shrub_cover-Sciurus_niger -0.1064 0.5069 -1.0868 -0.1076 0.8869
## shrub_cover-Procyon_lotor 0.3045 0.1578 -0.0045 0.3058 0.6067
## shrub_cover-Dasypus_novemcinctus 0.9160 0.3263 0.3230 0.8984 1.5905
## shrub_cover-Lynx_rufus 0.0358 0.3746 -0.7375 0.0549 0.7197
## shrub_cover-Didelphis_virginiana 1.0377 0.3921 0.3355 1.0142 1.8771
## shrub_cover-Sylvilagus_floridanus 0.6310 0.4259 -0.2255 0.6390 1.4471
## shrub_cover-Sciurus_carolinensis 0.9588 0.4243 0.1621 0.9554 1.8155
## shrub_cover-Vulpes_vulpes 0.2150 0.5939 -0.9960 0.2189 1.4153
## shrub_cover-Sus_scrofa 0.8027 0.7607 -0.6587 0.7894 2.3489
## veg_height-Odocoileus_virginianus -0.2950 0.0645 -0.4204 -0.2953 -0.1664
## veg_height-Canis_latrans -0.5764 0.1851 -0.9485 -0.5683 -0.2228
## veg_height-Sciurus_niger -0.0324 0.3924 -0.7910 -0.0499 0.8186
## veg_height-Procyon_lotor 0.3326 0.1216 0.0939 0.3305 0.5733
## veg_height-Dasypus_novemcinctus 0.2358 0.1341 -0.0232 0.2328 0.5035
## veg_height-Lynx_rufus 0.0322 0.2371 -0.4400 0.0318 0.4778
## veg_height-Didelphis_virginiana 0.3875 0.2387 -0.0582 0.3771 0.8848
## veg_height-Sylvilagus_floridanus 0.0496 0.2453 -0.4168 0.0441 0.5511
## veg_height-Sciurus_carolinensis 0.0860 0.2184 -0.3231 0.0768 0.5300
## veg_height-Vulpes_vulpes -0.1184 0.3167 -0.7830 -0.1085 0.4788
## veg_height-Sus_scrofa -0.1464 0.3223 -0.8037 -0.1357 0.4817
## week-Odocoileus_virginianus 0.2106 0.0609 0.0944 0.2095 0.3326
## week-Canis_latrans 0.0763 0.1261 -0.1736 0.0782 0.3150
## week-Sciurus_niger -0.2937 0.2984 -0.9753 -0.2677 0.1956
## week-Procyon_lotor -0.0475 0.1185 -0.2916 -0.0468 0.1780
## week-Dasypus_novemcinctus -0.1561 0.1359 -0.4395 -0.1512 0.0946
## week-Lynx_rufus -0.0299 0.1896 -0.4246 -0.0230 0.3214
## week-Didelphis_virginiana -0.2005 0.2147 -0.6585 -0.1885 0.1868
## week-Sylvilagus_floridanus -0.1404 0.2071 -0.5849 -0.1244 0.2289
## week-Sciurus_carolinensis 0.1428 0.1773 -0.2172 0.1438 0.4819
## week-Vulpes_vulpes -0.1158 0.2813 -0.7464 -0.0952 0.3845
## week-Sus_scrofa 0.0993 0.2312 -0.3533 0.0993 0.5564
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 5250
## (Intercept)-Canis_latrans 1.0006 1887
## (Intercept)-Sciurus_niger 1.0063 399
## (Intercept)-Procyon_lotor 1.0013 3934
## (Intercept)-Dasypus_novemcinctus 1.0015 2623
## (Intercept)-Lynx_rufus 1.0030 888
## (Intercept)-Didelphis_virginiana 1.0064 1865
## (Intercept)-Sylvilagus_floridanus 1.0034 1400
## (Intercept)-Sciurus_carolinensis 1.0031 1496
## (Intercept)-Vulpes_vulpes 1.0017 397
## (Intercept)-Sus_scrofa 1.0045 1011
## shrub_cover-Odocoileus_virginianus 1.0024 5250
## shrub_cover-Canis_latrans 1.0092 1979
## shrub_cover-Sciurus_niger 1.0005 958
## shrub_cover-Procyon_lotor 1.0045 3965
## shrub_cover-Dasypus_novemcinctus 1.0068 2055
## shrub_cover-Lynx_rufus 1.0024 1154
## shrub_cover-Didelphis_virginiana 1.0085 1264
## shrub_cover-Sylvilagus_floridanus 1.0026 1068
## shrub_cover-Sciurus_carolinensis 1.0006 1339
## shrub_cover-Vulpes_vulpes 1.0004 1109
## shrub_cover-Sus_scrofa 1.0048 1052
## veg_height-Odocoileus_virginianus 1.0019 5717
## veg_height-Canis_latrans 1.0001 2289
## veg_height-Sciurus_niger 1.0054 1472
## veg_height-Procyon_lotor 1.0012 4221
## veg_height-Dasypus_novemcinctus 1.0003 4016
## veg_height-Lynx_rufus 0.9998 2371
## veg_height-Didelphis_virginiana 1.0003 2953
## veg_height-Sylvilagus_floridanus 1.0044 1822
## veg_height-Sciurus_carolinensis 1.0024 2694
## veg_height-Vulpes_vulpes 1.0053 1865
## veg_height-Sus_scrofa 1.0047 3046
## week-Odocoileus_virginianus 1.0018 5118
## week-Canis_latrans 1.0019 4508
## week-Sciurus_niger 1.0065 1969
## week-Procyon_lotor 1.0006 4360
## week-Dasypus_novemcinctus 1.0058 4698
## week-Lynx_rufus 1.0022 3031
## week-Didelphis_virginiana 1.0050 3661
## week-Sylvilagus_floridanus 1.0015 2932
## week-Sciurus_carolinensis 1.0011 4551
## week-Vulpes_vulpes 1.0004 1981
## week-Sus_scrofa 1.0053 4186
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0338
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1697 0.6122 -1.3528 -0.1830 1.0665 1.0081 2633
## Veg_shannon_index 0.3595 0.2626 -0.1495 0.3529 0.8955 1.0042 1890
## Avg_Cogongrass_Cover 0.3239 0.2710 -0.1981 0.3239 0.8596 1.0047 1706
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7902 3.8985 0.7485 2.8338 12.1590 1.0226 1477
## Veg_shannon_index 0.2977 0.3542 0.0377 0.1896 1.1827 1.0162 2466
## Avg_Cogongrass_Cover 0.3084 0.4371 0.0376 0.1895 1.2388 1.0585 2131
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.797 0.8417 0.0709 0.5356 3.1073 1.0029 443
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6045 0.4332 -3.4695 -2.6106 -1.7373 1.0041 4157
## shrub_cover 0.2054 0.2413 -0.2580 0.2029 0.6964 1.0017 3371
## veg_height -0.0078 0.1567 -0.3257 -0.0068 0.2986 1.0060 3347
## week -0.0361 0.1176 -0.2873 -0.0312 0.1846 1.0086 2650
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0548 1.2299 0.7000 1.7524 5.2397 1.0033 2016
## shrub_cover 0.4506 0.3638 0.0931 0.3504 1.3778 1.0046 2408
## veg_height 0.1883 0.1265 0.0541 0.1568 0.5240 1.0032 3369
## week 0.0976 0.0780 0.0248 0.0765 0.2962 1.0146 2854
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5751 1.4516 1.3318 3.3998
## (Intercept)-Canis_latrans 0.3624 0.6699 -0.9880 0.3627
## (Intercept)-Sciurus_niger -0.1877 1.3107 -2.2561 -0.3823
## (Intercept)-Procyon_lotor 0.5480 0.6361 -0.7868 0.5581
## (Intercept)-Dasypus_novemcinctus -0.6790 0.6006 -1.9095 -0.6656
## (Intercept)-Lynx_rufus 0.1767 1.0395 -1.5835 0.0880
## (Intercept)-Didelphis_virginiana -1.3339 0.6783 -2.7225 -1.3167
## (Intercept)-Sylvilagus_floridanus -0.3270 0.7354 -1.7083 -0.3614
## (Intercept)-Sciurus_carolinensis -1.3504 0.6748 -2.7539 -1.3227
## (Intercept)-Vulpes_vulpes -0.9639 1.3394 -3.1180 -1.1341
## (Intercept)-Sus_scrofa -1.9707 0.8879 -3.8260 -1.9453
## Veg_shannon_index-Odocoileus_virginianus 0.3036 0.4959 -0.7563 0.3099
## Veg_shannon_index-Canis_latrans 0.6390 0.4017 -0.0879 0.6200
## Veg_shannon_index-Sciurus_niger 0.3930 0.5464 -0.6367 0.3706
## Veg_shannon_index-Procyon_lotor 0.4492 0.3733 -0.2590 0.4340
## Veg_shannon_index-Dasypus_novemcinctus 0.1927 0.3449 -0.4919 0.1969
## Veg_shannon_index-Lynx_rufus 0.2152 0.5064 -0.8533 0.2317
## Veg_shannon_index-Didelphis_virginiana 0.4983 0.3982 -0.2319 0.4801
## Veg_shannon_index-Sylvilagus_floridanus 0.4509 0.4264 -0.3187 0.4303
## Veg_shannon_index-Sciurus_carolinensis -0.0150 0.4109 -0.9072 0.0176
## Veg_shannon_index-Vulpes_vulpes 0.1223 0.4877 -0.8907 0.1424
## Veg_shannon_index-Sus_scrofa 0.7197 0.5362 -0.1651 0.6634
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3099 0.5228 -0.7192 0.3110
## Avg_Cogongrass_Cover-Canis_latrans 0.6070 0.4214 -0.1190 0.5607
## Avg_Cogongrass_Cover-Sciurus_niger 0.0056 0.6155 -1.3490 0.0519
## Avg_Cogongrass_Cover-Procyon_lotor 0.3824 0.3811 -0.3256 0.3700
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4472 0.3397 -0.1635 0.4355
## Avg_Cogongrass_Cover-Lynx_rufus 0.5627 0.4446 -0.1953 0.5266
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4569 0.3808 -0.2884 0.4509
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0697 0.4563 -1.0581 -0.0391
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4210 0.3639 -0.2822 0.4181
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4242 0.4965 -0.5029 0.4038
## Avg_Cogongrass_Cover-Sus_scrofa 0.0494 0.5505 -1.1758 0.1013
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9912 1.0001 1173
## (Intercept)-Canis_latrans 1.7388 1.0013 2589
## (Intercept)-Sciurus_niger 3.0048 1.0343 476
## (Intercept)-Procyon_lotor 1.7762 1.0023 2087
## (Intercept)-Dasypus_novemcinctus 0.5057 1.0004 2755
## (Intercept)-Lynx_rufus 2.4349 1.0152 805
## (Intercept)-Didelphis_virginiana -0.0097 1.0019 2777
## (Intercept)-Sylvilagus_floridanus 1.2248 1.0024 1858
## (Intercept)-Sciurus_carolinensis -0.0723 0.9999 2865
## (Intercept)-Vulpes_vulpes 2.0800 1.0215 464
## (Intercept)-Sus_scrofa -0.3198 1.0008 2233
## Veg_shannon_index-Odocoileus_virginianus 1.2943 1.0009 3596
## Veg_shannon_index-Canis_latrans 1.4939 1.0014 3236
## Veg_shannon_index-Sciurus_niger 1.5440 1.0073 2382
## Veg_shannon_index-Procyon_lotor 1.2389 1.0065 2518
## Veg_shannon_index-Dasypus_novemcinctus 0.8650 1.0049 4086
## Veg_shannon_index-Lynx_rufus 1.1577 1.0005 2568
## Veg_shannon_index-Didelphis_virginiana 1.3510 1.0035 3399
## Veg_shannon_index-Sylvilagus_floridanus 1.3683 1.0004 3330
## Veg_shannon_index-Sciurus_carolinensis 0.7200 1.0008 3360
## Veg_shannon_index-Vulpes_vulpes 1.0469 1.0032 1963
## Veg_shannon_index-Sus_scrofa 1.9545 1.0001 2676
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3740 1.0021 3092
## Avg_Cogongrass_Cover-Canis_latrans 1.5816 1.0006 3018
## Avg_Cogongrass_Cover-Sciurus_niger 1.1039 1.0033 1551
## Avg_Cogongrass_Cover-Procyon_lotor 1.1747 1.0045 3699
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1723 1.0038 3331
## Avg_Cogongrass_Cover-Lynx_rufus 1.5336 1.0023 2981
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2187 1.0029 3677
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7482 1.0051 2389
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1357 1.0007 3895
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4512 1.0027 2444
## Avg_Cogongrass_Cover-Sus_scrofa 0.9709 1.0093 2327
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0601 -0.1116 0.0050 0.1234
## (Intercept)-Canis_latrans -2.7415 0.1898 -3.1394 -2.7328 -2.3842
## (Intercept)-Sciurus_niger -4.2552 0.6579 -5.4904 -4.2669 -2.9487
## (Intercept)-Procyon_lotor -2.3047 0.1499 -2.6105 -2.2974 -2.0240
## (Intercept)-Dasypus_novemcinctus -1.7279 0.1580 -2.0463 -1.7294 -1.4281
## (Intercept)-Lynx_rufus -3.7037 0.3705 -4.4403 -3.6967 -2.9884
## (Intercept)-Didelphis_virginiana -2.5380 0.2875 -3.1355 -2.5234 -2.0112
## (Intercept)-Sylvilagus_floridanus -3.2356 0.3118 -3.8869 -3.2152 -2.6756
## (Intercept)-Sciurus_carolinensis -2.5958 0.3129 -3.2653 -2.5777 -2.0198
## (Intercept)-Vulpes_vulpes -4.2448 0.7535 -5.7318 -4.2107 -2.8816
## (Intercept)-Sus_scrofa -3.2248 0.5693 -4.3438 -3.2232 -2.1276
## shrub_cover-Odocoileus_virginianus -0.0520 0.0653 -0.1759 -0.0512 0.0783
## shrub_cover-Canis_latrans -0.2683 0.2146 -0.6845 -0.2653 0.1463
## shrub_cover-Sciurus_niger -0.3694 0.4593 -1.3146 -0.3627 0.5445
## shrub_cover-Procyon_lotor 0.2278 0.1733 -0.1323 0.2347 0.5576
## shrub_cover-Dasypus_novemcinctus 0.7896 0.2929 0.2454 0.7787 1.3813
## shrub_cover-Lynx_rufus -0.2541 0.3461 -0.9467 -0.2461 0.4198
## shrub_cover-Didelphis_virginiana 0.8652 0.3588 0.2357 0.8479 1.6165
## shrub_cover-Sylvilagus_floridanus 0.2193 0.3844 -0.4825 0.2010 1.0293
## shrub_cover-Sciurus_carolinensis 0.7472 0.3874 0.0175 0.7362 1.5404
## shrub_cover-Vulpes_vulpes -0.0730 0.5134 -1.1400 -0.0621 0.9337
## shrub_cover-Sus_scrofa 0.4522 0.6944 -0.9266 0.4376 1.8916
## veg_height-Odocoileus_virginianus -0.2978 0.0664 -0.4289 -0.2982 -0.1682
## veg_height-Canis_latrans -0.5790 0.1812 -0.9394 -0.5769 -0.2275
## veg_height-Sciurus_niger -0.0499 0.3826 -0.8087 -0.0624 0.7297
## veg_height-Procyon_lotor 0.3285 0.1251 0.0823 0.3286 0.5734
## veg_height-Dasypus_novemcinctus 0.2247 0.1301 -0.0231 0.2226 0.4829
## veg_height-Lynx_rufus -0.0046 0.2376 -0.4917 -0.0001 0.4565
## veg_height-Didelphis_virginiana 0.3920 0.2367 -0.0567 0.3821 0.8842
## veg_height-Sylvilagus_floridanus 0.1273 0.2448 -0.3505 0.1246 0.6025
## veg_height-Sciurus_carolinensis 0.0487 0.2053 -0.3449 0.0442 0.4732
## veg_height-Vulpes_vulpes -0.1444 0.3147 -0.8214 -0.1241 0.4303
## veg_height-Sus_scrofa -0.1317 0.3310 -0.8049 -0.1237 0.4925
## week-Odocoileus_virginianus 0.2117 0.0606 0.0954 0.2116 0.3338
## week-Canis_latrans 0.0752 0.1316 -0.1878 0.0760 0.3264
## week-Sciurus_niger -0.2772 0.2915 -0.9246 -0.2415 0.1957
## week-Procyon_lotor -0.0459 0.1175 -0.2876 -0.0426 0.1728
## week-Dasypus_novemcinctus -0.1542 0.1368 -0.4346 -0.1496 0.0993
## week-Lynx_rufus -0.0270 0.1922 -0.4417 -0.0186 0.3224
## week-Didelphis_virginiana -0.1981 0.2150 -0.6583 -0.1820 0.1731
## week-Sylvilagus_floridanus -0.1324 0.1983 -0.5464 -0.1244 0.2319
## week-Sciurus_carolinensis 0.1383 0.1766 -0.2124 0.1400 0.4856
## week-Vulpes_vulpes -0.0971 0.2669 -0.6693 -0.0806 0.3928
## week-Sus_scrofa 0.1009 0.2298 -0.3576 0.1029 0.5518
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0052 2211
## (Intercept)-Sciurus_niger 1.0339 419
## (Intercept)-Procyon_lotor 1.0060 3374
## (Intercept)-Dasypus_novemcinctus 1.0032 4361
## (Intercept)-Lynx_rufus 1.0025 837
## (Intercept)-Didelphis_virginiana 1.0064 2913
## (Intercept)-Sylvilagus_floridanus 1.0044 1424
## (Intercept)-Sciurus_carolinensis 1.0002 2803
## (Intercept)-Vulpes_vulpes 1.0236 449
## (Intercept)-Sus_scrofa 1.0011 2015
## shrub_cover-Odocoileus_virginianus 1.0052 5033
## shrub_cover-Canis_latrans 1.0024 2868
## shrub_cover-Sciurus_niger 1.0078 875
## shrub_cover-Procyon_lotor 1.0029 3525
## shrub_cover-Dasypus_novemcinctus 1.0045 3552
## shrub_cover-Lynx_rufus 1.0040 1342
## shrub_cover-Didelphis_virginiana 1.0026 2151
## shrub_cover-Sylvilagus_floridanus 1.0015 1864
## shrub_cover-Sciurus_carolinensis 1.0001 2512
## shrub_cover-Vulpes_vulpes 1.0020 2000
## shrub_cover-Sus_scrofa 1.0036 2815
## veg_height-Odocoileus_virginianus 1.0017 5250
## veg_height-Canis_latrans 1.0045 2629
## veg_height-Sciurus_niger 1.0021 1961
## veg_height-Procyon_lotor 0.9999 4236
## veg_height-Dasypus_novemcinctus 1.0006 4496
## veg_height-Lynx_rufus 1.0030 2256
## veg_height-Didelphis_virginiana 1.0013 2946
## veg_height-Sylvilagus_floridanus 1.0019 2438
## veg_height-Sciurus_carolinensis 1.0014 3729
## veg_height-Vulpes_vulpes 1.0013 1744
## veg_height-Sus_scrofa 1.0067 3441
## week-Odocoileus_virginianus 1.0014 5167
## week-Canis_latrans 1.0020 4341
## week-Sciurus_niger 1.0165 1781
## week-Procyon_lotor 1.0035 3989
## week-Dasypus_novemcinctus 1.0080 4249
## week-Lynx_rufus 1.0021 2886
## week-Didelphis_virginiana 1.0049 3467
## week-Sylvilagus_floridanus 1.0009 2769
## week-Sciurus_carolinensis 1.0001 5009
## week-Vulpes_vulpes 1.0064 2467
## week-Sus_scrofa 1.0012 4229
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8728
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8848 0.6271 -2.0833 -0.9070 0.4229 1.0042 2335
## Avg_Cogongrass_Cover -0.7890 0.3985 -1.6076 -0.7781 -0.0225 1.0014 1081
## I(Avg_Cogongrass_Cover^2) 0.8783 0.3442 0.2526 0.8598 1.5990 1.0036 1139
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7403 3.4621 0.7374 2.8557 11.6377 1.0355 1647
## Avg_Cogongrass_Cover 0.4386 0.5991 0.0424 0.2563 1.8904 1.0044 1418
## I(Avg_Cogongrass_Cover^2) 0.4812 1.0164 0.0369 0.2153 2.6301 1.0602 584
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4798 0.5242 0.041 0.3186 1.8504 1.0128 578
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5902 0.4169 -3.3788 -2.6004 -1.7344 1.0046 4075
## shrub_cover 0.2271 0.2429 -0.2667 0.2267 0.7096 1.0002 3432
## veg_height 0.0199 0.1570 -0.2914 0.0185 0.3354 1.0041 3279
## week -0.0403 0.1205 -0.2998 -0.0366 0.1792 1.0020 2717
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9458 1.2238 0.6722 1.6582 4.9128 1.0017 2481
## shrub_cover 0.4461 0.3830 0.0849 0.3442 1.4596 1.0008 2342
## veg_height 0.1887 0.1358 0.0528 0.1537 0.5335 1.0017 3483
## week 0.0977 0.0789 0.0253 0.0756 0.3023 1.0053 3068
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8405 1.4241 0.5403 2.6572
## (Intercept)-Canis_latrans -0.4470 0.6847 -1.8477 -0.4391
## (Intercept)-Sciurus_niger -0.8109 1.2381 -2.7939 -0.9458
## (Intercept)-Procyon_lotor -0.1206 0.6439 -1.3881 -0.1181
## (Intercept)-Dasypus_novemcinctus -1.3651 0.6319 -2.6516 -1.3484
## (Intercept)-Lynx_rufus -0.9921 0.9883 -2.7050 -1.0551
## (Intercept)-Didelphis_virginiana -1.8697 0.7136 -3.3331 -1.8480
## (Intercept)-Sylvilagus_floridanus -1.1029 0.7434 -2.5948 -1.1095
## (Intercept)-Sciurus_carolinensis -2.3818 0.7751 -3.9895 -2.3454
## (Intercept)-Vulpes_vulpes -2.1477 1.3095 -4.3743 -2.2262
## (Intercept)-Sus_scrofa -2.4571 0.9195 -4.3843 -2.4297
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7760 0.6711 -2.1334 -0.7644
## Avg_Cogongrass_Cover-Canis_latrans -0.3867 0.5594 -1.3814 -0.4262
## Avg_Cogongrass_Cover-Sciurus_niger -1.0843 0.7526 -2.8379 -1.0158
## Avg_Cogongrass_Cover-Procyon_lotor -0.7075 0.5288 -1.7588 -0.7017
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5747 0.5006 -1.5656 -0.5745
## Avg_Cogongrass_Cover-Lynx_rufus -0.7219 0.6074 -1.9705 -0.7142
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5320 0.5467 -1.5756 -0.5442
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2379 0.6581 -2.7121 -1.1762
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8535 0.5585 -2.0725 -0.8261
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8335 0.6467 -2.1827 -0.8095
## Avg_Cogongrass_Cover-Sus_scrofa -1.0727 0.7069 -2.6654 -1.0124
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1777 0.8354 0.1006 1.0342
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2434 0.7139 0.2588 1.0987
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4630 0.8191 -1.2343 0.4969
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0705 0.6179 0.2398 0.9763
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7645 0.3727 0.0672 0.7534
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2003 0.5383 0.3689 1.1284
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6195 0.4132 -0.1775 0.6128
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7853 0.4722 -0.0289 0.7520
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0237 0.4191 0.2994 0.9882
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0135 0.5736 0.1550 0.9414
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5110 0.6159 -0.9275 0.5643
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.1810 1.0074 1222
## (Intercept)-Canis_latrans 0.9078 1.0091 2976
## (Intercept)-Sciurus_niger 2.0800 1.0042 611
## (Intercept)-Procyon_lotor 1.1441 1.0011 3160
## (Intercept)-Dasypus_novemcinctus -0.1774 1.0047 3051
## (Intercept)-Lynx_rufus 1.1003 1.0165 1132
## (Intercept)-Didelphis_virginiana -0.5681 0.9998 2937
## (Intercept)-Sylvilagus_floridanus 0.3478 1.0043 2235
## (Intercept)-Sciurus_carolinensis -0.9453 1.0037 2425
## (Intercept)-Vulpes_vulpes 0.6828 1.0347 564
## (Intercept)-Sus_scrofa -0.7308 1.0005 1981
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5362 0.9999 2009
## Avg_Cogongrass_Cover-Canis_latrans 0.8198 1.0050 2240
## Avg_Cogongrass_Cover-Sciurus_niger 0.1877 1.0024 1143
## Avg_Cogongrass_Cover-Procyon_lotor 0.3352 1.0004 2696
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4031 1.0020 2273
## Avg_Cogongrass_Cover-Lynx_rufus 0.4499 1.0005 1897
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6207 1.0061 1961
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1095 1.0013 1454
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1669 1.0002 1892
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4112 1.0013 1776
## Avg_Cogongrass_Cover-Sus_scrofa 0.1246 1.0021 1509
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2303 1.0303 768
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0719 1.0034 1077
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.7760 1.0233 629
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5390 1.0251 814
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5358 1.0046 2303
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4579 1.0091 1566
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4808 1.0014 2307
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8024 1.0109 1586
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9440 1.0049 1929
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3145 1.0065 1055
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5689 1.0020 1250
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0604 -0.1160 0.0067 0.1223
## (Intercept)-Canis_latrans -2.7507 0.1898 -3.1346 -2.7465 -2.3958
## (Intercept)-Sciurus_niger -4.2016 0.6461 -5.4263 -4.2048 -2.9410
## (Intercept)-Procyon_lotor -2.3117 0.1503 -2.6213 -2.3058 -2.0317
## (Intercept)-Dasypus_novemcinctus -1.7256 0.1582 -2.0425 -1.7224 -1.4176
## (Intercept)-Lynx_rufus -3.6073 0.3680 -4.3717 -3.5977 -2.9269
## (Intercept)-Didelphis_virginiana -2.5683 0.2915 -3.1748 -2.5541 -2.0327
## (Intercept)-Sylvilagus_floridanus -3.2165 0.2989 -3.8414 -3.2077 -2.6658
## (Intercept)-Sciurus_carolinensis -2.5994 0.3175 -3.2731 -2.5862 -2.0395
## (Intercept)-Vulpes_vulpes -4.1104 0.7332 -5.6212 -4.0553 -2.8434
## (Intercept)-Sus_scrofa -3.2271 0.5745 -4.3449 -3.2330 -2.0982
## shrub_cover-Odocoileus_virginianus -0.0529 0.0656 -0.1810 -0.0532 0.0773
## shrub_cover-Canis_latrans -0.2366 0.2173 -0.6668 -0.2327 0.1860
## shrub_cover-Sciurus_niger -0.3186 0.4430 -1.2111 -0.2959 0.5133
## shrub_cover-Procyon_lotor 0.2304 0.1697 -0.1119 0.2304 0.5535
## shrub_cover-Dasypus_novemcinctus 0.7771 0.2841 0.2493 0.7727 1.3351
## shrub_cover-Lynx_rufus -0.2183 0.3642 -0.9494 -0.2128 0.4740
## shrub_cover-Didelphis_virginiana 0.9028 0.3704 0.2340 0.8779 1.6865
## shrub_cover-Sylvilagus_floridanus 0.2241 0.3966 -0.5099 0.2094 1.0427
## shrub_cover-Sciurus_carolinensis 0.7376 0.3874 0.0317 0.7245 1.5358
## shrub_cover-Vulpes_vulpes -0.0169 0.5315 -1.1060 -0.0068 1.0077
## shrub_cover-Sus_scrofa 0.4690 0.6949 -0.8938 0.4575 1.9003
## veg_height-Odocoileus_virginianus -0.2966 0.0651 -0.4211 -0.2967 -0.1695
## veg_height-Canis_latrans -0.5653 0.1828 -0.9479 -0.5601 -0.2163
## veg_height-Sciurus_niger 0.0219 0.3948 -0.7203 0.0003 0.8611
## veg_height-Procyon_lotor 0.3407 0.1221 0.0971 0.3419 0.5837
## veg_height-Dasypus_novemcinctus 0.2291 0.1329 -0.0253 0.2302 0.4979
## veg_height-Lynx_rufus 0.0800 0.2418 -0.4203 0.0853 0.5389
## veg_height-Didelphis_virginiana 0.3872 0.2454 -0.0725 0.3797 0.8974
## veg_height-Sylvilagus_floridanus 0.1481 0.2477 -0.3313 0.1461 0.6435
## veg_height-Sciurus_carolinensis 0.0626 0.2088 -0.3272 0.0599 0.4938
## veg_height-Vulpes_vulpes -0.1152 0.3129 -0.7485 -0.1081 0.4906
## veg_height-Sus_scrofa -0.1119 0.3261 -0.7750 -0.1028 0.5283
## week-Odocoileus_virginianus 0.2100 0.0622 0.0914 0.2095 0.3328
## week-Canis_latrans 0.0721 0.1294 -0.1910 0.0743 0.3165
## week-Sciurus_niger -0.2838 0.2954 -0.9612 -0.2495 0.1951
## week-Procyon_lotor -0.0463 0.1151 -0.2799 -0.0418 0.1608
## week-Dasypus_novemcinctus -0.1589 0.1367 -0.4452 -0.1531 0.0921
## week-Lynx_rufus -0.0313 0.1909 -0.4294 -0.0211 0.3050
## week-Didelphis_virginiana -0.1972 0.2144 -0.6656 -0.1811 0.1797
## week-Sylvilagus_floridanus -0.1416 0.2075 -0.5863 -0.1297 0.2383
## week-Sciurus_carolinensis 0.1418 0.1804 -0.2206 0.1468 0.4850
## week-Vulpes_vulpes -0.0934 0.2665 -0.6546 -0.0735 0.3844
## week-Sus_scrofa 0.1008 0.2311 -0.3620 0.1030 0.5598
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5728
## (Intercept)-Canis_latrans 1.0094 2447
## (Intercept)-Sciurus_niger 1.0136 421
## (Intercept)-Procyon_lotor 1.0021 2979
## (Intercept)-Dasypus_novemcinctus 1.0003 4396
## (Intercept)-Lynx_rufus 1.0083 916
## (Intercept)-Didelphis_virginiana 1.0035 2462
## (Intercept)-Sylvilagus_floridanus 1.0067 1630
## (Intercept)-Sciurus_carolinensis 1.0010 2606
## (Intercept)-Vulpes_vulpes 1.0123 490
## (Intercept)-Sus_scrofa 1.0018 1709
## shrub_cover-Odocoileus_virginianus 1.0032 5250
## shrub_cover-Canis_latrans 1.0001 2753
## shrub_cover-Sciurus_niger 1.0003 1316
## shrub_cover-Procyon_lotor 1.0018 3468
## shrub_cover-Dasypus_novemcinctus 1.0000 3826
## shrub_cover-Lynx_rufus 1.0017 1316
## shrub_cover-Didelphis_virginiana 1.0013 1935
## shrub_cover-Sylvilagus_floridanus 1.0003 1781
## shrub_cover-Sciurus_carolinensis 1.0020 2465
## shrub_cover-Vulpes_vulpes 1.0020 1968
## shrub_cover-Sus_scrofa 1.0014 2173
## veg_height-Odocoileus_virginianus 1.0065 5250
## veg_height-Canis_latrans 1.0080 2215
## veg_height-Sciurus_niger 1.0009 1552
## veg_height-Procyon_lotor 1.0005 4099
## veg_height-Dasypus_novemcinctus 1.0004 4853
## veg_height-Lynx_rufus 1.0004 2583
## veg_height-Didelphis_virginiana 0.9998 3159
## veg_height-Sylvilagus_floridanus 1.0038 2325
## veg_height-Sciurus_carolinensis 1.0007 3528
## veg_height-Vulpes_vulpes 1.0019 2070
## veg_height-Sus_scrofa 1.0032 2736
## week-Odocoileus_virginianus 0.9999 4956
## week-Canis_latrans 1.0002 4347
## week-Sciurus_niger 1.0006 1948
## week-Procyon_lotor 1.0015 4136
## week-Dasypus_novemcinctus 1.0041 5075
## week-Lynx_rufus 1.0022 3152
## week-Didelphis_virginiana 1.0004 3866
## week-Sylvilagus_floridanus 1.0037 2636
## week-Sciurus_carolinensis 1.0010 4671
## week-Vulpes_vulpes 1.0001 3282
## week-Sus_scrofa 1.0011 4459
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1298
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8601 1.1588 -3.0608 -0.8989 1.5053 1.0088 1643
## Cogon_Patch_Size -0.2683 0.7757 -1.9499 -0.2422 1.1750 1.0148 1004
## Veg_shannon_index 0.9507 0.4809 0.0361 0.9401 1.9304 1.0103 905
## total_shrub_cover -0.5108 0.5189 -1.5503 -0.5054 0.5041 1.0047 822
## Avg_Cogongrass_Cover -0.1384 0.9945 -2.1326 -0.1240 1.7604 1.0410 482
## Tree_Density -2.0346 0.8729 -3.8081 -2.0134 -0.3038 1.0048 648
## Avg_Canopy_Cover 2.0004 0.7215 0.6321 1.9595 3.5276 1.0094 707
## I(Avg_Cogongrass_Cover^2) 1.6596 0.6312 0.5287 1.6273 3.0486 1.0412 436
## avg_veg_height -0.1294 0.5232 -1.1660 -0.1280 0.8959 1.0114 758
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.2568 21.0451 4.2938 17.8866 79.9849 1.0057 441
## Cogon_Patch_Size 4.1778 6.0302 0.1606 2.2979 19.2492 1.0054 556
## Veg_shannon_index 0.9286 1.3882 0.0509 0.4677 4.6553 1.0483 819
## total_shrub_cover 0.9430 1.3713 0.0575 0.4991 4.7492 1.0212 842
## Avg_Cogongrass_Cover 1.5978 3.1362 0.0518 0.5985 9.2524 1.0056 649
## Tree_Density 4.9571 9.2315 0.0892 1.8482 29.1431 1.0132 330
## Avg_Canopy_Cover 3.9372 6.4491 0.1850 2.1701 17.7820 1.0906 261
## I(Avg_Cogongrass_Cover^2) 1.0786 2.2483 0.0474 0.4201 6.2734 1.0315 583
## avg_veg_height 0.5598 0.8809 0.0446 0.2977 2.6550 1.0086 936
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6292 2.3071 0.0545 0.8075 8.5026 1.0975 141
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6642 0.4580 -3.5262 -2.6837 -1.6712 1.0015 4310
## shrub_cover 0.3113 0.2598 -0.1904 0.3080 0.8408 0.9999 2191
## veg_height 0.0242 0.1558 -0.2914 0.0259 0.3316 1.0006 2778
## week -0.0396 0.1193 -0.2936 -0.0329 0.1829 1.0029 2827
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3395 1.4295 0.8245 1.9842 6.0377 1.0028 2889
## shrub_cover 0.5107 0.3963 0.1065 0.4039 1.5630 1.0042 1669
## veg_height 0.1906 0.1347 0.0552 0.1559 0.5243 1.0037 3673
## week 0.0972 0.0750 0.0252 0.0756 0.3044 1.0049 2714
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.1885 3.8610 2.6964
## (Intercept)-Canis_latrans -0.8670 1.2467 -3.3152
## (Intercept)-Sciurus_niger 1.0737 2.7241 -3.2065
## (Intercept)-Procyon_lotor -0.2875 1.1380 -2.6540
## (Intercept)-Dasypus_novemcinctus -2.7235 1.2728 -5.5810
## (Intercept)-Lynx_rufus 0.9226 3.0628 -3.4772
## (Intercept)-Didelphis_virginiana -4.2413 1.5465 -7.6403
## (Intercept)-Sylvilagus_floridanus -2.5157 1.5721 -5.9728
## (Intercept)-Sciurus_carolinensis -4.9522 1.7235 -8.8085
## (Intercept)-Vulpes_vulpes -4.0581 3.0005 -9.0601
## (Intercept)-Sus_scrofa -5.7871 2.2777 -10.7345
## Cogon_Patch_Size-Odocoileus_virginianus -0.0466 1.5916 -3.0248
## Cogon_Patch_Size-Canis_latrans 1.7385 1.6032 -0.4113
## Cogon_Patch_Size-Sciurus_niger -1.1315 2.1700 -6.0610
## Cogon_Patch_Size-Procyon_lotor -0.5931 0.8221 -2.3073
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0862 0.8680 -1.7896
## Cogon_Patch_Size-Lynx_rufus -0.3780 1.7458 -3.6569
## Cogon_Patch_Size-Didelphis_virginiana 1.6749 1.1202 -0.2101
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6533 1.8927 -6.3714
## Cogon_Patch_Size-Sciurus_carolinensis -1.2811 1.5828 -5.1107
## Cogon_Patch_Size-Vulpes_vulpes -0.8003 1.8781 -5.1471
## Cogon_Patch_Size-Sus_scrofa -1.0117 1.6861 -5.2633
## Veg_shannon_index-Odocoileus_virginianus 0.7841 0.9350 -1.2023
## Veg_shannon_index-Canis_latrans 1.3376 0.7356 0.1094
## Veg_shannon_index-Sciurus_niger 1.0756 1.0509 -0.8990
## Veg_shannon_index-Procyon_lotor 1.1966 0.6514 0.0512
## Veg_shannon_index-Dasypus_novemcinctus 0.5976 0.6056 -0.6006
## Veg_shannon_index-Lynx_rufus 1.0619 1.0085 -0.9458
## Veg_shannon_index-Didelphis_virginiana 1.1493 0.7648 -0.1976
## Veg_shannon_index-Sylvilagus_floridanus 1.0338 0.7645 -0.3755
## Veg_shannon_index-Sciurus_carolinensis 0.2969 0.8803 -1.7413
## Veg_shannon_index-Vulpes_vulpes 0.6134 0.9327 -1.5277
## Veg_shannon_index-Sus_scrofa 1.6101 1.0400 0.0986
## total_shrub_cover-Odocoileus_virginianus -0.2909 0.9307 -2.0580
## total_shrub_cover-Canis_latrans 0.1209 0.7697 -1.2106
## total_shrub_cover-Sciurus_niger -0.6787 1.0244 -2.9379
## total_shrub_cover-Procyon_lotor -1.1158 0.6895 -2.6831
## total_shrub_cover-Dasypus_novemcinctus -0.2314 0.6846 -1.6736
## total_shrub_cover-Lynx_rufus -0.7797 1.1027 -3.2054
## total_shrub_cover-Didelphis_virginiana -0.7949 0.8115 -2.5771
## total_shrub_cover-Sylvilagus_floridanus -0.5979 0.8758 -2.4874
## total_shrub_cover-Sciurus_carolinensis -0.4406 0.8379 -2.2369
## total_shrub_cover-Vulpes_vulpes -0.7324 0.9893 -2.9650
## total_shrub_cover-Sus_scrofa -0.2916 0.9464 -2.1215
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1823 1.4177 -3.0721
## Avg_Cogongrass_Cover-Canis_latrans 0.0739 1.3060 -2.4412
## Avg_Cogongrass_Cover-Sciurus_niger -0.6370 1.7645 -4.7935
## Avg_Cogongrass_Cover-Procyon_lotor 0.0262 1.2137 -2.3209
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6109 1.3947 -1.8538
## Avg_Cogongrass_Cover-Lynx_rufus -0.1201 1.4195 -2.9024
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1298 1.3069 -2.9242
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8016 1.4294 -3.8671
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1236 1.2830 -2.6933
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0306 1.3976 -2.6536
## Avg_Cogongrass_Cover-Sus_scrofa -0.5658 1.5367 -4.0602
## Tree_Density-Odocoileus_virginianus -0.8982 1.5655 -3.3499
## Tree_Density-Canis_latrans -3.0695 1.5779 -6.9956
## Tree_Density-Sciurus_niger -1.8878 2.0791 -5.6355
## Tree_Density-Procyon_lotor -2.0109 1.0421 -4.2007
## Tree_Density-Dasypus_novemcinctus -4.2752 2.4041 -10.5744
## Tree_Density-Lynx_rufus -0.5893 2.1766 -3.7115
## Tree_Density-Didelphis_virginiana -2.4342 1.4260 -5.8114
## Tree_Density-Sylvilagus_floridanus -2.7316 1.7015 -7.0352
## Tree_Density-Sciurus_carolinensis -2.8070 1.6742 -6.9050
## Tree_Density-Vulpes_vulpes -2.1667 1.8591 -6.2177
## Tree_Density-Sus_scrofa -2.7446 1.8870 -7.4218
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2362 1.6140 -2.1602
## Avg_Canopy_Cover-Canis_latrans 0.1219 0.7223 -1.3492
## Avg_Canopy_Cover-Sciurus_niger 2.4669 2.0823 -1.5582
## Avg_Canopy_Cover-Procyon_lotor 1.6913 0.8401 0.2026
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2355 0.9284 0.7705
## Avg_Canopy_Cover-Lynx_rufus 1.8379 1.7061 -1.3477
## Avg_Canopy_Cover-Didelphis_virginiana 3.2964 1.4753 1.3004
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9697 1.9388 1.2327
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0574 1.5961 1.0292
## Avg_Canopy_Cover-Vulpes_vulpes 2.7630 1.6891 0.4778
## Avg_Canopy_Cover-Sus_scrofa 2.2648 1.1193 0.5303
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9892 1.2717 0.1327
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1092 1.0534 0.6100
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2851 1.2504 -1.5361
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9609 0.8767 0.5606
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6040 0.7771 0.2562
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2039 1.0947 0.6036
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3102 0.7742 -0.1467
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3965 0.8489 -0.1326
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8727 0.8265 0.5196
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0040 0.9494 0.4984
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2242 1.0971 -1.3731
## avg_veg_height-Odocoileus_virginianus -0.1308 0.8389 -1.8302
## avg_veg_height-Canis_latrans -0.2417 0.6548 -1.5459
## avg_veg_height-Sciurus_niger -0.2822 0.8980 -2.2908
## avg_veg_height-Procyon_lotor 0.0604 0.6797 -1.2837
## avg_veg_height-Dasypus_novemcinctus 0.1997 0.6607 -1.0771
## avg_veg_height-Lynx_rufus -0.3138 0.8817 -2.2161
## avg_veg_height-Didelphis_virginiana -0.3097 0.7670 -1.9891
## avg_veg_height-Sylvilagus_floridanus -0.2530 0.7551 -1.8648
## avg_veg_height-Sciurus_carolinensis 0.1628 0.7554 -1.2588
## avg_veg_height-Vulpes_vulpes -0.2474 0.8407 -1.9785
## avg_veg_height-Sus_scrofa -0.1811 0.7884 -1.8714
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4392 17.6696 1.0006 342
## (Intercept)-Canis_latrans -0.8645 1.6725 1.0092 1705
## (Intercept)-Sciurus_niger 0.6591 7.6173 1.0152 327
## (Intercept)-Procyon_lotor -0.2686 1.9765 1.0077 1470
## (Intercept)-Dasypus_novemcinctus -2.5961 -0.6143 1.0175 546
## (Intercept)-Lynx_rufus 0.3702 8.6395 1.0276 249
## (Intercept)-Didelphis_virginiana -4.0938 -1.5936 1.0320 594
## (Intercept)-Sylvilagus_floridanus -2.3990 0.3218 1.0197 921
## (Intercept)-Sciurus_carolinensis -4.7987 -1.9873 1.0231 733
## (Intercept)-Vulpes_vulpes -4.2495 2.4986 1.0769 213
## (Intercept)-Sus_scrofa -5.6390 -1.7161 1.0447 408
## Cogon_Patch_Size-Odocoileus_virginianus -0.1087 3.5706 1.0051 1541
## Cogon_Patch_Size-Canis_latrans 1.4140 5.8533 1.0051 802
## Cogon_Patch_Size-Sciurus_niger -0.8583 2.4832 1.0040 490
## Cogon_Patch_Size-Procyon_lotor -0.5623 0.9186 1.0023 1024
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0987 1.7124 1.0070 1212
## Cogon_Patch_Size-Lynx_rufus -0.4448 3.4287 1.0134 654
## Cogon_Patch_Size-Didelphis_virginiana 1.5616 4.2702 1.0297 569
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2837 0.9247 1.0084 728
## Cogon_Patch_Size-Sciurus_carolinensis -0.9846 0.9548 1.0124 819
## Cogon_Patch_Size-Vulpes_vulpes -0.6455 2.7504 1.0210 672
## Cogon_Patch_Size-Sus_scrofa -0.7235 1.6028 1.0084 1044
## Veg_shannon_index-Odocoileus_virginianus 0.8193 2.5754 1.0031 2281
## Veg_shannon_index-Canis_latrans 1.2654 3.0725 1.0096 1262
## Veg_shannon_index-Sciurus_niger 1.0257 3.4127 1.0138 1175
## Veg_shannon_index-Procyon_lotor 1.1436 2.6294 1.0121 654
## Veg_shannon_index-Dasypus_novemcinctus 0.6093 1.7532 1.0091 2131
## Veg_shannon_index-Lynx_rufus 1.0250 3.1422 1.0140 1454
## Veg_shannon_index-Didelphis_virginiana 1.0949 2.8647 1.0105 1412
## Veg_shannon_index-Sylvilagus_floridanus 0.9816 2.7364 1.0110 1342
## Veg_shannon_index-Sciurus_carolinensis 0.4054 1.7458 1.0137 1695
## Veg_shannon_index-Vulpes_vulpes 0.6859 2.3272 1.0060 1257
## Veg_shannon_index-Sus_scrofa 1.4235 4.1992 1.0413 810
## total_shrub_cover-Odocoileus_virginianus -0.3232 1.6730 1.0049 1395
## total_shrub_cover-Canis_latrans 0.0531 1.9423 1.0052 1373
## total_shrub_cover-Sciurus_niger -0.6309 1.3129 1.0041 1113
## total_shrub_cover-Procyon_lotor -1.0462 0.0310 1.0143 1279
## total_shrub_cover-Dasypus_novemcinctus -0.1983 1.0286 1.0019 1388
## total_shrub_cover-Lynx_rufus -0.7069 1.2979 1.0132 778
## total_shrub_cover-Didelphis_virginiana -0.7189 0.6236 1.0091 1416
## total_shrub_cover-Sylvilagus_floridanus -0.5560 1.0190 1.0094 1047
## total_shrub_cover-Sciurus_carolinensis -0.3968 1.1066 1.0040 1041
## total_shrub_cover-Vulpes_vulpes -0.6509 0.9965 1.0056 1147
## total_shrub_cover-Sus_scrofa -0.3077 1.6752 1.0071 827
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1550 2.5369 1.0308 819
## Avg_Cogongrass_Cover-Canis_latrans 0.0531 2.6873 1.0177 817
## Avg_Cogongrass_Cover-Sciurus_niger -0.4561 2.2170 1.0119 515
## Avg_Cogongrass_Cover-Procyon_lotor 0.0207 2.4602 1.0259 727
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4987 3.8752 1.0119 595
## Avg_Cogongrass_Cover-Lynx_rufus -0.1076 2.5104 1.0133 730
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0794 2.3890 1.0243 656
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6945 1.7206 1.0364 752
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0937 2.3347 1.0317 741
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0130 3.0521 1.0252 780
## Avg_Cogongrass_Cover-Sus_scrofa -0.4398 2.0273 1.0331 701
## Tree_Density-Odocoileus_virginianus -1.1076 2.8531 1.0039 628
## Tree_Density-Canis_latrans -2.8202 -0.8099 1.0035 663
## Tree_Density-Sciurus_niger -1.9936 2.8684 1.0371 472
## Tree_Density-Procyon_lotor -1.9710 -0.0674 1.0007 853
## Tree_Density-Dasypus_novemcinctus -3.6776 -1.4176 1.0055 323
## Tree_Density-Lynx_rufus -0.9804 5.1097 1.0170 325
## Tree_Density-Didelphis_virginiana -2.2813 0.0138 1.0044 888
## Tree_Density-Sylvilagus_floridanus -2.4833 -0.0218 1.0047 884
## Tree_Density-Sciurus_carolinensis -2.5694 0.0535 1.0206 951
## Tree_Density-Vulpes_vulpes -2.1043 1.6417 1.0005 731
## Tree_Density-Sus_scrofa -2.4604 0.3277 1.0010 1027
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2883 4.4422 1.0032 910
## Avg_Canopy_Cover-Canis_latrans 0.1290 1.5244 1.0116 977
## Avg_Canopy_Cover-Sciurus_niger 2.3149 7.0536 1.0065 453
## Avg_Canopy_Cover-Procyon_lotor 1.6371 3.5463 1.0083 797
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0888 4.4805 1.0063 573
## Avg_Canopy_Cover-Lynx_rufus 1.7260 5.5587 1.0067 596
## Avg_Canopy_Cover-Didelphis_virginiana 3.0041 7.1197 1.0321 501
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6344 8.6695 1.0106 352
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7294 7.0948 1.0316 390
## Avg_Canopy_Cover-Vulpes_vulpes 2.4219 7.1324 1.0226 390
## Avg_Canopy_Cover-Sus_scrofa 2.1146 5.0135 1.0008 947
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8111 4.9228 1.0294 677
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9388 4.6120 1.0095 561
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3822 3.5455 1.0100 440
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8559 3.9859 1.0115 687
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5392 3.3803 1.0358 669
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0382 4.8518 1.0076 598
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2852 2.9100 1.0321 655
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3591 3.1812 1.0413 824
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7806 3.7444 1.0135 719
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8699 4.3708 1.0111 569
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2915 3.2528 1.0086 614
## avg_veg_height-Odocoileus_virginianus -0.1313 1.5588 1.0039 1360
## avg_veg_height-Canis_latrans -0.2318 1.0240 1.0013 1190
## avg_veg_height-Sciurus_niger -0.2222 1.3344 1.0070 1159
## avg_veg_height-Procyon_lotor 0.0548 1.4427 1.0067 1099
## avg_veg_height-Dasypus_novemcinctus 0.1711 1.5585 1.0156 1056
## avg_veg_height-Lynx_rufus -0.2872 1.2692 1.0088 1220
## avg_veg_height-Didelphis_virginiana -0.2662 1.1021 1.0025 1229
## avg_veg_height-Sylvilagus_floridanus -0.2195 1.1601 1.0094 1068
## avg_veg_height-Sciurus_carolinensis 0.1280 1.7586 1.0051 1270
## avg_veg_height-Vulpes_vulpes -0.2158 1.3923 1.0027 991
## avg_veg_height-Sus_scrofa -0.1584 1.3405 1.0059 1321
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0067 0.0605 -0.1110 0.0073 0.1264
## (Intercept)-Canis_latrans -2.7193 0.1838 -3.1071 -2.7111 -2.3808
## (Intercept)-Sciurus_niger -4.7659 0.4880 -5.6954 -4.7622 -3.8089
## (Intercept)-Procyon_lotor -2.3080 0.1470 -2.6081 -2.3019 -2.0397
## (Intercept)-Dasypus_novemcinctus -1.7572 0.1646 -2.0880 -1.7510 -1.4484
## (Intercept)-Lynx_rufus -3.9248 0.3682 -4.6515 -3.9362 -3.1930
## (Intercept)-Didelphis_virginiana -2.5727 0.2916 -3.1821 -2.5582 -2.0327
## (Intercept)-Sylvilagus_floridanus -3.2068 0.2731 -3.7663 -3.1950 -2.7060
## (Intercept)-Sciurus_carolinensis -2.6822 0.3225 -3.3523 -2.6696 -2.0774
## (Intercept)-Vulpes_vulpes -4.3313 0.6790 -5.7450 -4.2997 -3.0781
## (Intercept)-Sus_scrofa -3.3607 0.5901 -4.4998 -3.3695 -2.2021
## shrub_cover-Odocoileus_virginianus -0.0499 0.0656 -0.1777 -0.0510 0.0776
## shrub_cover-Canis_latrans -0.2802 0.2267 -0.7255 -0.2831 0.1658
## shrub_cover-Sciurus_niger -0.3270 0.4372 -1.2307 -0.3150 0.4953
## shrub_cover-Procyon_lotor 0.2683 0.1617 -0.0506 0.2699 0.5740
## shrub_cover-Dasypus_novemcinctus 0.8909 0.3118 0.2892 0.8905 1.5010
## shrub_cover-Lynx_rufus -0.2198 0.3522 -0.9324 -0.2171 0.4705
## shrub_cover-Didelphis_virginiana 0.9604 0.3707 0.2917 0.9412 1.7459
## shrub_cover-Sylvilagus_floridanus 0.4755 0.3919 -0.2930 0.4737 1.2375
## shrub_cover-Sciurus_carolinensis 0.8905 0.4122 0.0996 0.8793 1.7193
## shrub_cover-Vulpes_vulpes 0.1420 0.5357 -0.9475 0.1531 1.1767
## shrub_cover-Sus_scrofa 0.7192 0.7414 -0.6839 0.7040 2.2467
## veg_height-Odocoileus_virginianus -0.2960 0.0643 -0.4225 -0.2955 -0.1678
## veg_height-Canis_latrans -0.5397 0.1819 -0.9122 -0.5313 -0.2001
## veg_height-Sciurus_niger -0.0146 0.3269 -0.6638 -0.0231 0.6438
## veg_height-Procyon_lotor 0.3559 0.1230 0.1117 0.3577 0.5951
## veg_height-Dasypus_novemcinctus 0.2471 0.1345 -0.0143 0.2442 0.5154
## veg_height-Lynx_rufus 0.1471 0.2350 -0.3358 0.1496 0.5846
## veg_height-Didelphis_virginiana 0.4229 0.2375 -0.0157 0.4165 0.9074
## veg_height-Sylvilagus_floridanus 0.1446 0.2427 -0.3257 0.1437 0.6381
## veg_height-Sciurus_carolinensis 0.1031 0.2133 -0.3051 0.0983 0.5353
## veg_height-Vulpes_vulpes -0.1559 0.3168 -0.7972 -0.1483 0.4416
## veg_height-Sus_scrofa -0.1564 0.3189 -0.8122 -0.1437 0.4488
## week-Odocoileus_virginianus 0.2109 0.0607 0.0933 0.2105 0.3286
## week-Canis_latrans 0.0718 0.1293 -0.1918 0.0735 0.3183
## week-Sciurus_niger -0.2828 0.2961 -0.9444 -0.2548 0.2192
## week-Procyon_lotor -0.0436 0.1184 -0.2843 -0.0377 0.1810
## week-Dasypus_novemcinctus -0.1604 0.1370 -0.4368 -0.1547 0.0979
## week-Lynx_rufus -0.0314 0.1899 -0.4272 -0.0251 0.3186
## week-Didelphis_virginiana -0.1933 0.2111 -0.6529 -0.1810 0.1875
## week-Sylvilagus_floridanus -0.1402 0.2031 -0.5875 -0.1273 0.2193
## week-Sciurus_carolinensis 0.1474 0.1748 -0.1983 0.1484 0.4875
## week-Vulpes_vulpes -0.1108 0.2796 -0.7180 -0.0867 0.3969
## week-Sus_scrofa 0.0991 0.2349 -0.3663 0.0981 0.5612
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5465
## (Intercept)-Canis_latrans 1.0007 2305
## (Intercept)-Sciurus_niger 1.0129 531
## (Intercept)-Procyon_lotor 1.0093 2475
## (Intercept)-Dasypus_novemcinctus 1.0039 2845
## (Intercept)-Lynx_rufus 1.0055 510
## (Intercept)-Didelphis_virginiana 1.0040 1872
## (Intercept)-Sylvilagus_floridanus 1.0061 1691
## (Intercept)-Sciurus_carolinensis 1.0060 1554
## (Intercept)-Vulpes_vulpes 1.0055 494
## (Intercept)-Sus_scrofa 1.0071 939
## shrub_cover-Odocoileus_virginianus 1.0007 5250
## shrub_cover-Canis_latrans 1.0013 2017
## shrub_cover-Sciurus_niger 1.0022 934
## shrub_cover-Procyon_lotor 1.0000 3627
## shrub_cover-Dasypus_novemcinctus 1.0042 1880
## shrub_cover-Lynx_rufus 1.0018 760
## shrub_cover-Didelphis_virginiana 1.0058 1536
## shrub_cover-Sylvilagus_floridanus 1.0040 1554
## shrub_cover-Sciurus_carolinensis 1.0054 1519
## shrub_cover-Vulpes_vulpes 1.0042 1633
## shrub_cover-Sus_scrofa 1.0017 867
## veg_height-Odocoileus_virginianus 1.0005 4814
## veg_height-Canis_latrans 1.0046 2366
## veg_height-Sciurus_niger 1.0030 897
## veg_height-Procyon_lotor 1.0081 3692
## veg_height-Dasypus_novemcinctus 1.0006 3603
## veg_height-Lynx_rufus 1.0029 1881
## veg_height-Didelphis_virginiana 1.0020 3040
## veg_height-Sylvilagus_floridanus 1.0087 1903
## veg_height-Sciurus_carolinensis 1.0009 2665
## veg_height-Vulpes_vulpes 1.0043 1725
## veg_height-Sus_scrofa 1.0013 2940
## week-Odocoileus_virginianus 1.0020 5250
## week-Canis_latrans 1.0004 4437
## week-Sciurus_niger 1.0035 1285
## week-Procyon_lotor 1.0057 4470
## week-Dasypus_novemcinctus 1.0018 4510
## week-Lynx_rufus 1.0011 2689
## week-Didelphis_virginiana 1.0009 3609
## week-Sylvilagus_floridanus 1.0036 2809
## week-Sciurus_carolinensis 1.0006 4750
## week-Vulpes_vulpes 1.0019 2760
## week-Sus_scrofa 0.9999 4307
# Includes all covariates of occupancy and null for detection
ms_null_full <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5575
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1694 1.0365 -2.0978 -0.2102 2.0306 1.0059 2129
## Cogon_Patch_Size -0.8385 0.6193 -2.1611 -0.8035 0.3164 1.0148 1334
## Veg_shannon_index 0.8444 0.4535 -0.0281 0.8309 1.7804 1.0051 867
## total_shrub_cover -0.1750 0.3896 -0.9448 -0.1663 0.5863 1.0023 1406
## Avg_Cogongrass_Cover 2.0586 0.6525 0.8381 2.0423 3.3866 1.0008 589
## Tree_Density -1.8127 0.6674 -3.2121 -1.7900 -0.5497 1.0049 599
## Avg_Canopy_Cover 1.7695 0.5359 0.7760 1.7475 2.8963 1.0008 738
## avg_veg_height -0.5500 0.4334 -1.4327 -0.5532 0.2804 1.0011 835
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.6617 16.9637 3.6762 13.8523 61.9024 1.0173 489
## Cogon_Patch_Size 2.6810 3.6903 0.1148 1.4633 12.1169 1.0070 781
## Veg_shannon_index 0.8273 1.2355 0.0516 0.4271 3.9768 1.0079 1217
## total_shrub_cover 0.5758 0.8721 0.0457 0.3257 2.6692 1.0298 898
## Avg_Cogongrass_Cover 0.8819 1.5581 0.0499 0.4054 4.6963 1.0238 725
## Tree_Density 2.4587 5.2725 0.0606 0.9087 13.7270 1.0758 654
## Avg_Canopy_Cover 1.4884 1.8497 0.0842 0.8964 6.5410 1.0267 859
## avg_veg_height 0.3645 0.4557 0.0369 0.2177 1.6052 1.0081 2038
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3824 1.7983 0.0649 0.7888 6.1535 1.0141 262
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.532 0.4497 -3.4116 -2.5487 -1.6077 1.0054 4790
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1751 1.3098 0.7845 1.8311 5.8054 1.0012 2943
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1859 3.4392 3.4754 7.4419
## (Intercept)-Canis_latrans 0.6929 1.0808 -1.1411 0.5991
## (Intercept)-Sciurus_niger 1.5539 2.6565 -2.2900 1.1465
## (Intercept)-Procyon_lotor 0.8241 0.9245 -1.0574 0.8514
## (Intercept)-Dasypus_novemcinctus -1.4833 0.9121 -3.5547 -1.3924
## (Intercept)-Lynx_rufus 2.2052 2.8896 -1.8424 1.6125
## (Intercept)-Didelphis_virginiana -2.9553 1.0799 -5.2490 -2.8790
## (Intercept)-Sylvilagus_floridanus -1.3141 1.2242 -3.8501 -1.2853
## (Intercept)-Sciurus_carolinensis -3.1997 1.1752 -5.7961 -3.0810
## (Intercept)-Vulpes_vulpes -2.0802 2.1475 -5.6290 -2.3427
## (Intercept)-Sus_scrofa -4.6388 1.6521 -8.3024 -4.4955
## Cogon_Patch_Size-Odocoileus_virginianus -0.6046 1.2862 -2.9872 -0.6841
## Cogon_Patch_Size-Canis_latrans 0.6325 1.1830 -1.0062 0.4148
## Cogon_Patch_Size-Sciurus_niger -1.5223 1.6599 -5.3870 -1.3282
## Cogon_Patch_Size-Procyon_lotor -0.9881 0.7346 -2.3947 -0.9701
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7973 0.6105 -2.0866 -0.7684
## Cogon_Patch_Size-Lynx_rufus -0.9002 1.3456 -3.4267 -0.9362
## Cogon_Patch_Size-Didelphis_virginiana 0.7496 0.8690 -0.7161 0.6634
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0100 1.4701 -5.7983 -1.7262
## Cogon_Patch_Size-Sciurus_carolinensis -1.7438 1.2685 -4.9063 -1.4886
## Cogon_Patch_Size-Vulpes_vulpes -1.4659 1.5407 -5.1070 -1.2241
## Cogon_Patch_Size-Sus_scrofa -1.3935 1.4087 -4.8854 -1.1568
## Veg_shannon_index-Odocoileus_virginianus 0.7040 0.8692 -1.1716 0.7345
## Veg_shannon_index-Canis_latrans 1.2195 0.6431 0.1572 1.1492
## Veg_shannon_index-Sciurus_niger 0.9859 0.9853 -0.8527 0.9177
## Veg_shannon_index-Procyon_lotor 1.1322 0.5875 0.0735 1.1026
## Veg_shannon_index-Dasypus_novemcinctus 0.6359 0.5040 -0.3662 0.6419
## Veg_shannon_index-Lynx_rufus 0.7978 0.8681 -1.0489 0.8155
## Veg_shannon_index-Didelphis_virginiana 0.9998 0.6516 -0.1576 0.9536
## Veg_shannon_index-Sylvilagus_floridanus 0.9790 0.6605 -0.2185 0.9442
## Veg_shannon_index-Sciurus_carolinensis 0.2021 0.7094 -1.3830 0.2674
## Veg_shannon_index-Vulpes_vulpes 0.3202 0.8640 -1.6842 0.4085
## Veg_shannon_index-Sus_scrofa 1.5746 1.0100 0.1475 1.3658
## total_shrub_cover-Odocoileus_virginianus 0.0071 0.7127 -1.3422 -0.0329
## total_shrub_cover-Canis_latrans 0.1574 0.5775 -0.7931 0.1078
## total_shrub_cover-Sciurus_niger -0.4071 0.8398 -2.3173 -0.3304
## total_shrub_cover-Procyon_lotor -0.6510 0.5448 -1.8407 -0.5933
## total_shrub_cover-Dasypus_novemcinctus 0.0873 0.4746 -0.8032 0.0715
## total_shrub_cover-Lynx_rufus -0.4934 0.8400 -2.4077 -0.3965
## total_shrub_cover-Didelphis_virginiana -0.3327 0.5928 -1.6210 -0.2994
## total_shrub_cover-Sylvilagus_floridanus -0.1203 0.6318 -1.4041 -0.1121
## total_shrub_cover-Sciurus_carolinensis 0.0002 0.5713 -1.0866 -0.0119
## total_shrub_cover-Vulpes_vulpes -0.3302 0.7696 -2.0633 -0.2683
## total_shrub_cover-Sus_scrofa 0.1117 0.6863 -1.1629 0.0534
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0353 1.0061 0.1467 2.0123
## Avg_Cogongrass_Cover-Canis_latrans 2.3133 0.8581 0.8239 2.2424
## Avg_Cogongrass_Cover-Sciurus_niger 1.7010 1.2004 -1.0597 1.7916
## Avg_Cogongrass_Cover-Procyon_lotor 2.2906 0.8671 0.7858 2.2442
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5340 0.9179 0.9766 2.4556
## Avg_Cogongrass_Cover-Lynx_rufus 2.3853 1.0012 0.7251 2.3058
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1777 0.8238 0.6740 2.1382
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5904 0.9184 -0.2766 1.6125
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3354 0.8656 0.8209 2.2801
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4164 0.9835 0.7096 2.3391
## Avg_Cogongrass_Cover-Sus_scrofa 1.5946 1.0357 -0.7034 1.6539
## Tree_Density-Odocoileus_virginianus -0.8978 1.2983 -2.8902 -1.0800
## Tree_Density-Canis_latrans -2.3508 1.1073 -5.0562 -2.1739
## Tree_Density-Sciurus_niger -1.9385 1.4706 -5.1095 -1.8588
## Tree_Density-Procyon_lotor -1.4732 0.7585 -2.9788 -1.4849
## Tree_Density-Dasypus_novemcinctus -3.1505 1.6264 -7.3862 -2.7348
## Tree_Density-Lynx_rufus -0.7649 1.4701 -3.0442 -0.9723
## Tree_Density-Didelphis_virginiana -2.1780 1.0467 -4.7377 -2.0342
## Tree_Density-Sylvilagus_floridanus -2.3300 1.2463 -5.4287 -2.1327
## Tree_Density-Sciurus_carolinensis -2.3997 1.2828 -5.5741 -2.1489
## Tree_Density-Vulpes_vulpes -1.8397 1.4572 -4.8725 -1.8176
## Tree_Density-Sus_scrofa -2.2436 1.4247 -5.8540 -2.0083
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3609 1.1560 -1.0387 1.4230
## Avg_Canopy_Cover-Canis_latrans 0.4968 0.7055 -0.8810 0.4829
## Avg_Canopy_Cover-Sciurus_niger 2.0381 1.3693 -0.5373 1.9523
## Avg_Canopy_Cover-Procyon_lotor 1.7406 0.6622 0.5478 1.7030
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8955 0.6291 0.8037 1.8298
## Avg_Canopy_Cover-Lynx_rufus 1.4303 1.1917 -0.8818 1.4423
## Avg_Canopy_Cover-Didelphis_virginiana 2.4989 0.8564 1.1449 2.3773
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8603 1.2515 1.0935 2.6158
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1609 0.7515 0.9202 2.0682
## Avg_Canopy_Cover-Vulpes_vulpes 2.0374 1.0118 0.4254 1.8947
## Avg_Canopy_Cover-Sus_scrofa 2.0024 0.7738 0.6849 1.9312
## avg_veg_height-Odocoileus_virginianus -0.5741 0.6931 -2.0288 -0.5553
## avg_veg_height-Canis_latrans -0.7152 0.5628 -1.8642 -0.7047
## avg_veg_height-Sciurus_niger -0.6615 0.7131 -2.1451 -0.6432
## avg_veg_height-Procyon_lotor -0.3924 0.5351 -1.4406 -0.4063
## avg_veg_height-Dasypus_novemcinctus -0.3371 0.5477 -1.3731 -0.3510
## avg_veg_height-Lynx_rufus -0.5720 0.6910 -1.9407 -0.5708
## avg_veg_height-Didelphis_virginiana -0.6322 0.6078 -1.8880 -0.6186
## avg_veg_height-Sylvilagus_floridanus -0.7234 0.6157 -2.0503 -0.6840
## avg_veg_height-Sciurus_carolinensis -0.2642 0.6067 -1.3953 -0.2874
## avg_veg_height-Vulpes_vulpes -0.5707 0.6744 -1.9377 -0.5585
## avg_veg_height-Sus_scrofa -0.6601 0.6330 -2.0128 -0.6344
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 17.1690 1.0095 372
## (Intercept)-Canis_latrans 2.9620 1.0138 774
## (Intercept)-Sciurus_niger 8.1577 1.0225 252
## (Intercept)-Procyon_lotor 2.6336 1.0055 1450
## (Intercept)-Dasypus_novemcinctus 0.1011 1.0013 1135
## (Intercept)-Lynx_rufus 9.7464 1.0131 201
## (Intercept)-Didelphis_virginiana -1.0060 1.0055 1431
## (Intercept)-Sylvilagus_floridanus 1.1521 1.0060 1185
## (Intercept)-Sciurus_carolinensis -1.2079 1.0018 1088
## (Intercept)-Vulpes_vulpes 3.1390 1.0269 358
## (Intercept)-Sus_scrofa -1.7951 1.0097 632
## Cogon_Patch_Size-Odocoileus_virginianus 2.3041 1.0048 2267
## Cogon_Patch_Size-Canis_latrans 3.4849 1.0014 1073
## Cogon_Patch_Size-Sciurus_niger 1.2876 1.0237 738
## Cogon_Patch_Size-Procyon_lotor 0.3084 1.0163 885
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3340 1.0030 1402
## Cogon_Patch_Size-Lynx_rufus 2.0824 1.0074 761
## Cogon_Patch_Size-Didelphis_virginiana 2.6716 1.0007 1136
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0377 1.0032 877
## Cogon_Patch_Size-Sciurus_carolinensis 0.0024 1.0033 964
## Cogon_Patch_Size-Vulpes_vulpes 1.0491 1.0088 898
## Cogon_Patch_Size-Sus_scrofa 0.7038 1.0092 1211
## Veg_shannon_index-Odocoileus_virginianus 2.3780 1.0057 2027
## Veg_shannon_index-Canis_latrans 2.6846 1.0049 1208
## Veg_shannon_index-Sciurus_niger 3.2242 1.0039 1096
## Veg_shannon_index-Procyon_lotor 2.3887 1.0038 906
## Veg_shannon_index-Dasypus_novemcinctus 1.6251 1.0020 1681
## Veg_shannon_index-Lynx_rufus 2.5462 1.0022 1614
## Veg_shannon_index-Didelphis_virginiana 2.4324 1.0033 1378
## Veg_shannon_index-Sylvilagus_floridanus 2.4322 1.0029 1981
## Veg_shannon_index-Sciurus_carolinensis 1.4305 1.0052 1709
## Veg_shannon_index-Vulpes_vulpes 1.7844 1.0033 1303
## Veg_shannon_index-Sus_scrofa 4.1757 1.0018 1096
## total_shrub_cover-Odocoileus_virginianus 1.5511 1.0005 2450
## total_shrub_cover-Canis_latrans 1.4925 1.0003 1751
## total_shrub_cover-Sciurus_niger 1.0513 1.0049 1409
## total_shrub_cover-Procyon_lotor 0.2803 1.0019 2080
## total_shrub_cover-Dasypus_novemcinctus 1.0581 1.0016 2637
## total_shrub_cover-Lynx_rufus 0.9231 1.0087 880
## total_shrub_cover-Didelphis_virginiana 0.7529 1.0037 2427
## total_shrub_cover-Sylvilagus_floridanus 1.1163 1.0019 2087
## total_shrub_cover-Sciurus_carolinensis 1.1715 1.0029 2306
## total_shrub_cover-Vulpes_vulpes 1.0252 1.0057 1527
## total_shrub_cover-Sus_scrofa 1.6567 1.0030 2785
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.1969 1.0026 842
## Avg_Cogongrass_Cover-Canis_latrans 4.2008 1.0018 705
## Avg_Cogongrass_Cover-Sciurus_niger 3.8140 1.0070 748
## Avg_Cogongrass_Cover-Procyon_lotor 4.1975 1.0016 627
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6062 1.0063 597
## Avg_Cogongrass_Cover-Lynx_rufus 4.5109 1.0078 744
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9531 1.0058 742
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3393 1.0017 1125
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2702 1.0083 587
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.6266 1.0013 723
## Avg_Cogongrass_Cover-Sus_scrofa 3.4500 1.0015 1098
## Tree_Density-Odocoileus_virginianus 2.2445 1.0049 779
## Tree_Density-Canis_latrans -0.6945 1.0030 789
## Tree_Density-Sciurus_niger 0.9268 1.0006 874
## Tree_Density-Procyon_lotor 0.0488 1.0051 1252
## Tree_Density-Dasypus_novemcinctus -1.1444 1.0052 506
## Tree_Density-Lynx_rufus 2.9366 1.0210 465
## Tree_Density-Didelphis_virginiana -0.5457 1.0091 1267
## Tree_Density-Sylvilagus_floridanus -0.4607 1.0042 904
## Tree_Density-Sciurus_carolinensis -0.5819 1.0067 755
## Tree_Density-Vulpes_vulpes 1.0656 1.0074 655
## Tree_Density-Sus_scrofa -0.0722 1.0134 957
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6745 1.0003 1157
## Avg_Canopy_Cover-Canis_latrans 1.8973 1.0075 1148
## Avg_Canopy_Cover-Sciurus_niger 5.0769 1.0109 765
## Avg_Canopy_Cover-Procyon_lotor 3.1915 1.0056 1406
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2265 1.0033 1098
## Avg_Canopy_Cover-Lynx_rufus 3.8435 1.0217 547
## Avg_Canopy_Cover-Didelphis_virginiana 4.4450 1.0021 652
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.9885 1.0084 613
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8425 1.0011 1242
## Avg_Canopy_Cover-Vulpes_vulpes 4.5122 1.0020 1251
## Avg_Canopy_Cover-Sus_scrofa 3.7716 1.0012 1646
## avg_veg_height-Odocoileus_virginianus 0.7628 1.0014 1611
## avg_veg_height-Canis_latrans 0.3711 1.0003 1338
## avg_veg_height-Sciurus_niger 0.6581 1.0004 1274
## avg_veg_height-Procyon_lotor 0.6923 0.9999 1538
## avg_veg_height-Dasypus_novemcinctus 0.7799 1.0006 1277
## avg_veg_height-Lynx_rufus 0.7634 0.9998 1384
## avg_veg_height-Didelphis_virginiana 0.4773 0.9998 1330
## avg_veg_height-Sylvilagus_floridanus 0.4085 1.0073 1585
## avg_veg_height-Sciurus_carolinensis 1.0091 1.0000 1528
## avg_veg_height-Vulpes_vulpes 0.7211 1.0030 1394
## avg_veg_height-Sus_scrofa 0.5238 1.0020 1390
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0061 0.0591 -0.1096 0.0042 0.1212
## (Intercept)-Canis_latrans -2.6388 0.1819 -3.0175 -2.6318 -2.2983
## (Intercept)-Sciurus_niger -4.5219 0.4686 -5.4204 -4.5323 -3.5675
## (Intercept)-Procyon_lotor -2.2658 0.1301 -2.5310 -2.2614 -2.0207
## (Intercept)-Dasypus_novemcinctus -1.5715 0.1345 -1.8433 -1.5679 -1.3194
## (Intercept)-Lynx_rufus -3.8067 0.3381 -4.4572 -3.8187 -3.1475
## (Intercept)-Didelphis_virginiana -2.2853 0.2420 -2.7903 -2.2750 -1.8414
## (Intercept)-Sylvilagus_floridanus -3.1651 0.2772 -3.7314 -3.1466 -2.6602
## (Intercept)-Sciurus_carolinensis -2.4204 0.2572 -2.9550 -2.4109 -1.9447
## (Intercept)-Vulpes_vulpes -4.1903 0.6856 -5.6018 -4.1645 -2.9185
## (Intercept)-Sus_scrofa -2.8853 0.4590 -3.8961 -2.8514 -2.0926
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0013 2205
## (Intercept)-Sciurus_niger 1.0130 425
## (Intercept)-Procyon_lotor 1.0025 3858
## (Intercept)-Dasypus_novemcinctus 0.9999 5250
## (Intercept)-Lynx_rufus 1.0373 433
## (Intercept)-Didelphis_virginiana 1.0000 4069
## (Intercept)-Sylvilagus_floridanus 1.0042 1615
## (Intercept)-Sciurus_carolinensis 1.0033 3598
## (Intercept)-Vulpes_vulpes 1.0214 379
## (Intercept)-Sus_scrofa 1.0146 2000
# Includes cover covariates of occupancy and null for detection
ms_null_cover <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4155
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2213 0.6178 -1.4144 -0.2309 1.0761 1.0129 1992
## Avg_Cogongrass_Cover 0.1412 0.3056 -0.4781 0.1476 0.7198 1.0102 1250
## total_shrub_cover -0.2699 0.2702 -0.8269 -0.2707 0.2577 1.0017 2195
## avg_veg_height 0.0198 0.2824 -0.5373 0.0190 0.5827 1.0053 1389
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9518 3.0291 0.7699 3.0981 11.9538 1.0155 1694
## Avg_Cogongrass_Cover 0.2997 0.3480 0.0363 0.1922 1.2375 1.0055 2220
## total_shrub_cover 0.3606 0.4433 0.0406 0.2237 1.4673 1.0026 1858
## avg_veg_height 0.1994 0.2085 0.0321 0.1387 0.7532 1.0015 2960
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9152 0.9616 0.0698 0.6153 3.5012 1.0544 363
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4841 0.4222 -3.2965 -2.492 -1.6429 1.0007 3797
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9144 1.2237 0.6471 1.6136 5.124 1.0068 1595
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6806 1.4372 1.2749 3.5203
## (Intercept)-Canis_latrans 0.3411 0.6783 -0.9595 0.3191
## (Intercept)-Sciurus_niger -0.4912 1.3340 -2.5096 -0.6863
## (Intercept)-Procyon_lotor 0.6649 0.6993 -0.6937 0.6548
## (Intercept)-Dasypus_novemcinctus -0.7189 0.6130 -1.9777 -0.7178
## (Intercept)-Lynx_rufus -0.0361 0.9373 -1.6510 -0.1054
## (Intercept)-Didelphis_virginiana -1.4394 0.6797 -2.8149 -1.4172
## (Intercept)-Sylvilagus_floridanus -0.1989 0.8428 -1.6526 -0.2679
## (Intercept)-Sciurus_carolinensis -1.5205 0.7011 -2.9335 -1.5056
## (Intercept)-Vulpes_vulpes -0.9067 1.4380 -3.1663 -1.1031
## (Intercept)-Sus_scrofa -2.0093 0.8826 -3.7792 -1.9924
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1266 0.5327 -0.9435 0.1275
## Avg_Cogongrass_Cover-Canis_latrans 0.3778 0.4280 -0.4161 0.3581
## Avg_Cogongrass_Cover-Sciurus_niger -0.1727 0.6110 -1.5074 -0.1120
## Avg_Cogongrass_Cover-Procyon_lotor 0.0953 0.4225 -0.7457 0.0983
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2600 0.3824 -0.4708 0.2504
## Avg_Cogongrass_Cover-Lynx_rufus 0.4094 0.4629 -0.4295 0.3785
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3175 0.4122 -0.4585 0.3007
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1895 0.4878 -1.2461 -0.1579
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2404 0.4020 -0.5384 0.2366
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2301 0.5023 -0.7417 0.2192
## Avg_Cogongrass_Cover-Sus_scrofa -0.1577 0.5729 -1.4824 -0.0919
## total_shrub_cover-Odocoileus_virginianus -0.1939 0.5346 -1.2428 -0.2048
## total_shrub_cover-Canis_latrans 0.0908 0.4269 -0.6569 0.0600
## total_shrub_cover-Sciurus_niger -0.5000 0.5318 -1.6873 -0.4506
## total_shrub_cover-Procyon_lotor -0.7345 0.4723 -1.8017 -0.6838
## total_shrub_cover-Dasypus_novemcinctus -0.0618 0.3515 -0.7336 -0.0670
## total_shrub_cover-Lynx_rufus -0.6512 0.5581 -1.9827 -0.5809
## total_shrub_cover-Didelphis_virginiana -0.2124 0.3892 -0.9923 -0.2097
## total_shrub_cover-Sylvilagus_floridanus -0.3308 0.4993 -1.4302 -0.2924
## total_shrub_cover-Sciurus_carolinensis -0.1179 0.4019 -0.9155 -0.1272
## total_shrub_cover-Vulpes_vulpes -0.3215 0.5440 -1.5280 -0.2950
## total_shrub_cover-Sus_scrofa 0.0246 0.4906 -0.8768 -0.0069
## avg_veg_height-Odocoileus_virginianus -0.0046 0.4644 -0.9664 0.0067
## avg_veg_height-Canis_latrans -0.0435 0.3822 -0.8016 -0.0397
## avg_veg_height-Sciurus_niger -0.1236 0.4812 -1.1292 -0.1060
## avg_veg_height-Procyon_lotor 0.1149 0.3938 -0.6205 0.1056
## avg_veg_height-Dasypus_novemcinctus 0.1766 0.3738 -0.5657 0.1693
## avg_veg_height-Lynx_rufus 0.0258 0.4549 -0.8883 0.0218
## avg_veg_height-Didelphis_virginiana -0.0078 0.3915 -0.8026 -0.0069
## avg_veg_height-Sylvilagus_floridanus -0.1079 0.4152 -0.9537 -0.0947
## avg_veg_height-Sciurus_carolinensis 0.2531 0.4082 -0.4904 0.2333
## avg_veg_height-Vulpes_vulpes -0.0328 0.4520 -0.9486 -0.0290
## avg_veg_height-Sus_scrofa -0.0262 0.4334 -0.9031 -0.0196
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9422 1.0113 1262
## (Intercept)-Canis_latrans 1.7201 1.0059 3006
## (Intercept)-Sciurus_niger 2.6694 1.0759 397
## (Intercept)-Procyon_lotor 2.0968 1.0020 2373
## (Intercept)-Dasypus_novemcinctus 0.4821 1.0036 3059
## (Intercept)-Lynx_rufus 1.9625 1.0051 1118
## (Intercept)-Didelphis_virginiana -0.1147 1.0016 3161
## (Intercept)-Sylvilagus_floridanus 1.6590 1.0035 1270
## (Intercept)-Sciurus_carolinensis -0.1493 1.0014 3180
## (Intercept)-Vulpes_vulpes 2.6112 1.0134 371
## (Intercept)-Sus_scrofa -0.3137 1.0010 1841
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2105 1.0075 2618
## Avg_Cogongrass_Cover-Canis_latrans 1.3043 1.0022 2819
## Avg_Cogongrass_Cover-Sciurus_niger 0.8943 1.0076 1784
## Avg_Cogongrass_Cover-Procyon_lotor 0.9177 1.0023 2982
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0445 1.0015 2492
## Avg_Cogongrass_Cover-Lynx_rufus 1.4241 1.0018 2555
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1694 1.0051 2765
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6835 1.0056 1914
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0295 1.0049 2696
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2720 1.0079 2523
## Avg_Cogongrass_Cover-Sus_scrofa 0.7981 1.0023 1947
## total_shrub_cover-Odocoileus_virginianus 0.8996 1.0046 3475
## total_shrub_cover-Canis_latrans 1.0073 1.0030 3401
## total_shrub_cover-Sciurus_niger 0.4174 1.0010 2059
## total_shrub_cover-Procyon_lotor 0.0421 1.0023 2290
## total_shrub_cover-Dasypus_novemcinctus 0.6544 1.0007 4530
## total_shrub_cover-Lynx_rufus 0.2682 1.0054 1883
## total_shrub_cover-Didelphis_virginiana 0.5603 1.0010 4356
## total_shrub_cover-Sylvilagus_floridanus 0.5578 1.0028 2407
## total_shrub_cover-Sciurus_carolinensis 0.7112 1.0015 3849
## total_shrub_cover-Vulpes_vulpes 0.6996 1.0017 2620
## total_shrub_cover-Sus_scrofa 1.0669 1.0040 3765
## avg_veg_height-Odocoileus_virginianus 0.9091 1.0038 2306
## avg_veg_height-Canis_latrans 0.7251 1.0023 2677
## avg_veg_height-Sciurus_niger 0.8070 1.0018 2159
## avg_veg_height-Procyon_lotor 0.9344 1.0039 2459
## avg_veg_height-Dasypus_novemcinctus 0.9394 1.0018 2481
## avg_veg_height-Lynx_rufus 0.9311 1.0040 2207
## avg_veg_height-Didelphis_virginiana 0.7525 1.0009 2430
## avg_veg_height-Sylvilagus_floridanus 0.6852 1.0112 2157
## avg_veg_height-Sciurus_carolinensis 1.1091 1.0009 2331
## avg_veg_height-Vulpes_vulpes 0.8584 1.0059 2406
## avg_veg_height-Sus_scrofa 0.8314 1.0028 2361
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0042 0.0599 -0.1129 0.0041 0.1225
## (Intercept)-Canis_latrans -2.6316 0.1822 -3.0090 -2.6247 -2.2921
## (Intercept)-Sciurus_niger -3.9761 0.5878 -5.1559 -3.9538 -2.8949
## (Intercept)-Procyon_lotor -2.2733 0.1313 -2.5433 -2.2726 -2.0256
## (Intercept)-Dasypus_novemcinctus -1.5786 0.1341 -1.8433 -1.5765 -1.3198
## (Intercept)-Lynx_rufus -3.5464 0.3099 -4.1820 -3.5390 -2.9639
## (Intercept)-Didelphis_virginiana -2.3196 0.2543 -2.8628 -2.3085 -1.8441
## (Intercept)-Sylvilagus_floridanus -3.2553 0.3207 -3.9317 -3.2386 -2.6766
## (Intercept)-Sciurus_carolinensis -2.4493 0.2727 -3.0299 -2.4321 -1.9537
## (Intercept)-Vulpes_vulpes -4.1594 0.7712 -5.7071 -4.1180 -2.7664
## (Intercept)-Sus_scrofa -2.9276 0.4872 -4.0149 -2.8803 -2.1025
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0049 5468
## (Intercept)-Canis_latrans 1.0022 2759
## (Intercept)-Sciurus_niger 1.0402 468
## (Intercept)-Procyon_lotor 1.0027 4111
## (Intercept)-Dasypus_novemcinctus 1.0022 5006
## (Intercept)-Lynx_rufus 1.0036 1083
## (Intercept)-Didelphis_virginiana 1.0000 4096
## (Intercept)-Sylvilagus_floridanus 1.0106 1177
## (Intercept)-Sciurus_carolinensis 1.0010 3313
## (Intercept)-Vulpes_vulpes 1.0077 305
## (Intercept)-Sus_scrofa 1.0137 1540
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.432
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2163 0.7459 -1.6252 -0.2403 1.3259 1.0102 1463
## Tree_Density -0.7562 0.4095 -1.6672 -0.7218 -0.0439 1.0067 1010
## Avg_Canopy_Cover 1.0024 0.3278 0.4014 0.9876 1.6902 1.0013 1958
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.6666 6.0004 1.4967 5.0879 22.0379 1.0847 504
## Tree_Density 0.7177 1.1457 0.0451 0.3403 3.6719 1.0022 948
## Avg_Canopy_Cover 0.5403 0.6106 0.0578 0.3681 2.0977 1.0220 1745
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4023 0.4752 0.0446 0.254 1.6204 1.0187 643
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4869 0.4208 -3.2972 -2.4958 -1.6184 0.9997 5038
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9643 1.261 0.6695 1.6337 5.2176 1.0144 2044
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7001 1.7910 2.1336 4.3999 9.0822
## (Intercept)-Canis_latrans 0.3374 0.7105 -0.8031 0.2917 1.7274
## (Intercept)-Sciurus_niger -0.0899 1.5485 -2.3097 -0.3325 3.4771
## (Intercept)-Procyon_lotor 0.7719 0.6143 -0.4118 0.7662 1.9978
## (Intercept)-Dasypus_novemcinctus -1.0266 0.5845 -2.2601 -1.0006 0.0435
## (Intercept)-Lynx_rufus 1.2821 1.9654 -1.2252 0.8426 6.2341
## (Intercept)-Didelphis_virginiana -1.9313 0.6925 -3.3803 -1.8972 -0.6777
## (Intercept)-Sylvilagus_floridanus -0.6802 0.7233 -2.0855 -0.6867 0.8043
## (Intercept)-Sciurus_carolinensis -1.9944 0.6958 -3.4902 -1.9522 -0.7416
## (Intercept)-Vulpes_vulpes -1.3558 1.6891 -3.7473 -1.5863 2.5615
## (Intercept)-Sus_scrofa -2.7045 0.9152 -4.7022 -2.6418 -1.0349
## Tree_Density-Odocoileus_virginianus -0.3928 0.6582 -1.4739 -0.4554 1.1816
## Tree_Density-Canis_latrans -0.8804 0.5310 -2.0903 -0.8213 0.0111
## Tree_Density-Sciurus_niger -0.7790 0.7657 -2.5159 -0.7269 0.6009
## Tree_Density-Procyon_lotor -0.4760 0.4096 -1.3001 -0.4658 0.3401
## Tree_Density-Dasypus_novemcinctus -1.3287 0.8803 -3.6846 -1.1319 -0.1594
## Tree_Density-Lynx_rufus -0.0554 0.7951 -1.3342 -0.1506 1.8664
## Tree_Density-Didelphis_virginiana -1.0124 0.7486 -2.8987 -0.8761 0.0735
## Tree_Density-Sylvilagus_floridanus -1.0414 0.7216 -2.8238 -0.9263 0.0707
## Tree_Density-Sciurus_carolinensis -0.9365 0.7141 -2.7250 -0.8332 0.1648
## Tree_Density-Vulpes_vulpes -0.6702 0.8017 -2.2721 -0.6515 0.8157
## Tree_Density-Sus_scrofa -0.9685 0.8102 -3.0108 -0.8441 0.2577
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8008 0.6663 -0.5830 0.8153 2.1468
## Avg_Canopy_Cover-Canis_latrans 0.1512 0.4739 -0.7903 0.1498 1.0763
## Avg_Canopy_Cover-Sciurus_niger 0.9999 0.7601 -0.3568 0.9359 2.6743
## Avg_Canopy_Cover-Procyon_lotor 1.0283 0.4536 0.2014 1.0093 2.0094
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0075 0.4099 0.2491 0.9951 1.8772
## Avg_Canopy_Cover-Lynx_rufus 0.8927 0.7175 -0.4727 0.8725 2.4159
## Avg_Canopy_Cover-Didelphis_virginiana 1.2440 0.4815 0.3985 1.2050 2.3194
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6141 0.6961 0.5843 1.5054 3.2631
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2189 0.4795 0.3872 1.1786 2.2502
## Avg_Canopy_Cover-Vulpes_vulpes 1.0537 0.5788 0.0303 1.0193 2.3082
## Avg_Canopy_Cover-Sus_scrofa 1.2248 0.5180 0.3122 1.1870 2.3735
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0136 778
## (Intercept)-Canis_latrans 1.0304 1050
## (Intercept)-Sciurus_niger 1.0056 273
## (Intercept)-Procyon_lotor 1.0014 3395
## (Intercept)-Dasypus_novemcinctus 1.0005 2542
## (Intercept)-Lynx_rufus 1.0897 327
## (Intercept)-Didelphis_virginiana 1.0009 2981
## (Intercept)-Sylvilagus_floridanus 1.0033 2634
## (Intercept)-Sciurus_carolinensis 1.0041 2827
## (Intercept)-Vulpes_vulpes 1.0688 255
## (Intercept)-Sus_scrofa 1.0002 2027
## Tree_Density-Odocoileus_virginianus 1.0034 2221
## Tree_Density-Canis_latrans 1.0021 2667
## Tree_Density-Sciurus_niger 1.0046 1536
## Tree_Density-Procyon_lotor 1.0034 2892
## Tree_Density-Dasypus_novemcinctus 1.0016 1184
## Tree_Density-Lynx_rufus 1.0092 714
## Tree_Density-Didelphis_virginiana 1.0018 1502
## Tree_Density-Sylvilagus_floridanus 1.0037 1715
## Tree_Density-Sciurus_carolinensis 1.0094 1827
## Tree_Density-Vulpes_vulpes 1.0042 994
## Tree_Density-Sus_scrofa 1.0056 1852
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0041 3345
## Avg_Canopy_Cover-Canis_latrans 1.0057 2407
## Avg_Canopy_Cover-Sciurus_niger 1.0105 1501
## Avg_Canopy_Cover-Procyon_lotor 1.0005 3910
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0004 4376
## Avg_Canopy_Cover-Lynx_rufus 1.0020 1669
## Avg_Canopy_Cover-Didelphis_virginiana 1.0001 2840
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0031 1528
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0016 3145
## Avg_Canopy_Cover-Vulpes_vulpes 1.0009 2566
## Avg_Canopy_Cover-Sus_scrofa 1.0033 2953
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0037 0.0594 -0.1122 0.0030 0.1225
## (Intercept)-Canis_latrans -2.6456 0.1825 -3.0291 -2.6370 -2.3052
## (Intercept)-Sciurus_niger -4.1377 0.5758 -5.2088 -4.1587 -3.0033
## (Intercept)-Procyon_lotor -2.2671 0.1276 -2.5260 -2.2647 -2.0273
## (Intercept)-Dasypus_novemcinctus -1.5763 0.1360 -1.8600 -1.5749 -1.3103
## (Intercept)-Lynx_rufus -3.7545 0.3608 -4.4470 -3.7567 -3.0450
## (Intercept)-Didelphis_virginiana -2.2970 0.2498 -2.8133 -2.2860 -1.8326
## (Intercept)-Sylvilagus_floridanus -3.1277 0.2854 -3.7125 -3.1187 -2.5928
## (Intercept)-Sciurus_carolinensis -2.4261 0.2614 -2.9596 -2.4154 -1.9311
## (Intercept)-Vulpes_vulpes -4.0377 0.7476 -5.5970 -3.9901 -2.7254
## (Intercept)-Sus_scrofa -2.8568 0.4494 -3.8374 -2.8198 -2.0886
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5521
## (Intercept)-Canis_latrans 1.0066 2480
## (Intercept)-Sciurus_niger 1.0155 325
## (Intercept)-Procyon_lotor 1.0032 4048
## (Intercept)-Dasypus_novemcinctus 1.0006 4994
## (Intercept)-Lynx_rufus 1.0203 615
## (Intercept)-Didelphis_virginiana 1.0001 3970
## (Intercept)-Sylvilagus_floridanus 1.0042 2072
## (Intercept)-Sciurus_carolinensis 0.9998 3754
## (Intercept)-Vulpes_vulpes 1.0994 391
## (Intercept)-Sus_scrofa 1.0005 2335
# Includes movement covariates of occupancy and null for detection
ms_null_move <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4325
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2653 0.6419 -1.5189 -0.2765 1.0855 1.0052 2118
## Cogon_Patch_Size -0.2587 0.4040 -1.1083 -0.2366 0.4906 1.0051 1780
## Avg_Cogongrass_Cover 0.2474 0.2721 -0.2920 0.2439 0.7879 1.0008 1687
## total_shrub_cover -0.2291 0.2756 -0.7781 -0.2259 0.3146 1.0004 2224
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.3781 3.6976 0.8892 3.3744 14.3375 1.0066 1403
## Cogon_Patch_Size 0.9529 1.5682 0.0686 0.5250 4.2631 1.0053 1422
## Avg_Cogongrass_Cover 0.2617 0.2928 0.0360 0.1686 1.0408 1.0073 2467
## total_shrub_cover 0.3160 0.3852 0.0400 0.2008 1.3367 1.0128 1889
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0131 0.9152 0.077 0.7568 3.435 1.0003 473
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4818 0.4204 -3.2878 -2.4962 -1.6297 1.0009 4124
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8791 1.1689 0.6434 1.5787 4.9855 1.0037 2072
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8430 1.6211 1.2586 3.6318
## (Intercept)-Canis_latrans 0.4120 0.7177 -0.9717 0.3984
## (Intercept)-Sciurus_niger -0.5691 1.2721 -2.6644 -0.7293
## (Intercept)-Procyon_lotor 0.5907 0.7097 -0.8678 0.5969
## (Intercept)-Dasypus_novemcinctus -0.7604 0.6418 -2.0632 -0.7443
## (Intercept)-Lynx_rufus -0.0254 1.1209 -1.8458 -0.1364
## (Intercept)-Didelphis_virginiana -1.4569 0.7150 -2.9437 -1.4445
## (Intercept)-Sylvilagus_floridanus -0.3793 0.9209 -2.0076 -0.4241
## (Intercept)-Sciurus_carolinensis -1.6643 0.7377 -3.2189 -1.6217
## (Intercept)-Vulpes_vulpes -1.0814 1.4520 -3.5088 -1.2557
## (Intercept)-Sus_scrofa -2.1267 0.9123 -4.0102 -2.0801
## Cogon_Patch_Size-Odocoileus_virginianus -0.0685 0.7191 -1.3599 -0.1140
## Cogon_Patch_Size-Canis_latrans 0.6798 0.7463 -0.3812 0.5526
## Cogon_Patch_Size-Sciurus_niger -0.6598 0.9440 -2.7861 -0.5529
## Cogon_Patch_Size-Procyon_lotor -0.2662 0.4567 -1.1624 -0.2650
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1668 0.4138 -1.0110 -0.1618
## Cogon_Patch_Size-Lynx_rufus -0.2741 0.7614 -1.6149 -0.3116
## Cogon_Patch_Size-Didelphis_virginiana 0.5634 0.5196 -0.3281 0.5316
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9282 0.9233 -3.2371 -0.7565
## Cogon_Patch_Size-Sciurus_carolinensis -0.7639 0.7257 -2.4773 -0.6400
## Cogon_Patch_Size-Vulpes_vulpes -0.5879 0.9034 -2.6731 -0.4923
## Cogon_Patch_Size-Sus_scrofa -0.5258 0.8054 -2.5163 -0.4139
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2412 0.5147 -0.7883 0.2285
## Avg_Cogongrass_Cover-Canis_latrans 0.3013 0.3855 -0.4181 0.2860
## Avg_Cogongrass_Cover-Sciurus_niger -0.0246 0.5571 -1.2665 0.0046
## Avg_Cogongrass_Cover-Procyon_lotor 0.2821 0.4027 -0.4768 0.2646
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3993 0.3503 -0.2830 0.3919
## Avg_Cogongrass_Cover-Lynx_rufus 0.5189 0.4557 -0.2699 0.4839
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2543 0.3857 -0.5345 0.2590
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0244 0.4474 -0.9570 0.0030
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4675 0.3813 -0.2380 0.4495
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3423 0.4561 -0.5458 0.3355
## Avg_Cogongrass_Cover-Sus_scrofa -0.0101 0.5069 -1.1469 0.0326
## total_shrub_cover-Odocoileus_virginianus -0.1466 0.5187 -1.1152 -0.1585
## total_shrub_cover-Canis_latrans 0.0632 0.4155 -0.6731 0.0393
## total_shrub_cover-Sciurus_niger -0.4303 0.5180 -1.5420 -0.3862
## total_shrub_cover-Procyon_lotor -0.6562 0.4483 -1.6822 -0.6055
## total_shrub_cover-Dasypus_novemcinctus -0.0466 0.3531 -0.7202 -0.0551
## total_shrub_cover-Lynx_rufus -0.5275 0.5311 -1.7350 -0.4720
## total_shrub_cover-Didelphis_virginiana -0.2555 0.3975 -1.0793 -0.2480
## total_shrub_cover-Sylvilagus_floridanus -0.2292 0.4754 -1.1845 -0.2229
## total_shrub_cover-Sciurus_carolinensis -0.0699 0.4086 -0.8547 -0.0792
## total_shrub_cover-Vulpes_vulpes -0.2743 0.5289 -1.4581 -0.2563
## total_shrub_cover-Sus_scrofa 0.0611 0.4932 -0.8396 0.0184
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.6287 1.0135 806
## (Intercept)-Canis_latrans 1.8811 1.0005 2378
## (Intercept)-Sciurus_niger 2.4017 1.0260 452
## (Intercept)-Procyon_lotor 1.9939 1.0031 2406
## (Intercept)-Dasypus_novemcinctus 0.5001 1.0004 3226
## (Intercept)-Lynx_rufus 2.5308 1.0149 631
## (Intercept)-Didelphis_virginiana -0.0989 1.0019 2791
## (Intercept)-Sylvilagus_floridanus 1.4668 1.0227 1198
## (Intercept)-Sciurus_carolinensis -0.2834 1.0049 2641
## (Intercept)-Vulpes_vulpes 2.3539 1.0297 393
## (Intercept)-Sus_scrofa -0.4295 1.0117 1962
## Cogon_Patch_Size-Odocoileus_virginianus 1.5027 1.0013 3229
## Cogon_Patch_Size-Canis_latrans 2.4139 1.0029 1876
## Cogon_Patch_Size-Sciurus_niger 0.8739 1.0041 1474
## Cogon_Patch_Size-Procyon_lotor 0.6099 1.0014 2862
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6349 1.0031 4405
## Cogon_Patch_Size-Lynx_rufus 1.3643 1.0039 1911
## Cogon_Patch_Size-Didelphis_virginiana 1.6265 1.0045 2408
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3852 1.0033 1418
## Cogon_Patch_Size-Sciurus_carolinensis 0.2998 1.0036 1865
## Cogon_Patch_Size-Vulpes_vulpes 0.9467 1.0026 1287
## Cogon_Patch_Size-Sus_scrofa 0.7714 1.0028 2135
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3103 1.0011 3388
## Avg_Cogongrass_Cover-Canis_latrans 1.1051 1.0009 2947
## Avg_Cogongrass_Cover-Sciurus_niger 1.0303 1.0047 2190
## Avg_Cogongrass_Cover-Procyon_lotor 1.1294 1.0008 2695
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1177 1.0020 3670
## Avg_Cogongrass_Cover-Lynx_rufus 1.5200 1.0018 2548
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0184 1.0003 3219
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8183 1.0033 2363
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2585 1.0069 2624
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3059 1.0053 3330
## Avg_Cogongrass_Cover-Sus_scrofa 0.8745 1.0001 2376
## total_shrub_cover-Odocoileus_virginianus 0.9339 1.0033 3343
## total_shrub_cover-Canis_latrans 0.9744 1.0014 2774
## total_shrub_cover-Sciurus_niger 0.4916 1.0006 3047
## total_shrub_cover-Procyon_lotor 0.0853 1.0045 2529
## total_shrub_cover-Dasypus_novemcinctus 0.6779 1.0013 3870
## total_shrub_cover-Lynx_rufus 0.3619 1.0074 2416
## total_shrub_cover-Didelphis_virginiana 0.4731 1.0007 3820
## total_shrub_cover-Sylvilagus_floridanus 0.7188 1.0023 3020
## total_shrub_cover-Sciurus_carolinensis 0.7562 1.0005 3134
## total_shrub_cover-Vulpes_vulpes 0.7485 1.0013 2931
## total_shrub_cover-Sus_scrofa 1.1568 1.0012 2829
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0044 0.0600 -0.1149 0.0038 0.1232
## (Intercept)-Canis_latrans -2.6205 0.1748 -2.9788 -2.6174 -2.2894
## (Intercept)-Sciurus_niger -3.9889 0.6029 -5.1559 -3.9729 -2.8751
## (Intercept)-Procyon_lotor -2.2764 0.1298 -2.5395 -2.2756 -2.0268
## (Intercept)-Dasypus_novemcinctus -1.5738 0.1353 -1.8388 -1.5731 -1.3118
## (Intercept)-Lynx_rufus -3.5189 0.3260 -4.1842 -3.5032 -2.9150
## (Intercept)-Didelphis_virginiana -2.3063 0.2463 -2.8208 -2.2981 -1.8563
## (Intercept)-Sylvilagus_floridanus -3.2504 0.3187 -3.9088 -3.2402 -2.6643
## (Intercept)-Sciurus_carolinensis -2.4396 0.2611 -2.9787 -2.4273 -1.9579
## (Intercept)-Vulpes_vulpes -4.1102 0.7693 -5.6106 -4.0859 -2.7169
## (Intercept)-Sus_scrofa -2.9262 0.4724 -3.9822 -2.8842 -2.1158
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5514
## (Intercept)-Canis_latrans 1.0000 3090
## (Intercept)-Sciurus_niger 1.0215 426
## (Intercept)-Procyon_lotor 1.0000 3929
## (Intercept)-Dasypus_novemcinctus 1.0005 5253
## (Intercept)-Lynx_rufus 1.0032 779
## (Intercept)-Didelphis_virginiana 1.0015 3831
## (Intercept)-Sylvilagus_floridanus 1.0037 1219
## (Intercept)-Sciurus_carolinensis 1.0058 3666
## (Intercept)-Vulpes_vulpes 1.0124 380
## (Intercept)-Sus_scrofa 1.0091 1817
# Includes foraging covariates of occupancy and null for detection
ms_null_forage <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.362
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2485 0.6270 -1.4499 -0.2816 1.0974 1.0172 1406
## Veg_shannon_index 0.3702 0.2706 -0.1446 0.3581 0.9318 1.0058 1713
## Avg_Cogongrass_Cover 0.3189 0.2555 -0.1933 0.3174 0.8131 1.0017 1977
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0846 3.3617 0.8180 3.1772 13.3488 1.0031 1118
## Veg_shannon_index 0.2996 0.3702 0.0376 0.1924 1.2155 1.0053 2181
## Avg_Cogongrass_Cover 0.2740 0.3501 0.0362 0.1722 1.1815 1.0121 1814
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7988 0.8555 0.057 0.5327 3.1998 1.0118 419
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4834 0.4211 -3.2999 -2.4848 -1.6183 1.0077 4475
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.939 1.2537 0.6554 1.6108 5.122 1.0126 1607
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6783 1.4701 1.3218 3.4769
## (Intercept)-Canis_latrans 0.2497 0.6404 -1.0476 0.2526
## (Intercept)-Sciurus_niger -0.2574 1.4846 -2.3594 -0.5335
## (Intercept)-Procyon_lotor 0.5314 0.6481 -0.7856 0.5470
## (Intercept)-Dasypus_novemcinctus -0.7588 0.5846 -1.9951 -0.7376
## (Intercept)-Lynx_rufus 0.1270 1.1748 -1.7119 -0.0249
## (Intercept)-Didelphis_virginiana -1.5002 0.6751 -2.8542 -1.4934
## (Intercept)-Sylvilagus_floridanus -0.3367 0.8291 -1.7898 -0.3832
## (Intercept)-Sciurus_carolinensis -1.5021 0.6696 -2.9006 -1.4891
## (Intercept)-Vulpes_vulpes -1.0130 1.4093 -3.2136 -1.2198
## (Intercept)-Sus_scrofa -2.1666 0.8651 -3.9562 -2.1279
## Veg_shannon_index-Odocoileus_virginianus 0.3076 0.5096 -0.7446 0.3139
## Veg_shannon_index-Canis_latrans 0.6485 0.3898 -0.0309 0.6145
## Veg_shannon_index-Sciurus_niger 0.3801 0.5347 -0.6742 0.3677
## Veg_shannon_index-Procyon_lotor 0.4947 0.3932 -0.2184 0.4746
## Veg_shannon_index-Dasypus_novemcinctus 0.2128 0.3357 -0.4518 0.2117
## Veg_shannon_index-Lynx_rufus 0.1938 0.5301 -0.9471 0.2175
## Veg_shannon_index-Didelphis_virginiana 0.5083 0.3925 -0.1926 0.4876
## Veg_shannon_index-Sylvilagus_floridanus 0.4696 0.4455 -0.3303 0.4468
## Veg_shannon_index-Sciurus_carolinensis 0.0293 0.3958 -0.8126 0.0462
## Veg_shannon_index-Vulpes_vulpes 0.1342 0.4846 -0.8715 0.1571
## Veg_shannon_index-Sus_scrofa 0.7301 0.5390 -0.1140 0.6560
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3104 0.4880 -0.6537 0.3100
## Avg_Cogongrass_Cover-Canis_latrans 0.5366 0.3784 -0.1328 0.5092
## Avg_Cogongrass_Cover-Sciurus_niger 0.0062 0.5577 -1.2587 0.0584
## Avg_Cogongrass_Cover-Procyon_lotor 0.4236 0.3877 -0.2897 0.4057
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4351 0.3264 -0.1913 0.4240
## Avg_Cogongrass_Cover-Lynx_rufus 0.5573 0.4317 -0.1912 0.5203
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4590 0.3629 -0.2458 0.4432
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0262 0.4405 -0.9666 0.0036
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4091 0.3617 -0.3010 0.4045
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3939 0.4558 -0.4840 0.3756
## Avg_Cogongrass_Cover-Sus_scrofa 0.0235 0.5128 -1.1999 0.0892
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1242 1.0102 969
## (Intercept)-Canis_latrans 1.5519 1.0014 2704
## (Intercept)-Sciurus_niger 3.5120 1.0386 291
## (Intercept)-Procyon_lotor 1.7888 1.0030 2158
## (Intercept)-Dasypus_novemcinctus 0.3412 1.0010 2165
## (Intercept)-Lynx_rufus 2.9718 1.0351 512
## (Intercept)-Didelphis_virginiana -0.2220 1.0025 2827
## (Intercept)-Sylvilagus_floridanus 1.3209 1.0228 1190
## (Intercept)-Sciurus_carolinensis -0.2208 1.0021 3224
## (Intercept)-Vulpes_vulpes 2.5385 1.0071 342
## (Intercept)-Sus_scrofa -0.5451 1.0046 1881
## Veg_shannon_index-Odocoileus_virginianus 1.3124 1.0012 3614
## Veg_shannon_index-Canis_latrans 1.5119 1.0006 2908
## Veg_shannon_index-Sciurus_niger 1.4691 1.0016 2325
## Veg_shannon_index-Procyon_lotor 1.3218 1.0017 2333
## Veg_shannon_index-Dasypus_novemcinctus 0.8754 1.0045 3454
## Veg_shannon_index-Lynx_rufus 1.1707 1.0021 2411
## Veg_shannon_index-Didelphis_virginiana 1.3487 1.0012 3436
## Veg_shannon_index-Sylvilagus_floridanus 1.3825 1.0056 2911
## Veg_shannon_index-Sciurus_carolinensis 0.7651 1.0006 3549
## Veg_shannon_index-Vulpes_vulpes 1.0534 1.0068 2419
## Veg_shannon_index-Sus_scrofa 2.0116 1.0002 2162
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2739 1.0000 4076
## Avg_Cogongrass_Cover-Canis_latrans 1.3639 1.0013 3053
## Avg_Cogongrass_Cover-Sciurus_niger 0.9775 1.0029 2068
## Avg_Cogongrass_Cover-Procyon_lotor 1.2285 1.0007 2243
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0948 1.0017 3837
## Avg_Cogongrass_Cover-Lynx_rufus 1.5257 1.0029 2887
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1975 1.0004 3839
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7710 1.0029 2853
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1417 1.0004 3841
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3635 1.0032 2858
## Avg_Cogongrass_Cover-Sus_scrofa 0.8820 1.0016 2765
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0589 -0.1099 0.0048 0.1219
## (Intercept)-Canis_latrans -2.6117 0.1707 -2.9618 -2.6070 -2.2949
## (Intercept)-Sciurus_niger -4.0512 0.6109 -5.2418 -4.0358 -2.9349
## (Intercept)-Procyon_lotor -2.2775 0.1328 -2.5467 -2.2762 -2.0255
## (Intercept)-Dasypus_novemcinctus -1.5748 0.1343 -1.8441 -1.5726 -1.3136
## (Intercept)-Lynx_rufus -3.5799 0.3303 -4.2544 -3.5688 -2.9709
## (Intercept)-Didelphis_virginiana -2.3103 0.2503 -2.8257 -2.2970 -1.8537
## (Intercept)-Sylvilagus_floridanus -3.2211 0.3249 -3.9227 -3.1995 -2.6502
## (Intercept)-Sciurus_carolinensis -2.4406 0.2633 -2.9797 -2.4342 -1.9502
## (Intercept)-Vulpes_vulpes -4.0879 0.7656 -5.6004 -4.0558 -2.7166
## (Intercept)-Sus_scrofa -2.8994 0.4598 -3.8987 -2.8673 -2.0873
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 6277
## (Intercept)-Canis_latrans 1.0058 3054
## (Intercept)-Sciurus_niger 1.0631 389
## (Intercept)-Procyon_lotor 1.0006 3981
## (Intercept)-Dasypus_novemcinctus 1.0049 5657
## (Intercept)-Lynx_rufus 1.0207 810
## (Intercept)-Didelphis_virginiana 1.0014 3525
## (Intercept)-Sylvilagus_floridanus 1.0109 1147
## (Intercept)-Sciurus_carolinensis 1.0003 3685
## (Intercept)-Vulpes_vulpes 1.0147 370
## (Intercept)-Sus_scrofa 1.0039 1862
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3822
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9812 0.6257 -2.1583 -1.0034 0.3654 1.0021 3193
## Avg_Cogongrass_Cover -0.7413 0.3805 -1.5195 -0.7370 -0.0078 1.0015 1332
## I(Avg_Cogongrass_Cover^2) 0.8457 0.3428 0.2385 0.8208 1.5704 1.0010 1337
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8163 3.5224 0.7219 2.8971 12.6215 1.0399 1083
## Avg_Cogongrass_Cover 0.3715 0.4699 0.0379 0.2256 1.5264 1.0034 2000
## I(Avg_Cogongrass_Cover^2) 0.5008 0.9412 0.0355 0.2188 2.8953 1.0220 653
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.562 0.5861 0.0551 0.3855 2.1018 1.0171 586
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4515 0.4033 -3.242 -2.4525 -1.629 1.0032 3469
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7623 1.1278 0.6071 1.4762 4.6778 1.0048 2608
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8517 1.5167 0.4735 2.6564
## (Intercept)-Canis_latrans -0.5231 0.6893 -1.9178 -0.5180
## (Intercept)-Sciurus_niger -1.0051 1.1719 -2.9843 -1.1279
## (Intercept)-Procyon_lotor -0.2320 0.6662 -1.6187 -0.2023
## (Intercept)-Dasypus_novemcinctus -1.4086 0.6369 -2.7116 -1.3919
## (Intercept)-Lynx_rufus -1.2380 0.8774 -2.8669 -1.2573
## (Intercept)-Didelphis_virginiana -2.0221 0.7141 -3.4944 -2.0075
## (Intercept)-Sylvilagus_floridanus -1.0828 0.7719 -2.5678 -1.0966
## (Intercept)-Sciurus_carolinensis -2.4620 0.7681 -4.0322 -2.4295
## (Intercept)-Vulpes_vulpes -2.3094 1.1528 -4.5287 -2.3519
## (Intercept)-Sus_scrofa -2.5398 0.9186 -4.4773 -2.4798
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7390 0.6264 -2.0184 -0.7209
## Avg_Cogongrass_Cover-Canis_latrans -0.4756 0.5085 -1.4325 -0.4981
## Avg_Cogongrass_Cover-Sciurus_niger -0.9736 0.6570 -2.4627 -0.9179
## Avg_Cogongrass_Cover-Procyon_lotor -0.6046 0.5021 -1.5806 -0.6146
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5618 0.4740 -1.4939 -0.5590
## Avg_Cogongrass_Cover-Lynx_rufus -0.6213 0.5555 -1.7449 -0.6227
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4878 0.5266 -1.4664 -0.5056
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1372 0.6053 -2.4658 -1.0857
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8088 0.5360 -1.9351 -0.7753
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8007 0.5969 -2.0775 -0.7723
## Avg_Cogongrass_Cover-Sus_scrofa -1.0225 0.6395 -2.4828 -0.9693
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1657 0.8206 0.0728 1.0088
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2337 0.7581 0.2558 1.0543
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3666 0.7198 -1.2922 0.4267
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1038 0.6654 0.2425 0.9808
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7321 0.3450 0.0672 0.7251
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1919 0.5532 0.3648 1.1076
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5960 0.4002 -0.1769 0.5951
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7663 0.4796 -0.0703 0.7293
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9791 0.3994 0.2868 0.9450
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9569 0.5233 0.1394 0.8909
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3712 0.6117 -1.1340 0.4475
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.3516 1.0291 1014
## (Intercept)-Canis_latrans 0.7779 1.0002 2795
## (Intercept)-Sciurus_niger 1.6614 1.0295 546
## (Intercept)-Procyon_lotor 1.0023 1.0019 2026
## (Intercept)-Dasypus_novemcinctus -0.1814 1.0010 3333
## (Intercept)-Lynx_rufus 0.5627 1.0073 1338
## (Intercept)-Didelphis_virginiana -0.6671 1.0039 3584
## (Intercept)-Sylvilagus_floridanus 0.4764 1.0169 1981
## (Intercept)-Sciurus_carolinensis -1.0444 1.0012 2430
## (Intercept)-Vulpes_vulpes 0.0115 1.0148 943
## (Intercept)-Sus_scrofa -0.8699 1.0084 2044
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4881 1.0006 2328
## Avg_Cogongrass_Cover-Canis_latrans 0.5598 1.0004 2719
## Avg_Cogongrass_Cover-Sciurus_niger 0.1569 1.0009 1824
## Avg_Cogongrass_Cover-Procyon_lotor 0.4053 1.0009 2504
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3905 1.0001 2395
## Avg_Cogongrass_Cover-Lynx_rufus 0.4708 1.0009 2613
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6333 1.0040 2408
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1017 1.0020 1820
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1615 1.0065 1802
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3311 1.0036 1968
## Avg_Cogongrass_Cover-Sus_scrofa 0.0808 1.0014 1814
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.3504 1.0006 944
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2473 1.0006 818
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.6056 1.0085 778
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.9339 1.0125 907
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4413 1.0003 2428
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4726 1.0024 1401
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4003 1.0003 2498
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8162 1.0050 1536
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8503 1.0049 1764
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2837 1.0192 1430
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3545 1.0001 1278
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0031 0.0593 -0.1122 0.0027 0.1217
## (Intercept)-Canis_latrans -2.6364 0.1721 -2.9847 -2.6310 -2.3127
## (Intercept)-Sciurus_niger -3.9091 0.5829 -5.0592 -3.8872 -2.8355
## (Intercept)-Procyon_lotor -2.2733 0.1316 -2.5381 -2.2725 -2.0206
## (Intercept)-Dasypus_novemcinctus -1.5764 0.1322 -1.8391 -1.5779 -1.3205
## (Intercept)-Lynx_rufus -3.4113 0.3215 -4.0910 -3.3908 -2.8362
## (Intercept)-Didelphis_virginiana -2.3267 0.2566 -2.8579 -2.3175 -1.8555
## (Intercept)-Sylvilagus_floridanus -3.1949 0.3143 -3.8464 -3.1808 -2.6125
## (Intercept)-Sciurus_carolinensis -2.4306 0.2642 -2.9783 -2.4176 -1.9561
## (Intercept)-Vulpes_vulpes -3.8681 0.6785 -5.2876 -3.8257 -2.6724
## (Intercept)-Sus_scrofa -2.8994 0.4667 -3.9361 -2.8629 -2.0967
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0019 2941
## (Intercept)-Sciurus_niger 1.0499 403
## (Intercept)-Procyon_lotor 1.0009 3948
## (Intercept)-Dasypus_novemcinctus 1.0009 5400
## (Intercept)-Lynx_rufus 1.0005 1103
## (Intercept)-Didelphis_virginiana 1.0017 3539
## (Intercept)-Sylvilagus_floridanus 1.0090 1381
## (Intercept)-Sciurus_carolinensis 1.0010 3518
## (Intercept)-Vulpes_vulpes 1.0377 598
## (Intercept)-Sus_scrofa 1.0046 1976
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5583
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9712 1.1290 -3.0736 -1.0173 1.4314 1.0082 1824
## Cogon_Patch_Size -0.2610 0.7256 -1.7484 -0.2258 1.1212 1.0178 858
## Veg_shannon_index 0.9074 0.4772 -0.0004 0.8893 1.9005 1.0068 946
## total_shrub_cover -0.2818 0.4018 -1.1026 -0.2741 0.4920 1.0030 1493
## Avg_Cogongrass_Cover 0.0895 0.9370 -1.6949 0.0794 1.9108 1.0479 368
## Tree_Density -1.9898 0.7537 -3.5405 -1.9806 -0.5312 1.0330 934
## Avg_Canopy_Cover 1.7837 0.5881 0.7145 1.7618 3.0187 1.0267 671
## I(Avg_Cogongrass_Cover^2) 1.4360 0.5375 0.4451 1.4010 2.5791 1.0108 509
## avg_veg_height -0.1897 0.5050 -1.2092 -0.1853 0.7657 1.0416 630
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.0733 17.4300 4.1908 16.0616 68.3911 1.0530 502
## Cogon_Patch_Size 3.6090 5.2868 0.1451 2.0510 17.0758 1.0105 624
## Veg_shannon_index 0.8221 1.1731 0.0510 0.4305 4.0111 1.0209 1057
## total_shrub_cover 0.6318 0.8459 0.0507 0.3649 2.7684 1.0136 1447
## Avg_Cogongrass_Cover 1.1523 1.8512 0.0518 0.5075 6.2573 1.0246 1085
## Tree_Density 3.1489 5.7503 0.0715 1.2855 17.3010 1.0283 503
## Avg_Canopy_Cover 1.8967 2.2958 0.1064 1.1888 8.2539 1.0185 572
## I(Avg_Cogongrass_Cover^2) 0.8588 1.8018 0.0446 0.3799 4.6298 1.1007 1012
## avg_veg_height 0.4568 0.6356 0.0425 0.2659 1.9382 1.0104 1710
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6576 2.0306 0.0683 1.01 6.7284 1.0092 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5269 0.4386 -3.3551 -2.5439 -1.6248 1.0032 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.134 1.305 0.7554 1.8009 5.5727 1.0089 2840
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.5903 3.3790 2.6273
## (Intercept)-Canis_latrans -0.8693 1.1907 -3.2438
## (Intercept)-Sciurus_niger 0.8644 2.7105 -3.2280
## (Intercept)-Procyon_lotor -0.4097 1.0510 -2.6278
## (Intercept)-Dasypus_novemcinctus -2.7281 1.1646 -5.3544
## (Intercept)-Lynx_rufus 0.1576 2.7251 -3.8108
## (Intercept)-Didelphis_virginiana -4.2167 1.3759 -7.2830
## (Intercept)-Sylvilagus_floridanus -2.3586 1.4275 -5.4238
## (Intercept)-Sciurus_carolinensis -4.9863 1.5720 -8.5128
## (Intercept)-Vulpes_vulpes -3.9806 2.4069 -8.3402
## (Intercept)-Sus_scrofa -5.9077 1.9671 -10.3389
## Cogon_Patch_Size-Odocoileus_virginianus -0.0780 1.4863 -2.9059
## Cogon_Patch_Size-Canis_latrans 1.5673 1.3737 -0.3656
## Cogon_Patch_Size-Sciurus_niger -0.9351 1.9440 -5.4109
## Cogon_Patch_Size-Procyon_lotor -0.5275 0.8300 -2.1456
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2489 0.7122 -1.7262
## Cogon_Patch_Size-Lynx_rufus -0.3906 1.5390 -3.1923
## Cogon_Patch_Size-Didelphis_virginiana 1.5963 0.9913 -0.0807
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5275 1.6556 -5.7760
## Cogon_Patch_Size-Sciurus_carolinensis -1.2578 1.4313 -4.8827
## Cogon_Patch_Size-Vulpes_vulpes -0.7355 1.7980 -4.8380
## Cogon_Patch_Size-Sus_scrofa -0.9381 1.6079 -4.8965
## Veg_shannon_index-Odocoileus_virginianus 0.7270 0.9006 -1.2430
## Veg_shannon_index-Canis_latrans 1.3079 0.6990 0.1590
## Veg_shannon_index-Sciurus_niger 1.0351 0.9710 -0.7528
## Veg_shannon_index-Procyon_lotor 1.1259 0.6088 0.0422
## Veg_shannon_index-Dasypus_novemcinctus 0.6297 0.5593 -0.5135
## Veg_shannon_index-Lynx_rufus 0.9404 0.9683 -0.9926
## Veg_shannon_index-Didelphis_virginiana 1.0501 0.6722 -0.1952
## Veg_shannon_index-Sylvilagus_floridanus 0.9685 0.7145 -0.3531
## Veg_shannon_index-Sciurus_carolinensis 0.2967 0.7835 -1.4786
## Veg_shannon_index-Vulpes_vulpes 0.5850 0.8426 -1.2963
## Veg_shannon_index-Sus_scrofa 1.5684 1.0404 0.0728
## total_shrub_cover-Odocoileus_virginianus -0.1121 0.7663 -1.5504
## total_shrub_cover-Canis_latrans -0.0097 0.5635 -1.0267
## total_shrub_cover-Sciurus_niger -0.5580 0.8213 -2.4289
## total_shrub_cover-Procyon_lotor -0.8045 0.5847 -2.0933
## total_shrub_cover-Dasypus_novemcinctus 0.0477 0.5019 -0.8722
## total_shrub_cover-Lynx_rufus -0.6856 0.8349 -2.5309
## total_shrub_cover-Didelphis_virginiana -0.4742 0.6307 -1.8585
## total_shrub_cover-Sylvilagus_floridanus -0.2265 0.6751 -1.5803
## total_shrub_cover-Sciurus_carolinensis -0.0270 0.6227 -1.2100
## total_shrub_cover-Vulpes_vulpes -0.3971 0.7723 -2.0893
## total_shrub_cover-Sus_scrofa 0.0247 0.7494 -1.3187
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0394 1.3085 -2.5402
## Avg_Cogongrass_Cover-Canis_latrans 0.1604 1.1612 -2.0635
## Avg_Cogongrass_Cover-Sciurus_niger -0.2331 1.4266 -3.3673
## Avg_Cogongrass_Cover-Procyon_lotor 0.3484 1.1693 -1.8460
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6622 1.2430 -1.5097
## Avg_Cogongrass_Cover-Lynx_rufus 0.3182 1.2802 -2.0948
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2075 1.1722 -2.0003
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4455 1.2823 -3.1626
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1292 1.1707 -2.2037
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2214 1.2715 -2.2727
## Avg_Cogongrass_Cover-Sus_scrofa -0.4126 1.3284 -3.3641
## Tree_Density-Odocoileus_virginianus -0.9781 1.4628 -3.2196
## Tree_Density-Canis_latrans -2.7156 1.2896 -5.8197
## Tree_Density-Sciurus_niger -1.9907 1.5912 -5.4188
## Tree_Density-Procyon_lotor -1.8272 0.9288 -3.7809
## Tree_Density-Dasypus_novemcinctus -3.6551 1.8772 -8.4102
## Tree_Density-Lynx_rufus -0.8142 1.6142 -3.2333
## Tree_Density-Didelphis_virginiana -2.3578 1.1763 -5.2232
## Tree_Density-Sylvilagus_floridanus -2.4818 1.4092 -5.8743
## Tree_Density-Sciurus_carolinensis -2.7096 1.4733 -6.4417
## Tree_Density-Vulpes_vulpes -2.0932 1.5394 -5.6352
## Tree_Density-Sus_scrofa -2.3797 1.5567 -6.1801
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3374 1.2529 -1.2110
## Avg_Canopy_Cover-Canis_latrans 0.3355 0.7225 -1.0838
## Avg_Canopy_Cover-Sciurus_niger 2.0706 1.5284 -0.8105
## Avg_Canopy_Cover-Procyon_lotor 1.7026 0.7098 0.3980
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9460 0.7162 0.7292
## Avg_Canopy_Cover-Lynx_rufus 1.4789 1.2520 -0.9120
## Avg_Canopy_Cover-Didelphis_virginiana 2.6007 0.9566 1.1276
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.1315 1.4598 1.0738
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2391 0.8671 0.8496
## Avg_Canopy_Cover-Vulpes_vulpes 2.1981 1.1815 0.3889
## Avg_Canopy_Cover-Sus_scrofa 2.0052 0.8665 0.5617
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6887 1.0071 0.0898
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8813 0.8701 0.5640
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1388 1.0955 -1.2654
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7372 0.8283 0.4307
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3970 0.6724 0.1810
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9278 0.9320 0.5050
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0833 0.6485 -0.1987
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1918 0.7539 -0.2098
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5918 0.6949 0.3735
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7296 0.8312 0.3732
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.8751 0.9451 -1.3249
## avg_veg_height-Odocoileus_virginianus -0.2013 0.7854 -1.7705
## avg_veg_height-Canis_latrans -0.4249 0.6391 -1.7990
## avg_veg_height-Sciurus_niger -0.2794 0.8345 -2.0777
## avg_veg_height-Procyon_lotor 0.0988 0.6302 -1.1057
## avg_veg_height-Dasypus_novemcinctus 0.1006 0.6140 -1.0499
## avg_veg_height-Lynx_rufus -0.2568 0.7896 -1.9106
## avg_veg_height-Didelphis_virginiana -0.3086 0.6777 -1.7101
## avg_veg_height-Sylvilagus_floridanus -0.3148 0.7097 -1.8401
## avg_veg_height-Sciurus_carolinensis 0.0669 0.6716 -1.1828
## avg_veg_height-Vulpes_vulpes -0.3329 0.8017 -2.0798
## avg_veg_height-Sus_scrofa -0.2914 0.7250 -1.8483
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0271 15.4620 1.0584 433
## (Intercept)-Canis_latrans -0.8700 1.5110 1.0025 1782
## (Intercept)-Sciurus_niger 0.4286 7.7374 1.0226 271
## (Intercept)-Procyon_lotor -0.3730 1.5596 1.0026 1300
## (Intercept)-Dasypus_novemcinctus -2.6046 -0.7923 1.0097 710
## (Intercept)-Lynx_rufus -0.3050 6.9303 1.0320 260
## (Intercept)-Didelphis_virginiana -4.1154 -1.7657 1.0272 1227
## (Intercept)-Sylvilagus_floridanus -2.3117 0.3559 1.0084 1062
## (Intercept)-Sciurus_carolinensis -4.8125 -2.3572 1.0233 563
## (Intercept)-Vulpes_vulpes -4.1229 1.5629 1.0314 384
## (Intercept)-Sus_scrofa -5.7488 -2.5559 1.0291 662
## Cogon_Patch_Size-Odocoileus_virginianus -0.1543 3.2381 1.0156 1826
## Cogon_Patch_Size-Canis_latrans 1.3164 5.0160 1.0075 1170
## Cogon_Patch_Size-Sciurus_niger -0.7933 2.6897 1.0185 582
## Cogon_Patch_Size-Procyon_lotor -0.5270 1.0068 1.0270 892
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2220 1.0712 1.0339 989
## Cogon_Patch_Size-Lynx_rufus -0.4582 3.1015 1.0091 904
## Cogon_Patch_Size-Didelphis_virginiana 1.5075 3.7446 1.0037 738
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2161 0.8538 1.0123 700
## Cogon_Patch_Size-Sciurus_carolinensis -0.9854 0.7360 1.0317 684
## Cogon_Patch_Size-Vulpes_vulpes -0.5894 2.3835 1.0116 662
## Cogon_Patch_Size-Sus_scrofa -0.6247 1.4734 1.0030 1129
## Veg_shannon_index-Odocoileus_virginianus 0.7597 2.4450 1.0023 1769
## Veg_shannon_index-Canis_latrans 1.2257 2.9370 1.0081 1280
## Veg_shannon_index-Sciurus_niger 0.9746 3.2481 1.0039 1386
## Veg_shannon_index-Procyon_lotor 1.0835 2.4563 1.0094 1030
## Veg_shannon_index-Dasypus_novemcinctus 0.6423 1.7207 1.0027 2022
## Veg_shannon_index-Lynx_rufus 0.9122 2.9999 1.0089 1160
## Veg_shannon_index-Didelphis_virginiana 1.0183 2.4722 1.0049 2078
## Veg_shannon_index-Sylvilagus_floridanus 0.9306 2.4906 1.0015 1832
## Veg_shannon_index-Sciurus_carolinensis 0.3695 1.6657 1.0016 1515
## Veg_shannon_index-Vulpes_vulpes 0.6449 2.0970 1.0026 1558
## Veg_shannon_index-Sus_scrofa 1.3820 4.0741 1.0051 972
## total_shrub_cover-Odocoileus_virginianus -0.1514 1.5373 1.0028 2518
## total_shrub_cover-Canis_latrans -0.0379 1.2004 1.0012 2506
## total_shrub_cover-Sciurus_niger -0.4827 0.9019 1.0037 1216
## total_shrub_cover-Procyon_lotor -0.7464 0.1835 1.0012 2051
## total_shrub_cover-Dasypus_novemcinctus 0.0207 1.1016 1.0002 2634
## total_shrub_cover-Lynx_rufus -0.6173 0.7980 1.0036 1201
## total_shrub_cover-Didelphis_virginiana -0.4316 0.6862 1.0008 2046
## total_shrub_cover-Sylvilagus_floridanus -0.2238 1.1196 1.0043 2324
## total_shrub_cover-Sciurus_carolinensis -0.0618 1.2951 1.0030 2757
## total_shrub_cover-Vulpes_vulpes -0.3559 1.0301 1.0078 2379
## total_shrub_cover-Sus_scrofa -0.0302 1.6455 1.0021 2460
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0222 2.6002 1.0301 672
## Avg_Cogongrass_Cover-Canis_latrans 0.1431 2.4954 1.0280 496
## Avg_Cogongrass_Cover-Sciurus_niger -0.1457 2.3544 1.0262 586
## Avg_Cogongrass_Cover-Procyon_lotor 0.3076 2.7385 1.0478 530
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5721 3.4391 1.0528 525
## Avg_Cogongrass_Cover-Lynx_rufus 0.2672 3.0466 1.0327 772
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1798 2.6525 1.0245 577
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3707 1.8537 1.0267 625
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1030 2.4951 1.0396 514
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2053 2.8260 1.0299 563
## Avg_Cogongrass_Cover-Sus_scrofa -0.3368 1.9018 1.0129 741
## Tree_Density-Odocoileus_virginianus -1.1857 2.4594 1.0120 831
## Tree_Density-Canis_latrans -2.5277 -0.7712 1.0665 706
## Tree_Density-Sciurus_niger -1.9581 1.1321 1.0356 833
## Tree_Density-Procyon_lotor -1.8055 -0.0825 1.0211 935
## Tree_Density-Dasypus_novemcinctus -3.1817 -1.2942 1.0763 382
## Tree_Density-Lynx_rufus -1.0407 3.2363 1.0109 546
## Tree_Density-Didelphis_virginiana -2.2039 -0.5127 1.0412 665
## Tree_Density-Sylvilagus_floridanus -2.2848 -0.2241 1.0622 860
## Tree_Density-Sciurus_carolinensis -2.4362 -0.5969 1.0591 717
## Tree_Density-Vulpes_vulpes -2.0073 0.9004 1.0086 1134
## Tree_Density-Sus_scrofa -2.1871 0.2307 1.0199 1035
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3755 3.8780 1.0152 1601
## Avg_Canopy_Cover-Canis_latrans 0.3265 1.7485 1.0000 1365
## Avg_Canopy_Cover-Sciurus_niger 1.9458 5.3961 1.0170 800
## Avg_Canopy_Cover-Procyon_lotor 1.6590 3.2185 1.0060 1282
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8904 3.5668 1.0210 777
## Avg_Canopy_Cover-Lynx_rufus 1.4474 4.1452 1.0112 806
## Avg_Canopy_Cover-Didelphis_virginiana 2.4518 4.9484 1.0321 545
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8701 6.6768 1.0209 495
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1326 4.2274 1.0304 1049
## Avg_Canopy_Cover-Vulpes_vulpes 1.9989 5.0694 1.0079 603
## Avg_Canopy_Cover-Sus_scrofa 1.9275 3.9782 1.0157 923
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.5548 4.0937 1.0007 1016
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.7494 4.0371 1.0064 735
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1670 3.2298 1.0062 584
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.6367 3.6562 1.0101 732
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3474 2.8368 1.0052 853
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.7923 4.1605 1.0098 852
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0891 2.4064 1.0008 972
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1732 2.7912 1.0185 735
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5366 3.1404 1.0126 817
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6280 3.6652 1.0160 775
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9782 2.5001 1.0037 825
## avg_veg_height-Odocoileus_virginianus -0.1865 1.3258 1.0175 1166
## avg_veg_height-Canis_latrans -0.3934 0.7201 1.0426 851
## avg_veg_height-Sciurus_niger -0.2508 1.3027 1.0150 1136
## avg_veg_height-Procyon_lotor 0.0848 1.3911 1.0148 1027
## avg_veg_height-Dasypus_novemcinctus 0.0846 1.3571 1.0212 982
## avg_veg_height-Lynx_rufus -0.2242 1.1969 1.0375 1024
## avg_veg_height-Didelphis_virginiana -0.2818 0.9658 1.0265 1075
## avg_veg_height-Sylvilagus_floridanus -0.2786 1.0004 1.0335 1044
## avg_veg_height-Sciurus_carolinensis 0.0382 1.4521 1.0208 1335
## avg_veg_height-Vulpes_vulpes -0.3160 1.1192 1.0305 941
## avg_veg_height-Sus_scrofa -0.2666 1.0470 1.0221 941
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0047 0.0588 -0.1094 0.0043 0.1182
## (Intercept)-Canis_latrans -2.6126 0.1725 -2.9628 -2.6104 -2.2814
## (Intercept)-Sciurus_niger -4.5387 0.4606 -5.4244 -4.5472 -3.6007
## (Intercept)-Procyon_lotor -2.2709 0.1307 -2.5324 -2.2670 -2.0254
## (Intercept)-Dasypus_novemcinctus -1.5761 0.1331 -1.8443 -1.5742 -1.3197
## (Intercept)-Lynx_rufus -3.7472 0.3303 -4.3826 -3.7473 -3.1006
## (Intercept)-Didelphis_virginiana -2.2934 0.2440 -2.7951 -2.2856 -1.8469
## (Intercept)-Sylvilagus_floridanus -3.2068 0.2852 -3.8049 -3.1962 -2.6841
## (Intercept)-Sciurus_carolinensis -2.4283 0.2601 -2.9682 -2.4170 -1.9405
## (Intercept)-Vulpes_vulpes -4.1607 0.6668 -5.5767 -4.1117 -2.9684
## (Intercept)-Sus_scrofa -2.8667 0.4445 -3.8566 -2.8370 -2.0974
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5040
## (Intercept)-Canis_latrans 0.9999 2853
## (Intercept)-Sciurus_niger 1.0191 445
## (Intercept)-Procyon_lotor 1.0042 4001
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## (Intercept)-Lynx_rufus 1.0041 547
## (Intercept)-Didelphis_virginiana 1.0022 4244
## (Intercept)-Sylvilagus_floridanus 1.0047 1418
## (Intercept)-Sciurus_carolinensis 1.0023 3762
## (Intercept)-Vulpes_vulpes 1.0140 447
## (Intercept)-Sus_scrofa 1.0282 1911
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0865
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1940 0.5772 -1.3280 -0.205 0.9644 1.0114 2299
## Avg_Cogongrass_Cover 0.1994 0.2430 -0.2829 0.204 0.6816 1.0284 2196
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3088 3.0580 0.6198 2.5045 11.0577 1.0385 875
## Avg_Cogongrass_Cover 0.2646 0.3466 0.0375 0.1656 1.0577 1.0062 2343
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.738 0.7829 0.0664 0.5021 2.7706 1.1293 513
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5949 0.4260 -3.4180 -2.5954 -1.7086 1.0004 4126
## shrub_cover 0.2209 0.2390 -0.2413 0.2164 0.7014 1.0017 3205
## veg_height -0.0050 0.1537 -0.3175 -0.0047 0.2899 1.0052 3121
## week -0.0368 0.1183 -0.2828 -0.0343 0.1838 1.0005 2844
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9971 1.2639 0.6461 1.6837 5.2486 1.0066 2308
## shrub_cover 0.4470 0.3745 0.0842 0.3466 1.3830 1.0045 1765
## veg_height 0.1897 0.1304 0.0551 0.1542 0.5252 1.0015 3387
## week 0.0970 0.0794 0.0251 0.0758 0.3014 1.0014 2509
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3786 1.4875 1.1446 3.1908
## (Intercept)-Canis_latrans 0.4325 0.6525 -0.8116 0.4078
## (Intercept)-Sciurus_niger -0.4555 1.1129 -2.2475 -0.5823
## (Intercept)-Procyon_lotor 0.5419 0.6033 -0.6779 0.5516
## (Intercept)-Dasypus_novemcinctus -0.6175 0.5744 -1.7778 -0.6156
## (Intercept)-Lynx_rufus 0.1443 1.1154 -1.5569 0.0052
## (Intercept)-Didelphis_virginiana -1.2243 0.6331 -2.5014 -1.2231
## (Intercept)-Sylvilagus_floridanus -0.3377 0.6926 -1.6340 -0.3542
## (Intercept)-Sciurus_carolinensis -1.3106 0.6583 -2.6512 -1.2919
## (Intercept)-Vulpes_vulpes -1.0619 1.1678 -3.0750 -1.1727
## (Intercept)-Sus_scrofa -1.6766 0.8109 -3.3281 -1.6506
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1754 0.4672 -0.7563 0.1752
## Avg_Cogongrass_Cover-Canis_latrans 0.4208 0.3802 -0.2424 0.3900
## Avg_Cogongrass_Cover-Sciurus_niger -0.1272 0.5601 -1.4004 -0.0728
## Avg_Cogongrass_Cover-Procyon_lotor 0.2183 0.3436 -0.4371 0.2158
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3401 0.3231 -0.2668 0.3267
## Avg_Cogongrass_Cover-Lynx_rufus 0.4111 0.4098 -0.3182 0.3771
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3162 0.3475 -0.3489 0.3125
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1524 0.4238 -1.0917 -0.1199
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3373 0.3472 -0.3251 0.3327
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2892 0.4303 -0.5512 0.2796
## Avg_Cogongrass_Cover-Sus_scrofa -0.0549 0.4865 -1.1706 -0.0136
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5848 1.0568 640
## (Intercept)-Canis_latrans 1.7978 1.0054 2800
## (Intercept)-Sciurus_niger 2.0945 1.0108 600
## (Intercept)-Procyon_lotor 1.7172 1.0049 2733
## (Intercept)-Dasypus_novemcinctus 0.5109 1.0021 3682
## (Intercept)-Lynx_rufus 2.9869 1.0079 532
## (Intercept)-Didelphis_virginiana 0.0123 1.0025 2612
## (Intercept)-Sylvilagus_floridanus 1.1040 1.0015 2383
## (Intercept)-Sciurus_carolinensis -0.0852 1.0041 3145
## (Intercept)-Vulpes_vulpes 1.5365 1.0061 520
## (Intercept)-Sus_scrofa -0.1680 1.0042 2558
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1221 1.0091 3722
## Avg_Cogongrass_Cover-Canis_latrans 1.2818 1.0049 3738
## Avg_Cogongrass_Cover-Sciurus_niger 0.8327 1.0092 1802
## Avg_Cogongrass_Cover-Procyon_lotor 0.9368 1.0079 4089
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0136 1.0061 4321
## Avg_Cogongrass_Cover-Lynx_rufus 1.3032 1.0157 3163
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0218 1.0076 3591
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6031 1.0055 2435
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0482 1.0095 4288
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2034 1.0084 3191
## Avg_Cogongrass_Cover-Sus_scrofa 0.7618 1.0130 2507
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0054 0.0591 -0.1098 0.0055 0.1222
## (Intercept)-Canis_latrans -2.7623 0.1932 -3.1610 -2.7559 -2.3979
## (Intercept)-Sciurus_niger -4.1180 0.6494 -5.3980 -4.1171 -2.8878
## (Intercept)-Procyon_lotor -2.2983 0.1465 -2.5967 -2.2930 -2.0261
## (Intercept)-Dasypus_novemcinctus -1.7277 0.1547 -2.0454 -1.7246 -1.4409
## (Intercept)-Lynx_rufus -3.6846 0.3861 -4.4570 -3.6769 -2.9635
## (Intercept)-Didelphis_virginiana -2.5438 0.2883 -3.1369 -2.5337 -2.0024
## (Intercept)-Sylvilagus_floridanus -3.1968 0.2981 -3.8251 -3.1790 -2.6610
## (Intercept)-Sciurus_carolinensis -2.5901 0.3117 -3.2522 -2.5755 -2.0158
## (Intercept)-Vulpes_vulpes -4.1851 0.7803 -5.8361 -4.1422 -2.7944
## (Intercept)-Sus_scrofa -3.2467 0.5706 -4.4088 -3.2468 -2.1413
## shrub_cover-Odocoileus_virginianus -0.0521 0.0646 -0.1792 -0.0529 0.0760
## shrub_cover-Canis_latrans -0.2631 0.2174 -0.6910 -0.2643 0.1561
## shrub_cover-Sciurus_niger -0.3187 0.4519 -1.2412 -0.3098 0.5508
## shrub_cover-Procyon_lotor 0.2451 0.1639 -0.0869 0.2505 0.5567
## shrub_cover-Dasypus_novemcinctus 0.7849 0.2863 0.2457 0.7797 1.3535
## shrub_cover-Lynx_rufus -0.2314 0.3455 -0.9053 -0.2324 0.4551
## shrub_cover-Didelphis_virginiana 0.8801 0.3623 0.2326 0.8550 1.6638
## shrub_cover-Sylvilagus_floridanus 0.2774 0.3955 -0.4560 0.2579 1.1032
## shrub_cover-Sciurus_carolinensis 0.7427 0.3874 0.0239 0.7361 1.5280
## shrub_cover-Vulpes_vulpes -0.0775 0.5354 -1.1911 -0.0502 0.9428
## shrub_cover-Sus_scrofa 0.4761 0.6949 -0.8925 0.4540 1.8918
## veg_height-Odocoileus_virginianus -0.2964 0.0649 -0.4234 -0.2957 -0.1748
## veg_height-Canis_latrans -0.5888 0.1866 -0.9669 -0.5842 -0.2427
## veg_height-Sciurus_niger -0.0517 0.4043 -0.8372 -0.0555 0.7756
## veg_height-Procyon_lotor 0.3316 0.1222 0.1032 0.3308 0.5768
## veg_height-Dasypus_novemcinctus 0.2239 0.1302 -0.0255 0.2219 0.4875
## veg_height-Lynx_rufus 0.0055 0.2417 -0.4937 0.0101 0.4745
## veg_height-Didelphis_virginiana 0.4020 0.2336 -0.0300 0.3931 0.8832
## veg_height-Sylvilagus_floridanus 0.1240 0.2435 -0.3509 0.1209 0.5959
## veg_height-Sciurus_carolinensis 0.0468 0.2052 -0.3439 0.0450 0.4661
## veg_height-Vulpes_vulpes -0.1351 0.3147 -0.8120 -0.1175 0.4392
## veg_height-Sus_scrofa -0.1337 0.3197 -0.7926 -0.1304 0.4827
## week-Odocoileus_virginianus 0.2105 0.0595 0.0984 0.2090 0.3273
## week-Canis_latrans 0.0763 0.1304 -0.1810 0.0793 0.3239
## week-Sciurus_niger -0.2825 0.3002 -0.9839 -0.2526 0.2145
## week-Procyon_lotor -0.0440 0.1162 -0.2820 -0.0411 0.1768
## week-Dasypus_novemcinctus -0.1565 0.1385 -0.4431 -0.1522 0.1012
## week-Lynx_rufus -0.0224 0.1912 -0.4198 -0.0152 0.3415
## week-Didelphis_virginiana -0.1944 0.2152 -0.6639 -0.1793 0.1834
## week-Sylvilagus_floridanus -0.1454 0.2075 -0.5870 -0.1337 0.2256
## week-Sciurus_carolinensis 0.1477 0.1793 -0.2083 0.1468 0.4954
## week-Vulpes_vulpes -0.1005 0.2710 -0.6721 -0.0848 0.3871
## week-Sus_scrofa 0.1031 0.2250 -0.3549 0.1015 0.5511
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0040 2124
## (Intercept)-Sciurus_niger 1.0447 531
## (Intercept)-Procyon_lotor 1.0026 3476
## (Intercept)-Dasypus_novemcinctus 1.0008 4387
## (Intercept)-Lynx_rufus 1.0011 641
## (Intercept)-Didelphis_virginiana 1.0017 2857
## (Intercept)-Sylvilagus_floridanus 1.0014 1577
## (Intercept)-Sciurus_carolinensis 1.0006 2338
## (Intercept)-Vulpes_vulpes 1.0060 453
## (Intercept)-Sus_scrofa 1.0081 1822
## shrub_cover-Odocoileus_virginianus 1.0001 5010
## shrub_cover-Canis_latrans 1.0022 2652
## shrub_cover-Sciurus_niger 1.0179 1326
## shrub_cover-Procyon_lotor 1.0013 3840
## shrub_cover-Dasypus_novemcinctus 1.0035 3594
## shrub_cover-Lynx_rufus 1.0021 1384
## shrub_cover-Didelphis_virginiana 1.0161 2169
## shrub_cover-Sylvilagus_floridanus 1.0017 1851
## shrub_cover-Sciurus_carolinensis 1.0015 2196
## shrub_cover-Vulpes_vulpes 1.0018 1865
## shrub_cover-Sus_scrofa 1.0045 2325
## veg_height-Odocoileus_virginianus 1.0013 5250
## veg_height-Canis_latrans 1.0008 1938
## veg_height-Sciurus_niger 1.0115 1519
## veg_height-Procyon_lotor 1.0083 4192
## veg_height-Dasypus_novemcinctus 1.0011 4771
## veg_height-Lynx_rufus 1.0089 2358
## veg_height-Didelphis_virginiana 1.0021 3793
## veg_height-Sylvilagus_floridanus 1.0026 2513
## veg_height-Sciurus_carolinensis 1.0005 3622
## veg_height-Vulpes_vulpes 1.0023 2054
## veg_height-Sus_scrofa 1.0009 3250
## week-Odocoileus_virginianus 1.0025 5250
## week-Canis_latrans 1.0001 3700
## week-Sciurus_niger 1.0035 1936
## week-Procyon_lotor 1.0007 4465
## week-Dasypus_novemcinctus 1.0003 4793
## week-Lynx_rufus 1.0010 3053
## week-Didelphis_virginiana 1.0013 3505
## week-Sylvilagus_floridanus 1.0024 3064
## week-Sciurus_carolinensis 1.0010 4594
## week-Vulpes_vulpes 1.0004 3080
## week-Sus_scrofa 1.0004 4574
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.378
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2591 0.5765 -1.3517 -0.2755 0.9289 1.0047 2561
## Avg_Cogongrass_Cover 0.2002 0.2370 -0.2937 0.2035 0.6585 1.0047 2647
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4912 3.4100 0.7274 2.7145 10.6996 1.0301 1353
## Avg_Cogongrass_Cover 0.2511 0.2951 0.0352 0.1655 0.9590 1.0022 2611
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7311 0.7326 0.0651 0.5086 2.6909 1.0007 505
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4669 0.4107 -3.2585 -2.4704 -1.6469 1.0006 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8481 1.1633 0.6345 1.5469 4.9182 1.0075 1865
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4305 1.3957 1.3070 3.2412
## (Intercept)-Canis_latrans 0.2986 0.5996 -0.8783 0.2843
## (Intercept)-Sciurus_niger -0.5071 1.1909 -2.3083 -0.6763
## (Intercept)-Procyon_lotor 0.5122 0.6031 -0.7719 0.5232
## (Intercept)-Dasypus_novemcinctus -0.7067 0.5655 -1.8526 -0.6968
## (Intercept)-Lynx_rufus -0.0870 0.8614 -1.6488 -0.1360
## (Intercept)-Didelphis_virginiana -1.3761 0.6208 -2.6370 -1.3639
## (Intercept)-Sylvilagus_floridanus -0.3343 0.7322 -1.7015 -0.3672
## (Intercept)-Sciurus_carolinensis -1.4614 0.6446 -2.8240 -1.4347
## (Intercept)-Vulpes_vulpes -1.0946 1.3082 -3.1087 -1.2559
## (Intercept)-Sus_scrofa -1.9001 0.8057 -3.5583 -1.8739
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2023 0.4613 -0.6993 0.2002
## Avg_Cogongrass_Cover-Canis_latrans 0.3656 0.3486 -0.2708 0.3404
## Avg_Cogongrass_Cover-Sciurus_niger -0.0981 0.5192 -1.2665 -0.0499
## Avg_Cogongrass_Cover-Procyon_lotor 0.2514 0.3473 -0.3953 0.2383
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3364 0.3117 -0.2528 0.3308
## Avg_Cogongrass_Cover-Lynx_rufus 0.4451 0.3797 -0.2422 0.4180
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3281 0.3474 -0.3487 0.3225
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1368 0.4160 -1.0756 -0.1026
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3399 0.3448 -0.3219 0.3401
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2788 0.4144 -0.5161 0.2671
## Avg_Cogongrass_Cover-Sus_scrofa -0.0776 0.4888 -1.1934 -0.0276
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.6309 1.0038 739
## (Intercept)-Canis_latrans 1.5103 1.0062 2823
## (Intercept)-Sciurus_niger 2.4885 1.0161 414
## (Intercept)-Procyon_lotor 1.6520 1.0016 2750
## (Intercept)-Dasypus_novemcinctus 0.4130 1.0024 3276
## (Intercept)-Lynx_rufus 1.7954 1.0030 1095
## (Intercept)-Didelphis_virginiana -0.1859 1.0016 3005
## (Intercept)-Sylvilagus_floridanus 1.2136 1.0021 1949
## (Intercept)-Sciurus_carolinensis -0.2432 1.0012 3822
## (Intercept)-Vulpes_vulpes 1.7915 1.0246 430
## (Intercept)-Sus_scrofa -0.3485 1.0012 2378
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1518 1.0039 3883
## Avg_Cogongrass_Cover-Canis_latrans 1.1353 1.0003 4605
## Avg_Cogongrass_Cover-Sciurus_niger 0.8342 1.0020 2173
## Avg_Cogongrass_Cover-Procyon_lotor 0.9886 1.0033 4230
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9913 1.0004 4532
## Avg_Cogongrass_Cover-Lynx_rufus 1.2808 1.0057 4250
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0533 1.0040 4556
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5773 1.0055 3209
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0375 1.0018 4104
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1447 1.0143 3428
## Avg_Cogongrass_Cover-Sus_scrofa 0.7533 1.0012 2870
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0037 0.0584 -0.1083 0.0048 0.1179
## (Intercept)-Canis_latrans -2.6188 0.1776 -2.9814 -2.6117 -2.2837
## (Intercept)-Sciurus_niger -3.9345 0.5959 -5.1122 -3.8988 -2.8779
## (Intercept)-Procyon_lotor -2.2693 0.1302 -2.5346 -2.2655 -2.0161
## (Intercept)-Dasypus_novemcinctus -1.5744 0.1333 -1.8438 -1.5711 -1.3255
## (Intercept)-Lynx_rufus -3.5222 0.3271 -4.1994 -3.5135 -2.9056
## (Intercept)-Didelphis_virginiana -2.3085 0.2490 -2.8267 -2.2985 -1.8502
## (Intercept)-Sylvilagus_floridanus -3.1850 0.3143 -3.8480 -3.1656 -2.6286
## (Intercept)-Sciurus_carolinensis -2.4391 0.2675 -2.9845 -2.4237 -1.9520
## (Intercept)-Vulpes_vulpes -4.0236 0.7842 -5.7146 -3.9549 -2.6943
## (Intercept)-Sus_scrofa -2.9179 0.4765 -3.9585 -2.8798 -2.0941
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5668
## (Intercept)-Canis_latrans 1.0040 3098
## (Intercept)-Sciurus_niger 1.0059 370
## (Intercept)-Procyon_lotor 1.0038 4056
## (Intercept)-Dasypus_novemcinctus 1.0008 5014
## (Intercept)-Lynx_rufus 1.0131 1122
## (Intercept)-Didelphis_virginiana 1.0032 3902
## (Intercept)-Sylvilagus_floridanus 1.0020 1263
## (Intercept)-Sciurus_carolinensis 1.0004 3376
## (Intercept)-Vulpes_vulpes 1.0490 438
## (Intercept)-Sus_scrofa 1.0020 1820
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7495
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2712 0.5718 -1.3604 -0.2773 0.9116 1.0036 3511
## Avg_Cogongrass_Cover 0.1936 0.2389 -0.2829 0.1996 0.6451 1.0008 1969
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3505 2.7227 0.7170 2.6523 10.2630 1.0039 2000
## Avg_Cogongrass_Cover 0.2613 0.3285 0.0345 0.1680 1.0384 1.0499 2418
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7019 0.7309 0.056 0.4682 2.674 1.0041 529
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4897 0.4045 -3.2668 -2.4987 -1.6598 1.0001 4369
## week -0.0351 0.1157 -0.2731 -0.0322 0.1852 1.0003 3598
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8061 1.1028 0.6241 1.5087 4.8076 1.0002 2238
## week 0.0963 0.0761 0.0246 0.0759 0.2960 1.0110 2902
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3708 1.3260 1.2276 3.1888
## (Intercept)-Canis_latrans 0.2717 0.6017 -0.9061 0.2648
## (Intercept)-Sciurus_niger -0.6168 1.1176 -2.4158 -0.7670
## (Intercept)-Procyon_lotor 0.5186 0.6047 -0.7022 0.5310
## (Intercept)-Dasypus_novemcinctus -0.7223 0.5681 -1.8852 -0.7046
## (Intercept)-Lynx_rufus -0.0512 0.9109 -1.6196 -0.1386
## (Intercept)-Didelphis_virginiana -1.3738 0.6245 -2.6392 -1.3713
## (Intercept)-Sylvilagus_floridanus -0.3292 0.7543 -1.6965 -0.3692
## (Intercept)-Sciurus_carolinensis -1.4389 0.6438 -2.7910 -1.4240
## (Intercept)-Vulpes_vulpes -1.1676 1.1290 -3.1084 -1.2639
## (Intercept)-Sus_scrofa -1.8700 0.7800 -3.4615 -1.8619
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1902 0.4731 -0.7227 0.1822
## Avg_Cogongrass_Cover-Canis_latrans 0.3587 0.3479 -0.2831 0.3494
## Avg_Cogongrass_Cover-Sciurus_niger -0.1195 0.5326 -1.2986 -0.0766
## Avg_Cogongrass_Cover-Procyon_lotor 0.2545 0.3427 -0.3913 0.2438
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3380 0.3130 -0.2763 0.3289
## Avg_Cogongrass_Cover-Lynx_rufus 0.4366 0.3933 -0.2440 0.4098
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3331 0.3476 -0.3520 0.3246
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1558 0.4195 -1.0629 -0.1224
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3370 0.3383 -0.3171 0.3284
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2743 0.4203 -0.5339 0.2697
## Avg_Cogongrass_Cover-Sus_scrofa -0.0947 0.4936 -1.1745 -0.0387
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5252 1.0064 1215
## (Intercept)-Canis_latrans 1.4786 1.0003 2849
## (Intercept)-Sciurus_niger 2.0901 1.0018 669
## (Intercept)-Procyon_lotor 1.6660 1.0020 2833
## (Intercept)-Dasypus_novemcinctus 0.3930 1.0011 3585
## (Intercept)-Lynx_rufus 2.0438 1.0025 1152
## (Intercept)-Didelphis_virginiana -0.1355 1.0035 3613
## (Intercept)-Sylvilagus_floridanus 1.2456 1.0056 1761
## (Intercept)-Sciurus_carolinensis -0.2187 1.0019 3088
## (Intercept)-Vulpes_vulpes 1.3590 1.0243 563
## (Intercept)-Sus_scrofa -0.3803 1.0025 2174
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1772 1.0012 3683
## Avg_Cogongrass_Cover-Canis_latrans 1.0921 1.0008 3536
## Avg_Cogongrass_Cover-Sciurus_niger 0.8221 1.0052 2169
## Avg_Cogongrass_Cover-Procyon_lotor 0.9717 1.0069 4031
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9879 1.0026 3563
## Avg_Cogongrass_Cover-Lynx_rufus 1.3101 1.0014 3287
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0371 1.0005 3586
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5877 1.0084 2842
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0285 1.0001 3969
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1532 1.0006 3554
## Avg_Cogongrass_Cover-Sus_scrofa 0.7160 1.0035 2798
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0067 0.0580 -0.1053 0.0057 0.1203
## (Intercept)-Canis_latrans -2.6187 0.1724 -2.9724 -2.6116 -2.2998
## (Intercept)-Sciurus_niger -3.9366 0.5903 -5.1542 -3.8905 -2.9003
## (Intercept)-Procyon_lotor -2.2736 0.1335 -2.5462 -2.2718 -2.0280
## (Intercept)-Dasypus_novemcinctus -1.5875 0.1350 -1.8613 -1.5838 -1.3358
## (Intercept)-Lynx_rufus -3.5333 0.3319 -4.2146 -3.5182 -2.9310
## (Intercept)-Didelphis_virginiana -2.3300 0.2459 -2.8400 -2.3189 -1.8790
## (Intercept)-Sylvilagus_floridanus -3.2249 0.3218 -3.9061 -3.2077 -2.6415
## (Intercept)-Sciurus_carolinensis -2.4623 0.2737 -3.0255 -2.4503 -1.9514
## (Intercept)-Vulpes_vulpes -4.0308 0.7169 -5.5154 -4.0042 -2.7197
## (Intercept)-Sus_scrofa -2.9499 0.4705 -3.9498 -2.9111 -2.1221
## week-Odocoileus_virginianus 0.2073 0.0611 0.0903 0.2071 0.3256
## week-Canis_latrans 0.0713 0.1299 -0.1925 0.0737 0.3146
## week-Sciurus_niger -0.2794 0.2902 -0.9342 -0.2512 0.2092
## week-Procyon_lotor -0.0443 0.1146 -0.2775 -0.0394 0.1711
## week-Dasypus_novemcinctus -0.1523 0.1360 -0.4321 -0.1466 0.0981
## week-Lynx_rufus -0.0286 0.1913 -0.4280 -0.0193 0.3190
## week-Didelphis_virginiana -0.1933 0.2071 -0.6339 -0.1799 0.1777
## week-Sylvilagus_floridanus -0.1415 0.1996 -0.5547 -0.1288 0.2220
## week-Sciurus_carolinensis 0.1388 0.1786 -0.2156 0.1375 0.4857
## week-Vulpes_vulpes -0.0985 0.2666 -0.6705 -0.0846 0.3857
## week-Sus_scrofa 0.1053 0.2320 -0.3560 0.1050 0.5753
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 4842
## (Intercept)-Canis_latrans 1.0069 3052
## (Intercept)-Sciurus_niger 1.0063 436
## (Intercept)-Procyon_lotor 1.0013 4076
## (Intercept)-Dasypus_novemcinctus 1.0017 5250
## (Intercept)-Lynx_rufus 1.0015 1021
## (Intercept)-Didelphis_virginiana 1.0037 3733
## (Intercept)-Sylvilagus_floridanus 1.0011 1326
## (Intercept)-Sciurus_carolinensis 1.0007 3497
## (Intercept)-Vulpes_vulpes 1.0285 499
## (Intercept)-Sus_scrofa 1.0011 1681
## week-Odocoileus_virginianus 1.0036 5250
## week-Canis_latrans 1.0007 4244
## week-Sciurus_niger 1.0004 1977
## week-Procyon_lotor 1.0032 4741
## week-Dasypus_novemcinctus 1.0014 4772
## week-Lynx_rufus 1.0008 3247
## week-Didelphis_virginiana 1.0008 3916
## week-Sylvilagus_floridanus 1.0008 2951
## week-Sciurus_carolinensis 0.9998 4962
## week-Vulpes_vulpes 1.0018 3148
## week-Sus_scrofa 1.0009 4577
# Includes week covariate for detection and all covariates for occupancy
ms_week_full <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9275
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1432 1.0553 -2.1149 -0.1955 2.0795 1.0034 1737
## Cogon_Patch_Size -0.7834 0.6447 -2.1688 -0.7577 0.3992 1.0002 1160
## Veg_shannon_index 0.8601 0.4393 0.0221 0.8468 1.7964 1.0093 908
## total_shrub_cover -0.1714 0.3783 -0.9242 -0.1712 0.5505 1.0083 1561
## Avg_Cogongrass_Cover 2.0438 0.6519 0.8401 2.0136 3.3841 1.0016 643
## Tree_Density -1.8525 0.6546 -3.2082 -1.8233 -0.6443 1.0048 850
## Avg_Canopy_Cover 1.7646 0.5197 0.8019 1.7461 2.8377 1.0004 1251
## avg_veg_height -0.5383 0.4330 -1.3959 -0.5428 0.3416 1.0068 776
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.3630 17.5452 3.8099 14.3559 65.7702 1.0082 452
## Cogon_Patch_Size 2.6261 3.8638 0.1147 1.4402 12.3753 1.0072 709
## Veg_shannon_index 0.8589 1.2299 0.0536 0.4554 4.3071 1.0041 951
## total_shrub_cover 0.5200 0.6890 0.0456 0.2983 2.2897 1.0067 1644
## Avg_Cogongrass_Cover 0.8927 1.8231 0.0479 0.4127 4.5246 1.0851 1302
## Tree_Density 2.1767 4.5192 0.0641 0.9825 11.5629 1.0850 862
## Avg_Canopy_Cover 1.5864 2.2801 0.0834 0.9034 6.9436 1.0218 900
## avg_veg_height 0.3604 0.4535 0.0373 0.2121 1.5891 1.0135 2288
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4258 1.7376 0.0677 0.8362 6.1156 1.0341 192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5489 0.4530 -3.3945 -2.5535 -1.5949 1.0014 5250
## week -0.0364 0.1208 -0.2866 -0.0305 0.1847 1.0028 3159
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2464 1.3972 0.7784 1.8936 5.7145 1.0052 1794
## week 0.0980 0.0862 0.0251 0.0753 0.3007 1.0311 2011
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.2195 3.2660 3.5203 7.6016
## (Intercept)-Canis_latrans 0.6745 1.0120 -1.0914 0.5953
## (Intercept)-Sciurus_niger 1.4560 2.4715 -2.2195 1.0463
## (Intercept)-Procyon_lotor 0.8063 0.9159 -1.0774 0.8488
## (Intercept)-Dasypus_novemcinctus -1.5113 0.9015 -3.5441 -1.4223
## (Intercept)-Lynx_rufus 2.3051 3.1472 -1.9662 1.6245
## (Intercept)-Didelphis_virginiana -2.9513 1.0937 -5.2714 -2.8843
## (Intercept)-Sylvilagus_floridanus -1.2787 1.2416 -3.7361 -1.2604
## (Intercept)-Sciurus_carolinensis -3.2181 1.1992 -5.9563 -3.0909
## (Intercept)-Vulpes_vulpes -1.4856 3.4477 -5.6252 -2.1242
## (Intercept)-Sus_scrofa -4.7231 1.7065 -8.6776 -4.5411
## Cogon_Patch_Size-Odocoileus_virginianus -0.6306 1.2926 -3.1692 -0.6551
## Cogon_Patch_Size-Canis_latrans 0.6281 1.1341 -0.9787 0.4174
## Cogon_Patch_Size-Sciurus_niger -1.4425 1.7158 -5.1046 -1.2645
## Cogon_Patch_Size-Procyon_lotor -0.9886 0.6970 -2.4216 -0.9685
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7882 0.6112 -2.0545 -0.7729
## Cogon_Patch_Size-Lynx_rufus -0.8842 1.3048 -3.4510 -0.8854
## Cogon_Patch_Size-Didelphis_virginiana 0.7365 0.8523 -0.7146 0.6553
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9659 1.4787 -5.6609 -1.6735
## Cogon_Patch_Size-Sciurus_carolinensis -1.7135 1.2460 -4.7805 -1.4862
## Cogon_Patch_Size-Vulpes_vulpes -1.2039 1.6661 -4.6750 -1.0850
## Cogon_Patch_Size-Sus_scrofa -1.3231 1.3313 -4.5796 -1.0869
## Veg_shannon_index-Odocoileus_virginianus 0.7006 0.9155 -1.3041 0.7258
## Veg_shannon_index-Canis_latrans 1.2377 0.6491 0.1473 1.1624
## Veg_shannon_index-Sciurus_niger 1.0082 0.9859 -0.8055 0.9447
## Veg_shannon_index-Procyon_lotor 1.1378 0.5872 0.0953 1.0954
## Veg_shannon_index-Dasypus_novemcinctus 0.6352 0.4932 -0.3690 0.6392
## Veg_shannon_index-Lynx_rufus 0.7852 0.9175 -1.1918 0.8058
## Veg_shannon_index-Didelphis_virginiana 1.0180 0.6307 -0.1384 0.9797
## Veg_shannon_index-Sylvilagus_floridanus 1.0215 0.6745 -0.1985 0.9725
## Veg_shannon_index-Sciurus_carolinensis 0.1875 0.7179 -1.4544 0.2664
## Veg_shannon_index-Vulpes_vulpes 0.3417 0.8472 -1.6313 0.4385
## Veg_shannon_index-Sus_scrofa 1.6066 1.0090 0.1966 1.4125
## total_shrub_cover-Odocoileus_virginianus -0.0086 0.7028 -1.3424 -0.0427
## total_shrub_cover-Canis_latrans 0.1423 0.5507 -0.7992 0.0997
## total_shrub_cover-Sciurus_niger -0.3719 0.7373 -2.0279 -0.3323
## total_shrub_cover-Procyon_lotor -0.6372 0.5303 -1.8133 -0.5996
## total_shrub_cover-Dasypus_novemcinctus 0.0765 0.4665 -0.8141 0.0636
## total_shrub_cover-Lynx_rufus -0.4819 0.7866 -2.3349 -0.4047
## total_shrub_cover-Didelphis_virginiana -0.3317 0.5779 -1.6207 -0.3058
## total_shrub_cover-Sylvilagus_floridanus -0.1196 0.6288 -1.3740 -0.1242
## total_shrub_cover-Sciurus_carolinensis -0.0105 0.5631 -1.1018 -0.0227
## total_shrub_cover-Vulpes_vulpes -0.3031 0.7341 -1.9480 -0.2711
## total_shrub_cover-Sus_scrofa 0.0931 0.6863 -1.1661 0.0443
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9872 0.9874 0.0450 1.9557
## Avg_Cogongrass_Cover-Canis_latrans 2.3076 0.8534 0.8363 2.2479
## Avg_Cogongrass_Cover-Sciurus_niger 1.6642 1.2122 -1.1895 1.7684
## Avg_Cogongrass_Cover-Procyon_lotor 2.2608 0.8496 0.7804 2.1762
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5224 0.8982 1.0005 2.4266
## Avg_Cogongrass_Cover-Lynx_rufus 2.3928 0.9591 0.7582 2.3017
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1744 0.8030 0.7450 2.1284
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5722 0.9027 -0.2386 1.5873
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3105 0.8620 0.8218 2.2412
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3708 0.9568 0.7346 2.2966
## Avg_Cogongrass_Cover-Sus_scrofa 1.5700 1.0301 -0.7389 1.6389
## Tree_Density-Odocoileus_virginianus -0.9354 1.2099 -2.9145 -1.0713
## Tree_Density-Canis_latrans -2.3580 1.0727 -4.9051 -2.2045
## Tree_Density-Sciurus_niger -1.9893 1.3070 -4.9309 -1.8931
## Tree_Density-Procyon_lotor -1.4627 0.7534 -2.9185 -1.4708
## Tree_Density-Dasypus_novemcinctus -3.1792 1.5794 -7.3138 -2.8136
## Tree_Density-Lynx_rufus -0.8563 1.2605 -2.9323 -1.0154
## Tree_Density-Didelphis_virginiana -2.2093 1.0559 -4.7497 -2.0626
## Tree_Density-Sylvilagus_floridanus -2.3245 1.2231 -5.2832 -2.1618
## Tree_Density-Sciurus_carolinensis -2.3896 1.2235 -5.4602 -2.1908
## Tree_Density-Vulpes_vulpes -1.8499 1.3379 -4.7352 -1.8002
## Tree_Density-Sus_scrofa -2.2049 1.4207 -5.6662 -2.0028
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3502 1.1397 -1.1438 1.4195
## Avg_Canopy_Cover-Canis_latrans 0.4789 0.6980 -0.8792 0.4621
## Avg_Canopy_Cover-Sciurus_niger 2.0681 1.3667 -0.2981 1.9450
## Avg_Canopy_Cover-Procyon_lotor 1.7133 0.6517 0.5182 1.6841
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9136 0.6318 0.8179 1.8618
## Avg_Canopy_Cover-Lynx_rufus 1.4545 1.1935 -0.8207 1.4476
## Avg_Canopy_Cover-Didelphis_virginiana 2.4975 0.8437 1.1448 2.3926
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8772 1.3358 1.0892 2.5988
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1836 0.7495 0.9453 2.0947
## Avg_Canopy_Cover-Vulpes_vulpes 2.0420 1.0489 0.3401 1.9195
## Avg_Canopy_Cover-Sus_scrofa 2.0166 0.7790 0.6703 1.9441
## avg_veg_height-Odocoileus_virginianus -0.5727 0.7039 -2.0427 -0.5645
## avg_veg_height-Canis_latrans -0.6988 0.5619 -1.8367 -0.6851
## avg_veg_height-Sciurus_niger -0.6560 0.7017 -2.1426 -0.6183
## avg_veg_height-Procyon_lotor -0.3869 0.5375 -1.4359 -0.4006
## avg_veg_height-Dasypus_novemcinctus -0.3174 0.5384 -1.3260 -0.3264
## avg_veg_height-Lynx_rufus -0.5704 0.6590 -1.8833 -0.5515
## avg_veg_height-Didelphis_virginiana -0.6347 0.5993 -1.8655 -0.6198
## avg_veg_height-Sylvilagus_floridanus -0.7104 0.6102 -1.9648 -0.6862
## avg_veg_height-Sciurus_carolinensis -0.2438 0.5995 -1.3462 -0.2725
## avg_veg_height-Vulpes_vulpes -0.5525 0.6594 -1.8426 -0.5612
## avg_veg_height-Sus_scrofa -0.6389 0.6388 -1.9686 -0.6185
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.1763 1.0046 416
## (Intercept)-Canis_latrans 2.9507 1.0012 1479
## (Intercept)-Sciurus_niger 7.5033 1.0073 345
## (Intercept)-Procyon_lotor 2.5313 1.0360 1336
## (Intercept)-Dasypus_novemcinctus 0.0918 1.0064 1150
## (Intercept)-Lynx_rufus 10.2565 1.0687 217
## (Intercept)-Didelphis_virginiana -0.9556 1.0016 1605
## (Intercept)-Sylvilagus_floridanus 1.2145 1.0063 1292
## (Intercept)-Sciurus_carolinensis -1.1631 1.0261 858
## (Intercept)-Vulpes_vulpes 7.3578 1.0505 104
## (Intercept)-Sus_scrofa -1.9663 1.0064 702
## Cogon_Patch_Size-Odocoileus_virginianus 2.2184 1.0014 2117
## Cogon_Patch_Size-Canis_latrans 3.5139 1.0046 1576
## Cogon_Patch_Size-Sciurus_niger 1.5904 1.0034 673
## Cogon_Patch_Size-Procyon_lotor 0.3207 1.0127 902
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3232 1.0016 1493
## Cogon_Patch_Size-Lynx_rufus 1.7675 1.0060 1263
## Cogon_Patch_Size-Didelphis_virginiana 2.6336 1.0099 1519
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1097 1.0059 759
## Cogon_Patch_Size-Sciurus_carolinensis 0.0054 1.0046 724
## Cogon_Patch_Size-Vulpes_vulpes 1.6476 1.0096 644
## Cogon_Patch_Size-Sus_scrofa 0.7253 1.0117 1073
## Veg_shannon_index-Odocoileus_virginianus 2.4646 1.0022 1915
## Veg_shannon_index-Canis_latrans 2.7443 1.0031 1498
## Veg_shannon_index-Sciurus_niger 3.2556 1.0046 1386
## Veg_shannon_index-Procyon_lotor 2.4006 1.0054 970
## Veg_shannon_index-Dasypus_novemcinctus 1.6007 1.0047 1977
## Veg_shannon_index-Lynx_rufus 2.5417 1.0035 1419
## Veg_shannon_index-Didelphis_virginiana 2.3945 1.0049 2339
## Veg_shannon_index-Sylvilagus_floridanus 2.4624 1.0033 1686
## Veg_shannon_index-Sciurus_carolinensis 1.4001 1.0008 1428
## Veg_shannon_index-Vulpes_vulpes 1.8004 1.0112 969
## Veg_shannon_index-Sus_scrofa 4.2345 1.0017 896
## total_shrub_cover-Odocoileus_virginianus 1.5005 1.0052 2111
## total_shrub_cover-Canis_latrans 1.3616 1.0045 2368
## total_shrub_cover-Sciurus_niger 0.9665 1.0035 1720
## total_shrub_cover-Procyon_lotor 0.3008 1.0028 2207
## total_shrub_cover-Dasypus_novemcinctus 1.0494 1.0013 2555
## total_shrub_cover-Lynx_rufus 0.8778 1.0101 1341
## total_shrub_cover-Didelphis_virginiana 0.7244 1.0021 2432
## total_shrub_cover-Sylvilagus_floridanus 1.1475 1.0012 2578
## total_shrub_cover-Sciurus_carolinensis 1.1604 1.0056 2630
## total_shrub_cover-Vulpes_vulpes 1.0238 1.0053 1870
## total_shrub_cover-Sus_scrofa 1.6680 1.0010 2711
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.0001 1.0111 1026
## Avg_Cogongrass_Cover-Canis_latrans 4.2117 1.0032 945
## Avg_Cogongrass_Cover-Sciurus_niger 3.7396 1.0037 682
## Avg_Cogongrass_Cover-Procyon_lotor 4.0712 1.0046 867
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5288 1.0010 696
## Avg_Cogongrass_Cover-Lynx_rufus 4.5156 1.0020 880
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8975 1.0001 1052
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3230 1.0021 833
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1807 1.0039 789
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4516 1.0019 779
## Avg_Cogongrass_Cover-Sus_scrofa 3.3831 1.0034 907
## Tree_Density-Odocoileus_virginianus 1.8454 1.0116 877
## Tree_Density-Canis_latrans -0.7100 1.0000 984
## Tree_Density-Sciurus_niger 0.3164 1.0129 1078
## Tree_Density-Procyon_lotor 0.1048 1.0068 1335
## Tree_Density-Dasypus_novemcinctus -1.1893 1.0026 598
## Tree_Density-Lynx_rufus 2.0934 1.0144 606
## Tree_Density-Didelphis_virginiana -0.6097 1.0013 871
## Tree_Density-Sylvilagus_floridanus -0.4327 1.0030 1212
## Tree_Density-Sciurus_carolinensis -0.5770 1.0004 1002
## Tree_Density-Vulpes_vulpes 0.8060 1.0058 848
## Tree_Density-Sus_scrofa -0.0019 1.0033 1159
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5610 1.0074 1884
## Avg_Canopy_Cover-Canis_latrans 1.8845 1.0178 1380
## Avg_Canopy_Cover-Sciurus_niger 5.1575 1.0011 901
## Avg_Canopy_Cover-Procyon_lotor 3.1288 1.0017 1516
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2964 1.0013 1198
## Avg_Canopy_Cover-Lynx_rufus 3.9289 1.0070 832
## Avg_Canopy_Cover-Didelphis_virginiana 4.4123 1.0058 880
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.2331 1.0161 756
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8941 1.0016 1138
## Avg_Canopy_Cover-Vulpes_vulpes 4.4103 1.0168 1408
## Avg_Canopy_Cover-Sus_scrofa 3.8045 1.0052 1754
## avg_veg_height-Odocoileus_virginianus 0.7967 1.0046 1791
## avg_veg_height-Canis_latrans 0.3821 1.0027 1117
## avg_veg_height-Sciurus_niger 0.6646 1.0124 1306
## avg_veg_height-Procyon_lotor 0.6899 1.0052 1277
## avg_veg_height-Dasypus_novemcinctus 0.7851 1.0028 1296
## avg_veg_height-Lynx_rufus 0.7624 1.0040 1259
## avg_veg_height-Didelphis_virginiana 0.5245 1.0045 1335
## avg_veg_height-Sylvilagus_floridanus 0.4526 1.0072 1434
## avg_veg_height-Sciurus_carolinensis 1.0324 1.0042 1435
## avg_veg_height-Vulpes_vulpes 0.7703 1.0044 919
## avg_veg_height-Sus_scrofa 0.5648 1.0023 1552
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0065 0.0591 -0.1105 0.0061 0.1250
## (Intercept)-Canis_latrans -2.6529 0.1812 -3.0179 -2.6488 -2.3188
## (Intercept)-Sciurus_niger -4.5936 0.4476 -5.4544 -4.5967 -3.7278
## (Intercept)-Procyon_lotor -2.2739 0.1321 -2.5376 -2.2691 -2.0208
## (Intercept)-Dasypus_novemcinctus -1.5890 0.1349 -1.8603 -1.5874 -1.3281
## (Intercept)-Lynx_rufus -3.8283 0.3487 -4.4834 -3.8390 -3.1342
## (Intercept)-Didelphis_virginiana -2.3129 0.2405 -2.7927 -2.3071 -1.8619
## (Intercept)-Sylvilagus_floridanus -3.2032 0.2898 -3.8013 -3.1903 -2.6628
## (Intercept)-Sciurus_carolinensis -2.4465 0.2634 -2.9954 -2.4359 -1.9649
## (Intercept)-Vulpes_vulpes -4.2863 0.7522 -5.8837 -4.2441 -2.9476
## (Intercept)-Sus_scrofa -2.8918 0.4483 -3.8556 -2.8590 -2.1029
## week-Odocoileus_virginianus 0.2080 0.0605 0.0898 0.2067 0.3291
## week-Canis_latrans 0.0723 0.1281 -0.1854 0.0758 0.3171
## week-Sciurus_niger -0.2763 0.2920 -0.9478 -0.2427 0.1959
## week-Procyon_lotor -0.0464 0.1166 -0.2865 -0.0452 0.1748
## week-Dasypus_novemcinctus -0.1535 0.1356 -0.4304 -0.1479 0.1034
## week-Lynx_rufus -0.0286 0.1902 -0.4360 -0.0192 0.3188
## week-Didelphis_virginiana -0.1957 0.2118 -0.6542 -0.1798 0.1759
## week-Sylvilagus_floridanus -0.1375 0.2032 -0.5746 -0.1247 0.2309
## week-Sciurus_carolinensis 0.1451 0.1755 -0.2017 0.1470 0.4810
## week-Vulpes_vulpes -0.1017 0.2699 -0.7099 -0.0816 0.3786
## week-Sus_scrofa 0.1119 0.2328 -0.3468 0.1129 0.5671
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0000 2387
## (Intercept)-Sciurus_niger 1.0243 537
## (Intercept)-Procyon_lotor 1.0025 4140
## (Intercept)-Dasypus_novemcinctus 1.0023 5250
## (Intercept)-Lynx_rufus 1.0324 378
## (Intercept)-Didelphis_virginiana 1.0008 4212
## (Intercept)-Sylvilagus_floridanus 1.0051 1642
## (Intercept)-Sciurus_carolinensis 1.0040 3693
## (Intercept)-Vulpes_vulpes 1.0347 259
## (Intercept)-Sus_scrofa 1.0052 1926
## week-Odocoileus_virginianus 1.0010 5250
## week-Canis_latrans 1.0002 4302
## week-Sciurus_niger 1.0009 1338
## week-Procyon_lotor 1.0016 4097
## week-Dasypus_novemcinctus 0.9998 4829
## week-Lynx_rufus 1.0024 2511
## week-Didelphis_virginiana 1.0025 3950
## week-Sylvilagus_floridanus 1.0000 3131
## week-Sciurus_carolinensis 1.0006 5250
## week-Vulpes_vulpes 1.0021 2442
## week-Sus_scrofa 1.0099 4209
# Includes week covariate for detection and only cover for occupancy
ms_week_cover <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8055
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2207 0.6398 -1.4458 -0.2550 1.0802 1.0006 2446
## Avg_Cogongrass_Cover 0.1398 0.3090 -0.5143 0.1465 0.7282 1.0005 1402
## total_shrub_cover -0.2765 0.2800 -0.8531 -0.2662 0.2367 1.0014 2340
## avg_veg_height 0.0215 0.2900 -0.5353 0.0217 0.6038 1.0075 1246
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0752 3.5328 0.7304 3.1970 12.7514 1.0220 1635
## Avg_Cogongrass_Cover 0.3103 0.3972 0.0371 0.1921 1.3009 1.0022 1869
## total_shrub_cover 0.3811 0.4531 0.0432 0.2371 1.6289 1.0049 1821
## avg_veg_height 0.1981 0.1961 0.0336 0.1406 0.7102 1.0018 2777
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9602 1.0213 0.0686 0.6368 3.7269 1.0634 380
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5147 0.4276 -3.3537 -2.5214 -1.6532 1.0007 3422
## week -0.0390 0.1217 -0.2919 -0.0326 0.1812 1.0030 3083
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9359 1.2491 0.6562 1.6236 5.0077 1.0006 1527
## week 0.0990 0.0858 0.0255 0.0773 0.2975 1.0146 2032
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7675 1.5676 1.2303 3.5853
## (Intercept)-Canis_latrans 0.3312 0.6808 -0.9947 0.3052
## (Intercept)-Sciurus_niger -0.4582 1.2932 -2.4198 -0.6417
## (Intercept)-Procyon_lotor 0.6238 0.6871 -0.7929 0.6297
## (Intercept)-Dasypus_novemcinctus -0.7325 0.6166 -1.9821 -0.7146
## (Intercept)-Lynx_rufus -0.0286 0.9325 -1.7206 -0.0906
## (Intercept)-Didelphis_virginiana -1.4480 0.7004 -2.8885 -1.4431
## (Intercept)-Sylvilagus_floridanus -0.2238 0.8475 -1.7409 -0.2933
## (Intercept)-Sciurus_carolinensis -1.5552 0.7192 -3.0595 -1.5276
## (Intercept)-Vulpes_vulpes -0.8856 1.4130 -3.1466 -1.0768
## (Intercept)-Sus_scrofa -2.0299 0.8640 -3.8116 -2.0091
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1319 0.5371 -0.9483 0.1308
## Avg_Cogongrass_Cover-Canis_latrans 0.3814 0.4288 -0.4045 0.3623
## Avg_Cogongrass_Cover-Sciurus_niger -0.1751 0.6276 -1.5481 -0.1162
## Avg_Cogongrass_Cover-Procyon_lotor 0.1020 0.4260 -0.7458 0.0961
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2548 0.3845 -0.5076 0.2530
## Avg_Cogongrass_Cover-Lynx_rufus 0.4064 0.4736 -0.4277 0.3769
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3151 0.4178 -0.5017 0.3089
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2106 0.5011 -1.3036 -0.1703
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2450 0.4074 -0.5731 0.2424
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2383 0.4972 -0.7436 0.2306
## Avg_Cogongrass_Cover-Sus_scrofa -0.1444 0.5869 -1.5277 -0.0796
## total_shrub_cover-Odocoileus_virginianus -0.1911 0.5335 -1.2305 -0.2030
## total_shrub_cover-Canis_latrans 0.0954 0.4239 -0.6568 0.0663
## total_shrub_cover-Sciurus_niger -0.5155 0.5591 -1.7902 -0.4667
## total_shrub_cover-Procyon_lotor -0.7569 0.4719 -1.8497 -0.7023
## total_shrub_cover-Dasypus_novemcinctus -0.0545 0.3491 -0.7380 -0.0615
## total_shrub_cover-Lynx_rufus -0.6749 0.5801 -2.1048 -0.6031
## total_shrub_cover-Didelphis_virginiana -0.2192 0.3956 -1.0251 -0.2102
## total_shrub_cover-Sylvilagus_floridanus -0.3282 0.5071 -1.4413 -0.2970
## total_shrub_cover-Sciurus_carolinensis -0.1156 0.4013 -0.8796 -0.1290
## total_shrub_cover-Vulpes_vulpes -0.3280 0.5779 -1.6120 -0.2908
## total_shrub_cover-Sus_scrofa 0.0399 0.4927 -0.8487 0.0148
## avg_veg_height-Odocoileus_virginianus 0.0114 0.4787 -0.9348 0.0148
## avg_veg_height-Canis_latrans -0.0593 0.3831 -0.8104 -0.0576
## avg_veg_height-Sciurus_niger -0.1262 0.4887 -1.1357 -0.1023
## avg_veg_height-Procyon_lotor 0.1091 0.3926 -0.6477 0.0980
## avg_veg_height-Dasypus_novemcinctus 0.1758 0.3803 -0.5527 0.1668
## avg_veg_height-Lynx_rufus 0.0335 0.4605 -0.8930 0.0260
## avg_veg_height-Didelphis_virginiana -0.0137 0.4048 -0.8500 -0.0054
## avg_veg_height-Sylvilagus_floridanus -0.0967 0.4275 -0.9587 -0.0895
## avg_veg_height-Sciurus_carolinensis 0.2608 0.4127 -0.4930 0.2439
## avg_veg_height-Vulpes_vulpes -0.0229 0.4543 -0.9448 -0.0131
## avg_veg_height-Sus_scrofa -0.0110 0.4416 -0.9323 -0.0061
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3858 1.0025 1150
## (Intercept)-Canis_latrans 1.7563 1.0033 2485
## (Intercept)-Sciurus_niger 2.6194 1.0190 501
## (Intercept)-Procyon_lotor 1.9741 1.0028 2215
## (Intercept)-Dasypus_novemcinctus 0.4712 1.0011 2897
## (Intercept)-Lynx_rufus 1.9411 1.0007 1489
## (Intercept)-Didelphis_virginiana -0.0761 1.0020 2622
## (Intercept)-Sylvilagus_floridanus 1.6998 1.0100 1353
## (Intercept)-Sciurus_carolinensis -0.2091 1.0035 2602
## (Intercept)-Vulpes_vulpes 2.3543 1.0120 358
## (Intercept)-Sus_scrofa -0.3690 1.0038 2072
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2242 1.0024 2552
## Avg_Cogongrass_Cover-Canis_latrans 1.3123 1.0003 2464
## Avg_Cogongrass_Cover-Sciurus_niger 0.8995 1.0022 1461
## Avg_Cogongrass_Cover-Procyon_lotor 0.9569 1.0009 2795
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0164 1.0008 2831
## Avg_Cogongrass_Cover-Lynx_rufus 1.4323 1.0023 2404
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1572 1.0018 2737
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6814 1.0009 1851
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0509 1.0003 2366
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2224 1.0006 2331
## Avg_Cogongrass_Cover-Sus_scrofa 0.8277 1.0014 1955
## total_shrub_cover-Odocoileus_virginianus 0.9343 1.0008 3426
## total_shrub_cover-Canis_latrans 1.0109 1.0003 3302
## total_shrub_cover-Sciurus_niger 0.4887 1.0046 2176
## total_shrub_cover-Procyon_lotor 0.0210 1.0044 2327
## total_shrub_cover-Dasypus_novemcinctus 0.6505 1.0014 3645
## total_shrub_cover-Lynx_rufus 0.2642 1.0036 1984
## total_shrub_cover-Didelphis_virginiana 0.5731 1.0021 3906
## total_shrub_cover-Sylvilagus_floridanus 0.5749 1.0034 2089
## total_shrub_cover-Sciurus_carolinensis 0.7111 1.0009 4391
## total_shrub_cover-Vulpes_vulpes 0.7432 1.0055 2140
## total_shrub_cover-Sus_scrofa 1.1135 1.0023 3442
## avg_veg_height-Odocoileus_virginianus 0.9244 1.0045 2466
## avg_veg_height-Canis_latrans 0.7000 1.0053 2303
## avg_veg_height-Sciurus_niger 0.7954 1.0020 2110
## avg_veg_height-Procyon_lotor 0.9020 1.0011 2527
## avg_veg_height-Dasypus_novemcinctus 0.9198 1.0009 2291
## avg_veg_height-Lynx_rufus 0.9420 1.0013 2269
## avg_veg_height-Didelphis_virginiana 0.7818 1.0018 2431
## avg_veg_height-Sylvilagus_floridanus 0.7184 1.0012 2308
## avg_veg_height-Sciurus_carolinensis 1.0929 1.0080 2524
## avg_veg_height-Vulpes_vulpes 0.8608 1.0064 2272
## avg_veg_height-Sus_scrofa 0.8556 1.0091 2387
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0062 0.0593 -0.1100 0.0066 0.1199
## (Intercept)-Canis_latrans -2.6447 0.1774 -3.0100 -2.6371 -2.3219
## (Intercept)-Sciurus_niger -4.0563 0.5995 -5.2470 -4.0389 -2.9655
## (Intercept)-Procyon_lotor -2.2823 0.1301 -2.5541 -2.2803 -2.0311
## (Intercept)-Dasypus_novemcinctus -1.5902 0.1345 -1.8575 -1.5872 -1.3294
## (Intercept)-Lynx_rufus -3.5718 0.3144 -4.2252 -3.5685 -2.9771
## (Intercept)-Didelphis_virginiana -2.3381 0.2535 -2.8773 -2.3224 -1.8773
## (Intercept)-Sylvilagus_floridanus -3.2680 0.3305 -3.9664 -3.2462 -2.6752
## (Intercept)-Sciurus_carolinensis -2.4675 0.2735 -3.0407 -2.4546 -1.9803
## (Intercept)-Vulpes_vulpes -4.2125 0.7806 -5.7383 -4.1990 -2.8282
## (Intercept)-Sus_scrofa -2.9522 0.4720 -3.9709 -2.9201 -2.1158
## week-Odocoileus_virginianus 0.2085 0.0605 0.0933 0.2079 0.3308
## week-Canis_latrans 0.0705 0.1312 -0.1921 0.0737 0.3204
## week-Sciurus_niger -0.2937 0.3035 -0.9658 -0.2632 0.2087
## week-Procyon_lotor -0.0455 0.1190 -0.2881 -0.0412 0.1773
## week-Dasypus_novemcinctus -0.1548 0.1339 -0.4335 -0.1486 0.0902
## week-Lynx_rufus -0.0249 0.1921 -0.4328 -0.0150 0.3257
## week-Didelphis_virginiana -0.1954 0.2120 -0.6559 -0.1806 0.1793
## week-Sylvilagus_floridanus -0.1379 0.2018 -0.5669 -0.1281 0.2244
## week-Sciurus_carolinensis 0.1385 0.1755 -0.2040 0.1368 0.4940
## week-Vulpes_vulpes -0.1065 0.2782 -0.7267 -0.0844 0.3903
## week-Sus_scrofa 0.1064 0.2318 -0.3458 0.1096 0.5578
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0008 2529
## (Intercept)-Sciurus_niger 1.0009 487
## (Intercept)-Procyon_lotor 1.0005 4389
## (Intercept)-Dasypus_novemcinctus 1.0014 4911
## (Intercept)-Lynx_rufus 1.0035 1125
## (Intercept)-Didelphis_virginiana 1.0008 4320
## (Intercept)-Sylvilagus_floridanus 1.0044 1003
## (Intercept)-Sciurus_carolinensis 1.0035 3519
## (Intercept)-Vulpes_vulpes 1.0074 329
## (Intercept)-Sus_scrofa 1.0008 1881
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0019 4756
## week-Sciurus_niger 1.0031 1670
## week-Procyon_lotor 1.0006 4993
## week-Dasypus_novemcinctus 1.0017 4361
## week-Lynx_rufus 1.0009 2903
## week-Didelphis_virginiana 1.0013 3355
## week-Sylvilagus_floridanus 1.0050 2832
## week-Sciurus_carolinensis 0.9999 4691
## week-Vulpes_vulpes 1.0002 2318
## week-Sus_scrofa 1.0015 4293
# Includes week covariate for detection and none for occupancy
ms_week_null <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7368
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2021 0.5437 -1.2188 -0.2136 0.9522 1.0043 2355
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2205 2.5404 0.835 2.5588 9.706 1.0327 1061
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4802 0.4053 -3.2696 -2.4868 -1.6680 1.0025 4221
## week -0.0403 0.1173 -0.2833 -0.0362 0.1783 1.0034 3423
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7940 1.1178 0.6368 1.4945 4.6768 1.0063 2452
## week 0.0973 0.0748 0.0255 0.0766 0.2981 1.0030 3102
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4250 1.1239 1.7595 3.2489 6.1333
## (Intercept)-Canis_latrans 0.3136 0.4012 -0.4492 0.3032 1.1771
## (Intercept)-Sciurus_niger -0.6823 0.9816 -2.0892 -0.8322 1.5357
## (Intercept)-Procyon_lotor 0.6956 0.3949 -0.0480 0.6758 1.5119
## (Intercept)-Dasypus_novemcinctus -0.6384 0.3710 -1.4019 -0.6288 0.0646
## (Intercept)-Lynx_rufus 0.4438 1.0374 -0.8626 0.2377 2.9149
## (Intercept)-Didelphis_virginiana -1.3587 0.4442 -2.2703 -1.3439 -0.5220
## (Intercept)-Sylvilagus_floridanus -0.2982 0.6218 -1.2518 -0.3607 1.0064
## (Intercept)-Sciurus_carolinensis -1.3548 0.4472 -2.2720 -1.3391 -0.5226
## (Intercept)-Vulpes_vulpes -1.1575 1.0476 -2.7854 -1.3157 1.4454
## (Intercept)-Sus_scrofa -1.9061 0.6243 -3.2093 -1.8782 -0.7500
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0035 1550
## (Intercept)-Canis_latrans 1.0009 4294
## (Intercept)-Sciurus_niger 1.0129 509
## (Intercept)-Procyon_lotor 1.0030 5250
## (Intercept)-Dasypus_novemcinctus 1.0018 5327
## (Intercept)-Lynx_rufus 1.0754 458
## (Intercept)-Didelphis_virginiana 1.0025 4905
## (Intercept)-Sylvilagus_floridanus 1.0322 1097
## (Intercept)-Sciurus_carolinensis 1.0002 5103
## (Intercept)-Vulpes_vulpes 1.0290 529
## (Intercept)-Sus_scrofa 1.0066 3571
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0064 0.0585 -0.1088 0.0060 0.1197
## (Intercept)-Canis_latrans -2.6219 0.1766 -2.9881 -2.6183 -2.2953
## (Intercept)-Sciurus_niger -3.8875 0.5574 -5.0828 -3.8529 -2.9008
## (Intercept)-Procyon_lotor -2.2696 0.1294 -2.5256 -2.2685 -2.0164
## (Intercept)-Dasypus_novemcinctus -1.5891 0.1367 -1.8593 -1.5863 -1.3318
## (Intercept)-Lynx_rufus -3.6281 0.3565 -4.3412 -3.6169 -2.9708
## (Intercept)-Didelphis_virginiana -2.3381 0.2518 -2.8512 -2.3278 -1.8725
## (Intercept)-Sylvilagus_floridanus -3.2042 0.3171 -3.8981 -3.1877 -2.6347
## (Intercept)-Sciurus_carolinensis -2.4516 0.2675 -3.0021 -2.4466 -1.9588
## (Intercept)-Vulpes_vulpes -3.9694 0.7332 -5.4910 -3.9110 -2.7212
## (Intercept)-Sus_scrofa -2.9571 0.4738 -3.9817 -2.9277 -2.1209
## week-Odocoileus_virginianus 0.2073 0.0594 0.0930 0.2072 0.3228
## week-Canis_latrans 0.0736 0.1285 -0.1847 0.0750 0.3149
## week-Sciurus_niger -0.2912 0.2933 -0.9589 -0.2611 0.1923
## week-Procyon_lotor -0.0456 0.1163 -0.2813 -0.0409 0.1761
## week-Dasypus_novemcinctus -0.1568 0.1379 -0.4410 -0.1529 0.0966
## week-Lynx_rufus -0.0243 0.1900 -0.4162 -0.0189 0.3313
## week-Didelphis_virginiana -0.1992 0.2110 -0.6446 -0.1877 0.1875
## week-Sylvilagus_floridanus -0.1431 0.2020 -0.5729 -0.1338 0.2166
## week-Sciurus_carolinensis 0.1441 0.1771 -0.2095 0.1452 0.4845
## week-Vulpes_vulpes -0.1065 0.2689 -0.6905 -0.0875 0.3772
## week-Sus_scrofa 0.1022 0.2335 -0.3590 0.1066 0.5456
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 4678
## (Intercept)-Canis_latrans 1.0076 3017
## (Intercept)-Sciurus_niger 1.0022 635
## (Intercept)-Procyon_lotor 1.0062 4280
## (Intercept)-Dasypus_novemcinctus 1.0045 5027
## (Intercept)-Lynx_rufus 1.0441 809
## (Intercept)-Didelphis_virginiana 1.0004 3891
## (Intercept)-Sylvilagus_floridanus 1.0029 1376
## (Intercept)-Sciurus_carolinensis 1.0036 3787
## (Intercept)-Vulpes_vulpes 1.0270 503
## (Intercept)-Sus_scrofa 1.0020 1695
## week-Odocoileus_virginianus 1.0006 5358
## week-Canis_latrans 1.0033 4172
## week-Sciurus_niger 1.0025 2208
## week-Procyon_lotor 1.0019 4876
## week-Dasypus_novemcinctus 1.0001 4897
## week-Lynx_rufus 1.0049 2925
## week-Didelphis_virginiana 1.0050 3921
## week-Sylvilagus_floridanus 1.0082 3119
## week-Sciurus_carolinensis 1.0034 4889
## week-Vulpes_vulpes 1.0041 3356
## week-Sus_scrofa 1.0034 4455
#Includes week for detection and only foraging for occupancy
ms_week_forage <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7907
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2320 0.6385 -1.4358 -0.2453 1.1065 1.0049 1558
## Veg_shannon_index 0.3835 0.2656 -0.1220 0.3728 0.9308 1.0007 1739
## Avg_Cogongrass_Cover 0.3221 0.2576 -0.1784 0.3244 0.8447 1.0009 1917
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0419 3.4382 0.8293 3.1251 12.5101 1.0043 1389
## Veg_shannon_index 0.3023 0.4392 0.0391 0.1903 1.1638 1.1551 891
## Avg_Cogongrass_Cover 0.2831 0.3368 0.0364 0.1767 1.1889 1.0134 2227
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7818 0.7874 0.0685 0.5457 2.791 1.0138 549
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5130 0.4243 -3.3307 -2.5169 -1.6863 1.0054 3270
## week -0.0396 0.1214 -0.3005 -0.0351 0.1864 1.0022 2884
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9555 1.3202 0.6450 1.6250 5.1773 1.0090 1703
## week 0.0994 0.0813 0.0258 0.0765 0.3115 1.0037 3390
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6887 1.4737 1.3202 3.5130
## (Intercept)-Canis_latrans 0.2599 0.6401 -0.9915 0.2551
## (Intercept)-Sciurus_niger -0.3023 1.4592 -2.3954 -0.5486
## (Intercept)-Procyon_lotor 0.5519 0.6349 -0.6996 0.5585
## (Intercept)-Dasypus_novemcinctus -0.7334 0.5978 -1.9472 -0.7198
## (Intercept)-Lynx_rufus 0.0735 1.0168 -1.6222 -0.0327
## (Intercept)-Didelphis_virginiana -1.5171 0.6739 -2.8849 -1.5039
## (Intercept)-Sylvilagus_floridanus -0.2982 0.8311 -1.7415 -0.3615
## (Intercept)-Sciurus_carolinensis -1.4945 0.6783 -2.9051 -1.4680
## (Intercept)-Vulpes_vulpes -0.9396 1.4300 -3.1557 -1.1393
## (Intercept)-Sus_scrofa -2.2033 0.8807 -4.0413 -2.1746
## Veg_shannon_index-Odocoileus_virginianus 0.3216 0.4951 -0.7102 0.3372
## Veg_shannon_index-Canis_latrans 0.6540 0.3845 -0.0281 0.6296
## Veg_shannon_index-Sciurus_niger 0.4050 0.5901 -0.5948 0.3721
## Veg_shannon_index-Procyon_lotor 0.4919 0.3915 -0.2291 0.4763
## Veg_shannon_index-Dasypus_novemcinctus 0.2229 0.3388 -0.4590 0.2229
## Veg_shannon_index-Lynx_rufus 0.2212 0.5088 -0.8923 0.2365
## Veg_shannon_index-Didelphis_virginiana 0.5254 0.4000 -0.1916 0.5094
## Veg_shannon_index-Sylvilagus_floridanus 0.4864 0.4498 -0.3298 0.4581
## Veg_shannon_index-Sciurus_carolinensis 0.0399 0.3932 -0.8017 0.0654
## Veg_shannon_index-Vulpes_vulpes 0.1306 0.4927 -0.9313 0.1491
## Veg_shannon_index-Sus_scrofa 0.7543 0.5582 -0.1287 0.6741
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3125 0.4903 -0.6359 0.3077
## Avg_Cogongrass_Cover-Canis_latrans 0.5423 0.3873 -0.1490 0.5197
## Avg_Cogongrass_Cover-Sciurus_niger 0.0096 0.5602 -1.2733 0.0617
## Avg_Cogongrass_Cover-Procyon_lotor 0.4359 0.3978 -0.2809 0.4085
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4360 0.3302 -0.2019 0.4318
## Avg_Cogongrass_Cover-Lynx_rufus 0.5754 0.4338 -0.1804 0.5399
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4774 0.3593 -0.2114 0.4690
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0245 0.4494 -1.0135 0.0050
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4177 0.3624 -0.2912 0.4129
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3966 0.4652 -0.4796 0.3812
## Avg_Cogongrass_Cover-Sus_scrofa 0.0331 0.5201 -1.1728 0.0813
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1298 1.0016 1084
## (Intercept)-Canis_latrans 1.5348 1.0033 3128
## (Intercept)-Sciurus_niger 3.2541 1.0376 310
## (Intercept)-Procyon_lotor 1.8235 1.0026 1888
## (Intercept)-Dasypus_novemcinctus 0.4241 1.0052 2734
## (Intercept)-Lynx_rufus 2.3614 1.0064 828
## (Intercept)-Didelphis_virginiana -0.2087 1.0012 3288
## (Intercept)-Sylvilagus_floridanus 1.5586 1.0039 1245
## (Intercept)-Sciurus_carolinensis -0.1991 1.0024 3197
## (Intercept)-Vulpes_vulpes 2.5199 1.0103 396
## (Intercept)-Sus_scrofa -0.5612 1.0037 2058
## Veg_shannon_index-Odocoileus_virginianus 1.2975 1.0026 3500
## Veg_shannon_index-Canis_latrans 1.4740 1.0018 3086
## Veg_shannon_index-Sciurus_niger 1.5310 1.0400 918
## Veg_shannon_index-Procyon_lotor 1.3193 1.0027 2762
## Veg_shannon_index-Dasypus_novemcinctus 0.8799 1.0017 4256
## Veg_shannon_index-Lynx_rufus 1.1698 1.0053 2529
## Veg_shannon_index-Didelphis_virginiana 1.3815 1.0015 3327
## Veg_shannon_index-Sylvilagus_floridanus 1.4484 1.0004 2225
## Veg_shannon_index-Sciurus_carolinensis 0.7613 1.0020 3407
## Veg_shannon_index-Vulpes_vulpes 1.0575 1.0094 2291
## Veg_shannon_index-Sus_scrofa 2.0504 1.0029 1661
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3227 1.0005 3413
## Avg_Cogongrass_Cover-Canis_latrans 1.3504 1.0016 3286
## Avg_Cogongrass_Cover-Sciurus_niger 1.0072 1.0020 1611
## Avg_Cogongrass_Cover-Procyon_lotor 1.3127 1.0029 3298
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0925 1.0001 3398
## Avg_Cogongrass_Cover-Lynx_rufus 1.5364 1.0009 2388
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2235 1.0030 3948
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8140 1.0007 2263
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1494 1.0028 2971
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3920 1.0001 2889
## Avg_Cogongrass_Cover-Sus_scrofa 0.8981 1.0046 2560
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0590 -0.1099 0.0054 0.1208
## (Intercept)-Canis_latrans -2.6129 0.1704 -2.9613 -2.6064 -2.2859
## (Intercept)-Sciurus_niger -4.0926 0.6002 -5.2396 -4.0936 -2.9770
## (Intercept)-Procyon_lotor -2.2827 0.1304 -2.5420 -2.2792 -2.0398
## (Intercept)-Dasypus_novemcinctus -1.5925 0.1363 -1.8715 -1.5915 -1.3313
## (Intercept)-Lynx_rufus -3.5844 0.3297 -4.2704 -3.5651 -2.9819
## (Intercept)-Didelphis_virginiana -2.3334 0.2526 -2.8669 -2.3214 -1.8734
## (Intercept)-Sylvilagus_floridanus -3.2573 0.3223 -3.9387 -3.2378 -2.6691
## (Intercept)-Sciurus_carolinensis -2.4618 0.2686 -3.0178 -2.4484 -1.9711
## (Intercept)-Vulpes_vulpes -4.1817 0.7854 -5.8078 -4.1390 -2.7869
## (Intercept)-Sus_scrofa -2.9266 0.4695 -3.9673 -2.8906 -2.1119
## week-Odocoileus_virginianus 0.2069 0.0591 0.0924 0.2061 0.3229
## week-Canis_latrans 0.0726 0.1291 -0.1891 0.0744 0.3146
## week-Sciurus_niger -0.2898 0.2950 -0.9802 -0.2554 0.2022
## week-Procyon_lotor -0.0453 0.1155 -0.2770 -0.0422 0.1721
## week-Dasypus_novemcinctus -0.1555 0.1345 -0.4376 -0.1519 0.0986
## week-Lynx_rufus -0.0310 0.1888 -0.4251 -0.0234 0.3167
## week-Didelphis_virginiana -0.1965 0.2079 -0.6428 -0.1820 0.1766
## week-Sylvilagus_floridanus -0.1428 0.2003 -0.5640 -0.1337 0.2252
## week-Sciurus_carolinensis 0.1398 0.1779 -0.2136 0.1417 0.4821
## week-Vulpes_vulpes -0.1011 0.2692 -0.6782 -0.0886 0.3879
## week-Sus_scrofa 0.1026 0.2345 -0.3665 0.1009 0.5617
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0023 3029
## (Intercept)-Sciurus_niger 1.0179 386
## (Intercept)-Procyon_lotor 1.0011 4047
## (Intercept)-Dasypus_novemcinctus 1.0017 5042
## (Intercept)-Lynx_rufus 1.0087 868
## (Intercept)-Didelphis_virginiana 1.0058 4222
## (Intercept)-Sylvilagus_floridanus 1.0066 1158
## (Intercept)-Sciurus_carolinensis 1.0066 3496
## (Intercept)-Vulpes_vulpes 1.0055 386
## (Intercept)-Sus_scrofa 1.0013 2040
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0000 4695
## week-Sciurus_niger 1.0035 1792
## week-Procyon_lotor 1.0004 4441
## week-Dasypus_novemcinctus 1.0040 4265
## week-Lynx_rufus 0.9998 3124
## week-Didelphis_virginiana 1.0055 3525
## week-Sylvilagus_floridanus 1.0003 2998
## week-Sciurus_carolinensis 1.0009 4768
## week-Vulpes_vulpes 1.0018 2515
## week-Sus_scrofa 1.0008 4638
# Includes movement covariates of occupancy and week for detection
ms_week_move <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7617
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2811 0.6571 -1.5303 -0.3031 1.0390 1.0045 2099
## Cogon_Patch_Size -0.2766 0.4093 -1.1700 -0.2516 0.4851 1.0013 1928
## Avg_Cogongrass_Cover 0.2591 0.2804 -0.3020 0.2598 0.8191 1.0063 1386
## total_shrub_cover -0.2168 0.2700 -0.7523 -0.2099 0.3156 1.0016 2143
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.5754 3.8548 0.8350 3.5244 15.0578 1.0010 1301
## Cogon_Patch_Size 0.9453 1.2687 0.0658 0.5613 4.2379 1.0100 1372
## Avg_Cogongrass_Cover 0.2649 0.3015 0.0357 0.1725 1.0691 1.0193 2266
## total_shrub_cover 0.3230 0.3880 0.0395 0.2055 1.3575 1.0251 1964
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0738 1.0442 0.0793 0.7734 3.9836 1.0025 402
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4900 0.4157 -3.3085 -2.4984 -1.6349 1.0035 4241
## week -0.0384 0.1197 -0.2843 -0.0339 0.1852 1.0020 3171
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8940 1.1781 0.6517 1.6011 4.8966 1.0127 2294
## week 0.0984 0.0793 0.0246 0.0761 0.3031 1.0030 2907
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8849 1.6223 1.3230 3.6765
## (Intercept)-Canis_latrans 0.4197 0.7101 -0.9501 0.4020
## (Intercept)-Sciurus_niger -0.5057 1.5143 -2.6398 -0.7563
## (Intercept)-Procyon_lotor 0.6025 0.7080 -0.8722 0.6202
## (Intercept)-Dasypus_novemcinctus -0.7815 0.6607 -2.1255 -0.7634
## (Intercept)-Lynx_rufus -0.1247 1.0482 -1.9073 -0.1941
## (Intercept)-Didelphis_virginiana -1.4576 0.7273 -2.9398 -1.4548
## (Intercept)-Sylvilagus_floridanus -0.4017 0.8856 -1.9943 -0.4633
## (Intercept)-Sciurus_carolinensis -1.6755 0.7479 -3.2371 -1.6359
## (Intercept)-Vulpes_vulpes -1.1951 1.4669 -3.5472 -1.3852
## (Intercept)-Sus_scrofa -2.1537 0.9185 -4.0480 -2.1287
## Cogon_Patch_Size-Odocoileus_virginianus -0.1042 0.7217 -1.4148 -0.1547
## Cogon_Patch_Size-Canis_latrans 0.6683 0.7218 -0.3556 0.5389
## Cogon_Patch_Size-Sciurus_niger -0.6754 0.9143 -2.8440 -0.5547
## Cogon_Patch_Size-Procyon_lotor -0.2805 0.4645 -1.2240 -0.2784
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1810 0.4183 -1.0521 -0.1613
## Cogon_Patch_Size-Lynx_rufus -0.3057 0.7719 -1.7217 -0.3357
## Cogon_Patch_Size-Didelphis_virginiana 0.5582 0.5025 -0.3270 0.5262
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9575 0.8735 -3.0416 -0.8064
## Cogon_Patch_Size-Sciurus_carolinensis -0.7861 0.7399 -2.6244 -0.6640
## Cogon_Patch_Size-Vulpes_vulpes -0.6101 0.8896 -2.7229 -0.5024
## Cogon_Patch_Size-Sus_scrofa -0.5421 0.8369 -2.5329 -0.4161
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2497 0.5071 -0.7480 0.2502
## Avg_Cogongrass_Cover-Canis_latrans 0.3022 0.3877 -0.4283 0.2964
## Avg_Cogongrass_Cover-Sciurus_niger -0.0166 0.5529 -1.2060 0.0143
## Avg_Cogongrass_Cover-Procyon_lotor 0.2907 0.4172 -0.4962 0.2831
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4123 0.3670 -0.2857 0.4017
## Avg_Cogongrass_Cover-Lynx_rufus 0.5253 0.4505 -0.2826 0.5020
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2639 0.3951 -0.5277 0.2650
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0130 0.4461 -0.9454 -0.0024
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4795 0.3934 -0.2259 0.4653
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3483 0.4551 -0.5400 0.3307
## Avg_Cogongrass_Cover-Sus_scrofa -0.0071 0.5289 -1.1863 0.0455
## total_shrub_cover-Odocoileus_virginianus -0.1319 0.5148 -1.1487 -0.1352
## total_shrub_cover-Canis_latrans 0.0783 0.4066 -0.6665 0.0586
## total_shrub_cover-Sciurus_niger -0.4145 0.5211 -1.5560 -0.3817
## total_shrub_cover-Procyon_lotor -0.6617 0.4520 -1.6910 -0.6163
## total_shrub_cover-Dasypus_novemcinctus -0.0446 0.3443 -0.6983 -0.0505
## total_shrub_cover-Lynx_rufus -0.5345 0.5411 -1.8036 -0.4783
## total_shrub_cover-Didelphis_virginiana -0.2453 0.3956 -1.0560 -0.2369
## total_shrub_cover-Sylvilagus_floridanus -0.2187 0.4762 -1.1837 -0.2100
## total_shrub_cover-Sciurus_carolinensis -0.0654 0.3969 -0.8362 -0.0761
## total_shrub_cover-Vulpes_vulpes -0.2210 0.5366 -1.3004 -0.2027
## total_shrub_cover-Sus_scrofa 0.0583 0.4903 -0.8416 0.0209
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.6974 1.0023 1027
## (Intercept)-Canis_latrans 1.8934 1.0017 2366
## (Intercept)-Sciurus_niger 3.4870 1.0585 304
## (Intercept)-Procyon_lotor 1.9389 1.0013 2443
## (Intercept)-Dasypus_novemcinctus 0.4879 1.0039 2306
## (Intercept)-Lynx_rufus 2.0367 1.0057 835
## (Intercept)-Didelphis_virginiana -0.0156 1.0007 2660
## (Intercept)-Sylvilagus_floridanus 1.5776 1.0018 1583
## (Intercept)-Sciurus_carolinensis -0.2904 1.0033 2193
## (Intercept)-Vulpes_vulpes 2.2878 1.0039 381
## (Intercept)-Sus_scrofa -0.3968 1.0005 1963
## Cogon_Patch_Size-Odocoileus_virginianus 1.5022 1.0009 2995
## Cogon_Patch_Size-Canis_latrans 2.4397 1.0005 2031
## Cogon_Patch_Size-Sciurus_niger 0.8449 1.0037 1331
## Cogon_Patch_Size-Procyon_lotor 0.6137 1.0038 3563
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6111 1.0006 3816
## Cogon_Patch_Size-Lynx_rufus 1.4851 1.0034 1927
## Cogon_Patch_Size-Didelphis_virginiana 1.6363 1.0016 2484
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3222 1.0029 1425
## Cogon_Patch_Size-Sciurus_carolinensis 0.2717 1.0032 1604
## Cogon_Patch_Size-Vulpes_vulpes 0.8781 1.0006 1635
## Cogon_Patch_Size-Sus_scrofa 0.7534 1.0033 2106
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2763 1.0030 3195
## Avg_Cogongrass_Cover-Canis_latrans 1.1077 1.0092 2848
## Avg_Cogongrass_Cover-Sciurus_niger 0.9792 1.0139 1532
## Avg_Cogongrass_Cover-Procyon_lotor 1.1849 1.0018 3116
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1885 1.0008 2663
## Avg_Cogongrass_Cover-Lynx_rufus 1.5323 1.0022 2497
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0194 1.0053 3160
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8169 1.0027 2468
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3128 1.0031 2190
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2960 1.0058 2805
## Avg_Cogongrass_Cover-Sus_scrofa 0.8980 1.0064 2234
## total_shrub_cover-Odocoileus_virginianus 0.9343 1.0044 3652
## total_shrub_cover-Canis_latrans 0.9211 1.0074 3438
## total_shrub_cover-Sciurus_niger 0.5239 1.0059 2468
## total_shrub_cover-Procyon_lotor 0.0872 1.0097 2508
## total_shrub_cover-Dasypus_novemcinctus 0.6541 1.0023 4166
## total_shrub_cover-Lynx_rufus 0.3497 1.0051 2361
## total_shrub_cover-Didelphis_virginiana 0.5249 1.0001 3742
## total_shrub_cover-Sylvilagus_floridanus 0.7042 1.0058 3078
## total_shrub_cover-Sciurus_carolinensis 0.7394 1.0059 3378
## total_shrub_cover-Vulpes_vulpes 0.7904 1.0040 2379
## total_shrub_cover-Sus_scrofa 1.1158 1.0026 3162
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0072 0.0594 -0.1070 0.0066 0.1240
## (Intercept)-Canis_latrans -2.6244 0.1742 -2.9758 -2.6194 -2.2937
## (Intercept)-Sciurus_niger -4.0542 0.5993 -5.2619 -4.0352 -2.9815
## (Intercept)-Procyon_lotor -2.2828 0.1345 -2.5538 -2.2813 -2.0259
## (Intercept)-Dasypus_novemcinctus -1.5912 0.1345 -1.8678 -1.5864 -1.3383
## (Intercept)-Lynx_rufus -3.5321 0.3206 -4.1864 -3.5260 -2.9384
## (Intercept)-Didelphis_virginiana -2.3425 0.2504 -2.8593 -2.3267 -1.8950
## (Intercept)-Sylvilagus_floridanus -3.2665 0.3280 -3.9604 -3.2432 -2.6779
## (Intercept)-Sciurus_carolinensis -2.4666 0.2697 -3.0253 -2.4540 -1.9679
## (Intercept)-Vulpes_vulpes -4.0773 0.7623 -5.6055 -4.0281 -2.7469
## (Intercept)-Sus_scrofa -2.9485 0.4875 -3.9926 -2.9071 -2.1061
## week-Odocoileus_virginianus 0.2073 0.0603 0.0899 0.2072 0.3259
## week-Canis_latrans 0.0693 0.1292 -0.1914 0.0714 0.3120
## week-Sciurus_niger -0.2919 0.2931 -0.9566 -0.2576 0.1984
## week-Procyon_lotor -0.0450 0.1163 -0.2806 -0.0406 0.1745
## week-Dasypus_novemcinctus -0.1544 0.1345 -0.4377 -0.1474 0.0899
## week-Lynx_rufus -0.0286 0.1907 -0.4314 -0.0231 0.3221
## week-Didelphis_virginiana -0.2001 0.2164 -0.6736 -0.1833 0.1886
## week-Sylvilagus_floridanus -0.1394 0.1968 -0.5593 -0.1285 0.2158
## week-Sciurus_carolinensis 0.1428 0.1805 -0.2239 0.1465 0.4927
## week-Vulpes_vulpes -0.1007 0.2666 -0.6757 -0.0843 0.3858
## week-Sus_scrofa 0.1018 0.2353 -0.3613 0.1044 0.5665
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0004 3219
## (Intercept)-Sciurus_niger 1.0213 342
## (Intercept)-Procyon_lotor 1.0004 4118
## (Intercept)-Dasypus_novemcinctus 1.0001 4743
## (Intercept)-Lynx_rufus 1.0045 1149
## (Intercept)-Didelphis_virginiana 1.0008 3718
## (Intercept)-Sylvilagus_floridanus 1.0055 1152
## (Intercept)-Sciurus_carolinensis 1.0119 3538
## (Intercept)-Vulpes_vulpes 1.0323 385
## (Intercept)-Sus_scrofa 1.0027 1946
## week-Odocoileus_virginianus 1.0025 5201
## week-Canis_latrans 1.0003 4497
## week-Sciurus_niger 1.0037 1869
## week-Procyon_lotor 1.0002 5608
## week-Dasypus_novemcinctus 1.0001 4870
## week-Lynx_rufus 1.0011 3091
## week-Didelphis_virginiana 1.0023 3688
## week-Sylvilagus_floridanus 1.0018 3016
## week-Sciurus_carolinensis 1.0044 4838
## week-Vulpes_vulpes 1.0010 2738
## week-Sus_scrofa 0.9998 5114
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7647
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2078 0.7346 -1.5588 -0.2476 1.3456 1.0093 1508
## Tree_Density -0.7649 0.3883 -1.6116 -0.7388 -0.0639 1.0023 1442
## Avg_Canopy_Cover 1.0185 0.3404 0.3938 1.0036 1.7396 1.0124 1342
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5946 5.6873 1.5146 5.0125 20.7119 1.0154 935
## Tree_Density 0.6874 1.1053 0.0439 0.3373 3.3867 1.0597 868
## Avg_Canopy_Cover 0.5607 0.6144 0.0556 0.3739 2.2162 1.0330 1802
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.3983 0.4489 0.0396 0.2504 1.6195 1.0372 580
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5041 0.4237 -3.3074 -2.5107 -1.6234 1.0031 4770
## week -0.0370 0.1190 -0.2920 -0.0337 0.1881 1.0014 3392
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9959 1.2719 0.6825 1.6808 4.9822 1.0164 2017
## week 0.0973 0.0835 0.0251 0.0753 0.2936 1.0077 3069
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6871 1.7400 2.1396 4.3908 8.9230
## (Intercept)-Canis_latrans 0.3406 0.6830 -0.8334 0.2985 1.7918
## (Intercept)-Sciurus_niger 0.0087 1.5943 -2.3140 -0.2532 3.9350
## (Intercept)-Procyon_lotor 0.7610 0.5908 -0.3680 0.7461 1.9816
## (Intercept)-Dasypus_novemcinctus -1.0368 0.6039 -2.3098 -1.0255 0.0777
## (Intercept)-Lynx_rufus 1.2584 1.9214 -1.1808 0.8459 5.9822
## (Intercept)-Didelphis_virginiana -1.9249 0.6843 -3.3794 -1.8971 -0.6672
## (Intercept)-Sylvilagus_floridanus -0.6817 0.7124 -2.0570 -0.6914 0.7252
## (Intercept)-Sciurus_carolinensis -1.9896 0.7040 -3.5165 -1.9395 -0.7270
## (Intercept)-Vulpes_vulpes -1.3256 1.7244 -3.6528 -1.6377 3.3414
## (Intercept)-Sus_scrofa -2.7588 0.9249 -4.7021 -2.6936 -1.0889
## Tree_Density-Odocoileus_virginianus -0.3942 0.6369 -1.4994 -0.4504 1.0738
## Tree_Density-Canis_latrans -0.8952 0.5485 -2.1779 -0.8326 -0.0016
## Tree_Density-Sciurus_niger -0.8020 0.7577 -2.4758 -0.7466 0.5937
## Tree_Density-Procyon_lotor -0.4927 0.3973 -1.2756 -0.4980 0.2962
## Tree_Density-Dasypus_novemcinctus -1.3205 0.8492 -3.5011 -1.1421 -0.2018
## Tree_Density-Lynx_rufus -0.0686 0.7424 -1.2662 -0.1478 1.7232
## Tree_Density-Didelphis_virginiana -1.0148 0.7301 -2.8919 -0.8982 0.0667
## Tree_Density-Sylvilagus_floridanus -1.0335 0.7032 -2.7898 -0.9296 0.0509
## Tree_Density-Sciurus_carolinensis -0.9454 0.7184 -2.6981 -0.8394 0.1655
## Tree_Density-Vulpes_vulpes -0.6982 0.7530 -2.3495 -0.6534 0.6679
## Tree_Density-Sus_scrofa -0.9696 0.8007 -2.9711 -0.8469 0.2639
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8027 0.6672 -0.5208 0.8161 2.0879
## Avg_Canopy_Cover-Canis_latrans 0.1659 0.4770 -0.7580 0.1627 1.1202
## Avg_Canopy_Cover-Sciurus_niger 1.0179 0.7703 -0.3901 0.9615 2.8331
## Avg_Canopy_Cover-Procyon_lotor 1.0331 0.4495 0.2030 1.0160 2.0043
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0130 0.4170 0.2464 0.9985 1.8798
## Avg_Canopy_Cover-Lynx_rufus 0.8983 0.7308 -0.4911 0.8713 2.4299
## Avg_Canopy_Cover-Didelphis_virginiana 1.2505 0.4849 0.4173 1.2108 2.3226
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6220 0.7078 0.5765 1.5172 3.3209
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2422 0.4830 0.4080 1.2021 2.2834
## Avg_Canopy_Cover-Vulpes_vulpes 1.0726 0.6063 0.0110 1.0253 2.4550
## Avg_Canopy_Cover-Sus_scrofa 1.2519 0.5336 0.3328 1.2027 2.4234
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0150 840
## (Intercept)-Canis_latrans 1.0053 1910
## (Intercept)-Sciurus_niger 1.0362 423
## (Intercept)-Procyon_lotor 1.0060 3430
## (Intercept)-Dasypus_novemcinctus 1.0113 3135
## (Intercept)-Lynx_rufus 1.0113 330
## (Intercept)-Didelphis_virginiana 1.0028 3628
## (Intercept)-Sylvilagus_floridanus 1.0009 2656
## (Intercept)-Sciurus_carolinensis 1.0035 3094
## (Intercept)-Vulpes_vulpes 1.1658 213
## (Intercept)-Sus_scrofa 1.0065 2086
## Tree_Density-Odocoileus_virginianus 1.0088 1727
## Tree_Density-Canis_latrans 1.0012 2572
## Tree_Density-Sciurus_niger 1.0072 1492
## Tree_Density-Procyon_lotor 1.0052 3354
## Tree_Density-Dasypus_novemcinctus 1.0158 1329
## Tree_Density-Lynx_rufus 1.0033 840
## Tree_Density-Didelphis_virginiana 1.0061 2063
## Tree_Density-Sylvilagus_floridanus 1.0005 1843
## Tree_Density-Sciurus_carolinensis 1.0142 1938
## Tree_Density-Vulpes_vulpes 1.0153 2105
## Tree_Density-Sus_scrofa 1.0041 1585
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0051 2734
## Avg_Canopy_Cover-Canis_latrans 1.0097 2371
## Avg_Canopy_Cover-Sciurus_niger 1.0293 1595
## Avg_Canopy_Cover-Procyon_lotor 1.0023 3620
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0015 4174
## Avg_Canopy_Cover-Lynx_rufus 1.0090 1585
## Avg_Canopy_Cover-Didelphis_virginiana 1.0074 2640
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0119 1840
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0109 3049
## Avg_Canopy_Cover-Vulpes_vulpes 1.0127 2249
## Avg_Canopy_Cover-Sus_scrofa 1.0089 2665
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0082 0.0588 -0.1092 0.0083 0.1194
## (Intercept)-Canis_latrans -2.6602 0.1920 -3.0730 -2.6532 -2.3104
## (Intercept)-Sciurus_niger -4.2320 0.5871 -5.3273 -4.2641 -3.0586
## (Intercept)-Procyon_lotor -2.2679 0.1326 -2.5490 -2.2654 -2.0127
## (Intercept)-Dasypus_novemcinctus -1.5868 0.1321 -1.8472 -1.5841 -1.3368
## (Intercept)-Lynx_rufus -3.7779 0.3582 -4.4562 -3.7944 -3.0599
## (Intercept)-Didelphis_virginiana -2.3300 0.2543 -2.8721 -2.3212 -1.8781
## (Intercept)-Sylvilagus_floridanus -3.1361 0.2799 -3.7195 -3.1199 -2.6234
## (Intercept)-Sciurus_carolinensis -2.4527 0.2600 -2.9843 -2.4432 -1.9636
## (Intercept)-Vulpes_vulpes -4.0675 0.7844 -5.7698 -3.9972 -2.7288
## (Intercept)-Sus_scrofa -2.8802 0.4519 -3.8414 -2.8502 -2.0872
## week-Odocoileus_virginianus 0.2072 0.0607 0.0925 0.2065 0.3270
## week-Canis_latrans 0.0734 0.1295 -0.1913 0.0766 0.3131
## week-Sciurus_niger -0.2780 0.2853 -0.9079 -0.2494 0.2050
## week-Procyon_lotor -0.0441 0.1167 -0.2791 -0.0418 0.1835
## week-Dasypus_novemcinctus -0.1561 0.1347 -0.4371 -0.1494 0.0943
## week-Lynx_rufus -0.0253 0.1890 -0.4204 -0.0177 0.3233
## week-Didelphis_virginiana -0.1924 0.2134 -0.6459 -0.1808 0.1807
## week-Sylvilagus_floridanus -0.1370 0.1980 -0.5571 -0.1277 0.2214
## week-Sciurus_carolinensis 0.1371 0.1787 -0.2089 0.1385 0.4870
## week-Vulpes_vulpes -0.1027 0.2724 -0.7146 -0.0833 0.3778
## week-Sus_scrofa 0.1004 0.2313 -0.3578 0.1007 0.5582
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5286
## (Intercept)-Canis_latrans 1.0019 2076
## (Intercept)-Sciurus_niger 1.0131 395
## (Intercept)-Procyon_lotor 1.0027 4022
## (Intercept)-Dasypus_novemcinctus 1.0023 5608
## (Intercept)-Lynx_rufus 1.0038 596
## (Intercept)-Didelphis_virginiana 0.9999 3772
## (Intercept)-Sylvilagus_floridanus 1.0036 2055
## (Intercept)-Sciurus_carolinensis 1.0016 3543
## (Intercept)-Vulpes_vulpes 1.1337 267
## (Intercept)-Sus_scrofa 1.0098 2277
## week-Odocoileus_virginianus 1.0000 5530
## week-Canis_latrans 0.9999 4438
## week-Sciurus_niger 1.0041 1911
## week-Procyon_lotor 1.0118 4756
## week-Dasypus_novemcinctus 1.0000 4741
## week-Lynx_rufus 1.0035 2820
## week-Didelphis_virginiana 1.0016 3552
## week-Sylvilagus_floridanus 1.0033 3323
## week-Sciurus_carolinensis 1.0007 4729
## week-Vulpes_vulpes 1.0013 2967
## week-Sus_scrofa 1.0012 4804
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7005
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9454 0.6324 -2.1075 -0.9702 0.3883 1.0006 1834
## Avg_Cogongrass_Cover -0.7281 0.3703 -1.4679 -0.7202 -0.0372 0.9999 1332
## I(Avg_Cogongrass_Cover^2) 0.8450 0.3358 0.2579 0.8213 1.5748 1.0024 1116
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7763 3.1796 0.7659 2.8756 12.1673 1.0071 760
## Avg_Cogongrass_Cover 0.3756 0.5430 0.0406 0.2244 1.5576 1.0219 2379
## I(Avg_Cogongrass_Cover^2) 0.4903 1.1630 0.0375 0.2085 2.7741 1.1011 574
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5476 0.5738 0.0505 0.3573 2.0486 1.0161 552
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4884 0.4117 -3.2916 -2.4943 -1.6453 1.0010 4407
## week -0.0380 0.1175 -0.2746 -0.0346 0.1842 1.0016 3132
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8267 1.1459 0.6317 1.5391 4.6869 1.0018 1892
## week 0.0958 0.0745 0.0255 0.0745 0.3009 1.0022 2550
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8174 1.4331 0.5798 2.6281
## (Intercept)-Canis_latrans -0.5277 0.6742 -1.8739 -0.5213
## (Intercept)-Sciurus_niger -0.8305 1.3981 -2.8652 -1.0612
## (Intercept)-Procyon_lotor -0.2110 0.6428 -1.4862 -0.1935
## (Intercept)-Dasypus_novemcinctus -1.4217 0.6274 -2.7146 -1.4133
## (Intercept)-Lynx_rufus -1.2269 0.8972 -2.8878 -1.2525
## (Intercept)-Didelphis_virginiana -2.0127 0.7109 -3.4143 -1.9936
## (Intercept)-Sylvilagus_floridanus -1.0327 0.9250 -2.5593 -1.0818
## (Intercept)-Sciurus_carolinensis -2.4495 0.7660 -4.0378 -2.4203
## (Intercept)-Vulpes_vulpes -2.1882 1.2580 -4.3792 -2.2815
## (Intercept)-Sus_scrofa -2.5385 0.8813 -4.3484 -2.5060
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7266 0.6378 -2.0227 -0.7135
## Avg_Cogongrass_Cover-Canis_latrans -0.4673 0.5155 -1.4153 -0.4882
## Avg_Cogongrass_Cover-Sciurus_niger -0.9633 0.6568 -2.4494 -0.9058
## Avg_Cogongrass_Cover-Procyon_lotor -0.5954 0.5047 -1.5533 -0.6104
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5634 0.4690 -1.4742 -0.5686
## Avg_Cogongrass_Cover-Lynx_rufus -0.6230 0.5475 -1.6703 -0.6253
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4804 0.5293 -1.4811 -0.4938
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1295 0.5972 -2.4159 -1.0807
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7944 0.5396 -1.9415 -0.7654
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7710 0.6126 -2.0181 -0.7607
## Avg_Cogongrass_Cover-Sus_scrofa -1.0409 0.6509 -2.5130 -0.9715
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1532 0.8464 0.0811 1.0018
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2135 0.7724 0.2513 1.0262
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4249 0.7256 -1.2045 0.4763
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0826 0.6454 0.2143 0.9642
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7310 0.3443 0.0758 0.7188
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1940 0.5599 0.3614 1.1213
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6014 0.4016 -0.1553 0.5945
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7607 0.4567 -0.0454 0.7235
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9644 0.3930 0.2753 0.9339
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9477 0.5016 0.1629 0.8940
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4005 0.5769 -0.9051 0.4641
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.2076 1.0043 1093
## (Intercept)-Canis_latrans 0.7938 1.0008 2138
## (Intercept)-Sciurus_niger 2.5345 1.0275 229
## (Intercept)-Procyon_lotor 1.0113 1.0011 2996
## (Intercept)-Dasypus_novemcinctus -0.2088 1.0002 3453
## (Intercept)-Lynx_rufus 0.6261 1.0019 1470
## (Intercept)-Didelphis_virginiana -0.6563 1.0014 2822
## (Intercept)-Sylvilagus_floridanus 0.6176 1.0418 478
## (Intercept)-Sciurus_carolinensis -1.0189 1.0001 2403
## (Intercept)-Vulpes_vulpes 0.5085 1.0440 585
## (Intercept)-Sus_scrofa -0.8280 1.0038 2177
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5110 1.0014 2475
## Avg_Cogongrass_Cover-Canis_latrans 0.6288 1.0012 2451
## Avg_Cogongrass_Cover-Sciurus_niger 0.1850 1.0078 1875
## Avg_Cogongrass_Cover-Procyon_lotor 0.4272 1.0017 2595
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3631 1.0022 2234
## Avg_Cogongrass_Cover-Lynx_rufus 0.4660 1.0028 2188
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6094 1.0021 2312
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0996 1.0047 1797
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1998 1.0009 1973
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4180 1.0007 2108
## Avg_Cogongrass_Cover-Sus_scrofa 0.0579 1.0082 1772
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2438 1.0422 589
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3693 1.0253 703
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.7134 1.0285 584
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.7064 1.0209 916
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4248 1.0006 2548
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4815 1.0117 1001
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4183 1.0024 2436
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7706 1.0036 1741
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8464 1.0011 1754
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0883 1.0108 1426
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3245 1.0217 1258
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0075 0.0594 -0.1075 0.0065 0.1234
## (Intercept)-Canis_latrans -2.6464 0.1759 -3.0085 -2.6421 -2.3124
## (Intercept)-Sciurus_niger -4.0604 0.6397 -5.3385 -4.0279 -2.9375
## (Intercept)-Procyon_lotor -2.2787 0.1328 -2.5453 -2.2766 -2.0320
## (Intercept)-Dasypus_novemcinctus -1.5874 0.1347 -1.8586 -1.5844 -1.3243
## (Intercept)-Lynx_rufus -3.4361 0.3196 -4.0930 -3.4272 -2.8556
## (Intercept)-Didelphis_virginiana -2.3624 0.2668 -2.9352 -2.3484 -1.8667
## (Intercept)-Sylvilagus_floridanus -3.2349 0.3064 -3.8643 -3.2250 -2.6771
## (Intercept)-Sciurus_carolinensis -2.4532 0.2609 -3.0075 -2.4394 -1.9767
## (Intercept)-Vulpes_vulpes -3.9709 0.7150 -5.5049 -3.9184 -2.7207
## (Intercept)-Sus_scrofa -2.9425 0.4653 -3.9606 -2.9082 -2.1345
## week-Odocoileus_virginianus 0.2079 0.0598 0.0930 0.2066 0.3260
## week-Canis_latrans 0.0724 0.1263 -0.1871 0.0728 0.3155
## week-Sciurus_niger -0.2844 0.2987 -0.9710 -0.2485 0.2021
## week-Procyon_lotor -0.0443 0.1147 -0.2770 -0.0407 0.1638
## week-Dasypus_novemcinctus -0.1552 0.1352 -0.4379 -0.1493 0.0943
## week-Lynx_rufus -0.0318 0.1939 -0.4211 -0.0243 0.3267
## week-Didelphis_virginiana -0.1989 0.2115 -0.6426 -0.1834 0.1839
## week-Sylvilagus_floridanus -0.1337 0.2030 -0.5628 -0.1213 0.2288
## week-Sciurus_carolinensis 0.1411 0.1733 -0.2009 0.1404 0.4752
## week-Vulpes_vulpes -0.1024 0.2685 -0.6796 -0.0869 0.3961
## week-Sus_scrofa 0.1053 0.2340 -0.3701 0.1045 0.5769
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0043 5250
## (Intercept)-Canis_latrans 1.0027 3138
## (Intercept)-Sciurus_niger 1.0098 382
## (Intercept)-Procyon_lotor 1.0032 4193
## (Intercept)-Dasypus_novemcinctus 1.0011 5250
## (Intercept)-Lynx_rufus 1.0042 1228
## (Intercept)-Didelphis_virginiana 1.0006 3554
## (Intercept)-Sylvilagus_floridanus 1.0053 1301
## (Intercept)-Sciurus_carolinensis 1.0046 3624
## (Intercept)-Vulpes_vulpes 1.0359 506
## (Intercept)-Sus_scrofa 1.0071 1990
## week-Odocoileus_virginianus 1.0018 5250
## week-Canis_latrans 0.9999 4403
## week-Sciurus_niger 1.0009 1779
## week-Procyon_lotor 1.0007 4582
## week-Dasypus_novemcinctus 1.0018 5211
## week-Lynx_rufus 1.0033 3195
## week-Didelphis_virginiana 1.0040 3963
## week-Sylvilagus_floridanus 1.0002 2991
## week-Sciurus_carolinensis 1.0001 4312
## week-Vulpes_vulpes 1.0032 2951
## week-Sus_scrofa 1.0016 4787
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8437
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9672 1.1594 -3.1544 -1.0172 1.4064 1.0005 1791
## Cogon_Patch_Size -0.2643 0.7490 -1.7853 -0.2257 1.1513 1.0035 1289
## Veg_shannon_index 0.9123 0.4846 -0.0118 0.8880 1.9292 1.0170 1196
## total_shrub_cover -0.2895 0.4220 -1.1439 -0.2836 0.5340 1.0128 1495
## Avg_Cogongrass_Cover 0.0259 0.9186 -1.7704 0.0273 1.8922 1.0026 471
## Tree_Density -2.0075 0.7844 -3.5833 -1.9963 -0.4243 1.0041 672
## Avg_Canopy_Cover 1.8474 0.6242 0.7007 1.8098 3.2044 1.0146 781
## I(Avg_Cogongrass_Cover^2) 1.5444 0.5646 0.5130 1.5132 2.7269 1.0161 567
## avg_veg_height -0.1924 0.5039 -1.2010 -0.1879 0.7921 1.0014 723
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.1182 21.0519 4.2948 18.2574 80.4178 1.0010 468
## Cogon_Patch_Size 4.1570 5.8243 0.1892 2.3433 19.9860 1.0160 478
## Veg_shannon_index 0.9381 1.4322 0.0525 0.4576 5.1323 1.0430 721
## total_shrub_cover 0.6639 0.9089 0.0487 0.3669 3.1704 1.0001 1336
## Avg_Cogongrass_Cover 1.1637 1.7920 0.0539 0.5391 6.0356 1.0025 734
## Tree_Density 3.7663 7.0967 0.0686 1.3674 22.7962 1.0680 330
## Avg_Canopy_Cover 2.2808 3.0876 0.1029 1.2769 10.2555 1.0358 481
## I(Avg_Cogongrass_Cover^2) 0.9519 1.8971 0.0452 0.4070 5.2612 1.0295 600
## avg_veg_height 0.4744 0.6343 0.0450 0.2686 2.1948 1.0100 1856
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5964 2.0319 0.0613 0.9003 7.0974 1.2382 165
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5540 0.4484 -3.3983 -2.5678 -1.6102 1.0023 4875
## week -0.0382 0.1181 -0.2883 -0.0327 0.1796 1.0054 2662
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2114 1.3441 0.7951 1.8877 5.6332 1.0057 3551
## week 0.0966 0.0793 0.0251 0.0751 0.2905 1.0063 2997
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.0366 3.8909 2.6508
## (Intercept)-Canis_latrans -0.9446 1.1998 -3.3351
## (Intercept)-Sciurus_niger 1.3413 2.9329 -2.9354
## (Intercept)-Procyon_lotor -0.4309 1.1450 -2.6755
## (Intercept)-Dasypus_novemcinctus -2.7951 1.1976 -5.5489
## (Intercept)-Lynx_rufus 0.2523 2.5793 -3.9265
## (Intercept)-Didelphis_virginiana -4.4257 1.4610 -7.6109
## (Intercept)-Sylvilagus_floridanus -2.5419 1.5315 -5.7941
## (Intercept)-Sciurus_carolinensis -5.1437 1.5881 -8.6518
## (Intercept)-Vulpes_vulpes -4.1396 2.6628 -9.1222
## (Intercept)-Sus_scrofa -6.1886 2.0783 -10.9322
## Cogon_Patch_Size-Odocoileus_virginianus 0.0208 1.6560 -2.9556
## Cogon_Patch_Size-Canis_latrans 1.7412 1.4974 -0.2818
## Cogon_Patch_Size-Sciurus_niger -0.9995 2.1338 -5.7085
## Cogon_Patch_Size-Procyon_lotor -0.4932 0.9081 -2.1416
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2270 0.7338 -1.7398
## Cogon_Patch_Size-Lynx_rufus -0.4084 1.6420 -3.5423
## Cogon_Patch_Size-Didelphis_virginiana 1.7328 1.0767 -0.0042
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7111 1.8168 -6.2046
## Cogon_Patch_Size-Sciurus_carolinensis -1.3029 1.4435 -4.8421
## Cogon_Patch_Size-Vulpes_vulpes -0.8571 1.8198 -5.1485
## Cogon_Patch_Size-Sus_scrofa -1.0095 1.6761 -5.3632
## Veg_shannon_index-Odocoileus_virginianus 0.6967 0.9331 -1.3460
## Veg_shannon_index-Canis_latrans 1.3330 0.7163 0.1805
## Veg_shannon_index-Sciurus_niger 1.0283 1.0598 -1.0134
## Veg_shannon_index-Procyon_lotor 1.1208 0.6207 0.0349
## Veg_shannon_index-Dasypus_novemcinctus 0.6221 0.5721 -0.5364
## Veg_shannon_index-Lynx_rufus 0.9736 0.9873 -1.0268
## Veg_shannon_index-Didelphis_virginiana 1.0487 0.7101 -0.2363
## Veg_shannon_index-Sylvilagus_floridanus 1.0126 0.7331 -0.3267
## Veg_shannon_index-Sciurus_carolinensis 0.2501 0.8341 -1.7028
## Veg_shannon_index-Vulpes_vulpes 0.5752 0.9368 -1.5216
## Veg_shannon_index-Sus_scrofa 1.6446 1.0614 0.1769
## total_shrub_cover-Odocoileus_virginianus -0.1567 0.8184 -1.8062
## total_shrub_cover-Canis_latrans -0.0208 0.5921 -1.1630
## total_shrub_cover-Sciurus_niger -0.5380 0.8696 -2.5102
## total_shrub_cover-Procyon_lotor -0.8242 0.6016 -2.1780
## total_shrub_cover-Dasypus_novemcinctus 0.0394 0.5121 -0.9293
## total_shrub_cover-Lynx_rufus -0.6897 0.8619 -2.7102
## total_shrub_cover-Didelphis_virginiana -0.4685 0.6555 -1.8979
## total_shrub_cover-Sylvilagus_floridanus -0.2155 0.7046 -1.6111
## total_shrub_cover-Sciurus_carolinensis -0.0329 0.6224 -1.1974
## total_shrub_cover-Vulpes_vulpes -0.4251 0.8031 -2.2081
## total_shrub_cover-Sus_scrofa 0.0493 0.7708 -1.3225
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0062 1.3167 -2.5574
## Avg_Cogongrass_Cover-Canis_latrans 0.0578 1.1329 -2.2119
## Avg_Cogongrass_Cover-Sciurus_niger -0.2506 1.4667 -3.5041
## Avg_Cogongrass_Cover-Procyon_lotor 0.2793 1.1287 -1.8465
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6365 1.2415 -1.5789
## Avg_Cogongrass_Cover-Lynx_rufus 0.2148 1.2712 -2.1960
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0978 1.1900 -2.1728
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5024 1.2803 -3.2494
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0580 1.1991 -2.3162
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1604 1.2795 -2.2950
## Avg_Cogongrass_Cover-Sus_scrofa -0.4755 1.3259 -3.4119
## Tree_Density-Odocoileus_virginianus -0.9812 1.5632 -3.2736
## Tree_Density-Canis_latrans -2.8043 1.3193 -6.1008
## Tree_Density-Sciurus_niger -1.8539 1.8290 -5.3531
## Tree_Density-Procyon_lotor -1.8885 0.9506 -3.8865
## Tree_Density-Dasypus_novemcinctus -3.8513 2.0406 -9.1605
## Tree_Density-Lynx_rufus -0.7900 1.7795 -3.3142
## Tree_Density-Didelphis_virginiana -2.4398 1.2573 -5.4204
## Tree_Density-Sylvilagus_floridanus -2.6082 1.4537 -6.1297
## Tree_Density-Sciurus_carolinensis -2.8364 1.5766 -6.9112
## Tree_Density-Vulpes_vulpes -1.9208 1.7934 -5.3676
## Tree_Density-Sus_scrofa -2.4726 1.6916 -6.6513
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3165 1.3450 -1.5888
## Avg_Canopy_Cover-Canis_latrans 0.3166 0.7593 -1.1703
## Avg_Canopy_Cover-Sciurus_niger 2.2746 1.6370 -0.5877
## Avg_Canopy_Cover-Procyon_lotor 1.7179 0.7333 0.4165
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9861 0.7432 0.7358
## Avg_Canopy_Cover-Lynx_rufus 1.6119 1.4111 -0.9418
## Avg_Canopy_Cover-Didelphis_virginiana 2.7146 1.0524 1.1332
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3414 1.6430 1.0988
## Avg_Canopy_Cover-Sciurus_carolinensis 2.3119 0.9246 0.9115
## Avg_Canopy_Cover-Vulpes_vulpes 2.3613 1.3876 0.3957
## Avg_Canopy_Cover-Sus_scrofa 2.0448 0.8748 0.5911
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8225 1.1182 0.0845
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0148 0.9458 0.6092
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3360 1.2110 -1.2522
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8396 0.8771 0.4786
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4914 0.7041 0.2735
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0382 0.9992 0.5527
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1760 0.6803 -0.1636
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2817 0.8085 -0.2000
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7048 0.7304 0.4389
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8604 0.9312 0.4148
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9258 0.9754 -1.2714
## avg_veg_height-Odocoileus_virginianus -0.1971 0.7982 -1.8286
## avg_veg_height-Canis_latrans -0.4419 0.6323 -1.7438
## avg_veg_height-Sciurus_niger -0.2766 0.8531 -2.0948
## avg_veg_height-Procyon_lotor 0.1194 0.6500 -1.1123
## avg_veg_height-Dasypus_novemcinctus 0.0912 0.6227 -1.0671
## avg_veg_height-Lynx_rufus -0.2582 0.8022 -1.9165
## avg_veg_height-Didelphis_virginiana -0.3341 0.6991 -1.8224
## avg_veg_height-Sylvilagus_floridanus -0.3202 0.6882 -1.7618
## avg_veg_height-Sciurus_carolinensis 0.0592 0.7068 -1.2474
## avg_veg_height-Vulpes_vulpes -0.3179 0.7888 -1.9627
## avg_veg_height-Sus_scrofa -0.3055 0.7331 -1.9077
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.2915 17.5830 1.0108 356
## (Intercept)-Canis_latrans -0.9432 1.5112 1.0053 1367
## (Intercept)-Sciurus_niger 0.8303 8.3381 1.0263 295
## (Intercept)-Procyon_lotor -0.4228 1.7435 1.0289 976
## (Intercept)-Dasypus_novemcinctus -2.6789 -0.7458 1.0454 443
## (Intercept)-Lynx_rufus -0.1338 6.1491 1.0295 279
## (Intercept)-Didelphis_virginiana -4.3255 -1.8999 1.0079 858
## (Intercept)-Sylvilagus_floridanus -2.4745 0.3326 1.0089 843
## (Intercept)-Sciurus_carolinensis -4.9936 -2.4783 1.0201 720
## (Intercept)-Vulpes_vulpes -4.2834 1.9325 1.0121 250
## (Intercept)-Sus_scrofa -5.9730 -2.8137 1.0315 537
## Cogon_Patch_Size-Odocoileus_virginianus -0.0921 3.7396 1.0033 1665
## Cogon_Patch_Size-Canis_latrans 1.4469 5.4405 1.0155 1104
## Cogon_Patch_Size-Sciurus_niger -0.8263 2.8612 1.0044 557
## Cogon_Patch_Size-Procyon_lotor -0.4991 1.1251 1.0193 936
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1934 1.0902 1.0008 1128
## Cogon_Patch_Size-Lynx_rufus -0.4656 3.2190 1.0083 972
## Cogon_Patch_Size-Didelphis_virginiana 1.6212 4.1907 1.0341 604
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3492 0.7412 1.0048 740
## Cogon_Patch_Size-Sciurus_carolinensis -1.0243 0.7262 0.9999 939
## Cogon_Patch_Size-Vulpes_vulpes -0.6713 2.4031 1.0040 931
## Cogon_Patch_Size-Sus_scrofa -0.6962 1.3457 1.0031 1203
## Veg_shannon_index-Odocoileus_virginianus 0.7316 2.4971 1.0062 2126
## Veg_shannon_index-Canis_latrans 1.2511 3.0125 1.0378 1378
## Veg_shannon_index-Sciurus_niger 0.9711 3.3316 1.0055 1318
## Veg_shannon_index-Procyon_lotor 1.0802 2.4904 1.0272 920
## Veg_shannon_index-Dasypus_novemcinctus 0.6338 1.7074 1.0086 2169
## Veg_shannon_index-Lynx_rufus 0.9599 3.0000 1.0024 1322
## Veg_shannon_index-Didelphis_virginiana 1.0073 2.5984 1.0061 2217
## Veg_shannon_index-Sylvilagus_floridanus 0.9782 2.6085 1.0162 1916
## Veg_shannon_index-Sciurus_carolinensis 0.3430 1.6437 1.0030 1443
## Veg_shannon_index-Vulpes_vulpes 0.6437 2.2645 1.0075 1431
## Veg_shannon_index-Sus_scrofa 1.4357 4.3513 1.0208 1106
## total_shrub_cover-Odocoileus_virginianus -0.1577 1.4623 1.0065 2501
## total_shrub_cover-Canis_latrans -0.0484 1.2867 1.0042 2523
## total_shrub_cover-Sciurus_niger -0.4758 1.0068 1.0022 1685
## total_shrub_cover-Procyon_lotor -0.7574 0.1760 1.0035 1610
## total_shrub_cover-Dasypus_novemcinctus 0.0257 1.0942 1.0066 3051
## total_shrub_cover-Lynx_rufus -0.5952 0.7840 1.0047 1181
## total_shrub_cover-Didelphis_virginiana -0.4174 0.7386 1.0063 1901
## total_shrub_cover-Sylvilagus_floridanus -0.2191 1.2301 1.0035 2263
## total_shrub_cover-Sciurus_carolinensis -0.0598 1.2866 1.0021 2947
## total_shrub_cover-Vulpes_vulpes -0.3693 1.0234 1.0055 1768
## total_shrub_cover-Sus_scrofa -0.0107 1.8159 1.0122 2627
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0106 2.7214 1.0070 973
## Avg_Cogongrass_Cover-Canis_latrans 0.0779 2.2880 1.0032 698
## Avg_Cogongrass_Cover-Sciurus_niger -0.1589 2.3852 1.0110 736
## Avg_Cogongrass_Cover-Procyon_lotor 0.2425 2.7059 1.0071 789
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5508 3.4306 1.0009 702
## Avg_Cogongrass_Cover-Lynx_rufus 0.1739 2.7991 1.0016 773
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0792 2.5149 1.0035 762
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4064 1.7766 1.0027 712
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0594 2.4715 1.0054 718
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1233 2.9050 1.0020 816
## Avg_Cogongrass_Cover-Sus_scrofa -0.3943 1.9450 1.0007 828
## Tree_Density-Odocoileus_virginianus -1.2100 2.7939 1.0180 588
## Tree_Density-Canis_latrans -2.5983 -0.7673 1.0145 579
## Tree_Density-Sciurus_niger -1.9330 2.3022 1.0208 714
## Tree_Density-Procyon_lotor -1.8764 -0.0428 1.0023 1050
## Tree_Density-Dasypus_novemcinctus -3.3325 -1.3245 1.0385 313
## Tree_Density-Lynx_rufus -1.0851 3.6566 1.0210 541
## Tree_Density-Didelphis_virginiana -2.2715 -0.4418 1.0038 945
## Tree_Density-Sylvilagus_floridanus -2.3839 -0.3548 1.0277 591
## Tree_Density-Sciurus_carolinensis -2.5270 -0.6380 1.0103 512
## Tree_Density-Vulpes_vulpes -1.9806 1.9166 1.0053 719
## Tree_Density-Sus_scrofa -2.2339 0.1887 1.0070 704
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3643 3.9707 1.0028 1458
## Avg_Canopy_Cover-Canis_latrans 0.2992 1.8313 1.0256 1313
## Avg_Canopy_Cover-Sciurus_niger 2.1111 6.1598 1.0177 772
## Avg_Canopy_Cover-Procyon_lotor 1.6708 3.2907 1.0087 864
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9086 3.6516 1.0270 559
## Avg_Canopy_Cover-Lynx_rufus 1.5448 4.6636 1.0069 724
## Avg_Canopy_Cover-Didelphis_virginiana 2.5474 5.2513 1.0207 480
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9857 7.3919 1.0611 434
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1802 4.5003 1.0224 844
## Avg_Canopy_Cover-Vulpes_vulpes 2.0923 5.8073 1.0128 597
## Avg_Canopy_Cover-Sus_scrofa 1.9569 4.0638 1.0210 815
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6720 4.5353 1.0114 845
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8580 4.2632 1.0053 820
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3686 3.5533 1.0033 516
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7273 3.8597 1.0063 805
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4317 3.0415 1.0133 679
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.8750 4.5255 1.0073 759
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1819 2.5512 1.0185 711
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2399 3.0425 1.0033 985
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6388 3.3849 1.0077 934
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7363 3.9424 1.0139 804
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9974 2.5695 1.0217 740
## avg_veg_height-Odocoileus_virginianus -0.1913 1.3983 1.0029 1222
## avg_veg_height-Canis_latrans -0.4092 0.7524 1.0004 983
## avg_veg_height-Sciurus_niger -0.2436 1.3533 1.0018 1093
## avg_veg_height-Procyon_lotor 0.0950 1.4868 1.0099 1166
## avg_veg_height-Dasypus_novemcinctus 0.0611 1.4050 1.0008 1138
## avg_veg_height-Lynx_rufus -0.2480 1.2929 1.0014 1249
## avg_veg_height-Didelphis_virginiana -0.3068 0.9606 1.0000 895
## avg_veg_height-Sylvilagus_floridanus -0.2926 0.9937 1.0016 1133
## avg_veg_height-Sciurus_carolinensis 0.0249 1.6083 1.0091 1479
## avg_veg_height-Vulpes_vulpes -0.2969 1.1634 1.0044 1211
## avg_veg_height-Sus_scrofa -0.2878 1.0834 1.0008 1081
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0054 0.0586 -0.1078 0.0065 0.1199
## (Intercept)-Canis_latrans -2.6229 0.1720 -2.9780 -2.6168 -2.2957
## (Intercept)-Sciurus_niger -4.6824 0.4250 -5.5392 -4.6868 -3.8517
## (Intercept)-Procyon_lotor -2.2772 0.1314 -2.5424 -2.2746 -2.0340
## (Intercept)-Dasypus_novemcinctus -1.5857 0.1343 -1.8454 -1.5849 -1.3278
## (Intercept)-Lynx_rufus -3.7944 0.3308 -4.4288 -3.7946 -3.1260
## (Intercept)-Didelphis_virginiana -2.3143 0.2444 -2.8130 -2.3050 -1.8666
## (Intercept)-Sylvilagus_floridanus -3.2274 0.2827 -3.8024 -3.2150 -2.6913
## (Intercept)-Sciurus_carolinensis -2.4505 0.2653 -3.0050 -2.4384 -1.9704
## (Intercept)-Vulpes_vulpes -4.2060 0.6655 -5.5581 -4.1870 -2.9977
## (Intercept)-Sus_scrofa -2.8866 0.4484 -3.8400 -2.8603 -2.0890
## week-Odocoileus_virginianus 0.2068 0.0606 0.0902 0.2054 0.3293
## week-Canis_latrans 0.0732 0.1291 -0.1842 0.0747 0.3172
## week-Sciurus_niger -0.2913 0.2852 -0.9339 -0.2605 0.1920
## week-Procyon_lotor -0.0457 0.1170 -0.2807 -0.0437 0.1739
## week-Dasypus_novemcinctus -0.1570 0.1377 -0.4444 -0.1535 0.0950
## week-Lynx_rufus -0.0292 0.1889 -0.4152 -0.0176 0.3060
## week-Didelphis_virginiana -0.1935 0.2125 -0.6563 -0.1775 0.1830
## week-Sylvilagus_floridanus -0.1392 0.2056 -0.5722 -0.1291 0.2345
## week-Sciurus_carolinensis 0.1372 0.1778 -0.2130 0.1413 0.4814
## week-Vulpes_vulpes -0.1006 0.2734 -0.6851 -0.0875 0.3992
## week-Sus_scrofa 0.1032 0.2290 -0.3476 0.0999 0.5654
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0017 2915
## (Intercept)-Sciurus_niger 1.0052 567
## (Intercept)-Procyon_lotor 1.0043 3332
## (Intercept)-Dasypus_novemcinctus 1.0029 5016
## (Intercept)-Lynx_rufus 1.0066 516
## (Intercept)-Didelphis_virginiana 1.0023 3721
## (Intercept)-Sylvilagus_floridanus 1.0017 1543
## (Intercept)-Sciurus_carolinensis 1.0033 3654
## (Intercept)-Vulpes_vulpes 1.0215 315
## (Intercept)-Sus_scrofa 1.0082 2330
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0015 4407
## week-Sciurus_niger 1.0080 1346
## week-Procyon_lotor 1.0008 4313
## week-Dasypus_novemcinctus 1.0011 4787
## week-Lynx_rufus 1.0001 2845
## week-Didelphis_virginiana 1.0031 3449
## week-Sylvilagus_floridanus 1.0023 2787
## week-Sciurus_carolinensis 1.0031 4772
## week-Vulpes_vulpes 1.0009 2826
## week-Sus_scrofa 1.0028 4629
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3585
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1964 0.5643 -1.2780 -0.2186 0.9868 1.0020 2495
## Avg_Cogongrass_Cover 0.2054 0.2459 -0.2793 0.2082 0.6861 1.0071 2634
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2355 2.5984 0.6315 2.5508 9.9358 1.0050 1627
## Avg_Cogongrass_Cover 0.2658 0.3255 0.0368 0.1733 1.0401 1.0192 2245
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7342 0.7013 0.0593 0.5303 2.5169 1.0351 461
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5666 0.4200 -3.3705 -2.5756 -1.6869 1.0037 4974
## shrub_cover 0.2222 0.2383 -0.2449 0.2220 0.7089 1.0045 2762
## veg_height -0.0052 0.1559 -0.3137 -0.0092 0.3000 1.0027 3502
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9398 1.2638 0.6559 1.6104 5.0832 1.0010 2810
## shrub_cover 0.4373 0.3671 0.0840 0.3356 1.3866 1.0044 2294
## veg_height 0.1877 0.1395 0.0521 0.1520 0.5350 1.0031 3379
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3706 1.3711 1.1536 3.1958
## (Intercept)-Canis_latrans 0.4318 0.6316 -0.7844 0.4281
## (Intercept)-Sciurus_niger -0.4301 1.1742 -2.3278 -0.5789
## (Intercept)-Procyon_lotor 0.5441 0.6160 -0.6655 0.5471
## (Intercept)-Dasypus_novemcinctus -0.6143 0.5857 -1.7923 -0.5951
## (Intercept)-Lynx_rufus 0.1202 1.0082 -1.5078 0.0101
## (Intercept)-Didelphis_virginiana -1.2442 0.6382 -2.5265 -1.2351
## (Intercept)-Sylvilagus_floridanus -0.3697 0.6703 -1.6373 -0.3750
## (Intercept)-Sciurus_carolinensis -1.2979 0.6619 -2.6514 -1.2832
## (Intercept)-Vulpes_vulpes -1.1290 1.1422 -3.1299 -1.2272
## (Intercept)-Sus_scrofa -1.6981 0.8173 -3.4149 -1.6766
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2004 0.4757 -0.7196 0.1894
## Avg_Cogongrass_Cover-Canis_latrans 0.4259 0.3757 -0.2168 0.3950
## Avg_Cogongrass_Cover-Sciurus_niger -0.1150 0.5400 -1.3154 -0.0671
## Avg_Cogongrass_Cover-Procyon_lotor 0.2330 0.3412 -0.4307 0.2260
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3428 0.3183 -0.2641 0.3267
## Avg_Cogongrass_Cover-Lynx_rufus 0.4171 0.4087 -0.3394 0.3881
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3322 0.3543 -0.3642 0.3190
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1574 0.4221 -1.0851 -0.1210
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3454 0.3458 -0.3174 0.3371
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2987 0.4213 -0.5027 0.2855
## Avg_Cogongrass_Cover-Sus_scrofa -0.0472 0.4980 -1.2180 -0.0042
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.4760 1.0120 927
## (Intercept)-Canis_latrans 1.7195 1.0000 2807
## (Intercept)-Sciurus_niger 2.4445 1.0075 509
## (Intercept)-Procyon_lotor 1.7597 1.0034 2753
## (Intercept)-Dasypus_novemcinctus 0.5385 1.0012 3385
## (Intercept)-Lynx_rufus 2.4535 1.0073 851
## (Intercept)-Didelphis_virginiana -0.0069 1.0005 3352
## (Intercept)-Sylvilagus_floridanus 0.9732 1.0070 2556
## (Intercept)-Sciurus_carolinensis -0.0303 1.0013 3256
## (Intercept)-Vulpes_vulpes 1.5589 1.0076 567
## (Intercept)-Sus_scrofa -0.1412 1.0024 2107
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1879 1.0024 3829
## Avg_Cogongrass_Cover-Canis_latrans 1.2576 1.0006 3355
## Avg_Cogongrass_Cover-Sciurus_niger 0.8396 1.0030 2133
## Avg_Cogongrass_Cover-Procyon_lotor 0.9254 1.0023 4738
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9985 1.0030 4069
## Avg_Cogongrass_Cover-Lynx_rufus 1.3156 1.0065 3069
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0656 1.0014 3968
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5961 1.0061 2771
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0704 1.0056 3855
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1895 1.0020 3493
## Avg_Cogongrass_Cover-Sus_scrofa 0.7995 1.0053 2464
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0040 0.0592 -0.1107 0.0038 0.1215
## (Intercept)-Canis_latrans -2.7461 0.1901 -3.1297 -2.7431 -2.3832
## (Intercept)-Sciurus_niger -4.0690 0.6385 -5.3573 -4.0614 -2.8189
## (Intercept)-Procyon_lotor -2.2891 0.1443 -2.5821 -2.2857 -2.0231
## (Intercept)-Dasypus_novemcinctus -1.7155 0.1573 -2.0280 -1.7133 -1.4174
## (Intercept)-Lynx_rufus -3.6532 0.3650 -4.3965 -3.6508 -2.9730
## (Intercept)-Didelphis_virginiana -2.5049 0.2855 -3.1015 -2.4851 -1.9863
## (Intercept)-Sylvilagus_floridanus -3.1552 0.2920 -3.7682 -3.1422 -2.6341
## (Intercept)-Sciurus_carolinensis -2.5691 0.3032 -3.2008 -2.5558 -2.0029
## (Intercept)-Vulpes_vulpes -4.0971 0.7472 -5.6668 -4.0380 -2.7971
## (Intercept)-Sus_scrofa -3.2396 0.5701 -4.3712 -3.2290 -2.1365
## shrub_cover-Odocoileus_virginianus -0.0527 0.0638 -0.1775 -0.0524 0.0706
## shrub_cover-Canis_latrans -0.2620 0.2137 -0.6823 -0.2622 0.1451
## shrub_cover-Sciurus_niger -0.3136 0.4619 -1.2631 -0.2986 0.5499
## shrub_cover-Procyon_lotor 0.2445 0.1644 -0.0780 0.2454 0.5558
## shrub_cover-Dasypus_novemcinctus 0.7953 0.2943 0.2327 0.7935 1.3784
## shrub_cover-Lynx_rufus -0.2280 0.3414 -0.9236 -0.2203 0.4357
## shrub_cover-Didelphis_virginiana 0.8664 0.3587 0.2248 0.8447 1.6460
## shrub_cover-Sylvilagus_floridanus 0.2756 0.3843 -0.4648 0.2664 1.0526
## shrub_cover-Sciurus_carolinensis 0.7423 0.3870 0.0025 0.7309 1.5446
## shrub_cover-Vulpes_vulpes -0.0495 0.5276 -1.1296 -0.0354 0.9430
## shrub_cover-Sus_scrofa 0.4863 0.6875 -0.8324 0.4678 1.8851
## veg_height-Odocoileus_virginianus -0.2946 0.0636 -0.4218 -0.2933 -0.1727
## veg_height-Canis_latrans -0.5775 0.1828 -0.9458 -0.5718 -0.2288
## veg_height-Sciurus_niger -0.0459 0.3928 -0.8190 -0.0516 0.7951
## veg_height-Procyon_lotor 0.3279 0.1212 0.0910 0.3292 0.5622
## veg_height-Dasypus_novemcinctus 0.2266 0.1295 -0.0216 0.2241 0.4813
## veg_height-Lynx_rufus 0.0114 0.2401 -0.4595 0.0131 0.4728
## veg_height-Didelphis_virginiana 0.3891 0.2347 -0.0396 0.3793 0.8828
## veg_height-Sylvilagus_floridanus 0.1158 0.2397 -0.3549 0.1146 0.5920
## veg_height-Sciurus_carolinensis 0.0437 0.2020 -0.3367 0.0386 0.4598
## veg_height-Vulpes_vulpes -0.1341 0.3053 -0.7829 -0.1236 0.4271
## veg_height-Sus_scrofa -0.1243 0.3206 -0.7891 -0.1169 0.4783
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0036 5250
## (Intercept)-Canis_latrans 1.0045 1983
## (Intercept)-Sciurus_niger 1.0184 447
## (Intercept)-Procyon_lotor 1.0005 3820
## (Intercept)-Dasypus_novemcinctus 1.0047 4297
## (Intercept)-Lynx_rufus 1.0105 855
## (Intercept)-Didelphis_virginiana 1.0033 2704
## (Intercept)-Sylvilagus_floridanus 1.0051 1555
## (Intercept)-Sciurus_carolinensis 1.0000 2482
## (Intercept)-Vulpes_vulpes 1.0078 482
## (Intercept)-Sus_scrofa 1.0033 1944
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0021 2811
## shrub_cover-Sciurus_niger 1.0371 1197
## shrub_cover-Procyon_lotor 1.0027 4644
## shrub_cover-Dasypus_novemcinctus 1.0011 3441
## shrub_cover-Lynx_rufus 1.0014 1458
## shrub_cover-Didelphis_virginiana 1.0061 2035
## shrub_cover-Sylvilagus_floridanus 1.0024 1943
## shrub_cover-Sciurus_carolinensis 1.0019 2275
## shrub_cover-Vulpes_vulpes 1.0067 1819
## shrub_cover-Sus_scrofa 1.0029 2557
## veg_height-Odocoileus_virginianus 1.0013 5250
## veg_height-Canis_latrans 1.0049 2153
## veg_height-Sciurus_niger 1.0034 2139
## veg_height-Procyon_lotor 1.0001 4050
## veg_height-Dasypus_novemcinctus 1.0016 5089
## veg_height-Lynx_rufus 1.0017 2322
## veg_height-Didelphis_virginiana 1.0028 3712
## veg_height-Sylvilagus_floridanus 1.0040 2611
## veg_height-Sciurus_carolinensis 1.0011 3787
## veg_height-Vulpes_vulpes 1.0026 2336
## veg_height-Sus_scrofa 1.0033 3389
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5363
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0420 1.0325 -2.0636 -0.0565 2.1263 1.0138 2022
## Cogon_Patch_Size -0.7494 0.6345 -2.0817 -0.7296 0.4737 1.0080 1129
## Veg_shannon_index 0.8544 0.4721 -0.0699 0.8488 1.8060 1.0008 719
## total_shrub_cover -0.3304 0.4832 -1.3300 -0.3129 0.5336 1.0155 823
## Avg_Cogongrass_Cover 1.9896 0.6825 0.7691 1.9572 3.4713 1.0029 525
## Tree_Density -1.8074 0.7336 -3.2713 -1.7867 -0.4197 1.0076 883
## Avg_Canopy_Cover 1.8720 0.6008 0.7554 1.8405 3.1252 1.0036 1006
## avg_veg_height -0.4696 0.4567 -1.3665 -0.4757 0.4497 1.0140 768
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.8731 15.4355 3.5878 13.4777 58.4183 1.0329 653
## Cogon_Patch_Size 2.6235 4.0010 0.1007 1.3698 12.8879 1.0685 875
## Veg_shannon_index 0.8439 1.2557 0.0522 0.4304 4.1442 1.0170 1113
## total_shrub_cover 0.8213 1.5262 0.0503 0.4408 3.8410 1.2004 595
## Avg_Cogongrass_Cover 1.0028 1.5753 0.0538 0.4721 5.1834 1.0480 953
## Tree_Density 3.1675 5.6958 0.0714 1.3264 17.5684 1.0512 400
## Avg_Canopy_Cover 2.2048 3.1602 0.1223 1.3273 10.0181 1.0037 769
## avg_veg_height 0.4146 0.7567 0.0414 0.2379 1.7527 1.1192 911
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3969 2.1375 0.0574 0.7196 6.8806 1.0148 291
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6386 0.4556 -3.5019 -2.6525 -1.6869 1.0025 4324
## shrub_cover 0.2684 0.2544 -0.2366 0.2620 0.7841 1.0051 1890
## veg_height 0.0053 0.1551 -0.3013 0.0072 0.3074 1.0004 2657
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2560 1.3556 0.8059 1.9157 5.6348 1.0024 2967
## shrub_cover 0.4858 0.3961 0.1005 0.3836 1.5148 1.0111 1497
## veg_height 0.1976 0.1387 0.0553 0.1602 0.5392 1.0035 3660
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1641 3.2075 3.5596 7.5740
## (Intercept)-Canis_latrans 0.9086 1.0449 -0.8744 0.8072
## (Intercept)-Sciurus_niger 1.5192 2.3966 -2.3518 1.2258
## (Intercept)-Procyon_lotor 0.9901 0.9630 -0.8552 0.9796
## (Intercept)-Dasypus_novemcinctus -1.4327 0.9805 -3.6753 -1.3399
## (Intercept)-Lynx_rufus 2.5934 3.0201 -1.7820 2.0157
## (Intercept)-Didelphis_virginiana -2.8088 1.1269 -5.2144 -2.7422
## (Intercept)-Sylvilagus_floridanus -1.1661 1.2121 -3.5430 -1.1747
## (Intercept)-Sciurus_carolinensis -2.9762 1.2922 -5.9304 -2.8577
## (Intercept)-Vulpes_vulpes -1.8748 2.1381 -5.3544 -2.0987
## (Intercept)-Sus_scrofa -4.2041 1.7727 -8.0874 -4.0584
## Cogon_Patch_Size-Odocoileus_virginianus -0.5877 1.3080 -2.9792 -0.6389
## Cogon_Patch_Size-Canis_latrans 0.6155 1.2678 -1.1236 0.3824
## Cogon_Patch_Size-Sciurus_niger -1.4350 1.6753 -5.5173 -1.2057
## Cogon_Patch_Size-Procyon_lotor -0.9913 0.6781 -2.3873 -0.9561
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5980 0.7173 -2.0095 -0.6152
## Cogon_Patch_Size-Lynx_rufus -0.7612 1.4598 -3.4610 -0.8097
## Cogon_Patch_Size-Didelphis_virginiana 0.6670 0.8809 -0.7431 0.5705
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8732 1.4788 -5.6213 -1.5891
## Cogon_Patch_Size-Sciurus_carolinensis -1.6617 1.3182 -5.0968 -1.4165
## Cogon_Patch_Size-Vulpes_vulpes -1.2567 1.5150 -4.8029 -1.0858
## Cogon_Patch_Size-Sus_scrofa -1.3143 1.3770 -4.7216 -1.0896
## Veg_shannon_index-Odocoileus_virginianus 0.7121 0.8920 -1.2271 0.7467
## Veg_shannon_index-Canis_latrans 1.2362 0.6641 0.1080 1.1888
## Veg_shannon_index-Sciurus_niger 1.0260 1.0321 -0.9296 0.9731
## Veg_shannon_index-Procyon_lotor 1.1362 0.5904 0.0675 1.1139
## Veg_shannon_index-Dasypus_novemcinctus 0.6033 0.5478 -0.5079 0.6153
## Veg_shannon_index-Lynx_rufus 0.8282 0.9011 -1.0845 0.8436
## Veg_shannon_index-Didelphis_virginiana 1.0862 0.6868 -0.1606 1.0327
## Veg_shannon_index-Sylvilagus_floridanus 0.9943 0.6834 -0.2538 0.9598
## Veg_shannon_index-Sciurus_carolinensis 0.1826 0.7749 -1.5629 0.2715
## Veg_shannon_index-Vulpes_vulpes 0.3551 0.8790 -1.6258 0.4548
## Veg_shannon_index-Sus_scrofa 1.5251 0.9660 0.0947 1.3639
## total_shrub_cover-Odocoileus_virginianus -0.0889 0.8404 -1.6809 -0.1286
## total_shrub_cover-Canis_latrans 0.3461 0.7274 -0.8125 0.2501
## total_shrub_cover-Sciurus_niger -0.4929 0.9673 -2.5601 -0.4278
## total_shrub_cover-Procyon_lotor -0.8410 0.5891 -2.1371 -0.7938
## total_shrub_cover-Dasypus_novemcinctus -0.1192 0.5956 -1.3005 -0.1089
## total_shrub_cover-Lynx_rufus -0.5195 1.0206 -2.8082 -0.4570
## total_shrub_cover-Didelphis_virginiana -0.5743 0.7316 -2.2048 -0.5000
## total_shrub_cover-Sylvilagus_floridanus -0.4573 0.9108 -2.3573 -0.3769
## total_shrub_cover-Sciurus_carolinensis -0.3138 0.7648 -1.9748 -0.2692
## total_shrub_cover-Vulpes_vulpes -0.5383 0.9768 -2.8214 -0.4475
## total_shrub_cover-Sus_scrofa -0.1298 0.8574 -1.8061 -0.1404
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9740 1.0342 -0.0283 1.9581
## Avg_Cogongrass_Cover-Canis_latrans 2.3588 0.9233 0.7874 2.2671
## Avg_Cogongrass_Cover-Sciurus_niger 1.5748 1.2842 -1.4483 1.6757
## Avg_Cogongrass_Cover-Procyon_lotor 2.1645 0.8438 0.6639 2.1115
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5121 0.9457 0.9318 2.4340
## Avg_Cogongrass_Cover-Lynx_rufus 2.2757 1.0012 0.5501 2.1996
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0745 0.8608 0.4995 2.0375
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4259 0.9838 -0.5750 1.4501
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2428 0.8951 0.6898 2.1787
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4117 1.0307 0.6105 2.3301
## Avg_Cogongrass_Cover-Sus_scrofa 1.6224 1.1283 -0.7951 1.6739
## Tree_Density-Odocoileus_virginianus -0.7714 1.3290 -2.8294 -0.9318
## Tree_Density-Canis_latrans -2.5718 1.2792 -5.7450 -2.3473
## Tree_Density-Sciurus_niger -1.9532 1.6370 -5.6001 -1.8374
## Tree_Density-Procyon_lotor -1.4573 0.7611 -2.9806 -1.4650
## Tree_Density-Dasypus_novemcinctus -3.4741 1.8763 -8.4541 -3.0255
## Tree_Density-Lynx_rufus -0.6121 1.5538 -2.9468 -0.8402
## Tree_Density-Didelphis_virginiana -2.1866 1.2137 -5.0946 -2.0360
## Tree_Density-Sylvilagus_floridanus -2.4015 1.4060 -5.9065 -2.2024
## Tree_Density-Sciurus_carolinensis -2.4059 1.5043 -6.1304 -2.1702
## Tree_Density-Vulpes_vulpes -1.7658 1.6272 -5.2004 -1.7306
## Tree_Density-Sus_scrofa -2.3794 1.7439 -6.5977 -2.1063
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3278 1.2783 -1.3749 1.3651
## Avg_Canopy_Cover-Canis_latrans 0.3295 0.6764 -1.0063 0.3305
## Avg_Canopy_Cover-Sciurus_niger 2.1137 1.5613 -0.7925 1.9846
## Avg_Canopy_Cover-Procyon_lotor 1.7147 0.7045 0.4074 1.6766
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0770 0.7223 0.8568 2.0078
## Avg_Canopy_Cover-Lynx_rufus 1.5421 1.3557 -0.9957 1.5001
## Avg_Canopy_Cover-Didelphis_virginiana 2.8256 1.1235 1.2285 2.6218
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2398 1.4592 1.1718 3.0034
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5203 1.0429 0.9975 2.3510
## Avg_Canopy_Cover-Vulpes_vulpes 2.2162 1.1777 0.3605 2.0560
## Avg_Canopy_Cover-Sus_scrofa 2.1391 0.9269 0.5865 2.0300
## avg_veg_height-Odocoileus_virginianus -0.4924 0.7182 -1.9840 -0.4855
## avg_veg_height-Canis_latrans -0.5136 0.5967 -1.6894 -0.5141
## avg_veg_height-Sciurus_niger -0.6243 0.8735 -2.3082 -0.5865
## avg_veg_height-Procyon_lotor -0.4060 0.5663 -1.5223 -0.4072
## avg_veg_height-Dasypus_novemcinctus -0.2487 0.5683 -1.3101 -0.2671
## avg_veg_height-Lynx_rufus -0.5722 0.7814 -2.1892 -0.5605
## avg_veg_height-Didelphis_virginiana -0.6059 0.6418 -1.9221 -0.5864
## avg_veg_height-Sylvilagus_floridanus -0.6527 0.6539 -2.0256 -0.6326
## avg_veg_height-Sciurus_carolinensis -0.1676 0.6368 -1.3301 -0.2070
## avg_veg_height-Vulpes_vulpes -0.4730 0.6886 -1.8238 -0.4833
## avg_veg_height-Sus_scrofa -0.5336 0.6455 -1.8952 -0.5178
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8948 1.0439 471
## (Intercept)-Canis_latrans 3.2769 1.0059 1284
## (Intercept)-Sciurus_niger 7.2511 1.0233 394
## (Intercept)-Procyon_lotor 2.8901 1.0059 1257
## (Intercept)-Dasypus_novemcinctus 0.2885 1.0028 783
## (Intercept)-Lynx_rufus 9.8693 1.0115 255
## (Intercept)-Didelphis_virginiana -0.8011 1.0114 1523
## (Intercept)-Sylvilagus_floridanus 1.3200 1.0036 1228
## (Intercept)-Sciurus_carolinensis -0.7693 1.0008 745
## (Intercept)-Vulpes_vulpes 3.1319 1.0766 312
## (Intercept)-Sus_scrofa -1.0770 1.0013 725
## Cogon_Patch_Size-Odocoileus_virginianus 2.3058 1.0049 1701
## Cogon_Patch_Size-Canis_latrans 3.9192 1.0234 1197
## Cogon_Patch_Size-Sciurus_niger 1.3792 1.0320 775
## Cogon_Patch_Size-Procyon_lotor 0.2515 1.0031 947
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9144 1.0002 2018
## Cogon_Patch_Size-Lynx_rufus 2.4604 1.0171 897
## Cogon_Patch_Size-Didelphis_virginiana 2.6557 1.0116 1353
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1839 1.0087 737
## Cogon_Patch_Size-Sciurus_carolinensis 0.2075 1.0112 852
## Cogon_Patch_Size-Vulpes_vulpes 1.3818 1.0185 851
## Cogon_Patch_Size-Sus_scrofa 0.7740 1.0016 1325
## Veg_shannon_index-Odocoileus_virginianus 2.4451 1.0009 2162
## Veg_shannon_index-Canis_latrans 2.6949 1.0027 1381
## Veg_shannon_index-Sciurus_niger 3.3565 1.0016 1197
## Veg_shannon_index-Procyon_lotor 2.3887 1.0020 1118
## Veg_shannon_index-Dasypus_novemcinctus 1.6420 1.0005 1410
## Veg_shannon_index-Lynx_rufus 2.6174 1.0012 1613
## Veg_shannon_index-Didelphis_virginiana 2.6117 1.0004 1156
## Veg_shannon_index-Sylvilagus_floridanus 2.4350 1.0021 1432
## Veg_shannon_index-Sciurus_carolinensis 1.4893 1.0054 1345
## Veg_shannon_index-Vulpes_vulpes 1.8633 1.0061 1050
## Veg_shannon_index-Sus_scrofa 3.9002 1.0006 1146
## total_shrub_cover-Odocoileus_virginianus 1.7232 1.0145 2048
## total_shrub_cover-Canis_latrans 2.0565 1.0319 1374
## total_shrub_cover-Sciurus_niger 1.2479 1.0173 789
## total_shrub_cover-Procyon_lotor 0.1897 1.0020 1956
## total_shrub_cover-Dasypus_novemcinctus 1.0449 1.0112 1513
## total_shrub_cover-Lynx_rufus 1.3217 1.0064 732
## total_shrub_cover-Didelphis_virginiana 0.6730 1.0052 1149
## total_shrub_cover-Sylvilagus_floridanus 1.1001 1.0181 600
## total_shrub_cover-Sciurus_carolinensis 1.0238 1.0314 928
## total_shrub_cover-Vulpes_vulpes 1.1039 1.0150 755
## total_shrub_cover-Sus_scrofa 1.6982 1.0150 1501
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.1873 1.0048 993
## Avg_Cogongrass_Cover-Canis_latrans 4.3497 1.0074 684
## Avg_Cogongrass_Cover-Sciurus_niger 3.8391 1.0079 729
## Avg_Cogongrass_Cover-Procyon_lotor 3.9317 1.0065 704
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5812 1.0098 679
## Avg_Cogongrass_Cover-Lynx_rufus 4.6189 1.0097 858
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9019 1.0045 828
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3151 1.0012 930
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2080 1.0038 697
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7095 1.0055 711
## Avg_Cogongrass_Cover-Sus_scrofa 3.6692 1.0024 937
## Tree_Density-Odocoileus_virginianus 2.2837 1.0232 823
## Tree_Density-Canis_latrans -0.6932 1.0058 771
## Tree_Density-Sciurus_niger 1.0636 1.0051 692
## Tree_Density-Procyon_lotor 0.0389 1.0025 1465
## Tree_Density-Dasypus_novemcinctus -1.1685 1.0075 426
## Tree_Density-Lynx_rufus 3.2074 1.0192 482
## Tree_Density-Didelphis_virginiana -0.2348 1.0075 1214
## Tree_Density-Sylvilagus_floridanus -0.1149 1.0057 809
## Tree_Density-Sciurus_carolinensis -0.1214 1.0010 666
## Tree_Density-Vulpes_vulpes 1.3017 1.0240 826
## Tree_Density-Sus_scrofa 0.1366 1.0030 786
## Avg_Canopy_Cover-Odocoileus_virginianus 3.9212 1.0044 2009
## Avg_Canopy_Cover-Canis_latrans 1.6665 1.0068 1184
## Avg_Canopy_Cover-Sciurus_niger 5.6836 1.0089 807
## Avg_Canopy_Cover-Procyon_lotor 3.2005 1.0066 1570
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.6799 1.0020 860
## Avg_Canopy_Cover-Lynx_rufus 4.4072 1.0148 648
## Avg_Canopy_Cover-Didelphis_virginiana 5.5041 1.0024 603
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.8049 1.0014 786
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1362 1.0053 648
## Avg_Canopy_Cover-Vulpes_vulpes 5.1137 1.0019 1212
## Avg_Canopy_Cover-Sus_scrofa 4.2861 1.0096 1214
## avg_veg_height-Odocoileus_virginianus 0.9369 1.0070 1571
## avg_veg_height-Canis_latrans 0.6565 1.0135 1147
## avg_veg_height-Sciurus_niger 0.7883 1.0309 950
## avg_veg_height-Procyon_lotor 0.7191 1.0107 1132
## avg_veg_height-Dasypus_novemcinctus 0.9043 1.0020 1457
## avg_veg_height-Lynx_rufus 0.9393 1.0063 1235
## avg_veg_height-Didelphis_virginiana 0.6392 1.0074 1243
## avg_veg_height-Sylvilagus_floridanus 0.5824 1.0067 1139
## avg_veg_height-Sciurus_carolinensis 1.1899 1.0056 1355
## avg_veg_height-Vulpes_vulpes 0.8867 1.0153 1225
## avg_veg_height-Sus_scrofa 0.7175 1.0052 1189
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0033 0.0593 -0.1130 0.0033 0.1195
## (Intercept)-Canis_latrans -2.7468 0.1846 -3.1183 -2.7386 -2.3972
## (Intercept)-Sciurus_niger -4.6874 0.5051 -5.6909 -4.6924 -3.6305
## (Intercept)-Procyon_lotor -2.2934 0.1439 -2.5964 -2.2881 -2.0273
## (Intercept)-Dasypus_novemcinctus -1.7316 0.1595 -2.0503 -1.7278 -1.4258
## (Intercept)-Lynx_rufus -3.9297 0.3814 -4.6351 -3.9515 -3.1534
## (Intercept)-Didelphis_virginiana -2.5163 0.2802 -3.0888 -2.5046 -2.0021
## (Intercept)-Sylvilagus_floridanus -3.1704 0.2722 -3.7386 -3.1619 -2.6638
## (Intercept)-Sciurus_carolinensis -2.6294 0.3220 -3.3074 -2.6128 -2.0487
## (Intercept)-Vulpes_vulpes -4.3015 0.7147 -5.7572 -4.2772 -2.9924
## (Intercept)-Sus_scrofa -3.2431 0.5839 -4.4164 -3.2246 -2.1362
## shrub_cover-Odocoileus_virginianus -0.0530 0.0639 -0.1775 -0.0519 0.0708
## shrub_cover-Canis_latrans -0.3236 0.2173 -0.7538 -0.3252 0.1013
## shrub_cover-Sciurus_niger -0.3307 0.4460 -1.2525 -0.3116 0.4916
## shrub_cover-Procyon_lotor 0.2634 0.1577 -0.0495 0.2679 0.5651
## shrub_cover-Dasypus_novemcinctus 0.8526 0.3033 0.2830 0.8425 1.4642
## shrub_cover-Lynx_rufus -0.2300 0.3603 -0.9148 -0.2380 0.4998
## shrub_cover-Didelphis_virginiana 0.8991 0.3539 0.2564 0.8853 1.6398
## shrub_cover-Sylvilagus_floridanus 0.4072 0.3972 -0.3513 0.4032 1.2128
## shrub_cover-Sciurus_carolinensis 0.8360 0.4073 0.0678 0.8257 1.6394
## shrub_cover-Vulpes_vulpes 0.0984 0.5423 -1.0181 0.1086 1.1082
## shrub_cover-Sus_scrofa 0.5970 0.7363 -0.7925 0.5620 2.1881
## veg_height-Odocoileus_virginianus -0.2946 0.0639 -0.4237 -0.2944 -0.1716
## veg_height-Canis_latrans -0.5846 0.1791 -0.9465 -0.5833 -0.2434
## veg_height-Sciurus_niger -0.0545 0.3388 -0.7098 -0.0550 0.6112
## veg_height-Procyon_lotor 0.3448 0.1236 0.0972 0.3433 0.5853
## veg_height-Dasypus_novemcinctus 0.2387 0.1313 -0.0102 0.2373 0.5006
## veg_height-Lynx_rufus 0.0849 0.2368 -0.3911 0.0841 0.5423
## veg_height-Didelphis_virginiana 0.4194 0.2338 -0.0150 0.4191 0.8983
## veg_height-Sylvilagus_floridanus 0.1584 0.2425 -0.3051 0.1567 0.6307
## veg_height-Sciurus_carolinensis 0.0786 0.2092 -0.3082 0.0740 0.4933
## veg_height-Vulpes_vulpes -0.1905 0.3174 -0.8614 -0.1768 0.4099
## veg_height-Sus_scrofa -0.1437 0.3257 -0.8025 -0.1384 0.4603
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0016 5937
## (Intercept)-Canis_latrans 1.0005 2435
## (Intercept)-Sciurus_niger 1.0623 431
## (Intercept)-Procyon_lotor 1.0011 3351
## (Intercept)-Dasypus_novemcinctus 1.0106 3449
## (Intercept)-Lynx_rufus 1.0230 435
## (Intercept)-Didelphis_virginiana 1.0064 2200
## (Intercept)-Sylvilagus_floridanus 1.0091 1983
## (Intercept)-Sciurus_carolinensis 1.0004 1669
## (Intercept)-Vulpes_vulpes 1.0480 430
## (Intercept)-Sus_scrofa 1.0054 1293
## shrub_cover-Odocoileus_virginianus 1.0002 5250
## shrub_cover-Canis_latrans 1.0129 2377
## shrub_cover-Sciurus_niger 1.0146 923
## shrub_cover-Procyon_lotor 1.0011 4067
## shrub_cover-Dasypus_novemcinctus 1.0081 2648
## shrub_cover-Lynx_rufus 1.0066 638
## shrub_cover-Didelphis_virginiana 1.0014 1471
## shrub_cover-Sylvilagus_floridanus 1.0002 1353
## shrub_cover-Sciurus_carolinensis 1.0086 1329
## shrub_cover-Vulpes_vulpes 1.0047 1392
## shrub_cover-Sus_scrofa 1.0019 1343
## veg_height-Odocoileus_virginianus 1.0009 5474
## veg_height-Canis_latrans 1.0025 2630
## veg_height-Sciurus_niger 1.0019 1288
## veg_height-Procyon_lotor 1.0012 4074
## veg_height-Dasypus_novemcinctus 1.0025 4629
## veg_height-Lynx_rufus 1.0005 1678
## veg_height-Didelphis_virginiana 1.0012 3576
## veg_height-Sylvilagus_floridanus 1.0038 2072
## veg_height-Sciurus_carolinensis 1.0024 2928
## veg_height-Vulpes_vulpes 1.0033 1826
## veg_height-Sus_scrofa 1.0011 2783
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4975
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0231 0.6400 -1.1829 0.0115 1.3588 1.0107 1142
## Avg_Cogongrass_Cover 0.0348 0.3435 -0.6407 0.0387 0.7026 1.0025 1256
## total_shrub_cover -0.6489 0.4373 -1.6564 -0.6096 0.1034 1.0531 526
## avg_veg_height 0.1591 0.3363 -0.5103 0.1502 0.8436 1.0062 954
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5838 3.1782 0.4266 2.7497 11.9782 1.0043 1356
## Avg_Cogongrass_Cover 0.3798 0.5854 0.0396 0.2211 1.5794 1.0408 2186
## total_shrub_cover 0.7451 0.9646 0.0542 0.4370 3.1790 1.0090 652
## avg_veg_height 0.2546 0.2931 0.0344 0.1635 1.0189 1.0123 2396
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3186 1.4286 0.0712 0.8819 5.1349 1.035 233
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6433 0.4312 -3.4926 -2.6462 -1.7964 1.0009 2653
## shrub_cover 0.4433 0.2816 -0.0994 0.4401 1.0058 1.0235 1275
## veg_height -0.0063 0.1636 -0.3365 -0.0070 0.3057 1.0002 2559
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9934 1.2705 0.6774 1.6578 5.0877 1.0059 2217
## shrub_cover 0.5656 0.4289 0.1246 0.4524 1.6970 1.0028 1303
## veg_height 0.1998 0.1395 0.0562 0.1640 0.5601 1.0032 3408
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5995 1.5448 0.9083 3.4472
## (Intercept)-Canis_latrans 0.5829 0.7648 -0.8252 0.5462
## (Intercept)-Sciurus_niger -0.3510 1.2927 -2.5080 -0.4856
## (Intercept)-Procyon_lotor 0.8035 0.7526 -0.6689 0.7869
## (Intercept)-Dasypus_novemcinctus -0.4562 0.7468 -1.8640 -0.4830
## (Intercept)-Lynx_rufus 0.1958 1.1712 -1.6836 0.0920
## (Intercept)-Didelphis_virginiana -1.0023 0.8149 -2.5423 -1.0229
## (Intercept)-Sylvilagus_floridanus 0.1855 0.9144 -1.4384 0.1314
## (Intercept)-Sciurus_carolinensis -1.0381 0.8631 -2.6955 -1.0687
## (Intercept)-Vulpes_vulpes -0.7568 1.3971 -3.1447 -0.8630
## (Intercept)-Sus_scrofa -1.3479 1.0895 -3.4461 -1.3673
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0177 0.5883 -1.1307 0.0116
## Avg_Cogongrass_Cover-Canis_latrans 0.3488 0.5016 -0.5322 0.3139
## Avg_Cogongrass_Cover-Sciurus_niger -0.3342 0.6934 -1.9315 -0.2660
## Avg_Cogongrass_Cover-Procyon_lotor -0.0520 0.4562 -0.9753 -0.0413
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1634 0.4298 -0.6557 0.1505
## Avg_Cogongrass_Cover-Lynx_rufus 0.3361 0.5300 -0.5963 0.2982
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1796 0.4718 -0.7422 0.1691
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3292 0.5706 -1.5597 -0.2921
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0809 0.4592 -0.8512 0.0942
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1647 0.5700 -0.9398 0.1484
## Avg_Cogongrass_Cover-Sus_scrofa -0.2279 0.6512 -1.7576 -0.1719
## total_shrub_cover-Odocoileus_virginianus -0.3695 0.6586 -1.6615 -0.3854
## total_shrub_cover-Canis_latrans 0.1434 0.6346 -0.9335 0.0802
## total_shrub_cover-Sciurus_niger -0.8064 0.7625 -2.4873 -0.7548
## total_shrub_cover-Procyon_lotor -1.1366 0.5941 -2.5479 -1.0662
## total_shrub_cover-Dasypus_novemcinctus -0.3851 0.6253 -1.9525 -0.3050
## total_shrub_cover-Lynx_rufus -1.0521 0.8182 -2.8651 -0.9801
## total_shrub_cover-Didelphis_virginiana -0.6835 0.6229 -2.1358 -0.6150
## total_shrub_cover-Sylvilagus_floridanus -1.1271 0.8534 -3.1402 -0.9868
## total_shrub_cover-Sciurus_carolinensis -0.6674 0.6942 -2.3440 -0.5799
## total_shrub_cover-Vulpes_vulpes -0.7669 0.8946 -2.8113 -0.6794
## total_shrub_cover-Sus_scrofa -0.4867 0.8146 -2.2158 -0.4448
## avg_veg_height-Odocoileus_virginianus 0.1228 0.5252 -0.9478 0.1192
## avg_veg_height-Canis_latrans 0.1604 0.4620 -0.7117 0.1488
## avg_veg_height-Sciurus_niger -0.0615 0.5878 -1.3415 -0.0262
## avg_veg_height-Procyon_lotor 0.1739 0.4405 -0.6809 0.1604
## avg_veg_height-Dasypus_novemcinctus 0.3371 0.4377 -0.4637 0.3140
## avg_veg_height-Lynx_rufus 0.1192 0.5380 -0.9516 0.1192
## avg_veg_height-Didelphis_virginiana 0.0699 0.4613 -0.8671 0.0734
## avg_veg_height-Sylvilagus_floridanus 0.0975 0.4975 -0.8531 0.0932
## avg_veg_height-Sciurus_carolinensis 0.4470 0.4779 -0.3990 0.4146
## avg_veg_height-Vulpes_vulpes 0.1069 0.5268 -0.9523 0.1218
## avg_veg_height-Sus_scrofa 0.1689 0.5035 -0.8161 0.1756
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1219 1.0009 971
## (Intercept)-Canis_latrans 2.1887 1.0005 2025
## (Intercept)-Sciurus_niger 2.5316 1.0093 522
## (Intercept)-Procyon_lotor 2.3379 1.0018 1942
## (Intercept)-Dasypus_novemcinctus 1.1304 1.0199 1030
## (Intercept)-Lynx_rufus 2.6484 1.0183 548
## (Intercept)-Didelphis_virginiana 0.6891 1.0026 994
## (Intercept)-Sylvilagus_floridanus 2.1842 1.0026 1051
## (Intercept)-Sciurus_carolinensis 0.7559 1.0132 685
## (Intercept)-Vulpes_vulpes 2.3947 1.0476 325
## (Intercept)-Sus_scrofa 0.7983 1.0153 650
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2385 1.0004 2867
## Avg_Cogongrass_Cover-Canis_latrans 1.4553 1.0017 2085
## Avg_Cogongrass_Cover-Sciurus_niger 0.8448 1.0027 1316
## Avg_Cogongrass_Cover-Procyon_lotor 0.8128 1.0028 2180
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0391 1.0037 2337
## Avg_Cogongrass_Cover-Lynx_rufus 1.5284 1.0015 2309
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1706 1.0019 2217
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6515 1.0033 1680
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9772 1.0049 2100
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3505 1.0022 1888
## Avg_Cogongrass_Cover-Sus_scrofa 0.8716 1.0010 1917
## total_shrub_cover-Odocoileus_virginianus 0.9997 1.0105 2736
## total_shrub_cover-Canis_latrans 1.6181 1.0031 1251
## total_shrub_cover-Sciurus_niger 0.5688 1.0217 1068
## total_shrub_cover-Procyon_lotor -0.2138 1.0267 686
## total_shrub_cover-Dasypus_novemcinctus 0.5761 1.0405 490
## total_shrub_cover-Lynx_rufus 0.3203 1.0442 711
## total_shrub_cover-Didelphis_virginiana 0.3353 1.0171 844
## total_shrub_cover-Sylvilagus_floridanus 0.2179 1.0098 527
## total_shrub_cover-Sciurus_carolinensis 0.4687 1.0327 628
## total_shrub_cover-Vulpes_vulpes 0.7714 1.0231 887
## total_shrub_cover-Sus_scrofa 1.0550 1.0305 736
## avg_veg_height-Odocoileus_virginianus 1.1706 1.0018 1960
## avg_veg_height-Canis_latrans 1.1339 1.0007 1925
## avg_veg_height-Sciurus_niger 1.0412 1.0029 1739
## avg_veg_height-Procyon_lotor 1.0739 1.0008 2262
## avg_veg_height-Dasypus_novemcinctus 1.2716 1.0114 1455
## avg_veg_height-Lynx_rufus 1.1903 1.0021 1417
## avg_veg_height-Didelphis_virginiana 0.9691 1.0015 2011
## avg_veg_height-Sylvilagus_floridanus 1.1274 1.0038 1239
## avg_veg_height-Sciurus_carolinensis 1.4900 1.0034 1958
## avg_veg_height-Vulpes_vulpes 1.1429 1.0084 1539
## avg_veg_height-Sus_scrofa 1.1839 1.0012 1802
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0038 0.0590 -0.1107 0.0026 0.1199
## (Intercept)-Canis_latrans -2.7827 0.1964 -3.1772 -2.7744 -2.4214
## (Intercept)-Sciurus_niger -4.0535 0.6741 -5.3493 -4.0600 -2.7800
## (Intercept)-Procyon_lotor -2.2865 0.1401 -2.5610 -2.2837 -2.0213
## (Intercept)-Dasypus_novemcinctus -1.7805 0.1769 -2.1456 -1.7708 -1.4567
## (Intercept)-Lynx_rufus -3.6092 0.3729 -4.3861 -3.5917 -2.9265
## (Intercept)-Didelphis_virginiana -2.6461 0.3158 -3.2958 -2.6300 -2.0727
## (Intercept)-Sylvilagus_floridanus -3.2991 0.2850 -3.8729 -3.2938 -2.7694
## (Intercept)-Sciurus_carolinensis -2.7236 0.3432 -3.4567 -2.7057 -2.1147
## (Intercept)-Vulpes_vulpes -4.2577 0.7698 -5.7502 -4.2338 -2.8643
## (Intercept)-Sus_scrofa -3.5112 0.5957 -4.6341 -3.5223 -2.2894
## shrub_cover-Odocoileus_virginianus -0.0519 0.0648 -0.1812 -0.0514 0.0751
## shrub_cover-Canis_latrans -0.2654 0.2435 -0.7272 -0.2656 0.2170
## shrub_cover-Sciurus_niger -0.0967 0.5357 -1.1738 -0.0912 0.9749
## shrub_cover-Procyon_lotor 0.3171 0.1590 -0.0007 0.3196 0.6157
## shrub_cover-Dasypus_novemcinctus 0.9960 0.3638 0.3482 0.9683 1.7487
## shrub_cover-Lynx_rufus 0.0493 0.3839 -0.7283 0.0668 0.7521
## shrub_cover-Didelphis_virginiana 1.1172 0.4045 0.3860 1.0995 1.9369
## shrub_cover-Sylvilagus_floridanus 0.7023 0.4353 -0.2077 0.7184 1.5272
## shrub_cover-Sciurus_carolinensis 1.0440 0.4317 0.2128 1.0367 1.9112
## shrub_cover-Vulpes_vulpes 0.2446 0.6141 -0.9812 0.2480 1.4713
## shrub_cover-Sus_scrofa 0.9283 0.7671 -0.6678 0.9408 2.3585
## veg_height-Odocoileus_virginianus -0.2937 0.0649 -0.4188 -0.2935 -0.1643
## veg_height-Canis_latrans -0.5957 0.1833 -0.9719 -0.5924 -0.2585
## veg_height-Sciurus_niger 0.0020 0.4336 -0.8065 -0.0155 0.9041
## veg_height-Procyon_lotor 0.3340 0.1218 0.0887 0.3357 0.5684
## veg_height-Dasypus_novemcinctus 0.2433 0.1384 -0.0233 0.2419 0.5192
## veg_height-Lynx_rufus 0.0281 0.2502 -0.4683 0.0310 0.5103
## veg_height-Didelphis_virginiana 0.4028 0.2502 -0.0703 0.3993 0.9084
## veg_height-Sylvilagus_floridanus 0.0422 0.2483 -0.4254 0.0340 0.5341
## veg_height-Sciurus_carolinensis 0.0697 0.2172 -0.3469 0.0636 0.5037
## veg_height-Vulpes_vulpes -0.1349 0.3239 -0.8290 -0.1161 0.4523
## veg_height-Sus_scrofa -0.1595 0.3246 -0.8155 -0.1523 0.4721
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0012 1800
## (Intercept)-Sciurus_niger 1.0007 454
## (Intercept)-Procyon_lotor 1.0033 3925
## (Intercept)-Dasypus_novemcinctus 1.0173 1709
## (Intercept)-Lynx_rufus 1.0151 829
## (Intercept)-Didelphis_virginiana 1.0047 1135
## (Intercept)-Sylvilagus_floridanus 1.0036 1365
## (Intercept)-Sciurus_carolinensis 1.0337 803
## (Intercept)-Vulpes_vulpes 1.0372 375
## (Intercept)-Sus_scrofa 1.0285 759
## shrub_cover-Odocoileus_virginianus 1.0013 5250
## shrub_cover-Canis_latrans 1.0022 1677
## shrub_cover-Sciurus_niger 1.0036 902
## shrub_cover-Procyon_lotor 1.0031 4092
## shrub_cover-Dasypus_novemcinctus 1.0205 761
## shrub_cover-Lynx_rufus 1.0325 967
## shrub_cover-Didelphis_virginiana 1.0084 810
## shrub_cover-Sylvilagus_floridanus 1.0033 834
## shrub_cover-Sciurus_carolinensis 1.0271 824
## shrub_cover-Vulpes_vulpes 1.0094 1004
## shrub_cover-Sus_scrofa 1.0321 850
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0023 2282
## veg_height-Sciurus_niger 1.0055 1466
## veg_height-Procyon_lotor 1.0020 4082
## veg_height-Dasypus_novemcinctus 1.0017 4052
## veg_height-Lynx_rufus 1.0017 2048
## veg_height-Didelphis_virginiana 1.0019 2883
## veg_height-Sylvilagus_floridanus 1.0013 1834
## veg_height-Sciurus_carolinensis 1.0028 2881
## veg_height-Vulpes_vulpes 1.0018 1569
## veg_height-Sus_scrofa 1.0045 2510
# Includes cover covariate for detection and none for occupancy
ms_cover_null <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3537
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1111 0.5325 -1.1338 -0.1309 0.9731 1.0019 2531
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0707 2.461 0.8193 2.4105 9.1151 1.0062 2068
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5769 0.4237 -3.4042 -2.5793 -1.7056 1.0009 4330
## shrub_cover 0.2134 0.2448 -0.2591 0.2110 0.7196 1.0022 3404
## veg_height -0.0010 0.1576 -0.3164 0.0009 0.3109 1.0016 3767
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9261 1.1897 0.6586 1.6332 5.0211 1.0040 2699
## shrub_cover 0.4686 0.4036 0.0914 0.3578 1.5444 1.0037 2134
## veg_height 0.1925 0.1393 0.0544 0.1561 0.5538 1.0041 3731
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.3688 1.0607 1.7611 3.2065 5.8551
## (Intercept)-Canis_latrans 0.3987 0.4285 -0.3857 0.3786 1.3447
## (Intercept)-Sciurus_niger -0.4737 1.0836 -1.9830 -0.6508 2.1106
## (Intercept)-Procyon_lotor 0.7443 0.4109 -0.0143 0.7234 1.6062
## (Intercept)-Dasypus_novemcinctus -0.5699 0.3820 -1.3565 -0.5687 0.1760
## (Intercept)-Lynx_rufus 0.6006 0.9688 -0.7508 0.4366 3.0411
## (Intercept)-Didelphis_virginiana -1.2156 0.4619 -2.1559 -1.2064 -0.3564
## (Intercept)-Sylvilagus_floridanus -0.3008 0.5125 -1.2428 -0.3226 0.7678
## (Intercept)-Sciurus_carolinensis -1.2165 0.4648 -2.1711 -1.2026 -0.3477
## (Intercept)-Vulpes_vulpes -1.1616 0.9922 -2.8011 -1.2735 1.1482
## (Intercept)-Sus_scrofa -1.6754 0.6462 -2.9429 -1.6664 -0.4471
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0101 1717
## (Intercept)-Canis_latrans 1.0072 4156
## (Intercept)-Sciurus_niger 1.0486 381
## (Intercept)-Procyon_lotor 1.0022 5250
## (Intercept)-Dasypus_novemcinctus 1.0001 4916
## (Intercept)-Lynx_rufus 1.0268 713
## (Intercept)-Didelphis_virginiana 1.0025 4036
## (Intercept)-Sylvilagus_floridanus 1.0120 2662
## (Intercept)-Sciurus_carolinensis 1.0019 4320
## (Intercept)-Vulpes_vulpes 1.0046 616
## (Intercept)-Sus_scrofa 1.0000 2636
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0038 0.0585 -0.1136 0.0040 0.1167
## (Intercept)-Canis_latrans -2.7359 0.1866 -3.1122 -2.7308 -2.3872
## (Intercept)-Sciurus_niger -4.0387 0.6619 -5.3293 -4.0305 -2.7893
## (Intercept)-Procyon_lotor -2.2843 0.1451 -2.5859 -2.2817 -2.0143
## (Intercept)-Dasypus_novemcinctus -1.7105 0.1527 -2.0189 -1.7088 -1.4240
## (Intercept)-Lynx_rufus -3.7699 0.3747 -4.5172 -3.7671 -3.0466
## (Intercept)-Didelphis_virginiana -2.5154 0.2853 -3.1013 -2.5006 -1.9896
## (Intercept)-Sylvilagus_floridanus -3.1782 0.3078 -3.8415 -3.1613 -2.6362
## (Intercept)-Sciurus_carolinensis -2.5659 0.3113 -3.2007 -2.5440 -2.0009
## (Intercept)-Vulpes_vulpes -4.0346 0.7395 -5.5901 -3.9684 -2.7600
## (Intercept)-Sus_scrofa -3.2349 0.5685 -4.3553 -3.2346 -2.1133
## shrub_cover-Odocoileus_virginianus -0.0518 0.0639 -0.1782 -0.0512 0.0731
## shrub_cover-Canis_latrans -0.2837 0.2186 -0.7227 -0.2857 0.1430
## shrub_cover-Sciurus_niger -0.3135 0.4627 -1.2274 -0.3019 0.5942
## shrub_cover-Procyon_lotor 0.2497 0.1655 -0.0784 0.2546 0.5660
## shrub_cover-Dasypus_novemcinctus 0.7771 0.2893 0.2336 0.7696 1.3663
## shrub_cover-Lynx_rufus -0.3168 0.3473 -1.0116 -0.3155 0.3600
## shrub_cover-Didelphis_virginiana 0.8824 0.3601 0.2462 0.8612 1.6560
## shrub_cover-Sylvilagus_floridanus 0.2504 0.3896 -0.5071 0.2426 1.0357
## shrub_cover-Sciurus_carolinensis 0.7362 0.3901 0.0141 0.7158 1.5293
## shrub_cover-Vulpes_vulpes -0.0805 0.5420 -1.2012 -0.0712 0.9476
## shrub_cover-Sus_scrofa 0.4920 0.7036 -0.8863 0.4714 1.9373
## veg_height-Odocoileus_virginianus -0.2931 0.0639 -0.4171 -0.2927 -0.1698
## veg_height-Canis_latrans -0.5728 0.1828 -0.9416 -0.5655 -0.2376
## veg_height-Sciurus_niger -0.0371 0.3907 -0.8169 -0.0451 0.7749
## veg_height-Procyon_lotor 0.3312 0.1244 0.0822 0.3323 0.5781
## veg_height-Dasypus_novemcinctus 0.2255 0.1304 -0.0275 0.2224 0.4790
## veg_height-Lynx_rufus 0.0474 0.2382 -0.4315 0.0513 0.5039
## veg_height-Didelphis_virginiana 0.4118 0.2353 -0.0278 0.4021 0.8883
## veg_height-Sylvilagus_floridanus 0.1079 0.2367 -0.3545 0.1060 0.5733
## veg_height-Sciurus_carolinensis 0.0494 0.2029 -0.3351 0.0452 0.4605
## veg_height-Vulpes_vulpes -0.1157 0.3046 -0.7543 -0.1057 0.4560
## veg_height-Sus_scrofa -0.1339 0.3283 -0.7936 -0.1272 0.4959
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0094 2442
## (Intercept)-Sciurus_niger 1.0145 494
## (Intercept)-Procyon_lotor 1.0059 3607
## (Intercept)-Dasypus_novemcinctus 1.0038 4347
## (Intercept)-Lynx_rufus 1.0076 773
## (Intercept)-Didelphis_virginiana 1.0023 2642
## (Intercept)-Sylvilagus_floridanus 1.0128 1636
## (Intercept)-Sciurus_carolinensis 1.0004 2499
## (Intercept)-Vulpes_vulpes 1.0012 559
## (Intercept)-Sus_scrofa 0.9998 1954
## shrub_cover-Odocoileus_virginianus 1.0038 5250
## shrub_cover-Canis_latrans 1.0030 2878
## shrub_cover-Sciurus_niger 1.0062 1331
## shrub_cover-Procyon_lotor 1.0035 3784
## shrub_cover-Dasypus_novemcinctus 1.0001 3763
## shrub_cover-Lynx_rufus 1.0020 1218
## shrub_cover-Didelphis_virginiana 1.0027 2114
## shrub_cover-Sylvilagus_floridanus 1.0043 1709
## shrub_cover-Sciurus_carolinensis 1.0016 2380
## shrub_cover-Vulpes_vulpes 1.0012 2234
## shrub_cover-Sus_scrofa 1.0008 2343
## veg_height-Odocoileus_virginianus 1.0013 5250
## veg_height-Canis_latrans 1.0021 2417
## veg_height-Sciurus_niger 0.9999 1960
## veg_height-Procyon_lotor 1.0037 3844
## veg_height-Dasypus_novemcinctus 1.0004 4546
## veg_height-Lynx_rufus 1.0003 2265
## veg_height-Didelphis_virginiana 1.0015 3781
## veg_height-Sylvilagus_floridanus 1.0039 2774
## veg_height-Sciurus_carolinensis 1.0013 3681
## veg_height-Vulpes_vulpes 1.0031 2448
## veg_height-Sus_scrofa 1.0026 3117
#Includes cover for detection and only foraging for occupancy
ms_cover_forage <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4052
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1253 0.6247 -1.2630 -0.1471 1.1596 1.0120 1742
## Veg_shannon_index 0.3590 0.2691 -0.1728 0.3571 0.8955 1.0028 2147
## Avg_Cogongrass_Cover 0.3336 0.2666 -0.1969 0.3332 0.8553 1.0037 2014
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8492 3.2039 0.8107 2.9729 12.1473 1.0355 1449
## Veg_shannon_index 0.3039 0.3458 0.0386 0.1939 1.1997 1.0014 1288
## Avg_Cogongrass_Cover 0.2946 0.3957 0.0378 0.1885 1.1551 1.0573 1807
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7758 0.9087 0.0599 0.5068 3.1685 1.022 293
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5893 0.4478 -3.4783 -2.5904 -1.6617 1.0037 4309
## shrub_cover 0.1984 0.2368 -0.2628 0.1977 0.6720 1.0005 3599
## veg_height -0.0174 0.1561 -0.3273 -0.0157 0.2912 1.0031 2910
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1120 1.4519 0.7102 1.7500 5.6312 1.0108 2434
## shrub_cover 0.4478 0.3533 0.0917 0.3511 1.3731 1.0070 1593
## veg_height 0.1887 0.1295 0.0516 0.1541 0.5364 1.0034 3378
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6535 1.4219 1.2963 3.4941
## (Intercept)-Canis_latrans 0.4025 0.6432 -0.8730 0.3929
## (Intercept)-Sciurus_niger -0.0638 1.3231 -2.1748 -0.2340
## (Intercept)-Procyon_lotor 0.5774 0.6355 -0.7297 0.5831
## (Intercept)-Dasypus_novemcinctus -0.6590 0.5965 -1.8658 -0.6404
## (Intercept)-Lynx_rufus 0.3087 1.1967 -1.5300 0.1496
## (Intercept)-Didelphis_virginiana -1.3517 0.6724 -2.7112 -1.3442
## (Intercept)-Sylvilagus_floridanus -0.3064 0.7850 -1.7300 -0.3514
## (Intercept)-Sciurus_carolinensis -1.3497 0.6778 -2.7542 -1.3468
## (Intercept)-Vulpes_vulpes -0.8190 1.4184 -3.0333 -1.0313
## (Intercept)-Sus_scrofa -1.9900 0.8906 -3.8064 -1.9606
## Veg_shannon_index-Odocoileus_virginianus 0.2940 0.5037 -0.7540 0.3075
## Veg_shannon_index-Canis_latrans 0.6408 0.4052 -0.0705 0.6165
## Veg_shannon_index-Sciurus_niger 0.4108 0.5593 -0.6547 0.3854
## Veg_shannon_index-Procyon_lotor 0.4504 0.3783 -0.2426 0.4353
## Veg_shannon_index-Dasypus_novemcinctus 0.1923 0.3481 -0.5067 0.1943
## Veg_shannon_index-Lynx_rufus 0.2306 0.5267 -0.8765 0.2517
## Veg_shannon_index-Didelphis_virginiana 0.5103 0.4014 -0.2210 0.4891
## Veg_shannon_index-Sylvilagus_floridanus 0.4524 0.4295 -0.3480 0.4330
## Veg_shannon_index-Sciurus_carolinensis -0.0117 0.4116 -0.8951 0.0140
## Veg_shannon_index-Vulpes_vulpes 0.1223 0.4936 -0.9127 0.1485
## Veg_shannon_index-Sus_scrofa 0.7214 0.5428 -0.1784 0.6481
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3224 0.5015 -0.6888 0.3205
## Avg_Cogongrass_Cover-Canis_latrans 0.6233 0.4128 -0.0919 0.5882
## Avg_Cogongrass_Cover-Sciurus_niger 0.0373 0.5888 -1.2973 0.0975
## Avg_Cogongrass_Cover-Procyon_lotor 0.3825 0.3825 -0.3385 0.3670
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4364 0.3415 -0.2140 0.4253
## Avg_Cogongrass_Cover-Lynx_rufus 0.5666 0.4454 -0.2163 0.5340
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4505 0.3723 -0.2485 0.4410
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0451 0.4403 -0.9877 -0.0227
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4120 0.3703 -0.3159 0.4055
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4394 0.4899 -0.4976 0.4193
## Avg_Cogongrass_Cover-Sus_scrofa 0.0532 0.5377 -1.1598 0.1089
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9161 1.0215 1118
## (Intercept)-Canis_latrans 1.7054 1.0054 2876
## (Intercept)-Sciurus_niger 3.0879 1.0047 438
## (Intercept)-Procyon_lotor 1.8429 1.0008 2470
## (Intercept)-Dasypus_novemcinctus 0.4736 1.0006 3409
## (Intercept)-Lynx_rufus 3.1598 1.0079 615
## (Intercept)-Didelphis_virginiana -0.0709 1.0013 3170
## (Intercept)-Sylvilagus_floridanus 1.3622 1.0017 1691
## (Intercept)-Sciurus_carolinensis -0.0299 1.0014 2843
## (Intercept)-Vulpes_vulpes 2.4556 1.0363 370
## (Intercept)-Sus_scrofa -0.3107 1.0050 2225
## Veg_shannon_index-Odocoileus_virginianus 1.2646 1.0055 3694
## Veg_shannon_index-Canis_latrans 1.5166 1.0006 2649
## Veg_shannon_index-Sciurus_niger 1.6100 1.0019 2017
## Veg_shannon_index-Procyon_lotor 1.2502 1.0035 2956
## Veg_shannon_index-Dasypus_novemcinctus 0.8496 1.0027 3982
## Veg_shannon_index-Lynx_rufus 1.2266 1.0015 2329
## Veg_shannon_index-Didelphis_virginiana 1.3726 1.0009 3322
## Veg_shannon_index-Sylvilagus_floridanus 1.3747 1.0033 3065
## Veg_shannon_index-Sciurus_carolinensis 0.7315 1.0006 2677
## Veg_shannon_index-Vulpes_vulpes 1.0763 1.0062 2516
## Veg_shannon_index-Sus_scrofa 1.9832 1.0010 2370
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3558 1.0006 2709
## Avg_Cogongrass_Cover-Canis_latrans 1.5271 1.0092 2582
## Avg_Cogongrass_Cover-Sciurus_niger 1.0669 1.0070 1555
## Avg_Cogongrass_Cover-Procyon_lotor 1.1903 1.0031 3210
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1484 1.0008 4330
## Avg_Cogongrass_Cover-Lynx_rufus 1.5546 1.0113 2894
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2255 1.0016 3932
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7407 1.0054 2191
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1652 0.9998 3805
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4679 1.0078 2701
## Avg_Cogongrass_Cover-Sus_scrofa 0.9371 1.0156 2224
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0040 0.0594 -0.1100 0.0035 0.1188
## (Intercept)-Canis_latrans -2.7325 0.1880 -3.1252 -2.7275 -2.3837
## (Intercept)-Sciurus_niger -4.3053 0.6634 -5.5921 -4.3090 -3.0026
## (Intercept)-Procyon_lotor -2.2960 0.1462 -2.5943 -2.2908 -2.0230
## (Intercept)-Dasypus_novemcinctus -1.7104 0.1548 -2.0211 -1.7061 -1.4181
## (Intercept)-Lynx_rufus -3.7131 0.3678 -4.4575 -3.7069 -3.0120
## (Intercept)-Didelphis_virginiana -2.5121 0.2809 -3.0953 -2.4977 -1.9949
## (Intercept)-Sylvilagus_floridanus -3.2241 0.3211 -3.9137 -3.2062 -2.6550
## (Intercept)-Sciurus_carolinensis -2.5723 0.3061 -3.2082 -2.5525 -1.9949
## (Intercept)-Vulpes_vulpes -4.2974 0.8302 -5.9837 -4.2675 -2.8176
## (Intercept)-Sus_scrofa -3.2020 0.5618 -4.3503 -3.1872 -2.1470
## shrub_cover-Odocoileus_virginianus -0.0539 0.0625 -0.1731 -0.0535 0.0687
## shrub_cover-Canis_latrans -0.2620 0.2106 -0.6813 -0.2571 0.1439
## shrub_cover-Sciurus_niger -0.3754 0.4504 -1.3346 -0.3521 0.4794
## shrub_cover-Procyon_lotor 0.2277 0.1688 -0.1269 0.2328 0.5368
## shrub_cover-Dasypus_novemcinctus 0.7800 0.2832 0.2360 0.7780 1.3498
## shrub_cover-Lynx_rufus -0.2783 0.3404 -0.9795 -0.2694 0.3726
## shrub_cover-Didelphis_virginiana 0.8740 0.3591 0.2242 0.8481 1.6302
## shrub_cover-Sylvilagus_floridanus 0.2194 0.3870 -0.5080 0.2107 1.0056
## shrub_cover-Sciurus_carolinensis 0.7451 0.3864 0.0249 0.7277 1.5313
## shrub_cover-Vulpes_vulpes -0.0701 0.5073 -1.0968 -0.0591 0.9364
## shrub_cover-Sus_scrofa 0.4569 0.6802 -0.8845 0.4442 1.8456
## veg_height-Odocoileus_virginianus -0.2957 0.0649 -0.4245 -0.2962 -0.1736
## veg_height-Canis_latrans -0.5770 0.1819 -0.9450 -0.5722 -0.2324
## veg_height-Sciurus_niger -0.0857 0.3839 -0.8488 -0.0936 0.6995
## veg_height-Procyon_lotor 0.3236 0.1230 0.0796 0.3247 0.5625
## veg_height-Dasypus_novemcinctus 0.2202 0.1321 -0.0391 0.2210 0.4814
## veg_height-Lynx_rufus -0.0038 0.2401 -0.4863 0.0005 0.4592
## veg_height-Didelphis_virginiana 0.3939 0.2390 -0.0736 0.3862 0.8755
## veg_height-Sylvilagus_floridanus 0.1097 0.2411 -0.3643 0.1062 0.5818
## veg_height-Sciurus_carolinensis 0.0430 0.2067 -0.3530 0.0385 0.4631
## veg_height-Vulpes_vulpes -0.1587 0.3152 -0.8421 -0.1486 0.4124
## veg_height-Sus_scrofa -0.1314 0.3228 -0.7905 -0.1160 0.4787
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0002 2447
## (Intercept)-Sciurus_niger 1.0027 380
## (Intercept)-Procyon_lotor 1.0020 3569
## (Intercept)-Dasypus_novemcinctus 1.0011 4652
## (Intercept)-Lynx_rufus 1.0106 797
## (Intercept)-Didelphis_virginiana 1.0029 2839
## (Intercept)-Sylvilagus_floridanus 1.0014 1377
## (Intercept)-Sciurus_carolinensis 1.0085 2758
## (Intercept)-Vulpes_vulpes 1.0052 332
## (Intercept)-Sus_scrofa 1.0027 2075
## shrub_cover-Odocoileus_virginianus 1.0040 5250
## shrub_cover-Canis_latrans 1.0009 2594
## shrub_cover-Sciurus_niger 1.0175 1010
## shrub_cover-Procyon_lotor 1.0008 3324
## shrub_cover-Dasypus_novemcinctus 1.0003 3273
## shrub_cover-Lynx_rufus 1.0001 1195
## shrub_cover-Didelphis_virginiana 1.0028 1934
## shrub_cover-Sylvilagus_floridanus 1.0007 1706
## shrub_cover-Sciurus_carolinensis 1.0035 2428
## shrub_cover-Vulpes_vulpes 1.0010 1810
## shrub_cover-Sus_scrofa 1.0028 2877
## veg_height-Odocoileus_virginianus 1.0030 5250
## veg_height-Canis_latrans 0.9998 2134
## veg_height-Sciurus_niger 1.0040 1295
## veg_height-Procyon_lotor 1.0009 4605
## veg_height-Dasypus_novemcinctus 1.0025 4903
## veg_height-Lynx_rufus 1.0061 2387
## veg_height-Didelphis_virginiana 1.0013 3606
## veg_height-Sylvilagus_floridanus 1.0006 2572
## veg_height-Sciurus_carolinensis 1.0023 3665
## veg_height-Vulpes_vulpes 1.0002 1867
## veg_height-Sus_scrofa 1.0069 3120
# Includes movement covariates of occupancy and cover for detection
ms_cover_move <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4608
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0731 0.6369 -1.2958 -0.0739 1.2209 1.0012 1785
## Cogon_Patch_Size -0.2415 0.4030 -1.1320 -0.2132 0.5036 1.0014 1674
## Avg_Cogongrass_Cover 0.2384 0.3075 -0.3713 0.2363 0.8499 1.0063 1225
## total_shrub_cover -0.5606 0.3965 -1.3946 -0.5396 0.1665 1.0153 880
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7852 3.2496 0.3888 2.9624 12.0174 1.0364 1113
## Cogon_Patch_Size 0.8400 1.2054 0.0619 0.4734 3.9013 1.0200 1282
## Avg_Cogongrass_Cover 0.3063 0.3854 0.0354 0.1899 1.2699 1.0369 2150
## total_shrub_cover 0.5784 0.7406 0.0489 0.3404 2.6014 1.0478 1144
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.574 1.5402 0.0884 1.1595 5.4538 1.0458 399
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6164 0.4238 -3.4415 -2.6227 -1.7614 1.0029 2962
## shrub_cover 0.4084 0.2673 -0.1109 0.3998 0.9481 1.0145 1565
## veg_height -0.0039 0.1558 -0.3154 -0.0031 0.3056 1.0073 2834
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9986 1.2623 0.6751 1.6971 5.1410 1.0062 2462
## shrub_cover 0.5138 0.4552 0.1032 0.3977 1.6026 1.0294 1503
## veg_height 0.1893 0.1373 0.0548 0.1529 0.5455 1.0098 3543
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5948 1.6100 0.6348 3.4840
## (Intercept)-Canis_latrans 0.5843 0.7996 -0.9154 0.5522
## (Intercept)-Sciurus_niger -0.4448 1.3308 -2.5824 -0.5818
## (Intercept)-Procyon_lotor 0.7222 0.8024 -0.8389 0.7056
## (Intercept)-Dasypus_novemcinctus -0.5789 0.7221 -2.0423 -0.5624
## (Intercept)-Lynx_rufus -0.0299 1.0368 -1.8558 -0.1112
## (Intercept)-Didelphis_virginiana -1.1070 0.8228 -2.7328 -1.1053
## (Intercept)-Sylvilagus_floridanus -0.0546 0.9548 -1.8377 -0.0964
## (Intercept)-Sciurus_carolinensis -1.2572 0.9061 -3.1251 -1.2341
## (Intercept)-Vulpes_vulpes -0.7703 1.4352 -3.1911 -0.9131
## (Intercept)-Sus_scrofa -1.5418 1.1061 -3.7940 -1.5382
## Cogon_Patch_Size-Odocoileus_virginianus -0.0707 0.7041 -1.3482 -0.1162
## Cogon_Patch_Size-Canis_latrans 0.6127 0.6857 -0.4064 0.4967
## Cogon_Patch_Size-Sciurus_niger -0.5731 0.9058 -2.7431 -0.4540
## Cogon_Patch_Size-Procyon_lotor -0.2840 0.4607 -1.2568 -0.2672
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1166 0.4316 -0.9628 -0.1222
## Cogon_Patch_Size-Lynx_rufus -0.2440 0.7213 -1.6448 -0.2651
## Cogon_Patch_Size-Didelphis_virginiana 0.5142 0.5048 -0.3963 0.4847
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8373 0.8555 -3.0113 -0.6835
## Cogon_Patch_Size-Sciurus_carolinensis -0.7229 0.7545 -2.5281 -0.6065
## Cogon_Patch_Size-Vulpes_vulpes -0.5302 0.8892 -2.5912 -0.4327
## Cogon_Patch_Size-Sus_scrofa -0.4702 0.8223 -2.4504 -0.3593
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2147 0.5385 -0.8233 0.2223
## Avg_Cogongrass_Cover-Canis_latrans 0.3779 0.4310 -0.4003 0.3567
## Avg_Cogongrass_Cover-Sciurus_niger -0.0694 0.6305 -1.5197 -0.0261
## Avg_Cogongrass_Cover-Procyon_lotor 0.2130 0.4427 -0.6597 0.2081
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3989 0.3781 -0.3143 0.3857
## Avg_Cogongrass_Cover-Lynx_rufus 0.5086 0.4926 -0.3458 0.4649
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2240 0.4280 -0.6239 0.2221
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0323 0.5169 -1.1266 -0.0111
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4334 0.4228 -0.3539 0.4192
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3494 0.5012 -0.5870 0.3290
## Avg_Cogongrass_Cover-Sus_scrofa 0.0283 0.5896 -1.2711 0.0796
## total_shrub_cover-Odocoileus_virginianus -0.3301 0.6153 -1.5473 -0.3573
## total_shrub_cover-Canis_latrans 0.0456 0.5802 -0.9261 -0.0068
## total_shrub_cover-Sciurus_niger -0.6949 0.6996 -2.2229 -0.6536
## total_shrub_cover-Procyon_lotor -1.0167 0.5811 -2.3798 -0.9355
## total_shrub_cover-Dasypus_novemcinctus -0.2854 0.4773 -1.3021 -0.2625
## total_shrub_cover-Lynx_rufus -0.9326 0.7507 -2.6623 -0.8563
## total_shrub_cover-Didelphis_virginiana -0.6299 0.5360 -1.8039 -0.5854
## total_shrub_cover-Sylvilagus_floridanus -0.8967 0.7655 -2.7577 -0.7917
## total_shrub_cover-Sciurus_carolinensis -0.5113 0.6035 -1.8519 -0.4750
## total_shrub_cover-Vulpes_vulpes -0.6476 0.8103 -2.4907 -0.5938
## total_shrub_cover-Sus_scrofa -0.3588 0.7158 -1.8395 -0.3503
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1745 1.0172 811
## (Intercept)-Canis_latrans 2.3017 1.0032 1851
## (Intercept)-Sciurus_niger 2.6758 1.0068 530
## (Intercept)-Procyon_lotor 2.3628 1.0013 1720
## (Intercept)-Dasypus_novemcinctus 0.8185 1.0027 2120
## (Intercept)-Lynx_rufus 2.3070 1.0337 1005
## (Intercept)-Didelphis_virginiana 0.5029 1.0087 1900
## (Intercept)-Sylvilagus_floridanus 1.9772 1.0020 1268
## (Intercept)-Sciurus_carolinensis 0.4841 1.0063 1169
## (Intercept)-Vulpes_vulpes 2.4930 1.0127 476
## (Intercept)-Sus_scrofa 0.5981 1.0284 877
## Cogon_Patch_Size-Odocoileus_virginianus 1.4609 1.0018 3271
## Cogon_Patch_Size-Canis_latrans 2.2391 1.0046 2206
## Cogon_Patch_Size-Sciurus_niger 0.9013 1.0027 1375
## Cogon_Patch_Size-Procyon_lotor 0.6086 1.0010 2910
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7250 1.0000 3800
## Cogon_Patch_Size-Lynx_rufus 1.2744 1.0036 1985
## Cogon_Patch_Size-Didelphis_virginiana 1.5992 1.0022 2688
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4029 1.0043 1271
## Cogon_Patch_Size-Sciurus_carolinensis 0.3573 1.0038 1471
## Cogon_Patch_Size-Vulpes_vulpes 0.9468 1.0009 1438
## Cogon_Patch_Size-Sus_scrofa 0.8158 1.0079 1816
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2618 1.0046 2393
## Avg_Cogongrass_Cover-Canis_latrans 1.2957 1.0000 2363
## Avg_Cogongrass_Cover-Sciurus_niger 1.0772 1.0180 1290
## Avg_Cogongrass_Cover-Procyon_lotor 1.0754 0.9999 2475
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1697 1.0004 2685
## Avg_Cogongrass_Cover-Lynx_rufus 1.5937 1.0009 2296
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0706 1.0002 2295
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9171 1.0037 1753
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3203 1.0023 2190
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3887 1.0025 2314
## Avg_Cogongrass_Cover-Sus_scrofa 1.0434 1.0093 1901
## total_shrub_cover-Odocoileus_virginianus 0.9311 1.0009 3018
## total_shrub_cover-Canis_latrans 1.3784 1.0029 1652
## total_shrub_cover-Sciurus_niger 0.5800 1.0039 1196
## total_shrub_cover-Procyon_lotor -0.0892 1.0094 1411
## total_shrub_cover-Dasypus_novemcinctus 0.5729 1.0041 1653
## total_shrub_cover-Lynx_rufus 0.3113 1.0197 1074
## total_shrub_cover-Didelphis_virginiana 0.2870 1.0095 1530
## total_shrub_cover-Sylvilagus_floridanus 0.3365 1.0113 916
## total_shrub_cover-Sciurus_carolinensis 0.5505 1.0299 1165
## total_shrub_cover-Vulpes_vulpes 0.7685 1.0091 943
## total_shrub_cover-Sus_scrofa 1.0393 1.0235 1276
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0034 0.0588 -0.1109 0.0039 0.1167
## (Intercept)-Canis_latrans -2.7542 0.1939 -3.1621 -2.7444 -2.3901
## (Intercept)-Sciurus_niger -4.0340 0.6520 -5.3224 -4.0209 -2.8067
## (Intercept)-Procyon_lotor -2.2963 0.1405 -2.5888 -2.2924 -2.0372
## (Intercept)-Dasypus_novemcinctus -1.7537 0.1738 -2.1124 -1.7444 -1.4335
## (Intercept)-Lynx_rufus -3.5824 0.3608 -4.3457 -3.5658 -2.9195
## (Intercept)-Didelphis_virginiana -2.5806 0.3033 -3.2057 -2.5663 -2.0226
## (Intercept)-Sylvilagus_floridanus -3.2809 0.2896 -3.8728 -3.2694 -2.7402
## (Intercept)-Sciurus_carolinensis -2.7024 0.3460 -3.4347 -2.6784 -2.0759
## (Intercept)-Vulpes_vulpes -4.3021 0.7467 -5.7585 -4.2806 -2.8983
## (Intercept)-Sus_scrofa -3.4479 0.5939 -4.6054 -3.4471 -2.2588
## shrub_cover-Odocoileus_virginianus -0.0522 0.0646 -0.1767 -0.0520 0.0709
## shrub_cover-Canis_latrans -0.2515 0.2381 -0.7118 -0.2561 0.2122
## shrub_cover-Sciurus_niger -0.1090 0.5010 -1.1026 -0.1011 0.8875
## shrub_cover-Procyon_lotor 0.3054 0.1622 -0.0228 0.3047 0.6180
## shrub_cover-Dasypus_novemcinctus 0.9220 0.3353 0.3214 0.9009 1.6236
## shrub_cover-Lynx_rufus 0.0514 0.3798 -0.7256 0.0625 0.7852
## shrub_cover-Didelphis_virginiana 1.0344 0.3923 0.3261 1.0105 1.8795
## shrub_cover-Sylvilagus_floridanus 0.6420 0.4193 -0.2051 0.6478 1.4692
## shrub_cover-Sciurus_carolinensis 0.9722 0.4267 0.1588 0.9613 1.8331
## shrub_cover-Vulpes_vulpes 0.2111 0.5771 -0.9504 0.2098 1.3713
## shrub_cover-Sus_scrofa 0.8360 0.7377 -0.6063 0.8237 2.3249
## veg_height-Odocoileus_virginianus -0.2928 0.0640 -0.4166 -0.2926 -0.1688
## veg_height-Canis_latrans -0.5695 0.1842 -0.9505 -0.5637 -0.2185
## veg_height-Sciurus_niger -0.0242 0.3892 -0.7577 -0.0394 0.7964
## veg_height-Procyon_lotor 0.3351 0.1213 0.1051 0.3336 0.5788
## veg_height-Dasypus_novemcinctus 0.2389 0.1391 -0.0209 0.2347 0.5176
## veg_height-Lynx_rufus 0.0395 0.2392 -0.4490 0.0405 0.5070
## veg_height-Didelphis_virginiana 0.3890 0.2332 -0.0457 0.3824 0.8695
## veg_height-Sylvilagus_floridanus 0.0491 0.2399 -0.4103 0.0457 0.5460
## veg_height-Sciurus_carolinensis 0.0884 0.2128 -0.3200 0.0814 0.5224
## veg_height-Vulpes_vulpes -0.1284 0.3177 -0.8011 -0.1178 0.4722
## veg_height-Sus_scrofa -0.1402 0.3193 -0.7782 -0.1312 0.4718
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0039 5250
## (Intercept)-Canis_latrans 1.0071 2084
## (Intercept)-Sciurus_niger 1.0023 512
## (Intercept)-Procyon_lotor 1.0019 3692
## (Intercept)-Dasypus_novemcinctus 1.0067 2169
## (Intercept)-Lynx_rufus 1.0148 869
## (Intercept)-Didelphis_virginiana 1.0137 1640
## (Intercept)-Sylvilagus_floridanus 1.0071 1404
## (Intercept)-Sciurus_carolinensis 1.0237 1191
## (Intercept)-Vulpes_vulpes 1.0146 434
## (Intercept)-Sus_scrofa 1.0203 966
## shrub_cover-Odocoileus_virginianus 1.0016 5250
## shrub_cover-Canis_latrans 1.0024 2099
## shrub_cover-Sciurus_niger 1.0009 1137
## shrub_cover-Procyon_lotor 1.0012 3755
## shrub_cover-Dasypus_novemcinctus 1.0092 1539
## shrub_cover-Lynx_rufus 1.0022 1037
## shrub_cover-Didelphis_virginiana 1.0186 1243
## shrub_cover-Sylvilagus_floridanus 1.0074 1044
## shrub_cover-Sciurus_carolinensis 1.0328 953
## shrub_cover-Vulpes_vulpes 1.0121 1076
## shrub_cover-Sus_scrofa 1.0231 932
## veg_height-Odocoileus_virginianus 1.0019 5250
## veg_height-Canis_latrans 1.0021 2225
## veg_height-Sciurus_niger 1.0173 1861
## veg_height-Procyon_lotor 1.0012 4156
## veg_height-Dasypus_novemcinctus 1.0021 3116
## veg_height-Lynx_rufus 1.0031 2443
## veg_height-Didelphis_virginiana 1.0004 3361
## veg_height-Sylvilagus_floridanus 1.0063 1932
## veg_height-Sciurus_carolinensis 1.0037 2917
## veg_height-Vulpes_vulpes 1.0006 1818
## veg_height-Sus_scrofa 1.0001 3004
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4388
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0640 0.7508 -1.4616 -0.0932 1.5190 1.0095 1190
## Tree_Density -0.7594 0.4103 -1.6631 -0.7300 -0.0024 1.0066 1260
## Avg_Canopy_Cover 1.0845 0.3776 0.3913 1.0667 1.8960 1.0074 1625
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.9114 8.2812 1.3633 4.9289 24.4396 1.1756 207
## Tree_Density 0.7257 1.2213 0.0456 0.3349 3.8276 1.0105 1134
## Avg_Canopy_Cover 0.7420 0.8222 0.0688 0.4910 2.9594 1.0434 1610
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.3978 0.433 0.0398 0.2479 1.6426 1.0062 522
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6181 0.4558 -3.4902 -2.6339 -1.6797 1.0016 4497
## shrub_cover 0.2310 0.2488 -0.2602 0.2249 0.7473 1.0134 3198
## veg_height 0.0225 0.1590 -0.2958 0.0234 0.3280 1.0000 3425
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1549 1.4461 0.7258 1.7939 5.7039 1.0055 1260
## shrub_cover 0.4907 0.4152 0.1009 0.3773 1.5594 1.0017 2400
## veg_height 0.1940 0.1295 0.0545 0.1603 0.5227 1.0066 3639
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7789 2.0652 2.0637 4.4058 9.6584
## (Intercept)-Canis_latrans 0.4168 0.6235 -0.7239 0.3972 1.7109
## (Intercept)-Sciurus_niger 0.2873 1.8739 -2.1690 -0.0259 4.7192
## (Intercept)-Procyon_lotor 0.8567 0.6400 -0.3475 0.8325 2.1665
## (Intercept)-Dasypus_novemcinctus -0.9140 0.6065 -2.1689 -0.8866 0.2221
## (Intercept)-Lynx_rufus 1.7434 2.2587 -0.9642 1.2355 7.8556
## (Intercept)-Didelphis_virginiana -1.6992 0.7261 -3.2066 -1.6710 -0.3645
## (Intercept)-Sylvilagus_floridanus -0.6033 0.7150 -1.9805 -0.6063 0.8362
## (Intercept)-Sciurus_carolinensis -1.7657 0.7281 -3.2681 -1.7449 -0.3940
## (Intercept)-Vulpes_vulpes -1.2881 1.4859 -3.6847 -1.4982 2.2402
## (Intercept)-Sus_scrofa -2.5017 0.9849 -4.5651 -2.4519 -0.6668
## Tree_Density-Odocoileus_virginianus -0.3878 0.6804 -1.5326 -0.4591 1.1817
## Tree_Density-Canis_latrans -0.9256 0.5741 -2.2306 -0.8668 0.0367
## Tree_Density-Sciurus_niger -0.7807 0.7946 -2.5416 -0.7381 0.7509
## Tree_Density-Procyon_lotor -0.5027 0.4121 -1.2993 -0.4997 0.3002
## Tree_Density-Dasypus_novemcinctus -1.3221 0.8652 -3.5561 -1.1442 -0.1561
## Tree_Density-Lynx_rufus -0.0864 0.8097 -1.4307 -0.1853 1.8096
## Tree_Density-Didelphis_virginiana -0.9848 0.7237 -2.7708 -0.8830 0.1540
## Tree_Density-Sylvilagus_floridanus -1.0120 0.7190 -2.6966 -0.9041 0.1368
## Tree_Density-Sciurus_carolinensis -0.8930 0.7202 -2.5771 -0.8011 0.2874
## Tree_Density-Vulpes_vulpes -0.6929 0.7970 -2.3301 -0.6737 0.8094
## Tree_Density-Sus_scrofa -0.9512 0.7852 -2.7860 -0.8338 0.2852
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8313 0.7297 -0.6364 0.8324 2.2857
## Avg_Canopy_Cover-Canis_latrans 0.0470 0.4900 -0.9205 0.0336 1.0239
## Avg_Canopy_Cover-Sciurus_niger 1.1136 0.8855 -0.3644 1.0406 3.1327
## Avg_Canopy_Cover-Procyon_lotor 1.0735 0.4837 0.1943 1.0496 2.1160
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0739 0.4472 0.2488 1.0472 2.0004
## Avg_Canopy_Cover-Lynx_rufus 1.0175 0.7995 -0.4852 0.9824 2.7492
## Avg_Canopy_Cover-Didelphis_virginiana 1.4507 0.6071 0.4635 1.3751 2.8757
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8617 0.8197 0.6323 1.7115 3.8289
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3843 0.5822 0.4014 1.3223 2.7104
## Avg_Canopy_Cover-Vulpes_vulpes 1.1193 0.6617 -0.0808 1.0719 2.5647
## Avg_Canopy_Cover-Sus_scrofa 1.3229 0.5912 0.2988 1.2678 2.6637
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0632 381
## (Intercept)-Canis_latrans 1.0005 3252
## (Intercept)-Sciurus_niger 1.1253 244
## (Intercept)-Procyon_lotor 1.0073 2192
## (Intercept)-Dasypus_novemcinctus 1.0007 2650
## (Intercept)-Lynx_rufus 1.1284 104
## (Intercept)-Didelphis_virginiana 1.0036 2614
## (Intercept)-Sylvilagus_floridanus 1.0034 2850
## (Intercept)-Sciurus_carolinensis 1.0038 1645
## (Intercept)-Vulpes_vulpes 1.0103 385
## (Intercept)-Sus_scrofa 1.0120 1561
## Tree_Density-Odocoileus_virginianus 1.0039 2176
## Tree_Density-Canis_latrans 1.0024 2185
## Tree_Density-Sciurus_niger 1.0004 1601
## Tree_Density-Procyon_lotor 1.0093 3255
## Tree_Density-Dasypus_novemcinctus 1.0003 1340
## Tree_Density-Lynx_rufus 1.0151 835
## Tree_Density-Didelphis_virginiana 1.0052 1813
## Tree_Density-Sylvilagus_floridanus 1.0048 1829
## Tree_Density-Sciurus_carolinensis 1.0007 1862
## Tree_Density-Vulpes_vulpes 1.0079 1731
## Tree_Density-Sus_scrofa 1.0005 1898
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0042 2688
## Avg_Canopy_Cover-Canis_latrans 1.0167 2483
## Avg_Canopy_Cover-Sciurus_niger 1.0190 1345
## Avg_Canopy_Cover-Procyon_lotor 1.0019 3563
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0008 3718
## Avg_Canopy_Cover-Lynx_rufus 1.0277 1436
## Avg_Canopy_Cover-Didelphis_virginiana 1.0051 2378
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0207 1416
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0018 2511
## Avg_Canopy_Cover-Vulpes_vulpes 1.0025 2159
## Avg_Canopy_Cover-Sus_scrofa 1.0075 2607
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0038 0.0603 -0.1157 0.0036 0.1222
## (Intercept)-Canis_latrans -2.7608 0.1918 -3.1581 -2.7545 -2.4014
## (Intercept)-Sciurus_niger -4.4003 0.6191 -5.5546 -4.4163 -3.1010
## (Intercept)-Procyon_lotor -2.2990 0.1461 -2.5899 -2.2923 -2.0272
## (Intercept)-Dasypus_novemcinctus -1.7257 0.1596 -2.0461 -1.7197 -1.4223
## (Intercept)-Lynx_rufus -3.9437 0.3585 -4.6250 -3.9551 -3.2096
## (Intercept)-Didelphis_virginiana -2.5639 0.2885 -3.1535 -2.5497 -2.0217
## (Intercept)-Sylvilagus_floridanus -3.1239 0.2736 -3.7014 -3.1081 -2.6270
## (Intercept)-Sciurus_carolinensis -2.6277 0.3245 -3.3119 -2.6128 -2.0302
## (Intercept)-Vulpes_vulpes -4.2256 0.7778 -5.8413 -4.1754 -2.8391
## (Intercept)-Sus_scrofa -3.1775 0.5858 -4.3035 -3.1687 -2.0614
## shrub_cover-Odocoileus_virginianus -0.0534 0.0642 -0.1798 -0.0549 0.0742
## shrub_cover-Canis_latrans -0.2780 0.2179 -0.7039 -0.2785 0.1528
## shrub_cover-Sciurus_niger -0.3742 0.4374 -1.2675 -0.3617 0.4494
## shrub_cover-Procyon_lotor 0.2468 0.1604 -0.0791 0.2497 0.5623
## shrub_cover-Dasypus_novemcinctus 0.8201 0.2881 0.2848 0.8097 1.4088
## shrub_cover-Lynx_rufus -0.3192 0.3059 -0.9446 -0.3133 0.2744
## shrub_cover-Didelphis_virginiana 0.9185 0.3514 0.2731 0.9028 1.6447
## shrub_cover-Sylvilagus_floridanus 0.3836 0.3766 -0.3409 0.3767 1.1246
## shrub_cover-Sciurus_carolinensis 0.8127 0.3953 0.0712 0.7999 1.6159
## shrub_cover-Vulpes_vulpes -0.0559 0.5399 -1.1705 -0.0432 0.9670
## shrub_cover-Sus_scrofa 0.4881 0.7161 -0.9356 0.4668 1.9757
## veg_height-Odocoileus_virginianus -0.2948 0.0638 -0.4246 -0.2930 -0.1726
## veg_height-Canis_latrans -0.5801 0.1873 -0.9582 -0.5761 -0.2289
## veg_height-Sciurus_niger -0.0554 0.3623 -0.7741 -0.0547 0.6709
## veg_height-Procyon_lotor 0.3430 0.1218 0.1075 0.3433 0.5899
## veg_height-Dasypus_novemcinctus 0.2388 0.1325 -0.0172 0.2374 0.5038
## veg_height-Lynx_rufus 0.0998 0.2337 -0.3681 0.1072 0.5406
## veg_height-Didelphis_virginiana 0.4513 0.2368 0.0170 0.4444 0.9392
## veg_height-Sylvilagus_floridanus 0.1497 0.2310 -0.3125 0.1535 0.5889
## veg_height-Sciurus_carolinensis 0.0949 0.2128 -0.3030 0.0888 0.5471
## veg_height-Vulpes_vulpes -0.1114 0.3080 -0.7416 -0.1002 0.4642
## veg_height-Sus_scrofa -0.1139 0.3188 -0.7669 -0.1029 0.4914
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 6119
## (Intercept)-Canis_latrans 1.0056 2094
## (Intercept)-Sciurus_niger 1.0783 348
## (Intercept)-Procyon_lotor 1.0051 3588
## (Intercept)-Dasypus_novemcinctus 1.0036 3833
## (Intercept)-Lynx_rufus 1.0295 609
## (Intercept)-Didelphis_virginiana 1.0035 2335
## (Intercept)-Sylvilagus_floridanus 0.9998 2051
## (Intercept)-Sciurus_carolinensis 1.0011 2184
## (Intercept)-Vulpes_vulpes 1.0017 420
## (Intercept)-Sus_scrofa 1.0056 2005
## shrub_cover-Odocoileus_virginianus 1.0033 5250
## shrub_cover-Canis_latrans 1.0012 2560
## shrub_cover-Sciurus_niger 1.0428 1037
## shrub_cover-Procyon_lotor 1.0010 3920
## shrub_cover-Dasypus_novemcinctus 1.0009 3452
## shrub_cover-Lynx_rufus 1.0084 1536
## shrub_cover-Didelphis_virginiana 1.0006 2152
## shrub_cover-Sylvilagus_floridanus 1.0065 2133
## shrub_cover-Sciurus_carolinensis 1.0002 2133
## shrub_cover-Vulpes_vulpes 1.0046 1840
## shrub_cover-Sus_scrofa 1.0073 2655
## veg_height-Odocoileus_virginianus 1.0002 5250
## veg_height-Canis_latrans 1.0046 2357
## veg_height-Sciurus_niger 1.0040 1783
## veg_height-Procyon_lotor 1.0021 3999
## veg_height-Dasypus_novemcinctus 1.0009 4045
## veg_height-Lynx_rufus 1.0030 2086
## veg_height-Didelphis_virginiana 0.9998 3246
## veg_height-Sylvilagus_floridanus 1.0022 3283
## veg_height-Sciurus_carolinensis 1.0000 3039
## veg_height-Vulpes_vulpes 1.0100 1970
## veg_height-Sus_scrofa 1.0025 4169
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9025 0.6164 -2.1046 -0.9071 0.3426 1.0014 3566
## Avg_Cogongrass_Cover -0.7720 0.3894 -1.5556 -0.7683 -0.0250 1.0047 1160
## I(Avg_Cogongrass_Cover^2) 0.8643 0.3288 0.2840 0.8487 1.5734 1.0001 947
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6061 3.1150 0.7208 2.7630 11.4770 1.0104 1889
## Avg_Cogongrass_Cover 0.4349 0.5439 0.0432 0.2585 1.8683 1.0012 1328
## I(Avg_Cogongrass_Cover^2) 0.4276 0.7305 0.0373 0.2176 2.0865 1.0343 874
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.559 0.5652 0.05 0.3825 2.0727 1.003 595
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5681 0.4089 -3.3516 -2.5720 -1.7284 1.0019 4143
## shrub_cover 0.2263 0.2439 -0.2490 0.2245 0.7250 1.0006 2920
## veg_height 0.0179 0.1569 -0.2864 0.0205 0.3257 1.0003 2975
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8499 1.1051 0.6584 1.5672 4.7324 1.0126 2171
## shrub_cover 0.4477 0.3665 0.0875 0.3439 1.4543 1.0061 1886
## veg_height 0.1856 0.1314 0.0514 0.1526 0.5215 1.0029 3442
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7856 1.3870 0.5136 2.5991
## (Intercept)-Canis_latrans -0.4475 0.6922 -1.8262 -0.4469
## (Intercept)-Sciurus_niger -0.9348 1.0777 -2.7883 -1.0160
## (Intercept)-Procyon_lotor -0.1305 0.6453 -1.3982 -0.1229
## (Intercept)-Dasypus_novemcinctus -1.3430 0.6383 -2.6389 -1.3210
## (Intercept)-Lynx_rufus -1.0118 0.9525 -2.7379 -1.0746
## (Intercept)-Didelphis_virginiana -1.8638 0.7286 -3.3586 -1.8432
## (Intercept)-Sylvilagus_floridanus -1.0798 0.7592 -2.5703 -1.0755
## (Intercept)-Sciurus_carolinensis -2.3462 0.7757 -3.9768 -2.3282
## (Intercept)-Vulpes_vulpes -2.2127 1.1392 -4.3144 -2.2644
## (Intercept)-Sus_scrofa -2.3867 0.9122 -4.2418 -2.3603
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7575 0.6653 -2.1135 -0.7501
## Avg_Cogongrass_Cover-Canis_latrans -0.3738 0.5575 -1.3832 -0.4146
## Avg_Cogongrass_Cover-Sciurus_niger -1.0866 0.7629 -2.8528 -1.0123
## Avg_Cogongrass_Cover-Procyon_lotor -0.6983 0.5058 -1.7099 -0.6898
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5598 0.4991 -1.5241 -0.5704
## Avg_Cogongrass_Cover-Lynx_rufus -0.7064 0.5955 -1.9348 -0.6925
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5076 0.5465 -1.5291 -0.5298
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2274 0.6544 -2.7010 -1.1631
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8494 0.5562 -1.9881 -0.8274
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7985 0.6261 -2.0746 -0.7953
## Avg_Cogongrass_Cover-Sus_scrofa -1.0398 0.6788 -2.5790 -0.9902
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1497 0.7371 0.1129 1.0186
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2163 0.6875 0.2528 1.0906
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4061 0.6639 -1.0823 0.4531
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0459 0.5767 0.2096 0.9647
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7584 0.3678 0.0690 0.7484
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1892 0.5414 0.3209 1.1220
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6360 0.4257 -0.1523 0.6106
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7679 0.4687 -0.0413 0.7285
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0113 0.4158 0.2765 0.9791
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9907 0.5308 0.1778 0.9196
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5126 0.6044 -0.8389 0.5548
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.9854 1.0158 1373
## (Intercept)-Canis_latrans 0.9386 1.0018 2671
## (Intercept)-Sciurus_niger 1.4245 1.0335 839
## (Intercept)-Procyon_lotor 1.1100 1.0010 2930
## (Intercept)-Dasypus_novemcinctus -0.1320 1.0009 3292
## (Intercept)-Lynx_rufus 1.0418 1.0067 1450
## (Intercept)-Didelphis_virginiana -0.4735 1.0012 2883
## (Intercept)-Sylvilagus_floridanus 0.4573 1.0009 2714
## (Intercept)-Sciurus_carolinensis -0.9015 1.0016 2706
## (Intercept)-Vulpes_vulpes 0.2335 1.0043 967
## (Intercept)-Sus_scrofa -0.6518 1.0005 2217
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5228 1.0025 2633
## Avg_Cogongrass_Cover-Canis_latrans 0.8307 1.0020 2572
## Avg_Cogongrass_Cover-Sciurus_niger 0.1936 1.0035 1266
## Avg_Cogongrass_Cover-Procyon_lotor 0.2962 1.0006 2578
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4747 1.0053 2486
## Avg_Cogongrass_Cover-Lynx_rufus 0.4521 1.0015 1957
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6264 1.0009 2317
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1130 1.0021 1608
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1728 1.0015 1840
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3904 1.0031 1755
## Avg_Cogongrass_Cover-Sus_scrofa 0.1496 1.0016 1494
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.9699 1.0054 906
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9735 1.0021 977
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5899 1.0026 970
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4298 1.0057 1233
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5348 1.0073 2629
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4809 1.0040 1400
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5961 1.0001 2126
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7505 1.0013 1367
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9326 1.0008 1603
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2801 1.0047 1294
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5748 1.0031 1567
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0029 0.0594 -0.1113 0.0026 0.1202
## (Intercept)-Canis_latrans -2.7361 0.1815 -3.1048 -2.7289 -2.3928
## (Intercept)-Sciurus_niger -4.0696 0.6277 -5.2678 -4.0831 -2.8375
## (Intercept)-Procyon_lotor -2.3077 0.1485 -2.6104 -2.3028 -2.0289
## (Intercept)-Dasypus_novemcinctus -1.7162 0.1559 -2.0338 -1.7126 -1.4186
## (Intercept)-Lynx_rufus -3.5859 0.3638 -4.3157 -3.5703 -2.9188
## (Intercept)-Didelphis_virginiana -2.5416 0.2930 -3.1537 -2.5317 -2.0006
## (Intercept)-Sylvilagus_floridanus -3.1910 0.2975 -3.8419 -3.1724 -2.6491
## (Intercept)-Sciurus_carolinensis -2.5678 0.3075 -3.2056 -2.5557 -2.0051
## (Intercept)-Vulpes_vulpes -4.0111 0.6979 -5.4777 -3.9685 -2.7959
## (Intercept)-Sus_scrofa -3.2316 0.5862 -4.3855 -3.2263 -2.1115
## shrub_cover-Odocoileus_virginianus -0.0511 0.0646 -0.1780 -0.0526 0.0743
## shrub_cover-Canis_latrans -0.2361 0.2102 -0.6472 -0.2345 0.1826
## shrub_cover-Sciurus_niger -0.3255 0.4621 -1.2740 -0.3149 0.5478
## shrub_cover-Procyon_lotor 0.2289 0.1687 -0.1169 0.2344 0.5496
## shrub_cover-Dasypus_novemcinctus 0.7861 0.2883 0.2441 0.7717 1.3625
## shrub_cover-Lynx_rufus -0.2173 0.3604 -0.9532 -0.2092 0.4597
## shrub_cover-Didelphis_virginiana 0.9103 0.3684 0.2487 0.8890 1.6914
## shrub_cover-Sylvilagus_floridanus 0.2246 0.3957 -0.5068 0.2067 1.0495
## shrub_cover-Sciurus_carolinensis 0.7259 0.3860 0.0008 0.7161 1.5196
## shrub_cover-Vulpes_vulpes -0.0453 0.5384 -1.1575 -0.0332 0.9926
## shrub_cover-Sus_scrofa 0.4922 0.7253 -0.9353 0.4667 2.0147
## veg_height-Odocoileus_virginianus -0.2927 0.0648 -0.4173 -0.2932 -0.1648
## veg_height-Canis_latrans -0.5583 0.1786 -0.9228 -0.5514 -0.2262
## veg_height-Sciurus_niger 0.0384 0.3952 -0.7071 0.0266 0.8508
## veg_height-Procyon_lotor 0.3405 0.1224 0.1032 0.3415 0.5807
## veg_height-Dasypus_novemcinctus 0.2284 0.1307 -0.0256 0.2282 0.4850
## veg_height-Lynx_rufus 0.0727 0.2426 -0.4137 0.0783 0.5384
## veg_height-Didelphis_virginiana 0.3825 0.2427 -0.0694 0.3747 0.8866
## veg_height-Sylvilagus_floridanus 0.1431 0.2387 -0.3272 0.1419 0.6102
## veg_height-Sciurus_carolinensis 0.0604 0.2044 -0.3294 0.0561 0.4703
## veg_height-Vulpes_vulpes -0.1137 0.3029 -0.7506 -0.1000 0.4378
## veg_height-Sus_scrofa -0.1106 0.3246 -0.7697 -0.1055 0.5158
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0003 2570
## (Intercept)-Sciurus_niger 1.0308 512
## (Intercept)-Procyon_lotor 1.0015 3109
## (Intercept)-Dasypus_novemcinctus 1.0007 4798
## (Intercept)-Lynx_rufus 1.0148 882
## (Intercept)-Didelphis_virginiana 1.0099 1832
## (Intercept)-Sylvilagus_floridanus 1.0054 1489
## (Intercept)-Sciurus_carolinensis 1.0010 2296
## (Intercept)-Vulpes_vulpes 1.0213 630
## (Intercept)-Sus_scrofa 1.0021 1473
## shrub_cover-Odocoileus_virginianus 1.0006 4967
## shrub_cover-Canis_latrans 1.0024 2950
## shrub_cover-Sciurus_niger 1.0000 1028
## shrub_cover-Procyon_lotor 1.0039 3683
## shrub_cover-Dasypus_novemcinctus 1.0010 3692
## shrub_cover-Lynx_rufus 1.0043 1311
## shrub_cover-Didelphis_virginiana 1.0054 1695
## shrub_cover-Sylvilagus_floridanus 1.0004 1727
## shrub_cover-Sciurus_carolinensis 1.0008 2855
## shrub_cover-Vulpes_vulpes 1.0034 2209
## shrub_cover-Sus_scrofa 1.0035 1795
## veg_height-Odocoileus_virginianus 1.0005 5478
## veg_height-Canis_latrans 1.0018 2778
## veg_height-Sciurus_niger 1.0017 1659
## veg_height-Procyon_lotor 1.0003 4311
## veg_height-Dasypus_novemcinctus 1.0018 4723
## veg_height-Lynx_rufus 1.0019 2512
## veg_height-Didelphis_virginiana 1.0022 3260
## veg_height-Sylvilagus_floridanus 1.0013 2390
## veg_height-Sciurus_carolinensis 1.0000 2775
## veg_height-Vulpes_vulpes 1.0046 2410
## veg_height-Sus_scrofa 1.0069 3208
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5422
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8524 1.1528 -3.0237 -0.9033 1.6321 1.0010 1384
## Cogon_Patch_Size -0.2799 0.8032 -2.0173 -0.2293 1.1646 1.0021 718
## Veg_shannon_index 0.9539 0.5322 -0.0906 0.9329 2.0522 1.0055 597
## total_shrub_cover -0.5336 0.5418 -1.6779 -0.5050 0.4721 1.0007 642
## Avg_Cogongrass_Cover -0.0785 0.9676 -1.9682 -0.0808 1.8063 1.0014 412
## Tree_Density -1.9755 0.8340 -3.6271 -1.9702 -0.2454 1.0066 705
## Avg_Canopy_Cover 1.9900 0.7172 0.6766 1.9401 3.4875 1.0070 1052
## I(Avg_Cogongrass_Cover^2) 1.6397 0.6016 0.5734 1.5860 2.9704 1.0176 461
## avg_veg_height -0.1384 0.5295 -1.1932 -0.1325 0.8876 1.0033 615
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.9244 23.0661 4.0088 17.5063 80.2196 1.0324 353
## Cogon_Patch_Size 4.4758 7.7642 0.1303 2.2473 23.6128 1.1002 353
## Veg_shannon_index 1.0562 1.9953 0.0505 0.4690 5.9602 1.0767 669
## total_shrub_cover 1.0805 2.1292 0.0582 0.5213 5.3505 1.0889 460
## Avg_Cogongrass_Cover 1.3648 2.4033 0.0542 0.5490 7.9545 1.0252 1134
## Tree_Density 4.8017 9.3541 0.0750 1.8625 28.7136 1.0286 262
## Avg_Canopy_Cover 3.7097 5.4549 0.1805 2.1379 17.4733 1.0402 360
## I(Avg_Cogongrass_Cover^2) 0.9047 1.6231 0.0465 0.4055 4.8626 1.0103 701
## avg_veg_height 0.5426 1.0225 0.0463 0.2909 2.5192 1.0582 1651
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9727 3.9331 0.0574 0.7956 10.506 1.2457 141
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6508 0.4574 -3.5213 -2.6657 -1.7063 1.0026 5027
## shrub_cover 0.3196 0.2591 -0.1641 0.3125 0.8463 1.0010 1702
## veg_height 0.0167 0.1522 -0.2863 0.0170 0.3184 1.0038 3053
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2762 1.4161 0.8048 1.9242 5.7928 1.0119 2114
## shrub_cover 0.5248 0.4729 0.1088 0.4051 1.6549 1.0309 1348
## veg_height 0.1921 0.1412 0.0547 0.1571 0.5586 1.0068 3408
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.0353 3.8810 2.4429
## (Intercept)-Canis_latrans -0.8183 1.3091 -3.2520
## (Intercept)-Sciurus_niger 1.3278 3.0939 -3.1441
## (Intercept)-Procyon_lotor -0.3275 1.1737 -2.7923
## (Intercept)-Dasypus_novemcinctus -2.7046 1.2970 -5.6509
## (Intercept)-Lynx_rufus 0.7095 2.9553 -3.8331
## (Intercept)-Didelphis_virginiana -4.1532 1.5490 -7.4043
## (Intercept)-Sylvilagus_floridanus -2.4319 1.6304 -6.0466
## (Intercept)-Sciurus_carolinensis -4.7789 1.7461 -8.5503
## (Intercept)-Vulpes_vulpes -4.3187 2.6299 -9.1623
## (Intercept)-Sus_scrofa -5.6539 2.2934 -10.7573
## Cogon_Patch_Size-Odocoileus_virginianus -0.0580 1.5460 -3.1040
## Cogon_Patch_Size-Canis_latrans 1.6956 1.5735 -0.3490
## Cogon_Patch_Size-Sciurus_niger -1.1758 2.3154 -6.7875
## Cogon_Patch_Size-Procyon_lotor -0.5707 0.8550 -2.2792
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0531 0.8387 -1.6724
## Cogon_Patch_Size-Lynx_rufus -0.4944 1.6874 -3.8250
## Cogon_Patch_Size-Didelphis_virginiana 1.6596 1.1365 -0.2085
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7043 2.0901 -6.7809
## Cogon_Patch_Size-Sciurus_carolinensis -1.2994 1.7231 -5.5909
## Cogon_Patch_Size-Vulpes_vulpes -0.8283 1.9697 -5.3685
## Cogon_Patch_Size-Sus_scrofa -0.9934 1.8110 -5.3529
## Veg_shannon_index-Odocoileus_virginianus 0.7608 1.0044 -1.3341
## Veg_shannon_index-Canis_latrans 1.3682 0.7763 0.0993
## Veg_shannon_index-Sciurus_niger 1.0408 1.1638 -1.0500
## Veg_shannon_index-Procyon_lotor 1.2066 0.6861 0.0467
## Veg_shannon_index-Dasypus_novemcinctus 0.5969 0.6246 -0.6788
## Veg_shannon_index-Lynx_rufus 1.0979 1.0507 -0.7678
## Veg_shannon_index-Didelphis_virginiana 1.1666 0.7806 -0.2430
## Veg_shannon_index-Sylvilagus_floridanus 1.0552 0.8100 -0.4136
## Veg_shannon_index-Sciurus_carolinensis 0.2831 0.9390 -1.8971
## Veg_shannon_index-Vulpes_vulpes 0.5955 1.0346 -1.7453
## Veg_shannon_index-Sus_scrofa 1.6322 1.1388 0.0541
## total_shrub_cover-Odocoileus_virginianus -0.2798 0.9485 -2.0665
## total_shrub_cover-Canis_latrans 0.1888 0.8523 -1.1903
## total_shrub_cover-Sciurus_niger -0.6989 1.1317 -3.1654
## total_shrub_cover-Procyon_lotor -1.1411 0.6918 -2.6938
## total_shrub_cover-Dasypus_novemcinctus -0.2372 0.6871 -1.7026
## total_shrub_cover-Lynx_rufus -0.7998 1.1565 -3.3327
## total_shrub_cover-Didelphis_virginiana -0.8469 0.9014 -2.9164
## total_shrub_cover-Sylvilagus_floridanus -0.6542 0.9485 -2.7184
## total_shrub_cover-Sciurus_carolinensis -0.4534 0.8470 -2.2320
## total_shrub_cover-Vulpes_vulpes -0.7515 1.0853 -3.1567
## total_shrub_cover-Sus_scrofa -0.3564 0.9866 -2.4353
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1189 1.3889 -2.8752
## Avg_Cogongrass_Cover-Canis_latrans 0.1323 1.2195 -2.2164
## Avg_Cogongrass_Cover-Sciurus_niger -0.4450 1.5792 -4.0206
## Avg_Cogongrass_Cover-Procyon_lotor 0.0255 1.1683 -2.2738
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5507 1.2929 -1.7630
## Avg_Cogongrass_Cover-Lynx_rufus 0.0192 1.3329 -2.5314
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0909 1.2569 -2.5921
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6999 1.3938 -3.7607
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0836 1.2722 -2.5485
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0829 1.3340 -2.5078
## Avg_Cogongrass_Cover-Sus_scrofa -0.4357 1.4559 -3.6361
## Tree_Density-Odocoileus_virginianus -0.8651 1.6352 -3.2928
## Tree_Density-Canis_latrans -2.9925 1.5370 -6.8474
## Tree_Density-Sciurus_niger -1.9876 1.9719 -6.1653
## Tree_Density-Procyon_lotor -1.9693 0.9980 -4.0705
## Tree_Density-Dasypus_novemcinctus -4.1945 2.4228 -10.5779
## Tree_Density-Lynx_rufus -0.5472 2.1010 -3.4437
## Tree_Density-Didelphis_virginiana -2.3682 1.3513 -5.4234
## Tree_Density-Sylvilagus_floridanus -2.6687 1.6928 -6.9770
## Tree_Density-Sciurus_carolinensis -2.7349 1.6728 -6.8066
## Tree_Density-Vulpes_vulpes -1.9957 1.8715 -5.7473
## Tree_Density-Sus_scrofa -2.6092 1.8704 -7.3498
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2246 1.5489 -2.0750
## Avg_Canopy_Cover-Canis_latrans 0.1469 0.7527 -1.3871
## Avg_Canopy_Cover-Sciurus_niger 2.4450 2.0021 -1.0801
## Avg_Canopy_Cover-Procyon_lotor 1.7173 0.8597 0.1825
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2184 0.9179 0.7513
## Avg_Canopy_Cover-Lynx_rufus 1.7037 1.6388 -1.1949
## Avg_Canopy_Cover-Didelphis_virginiana 3.2840 1.4640 1.2831
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9177 1.9434 1.2398
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9494 1.4224 1.0481
## Avg_Canopy_Cover-Vulpes_vulpes 2.6917 1.6089 0.4518
## Avg_Canopy_Cover-Sus_scrofa 2.2594 1.1360 0.4868
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9186 1.1031 0.1603
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0162 0.9352 0.6003
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3710 1.1664 -1.1013
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9266 0.8755 0.5409
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5784 0.7514 0.3053
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1185 0.9909 0.6003
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2744 0.7378 -0.1661
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3632 0.8461 -0.1945
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8071 0.8124 0.4543
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9477 0.8657 0.5445
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2671 1.0893 -1.0206
## avg_veg_height-Odocoileus_virginianus -0.1556 0.8529 -1.9876
## avg_veg_height-Canis_latrans -0.2468 0.6509 -1.5855
## avg_veg_height-Sciurus_niger -0.2633 0.9077 -2.2383
## avg_veg_height-Procyon_lotor 0.0619 0.6666 -1.2547
## avg_veg_height-Dasypus_novemcinctus 0.1934 0.6582 -1.0380
## avg_veg_height-Lynx_rufus -0.3222 0.8756 -2.2206
## avg_veg_height-Didelphis_virginiana -0.3440 0.7755 -2.0420
## avg_veg_height-Sylvilagus_floridanus -0.2539 0.7536 -1.8139
## avg_veg_height-Sciurus_carolinensis 0.1585 0.7324 -1.1486
## avg_veg_height-Vulpes_vulpes -0.2497 0.8373 -2.0178
## avg_veg_height-Sus_scrofa -0.1775 0.7726 -1.7832
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3374 17.6018 1.0222 408
## (Intercept)-Canis_latrans -0.8517 1.9483 1.0045 1006
## (Intercept)-Sciurus_niger 0.7694 9.2263 1.0130 238
## (Intercept)-Procyon_lotor -0.2807 1.9205 1.0094 849
## (Intercept)-Dasypus_novemcinctus -2.5686 -0.5807 1.0040 504
## (Intercept)-Lynx_rufus 0.2585 7.6566 1.0096 205
## (Intercept)-Didelphis_virginiana -4.0550 -1.3264 1.0173 1115
## (Intercept)-Sylvilagus_floridanus -2.3360 0.6346 1.0119 668
## (Intercept)-Sciurus_carolinensis -4.6165 -1.8148 1.0192 436
## (Intercept)-Vulpes_vulpes -4.4369 0.8810 1.0056 269
## (Intercept)-Sus_scrofa -5.4494 -1.7907 1.0062 391
## Cogon_Patch_Size-Odocoileus_virginianus -0.0965 3.2554 1.0068 1205
## Cogon_Patch_Size-Canis_latrans 1.3712 5.5961 1.0181 608
## Cogon_Patch_Size-Sciurus_niger -0.8147 2.5155 1.0094 424
## Cogon_Patch_Size-Procyon_lotor -0.5483 0.9708 1.0039 518
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0690 1.6915 1.0026 1446
## Cogon_Patch_Size-Lynx_rufus -0.4681 2.9043 1.0165 555
## Cogon_Patch_Size-Didelphis_virginiana 1.5194 4.3354 1.0156 481
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2825 0.9027 1.0538 306
## Cogon_Patch_Size-Sciurus_carolinensis -0.9636 1.0181 1.0275 540
## Cogon_Patch_Size-Vulpes_vulpes -0.5983 2.5308 1.0243 624
## Cogon_Patch_Size-Sus_scrofa -0.6695 1.6309 1.0249 578
## Veg_shannon_index-Odocoileus_virginianus 0.8113 2.6505 1.0111 1485
## Veg_shannon_index-Canis_latrans 1.2709 3.1891 1.0058 585
## Veg_shannon_index-Sciurus_niger 0.9889 3.5646 1.0131 774
## Veg_shannon_index-Procyon_lotor 1.1389 2.8032 1.0064 409
## Veg_shannon_index-Dasypus_novemcinctus 0.6186 1.7661 1.0016 1791
## Veg_shannon_index-Lynx_rufus 1.0244 3.4236 1.0063 987
## Veg_shannon_index-Didelphis_virginiana 1.1110 2.9374 1.0088 1011
## Veg_shannon_index-Sylvilagus_floridanus 1.0010 2.8466 1.0042 1107
## Veg_shannon_index-Sciurus_carolinensis 0.3923 1.8322 1.0110 1179
## Veg_shannon_index-Vulpes_vulpes 0.6804 2.3337 1.0123 987
## Veg_shannon_index-Sus_scrofa 1.4175 4.4207 1.0289 698
## total_shrub_cover-Odocoileus_virginianus -0.3254 1.7553 1.0057 1575
## total_shrub_cover-Canis_latrans 0.0846 2.2527 1.0080 856
## total_shrub_cover-Sciurus_niger -0.6238 1.2641 1.0121 493
## total_shrub_cover-Procyon_lotor -1.0768 0.0365 1.0033 1261
## total_shrub_cover-Dasypus_novemcinctus -0.2006 1.0088 1.0028 1393
## total_shrub_cover-Lynx_rufus -0.7181 1.2817 1.0120 562
## total_shrub_cover-Didelphis_virginiana -0.7472 0.5539 1.0148 792
## total_shrub_cover-Sylvilagus_floridanus -0.5769 0.9526 1.0056 840
## total_shrub_cover-Sciurus_carolinensis -0.4308 1.1355 1.0015 1197
## total_shrub_cover-Vulpes_vulpes -0.6391 1.0374 1.0093 773
## total_shrub_cover-Sus_scrofa -0.3517 1.6202 1.0026 825
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1066 2.6026 1.0033 832
## Avg_Cogongrass_Cover-Canis_latrans 0.0974 2.5469 1.0022 655
## Avg_Cogongrass_Cover-Sciurus_niger -0.3188 2.1801 1.0226 640
## Avg_Cogongrass_Cover-Procyon_lotor 0.0238 2.3460 1.0028 622
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4831 3.4091 1.0074 582
## Avg_Cogongrass_Cover-Lynx_rufus -0.0110 2.7396 1.0028 620
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0776 2.3336 1.0016 642
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5958 1.8037 1.0044 627
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0796 2.4618 1.0012 627
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0744 2.8142 1.0050 698
## Avg_Cogongrass_Cover-Sus_scrofa -0.3377 2.1025 1.0153 622
## Tree_Density-Odocoileus_virginianus -1.1212 3.3535 1.0170 475
## Tree_Density-Canis_latrans -2.7086 -0.7595 1.0037 573
## Tree_Density-Sciurus_niger -1.9608 2.1072 1.0110 630
## Tree_Density-Procyon_lotor -1.9535 -0.0944 1.0037 751
## Tree_Density-Dasypus_novemcinctus -3.5469 -1.3574 1.0088 238
## Tree_Density-Lynx_rufus -0.9333 4.6730 1.0240 356
## Tree_Density-Didelphis_virginiana -2.2645 -0.0254 1.0051 1001
## Tree_Density-Sylvilagus_floridanus -2.4281 0.0590 1.0025 575
## Tree_Density-Sciurus_carolinensis -2.4649 -0.0959 1.0007 784
## Tree_Density-Vulpes_vulpes -2.0316 1.8907 1.0218 680
## Tree_Density-Sus_scrofa -2.3411 0.4039 1.0051 781
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3043 4.2128 1.0060 1601
## Avg_Canopy_Cover-Canis_latrans 0.1586 1.6062 1.0079 1057
## Avg_Canopy_Cover-Sciurus_niger 2.2799 6.8725 1.0137 597
## Avg_Canopy_Cover-Procyon_lotor 1.6581 3.5706 1.0069 588
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1127 4.3856 1.0074 422
## Avg_Canopy_Cover-Lynx_rufus 1.6248 5.2465 1.0036 654
## Avg_Canopy_Cover-Didelphis_virginiana 2.9913 6.9405 1.0150 392
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5353 8.8357 1.0295 515
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6460 6.6047 1.0077 426
## Avg_Canopy_Cover-Vulpes_vulpes 2.3742 6.8734 1.0230 435
## Avg_Canopy_Cover-Sus_scrofa 2.1254 4.7660 1.0077 876
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7941 4.5224 1.0015 872
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8560 4.3230 1.0040 667
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3936 3.6405 1.0298 527
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8167 3.9222 1.0070 717
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5056 3.2575 1.0025 806
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9757 4.4842 1.0049 698
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2484 2.7929 1.0096 637
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3165 3.2089 1.0072 677
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7103 3.6086 1.0081 799
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8453 3.9486 1.0041 818
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2953 3.2908 1.0179 666
## avg_veg_height-Odocoileus_virginianus -0.1215 1.4595 1.0027 1156
## avg_veg_height-Canis_latrans -0.2311 1.0428 1.0018 1073
## avg_veg_height-Sciurus_niger -0.2169 1.3355 1.0076 984
## avg_veg_height-Procyon_lotor 0.0567 1.3967 1.0015 1065
## avg_veg_height-Dasypus_novemcinctus 0.1726 1.5916 1.0013 1049
## avg_veg_height-Lynx_rufus -0.2699 1.2602 1.0050 892
## avg_veg_height-Didelphis_virginiana -0.2884 1.0471 1.0041 1106
## avg_veg_height-Sylvilagus_floridanus -0.2277 1.1898 1.0037 1088
## avg_veg_height-Sciurus_carolinensis 0.1247 1.7600 1.0007 1221
## avg_veg_height-Vulpes_vulpes -0.2162 1.3306 1.0018 832
## avg_veg_height-Sus_scrofa -0.1435 1.2767 1.0000 1051
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0047 0.0603 -0.1152 0.0041 0.1232
## (Intercept)-Canis_latrans -2.7077 0.1843 -3.0926 -2.7003 -2.3660
## (Intercept)-Sciurus_niger -4.7426 0.5080 -5.7821 -4.7436 -3.7138
## (Intercept)-Procyon_lotor -2.3015 0.1456 -2.5886 -2.2963 -2.0275
## (Intercept)-Dasypus_novemcinctus -1.7423 0.1590 -2.0523 -1.7383 -1.4293
## (Intercept)-Lynx_rufus -3.8862 0.3692 -4.5859 -3.8930 -3.1541
## (Intercept)-Didelphis_virginiana -2.5655 0.2989 -3.2020 -2.5487 -2.0103
## (Intercept)-Sylvilagus_floridanus -3.1924 0.2668 -3.7422 -3.1784 -2.6960
## (Intercept)-Sciurus_carolinensis -2.6751 0.3187 -3.3207 -2.6667 -2.0671
## (Intercept)-Vulpes_vulpes -4.2283 0.6593 -5.5789 -4.2056 -3.0056
## (Intercept)-Sus_scrofa -3.3533 0.6058 -4.5741 -3.3375 -2.2067
## shrub_cover-Odocoileus_virginianus -0.0525 0.0646 -0.1786 -0.0523 0.0736
## shrub_cover-Canis_latrans -0.2868 0.2238 -0.7211 -0.2840 0.1490
## shrub_cover-Sciurus_niger -0.3305 0.4453 -1.2736 -0.3077 0.5048
## shrub_cover-Procyon_lotor 0.2675 0.1621 -0.0563 0.2695 0.5758
## shrub_cover-Dasypus_novemcinctus 0.8828 0.3132 0.2982 0.8791 1.5040
## shrub_cover-Lynx_rufus -0.2021 0.3579 -0.9065 -0.2112 0.5168
## shrub_cover-Didelphis_virginiana 0.9760 0.3734 0.3034 0.9533 1.7707
## shrub_cover-Sylvilagus_floridanus 0.4974 0.3866 -0.2441 0.4920 1.2665
## shrub_cover-Sciurus_carolinensis 0.9065 0.4103 0.1537 0.8945 1.7704
## shrub_cover-Vulpes_vulpes 0.1631 0.5506 -0.9500 0.1659 1.2527
## shrub_cover-Sus_scrofa 0.7457 0.7850 -0.6994 0.7051 2.4002
## veg_height-Odocoileus_virginianus -0.2932 0.0659 -0.4230 -0.2931 -0.1650
## veg_height-Canis_latrans -0.5411 0.1784 -0.9066 -0.5370 -0.2041
## veg_height-Sciurus_niger -0.0540 0.3297 -0.7270 -0.0522 0.6095
## veg_height-Procyon_lotor 0.3518 0.1232 0.1086 0.3521 0.5933
## veg_height-Dasypus_novemcinctus 0.2444 0.1325 -0.0089 0.2434 0.5086
## veg_height-Lynx_rufus 0.1481 0.2359 -0.3204 0.1496 0.6001
## veg_height-Didelphis_virginiana 0.4225 0.2413 -0.0241 0.4149 0.9200
## veg_height-Sylvilagus_floridanus 0.1392 0.2356 -0.3046 0.1360 0.6268
## veg_height-Sciurus_carolinensis 0.1005 0.2130 -0.3183 0.0940 0.5189
## veg_height-Vulpes_vulpes -0.1713 0.3144 -0.8304 -0.1593 0.4090
## veg_height-Sus_scrofa -0.1568 0.3219 -0.8234 -0.1453 0.4528
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0038 5250
## (Intercept)-Canis_latrans 1.0000 2347
## (Intercept)-Sciurus_niger 1.0069 477
## (Intercept)-Procyon_lotor 1.0018 3490
## (Intercept)-Dasypus_novemcinctus 1.0031 3195
## (Intercept)-Lynx_rufus 1.0084 398
## (Intercept)-Didelphis_virginiana 1.0059 1653
## (Intercept)-Sylvilagus_floridanus 1.0030 1816
## (Intercept)-Sciurus_carolinensis 1.0035 1692
## (Intercept)-Vulpes_vulpes 1.0102 496
## (Intercept)-Sus_scrofa 1.0082 744
## shrub_cover-Odocoileus_virginianus 1.0020 5250
## shrub_cover-Canis_latrans 1.0015 1940
## shrub_cover-Sciurus_niger 1.0020 881
## shrub_cover-Procyon_lotor 1.0026 3526
## shrub_cover-Dasypus_novemcinctus 1.0010 1987
## shrub_cover-Lynx_rufus 1.0003 715
## shrub_cover-Didelphis_virginiana 1.0005 1445
## shrub_cover-Sylvilagus_floridanus 1.0018 1341
## shrub_cover-Sciurus_carolinensis 1.0032 1335
## shrub_cover-Vulpes_vulpes 1.0011 1638
## shrub_cover-Sus_scrofa 1.0038 806
## veg_height-Odocoileus_virginianus 1.0032 4774
## veg_height-Canis_latrans 1.0020 2316
## veg_height-Sciurus_niger 1.0060 1004
## veg_height-Procyon_lotor 1.0008 3933
## veg_height-Dasypus_novemcinctus 1.0021 4611
## veg_height-Lynx_rufus 1.0012 1462
## veg_height-Didelphis_virginiana 1.0030 2896
## veg_height-Sylvilagus_floridanus 1.0017 2409
## veg_height-Sciurus_carolinensis 1.0017 2859
## veg_height-Vulpes_vulpes 1.0031 1855
## veg_height-Sus_scrofa 1.0118 2634
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7325
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2091 0.557 -1.2847 -0.2173 0.9196 1.0028 3365
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2617 2.5936 0.8552 2.5582 10.0594 1.0033 1541
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2865 0.4403 -3.1381 -2.3022 -1.3909 1.0006 4678
## week 0.3546 0.2423 -0.1484 0.3613 0.8216 1.0024 2750
## I(week^2) -0.2845 0.1029 -0.4934 -0.2824 -0.0866 1.0132 2305
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1097 1.3140 0.7399 1.7609 5.5423 1.0032 2496
## week 0.4300 0.3458 0.1050 0.3429 1.2611 1.0056 2082
## I(week^2) 0.0744 0.0603 0.0223 0.0591 0.2287 1.0053 2193
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4522 1.2321 1.7222 3.2502 6.4871
## (Intercept)-Canis_latrans 0.3077 0.4044 -0.4392 0.2896 1.1569
## (Intercept)-Sciurus_niger -0.6348 0.9638 -2.0565 -0.7831 1.8079
## (Intercept)-Procyon_lotor 0.7163 0.3933 -0.0134 0.7083 1.5270
## (Intercept)-Dasypus_novemcinctus -0.6345 0.3636 -1.3765 -0.6216 0.0589
## (Intercept)-Lynx_rufus 0.4225 0.9458 -0.8585 0.2553 2.8960
## (Intercept)-Didelphis_virginiana -1.3789 0.4450 -2.3059 -1.3593 -0.5533
## (Intercept)-Sylvilagus_floridanus -0.2987 0.5506 -1.2369 -0.3403 0.8818
## (Intercept)-Sciurus_carolinensis -1.3403 0.4506 -2.2739 -1.3189 -0.5171
## (Intercept)-Vulpes_vulpes -1.1378 1.0078 -2.7814 -1.2616 1.2271
## (Intercept)-Sus_scrofa -1.8988 0.6270 -3.1869 -1.8788 -0.6949
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0075 1266
## (Intercept)-Canis_latrans 1.0027 4750
## (Intercept)-Sciurus_niger 1.0538 503
## (Intercept)-Procyon_lotor 1.0031 5250
## (Intercept)-Dasypus_novemcinctus 1.0009 5250
## (Intercept)-Lynx_rufus 1.0076 829
## (Intercept)-Didelphis_virginiana 1.0010 4587
## (Intercept)-Sylvilagus_floridanus 1.0061 1932
## (Intercept)-Sciurus_carolinensis 1.0020 5250
## (Intercept)-Vulpes_vulpes 1.0208 565
## (Intercept)-Sus_scrofa 1.0002 3187
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5258 0.0791 0.3721 0.5259 0.6763
## (Intercept)-Canis_latrans -2.4356 0.1894 -2.8219 -2.4329 -2.0762
## (Intercept)-Sciurus_niger -3.8667 0.6104 -5.1534 -3.8308 -2.7862
## (Intercept)-Procyon_lotor -2.1538 0.1514 -2.4566 -2.1517 -1.8608
## (Intercept)-Dasypus_novemcinctus -1.4402 0.1550 -1.7495 -1.4396 -1.1500
## (Intercept)-Lynx_rufus -3.4508 0.3575 -4.1683 -3.4508 -2.7577
## (Intercept)-Didelphis_virginiana -2.1109 0.2706 -2.6665 -2.0983 -1.6154
## (Intercept)-Sylvilagus_floridanus -3.0828 0.3286 -3.7780 -3.0660 -2.4893
## (Intercept)-Sciurus_carolinensis -2.2549 0.2896 -2.8459 -2.2475 -1.7313
## (Intercept)-Vulpes_vulpes -3.8163 0.7475 -5.3824 -3.7609 -2.5392
## (Intercept)-Sus_scrofa -2.8124 0.5050 -3.8958 -2.7685 -1.9332
## week-Odocoileus_virginianus 1.2791 0.1234 1.0399 1.2754 1.5267
## week-Canis_latrans 0.5858 0.2558 0.0922 0.5789 1.1068
## week-Sciurus_niger -0.4236 0.5589 -1.6365 -0.3682 0.5263
## week-Procyon_lotor 0.2019 0.2123 -0.2024 0.2000 0.6159
## week-Dasypus_novemcinctus 0.1050 0.2247 -0.3249 0.1057 0.5520
## week-Lynx_rufus 0.3991 0.3467 -0.2846 0.4030 1.0810
## week-Didelphis_virginiana 0.0553 0.3780 -0.7123 0.0700 0.7695
## week-Sylvilagus_floridanus 0.0615 0.3527 -0.6408 0.0599 0.7578
## week-Sciurus_carolinensis 0.7907 0.3621 0.1147 0.7763 1.5454
## week-Vulpes_vulpes 0.1923 0.5279 -0.9092 0.2139 1.1685
## week-Sus_scrofa 0.6912 0.4491 -0.1506 0.6781 1.6206
## I(week^2)-Odocoileus_virginianus -0.5275 0.0505 -0.6256 -0.5270 -0.4272
## I(week^2)-Canis_latrans -0.2415 0.1060 -0.4566 -0.2410 -0.0355
## I(week^2)-Sciurus_niger -0.2877 0.2375 -0.7954 -0.2771 0.1542
## I(week^2)-Procyon_lotor -0.1313 0.0916 -0.3153 -0.1311 0.0469
## I(week^2)-Dasypus_novemcinctus -0.1793 0.1044 -0.3890 -0.1780 0.0222
## I(week^2)-Lynx_rufus -0.2422 0.1513 -0.5531 -0.2387 0.0414
## I(week^2)-Didelphis_virginiana -0.4163 0.2108 -0.8842 -0.3964 -0.0614
## I(week^2)-Sylvilagus_floridanus -0.1800 0.1609 -0.5116 -0.1784 0.1286
## I(week^2)-Sciurus_carolinensis -0.2800 0.1429 -0.5665 -0.2776 -0.0068
## I(week^2)-Vulpes_vulpes -0.4082 0.2596 -0.9892 -0.3840 0.0219
## I(week^2)-Sus_scrofa -0.2437 0.1766 -0.6131 -0.2374 0.0881
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 4909
## (Intercept)-Canis_latrans 1.0005 3652
## (Intercept)-Sciurus_niger 1.0394 539
## (Intercept)-Procyon_lotor 1.0001 4526
## (Intercept)-Dasypus_novemcinctus 1.0008 4997
## (Intercept)-Lynx_rufus 1.0011 1007
## (Intercept)-Didelphis_virginiana 1.0007 4032
## (Intercept)-Sylvilagus_floridanus 1.0002 1427
## (Intercept)-Sciurus_carolinensis 1.0031 3852
## (Intercept)-Vulpes_vulpes 1.0162 577
## (Intercept)-Sus_scrofa 1.0046 2109
## week-Odocoileus_virginianus 1.0008 4100
## week-Canis_latrans 1.0047 3802
## week-Sciurus_niger 1.0169 1026
## week-Procyon_lotor 1.0012 4363
## week-Dasypus_novemcinctus 1.0003 4220
## week-Lynx_rufus 1.0007 2815
## week-Didelphis_virginiana 1.0023 3153
## week-Sylvilagus_floridanus 1.0001 2849
## week-Sciurus_carolinensis 1.0072 4304
## week-Vulpes_vulpes 1.0055 1585
## week-Sus_scrofa 1.0044 3669
## I(week^2)-Odocoileus_virginianus 1.0004 4928
## I(week^2)-Canis_latrans 1.0022 4220
## I(week^2)-Sciurus_niger 1.0050 1348
## I(week^2)-Procyon_lotor 1.0006 4554
## I(week^2)-Dasypus_novemcinctus 1.0001 4311
## I(week^2)-Lynx_rufus 1.0024 2555
## I(week^2)-Didelphis_virginiana 1.0086 2026
## I(week^2)-Sylvilagus_floridanus 1.0015 2815
## I(week^2)-Sciurus_carolinensis 1.0083 4140
## I(week^2)-Vulpes_vulpes 1.0044 1283
## I(week^2)-Sus_scrofa 1.0035 4335
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full<- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1449 1.0327 -2.0506 -0.1856 1.9998 1.0209 1498
## Cogon_Patch_Size -0.7966 0.6317 -2.1723 -0.7585 0.4116 1.0049 1193
## Veg_shannon_index 0.8095 0.4562 -0.0714 0.7971 1.7428 1.0014 797
## total_shrub_cover -0.1707 0.3787 -0.9139 -0.1740 0.5819 1.0203 1385
## Avg_Cogongrass_Cover 2.0208 0.6400 0.8284 1.9919 3.3692 1.0006 527
## Tree_Density -1.7928 0.6425 -3.0771 -1.7802 -0.5329 1.0384 808
## Avg_Canopy_Cover 1.7450 0.5127 0.7383 1.7268 2.8090 1.0174 880
## avg_veg_height -0.5231 0.4276 -1.3540 -0.5245 0.3324 1.0008 864
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.7917 17.0138 3.7621 13.8863 65.6950 1.0182 422
## Cogon_Patch_Size 2.6959 3.7068 0.1313 1.5697 12.6064 1.0069 811
## Veg_shannon_index 0.8721 1.4261 0.0518 0.4270 4.4219 1.0260 960
## total_shrub_cover 0.4905 0.6559 0.0438 0.2920 2.1473 1.0027 1285
## Avg_Cogongrass_Cover 0.8393 1.6656 0.0498 0.4038 4.1322 1.0775 902
## Tree_Density 2.0466 3.9472 0.0624 0.8348 11.0314 1.0452 535
## Avg_Canopy_Cover 1.4949 1.9176 0.0865 0.8987 6.6065 1.0215 817
## avg_veg_height 0.3640 0.4548 0.0373 0.2167 1.5905 1.0144 2346
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2257 2.0653 0.0528 0.6283 5.347 1.1199 218
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3524 0.4795 -3.2652 -2.3684 -1.3710 1.0015 4976
## week 0.3601 0.2357 -0.1176 0.3654 0.8140 1.0022 2652
## I(week^2) -0.2789 0.1020 -0.4823 -0.2778 -0.0788 1.0023 2647
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5547 1.6729 0.9029 2.1602 6.5917 1.0055 2705
## week 0.4159 0.3482 0.1004 0.3245 1.3009 1.0341 1839
## I(week^2) 0.0715 0.0526 0.0217 0.0578 0.2035 1.0095 2617
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1406 3.4038 3.4180 7.4732
## (Intercept)-Canis_latrans 0.6252 1.0140 -1.1271 0.5444
## (Intercept)-Sciurus_niger 1.6762 2.7967 -2.2576 1.2116
## (Intercept)-Procyon_lotor 0.8552 0.8947 -0.9502 0.8685
## (Intercept)-Dasypus_novemcinctus -1.4550 0.8651 -3.3332 -1.3866
## (Intercept)-Lynx_rufus 2.3461 2.9696 -1.5822 1.7842
## (Intercept)-Didelphis_virginiana -2.9165 1.0595 -5.1019 -2.8805
## (Intercept)-Sylvilagus_floridanus -1.2784 1.1937 -3.6357 -1.2936
## (Intercept)-Sciurus_carolinensis -3.1295 1.1493 -5.5481 -3.0321
## (Intercept)-Vulpes_vulpes -1.6208 2.7119 -5.4128 -2.0932
## (Intercept)-Sus_scrofa -4.5780 1.5871 -8.2156 -4.4334
## Cogon_Patch_Size-Odocoileus_virginianus -0.6654 1.2642 -3.1268 -0.7041
## Cogon_Patch_Size-Canis_latrans 0.6776 1.1655 -0.9446 0.4645
## Cogon_Patch_Size-Sciurus_niger -1.5020 1.7158 -5.5850 -1.2942
## Cogon_Patch_Size-Procyon_lotor -0.9595 0.6945 -2.3603 -0.9508
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7747 0.6099 -2.0484 -0.7494
## Cogon_Patch_Size-Lynx_rufus -0.7814 1.3533 -3.3565 -0.8386
## Cogon_Patch_Size-Didelphis_virginiana 0.7601 0.8239 -0.6120 0.6798
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9894 1.5043 -5.7396 -1.6992
## Cogon_Patch_Size-Sciurus_carolinensis -1.6936 1.2001 -4.6993 -1.4855
## Cogon_Patch_Size-Vulpes_vulpes -1.3403 1.5748 -4.9409 -1.1890
## Cogon_Patch_Size-Sus_scrofa -1.3755 1.3578 -4.8415 -1.1273
## Veg_shannon_index-Odocoileus_virginianus 0.6446 0.9085 -1.3363 0.7031
## Veg_shannon_index-Canis_latrans 1.1931 0.6247 0.1477 1.1396
## Veg_shannon_index-Sciurus_niger 0.9367 1.0213 -1.0821 0.8898
## Veg_shannon_index-Procyon_lotor 1.0788 0.5830 0.0203 1.0523
## Veg_shannon_index-Dasypus_novemcinctus 0.6117 0.4997 -0.3826 0.6144
## Veg_shannon_index-Lynx_rufus 0.7239 0.9769 -1.5409 0.7692
## Veg_shannon_index-Didelphis_virginiana 0.9660 0.6395 -0.1980 0.9300
## Veg_shannon_index-Sylvilagus_floridanus 0.9722 0.6554 -0.2367 0.9394
## Veg_shannon_index-Sciurus_carolinensis 0.1622 0.7101 -1.4279 0.2307
## Veg_shannon_index-Vulpes_vulpes 0.2885 0.8708 -1.6885 0.3747
## Veg_shannon_index-Sus_scrofa 1.5550 1.0136 0.1099 1.3649
## total_shrub_cover-Odocoileus_virginianus -0.0096 0.6864 -1.3205 -0.0406
## total_shrub_cover-Canis_latrans 0.1180 0.5348 -0.8219 0.0935
## total_shrub_cover-Sciurus_niger -0.3704 0.7203 -1.9555 -0.3307
## total_shrub_cover-Procyon_lotor -0.6238 0.5257 -1.8028 -0.5847
## total_shrub_cover-Dasypus_novemcinctus 0.0710 0.4692 -0.8191 0.0594
## total_shrub_cover-Lynx_rufus -0.4562 0.7898 -2.2257 -0.4054
## total_shrub_cover-Didelphis_virginiana -0.3133 0.5625 -1.5059 -0.2931
## total_shrub_cover-Sylvilagus_floridanus -0.1040 0.5851 -1.2821 -0.1008
## total_shrub_cover-Sciurus_carolinensis -0.0168 0.5494 -1.0948 -0.0264
## total_shrub_cover-Vulpes_vulpes -0.3033 0.7190 -1.8306 -0.2784
## total_shrub_cover-Sus_scrofa 0.0731 0.6574 -1.1654 0.0317
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9655 1.0064 -0.0457 1.9389
## Avg_Cogongrass_Cover-Canis_latrans 2.2558 0.8148 0.7840 2.2017
## Avg_Cogongrass_Cover-Sciurus_niger 1.6594 1.1956 -0.9525 1.7382
## Avg_Cogongrass_Cover-Procyon_lotor 2.2326 0.8167 0.8213 2.1452
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4768 0.8679 1.0070 2.4044
## Avg_Cogongrass_Cover-Lynx_rufus 2.3580 0.9309 0.7620 2.2647
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1289 0.7866 0.7085 2.0793
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5640 0.8661 -0.1848 1.5797
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2780 0.8391 0.8144 2.2110
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3207 0.9520 0.6583 2.2427
## Avg_Cogongrass_Cover-Sus_scrofa 1.5682 1.0020 -0.6747 1.6281
## Tree_Density-Odocoileus_virginianus -0.9626 1.1714 -2.8059 -1.1046
## Tree_Density-Canis_latrans -2.3053 1.0308 -4.7846 -2.1466
## Tree_Density-Sciurus_niger -1.8298 1.3353 -4.5621 -1.8339
## Tree_Density-Procyon_lotor -1.4595 0.7263 -2.8587 -1.4731
## Tree_Density-Dasypus_novemcinctus -3.0175 1.4661 -6.6144 -2.6710
## Tree_Density-Lynx_rufus -0.8604 1.3635 -2.9371 -1.0639
## Tree_Density-Didelphis_virginiana -2.1298 1.0099 -4.5945 -1.9784
## Tree_Density-Sylvilagus_floridanus -2.2910 1.1741 -5.1132 -2.1376
## Tree_Density-Sciurus_carolinensis -2.2912 1.1258 -5.0193 -2.1190
## Tree_Density-Vulpes_vulpes -1.7739 1.4111 -4.6599 -1.7763
## Tree_Density-Sus_scrofa -2.1334 1.3124 -5.3231 -1.9628
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3289 1.1125 -1.0253 1.3953
## Avg_Canopy_Cover-Canis_latrans 0.4934 0.6751 -0.8913 0.4952
## Avg_Canopy_Cover-Sciurus_niger 1.9628 1.3869 -0.6240 1.8700
## Avg_Canopy_Cover-Procyon_lotor 1.6975 0.6637 0.5083 1.6573
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8731 0.6109 0.7956 1.8276
## Avg_Canopy_Cover-Lynx_rufus 1.4225 1.1803 -0.8368 1.4181
## Avg_Canopy_Cover-Didelphis_virginiana 2.4641 0.8178 1.1580 2.3756
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8343 1.2393 1.0688 2.6080
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1533 0.7229 0.9410 2.0840
## Avg_Canopy_Cover-Vulpes_vulpes 2.0368 0.9862 0.3899 1.9211
## Avg_Canopy_Cover-Sus_scrofa 1.9780 0.7513 0.6882 1.9195
## avg_veg_height-Odocoileus_virginianus -0.5507 0.6917 -2.0191 -0.5422
## avg_veg_height-Canis_latrans -0.6960 0.5619 -1.8524 -0.6877
## avg_veg_height-Sciurus_niger -0.6421 0.7234 -2.2139 -0.6172
## avg_veg_height-Procyon_lotor -0.3747 0.5316 -1.3768 -0.3878
## avg_veg_height-Dasypus_novemcinctus -0.3200 0.5416 -1.3484 -0.3374
## avg_veg_height-Lynx_rufus -0.5393 0.6925 -1.9009 -0.5393
## avg_veg_height-Didelphis_virginiana -0.6021 0.5991 -1.8331 -0.5908
## avg_veg_height-Sylvilagus_floridanus -0.6983 0.6050 -1.9371 -0.6743
## avg_veg_height-Sciurus_carolinensis -0.2384 0.5938 -1.3356 -0.2770
## avg_veg_height-Vulpes_vulpes -0.5472 0.6526 -1.8449 -0.5552
## avg_veg_height-Sus_scrofa -0.6200 0.6293 -1.9210 -0.6058
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.2971 1.0408 337
## (Intercept)-Canis_latrans 2.9015 1.0111 1486
## (Intercept)-Sciurus_niger 9.0035 1.0510 286
## (Intercept)-Procyon_lotor 2.6184 1.0105 1273
## (Intercept)-Dasypus_novemcinctus 0.0392 1.0014 1236
## (Intercept)-Lynx_rufus 9.4699 1.0392 185
## (Intercept)-Didelphis_virginiana -0.9518 1.0154 973
## (Intercept)-Sylvilagus_floridanus 1.1405 1.0043 1436
## (Intercept)-Sciurus_carolinensis -1.2012 1.0082 861
## (Intercept)-Vulpes_vulpes 5.4658 1.0517 171
## (Intercept)-Sus_scrofa -1.8708 1.0198 838
## Cogon_Patch_Size-Odocoileus_virginianus 2.0976 1.0033 2046
## Cogon_Patch_Size-Canis_latrans 3.5372 1.0108 1752
## Cogon_Patch_Size-Sciurus_niger 1.4611 1.0143 588
## Cogon_Patch_Size-Procyon_lotor 0.3248 1.0047 820
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3924 1.0011 1506
## Cogon_Patch_Size-Lynx_rufus 2.1820 1.0172 1015
## Cogon_Patch_Size-Didelphis_virginiana 2.5639 1.0025 1562
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0555 1.0048 859
## Cogon_Patch_Size-Sciurus_carolinensis 0.0013 1.0048 1145
## Cogon_Patch_Size-Vulpes_vulpes 1.5100 1.0045 821
## Cogon_Patch_Size-Sus_scrofa 0.6105 1.0007 1302
## Veg_shannon_index-Odocoileus_virginianus 2.2680 1.0019 1663
## Veg_shannon_index-Canis_latrans 2.5680 1.0012 1382
## Veg_shannon_index-Sciurus_niger 3.1750 1.0215 954
## Veg_shannon_index-Procyon_lotor 2.3379 1.0007 847
## Veg_shannon_index-Dasypus_novemcinctus 1.5955 1.0017 1600
## Veg_shannon_index-Lynx_rufus 2.5485 1.0086 1285
## Veg_shannon_index-Didelphis_virginiana 2.3384 0.9998 2360
## Veg_shannon_index-Sylvilagus_floridanus 2.3790 1.0004 1317
## Veg_shannon_index-Sciurus_carolinensis 1.3607 1.0042 1356
## Veg_shannon_index-Vulpes_vulpes 1.7850 1.0058 1077
## Veg_shannon_index-Sus_scrofa 4.1711 1.0061 838
## total_shrub_cover-Odocoileus_virginianus 1.4643 1.0051 2423
## total_shrub_cover-Canis_latrans 1.2906 1.0005 2613
## total_shrub_cover-Sciurus_niger 0.9547 1.0179 1373
## total_shrub_cover-Procyon_lotor 0.3040 1.0054 2247
## total_shrub_cover-Dasypus_novemcinctus 1.0467 1.0030 2558
## total_shrub_cover-Lynx_rufus 0.9705 1.0071 1054
## total_shrub_cover-Didelphis_virginiana 0.7198 1.0016 3011
## total_shrub_cover-Sylvilagus_floridanus 1.0783 1.0100 2623
## total_shrub_cover-Sciurus_carolinensis 1.1067 1.0032 2676
## total_shrub_cover-Vulpes_vulpes 1.0186 1.0182 1820
## total_shrub_cover-Sus_scrofa 1.4750 1.0064 2675
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.9924 1.0004 1254
## Avg_Cogongrass_Cover-Canis_latrans 4.0196 1.0008 901
## Avg_Cogongrass_Cover-Sciurus_niger 3.7064 1.0125 754
## Avg_Cogongrass_Cover-Procyon_lotor 3.9830 1.0024 770
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.4336 1.0012 555
## Avg_Cogongrass_Cover-Lynx_rufus 4.4606 1.0040 723
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7965 1.0030 783
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2650 1.0024 1104
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0889 1.0005 688
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4359 1.0001 729
## Avg_Cogongrass_Cover-Sus_scrofa 3.3930 1.0042 925
## Tree_Density-Odocoileus_virginianus 1.7744 1.0352 732
## Tree_Density-Canis_latrans -0.6908 1.0038 844
## Tree_Density-Sciurus_niger 0.9130 1.0056 952
## Tree_Density-Procyon_lotor 0.0064 1.0171 1365
## Tree_Density-Dasypus_novemcinctus -1.1512 1.0010 531
## Tree_Density-Lynx_rufus 2.4618 1.0362 470
## Tree_Density-Didelphis_virginiana -0.5373 1.0040 1229
## Tree_Density-Sylvilagus_floridanus -0.4802 1.0045 1012
## Tree_Density-Sciurus_carolinensis -0.6124 1.0034 1131
## Tree_Density-Vulpes_vulpes 1.1622 1.0120 721
## Tree_Density-Sus_scrofa -0.0506 1.0169 1165
## Avg_Canopy_Cover-Odocoileus_virginianus 3.4146 1.0070 1653
## Avg_Canopy_Cover-Canis_latrans 1.8490 1.0132 1744
## Avg_Canopy_Cover-Sciurus_niger 5.1649 1.0245 754
## Avg_Canopy_Cover-Procyon_lotor 3.1078 1.0060 1681
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2140 1.0026 1456
## Avg_Canopy_Cover-Lynx_rufus 3.9351 1.0279 1046
## Avg_Canopy_Cover-Didelphis_virginiana 4.3306 1.0023 983
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.8600 1.0013 783
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7981 1.0041 1317
## Avg_Canopy_Cover-Vulpes_vulpes 4.4185 1.0001 1042
## Avg_Canopy_Cover-Sus_scrofa 3.6567 1.0084 1968
## avg_veg_height-Odocoileus_virginianus 0.7864 1.0025 1776
## avg_veg_height-Canis_latrans 0.3826 1.0021 1489
## avg_veg_height-Sciurus_niger 0.6877 1.0049 1486
## avg_veg_height-Procyon_lotor 0.7343 1.0035 1531
## avg_veg_height-Dasypus_novemcinctus 0.7842 1.0006 1420
## avg_veg_height-Lynx_rufus 0.8644 1.0009 1475
## avg_veg_height-Didelphis_virginiana 0.5365 1.0010 1549
## avg_veg_height-Sylvilagus_floridanus 0.4187 1.0007 1435
## avg_veg_height-Sciurus_carolinensis 1.0601 1.0032 1490
## avg_veg_height-Vulpes_vulpes 0.7542 1.0005 1339
## avg_veg_height-Sus_scrofa 0.5994 1.0020 1743
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5242 0.0792 0.3702 0.5235 0.6765
## (Intercept)-Canis_latrans -2.4515 0.1981 -2.8541 -2.4447 -2.0813
## (Intercept)-Sciurus_niger -4.5375 0.5070 -5.5490 -4.5311 -3.5387
## (Intercept)-Procyon_lotor -2.1596 0.1535 -2.4771 -2.1552 -1.8657
## (Intercept)-Dasypus_novemcinctus -1.4336 0.1576 -1.7544 -1.4298 -1.1323
## (Intercept)-Lynx_rufus -3.6645 0.3544 -4.3206 -3.6710 -2.9503
## (Intercept)-Didelphis_virginiana -2.0903 0.2645 -2.6342 -2.0840 -1.5920
## (Intercept)-Sylvilagus_floridanus -3.0642 0.3083 -3.7047 -3.0535 -2.4833
## (Intercept)-Sciurus_carolinensis -2.2473 0.2876 -2.8501 -2.2336 -1.7182
## (Intercept)-Vulpes_vulpes -4.0762 0.7210 -5.5055 -4.0422 -2.7668
## (Intercept)-Sus_scrofa -2.7512 0.4730 -3.7573 -2.7292 -1.9158
## week-Odocoileus_virginianus 1.2754 0.1231 1.0355 1.2751 1.5201
## week-Canis_latrans 0.5801 0.2586 0.0716 0.5746 1.0867
## week-Sciurus_niger -0.3557 0.5377 -1.5594 -0.3139 0.5861
## week-Procyon_lotor 0.2033 0.2080 -0.2031 0.2016 0.6132
## week-Dasypus_novemcinctus 0.1103 0.2238 -0.3292 0.1101 0.5487
## week-Lynx_rufus 0.3663 0.3522 -0.3233 0.3684 1.0443
## week-Didelphis_virginiana 0.0635 0.3668 -0.6752 0.0746 0.7748
## week-Sylvilagus_floridanus 0.0736 0.3419 -0.6271 0.0802 0.7294
## week-Sciurus_carolinensis 0.7869 0.3708 0.0863 0.7827 1.5424
## week-Vulpes_vulpes 0.2113 0.5109 -0.8428 0.2371 1.1746
## week-Sus_scrofa 0.6687 0.4458 -0.1798 0.6593 1.5873
## I(week^2)-Odocoileus_virginianus -0.5262 0.0511 -0.6262 -0.5254 -0.4278
## I(week^2)-Canis_latrans -0.2409 0.1082 -0.4538 -0.2423 -0.0307
## I(week^2)-Sciurus_niger -0.2798 0.2278 -0.7598 -0.2687 0.1380
## I(week^2)-Procyon_lotor -0.1316 0.0900 -0.3083 -0.1310 0.0410
## I(week^2)-Dasypus_novemcinctus -0.1803 0.1033 -0.3845 -0.1772 0.0188
## I(week^2)-Lynx_rufus -0.2427 0.1534 -0.5535 -0.2405 0.0532
## I(week^2)-Didelphis_virginiana -0.4029 0.2086 -0.8614 -0.3818 -0.0568
## I(week^2)-Sylvilagus_floridanus -0.1814 0.1575 -0.5017 -0.1785 0.1175
## I(week^2)-Sciurus_carolinensis -0.2786 0.1438 -0.5701 -0.2749 -0.0013
## I(week^2)-Vulpes_vulpes -0.3794 0.2406 -0.9053 -0.3620 0.0410
## I(week^2)-Sus_scrofa -0.2345 0.1800 -0.5966 -0.2320 0.1095
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 0.9999 2668
## (Intercept)-Sciurus_niger 1.0195 509
## (Intercept)-Procyon_lotor 1.0081 4067
## (Intercept)-Dasypus_novemcinctus 1.0022 4858
## (Intercept)-Lynx_rufus 1.0524 588
## (Intercept)-Didelphis_virginiana 1.0000 4203
## (Intercept)-Sylvilagus_floridanus 1.0050 1973
## (Intercept)-Sciurus_carolinensis 1.0058 3612
## (Intercept)-Vulpes_vulpes 1.0179 397
## (Intercept)-Sus_scrofa 1.0010 2444
## week-Odocoileus_virginianus 1.0008 4757
## week-Canis_latrans 1.0005 3992
## week-Sciurus_niger 1.0304 738
## week-Procyon_lotor 1.0005 4517
## week-Dasypus_novemcinctus 1.0000 4142
## week-Lynx_rufus 1.0040 2164
## week-Didelphis_virginiana 1.0028 3107
## week-Sylvilagus_floridanus 1.0004 2756
## week-Sciurus_carolinensis 1.0008 3646
## week-Vulpes_vulpes 1.0098 1879
## week-Sus_scrofa 1.0011 4439
## I(week^2)-Odocoileus_virginianus 1.0005 4965
## I(week^2)-Canis_latrans 1.0002 4091
## I(week^2)-Sciurus_niger 1.0085 809
## I(week^2)-Procyon_lotor 1.0004 4209
## I(week^2)-Dasypus_novemcinctus 1.0005 3951
## I(week^2)-Lynx_rufus 1.0021 2036
## I(week^2)-Didelphis_virginiana 1.0019 1748
## I(week^2)-Sylvilagus_floridanus 1.0005 2791
## I(week^2)-Sciurus_carolinensis 1.0005 3976
## I(week^2)-Vulpes_vulpes 1.0024 1121
## I(week^2)-Sus_scrofa 1.0019 4406
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover<- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7752
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2300 0.6040 -1.4070 -0.2390 1.0293 1.0027 2847
## Avg_Cogongrass_Cover 0.1437 0.3057 -0.4711 0.1466 0.7497 1.0030 1405
## total_shrub_cover -0.2687 0.2710 -0.8049 -0.2635 0.2546 1.0005 2459
## avg_veg_height 0.0235 0.2948 -0.5463 0.0270 0.6127 1.0033 1310
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8935 3.2407 0.7930 3.0650 12.0363 1.0258 1579
## Avg_Cogongrass_Cover 0.3043 0.3864 0.0382 0.1901 1.2784 1.0097 2271
## total_shrub_cover 0.3566 0.4045 0.0436 0.2304 1.4051 1.0118 1922
## avg_veg_height 0.2072 0.2210 0.0328 0.1422 0.7458 1.0031 3366
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9636 0.9521 0.0753 0.6907 3.4616 1.0504 497
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3081 0.4462 -3.1595 -2.3090 -1.3961 1.0001 3945
## week 0.3627 0.2382 -0.1146 0.3695 0.8231 1.0035 3884
## I(week^2) -0.2802 0.1007 -0.4875 -0.2798 -0.0857 1.0016 3166
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2212 1.3796 0.7819 1.8659 5.7905 1.0053 2774
## week 0.4242 0.3165 0.1027 0.3378 1.3040 1.0015 2644
## I(week^2) 0.0705 0.0486 0.0222 0.0575 0.1920 1.0002 3221
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6541 1.4411 1.2735 3.4988
## (Intercept)-Canis_latrans 0.3241 0.6868 -0.9642 0.2940
## (Intercept)-Sciurus_niger -0.4968 1.2115 -2.4808 -0.6422
## (Intercept)-Procyon_lotor 0.6230 0.7152 -0.8598 0.6308
## (Intercept)-Dasypus_novemcinctus -0.7242 0.6126 -2.0033 -0.7061
## (Intercept)-Lynx_rufus -0.0598 0.9356 -1.7035 -0.1183
## (Intercept)-Didelphis_virginiana -1.4306 0.6975 -2.8369 -1.4261
## (Intercept)-Sylvilagus_floridanus -0.2290 0.8342 -1.7294 -0.2823
## (Intercept)-Sciurus_carolinensis -1.5213 0.7192 -3.0066 -1.4945
## (Intercept)-Vulpes_vulpes -0.9537 1.4360 -3.2731 -1.1149
## (Intercept)-Sus_scrofa -1.9960 0.8735 -3.7670 -1.9771
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1397 0.5349 -0.9418 0.1380
## Avg_Cogongrass_Cover-Canis_latrans 0.3820 0.4349 -0.4195 0.3581
## Avg_Cogongrass_Cover-Sciurus_niger -0.1774 0.6186 -1.6552 -0.1147
## Avg_Cogongrass_Cover-Procyon_lotor 0.1037 0.4244 -0.7427 0.1070
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2542 0.3791 -0.4761 0.2463
## Avg_Cogongrass_Cover-Lynx_rufus 0.4181 0.4803 -0.4338 0.3833
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3120 0.4156 -0.4681 0.2977
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1910 0.4935 -1.2850 -0.1526
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2471 0.4065 -0.5516 0.2422
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2451 0.5100 -0.7685 0.2295
## Avg_Cogongrass_Cover-Sus_scrofa -0.1610 0.5790 -1.5413 -0.0967
## total_shrub_cover-Odocoileus_virginianus -0.1899 0.5148 -1.1904 -0.1962
## total_shrub_cover-Canis_latrans 0.0826 0.4052 -0.6604 0.0535
## total_shrub_cover-Sciurus_niger -0.5133 0.5469 -1.7335 -0.4680
## total_shrub_cover-Procyon_lotor -0.7398 0.4759 -1.8012 -0.6837
## total_shrub_cover-Dasypus_novemcinctus -0.0545 0.3478 -0.7254 -0.0645
## total_shrub_cover-Lynx_rufus -0.6485 0.5578 -1.9879 -0.5786
## total_shrub_cover-Didelphis_virginiana -0.2189 0.3953 -1.0120 -0.2203
## total_shrub_cover-Sylvilagus_floridanus -0.3213 0.4722 -1.3322 -0.2996
## total_shrub_cover-Sciurus_carolinensis -0.1106 0.3958 -0.8663 -0.1214
## total_shrub_cover-Vulpes_vulpes -0.3313 0.5643 -1.5125 -0.3057
## total_shrub_cover-Sus_scrofa 0.0444 0.4816 -0.8031 0.0053
## avg_veg_height-Odocoileus_virginianus 0.0103 0.4797 -0.9549 0.0070
## avg_veg_height-Canis_latrans -0.0564 0.3990 -0.8625 -0.0551
## avg_veg_height-Sciurus_niger -0.1274 0.4927 -1.1875 -0.1063
## avg_veg_height-Procyon_lotor 0.1035 0.4005 -0.6765 0.1049
## avg_veg_height-Dasypus_novemcinctus 0.1841 0.3762 -0.5508 0.1803
## avg_veg_height-Lynx_rufus 0.0395 0.4718 -0.8914 0.0333
## avg_veg_height-Didelphis_virginiana -0.0042 0.4021 -0.8183 -0.0011
## avg_veg_height-Sylvilagus_floridanus -0.1032 0.4253 -0.9821 -0.0896
## avg_veg_height-Sciurus_carolinensis 0.2692 0.4153 -0.5143 0.2557
## avg_veg_height-Vulpes_vulpes -0.0292 0.4576 -0.9388 -0.0183
## avg_veg_height-Sus_scrofa -0.0128 0.4397 -0.9024 -0.0071
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9269 1.0244 1245
## (Intercept)-Canis_latrans 1.7615 1.0012 2509
## (Intercept)-Sciurus_niger 2.3145 1.0143 579
## (Intercept)-Procyon_lotor 2.0595 1.0077 2029
## (Intercept)-Dasypus_novemcinctus 0.4616 1.0041 3322
## (Intercept)-Lynx_rufus 1.9736 1.0047 1402
## (Intercept)-Didelphis_virginiana -0.0772 1.0003 2898
## (Intercept)-Sylvilagus_floridanus 1.6174 1.0000 1583
## (Intercept)-Sciurus_carolinensis -0.1523 1.0005 2756
## (Intercept)-Vulpes_vulpes 2.5178 1.0333 448
## (Intercept)-Sus_scrofa -0.3307 1.0009 2171
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2067 1.0002 2423
## Avg_Cogongrass_Cover-Canis_latrans 1.3241 1.0010 2482
## Avg_Cogongrass_Cover-Sciurus_niger 0.8824 1.0045 1992
## Avg_Cogongrass_Cover-Procyon_lotor 0.9412 1.0006 2382
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0350 1.0009 2534
## Avg_Cogongrass_Cover-Lynx_rufus 1.4632 1.0070 2016
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1726 1.0002 2236
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6801 1.0030 2146
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0679 1.0006 2292
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3142 1.0056 2258
## Avg_Cogongrass_Cover-Sus_scrofa 0.7785 1.0019 2036
## total_shrub_cover-Odocoileus_virginianus 0.8694 1.0010 4132
## total_shrub_cover-Canis_latrans 0.9715 1.0016 3699
## total_shrub_cover-Sciurus_niger 0.4414 1.0016 2567
## total_shrub_cover-Procyon_lotor 0.0477 1.0039 2844
## total_shrub_cover-Dasypus_novemcinctus 0.6470 1.0004 4522
## total_shrub_cover-Lynx_rufus 0.2741 1.0008 2193
## total_shrub_cover-Didelphis_virginiana 0.5329 1.0003 4127
## total_shrub_cover-Sylvilagus_floridanus 0.5568 1.0013 2542
## total_shrub_cover-Sciurus_carolinensis 0.7036 1.0005 4341
## total_shrub_cover-Vulpes_vulpes 0.7117 1.0033 2273
## total_shrub_cover-Sus_scrofa 1.1077 1.0013 3455
## avg_veg_height-Odocoileus_virginianus 0.9786 1.0013 2323
## avg_veg_height-Canis_latrans 0.7273 1.0024 2714
## avg_veg_height-Sciurus_niger 0.7980 1.0009 2207
## avg_veg_height-Procyon_lotor 0.9136 1.0000 2455
## avg_veg_height-Dasypus_novemcinctus 0.9503 1.0008 2167
## avg_veg_height-Lynx_rufus 1.0247 1.0015 1894
## avg_veg_height-Didelphis_virginiana 0.7780 1.0005 2123
## avg_veg_height-Sylvilagus_floridanus 0.6849 1.0011 2528
## avg_veg_height-Sciurus_carolinensis 1.1240 1.0013 2434
## avg_veg_height-Vulpes_vulpes 0.8511 1.0039 2154
## avg_veg_height-Sus_scrofa 0.8416 1.0025 2472
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5239 0.0788 0.3728 0.5220 0.6792
## (Intercept)-Canis_latrans -2.4528 0.1950 -2.8603 -2.4445 -2.0911
## (Intercept)-Sciurus_niger -3.9724 0.6240 -5.2156 -3.9620 -2.8114
## (Intercept)-Procyon_lotor -2.1632 0.1515 -2.4668 -2.1598 -1.8783
## (Intercept)-Dasypus_novemcinctus -1.4366 0.1563 -1.7480 -1.4323 -1.1430
## (Intercept)-Lynx_rufus -3.3867 0.3345 -4.0747 -3.3809 -2.7569
## (Intercept)-Didelphis_virginiana -2.1212 0.2812 -2.6938 -2.1096 -1.6113
## (Intercept)-Sylvilagus_floridanus -3.1395 0.3363 -3.8507 -3.1303 -2.5362
## (Intercept)-Sciurus_carolinensis -2.2684 0.2910 -2.8716 -2.2545 -1.7290
## (Intercept)-Vulpes_vulpes -3.9920 0.7716 -5.5010 -3.9611 -2.5787
## (Intercept)-Sus_scrofa -2.8102 0.5173 -3.9491 -2.7791 -1.9064
## week-Odocoileus_virginianus 1.2789 0.1227 1.0438 1.2777 1.5262
## week-Canis_latrans 0.5875 0.2598 0.0733 0.5870 1.1023
## week-Sciurus_niger -0.3859 0.5345 -1.5372 -0.3419 0.5359
## week-Procyon_lotor 0.2082 0.2084 -0.2054 0.2111 0.6184
## week-Dasypus_novemcinctus 0.1064 0.2260 -0.3303 0.1070 0.5579
## week-Lynx_rufus 0.3855 0.3461 -0.2853 0.3849 1.0707
## week-Didelphis_virginiana 0.0632 0.3740 -0.7085 0.0763 0.7799
## week-Sylvilagus_floridanus 0.0654 0.3400 -0.6305 0.0714 0.7133
## week-Sciurus_carolinensis 0.7949 0.3568 0.1185 0.7823 1.5077
## week-Vulpes_vulpes 0.2055 0.5086 -0.8665 0.2196 1.1606
## week-Sus_scrofa 0.6793 0.4471 -0.1542 0.6661 1.6003
## I(week^2)-Odocoileus_virginianus -0.5268 0.0505 -0.6263 -0.5261 -0.4289
## I(week^2)-Canis_latrans -0.2449 0.1070 -0.4563 -0.2428 -0.0354
## I(week^2)-Sciurus_niger -0.2730 0.2270 -0.7300 -0.2657 0.1430
## I(week^2)-Procyon_lotor -0.1340 0.0902 -0.3118 -0.1344 0.0430
## I(week^2)-Dasypus_novemcinctus -0.1798 0.1048 -0.3936 -0.1769 0.0189
## I(week^2)-Lynx_rufus -0.2366 0.1505 -0.5327 -0.2351 0.0486
## I(week^2)-Didelphis_virginiana -0.4058 0.2049 -0.8571 -0.3924 -0.0450
## I(week^2)-Sylvilagus_floridanus -0.1819 0.1529 -0.4871 -0.1806 0.1121
## I(week^2)-Sciurus_carolinensis -0.2821 0.1433 -0.5702 -0.2809 -0.0033
## I(week^2)-Vulpes_vulpes -0.4014 0.2491 -0.9489 -0.3782 0.0238
## I(week^2)-Sus_scrofa -0.2387 0.1773 -0.5924 -0.2354 0.1026
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0031 5250
## (Intercept)-Canis_latrans 1.0008 3080
## (Intercept)-Sciurus_niger 1.0135 492
## (Intercept)-Procyon_lotor 1.0002 4745
## (Intercept)-Dasypus_novemcinctus 1.0026 5382
## (Intercept)-Lynx_rufus 1.0028 1177
## (Intercept)-Didelphis_virginiana 1.0010 3772
## (Intercept)-Sylvilagus_floridanus 1.0009 1400
## (Intercept)-Sciurus_carolinensis 1.0000 3711
## (Intercept)-Vulpes_vulpes 1.0379 472
## (Intercept)-Sus_scrofa 1.0050 2016
## week-Odocoileus_virginianus 1.0020 5016
## week-Canis_latrans 0.9999 3914
## week-Sciurus_niger 1.0069 1248
## week-Procyon_lotor 1.0005 4444
## week-Dasypus_novemcinctus 1.0004 4782
## week-Lynx_rufus 1.0024 2925
## week-Didelphis_virginiana 1.0017 2959
## week-Sylvilagus_floridanus 1.0003 2921
## week-Sciurus_carolinensis 1.0016 3820
## week-Vulpes_vulpes 1.0003 1767
## week-Sus_scrofa 1.0008 3912
## I(week^2)-Odocoileus_virginianus 1.0011 4978
## I(week^2)-Canis_latrans 1.0008 4179
## I(week^2)-Sciurus_niger 1.0063 1457
## I(week^2)-Procyon_lotor 1.0006 4429
## I(week^2)-Dasypus_novemcinctus 1.0012 4287
## I(week^2)-Lynx_rufus 1.0054 2704
## I(week^2)-Didelphis_virginiana 1.0012 1994
## I(week^2)-Sylvilagus_floridanus 1.0025 2638
## I(week^2)-Sciurus_carolinensis 1.0006 4153
## I(week^2)-Vulpes_vulpes 1.0051 1272
## I(week^2)-Sus_scrofa 1.0000 3746
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy<- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8065
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2098 0.7566 -1.5886 -0.2436 1.3997 1.0091 1349
## Tree_Density -0.7511 0.3965 -1.6305 -0.7170 -0.0501 1.0010 1198
## Avg_Canopy_Cover 1.0032 0.3345 0.3924 0.9840 1.6997 1.0029 1558
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5694 5.7516 1.4540 4.9318 20.9563 1.0069 757
## Tree_Density 0.6899 1.2139 0.0406 0.3150 3.7034 1.0394 1043
## Avg_Canopy_Cover 0.5436 0.6102 0.0584 0.3566 2.1256 1.0122 1881
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.3845 0.4185 0.0435 0.2416 1.5865 1.0044 549
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3133 0.4620 -3.1991 -2.3224 -1.3711 1.0019 3937
## week 0.3586 0.2346 -0.1417 0.3677 0.7956 1.0056 2846
## I(week^2) -0.2825 0.0978 -0.4784 -0.2823 -0.0893 1.0044 2783
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3128 1.4511 0.8087 1.9357 6.0777 1.0021 2080
## week 0.4179 0.3147 0.1027 0.3362 1.2197 1.0070 1710
## I(week^2) 0.0697 0.0471 0.0214 0.0568 0.1965 1.0059 3291
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6070 1.7554 2.0540 4.3093 9.0889
## (Intercept)-Canis_latrans 0.3385 0.6747 -0.8111 0.2973 1.8089
## (Intercept)-Sciurus_niger 0.0408 1.5218 -2.2600 -0.1739 3.7422
## (Intercept)-Procyon_lotor 0.7499 0.5995 -0.4746 0.7415 1.9713
## (Intercept)-Dasypus_novemcinctus -1.0138 0.5951 -2.2700 -0.9980 0.0911
## (Intercept)-Lynx_rufus 1.2532 1.9487 -1.2584 0.8152 6.3072
## (Intercept)-Didelphis_virginiana -1.9235 0.6880 -3.3763 -1.8803 -0.6719
## (Intercept)-Sylvilagus_floridanus -0.6691 0.7104 -2.0383 -0.6858 0.7945
## (Intercept)-Sciurus_carolinensis -1.9735 0.6874 -3.3966 -1.9283 -0.7638
## (Intercept)-Vulpes_vulpes -1.3041 1.6280 -3.7063 -1.5593 2.6314
## (Intercept)-Sus_scrofa -2.7100 0.8952 -4.6807 -2.6354 -1.1353
## Tree_Density-Odocoileus_virginianus -0.4080 0.6228 -1.4967 -0.4604 1.0207
## Tree_Density-Canis_latrans -0.8397 0.5631 -2.1325 -0.7846 0.0211
## Tree_Density-Sciurus_niger -0.7865 0.7241 -2.4154 -0.7294 0.5195
## Tree_Density-Procyon_lotor -0.4933 0.3846 -1.2646 -0.4918 0.2404
## Tree_Density-Dasypus_novemcinctus -1.2853 0.8516 -3.4617 -1.0966 -0.1672
## Tree_Density-Lynx_rufus -0.0591 0.7978 -1.3299 -0.1629 1.8266
## Tree_Density-Didelphis_virginiana -0.9880 0.7279 -2.7947 -0.8622 0.1034
## Tree_Density-Sylvilagus_floridanus -1.0136 0.7184 -2.7950 -0.9024 0.0720
## Tree_Density-Sciurus_carolinensis -0.9262 0.6977 -2.6341 -0.8210 0.1607
## Tree_Density-Vulpes_vulpes -0.6774 0.7965 -2.2883 -0.6416 0.7767
## Tree_Density-Sus_scrofa -0.9588 0.7975 -2.9157 -0.8319 0.2140
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7983 0.6416 -0.5464 0.8120 2.0274
## Avg_Canopy_Cover-Canis_latrans 0.1645 0.4675 -0.7537 0.1565 1.0763
## Avg_Canopy_Cover-Sciurus_niger 1.0151 0.7770 -0.3480 0.9580 2.7935
## Avg_Canopy_Cover-Procyon_lotor 1.0181 0.4458 0.2098 0.9877 1.9885
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9994 0.4047 0.2563 0.9813 1.8439
## Avg_Canopy_Cover-Lynx_rufus 0.9082 0.6824 -0.3430 0.8700 2.4149
## Avg_Canopy_Cover-Didelphis_virginiana 1.2405 0.4806 0.4148 1.1948 2.3220
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6127 0.7252 0.5341 1.5018 3.3282
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2293 0.4825 0.4028 1.1844 2.3060
## Avg_Canopy_Cover-Vulpes_vulpes 1.0435 0.5782 -0.0196 1.0074 2.3200
## Avg_Canopy_Cover-Sus_scrofa 1.2308 0.5262 0.2941 1.1822 2.4072
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 540
## (Intercept)-Canis_latrans 1.0179 1586
## (Intercept)-Sciurus_niger 1.0069 429
## (Intercept)-Procyon_lotor 1.0004 2487
## (Intercept)-Dasypus_novemcinctus 1.0078 2807
## (Intercept)-Lynx_rufus 1.0060 300
## (Intercept)-Didelphis_virginiana 1.0012 3101
## (Intercept)-Sylvilagus_floridanus 1.0005 2247
## (Intercept)-Sciurus_carolinensis 1.0075 2770
## (Intercept)-Vulpes_vulpes 1.0214 268
## (Intercept)-Sus_scrofa 1.0048 2298
## Tree_Density-Odocoileus_virginianus 1.0025 1882
## Tree_Density-Canis_latrans 1.0084 2083
## Tree_Density-Sciurus_niger 1.0055 1522
## Tree_Density-Procyon_lotor 1.0073 3611
## Tree_Density-Dasypus_novemcinctus 1.0130 1185
## Tree_Density-Lynx_rufus 1.0085 872
## Tree_Density-Didelphis_virginiana 1.0024 1581
## Tree_Density-Sylvilagus_floridanus 1.0043 1566
## Tree_Density-Sciurus_carolinensis 1.0027 1834
## Tree_Density-Vulpes_vulpes 1.0032 1608
## Tree_Density-Sus_scrofa 1.0055 1401
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0054 2688
## Avg_Canopy_Cover-Canis_latrans 1.0014 2193
## Avg_Canopy_Cover-Sciurus_niger 1.0028 1266
## Avg_Canopy_Cover-Procyon_lotor 1.0004 3389
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0004 3275
## Avg_Canopy_Cover-Lynx_rufus 1.0015 1566
## Avg_Canopy_Cover-Didelphis_virginiana 1.0014 2799
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0030 1996
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0028 2901
## Avg_Canopy_Cover-Vulpes_vulpes 0.9999 2918
## Avg_Canopy_Cover-Sus_scrofa 1.0034 2891
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5246 0.0800 0.3702 0.5243 0.6853
## (Intercept)-Canis_latrans -2.4584 0.2010 -2.8805 -2.4515 -2.0845
## (Intercept)-Sciurus_niger -4.1771 0.5925 -5.3229 -4.1874 -3.0007
## (Intercept)-Procyon_lotor -2.1534 0.1503 -2.4548 -2.1502 -1.8708
## (Intercept)-Dasypus_novemcinctus -1.4387 0.1548 -1.7495 -1.4338 -1.1460
## (Intercept)-Lynx_rufus -3.5960 0.3758 -4.3213 -3.5961 -2.8790
## (Intercept)-Didelphis_virginiana -2.0999 0.2666 -2.6587 -2.0845 -1.6160
## (Intercept)-Sylvilagus_floridanus -3.0351 0.3074 -3.6679 -3.0172 -2.4705
## (Intercept)-Sciurus_carolinensis -2.2510 0.2849 -2.8473 -2.2377 -1.7289
## (Intercept)-Vulpes_vulpes -3.9377 0.7879 -5.5777 -3.8909 -2.5324
## (Intercept)-Sus_scrofa -2.7421 0.4738 -3.7619 -2.7128 -1.8931
## week-Odocoileus_virginianus 1.2775 0.1206 1.0426 1.2773 1.5175
## week-Canis_latrans 0.5837 0.2596 0.0922 0.5798 1.0950
## week-Sciurus_niger -0.3667 0.5464 -1.5824 -0.3214 0.5830
## week-Procyon_lotor 0.2069 0.2136 -0.2030 0.2042 0.6345
## week-Dasypus_novemcinctus 0.1084 0.2280 -0.3484 0.1088 0.5472
## week-Lynx_rufus 0.3838 0.3485 -0.2804 0.3799 1.0673
## week-Didelphis_virginiana 0.0728 0.3676 -0.6813 0.0810 0.7877
## week-Sylvilagus_floridanus 0.0644 0.3521 -0.6504 0.0751 0.7302
## week-Sciurus_carolinensis 0.7924 0.3648 0.1161 0.7808 1.5458
## week-Vulpes_vulpes 0.2209 0.5130 -0.8597 0.2428 1.1739
## week-Sus_scrofa 0.6737 0.4360 -0.1622 0.6600 1.5525
## I(week^2)-Odocoileus_virginianus -0.5264 0.0499 -0.6269 -0.5255 -0.4294
## I(week^2)-Canis_latrans -0.2437 0.1064 -0.4589 -0.2425 -0.0433
## I(week^2)-Sciurus_niger -0.2809 0.2245 -0.7543 -0.2748 0.1357
## I(week^2)-Procyon_lotor -0.1334 0.0904 -0.3127 -0.1326 0.0438
## I(week^2)-Dasypus_novemcinctus -0.1792 0.1053 -0.3942 -0.1790 0.0265
## I(week^2)-Lynx_rufus -0.2400 0.1495 -0.5477 -0.2376 0.0456
## I(week^2)-Didelphis_virginiana -0.3980 0.2035 -0.8555 -0.3793 -0.0539
## I(week^2)-Sylvilagus_floridanus -0.1806 0.1577 -0.5108 -0.1756 0.1238
## I(week^2)-Sciurus_carolinensis -0.2809 0.1443 -0.5706 -0.2766 -0.0111
## I(week^2)-Vulpes_vulpes -0.3980 0.2432 -0.9377 -0.3751 0.0168
## I(week^2)-Sus_scrofa -0.2386 0.1742 -0.5893 -0.2361 0.1000
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0092 2676
## (Intercept)-Sciurus_niger 1.0059 374
## (Intercept)-Procyon_lotor 1.0012 4240
## (Intercept)-Dasypus_novemcinctus 1.0016 5002
## (Intercept)-Lynx_rufus 1.0098 624
## (Intercept)-Didelphis_virginiana 1.0040 4153
## (Intercept)-Sylvilagus_floridanus 1.0057 1681
## (Intercept)-Sciurus_carolinensis 1.0009 3503
## (Intercept)-Vulpes_vulpes 1.0018 397
## (Intercept)-Sus_scrofa 1.0024 2431
## week-Odocoileus_virginianus 1.0025 5518
## week-Canis_latrans 1.0030 3724
## week-Sciurus_niger 1.0073 918
## week-Procyon_lotor 1.0016 4232
## week-Dasypus_novemcinctus 1.0001 4074
## week-Lynx_rufus 1.0029 2406
## week-Didelphis_virginiana 1.0026 3268
## week-Sylvilagus_floridanus 1.0001 2823
## week-Sciurus_carolinensis 1.0017 3846
## week-Vulpes_vulpes 1.0033 1631
## week-Sus_scrofa 1.0006 3347
## I(week^2)-Odocoileus_virginianus 1.0027 4965
## I(week^2)-Canis_latrans 1.0021 3869
## I(week^2)-Sciurus_niger 1.0110 1201
## I(week^2)-Procyon_lotor 1.0026 4163
## I(week^2)-Dasypus_novemcinctus 1.0008 4505
## I(week^2)-Lynx_rufus 1.0020 2321
## I(week^2)-Didelphis_virginiana 1.0018 2068
## I(week^2)-Sylvilagus_floridanus 1.0018 2699
## I(week^2)-Sciurus_carolinensis 1.0028 3747
## I(week^2)-Vulpes_vulpes 1.0136 1400
## I(week^2)-Sus_scrofa 1.0007 4614
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move<- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7825
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2785 0.6396 -1.4990 -0.2909 1.0401 1.0039 2196
## Cogon_Patch_Size -0.2843 0.4096 -1.1841 -0.2631 0.4708 1.0033 1768
## Avg_Cogongrass_Cover 0.2603 0.2790 -0.2972 0.2570 0.8234 1.0183 1305
## total_shrub_cover -0.2253 0.2757 -0.7743 -0.2240 0.3117 1.0016 2252
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2471 3.3697 0.8349 3.3429 12.9948 1.0308 1316
## Cogon_Patch_Size 0.9826 1.4772 0.0691 0.5645 4.3019 1.0052 1476
## Avg_Cogongrass_Cover 0.2701 0.3085 0.0360 0.1736 1.0817 1.0014 2307
## total_shrub_cover 0.3181 0.3951 0.0370 0.2039 1.3260 1.0414 2195
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1278 1.0404 0.1058 0.8487 3.9428 1.0483 560
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3155 0.4558 -3.1664 -2.3250 -1.3879 1.0003 4686
## week 0.3504 0.2347 -0.1385 0.3583 0.7737 1.0012 2825
## I(week^2) -0.2801 0.1013 -0.4866 -0.2777 -0.0918 1.0056 2788
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2634 1.4087 0.8010 1.9028 5.9626 1.0070 1735
## week 0.4161 0.3263 0.1060 0.3227 1.2369 1.0077 2600
## I(week^2) 0.0725 0.0550 0.0216 0.0576 0.2189 1.0103 1885
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7618 1.4936 1.1781 3.6265
## (Intercept)-Canis_latrans 0.4148 0.7341 -0.9746 0.3925
## (Intercept)-Sciurus_niger -0.5228 1.3249 -2.6771 -0.6756
## (Intercept)-Procyon_lotor 0.5960 0.7068 -0.8289 0.6070
## (Intercept)-Dasypus_novemcinctus -0.7516 0.6419 -2.0956 -0.7405
## (Intercept)-Lynx_rufus -0.1129 0.9770 -1.9026 -0.1554
## (Intercept)-Didelphis_virginiana -1.4633 0.7313 -2.9495 -1.4515
## (Intercept)-Sylvilagus_floridanus -0.3681 0.9492 -2.0243 -0.4304
## (Intercept)-Sciurus_carolinensis -1.6694 0.7669 -3.3001 -1.6328
## (Intercept)-Vulpes_vulpes -1.0694 1.4253 -3.4844 -1.2034
## (Intercept)-Sus_scrofa -2.1510 0.9290 -4.0581 -2.1335
## Cogon_Patch_Size-Odocoileus_virginianus -0.0750 0.7189 -1.3332 -0.1240
## Cogon_Patch_Size-Canis_latrans 0.6829 0.7332 -0.3652 0.5555
## Cogon_Patch_Size-Sciurus_niger -0.6875 0.9287 -3.0082 -0.5644
## Cogon_Patch_Size-Procyon_lotor -0.2777 0.4617 -1.1737 -0.2774
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1689 0.4191 -1.0383 -0.1610
## Cogon_Patch_Size-Lynx_rufus -0.2841 0.7772 -1.6825 -0.3303
## Cogon_Patch_Size-Didelphis_virginiana 0.5777 0.5007 -0.3044 0.5398
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9425 0.8856 -3.0978 -0.7806
## Cogon_Patch_Size-Sciurus_carolinensis -0.7907 0.7458 -2.5618 -0.6702
## Cogon_Patch_Size-Vulpes_vulpes -0.6300 0.9712 -2.8713 -0.4988
## Cogon_Patch_Size-Sus_scrofa -0.5476 0.8260 -2.5765 -0.4191
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2609 0.5105 -0.7417 0.2513
## Avg_Cogongrass_Cover-Canis_latrans 0.3187 0.3964 -0.4369 0.3059
## Avg_Cogongrass_Cover-Sciurus_niger -0.0306 0.5781 -1.3362 0.0185
## Avg_Cogongrass_Cover-Procyon_lotor 0.2808 0.4010 -0.4690 0.2643
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4130 0.3528 -0.2474 0.4059
## Avg_Cogongrass_Cover-Lynx_rufus 0.5355 0.4585 -0.2739 0.4959
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2612 0.3928 -0.5461 0.2677
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0178 0.4557 -1.0076 0.0016
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4835 0.3933 -0.2481 0.4584
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3489 0.4575 -0.5171 0.3401
## Avg_Cogongrass_Cover-Sus_scrofa -0.0058 0.5350 -1.2044 0.0427
## total_shrub_cover-Odocoileus_virginianus -0.1381 0.5082 -1.1336 -0.1449
## total_shrub_cover-Canis_latrans 0.0615 0.4120 -0.6714 0.0383
## total_shrub_cover-Sciurus_niger -0.4180 0.5214 -1.5515 -0.3780
## total_shrub_cover-Procyon_lotor -0.6517 0.4511 -1.6749 -0.6140
## total_shrub_cover-Dasypus_novemcinctus -0.0441 0.3465 -0.7175 -0.0442
## total_shrub_cover-Lynx_rufus -0.5463 0.5430 -1.8021 -0.4886
## total_shrub_cover-Didelphis_virginiana -0.2614 0.3912 -1.0542 -0.2621
## total_shrub_cover-Sylvilagus_floridanus -0.2257 0.4812 -1.1870 -0.2239
## total_shrub_cover-Sciurus_carolinensis -0.0600 0.4064 -0.8372 -0.0688
## total_shrub_cover-Vulpes_vulpes -0.2465 0.5359 -1.3526 -0.2288
## total_shrub_cover-Sus_scrofa 0.0581 0.4818 -0.7975 0.0216
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1813 1.0219 849
## (Intercept)-Canis_latrans 1.8615 1.0216 2177
## (Intercept)-Sciurus_niger 2.5405 1.0811 526
## (Intercept)-Procyon_lotor 1.9658 1.0047 2480
## (Intercept)-Dasypus_novemcinctus 0.5041 1.0088 2940
## (Intercept)-Lynx_rufus 2.0076 1.0070 1256
## (Intercept)-Didelphis_virginiana 0.0029 1.0024 2599
## (Intercept)-Sylvilagus_floridanus 1.7218 1.0236 1239
## (Intercept)-Sciurus_carolinensis -0.2519 1.0099 2139
## (Intercept)-Vulpes_vulpes 2.1312 1.0056 470
## (Intercept)-Sus_scrofa -0.3335 1.0058 1521
## Cogon_Patch_Size-Odocoileus_virginianus 1.5216 1.0071 3220
## Cogon_Patch_Size-Canis_latrans 2.5240 1.0135 2375
## Cogon_Patch_Size-Sciurus_niger 0.8332 1.0025 1303
## Cogon_Patch_Size-Procyon_lotor 0.6294 1.0028 3360
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6252 1.0004 4446
## Cogon_Patch_Size-Lynx_rufus 1.4682 1.0046 2144
## Cogon_Patch_Size-Didelphis_virginiana 1.6594 1.0029 2317
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3538 1.0020 1481
## Cogon_Patch_Size-Sciurus_carolinensis 0.2890 1.0035 1677
## Cogon_Patch_Size-Vulpes_vulpes 0.9410 1.0017 1366
## Cogon_Patch_Size-Sus_scrofa 0.6951 1.0047 2148
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2992 1.0124 2516
## Avg_Cogongrass_Cover-Canis_latrans 1.1492 1.0033 3045
## Avg_Cogongrass_Cover-Sciurus_niger 0.9963 1.0165 1826
## Avg_Cogongrass_Cover-Procyon_lotor 1.1351 1.0055 3030
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1550 1.0041 3276
## Avg_Cogongrass_Cover-Lynx_rufus 1.5449 1.0024 2704
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0352 1.0019 3102
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8300 1.0089 2087
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2927 1.0074 2891
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2989 1.0111 2759
## Avg_Cogongrass_Cover-Sus_scrofa 0.9131 1.0051 1773
## total_shrub_cover-Odocoileus_virginianus 0.8570 1.0053 3363
## total_shrub_cover-Canis_latrans 0.9503 1.0009 3503
## total_shrub_cover-Sciurus_niger 0.4878 1.0032 2259
## total_shrub_cover-Procyon_lotor 0.1280 1.0058 2524
## total_shrub_cover-Dasypus_novemcinctus 0.6498 1.0019 4098
## total_shrub_cover-Lynx_rufus 0.3532 1.0107 1873
## total_shrub_cover-Didelphis_virginiana 0.5116 1.0020 4046
## total_shrub_cover-Sylvilagus_floridanus 0.7343 1.0013 3016
## total_shrub_cover-Sciurus_carolinensis 0.7403 1.0015 4011
## total_shrub_cover-Vulpes_vulpes 0.7711 1.0008 2678
## total_shrub_cover-Sus_scrofa 1.1276 1.0008 3182
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5254 0.0793 0.3703 0.5254 0.6815
## (Intercept)-Canis_latrans -2.4331 0.1919 -2.8329 -2.4273 -2.0823
## (Intercept)-Sciurus_niger -3.9965 0.6098 -5.2305 -3.9869 -2.8434
## (Intercept)-Procyon_lotor -2.1649 0.1500 -2.4629 -2.1632 -1.8816
## (Intercept)-Dasypus_novemcinctus -1.4427 0.1555 -1.7585 -1.4379 -1.1533
## (Intercept)-Lynx_rufus -3.3936 0.3394 -4.0741 -3.3838 -2.7717
## (Intercept)-Didelphis_virginiana -2.1126 0.2717 -2.6864 -2.1038 -1.6067
## (Intercept)-Sylvilagus_floridanus -3.1752 0.3495 -3.9048 -3.1539 -2.5273
## (Intercept)-Sciurus_carolinensis -2.2577 0.2913 -2.8590 -2.2449 -1.7271
## (Intercept)-Vulpes_vulpes -4.0269 0.7880 -5.5879 -3.9976 -2.5924
## (Intercept)-Sus_scrofa -2.8118 0.4984 -3.8921 -2.7695 -1.9600
## week-Odocoileus_virginianus 1.2787 0.1202 1.0454 1.2782 1.5161
## week-Canis_latrans 0.5792 0.2583 0.0888 0.5751 1.1050
## week-Sciurus_niger -0.3779 0.5383 -1.5580 -0.3280 0.5375
## week-Procyon_lotor 0.2079 0.2097 -0.1979 0.2064 0.6180
## week-Dasypus_novemcinctus 0.1034 0.2226 -0.3261 0.1040 0.5327
## week-Lynx_rufus 0.3769 0.3468 -0.2827 0.3779 1.0612
## week-Didelphis_virginiana 0.0587 0.3711 -0.7173 0.0733 0.7598
## week-Sylvilagus_floridanus 0.0617 0.3453 -0.6225 0.0676 0.7240
## week-Sciurus_carolinensis 0.7808 0.3605 0.0959 0.7698 1.5052
## week-Vulpes_vulpes 0.2037 0.5097 -0.8598 0.2284 1.1696
## week-Sus_scrofa 0.6594 0.4415 -0.1947 0.6512 1.5453
## I(week^2)-Odocoileus_virginianus -0.5273 0.0491 -0.6232 -0.5266 -0.4303
## I(week^2)-Canis_latrans -0.2433 0.1075 -0.4590 -0.2421 -0.0382
## I(week^2)-Sciurus_niger -0.2800 0.2354 -0.7891 -0.2708 0.1560
## I(week^2)-Procyon_lotor -0.1324 0.0892 -0.3055 -0.1327 0.0421
## I(week^2)-Dasypus_novemcinctus -0.1780 0.1032 -0.3815 -0.1764 0.0212
## I(week^2)-Lynx_rufus -0.2394 0.1526 -0.5536 -0.2350 0.0458
## I(week^2)-Didelphis_virginiana -0.4086 0.2136 -0.8909 -0.3891 -0.0564
## I(week^2)-Sylvilagus_floridanus -0.1788 0.1597 -0.5074 -0.1764 0.1352
## I(week^2)-Sciurus_carolinensis -0.2753 0.1402 -0.5620 -0.2738 -0.0078
## I(week^2)-Vulpes_vulpes -0.4016 0.2515 -0.9468 -0.3815 0.0231
## I(week^2)-Sus_scrofa -0.2366 0.1769 -0.5949 -0.2356 0.0967
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0017 3112
## (Intercept)-Sciurus_niger 1.0369 417
## (Intercept)-Procyon_lotor 0.9999 4141
## (Intercept)-Dasypus_novemcinctus 1.0009 4740
## (Intercept)-Lynx_rufus 1.0063 1249
## (Intercept)-Didelphis_virginiana 0.9999 4080
## (Intercept)-Sylvilagus_floridanus 1.0157 1112
## (Intercept)-Sciurus_carolinensis 1.0028 3553
## (Intercept)-Vulpes_vulpes 1.0030 360
## (Intercept)-Sus_scrofa 1.0012 1715
## week-Odocoileus_virginianus 1.0013 4506
## week-Canis_latrans 1.0013 3413
## week-Sciurus_niger 1.0162 961
## week-Procyon_lotor 1.0009 3881
## week-Dasypus_novemcinctus 1.0039 4198
## week-Lynx_rufus 1.0012 2502
## week-Didelphis_virginiana 1.0069 3064
## week-Sylvilagus_floridanus 1.0008 2873
## week-Sciurus_carolinensis 1.0030 3953
## week-Vulpes_vulpes 1.0013 1806
## week-Sus_scrofa 1.0006 4074
## I(week^2)-Odocoileus_virginianus 1.0003 5162
## I(week^2)-Canis_latrans 1.0012 3533
## I(week^2)-Sciurus_niger 1.0069 1202
## I(week^2)-Procyon_lotor 1.0006 4143
## I(week^2)-Dasypus_novemcinctus 1.0055 4135
## I(week^2)-Lynx_rufus 1.0097 2276
## I(week^2)-Didelphis_virginiana 1.0044 1637
## I(week^2)-Sylvilagus_floridanus 1.0016 2471
## I(week^2)-Sciurus_carolinensis 1.0000 4124
## I(week^2)-Vulpes_vulpes 1.0009 1175
## I(week^2)-Sus_scrofa 1.0001 4189
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage<- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.799
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1945 0.6736 -1.4680 -0.2181 1.2356 1.0234 1054
## Veg_shannon_index 0.3793 0.2644 -0.1305 0.3730 0.9280 1.0053 1864
## Avg_Cogongrass_Cover 0.3252 0.2612 -0.1938 0.3287 0.8550 1.0018 1774
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.3553 3.8798 0.8952 3.3339 13.7798 1.0497 941
## Veg_shannon_index 0.2946 0.3409 0.0382 0.1915 1.1771 1.0036 2128
## Avg_Cogongrass_Cover 0.2877 0.3623 0.0369 0.1825 1.1689 1.0144 1821
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7636 0.8319 0.0671 0.5273 2.7803 1.0331 478
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3169 0.4552 -3.1868 -2.3226 -1.3905 1.0068 3914
## week 0.3584 0.2367 -0.1292 0.3634 0.8084 1.0022 3116
## I(week^2) -0.2834 0.1007 -0.4936 -0.2809 -0.0933 1.0014 2562
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3158 1.5204 0.7782 1.9238 6.2311 1.0221 1534
## week 0.4279 0.3526 0.1023 0.3378 1.2995 1.0099 2338
## I(week^2) 0.0720 0.0605 0.0227 0.0577 0.2002 1.0215 1938
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7853 1.5689 1.4042 3.5459
## (Intercept)-Canis_latrans 0.2710 0.6421 -1.0199 0.2647
## (Intercept)-Sciurus_niger -0.2225 1.5073 -2.4247 -0.5136
## (Intercept)-Procyon_lotor 0.5512 0.6339 -0.7005 0.5613
## (Intercept)-Dasypus_novemcinctus -0.7626 0.5935 -1.9900 -0.7475
## (Intercept)-Lynx_rufus 0.1848 1.1914 -1.6345 0.0303
## (Intercept)-Didelphis_virginiana -1.5101 0.6553 -2.8863 -1.4865
## (Intercept)-Sylvilagus_floridanus -0.3000 0.7953 -1.7032 -0.3512
## (Intercept)-Sciurus_carolinensis -1.5089 0.6669 -2.8991 -1.5033
## (Intercept)-Vulpes_vulpes -0.5740 1.8811 -3.0415 -0.9812
## (Intercept)-Sus_scrofa -2.2361 0.8699 -4.0730 -2.1676
## Veg_shannon_index-Odocoileus_virginianus 0.3314 0.5111 -0.6875 0.3392
## Veg_shannon_index-Canis_latrans 0.6545 0.3889 -0.0149 0.6263
## Veg_shannon_index-Sciurus_niger 0.3839 0.5316 -0.6323 0.3651
## Veg_shannon_index-Procyon_lotor 0.4959 0.3873 -0.2099 0.4734
## Veg_shannon_index-Dasypus_novemcinctus 0.2241 0.3345 -0.4485 0.2279
## Veg_shannon_index-Lynx_rufus 0.1979 0.5272 -0.9366 0.2231
## Veg_shannon_index-Didelphis_virginiana 0.5226 0.3924 -0.1984 0.5084
## Veg_shannon_index-Sylvilagus_floridanus 0.4992 0.4518 -0.3192 0.4722
## Veg_shannon_index-Sciurus_carolinensis 0.0368 0.3940 -0.8055 0.0573
## Veg_shannon_index-Vulpes_vulpes 0.1444 0.4984 -0.8814 0.1619
## Veg_shannon_index-Sus_scrofa 0.7478 0.5353 -0.1106 0.6780
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3172 0.5109 -0.6599 0.3131
## Avg_Cogongrass_Cover-Canis_latrans 0.5433 0.3812 -0.1318 0.5170
## Avg_Cogongrass_Cover-Sciurus_niger 0.0104 0.5851 -1.2696 0.0553
## Avg_Cogongrass_Cover-Procyon_lotor 0.4321 0.3881 -0.2543 0.4077
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4320 0.3319 -0.1861 0.4180
## Avg_Cogongrass_Cover-Lynx_rufus 0.5670 0.4353 -0.2084 0.5390
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4720 0.3696 -0.2321 0.4653
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0181 0.4435 -0.9776 0.0100
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4213 0.3638 -0.2755 0.4179
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3892 0.4753 -0.5400 0.3766
## Avg_Cogongrass_Cover-Sus_scrofa 0.0206 0.5327 -1.1997 0.0855
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.6458 1.0490 594
## (Intercept)-Canis_latrans 1.5756 1.0025 2632
## (Intercept)-Sciurus_niger 3.6179 1.0502 321
## (Intercept)-Procyon_lotor 1.7957 1.0013 2303
## (Intercept)-Dasypus_novemcinctus 0.3719 1.0000 3321
## (Intercept)-Lynx_rufus 3.0108 1.0294 580
## (Intercept)-Didelphis_virginiana -0.2679 1.0014 3566
## (Intercept)-Sylvilagus_floridanus 1.4833 1.0069 1641
## (Intercept)-Sciurus_carolinensis -0.2842 1.0009 3069
## (Intercept)-Vulpes_vulpes 4.7168 1.1178 181
## (Intercept)-Sus_scrofa -0.6414 1.0052 1940
## Veg_shannon_index-Odocoileus_virginianus 1.3260 1.0014 3183
## Veg_shannon_index-Canis_latrans 1.5040 1.0035 2545
## Veg_shannon_index-Sciurus_niger 1.4838 1.0030 2159
## Veg_shannon_index-Procyon_lotor 1.3243 1.0034 3073
## Veg_shannon_index-Dasypus_novemcinctus 0.8624 1.0012 3835
## Veg_shannon_index-Lynx_rufus 1.1515 1.0015 2357
## Veg_shannon_index-Didelphis_virginiana 1.3336 1.0019 3504
## Veg_shannon_index-Sylvilagus_floridanus 1.4941 1.0000 2780
## Veg_shannon_index-Sciurus_carolinensis 0.7668 1.0056 3601
## Veg_shannon_index-Vulpes_vulpes 1.1198 1.0101 2092
## Veg_shannon_index-Sus_scrofa 2.0157 1.0059 2745
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3746 1.0002 3271
## Avg_Cogongrass_Cover-Canis_latrans 1.3806 1.0028 3073
## Avg_Cogongrass_Cover-Sciurus_niger 1.0673 1.0006 1304
## Avg_Cogongrass_Cover-Procyon_lotor 1.2770 1.0012 3246
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1180 1.0009 3786
## Avg_Cogongrass_Cover-Lynx_rufus 1.5161 1.0023 3239
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2524 1.0010 3841
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7989 1.0009 2728
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1663 1.0012 3045
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3798 1.0018 2873
## Avg_Cogongrass_Cover-Sus_scrofa 0.8823 1.0026 2012
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5244 0.0781 0.3714 0.5241 0.6783
## (Intercept)-Canis_latrans -2.4233 0.1912 -2.8165 -2.4174 -2.0652
## (Intercept)-Sciurus_niger -4.0604 0.6788 -5.3895 -4.0409 -2.8197
## (Intercept)-Procyon_lotor -2.1666 0.1521 -2.4705 -2.1648 -1.8816
## (Intercept)-Dasypus_novemcinctus -1.4382 0.1571 -1.7618 -1.4370 -1.1364
## (Intercept)-Lynx_rufus -3.4410 0.3542 -4.1672 -3.4314 -2.7996
## (Intercept)-Didelphis_virginiana -2.1111 0.2733 -2.6754 -2.1050 -1.5964
## (Intercept)-Sylvilagus_floridanus -3.1397 0.3474 -3.8714 -3.1178 -2.5138
## (Intercept)-Sciurus_carolinensis -2.2668 0.2857 -2.8671 -2.2560 -1.7348
## (Intercept)-Vulpes_vulpes -4.1059 0.8346 -5.7633 -4.0616 -2.6164
## (Intercept)-Sus_scrofa -2.7754 0.4895 -3.8197 -2.7509 -1.9028
## week-Odocoileus_virginianus 1.2803 0.1208 1.0451 1.2804 1.5136
## week-Canis_latrans 0.5862 0.2581 0.0912 0.5813 1.1107
## week-Sciurus_niger -0.3989 0.5642 -1.6159 -0.3579 0.5573
## week-Procyon_lotor 0.2009 0.2093 -0.2109 0.2017 0.6117
## week-Dasypus_novemcinctus 0.1086 0.2232 -0.3266 0.1152 0.5426
## week-Lynx_rufus 0.3839 0.3521 -0.3219 0.3810 1.0847
## week-Didelphis_virginiana 0.0715 0.3742 -0.6924 0.0844 0.7827
## week-Sylvilagus_floridanus 0.0601 0.3440 -0.6294 0.0698 0.7096
## week-Sciurus_carolinensis 0.7840 0.3674 0.0990 0.7606 1.5295
## week-Vulpes_vulpes 0.1944 0.5171 -0.8825 0.2185 1.1505
## week-Sus_scrofa 0.6853 0.4472 -0.1509 0.6757 1.5944
## I(week^2)-Odocoileus_virginianus -0.5278 0.0493 -0.6263 -0.5274 -0.4316
## I(week^2)-Canis_latrans -0.2427 0.1053 -0.4543 -0.2412 -0.0426
## I(week^2)-Sciurus_niger -0.2868 0.2377 -0.7764 -0.2770 0.1480
## I(week^2)-Procyon_lotor -0.1312 0.0901 -0.3093 -0.1312 0.0411
## I(week^2)-Dasypus_novemcinctus -0.1812 0.1043 -0.3917 -0.1827 0.0232
## I(week^2)-Lynx_rufus -0.2384 0.1512 -0.5475 -0.2368 0.0562
## I(week^2)-Didelphis_virginiana -0.4082 0.2061 -0.8760 -0.3897 -0.0517
## I(week^2)-Sylvilagus_floridanus -0.1823 0.1565 -0.4930 -0.1807 0.1121
## I(week^2)-Sciurus_carolinensis -0.2804 0.1453 -0.5733 -0.2772 -0.0107
## I(week^2)-Vulpes_vulpes -0.4055 0.2618 -0.9786 -0.3764 0.0187
## I(week^2)-Sus_scrofa -0.2425 0.1782 -0.5922 -0.2374 0.1019
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0024 3529
## (Intercept)-Sciurus_niger 1.0325 337
## (Intercept)-Procyon_lotor 1.0005 4128
## (Intercept)-Dasypus_novemcinctus 1.0015 4432
## (Intercept)-Lynx_rufus 1.0149 938
## (Intercept)-Didelphis_virginiana 1.0015 4363
## (Intercept)-Sylvilagus_floridanus 1.0027 1277
## (Intercept)-Sciurus_carolinensis 1.0084 3747
## (Intercept)-Vulpes_vulpes 1.1035 274
## (Intercept)-Sus_scrofa 1.0011 2428
## week-Odocoileus_virginianus 1.0004 5266
## week-Canis_latrans 1.0021 4044
## week-Sciurus_niger 1.0070 657
## week-Procyon_lotor 1.0008 4634
## week-Dasypus_novemcinctus 1.0008 4855
## week-Lynx_rufus 1.0005 2521
## week-Didelphis_virginiana 1.0025 3232
## week-Sylvilagus_floridanus 1.0010 2859
## week-Sciurus_carolinensis 1.0008 4140
## week-Vulpes_vulpes 1.0010 1706
## week-Sus_scrofa 1.0026 4000
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0009 3567
## I(week^2)-Sciurus_niger 1.0029 1242
## I(week^2)-Procyon_lotor 1.0010 4065
## I(week^2)-Dasypus_novemcinctus 1.0010 4414
## I(week^2)-Lynx_rufus 1.0096 2436
## I(week^2)-Didelphis_virginiana 1.0033 2070
## I(week^2)-Sylvilagus_floridanus 1.0038 2316
## I(week^2)-Sciurus_carolinensis 1.0023 4463
## I(week^2)-Vulpes_vulpes 1.0064 831
## I(week^2)-Sus_scrofa 1.0005 4201
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon<- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7403
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2829 0.5969 -1.4110 -0.3102 0.9940 1.0011 2114
## Avg_Cogongrass_Cover 0.1874 0.2371 -0.2788 0.1922 0.6567 1.0052 2636
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4995 2.9865 0.7253 2.7040 10.7348 1.0351 1848
## Avg_Cogongrass_Cover 0.2574 0.3080 0.0349 0.1692 1.0121 1.0066 2695
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7196 0.7075 0.0613 0.5072 2.6401 1.0074 610
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2885 0.4521 -3.1361 -2.3063 -1.3342 1.0028 4755
## week 0.3535 0.2443 -0.1575 0.3587 0.8188 1.0009 2926
## I(week^2) -0.2824 0.1016 -0.4903 -0.2786 -0.0920 1.0042 2568
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1584 1.3809 0.7481 1.7816 5.8879 1.0004 2333
## week 0.4251 0.3327 0.0987 0.3327 1.3096 1.0074 2073
## I(week^2) 0.0709 0.0511 0.0222 0.0572 0.2011 1.0113 2372
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4050 1.3407 1.2205 3.2601
## (Intercept)-Canis_latrans 0.2763 0.6116 -0.8978 0.2698
## (Intercept)-Sciurus_niger -0.5780 1.1842 -2.3838 -0.7654
## (Intercept)-Procyon_lotor 0.5077 0.6113 -0.7714 0.5092
## (Intercept)-Dasypus_novemcinctus -0.7249 0.5792 -1.9078 -0.7149
## (Intercept)-Lynx_rufus -0.0165 0.9853 -1.6386 -0.1004
## (Intercept)-Didelphis_virginiana -1.3950 0.6301 -2.6513 -1.3755
## (Intercept)-Sylvilagus_floridanus -0.3179 0.7467 -1.6406 -0.3697
## (Intercept)-Sciurus_carolinensis -1.4533 0.6414 -2.7333 -1.4428
## (Intercept)-Vulpes_vulpes -1.0918 1.2907 -3.1042 -1.2465
## (Intercept)-Sus_scrofa -1.9343 0.8174 -3.6848 -1.9139
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1677 0.4753 -0.7912 0.1718
## Avg_Cogongrass_Cover-Canis_latrans 0.3637 0.3371 -0.2513 0.3429
## Avg_Cogongrass_Cover-Sciurus_niger -0.1247 0.5074 -1.2738 -0.0804
## Avg_Cogongrass_Cover-Procyon_lotor 0.2493 0.3484 -0.3981 0.2362
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3337 0.3089 -0.2454 0.3285
## Avg_Cogongrass_Cover-Lynx_rufus 0.4347 0.3982 -0.2730 0.4081
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3331 0.3344 -0.3091 0.3224
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1608 0.4118 -1.0549 -0.1302
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3291 0.3441 -0.3396 0.3270
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2697 0.4151 -0.5714 0.2682
## Avg_Cogongrass_Cover-Sus_scrofa -0.0989 0.4925 -1.2018 -0.0395
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5433 1.0142 1404
## (Intercept)-Canis_latrans 1.5373 0.9999 2646
## (Intercept)-Sciurus_niger 2.4394 1.0042 491
## (Intercept)-Procyon_lotor 1.6811 1.0074 2325
## (Intercept)-Dasypus_novemcinctus 0.3660 1.0003 3220
## (Intercept)-Lynx_rufus 2.1274 1.0101 809
## (Intercept)-Didelphis_virginiana -0.1930 1.0007 3149
## (Intercept)-Sylvilagus_floridanus 1.2623 1.0051 1382
## (Intercept)-Sciurus_carolinensis -0.2430 1.0007 3418
## (Intercept)-Vulpes_vulpes 2.1543 1.0043 387
## (Intercept)-Sus_scrofa -0.3671 1.0013 2387
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1292 1.0008 3873
## Avg_Cogongrass_Cover-Canis_latrans 1.0586 1.0012 4512
## Avg_Cogongrass_Cover-Sciurus_niger 0.7700 1.0002 2248
## Avg_Cogongrass_Cover-Procyon_lotor 0.9817 1.0031 4196
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9597 1.0011 4690
## Avg_Cogongrass_Cover-Lynx_rufus 1.2944 1.0008 2961
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0163 1.0017 4209
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5713 1.0020 2983
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0124 1.0038 4338
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1207 1.0003 3765
## Avg_Cogongrass_Cover-Sus_scrofa 0.6960 1.0057 2785
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5237 0.0804 0.3676 0.5243 0.6846
## (Intercept)-Canis_latrans -2.4352 0.1907 -2.8232 -2.4271 -2.0740
## (Intercept)-Sciurus_niger -3.8875 0.6243 -5.1812 -3.8445 -2.7885
## (Intercept)-Procyon_lotor -2.1595 0.1521 -2.4655 -2.1563 -1.8805
## (Intercept)-Dasypus_novemcinctus -1.4408 0.1543 -1.7516 -1.4388 -1.1532
## (Intercept)-Lynx_rufus -3.3834 0.3551 -4.0896 -3.3761 -2.7179
## (Intercept)-Didelphis_virginiana -2.1119 0.2707 -2.6708 -2.0946 -1.6101
## (Intercept)-Sylvilagus_floridanus -3.1006 0.3358 -3.8027 -3.0845 -2.4897
## (Intercept)-Sciurus_carolinensis -2.2607 0.2877 -2.8576 -2.2490 -1.7363
## (Intercept)-Vulpes_vulpes -3.8597 0.7655 -5.4800 -3.8197 -2.5198
## (Intercept)-Sus_scrofa -2.8119 0.5070 -3.9255 -2.7781 -1.9197
## week-Odocoileus_virginianus 1.2783 0.1230 1.0402 1.2763 1.5247
## week-Canis_latrans 0.5774 0.2583 0.0806 0.5751 1.0963
## week-Sciurus_niger -0.3947 0.5481 -1.5940 -0.3429 0.5566
## week-Procyon_lotor 0.2042 0.2107 -0.1978 0.2017 0.6227
## week-Dasypus_novemcinctus 0.1033 0.2247 -0.3363 0.1023 0.5417
## week-Lynx_rufus 0.3818 0.3440 -0.2917 0.3837 1.0538
## week-Didelphis_virginiana 0.0568 0.3698 -0.6868 0.0631 0.7840
## week-Sylvilagus_floridanus 0.0604 0.3421 -0.6419 0.0650 0.7057
## week-Sciurus_carolinensis 0.7850 0.3673 0.1021 0.7705 1.5439
## week-Vulpes_vulpes 0.1903 0.5170 -0.9107 0.2190 1.1643
## week-Sus_scrofa 0.6689 0.4392 -0.1556 0.6537 1.5649
## I(week^2)-Odocoileus_virginianus -0.5270 0.0506 -0.6280 -0.5265 -0.4294
## I(week^2)-Canis_latrans -0.2407 0.1073 -0.4528 -0.2385 -0.0372
## I(week^2)-Sciurus_niger -0.2768 0.2292 -0.7658 -0.2672 0.1408
## I(week^2)-Procyon_lotor -0.1333 0.0905 -0.3124 -0.1330 0.0407
## I(week^2)-Dasypus_novemcinctus -0.1780 0.1030 -0.3859 -0.1768 0.0230
## I(week^2)-Lynx_rufus -0.2423 0.1502 -0.5452 -0.2400 0.0401
## I(week^2)-Didelphis_virginiana -0.4158 0.2098 -0.8724 -0.3947 -0.0638
## I(week^2)-Sylvilagus_floridanus -0.1805 0.1559 -0.4896 -0.1779 0.1180
## I(week^2)-Sciurus_carolinensis -0.2772 0.1439 -0.5769 -0.2731 -0.0036
## I(week^2)-Vulpes_vulpes -0.4030 0.2395 -0.9551 -0.3786 0.0032
## I(week^2)-Sus_scrofa -0.2364 0.1735 -0.5815 -0.2350 0.0958
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0023 5250
## (Intercept)-Canis_latrans 1.0013 3179
## (Intercept)-Sciurus_niger 1.0061 496
## (Intercept)-Procyon_lotor 1.0014 4414
## (Intercept)-Dasypus_novemcinctus 1.0004 5250
## (Intercept)-Lynx_rufus 1.0047 938
## (Intercept)-Didelphis_virginiana 1.0009 4273
## (Intercept)-Sylvilagus_floridanus 1.0038 1509
## (Intercept)-Sciurus_carolinensis 1.0014 3476
## (Intercept)-Vulpes_vulpes 1.0172 391
## (Intercept)-Sus_scrofa 1.0014 2060
## week-Odocoileus_virginianus 1.0035 4507
## week-Canis_latrans 1.0035 3644
## week-Sciurus_niger 1.0162 1022
## week-Procyon_lotor 1.0000 4464
## week-Dasypus_novemcinctus 1.0002 4911
## week-Lynx_rufus 1.0050 2830
## week-Didelphis_virginiana 1.0003 2972
## week-Sylvilagus_floridanus 1.0006 2597
## week-Sciurus_carolinensis 0.9998 4097
## week-Vulpes_vulpes 1.0084 2406
## week-Sus_scrofa 1.0000 3712
## I(week^2)-Odocoileus_virginianus 1.0011 4214
## I(week^2)-Canis_latrans 1.0059 3705
## I(week^2)-Sciurus_niger 1.0012 1335
## I(week^2)-Procyon_lotor 1.0008 4463
## I(week^2)-Dasypus_novemcinctus 1.0015 4152
## I(week^2)-Lynx_rufus 1.0009 2153
## I(week^2)-Didelphis_virginiana 1.0028 1808
## I(week^2)-Sylvilagus_floridanus 1.0004 1982
## I(week^2)-Sciurus_carolinensis 1.0007 4038
## I(week^2)-Vulpes_vulpes 1.0090 1492
## I(week^2)-Sus_scrofa 1.0039 4053
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ<- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7495
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9420 0.6302 -2.1301 -0.9665 0.4024 1.0001 2426
## Avg_Cogongrass_Cover -0.7449 0.3745 -1.5046 -0.7296 -0.0321 1.0075 1239
## I(Avg_Cogongrass_Cover^2) 0.8473 0.3389 0.2463 0.8261 1.6023 1.0065 922
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7821 3.3564 0.7923 2.9098 11.5225 1.0181 1715
## Avg_Cogongrass_Cover 0.3571 0.4453 0.0389 0.2193 1.4114 1.0003 2116
## I(Avg_Cogongrass_Cover^2) 0.4744 0.9448 0.0374 0.2125 2.6685 1.0438 592
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5161 0.5107 0.0525 0.3555 1.9112 1.0017 540
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2839 0.4376 -3.1272 -2.2896 -1.3749 1.0037 4953
## week 0.3594 0.2296 -0.1197 0.3661 0.7953 1.0041 3338
## I(week^2) -0.2853 0.1024 -0.4931 -0.2828 -0.0847 1.0004 2343
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0954 1.3073 0.7376 1.7642 5.4681 1.0047 3312
## week 0.4230 0.3305 0.1024 0.3364 1.2621 1.0035 1517
## I(week^2) 0.0716 0.0529 0.0221 0.0580 0.2049 1.0025 2366
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8761 1.4301 0.6299 2.6904
## (Intercept)-Canis_latrans -0.5239 0.6606 -1.8698 -0.5135
## (Intercept)-Sciurus_niger -0.9646 1.1742 -2.8439 -1.1093
## (Intercept)-Procyon_lotor -0.1891 0.6431 -1.4921 -0.1848
## (Intercept)-Dasypus_novemcinctus -1.3976 0.6366 -2.7254 -1.3726
## (Intercept)-Lynx_rufus -1.2090 0.8884 -2.9095 -1.2301
## (Intercept)-Didelphis_virginiana -2.0169 0.7104 -3.5187 -1.9798
## (Intercept)-Sylvilagus_floridanus -1.0556 0.7791 -2.5436 -1.0734
## (Intercept)-Sciurus_carolinensis -2.4737 0.7589 -4.0303 -2.4444
## (Intercept)-Vulpes_vulpes -2.1730 1.2375 -4.3197 -2.2453
## (Intercept)-Sus_scrofa -2.5178 0.8853 -4.3403 -2.5119
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7485 0.6208 -2.0446 -0.7293
## Avg_Cogongrass_Cover-Canis_latrans -0.4912 0.5088 -1.4465 -0.5110
## Avg_Cogongrass_Cover-Sciurus_niger -0.9721 0.6399 -2.4234 -0.9161
## Avg_Cogongrass_Cover-Procyon_lotor -0.6082 0.4911 -1.5436 -0.6125
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5772 0.4750 -1.4966 -0.5774
## Avg_Cogongrass_Cover-Lynx_rufus -0.6430 0.5437 -1.7280 -0.6313
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4939 0.5180 -1.4768 -0.5062
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1334 0.6099 -2.4846 -1.0629
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8018 0.5245 -1.8901 -0.7767
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7876 0.6005 -2.0176 -0.7681
## Avg_Cogongrass_Cover-Sus_scrofa -1.0273 0.6414 -2.4644 -0.9659
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1553 0.7924 0.0795 1.0190
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2161 0.7615 0.2243 1.0568
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3698 0.7118 -1.3553 0.4163
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0716 0.6305 0.2077 0.9618
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7360 0.3447 0.0785 0.7239
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1771 0.5366 0.3336 1.1051
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6023 0.4141 -0.1583 0.5861
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7808 0.4843 -0.0635 0.7374
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9717 0.3922 0.2878 0.9512
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9619 0.5292 0.1599 0.8925
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3812 0.5843 -0.9366 0.4419
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.2024 1.0006 1256
## (Intercept)-Canis_latrans 0.7488 1.0011 2232
## (Intercept)-Sciurus_niger 1.7832 1.0125 616
## (Intercept)-Procyon_lotor 1.0479 1.0004 2789
## (Intercept)-Dasypus_novemcinctus -0.2222 1.0015 3016
## (Intercept)-Lynx_rufus 0.5649 1.0076 1700
## (Intercept)-Didelphis_virginiana -0.6736 1.0012 2765
## (Intercept)-Sylvilagus_floridanus 0.5695 1.0071 2201
## (Intercept)-Sciurus_carolinensis -1.0506 1.0051 2606
## (Intercept)-Vulpes_vulpes 0.3634 1.0105 637
## (Intercept)-Sus_scrofa -0.8011 1.0084 1876
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4609 1.0066 2079
## Avg_Cogongrass_Cover-Canis_latrans 0.6054 1.0029 2981
## Avg_Cogongrass_Cover-Sciurus_niger 0.1664 1.0025 1950
## Avg_Cogongrass_Cover-Procyon_lotor 0.3946 1.0018 2818
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3715 1.0017 2341
## Avg_Cogongrass_Cover-Lynx_rufus 0.4451 1.0088 1833
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5628 1.0023 2505
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0941 1.0036 1716
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1753 1.0004 1735
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3604 1.0012 1925
## Avg_Cogongrass_Cover-Sus_scrofa 0.0428 1.0061 1754
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2015 1.0032 858
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1526 1.0083 557
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.6080 1.0074 763
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6820 1.0067 752
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4503 1.0027 2562
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4242 1.0019 1073
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4441 1.0032 1837
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8846 1.0032 1725
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8371 1.0008 1748
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2460 1.0005 1245
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3038 1.0031 1339
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5226 0.0801 0.3685 0.5205 0.6839
## (Intercept)-Canis_latrans -2.4499 0.1898 -2.8325 -2.4467 -2.0809
## (Intercept)-Sciurus_niger -3.9455 0.6318 -5.1877 -3.9168 -2.8191
## (Intercept)-Procyon_lotor -2.1620 0.1511 -2.4620 -2.1608 -1.8683
## (Intercept)-Dasypus_novemcinctus -1.4387 0.1565 -1.7479 -1.4395 -1.1324
## (Intercept)-Lynx_rufus -3.2715 0.3298 -3.9673 -3.2560 -2.6614
## (Intercept)-Didelphis_virginiana -2.1373 0.2836 -2.7148 -2.1265 -1.6202
## (Intercept)-Sylvilagus_floridanus -3.1146 0.3365 -3.8098 -3.1009 -2.5109
## (Intercept)-Sciurus_carolinensis -2.2538 0.2822 -2.8404 -2.2439 -1.7345
## (Intercept)-Vulpes_vulpes -3.8140 0.7033 -5.2596 -3.7921 -2.5217
## (Intercept)-Sus_scrofa -2.8101 0.5131 -3.9471 -2.7687 -1.9074
## week-Odocoileus_virginianus 1.2776 0.1225 1.0417 1.2760 1.5268
## week-Canis_latrans 0.5781 0.2590 0.0849 0.5726 1.0977
## week-Sciurus_niger -0.4040 0.5468 -1.5931 -0.3538 0.5290
## week-Procyon_lotor 0.2095 0.2103 -0.2063 0.2080 0.6302
## week-Dasypus_novemcinctus 0.1145 0.2247 -0.3260 0.1127 0.5706
## week-Lynx_rufus 0.3807 0.3550 -0.3123 0.3848 1.0872
## week-Didelphis_virginiana 0.0521 0.3716 -0.7195 0.0684 0.7381
## week-Sylvilagus_floridanus 0.0659 0.3422 -0.6265 0.0782 0.7176
## week-Sciurus_carolinensis 0.7952 0.3644 0.1223 0.7767 1.5350
## week-Vulpes_vulpes 0.2088 0.5153 -0.8767 0.2292 1.1730
## week-Sus_scrofa 0.6996 0.4487 -0.1512 0.6871 1.6302
## I(week^2)-Odocoileus_virginianus -0.5268 0.0506 -0.6269 -0.5258 -0.4309
## I(week^2)-Canis_latrans -0.2407 0.1052 -0.4486 -0.2404 -0.0360
## I(week^2)-Sciurus_niger -0.2961 0.2315 -0.7823 -0.2846 0.1210
## I(week^2)-Procyon_lotor -0.1342 0.0901 -0.3151 -0.1332 0.0403
## I(week^2)-Dasypus_novemcinctus -0.1803 0.1039 -0.3892 -0.1776 0.0174
## I(week^2)-Lynx_rufus -0.2423 0.1531 -0.5588 -0.2380 0.0464
## I(week^2)-Didelphis_virginiana -0.4154 0.2116 -0.8924 -0.3939 -0.0625
## I(week^2)-Sylvilagus_floridanus -0.1831 0.1572 -0.5099 -0.1831 0.1179
## I(week^2)-Sciurus_carolinensis -0.2804 0.1432 -0.5745 -0.2767 -0.0043
## I(week^2)-Vulpes_vulpes -0.4067 0.2567 -0.9776 -0.3859 0.0265
## I(week^2)-Sus_scrofa -0.2434 0.1776 -0.6036 -0.2398 0.1008
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0001 3254
## (Intercept)-Sciurus_niger 1.0075 396
## (Intercept)-Procyon_lotor 1.0003 4283
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## (Intercept)-Lynx_rufus 1.0059 1569
## (Intercept)-Didelphis_virginiana 1.0000 3575
## (Intercept)-Sylvilagus_floridanus 1.0076 1435
## (Intercept)-Sciurus_carolinensis 1.0013 3971
## (Intercept)-Vulpes_vulpes 1.0085 550
## (Intercept)-Sus_scrofa 1.0053 1569
## week-Odocoileus_virginianus 1.0055 5138
## week-Canis_latrans 1.0037 3944
## week-Sciurus_niger 1.0029 1037
## week-Procyon_lotor 1.0020 4266
## week-Dasypus_novemcinctus 1.0020 4954
## week-Lynx_rufus 1.0041 2622
## week-Didelphis_virginiana 1.0007 2656
## week-Sylvilagus_floridanus 1.0037 2867
## week-Sciurus_carolinensis 1.0005 4306
## week-Vulpes_vulpes 1.0019 2171
## week-Sus_scrofa 0.9999 3818
## I(week^2)-Odocoileus_virginianus 1.0049 3779
## I(week^2)-Canis_latrans 1.0015 4035
## I(week^2)-Sciurus_niger 1.0001 1470
## I(week^2)-Procyon_lotor 1.0027 4282
## I(week^2)-Dasypus_novemcinctus 1.0050 4736
## I(week^2)-Lynx_rufus 1.0022 2505
## I(week^2)-Didelphis_virginiana 1.0003 1681
## I(week^2)-Sylvilagus_floridanus 1.0015 2591
## I(week^2)-Sciurus_carolinensis 1.0014 4359
## I(week^2)-Vulpes_vulpes 1.0023 1403
## I(week^2)-Sus_scrofa 1.0006 4366
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ<- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8997
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9780 1.1469 -3.0856 -1.0382 1.3810 1.0047 1724
## Cogon_Patch_Size -0.2519 0.7173 -1.7496 -0.2225 1.1365 1.0093 1269
## Veg_shannon_index 0.9176 0.4698 0.0261 0.9057 1.8657 1.0017 1210
## total_shrub_cover -0.2776 0.4093 -1.1122 -0.2705 0.5324 1.0008 1449
## Avg_Cogongrass_Cover 0.0685 0.9440 -1.7663 0.0630 1.9040 1.0014 470
## Tree_Density -2.0026 0.7964 -3.5881 -1.9775 -0.5175 1.0221 531
## Avg_Canopy_Cover 1.8172 0.5977 0.7091 1.7826 3.0888 1.0031 791
## I(Avg_Cogongrass_Cover^2) 1.4719 0.5561 0.4729 1.4499 2.6368 1.0337 556
## avg_veg_height -0.2108 0.4806 -1.1415 -0.2215 0.7298 1.0025 707
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.9802 20.2384 4.4390 16.9080 77.6968 1.0323 306
## Cogon_Patch_Size 3.7809 6.2370 0.1677 2.0623 18.0677 1.0479 488
## Veg_shannon_index 0.8164 1.1976 0.0483 0.4090 4.0291 1.0163 1166
## total_shrub_cover 0.6362 0.8172 0.0494 0.3668 2.8795 1.0268 1255
## Avg_Cogongrass_Cover 1.2317 2.0263 0.0536 0.5527 6.4682 1.0196 935
## Tree_Density 3.3938 6.0162 0.0759 1.3558 19.2210 1.0078 360
## Avg_Canopy_Cover 2.1698 2.9791 0.1124 1.2984 9.8455 1.0292 668
## I(Avg_Cogongrass_Cover^2) 0.8266 1.5595 0.0462 0.3795 4.2953 1.0265 781
## avg_veg_height 0.4482 0.5631 0.0436 0.2660 1.9591 1.0028 1665
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5775 1.9802 0.063 0.8916 7.1985 1.0094 235
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3370 0.4694 -3.2319 -2.3435 -1.3708 1.0034 5034
## week 0.3570 0.2341 -0.1241 0.3626 0.8030 1.0015 3193
## I(week^2) -0.2801 0.1023 -0.4818 -0.2793 -0.0855 1.0096 2324
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5029 1.4963 0.9049 2.1089 6.3608 1.0007 2852
## week 0.4167 0.3161 0.1068 0.3310 1.2640 1.0009 2043
## I(week^2) 0.0704 0.0534 0.0213 0.0570 0.1974 1.0129 3063
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.8778 3.7855 2.6732
## (Intercept)-Canis_latrans -0.9297 1.1694 -3.2227
## (Intercept)-Sciurus_niger 1.2752 2.9399 -2.9371
## (Intercept)-Procyon_lotor -0.3953 1.0924 -2.6661
## (Intercept)-Dasypus_novemcinctus -2.7713 1.1742 -5.4243
## (Intercept)-Lynx_rufus 0.3934 3.1143 -3.6295
## (Intercept)-Didelphis_virginiana -4.2848 1.4308 -7.3834
## (Intercept)-Sylvilagus_floridanus -2.4726 1.4093 -5.4864
## (Intercept)-Sciurus_carolinensis -4.9982 1.6080 -8.7190
## (Intercept)-Vulpes_vulpes -4.0459 2.3711 -8.4735
## (Intercept)-Sus_scrofa -6.0548 2.1092 -10.7693
## Cogon_Patch_Size-Odocoileus_virginianus -0.0506 1.5857 -3.0065
## Cogon_Patch_Size-Canis_latrans 1.6084 1.4711 -0.4169
## Cogon_Patch_Size-Sciurus_niger -1.0155 2.1116 -5.8170
## Cogon_Patch_Size-Procyon_lotor -0.4964 0.8812 -2.1371
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2506 0.7147 -1.7623
## Cogon_Patch_Size-Lynx_rufus -0.3518 1.5407 -3.1398
## Cogon_Patch_Size-Didelphis_virginiana 1.6381 1.0487 -0.0816
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5157 1.6893 -5.6548
## Cogon_Patch_Size-Sciurus_carolinensis -1.2463 1.4015 -4.6983
## Cogon_Patch_Size-Vulpes_vulpes -0.8547 1.7989 -4.8296
## Cogon_Patch_Size-Sus_scrofa -0.8793 1.5760 -4.6351
## Veg_shannon_index-Odocoileus_virginianus 0.7541 0.8870 -1.1570
## Veg_shannon_index-Canis_latrans 1.3085 0.7153 0.1870
## Veg_shannon_index-Sciurus_niger 1.0179 0.9680 -0.8799
## Veg_shannon_index-Procyon_lotor 1.1077 0.6150 0.0275
## Veg_shannon_index-Dasypus_novemcinctus 0.6434 0.5491 -0.4706
## Veg_shannon_index-Lynx_rufus 0.9777 0.9175 -0.7711
## Veg_shannon_index-Didelphis_virginiana 1.0454 0.6830 -0.2550
## Veg_shannon_index-Sylvilagus_floridanus 0.9918 0.6962 -0.3257
## Veg_shannon_index-Sciurus_carolinensis 0.3011 0.7964 -1.5443
## Veg_shannon_index-Vulpes_vulpes 0.5783 0.8844 -1.3665
## Veg_shannon_index-Sus_scrofa 1.5806 0.9970 0.1736
## total_shrub_cover-Odocoileus_virginianus -0.1195 0.7991 -1.6641
## total_shrub_cover-Canis_latrans -0.0160 0.5643 -1.0830
## total_shrub_cover-Sciurus_niger -0.4946 0.8558 -2.3892
## total_shrub_cover-Procyon_lotor -0.8201 0.6177 -2.1818
## total_shrub_cover-Dasypus_novemcinctus 0.0588 0.5087 -0.9097
## total_shrub_cover-Lynx_rufus -0.6846 0.8429 -2.6950
## total_shrub_cover-Didelphis_virginiana -0.4492 0.6388 -1.8435
## total_shrub_cover-Sylvilagus_floridanus -0.2171 0.6812 -1.6071
## total_shrub_cover-Sciurus_carolinensis -0.0211 0.6238 -1.2136
## total_shrub_cover-Vulpes_vulpes -0.4273 0.7971 -2.2447
## total_shrub_cover-Sus_scrofa 0.0621 0.7451 -1.2735
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0395 1.3625 -2.5754
## Avg_Cogongrass_Cover-Canis_latrans 0.1291 1.1858 -2.2328
## Avg_Cogongrass_Cover-Sciurus_niger -0.2391 1.4883 -3.4436
## Avg_Cogongrass_Cover-Procyon_lotor 0.3318 1.1262 -1.8049
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6773 1.2351 -1.5402
## Avg_Cogongrass_Cover-Lynx_rufus 0.2828 1.2947 -2.2201
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1548 1.2014 -2.1483
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4966 1.2955 -3.1509
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1200 1.1940 -2.1576
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2435 1.3151 -2.1919
## Avg_Cogongrass_Cover-Sus_scrofa -0.4550 1.3917 -3.5344
## Tree_Density-Odocoileus_virginianus -1.0241 1.4334 -3.3970
## Tree_Density-Canis_latrans -2.8069 1.3676 -6.0980
## Tree_Density-Sciurus_niger -1.8900 1.8191 -5.5827
## Tree_Density-Procyon_lotor -1.8196 0.9712 -3.8325
## Tree_Density-Dasypus_novemcinctus -3.7584 1.9633 -8.8618
## Tree_Density-Lynx_rufus -0.8683 1.6501 -3.5578
## Tree_Density-Didelphis_virginiana -2.3621 1.1957 -5.2452
## Tree_Density-Sylvilagus_floridanus -2.5783 1.4222 -5.9979
## Tree_Density-Sciurus_carolinensis -2.8147 1.5969 -6.8086
## Tree_Density-Vulpes_vulpes -2.0627 1.6448 -5.5927
## Tree_Density-Sus_scrofa -2.5079 1.7283 -6.7622
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2911 1.3033 -1.5125
## Avg_Canopy_Cover-Canis_latrans 0.2953 0.7367 -1.1476
## Avg_Canopy_Cover-Sciurus_niger 2.1987 1.6298 -0.7717
## Avg_Canopy_Cover-Procyon_lotor 1.6947 0.7323 0.3651
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9621 0.7337 0.7173
## Avg_Canopy_Cover-Lynx_rufus 1.5240 1.3154 -0.8871
## Avg_Canopy_Cover-Didelphis_virginiana 2.6246 0.9683 1.1001
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2246 1.5005 1.1090
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2858 0.9110 0.8509
## Avg_Canopy_Cover-Vulpes_vulpes 2.3429 1.2885 0.3823
## Avg_Canopy_Cover-Sus_scrofa 2.0184 0.8863 0.5395
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6919 1.0159 0.0251
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9245 0.8935 0.5388
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2415 1.0841 -1.0490
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7761 0.8985 0.4408
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4484 0.6942 0.2019
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9409 0.9805 0.4748
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1439 0.6789 -0.1953
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2122 0.7621 -0.2465
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6350 0.7192 0.4051
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7779 0.8484 0.4205
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.8986 0.9501 -1.2718
## avg_veg_height-Odocoileus_virginianus -0.2240 0.7847 -1.7680
## avg_veg_height-Canis_latrans -0.4304 0.6181 -1.7379
## avg_veg_height-Sciurus_niger -0.3042 0.8014 -2.0078
## avg_veg_height-Procyon_lotor 0.0810 0.6216 -1.1079
## avg_veg_height-Dasypus_novemcinctus 0.0828 0.6054 -1.0693
## avg_veg_height-Lynx_rufus -0.2824 0.7335 -1.8365
## avg_veg_height-Didelphis_virginiana -0.3258 0.6722 -1.7221
## avg_veg_height-Sylvilagus_floridanus -0.3388 0.6759 -1.7425
## avg_veg_height-Sciurus_carolinensis 0.0524 0.6659 -1.2011
## avg_veg_height-Vulpes_vulpes -0.3430 0.7695 -1.9970
## avg_veg_height-Sus_scrofa -0.2949 0.6999 -1.7593
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1180 17.4766 1.0341 293
## (Intercept)-Canis_latrans -0.9225 1.3432 1.0032 1880
## (Intercept)-Sciurus_niger 0.8144 8.3809 1.0199 318
## (Intercept)-Procyon_lotor -0.3650 1.6252 1.0047 1294
## (Intercept)-Dasypus_novemcinctus -2.6475 -0.7699 1.0046 887
## (Intercept)-Lynx_rufus -0.2167 8.8284 1.0155 222
## (Intercept)-Didelphis_virginiana -4.1837 -1.7550 1.0196 1090
## (Intercept)-Sylvilagus_floridanus -2.4038 0.1926 1.0057 1110
## (Intercept)-Sciurus_carolinensis -4.7862 -2.3971 1.0252 436
## (Intercept)-Vulpes_vulpes -4.1191 1.1188 1.0152 401
## (Intercept)-Sus_scrofa -5.8547 -2.5252 1.0139 488
## Cogon_Patch_Size-Odocoileus_virginianus -0.1298 3.4438 1.0063 1577
## Cogon_Patch_Size-Canis_latrans 1.3330 5.1184 1.0248 721
## Cogon_Patch_Size-Sciurus_niger -0.7825 2.5358 1.0206 429
## Cogon_Patch_Size-Procyon_lotor -0.5055 1.1340 1.0106 1235
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2214 1.0667 1.0023 1379
## Cogon_Patch_Size-Lynx_rufus -0.4109 2.9910 1.0208 962
## Cogon_Patch_Size-Didelphis_virginiana 1.5178 4.0199 1.0214 696
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2101 0.8185 1.0031 771
## Cogon_Patch_Size-Sciurus_carolinensis -0.9912 0.7317 1.0042 1158
## Cogon_Patch_Size-Vulpes_vulpes -0.6690 2.2895 1.0128 766
## Cogon_Patch_Size-Sus_scrofa -0.6273 1.5453 1.0111 1237
## Veg_shannon_index-Odocoileus_virginianus 0.7627 2.4420 1.0026 2205
## Veg_shannon_index-Canis_latrans 1.2160 2.9427 1.0115 1219
## Veg_shannon_index-Sciurus_niger 0.9722 3.1005 1.0057 1365
## Veg_shannon_index-Procyon_lotor 1.0678 2.4893 1.0111 1086
## Veg_shannon_index-Dasypus_novemcinctus 0.6538 1.7343 1.0031 2409
## Veg_shannon_index-Lynx_rufus 0.9366 2.9771 1.0013 1436
## Veg_shannon_index-Didelphis_virginiana 1.0178 2.5268 1.0054 2103
## Veg_shannon_index-Sylvilagus_floridanus 0.9601 2.4434 1.0048 1921
## Veg_shannon_index-Sciurus_carolinensis 0.4036 1.6444 1.0063 1914
## Veg_shannon_index-Vulpes_vulpes 0.6591 2.1456 1.0060 1580
## Veg_shannon_index-Sus_scrofa 1.4041 4.0206 1.0061 1133
## total_shrub_cover-Odocoileus_virginianus -0.1409 1.6221 1.0024 2758
## total_shrub_cover-Canis_latrans -0.0348 1.1466 1.0011 2721
## total_shrub_cover-Sciurus_niger -0.4357 1.0712 1.0066 1412
## total_shrub_cover-Procyon_lotor -0.7500 0.2095 1.0097 1890
## total_shrub_cover-Dasypus_novemcinctus 0.0435 1.1291 1.0030 2726
## total_shrub_cover-Lynx_rufus -0.5885 0.7424 1.0106 1251
## total_shrub_cover-Didelphis_virginiana -0.4177 0.7003 1.0047 2087
## total_shrub_cover-Sylvilagus_floridanus -0.2118 1.1242 1.0037 2144
## total_shrub_cover-Sciurus_carolinensis -0.0447 1.2947 1.0015 2854
## total_shrub_cover-Vulpes_vulpes -0.3789 1.0195 1.0121 1761
## total_shrub_cover-Sus_scrofa 0.0052 1.7378 1.0071 2351
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0239 2.7941 1.0013 891
## Avg_Cogongrass_Cover-Canis_latrans 0.1346 2.5120 1.0018 775
## Avg_Cogongrass_Cover-Sciurus_niger -0.1715 2.4685 1.0105 718
## Avg_Cogongrass_Cover-Procyon_lotor 0.3032 2.6257 1.0014 781
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6133 3.2840 1.0003 717
## Avg_Cogongrass_Cover-Lynx_rufus 0.2523 2.9750 1.0010 790
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1409 2.5206 1.0039 696
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4378 1.8180 1.0025 776
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1141 2.4694 1.0014 801
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2190 2.9305 1.0022 855
## Avg_Cogongrass_Cover-Sus_scrofa -0.3751 2.0169 1.0068 711
## Tree_Density-Odocoileus_virginianus -1.1892 2.3252 1.0190 584
## Tree_Density-Canis_latrans -2.5931 -0.7491 1.0092 715
## Tree_Density-Sciurus_niger -1.9027 2.0207 1.0240 526
## Tree_Density-Procyon_lotor -1.7951 0.0198 1.0072 848
## Tree_Density-Dasypus_novemcinctus -3.3330 -1.2685 1.0007 283
## Tree_Density-Lynx_rufus -1.0590 3.1078 1.0280 415
## Tree_Density-Didelphis_virginiana -2.2110 -0.4494 1.0000 805
## Tree_Density-Sylvilagus_floridanus -2.3682 -0.3258 1.0030 687
## Tree_Density-Sciurus_carolinensis -2.5052 -0.6028 1.0114 568
## Tree_Density-Vulpes_vulpes -2.0203 1.4354 1.0163 798
## Tree_Density-Sus_scrofa -2.2472 0.1112 1.0058 956
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3415 3.8695 1.0035 1678
## Avg_Canopy_Cover-Canis_latrans 0.2838 1.7627 1.0163 1482
## Avg_Canopy_Cover-Sciurus_niger 2.0419 6.0509 1.0070 839
## Avg_Canopy_Cover-Procyon_lotor 1.6487 3.2915 1.0061 1295
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8801 3.6007 1.0060 787
## Avg_Canopy_Cover-Lynx_rufus 1.4639 4.3242 1.0142 721
## Avg_Canopy_Cover-Didelphis_virginiana 2.4780 4.8679 1.0076 624
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9476 6.9233 1.0188 555
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1724 4.4331 1.0027 783
## Avg_Canopy_Cover-Vulpes_vulpes 2.1046 5.6617 1.0080 706
## Avg_Canopy_Cover-Sus_scrofa 1.9220 3.9447 1.0012 891
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.5802 4.1898 1.0105 1071
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.7989 4.0824 1.0344 786
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2883 3.3453 1.0239 502
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.6519 3.9232 1.0140 820
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4085 2.9836 1.0129 915
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.8087 4.2871 1.0247 788
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1501 2.4347 1.0115 926
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1809 2.7844 1.0043 958
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5691 3.2632 1.0286 879
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6699 3.7528 1.0249 845
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9699 2.5440 1.0085 937
## avg_veg_height-Odocoileus_virginianus -0.2159 1.3137 1.0030 1258
## avg_veg_height-Canis_latrans -0.4052 0.7222 1.0006 1074
## avg_veg_height-Sciurus_niger -0.2816 1.2359 1.0025 1042
## avg_veg_height-Procyon_lotor 0.0565 1.3612 1.0011 1117
## avg_veg_height-Dasypus_novemcinctus 0.0588 1.3440 1.0005 1081
## avg_veg_height-Lynx_rufus -0.2621 1.1160 1.0035 1290
## avg_veg_height-Didelphis_virginiana -0.3163 0.9756 1.0009 1015
## avg_veg_height-Sylvilagus_floridanus -0.3246 0.9808 1.0026 1081
## avg_veg_height-Sciurus_carolinensis 0.0209 1.4516 1.0073 1302
## avg_veg_height-Vulpes_vulpes -0.3102 1.1008 1.0023 949
## avg_veg_height-Sus_scrofa -0.2861 1.0481 1.0037 1355
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5255 0.0801 0.3667 0.5260 0.6811
## (Intercept)-Canis_latrans -2.4343 0.1876 -2.8097 -2.4294 -2.0772
## (Intercept)-Sciurus_niger -4.5675 0.4729 -5.5136 -4.5592 -3.6431
## (Intercept)-Procyon_lotor -2.1575 0.1493 -2.4544 -2.1552 -1.8695
## (Intercept)-Dasypus_novemcinctus -1.4370 0.1546 -1.7496 -1.4364 -1.1435
## (Intercept)-Lynx_rufus -3.6067 0.3577 -4.3142 -3.6072 -2.8990
## (Intercept)-Didelphis_virginiana -2.1013 0.2672 -2.6555 -2.0905 -1.6033
## (Intercept)-Sylvilagus_floridanus -3.0984 0.2986 -3.7065 -3.0914 -2.5424
## (Intercept)-Sciurus_carolinensis -2.2492 0.2876 -2.8445 -2.2379 -1.7248
## (Intercept)-Vulpes_vulpes -4.0472 0.6745 -5.4974 -4.0071 -2.8296
## (Intercept)-Sus_scrofa -2.7540 0.4867 -3.8040 -2.7169 -1.9164
## week-Odocoileus_virginianus 1.2792 0.1228 1.0367 1.2794 1.5179
## week-Canis_latrans 0.5896 0.2600 0.0877 0.5846 1.1094
## week-Sciurus_niger -0.3527 0.5181 -1.4564 -0.3256 0.5563
## week-Procyon_lotor 0.2053 0.2082 -0.1939 0.2025 0.6114
## week-Dasypus_novemcinctus 0.1046 0.2206 -0.3286 0.1072 0.5427
## week-Lynx_rufus 0.3855 0.3535 -0.3226 0.3919 1.0697
## week-Didelphis_virginiana 0.0584 0.3618 -0.6904 0.0735 0.7486
## week-Sylvilagus_floridanus 0.0681 0.3408 -0.6249 0.0726 0.7097
## week-Sciurus_carolinensis 0.7963 0.3684 0.1097 0.7839 1.5621
## week-Vulpes_vulpes 0.1917 0.5057 -0.8537 0.2097 1.1481
## week-Sus_scrofa 0.6812 0.4353 -0.1377 0.6687 1.5958
## I(week^2)-Odocoileus_virginianus -0.5273 0.0503 -0.6255 -0.5277 -0.4301
## I(week^2)-Canis_latrans -0.2430 0.1059 -0.4543 -0.2417 -0.0401
## I(week^2)-Sciurus_niger -0.2769 0.2247 -0.7465 -0.2665 0.1399
## I(week^2)-Procyon_lotor -0.1323 0.0907 -0.3126 -0.1305 0.0428
## I(week^2)-Dasypus_novemcinctus -0.1802 0.1028 -0.3831 -0.1781 0.0196
## I(week^2)-Lynx_rufus -0.2365 0.1520 -0.5491 -0.2339 0.0550
## I(week^2)-Didelphis_virginiana -0.3997 0.2020 -0.8523 -0.3813 -0.0558
## I(week^2)-Sylvilagus_floridanus -0.1773 0.1576 -0.5036 -0.1729 0.1208
## I(week^2)-Sciurus_carolinensis -0.2830 0.1447 -0.5715 -0.2811 0.0000
## I(week^2)-Vulpes_vulpes -0.4027 0.2504 -0.9810 -0.3790 0.0212
## I(week^2)-Sus_scrofa -0.2414 0.1760 -0.6076 -0.2327 0.1012
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0023 3674
## (Intercept)-Sciurus_niger 1.0123 605
## (Intercept)-Procyon_lotor 1.0070 4087
## (Intercept)-Dasypus_novemcinctus 1.0018 4999
## (Intercept)-Lynx_rufus 1.0067 592
## (Intercept)-Didelphis_virginiana 1.0032 4142
## (Intercept)-Sylvilagus_floridanus 1.0039 1953
## (Intercept)-Sciurus_carolinensis 1.0002 3696
## (Intercept)-Vulpes_vulpes 1.0364 457
## (Intercept)-Sus_scrofa 1.0011 1601
## week-Odocoileus_virginianus 1.0011 4516
## week-Canis_latrans 1.0025 3805
## week-Sciurus_niger 1.0039 778
## week-Procyon_lotor 1.0038 4143
## week-Dasypus_novemcinctus 1.0008 4940
## week-Lynx_rufus 1.0036 2455
## week-Didelphis_virginiana 1.0029 3208
## week-Sylvilagus_floridanus 1.0012 3344
## week-Sciurus_carolinensis 1.0016 4175
## week-Vulpes_vulpes 1.0027 1765
## week-Sus_scrofa 1.0029 3718
## I(week^2)-Odocoileus_virginianus 1.0018 4636
## I(week^2)-Canis_latrans 1.0016 4172
## I(week^2)-Sciurus_niger 1.0205 848
## I(week^2)-Procyon_lotor 1.0033 3945
## I(week^2)-Dasypus_novemcinctus 1.0027 4214
## I(week^2)-Lynx_rufus 1.0012 2005
## I(week^2)-Didelphis_virginiana 1.0023 2058
## I(week^2)-Sylvilagus_floridanus 1.0048 2531
## I(week^2)-Sciurus_carolinensis 1.0008 4370
## I(week^2)-Vulpes_vulpes 1.0136 1432
## I(week^2)-Sus_scrofa 1.0023 3913
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null<- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.95
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.107 0.5262 -1.1108 -0.12 1.0045 1.0049 2694
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9983 2.2938 0.8136 2.395 8.5829 1.0084 2715
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4035 0.4621 -3.2982 -2.4053 -1.4746 1.0000 4295
## shrub_cover 0.2005 0.2480 -0.2863 0.1971 0.6927 1.0005 3059
## veg_height -0.0085 0.1565 -0.3271 -0.0092 0.2963 1.0058 3512
## week 0.3555 0.2449 -0.1535 0.3598 0.8187 1.0000 3119
## I(week^2) -0.2864 0.1029 -0.4996 -0.2850 -0.0900 1.0065 2454
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3471 1.4185 0.8280 1.9891 6.0580 1.0061 1851
## shrub_cover 0.4834 0.4132 0.0886 0.3685 1.5766 1.0022 1883
## veg_height 0.1969 0.1369 0.0554 0.1618 0.5607 1.0047 3559
## week 0.4531 0.3635 0.1076 0.3524 1.4322 1.0172 1995
## I(week^2) 0.0724 0.0532 0.0222 0.0582 0.2116 1.0000 2521
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.3845 1.0594 1.7695 3.2430 5.8345
## (Intercept)-Canis_latrans 0.4065 0.4230 -0.3850 0.3991 1.2946
## (Intercept)-Sciurus_niger -0.4792 1.0131 -1.9764 -0.6520 1.9765
## (Intercept)-Procyon_lotor 0.7414 0.4129 -0.0160 0.7226 1.6164
## (Intercept)-Dasypus_novemcinctus -0.5780 0.3764 -1.3305 -0.5742 0.1346
## (Intercept)-Lynx_rufus 0.5906 0.9127 -0.7187 0.4452 2.8184
## (Intercept)-Didelphis_virginiana -1.2323 0.4704 -2.2002 -1.2190 -0.3374
## (Intercept)-Sylvilagus_floridanus -0.3128 0.5068 -1.2278 -0.3453 0.7788
## (Intercept)-Sciurus_carolinensis -1.2243 0.4789 -2.2157 -1.2083 -0.3130
## (Intercept)-Vulpes_vulpes -0.8580 1.1994 -2.6366 -1.0451 2.1533
## (Intercept)-Sus_scrofa -1.6779 0.6714 -3.0374 -1.6668 -0.3798
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 1785
## (Intercept)-Canis_latrans 1.0016 4747
## (Intercept)-Sciurus_niger 1.0043 559
## (Intercept)-Procyon_lotor 1.0003 5250
## (Intercept)-Dasypus_novemcinctus 0.9999 4971
## (Intercept)-Lynx_rufus 1.0022 1009
## (Intercept)-Didelphis_virginiana 1.0006 4140
## (Intercept)-Sylvilagus_floridanus 1.0006 2618
## (Intercept)-Sciurus_carolinensis 1.0042 4619
## (Intercept)-Vulpes_vulpes 1.0238 305
## (Intercept)-Sus_scrofa 1.0010 2299
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5364 0.0811 0.3718 0.5367 0.6941
## (Intercept)-Canis_latrans -2.5582 0.2012 -2.9580 -2.5551 -2.1735
## (Intercept)-Sciurus_niger -4.0510 0.6791 -5.3624 -4.0437 -2.7450
## (Intercept)-Procyon_lotor -2.1782 0.1652 -2.5232 -2.1736 -1.8717
## (Intercept)-Dasypus_novemcinctus -1.5762 0.1768 -1.9325 -1.5699 -1.2395
## (Intercept)-Lynx_rufus -3.6205 0.3772 -4.3556 -3.6190 -2.9021
## (Intercept)-Didelphis_virginiana -2.3053 0.3018 -2.9308 -2.2986 -1.7467
## (Intercept)-Sylvilagus_floridanus -3.0792 0.3233 -3.7623 -3.0672 -2.4884
## (Intercept)-Sciurus_carolinensis -2.3937 0.3280 -3.0813 -2.3806 -1.7900
## (Intercept)-Vulpes_vulpes -4.0815 0.8216 -5.7779 -4.0338 -2.6085
## (Intercept)-Sus_scrofa -3.1315 0.6222 -4.3872 -3.1248 -1.9458
## shrub_cover-Odocoileus_virginianus -0.0607 0.0680 -0.1950 -0.0609 0.0738
## shrub_cover-Canis_latrans -0.2873 0.2169 -0.7193 -0.2874 0.1285
## shrub_cover-Sciurus_niger -0.3252 0.4663 -1.2875 -0.3073 0.5584
## shrub_cover-Procyon_lotor 0.2474 0.1649 -0.0997 0.2510 0.5612
## shrub_cover-Dasypus_novemcinctus 0.7833 0.2920 0.2399 0.7787 1.3632
## shrub_cover-Lynx_rufus -0.3410 0.3432 -1.0435 -0.3349 0.3269
## shrub_cover-Didelphis_virginiana 0.8764 0.3620 0.2254 0.8573 1.6423
## shrub_cover-Sylvilagus_floridanus 0.2302 0.4019 -0.5400 0.2184 1.0318
## shrub_cover-Sciurus_carolinensis 0.7484 0.4049 0.0027 0.7355 1.5751
## shrub_cover-Vulpes_vulpes -0.1175 0.5510 -1.2683 -0.1006 0.9356
## shrub_cover-Sus_scrofa 0.4848 0.7234 -0.9152 0.4671 1.9725
## veg_height-Odocoileus_virginianus -0.3321 0.0681 -0.4657 -0.3321 -0.1977
## veg_height-Canis_latrans -0.5870 0.1843 -0.9609 -0.5830 -0.2412
## veg_height-Sciurus_niger -0.0640 0.4024 -0.8322 -0.0721 0.7999
## veg_height-Procyon_lotor 0.3328 0.1226 0.0889 0.3327 0.5748
## veg_height-Dasypus_novemcinctus 0.2273 0.1304 -0.0176 0.2239 0.4863
## veg_height-Lynx_rufus 0.0278 0.2393 -0.4588 0.0325 0.4805
## veg_height-Didelphis_virginiana 0.4110 0.2382 -0.0413 0.4019 0.8847
## veg_height-Sylvilagus_floridanus 0.1142 0.2444 -0.3562 0.1052 0.5894
## veg_height-Sciurus_carolinensis 0.0475 0.2050 -0.3480 0.0451 0.4663
## veg_height-Vulpes_vulpes -0.1249 0.3117 -0.7705 -0.1052 0.4422
## veg_height-Sus_scrofa -0.1388 0.3234 -0.7962 -0.1308 0.4686
## week-Odocoileus_virginianus 1.3108 0.1240 1.0733 1.3106 1.5576
## week-Canis_latrans 0.5914 0.2637 0.0745 0.5913 1.1177
## week-Sciurus_niger -0.4447 0.5806 -1.6956 -0.3946 0.5701
## week-Procyon_lotor 0.2015 0.2130 -0.2149 0.2005 0.6154
## week-Dasypus_novemcinctus 0.1068 0.2275 -0.3459 0.1066 0.5549
## week-Lynx_rufus 0.3867 0.3539 -0.3099 0.3855 1.0763
## week-Didelphis_virginiana 0.0651 0.3717 -0.6912 0.0763 0.7640
## week-Sylvilagus_floridanus 0.0560 0.3504 -0.6309 0.0641 0.7265
## week-Sciurus_carolinensis 0.8098 0.3680 0.1182 0.8009 1.5621
## week-Vulpes_vulpes 0.1995 0.5245 -0.9074 0.2288 1.1775
## week-Sus_scrofa 0.6926 0.4586 -0.1894 0.6770 1.6404
## I(week^2)-Odocoileus_virginianus -0.5403 0.0510 -0.6402 -0.5400 -0.4410
## I(week^2)-Canis_latrans -0.2465 0.1073 -0.4594 -0.2443 -0.0431
## I(week^2)-Sciurus_niger -0.2913 0.2364 -0.7908 -0.2820 0.1438
## I(week^2)-Procyon_lotor -0.1323 0.0927 -0.3127 -0.1319 0.0520
## I(week^2)-Dasypus_novemcinctus -0.1807 0.1055 -0.3977 -0.1784 0.0190
## I(week^2)-Lynx_rufus -0.2439 0.1560 -0.5622 -0.2413 0.0480
## I(week^2)-Didelphis_virginiana -0.4039 0.2048 -0.8677 -0.3852 -0.0559
## I(week^2)-Sylvilagus_floridanus -0.1832 0.1582 -0.4932 -0.1771 0.1177
## I(week^2)-Sciurus_carolinensis -0.2848 0.1467 -0.5822 -0.2807 -0.0023
## I(week^2)-Vulpes_vulpes -0.4032 0.2498 -0.9635 -0.3813 0.0313
## I(week^2)-Sus_scrofa -0.2476 0.1794 -0.6149 -0.2457 0.0921
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0098 2988
## (Intercept)-Sciurus_niger 1.0148 595
## (Intercept)-Procyon_lotor 1.0013 4014
## (Intercept)-Dasypus_novemcinctus 1.0003 4492
## (Intercept)-Lynx_rufus 1.0074 860
## (Intercept)-Didelphis_virginiana 1.0056 2855
## (Intercept)-Sylvilagus_floridanus 1.0012 1862
## (Intercept)-Sciurus_carolinensis 1.0013 2908
## (Intercept)-Vulpes_vulpes 1.0148 329
## (Intercept)-Sus_scrofa 1.0013 1590
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0032 2348
## shrub_cover-Sciurus_niger 1.0068 1150
## shrub_cover-Procyon_lotor 1.0045 3802
## shrub_cover-Dasypus_novemcinctus 1.0003 3888
## shrub_cover-Lynx_rufus 1.0019 1351
## shrub_cover-Didelphis_virginiana 1.0011 2051
## shrub_cover-Sylvilagus_floridanus 1.0032 1791
## shrub_cover-Sciurus_carolinensis 1.0035 2327
## shrub_cover-Vulpes_vulpes 1.0013 1730
## shrub_cover-Sus_scrofa 1.0001 2197
## veg_height-Odocoileus_virginianus 1.0001 5276
## veg_height-Canis_latrans 1.0151 2362
## veg_height-Sciurus_niger 1.0006 2139
## veg_height-Procyon_lotor 1.0001 4198
## veg_height-Dasypus_novemcinctus 1.0004 4830
## veg_height-Lynx_rufus 1.0057 2393
## veg_height-Didelphis_virginiana 1.0016 3505
## veg_height-Sylvilagus_floridanus 1.0006 2696
## veg_height-Sciurus_carolinensis 1.0025 3460
## veg_height-Vulpes_vulpes 1.0015 2053
## veg_height-Sus_scrofa 1.0007 3662
## week-Odocoileus_virginianus 1.0008 3945
## week-Canis_latrans 1.0076 3869
## week-Sciurus_niger 1.0073 870
## week-Procyon_lotor 1.0059 4761
## week-Dasypus_novemcinctus 1.0011 4245
## week-Lynx_rufus 1.0010 2582
## week-Didelphis_virginiana 1.0046 3101
## week-Sylvilagus_floridanus 1.0001 2793
## week-Sciurus_carolinensis 1.0002 3487
## week-Vulpes_vulpes 1.0002 1419
## week-Sus_scrofa 1.0006 3758
## I(week^2)-Odocoileus_virginianus 1.0014 3995
## I(week^2)-Canis_latrans 1.0059 3752
## I(week^2)-Sciurus_niger 1.0089 1380
## I(week^2)-Procyon_lotor 0.9998 4280
## I(week^2)-Dasypus_novemcinctus 1.0002 4361
## I(week^2)-Lynx_rufus 1.0073 2589
## I(week^2)-Didelphis_virginiana 1.0080 1555
## I(week^2)-Sylvilagus_floridanus 1.0050 2439
## I(week^2)-Sciurus_carolinensis 1.0016 4228
## I(week^2)-Vulpes_vulpes 1.0042 1384
## I(week^2)-Sus_scrofa 1.0003 4221
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full<- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
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summary(ms_fullQ_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2172
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0924 1.0230 -2.0429 -0.1072 2.0506 1.0033 1664
## Cogon_Patch_Size -0.7770 0.6792 -2.2273 -0.7370 0.4486 1.0022 1020
## Veg_shannon_index 0.8619 0.4891 -0.0502 0.8440 1.8401 1.0024 703
## total_shrub_cover -0.3235 0.4797 -1.3695 -0.3031 0.5931 1.0080 820
## Avg_Cogongrass_Cover 2.0454 0.6914 0.7661 2.0323 3.4381 1.0072 419
## Tree_Density -1.8375 0.7240 -3.3297 -1.7993 -0.4608 1.0178 714
## Avg_Canopy_Cover 1.8755 0.5981 0.7663 1.8392 3.1435 1.0031 750
## avg_veg_height -0.5275 0.4475 -1.4147 -0.5214 0.3453 1.0090 759
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.2035 17.4320 3.2027 13.3953 63.0114 1.0360 283
## Cogon_Patch_Size 2.7543 4.6449 0.1105 1.4287 13.0746 1.1529 556
## Veg_shannon_index 0.9808 1.6737 0.0551 0.4880 4.8844 1.0326 585
## total_shrub_cover 0.7802 1.1433 0.0501 0.4211 3.6610 1.0232 712
## Avg_Cogongrass_Cover 1.0967 1.9363 0.0505 0.4754 6.0661 1.0035 710
## Tree_Density 3.0316 5.4513 0.0694 1.3032 16.1030 1.0403 418
## Avg_Canopy_Cover 2.0661 2.4778 0.1438 1.3206 8.9151 1.0172 492
## avg_veg_height 0.4024 0.5653 0.0395 0.2286 1.8449 1.0043 1728
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7009 2.4391 0.0617 0.8639 8.3545 1.0163 198
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4447 0.4775 -3.3726 -2.4515 -1.4576 1.0003 4917
## shrub_cover 0.2715 0.2575 -0.2140 0.2638 0.8086 1.0045 2060
## veg_height 0.0004 0.1609 -0.3257 0.0036 0.3115 1.0028 3280
## week 0.3563 0.2427 -0.1522 0.3641 0.8122 1.0043 3099
## I(week^2) -0.2877 0.1026 -0.4957 -0.2862 -0.0986 1.0018 2561
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6170 1.6318 0.9301 2.1937 6.6925 1.0002 3456
## shrub_cover 0.4811 0.3770 0.1014 0.3829 1.4224 1.0040 2117
## veg_height 0.2023 0.1350 0.0577 0.1679 0.5587 1.0069 3152
## week 0.4495 0.3510 0.1092 0.3545 1.3271 1.0023 1653
## I(week^2) 0.0738 0.0515 0.0226 0.0598 0.2116 1.0012 2555
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1130 3.4179 3.2484 7.4755
## (Intercept)-Canis_latrans 0.9014 1.2061 -1.0996 0.7894
## (Intercept)-Sciurus_niger 1.4679 2.5253 -2.3788 1.1260
## (Intercept)-Procyon_lotor 0.9538 1.0254 -1.1862 0.9466
## (Intercept)-Dasypus_novemcinctus -1.4256 1.0311 -3.6217 -1.3413
## (Intercept)-Lynx_rufus 2.4328 2.9181 -1.8678 1.9862
## (Intercept)-Didelphis_virginiana -2.7708 1.1821 -5.3098 -2.7242
## (Intercept)-Sylvilagus_floridanus -1.2045 1.2140 -3.7153 -1.1729
## (Intercept)-Sciurus_carolinensis -2.9744 1.3569 -6.0440 -2.8488
## (Intercept)-Vulpes_vulpes -2.0164 2.0464 -5.5054 -2.1408
## (Intercept)-Sus_scrofa -4.3080 1.9951 -8.9104 -4.0743
## Cogon_Patch_Size-Odocoileus_virginianus -0.6097 1.2546 -2.9960 -0.6482
## Cogon_Patch_Size-Canis_latrans 0.5851 1.2268 -1.1197 0.3587
## Cogon_Patch_Size-Sciurus_niger -1.4255 1.7940 -5.7049 -1.2197
## Cogon_Patch_Size-Procyon_lotor -1.0354 0.7075 -2.4940 -1.0055
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6243 0.7300 -2.0467 -0.6371
## Cogon_Patch_Size-Lynx_rufus -0.8713 1.3959 -3.6610 -0.9038
## Cogon_Patch_Size-Didelphis_virginiana 0.7052 0.8775 -0.7785 0.6058
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9172 1.5475 -5.9485 -1.5963
## Cogon_Patch_Size-Sciurus_carolinensis -1.6776 1.4049 -5.2054 -1.4160
## Cogon_Patch_Size-Vulpes_vulpes -1.2381 1.6072 -4.8680 -1.1054
## Cogon_Patch_Size-Sus_scrofa -1.3464 1.5505 -5.2753 -1.0676
## Veg_shannon_index-Odocoileus_virginianus 0.7285 0.9663 -1.3227 0.7633
## Veg_shannon_index-Canis_latrans 1.2997 0.7237 0.0728 1.2316
## Veg_shannon_index-Sciurus_niger 1.0121 1.1498 -1.3212 0.9878
## Veg_shannon_index-Procyon_lotor 1.1835 0.6273 0.1111 1.1299
## Veg_shannon_index-Dasypus_novemcinctus 0.5922 0.5707 -0.5791 0.6008
## Veg_shannon_index-Lynx_rufus 0.8366 0.9431 -1.2409 0.8651
## Veg_shannon_index-Didelphis_virginiana 1.0808 0.6970 -0.1373 1.0362
## Veg_shannon_index-Sylvilagus_floridanus 1.0278 0.6978 -0.2517 0.9851
## Veg_shannon_index-Sciurus_carolinensis 0.1468 0.7860 -1.6179 0.2191
## Veg_shannon_index-Vulpes_vulpes 0.2918 0.9223 -1.8386 0.3942
## Veg_shannon_index-Sus_scrofa 1.6169 1.1250 0.0911 1.4102
## total_shrub_cover-Odocoileus_virginianus -0.0793 0.8273 -1.6778 -0.1208
## total_shrub_cover-Canis_latrans 0.3287 0.7355 -0.8516 0.2274
## total_shrub_cover-Sciurus_niger -0.4593 0.9384 -2.5502 -0.4041
## total_shrub_cover-Procyon_lotor -0.8365 0.6185 -2.2809 -0.7824
## total_shrub_cover-Dasypus_novemcinctus -0.1249 0.5964 -1.3510 -0.1157
## total_shrub_cover-Lynx_rufus -0.5368 1.0136 -2.7997 -0.4836
## total_shrub_cover-Didelphis_virginiana -0.5653 0.7257 -2.1694 -0.4953
## total_shrub_cover-Sylvilagus_floridanus -0.4553 0.8052 -2.3169 -0.3895
## total_shrub_cover-Sciurus_carolinensis -0.3059 0.7844 -2.0244 -0.2528
## total_shrub_cover-Vulpes_vulpes -0.5353 0.9622 -2.8337 -0.4332
## total_shrub_cover-Sus_scrofa -0.1022 0.8538 -1.7969 -0.1178
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0036 1.0627 -0.0651 1.9971
## Avg_Cogongrass_Cover-Canis_latrans 2.4681 0.9829 0.8647 2.3588
## Avg_Cogongrass_Cover-Sciurus_niger 1.5491 1.3871 -1.8419 1.7314
## Avg_Cogongrass_Cover-Procyon_lotor 2.2404 0.8689 0.6772 2.1990
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5851 0.9792 0.9413 2.4817
## Avg_Cogongrass_Cover-Lynx_rufus 2.3683 1.0506 0.5947 2.2853
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1308 0.8695 0.5169 2.0978
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4902 0.9669 -0.5123 1.5375
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3159 0.9177 0.7091 2.2403
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4890 1.0733 0.7070 2.3952
## Avg_Cogongrass_Cover-Sus_scrofa 1.6769 1.1176 -0.8094 1.7455
## Tree_Density-Odocoileus_virginianus -0.8057 1.3068 -2.8072 -0.9720
## Tree_Density-Canis_latrans -2.6452 1.3259 -5.9206 -2.3985
## Tree_Density-Sciurus_niger -1.8550 1.6930 -5.1574 -1.8431
## Tree_Density-Procyon_lotor -1.5133 0.7688 -3.0372 -1.5116
## Tree_Density-Dasypus_novemcinctus -3.4784 1.8307 -8.3727 -3.0255
## Tree_Density-Lynx_rufus -0.7136 1.4572 -3.0260 -0.9171
## Tree_Density-Didelphis_virginiana -2.2488 1.1591 -5.0043 -2.1169
## Tree_Density-Sylvilagus_floridanus -2.3545 1.3871 -5.8789 -2.1702
## Tree_Density-Sciurus_carolinensis -2.3807 1.4576 -5.9785 -2.1731
## Tree_Density-Vulpes_vulpes -1.7564 1.6232 -4.8518 -1.7947
## Tree_Density-Sus_scrofa -2.3883 1.6659 -6.5954 -2.1156
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3276 1.2695 -1.4227 1.3863
## Avg_Canopy_Cover-Canis_latrans 0.3571 0.6883 -0.9906 0.3520
## Avg_Canopy_Cover-Sciurus_niger 2.0861 1.5562 -0.8938 1.9773
## Avg_Canopy_Cover-Procyon_lotor 1.7861 0.7570 0.4704 1.7341
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0734 0.7179 0.8533 1.9930
## Avg_Canopy_Cover-Lynx_rufus 1.5080 1.3426 -1.1150 1.5208
## Avg_Canopy_Cover-Didelphis_virginiana 2.8051 1.0648 1.2331 2.6322
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2327 1.4222 1.2136 2.9730
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5765 1.0671 1.0600 2.3923
## Avg_Canopy_Cover-Vulpes_vulpes 2.2359 1.1169 0.4559 2.0921
## Avg_Canopy_Cover-Sus_scrofa 2.1139 0.9116 0.5908 1.9999
## avg_veg_height-Odocoileus_virginianus -0.5648 0.7145 -2.0145 -0.5451
## avg_veg_height-Canis_latrans -0.5595 0.5791 -1.6869 -0.5514
## avg_veg_height-Sciurus_niger -0.6913 0.8284 -2.4814 -0.6445
## avg_veg_height-Procyon_lotor -0.4407 0.5650 -1.5506 -0.4449
## avg_veg_height-Dasypus_novemcinctus -0.3194 0.5582 -1.3826 -0.3303
## avg_veg_height-Lynx_rufus -0.6317 0.7397 -2.2059 -0.5887
## avg_veg_height-Didelphis_virginiana -0.6482 0.6254 -1.9577 -0.6198
## avg_veg_height-Sylvilagus_floridanus -0.7118 0.6435 -2.1165 -0.6877
## avg_veg_height-Sciurus_carolinensis -0.2171 0.6161 -1.3658 -0.2436
## avg_veg_height-Vulpes_vulpes -0.5283 0.6819 -1.9114 -0.5206
## avg_veg_height-Sus_scrofa -0.5840 0.6641 -2.0006 -0.5730
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.5026 1.0248 299
## (Intercept)-Canis_latrans 3.4323 1.0274 737
## (Intercept)-Sciurus_niger 7.5719 1.0160 312
## (Intercept)-Procyon_lotor 3.0434 1.0095 1150
## (Intercept)-Dasypus_novemcinctus 0.3460 1.0050 705
## (Intercept)-Lynx_rufus 9.3135 1.0200 227
## (Intercept)-Didelphis_virginiana -0.6038 1.0141 1258
## (Intercept)-Sylvilagus_floridanus 1.1297 1.0159 1113
## (Intercept)-Sciurus_carolinensis -0.6383 1.0149 509
## (Intercept)-Vulpes_vulpes 2.6486 1.0387 375
## (Intercept)-Sus_scrofa -0.8975 1.0103 515
## Cogon_Patch_Size-Odocoileus_virginianus 2.0555 1.0031 1611
## Cogon_Patch_Size-Canis_latrans 3.5003 1.0138 1109
## Cogon_Patch_Size-Sciurus_niger 1.5895 1.0080 570
## Cogon_Patch_Size-Procyon_lotor 0.2695 1.0012 744
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9501 1.0025 1579
## Cogon_Patch_Size-Lynx_rufus 2.0787 1.0112 923
## Cogon_Patch_Size-Didelphis_virginiana 2.6257 1.0015 1268
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2124 1.0380 599
## Cogon_Patch_Size-Sciurus_carolinensis 0.2964 1.0276 827
## Cogon_Patch_Size-Vulpes_vulpes 1.6135 1.0107 714
## Cogon_Patch_Size-Sus_scrofa 0.8705 1.0261 697
## Veg_shannon_index-Odocoileus_virginianus 2.4840 1.0031 1178
## Veg_shannon_index-Canis_latrans 2.9271 1.0004 984
## Veg_shannon_index-Sciurus_niger 3.3357 1.0083 813
## Veg_shannon_index-Procyon_lotor 2.5441 1.0039 697
## Veg_shannon_index-Dasypus_novemcinctus 1.6920 1.0033 1219
## Veg_shannon_index-Lynx_rufus 2.6330 1.0021 1352
## Veg_shannon_index-Didelphis_virginiana 2.6436 1.0028 1491
## Veg_shannon_index-Sylvilagus_floridanus 2.4965 1.0018 1427
## Veg_shannon_index-Sciurus_carolinensis 1.5053 1.0149 1170
## Veg_shannon_index-Vulpes_vulpes 1.8050 1.0024 859
## Veg_shannon_index-Sus_scrofa 4.4673 1.0170 760
## total_shrub_cover-Odocoileus_virginianus 1.7619 1.0112 1945
## total_shrub_cover-Canis_latrans 2.1442 1.0181 1296
## total_shrub_cover-Sciurus_niger 1.3172 1.0032 1031
## total_shrub_cover-Procyon_lotor 0.2061 1.0076 1230
## total_shrub_cover-Dasypus_novemcinctus 1.0121 1.0084 2079
## total_shrub_cover-Lynx_rufus 1.3297 1.0021 792
## total_shrub_cover-Didelphis_virginiana 0.6326 1.0053 1262
## total_shrub_cover-Sylvilagus_floridanus 0.9528 1.0021 1122
## total_shrub_cover-Sciurus_carolinensis 1.0991 1.0028 1271
## total_shrub_cover-Vulpes_vulpes 1.0653 1.0064 789
## total_shrub_cover-Sus_scrofa 1.6732 1.0118 1548
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.1551 1.0104 964
## Avg_Cogongrass_Cover-Canis_latrans 4.7797 1.0074 613
## Avg_Cogongrass_Cover-Sciurus_niger 3.7608 1.0030 706
## Avg_Cogongrass_Cover-Procyon_lotor 4.1634 1.0025 720
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8509 1.0137 620
## Avg_Cogongrass_Cover-Lynx_rufus 4.6937 1.0091 796
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9677 1.0040 749
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3424 1.0023 776
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3587 1.0022 615
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.9397 1.0057 685
## Avg_Cogongrass_Cover-Sus_scrofa 3.6538 1.0017 919
## Tree_Density-Odocoileus_virginianus 2.2649 1.0022 910
## Tree_Density-Canis_latrans -0.7218 1.0179 796
## Tree_Density-Sciurus_niger 1.7961 1.0262 537
## Tree_Density-Procyon_lotor -0.0246 1.0064 1307
## Tree_Density-Dasypus_novemcinctus -1.1909 1.0083 419
## Tree_Density-Lynx_rufus 2.7022 1.0145 484
## Tree_Density-Didelphis_virginiana -0.3466 1.0029 1169
## Tree_Density-Sylvilagus_floridanus -0.0596 1.0291 1042
## Tree_Density-Sciurus_carolinensis -0.0639 1.0045 691
## Tree_Density-Vulpes_vulpes 1.4723 1.0191 685
## Tree_Density-Sus_scrofa 0.1813 1.0054 596
## Avg_Canopy_Cover-Odocoileus_virginianus 3.7727 1.0074 1730
## Avg_Canopy_Cover-Canis_latrans 1.7159 1.0268 1265
## Avg_Canopy_Cover-Sciurus_niger 5.5911 1.0103 536
## Avg_Canopy_Cover-Procyon_lotor 3.4447 1.0027 1046
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7214 1.0033 910
## Avg_Canopy_Cover-Lynx_rufus 4.3178 1.0211 837
## Avg_Canopy_Cover-Didelphis_virginiana 5.4645 1.0053 544
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.7060 1.0139 762
## Avg_Canopy_Cover-Sciurus_carolinensis 5.1973 1.0177 525
## Avg_Canopy_Cover-Vulpes_vulpes 4.9454 1.0011 1111
## Avg_Canopy_Cover-Sus_scrofa 4.1593 1.0009 1034
## avg_veg_height-Odocoileus_virginianus 0.7939 1.0028 1662
## avg_veg_height-Canis_latrans 0.5484 1.0142 1226
## avg_veg_height-Sciurus_niger 0.7451 1.0091 1089
## avg_veg_height-Procyon_lotor 0.6838 1.0022 1216
## avg_veg_height-Dasypus_novemcinctus 0.8295 1.0032 1374
## avg_veg_height-Lynx_rufus 0.7495 1.0101 1091
## avg_veg_height-Didelphis_virginiana 0.5444 1.0050 1237
## avg_veg_height-Sylvilagus_floridanus 0.5012 1.0110 1309
## avg_veg_height-Sciurus_carolinensis 1.1380 1.0044 1701
## avg_veg_height-Vulpes_vulpes 0.8712 1.0076 1241
## avg_veg_height-Sus_scrofa 0.7031 1.0113 1350
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5371 0.0808 0.3765 0.5375 0.6973
## (Intercept)-Canis_latrans -2.5681 0.2032 -2.9945 -2.5634 -2.1838
## (Intercept)-Sciurus_niger -4.6553 0.5457 -5.7068 -4.6503 -3.5602
## (Intercept)-Procyon_lotor -2.1829 0.1606 -2.5213 -2.1761 -1.8791
## (Intercept)-Dasypus_novemcinctus -1.5935 0.1793 -1.9570 -1.5893 -1.2529
## (Intercept)-Lynx_rufus -3.7849 0.3885 -4.5165 -3.7946 -3.0040
## (Intercept)-Didelphis_virginiana -2.3050 0.2963 -2.9030 -2.2926 -1.7718
## (Intercept)-Sylvilagus_floridanus -3.0611 0.3010 -3.6681 -3.0528 -2.4881
## (Intercept)-Sciurus_carolinensis -2.4498 0.3374 -3.1385 -2.4436 -1.8215
## (Intercept)-Vulpes_vulpes -4.1207 0.7240 -5.5472 -4.1015 -2.7975
## (Intercept)-Sus_scrofa -3.1056 0.6094 -4.3596 -3.0924 -1.9644
## shrub_cover-Odocoileus_virginianus -0.0585 0.0682 -0.1882 -0.0583 0.0757
## shrub_cover-Canis_latrans -0.3275 0.2233 -0.7638 -0.3295 0.1106
## shrub_cover-Sciurus_niger -0.3201 0.4401 -1.2152 -0.3056 0.5124
## shrub_cover-Procyon_lotor 0.2631 0.1634 -0.0737 0.2678 0.5725
## shrub_cover-Dasypus_novemcinctus 0.8476 0.3038 0.2808 0.8376 1.4608
## shrub_cover-Lynx_rufus -0.2166 0.3605 -0.9165 -0.2237 0.5044
## shrub_cover-Didelphis_virginiana 0.8894 0.3417 0.2715 0.8749 1.5890
## shrub_cover-Sylvilagus_floridanus 0.4244 0.3954 -0.3210 0.4182 1.2207
## shrub_cover-Sciurus_carolinensis 0.8361 0.4079 0.0700 0.8321 1.6537
## shrub_cover-Vulpes_vulpes 0.0921 0.5495 -1.0442 0.1010 1.1585
## shrub_cover-Sus_scrofa 0.5655 0.7299 -0.8558 0.5381 2.0870
## veg_height-Odocoileus_virginianus -0.3294 0.0682 -0.4622 -0.3295 -0.1986
## veg_height-Canis_latrans -0.5890 0.1801 -0.9612 -0.5808 -0.2564
## veg_height-Sciurus_niger -0.0568 0.3531 -0.7391 -0.0561 0.6452
## veg_height-Procyon_lotor 0.3451 0.1229 0.1062 0.3431 0.5861
## veg_height-Dasypus_novemcinctus 0.2392 0.1318 -0.0196 0.2404 0.5065
## veg_height-Lynx_rufus 0.1002 0.2372 -0.3692 0.1045 0.5565
## veg_height-Didelphis_virginiana 0.4236 0.2365 -0.0251 0.4186 0.9082
## veg_height-Sylvilagus_floridanus 0.1504 0.2407 -0.3236 0.1510 0.6319
## veg_height-Sciurus_carolinensis 0.0713 0.2124 -0.3361 0.0674 0.5069
## veg_height-Vulpes_vulpes -0.2031 0.3329 -0.9073 -0.1808 0.4082
## veg_height-Sus_scrofa -0.1490 0.3280 -0.8201 -0.1467 0.4831
## week-Odocoileus_virginianus 1.3112 0.1229 1.0725 1.3114 1.5529
## week-Canis_latrans 0.5990 0.2679 0.0937 0.5969 1.1301
## week-Sciurus_niger -0.4162 0.5542 -1.6139 -0.3694 0.5518
## week-Procyon_lotor 0.2045 0.2082 -0.2126 0.2075 0.6094
## week-Dasypus_novemcinctus 0.1094 0.2306 -0.3478 0.1123 0.5546
## week-Lynx_rufus 0.3708 0.3487 -0.3205 0.3704 1.0628
## week-Didelphis_virginiana 0.0713 0.3789 -0.7099 0.0873 0.7684
## week-Sylvilagus_floridanus 0.0621 0.3486 -0.6277 0.0641 0.7405
## week-Sciurus_carolinensis 0.8091 0.3694 0.1091 0.8017 1.5824
## week-Vulpes_vulpes 0.1821 0.5271 -0.9141 0.1953 1.1601
## week-Sus_scrofa 0.6996 0.4546 -0.1618 0.6924 1.6264
## I(week^2)-Odocoileus_virginianus -0.5408 0.0507 -0.6395 -0.5405 -0.4410
## I(week^2)-Canis_latrans -0.2464 0.1094 -0.4639 -0.2446 -0.0377
## I(week^2)-Sciurus_niger -0.2953 0.2434 -0.8204 -0.2819 0.1374
## I(week^2)-Procyon_lotor -0.1326 0.0906 -0.3110 -0.1307 0.0408
## I(week^2)-Dasypus_novemcinctus -0.1823 0.1056 -0.3879 -0.1813 0.0209
## I(week^2)-Lynx_rufus -0.2376 0.1521 -0.5429 -0.2343 0.0499
## I(week^2)-Didelphis_virginiana -0.4111 0.2119 -0.8904 -0.3898 -0.0597
## I(week^2)-Sylvilagus_floridanus -0.1800 0.1606 -0.5047 -0.1763 0.1330
## I(week^2)-Sciurus_carolinensis -0.2870 0.1455 -0.5858 -0.2837 -0.0166
## I(week^2)-Vulpes_vulpes -0.4075 0.2499 -0.9626 -0.3889 0.0264
## I(week^2)-Sus_scrofa -0.2434 0.1789 -0.6044 -0.2383 0.0950
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0027 1878
## (Intercept)-Sciurus_niger 1.0077 436
## (Intercept)-Procyon_lotor 1.0020 3610
## (Intercept)-Dasypus_novemcinctus 1.0022 4480
## (Intercept)-Lynx_rufus 1.0077 393
## (Intercept)-Didelphis_virginiana 1.0019 2583
## (Intercept)-Sylvilagus_floridanus 1.0139 2182
## (Intercept)-Sciurus_carolinensis 1.0073 2066
## (Intercept)-Vulpes_vulpes 1.0212 510
## (Intercept)-Sus_scrofa 1.0051 1363
## shrub_cover-Odocoileus_virginianus 1.0007 5250
## shrub_cover-Canis_latrans 1.0021 2127
## shrub_cover-Sciurus_niger 1.0019 1013
## shrub_cover-Procyon_lotor 1.0011 3960
## shrub_cover-Dasypus_novemcinctus 1.0027 2466
## shrub_cover-Lynx_rufus 1.0017 857
## shrub_cover-Didelphis_virginiana 1.0006 1993
## shrub_cover-Sylvilagus_floridanus 1.0105 1397
## shrub_cover-Sciurus_carolinensis 1.0009 1736
## shrub_cover-Vulpes_vulpes 1.0031 1392
## shrub_cover-Sus_scrofa 1.0075 1574
## veg_height-Odocoileus_virginianus 1.0008 5250
## veg_height-Canis_latrans 1.0055 2506
## veg_height-Sciurus_niger 1.0013 1108
## veg_height-Procyon_lotor 1.0016 4037
## veg_height-Dasypus_novemcinctus 1.0018 4387
## veg_height-Lynx_rufus 1.0038 2022
## veg_height-Didelphis_virginiana 1.0020 3407
## veg_height-Sylvilagus_floridanus 1.0023 2711
## veg_height-Sciurus_carolinensis 1.0007 2525
## veg_height-Vulpes_vulpes 1.0063 1807
## veg_height-Sus_scrofa 1.0018 3017
## week-Odocoileus_virginianus 1.0011 5250
## week-Canis_latrans 1.0003 3790
## week-Sciurus_niger 1.0080 637
## week-Procyon_lotor 1.0012 4172
## week-Dasypus_novemcinctus 1.0004 4698
## week-Lynx_rufus 1.0001 2334
## week-Didelphis_virginiana 1.0045 2805
## week-Sylvilagus_floridanus 1.0021 3062
## week-Sciurus_carolinensis 1.0005 3513
## week-Vulpes_vulpes 1.0034 1741
## week-Sus_scrofa 1.0011 4141
## I(week^2)-Odocoileus_virginianus 1.0032 5250
## I(week^2)-Canis_latrans 1.0007 3836
## I(week^2)-Sciurus_niger 1.0163 786
## I(week^2)-Procyon_lotor 1.0024 4262
## I(week^2)-Dasypus_novemcinctus 1.0055 4518
## I(week^2)-Lynx_rufus 1.0071 2135
## I(week^2)-Didelphis_virginiana 1.0009 1751
## I(week^2)-Sylvilagus_floridanus 1.0025 2688
## I(week^2)-Sciurus_carolinensis 1.0003 3443
## I(week^2)-Vulpes_vulpes 1.0012 1444
## I(week^2)-Sus_scrofa 1.0004 3974
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover<- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2067
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0688 0.6366 -1.1230 0.0595 1.4039 1.0080 1451
## Avg_Cogongrass_Cover 0.0124 0.3343 -0.6731 0.0170 0.6673 1.0004 1386
## total_shrub_cover -0.6385 0.4195 -1.5558 -0.6118 0.0889 1.0001 511
## avg_veg_height 0.1667 0.3281 -0.4894 0.1688 0.8091 1.0028 862
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5831 3.2253 0.4503 2.7178 11.8672 1.0401 1123
## Avg_Cogongrass_Cover 0.3616 0.4713 0.0383 0.2124 1.6734 1.0053 1717
## total_shrub_cover 0.7531 1.0584 0.0553 0.4332 3.3514 1.0079 893
## avg_veg_height 0.2512 0.2960 0.0348 0.1621 0.9801 1.0022 2438
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.251 1.3226 0.0724 0.8225 4.9479 1.0125 285
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4708 0.4655 -3.3752 -2.4757 -1.5281 1.0000 3835
## shrub_cover 0.4382 0.2862 -0.1181 0.4318 1.0324 1.0078 840
## veg_height -0.0178 0.1645 -0.3490 -0.0205 0.3176 1.0009 2978
## week 0.3522 0.2386 -0.1315 0.3606 0.7944 1.0061 2992
## I(week^2) -0.2860 0.1039 -0.4902 -0.2835 -0.0827 1.0103 2057
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4095 1.4489 0.8438 2.0455 6.3682 1.0057 2525
## shrub_cover 0.6023 0.4900 0.1178 0.4680 1.9286 1.0031 1036
## veg_height 0.2151 0.1628 0.0575 0.1717 0.6240 1.0033 3204
## week 0.4548 0.3490 0.1105 0.3560 1.4005 1.0133 1537
## I(week^2) 0.0740 0.0577 0.0224 0.0592 0.2166 1.0480 1287
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6583 1.6698 0.8576 3.4706
## (Intercept)-Canis_latrans 0.6037 0.7818 -0.8425 0.5621
## (Intercept)-Sciurus_niger -0.3731 1.2740 -2.3981 -0.5201
## (Intercept)-Procyon_lotor 0.8356 0.7698 -0.6712 0.8151
## (Intercept)-Dasypus_novemcinctus -0.4731 0.7268 -1.8508 -0.4888
## (Intercept)-Lynx_rufus 0.1652 1.0084 -1.6521 0.1061
## (Intercept)-Didelphis_virginiana -0.9684 0.8128 -2.5442 -0.9822
## (Intercept)-Sylvilagus_floridanus 0.1903 0.9342 -1.5380 0.1243
## (Intercept)-Sciurus_carolinensis -1.0190 0.8499 -2.7043 -1.0200
## (Intercept)-Vulpes_vulpes -0.4992 1.3868 -3.0014 -0.5909
## (Intercept)-Sus_scrofa -1.3706 1.0821 -3.4788 -1.3915
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0023 0.5709 -1.1337 -0.0042
## Avg_Cogongrass_Cover-Canis_latrans 0.3188 0.4994 -0.5551 0.2831
## Avg_Cogongrass_Cover-Sciurus_niger -0.3322 0.6833 -1.8942 -0.2644
## Avg_Cogongrass_Cover-Procyon_lotor -0.0630 0.4559 -1.0158 -0.0506
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1549 0.4231 -0.6697 0.1456
## Avg_Cogongrass_Cover-Lynx_rufus 0.3147 0.5187 -0.6183 0.2819
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1460 0.4699 -0.7826 0.1396
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3463 0.5527 -1.5764 -0.2945
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0607 0.4534 -0.8695 0.0711
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1416 0.5638 -0.9244 0.1232
## Avg_Cogongrass_Cover-Sus_scrofa -0.2397 0.6260 -1.7125 -0.1792
## total_shrub_cover-Odocoileus_virginianus -0.3580 0.6887 -1.6887 -0.3724
## total_shrub_cover-Canis_latrans 0.1498 0.6559 -0.9513 0.0852
## total_shrub_cover-Sciurus_niger -0.7752 0.7491 -2.4502 -0.7247
## total_shrub_cover-Procyon_lotor -1.1486 0.6111 -2.5559 -1.0663
## total_shrub_cover-Dasypus_novemcinctus -0.3602 0.5862 -1.8229 -0.2939
## total_shrub_cover-Lynx_rufus -1.0452 0.7903 -2.8303 -0.9697
## total_shrub_cover-Didelphis_virginiana -0.7006 0.6107 -2.1402 -0.6380
## total_shrub_cover-Sylvilagus_floridanus -1.1173 0.8729 -3.2245 -0.9806
## total_shrub_cover-Sciurus_carolinensis -0.6685 0.6638 -2.2149 -0.5901
## total_shrub_cover-Vulpes_vulpes -0.8106 0.9594 -2.9616 -0.7132
## total_shrub_cover-Sus_scrofa -0.4252 0.7784 -2.0528 -0.3995
## avg_veg_height-Odocoileus_virginianus 0.1412 0.5164 -0.8777 0.1429
## avg_veg_height-Canis_latrans 0.1710 0.4549 -0.7181 0.1675
## avg_veg_height-Sciurus_niger -0.0443 0.5770 -1.3132 -0.0242
## avg_veg_height-Procyon_lotor 0.1815 0.4402 -0.6792 0.1704
## avg_veg_height-Dasypus_novemcinctus 0.3385 0.4363 -0.4784 0.3178
## avg_veg_height-Lynx_rufus 0.1406 0.5397 -0.9314 0.1377
## avg_veg_height-Didelphis_virginiana 0.0805 0.4575 -0.8701 0.0901
## avg_veg_height-Sylvilagus_floridanus 0.0960 0.4868 -0.8931 0.1031
## avg_veg_height-Sciurus_carolinensis 0.4605 0.4789 -0.3854 0.4315
## avg_veg_height-Vulpes_vulpes 0.1254 0.5213 -0.9056 0.1257
## avg_veg_height-Sus_scrofa 0.1736 0.4947 -0.8258 0.1645
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3648 1.0230 805
## (Intercept)-Canis_latrans 2.2551 1.0031 1638
## (Intercept)-Sciurus_niger 2.6466 1.0326 528
## (Intercept)-Procyon_lotor 2.4407 1.0045 2151
## (Intercept)-Dasypus_novemcinctus 1.0578 1.0024 1462
## (Intercept)-Lynx_rufus 2.3803 1.0083 1074
## (Intercept)-Didelphis_virginiana 0.6470 1.0030 1237
## (Intercept)-Sylvilagus_floridanus 2.2769 1.0034 1096
## (Intercept)-Sciurus_carolinensis 0.7328 1.0022 1274
## (Intercept)-Vulpes_vulpes 2.5510 1.0419 525
## (Intercept)-Sus_scrofa 0.8085 1.0008 866
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1680 1.0003 2665
## Avg_Cogongrass_Cover-Canis_latrans 1.4315 1.0062 1998
## Avg_Cogongrass_Cover-Sciurus_niger 0.8318 1.0052 1312
## Avg_Cogongrass_Cover-Procyon_lotor 0.8266 1.0018 2080
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9880 1.0000 2218
## Avg_Cogongrass_Cover-Lynx_rufus 1.4403 1.0005 2320
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0737 1.0001 2335
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6195 1.0020 2110
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9283 1.0005 2086
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3191 1.0003 2338
## Avg_Cogongrass_Cover-Sus_scrofa 0.8211 1.0096 1799
## total_shrub_cover-Odocoileus_virginianus 1.0765 1.0095 2392
## total_shrub_cover-Canis_latrans 1.6832 1.0102 1356
## total_shrub_cover-Sciurus_niger 0.6290 1.0003 1194
## total_shrub_cover-Procyon_lotor -0.1765 1.0005 977
## total_shrub_cover-Dasypus_novemcinctus 0.5756 1.0243 1001
## total_shrub_cover-Lynx_rufus 0.3201 1.0018 730
## total_shrub_cover-Didelphis_virginiana 0.3208 1.0020 765
## total_shrub_cover-Sylvilagus_floridanus 0.2253 1.0022 493
## total_shrub_cover-Sciurus_carolinensis 0.4375 1.0030 784
## total_shrub_cover-Vulpes_vulpes 0.8487 1.0079 772
## total_shrub_cover-Sus_scrofa 1.0836 1.0047 747
## avg_veg_height-Odocoileus_virginianus 1.1637 1.0019 2147
## avg_veg_height-Canis_latrans 1.1087 1.0008 1608
## avg_veg_height-Sciurus_niger 1.0110 1.0095 1456
## avg_veg_height-Procyon_lotor 1.0816 1.0026 2160
## avg_veg_height-Dasypus_novemcinctus 1.2861 1.0029 1166
## avg_veg_height-Lynx_rufus 1.2116 1.0015 1619
## avg_veg_height-Didelphis_virginiana 0.9844 1.0046 1927
## avg_veg_height-Sylvilagus_floridanus 1.0447 1.0004 1379
## avg_veg_height-Sciurus_carolinensis 1.4826 1.0016 1409
## avg_veg_height-Vulpes_vulpes 1.1646 1.0020 1746
## avg_veg_height-Sus_scrofa 1.1534 1.0030 1790
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5383 0.0814 0.3796 0.5372 0.6918
## (Intercept)-Canis_latrans -2.6079 0.2140 -3.0446 -2.6029 -2.2098
## (Intercept)-Sciurus_niger -4.0572 0.7147 -5.4494 -4.0530 -2.7470
## (Intercept)-Procyon_lotor -2.1825 0.1585 -2.5085 -2.1760 -1.8800
## (Intercept)-Dasypus_novemcinctus -1.6347 0.1943 -2.0304 -1.6274 -1.2656
## (Intercept)-Lynx_rufus -3.4561 0.3848 -4.2345 -3.4372 -2.7386
## (Intercept)-Didelphis_virginiana -2.4609 0.3437 -3.1700 -2.4472 -1.8318
## (Intercept)-Sylvilagus_floridanus -3.1894 0.3077 -3.8181 -3.1778 -2.6031
## (Intercept)-Sciurus_carolinensis -2.5578 0.3624 -3.3128 -2.5438 -1.8982
## (Intercept)-Vulpes_vulpes -4.2593 0.7574 -5.7428 -4.2541 -2.8270
## (Intercept)-Sus_scrofa -3.4045 0.6469 -4.7260 -3.3874 -2.1475
## shrub_cover-Odocoileus_virginianus -0.0568 0.0678 -0.1906 -0.0576 0.0770
## shrub_cover-Canis_latrans -0.2802 0.2465 -0.7596 -0.2806 0.1999
## shrub_cover-Sciurus_niger -0.1024 0.5420 -1.1998 -0.0983 0.9568
## shrub_cover-Procyon_lotor 0.3167 0.1593 0.0037 0.3179 0.6254
## shrub_cover-Dasypus_novemcinctus 0.9869 0.3653 0.3378 0.9628 1.7546
## shrub_cover-Lynx_rufus 0.0331 0.3786 -0.7447 0.0453 0.7430
## shrub_cover-Didelphis_virginiana 1.1499 0.4182 0.4062 1.1304 2.0136
## shrub_cover-Sylvilagus_floridanus 0.6784 0.4261 -0.2119 0.6924 1.4810
## shrub_cover-Sciurus_carolinensis 1.0775 0.4430 0.2275 1.0777 1.9670
## shrub_cover-Vulpes_vulpes 0.2388 0.6108 -0.9607 0.2318 1.4698
## shrub_cover-Sus_scrofa 0.9310 0.8041 -0.6891 0.9228 2.5046
## veg_height-Odocoileus_virginianus -0.3323 0.0685 -0.4654 -0.3329 -0.2005
## veg_height-Canis_latrans -0.6099 0.1887 -0.9979 -0.6037 -0.2564
## veg_height-Sciurus_niger 0.0011 0.4509 -0.8373 -0.0214 0.9895
## veg_height-Procyon_lotor 0.3349 0.1248 0.0900 0.3345 0.5773
## veg_height-Dasypus_novemcinctus 0.2404 0.1401 -0.0298 0.2385 0.5185
## veg_height-Lynx_rufus 0.0220 0.2494 -0.4783 0.0235 0.4995
## veg_height-Didelphis_virginiana 0.4091 0.2525 -0.0663 0.3974 0.9261
## veg_height-Sylvilagus_floridanus 0.0433 0.2535 -0.4503 0.0382 0.5476
## veg_height-Sciurus_carolinensis 0.0711 0.2252 -0.3534 0.0610 0.5304
## veg_height-Vulpes_vulpes -0.1746 0.3473 -0.9228 -0.1608 0.4869
## veg_height-Sus_scrofa -0.1786 0.3401 -0.8708 -0.1699 0.4684
## week-Odocoileus_virginianus 1.3109 0.1254 1.0682 1.3082 1.5566
## week-Canis_latrans 0.5989 0.2642 0.0822 0.5950 1.1062
## week-Sciurus_niger -0.4579 0.5739 -1.7660 -0.3970 0.5085
## week-Procyon_lotor 0.2039 0.2112 -0.2100 0.2039 0.6199
## week-Dasypus_novemcinctus 0.1053 0.2244 -0.3304 0.1034 0.5558
## week-Lynx_rufus 0.3786 0.3512 -0.3261 0.3802 1.0628
## week-Didelphis_virginiana 0.0642 0.3790 -0.7254 0.0772 0.7814
## week-Sylvilagus_floridanus 0.0646 0.3432 -0.6437 0.0737 0.7124
## week-Sciurus_carolinensis 0.8101 0.3732 0.1109 0.8037 1.5880
## week-Vulpes_vulpes 0.1870 0.5187 -0.8975 0.2045 1.1569
## week-Sus_scrofa 0.6836 0.4495 -0.1756 0.6728 1.6138
## I(week^2)-Odocoileus_virginianus -0.5408 0.0511 -0.6430 -0.5397 -0.4453
## I(week^2)-Canis_latrans -0.2467 0.1072 -0.4603 -0.2448 -0.0388
## I(week^2)-Sciurus_niger -0.2856 0.2457 -0.7786 -0.2726 0.1433
## I(week^2)-Procyon_lotor -0.1310 0.0903 -0.3104 -0.1302 0.0435
## I(week^2)-Dasypus_novemcinctus -0.1830 0.1042 -0.3904 -0.1829 0.0155
## I(week^2)-Lynx_rufus -0.2375 0.1532 -0.5412 -0.2377 0.0614
## I(week^2)-Didelphis_virginiana -0.4186 0.2159 -0.9095 -0.3953 -0.0591
## I(week^2)-Sylvilagus_floridanus -0.1774 0.1579 -0.4899 -0.1727 0.1252
## I(week^2)-Sciurus_carolinensis -0.2855 0.1469 -0.5841 -0.2820 -0.0091
## I(week^2)-Vulpes_vulpes -0.4014 0.2616 -0.9701 -0.3691 0.0154
## I(week^2)-Sus_scrofa -0.2434 0.1796 -0.6065 -0.2415 0.1113
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0018 5027
## (Intercept)-Canis_latrans 1.0028 2151
## (Intercept)-Sciurus_niger 1.0080 490
## (Intercept)-Procyon_lotor 1.0013 3986
## (Intercept)-Dasypus_novemcinctus 1.0177 1633
## (Intercept)-Lynx_rufus 1.0150 1073
## (Intercept)-Didelphis_virginiana 1.0127 1112
## (Intercept)-Sylvilagus_floridanus 1.0005 1375
## (Intercept)-Sciurus_carolinensis 1.0005 1286
## (Intercept)-Vulpes_vulpes 1.0350 415
## (Intercept)-Sus_scrofa 1.0012 715
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0051 1493
## shrub_cover-Sciurus_niger 1.0122 948
## shrub_cover-Procyon_lotor 1.0031 3387
## shrub_cover-Dasypus_novemcinctus 1.0202 947
## shrub_cover-Lynx_rufus 1.0026 1236
## shrub_cover-Didelphis_virginiana 1.0026 912
## shrub_cover-Sylvilagus_floridanus 1.0011 722
## shrub_cover-Sciurus_carolinensis 1.0024 829
## shrub_cover-Vulpes_vulpes 1.0031 959
## shrub_cover-Sus_scrofa 1.0023 559
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0042 2049
## veg_height-Sciurus_niger 1.0008 1337
## veg_height-Procyon_lotor 1.0002 4008
## veg_height-Dasypus_novemcinctus 1.0010 4017
## veg_height-Lynx_rufus 1.0029 2090
## veg_height-Didelphis_virginiana 1.0114 2621
## veg_height-Sylvilagus_floridanus 1.0004 1486
## veg_height-Sciurus_carolinensis 1.0007 2133
## veg_height-Vulpes_vulpes 1.0017 1684
## veg_height-Sus_scrofa 1.0003 2285
## week-Odocoileus_virginianus 1.0010 4845
## week-Canis_latrans 1.0019 3549
## week-Sciurus_niger 1.0257 768
## week-Procyon_lotor 1.0006 4465
## week-Dasypus_novemcinctus 1.0075 4565
## week-Lynx_rufus 1.0045 2514
## week-Didelphis_virginiana 1.0061 2205
## week-Sylvilagus_floridanus 1.0048 2614
## week-Sciurus_carolinensis 1.0008 3272
## week-Vulpes_vulpes 1.0103 1229
## week-Sus_scrofa 1.0026 3635
## I(week^2)-Odocoileus_virginianus 1.0012 4934
## I(week^2)-Canis_latrans 1.0017 3766
## I(week^2)-Sciurus_niger 1.0280 942
## I(week^2)-Procyon_lotor 1.0000 4454
## I(week^2)-Dasypus_novemcinctus 1.0032 3958
## I(week^2)-Lynx_rufus 1.0037 2408
## I(week^2)-Didelphis_virginiana 1.0020 1241
## I(week^2)-Sylvilagus_floridanus 1.0031 2209
## I(week^2)-Sciurus_carolinensis 1.0002 3532
## I(week^2)-Vulpes_vulpes 1.0261 1088
## I(week^2)-Sus_scrofa 1.0001 3880
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy<- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.043
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0458 0.7642 -1.4819 -0.0831 1.5920 1.0093 1435
## Tree_Density -0.7621 0.3961 -1.6235 -0.7417 -0.0349 1.0072 1229
## Avg_Canopy_Cover 1.0938 0.3819 0.3840 1.0657 1.9020 1.0110 1374
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.9088 5.9983 1.3836 5.1550 22.4773 1.0063 645
## Tree_Density 0.6586 1.1773 0.0442 0.3178 3.2388 1.0187 1299
## Avg_Canopy_Cover 0.7770 0.8171 0.0817 0.5264 2.9620 1.0393 1628
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4109 0.5041 0.0397 0.2453 1.7407 1.0304 503
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4360 0.4875 -3.3693 -2.4554 -1.4155 1.0044 3664
## shrub_cover 0.2313 0.2436 -0.2291 0.2218 0.7286 1.0005 3244
## veg_height 0.0185 0.1584 -0.2949 0.0176 0.3335 0.9999 3317
## week 0.3530 0.2405 -0.1592 0.3617 0.8128 1.0029 3257
## I(week^2) -0.2876 0.1025 -0.4972 -0.2851 -0.0911 1.0005 2694
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5444 1.5882 0.8892 2.1353 6.7463 1.0078 2370
## shrub_cover 0.4912 0.4074 0.1074 0.3867 1.4868 1.0217 2604
## veg_height 0.2064 0.1577 0.0582 0.1679 0.5738 1.0075 4422
## week 0.4557 0.3507 0.1154 0.3577 1.3863 1.0039 2052
## I(week^2) 0.0745 0.0557 0.0224 0.0597 0.2090 1.0110 2592
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8028 1.9028 2.1002 4.4956 9.6434
## (Intercept)-Canis_latrans 0.4441 0.6716 -0.7505 0.4075 1.8018
## (Intercept)-Sciurus_niger 0.2761 1.6948 -2.1467 -0.0011 4.4973
## (Intercept)-Procyon_lotor 0.8547 0.6416 -0.3280 0.8281 2.1957
## (Intercept)-Dasypus_novemcinctus -0.9124 0.6163 -2.1701 -0.8957 0.2609
## (Intercept)-Lynx_rufus 1.8044 2.0213 -0.9099 1.3833 6.6802
## (Intercept)-Didelphis_virginiana -1.7240 0.7247 -3.2234 -1.6963 -0.3886
## (Intercept)-Sylvilagus_floridanus -0.6140 0.7182 -2.0558 -0.6140 0.8610
## (Intercept)-Sciurus_carolinensis -1.7773 0.7506 -3.3603 -1.7448 -0.3923
## (Intercept)-Vulpes_vulpes -1.0792 1.7026 -3.6359 -1.3317 3.3120
## (Intercept)-Sus_scrofa -2.5196 1.0029 -4.6672 -2.4631 -0.6778
## Tree_Density-Odocoileus_virginianus -0.4466 0.6310 -1.5412 -0.4897 0.9928
## Tree_Density-Canis_latrans -0.9206 0.5421 -2.1763 -0.8597 -0.0286
## Tree_Density-Sciurus_niger -0.7716 0.7637 -2.3481 -0.7394 0.6846
## Tree_Density-Procyon_lotor -0.5234 0.4065 -1.3446 -0.5273 0.2771
## Tree_Density-Dasypus_novemcinctus -1.2920 0.8325 -3.3797 -1.1220 -0.1664
## Tree_Density-Lynx_rufus -0.1535 0.7925 -1.4125 -0.2569 1.7535
## Tree_Density-Didelphis_virginiana -0.9638 0.7118 -2.6652 -0.8563 0.1332
## Tree_Density-Sylvilagus_floridanus -1.0047 0.6918 -2.6899 -0.9081 0.1096
## Tree_Density-Sciurus_carolinensis -0.9131 0.6992 -2.5710 -0.8211 0.2154
## Tree_Density-Vulpes_vulpes -0.6968 0.8107 -2.4737 -0.6763 0.8716
## Tree_Density-Sus_scrofa -0.9461 0.7935 -2.8609 -0.8507 0.3069
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8114 0.7437 -0.7148 0.8156 2.2828
## Avg_Canopy_Cover-Canis_latrans 0.0231 0.4991 -0.9444 0.0186 1.0187
## Avg_Canopy_Cover-Sciurus_niger 1.1105 0.9270 -0.5277 1.0310 3.2232
## Avg_Canopy_Cover-Procyon_lotor 1.0813 0.4880 0.2045 1.0516 2.1386
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0641 0.4449 0.2486 1.0520 2.0111
## Avg_Canopy_Cover-Lynx_rufus 1.0775 0.8294 -0.4093 1.0150 2.9742
## Avg_Canopy_Cover-Didelphis_virginiana 1.4694 0.6144 0.4754 1.3937 2.9468
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8930 0.8471 0.6389 1.7469 3.9038
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4077 0.6040 0.4320 1.3333 2.7727
## Avg_Canopy_Cover-Vulpes_vulpes 1.1268 0.6864 -0.0677 1.0685 2.6521
## Avg_Canopy_Cover-Sus_scrofa 1.3307 0.5957 0.2983 1.2887 2.6700
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0154 657
## (Intercept)-Canis_latrans 1.0046 2180
## (Intercept)-Sciurus_niger 1.0180 353
## (Intercept)-Procyon_lotor 1.0014 3470
## (Intercept)-Dasypus_novemcinctus 1.0001 3178
## (Intercept)-Lynx_rufus 1.0360 314
## (Intercept)-Didelphis_virginiana 1.0007 2917
## (Intercept)-Sylvilagus_floridanus 1.0019 2804
## (Intercept)-Sciurus_carolinensis 1.0007 2707
## (Intercept)-Vulpes_vulpes 1.1183 274
## (Intercept)-Sus_scrofa 1.0002 1820
## Tree_Density-Odocoileus_virginianus 1.0013 1966
## Tree_Density-Canis_latrans 1.0058 2398
## Tree_Density-Sciurus_niger 1.0048 1681
## Tree_Density-Procyon_lotor 1.0001 3138
## Tree_Density-Dasypus_novemcinctus 1.0042 1252
## Tree_Density-Lynx_rufus 1.0045 765
## Tree_Density-Didelphis_virginiana 1.0012 1749
## Tree_Density-Sylvilagus_floridanus 1.0071 2131
## Tree_Density-Sciurus_carolinensis 1.0014 2123
## Tree_Density-Vulpes_vulpes 1.0145 1318
## Tree_Density-Sus_scrofa 1.0066 1772
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0010 2402
## Avg_Canopy_Cover-Canis_latrans 1.0073 2183
## Avg_Canopy_Cover-Sciurus_niger 1.0164 1016
## Avg_Canopy_Cover-Procyon_lotor 1.0008 3359
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0062 3783
## Avg_Canopy_Cover-Lynx_rufus 1.0108 1384
## Avg_Canopy_Cover-Didelphis_virginiana 1.0027 1830
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0115 1678
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0034 2482
## Avg_Canopy_Cover-Vulpes_vulpes 1.0095 2547
## Avg_Canopy_Cover-Sus_scrofa 1.0039 2134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5391 0.0801 0.3834 0.5386 0.6955
## (Intercept)-Canis_latrans -2.5839 0.2125 -3.0316 -2.5771 -2.1971
## (Intercept)-Sciurus_niger -4.3857 0.6470 -5.5713 -4.4216 -3.0307
## (Intercept)-Procyon_lotor -2.1853 0.1650 -2.5301 -2.1803 -1.8800
## (Intercept)-Dasypus_novemcinctus -1.5874 0.1765 -1.9427 -1.5829 -1.2547
## (Intercept)-Lynx_rufus -3.8196 0.3805 -4.5367 -3.8310 -3.0472
## (Intercept)-Didelphis_virginiana -2.3510 0.3101 -2.9825 -2.3383 -1.7685
## (Intercept)-Sylvilagus_floridanus -3.0207 0.3003 -3.6501 -3.0058 -2.4733
## (Intercept)-Sciurus_carolinensis -2.4386 0.3346 -3.1297 -2.4226 -1.8312
## (Intercept)-Vulpes_vulpes -4.1339 0.7998 -5.6986 -4.1139 -2.6629
## (Intercept)-Sus_scrofa -3.0741 0.6071 -4.3241 -3.0551 -1.9219
## shrub_cover-Odocoileus_virginianus -0.0600 0.0687 -0.1895 -0.0605 0.0757
## shrub_cover-Canis_latrans -0.2812 0.2240 -0.7203 -0.2761 0.1585
## shrub_cover-Sciurus_niger -0.3621 0.4211 -1.2435 -0.3533 0.4408
## shrub_cover-Procyon_lotor 0.2509 0.1637 -0.0796 0.2518 0.5606
## shrub_cover-Dasypus_novemcinctus 0.8253 0.2969 0.2643 0.8223 1.4350
## shrub_cover-Lynx_rufus -0.3379 0.3155 -0.9704 -0.3378 0.2929
## shrub_cover-Didelphis_virginiana 0.9216 0.3525 0.2650 0.9051 1.6469
## shrub_cover-Sylvilagus_floridanus 0.3711 0.3780 -0.3539 0.3636 1.1561
## shrub_cover-Sciurus_carolinensis 0.8070 0.4057 0.0477 0.7951 1.6253
## shrub_cover-Vulpes_vulpes -0.0544 0.5240 -1.1492 -0.0465 0.9680
## shrub_cover-Sus_scrofa 0.5094 0.7352 -0.9141 0.4801 2.0381
## veg_height-Odocoileus_virginianus -0.3303 0.0686 -0.4622 -0.3302 -0.1966
## veg_height-Canis_latrans -0.5919 0.1867 -0.9667 -0.5924 -0.2371
## veg_height-Sciurus_niger -0.0455 0.3721 -0.7710 -0.0533 0.7160
## veg_height-Procyon_lotor 0.3433 0.1230 0.1058 0.3450 0.5877
## veg_height-Dasypus_novemcinctus 0.2405 0.1339 -0.0186 0.2357 0.5077
## veg_height-Lynx_rufus 0.0947 0.2411 -0.3832 0.0983 0.5611
## veg_height-Didelphis_virginiana 0.4563 0.2365 0.0020 0.4490 0.9342
## veg_height-Sylvilagus_floridanus 0.1500 0.2345 -0.3054 0.1469 0.6145
## veg_height-Sciurus_carolinensis 0.0859 0.2128 -0.3261 0.0855 0.5112
## veg_height-Vulpes_vulpes -0.1233 0.3195 -0.8103 -0.1086 0.4717
## veg_height-Sus_scrofa -0.1161 0.3296 -0.8012 -0.1080 0.5216
## week-Odocoileus_virginianus 1.3144 0.1242 1.0713 1.3139 1.5592
## week-Canis_latrans 0.5955 0.2656 0.0855 0.5946 1.1221
## week-Sciurus_niger -0.4266 0.5552 -1.6402 -0.3780 0.5129
## week-Procyon_lotor 0.2087 0.2125 -0.2069 0.2108 0.6262
## week-Dasypus_novemcinctus 0.1032 0.2272 -0.3326 0.1029 0.5411
## week-Lynx_rufus 0.3844 0.3525 -0.2688 0.3776 1.0966
## week-Didelphis_virginiana 0.0571 0.3692 -0.6934 0.0714 0.7621
## week-Sylvilagus_floridanus 0.0489 0.3475 -0.6427 0.0521 0.7213
## week-Sciurus_carolinensis 0.8060 0.3622 0.1276 0.7950 1.5384
## week-Vulpes_vulpes 0.1821 0.5348 -0.9641 0.2042 1.1795
## week-Sus_scrofa 0.6885 0.4494 -0.1810 0.6769 1.6074
## I(week^2)-Odocoileus_virginianus -0.5416 0.0512 -0.6442 -0.5403 -0.4416
## I(week^2)-Canis_latrans -0.2462 0.1085 -0.4580 -0.2448 -0.0383
## I(week^2)-Sciurus_niger -0.2916 0.2423 -0.8251 -0.2797 0.1494
## I(week^2)-Procyon_lotor -0.1343 0.0905 -0.3128 -0.1333 0.0402
## I(week^2)-Dasypus_novemcinctus -0.1809 0.1041 -0.3907 -0.1802 0.0189
## I(week^2)-Lynx_rufus -0.2441 0.1573 -0.5636 -0.2392 0.0494
## I(week^2)-Didelphis_virginiana -0.4142 0.2167 -0.8884 -0.3916 -0.0409
## I(week^2)-Sylvilagus_floridanus -0.1760 0.1596 -0.4969 -0.1734 0.1275
## I(week^2)-Sciurus_carolinensis -0.2840 0.1427 -0.5648 -0.2801 -0.0142
## I(week^2)-Vulpes_vulpes -0.4069 0.2579 -0.9952 -0.3836 0.0242
## I(week^2)-Sus_scrofa -0.2414 0.1808 -0.6212 -0.2389 0.1008
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 4787
## (Intercept)-Canis_latrans 1.0005 2323
## (Intercept)-Sciurus_niger 1.0299 412
## (Intercept)-Procyon_lotor 1.0039 4050
## (Intercept)-Dasypus_novemcinctus 1.0001 4550
## (Intercept)-Lynx_rufus 1.0037 623
## (Intercept)-Didelphis_virginiana 1.0034 2381
## (Intercept)-Sylvilagus_floridanus 1.0047 2271
## (Intercept)-Sciurus_carolinensis 1.0006 2466
## (Intercept)-Vulpes_vulpes 1.0632 398
## (Intercept)-Sus_scrofa 1.0049 1663
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0019 2558
## shrub_cover-Sciurus_niger 1.0075 1364
## shrub_cover-Procyon_lotor 1.0002 4000
## shrub_cover-Dasypus_novemcinctus 1.0010 3079
## shrub_cover-Lynx_rufus 1.0131 1271
## shrub_cover-Didelphis_virginiana 1.0025 2186
## shrub_cover-Sylvilagus_floridanus 1.0014 2182
## shrub_cover-Sciurus_carolinensis 1.0019 2163
## shrub_cover-Vulpes_vulpes 1.0082 1995
## shrub_cover-Sus_scrofa 1.0041 2380
## veg_height-Odocoileus_virginianus 1.0018 5250
## veg_height-Canis_latrans 1.0086 2525
## veg_height-Sciurus_niger 1.0132 1968
## veg_height-Procyon_lotor 1.0002 4065
## veg_height-Dasypus_novemcinctus 1.0002 4920
## veg_height-Lynx_rufus 1.0110 1910
## veg_height-Didelphis_virginiana 1.0009 3423
## veg_height-Sylvilagus_floridanus 1.0009 3365
## veg_height-Sciurus_carolinensis 1.0016 3005
## veg_height-Vulpes_vulpes 1.0074 1903
## veg_height-Sus_scrofa 1.0009 4156
## week-Odocoileus_virginianus 1.0001 4820
## week-Canis_latrans 1.0017 3531
## week-Sciurus_niger 1.0091 800
## week-Procyon_lotor 1.0018 4403
## week-Dasypus_novemcinctus 1.0014 4282
## week-Lynx_rufus 1.0013 2496
## week-Didelphis_virginiana 1.0017 2950
## week-Sylvilagus_floridanus 1.0026 2726
## week-Sciurus_carolinensis 1.0017 3086
## week-Vulpes_vulpes 1.0084 1616
## week-Sus_scrofa 1.0001 3258
## I(week^2)-Odocoileus_virginianus 1.0001 4747
## I(week^2)-Canis_latrans 1.0009 3124
## I(week^2)-Sciurus_niger 1.0047 966
## I(week^2)-Procyon_lotor 1.0021 4371
## I(week^2)-Dasypus_novemcinctus 1.0029 4381
## I(week^2)-Lynx_rufus 1.0106 2059
## I(week^2)-Didelphis_virginiana 1.0046 1570
## I(week^2)-Sylvilagus_floridanus 1.0005 2905
## I(week^2)-Sciurus_carolinensis 1.0014 4059
## I(week^2)-Vulpes_vulpes 1.0050 1244
## I(week^2)-Sus_scrofa 1.0005 3867
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move<- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0292
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0590 0.6265 -1.2402 -0.0835 1.2166 1.0245 1469
## Cogon_Patch_Size -0.2240 0.4006 -1.0906 -0.2110 0.5149 1.0022 1280
## Avg_Cogongrass_Cover 0.2214 0.3013 -0.3807 0.2214 0.8022 1.0088 1397
## total_shrub_cover -0.5392 0.3966 -1.4221 -0.5069 0.1738 1.0087 778
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9252 3.6978 0.5196 2.9711 13.2716 1.0692 800
## Cogon_Patch_Size 0.8434 1.3349 0.0597 0.4656 4.0447 1.0370 1435
## Avg_Cogongrass_Cover 0.3174 0.4103 0.0378 0.1925 1.3448 1.0074 2088
## total_shrub_cover 0.5737 0.7762 0.0480 0.3333 2.6233 1.0128 780
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4096 1.3619 0.083 1.0196 4.9681 1.0123 403
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4482 0.4752 -3.3424 -2.4574 -1.4634 1.0005 4284
## shrub_cover 0.4019 0.2809 -0.1333 0.3932 0.9748 1.0016 1266
## veg_height -0.0125 0.1609 -0.3269 -0.0123 0.3045 1.0027 3222
## week 0.3609 0.2355 -0.1080 0.3628 0.8165 1.0020 3067
## I(week^2) -0.2877 0.0994 -0.4923 -0.2868 -0.0994 1.0024 2692
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4474 1.5238 0.8386 2.0539 6.4109 1.0083 2326
## shrub_cover 0.5476 0.4486 0.1079 0.4234 1.7428 1.0127 1417
## veg_height 0.1968 0.1365 0.0565 0.1608 0.5609 1.0004 3569
## week 0.4445 0.3558 0.1111 0.3493 1.3446 1.0043 1715
## I(week^2) 0.0741 0.0570 0.0225 0.0599 0.2102 1.0107 2404
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6863 1.6570 0.8686 3.5113
## (Intercept)-Canis_latrans 0.5934 0.7792 -0.8837 0.5596
## (Intercept)-Sciurus_niger -0.3706 1.3740 -2.5640 -0.5330
## (Intercept)-Procyon_lotor 0.7130 0.7629 -0.7826 0.7105
## (Intercept)-Dasypus_novemcinctus -0.5838 0.7253 -2.0647 -0.5747
## (Intercept)-Lynx_rufus 0.0877 1.2434 -1.8423 -0.0569
## (Intercept)-Didelphis_virginiana -1.1363 0.8096 -2.7513 -1.1324
## (Intercept)-Sylvilagus_floridanus -0.0758 0.9288 -1.7997 -0.1121
## (Intercept)-Sciurus_carolinensis -1.2509 0.8617 -2.9825 -1.2482
## (Intercept)-Vulpes_vulpes -0.6916 1.6474 -3.2026 -0.8813
## (Intercept)-Sus_scrofa -1.5473 1.1147 -3.7345 -1.5623
## Cogon_Patch_Size-Odocoileus_virginianus -0.0629 0.6798 -1.2806 -0.1022
## Cogon_Patch_Size-Canis_latrans 0.6172 0.7299 -0.4396 0.4840
## Cogon_Patch_Size-Sciurus_niger -0.5396 0.8937 -2.5585 -0.4522
## Cogon_Patch_Size-Procyon_lotor -0.2745 0.4645 -1.2248 -0.2740
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1120 0.4272 -0.9819 -0.1070
## Cogon_Patch_Size-Lynx_rufus -0.2283 0.7544 -1.5693 -0.2619
## Cogon_Patch_Size-Didelphis_virginiana 0.5033 0.5023 -0.3990 0.4745
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8283 0.8363 -2.8810 -0.6862
## Cogon_Patch_Size-Sciurus_carolinensis -0.7105 0.7338 -2.5528 -0.5872
## Cogon_Patch_Size-Vulpes_vulpes -0.4562 0.8606 -2.4183 -0.3835
## Cogon_Patch_Size-Sus_scrofa -0.4955 0.8313 -2.4408 -0.3838
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2009 0.5426 -0.8864 0.2058
## Avg_Cogongrass_Cover-Canis_latrans 0.3723 0.4312 -0.3923 0.3503
## Avg_Cogongrass_Cover-Sciurus_niger -0.0905 0.6337 -1.5226 -0.0368
## Avg_Cogongrass_Cover-Procyon_lotor 0.2009 0.4299 -0.6438 0.1974
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3886 0.3836 -0.3392 0.3780
## Avg_Cogongrass_Cover-Lynx_rufus 0.4944 0.5007 -0.3955 0.4584
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2059 0.4294 -0.6564 0.2133
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0387 0.5124 -1.1378 -0.0163
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4320 0.4244 -0.3555 0.4152
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3383 0.5003 -0.6504 0.3315
## Avg_Cogongrass_Cover-Sus_scrofa 0.0044 0.5914 -1.3446 0.0550
## total_shrub_cover-Odocoileus_virginianus -0.3204 0.6269 -1.5334 -0.3344
## total_shrub_cover-Canis_latrans 0.0808 0.5832 -0.9186 0.0203
## total_shrub_cover-Sciurus_niger -0.6655 0.7014 -2.1909 -0.6185
## total_shrub_cover-Procyon_lotor -0.9927 0.5713 -2.3493 -0.9216
## total_shrub_cover-Dasypus_novemcinctus -0.2924 0.4779 -1.3341 -0.2642
## total_shrub_cover-Lynx_rufus -0.8615 0.7636 -2.5704 -0.7885
## total_shrub_cover-Didelphis_virginiana -0.6242 0.5329 -1.8512 -0.5753
## total_shrub_cover-Sylvilagus_floridanus -0.8709 0.7804 -2.7019 -0.7643
## total_shrub_cover-Sciurus_carolinensis -0.4987 0.5897 -1.8249 -0.4506
## total_shrub_cover-Vulpes_vulpes -0.6300 0.7951 -2.3522 -0.5714
## total_shrub_cover-Sus_scrofa -0.3502 0.7385 -1.8350 -0.3393
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4101 1.0453 806
## (Intercept)-Canis_latrans 2.2062 1.0166 1685
## (Intercept)-Sciurus_niger 3.0283 1.0097 494
## (Intercept)-Procyon_lotor 2.2787 1.0076 1897
## (Intercept)-Dasypus_novemcinctus 0.8389 1.0031 2037
## (Intercept)-Lynx_rufus 2.9501 1.0871 482
## (Intercept)-Didelphis_virginiana 0.4894 1.0037 1822
## (Intercept)-Sylvilagus_floridanus 1.9284 1.0035 1265
## (Intercept)-Sciurus_carolinensis 0.4245 1.0015 1126
## (Intercept)-Vulpes_vulpes 3.0194 1.1054 322
## (Intercept)-Sus_scrofa 0.6786 1.0050 871
## Cogon_Patch_Size-Odocoileus_virginianus 1.4388 1.0043 3341
## Cogon_Patch_Size-Canis_latrans 2.3917 1.0093 2027
## Cogon_Patch_Size-Sciurus_niger 0.9807 1.0008 1372
## Cogon_Patch_Size-Procyon_lotor 0.6290 1.0004 2785
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7206 1.0009 3542
## Cogon_Patch_Size-Lynx_rufus 1.3910 1.0041 1466
## Cogon_Patch_Size-Didelphis_virginiana 1.5920 1.0081 2229
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3881 1.0027 1386
## Cogon_Patch_Size-Sciurus_carolinensis 0.3652 1.0091 1650
## Cogon_Patch_Size-Vulpes_vulpes 1.1083 1.0041 1564
## Cogon_Patch_Size-Sus_scrofa 0.8120 1.0047 1355
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2914 1.0022 2870
## Avg_Cogongrass_Cover-Canis_latrans 1.2947 1.0047 3131
## Avg_Cogongrass_Cover-Sciurus_niger 0.9862 1.0087 1286
## Avg_Cogongrass_Cover-Procyon_lotor 1.0465 1.0066 2161
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1821 1.0025 3242
## Avg_Cogongrass_Cover-Lynx_rufus 1.5906 1.0060 2412
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0387 1.0019 2837
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9049 1.0049 1792
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3181 1.0024 2392
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3620 1.0036 2622
## Avg_Cogongrass_Cover-Sus_scrofa 1.0316 1.0059 1743
## total_shrub_cover-Odocoileus_virginianus 1.0029 1.0139 2153
## total_shrub_cover-Canis_latrans 1.4509 1.0015 1432
## total_shrub_cover-Sciurus_niger 0.5824 1.0046 950
## total_shrub_cover-Procyon_lotor -0.0935 1.0093 1193
## total_shrub_cover-Dasypus_novemcinctus 0.5782 1.0172 1469
## total_shrub_cover-Lynx_rufus 0.4135 1.0023 654
## total_shrub_cover-Didelphis_virginiana 0.3050 1.0055 1303
## total_shrub_cover-Sylvilagus_floridanus 0.3767 1.0032 599
## total_shrub_cover-Sciurus_carolinensis 0.5265 1.0058 892
## total_shrub_cover-Vulpes_vulpes 0.8747 1.0037 1109
## total_shrub_cover-Sus_scrofa 1.1129 1.0067 767
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5369 0.0814 0.3782 0.5354 0.7006
## (Intercept)-Canis_latrans -2.5738 0.2097 -3.0082 -2.5688 -2.1872
## (Intercept)-Sciurus_niger -4.1206 0.7197 -5.5066 -4.1251 -2.7674
## (Intercept)-Procyon_lotor -2.1813 0.1583 -2.4991 -2.1772 -1.8784
## (Intercept)-Dasypus_novemcinctus -1.6232 0.1866 -2.0110 -1.6176 -1.2768
## (Intercept)-Lynx_rufus -3.4468 0.3824 -4.2257 -3.4357 -2.7356
## (Intercept)-Didelphis_virginiana -2.3777 0.3214 -3.0454 -2.3637 -1.7980
## (Intercept)-Sylvilagus_floridanus -3.1817 0.3188 -3.8347 -3.1683 -2.5845
## (Intercept)-Sciurus_carolinensis -2.5307 0.3630 -3.2886 -2.5127 -1.8745
## (Intercept)-Vulpes_vulpes -4.2200 0.7986 -5.7761 -4.2074 -2.7626
## (Intercept)-Sus_scrofa -3.3731 0.6567 -4.6855 -3.3677 -2.0666
## shrub_cover-Odocoileus_virginianus -0.0579 0.0678 -0.1894 -0.0586 0.0771
## shrub_cover-Canis_latrans -0.2694 0.2374 -0.7267 -0.2687 0.2043
## shrub_cover-Sciurus_niger -0.1384 0.5080 -1.1463 -0.1268 0.8230
## shrub_cover-Procyon_lotor 0.3073 0.1625 -0.0201 0.3081 0.6202
## shrub_cover-Dasypus_novemcinctus 0.9424 0.3371 0.3133 0.9318 1.6317
## shrub_cover-Lynx_rufus -0.0015 0.3786 -0.7729 0.0063 0.7112
## shrub_cover-Didelphis_virginiana 1.0382 0.3876 0.3384 1.0175 1.8444
## shrub_cover-Sylvilagus_floridanus 0.6281 0.4313 -0.2661 0.6433 1.4277
## shrub_cover-Sciurus_carolinensis 0.9911 0.4308 0.1911 0.9818 1.8634
## shrub_cover-Vulpes_vulpes 0.1665 0.6022 -1.0575 0.1738 1.3370
## shrub_cover-Sus_scrofa 0.8826 0.8093 -0.6676 0.8552 2.5245
## veg_height-Odocoileus_virginianus -0.3309 0.0691 -0.4719 -0.3302 -0.1965
## veg_height-Canis_latrans -0.5870 0.1849 -0.9724 -0.5811 -0.2287
## veg_height-Sciurus_niger -0.0406 0.3945 -0.7760 -0.0546 0.8039
## veg_height-Procyon_lotor 0.3360 0.1230 0.0961 0.3347 0.5748
## veg_height-Dasypus_novemcinctus 0.2407 0.1382 -0.0275 0.2381 0.5173
## veg_height-Lynx_rufus 0.0258 0.2462 -0.4729 0.0308 0.4945
## veg_height-Didelphis_virginiana 0.3970 0.2387 -0.0459 0.3871 0.8932
## veg_height-Sylvilagus_floridanus 0.0462 0.2436 -0.4266 0.0419 0.5300
## veg_height-Sciurus_carolinensis 0.0836 0.2210 -0.3263 0.0747 0.5489
## veg_height-Vulpes_vulpes -0.1532 0.3207 -0.8323 -0.1407 0.4422
## veg_height-Sus_scrofa -0.1573 0.3261 -0.8310 -0.1462 0.4568
## week-Odocoileus_virginianus 1.3106 0.1269 1.0617 1.3084 1.5650
## week-Canis_latrans 0.5995 0.2623 0.0932 0.5959 1.1309
## week-Sciurus_niger -0.4144 0.5655 -1.6922 -0.3589 0.5322
## week-Procyon_lotor 0.2050 0.2076 -0.2036 0.2059 0.6128
## week-Dasypus_novemcinctus 0.1094 0.2277 -0.3386 0.1117 0.5550
## week-Lynx_rufus 0.3768 0.3527 -0.3183 0.3771 1.0769
## week-Didelphis_virginiana 0.0571 0.3668 -0.6667 0.0605 0.7601
## week-Sylvilagus_floridanus 0.0529 0.3471 -0.6308 0.0557 0.7086
## week-Sciurus_carolinensis 0.8068 0.3694 0.1184 0.7892 1.5723
## week-Vulpes_vulpes 0.2009 0.5257 -0.8986 0.2267 1.1758
## week-Sus_scrofa 0.6998 0.4514 -0.1475 0.6862 1.6342
## I(week^2)-Odocoileus_virginianus -0.5403 0.0519 -0.6427 -0.5398 -0.4398
## I(week^2)-Canis_latrans -0.2495 0.1087 -0.4644 -0.2477 -0.0417
## I(week^2)-Sciurus_niger -0.2861 0.2363 -0.7791 -0.2754 0.1456
## I(week^2)-Procyon_lotor -0.1346 0.0909 -0.3100 -0.1345 0.0433
## I(week^2)-Dasypus_novemcinctus -0.1809 0.1047 -0.3929 -0.1801 0.0185
## I(week^2)-Lynx_rufus -0.2396 0.1535 -0.5453 -0.2349 0.0499
## I(week^2)-Didelphis_virginiana -0.4199 0.2090 -0.8788 -0.4023 -0.0642
## I(week^2)-Sylvilagus_floridanus -0.1831 0.1604 -0.5155 -0.1812 0.1245
## I(week^2)-Sciurus_carolinensis -0.2845 0.1473 -0.5904 -0.2784 -0.0057
## I(week^2)-Vulpes_vulpes -0.4072 0.2545 -0.9635 -0.3858 0.0430
## I(week^2)-Sus_scrofa -0.2485 0.1797 -0.6170 -0.2463 0.0965
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0024 2491
## (Intercept)-Sciurus_niger 1.0072 417
## (Intercept)-Procyon_lotor 1.0013 4220
## (Intercept)-Dasypus_novemcinctus 1.0067 2730
## (Intercept)-Lynx_rufus 1.0027 924
## (Intercept)-Didelphis_virginiana 1.0021 1778
## (Intercept)-Sylvilagus_floridanus 1.0049 1316
## (Intercept)-Sciurus_carolinensis 1.0057 1299
## (Intercept)-Vulpes_vulpes 1.0524 356
## (Intercept)-Sus_scrofa 1.0013 833
## shrub_cover-Odocoileus_virginianus 0.9999 4987
## shrub_cover-Canis_latrans 1.0011 1788
## shrub_cover-Sciurus_niger 1.0019 928
## shrub_cover-Procyon_lotor 1.0028 3333
## shrub_cover-Dasypus_novemcinctus 1.0100 1322
## shrub_cover-Lynx_rufus 1.0081 873
## shrub_cover-Didelphis_virginiana 1.0023 1330
## shrub_cover-Sylvilagus_floridanus 1.0081 911
## shrub_cover-Sciurus_carolinensis 1.0028 1268
## shrub_cover-Vulpes_vulpes 1.0053 1068
## shrub_cover-Sus_scrofa 1.0085 808
## veg_height-Odocoileus_virginianus 1.0019 5250
## veg_height-Canis_latrans 1.0011 2299
## veg_height-Sciurus_niger 1.0042 1769
## veg_height-Procyon_lotor 1.0007 4391
## veg_height-Dasypus_novemcinctus 1.0011 4737
## veg_height-Lynx_rufus 1.0008 2415
## veg_height-Didelphis_virginiana 1.0023 3332
## veg_height-Sylvilagus_floridanus 1.0019 2021
## veg_height-Sciurus_carolinensis 1.0041 2412
## veg_height-Vulpes_vulpes 1.0024 1869
## veg_height-Sus_scrofa 1.0024 2593
## week-Odocoileus_virginianus 1.0002 4822
## week-Canis_latrans 1.0075 3655
## week-Sciurus_niger 1.0118 906
## week-Procyon_lotor 0.9999 4138
## week-Dasypus_novemcinctus 1.0037 4253
## week-Lynx_rufus 1.0017 2761
## week-Didelphis_virginiana 1.0015 2856
## week-Sylvilagus_floridanus 1.0016 2644
## week-Sciurus_carolinensis 1.0017 3206
## week-Vulpes_vulpes 1.0020 1523
## week-Sus_scrofa 1.0015 3390
## I(week^2)-Odocoileus_virginianus 0.9999 4897
## I(week^2)-Canis_latrans 1.0020 3911
## I(week^2)-Sciurus_niger 1.0229 1024
## I(week^2)-Procyon_lotor 1.0010 4215
## I(week^2)-Dasypus_novemcinctus 1.0006 4226
## I(week^2)-Lynx_rufus 1.0015 2624
## I(week^2)-Didelphis_virginiana 1.0061 1974
## I(week^2)-Sylvilagus_floridanus 1.0029 2206
## I(week^2)-Sciurus_carolinensis 1.0024 3759
## I(week^2)-Vulpes_vulpes 1.0030 1201
## I(week^2)-Sus_scrofa 1.0007 4430
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage<- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9672
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1421 0.6121 -1.3254 -0.1575 1.1606 1.0058 1941
## Veg_shannon_index 0.3598 0.2664 -0.1639 0.3561 0.8983 1.0027 2160
## Avg_Cogongrass_Cover 0.3282 0.2726 -0.2125 0.3254 0.8754 1.0047 1851
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8128 3.1180 0.7229 2.9531 12.1972 1.0023 1236
## Veg_shannon_index 0.2938 0.3357 0.0405 0.1932 1.1652 1.0058 2162
## Avg_Cogongrass_Cover 0.3184 0.4570 0.0370 0.1911 1.3370 1.0102 1708
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7389 0.7324 0.0657 0.522 2.7488 1.0049 588
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4153 0.4776 -3.3145 -2.4179 -1.4127 1.0052 4700
## shrub_cover 0.2094 0.2447 -0.2664 0.2049 0.6997 1.0013 3369
## veg_height -0.0186 0.1605 -0.3402 -0.0170 0.3075 1.0022 2763
## week 0.3699 0.2362 -0.1191 0.3782 0.8066 1.0014 3179
## I(week^2) -0.2863 0.1033 -0.4953 -0.2839 -0.0871 1.0012 2352
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4768 1.5548 0.8677 2.0903 6.4443 1.0060 2326
## shrub_cover 0.4801 0.4089 0.0917 0.3809 1.4965 1.0084 2058
## veg_height 0.1989 0.1395 0.0563 0.1625 0.5413 1.0146 3838
## week 0.4323 0.3247 0.1045 0.3392 1.3110 1.0019 1876
## I(week^2) 0.0727 0.0532 0.0224 0.0587 0.2001 1.0024 2713
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6243 1.4482 1.2949 3.4357
## (Intercept)-Canis_latrans 0.3827 0.6592 -0.8789 0.3751
## (Intercept)-Sciurus_niger -0.0780 1.3484 -2.1519 -0.2608
## (Intercept)-Procyon_lotor 0.5875 0.6423 -0.7343 0.5857
## (Intercept)-Dasypus_novemcinctus -0.6436 0.5927 -1.8578 -0.6428
## (Intercept)-Lynx_rufus 0.3127 1.1739 -1.5273 0.1394
## (Intercept)-Didelphis_virginiana -1.3534 0.6784 -2.7568 -1.3359
## (Intercept)-Sylvilagus_floridanus -0.3162 0.7588 -1.7122 -0.3569
## (Intercept)-Sciurus_carolinensis -1.3343 0.6675 -2.6722 -1.3264
## (Intercept)-Vulpes_vulpes -0.7530 1.4227 -2.9482 -0.9625
## (Intercept)-Sus_scrofa -1.9620 0.8993 -3.8542 -1.9091
## Veg_shannon_index-Odocoileus_virginianus 0.2996 0.4931 -0.7022 0.3132
## Veg_shannon_index-Canis_latrans 0.6369 0.3966 -0.0915 0.6137
## Veg_shannon_index-Sciurus_niger 0.4000 0.5385 -0.6458 0.3862
## Veg_shannon_index-Procyon_lotor 0.4588 0.3748 -0.2394 0.4437
## Veg_shannon_index-Dasypus_novemcinctus 0.1986 0.3400 -0.4950 0.2027
## Veg_shannon_index-Lynx_rufus 0.2270 0.5227 -0.9105 0.2520
## Veg_shannon_index-Didelphis_virginiana 0.5081 0.3878 -0.2091 0.4914
## Veg_shannon_index-Sylvilagus_floridanus 0.4511 0.4297 -0.3555 0.4302
## Veg_shannon_index-Sciurus_carolinensis -0.0036 0.4158 -0.8978 0.0273
## Veg_shannon_index-Vulpes_vulpes 0.1416 0.4805 -0.8647 0.1566
## Veg_shannon_index-Sus_scrofa 0.7201 0.5270 -0.1482 0.6625
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3264 0.5187 -0.6955 0.3187
## Avg_Cogongrass_Cover-Canis_latrans 0.6266 0.4311 -0.0843 0.5779
## Avg_Cogongrass_Cover-Sciurus_niger -0.0010 0.6548 -1.4420 0.0586
## Avg_Cogongrass_Cover-Procyon_lotor 0.3945 0.3763 -0.3176 0.3848
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4529 0.3323 -0.1958 0.4436
## Avg_Cogongrass_Cover-Lynx_rufus 0.5585 0.4552 -0.2435 0.5177
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4583 0.3773 -0.2576 0.4451
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0566 0.4709 -1.0704 -0.0251
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4135 0.3690 -0.3013 0.4111
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4431 0.5064 -0.4592 0.4096
## Avg_Cogongrass_Cover-Sus_scrofa 0.0413 0.5416 -1.2193 0.0983
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0484 0.9999 1041
## (Intercept)-Canis_latrans 1.7135 1.0020 3029
## (Intercept)-Sciurus_niger 3.1801 1.0183 479
## (Intercept)-Procyon_lotor 1.8503 1.0028 2707
## (Intercept)-Dasypus_novemcinctus 0.5167 1.0014 3531
## (Intercept)-Lynx_rufus 3.2052 1.0025 594
## (Intercept)-Didelphis_virginiana -0.0271 1.0006 3207
## (Intercept)-Sylvilagus_floridanus 1.3110 1.0026 1822
## (Intercept)-Sciurus_carolinensis -0.0740 1.0012 3056
## (Intercept)-Vulpes_vulpes 2.7723 1.0594 370
## (Intercept)-Sus_scrofa -0.2784 1.0131 1703
## Veg_shannon_index-Odocoileus_virginianus 1.2714 1.0011 3283
## Veg_shannon_index-Canis_latrans 1.4896 1.0021 3151
## Veg_shannon_index-Sciurus_niger 1.5394 1.0003 2627
## Veg_shannon_index-Procyon_lotor 1.2430 1.0026 3742
## Veg_shannon_index-Dasypus_novemcinctus 0.8542 1.0019 3975
## Veg_shannon_index-Lynx_rufus 1.2044 1.0038 2468
## Veg_shannon_index-Didelphis_virginiana 1.3264 1.0002 3884
## Veg_shannon_index-Sylvilagus_floridanus 1.3669 1.0015 3527
## Veg_shannon_index-Sciurus_carolinensis 0.7387 1.0047 3186
## Veg_shannon_index-Vulpes_vulpes 1.0543 1.0079 2140
## Veg_shannon_index-Sus_scrofa 1.9629 1.0040 2539
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3813 1.0007 3243
## Avg_Cogongrass_Cover-Canis_latrans 1.6190 1.0044 2959
## Avg_Cogongrass_Cover-Sciurus_niger 1.1420 1.0111 1361
## Avg_Cogongrass_Cover-Procyon_lotor 1.1651 1.0025 3572
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1122 1.0010 4287
## Avg_Cogongrass_Cover-Lynx_rufus 1.5412 0.9999 2570
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2321 1.0013 2986
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8004 1.0005 2352
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1721 1.0080 3261
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.5490 1.0102 2268
## Avg_Cogongrass_Cover-Sus_scrofa 0.9385 1.0004 2060
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5356 0.0825 0.3770 0.5354 0.6965
## (Intercept)-Canis_latrans -2.5522 0.2044 -2.9684 -2.5460 -2.1729
## (Intercept)-Sciurus_niger -4.2672 0.6736 -5.5333 -4.2878 -2.9335
## (Intercept)-Procyon_lotor -2.1806 0.1655 -2.5171 -2.1756 -1.8730
## (Intercept)-Dasypus_novemcinctus -1.5727 0.1755 -1.9206 -1.5682 -1.2323
## (Intercept)-Lynx_rufus -3.5644 0.3891 -4.3414 -3.5559 -2.8242
## (Intercept)-Didelphis_virginiana -2.3130 0.3054 -2.9577 -2.3023 -1.7481
## (Intercept)-Sylvilagus_floridanus -3.1244 0.3420 -3.8550 -3.1076 -2.5171
## (Intercept)-Sciurus_carolinensis -2.3954 0.3277 -3.0866 -2.3825 -1.8050
## (Intercept)-Vulpes_vulpes -4.1839 0.8188 -5.8301 -4.1521 -2.7049
## (Intercept)-Sus_scrofa -3.0901 0.6022 -4.2813 -3.0738 -1.9345
## shrub_cover-Odocoileus_virginianus -0.0593 0.0670 -0.1915 -0.0593 0.0708
## shrub_cover-Canis_latrans -0.2707 0.2121 -0.6798 -0.2689 0.1422
## shrub_cover-Sciurus_niger -0.3798 0.4444 -1.3147 -0.3627 0.4761
## shrub_cover-Procyon_lotor 0.2351 0.1720 -0.1174 0.2422 0.5544
## shrub_cover-Dasypus_novemcinctus 0.7943 0.2966 0.2393 0.7838 1.3949
## shrub_cover-Lynx_rufus -0.2742 0.3464 -0.9891 -0.2581 0.3893
## shrub_cover-Didelphis_virginiana 0.8855 0.3591 0.2444 0.8672 1.6511
## shrub_cover-Sylvilagus_floridanus 0.2327 0.3948 -0.4935 0.2123 1.0440
## shrub_cover-Sciurus_carolinensis 0.7667 0.4009 0.0170 0.7540 1.5818
## shrub_cover-Vulpes_vulpes -0.1201 0.5364 -1.2523 -0.1036 0.8935
## shrub_cover-Sus_scrofa 0.4808 0.7423 -0.9357 0.4562 2.0484
## veg_height-Odocoileus_virginianus -0.3308 0.0685 -0.4667 -0.3303 -0.1997
## veg_height-Canis_latrans -0.5880 0.1830 -0.9567 -0.5834 -0.2455
## veg_height-Sciurus_niger -0.0867 0.3833 -0.8154 -0.0964 0.7036
## veg_height-Procyon_lotor 0.3291 0.1214 0.0919 0.3298 0.5641
## veg_height-Dasypus_novemcinctus 0.2247 0.1329 -0.0345 0.2196 0.4873
## veg_height-Lynx_rufus -0.0022 0.2469 -0.4954 0.0013 0.4647
## veg_height-Didelphis_virginiana 0.4032 0.2377 -0.0413 0.3903 0.8959
## veg_height-Sylvilagus_floridanus 0.1179 0.2471 -0.3636 0.1194 0.6055
## veg_height-Sciurus_carolinensis 0.0451 0.2072 -0.3596 0.0420 0.4714
## veg_height-Vulpes_vulpes -0.1637 0.3323 -0.8688 -0.1472 0.4587
## veg_height-Sus_scrofa -0.1398 0.3295 -0.8082 -0.1337 0.5073
## week-Odocoileus_virginianus 1.3115 0.1254 1.0673 1.3094 1.5565
## week-Canis_latrans 0.5927 0.2605 0.0845 0.5882 1.0941
## week-Sciurus_niger -0.3916 0.5631 -1.6217 -0.3390 0.5459
## week-Procyon_lotor 0.2115 0.2128 -0.1937 0.2122 0.6317
## week-Dasypus_novemcinctus 0.1127 0.2254 -0.3135 0.1157 0.5640
## week-Lynx_rufus 0.3955 0.3466 -0.2727 0.3965 1.0773
## week-Didelphis_virginiana 0.0774 0.3697 -0.6895 0.0863 0.7744
## week-Sylvilagus_floridanus 0.0654 0.3502 -0.6332 0.0727 0.7379
## week-Sciurus_carolinensis 0.8088 0.3671 0.1126 0.7980 1.5524
## week-Vulpes_vulpes 0.2163 0.5086 -0.8741 0.2373 1.1476
## week-Sus_scrofa 0.6953 0.4462 -0.1459 0.6782 1.6248
## I(week^2)-Odocoileus_virginianus -0.5404 0.0517 -0.6445 -0.5393 -0.4420
## I(week^2)-Canis_latrans -0.2467 0.1070 -0.4631 -0.2454 -0.0433
## I(week^2)-Sciurus_niger -0.2838 0.2398 -0.7970 -0.2693 0.1541
## I(week^2)-Procyon_lotor -0.1349 0.0922 -0.3194 -0.1342 0.0425
## I(week^2)-Dasypus_novemcinctus -0.1836 0.1039 -0.3949 -0.1822 0.0132
## I(week^2)-Lynx_rufus -0.2469 0.1537 -0.5481 -0.2443 0.0470
## I(week^2)-Didelphis_virginiana -0.4076 0.2010 -0.8520 -0.3934 -0.0582
## I(week^2)-Sylvilagus_floridanus -0.1844 0.1589 -0.5058 -0.1824 0.1174
## I(week^2)-Sciurus_carolinensis -0.2875 0.1439 -0.5859 -0.2830 -0.0152
## I(week^2)-Vulpes_vulpes -0.3972 0.2457 -0.9319 -0.3749 0.0283
## I(week^2)-Sus_scrofa -0.2439 0.1767 -0.6012 -0.2418 0.0903
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5540
## (Intercept)-Canis_latrans 1.0022 2603
## (Intercept)-Sciurus_niger 1.0174 498
## (Intercept)-Procyon_lotor 1.0008 3829
## (Intercept)-Dasypus_novemcinctus 1.0018 4490
## (Intercept)-Lynx_rufus 1.0074 808
## (Intercept)-Didelphis_virginiana 1.0001 3003
## (Intercept)-Sylvilagus_floridanus 1.0052 1310
## (Intercept)-Sciurus_carolinensis 1.0016 2718
## (Intercept)-Vulpes_vulpes 1.0370 329
## (Intercept)-Sus_scrofa 1.0045 1982
## shrub_cover-Odocoileus_virginianus 1.0004 5250
## shrub_cover-Canis_latrans 1.0011 2896
## shrub_cover-Sciurus_niger 1.0073 1210
## shrub_cover-Procyon_lotor 1.0033 3418
## shrub_cover-Dasypus_novemcinctus 1.0032 3204
## shrub_cover-Lynx_rufus 1.0009 1375
## shrub_cover-Didelphis_virginiana 1.0040 2300
## shrub_cover-Sylvilagus_floridanus 1.0054 1826
## shrub_cover-Sciurus_carolinensis 1.0070 2376
## shrub_cover-Vulpes_vulpes 1.0019 1464
## shrub_cover-Sus_scrofa 1.0006 2468
## veg_height-Odocoileus_virginianus 1.0030 5250
## veg_height-Canis_latrans 1.0002 2328
## veg_height-Sciurus_niger 1.0041 1739
## veg_height-Procyon_lotor 1.0008 3993
## veg_height-Dasypus_novemcinctus 1.0008 4158
## veg_height-Lynx_rufus 1.0071 2412
## veg_height-Didelphis_virginiana 1.0001 3691
## veg_height-Sylvilagus_floridanus 1.0019 2447
## veg_height-Sciurus_carolinensis 1.0056 3744
## veg_height-Vulpes_vulpes 1.0099 1615
## veg_height-Sus_scrofa 1.0010 3792
## week-Odocoileus_virginianus 1.0001 4666
## week-Canis_latrans 1.0035 3727
## week-Sciurus_niger 1.0008 841
## week-Procyon_lotor 1.0023 4365
## week-Dasypus_novemcinctus 1.0004 4624
## week-Lynx_rufus 1.0005 2668
## week-Didelphis_virginiana 1.0032 2958
## week-Sylvilagus_floridanus 1.0028 2912
## week-Sciurus_carolinensis 1.0029 3537
## week-Vulpes_vulpes 1.0023 1693
## week-Sus_scrofa 1.0012 3893
## I(week^2)-Odocoileus_virginianus 1.0008 5250
## I(week^2)-Canis_latrans 1.0056 3798
## I(week^2)-Sciurus_niger 1.0070 1238
## I(week^2)-Procyon_lotor 1.0015 4286
## I(week^2)-Dasypus_novemcinctus 1.0006 4341
## I(week^2)-Lynx_rufus 1.0004 2206
## I(week^2)-Didelphis_virginiana 1.0009 2227
## I(week^2)-Sylvilagus_floridanus 1.0010 2463
## I(week^2)-Sciurus_carolinensis 1.0031 4037
## I(week^2)-Vulpes_vulpes 1.0094 1305
## I(week^2)-Sus_scrofa 1.0004 4439
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon<- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9315
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1718 0.5615 -1.2279 -0.1894 0.9953 1.0053 2984
## Avg_Cogongrass_Cover 0.2076 0.2462 -0.2726 0.2084 0.6940 1.0047 2432
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2513 2.8256 0.6139 2.5535 10.0370 1.0410 1227
## Avg_Cogongrass_Cover 0.2697 0.3391 0.0345 0.1758 1.0937 1.0226 2240
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7194 0.6914 0.0637 0.5156 2.5325 1.0117 545
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3966 0.4582 -3.2881 -2.4034 -1.4617 1.0057 4452
## shrub_cover 0.2215 0.2394 -0.2452 0.2154 0.7119 1.0033 3059
## veg_height -0.0146 0.1616 -0.3373 -0.0137 0.3066 1.0021 3017
## week 0.3675 0.2368 -0.1107 0.3742 0.8178 1.0004 3420
## I(week^2) -0.2849 0.1008 -0.4896 -0.2856 -0.0883 1.0008 2489
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3419 1.4786 0.8083 1.9559 6.1395 1.0194 2592
## shrub_cover 0.4447 0.3578 0.0867 0.3514 1.3370 1.0022 2295
## veg_height 0.1981 0.1425 0.0566 0.1604 0.5610 1.0071 3705
## week 0.4348 0.3274 0.1046 0.3471 1.2788 1.0027 2652
## I(week^2) 0.0719 0.0531 0.0219 0.0578 0.2074 1.0094 2374
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3795 1.4025 1.1093 3.2255
## (Intercept)-Canis_latrans 0.4288 0.6274 -0.8051 0.4084
## (Intercept)-Sciurus_niger -0.4150 1.1058 -2.2354 -0.5349
## (Intercept)-Procyon_lotor 0.5315 0.6088 -0.6928 0.5375
## (Intercept)-Dasypus_novemcinctus -0.6393 0.5686 -1.8132 -0.6210
## (Intercept)-Lynx_rufus 0.1534 1.0256 -1.5730 0.0320
## (Intercept)-Didelphis_virginiana -1.2305 0.6574 -2.5569 -1.2246
## (Intercept)-Sylvilagus_floridanus -0.3442 0.6982 -1.6375 -0.3661
## (Intercept)-Sciurus_carolinensis -1.3170 0.6556 -2.6929 -1.2965
## (Intercept)-Vulpes_vulpes -0.9543 1.2263 -2.9881 -1.0708
## (Intercept)-Sus_scrofa -1.6925 0.8072 -3.3444 -1.6865
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1766 0.4738 -0.7484 0.1684
## Avg_Cogongrass_Cover-Canis_latrans 0.4331 0.3913 -0.2237 0.3968
## Avg_Cogongrass_Cover-Sciurus_niger -0.1200 0.5593 -1.3941 -0.0712
## Avg_Cogongrass_Cover-Procyon_lotor 0.2279 0.3386 -0.4142 0.2175
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3480 0.3234 -0.2548 0.3342
## Avg_Cogongrass_Cover-Lynx_rufus 0.4193 0.4083 -0.3280 0.3939
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3212 0.3524 -0.3851 0.3147
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1696 0.4285 -1.1114 -0.1345
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3481 0.3415 -0.3273 0.3438
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2920 0.4364 -0.5560 0.2755
## Avg_Cogongrass_Cover-Sus_scrofa -0.0431 0.5016 -1.1792 0.0087
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5598 1.0283 998
## (Intercept)-Canis_latrans 1.6958 1.0019 3008
## (Intercept)-Sciurus_niger 2.1984 1.0130 578
## (Intercept)-Procyon_lotor 1.6964 1.0041 2446
## (Intercept)-Dasypus_novemcinctus 0.4686 1.0015 3178
## (Intercept)-Lynx_rufus 2.5742 1.0172 914
## (Intercept)-Didelphis_virginiana 0.0510 1.0022 3282
## (Intercept)-Sylvilagus_floridanus 1.0710 1.0080 2089
## (Intercept)-Sciurus_carolinensis -0.0806 1.0010 3115
## (Intercept)-Vulpes_vulpes 1.8039 1.0594 434
## (Intercept)-Sus_scrofa -0.1715 1.0057 1924
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1550 1.0018 4177
## Avg_Cogongrass_Cover-Canis_latrans 1.2909 1.0032 3723
## Avg_Cogongrass_Cover-Sciurus_niger 0.8383 1.0019 1641
## Avg_Cogongrass_Cover-Procyon_lotor 0.9327 1.0035 3489
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0025 1.0012 4021
## Avg_Cogongrass_Cover-Lynx_rufus 1.2845 1.0031 3301
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0290 1.0012 4140
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5746 1.0018 2559
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0489 1.0037 4231
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2148 1.0010 3295
## Avg_Cogongrass_Cover-Sus_scrofa 0.7893 1.0045 2599
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5379 0.0794 0.3803 0.5382 0.6912
## (Intercept)-Canis_latrans -2.5711 0.2070 -3.0015 -2.5647 -2.1864
## (Intercept)-Sciurus_niger -4.0990 0.6704 -5.4407 -4.0797 -2.7938
## (Intercept)-Procyon_lotor -2.1758 0.1618 -2.5024 -2.1724 -1.8618
## (Intercept)-Dasypus_novemcinctus -1.5753 0.1773 -1.9353 -1.5719 -1.2437
## (Intercept)-Lynx_rufus -3.5240 0.3878 -4.2749 -3.5184 -2.7872
## (Intercept)-Didelphis_virginiana -2.3130 0.3066 -2.9464 -2.3007 -1.7431
## (Intercept)-Sylvilagus_floridanus -3.0645 0.3260 -3.7461 -3.0470 -2.4752
## (Intercept)-Sciurus_carolinensis -2.3861 0.3249 -3.0454 -2.3775 -1.7713
## (Intercept)-Vulpes_vulpes -4.0357 0.7850 -5.6046 -3.9940 -2.6507
## (Intercept)-Sus_scrofa -3.1192 0.6143 -4.3436 -3.1253 -1.9178
## shrub_cover-Odocoileus_virginianus -0.0588 0.0665 -0.1935 -0.0587 0.0708
## shrub_cover-Canis_latrans -0.2636 0.2203 -0.7025 -0.2551 0.1512
## shrub_cover-Sciurus_niger -0.3345 0.4626 -1.2575 -0.3269 0.5840
## shrub_cover-Procyon_lotor 0.2495 0.1672 -0.0934 0.2553 0.5634
## shrub_cover-Dasypus_novemcinctus 0.7959 0.2921 0.2509 0.7871 1.3957
## shrub_cover-Lynx_rufus -0.2296 0.3455 -0.9150 -0.2264 0.4472
## shrub_cover-Didelphis_virginiana 0.8762 0.3610 0.2128 0.8564 1.6414
## shrub_cover-Sylvilagus_floridanus 0.2608 0.3956 -0.4891 0.2535 1.0711
## shrub_cover-Sciurus_carolinensis 0.7535 0.3835 -0.0034 0.7496 1.5197
## shrub_cover-Vulpes_vulpes -0.0598 0.5253 -1.1430 -0.0473 0.9881
## shrub_cover-Sus_scrofa 0.4833 0.6948 -0.8933 0.4705 1.8979
## veg_height-Odocoileus_virginianus -0.3305 0.0681 -0.4646 -0.3309 -0.1992
## veg_height-Canis_latrans -0.5930 0.1830 -0.9654 -0.5863 -0.2538
## veg_height-Sciurus_niger -0.0452 0.4056 -0.8187 -0.0516 0.7975
## veg_height-Procyon_lotor 0.3280 0.1243 0.0872 0.3276 0.5752
## veg_height-Dasypus_novemcinctus 0.2284 0.1337 -0.0314 0.2258 0.4947
## veg_height-Lynx_rufus 0.0102 0.2491 -0.4916 0.0118 0.4845
## veg_height-Didelphis_virginiana 0.3986 0.2400 -0.0463 0.3903 0.8885
## veg_height-Sylvilagus_floridanus 0.1232 0.2468 -0.3582 0.1191 0.6110
## veg_height-Sciurus_carolinensis 0.0410 0.2061 -0.3521 0.0384 0.4521
## veg_height-Vulpes_vulpes -0.1511 0.3235 -0.8533 -0.1334 0.4399
## veg_height-Sus_scrofa -0.1297 0.3279 -0.7869 -0.1279 0.5113
## week-Odocoileus_virginianus 1.3100 0.1236 1.0668 1.3078 1.5558
## week-Canis_latrans 0.5978 0.2631 0.0882 0.5934 1.1262
## week-Sciurus_niger -0.3948 0.5462 -1.6086 -0.3492 0.5371
## week-Procyon_lotor 0.2064 0.2156 -0.2184 0.2071 0.6277
## week-Dasypus_novemcinctus 0.1151 0.2281 -0.3242 0.1131 0.5646
## week-Lynx_rufus 0.3811 0.3556 -0.2937 0.3767 1.0933
## week-Didelphis_virginiana 0.0729 0.3686 -0.6755 0.0809 0.7744
## week-Sylvilagus_floridanus 0.0659 0.3482 -0.6343 0.0634 0.7376
## week-Sciurus_carolinensis 0.8046 0.3707 0.1236 0.7947 1.5769
## week-Vulpes_vulpes 0.2255 0.5149 -0.8612 0.2487 1.1952
## week-Sus_scrofa 0.6972 0.4461 -0.1440 0.6760 1.6650
## I(week^2)-Odocoileus_virginianus -0.5400 0.0511 -0.6395 -0.5395 -0.4406
## I(week^2)-Canis_latrans -0.2461 0.1080 -0.4657 -0.2445 -0.0377
## I(week^2)-Sciurus_niger -0.2910 0.2360 -0.7898 -0.2804 0.1520
## I(week^2)-Procyon_lotor -0.1342 0.0918 -0.3179 -0.1326 0.0458
## I(week^2)-Dasypus_novemcinctus -0.1816 0.1057 -0.3953 -0.1811 0.0201
## I(week^2)-Lynx_rufus -0.2378 0.1514 -0.5338 -0.2380 0.0562
## I(week^2)-Didelphis_virginiana -0.4104 0.2132 -0.8920 -0.3893 -0.0676
## I(week^2)-Sylvilagus_floridanus -0.1854 0.1588 -0.5084 -0.1829 0.1208
## I(week^2)-Sciurus_carolinensis -0.2848 0.1461 -0.5870 -0.2785 -0.0091
## I(week^2)-Vulpes_vulpes -0.3990 0.2427 -0.9474 -0.3777 0.0129
## I(week^2)-Sus_scrofa -0.2483 0.1780 -0.6044 -0.2447 0.0917
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0094 2571
## (Intercept)-Sciurus_niger 1.0046 547
## (Intercept)-Procyon_lotor 0.9998 3735
## (Intercept)-Dasypus_novemcinctus 1.0108 4326
## (Intercept)-Lynx_rufus 1.0114 873
## (Intercept)-Didelphis_virginiana 1.0073 2921
## (Intercept)-Sylvilagus_floridanus 1.0101 1632
## (Intercept)-Sciurus_carolinensis 1.0005 2685
## (Intercept)-Vulpes_vulpes 1.0326 423
## (Intercept)-Sus_scrofa 1.0015 1826
## shrub_cover-Odocoileus_virginianus 1.0008 4939
## shrub_cover-Canis_latrans 1.0023 2886
## shrub_cover-Sciurus_niger 1.0017 1270
## shrub_cover-Procyon_lotor 1.0027 3854
## shrub_cover-Dasypus_novemcinctus 1.0061 3442
## shrub_cover-Lynx_rufus 1.0109 1431
## shrub_cover-Didelphis_virginiana 1.0087 2261
## shrub_cover-Sylvilagus_floridanus 1.0046 1778
## shrub_cover-Sciurus_carolinensis 0.9999 2586
## shrub_cover-Vulpes_vulpes 1.0041 1775
## shrub_cover-Sus_scrofa 1.0004 2353
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0017 2393
## veg_height-Sciurus_niger 1.0013 2091
## veg_height-Procyon_lotor 0.9998 3870
## veg_height-Dasypus_novemcinctus 1.0005 4789
## veg_height-Lynx_rufus 1.0002 2356
## veg_height-Didelphis_virginiana 1.0001 3502
## veg_height-Sylvilagus_floridanus 1.0013 2351
## veg_height-Sciurus_carolinensis 1.0013 3565
## veg_height-Vulpes_vulpes 1.0078 1951
## veg_height-Sus_scrofa 1.0001 2871
## week-Odocoileus_virginianus 0.9998 5018
## week-Canis_latrans 1.0011 3953
## week-Sciurus_niger 1.0002 1180
## week-Procyon_lotor 1.0006 4410
## week-Dasypus_novemcinctus 1.0022 4990
## week-Lynx_rufus 1.0062 2512
## week-Didelphis_virginiana 1.0050 2628
## week-Sylvilagus_floridanus 1.0014 3070
## week-Sciurus_carolinensis 1.0007 3879
## week-Vulpes_vulpes 1.0009 1936
## week-Sus_scrofa 0.9999 4029
## I(week^2)-Odocoileus_virginianus 1.0004 4899
## I(week^2)-Canis_latrans 1.0024 4107
## I(week^2)-Sciurus_niger 1.0027 1350
## I(week^2)-Procyon_lotor 1.0001 4246
## I(week^2)-Dasypus_novemcinctus 1.0047 4468
## I(week^2)-Lynx_rufus 1.0024 2356
## I(week^2)-Didelphis_virginiana 1.0090 1494
## I(week^2)-Sylvilagus_floridanus 1.0014 2291
## I(week^2)-Sciurus_carolinensis 1.0015 4156
## I(week^2)-Vulpes_vulpes 1.0006 1698
## I(week^2)-Sus_scrofa 1.0020 4365
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ<- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9088
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9053 0.5997 -2.0601 -0.9245 0.3026 1.0016 3042
## Avg_Cogongrass_Cover -0.7638 0.3854 -1.5468 -0.7580 -0.0510 1.0033 1240
## I(Avg_Cogongrass_Cover^2) 0.8528 0.3360 0.2463 0.8338 1.5635 1.0023 1181
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5751 3.2201 0.6377 2.7347 11.5835 1.0262 1157
## Avg_Cogongrass_Cover 0.4103 0.5455 0.0416 0.2442 1.7511 1.0127 1548
## I(Avg_Cogongrass_Cover^2) 0.4212 0.6883 0.0396 0.2102 2.2753 1.0026 973
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5394 0.5362 0.0488 0.3653 2.0257 1.0049 390
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3888 0.4463 -3.2619 -2.3916 -1.4859 1.0002 4090
## shrub_cover 0.2214 0.2416 -0.2576 0.2199 0.7044 1.0010 2923
## veg_height 0.0137 0.1618 -0.3035 0.0131 0.3369 1.0005 3317
## week 0.3721 0.2395 -0.1276 0.3794 0.8345 1.0022 2884
## I(week^2) -0.2868 0.1006 -0.4914 -0.2834 -0.0900 1.0005 2806
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2293 1.3762 0.7856 1.8956 5.7013 1.0027 3258
## shrub_cover 0.4726 0.4099 0.0889 0.3627 1.5627 1.0006 2085
## veg_height 0.1954 0.1359 0.0549 0.1599 0.5446 1.0028 3693
## week 0.4338 0.3481 0.1078 0.3432 1.3122 1.0089 2106
## I(week^2) 0.0725 0.0534 0.0223 0.0581 0.2071 1.0084 2979
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7778 1.3895 0.4848 2.6244
## (Intercept)-Canis_latrans -0.4486 0.6773 -1.7773 -0.4501
## (Intercept)-Sciurus_niger -0.8935 1.2272 -2.8872 -1.0214
## (Intercept)-Procyon_lotor -0.1536 0.6547 -1.5118 -0.1312
## (Intercept)-Dasypus_novemcinctus -1.3522 0.6264 -2.6132 -1.3457
## (Intercept)-Lynx_rufus -1.0008 0.9514 -2.7583 -1.0421
## (Intercept)-Didelphis_virginiana -1.8562 0.7135 -3.3158 -1.8393
## (Intercept)-Sylvilagus_floridanus -1.0878 0.7336 -2.5498 -1.0892
## (Intercept)-Sciurus_carolinensis -2.3257 0.7666 -3.9271 -2.3023
## (Intercept)-Vulpes_vulpes -2.1610 1.1815 -4.3656 -2.2024
## (Intercept)-Sus_scrofa -2.4010 0.9057 -4.2477 -2.3689
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7584 0.6604 -2.1421 -0.7452
## Avg_Cogongrass_Cover-Canis_latrans -0.3826 0.5490 -1.3642 -0.4212
## Avg_Cogongrass_Cover-Sciurus_niger -1.0618 0.7068 -2.7146 -0.9991
## Avg_Cogongrass_Cover-Procyon_lotor -0.6960 0.5115 -1.7278 -0.6933
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5719 0.4846 -1.5145 -0.5844
## Avg_Cogongrass_Cover-Lynx_rufus -0.6901 0.5811 -1.8507 -0.6916
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5189 0.5337 -1.5312 -0.5269
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1993 0.6234 -2.5941 -1.1363
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8201 0.5522 -2.0029 -0.7929
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7863 0.6267 -2.0505 -0.7736
## Avg_Cogongrass_Cover-Sus_scrofa -1.0305 0.6759 -2.5799 -0.9713
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1285 0.7243 0.1017 1.0078
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2194 0.6931 0.2457 1.0798
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4217 0.6944 -1.0636 0.4554
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0513 0.5607 0.2219 0.9731
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7536 0.3614 0.0672 0.7369
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1697 0.5269 0.3290 1.1092
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6233 0.4053 -0.1463 0.6115
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7581 0.4586 -0.0287 0.7222
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0003 0.4134 0.2738 0.9723
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9594 0.5000 0.1527 0.9006
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4781 0.6023 -0.8773 0.5201
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.0636 1.0118 1327
## (Intercept)-Canis_latrans 0.9007 1.0017 2431
## (Intercept)-Sciurus_niger 1.8716 1.0498 383
## (Intercept)-Procyon_lotor 1.1222 1.0005 2848
## (Intercept)-Dasypus_novemcinctus -0.1626 1.0026 3517
## (Intercept)-Lynx_rufus 0.9021 1.0172 1116
## (Intercept)-Didelphis_virginiana -0.4921 1.0040 2879
## (Intercept)-Sylvilagus_floridanus 0.3263 1.0042 3031
## (Intercept)-Sciurus_carolinensis -0.8648 1.0007 2266
## (Intercept)-Vulpes_vulpes 0.2757 1.0089 696
## (Intercept)-Sus_scrofa -0.7057 1.0112 1886
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5215 1.0017 2357
## Avg_Cogongrass_Cover-Canis_latrans 0.8073 1.0078 2283
## Avg_Cogongrass_Cover-Sciurus_niger 0.1343 1.0032 1410
## Avg_Cogongrass_Cover-Procyon_lotor 0.3358 1.0008 2196
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3929 1.0055 2236
## Avg_Cogongrass_Cover-Lynx_rufus 0.4419 1.0033 2017
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5563 1.0018 2080
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1274 1.0027 1537
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1729 1.0023 1803
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4379 1.0001 1863
## Avg_Cogongrass_Cover-Sus_scrofa 0.1019 1.0015 1825
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.9866 1.0038 1134
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0385 1.0031 960
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.6736 1.0068 1027
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4429 1.0032 1311
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5047 1.0020 2567
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3965 1.0002 1284
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4815 1.0011 2092
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8047 1.0101 1985
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9022 1.0014 1766
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0936 1.0034 1499
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5575 1.0055 1531
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5356 0.0817 0.3802 0.5342 0.7002
## (Intercept)-Canis_latrans -2.5535 0.2006 -2.9658 -2.5476 -2.1735
## (Intercept)-Sciurus_niger -4.0941 0.6699 -5.4229 -4.0854 -2.8103
## (Intercept)-Procyon_lotor -2.1884 0.1668 -2.5236 -2.1839 -1.8626
## (Intercept)-Dasypus_novemcinctus -1.5756 0.1773 -1.9288 -1.5694 -1.2439
## (Intercept)-Lynx_rufus -3.4331 0.3696 -4.1756 -3.4225 -2.7427
## (Intercept)-Didelphis_virginiana -2.3498 0.3069 -2.9834 -2.3434 -1.7840
## (Intercept)-Sylvilagus_floridanus -3.0742 0.3152 -3.7206 -3.0575 -2.4886
## (Intercept)-Sciurus_carolinensis -2.3773 0.3231 -3.0414 -2.3661 -1.7726
## (Intercept)-Vulpes_vulpes -3.9042 0.7449 -5.4865 -3.8685 -2.5960
## (Intercept)-Sus_scrofa -3.1368 0.6147 -4.3958 -3.1242 -1.9516
## shrub_cover-Odocoileus_virginianus -0.0580 0.0679 -0.1912 -0.0571 0.0743
## shrub_cover-Canis_latrans -0.2492 0.2201 -0.7006 -0.2470 0.1753
## shrub_cover-Sciurus_niger -0.3279 0.4629 -1.2503 -0.3173 0.5793
## shrub_cover-Procyon_lotor 0.2337 0.1692 -0.1049 0.2373 0.5527
## shrub_cover-Dasypus_novemcinctus 0.7898 0.2914 0.2488 0.7836 1.3764
## shrub_cover-Lynx_rufus -0.2192 0.3620 -0.9402 -0.2135 0.4599
## shrub_cover-Didelphis_virginiana 0.9255 0.3670 0.2550 0.9041 1.7099
## shrub_cover-Sylvilagus_floridanus 0.2500 0.3966 -0.4715 0.2257 1.1011
## shrub_cover-Sciurus_carolinensis 0.7506 0.3884 0.0435 0.7279 1.5668
## shrub_cover-Vulpes_vulpes -0.0482 0.5480 -1.1830 -0.0316 1.0083
## shrub_cover-Sus_scrofa 0.5164 0.7254 -0.9168 0.5000 2.0338
## veg_height-Odocoileus_virginianus -0.3294 0.0689 -0.4640 -0.3293 -0.1924
## veg_height-Canis_latrans -0.5765 0.1838 -0.9467 -0.5720 -0.2341
## veg_height-Sciurus_niger 0.0257 0.4047 -0.7272 0.0120 0.8746
## veg_height-Procyon_lotor 0.3436 0.1233 0.1009 0.3427 0.5828
## veg_height-Dasypus_novemcinctus 0.2295 0.1346 -0.0273 0.2288 0.4951
## veg_height-Lynx_rufus 0.0797 0.2385 -0.3993 0.0841 0.5431
## veg_height-Didelphis_virginiana 0.3964 0.2512 -0.0801 0.3901 0.9165
## veg_height-Sylvilagus_floridanus 0.1486 0.2397 -0.3176 0.1442 0.6340
## veg_height-Sciurus_carolinensis 0.0541 0.2086 -0.3424 0.0530 0.4724
## veg_height-Vulpes_vulpes -0.1106 0.3118 -0.7608 -0.0967 0.4731
## veg_height-Sus_scrofa -0.1159 0.3277 -0.7692 -0.1113 0.5326
## week-Odocoileus_virginianus 1.3093 0.1238 1.0670 1.3069 1.5534
## week-Canis_latrans 0.5971 0.2606 0.0899 0.5975 1.1109
## week-Sciurus_niger -0.3806 0.5609 -1.6056 -0.3315 0.5961
## week-Procyon_lotor 0.2171 0.2126 -0.1943 0.2167 0.6388
## week-Dasypus_novemcinctus 0.1140 0.2303 -0.3336 0.1144 0.5594
## week-Lynx_rufus 0.3887 0.3564 -0.2964 0.3825 1.0886
## week-Didelphis_virginiana 0.0822 0.3780 -0.7085 0.0961 0.7841
## week-Sylvilagus_floridanus 0.0775 0.3416 -0.6071 0.0865 0.7252
## week-Sciurus_carolinensis 0.8156 0.3705 0.1160 0.8048 1.5666
## week-Vulpes_vulpes 0.2290 0.5148 -0.8219 0.2406 1.2075
## week-Sus_scrofa 0.6967 0.4588 -0.1489 0.6862 1.6469
## I(week^2)-Odocoileus_virginianus -0.5397 0.0513 -0.6425 -0.5382 -0.4398
## I(week^2)-Canis_latrans -0.2484 0.1076 -0.4630 -0.2481 -0.0400
## I(week^2)-Sciurus_niger -0.2869 0.2412 -0.8294 -0.2731 0.1356
## I(week^2)-Procyon_lotor -0.1384 0.0922 -0.3164 -0.1396 0.0396
## I(week^2)-Dasypus_novemcinctus -0.1829 0.1056 -0.3959 -0.1806 0.0210
## I(week^2)-Lynx_rufus -0.2413 0.1551 -0.5539 -0.2384 0.0470
## I(week^2)-Didelphis_virginiana -0.4148 0.2103 -0.8866 -0.3935 -0.0569
## I(week^2)-Sylvilagus_floridanus -0.1813 0.1570 -0.5003 -0.1786 0.1239
## I(week^2)-Sciurus_carolinensis -0.2876 0.1473 -0.5874 -0.2869 -0.0084
## I(week^2)-Vulpes_vulpes -0.3966 0.2448 -0.9399 -0.3766 0.0336
## I(week^2)-Sus_scrofa -0.2464 0.1791 -0.6145 -0.2450 0.0969
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0043 2704
## (Intercept)-Sciurus_niger 1.0161 518
## (Intercept)-Procyon_lotor 1.0003 3456
## (Intercept)-Dasypus_novemcinctus 1.0005 4357
## (Intercept)-Lynx_rufus 1.0079 1041
## (Intercept)-Didelphis_virginiana 1.0028 2603
## (Intercept)-Sylvilagus_floridanus 1.0014 1947
## (Intercept)-Sciurus_carolinensis 1.0031 3183
## (Intercept)-Vulpes_vulpes 1.0189 589
## (Intercept)-Sus_scrofa 1.0020 1598
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0010 2402
## shrub_cover-Sciurus_niger 1.0020 918
## shrub_cover-Procyon_lotor 1.0005 3216
## shrub_cover-Dasypus_novemcinctus 1.0001 3559
## shrub_cover-Lynx_rufus 1.0071 1466
## shrub_cover-Didelphis_virginiana 1.0009 1997
## shrub_cover-Sylvilagus_floridanus 1.0004 1580
## shrub_cover-Sciurus_carolinensis 1.0008 2719
## shrub_cover-Vulpes_vulpes 1.0066 2064
## shrub_cover-Sus_scrofa 1.0015 2152
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0003 2431
## veg_height-Sciurus_niger 0.9999 1860
## veg_height-Procyon_lotor 1.0008 3948
## veg_height-Dasypus_novemcinctus 0.9999 4659
## veg_height-Lynx_rufus 1.0024 2278
## veg_height-Didelphis_virginiana 1.0002 3016
## veg_height-Sylvilagus_floridanus 1.0026 2346
## veg_height-Sciurus_carolinensis 1.0024 3764
## veg_height-Vulpes_vulpes 1.0005 2210
## veg_height-Sus_scrofa 1.0023 3206
## week-Odocoileus_virginianus 1.0001 5250
## week-Canis_latrans 1.0016 3434
## week-Sciurus_niger 1.0064 970
## week-Procyon_lotor 1.0056 3951
## week-Dasypus_novemcinctus 1.0004 4341
## week-Lynx_rufus 1.0001 3044
## week-Didelphis_virginiana 1.0065 3263
## week-Sylvilagus_floridanus 1.0087 2422
## week-Sciurus_carolinensis 1.0021 3903
## week-Vulpes_vulpes 1.0032 2229
## week-Sus_scrofa 1.0028 3446
## I(week^2)-Odocoileus_virginianus 1.0005 5250
## I(week^2)-Canis_latrans 1.0072 3844
## I(week^2)-Sciurus_niger 1.0067 996
## I(week^2)-Procyon_lotor 1.0113 4060
## I(week^2)-Dasypus_novemcinctus 1.0008 3934
## I(week^2)-Lynx_rufus 1.0037 2444
## I(week^2)-Didelphis_virginiana 1.0186 1804
## I(week^2)-Sylvilagus_floridanus 1.0013 2470
## I(week^2)-Sciurus_carolinensis 1.0026 4341
## I(week^2)-Vulpes_vulpes 1.0008 1768
## I(week^2)-Sus_scrofa 1.0012 3200
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ<- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 11 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8684 1.1640 -3.1207 -0.8921 1.5228 1.0031 1789
## Cogon_Patch_Size -0.2297 0.7723 -1.8625 -0.1827 1.1648 1.0918 863
## Veg_shannon_index 0.9654 0.5064 0.0071 0.9451 2.0538 1.0297 641
## total_shrub_cover -0.5459 0.5248 -1.6740 -0.5256 0.4403 1.0287 582
## Avg_Cogongrass_Cover -0.1833 0.9355 -1.9776 -0.2035 1.6903 1.0445 473
## Tree_Density -2.0447 0.8297 -3.8124 -2.0095 -0.4280 1.0288 837
## Avg_Canopy_Cover 1.9902 0.7184 0.6411 1.9473 3.5634 1.0216 738
## I(Avg_Cogongrass_Cover^2) 1.6787 0.6143 0.6061 1.6374 2.9346 1.0168 429
## avg_veg_height -0.1287 0.5230 -1.2371 -0.1148 0.8600 1.0207 686
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.7704 20.2177 4.2730 18.2633 77.3259 1.0817 473
## Cogon_Patch_Size 4.0946 6.2264 0.1368 2.2158 19.8573 1.0707 371
## Veg_shannon_index 0.8807 1.3606 0.0486 0.4404 4.2771 1.0205 917
## total_shrub_cover 0.9407 1.3425 0.0555 0.5093 4.6424 1.1064 544
## Avg_Cogongrass_Cover 1.4625 2.9370 0.0546 0.5881 8.0223 1.1077 651
## Tree_Density 4.1862 7.7422 0.0799 1.5520 25.1342 1.0901 250
## Avg_Canopy_Cover 3.7180 5.3728 0.1521 2.1109 16.3173 1.1437 379
## I(Avg_Cogongrass_Cover^2) 1.1713 3.1064 0.0471 0.4145 7.0620 1.0511 297
## avg_veg_height 0.5347 0.8152 0.0453 0.2775 2.5725 1.0422 1171
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8625 2.7697 0.0602 0.8441 10.579 1.077 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4610 0.4870 -3.3959 -2.4737 -1.4668 1.0019 3807
## shrub_cover 0.3208 0.2594 -0.1866 0.3187 0.8637 1.0083 1932
## veg_height 0.0132 0.1565 -0.3073 0.0168 0.3149 1.0008 2746
## week 0.3609 0.2410 -0.1307 0.3677 0.8150 1.0034 3272
## I(week^2) -0.2855 0.1005 -0.4911 -0.2822 -0.0971 1.0023 2807
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6451 1.5610 0.9539 2.2431 6.6615 1.0009 2789
## shrub_cover 0.5337 0.4292 0.1112 0.4141 1.6585 1.0185 1450
## veg_height 0.1984 0.1366 0.0580 0.1641 0.5488 1.0012 3590
## week 0.4406 0.3420 0.1064 0.3477 1.3504 1.0128 1488
## I(week^2) 0.0723 0.0522 0.0223 0.0581 0.2099 1.0024 3214
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.1586 3.8278 2.5585
## (Intercept)-Canis_latrans -0.8087 1.3470 -3.3486
## (Intercept)-Sciurus_niger 1.2631 2.7090 -2.8826
## (Intercept)-Procyon_lotor -0.3588 1.2830 -2.9875
## (Intercept)-Dasypus_novemcinctus -2.6923 1.2634 -5.5550
## (Intercept)-Lynx_rufus 0.6999 2.9052 -3.9168
## (Intercept)-Didelphis_virginiana -4.2008 1.5487 -7.4999
## (Intercept)-Sylvilagus_floridanus -2.4384 1.5906 -5.9079
## (Intercept)-Sciurus_carolinensis -4.9400 1.7836 -9.1548
## (Intercept)-Vulpes_vulpes -4.1939 2.6742 -9.1022
## (Intercept)-Sus_scrofa -5.7989 2.2123 -10.9140
## Cogon_Patch_Size-Odocoileus_virginianus -0.0616 1.5813 -3.0669
## Cogon_Patch_Size-Canis_latrans 1.7448 1.5514 -0.4000
## Cogon_Patch_Size-Sciurus_niger -0.8688 2.0843 -5.7348
## Cogon_Patch_Size-Procyon_lotor -0.5589 0.8657 -2.4067
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0255 0.8700 -1.7627
## Cogon_Patch_Size-Lynx_rufus -0.3914 1.5883 -3.5715
## Cogon_Patch_Size-Didelphis_virginiana 1.6974 1.0990 -0.0445
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5795 1.9107 -6.5627
## Cogon_Patch_Size-Sciurus_carolinensis -1.2803 1.6105 -5.2356
## Cogon_Patch_Size-Vulpes_vulpes -0.7017 1.9351 -5.0859
## Cogon_Patch_Size-Sus_scrofa -0.9131 1.6855 -5.2907
## Veg_shannon_index-Odocoileus_virginianus 0.8037 0.9571 -1.2729
## Veg_shannon_index-Canis_latrans 1.3462 0.7460 0.1163
## Veg_shannon_index-Sciurus_niger 1.1103 1.0485 -0.9171
## Veg_shannon_index-Procyon_lotor 1.1910 0.6508 0.0854
## Veg_shannon_index-Dasypus_novemcinctus 0.6326 0.6273 -0.6316
## Veg_shannon_index-Lynx_rufus 1.0426 0.9805 -0.8451
## Veg_shannon_index-Didelphis_virginiana 1.1621 0.7460 -0.1745
## Veg_shannon_index-Sylvilagus_floridanus 1.0654 0.7481 -0.3158
## Veg_shannon_index-Sciurus_carolinensis 0.3504 0.8571 -1.6234
## Veg_shannon_index-Vulpes_vulpes 0.6544 0.9568 -1.4755
## Veg_shannon_index-Sus_scrofa 1.6039 1.0275 0.0857
## total_shrub_cover-Odocoileus_virginianus -0.3467 0.9325 -2.2101
## total_shrub_cover-Canis_latrans 0.1109 0.7893 -1.2493
## total_shrub_cover-Sciurus_niger -0.7228 1.0759 -3.1211
## total_shrub_cover-Procyon_lotor -1.1331 0.6799 -2.6075
## total_shrub_cover-Dasypus_novemcinctus -0.2698 0.6777 -1.7457
## total_shrub_cover-Lynx_rufus -0.8076 1.0573 -3.1577
## total_shrub_cover-Didelphis_virginiana -0.8369 0.8319 -2.7074
## total_shrub_cover-Sylvilagus_floridanus -0.6672 0.9390 -2.8195
## total_shrub_cover-Sciurus_carolinensis -0.4622 0.8361 -2.2358
## total_shrub_cover-Vulpes_vulpes -0.7610 1.0182 -3.0043
## total_shrub_cover-Sus_scrofa -0.3033 0.9410 -2.2688
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2502 1.4119 -3.2259
## Avg_Cogongrass_Cover-Canis_latrans 0.0202 1.2447 -2.2982
## Avg_Cogongrass_Cover-Sciurus_niger -0.5833 1.6742 -4.3595
## Avg_Cogongrass_Cover-Procyon_lotor -0.0125 1.1860 -2.1975
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4950 1.3339 -1.7254
## Avg_Cogongrass_Cover-Lynx_rufus -0.0982 1.3208 -2.6063
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1780 1.2266 -2.5943
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8014 1.3709 -3.8096
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1683 1.2686 -2.6193
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0133 1.3566 -2.4550
## Avg_Cogongrass_Cover-Sus_scrofa -0.5692 1.4932 -3.8748
## Tree_Density-Odocoileus_virginianus -0.9979 1.5526 -3.3745
## Tree_Density-Canis_latrans -2.9270 1.5710 -7.0018
## Tree_Density-Sciurus_niger -2.1441 1.8870 -6.0448
## Tree_Density-Procyon_lotor -2.0080 1.0392 -4.2531
## Tree_Density-Dasypus_novemcinctus -4.1227 2.3525 -10.2848
## Tree_Density-Lynx_rufus -0.8488 1.7595 -3.4796
## Tree_Density-Didelphis_virginiana -2.3776 1.3563 -5.5460
## Tree_Density-Sylvilagus_floridanus -2.5706 1.5456 -6.1524
## Tree_Density-Sciurus_carolinensis -2.7675 1.6883 -6.9412
## Tree_Density-Vulpes_vulpes -2.1317 1.8872 -6.0453
## Tree_Density-Sus_scrofa -2.6281 1.8694 -7.2975
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2133 1.5882 -2.0666
## Avg_Canopy_Cover-Canis_latrans 0.1309 0.7646 -1.4377
## Avg_Canopy_Cover-Sciurus_niger 2.4466 1.9844 -1.3911
## Avg_Canopy_Cover-Procyon_lotor 1.7072 0.8554 0.2527
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2289 0.9363 0.7692
## Avg_Canopy_Cover-Lynx_rufus 1.8228 1.6680 -1.2981
## Avg_Canopy_Cover-Didelphis_virginiana 3.2562 1.4483 1.2373
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9500 1.9411 1.2977
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0034 1.4769 1.0654
## Avg_Canopy_Cover-Vulpes_vulpes 2.7049 1.7114 0.3909
## Avg_Canopy_Cover-Sus_scrofa 2.2286 1.1204 0.4976
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.0117 1.2934 0.1640
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0700 0.9724 0.6278
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3358 1.3239 -1.5071
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0112 0.9891 0.5900
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5927 0.7724 0.2586
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2585 1.2422 0.6437
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3066 0.7408 -0.1362
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4429 0.9031 -0.0666
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8671 0.8365 0.4981
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0228 0.9756 0.5501
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3046 1.1154 -1.2444
## avg_veg_height-Odocoileus_virginianus -0.1495 0.8421 -1.9635
## avg_veg_height-Canis_latrans -0.2307 0.6548 -1.5928
## avg_veg_height-Sciurus_niger -0.2560 0.9101 -2.3650
## avg_veg_height-Procyon_lotor 0.0587 0.6568 -1.2247
## avg_veg_height-Dasypus_novemcinctus 0.2127 0.6604 -0.9859
## avg_veg_height-Lynx_rufus -0.3384 0.8780 -2.3174
## avg_veg_height-Didelphis_virginiana -0.3167 0.7477 -1.9287
## avg_veg_height-Sylvilagus_floridanus -0.2268 0.7430 -1.8031
## avg_veg_height-Sciurus_carolinensis 0.1758 0.7309 -1.1657
## avg_veg_height-Vulpes_vulpes -0.2172 0.8391 -2.0194
## avg_veg_height-Sus_scrofa -0.1771 0.7753 -1.7903
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4925 17.6135 1.0995 396
## (Intercept)-Canis_latrans -0.8663 2.0369 1.0052 1109
## (Intercept)-Sciurus_niger 0.8692 7.7186 1.0143 414
## (Intercept)-Procyon_lotor -0.3237 1.9089 1.0092 788
## (Intercept)-Dasypus_novemcinctus -2.5971 -0.5152 1.0576 717
## (Intercept)-Lynx_rufus 0.2796 7.6168 1.0160 209
## (Intercept)-Didelphis_virginiana -4.1064 -1.4956 1.0136 1039
## (Intercept)-Sylvilagus_floridanus -2.3710 0.5407 1.0245 898
## (Intercept)-Sciurus_carolinensis -4.7337 -2.0535 1.0672 444
## (Intercept)-Vulpes_vulpes -4.3385 1.7119 1.0459 285
## (Intercept)-Sus_scrofa -5.5963 -1.9786 1.0171 462
## Cogon_Patch_Size-Odocoileus_virginianus -0.1067 3.4259 1.0206 1820
## Cogon_Patch_Size-Canis_latrans 1.4515 5.5702 1.0178 728
## Cogon_Patch_Size-Sciurus_niger -0.6307 2.7853 1.0616 572
## Cogon_Patch_Size-Procyon_lotor -0.5285 1.0173 1.0425 578
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0145 1.7366 1.0211 1384
## Cogon_Patch_Size-Lynx_rufus -0.4072 2.8373 1.0240 950
## Cogon_Patch_Size-Didelphis_virginiana 1.5775 4.2129 1.0165 588
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2030 0.9525 1.0908 678
## Cogon_Patch_Size-Sciurus_carolinensis -0.9901 0.9472 1.0781 660
## Cogon_Patch_Size-Vulpes_vulpes -0.5550 2.8205 1.1369 607
## Cogon_Patch_Size-Sus_scrofa -0.6298 1.6290 1.0168 958
## Veg_shannon_index-Odocoileus_virginianus 0.8414 2.6456 1.0197 1717
## Veg_shannon_index-Canis_latrans 1.2638 3.0688 1.0199 990
## Veg_shannon_index-Sciurus_niger 1.0562 3.4333 1.0096 1215
## Veg_shannon_index-Procyon_lotor 1.1317 2.6519 1.0239 787
## Veg_shannon_index-Dasypus_novemcinctus 0.6420 1.8711 1.0221 1308
## Veg_shannon_index-Lynx_rufus 1.0361 3.0674 1.0219 950
## Veg_shannon_index-Didelphis_virginiana 1.1059 2.8226 1.0223 1277
## Veg_shannon_index-Sylvilagus_floridanus 1.0249 2.6928 1.0146 1315
## Veg_shannon_index-Sciurus_carolinensis 0.4407 1.8176 1.0102 1071
## Veg_shannon_index-Vulpes_vulpes 0.7207 2.3419 1.0078 1000
## Veg_shannon_index-Sus_scrofa 1.4277 4.1258 1.0214 1030
## total_shrub_cover-Odocoileus_virginianus -0.3719 1.6373 1.0142 1559
## total_shrub_cover-Canis_latrans 0.0320 1.9119 1.0182 854
## total_shrub_cover-Sciurus_niger -0.6526 1.2679 1.0131 914
## total_shrub_cover-Procyon_lotor -1.0703 0.0253 1.0053 1086
## total_shrub_cover-Dasypus_novemcinctus -0.2432 1.0111 1.0040 1255
## total_shrub_cover-Lynx_rufus -0.7311 1.1246 1.0459 469
## total_shrub_cover-Didelphis_virginiana -0.7616 0.5769 1.0106 1131
## total_shrub_cover-Sylvilagus_floridanus -0.5954 0.9815 1.0437 837
## total_shrub_cover-Sciurus_carolinensis -0.4187 1.0920 1.0309 1290
## total_shrub_cover-Vulpes_vulpes -0.6851 0.9745 1.0238 1012
## total_shrub_cover-Sus_scrofa -0.3147 1.6223 1.0146 1141
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2482 2.5816 1.0149 742
## Avg_Cogongrass_Cover-Canis_latrans -0.0080 2.6093 1.0227 809
## Avg_Cogongrass_Cover-Sciurus_niger -0.4611 2.2290 1.0220 530
## Avg_Cogongrass_Cover-Procyon_lotor -0.0723 2.4996 1.0405 704
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3531 3.4953 1.0588 620
## Avg_Cogongrass_Cover-Lynx_rufus -0.1170 2.5993 1.0301 843
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1843 2.3134 1.0194 759
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6992 1.6535 1.0319 650
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1797 2.4565 1.0310 646
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0332 2.8793 1.0198 761
## Avg_Cogongrass_Cover-Sus_scrofa -0.4792 2.0238 1.0181 620
## Tree_Density-Odocoileus_virginianus -1.2350 2.8268 1.0043 658
## Tree_Density-Canis_latrans -2.6485 -0.6791 1.0456 488
## Tree_Density-Sciurus_niger -2.0744 1.4358 1.0267 612
## Tree_Density-Procyon_lotor -1.9296 -0.1701 1.0355 661
## Tree_Density-Dasypus_novemcinctus -3.4759 -1.3227 1.1111 313
## Tree_Density-Lynx_rufus -1.1265 3.4306 1.0177 504
## Tree_Density-Didelphis_virginiana -2.2302 -0.0553 1.0383 987
## Tree_Density-Sylvilagus_floridanus -2.3743 -0.0113 1.0175 740
## Tree_Density-Sciurus_carolinensis -2.4930 -0.1511 1.0603 568
## Tree_Density-Vulpes_vulpes -2.0814 1.5423 1.0331 562
## Tree_Density-Sus_scrofa -2.3581 0.2820 1.0788 695
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3017 4.3310 1.0079 1580
## Avg_Canopy_Cover-Canis_latrans 0.1166 1.6644 1.0073 1120
## Avg_Canopy_Cover-Sciurus_niger 2.2974 7.0451 1.0220 686
## Avg_Canopy_Cover-Procyon_lotor 1.6602 3.5785 1.0178 965
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0918 4.5148 1.0704 548
## Avg_Canopy_Cover-Lynx_rufus 1.7405 5.4607 1.0089 546
## Avg_Canopy_Cover-Didelphis_virginiana 2.9815 6.8829 1.0794 621
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5832 8.9410 1.1275 424
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6793 6.7555 1.0804 472
## Avg_Canopy_Cover-Vulpes_vulpes 2.3740 6.6194 1.0932 358
## Avg_Canopy_Cover-Sus_scrofa 2.0915 4.8729 1.0750 922
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8229 5.1617 1.0009 437
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9211 4.4536 1.0182 551
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.4239 3.5451 1.0037 310
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8727 4.3515 1.0270 449
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5285 3.2692 1.0281 698
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0309 5.3563 1.0558 430
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3075 2.8050 1.0034 671
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3690 3.4552 1.0116 644
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7675 3.7566 1.0344 573
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8745 4.4032 1.0242 555
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3575 3.3433 1.0114 463
## avg_veg_height-Odocoileus_virginianus -0.1226 1.4443 1.0096 1675
## avg_veg_height-Canis_latrans -0.2147 1.0320 1.0318 878
## avg_veg_height-Sciurus_niger -0.1942 1.3887 1.0138 998
## avg_veg_height-Procyon_lotor 0.0569 1.3636 1.0129 1229
## avg_veg_height-Dasypus_novemcinctus 0.1808 1.5941 1.0137 1085
## avg_veg_height-Lynx_rufus -0.2748 1.2221 1.0081 1056
## avg_veg_height-Didelphis_virginiana -0.2742 1.0209 1.0175 1173
## avg_veg_height-Sylvilagus_floridanus -0.2026 1.1498 1.0150 1032
## avg_veg_height-Sciurus_carolinensis 0.1237 1.7758 1.0070 1437
## avg_veg_height-Vulpes_vulpes -0.1864 1.3930 1.0175 1068
## avg_veg_height-Sus_scrofa -0.1525 1.3321 1.0116 1303
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5391 0.0796 0.3872 0.5380 0.6985
## (Intercept)-Canis_latrans -2.5378 0.1999 -2.9436 -2.5314 -2.1634
## (Intercept)-Sciurus_niger -4.7460 0.5226 -5.7902 -4.7447 -3.7312
## (Intercept)-Procyon_lotor -2.1960 0.1657 -2.5353 -2.1886 -1.8886
## (Intercept)-Dasypus_novemcinctus -1.6132 0.1847 -1.9813 -1.6094 -1.2689
## (Intercept)-Lynx_rufus -3.7371 0.3778 -4.4725 -3.7433 -2.9677
## (Intercept)-Didelphis_virginiana -2.3485 0.3058 -2.9769 -2.3471 -1.7623
## (Intercept)-Sylvilagus_floridanus -3.0978 0.2991 -3.7269 -3.0811 -2.5567
## (Intercept)-Sciurus_carolinensis -2.5031 0.3430 -3.1787 -2.5019 -1.8490
## (Intercept)-Vulpes_vulpes -4.1105 0.7000 -5.5917 -4.0697 -2.8654
## (Intercept)-Sus_scrofa -3.2258 0.6121 -4.4368 -3.2234 -2.0036
## shrub_cover-Odocoileus_virginianus -0.0583 0.0678 -0.1908 -0.0575 0.0785
## shrub_cover-Canis_latrans -0.2776 0.2302 -0.7228 -0.2807 0.1701
## shrub_cover-Sciurus_niger -0.3352 0.4474 -1.2395 -0.3219 0.5084
## shrub_cover-Procyon_lotor 0.2676 0.1639 -0.0642 0.2709 0.5792
## shrub_cover-Dasypus_novemcinctus 0.9074 0.3107 0.3259 0.9030 1.5168
## shrub_cover-Lynx_rufus -0.1960 0.3612 -0.8882 -0.2023 0.5332
## shrub_cover-Didelphis_virginiana 0.9754 0.3665 0.3160 0.9516 1.7437
## shrub_cover-Sylvilagus_floridanus 0.4946 0.3913 -0.2678 0.4903 1.2826
## shrub_cover-Sciurus_carolinensis 0.9170 0.4134 0.1343 0.9054 1.7666
## shrub_cover-Vulpes_vulpes 0.1398 0.5355 -0.9473 0.1526 1.1807
## shrub_cover-Sus_scrofa 0.7482 0.7512 -0.6753 0.7160 2.2821
## veg_height-Odocoileus_virginianus -0.3311 0.0686 -0.4640 -0.3307 -0.1983
## veg_height-Canis_latrans -0.5533 0.1835 -0.9239 -0.5532 -0.2045
## veg_height-Sciurus_niger -0.0461 0.3259 -0.6829 -0.0477 0.5931
## veg_height-Procyon_lotor 0.3576 0.1248 0.1144 0.3558 0.6060
## veg_height-Dasypus_novemcinctus 0.2497 0.1349 -0.0024 0.2478 0.5261
## veg_height-Lynx_rufus 0.1457 0.2355 -0.3304 0.1500 0.5936
## veg_height-Didelphis_virginiana 0.4326 0.2351 -0.0098 0.4242 0.9145
## veg_height-Sylvilagus_floridanus 0.1391 0.2440 -0.3367 0.1380 0.6229
## veg_height-Sciurus_carolinensis 0.1067 0.2141 -0.3070 0.1040 0.5322
## veg_height-Vulpes_vulpes -0.1694 0.3332 -0.8896 -0.1551 0.4406
## veg_height-Sus_scrofa -0.1674 0.3251 -0.8263 -0.1586 0.4594
## week-Odocoileus_virginianus 1.3111 0.1252 1.0743 1.3094 1.5641
## week-Canis_latrans 0.5881 0.2623 0.0839 0.5782 1.1237
## week-Sciurus_niger -0.4008 0.5652 -1.6085 -0.3479 0.5402
## week-Procyon_lotor 0.2043 0.2087 -0.2132 0.2078 0.6139
## week-Dasypus_novemcinctus 0.1124 0.2292 -0.3336 0.1129 0.5565
## week-Lynx_rufus 0.3857 0.3492 -0.3248 0.3859 1.0832
## week-Didelphis_virginiana 0.0778 0.3685 -0.6638 0.0882 0.7722
## week-Sylvilagus_floridanus 0.0665 0.3472 -0.6243 0.0718 0.7404
## week-Sciurus_carolinensis 0.8157 0.3684 0.1158 0.8035 1.5742
## week-Vulpes_vulpes 0.2051 0.5178 -0.8867 0.2325 1.1694
## week-Sus_scrofa 0.6973 0.4407 -0.1380 0.6892 1.5911
## I(week^2)-Odocoileus_virginianus -0.5408 0.0516 -0.6436 -0.5409 -0.4424
## I(week^2)-Canis_latrans -0.2448 0.1075 -0.4612 -0.2438 -0.0361
## I(week^2)-Sciurus_niger -0.2816 0.2364 -0.7939 -0.2678 0.1458
## I(week^2)-Procyon_lotor -0.1327 0.0909 -0.3122 -0.1341 0.0470
## I(week^2)-Dasypus_novemcinctus -0.1829 0.1050 -0.3870 -0.1838 0.0207
## I(week^2)-Lynx_rufus -0.2433 0.1550 -0.5610 -0.2392 0.0422
## I(week^2)-Didelphis_virginiana -0.4094 0.2071 -0.8690 -0.3905 -0.0536
## I(week^2)-Sylvilagus_floridanus -0.1804 0.1568 -0.4947 -0.1769 0.1203
## I(week^2)-Sciurus_carolinensis -0.2871 0.1461 -0.5928 -0.2859 -0.0089
## I(week^2)-Vulpes_vulpes -0.4011 0.2470 -0.9665 -0.3796 0.0262
## I(week^2)-Sus_scrofa -0.2472 0.1760 -0.6031 -0.2465 0.0971
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 6400
## (Intercept)-Canis_latrans 1.0047 2325
## (Intercept)-Sciurus_niger 1.0169 614
## (Intercept)-Procyon_lotor 1.0014 3072
## (Intercept)-Dasypus_novemcinctus 1.0058 2889
## (Intercept)-Lynx_rufus 1.0088 442
## (Intercept)-Didelphis_virginiana 1.0046 2167
## (Intercept)-Sylvilagus_floridanus 1.0013 2152
## (Intercept)-Sciurus_carolinensis 1.0057 1757
## (Intercept)-Vulpes_vulpes 1.0081 434
## (Intercept)-Sus_scrofa 1.0028 979
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0095 1551
## shrub_cover-Sciurus_niger 1.0351 843
## shrub_cover-Procyon_lotor 1.0001 3408
## shrub_cover-Dasypus_novemcinctus 1.0045 1802
## shrub_cover-Lynx_rufus 1.0144 651
## shrub_cover-Didelphis_virginiana 1.0096 1704
## shrub_cover-Sylvilagus_floridanus 1.0256 1280
## shrub_cover-Sciurus_carolinensis 1.0108 1432
## shrub_cover-Vulpes_vulpes 1.0004 1331
## shrub_cover-Sus_scrofa 1.0077 753
## veg_height-Odocoileus_virginianus 1.0002 5165
## veg_height-Canis_latrans 1.0023 2397
## veg_height-Sciurus_niger 1.0049 1137
## veg_height-Procyon_lotor 1.0032 3644
## veg_height-Dasypus_novemcinctus 1.0063 3880
## veg_height-Lynx_rufus 1.0055 1881
## veg_height-Didelphis_virginiana 1.0013 2891
## veg_height-Sylvilagus_floridanus 1.0031 2005
## veg_height-Sciurus_carolinensis 1.0068 2614
## veg_height-Vulpes_vulpes 1.0013 1670
## veg_height-Sus_scrofa 1.0021 2484
## week-Odocoileus_virginianus 0.9999 4406
## week-Canis_latrans 1.0024 3624
## week-Sciurus_niger 1.0095 661
## week-Procyon_lotor 1.0006 4321
## week-Dasypus_novemcinctus 0.9997 4806
## week-Lynx_rufus 1.0047 2263
## week-Didelphis_virginiana 1.0058 2999
## week-Sylvilagus_floridanus 1.0017 2874
## week-Sciurus_carolinensis 1.0028 4098
## week-Vulpes_vulpes 1.0086 1646
## week-Sus_scrofa 1.0011 3888
## I(week^2)-Odocoileus_virginianus 1.0002 4233
## I(week^2)-Canis_latrans 1.0006 3771
## I(week^2)-Sciurus_niger 1.0185 808
## I(week^2)-Procyon_lotor 1.0001 4058
## I(week^2)-Dasypus_novemcinctus 1.0014 4236
## I(week^2)-Lynx_rufus 1.0000 1981
## I(week^2)-Didelphis_virginiana 1.0013 1666
## I(week^2)-Sylvilagus_floridanus 1.0001 2413
## I(week^2)-Sciurus_carolinensis 1.0034 4104
## I(week^2)-Vulpes_vulpes 1.0036 1485
## I(week^2)-Sus_scrofa 1.0013 4074
waicOcc(ms_full_full, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1765.1155 122.9221 3776.0751
waicOcc(ms_full_cover, by.sp = FALSE)
## elpd pD WAIC
## -1805.1923 121.8764 3854.1373
waicOcc(ms_full_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1798.8875 104.3316 3806.4382
waicOcc(ms_full_move, by.sp = FALSE)
## elpd pD WAIC
## -1803.3585 117.7229 3842.1629
waicOcc(ms_full_forage, by.sp = FALSE)
## elpd pD WAIC
## -1812.1947 110.0026 3844.3946
waicOcc(ms_full_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1817.4772 103.6784 3842.3112
waicOcc(ms_full_null, by.sp = FALSE)
## elpd pD WAIC
## -1829.47689 90.69082 3840.33542
waicOcc(ms_full_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1810.0422 107.1644 3834.4131
waicOcc(ms_full_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1759.4679 124.1157 3767.1672
waicOcc(ms_null_null, by.sp = FALSE)
## elpd pD WAIC
## -1888.37903 39.63343 3856.02491
waicOcc(ms_null_full, by.sp = FALSE)
## elpd pD WAIC
## -1826.47678 71.51929 3795.99215
waicOcc(ms_null_cover, by.sp = FALSE)
## elpd pD WAIC
## -1869.40269 63.29355 3865.39247
waicOcc(ms_null_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1859.00922 53.73138 3825.48120
waicOcc(ms_null_move, by.sp = FALSE)
## elpd pD WAIC
## -1864.87004 62.64658 3855.03324
waicOcc(ms_null_forage, by.sp = FALSE)
## elpd pD WAIC
## -1870.70536 57.82196 3857.05464
waicOcc(ms_null_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1875.21969 53.19213 3856.82364
waicOcc(ms_null_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1867.77843 55.54222 3846.64131
waicOcc(ms_null_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1819.59496 72.42328 3784.03649
waicOcc(ms_week_full, by.sp = FALSE)
## elpd pD WAIC
## -1815.48052 79.97089 3790.90282
waicOcc(ms_week_cover, by.sp = FALSE)
## elpd pD WAIC
## -1858.26517 71.80711 3860.14456
waicOcc(ms_week_null, by.sp = FALSE)
## elpd pD WAIC
## -1877.43146 48.18202 3851.22695
waicOcc(ms_week_forage, by.sp = FALSE)
## elpd pD WAIC
## -1859.61376 66.53506 3852.29763
waicOcc(ms_week_move, by.sp = FALSE)
## elpd pD WAIC
## -1853.591 71.669 3850.519
waicOcc(ms_week_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1848.37202 61.86644 3820.47691
waicOcc(ms_week_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1864.36046 61.74896 3852.21885
waicOcc(ms_week_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1856.71224 64.57995 3842.58438
waicOcc(ms_week_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1808.18284 81.52182 3779.40932
waicOcc(ms_cover_full, by.sp = FALSE)
## elpd pD WAIC
## -1778.4720 112.2452 3781.4344
waicOcc(ms_cover_cover, by.sp = FALSE)
## elpd pD WAIC
## -1816.9892 112.9457 3859.8699
waicOcc(ms_cover_null, by.sp = FALSE)
## elpd pD WAIC
## -1840.9515 81.4515 3844.8061
waicOcc(ms_cover_forage, by.sp = FALSE)
## elpd pD WAIC
## -1824.24176 99.61615 3847.71581
waicOcc(ms_cover_move, by.sp = FALSE)
## elpd pD WAIC
## -1814.1597 110.0362 3848.3917
waicOcc(ms_cover_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1810.33432 94.29619 3809.26102
waicOcc(ms_cover_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1828.36585 94.80366 3846.33902
waicOcc(ms_cover_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1821.04490 97.87163 3837.83305
waicOcc(ms_cover_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1770.9871 115.8109 3773.5961
waicOcc(ms_weekQ_full, by.sp = FALSE)
## elpd pD WAIC
## -1742.45609 93.46293 3671.83805
waicOcc(ms_weekQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -1784.29879 85.28669 3739.17096
waicOcc(ms_weekQ_null, by.sp = FALSE)
## elpd pD WAIC
## -1803.14538 61.90162 3730.09399
waicOcc(ms_weekQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -1785.60885 80.42088 3732.05945
waicOcc(ms_weekQ_move, by.sp = FALSE)
## elpd pD WAIC
## -1779.45627 85.27812 3729.46879
waicOcc(ms_weekQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1774.42053 75.84419 3700.52945
waicOcc(ms_weekQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1789.95150 75.89883 3731.70065
waicOcc(ms_weekQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1782.72289 78.28339 3722.01257
waicOcc(ms_weekQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1734.32718 94.99506 3658.64448
waicOcc(ms_fullQ_full, by.sp = FALSE)
## elpd pD WAIC
## -1690.077 137.431 3655.015
waicOcc(ms_fullQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -1729.2932 137.4499 3733.4863
waicOcc(ms_fullQ_null, by.sp = FALSE)
## elpd pD WAIC
## -1752.9039 107.3217 3720.4513
waicOcc(ms_fullQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -1736.0865 125.8356 3723.8441
waicOcc(ms_fullQ_move, by.sp = FALSE)
## elpd pD WAIC
## -1726.6673 134.3722 3722.0791
waicOcc(ms_fullQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1722.3484 119.9926 3684.6820
waicOcc(ms_fullQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1740.5038 120.6322 3722.2718
waicOcc(ms_fullQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1733.5956 122.7889 3712.7690
waicOcc(ms_fullQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1683.4886 139.5051 3645.9874
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ <- ppcOcc(ms_fullQ_fullQ, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 11
## Currently on species 2 out of 11
## Currently on species 3 out of 11
## Currently on species 4 out of 11
## Currently on species 5 out of 11
## Currently on species 6 out of 11
## Currently on species 7 out of 11
## Currently on species 8 out of 11
## Currently on species 9 out of 11
## Currently on species 10 out of 11
## Currently on species 11 out of 11
summary(ppc.ms_fullQ_fullQ)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ, fit.stat = "freeman-tukey", group = 1)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.2856
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Odocoileus_virginianus Bayesian p-value: 0
## Canis_latrans Bayesian p-value: 0.609
## Sciurus_niger Bayesian p-value: 0.1181
## Procyon_lotor Bayesian p-value: 0.0619
## Dasypus_novemcinctus Bayesian p-value: 2e-04
## Lynx_rufus Bayesian p-value: 0.2808
## Didelphis_virginiana Bayesian p-value: 0.436
## Sylvilagus_floridanus Bayesian p-value: 0.4103
## Sciurus_carolinensis Bayesian p-value: 0.3823
## Vulpes_vulpes Bayesian p-value: 0.2688
## Sus_scrofa Bayesian p-value: 0.5739
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8684 1.1640 -3.1207 -0.8921 1.5228 1.0031 1789
## Cogon_Patch_Size -0.2297 0.7723 -1.8625 -0.1827 1.1648 1.0918 863
## Veg_shannon_index 0.9654 0.5064 0.0071 0.9451 2.0538 1.0297 641
## total_shrub_cover -0.5459 0.5248 -1.6740 -0.5256 0.4403 1.0287 582
## Avg_Cogongrass_Cover -0.1833 0.9355 -1.9776 -0.2035 1.6903 1.0445 473
## Tree_Density -2.0447 0.8297 -3.8124 -2.0095 -0.4280 1.0288 837
## Avg_Canopy_Cover 1.9902 0.7184 0.6411 1.9473 3.5634 1.0216 738
## I(Avg_Cogongrass_Cover^2) 1.6787 0.6143 0.6061 1.6374 2.9346 1.0168 429
## avg_veg_height -0.1287 0.5230 -1.2371 -0.1148 0.8600 1.0207 686
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.7704 20.2177 4.2730 18.2633 77.3259 1.0817 473
## Cogon_Patch_Size 4.0946 6.2264 0.1368 2.2158 19.8573 1.0707 371
## Veg_shannon_index 0.8807 1.3606 0.0486 0.4404 4.2771 1.0205 917
## total_shrub_cover 0.9407 1.3425 0.0555 0.5093 4.6424 1.1064 544
## Avg_Cogongrass_Cover 1.4625 2.9370 0.0546 0.5881 8.0223 1.1077 651
## Tree_Density 4.1862 7.7422 0.0799 1.5520 25.1342 1.0901 250
## Avg_Canopy_Cover 3.7180 5.3728 0.1521 2.1109 16.3173 1.1437 379
## I(Avg_Cogongrass_Cover^2) 1.1713 3.1064 0.0471 0.4145 7.0620 1.0511 297
## avg_veg_height 0.5347 0.8152 0.0453 0.2775 2.5725 1.0422 1171
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8625 2.7697 0.0602 0.8441 10.579 1.077 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4610 0.4870 -3.3959 -2.4737 -1.4668 1.0019 3807
## shrub_cover 0.3208 0.2594 -0.1866 0.3187 0.8637 1.0083 1932
## veg_height 0.0132 0.1565 -0.3073 0.0168 0.3149 1.0008 2746
## week 0.3609 0.2410 -0.1307 0.3677 0.8150 1.0034 3272
## I(week^2) -0.2855 0.1005 -0.4911 -0.2822 -0.0971 1.0023 2807
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6451 1.5610 0.9539 2.2431 6.6615 1.0009 2789
## shrub_cover 0.5337 0.4292 0.1112 0.4141 1.6585 1.0185 1450
## veg_height 0.1984 0.1366 0.0580 0.1641 0.5488 1.0012 3590
## week 0.4406 0.3420 0.1064 0.3477 1.3504 1.0128 1488
## I(week^2) 0.0723 0.0522 0.0223 0.0581 0.2099 1.0024 3214
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.1586 3.8278 2.5585
## (Intercept)-Canis_latrans -0.8087 1.3470 -3.3486
## (Intercept)-Sciurus_niger 1.2631 2.7090 -2.8826
## (Intercept)-Procyon_lotor -0.3588 1.2830 -2.9875
## (Intercept)-Dasypus_novemcinctus -2.6923 1.2634 -5.5550
## (Intercept)-Lynx_rufus 0.6999 2.9052 -3.9168
## (Intercept)-Didelphis_virginiana -4.2008 1.5487 -7.4999
## (Intercept)-Sylvilagus_floridanus -2.4384 1.5906 -5.9079
## (Intercept)-Sciurus_carolinensis -4.9400 1.7836 -9.1548
## (Intercept)-Vulpes_vulpes -4.1939 2.6742 -9.1022
## (Intercept)-Sus_scrofa -5.7989 2.2123 -10.9140
## Cogon_Patch_Size-Odocoileus_virginianus -0.0616 1.5813 -3.0669
## Cogon_Patch_Size-Canis_latrans 1.7448 1.5514 -0.4000
## Cogon_Patch_Size-Sciurus_niger -0.8688 2.0843 -5.7348
## Cogon_Patch_Size-Procyon_lotor -0.5589 0.8657 -2.4067
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0255 0.8700 -1.7627
## Cogon_Patch_Size-Lynx_rufus -0.3914 1.5883 -3.5715
## Cogon_Patch_Size-Didelphis_virginiana 1.6974 1.0990 -0.0445
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5795 1.9107 -6.5627
## Cogon_Patch_Size-Sciurus_carolinensis -1.2803 1.6105 -5.2356
## Cogon_Patch_Size-Vulpes_vulpes -0.7017 1.9351 -5.0859
## Cogon_Patch_Size-Sus_scrofa -0.9131 1.6855 -5.2907
## Veg_shannon_index-Odocoileus_virginianus 0.8037 0.9571 -1.2729
## Veg_shannon_index-Canis_latrans 1.3462 0.7460 0.1163
## Veg_shannon_index-Sciurus_niger 1.1103 1.0485 -0.9171
## Veg_shannon_index-Procyon_lotor 1.1910 0.6508 0.0854
## Veg_shannon_index-Dasypus_novemcinctus 0.6326 0.6273 -0.6316
## Veg_shannon_index-Lynx_rufus 1.0426 0.9805 -0.8451
## Veg_shannon_index-Didelphis_virginiana 1.1621 0.7460 -0.1745
## Veg_shannon_index-Sylvilagus_floridanus 1.0654 0.7481 -0.3158
## Veg_shannon_index-Sciurus_carolinensis 0.3504 0.8571 -1.6234
## Veg_shannon_index-Vulpes_vulpes 0.6544 0.9568 -1.4755
## Veg_shannon_index-Sus_scrofa 1.6039 1.0275 0.0857
## total_shrub_cover-Odocoileus_virginianus -0.3467 0.9325 -2.2101
## total_shrub_cover-Canis_latrans 0.1109 0.7893 -1.2493
## total_shrub_cover-Sciurus_niger -0.7228 1.0759 -3.1211
## total_shrub_cover-Procyon_lotor -1.1331 0.6799 -2.6075
## total_shrub_cover-Dasypus_novemcinctus -0.2698 0.6777 -1.7457
## total_shrub_cover-Lynx_rufus -0.8076 1.0573 -3.1577
## total_shrub_cover-Didelphis_virginiana -0.8369 0.8319 -2.7074
## total_shrub_cover-Sylvilagus_floridanus -0.6672 0.9390 -2.8195
## total_shrub_cover-Sciurus_carolinensis -0.4622 0.8361 -2.2358
## total_shrub_cover-Vulpes_vulpes -0.7610 1.0182 -3.0043
## total_shrub_cover-Sus_scrofa -0.3033 0.9410 -2.2688
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2502 1.4119 -3.2259
## Avg_Cogongrass_Cover-Canis_latrans 0.0202 1.2447 -2.2982
## Avg_Cogongrass_Cover-Sciurus_niger -0.5833 1.6742 -4.3595
## Avg_Cogongrass_Cover-Procyon_lotor -0.0125 1.1860 -2.1975
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4950 1.3339 -1.7254
## Avg_Cogongrass_Cover-Lynx_rufus -0.0982 1.3208 -2.6063
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1780 1.2266 -2.5943
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8014 1.3709 -3.8096
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1683 1.2686 -2.6193
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0133 1.3566 -2.4550
## Avg_Cogongrass_Cover-Sus_scrofa -0.5692 1.4932 -3.8748
## Tree_Density-Odocoileus_virginianus -0.9979 1.5526 -3.3745
## Tree_Density-Canis_latrans -2.9270 1.5710 -7.0018
## Tree_Density-Sciurus_niger -2.1441 1.8870 -6.0448
## Tree_Density-Procyon_lotor -2.0080 1.0392 -4.2531
## Tree_Density-Dasypus_novemcinctus -4.1227 2.3525 -10.2848
## Tree_Density-Lynx_rufus -0.8488 1.7595 -3.4796
## Tree_Density-Didelphis_virginiana -2.3776 1.3563 -5.5460
## Tree_Density-Sylvilagus_floridanus -2.5706 1.5456 -6.1524
## Tree_Density-Sciurus_carolinensis -2.7675 1.6883 -6.9412
## Tree_Density-Vulpes_vulpes -2.1317 1.8872 -6.0453
## Tree_Density-Sus_scrofa -2.6281 1.8694 -7.2975
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2133 1.5882 -2.0666
## Avg_Canopy_Cover-Canis_latrans 0.1309 0.7646 -1.4377
## Avg_Canopy_Cover-Sciurus_niger 2.4466 1.9844 -1.3911
## Avg_Canopy_Cover-Procyon_lotor 1.7072 0.8554 0.2527
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2289 0.9363 0.7692
## Avg_Canopy_Cover-Lynx_rufus 1.8228 1.6680 -1.2981
## Avg_Canopy_Cover-Didelphis_virginiana 3.2562 1.4483 1.2373
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9500 1.9411 1.2977
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0034 1.4769 1.0654
## Avg_Canopy_Cover-Vulpes_vulpes 2.7049 1.7114 0.3909
## Avg_Canopy_Cover-Sus_scrofa 2.2286 1.1204 0.4976
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.0117 1.2934 0.1640
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0700 0.9724 0.6278
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3358 1.3239 -1.5071
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0112 0.9891 0.5900
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5927 0.7724 0.2586
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2585 1.2422 0.6437
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3066 0.7408 -0.1362
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4429 0.9031 -0.0666
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8671 0.8365 0.4981
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0228 0.9756 0.5501
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3046 1.1154 -1.2444
## avg_veg_height-Odocoileus_virginianus -0.1495 0.8421 -1.9635
## avg_veg_height-Canis_latrans -0.2307 0.6548 -1.5928
## avg_veg_height-Sciurus_niger -0.2560 0.9101 -2.3650
## avg_veg_height-Procyon_lotor 0.0587 0.6568 -1.2247
## avg_veg_height-Dasypus_novemcinctus 0.2127 0.6604 -0.9859
## avg_veg_height-Lynx_rufus -0.3384 0.8780 -2.3174
## avg_veg_height-Didelphis_virginiana -0.3167 0.7477 -1.9287
## avg_veg_height-Sylvilagus_floridanus -0.2268 0.7430 -1.8031
## avg_veg_height-Sciurus_carolinensis 0.1758 0.7309 -1.1657
## avg_veg_height-Vulpes_vulpes -0.2172 0.8391 -2.0194
## avg_veg_height-Sus_scrofa -0.1771 0.7753 -1.7903
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4925 17.6135 1.0995 396
## (Intercept)-Canis_latrans -0.8663 2.0369 1.0052 1109
## (Intercept)-Sciurus_niger 0.8692 7.7186 1.0143 414
## (Intercept)-Procyon_lotor -0.3237 1.9089 1.0092 788
## (Intercept)-Dasypus_novemcinctus -2.5971 -0.5152 1.0576 717
## (Intercept)-Lynx_rufus 0.2796 7.6168 1.0160 209
## (Intercept)-Didelphis_virginiana -4.1064 -1.4956 1.0136 1039
## (Intercept)-Sylvilagus_floridanus -2.3710 0.5407 1.0245 898
## (Intercept)-Sciurus_carolinensis -4.7337 -2.0535 1.0672 444
## (Intercept)-Vulpes_vulpes -4.3385 1.7119 1.0459 285
## (Intercept)-Sus_scrofa -5.5963 -1.9786 1.0171 462
## Cogon_Patch_Size-Odocoileus_virginianus -0.1067 3.4259 1.0206 1820
## Cogon_Patch_Size-Canis_latrans 1.4515 5.5702 1.0178 728
## Cogon_Patch_Size-Sciurus_niger -0.6307 2.7853 1.0616 572
## Cogon_Patch_Size-Procyon_lotor -0.5285 1.0173 1.0425 578
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0145 1.7366 1.0211 1384
## Cogon_Patch_Size-Lynx_rufus -0.4072 2.8373 1.0240 950
## Cogon_Patch_Size-Didelphis_virginiana 1.5775 4.2129 1.0165 588
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2030 0.9525 1.0908 678
## Cogon_Patch_Size-Sciurus_carolinensis -0.9901 0.9472 1.0781 660
## Cogon_Patch_Size-Vulpes_vulpes -0.5550 2.8205 1.1369 607
## Cogon_Patch_Size-Sus_scrofa -0.6298 1.6290 1.0168 958
## Veg_shannon_index-Odocoileus_virginianus 0.8414 2.6456 1.0197 1717
## Veg_shannon_index-Canis_latrans 1.2638 3.0688 1.0199 990
## Veg_shannon_index-Sciurus_niger 1.0562 3.4333 1.0096 1215
## Veg_shannon_index-Procyon_lotor 1.1317 2.6519 1.0239 787
## Veg_shannon_index-Dasypus_novemcinctus 0.6420 1.8711 1.0221 1308
## Veg_shannon_index-Lynx_rufus 1.0361 3.0674 1.0219 950
## Veg_shannon_index-Didelphis_virginiana 1.1059 2.8226 1.0223 1277
## Veg_shannon_index-Sylvilagus_floridanus 1.0249 2.6928 1.0146 1315
## Veg_shannon_index-Sciurus_carolinensis 0.4407 1.8176 1.0102 1071
## Veg_shannon_index-Vulpes_vulpes 0.7207 2.3419 1.0078 1000
## Veg_shannon_index-Sus_scrofa 1.4277 4.1258 1.0214 1030
## total_shrub_cover-Odocoileus_virginianus -0.3719 1.6373 1.0142 1559
## total_shrub_cover-Canis_latrans 0.0320 1.9119 1.0182 854
## total_shrub_cover-Sciurus_niger -0.6526 1.2679 1.0131 914
## total_shrub_cover-Procyon_lotor -1.0703 0.0253 1.0053 1086
## total_shrub_cover-Dasypus_novemcinctus -0.2432 1.0111 1.0040 1255
## total_shrub_cover-Lynx_rufus -0.7311 1.1246 1.0459 469
## total_shrub_cover-Didelphis_virginiana -0.7616 0.5769 1.0106 1131
## total_shrub_cover-Sylvilagus_floridanus -0.5954 0.9815 1.0437 837
## total_shrub_cover-Sciurus_carolinensis -0.4187 1.0920 1.0309 1290
## total_shrub_cover-Vulpes_vulpes -0.6851 0.9745 1.0238 1012
## total_shrub_cover-Sus_scrofa -0.3147 1.6223 1.0146 1141
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2482 2.5816 1.0149 742
## Avg_Cogongrass_Cover-Canis_latrans -0.0080 2.6093 1.0227 809
## Avg_Cogongrass_Cover-Sciurus_niger -0.4611 2.2290 1.0220 530
## Avg_Cogongrass_Cover-Procyon_lotor -0.0723 2.4996 1.0405 704
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3531 3.4953 1.0588 620
## Avg_Cogongrass_Cover-Lynx_rufus -0.1170 2.5993 1.0301 843
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1843 2.3134 1.0194 759
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6992 1.6535 1.0319 650
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1797 2.4565 1.0310 646
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0332 2.8793 1.0198 761
## Avg_Cogongrass_Cover-Sus_scrofa -0.4792 2.0238 1.0181 620
## Tree_Density-Odocoileus_virginianus -1.2350 2.8268 1.0043 658
## Tree_Density-Canis_latrans -2.6485 -0.6791 1.0456 488
## Tree_Density-Sciurus_niger -2.0744 1.4358 1.0267 612
## Tree_Density-Procyon_lotor -1.9296 -0.1701 1.0355 661
## Tree_Density-Dasypus_novemcinctus -3.4759 -1.3227 1.1111 313
## Tree_Density-Lynx_rufus -1.1265 3.4306 1.0177 504
## Tree_Density-Didelphis_virginiana -2.2302 -0.0553 1.0383 987
## Tree_Density-Sylvilagus_floridanus -2.3743 -0.0113 1.0175 740
## Tree_Density-Sciurus_carolinensis -2.4930 -0.1511 1.0603 568
## Tree_Density-Vulpes_vulpes -2.0814 1.5423 1.0331 562
## Tree_Density-Sus_scrofa -2.3581 0.2820 1.0788 695
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3017 4.3310 1.0079 1580
## Avg_Canopy_Cover-Canis_latrans 0.1166 1.6644 1.0073 1120
## Avg_Canopy_Cover-Sciurus_niger 2.2974 7.0451 1.0220 686
## Avg_Canopy_Cover-Procyon_lotor 1.6602 3.5785 1.0178 965
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0918 4.5148 1.0704 548
## Avg_Canopy_Cover-Lynx_rufus 1.7405 5.4607 1.0089 546
## Avg_Canopy_Cover-Didelphis_virginiana 2.9815 6.8829 1.0794 621
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5832 8.9410 1.1275 424
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6793 6.7555 1.0804 472
## Avg_Canopy_Cover-Vulpes_vulpes 2.3740 6.6194 1.0932 358
## Avg_Canopy_Cover-Sus_scrofa 2.0915 4.8729 1.0750 922
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8229 5.1617 1.0009 437
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9211 4.4536 1.0182 551
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.4239 3.5451 1.0037 310
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8727 4.3515 1.0270 449
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5285 3.2692 1.0281 698
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0309 5.3563 1.0558 430
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3075 2.8050 1.0034 671
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3690 3.4552 1.0116 644
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7675 3.7566 1.0344 573
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8745 4.4032 1.0242 555
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3575 3.3433 1.0114 463
## avg_veg_height-Odocoileus_virginianus -0.1226 1.4443 1.0096 1675
## avg_veg_height-Canis_latrans -0.2147 1.0320 1.0318 878
## avg_veg_height-Sciurus_niger -0.1942 1.3887 1.0138 998
## avg_veg_height-Procyon_lotor 0.0569 1.3636 1.0129 1229
## avg_veg_height-Dasypus_novemcinctus 0.1808 1.5941 1.0137 1085
## avg_veg_height-Lynx_rufus -0.2748 1.2221 1.0081 1056
## avg_veg_height-Didelphis_virginiana -0.2742 1.0209 1.0175 1173
## avg_veg_height-Sylvilagus_floridanus -0.2026 1.1498 1.0150 1032
## avg_veg_height-Sciurus_carolinensis 0.1237 1.7758 1.0070 1437
## avg_veg_height-Vulpes_vulpes -0.1864 1.3930 1.0175 1068
## avg_veg_height-Sus_scrofa -0.1525 1.3321 1.0116 1303
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5391 0.0796 0.3872 0.5380 0.6985
## (Intercept)-Canis_latrans -2.5378 0.1999 -2.9436 -2.5314 -2.1634
## (Intercept)-Sciurus_niger -4.7460 0.5226 -5.7902 -4.7447 -3.7312
## (Intercept)-Procyon_lotor -2.1960 0.1657 -2.5353 -2.1886 -1.8886
## (Intercept)-Dasypus_novemcinctus -1.6132 0.1847 -1.9813 -1.6094 -1.2689
## (Intercept)-Lynx_rufus -3.7371 0.3778 -4.4725 -3.7433 -2.9677
## (Intercept)-Didelphis_virginiana -2.3485 0.3058 -2.9769 -2.3471 -1.7623
## (Intercept)-Sylvilagus_floridanus -3.0978 0.2991 -3.7269 -3.0811 -2.5567
## (Intercept)-Sciurus_carolinensis -2.5031 0.3430 -3.1787 -2.5019 -1.8490
## (Intercept)-Vulpes_vulpes -4.1105 0.7000 -5.5917 -4.0697 -2.8654
## (Intercept)-Sus_scrofa -3.2258 0.6121 -4.4368 -3.2234 -2.0036
## shrub_cover-Odocoileus_virginianus -0.0583 0.0678 -0.1908 -0.0575 0.0785
## shrub_cover-Canis_latrans -0.2776 0.2302 -0.7228 -0.2807 0.1701
## shrub_cover-Sciurus_niger -0.3352 0.4474 -1.2395 -0.3219 0.5084
## shrub_cover-Procyon_lotor 0.2676 0.1639 -0.0642 0.2709 0.5792
## shrub_cover-Dasypus_novemcinctus 0.9074 0.3107 0.3259 0.9030 1.5168
## shrub_cover-Lynx_rufus -0.1960 0.3612 -0.8882 -0.2023 0.5332
## shrub_cover-Didelphis_virginiana 0.9754 0.3665 0.3160 0.9516 1.7437
## shrub_cover-Sylvilagus_floridanus 0.4946 0.3913 -0.2678 0.4903 1.2826
## shrub_cover-Sciurus_carolinensis 0.9170 0.4134 0.1343 0.9054 1.7666
## shrub_cover-Vulpes_vulpes 0.1398 0.5355 -0.9473 0.1526 1.1807
## shrub_cover-Sus_scrofa 0.7482 0.7512 -0.6753 0.7160 2.2821
## veg_height-Odocoileus_virginianus -0.3311 0.0686 -0.4640 -0.3307 -0.1983
## veg_height-Canis_latrans -0.5533 0.1835 -0.9239 -0.5532 -0.2045
## veg_height-Sciurus_niger -0.0461 0.3259 -0.6829 -0.0477 0.5931
## veg_height-Procyon_lotor 0.3576 0.1248 0.1144 0.3558 0.6060
## veg_height-Dasypus_novemcinctus 0.2497 0.1349 -0.0024 0.2478 0.5261
## veg_height-Lynx_rufus 0.1457 0.2355 -0.3304 0.1500 0.5936
## veg_height-Didelphis_virginiana 0.4326 0.2351 -0.0098 0.4242 0.9145
## veg_height-Sylvilagus_floridanus 0.1391 0.2440 -0.3367 0.1380 0.6229
## veg_height-Sciurus_carolinensis 0.1067 0.2141 -0.3070 0.1040 0.5322
## veg_height-Vulpes_vulpes -0.1694 0.3332 -0.8896 -0.1551 0.4406
## veg_height-Sus_scrofa -0.1674 0.3251 -0.8263 -0.1586 0.4594
## week-Odocoileus_virginianus 1.3111 0.1252 1.0743 1.3094 1.5641
## week-Canis_latrans 0.5881 0.2623 0.0839 0.5782 1.1237
## week-Sciurus_niger -0.4008 0.5652 -1.6085 -0.3479 0.5402
## week-Procyon_lotor 0.2043 0.2087 -0.2132 0.2078 0.6139
## week-Dasypus_novemcinctus 0.1124 0.2292 -0.3336 0.1129 0.5565
## week-Lynx_rufus 0.3857 0.3492 -0.3248 0.3859 1.0832
## week-Didelphis_virginiana 0.0778 0.3685 -0.6638 0.0882 0.7722
## week-Sylvilagus_floridanus 0.0665 0.3472 -0.6243 0.0718 0.7404
## week-Sciurus_carolinensis 0.8157 0.3684 0.1158 0.8035 1.5742
## week-Vulpes_vulpes 0.2051 0.5178 -0.8867 0.2325 1.1694
## week-Sus_scrofa 0.6973 0.4407 -0.1380 0.6892 1.5911
## I(week^2)-Odocoileus_virginianus -0.5408 0.0516 -0.6436 -0.5409 -0.4424
## I(week^2)-Canis_latrans -0.2448 0.1075 -0.4612 -0.2438 -0.0361
## I(week^2)-Sciurus_niger -0.2816 0.2364 -0.7939 -0.2678 0.1458
## I(week^2)-Procyon_lotor -0.1327 0.0909 -0.3122 -0.1341 0.0470
## I(week^2)-Dasypus_novemcinctus -0.1829 0.1050 -0.3870 -0.1838 0.0207
## I(week^2)-Lynx_rufus -0.2433 0.1550 -0.5610 -0.2392 0.0422
## I(week^2)-Didelphis_virginiana -0.4094 0.2071 -0.8690 -0.3905 -0.0536
## I(week^2)-Sylvilagus_floridanus -0.1804 0.1568 -0.4947 -0.1769 0.1203
## I(week^2)-Sciurus_carolinensis -0.2871 0.1461 -0.5928 -0.2859 -0.0089
## I(week^2)-Vulpes_vulpes -0.4011 0.2470 -0.9665 -0.3796 0.0262
## I(week^2)-Sus_scrofa -0.2472 0.1760 -0.6031 -0.2465 0.0971
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 6400
## (Intercept)-Canis_latrans 1.0047 2325
## (Intercept)-Sciurus_niger 1.0169 614
## (Intercept)-Procyon_lotor 1.0014 3072
## (Intercept)-Dasypus_novemcinctus 1.0058 2889
## (Intercept)-Lynx_rufus 1.0088 442
## (Intercept)-Didelphis_virginiana 1.0046 2167
## (Intercept)-Sylvilagus_floridanus 1.0013 2152
## (Intercept)-Sciurus_carolinensis 1.0057 1757
## (Intercept)-Vulpes_vulpes 1.0081 434
## (Intercept)-Sus_scrofa 1.0028 979
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0095 1551
## shrub_cover-Sciurus_niger 1.0351 843
## shrub_cover-Procyon_lotor 1.0001 3408
## shrub_cover-Dasypus_novemcinctus 1.0045 1802
## shrub_cover-Lynx_rufus 1.0144 651
## shrub_cover-Didelphis_virginiana 1.0096 1704
## shrub_cover-Sylvilagus_floridanus 1.0256 1280
## shrub_cover-Sciurus_carolinensis 1.0108 1432
## shrub_cover-Vulpes_vulpes 1.0004 1331
## shrub_cover-Sus_scrofa 1.0077 753
## veg_height-Odocoileus_virginianus 1.0002 5165
## veg_height-Canis_latrans 1.0023 2397
## veg_height-Sciurus_niger 1.0049 1137
## veg_height-Procyon_lotor 1.0032 3644
## veg_height-Dasypus_novemcinctus 1.0063 3880
## veg_height-Lynx_rufus 1.0055 1881
## veg_height-Didelphis_virginiana 1.0013 2891
## veg_height-Sylvilagus_floridanus 1.0031 2005
## veg_height-Sciurus_carolinensis 1.0068 2614
## veg_height-Vulpes_vulpes 1.0013 1670
## veg_height-Sus_scrofa 1.0021 2484
## week-Odocoileus_virginianus 0.9999 4406
## week-Canis_latrans 1.0024 3624
## week-Sciurus_niger 1.0095 661
## week-Procyon_lotor 1.0006 4321
## week-Dasypus_novemcinctus 0.9997 4806
## week-Lynx_rufus 1.0047 2263
## week-Didelphis_virginiana 1.0058 2999
## week-Sylvilagus_floridanus 1.0017 2874
## week-Sciurus_carolinensis 1.0028 4098
## week-Vulpes_vulpes 1.0086 1646
## week-Sus_scrofa 1.0011 3888
## I(week^2)-Odocoileus_virginianus 1.0002 4233
## I(week^2)-Canis_latrans 1.0006 3771
## I(week^2)-Sciurus_niger 1.0185 808
## I(week^2)-Procyon_lotor 1.0001 4058
## I(week^2)-Dasypus_novemcinctus 1.0014 4236
## I(week^2)-Lynx_rufus 1.0000 1981
## I(week^2)-Didelphis_virginiana 1.0013 1666
## I(week^2)-Sylvilagus_floridanus 1.0001 2413
## I(week^2)-Sciurus_carolinensis 1.0034 4104
## I(week^2)-Vulpes_vulpes 1.0036 1485
## I(week^2)-Sus_scrofa 1.0013 4074
names(ms_fullQ_fullQ)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ$beta.samples)
## 'mcmc' num [1:5250, 1:99] 2.97 5.02 5.21 4.62 4.48 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:99] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.008380952
MCMCplot(ms_fullQ_fullQ$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:5250, 1:11, 1:100] 0.89 0.999 0.195 0.947 0.994 ...
## $ z.0.samples : int [1:5250, 1:11, 1:100] 1 1 0 1 1 1 1 1 1 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:5250, 1:11, 1:100] 0.89 0.999 0.195 0.947 0.994 ...
psi.0.quants <- apply(out.pred$psi.0.samples, c(3), function(x) quantile(x, prob = c(0.025, 0.5, 0.975)))
str(psi.0.quants)
## num [1:3, 1:100] 0.00019 0.134868 0.999972 0.000202 0.133893 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:3] "2.5%" "50%" "97.5%"
## ..$ : NULL
psi.plot.dat <- data.frame(
psi.med = psi.0.quants[2, ],
psi.low = psi.0.quants[1, ],
psi.high = psi.0.quants[3, ],
Avg_Cogongrass_Cover = cogon.pred.vals
)
str(psi.plot.dat)
## 'data.frame': 100 obs. of 4 variables:
## $ psi.med : num 0.135 0.134 0.134 0.136 0.134 ...
## $ psi.low : num 0.00019 0.000202 0.000201 0.000208 0.000223 ...
## $ psi.high : num 1 1 1 1 1 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8684 1.1640 -3.1207 -0.8921 1.5228 1.0031 1789
## Cogon_Patch_Size -0.2297 0.7723 -1.8625 -0.1827 1.1648 1.0918 863
## Veg_shannon_index 0.9654 0.5064 0.0071 0.9451 2.0538 1.0297 641
## total_shrub_cover -0.5459 0.5248 -1.6740 -0.5256 0.4403 1.0287 582
## Avg_Cogongrass_Cover -0.1833 0.9355 -1.9776 -0.2035 1.6903 1.0445 473
## Tree_Density -2.0447 0.8297 -3.8124 -2.0095 -0.4280 1.0288 837
## Avg_Canopy_Cover 1.9902 0.7184 0.6411 1.9473 3.5634 1.0216 738
## I(Avg_Cogongrass_Cover^2) 1.6787 0.6143 0.6061 1.6374 2.9346 1.0168 429
## avg_veg_height -0.1287 0.5230 -1.2371 -0.1148 0.8600 1.0207 686
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.7704 20.2177 4.2730 18.2633 77.3259 1.0817 473
## Cogon_Patch_Size 4.0946 6.2264 0.1368 2.2158 19.8573 1.0707 371
## Veg_shannon_index 0.8807 1.3606 0.0486 0.4404 4.2771 1.0205 917
## total_shrub_cover 0.9407 1.3425 0.0555 0.5093 4.6424 1.1064 544
## Avg_Cogongrass_Cover 1.4625 2.9370 0.0546 0.5881 8.0223 1.1077 651
## Tree_Density 4.1862 7.7422 0.0799 1.5520 25.1342 1.0901 250
## Avg_Canopy_Cover 3.7180 5.3728 0.1521 2.1109 16.3173 1.1437 379
## I(Avg_Cogongrass_Cover^2) 1.1713 3.1064 0.0471 0.4145 7.0620 1.0511 297
## avg_veg_height 0.5347 0.8152 0.0453 0.2775 2.5725 1.0422 1171
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8625 2.7697 0.0602 0.8441 10.579 1.077 184
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4610 0.4870 -3.3959 -2.4737 -1.4668 1.0019 3807
## shrub_cover 0.3208 0.2594 -0.1866 0.3187 0.8637 1.0083 1932
## veg_height 0.0132 0.1565 -0.3073 0.0168 0.3149 1.0008 2746
## week 0.3609 0.2410 -0.1307 0.3677 0.8150 1.0034 3272
## I(week^2) -0.2855 0.1005 -0.4911 -0.2822 -0.0971 1.0023 2807
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6451 1.5610 0.9539 2.2431 6.6615 1.0009 2789
## shrub_cover 0.5337 0.4292 0.1112 0.4141 1.6585 1.0185 1450
## veg_height 0.1984 0.1366 0.0580 0.1641 0.5488 1.0012 3590
## week 0.4406 0.3420 0.1064 0.3477 1.3504 1.0128 1488
## I(week^2) 0.0723 0.0522 0.0223 0.0581 0.2099 1.0024 3214
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.1586 3.8278 2.5585
## (Intercept)-Canis_latrans -0.8087 1.3470 -3.3486
## (Intercept)-Sciurus_niger 1.2631 2.7090 -2.8826
## (Intercept)-Procyon_lotor -0.3588 1.2830 -2.9875
## (Intercept)-Dasypus_novemcinctus -2.6923 1.2634 -5.5550
## (Intercept)-Lynx_rufus 0.6999 2.9052 -3.9168
## (Intercept)-Didelphis_virginiana -4.2008 1.5487 -7.4999
## (Intercept)-Sylvilagus_floridanus -2.4384 1.5906 -5.9079
## (Intercept)-Sciurus_carolinensis -4.9400 1.7836 -9.1548
## (Intercept)-Vulpes_vulpes -4.1939 2.6742 -9.1022
## (Intercept)-Sus_scrofa -5.7989 2.2123 -10.9140
## Cogon_Patch_Size-Odocoileus_virginianus -0.0616 1.5813 -3.0669
## Cogon_Patch_Size-Canis_latrans 1.7448 1.5514 -0.4000
## Cogon_Patch_Size-Sciurus_niger -0.8688 2.0843 -5.7348
## Cogon_Patch_Size-Procyon_lotor -0.5589 0.8657 -2.4067
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0255 0.8700 -1.7627
## Cogon_Patch_Size-Lynx_rufus -0.3914 1.5883 -3.5715
## Cogon_Patch_Size-Didelphis_virginiana 1.6974 1.0990 -0.0445
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5795 1.9107 -6.5627
## Cogon_Patch_Size-Sciurus_carolinensis -1.2803 1.6105 -5.2356
## Cogon_Patch_Size-Vulpes_vulpes -0.7017 1.9351 -5.0859
## Cogon_Patch_Size-Sus_scrofa -0.9131 1.6855 -5.2907
## Veg_shannon_index-Odocoileus_virginianus 0.8037 0.9571 -1.2729
## Veg_shannon_index-Canis_latrans 1.3462 0.7460 0.1163
## Veg_shannon_index-Sciurus_niger 1.1103 1.0485 -0.9171
## Veg_shannon_index-Procyon_lotor 1.1910 0.6508 0.0854
## Veg_shannon_index-Dasypus_novemcinctus 0.6326 0.6273 -0.6316
## Veg_shannon_index-Lynx_rufus 1.0426 0.9805 -0.8451
## Veg_shannon_index-Didelphis_virginiana 1.1621 0.7460 -0.1745
## Veg_shannon_index-Sylvilagus_floridanus 1.0654 0.7481 -0.3158
## Veg_shannon_index-Sciurus_carolinensis 0.3504 0.8571 -1.6234
## Veg_shannon_index-Vulpes_vulpes 0.6544 0.9568 -1.4755
## Veg_shannon_index-Sus_scrofa 1.6039 1.0275 0.0857
## total_shrub_cover-Odocoileus_virginianus -0.3467 0.9325 -2.2101
## total_shrub_cover-Canis_latrans 0.1109 0.7893 -1.2493
## total_shrub_cover-Sciurus_niger -0.7228 1.0759 -3.1211
## total_shrub_cover-Procyon_lotor -1.1331 0.6799 -2.6075
## total_shrub_cover-Dasypus_novemcinctus -0.2698 0.6777 -1.7457
## total_shrub_cover-Lynx_rufus -0.8076 1.0573 -3.1577
## total_shrub_cover-Didelphis_virginiana -0.8369 0.8319 -2.7074
## total_shrub_cover-Sylvilagus_floridanus -0.6672 0.9390 -2.8195
## total_shrub_cover-Sciurus_carolinensis -0.4622 0.8361 -2.2358
## total_shrub_cover-Vulpes_vulpes -0.7610 1.0182 -3.0043
## total_shrub_cover-Sus_scrofa -0.3033 0.9410 -2.2688
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2502 1.4119 -3.2259
## Avg_Cogongrass_Cover-Canis_latrans 0.0202 1.2447 -2.2982
## Avg_Cogongrass_Cover-Sciurus_niger -0.5833 1.6742 -4.3595
## Avg_Cogongrass_Cover-Procyon_lotor -0.0125 1.1860 -2.1975
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4950 1.3339 -1.7254
## Avg_Cogongrass_Cover-Lynx_rufus -0.0982 1.3208 -2.6063
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1780 1.2266 -2.5943
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8014 1.3709 -3.8096
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1683 1.2686 -2.6193
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0133 1.3566 -2.4550
## Avg_Cogongrass_Cover-Sus_scrofa -0.5692 1.4932 -3.8748
## Tree_Density-Odocoileus_virginianus -0.9979 1.5526 -3.3745
## Tree_Density-Canis_latrans -2.9270 1.5710 -7.0018
## Tree_Density-Sciurus_niger -2.1441 1.8870 -6.0448
## Tree_Density-Procyon_lotor -2.0080 1.0392 -4.2531
## Tree_Density-Dasypus_novemcinctus -4.1227 2.3525 -10.2848
## Tree_Density-Lynx_rufus -0.8488 1.7595 -3.4796
## Tree_Density-Didelphis_virginiana -2.3776 1.3563 -5.5460
## Tree_Density-Sylvilagus_floridanus -2.5706 1.5456 -6.1524
## Tree_Density-Sciurus_carolinensis -2.7675 1.6883 -6.9412
## Tree_Density-Vulpes_vulpes -2.1317 1.8872 -6.0453
## Tree_Density-Sus_scrofa -2.6281 1.8694 -7.2975
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2133 1.5882 -2.0666
## Avg_Canopy_Cover-Canis_latrans 0.1309 0.7646 -1.4377
## Avg_Canopy_Cover-Sciurus_niger 2.4466 1.9844 -1.3911
## Avg_Canopy_Cover-Procyon_lotor 1.7072 0.8554 0.2527
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2289 0.9363 0.7692
## Avg_Canopy_Cover-Lynx_rufus 1.8228 1.6680 -1.2981
## Avg_Canopy_Cover-Didelphis_virginiana 3.2562 1.4483 1.2373
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9500 1.9411 1.2977
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0034 1.4769 1.0654
## Avg_Canopy_Cover-Vulpes_vulpes 2.7049 1.7114 0.3909
## Avg_Canopy_Cover-Sus_scrofa 2.2286 1.1204 0.4976
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.0117 1.2934 0.1640
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0700 0.9724 0.6278
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3358 1.3239 -1.5071
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0112 0.9891 0.5900
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5927 0.7724 0.2586
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2585 1.2422 0.6437
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3066 0.7408 -0.1362
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4429 0.9031 -0.0666
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8671 0.8365 0.4981
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0228 0.9756 0.5501
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3046 1.1154 -1.2444
## avg_veg_height-Odocoileus_virginianus -0.1495 0.8421 -1.9635
## avg_veg_height-Canis_latrans -0.2307 0.6548 -1.5928
## avg_veg_height-Sciurus_niger -0.2560 0.9101 -2.3650
## avg_veg_height-Procyon_lotor 0.0587 0.6568 -1.2247
## avg_veg_height-Dasypus_novemcinctus 0.2127 0.6604 -0.9859
## avg_veg_height-Lynx_rufus -0.3384 0.8780 -2.3174
## avg_veg_height-Didelphis_virginiana -0.3167 0.7477 -1.9287
## avg_veg_height-Sylvilagus_floridanus -0.2268 0.7430 -1.8031
## avg_veg_height-Sciurus_carolinensis 0.1758 0.7309 -1.1657
## avg_veg_height-Vulpes_vulpes -0.2172 0.8391 -2.0194
## avg_veg_height-Sus_scrofa -0.1771 0.7753 -1.7903
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4925 17.6135 1.0995 396
## (Intercept)-Canis_latrans -0.8663 2.0369 1.0052 1109
## (Intercept)-Sciurus_niger 0.8692 7.7186 1.0143 414
## (Intercept)-Procyon_lotor -0.3237 1.9089 1.0092 788
## (Intercept)-Dasypus_novemcinctus -2.5971 -0.5152 1.0576 717
## (Intercept)-Lynx_rufus 0.2796 7.6168 1.0160 209
## (Intercept)-Didelphis_virginiana -4.1064 -1.4956 1.0136 1039
## (Intercept)-Sylvilagus_floridanus -2.3710 0.5407 1.0245 898
## (Intercept)-Sciurus_carolinensis -4.7337 -2.0535 1.0672 444
## (Intercept)-Vulpes_vulpes -4.3385 1.7119 1.0459 285
## (Intercept)-Sus_scrofa -5.5963 -1.9786 1.0171 462
## Cogon_Patch_Size-Odocoileus_virginianus -0.1067 3.4259 1.0206 1820
## Cogon_Patch_Size-Canis_latrans 1.4515 5.5702 1.0178 728
## Cogon_Patch_Size-Sciurus_niger -0.6307 2.7853 1.0616 572
## Cogon_Patch_Size-Procyon_lotor -0.5285 1.0173 1.0425 578
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0145 1.7366 1.0211 1384
## Cogon_Patch_Size-Lynx_rufus -0.4072 2.8373 1.0240 950
## Cogon_Patch_Size-Didelphis_virginiana 1.5775 4.2129 1.0165 588
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2030 0.9525 1.0908 678
## Cogon_Patch_Size-Sciurus_carolinensis -0.9901 0.9472 1.0781 660
## Cogon_Patch_Size-Vulpes_vulpes -0.5550 2.8205 1.1369 607
## Cogon_Patch_Size-Sus_scrofa -0.6298 1.6290 1.0168 958
## Veg_shannon_index-Odocoileus_virginianus 0.8414 2.6456 1.0197 1717
## Veg_shannon_index-Canis_latrans 1.2638 3.0688 1.0199 990
## Veg_shannon_index-Sciurus_niger 1.0562 3.4333 1.0096 1215
## Veg_shannon_index-Procyon_lotor 1.1317 2.6519 1.0239 787
## Veg_shannon_index-Dasypus_novemcinctus 0.6420 1.8711 1.0221 1308
## Veg_shannon_index-Lynx_rufus 1.0361 3.0674 1.0219 950
## Veg_shannon_index-Didelphis_virginiana 1.1059 2.8226 1.0223 1277
## Veg_shannon_index-Sylvilagus_floridanus 1.0249 2.6928 1.0146 1315
## Veg_shannon_index-Sciurus_carolinensis 0.4407 1.8176 1.0102 1071
## Veg_shannon_index-Vulpes_vulpes 0.7207 2.3419 1.0078 1000
## Veg_shannon_index-Sus_scrofa 1.4277 4.1258 1.0214 1030
## total_shrub_cover-Odocoileus_virginianus -0.3719 1.6373 1.0142 1559
## total_shrub_cover-Canis_latrans 0.0320 1.9119 1.0182 854
## total_shrub_cover-Sciurus_niger -0.6526 1.2679 1.0131 914
## total_shrub_cover-Procyon_lotor -1.0703 0.0253 1.0053 1086
## total_shrub_cover-Dasypus_novemcinctus -0.2432 1.0111 1.0040 1255
## total_shrub_cover-Lynx_rufus -0.7311 1.1246 1.0459 469
## total_shrub_cover-Didelphis_virginiana -0.7616 0.5769 1.0106 1131
## total_shrub_cover-Sylvilagus_floridanus -0.5954 0.9815 1.0437 837
## total_shrub_cover-Sciurus_carolinensis -0.4187 1.0920 1.0309 1290
## total_shrub_cover-Vulpes_vulpes -0.6851 0.9745 1.0238 1012
## total_shrub_cover-Sus_scrofa -0.3147 1.6223 1.0146 1141
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2482 2.5816 1.0149 742
## Avg_Cogongrass_Cover-Canis_latrans -0.0080 2.6093 1.0227 809
## Avg_Cogongrass_Cover-Sciurus_niger -0.4611 2.2290 1.0220 530
## Avg_Cogongrass_Cover-Procyon_lotor -0.0723 2.4996 1.0405 704
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3531 3.4953 1.0588 620
## Avg_Cogongrass_Cover-Lynx_rufus -0.1170 2.5993 1.0301 843
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1843 2.3134 1.0194 759
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6992 1.6535 1.0319 650
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1797 2.4565 1.0310 646
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0332 2.8793 1.0198 761
## Avg_Cogongrass_Cover-Sus_scrofa -0.4792 2.0238 1.0181 620
## Tree_Density-Odocoileus_virginianus -1.2350 2.8268 1.0043 658
## Tree_Density-Canis_latrans -2.6485 -0.6791 1.0456 488
## Tree_Density-Sciurus_niger -2.0744 1.4358 1.0267 612
## Tree_Density-Procyon_lotor -1.9296 -0.1701 1.0355 661
## Tree_Density-Dasypus_novemcinctus -3.4759 -1.3227 1.1111 313
## Tree_Density-Lynx_rufus -1.1265 3.4306 1.0177 504
## Tree_Density-Didelphis_virginiana -2.2302 -0.0553 1.0383 987
## Tree_Density-Sylvilagus_floridanus -2.3743 -0.0113 1.0175 740
## Tree_Density-Sciurus_carolinensis -2.4930 -0.1511 1.0603 568
## Tree_Density-Vulpes_vulpes -2.0814 1.5423 1.0331 562
## Tree_Density-Sus_scrofa -2.3581 0.2820 1.0788 695
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3017 4.3310 1.0079 1580
## Avg_Canopy_Cover-Canis_latrans 0.1166 1.6644 1.0073 1120
## Avg_Canopy_Cover-Sciurus_niger 2.2974 7.0451 1.0220 686
## Avg_Canopy_Cover-Procyon_lotor 1.6602 3.5785 1.0178 965
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0918 4.5148 1.0704 548
## Avg_Canopy_Cover-Lynx_rufus 1.7405 5.4607 1.0089 546
## Avg_Canopy_Cover-Didelphis_virginiana 2.9815 6.8829 1.0794 621
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5832 8.9410 1.1275 424
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6793 6.7555 1.0804 472
## Avg_Canopy_Cover-Vulpes_vulpes 2.3740 6.6194 1.0932 358
## Avg_Canopy_Cover-Sus_scrofa 2.0915 4.8729 1.0750 922
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8229 5.1617 1.0009 437
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9211 4.4536 1.0182 551
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.4239 3.5451 1.0037 310
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8727 4.3515 1.0270 449
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5285 3.2692 1.0281 698
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0309 5.3563 1.0558 430
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3075 2.8050 1.0034 671
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3690 3.4552 1.0116 644
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7675 3.7566 1.0344 573
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8745 4.4032 1.0242 555
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3575 3.3433 1.0114 463
## avg_veg_height-Odocoileus_virginianus -0.1226 1.4443 1.0096 1675
## avg_veg_height-Canis_latrans -0.2147 1.0320 1.0318 878
## avg_veg_height-Sciurus_niger -0.1942 1.3887 1.0138 998
## avg_veg_height-Procyon_lotor 0.0569 1.3636 1.0129 1229
## avg_veg_height-Dasypus_novemcinctus 0.1808 1.5941 1.0137 1085
## avg_veg_height-Lynx_rufus -0.2748 1.2221 1.0081 1056
## avg_veg_height-Didelphis_virginiana -0.2742 1.0209 1.0175 1173
## avg_veg_height-Sylvilagus_floridanus -0.2026 1.1498 1.0150 1032
## avg_veg_height-Sciurus_carolinensis 0.1237 1.7758 1.0070 1437
## avg_veg_height-Vulpes_vulpes -0.1864 1.3930 1.0175 1068
## avg_veg_height-Sus_scrofa -0.1525 1.3321 1.0116 1303
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5391 0.0796 0.3872 0.5380 0.6985
## (Intercept)-Canis_latrans -2.5378 0.1999 -2.9436 -2.5314 -2.1634
## (Intercept)-Sciurus_niger -4.7460 0.5226 -5.7902 -4.7447 -3.7312
## (Intercept)-Procyon_lotor -2.1960 0.1657 -2.5353 -2.1886 -1.8886
## (Intercept)-Dasypus_novemcinctus -1.6132 0.1847 -1.9813 -1.6094 -1.2689
## (Intercept)-Lynx_rufus -3.7371 0.3778 -4.4725 -3.7433 -2.9677
## (Intercept)-Didelphis_virginiana -2.3485 0.3058 -2.9769 -2.3471 -1.7623
## (Intercept)-Sylvilagus_floridanus -3.0978 0.2991 -3.7269 -3.0811 -2.5567
## (Intercept)-Sciurus_carolinensis -2.5031 0.3430 -3.1787 -2.5019 -1.8490
## (Intercept)-Vulpes_vulpes -4.1105 0.7000 -5.5917 -4.0697 -2.8654
## (Intercept)-Sus_scrofa -3.2258 0.6121 -4.4368 -3.2234 -2.0036
## shrub_cover-Odocoileus_virginianus -0.0583 0.0678 -0.1908 -0.0575 0.0785
## shrub_cover-Canis_latrans -0.2776 0.2302 -0.7228 -0.2807 0.1701
## shrub_cover-Sciurus_niger -0.3352 0.4474 -1.2395 -0.3219 0.5084
## shrub_cover-Procyon_lotor 0.2676 0.1639 -0.0642 0.2709 0.5792
## shrub_cover-Dasypus_novemcinctus 0.9074 0.3107 0.3259 0.9030 1.5168
## shrub_cover-Lynx_rufus -0.1960 0.3612 -0.8882 -0.2023 0.5332
## shrub_cover-Didelphis_virginiana 0.9754 0.3665 0.3160 0.9516 1.7437
## shrub_cover-Sylvilagus_floridanus 0.4946 0.3913 -0.2678 0.4903 1.2826
## shrub_cover-Sciurus_carolinensis 0.9170 0.4134 0.1343 0.9054 1.7666
## shrub_cover-Vulpes_vulpes 0.1398 0.5355 -0.9473 0.1526 1.1807
## shrub_cover-Sus_scrofa 0.7482 0.7512 -0.6753 0.7160 2.2821
## veg_height-Odocoileus_virginianus -0.3311 0.0686 -0.4640 -0.3307 -0.1983
## veg_height-Canis_latrans -0.5533 0.1835 -0.9239 -0.5532 -0.2045
## veg_height-Sciurus_niger -0.0461 0.3259 -0.6829 -0.0477 0.5931
## veg_height-Procyon_lotor 0.3576 0.1248 0.1144 0.3558 0.6060
## veg_height-Dasypus_novemcinctus 0.2497 0.1349 -0.0024 0.2478 0.5261
## veg_height-Lynx_rufus 0.1457 0.2355 -0.3304 0.1500 0.5936
## veg_height-Didelphis_virginiana 0.4326 0.2351 -0.0098 0.4242 0.9145
## veg_height-Sylvilagus_floridanus 0.1391 0.2440 -0.3367 0.1380 0.6229
## veg_height-Sciurus_carolinensis 0.1067 0.2141 -0.3070 0.1040 0.5322
## veg_height-Vulpes_vulpes -0.1694 0.3332 -0.8896 -0.1551 0.4406
## veg_height-Sus_scrofa -0.1674 0.3251 -0.8263 -0.1586 0.4594
## week-Odocoileus_virginianus 1.3111 0.1252 1.0743 1.3094 1.5641
## week-Canis_latrans 0.5881 0.2623 0.0839 0.5782 1.1237
## week-Sciurus_niger -0.4008 0.5652 -1.6085 -0.3479 0.5402
## week-Procyon_lotor 0.2043 0.2087 -0.2132 0.2078 0.6139
## week-Dasypus_novemcinctus 0.1124 0.2292 -0.3336 0.1129 0.5565
## week-Lynx_rufus 0.3857 0.3492 -0.3248 0.3859 1.0832
## week-Didelphis_virginiana 0.0778 0.3685 -0.6638 0.0882 0.7722
## week-Sylvilagus_floridanus 0.0665 0.3472 -0.6243 0.0718 0.7404
## week-Sciurus_carolinensis 0.8157 0.3684 0.1158 0.8035 1.5742
## week-Vulpes_vulpes 0.2051 0.5178 -0.8867 0.2325 1.1694
## week-Sus_scrofa 0.6973 0.4407 -0.1380 0.6892 1.5911
## I(week^2)-Odocoileus_virginianus -0.5408 0.0516 -0.6436 -0.5409 -0.4424
## I(week^2)-Canis_latrans -0.2448 0.1075 -0.4612 -0.2438 -0.0361
## I(week^2)-Sciurus_niger -0.2816 0.2364 -0.7939 -0.2678 0.1458
## I(week^2)-Procyon_lotor -0.1327 0.0909 -0.3122 -0.1341 0.0470
## I(week^2)-Dasypus_novemcinctus -0.1829 0.1050 -0.3870 -0.1838 0.0207
## I(week^2)-Lynx_rufus -0.2433 0.1550 -0.5610 -0.2392 0.0422
## I(week^2)-Didelphis_virginiana -0.4094 0.2071 -0.8690 -0.3905 -0.0536
## I(week^2)-Sylvilagus_floridanus -0.1804 0.1568 -0.4947 -0.1769 0.1203
## I(week^2)-Sciurus_carolinensis -0.2871 0.1461 -0.5928 -0.2859 -0.0089
## I(week^2)-Vulpes_vulpes -0.4011 0.2470 -0.9665 -0.3796 0.0262
## I(week^2)-Sus_scrofa -0.2472 0.1760 -0.6031 -0.2465 0.0971
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 6400
## (Intercept)-Canis_latrans 1.0047 2325
## (Intercept)-Sciurus_niger 1.0169 614
## (Intercept)-Procyon_lotor 1.0014 3072
## (Intercept)-Dasypus_novemcinctus 1.0058 2889
## (Intercept)-Lynx_rufus 1.0088 442
## (Intercept)-Didelphis_virginiana 1.0046 2167
## (Intercept)-Sylvilagus_floridanus 1.0013 2152
## (Intercept)-Sciurus_carolinensis 1.0057 1757
## (Intercept)-Vulpes_vulpes 1.0081 434
## (Intercept)-Sus_scrofa 1.0028 979
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0095 1551
## shrub_cover-Sciurus_niger 1.0351 843
## shrub_cover-Procyon_lotor 1.0001 3408
## shrub_cover-Dasypus_novemcinctus 1.0045 1802
## shrub_cover-Lynx_rufus 1.0144 651
## shrub_cover-Didelphis_virginiana 1.0096 1704
## shrub_cover-Sylvilagus_floridanus 1.0256 1280
## shrub_cover-Sciurus_carolinensis 1.0108 1432
## shrub_cover-Vulpes_vulpes 1.0004 1331
## shrub_cover-Sus_scrofa 1.0077 753
## veg_height-Odocoileus_virginianus 1.0002 5165
## veg_height-Canis_latrans 1.0023 2397
## veg_height-Sciurus_niger 1.0049 1137
## veg_height-Procyon_lotor 1.0032 3644
## veg_height-Dasypus_novemcinctus 1.0063 3880
## veg_height-Lynx_rufus 1.0055 1881
## veg_height-Didelphis_virginiana 1.0013 2891
## veg_height-Sylvilagus_floridanus 1.0031 2005
## veg_height-Sciurus_carolinensis 1.0068 2614
## veg_height-Vulpes_vulpes 1.0013 1670
## veg_height-Sus_scrofa 1.0021 2484
## week-Odocoileus_virginianus 0.9999 4406
## week-Canis_latrans 1.0024 3624
## week-Sciurus_niger 1.0095 661
## week-Procyon_lotor 1.0006 4321
## week-Dasypus_novemcinctus 0.9997 4806
## week-Lynx_rufus 1.0047 2263
## week-Didelphis_virginiana 1.0058 2999
## week-Sylvilagus_floridanus 1.0017 2874
## week-Sciurus_carolinensis 1.0028 4098
## week-Vulpes_vulpes 1.0086 1646
## week-Sus_scrofa 1.0011 3888
## I(week^2)-Odocoileus_virginianus 1.0002 4233
## I(week^2)-Canis_latrans 1.0006 3771
## I(week^2)-Sciurus_niger 1.0185 808
## I(week^2)-Procyon_lotor 1.0001 4058
## I(week^2)-Dasypus_novemcinctus 1.0014 4236
## I(week^2)-Lynx_rufus 1.0000 1981
## I(week^2)-Didelphis_virginiana 1.0013 1666
## I(week^2)-Sylvilagus_floridanus 1.0001 2413
## I(week^2)-Sciurus_carolinensis 1.0034 4104
## I(week^2)-Vulpes_vulpes 1.0036 1485
## I(week^2)-Sus_scrofa 1.0013 4074
names(ms_fullQ_fullQ)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ$beta.samples)
## 'mcmc' num [1:5250, 1:99] 2.97 5.02 5.21 4.62 4.48 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:99] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.008380952
MCMCplot(ms_fullQ_fullQ$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
for (i in seq(1, n_params, by = chunk_size)) {
end <- min(i + chunk_size - 1, n_params)
param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
# Set filename
file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
# Save plot to PNG
png(filename = file_name, width = 1200, height = 800, res = 150)
MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
ref_ovl = TRUE,
ci = c(50, 95),
main = paste0("Occupancy Parameters: ", i, " to ", end))
dev.off()
}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
for (i in seq(1, n_params, by = chunk_size)) {
end <- min(i + chunk_size - 1, n_params)
param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
# Set filename
file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
# Save plot to PNG
png(filename = file_name, width = 1200, height = 800, res = 150)
MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
ref_ovl = TRUE,
ci = c(50, 95),
main = paste0("Detection Parameters: ", i, " to ", end))
dev.off()
}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 174 × 2
## Plot Veg_shannon_index
## <chr> <dbl>
## 1 BI200 2.75
## 2 BI201 2.70
## 3 BI202 2.59
## 4 BI97 1.61
## 5 BI99 2.97
## 6 BN210 2.97
## 7 BN211 2.43
## 8 BN212 2.22
## 9 BN96 3.05
## 10 BN98 2.79
## # ℹ 164 more rows
if (!is.numeric(fuel_data$Height)) {
fuel_data$Height <- as.numeric(as.character(fuel_data$Height))
}
## Warning: NAs introduced by coercion
# Calculate average vegetation height per plot
veg_height <- fuel_data %>%
group_by(Plot) %>%
summarize(avg_veg_height = mean(Height, na.rm = TRUE), .groups = "drop")
# Tree density from point-centered quarter data
if (!is.numeric(tree_data$Distance)) {
tree_data$Distance <- as.numeric(as.character(tree_data$Distance))
}
tree_density_data <- tree_data %>%
group_by(Plot) %>%
summarize(Average_Distance = mean(Distance) / 100, # Convert to meters
Tree_Density = 10000 / (Average_Distance^2)) # Convert to trees per hectare
# Average canopy cover from vegetation quadrats
tree_canopy_data <- Veg_Cover %>%
distinct(Plot, Quadrat, .keep_all = TRUE) %>% # Ensure each quadrat counts once per plot
group_by(Plot) %>%
summarize(Avg_Canopy_Cover = mean(Canopy_Cover, na.rm = TRUE), .groups = "drop") # Calculate the average canopy cover per plot
cor(tree_density_data$Tree_Density, tree_canopy_data$Avg_Canopy_Cover)
## [1] 0.2836106
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter(Class == "Mammalia" | Name == "Meleagris_gallopavo") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 2) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 12
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$week)
week_matrix <- matrix(rep(week_vals, each = num_sites), nrow = num_sites, ncol = num_weeks, byrow = FALSE)
# Create detection covariate list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_matrix
)
# Remove dash and convert to numeric
week_numeric <- as.numeric(gsub("-", "", det.covs$week))
## Scale and center week_numeric
week_numeric <- scale(week_numeric)
# Reshape into the original 32x36 matrix
det.covs$week <- matrix(week_numeric, nrow = 32, ncol = 36)
str(det.covs)
## List of 3
## $ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
This requires combining the presence data and the site covariate data into a single list. This also means that the presence data is in a 3-d format.
# Combine into a named list
data_list <- list(
y = detection_array,
occ.covs = covariates_matrix,
det.covs = det.covs
)
str(data_list)
## List of 3
## $ y : num [1:32, 1:36, 1:12] 1 1 0 1 0 0 0 1 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## .. ..$ : chr [1:12] "Odocoileus_virginianus" "Canis_latrans" "Sciurus_niger" "Procyon_lotor" ...
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:12, 1:32, 1:36] 1 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:12] "Odocoileus_virginianus" "Canis_latrans" "Sciurus_niger" "Procyon_lotor" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.421
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2235 0.4931 -1.2006 -0.2272 0.7999 0.9998 3650
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.7079 1.9292 0.7689 2.2061 7.7143 1.0039 2766
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5507 0.3718 -3.2721 -2.5587 -1.7888 1.0002 3809
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6381 0.9009 0.602 1.4197 3.9955 1.0016 1926
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.2743 1.0222 1.7406 3.1260 5.7183
## (Intercept)-Canis_latrans 0.3027 0.4097 -0.4548 0.2892 1.1739
## (Intercept)-Sciurus_niger -0.7035 0.8653 -2.0634 -0.8128 1.3629
## (Intercept)-Procyon_lotor 0.7004 0.3965 -0.0338 0.6829 1.5204
## (Intercept)-Dasypus_novemcinctus -0.6303 0.3643 -1.3503 -0.6296 0.0654
## (Intercept)-Lynx_rufus 0.3424 0.8618 -0.8652 0.1959 2.4588
## (Intercept)-Didelphis_virginiana -1.3502 0.4307 -2.2090 -1.3436 -0.5218
## (Intercept)-Sylvilagus_floridanus -0.2993 0.5710 -1.2451 -0.3539 0.9496
## (Intercept)-Meleagris_gallopavo -0.2381 0.6638 -1.3016 -0.3127 1.3115
## (Intercept)-Sciurus_carolinensis -1.3323 0.4478 -2.2777 -1.3109 -0.5248
## (Intercept)-Vulpes_vulpes -1.1119 1.0635 -2.7661 -1.2466 1.4385
## (Intercept)-Sus_scrofa -1.8393 0.6251 -3.1088 -1.8287 -0.6574
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 2037
## (Intercept)-Canis_latrans 1.0005 5027
## (Intercept)-Sciurus_niger 1.0337 788
## (Intercept)-Procyon_lotor 1.0000 4982
## (Intercept)-Dasypus_novemcinctus 1.0000 5250
## (Intercept)-Lynx_rufus 1.0141 662
## (Intercept)-Didelphis_virginiana 1.0031 4711
## (Intercept)-Sylvilagus_floridanus 1.0003 1838
## (Intercept)-Meleagris_gallopavo 1.0032 925
## (Intercept)-Sciurus_carolinensis 1.0004 5250
## (Intercept)-Vulpes_vulpes 1.0245 579
## (Intercept)-Sus_scrofa 1.0025 2579
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0026 0.0593 -0.1125 0.0029 0.1198
## (Intercept)-Canis_latrans -2.6151 0.1735 -2.9778 -2.6115 -2.2920
## (Intercept)-Sciurus_niger -3.8123 0.5511 -4.9339 -3.7841 -2.8109
## (Intercept)-Procyon_lotor -2.2668 0.1285 -2.5317 -2.2653 -2.0206
## (Intercept)-Dasypus_novemcinctus -1.5745 0.1327 -1.8430 -1.5722 -1.3262
## (Intercept)-Lynx_rufus -3.5890 0.3487 -4.2973 -3.5737 -2.9486
## (Intercept)-Didelphis_virginiana -2.3078 0.2492 -2.8238 -2.2966 -1.8551
## (Intercept)-Sylvilagus_floridanus -3.1883 0.3177 -3.8597 -3.1695 -2.6166
## (Intercept)-Meleagris_gallopavo -3.4398 0.3619 -4.1984 -3.4254 -2.7807
## (Intercept)-Sciurus_carolinensis -2.4343 0.2644 -2.9954 -2.4220 -1.9554
## (Intercept)-Vulpes_vulpes -3.9710 0.7269 -5.5010 -3.9122 -2.6963
## (Intercept)-Sus_scrofa -2.9468 0.4880 -3.9843 -2.9140 -2.0828
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0017 5250
## (Intercept)-Canis_latrans 1.0009 3100
## (Intercept)-Sciurus_niger 1.0288 681
## (Intercept)-Procyon_lotor 1.0053 4158
## (Intercept)-Dasypus_novemcinctus 1.0094 5250
## (Intercept)-Lynx_rufus 1.0111 706
## (Intercept)-Didelphis_virginiana 1.0017 3989
## (Intercept)-Sylvilagus_floridanus 1.0004 1310
## (Intercept)-Meleagris_gallopavo 1.0063 875
## (Intercept)-Sciurus_carolinensis 1.0005 3576
## (Intercept)-Vulpes_vulpes 1.0263 497
## (Intercept)-Sus_scrofa 1.0037 1762
# Includes all covariates of detection and occupancy
ms_full_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2243
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0577 0.9816 -1.9109 -0.0896 1.9611 1.0036 1800
## Cogon_Patch_Size -0.6312 0.5958 -1.8697 -0.6161 0.4935 1.0097 1032
## Veg_shannon_index 0.9367 0.4659 0.0411 0.9228 1.9233 1.0073 504
## total_shrub_cover -0.5484 0.5293 -1.6669 -0.5209 0.4217 1.0046 701
## Avg_Cogongrass_Cover 1.8870 0.7082 0.5258 1.8470 3.3831 1.0021 334
## Tree_Density -1.8562 0.7049 -3.3328 -1.8213 -0.5402 1.0044 617
## Avg_Canopy_Cover 1.9326 0.6002 0.8764 1.8938 3.2193 1.0020 904
## avg_veg_height -0.4401 0.4674 -1.3507 -0.4354 0.4770 1.0032 569
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.5539 14.1894 3.2347 11.5715 53.1643 1.0706 433
## Cogon_Patch_Size 2.1553 3.5889 0.0903 1.1118 10.1629 1.0080 553
## Veg_shannon_index 0.9043 1.3617 0.0517 0.4433 4.5849 1.0102 810
## total_shrub_cover 1.3824 2.1450 0.0676 0.7284 6.8518 1.0506 442
## Avg_Cogongrass_Cover 1.2683 2.6019 0.0514 0.5141 6.8298 1.0782 430
## Tree_Density 2.6280 4.2226 0.0723 1.1796 13.7823 1.1279 531
## Avg_Canopy_Cover 2.1824 2.9926 0.1287 1.2871 9.8244 1.0035 678
## avg_veg_height 0.3830 0.4958 0.0373 0.2251 1.6634 1.0132 1705
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7858 2.3291 0.0692 0.9319 8.5035 1.0517 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7875 0.4413 -3.6472 -2.7982 -1.8691 1.0006 5030
## shrub_cover 0.1952 0.2606 -0.3127 0.1944 0.7353 1.0012 1652
## veg_height -0.0344 0.1533 -0.3455 -0.0329 0.2597 1.0017 2574
## week -0.0711 0.1178 -0.3127 -0.0685 0.1514 1.0023 3027
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2655 1.2986 0.8560 1.9434 5.6009 1.0029 1961
## shrub_cover 0.5950 0.4332 0.1357 0.4893 1.7190 0.9999 1477
## veg_height 0.1981 0.1326 0.0572 0.1644 0.5297 1.0049 2843
## week 0.1028 0.0817 0.0270 0.0811 0.2995 1.0047 1952
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.8142 3.3121 3.2021 7.1954
## (Intercept)-Canis_latrans 0.8814 1.0743 -1.1281 0.8063
## (Intercept)-Sciurus_niger 1.5652 2.5554 -2.3108 1.2297
## (Intercept)-Procyon_lotor 0.9666 1.0335 -1.0302 0.9566
## (Intercept)-Dasypus_novemcinctus -1.3792 1.0169 -3.6781 -1.2985
## (Intercept)-Lynx_rufus 2.2452 2.7780 -1.8630 1.7906
## (Intercept)-Didelphis_virginiana -2.7300 1.1534 -5.1389 -2.6738
## (Intercept)-Sylvilagus_floridanus -1.0256 1.2975 -3.6568 -1.0379
## (Intercept)-Meleagris_gallopavo -0.0761 1.9787 -3.4714 -0.2329
## (Intercept)-Sciurus_carolinensis -2.8373 1.3464 -5.9042 -2.7235
## (Intercept)-Vulpes_vulpes -1.7636 2.2736 -5.6703 -1.9982
## (Intercept)-Sus_scrofa -4.1006 1.8971 -8.4372 -3.9210
## Cogon_Patch_Size-Odocoileus_virginianus -0.4838 1.2174 -2.7407 -0.5376
## Cogon_Patch_Size-Canis_latrans 0.5256 1.1138 -1.0867 0.3300
## Cogon_Patch_Size-Sciurus_niger -1.1804 1.5484 -4.7943 -0.9893
## Cogon_Patch_Size-Procyon_lotor -0.9432 0.7440 -2.5031 -0.9126
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5743 0.7244 -1.9766 -0.5812
## Cogon_Patch_Size-Lynx_rufus -0.7305 1.3018 -3.2822 -0.7280
## Cogon_Patch_Size-Didelphis_virginiana 0.6667 0.8764 -0.7881 0.5663
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6777 1.5032 -5.5468 -1.3904
## Cogon_Patch_Size-Meleagris_gallopavo -0.2760 1.1821 -2.3624 -0.3681
## Cogon_Patch_Size-Sciurus_carolinensis -1.4170 1.1974 -4.4626 -1.2176
## Cogon_Patch_Size-Vulpes_vulpes -0.9890 1.4777 -4.2331 -0.8772
## Cogon_Patch_Size-Sus_scrofa -1.0848 1.2762 -4.0606 -0.9195
## Veg_shannon_index-Odocoileus_virginianus 0.7809 0.9263 -1.2599 0.8168
## Veg_shannon_index-Canis_latrans 1.2866 0.6852 0.0855 1.2357
## Veg_shannon_index-Sciurus_niger 1.0846 1.0497 -0.9334 1.0297
## Veg_shannon_index-Procyon_lotor 1.2328 0.6234 0.1202 1.2053
## Veg_shannon_index-Dasypus_novemcinctus 0.6288 0.5566 -0.5086 0.6499
## Veg_shannon_index-Lynx_rufus 0.8995 0.9395 -1.0974 0.9079
## Veg_shannon_index-Didelphis_virginiana 1.1496 0.6939 -0.0719 1.1037
## Veg_shannon_index-Sylvilagus_floridanus 1.0449 0.7015 -0.2630 1.0077
## Veg_shannon_index-Meleagris_gallopavo 1.2795 0.8588 -0.1089 1.1768
## Veg_shannon_index-Sciurus_carolinensis 0.2045 0.8159 -1.6819 0.2853
## Veg_shannon_index-Vulpes_vulpes 0.3988 0.8893 -1.6810 0.4941
## Veg_shannon_index-Sus_scrofa 1.6042 1.0272 0.1091 1.4038
## total_shrub_cover-Odocoileus_virginianus -0.1438 0.9735 -2.0221 -0.2094
## total_shrub_cover-Canis_latrans 0.4613 0.9032 -0.8621 0.3213
## total_shrub_cover-Sciurus_niger -0.6827 1.0621 -3.0787 -0.6250
## total_shrub_cover-Procyon_lotor -1.0418 0.6721 -2.5324 -0.9762
## total_shrub_cover-Dasypus_novemcinctus -0.1642 0.6505 -1.4873 -0.1557
## total_shrub_cover-Lynx_rufus -0.7961 1.2735 -3.7351 -0.6990
## total_shrub_cover-Didelphis_virginiana -0.8202 0.8492 -2.7981 -0.7100
## total_shrub_cover-Sylvilagus_floridanus -0.6737 1.0174 -3.0453 -0.5594
## total_shrub_cover-Meleagris_gallopavo -1.5404 1.3589 -4.7611 -1.2920
## total_shrub_cover-Sciurus_carolinensis -0.4941 0.9159 -2.5391 -0.4178
## total_shrub_cover-Vulpes_vulpes -0.8181 1.1690 -3.5611 -0.6863
## total_shrub_cover-Sus_scrofa -0.2188 1.0223 -2.2557 -0.2518
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8600 1.1092 -0.3372 1.8352
## Avg_Cogongrass_Cover-Canis_latrans 2.3884 1.0229 0.7180 2.2704
## Avg_Cogongrass_Cover-Sciurus_niger 1.3249 1.4790 -2.3616 1.5463
## Avg_Cogongrass_Cover-Procyon_lotor 2.0889 0.8632 0.5404 2.0194
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4849 1.0057 0.8339 2.3714
## Avg_Cogongrass_Cover-Lynx_rufus 2.2512 1.0545 0.4362 2.1370
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0312 0.8971 0.3996 1.9682
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3195 1.0013 -0.7434 1.3570
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5667 1.3280 -1.4546 1.6734
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1923 0.9287 0.5310 2.1276
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4098 1.1441 0.5167 2.2845
## Avg_Cogongrass_Cover-Sus_scrofa 1.4571 1.2101 -1.2760 1.5445
## Tree_Density-Odocoileus_virginianus -0.8090 1.2098 -2.8364 -0.9424
## Tree_Density-Canis_latrans -2.6031 1.3191 -5.7385 -2.3713
## Tree_Density-Sciurus_niger -1.9743 1.4475 -5.0622 -1.9171
## Tree_Density-Procyon_lotor -1.4613 0.7800 -2.9813 -1.4660
## Tree_Density-Dasypus_novemcinctus -3.3973 1.7982 -8.0774 -2.9701
## Tree_Density-Lynx_rufus -0.6791 1.4713 -2.9676 -0.9043
## Tree_Density-Didelphis_virginiana -2.1724 1.1465 -4.8013 -2.0465
## Tree_Density-Sylvilagus_floridanus -2.3311 1.3291 -5.6015 -2.1415
## Tree_Density-Meleagris_gallopavo -2.2117 1.3479 -5.1944 -2.0828
## Tree_Density-Sciurus_carolinensis -2.2938 1.3844 -5.6050 -2.1155
## Tree_Density-Vulpes_vulpes -1.8246 1.6056 -5.0083 -1.7878
## Tree_Density-Sus_scrofa -2.2131 1.4549 -5.8077 -2.0086
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3119 1.2674 -1.3147 1.3408
## Avg_Canopy_Cover-Canis_latrans 0.3420 0.6928 -1.0136 0.3197
## Avg_Canopy_Cover-Sciurus_niger 2.1541 1.6627 -0.9787 2.0649
## Avg_Canopy_Cover-Procyon_lotor 1.7684 0.7587 0.4142 1.7207
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0928 0.7298 0.8933 2.0143
## Avg_Canopy_Cover-Lynx_rufus 1.4891 1.3598 -1.0825 1.4710
## Avg_Canopy_Cover-Didelphis_virginiana 2.8011 1.0546 1.2226 2.6193
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2739 1.4698 1.2391 2.9754
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4372 1.2070 0.5617 2.2595
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5978 1.0704 1.0672 2.3922
## Avg_Canopy_Cover-Vulpes_vulpes 2.3283 1.2582 0.4078 2.1394
## Avg_Canopy_Cover-Sus_scrofa 2.1546 0.9353 0.6030 2.0562
## avg_veg_height-Odocoileus_virginianus -0.4814 0.7210 -1.9768 -0.4714
## avg_veg_height-Canis_latrans -0.4785 0.5961 -1.6541 -0.4772
## avg_veg_height-Sciurus_niger -0.5779 0.7760 -2.2740 -0.5412
## avg_veg_height-Procyon_lotor -0.3941 0.5595 -1.5048 -0.3880
## avg_veg_height-Dasypus_novemcinctus -0.2270 0.5660 -1.3180 -0.2365
## avg_veg_height-Lynx_rufus -0.5134 0.7508 -2.0724 -0.4944
## avg_veg_height-Didelphis_virginiana -0.5903 0.6331 -1.8639 -0.5716
## avg_veg_height-Sylvilagus_floridanus -0.6026 0.6575 -2.0202 -0.5816
## avg_veg_height-Meleagris_gallopavo -0.4497 0.7476 -1.9344 -0.4444
## avg_veg_height-Sciurus_carolinensis -0.1402 0.6383 -1.3030 -0.1662
## avg_veg_height-Vulpes_vulpes -0.4271 0.6905 -1.8362 -0.4291
## avg_veg_height-Sus_scrofa -0.5016 0.6685 -1.8957 -0.4777
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.2661 1.0572 392
## (Intercept)-Canis_latrans 3.2049 1.0027 1400
## (Intercept)-Sciurus_niger 7.9108 1.0496 285
## (Intercept)-Procyon_lotor 3.0335 1.0041 1267
## (Intercept)-Dasypus_novemcinctus 0.4401 1.0233 964
## (Intercept)-Lynx_rufus 9.2030 1.0582 243
## (Intercept)-Didelphis_virginiana -0.5700 1.0052 1541
## (Intercept)-Sylvilagus_floridanus 1.6282 1.0064 698
## (Intercept)-Meleagris_gallopavo 4.3223 1.0049 343
## (Intercept)-Sciurus_carolinensis -0.4825 1.0254 737
## (Intercept)-Vulpes_vulpes 3.6320 1.0128 335
## (Intercept)-Sus_scrofa -0.8671 1.0131 539
## Cogon_Patch_Size-Odocoileus_virginianus 2.1335 1.0031 1854
## Cogon_Patch_Size-Canis_latrans 3.3173 1.0048 1279
## Cogon_Patch_Size-Sciurus_niger 1.4277 1.0040 705
## Cogon_Patch_Size-Procyon_lotor 0.4163 1.0066 629
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8874 1.0041 1517
## Cogon_Patch_Size-Lynx_rufus 1.8932 1.0047 877
## Cogon_Patch_Size-Didelphis_virginiana 2.6218 1.0065 1167
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3608 1.0007 579
## Cogon_Patch_Size-Meleagris_gallopavo 2.5138 1.0023 1213
## Cogon_Patch_Size-Sciurus_carolinensis 0.3115 1.0240 747
## Cogon_Patch_Size-Vulpes_vulpes 1.6958 1.0244 538
## Cogon_Patch_Size-Sus_scrofa 0.9016 1.0139 876
## Veg_shannon_index-Odocoileus_virginianus 2.5944 1.0079 1233
## Veg_shannon_index-Canis_latrans 2.7973 1.0005 1044
## Veg_shannon_index-Sciurus_niger 3.3198 1.0147 1100
## Veg_shannon_index-Procyon_lotor 2.5891 1.0001 684
## Veg_shannon_index-Dasypus_novemcinctus 1.6653 1.0030 1185
## Veg_shannon_index-Lynx_rufus 2.7460 1.0062 1014
## Veg_shannon_index-Didelphis_virginiana 2.6707 1.0020 1395
## Veg_shannon_index-Sylvilagus_floridanus 2.5754 1.0010 1180
## Veg_shannon_index-Meleagris_gallopavo 3.2727 1.0043 913
## Veg_shannon_index-Sciurus_carolinensis 1.5921 1.0109 934
## Veg_shannon_index-Vulpes_vulpes 1.9338 1.0020 888
## Veg_shannon_index-Sus_scrofa 4.2139 1.0163 846
## total_shrub_cover-Odocoileus_virginianus 1.9936 1.0037 2399
## total_shrub_cover-Canis_latrans 2.7204 1.0310 575
## total_shrub_cover-Sciurus_niger 1.2772 1.0079 947
## total_shrub_cover-Procyon_lotor 0.1067 1.0025 1394
## total_shrub_cover-Dasypus_novemcinctus 1.0836 1.0017 1980
## total_shrub_cover-Lynx_rufus 1.5502 1.0514 507
## total_shrub_cover-Didelphis_virginiana 0.5340 1.0019 1077
## total_shrub_cover-Sylvilagus_floridanus 0.9992 1.0081 673
## total_shrub_cover-Meleagris_gallopavo 0.4521 1.0226 379
## total_shrub_cover-Sciurus_carolinensis 1.0380 1.0057 727
## total_shrub_cover-Vulpes_vulpes 1.1313 1.0036 787
## total_shrub_cover-Sus_scrofa 1.9418 1.0051 1188
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.2448 1.0025 667
## Avg_Cogongrass_Cover-Canis_latrans 4.6923 1.0147 540
## Avg_Cogongrass_Cover-Sciurus_niger 3.6059 1.0264 541
## Avg_Cogongrass_Cover-Procyon_lotor 3.9657 1.0038 759
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8039 1.0151 453
## Avg_Cogongrass_Cover-Lynx_rufus 4.6651 1.0089 856
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9166 1.0041 757
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2111 1.0035 770
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.9075 1.0077 615
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2086 1.0102 478
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.0061 1.0067 510
## Avg_Cogongrass_Cover-Sus_scrofa 3.5892 1.0117 634
## Tree_Density-Odocoileus_virginianus 1.9363 1.0078 876
## Tree_Density-Canis_latrans -0.6680 1.0449 562
## Tree_Density-Sciurus_niger 0.8522 1.0089 1010
## Tree_Density-Procyon_lotor 0.1202 1.0024 1570
## Tree_Density-Dasypus_novemcinctus -1.1497 1.0494 441
## Tree_Density-Lynx_rufus 2.8731 1.0239 534
## Tree_Density-Didelphis_virginiana -0.2283 1.0071 891
## Tree_Density-Sylvilagus_floridanus -0.0864 1.0045 948
## Tree_Density-Meleagris_gallopavo 0.2463 1.0041 987
## Tree_Density-Sciurus_carolinensis -0.0191 1.0270 681
## Tree_Density-Vulpes_vulpes 1.3801 1.0162 925
## Tree_Density-Sus_scrofa 0.1934 1.0084 891
## Avg_Canopy_Cover-Odocoileus_virginianus 3.8464 1.0028 1538
## Avg_Canopy_Cover-Canis_latrans 1.7376 1.0054 1311
## Avg_Canopy_Cover-Sciurus_niger 5.6774 1.0102 634
## Avg_Canopy_Cover-Procyon_lotor 3.3856 1.0036 1098
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7454 1.0083 654
## Avg_Canopy_Cover-Lynx_rufus 4.2931 1.0231 688
## Avg_Canopy_Cover-Didelphis_virginiana 5.3749 1.0091 540
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.9411 1.0023 571
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3626 1.0021 1012
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2950 1.0030 719
## Avg_Canopy_Cover-Vulpes_vulpes 5.3265 1.0017 961
## Avg_Canopy_Cover-Sus_scrofa 4.3428 1.0011 1591
## avg_veg_height-Odocoileus_virginianus 0.8967 1.0029 1307
## avg_veg_height-Canis_latrans 0.6654 1.0004 1017
## avg_veg_height-Sciurus_niger 0.8428 1.0056 974
## avg_veg_height-Procyon_lotor 0.7053 1.0009 1117
## avg_veg_height-Dasypus_novemcinctus 0.9080 1.0011 1350
## avg_veg_height-Lynx_rufus 0.9312 1.0021 1068
## avg_veg_height-Didelphis_virginiana 0.6231 1.0041 911
## avg_veg_height-Sylvilagus_floridanus 0.6408 1.0006 1171
## avg_veg_height-Meleagris_gallopavo 1.0395 1.0075 902
## avg_veg_height-Sciurus_carolinensis 1.2335 1.0013 977
## avg_veg_height-Vulpes_vulpes 0.9121 1.0032 991
## avg_veg_height-Sus_scrofa 0.7689 1.0028 1049
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0062 0.0601 -0.1093 0.0049 0.1220
## (Intercept)-Canis_latrans -2.7667 0.1853 -3.1426 -2.7584 -2.4239
## (Intercept)-Sciurus_niger -4.7924 0.5251 -5.8950 -4.7757 -3.7880
## (Intercept)-Procyon_lotor -2.3019 0.1438 -2.5920 -2.2974 -2.0321
## (Intercept)-Dasypus_novemcinctus -1.7535 0.1620 -2.0890 -1.7471 -1.4531
## (Intercept)-Lynx_rufus -3.9359 0.3795 -4.6542 -3.9474 -3.1775
## (Intercept)-Didelphis_virginiana -2.5634 0.2872 -3.1569 -2.5508 -2.0245
## (Intercept)-Sylvilagus_floridanus -3.2182 0.2777 -3.8015 -3.2105 -2.7120
## (Intercept)-Meleagris_gallopavo -4.0441 0.4995 -5.0659 -4.0400 -3.0991
## (Intercept)-Sciurus_carolinensis -2.6851 0.3316 -3.3844 -2.6681 -2.0839
## (Intercept)-Vulpes_vulpes -4.3910 0.7126 -5.8253 -4.3637 -3.1104
## (Intercept)-Sus_scrofa -3.2877 0.6001 -4.4313 -3.2836 -2.0946
## shrub_cover-Odocoileus_virginianus -0.0563 0.0652 -0.1853 -0.0566 0.0708
## shrub_cover-Canis_latrans -0.3574 0.2190 -0.7751 -0.3589 0.0816
## shrub_cover-Sciurus_niger -0.3931 0.4646 -1.3146 -0.3886 0.4777
## shrub_cover-Procyon_lotor 0.2595 0.1587 -0.0633 0.2658 0.5554
## shrub_cover-Dasypus_novemcinctus 0.8762 0.3014 0.3070 0.8764 1.4736
## shrub_cover-Lynx_rufus -0.2300 0.3611 -0.9421 -0.2313 0.4942
## shrub_cover-Didelphis_virginiana 0.9420 0.3661 0.2655 0.9244 1.6990
## shrub_cover-Sylvilagus_floridanus 0.4623 0.4058 -0.3102 0.4562 1.2544
## shrub_cover-Meleagris_gallopavo -0.6814 0.4475 -1.6004 -0.6713 0.1832
## shrub_cover-Sciurus_carolinensis 0.8928 0.4257 0.0988 0.8870 1.7652
## shrub_cover-Vulpes_vulpes 0.0707 0.5638 -1.0626 0.0830 1.1738
## shrub_cover-Sus_scrofa 0.5851 0.7864 -0.9659 0.5722 2.1399
## veg_height-Odocoileus_virginianus -0.2989 0.0648 -0.4258 -0.2980 -0.1728
## veg_height-Canis_latrans -0.5975 0.1770 -0.9565 -0.5918 -0.2649
## veg_height-Sciurus_niger -0.0877 0.3409 -0.7590 -0.0898 0.5895
## veg_height-Procyon_lotor 0.3368 0.1208 0.0953 0.3382 0.5701
## veg_height-Dasypus_novemcinctus 0.2366 0.1354 -0.0303 0.2367 0.5084
## veg_height-Lynx_rufus 0.0712 0.2344 -0.4035 0.0754 0.5186
## veg_height-Didelphis_virginiana 0.4178 0.2312 -0.0089 0.4105 0.8883
## veg_height-Sylvilagus_floridanus 0.1293 0.2449 -0.3582 0.1338 0.6140
## veg_height-Meleagris_gallopavo -0.2900 0.3406 -0.9895 -0.2760 0.3557
## veg_height-Sciurus_carolinensis 0.0766 0.2105 -0.3205 0.0729 0.5041
## veg_height-Vulpes_vulpes -0.2234 0.3294 -0.9370 -0.2073 0.3922
## veg_height-Sus_scrofa -0.1607 0.3242 -0.8231 -0.1511 0.4456
## week-Odocoileus_virginianus 0.2128 0.0615 0.0976 0.2115 0.3362
## week-Canis_latrans 0.0694 0.1334 -0.1964 0.0739 0.3174
## week-Sciurus_niger -0.3236 0.3083 -1.0340 -0.2852 0.1816
## week-Procyon_lotor -0.0520 0.1187 -0.2884 -0.0492 0.1718
## week-Dasypus_novemcinctus -0.1700 0.1382 -0.4666 -0.1642 0.0843
## week-Lynx_rufus -0.0410 0.1950 -0.4480 -0.0315 0.3182
## week-Didelphis_virginiana -0.2180 0.2172 -0.6945 -0.2031 0.1663
## week-Sylvilagus_floridanus -0.1610 0.2067 -0.6106 -0.1477 0.2126
## week-Meleagris_gallopavo -0.2761 0.2448 -0.8161 -0.2560 0.1556
## week-Sciurus_carolinensis 0.1323 0.1792 -0.2320 0.1332 0.4865
## week-Vulpes_vulpes -0.1239 0.2766 -0.7292 -0.1101 0.3732
## week-Sus_scrofa 0.0948 0.2342 -0.3776 0.0959 0.5550
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0033 2088
## (Intercept)-Sciurus_niger 1.0176 408
## (Intercept)-Procyon_lotor 1.0026 3350
## (Intercept)-Dasypus_novemcinctus 1.0003 3650
## (Intercept)-Lynx_rufus 1.0457 416
## (Intercept)-Didelphis_virginiana 1.0004 2084
## (Intercept)-Sylvilagus_floridanus 1.0054 1506
## (Intercept)-Meleagris_gallopavo 1.0051 479
## (Intercept)-Sciurus_carolinensis 1.0047 1437
## (Intercept)-Vulpes_vulpes 1.0038 392
## (Intercept)-Sus_scrofa 1.0100 1099
## shrub_cover-Odocoileus_virginianus 1.0007 5250
## shrub_cover-Canis_latrans 1.0018 1752
## shrub_cover-Sciurus_niger 1.0052 884
## shrub_cover-Procyon_lotor 1.0098 4096
## shrub_cover-Dasypus_novemcinctus 1.0017 2308
## shrub_cover-Lynx_rufus 1.0179 773
## shrub_cover-Didelphis_virginiana 1.0000 1720
## shrub_cover-Sylvilagus_floridanus 0.9999 1395
## shrub_cover-Meleagris_gallopavo 1.0035 590
## shrub_cover-Sciurus_carolinensis 1.0003 1165
## shrub_cover-Vulpes_vulpes 1.0019 1366
## shrub_cover-Sus_scrofa 1.0006 1134
## veg_height-Odocoileus_virginianus 1.0013 4887
## veg_height-Canis_latrans 1.0047 2478
## veg_height-Sciurus_niger 1.0014 1097
## veg_height-Procyon_lotor 1.0013 3655
## veg_height-Dasypus_novemcinctus 1.0012 4476
## veg_height-Lynx_rufus 1.0033 1640
## veg_height-Didelphis_virginiana 1.0048 3315
## veg_height-Sylvilagus_floridanus 1.0003 2670
## veg_height-Meleagris_gallopavo 1.0019 1232
## veg_height-Sciurus_carolinensis 1.0003 3344
## veg_height-Vulpes_vulpes 1.0022 1736
## veg_height-Sus_scrofa 1.0100 2461
## week-Odocoileus_virginianus 1.0014 5460
## week-Canis_latrans 1.0037 4104
## week-Sciurus_niger 1.0020 1281
## week-Procyon_lotor 1.0014 4571
## week-Dasypus_novemcinctus 1.0025 4640
## week-Lynx_rufus 1.0037 2529
## week-Didelphis_virginiana 1.0009 3329
## week-Sylvilagus_floridanus 1.0017 3008
## week-Meleagris_gallopavo 1.0034 2061
## week-Sciurus_carolinensis 1.0013 4524
## week-Vulpes_vulpes 1.0017 2486
## week-Sus_scrofa 1.0011 4387
#Includes all covariates of detection and only null for occupancy
ms_full_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
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## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1305
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0042 0.5279 -1.0048 -0.0193 1.087 1.0031 1823
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0215 2.3839 0.7703 2.3628 9.5525 1.0016 1218
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7529 0.4300 -3.5881 -2.7581 -1.8648 1.0007 4066
## shrub_cover 0.0843 0.2598 -0.4343 0.0828 0.6087 1.0046 3194
## veg_height -0.0541 0.1532 -0.3616 -0.0537 0.2417 1.0010 2896
## week -0.0736 0.1198 -0.3315 -0.0670 0.1457 1.0042 2760
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1757 1.2199 0.7991 1.8796 5.3853 1.0028 1918
## shrub_cover 0.6494 0.4812 0.1549 0.5238 1.9658 1.0014 1680
## veg_height 0.1973 0.1302 0.0588 0.1627 0.5312 1.0019 3048
## week 0.1037 0.0821 0.0267 0.0812 0.3193 1.0090 1994
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4219 1.1534 1.7772 3.2382 6.2021
## (Intercept)-Canis_latrans 0.4186 0.4262 -0.3592 0.4051 1.2980
## (Intercept)-Sciurus_niger -0.3942 0.9720 -1.9043 -0.5113 1.8779
## (Intercept)-Procyon_lotor 0.7413 0.4032 -0.0024 0.7184 1.5721
## (Intercept)-Dasypus_novemcinctus -0.5692 0.3795 -1.3345 -0.5668 0.1594
## (Intercept)-Lynx_rufus 0.6416 0.9657 -0.7192 0.4856 3.0310
## (Intercept)-Didelphis_virginiana -1.1973 0.4642 -2.1404 -1.1840 -0.3188
## (Intercept)-Sylvilagus_floridanus -0.2886 0.5196 -1.2127 -0.3099 0.8002
## (Intercept)-Meleagris_gallopavo 0.9946 1.2113 -0.7196 0.7685 4.0956
## (Intercept)-Sciurus_carolinensis -1.1953 0.4842 -2.1664 -1.1795 -0.2778
## (Intercept)-Vulpes_vulpes -0.8510 1.1332 -2.6512 -1.0226 1.9914
## (Intercept)-Sus_scrofa -1.6419 0.6574 -2.9944 -1.6284 -0.3697
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0030 1395
## (Intercept)-Canis_latrans 1.0015 4270
## (Intercept)-Sciurus_niger 1.0066 596
## (Intercept)-Procyon_lotor 1.0015 4822
## (Intercept)-Dasypus_novemcinctus 1.0010 5250
## (Intercept)-Lynx_rufus 1.0126 693
## (Intercept)-Didelphis_virginiana 1.0021 4641
## (Intercept)-Sylvilagus_floridanus 1.0086 2613
## (Intercept)-Meleagris_gallopavo 1.0184 639
## (Intercept)-Sciurus_carolinensis 1.0007 3679
## (Intercept)-Vulpes_vulpes 1.0093 472
## (Intercept)-Sus_scrofa 1.0005 1901
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0066 0.0602 -0.1107 0.0060 0.1269
## (Intercept)-Canis_latrans -2.7609 0.1924 -3.1525 -2.7553 -2.4003
## (Intercept)-Sciurus_niger -4.2580 0.6402 -5.4744 -4.2629 -3.0304
## (Intercept)-Procyon_lotor -2.2940 0.1435 -2.5923 -2.2875 -2.0204
## (Intercept)-Dasypus_novemcinctus -1.7267 0.1556 -2.0434 -1.7232 -1.4341
## (Intercept)-Lynx_rufus -3.8233 0.3567 -4.5058 -3.8277 -3.1217
## (Intercept)-Didelphis_virginiana -2.5585 0.2895 -3.1662 -2.5461 -2.0142
## (Intercept)-Sylvilagus_floridanus -3.2165 0.3118 -3.8849 -3.1995 -2.6624
## (Intercept)-Meleagris_gallopavo -4.3852 0.4705 -5.3158 -4.3928 -3.4529
## (Intercept)-Sciurus_carolinensis -2.6055 0.3168 -3.2568 -2.5833 -2.0248
## (Intercept)-Vulpes_vulpes -4.3327 0.7674 -5.9037 -4.2888 -2.9629
## (Intercept)-Sus_scrofa -3.3073 0.6306 -4.5840 -3.3115 -2.1043
## shrub_cover-Odocoileus_virginianus -0.0556 0.0641 -0.1795 -0.0569 0.0680
## shrub_cover-Canis_latrans -0.3197 0.2196 -0.7443 -0.3213 0.1061
## shrub_cover-Sciurus_niger -0.4897 0.4817 -1.4632 -0.4867 0.4694
## shrub_cover-Procyon_lotor 0.2412 0.1686 -0.0914 0.2440 0.5621
## shrub_cover-Dasypus_novemcinctus 0.8103 0.2918 0.2568 0.7997 1.4069
## shrub_cover-Lynx_rufus -0.4026 0.3380 -1.0968 -0.3957 0.2278
## shrub_cover-Didelphis_virginiana 0.9291 0.3725 0.2477 0.9094 1.7199
## shrub_cover-Sylvilagus_floridanus 0.2041 0.4158 -0.5958 0.1870 1.0560
## shrub_cover-Meleagris_gallopavo -0.9399 0.4061 -1.7347 -0.9384 -0.1574
## shrub_cover-Sciurus_carolinensis 0.7783 0.4051 -0.0109 0.7654 1.6111
## shrub_cover-Vulpes_vulpes -0.2588 0.5592 -1.4249 -0.2423 0.8231
## shrub_cover-Sus_scrofa 0.5096 0.8338 -1.1333 0.4918 2.2441
## veg_height-Odocoileus_virginianus -0.3003 0.0652 -0.4289 -0.2992 -0.1731
## veg_height-Canis_latrans -0.6013 0.1869 -0.9781 -0.5968 -0.2494
## veg_height-Sciurus_niger -0.1425 0.3853 -0.9000 -0.1393 0.6251
## veg_height-Procyon_lotor 0.3307 0.1219 0.0999 0.3272 0.5716
## veg_height-Dasypus_novemcinctus 0.2275 0.1310 -0.0258 0.2265 0.4905
## veg_height-Lynx_rufus 0.0049 0.2437 -0.4870 0.0069 0.4730
## veg_height-Didelphis_virginiana 0.3980 0.2404 -0.0517 0.3846 0.8967
## veg_height-Sylvilagus_floridanus 0.0891 0.2450 -0.3871 0.0869 0.5649
## veg_height-Meleagris_gallopavo -0.3646 0.3249 -1.0406 -0.3567 0.2679
## veg_height-Sciurus_carolinensis 0.0439 0.2123 -0.3634 0.0391 0.4707
## veg_height-Vulpes_vulpes -0.1611 0.3202 -0.8158 -0.1549 0.4432
## veg_height-Sus_scrofa -0.1583 0.3267 -0.8163 -0.1517 0.4760
## week-Odocoileus_virginianus 0.2104 0.0600 0.0914 0.2109 0.3254
## week-Canis_latrans 0.0673 0.1304 -0.1949 0.0699 0.3169
## week-Sciurus_niger -0.3229 0.3101 -1.0430 -0.2846 0.1737
## week-Procyon_lotor -0.0508 0.1185 -0.2887 -0.0477 0.1709
## week-Dasypus_novemcinctus -0.1676 0.1338 -0.4421 -0.1635 0.0807
## week-Lynx_rufus -0.0448 0.1918 -0.4333 -0.0387 0.3158
## week-Didelphis_virginiana -0.2183 0.2195 -0.6894 -0.2036 0.1783
## week-Sylvilagus_floridanus -0.1582 0.2096 -0.6094 -0.1450 0.2190
## week-Meleagris_gallopavo -0.2819 0.2459 -0.8445 -0.2594 0.1380
## week-Sciurus_carolinensis 0.1334 0.1806 -0.2295 0.1341 0.4808
## week-Vulpes_vulpes -0.1284 0.2840 -0.7479 -0.1052 0.3759
## week-Sus_scrofa 0.0824 0.2330 -0.3886 0.0789 0.5478
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0025 5352
## (Intercept)-Canis_latrans 1.0003 2236
## (Intercept)-Sciurus_niger 1.0008 606
## (Intercept)-Procyon_lotor 1.0033 3673
## (Intercept)-Dasypus_novemcinctus 1.0005 4395
## (Intercept)-Lynx_rufus 1.0053 804
## (Intercept)-Didelphis_virginiana 1.0014 2467
## (Intercept)-Sylvilagus_floridanus 1.0025 1653
## (Intercept)-Meleagris_gallopavo 1.0210 564
## (Intercept)-Sciurus_carolinensis 1.0059 2605
## (Intercept)-Vulpes_vulpes 1.0048 400
## (Intercept)-Sus_scrofa 1.0031 1229
## shrub_cover-Odocoileus_virginianus 1.0006 5250
## shrub_cover-Canis_latrans 1.0055 2625
## shrub_cover-Sciurus_niger 1.0047 1395
## shrub_cover-Procyon_lotor 1.0001 3899
## shrub_cover-Dasypus_novemcinctus 1.0019 3957
## shrub_cover-Lynx_rufus 1.0135 1345
## shrub_cover-Didelphis_virginiana 1.0052 2270
## shrub_cover-Sylvilagus_floridanus 1.0081 1590
## shrub_cover-Meleagris_gallopavo 1.0135 740
## shrub_cover-Sciurus_carolinensis 1.0046 2524
## shrub_cover-Vulpes_vulpes 1.0072 1597
## shrub_cover-Sus_scrofa 1.0108 1955
## veg_height-Odocoileus_virginianus 1.0003 5712
## veg_height-Canis_latrans 1.0002 2301
## veg_height-Sciurus_niger 0.9999 2230
## veg_height-Procyon_lotor 1.0000 4209
## veg_height-Dasypus_novemcinctus 1.0003 4619
## veg_height-Lynx_rufus 1.0006 2562
## veg_height-Didelphis_virginiana 1.0025 3404
## veg_height-Sylvilagus_floridanus 1.0111 2655
## veg_height-Meleagris_gallopavo 1.0045 1537
## veg_height-Sciurus_carolinensis 1.0014 3295
## veg_height-Vulpes_vulpes 1.0004 1922
## veg_height-Sus_scrofa 1.0051 3122
## week-Odocoileus_virginianus 1.0007 5250
## week-Canis_latrans 1.0014 4557
## week-Sciurus_niger 1.0031 1641
## week-Procyon_lotor 1.0012 4553
## week-Dasypus_novemcinctus 1.0024 4959
## week-Lynx_rufus 1.0032 2686
## week-Didelphis_virginiana 1.0042 3040
## week-Sylvilagus_floridanus 1.0054 3051
## week-Meleagris_gallopavo 1.0029 1523
## week-Sciurus_carolinensis 1.0035 4703
## week-Vulpes_vulpes 1.0026 2449
## week-Sus_scrofa 1.0008 4507
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2357
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1003 0.5912 -1.0173 0.0852 1.3132 1.0222 938
## Avg_Cogongrass_Cover -0.0675 0.3545 -0.7804 -0.0605 0.6153 1.0025 1379
## total_shrub_cover -0.8394 0.4748 -1.9041 -0.7986 -0.0349 1.0361 422
## avg_veg_height 0.1704 0.3435 -0.4980 0.1666 0.8837 1.0125 914
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0657 2.7330 0.2922 2.3372 10.2901 1.0192 906
## Avg_Cogongrass_Cover 0.4403 0.5111 0.0430 0.2704 1.8544 1.0129 1321
## total_shrub_cover 0.9766 1.2146 0.0630 0.5976 4.2147 1.0482 574
## avg_veg_height 0.2865 0.3482 0.0356 0.1761 1.1817 1.0144 1710
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8158 1.9312 0.0963 1.2565 6.6301 1.0904 245
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8042 0.4229 -3.6190 -2.8089 -1.9557 1.0049 3702
## shrub_cover 0.3870 0.3047 -0.1883 0.3810 1.0062 1.0055 1094
## veg_height -0.0429 0.1633 -0.3739 -0.0410 0.2725 1.0100 2168
## week -0.0757 0.1186 -0.3373 -0.0690 0.1392 0.9999 2760
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0803 1.2328 0.7633 1.7829 5.0393 1.0071 2098
## shrub_cover 0.7659 0.5786 0.1582 0.6190 2.2573 1.0053 1026
## veg_height 0.2041 0.1436 0.0582 0.1670 0.5928 1.0106 2869
## week 0.1036 0.0781 0.0259 0.0816 0.3205 1.0024 2353
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3770 1.6211 0.5227 3.2693
## (Intercept)-Canis_latrans 0.6128 0.8184 -0.9107 0.5836
## (Intercept)-Sciurus_niger -0.2241 1.3154 -2.3859 -0.3413
## (Intercept)-Procyon_lotor 0.7704 0.8048 -0.8237 0.7662
## (Intercept)-Dasypus_novemcinctus -0.3841 0.8178 -1.9767 -0.4031
## (Intercept)-Lynx_rufus 0.1413 1.0895 -1.8584 0.0813
## (Intercept)-Didelphis_virginiana -0.8125 0.8555 -2.4477 -0.8408
## (Intercept)-Sylvilagus_floridanus 0.2870 0.9377 -1.3993 0.2356
## (Intercept)-Meleagris_gallopavo -0.0947 1.1914 -2.1812 -0.1637
## (Intercept)-Sciurus_carolinensis -0.8999 0.9449 -2.8166 -0.8997
## (Intercept)-Vulpes_vulpes -0.4240 1.4216 -2.9069 -0.4900
## (Intercept)-Sus_scrofa -1.1456 1.1471 -3.4271 -1.1458
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0672 0.6068 -1.2684 -0.0643
## Avg_Cogongrass_Cover-Canis_latrans 0.3138 0.5277 -0.6013 0.2684
## Avg_Cogongrass_Cover-Sciurus_niger -0.4274 0.7353 -2.1560 -0.3436
## Avg_Cogongrass_Cover-Procyon_lotor -0.1459 0.4822 -1.1348 -0.1347
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1231 0.4512 -0.7448 0.1084
## Avg_Cogongrass_Cover-Lynx_rufus 0.3130 0.5683 -0.6943 0.2732
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1300 0.4887 -0.7982 0.1199
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4505 0.5884 -1.7433 -0.3899
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4599 0.7241 -2.1346 -0.3892
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0212 0.4895 -0.9761 0.0294
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0955 0.5945 -1.0197 0.0810
## Avg_Cogongrass_Cover-Sus_scrofa -0.3174 0.6915 -1.8890 -0.2480
## total_shrub_cover-Odocoileus_virginianus -0.4279 0.7268 -1.8382 -0.4428
## total_shrub_cover-Canis_latrans 0.1866 0.7205 -1.0914 0.1252
## total_shrub_cover-Sciurus_niger -0.9325 0.8657 -2.8695 -0.8630
## total_shrub_cover-Procyon_lotor -1.3076 0.6637 -2.8496 -1.2133
## total_shrub_cover-Dasypus_novemcinctus -0.4561 0.6797 -2.1567 -0.3637
## total_shrub_cover-Lynx_rufus -1.2513 0.8900 -3.3203 -1.1558
## total_shrub_cover-Didelphis_virginiana -0.8490 0.6646 -2.3634 -0.7693
## total_shrub_cover-Sylvilagus_floridanus -1.4192 0.9558 -3.7085 -1.2634
## total_shrub_cover-Meleagris_gallopavo -1.4013 0.8989 -3.4967 -1.3008
## total_shrub_cover-Sciurus_carolinensis -0.8744 0.8050 -2.7906 -0.7680
## total_shrub_cover-Vulpes_vulpes -0.9609 1.0804 -3.4368 -0.8535
## total_shrub_cover-Sus_scrofa -0.7164 0.9228 -2.7242 -0.6489
## avg_veg_height-Odocoileus_virginianus 0.1409 0.5376 -0.9547 0.1461
## avg_veg_height-Canis_latrans 0.1900 0.4682 -0.7052 0.1799
## avg_veg_height-Sciurus_niger -0.0399 0.6232 -1.3997 -0.0053
## avg_veg_height-Procyon_lotor 0.2023 0.4506 -0.6813 0.2016
## avg_veg_height-Dasypus_novemcinctus 0.3822 0.4696 -0.4468 0.3515
## avg_veg_height-Lynx_rufus 0.1482 0.5644 -0.9237 0.1400
## avg_veg_height-Didelphis_virginiana 0.0758 0.4744 -0.8872 0.0876
## avg_veg_height-Sylvilagus_floridanus 0.1303 0.5154 -0.8686 0.1270
## avg_veg_height-Meleagris_gallopavo 0.0046 0.6573 -1.3811 0.0324
## avg_veg_height-Sciurus_carolinensis 0.4893 0.5027 -0.4008 0.4451
## avg_veg_height-Vulpes_vulpes 0.1426 0.5461 -0.9429 0.1448
## avg_veg_height-Sus_scrofa 0.2005 0.5166 -0.7971 0.1867
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9831 1.0198 651
## (Intercept)-Canis_latrans 2.2860 1.0057 1799
## (Intercept)-Sciurus_niger 2.8100 1.0174 448
## (Intercept)-Procyon_lotor 2.4411 1.0075 2084
## (Intercept)-Dasypus_novemcinctus 1.3314 1.0132 1419
## (Intercept)-Lynx_rufus 2.4433 1.0226 841
## (Intercept)-Didelphis_virginiana 0.9527 1.0112 1292
## (Intercept)-Sylvilagus_floridanus 2.2654 1.0233 1201
## (Intercept)-Meleagris_gallopavo 2.5631 1.0249 719
## (Intercept)-Sciurus_carolinensis 0.9605 1.0217 878
## (Intercept)-Vulpes_vulpes 2.6143 1.0399 456
## (Intercept)-Sus_scrofa 1.0522 1.0091 673
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1676 1.0041 2534
## Avg_Cogongrass_Cover-Canis_latrans 1.4827 1.0074 2091
## Avg_Cogongrass_Cover-Sciurus_niger 0.8200 1.0042 1564
## Avg_Cogongrass_Cover-Procyon_lotor 0.7782 1.0034 2482
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0544 1.0019 2391
## Avg_Cogongrass_Cover-Lynx_rufus 1.5717 1.0081 2229
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1400 1.0036 1897
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5855 1.0014 1917
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7857 1.0023 1411
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9843 1.0075 2210
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3188 1.0028 2269
## Avg_Cogongrass_Cover-Sus_scrofa 0.8525 1.0033 1824
## total_shrub_cover-Odocoileus_virginianus 1.1133 1.0036 2291
## total_shrub_cover-Canis_latrans 1.7958 1.0124 1085
## total_shrub_cover-Sciurus_niger 0.6688 1.0012 1156
## total_shrub_cover-Procyon_lotor -0.2651 1.0239 712
## total_shrub_cover-Dasypus_novemcinctus 0.5831 1.0115 742
## total_shrub_cover-Lynx_rufus 0.2593 1.0274 785
## total_shrub_cover-Didelphis_virginiana 0.2511 1.0114 655
## total_shrub_cover-Sylvilagus_floridanus 0.0213 1.0864 428
## total_shrub_cover-Meleagris_gallopavo 0.0520 1.0153 620
## total_shrub_cover-Sciurus_carolinensis 0.4171 1.0683 531
## total_shrub_cover-Vulpes_vulpes 1.0236 1.0411 523
## total_shrub_cover-Sus_scrofa 0.9322 1.0105 668
## avg_veg_height-Odocoileus_virginianus 1.1740 1.0061 1424
## avg_veg_height-Canis_latrans 1.1423 1.0075 1694
## avg_veg_height-Sciurus_niger 1.0954 1.0139 1237
## avg_veg_height-Procyon_lotor 1.1105 1.0019 2212
## avg_veg_height-Dasypus_novemcinctus 1.4001 1.0092 1384
## avg_veg_height-Lynx_rufus 1.3016 1.0174 1390
## avg_veg_height-Didelphis_virginiana 0.9845 1.0073 1455
## avg_veg_height-Sylvilagus_floridanus 1.1604 1.0119 1389
## avg_veg_height-Meleagris_gallopavo 1.2133 1.0100 1229
## avg_veg_height-Sciurus_carolinensis 1.5926 1.0068 1585
## avg_veg_height-Vulpes_vulpes 1.2502 1.0043 1547
## avg_veg_height-Sus_scrofa 1.2492 1.0034 1279
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0053 0.0593 -0.1085 0.0047 0.1229
## (Intercept)-Canis_latrans -2.8080 0.1993 -3.2212 -2.7996 -2.4428
## (Intercept)-Sciurus_niger -4.2232 0.6924 -5.6093 -4.2170 -2.9144
## (Intercept)-Procyon_lotor -2.3065 0.1408 -2.5907 -2.3040 -2.0382
## (Intercept)-Dasypus_novemcinctus -1.8065 0.1855 -2.1838 -1.8010 -1.4592
## (Intercept)-Lynx_rufus -3.6388 0.3720 -4.4280 -3.6139 -2.9722
## (Intercept)-Didelphis_virginiana -2.7384 0.3244 -3.4016 -2.7250 -2.1263
## (Intercept)-Sylvilagus_floridanus -3.3436 0.2766 -3.9054 -3.3330 -2.8175
## (Intercept)-Meleagris_gallopavo -3.9047 0.5626 -4.9963 -3.9073 -2.8191
## (Intercept)-Sciurus_carolinensis -2.8211 0.3588 -3.5813 -2.8079 -2.1540
## (Intercept)-Vulpes_vulpes -4.4691 0.7403 -5.8744 -4.4645 -3.0693
## (Intercept)-Sus_scrofa -3.7109 0.6455 -4.9341 -3.7312 -2.3909
## shrub_cover-Odocoileus_virginianus -0.0521 0.0646 -0.1776 -0.0520 0.0750
## shrub_cover-Canis_latrans -0.3060 0.2474 -0.7821 -0.3099 0.1870
## shrub_cover-Sciurus_niger -0.1669 0.5624 -1.3219 -0.1580 0.9142
## shrub_cover-Procyon_lotor 0.3164 0.1621 -0.0086 0.3169 0.6313
## shrub_cover-Dasypus_novemcinctus 1.0446 0.3703 0.3604 1.0329 1.7803
## shrub_cover-Lynx_rufus 0.0119 0.3951 -0.8087 0.0285 0.7379
## shrub_cover-Didelphis_virginiana 1.2289 0.4246 0.4374 1.2156 2.0941
## shrub_cover-Sylvilagus_floridanus 0.7680 0.4180 -0.0908 0.7816 1.5393
## shrub_cover-Meleagris_gallopavo -0.5558 0.4887 -1.5238 -0.5618 0.3743
## shrub_cover-Sciurus_carolinensis 1.1570 0.4548 0.2740 1.1544 2.0528
## shrub_cover-Vulpes_vulpes 0.1717 0.6490 -1.1570 0.1795 1.4216
## shrub_cover-Sus_scrofa 1.1455 0.8927 -0.6811 1.1474 2.8585
## veg_height-Odocoileus_virginianus -0.2966 0.0629 -0.4229 -0.2968 -0.1739
## veg_height-Canis_latrans -0.6109 0.1870 -0.9959 -0.6002 -0.2630
## veg_height-Sciurus_niger -0.0285 0.4254 -0.8249 -0.0432 0.8637
## veg_height-Procyon_lotor 0.3284 0.1227 0.0900 0.3283 0.5639
## veg_height-Dasypus_novemcinctus 0.2435 0.1407 -0.0258 0.2403 0.5253
## veg_height-Lynx_rufus 0.0036 0.2455 -0.5013 0.0144 0.4693
## veg_height-Didelphis_virginiana 0.3983 0.2539 -0.0758 0.3901 0.9253
## veg_height-Sylvilagus_floridanus 0.0260 0.2524 -0.4596 0.0250 0.5354
## veg_height-Meleagris_gallopavo -0.2509 0.4050 -1.0407 -0.2559 0.5912
## veg_height-Sciurus_carolinensis 0.0853 0.2245 -0.3346 0.0749 0.5424
## veg_height-Vulpes_vulpes -0.1837 0.3359 -0.8831 -0.1681 0.4320
## veg_height-Sus_scrofa -0.2004 0.3304 -0.8671 -0.1944 0.4213
## week-Odocoileus_virginianus 0.2112 0.0620 0.0917 0.2102 0.3329
## week-Canis_latrans 0.0674 0.1328 -0.2063 0.0717 0.3189
## week-Sciurus_niger -0.3247 0.3181 -1.0607 -0.2853 0.1935
## week-Procyon_lotor -0.0516 0.1197 -0.2930 -0.0488 0.1681
## week-Dasypus_novemcinctus -0.1680 0.1381 -0.4579 -0.1633 0.0902
## week-Lynx_rufus -0.0398 0.1935 -0.4368 -0.0315 0.3109
## week-Didelphis_virginiana -0.2178 0.2174 -0.6852 -0.2009 0.1538
## week-Sylvilagus_floridanus -0.1603 0.2086 -0.6011 -0.1459 0.2104
## week-Meleagris_gallopavo -0.2938 0.2513 -0.8564 -0.2708 0.1266
## week-Sciurus_carolinensis 0.1326 0.1821 -0.2351 0.1348 0.4907
## week-Vulpes_vulpes -0.1338 0.2782 -0.7229 -0.1183 0.3631
## week-Sus_scrofa 0.0857 0.2349 -0.3961 0.0891 0.5402
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5004
## (Intercept)-Canis_latrans 1.0137 1618
## (Intercept)-Sciurus_niger 1.0061 550
## (Intercept)-Procyon_lotor 1.0011 4065
## (Intercept)-Dasypus_novemcinctus 1.0018 1333
## (Intercept)-Lynx_rufus 1.0130 800
## (Intercept)-Didelphis_virginiana 1.0113 1303
## (Intercept)-Sylvilagus_floridanus 1.0231 1591
## (Intercept)-Meleagris_gallopavo 1.0032 628
## (Intercept)-Sciurus_carolinensis 1.0521 766
## (Intercept)-Vulpes_vulpes 1.0548 418
## (Intercept)-Sus_scrofa 1.0198 731
## shrub_cover-Odocoileus_virginianus 1.0028 5250
## shrub_cover-Canis_latrans 1.0090 1543
## shrub_cover-Sciurus_niger 1.0022 885
## shrub_cover-Procyon_lotor 1.0041 3993
## shrub_cover-Dasypus_novemcinctus 1.0062 821
## shrub_cover-Lynx_rufus 1.0044 889
## shrub_cover-Didelphis_virginiana 1.0078 1009
## shrub_cover-Sylvilagus_floridanus 1.0281 1018
## shrub_cover-Meleagris_gallopavo 1.0028 596
## shrub_cover-Sciurus_carolinensis 1.0530 679
## shrub_cover-Vulpes_vulpes 1.0076 794
## shrub_cover-Sus_scrofa 1.0110 439
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0055 1775
## veg_height-Sciurus_niger 1.0067 1360
## veg_height-Procyon_lotor 1.0067 3900
## veg_height-Dasypus_novemcinctus 1.0015 3900
## veg_height-Lynx_rufus 1.0027 2269
## veg_height-Didelphis_virginiana 1.0048 2431
## veg_height-Sylvilagus_floridanus 1.0239 1552
## veg_height-Meleagris_gallopavo 1.0012 1241
## veg_height-Sciurus_carolinensis 1.0161 2248
## veg_height-Vulpes_vulpes 1.0045 1645
## veg_height-Sus_scrofa 1.0009 2424
## week-Odocoileus_virginianus 1.0012 5250
## week-Canis_latrans 1.0014 4106
## week-Sciurus_niger 1.0011 1536
## week-Procyon_lotor 1.0012 4291
## week-Dasypus_novemcinctus 1.0004 4723
## week-Lynx_rufus 1.0070 2571
## week-Didelphis_virginiana 1.0006 3042
## week-Sylvilagus_floridanus 1.0016 2258
## week-Meleagris_gallopavo 1.0024 1950
## week-Sciurus_carolinensis 1.0006 3856
## week-Vulpes_vulpes 1.0009 2457
## week-Sus_scrofa 1.0019 3408
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.177
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0444 0.6861 -1.3437 -0.0735 1.4121 1.0088 1580
## Tree_Density -0.7888 0.4046 -1.6825 -0.7573 -0.0594 1.0052 1155
## Avg_Canopy_Cover 1.1757 0.3826 0.4982 1.1545 1.9853 1.0037 868
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.8583 4.7614 1.2275 4.5380 18.1010 1.0145 816
## Tree_Density 0.7147 1.2719 0.0424 0.3332 3.7853 1.0608 610
## Avg_Canopy_Cover 0.7809 0.8546 0.0760 0.5280 2.8403 1.0067 1072
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5589 0.6624 0.0446 0.3268 2.3729 1.0124 422
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7690 0.4363 -3.5938 -2.7763 -1.8658 1.0034 4869
## shrub_cover 0.1181 0.2575 -0.3941 0.1147 0.6354 1.0041 2913
## veg_height -0.0187 0.1528 -0.3213 -0.0146 0.2875 1.0005 2689
## week -0.0710 0.1161 -0.3191 -0.0663 0.1450 1.0048 2662
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2628 1.3513 0.8370 1.9412 5.6638 1.0108 2414
## shrub_cover 0.6154 0.4498 0.1525 0.5048 1.7166 1.0038 2209
## veg_height 0.1949 0.1313 0.0565 0.1597 0.5405 1.0016 3442
## week 0.1015 0.0774 0.0274 0.0807 0.2995 1.0002 2328
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6361 1.7463 2.0397 4.3704 8.8130
## (Intercept)-Canis_latrans 0.4427 0.6579 -0.7759 0.4279 1.8208
## (Intercept)-Sciurus_niger 0.3180 1.6295 -2.1077 0.0608 4.2314
## (Intercept)-Procyon_lotor 0.8083 0.6563 -0.4872 0.7973 2.1502
## (Intercept)-Dasypus_novemcinctus -0.9201 0.6451 -2.2584 -0.8971 0.2821
## (Intercept)-Lynx_rufus 1.5998 1.7292 -0.9493 1.2935 5.9486
## (Intercept)-Didelphis_virginiana -1.6898 0.7611 -3.2564 -1.6742 -0.2431
## (Intercept)-Sylvilagus_floridanus -0.5912 0.7331 -2.0313 -0.5972 0.8872
## (Intercept)-Meleagris_gallopavo 0.5204 1.1983 -1.4644 0.3870 3.2044
## (Intercept)-Sciurus_carolinensis -1.7911 0.7840 -3.3829 -1.7635 -0.3198
## (Intercept)-Vulpes_vulpes -1.0787 1.5764 -3.6749 -1.2663 2.8258
## (Intercept)-Sus_scrofa -2.4943 1.0247 -4.6750 -2.4418 -0.6376
## Tree_Density-Odocoileus_virginianus -0.4138 0.6750 -1.5448 -0.4813 1.1970
## Tree_Density-Canis_latrans -0.9341 0.5572 -2.2246 -0.8761 -0.0432
## Tree_Density-Sciurus_niger -0.8125 0.8099 -2.5731 -0.7675 0.7506
## Tree_Density-Procyon_lotor -0.5215 0.4153 -1.3477 -0.5150 0.3073
## Tree_Density-Dasypus_novemcinctus -1.3430 0.8758 -3.6278 -1.1565 -0.1839
## Tree_Density-Lynx_rufus -0.1117 0.8126 -1.3620 -0.2246 1.9089
## Tree_Density-Didelphis_virginiana -0.9974 0.7298 -2.7801 -0.8911 0.1138
## Tree_Density-Sylvilagus_floridanus -1.0304 0.7252 -2.8051 -0.9304 0.1123
## Tree_Density-Meleagris_gallopavo -0.9702 0.7895 -2.7483 -0.8953 0.3372
## Tree_Density-Sciurus_carolinensis -0.9212 0.7134 -2.6171 -0.8309 0.2268
## Tree_Density-Vulpes_vulpes -0.7138 0.8819 -2.3745 -0.6985 0.8771
## Tree_Density-Sus_scrofa -0.9762 0.8452 -3.0448 -0.8472 0.2742
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8773 0.7619 -0.6390 0.8767 2.4409
## Avg_Canopy_Cover-Canis_latrans 0.0307 0.5036 -0.9443 0.0367 1.0132
## Avg_Canopy_Cover-Sciurus_niger 1.1347 0.9389 -0.5957 1.0664 3.2477
## Avg_Canopy_Cover-Procyon_lotor 1.1088 0.4925 0.2141 1.0856 2.1686
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1075 0.4572 0.2596 1.0776 2.0639
## Avg_Canopy_Cover-Lynx_rufus 1.1123 0.8525 -0.4423 1.0559 2.9501
## Avg_Canopy_Cover-Didelphis_virginiana 1.5102 0.6181 0.5158 1.4352 2.9792
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9726 0.8796 0.7026 1.8211 4.0944
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4986 0.7700 0.1698 1.4051 3.2596
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4647 0.5994 0.5073 1.3882 2.8403
## Avg_Canopy_Cover-Vulpes_vulpes 1.2058 0.6903 0.0110 1.1474 2.8157
## Avg_Canopy_Cover-Sus_scrofa 1.3666 0.6066 0.3510 1.3108 2.7436
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0102 724
## (Intercept)-Canis_latrans 1.0006 2959
## (Intercept)-Sciurus_niger 1.0022 423
## (Intercept)-Procyon_lotor 1.0000 2977
## (Intercept)-Dasypus_novemcinctus 1.0033 2629
## (Intercept)-Lynx_rufus 1.0062 379
## (Intercept)-Didelphis_virginiana 1.0057 2581
## (Intercept)-Sylvilagus_floridanus 1.0017 2555
## (Intercept)-Meleagris_gallopavo 1.0072 818
## (Intercept)-Sciurus_carolinensis 1.0041 2250
## (Intercept)-Vulpes_vulpes 1.0923 363
## (Intercept)-Sus_scrofa 1.0120 1266
## Tree_Density-Odocoileus_virginianus 1.0036 1614
## Tree_Density-Canis_latrans 1.0082 2377
## Tree_Density-Sciurus_niger 1.0094 1414
## Tree_Density-Procyon_lotor 1.0052 2998
## Tree_Density-Dasypus_novemcinctus 1.0199 908
## Tree_Density-Lynx_rufus 1.0036 850
## Tree_Density-Didelphis_virginiana 1.0065 1627
## Tree_Density-Sylvilagus_floridanus 1.0100 1356
## Tree_Density-Meleagris_gallopavo 1.0163 1380
## Tree_Density-Sciurus_carolinensis 1.0099 1744
## Tree_Density-Vulpes_vulpes 1.0085 1021
## Tree_Density-Sus_scrofa 1.0109 1392
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0035 2064
## Avg_Canopy_Cover-Canis_latrans 1.0028 2520
## Avg_Canopy_Cover-Sciurus_niger 1.0065 1055
## Avg_Canopy_Cover-Procyon_lotor 1.0006 3147
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0007 3384
## Avg_Canopy_Cover-Lynx_rufus 1.0033 1291
## Avg_Canopy_Cover-Didelphis_virginiana 1.0014 2022
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0115 927
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0065 1194
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0035 1562
## Avg_Canopy_Cover-Vulpes_vulpes 1.0009 1748
## Avg_Canopy_Cover-Sus_scrofa 1.0019 1394
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0062 0.0592 -0.1075 0.0057 0.1217
## (Intercept)-Canis_latrans -2.7818 0.1950 -3.1757 -2.7745 -2.4189
## (Intercept)-Sciurus_niger -4.5532 0.6122 -5.7381 -4.5673 -3.3313
## (Intercept)-Procyon_lotor -2.3020 0.1448 -2.6047 -2.2966 -2.0285
## (Intercept)-Dasypus_novemcinctus -1.7397 0.1591 -2.0617 -1.7376 -1.4425
## (Intercept)-Lynx_rufus -3.9971 0.3532 -4.6762 -4.0014 -3.2861
## (Intercept)-Didelphis_virginiana -2.5873 0.2921 -3.1909 -2.5746 -2.0604
## (Intercept)-Sylvilagus_floridanus -3.1566 0.2795 -3.7499 -3.1455 -2.6491
## (Intercept)-Meleagris_gallopavo -4.1993 0.4469 -5.0780 -4.1961 -3.3518
## (Intercept)-Sciurus_carolinensis -2.6528 0.3201 -3.3080 -2.6419 -2.0596
## (Intercept)-Vulpes_vulpes -4.4093 0.7942 -6.0337 -4.3654 -2.9774
## (Intercept)-Sus_scrofa -3.2178 0.6061 -4.4069 -3.2053 -2.0209
## shrub_cover-Odocoileus_virginianus -0.0566 0.0639 -0.1783 -0.0569 0.0675
## shrub_cover-Canis_latrans -0.3153 0.2232 -0.7449 -0.3151 0.1335
## shrub_cover-Sciurus_niger -0.4662 0.4472 -1.3723 -0.4604 0.4192
## shrub_cover-Procyon_lotor 0.2391 0.1650 -0.1020 0.2388 0.5531
## shrub_cover-Dasypus_novemcinctus 0.8316 0.2950 0.2635 0.8261 1.4199
## shrub_cover-Lynx_rufus -0.3843 0.3207 -1.0336 -0.3798 0.2359
## shrub_cover-Didelphis_virginiana 0.9423 0.3511 0.2907 0.9274 1.6641
## shrub_cover-Sylvilagus_floridanus 0.3580 0.3806 -0.3892 0.3590 1.1042
## shrub_cover-Meleagris_gallopavo -0.8312 0.3862 -1.6105 -0.8214 -0.1115
## shrub_cover-Sciurus_carolinensis 0.8293 0.3976 0.0818 0.8183 1.6393
## shrub_cover-Vulpes_vulpes -0.1562 0.5681 -1.3272 -0.1396 0.9402
## shrub_cover-Sus_scrofa 0.4810 0.7824 -1.0702 0.4652 2.0621
## veg_height-Odocoileus_virginianus -0.2980 0.0646 -0.4265 -0.2984 -0.1710
## veg_height-Canis_latrans -0.5973 0.1853 -0.9740 -0.5903 -0.2484
## veg_height-Sciurus_niger -0.1041 0.3548 -0.8199 -0.0954 0.5888
## veg_height-Procyon_lotor 0.3325 0.1235 0.0926 0.3327 0.5823
## veg_height-Dasypus_novemcinctus 0.2355 0.1333 -0.0262 0.2361 0.4942
## veg_height-Lynx_rufus 0.0693 0.2339 -0.3957 0.0720 0.5121
## veg_height-Didelphis_virginiana 0.4372 0.2406 0.0039 0.4261 0.9374
## veg_height-Sylvilagus_floridanus 0.1392 0.2362 -0.3111 0.1382 0.6040
## veg_height-Meleagris_gallopavo -0.2519 0.3278 -0.9321 -0.2385 0.3810
## veg_height-Sciurus_carolinensis 0.0855 0.2106 -0.3055 0.0775 0.5117
## veg_height-Vulpes_vulpes -0.1523 0.3202 -0.8391 -0.1380 0.4535
## veg_height-Sus_scrofa -0.1238 0.3228 -0.8102 -0.1153 0.4854
## week-Odocoileus_virginianus 0.2116 0.0603 0.0943 0.2110 0.3302
## week-Canis_latrans 0.0654 0.1333 -0.2039 0.0674 0.3188
## week-Sciurus_niger -0.3160 0.2978 -0.9812 -0.2831 0.1887
## week-Procyon_lotor -0.0516 0.1188 -0.2950 -0.0479 0.1741
## week-Dasypus_novemcinctus -0.1657 0.1379 -0.4480 -0.1601 0.0921
## week-Lynx_rufus -0.0413 0.1917 -0.4290 -0.0355 0.3170
## week-Didelphis_virginiana -0.2150 0.2155 -0.6791 -0.2029 0.1688
## week-Sylvilagus_floridanus -0.1578 0.2056 -0.5902 -0.1487 0.2176
## week-Meleagris_gallopavo -0.2740 0.2442 -0.7971 -0.2545 0.1535
## week-Sciurus_carolinensis 0.1379 0.1790 -0.2123 0.1413 0.4954
## week-Vulpes_vulpes -0.1254 0.2760 -0.7075 -0.1075 0.3650
## week-Sus_scrofa 0.0894 0.2364 -0.3771 0.0865 0.5579
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0018 5250
## (Intercept)-Canis_latrans 1.0011 2212
## (Intercept)-Sciurus_niger 1.0067 411
## (Intercept)-Procyon_lotor 1.0101 3203
## (Intercept)-Dasypus_novemcinctus 1.0016 3974
## (Intercept)-Lynx_rufus 1.0078 698
## (Intercept)-Didelphis_virginiana 1.0003 2318
## (Intercept)-Sylvilagus_floridanus 1.0157 2218
## (Intercept)-Meleagris_gallopavo 1.0307 674
## (Intercept)-Sciurus_carolinensis 1.0016 2264
## (Intercept)-Vulpes_vulpes 1.0594 355
## (Intercept)-Sus_scrofa 1.0070 1991
## shrub_cover-Odocoileus_virginianus 1.0024 5250
## shrub_cover-Canis_latrans 1.0005 2318
## shrub_cover-Sciurus_niger 1.0030 1166
## shrub_cover-Procyon_lotor 1.0002 4359
## shrub_cover-Dasypus_novemcinctus 0.9999 3229
## shrub_cover-Lynx_rufus 1.0140 1431
## shrub_cover-Didelphis_virginiana 1.0039 2257
## shrub_cover-Sylvilagus_floridanus 1.0020 1973
## shrub_cover-Meleagris_gallopavo 1.0150 880
## shrub_cover-Sciurus_carolinensis 1.0029 2144
## shrub_cover-Vulpes_vulpes 1.0027 1631
## shrub_cover-Sus_scrofa 1.0030 2575
## veg_height-Odocoileus_virginianus 0.9998 5250
## veg_height-Canis_latrans 1.0064 2264
## veg_height-Sciurus_niger 1.0053 1819
## veg_height-Procyon_lotor 1.0036 4059
## veg_height-Dasypus_novemcinctus 1.0026 4752
## veg_height-Lynx_rufus 1.0009 2062
## veg_height-Didelphis_virginiana 1.0018 3269
## veg_height-Sylvilagus_floridanus 1.0013 3262
## veg_height-Meleagris_gallopavo 1.0010 1625
## veg_height-Sciurus_carolinensis 1.0004 3225
## veg_height-Vulpes_vulpes 1.0007 1964
## veg_height-Sus_scrofa 1.0011 4067
## week-Odocoileus_virginianus 1.0021 5250
## week-Canis_latrans 1.0031 4281
## week-Sciurus_niger 0.9998 1536
## week-Procyon_lotor 1.0003 4406
## week-Dasypus_novemcinctus 1.0059 4744
## week-Lynx_rufus 1.0031 2516
## week-Didelphis_virginiana 1.0005 3852
## week-Sylvilagus_floridanus 1.0082 3313
## week-Meleagris_gallopavo 1.0006 2048
## week-Sciurus_carolinensis 1.0006 4762
## week-Vulpes_vulpes 1.0036 3168
## week-Sus_scrofa 1.0020 4489
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1737
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0422 0.6039 -1.2223 -0.0479 1.2068 1.0025 999
## Cogon_Patch_Size -0.1519 0.3804 -0.9629 -0.1368 0.5657 1.0299 1437
## Avg_Cogongrass_Cover 0.1108 0.3261 -0.5293 0.1124 0.7509 1.0105 1146
## total_shrub_cover -0.7223 0.4169 -1.6526 -0.6897 0.0206 1.0104 685
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3671 2.9987 0.4343 2.5860 11.4053 1.0192 907
## Cogon_Patch_Size 0.7294 0.9054 0.0575 0.4432 3.3037 1.0038 1257
## Avg_Cogongrass_Cover 0.4090 0.5249 0.0403 0.2376 1.8417 1.0042 1510
## total_shrub_cover 0.7393 0.9219 0.0547 0.4506 3.0704 1.0298 570
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8653 1.849 0.1222 1.3465 6.5865 1.0173 377
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7759 0.4178 -3.6077 -2.7796 -1.9374 1.0013 3409
## shrub_cover 0.3337 0.2779 -0.2016 0.3293 0.8931 1.0015 1603
## veg_height -0.0368 0.1578 -0.3399 -0.0352 0.2825 1.0045 2150
## week -0.0754 0.1234 -0.3421 -0.0692 0.1471 1.0040 2366
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0217 1.1666 0.6996 1.7476 4.9081 1.0006 1728
## shrub_cover 0.6648 0.5182 0.1426 0.5293 2.0089 1.0030 1200
## veg_height 0.1929 0.1281 0.0560 0.1592 0.5263 1.0044 3060
## week 0.1060 0.0833 0.0262 0.0834 0.3190 1.0029 2139
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4565 1.6421 0.6031 3.3112
## (Intercept)-Canis_latrans 0.6030 0.7917 -0.8985 0.5685
## (Intercept)-Sciurus_niger -0.4773 1.2931 -2.6890 -0.5902
## (Intercept)-Procyon_lotor 0.7389 0.8522 -0.9011 0.7245
## (Intercept)-Dasypus_novemcinctus -0.5308 0.7668 -2.0478 -0.5335
## (Intercept)-Lynx_rufus 0.0523 1.1332 -1.8403 -0.0275
## (Intercept)-Didelphis_virginiana -1.0022 0.8633 -2.6699 -1.0175
## (Intercept)-Sylvilagus_floridanus 0.0691 0.9441 -1.6749 0.0174
## (Intercept)-Meleagris_gallopavo -0.0020 1.2910 -2.1276 -0.1266
## (Intercept)-Sciurus_carolinensis -1.1203 0.9022 -2.9921 -1.1025
## (Intercept)-Vulpes_vulpes -0.6345 1.4607 -3.1102 -0.7541
## (Intercept)-Sus_scrofa -1.4527 1.1416 -3.7565 -1.4289
## Cogon_Patch_Size-Odocoileus_virginianus -0.0276 0.6733 -1.2603 -0.0609
## Cogon_Patch_Size-Canis_latrans 0.6205 0.6875 -0.4046 0.4975
## Cogon_Patch_Size-Sciurus_niger -0.4880 0.8708 -2.5704 -0.3924
## Cogon_Patch_Size-Procyon_lotor -0.2307 0.4692 -1.1982 -0.2101
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0515 0.4480 -0.9562 -0.0451
## Cogon_Patch_Size-Lynx_rufus -0.1381 0.7410 -1.5975 -0.1530
## Cogon_Patch_Size-Didelphis_virginiana 0.5508 0.5088 -0.3527 0.5174
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7091 0.8041 -2.6362 -0.5812
## Cogon_Patch_Size-Meleagris_gallopavo -0.0376 0.6922 -1.3904 -0.0408
## Cogon_Patch_Size-Sciurus_carolinensis -0.6221 0.6866 -2.2714 -0.5070
## Cogon_Patch_Size-Vulpes_vulpes -0.4126 0.8376 -2.3339 -0.3320
## Cogon_Patch_Size-Sus_scrofa -0.3611 0.7908 -2.2527 -0.2567
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0949 0.5810 -1.0640 0.0913
## Avg_Cogongrass_Cover-Canis_latrans 0.3319 0.4476 -0.4879 0.3014
## Avg_Cogongrass_Cover-Sciurus_niger -0.2336 0.7115 -1.8889 -0.1669
## Avg_Cogongrass_Cover-Procyon_lotor 0.1015 0.4656 -0.8263 0.0982
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3418 0.4192 -0.4366 0.3225
## Avg_Cogongrass_Cover-Lynx_rufus 0.4727 0.5459 -0.4707 0.4159
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1451 0.4586 -0.7773 0.1473
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1715 0.5609 -1.3651 -0.1425
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2728 0.7421 -1.9750 -0.2039
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3671 0.4600 -0.5048 0.3461
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2714 0.5334 -0.7311 0.2528
## Avg_Cogongrass_Cover-Sus_scrofa -0.1127 0.6607 -1.6255 -0.0478
## total_shrub_cover-Odocoileus_virginianus -0.4001 0.6701 -1.7181 -0.4230
## total_shrub_cover-Canis_latrans 0.0817 0.6401 -0.9939 0.0204
## total_shrub_cover-Sciurus_niger -0.8234 0.7824 -2.5604 -0.7642
## total_shrub_cover-Procyon_lotor -1.1802 0.6275 -2.6738 -1.0869
## total_shrub_cover-Dasypus_novemcinctus -0.3765 0.5444 -1.5847 -0.3321
## total_shrub_cover-Lynx_rufus -1.1035 0.8309 -3.0060 -1.0007
## total_shrub_cover-Didelphis_virginiana -0.7784 0.6174 -2.2125 -0.6987
## total_shrub_cover-Sylvilagus_floridanus -1.1350 0.8505 -3.1060 -0.9979
## total_shrub_cover-Meleagris_gallopavo -1.2266 0.8138 -3.0937 -1.1252
## total_shrub_cover-Sciurus_carolinensis -0.6441 0.6675 -2.1699 -0.5797
## total_shrub_cover-Vulpes_vulpes -0.8104 0.8960 -2.7753 -0.7396
## total_shrub_cover-Sus_scrofa -0.5122 0.7977 -2.1601 -0.4834
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1831 1.0043 827
## (Intercept)-Canis_latrans 2.2721 1.0020 1983
## (Intercept)-Sciurus_niger 2.3862 1.0076 626
## (Intercept)-Procyon_lotor 2.4852 1.0012 1876
## (Intercept)-Dasypus_novemcinctus 1.0237 1.0012 1700
## (Intercept)-Lynx_rufus 2.5175 1.0168 641
## (Intercept)-Didelphis_virginiana 0.7698 1.0012 1433
## (Intercept)-Sylvilagus_floridanus 2.0453 1.0113 1368
## (Intercept)-Meleagris_gallopavo 2.9401 1.0088 578
## (Intercept)-Sciurus_carolinensis 0.6417 1.0007 1126
## (Intercept)-Vulpes_vulpes 2.4722 1.0098 404
## (Intercept)-Sus_scrofa 0.6803 1.0063 907
## Cogon_Patch_Size-Odocoileus_virginianus 1.5071 1.0151 2984
## Cogon_Patch_Size-Canis_latrans 2.3165 1.0056 2132
## Cogon_Patch_Size-Sciurus_niger 0.9500 1.0169 1644
## Cogon_Patch_Size-Procyon_lotor 0.6290 1.0192 2267
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8351 1.0056 3210
## Cogon_Patch_Size-Lynx_rufus 1.4185 1.0092 1843
## Cogon_Patch_Size-Didelphis_virginiana 1.6207 1.0044 2457
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4977 1.0122 1330
## Cogon_Patch_Size-Meleagris_gallopavo 1.4108 1.0039 1890
## Cogon_Patch_Size-Sciurus_carolinensis 0.4593 1.0010 1723
## Cogon_Patch_Size-Vulpes_vulpes 1.0967 1.0078 1723
## Cogon_Patch_Size-Sus_scrofa 0.9451 1.0081 1798
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3070 1.0117 2813
## Avg_Cogongrass_Cover-Canis_latrans 1.3244 1.0037 2628
## Avg_Cogongrass_Cover-Sciurus_niger 0.9850 1.0034 1396
## Avg_Cogongrass_Cover-Procyon_lotor 1.0206 1.0171 2376
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2447 1.0056 3174
## Avg_Cogongrass_Cover-Lynx_rufus 1.7170 1.0050 2847
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0725 1.0018 2710
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8607 1.0028 1683
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0295 1.0053 1156
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3195 1.0021 2748
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4008 1.0082 2541
## Avg_Cogongrass_Cover-Sus_scrofa 1.0266 1.0113 1459
## total_shrub_cover-Odocoileus_virginianus 1.0030 1.0008 2363
## total_shrub_cover-Canis_latrans 1.5734 1.0007 1061
## total_shrub_cover-Sciurus_niger 0.6107 1.0151 1050
## total_shrub_cover-Procyon_lotor -0.2040 1.0003 899
## total_shrub_cover-Dasypus_novemcinctus 0.5567 1.0073 1452
## total_shrub_cover-Lynx_rufus 0.3199 1.0013 865
## total_shrub_cover-Didelphis_virginiana 0.2486 1.0005 1080
## total_shrub_cover-Sylvilagus_floridanus 0.1849 1.0105 741
## total_shrub_cover-Meleagris_gallopavo 0.1291 1.0172 888
## total_shrub_cover-Sciurus_carolinensis 0.4977 1.0002 1121
## total_shrub_cover-Vulpes_vulpes 0.8487 1.0151 792
## total_shrub_cover-Sus_scrofa 1.0480 1.0021 992
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0596 -0.1075 0.0040 0.1219
## (Intercept)-Canis_latrans -2.7709 0.1960 -3.1832 -2.7627 -2.4100
## (Intercept)-Sciurus_niger -4.1740 0.6470 -5.4048 -4.1807 -2.9318
## (Intercept)-Procyon_lotor -2.3071 0.1430 -2.5914 -2.3050 -2.0359
## (Intercept)-Dasypus_novemcinctus -1.7798 0.1740 -2.1303 -1.7755 -1.4445
## (Intercept)-Lynx_rufus -3.6142 0.3558 -4.3440 -3.5989 -2.9393
## (Intercept)-Didelphis_virginiana -2.6476 0.3095 -3.3015 -2.6335 -2.0809
## (Intercept)-Sylvilagus_floridanus -3.3362 0.2891 -3.9301 -3.3262 -2.7894
## (Intercept)-Meleagris_gallopavo -3.9724 0.5615 -5.0593 -3.9838 -2.8882
## (Intercept)-Sciurus_carolinensis -2.7720 0.3503 -3.5030 -2.7626 -2.1300
## (Intercept)-Vulpes_vulpes -4.4214 0.7697 -5.9725 -4.4049 -2.9829
## (Intercept)-Sus_scrofa -3.5379 0.6275 -4.7596 -3.5516 -2.2841
## shrub_cover-Odocoileus_virginianus -0.0550 0.0639 -0.1810 -0.0554 0.0670
## shrub_cover-Canis_latrans -0.2948 0.2397 -0.7635 -0.2922 0.1627
## shrub_cover-Sciurus_niger -0.1854 0.5235 -1.2063 -0.1816 0.8465
## shrub_cover-Procyon_lotor 0.3085 0.1593 -0.0144 0.3069 0.6177
## shrub_cover-Dasypus_novemcinctus 0.9636 0.3392 0.3496 0.9457 1.6520
## shrub_cover-Lynx_rufus 0.0167 0.3799 -0.7965 0.0331 0.7148
## shrub_cover-Didelphis_virginiana 1.0844 0.3959 0.3666 1.0633 1.8881
## shrub_cover-Sylvilagus_floridanus 0.7042 0.4163 -0.1425 0.7183 1.5044
## shrub_cover-Meleagris_gallopavo -0.5900 0.4814 -1.5285 -0.5833 0.3509
## shrub_cover-Sciurus_carolinensis 1.0518 0.4393 0.1904 1.0516 1.9140
## shrub_cover-Vulpes_vulpes 0.1577 0.6328 -1.1058 0.1559 1.4117
## shrub_cover-Sus_scrofa 0.9075 0.8334 -0.6924 0.8933 2.6117
## veg_height-Odocoileus_virginianus -0.2978 0.0637 -0.4220 -0.2981 -0.1731
## veg_height-Canis_latrans -0.5850 0.1845 -0.9587 -0.5781 -0.2424
## veg_height-Sciurus_niger -0.0562 0.3956 -0.8228 -0.0668 0.7877
## veg_height-Procyon_lotor 0.3344 0.1236 0.0919 0.3352 0.5766
## veg_height-Dasypus_novemcinctus 0.2402 0.1368 -0.0232 0.2383 0.5171
## veg_height-Lynx_rufus 0.0148 0.2376 -0.4711 0.0183 0.4666
## veg_height-Didelphis_virginiana 0.4004 0.2405 -0.0508 0.3922 0.8928
## veg_height-Sylvilagus_floridanus 0.0327 0.2456 -0.4434 0.0306 0.5190
## veg_height-Meleagris_gallopavo -0.2778 0.3720 -1.0206 -0.2770 0.4583
## veg_height-Sciurus_carolinensis 0.0908 0.2209 -0.3367 0.0832 0.5413
## veg_height-Vulpes_vulpes -0.1515 0.3246 -0.8318 -0.1437 0.4506
## veg_height-Sus_scrofa -0.1714 0.3270 -0.8491 -0.1664 0.4489
## week-Odocoileus_virginianus 0.2103 0.0605 0.0910 0.2102 0.3311
## week-Canis_latrans 0.0695 0.1308 -0.1966 0.0728 0.3145
## week-Sciurus_niger -0.3315 0.3075 -1.0646 -0.2922 0.1740
## week-Procyon_lotor -0.0521 0.1187 -0.3042 -0.0472 0.1660
## week-Dasypus_novemcinctus -0.1703 0.1370 -0.4449 -0.1675 0.0917
## week-Lynx_rufus -0.0355 0.1932 -0.4435 -0.0230 0.3207
## week-Didelphis_virginiana -0.2209 0.2188 -0.7080 -0.2086 0.1588
## week-Sylvilagus_floridanus -0.1532 0.2072 -0.5863 -0.1422 0.2317
## week-Meleagris_gallopavo -0.2894 0.2572 -0.8589 -0.2668 0.1522
## week-Sciurus_carolinensis 0.1360 0.1815 -0.2201 0.1366 0.4962
## week-Vulpes_vulpes -0.1336 0.2756 -0.7126 -0.1194 0.3636
## week-Sus_scrofa 0.0840 0.2408 -0.4015 0.0868 0.5521
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5529
## (Intercept)-Canis_latrans 1.0047 2133
## (Intercept)-Sciurus_niger 1.0177 573
## (Intercept)-Procyon_lotor 1.0025 3181
## (Intercept)-Dasypus_novemcinctus 1.0064 2149
## (Intercept)-Lynx_rufus 1.0079 959
## (Intercept)-Didelphis_virginiana 1.0014 1186
## (Intercept)-Sylvilagus_floridanus 1.0042 1436
## (Intercept)-Meleagris_gallopavo 1.0014 646
## (Intercept)-Sciurus_carolinensis 1.0051 1217
## (Intercept)-Vulpes_vulpes 1.0110 348
## (Intercept)-Sus_scrofa 1.0091 839
## shrub_cover-Odocoileus_virginianus 1.0004 4838
## shrub_cover-Canis_latrans 1.0009 1553
## shrub_cover-Sciurus_niger 1.0138 1004
## shrub_cover-Procyon_lotor 1.0040 3528
## shrub_cover-Dasypus_novemcinctus 1.0073 1666
## shrub_cover-Lynx_rufus 1.0022 1018
## shrub_cover-Didelphis_virginiana 1.0084 1145
## shrub_cover-Sylvilagus_floridanus 1.0115 1048
## shrub_cover-Meleagris_gallopavo 1.0001 698
## shrub_cover-Sciurus_carolinensis 1.0019 1085
## shrub_cover-Vulpes_vulpes 1.0021 1025
## shrub_cover-Sus_scrofa 1.0166 776
## veg_height-Odocoileus_virginianus 1.0015 5250
## veg_height-Canis_latrans 1.0104 2368
## veg_height-Sciurus_niger 1.0050 1862
## veg_height-Procyon_lotor 1.0004 4040
## veg_height-Dasypus_novemcinctus 1.0038 4532
## veg_height-Lynx_rufus 1.0016 2390
## veg_height-Didelphis_virginiana 1.0006 3197
## veg_height-Sylvilagus_floridanus 1.0027 2085
## veg_height-Meleagris_gallopavo 1.0020 1105
## veg_height-Sciurus_carolinensis 1.0006 3003
## veg_height-Vulpes_vulpes 1.0059 1700
## veg_height-Sus_scrofa 1.0108 2980
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0104 4290
## week-Sciurus_niger 1.0051 1633
## week-Procyon_lotor 1.0015 4113
## week-Dasypus_novemcinctus 1.0005 4553
## week-Lynx_rufus 1.0002 2882
## week-Didelphis_virginiana 1.0112 3413
## week-Sylvilagus_floridanus 1.0004 2299
## week-Meleagris_gallopavo 1.0036 1817
## week-Sciurus_carolinensis 1.0010 4226
## week-Vulpes_vulpes 1.0052 2300
## week-Sus_scrofa 1.0002 3897
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1532
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0617 0.5905 -1.1475 -0.0879 1.1510 1.0014 1424
## Veg_shannon_index 0.3990 0.2644 -0.0965 0.3922 0.9278 1.0049 1905
## Avg_Cogongrass_Cover 0.2583 0.2845 -0.3195 0.2656 0.7983 1.0041 1435
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5774 2.9917 0.6474 2.7871 11.3401 1.0111 1111
## Veg_shannon_index 0.2877 0.3395 0.0363 0.1868 1.1130 1.0108 1898
## Avg_Cogongrass_Cover 0.3623 0.4439 0.0403 0.2221 1.5408 1.0211 1443
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8522 0.9681 0.0643 0.5474 3.4119 1.0045 401
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7557 0.4425 -3.5996 -2.7662 -1.8516 1.0012 4272
## shrub_cover 0.0843 0.2601 -0.4370 0.0835 0.6066 1.0016 3286
## veg_height -0.0464 0.1564 -0.3587 -0.0438 0.2611 1.0017 2789
## week -0.0737 0.1187 -0.3225 -0.0684 0.1403 1.0029 2669
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2355 1.3657 0.8109 1.8922 5.6350 1.0230 1905
## shrub_cover 0.6130 0.4305 0.1451 0.5028 1.7351 1.0161 1655
## veg_height 0.1876 0.1180 0.0543 0.1582 0.5008 1.0031 3205
## week 0.1036 0.0847 0.0265 0.0800 0.3237 1.0063 2017
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5525 1.4343 1.0338 3.4321
## (Intercept)-Canis_latrans 0.3933 0.6533 -0.8544 0.3936
## (Intercept)-Sciurus_niger -0.1099 1.3073 -2.1955 -0.2833
## (Intercept)-Procyon_lotor 0.5623 0.6470 -0.7884 0.5844
## (Intercept)-Dasypus_novemcinctus -0.6253 0.5875 -1.8237 -0.6209
## (Intercept)-Lynx_rufus 0.4285 1.2316 -1.4817 0.2607
## (Intercept)-Didelphis_virginiana -1.3082 0.6681 -2.6515 -1.3009
## (Intercept)-Sylvilagus_floridanus -0.3199 0.7566 -1.7091 -0.3583
## (Intercept)-Meleagris_gallopavo 0.7176 1.2832 -1.3210 0.5446
## (Intercept)-Sciurus_carolinensis -1.3127 0.6874 -2.7405 -1.2929
## (Intercept)-Vulpes_vulpes -0.7238 1.4233 -2.9176 -0.9444
## (Intercept)-Sus_scrofa -1.9592 0.9102 -3.8404 -1.9333
## Veg_shannon_index-Odocoileus_virginianus 0.3272 0.4950 -0.7012 0.3300
## Veg_shannon_index-Canis_latrans 0.6582 0.3974 -0.0534 0.6274
## Veg_shannon_index-Sciurus_niger 0.4280 0.5441 -0.6159 0.4109
## Veg_shannon_index-Procyon_lotor 0.4716 0.3684 -0.2138 0.4588
## Veg_shannon_index-Dasypus_novemcinctus 0.2173 0.3495 -0.5017 0.2298
## Veg_shannon_index-Lynx_rufus 0.2898 0.5156 -0.7977 0.3107
## Veg_shannon_index-Didelphis_virginiana 0.5357 0.3963 -0.1999 0.5215
## Veg_shannon_index-Sylvilagus_floridanus 0.4624 0.4341 -0.3643 0.4464
## Veg_shannon_index-Meleagris_gallopavo 0.5463 0.5295 -0.3732 0.5067
## Veg_shannon_index-Sciurus_carolinensis 0.0245 0.4049 -0.8568 0.0488
## Veg_shannon_index-Vulpes_vulpes 0.1697 0.4877 -0.8390 0.1846
## Veg_shannon_index-Sus_scrofa 0.7354 0.5291 -0.1680 0.6784
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2793 0.5243 -0.7818 0.2781
## Avg_Cogongrass_Cover-Canis_latrans 0.6171 0.4439 -0.1199 0.5692
## Avg_Cogongrass_Cover-Sciurus_niger -0.1002 0.6574 -1.6122 -0.0278
## Avg_Cogongrass_Cover-Procyon_lotor 0.3616 0.3920 -0.3620 0.3418
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4231 0.3554 -0.2559 0.4129
## Avg_Cogongrass_Cover-Lynx_rufus 0.5440 0.4774 -0.3063 0.5061
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4351 0.3860 -0.3102 0.4235
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1301 0.4766 -1.1751 -0.0927
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0363 0.6894 -1.6291 0.0266
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4060 0.3730 -0.2994 0.3976
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3855 0.5234 -0.5981 0.3620
## Avg_Cogongrass_Cover-Sus_scrofa -0.0320 0.5900 -1.4043 0.0299
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8307 1.0019 814
## (Intercept)-Canis_latrans 1.7121 1.0002 2809
## (Intercept)-Sciurus_niger 2.9665 1.0205 495
## (Intercept)-Procyon_lotor 1.8193 1.0064 2230
## (Intercept)-Dasypus_novemcinctus 0.5214 1.0051 3404
## (Intercept)-Lynx_rufus 3.4173 1.0273 603
## (Intercept)-Didelphis_virginiana 0.0446 1.0005 2853
## (Intercept)-Sylvilagus_floridanus 1.3093 0.9998 1863
## (Intercept)-Meleagris_gallopavo 3.6917 1.0183 646
## (Intercept)-Sciurus_carolinensis 0.0146 1.0010 2719
## (Intercept)-Vulpes_vulpes 2.6535 1.0018 364
## (Intercept)-Sus_scrofa -0.2306 1.0009 1833
## Veg_shannon_index-Odocoileus_virginianus 1.3381 1.0017 3519
## Veg_shannon_index-Canis_latrans 1.5368 1.0006 3052
## Veg_shannon_index-Sciurus_niger 1.5515 1.0010 2284
## Veg_shannon_index-Procyon_lotor 1.2196 1.0028 2985
## Veg_shannon_index-Dasypus_novemcinctus 0.8982 1.0006 4005
## Veg_shannon_index-Lynx_rufus 1.3198 1.0002 2530
## Veg_shannon_index-Didelphis_virginiana 1.3954 1.0039 3184
## Veg_shannon_index-Sylvilagus_floridanus 1.3637 1.0088 2468
## Veg_shannon_index-Meleagris_gallopavo 1.7051 1.0081 2438
## Veg_shannon_index-Sciurus_carolinensis 0.7377 1.0004 3464
## Veg_shannon_index-Vulpes_vulpes 1.1104 1.0032 2421
## Veg_shannon_index-Sus_scrofa 1.9331 1.0039 2121
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3949 0.9998 3170
## Avg_Cogongrass_Cover-Canis_latrans 1.6260 1.0006 3020
## Avg_Cogongrass_Cover-Sciurus_niger 0.9919 1.0165 1396
## Avg_Cogongrass_Cover-Procyon_lotor 1.1913 1.0003 3224
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1737 1.0008 4134
## Avg_Cogongrass_Cover-Lynx_rufus 1.5915 1.0008 2891
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2298 1.0041 3099
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7066 1.0035 1746
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1280 1.0112 1266
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1707 1.0004 2433
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4869 1.0032 2531
## Avg_Cogongrass_Cover-Sus_scrofa 0.9554 1.0020 1842
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0589 -0.1097 0.0063 0.1235
## (Intercept)-Canis_latrans -2.7449 0.1836 -3.1324 -2.7370 -2.4066
## (Intercept)-Sciurus_niger -4.3911 0.6491 -5.6422 -4.3911 -3.1156
## (Intercept)-Procyon_lotor -2.3081 0.1479 -2.6133 -2.3033 -2.0283
## (Intercept)-Dasypus_novemcinctus -1.7276 0.1553 -2.0436 -1.7228 -1.4385
## (Intercept)-Lynx_rufus -3.8080 0.3872 -4.5719 -3.8035 -3.0816
## (Intercept)-Didelphis_virginiana -2.5457 0.2877 -3.1460 -2.5351 -2.0123
## (Intercept)-Sylvilagus_floridanus -3.2547 0.3245 -3.9485 -3.2334 -2.6729
## (Intercept)-Meleagris_gallopavo -4.2889 0.4423 -5.1630 -4.2896 -3.4382
## (Intercept)-Sciurus_carolinensis -2.6032 0.3141 -3.2602 -2.5840 -2.0324
## (Intercept)-Vulpes_vulpes -4.4051 0.7928 -6.0356 -4.3737 -2.9870
## (Intercept)-Sus_scrofa -3.2465 0.6151 -4.4875 -3.2358 -2.0595
## shrub_cover-Odocoileus_virginianus -0.0563 0.0644 -0.1835 -0.0568 0.0675
## shrub_cover-Canis_latrans -0.3037 0.2160 -0.7142 -0.3033 0.1184
## shrub_cover-Sciurus_niger -0.5299 0.4588 -1.4725 -0.5093 0.3500
## shrub_cover-Procyon_lotor 0.2204 0.1721 -0.1274 0.2271 0.5434
## shrub_cover-Dasypus_novemcinctus 0.8047 0.2990 0.2455 0.8014 1.4034
## shrub_cover-Lynx_rufus -0.3739 0.3626 -1.1132 -0.3668 0.3346
## shrub_cover-Didelphis_virginiana 0.9046 0.3657 0.2555 0.8917 1.6709
## shrub_cover-Sylvilagus_floridanus 0.1881 0.4069 -0.5638 0.1709 1.0225
## shrub_cover-Meleagris_gallopavo -0.8911 0.3800 -1.6608 -0.8807 -0.1888
## shrub_cover-Sciurus_carolinensis 0.7862 0.4043 -0.0013 0.7770 1.6358
## shrub_cover-Vulpes_vulpes -0.2161 0.5527 -1.3696 -0.1997 0.8539
## shrub_cover-Sus_scrofa 0.4460 0.7992 -1.1166 0.4315 2.0663
## veg_height-Odocoileus_virginianus -0.2994 0.0648 -0.4254 -0.2999 -0.1712
## veg_height-Canis_latrans -0.5896 0.1822 -0.9645 -0.5836 -0.2580
## veg_height-Sciurus_niger -0.1179 0.3910 -0.8863 -0.1178 0.6490
## veg_height-Procyon_lotor 0.3247 0.1239 0.0848 0.3244 0.5719
## veg_height-Dasypus_novemcinctus 0.2233 0.1318 -0.0365 0.2220 0.4826
## veg_height-Lynx_rufus -0.0142 0.2435 -0.5237 -0.0070 0.4412
## veg_height-Didelphis_virginiana 0.3941 0.2396 -0.0543 0.3886 0.8767
## veg_height-Sylvilagus_floridanus 0.1225 0.2427 -0.3627 0.1238 0.6035
## veg_height-Meleagris_gallopavo -0.2994 0.3350 -0.9720 -0.2981 0.3450
## veg_height-Sciurus_carolinensis 0.0376 0.2085 -0.3631 0.0322 0.4441
## veg_height-Vulpes_vulpes -0.1732 0.3070 -0.8236 -0.1593 0.3910
## veg_height-Sus_scrofa -0.1405 0.3356 -0.8477 -0.1245 0.4750
## week-Odocoileus_virginianus 0.2092 0.0614 0.0888 0.2092 0.3260
## week-Canis_latrans 0.0670 0.1314 -0.2040 0.0703 0.3183
## week-Sciurus_niger -0.3179 0.2997 -1.0199 -0.2842 0.1779
## week-Procyon_lotor -0.0483 0.1175 -0.2860 -0.0449 0.1668
## week-Dasypus_novemcinctus -0.1671 0.1401 -0.4614 -0.1601 0.0951
## week-Lynx_rufus -0.0438 0.1919 -0.4439 -0.0345 0.3126
## week-Didelphis_virginiana -0.2155 0.2144 -0.6746 -0.2070 0.1676
## week-Sylvilagus_floridanus -0.1595 0.2065 -0.5873 -0.1497 0.2027
## week-Meleagris_gallopavo -0.2758 0.2411 -0.8059 -0.2522 0.1399
## week-Sciurus_carolinensis 0.1384 0.1822 -0.2213 0.1395 0.4965
## week-Vulpes_vulpes -0.1328 0.2844 -0.7484 -0.1177 0.3807
## week-Sus_scrofa 0.0898 0.2403 -0.3907 0.0925 0.5578
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5877
## (Intercept)-Canis_latrans 1.0052 2293
## (Intercept)-Sciurus_niger 1.0236 431
## (Intercept)-Procyon_lotor 1.0039 3615
## (Intercept)-Dasypus_novemcinctus 1.0012 4309
## (Intercept)-Lynx_rufus 1.0208 656
## (Intercept)-Didelphis_virginiana 1.0005 2637
## (Intercept)-Sylvilagus_floridanus 1.0098 1230
## (Intercept)-Meleagris_gallopavo 1.0317 623
## (Intercept)-Sciurus_carolinensis 1.0085 2482
## (Intercept)-Vulpes_vulpes 1.0092 350
## (Intercept)-Sus_scrofa 1.0008 2051
## shrub_cover-Odocoileus_virginianus 0.9999 5007
## shrub_cover-Canis_latrans 1.0039 2824
## shrub_cover-Sciurus_niger 1.0071 1153
## shrub_cover-Procyon_lotor 1.0004 3756
## shrub_cover-Dasypus_novemcinctus 1.0045 3704
## shrub_cover-Lynx_rufus 1.0093 1162
## shrub_cover-Didelphis_virginiana 1.0003 2432
## shrub_cover-Sylvilagus_floridanus 1.0007 1796
## shrub_cover-Meleagris_gallopavo 1.0247 802
## shrub_cover-Sciurus_carolinensis 1.0027 2405
## shrub_cover-Vulpes_vulpes 1.0051 1475
## shrub_cover-Sus_scrofa 1.0007 2521
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0063 2485
## veg_height-Sciurus_niger 1.0096 1537
## veg_height-Procyon_lotor 1.0063 4075
## veg_height-Dasypus_novemcinctus 1.0001 4916
## veg_height-Lynx_rufus 1.0055 2333
## veg_height-Didelphis_virginiana 1.0010 3280
## veg_height-Sylvilagus_floridanus 1.0069 2684
## veg_height-Meleagris_gallopavo 1.0037 1226
## veg_height-Sciurus_carolinensis 1.0056 3597
## veg_height-Vulpes_vulpes 1.0044 1910
## veg_height-Sus_scrofa 1.0027 3140
## week-Odocoileus_virginianus 1.0003 5616
## week-Canis_latrans 1.0003 4240
## week-Sciurus_niger 1.0027 1540
## week-Procyon_lotor 1.0001 4612
## week-Dasypus_novemcinctus 1.0043 4611
## week-Lynx_rufus 1.0015 2948
## week-Didelphis_virginiana 1.0059 3221
## week-Sylvilagus_floridanus 1.0006 2729
## week-Meleagris_gallopavo 1.0123 1803
## week-Sciurus_carolinensis 1.0002 4591
## week-Vulpes_vulpes 1.0040 2217
## week-Sus_scrofa 1.0019 4159
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1038
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7464 0.6005 -1.8928 -0.7614 0.4974 1.0022 1742
## Avg_Cogongrass_Cover -0.8144 0.4046 -1.6489 -0.8073 -0.0244 1.0046 930
## I(Avg_Cogongrass_Cover^2) 0.7981 0.3382 0.1830 0.7820 1.5433 1.0096 1124
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5600 3.3010 0.6906 2.7080 11.4568 1.0450 540
## Avg_Cogongrass_Cover 0.4773 0.6507 0.0437 0.2725 2.0390 1.0203 1163
## I(Avg_Cogongrass_Cover^2) 0.5016 0.7611 0.0406 0.2481 2.5919 1.0319 871
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.578 0.6152 0.0488 0.3753 2.2623 1.0196 427
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7383 0.4097 -3.5234 -2.7481 -1.8936 1.0011 3932
## shrub_cover 0.1033 0.2580 -0.4044 0.1048 0.6177 1.0034 2766
## veg_height -0.0142 0.1513 -0.3179 -0.0124 0.2821 1.0018 2624
## week -0.0708 0.1201 -0.3257 -0.0633 0.1459 1.0040 2696
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0351 1.1857 0.7360 1.7435 5.1471 1.0023 2416
## shrub_cover 0.5994 0.4482 0.1356 0.4825 1.7777 1.0071 1810
## veg_height 0.1816 0.1258 0.0543 0.1494 0.4830 1.0031 3600
## week 0.1027 0.0804 0.0274 0.0807 0.3084 1.0040 2179
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8172 1.4117 0.4133 2.6792
## (Intercept)-Canis_latrans -0.4367 0.6904 -1.8010 -0.4416
## (Intercept)-Sciurus_niger -0.7322 1.2591 -2.7392 -0.8353
## (Intercept)-Procyon_lotor -0.1205 0.6597 -1.4315 -0.1236
## (Intercept)-Dasypus_novemcinctus -1.3137 0.6489 -2.6286 -1.3046
## (Intercept)-Lynx_rufus -0.8404 1.2378 -2.6826 -0.9373
## (Intercept)-Didelphis_virginiana -1.8037 0.7308 -3.2960 -1.7774
## (Intercept)-Sylvilagus_floridanus -1.0502 0.7491 -2.5324 -1.0492
## (Intercept)-Meleagris_gallopavo 0.2874 1.1960 -1.6674 0.1727
## (Intercept)-Sciurus_carolinensis -2.3191 0.7867 -3.9636 -2.2932
## (Intercept)-Vulpes_vulpes -2.0010 1.3052 -4.2863 -2.0916
## (Intercept)-Sus_scrofa -2.3605 0.9161 -4.3413 -2.2895
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7963 0.6710 -2.1841 -0.7809
## Avg_Cogongrass_Cover-Canis_latrans -0.3770 0.5631 -1.3670 -0.4197
## Avg_Cogongrass_Cover-Sciurus_niger -1.1246 0.7882 -2.9827 -1.0314
## Avg_Cogongrass_Cover-Procyon_lotor -0.7126 0.5404 -1.7756 -0.7069
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5579 0.5022 -1.5466 -0.5626
## Avg_Cogongrass_Cover-Lynx_rufus -0.7235 0.6132 -1.9871 -0.7310
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4885 0.5568 -1.5317 -0.5135
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2445 0.6702 -2.7644 -1.1712
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1138 0.7932 -2.9876 -1.0396
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8443 0.5793 -2.0576 -0.8174
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8292 0.6656 -2.2502 -0.8054
## Avg_Cogongrass_Cover-Sus_scrofa -1.0646 0.7018 -2.6684 -0.9930
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1258 0.7347 0.0409 1.0062
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2250 0.7501 0.2001 1.0648
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3040 0.7198 -1.3742 0.3705
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0510 0.6075 0.1886 0.9605
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7369 0.3740 0.0427 0.7231
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1755 0.5655 0.2815 1.0972
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6024 0.4326 -0.2059 0.5884
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7516 0.4764 -0.0614 0.7194
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4273 0.7202 -1.0256 0.4368
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9980 0.4249 0.2643 0.9667
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9586 0.5333 0.0937 0.9035
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4216 0.6305 -1.0590 0.4801
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.0956 1.0012 937
## (Intercept)-Canis_latrans 0.9267 1.0011 2498
## (Intercept)-Sciurus_niger 1.9149 1.0334 487
## (Intercept)-Procyon_lotor 1.1968 1.0007 2483
## (Intercept)-Dasypus_novemcinctus -0.0339 1.0022 3198
## (Intercept)-Lynx_rufus 1.5084 1.0765 531
## (Intercept)-Didelphis_virginiana -0.4185 1.0008 3103
## (Intercept)-Sylvilagus_floridanus 0.4378 1.0034 2293
## (Intercept)-Meleagris_gallopavo 3.1236 1.0179 544
## (Intercept)-Sciurus_carolinensis -0.8895 1.0039 2119
## (Intercept)-Vulpes_vulpes 0.8162 1.0163 431
## (Intercept)-Sus_scrofa -0.6999 1.0018 1671
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4899 1.0012 1995
## Avg_Cogongrass_Cover-Canis_latrans 0.8795 1.0008 2138
## Avg_Cogongrass_Cover-Sciurus_niger 0.1457 1.0087 1015
## Avg_Cogongrass_Cover-Procyon_lotor 0.3371 1.0047 2220
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4586 1.0006 2312
## Avg_Cogongrass_Cover-Lynx_rufus 0.5207 1.0032 1740
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6736 1.0011 2116
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1181 1.0132 1187
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2418 1.0028 827
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2254 1.0003 1518
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4040 1.0022 1494
## Avg_Cogongrass_Cover-Sus_scrofa 0.1080 1.0066 1290
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.9622 1.0046 1107
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1994 1.0105 941
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5785 1.0031 886
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5936 1.0123 1100
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5161 1.0005 2467
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5065 1.0008 1390
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5370 1.0026 1719
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8384 1.0030 1561
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8467 1.0377 708
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9493 1.0029 1800
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2219 1.0010 1363
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5367 1.0035 1607
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0058 0.0593 -0.1099 0.0057 0.1188
## (Intercept)-Canis_latrans -2.7505 0.1866 -3.1251 -2.7440 -2.3965
## (Intercept)-Sciurus_niger -4.2622 0.6355 -5.5382 -4.2639 -2.9944
## (Intercept)-Procyon_lotor -2.3172 0.1495 -2.6224 -2.3130 -2.0321
## (Intercept)-Dasypus_novemcinctus -1.7318 0.1578 -2.0474 -1.7293 -1.4249
## (Intercept)-Lynx_rufus -3.6713 0.3746 -4.4187 -3.6681 -2.9727
## (Intercept)-Didelphis_virginiana -2.5814 0.2912 -3.1766 -2.5707 -2.0424
## (Intercept)-Sylvilagus_floridanus -3.2290 0.3031 -3.8609 -3.2148 -2.6826
## (Intercept)-Meleagris_gallopavo -4.2433 0.4671 -5.1481 -4.2495 -3.3193
## (Intercept)-Sciurus_carolinensis -2.5926 0.3090 -3.2282 -2.5759 -2.0321
## (Intercept)-Vulpes_vulpes -4.1935 0.7286 -5.7746 -4.1347 -2.9280
## (Intercept)-Sus_scrofa -3.2441 0.6016 -4.4271 -3.2500 -2.0681
## shrub_cover-Odocoileus_virginianus -0.0546 0.0638 -0.1783 -0.0536 0.0702
## shrub_cover-Canis_latrans -0.2754 0.2175 -0.6992 -0.2751 0.1479
## shrub_cover-Sciurus_niger -0.4623 0.4708 -1.3805 -0.4551 0.4527
## shrub_cover-Procyon_lotor 0.2194 0.1690 -0.1206 0.2246 0.5301
## shrub_cover-Dasypus_novemcinctus 0.8010 0.2876 0.2438 0.7909 1.3864
## shrub_cover-Lynx_rufus -0.3054 0.3540 -1.0305 -0.2973 0.3672
## shrub_cover-Didelphis_virginiana 0.9431 0.3767 0.2700 0.9206 1.7296
## shrub_cover-Sylvilagus_floridanus 0.2045 0.4029 -0.5071 0.1875 1.0318
## shrub_cover-Meleagris_gallopavo -0.8568 0.3968 -1.6284 -0.8502 -0.0942
## shrub_cover-Sciurus_carolinensis 0.7606 0.3993 0.0367 0.7437 1.5912
## shrub_cover-Vulpes_vulpes -0.1897 0.5727 -1.3330 -0.1734 0.9100
## shrub_cover-Sus_scrofa 0.4583 0.7918 -1.0760 0.4440 2.1078
## veg_height-Odocoileus_virginianus -0.2981 0.0645 -0.4263 -0.2982 -0.1731
## veg_height-Canis_latrans -0.5739 0.1812 -0.9521 -0.5681 -0.2400
## veg_height-Sciurus_niger -0.0148 0.3862 -0.7575 -0.0221 0.7937
## veg_height-Procyon_lotor 0.3350 0.1246 0.0900 0.3340 0.5848
## veg_height-Dasypus_novemcinctus 0.2258 0.1326 -0.0322 0.2224 0.4829
## veg_height-Lynx_rufus 0.0499 0.2389 -0.4255 0.0528 0.5223
## veg_height-Didelphis_virginiana 0.3719 0.2423 -0.0850 0.3643 0.8662
## veg_height-Sylvilagus_floridanus 0.1461 0.2469 -0.3411 0.1426 0.6209
## veg_height-Meleagris_gallopavo -0.2060 0.3374 -0.8807 -0.2026 0.4478
## veg_height-Sciurus_carolinensis 0.0560 0.2062 -0.3462 0.0543 0.4773
## veg_height-Vulpes_vulpes -0.1220 0.3032 -0.7660 -0.1102 0.4501
## veg_height-Sus_scrofa -0.1203 0.3241 -0.7725 -0.1132 0.4854
## week-Odocoileus_virginianus 0.2110 0.0603 0.0944 0.2105 0.3291
## week-Canis_latrans 0.0715 0.1315 -0.1966 0.0738 0.3163
## week-Sciurus_niger -0.3160 0.2994 -1.0152 -0.2859 0.1966
## week-Procyon_lotor -0.0513 0.1180 -0.2950 -0.0482 0.1682
## week-Dasypus_novemcinctus -0.1692 0.1366 -0.4468 -0.1664 0.0917
## week-Lynx_rufus -0.0378 0.1928 -0.4252 -0.0304 0.3344
## week-Didelphis_virginiana -0.2161 0.2111 -0.6619 -0.2010 0.1551
## week-Sylvilagus_floridanus -0.1620 0.2053 -0.6005 -0.1485 0.1966
## week-Meleagris_gallopavo -0.2828 0.2497 -0.8435 -0.2597 0.1468
## week-Sciurus_carolinensis 0.1354 0.1811 -0.2221 0.1388 0.4804
## week-Vulpes_vulpes -0.1341 0.2771 -0.7412 -0.1129 0.3565
## week-Sus_scrofa 0.0905 0.2322 -0.3703 0.0898 0.5523
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 5250
## (Intercept)-Canis_latrans 1.0031 2263
## (Intercept)-Sciurus_niger 1.0077 491
## (Intercept)-Procyon_lotor 1.0016 3101
## (Intercept)-Dasypus_novemcinctus 1.0011 4322
## (Intercept)-Lynx_rufus 1.0132 842
## (Intercept)-Didelphis_virginiana 1.0007 2543
## (Intercept)-Sylvilagus_floridanus 1.0067 1685
## (Intercept)-Meleagris_gallopavo 1.0090 517
## (Intercept)-Sciurus_carolinensis 1.0016 2712
## (Intercept)-Vulpes_vulpes 1.0112 530
## (Intercept)-Sus_scrofa 1.0023 1641
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0039 2810
## shrub_cover-Sciurus_niger 1.0161 1104
## shrub_cover-Procyon_lotor 1.0038 3246
## shrub_cover-Dasypus_novemcinctus 1.0005 3936
## shrub_cover-Lynx_rufus 1.0101 1634
## shrub_cover-Didelphis_virginiana 1.0025 1954
## shrub_cover-Sylvilagus_floridanus 1.0202 1452
## shrub_cover-Meleagris_gallopavo 1.0036 648
## shrub_cover-Sciurus_carolinensis 1.0078 2626
## shrub_cover-Vulpes_vulpes 1.0004 1961
## shrub_cover-Sus_scrofa 1.0018 2012
## veg_height-Odocoileus_virginianus 1.0010 5250
## veg_height-Canis_latrans 1.0069 2134
## veg_height-Sciurus_niger 1.0026 1801
## veg_height-Procyon_lotor 1.0015 3726
## veg_height-Dasypus_novemcinctus 1.0018 4702
## veg_height-Lynx_rufus 1.0030 2528
## veg_height-Didelphis_virginiana 1.0005 3217
## veg_height-Sylvilagus_floridanus 1.0017 2291
## veg_height-Meleagris_gallopavo 1.0080 1281
## veg_height-Sciurus_carolinensis 1.0021 3228
## veg_height-Vulpes_vulpes 1.0014 2080
## veg_height-Sus_scrofa 1.0036 3302
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0003 3787
## week-Sciurus_niger 1.0005 1616
## week-Procyon_lotor 1.0006 4461
## week-Dasypus_novemcinctus 1.0009 5113
## week-Lynx_rufus 1.0004 2759
## week-Didelphis_virginiana 1.0012 3809
## week-Sylvilagus_floridanus 1.0035 2735
## week-Meleagris_gallopavo 1.0034 1646
## week-Sciurus_carolinensis 0.9999 4410
## week-Vulpes_vulpes 1.0007 2586
## week-Sus_scrofa 1.0028 4233
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2565
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8900 1.0791 -2.9589 -0.9264 1.3295 1.0078 1423
## Cogon_Patch_Size -0.0528 0.7219 -1.5493 -0.0177 1.2827 1.0066 816
## Veg_shannon_index 1.0122 0.4952 0.0773 0.9874 2.0523 1.0105 549
## total_shrub_cover -0.7517 0.5902 -2.0166 -0.7113 0.3126 1.0038 563
## Avg_Cogongrass_Cover -0.2837 0.9887 -2.2409 -0.2708 1.6391 1.0077 395
## Tree_Density -2.0083 0.8205 -3.6862 -1.9809 -0.4190 1.0303 663
## Avg_Canopy_Cover 2.0716 0.6829 0.8477 2.0311 3.5210 1.0251 742
## I(Avg_Cogongrass_Cover^2) 1.5413 0.6105 0.3326 1.5312 2.7637 1.0065 506
## avg_veg_height -0.0603 0.5441 -1.1787 -0.0505 0.9720 1.0221 633
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.1847 16.0936 3.6070 15.0097 60.7223 1.0763 404
## Cogon_Patch_Size 3.1312 4.4012 0.1209 1.7541 14.1092 1.0354 549
## Veg_shannon_index 0.9505 1.6874 0.0519 0.4781 4.8597 1.1225 654
## total_shrub_cover 1.5309 2.1543 0.0716 0.8154 7.0860 1.0259 432
## Avg_Cogongrass_Cover 1.4092 2.5023 0.0556 0.6090 7.6806 1.0267 597
## Tree_Density 3.4632 6.7959 0.0707 1.5576 18.6522 1.0351 449
## Avg_Canopy_Cover 3.3086 5.0213 0.1616 1.9752 14.1574 1.0306 449
## I(Avg_Cogongrass_Cover^2) 1.7918 6.0580 0.0497 0.5439 10.1898 1.2294 243
## avg_veg_height 0.5491 0.8430 0.0435 0.2842 2.6477 1.0220 1091
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.799 2.7445 0.0599 0.8151 9.9503 1.1181 170
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8012 0.4373 -3.6452 -2.8125 -1.8978 1.0001 4133
## shrub_cover 0.2400 0.2759 -0.3006 0.2357 0.8028 1.0020 1221
## veg_height -0.0114 0.1511 -0.3159 -0.0085 0.2761 1.0032 2568
## week -0.0689 0.1214 -0.3279 -0.0623 0.1549 1.0006 2749
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2847 1.3199 0.8617 1.9442 5.7329 1.0016 2302
## shrub_cover 0.6627 0.5005 0.1412 0.5331 2.0332 1.0079 1187
## veg_height 0.1930 0.1305 0.0577 0.1596 0.5380 1.0127 3578
## week 0.1014 0.0778 0.0258 0.0789 0.3093 1.0018 1987
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.4093 3.5216 2.2317 6.8404
## (Intercept)-Canis_latrans -0.7875 1.2888 -3.2576 -0.8223
## (Intercept)-Sciurus_niger 1.2135 2.7138 -3.0812 0.7847
## (Intercept)-Procyon_lotor -0.2751 1.1495 -2.6537 -0.2583
## (Intercept)-Dasypus_novemcinctus -2.5989 1.2200 -5.3612 -2.4758
## (Intercept)-Lynx_rufus 0.5356 2.7781 -3.7017 0.1498
## (Intercept)-Didelphis_virginiana -4.0511 1.5112 -7.2609 -3.9667
## (Intercept)-Sylvilagus_floridanus -2.1976 1.5187 -5.3677 -2.1475
## (Intercept)-Meleagris_gallopavo -0.6053 2.2761 -4.5165 -0.8135
## (Intercept)-Sciurus_carolinensis -4.6770 1.6495 -8.2721 -4.5024
## (Intercept)-Vulpes_vulpes -4.0741 2.5173 -8.8677 -4.1707
## (Intercept)-Sus_scrofa -5.3110 2.1256 -9.7450 -5.1425
## Cogon_Patch_Size-Odocoileus_virginianus 0.0220 1.3999 -2.7690 -0.0111
## Cogon_Patch_Size-Canis_latrans 1.6316 1.4201 -0.3598 1.3550
## Cogon_Patch_Size-Sciurus_niger -0.6333 1.7839 -4.5375 -0.4478
## Cogon_Patch_Size-Procyon_lotor -0.4405 0.8256 -2.1937 -0.4062
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0288 0.8297 -1.5733 0.0341
## Cogon_Patch_Size-Lynx_rufus -0.1662 1.6137 -3.3738 -0.1664
## Cogon_Patch_Size-Didelphis_virginiana 1.5837 1.0356 -0.1363 1.4652
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2239 1.6181 -5.3118 -0.9238
## Cogon_Patch_Size-Meleagris_gallopavo 0.5487 1.4324 -1.8567 0.3985
## Cogon_Patch_Size-Sciurus_carolinensis -0.9971 1.4457 -4.6599 -0.7465
## Cogon_Patch_Size-Vulpes_vulpes -0.4838 1.7052 -4.4798 -0.3381
## Cogon_Patch_Size-Sus_scrofa -0.6234 1.4569 -4.0540 -0.4193
## Veg_shannon_index-Odocoileus_virginianus 0.7842 0.9617 -1.3473 0.8498
## Veg_shannon_index-Canis_latrans 1.3634 0.7312 0.1385 1.2914
## Veg_shannon_index-Sciurus_niger 1.1462 1.1045 -0.9336 1.0821
## Veg_shannon_index-Procyon_lotor 1.2255 0.6608 0.0604 1.1831
## Veg_shannon_index-Dasypus_novemcinctus 0.6206 0.6256 -0.6477 0.6384
## Veg_shannon_index-Lynx_rufus 1.1136 0.9751 -0.7662 1.0773
## Veg_shannon_index-Didelphis_virginiana 1.2171 0.7637 -0.1685 1.1568
## Veg_shannon_index-Sylvilagus_floridanus 1.0720 0.7731 -0.3658 1.0455
## Veg_shannon_index-Meleagris_gallopavo 1.3194 0.9183 -0.2443 1.2253
## Veg_shannon_index-Sciurus_carolinensis 0.3267 0.8953 -1.7021 0.4274
## Veg_shannon_index-Vulpes_vulpes 0.6825 0.9460 -1.3810 0.7404
## Veg_shannon_index-Sus_scrofa 1.6442 1.0646 0.1483 1.4590
## total_shrub_cover-Odocoileus_virginianus -0.3467 1.0827 -2.4245 -0.4036
## total_shrub_cover-Canis_latrans 0.2251 0.8702 -1.2057 0.1127
## total_shrub_cover-Sciurus_niger -0.9301 1.2326 -3.7814 -0.8355
## total_shrub_cover-Procyon_lotor -1.3305 0.7340 -2.9671 -1.2552
## total_shrub_cover-Dasypus_novemcinctus -0.3052 0.7536 -1.9498 -0.2722
## total_shrub_cover-Lynx_rufus -1.0863 1.3056 -4.1835 -0.9254
## total_shrub_cover-Didelphis_virginiana -1.0789 0.9821 -3.4048 -0.9336
## total_shrub_cover-Sylvilagus_floridanus -0.8796 1.0715 -3.4000 -0.7702
## total_shrub_cover-Meleagris_gallopavo -1.7427 1.4426 -5.2486 -1.4884
## total_shrub_cover-Sciurus_carolinensis -0.6016 0.9951 -2.8450 -0.5280
## total_shrub_cover-Vulpes_vulpes -1.0099 1.2166 -3.9424 -0.8725
## total_shrub_cover-Sus_scrofa -0.4850 1.1607 -2.9294 -0.4610
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.3528 1.4032 -3.1939 -0.3287
## Avg_Cogongrass_Cover-Canis_latrans -0.0194 1.2457 -2.4369 -0.0407
## Avg_Cogongrass_Cover-Sciurus_niger -0.7054 1.6472 -4.4202 -0.5639
## Avg_Cogongrass_Cover-Procyon_lotor -0.1627 1.2220 -2.5484 -0.1551
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4284 1.3464 -1.9469 0.3187
## Avg_Cogongrass_Cover-Lynx_rufus -0.1753 1.3645 -2.7857 -0.1858
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1847 1.2678 -2.6714 -0.1659
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8797 1.3949 -4.0132 -0.7956
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4916 1.5412 -3.7160 -0.4651
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2833 1.3098 -2.8929 -0.2605
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0913 1.3710 -2.6948 -0.1242
## Avg_Cogongrass_Cover-Sus_scrofa -0.6029 1.4036 -3.5921 -0.5284
## Tree_Density-Odocoileus_virginianus -0.9519 1.4720 -3.3359 -1.1083
## Tree_Density-Canis_latrans -2.8502 1.3822 -6.2342 -2.6322
## Tree_Density-Sciurus_niger -1.9074 1.7078 -5.2725 -1.9358
## Tree_Density-Procyon_lotor -1.9536 1.0006 -4.0484 -1.9270
## Tree_Density-Dasypus_novemcinctus -3.8732 1.9937 -8.8212 -3.4468
## Tree_Density-Lynx_rufus -0.8426 1.7565 -3.5910 -1.0842
## Tree_Density-Didelphis_virginiana -2.2717 1.2873 -5.1295 -2.1745
## Tree_Density-Sylvilagus_floridanus -2.5625 1.5348 -6.2822 -2.3657
## Tree_Density-Meleagris_gallopavo -2.1807 1.5718 -5.5206 -2.1347
## Tree_Density-Sciurus_carolinensis -2.6267 1.5404 -6.2303 -2.4378
## Tree_Density-Vulpes_vulpes -1.9801 1.8016 -5.4734 -2.0049
## Tree_Density-Sus_scrofa -2.4766 1.6723 -6.5851 -2.2785
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2271 1.4888 -1.8511 1.2932
## Avg_Canopy_Cover-Canis_latrans 0.1745 0.7748 -1.2867 0.1582
## Avg_Canopy_Cover-Sciurus_niger 2.4785 2.0789 -1.1547 2.3257
## Avg_Canopy_Cover-Procyon_lotor 1.7046 0.8279 0.1858 1.6559
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2098 0.8599 0.7775 2.1138
## Avg_Canopy_Cover-Lynx_rufus 1.7874 1.5840 -1.1002 1.7352
## Avg_Canopy_Cover-Didelphis_virginiana 3.1684 1.3696 1.2802 2.9026
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8910 1.8533 1.2946 3.5797
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6609 1.4580 0.4259 2.4411
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0024 1.4685 1.0557 2.7033
## Avg_Canopy_Cover-Vulpes_vulpes 2.6862 1.4626 0.4640 2.4482
## Avg_Canopy_Cover-Sus_scrofa 2.2874 1.1679 0.5242 2.1272
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.0654 1.8455 0.0583 1.7792
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0986 1.0782 0.6092 1.9122
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1563 1.4530 -2.1258 1.3004
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9996 1.0332 0.5231 1.8447
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5303 0.7389 0.2012 1.4912
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3069 1.2632 0.6612 2.0555
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2148 0.7326 -0.2701 1.2226
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3458 0.9308 -0.3540 1.3018
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.8240 1.4464 -2.7933 1.0851
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8165 0.8221 0.4701 1.7321
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9967 0.9556 0.4754 1.8649
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0053 1.3515 -2.3242 1.1614
## avg_veg_height-Odocoileus_virginianus -0.0750 0.8524 -1.8413 -0.0564
## avg_veg_height-Canis_latrans -0.1496 0.6604 -1.4794 -0.1344
## avg_veg_height-Sciurus_niger -0.2093 0.9076 -2.2354 -0.1540
## avg_veg_height-Procyon_lotor 0.0929 0.6713 -1.2407 0.0874
## avg_veg_height-Dasypus_novemcinctus 0.2825 0.6729 -0.9729 0.2569
## avg_veg_height-Lynx_rufus -0.2771 0.9042 -2.2919 -0.2250
## avg_veg_height-Didelphis_virginiana -0.2425 0.7652 -1.9139 -0.1904
## avg_veg_height-Sylvilagus_floridanus -0.1576 0.7640 -1.7673 -0.1302
## avg_veg_height-Meleagris_gallopavo -0.0105 0.8925 -1.8271 -0.0103
## avg_veg_height-Sciurus_carolinensis 0.2534 0.7599 -1.0863 0.2152
## avg_veg_height-Vulpes_vulpes -0.1446 0.8541 -1.9621 -0.1283
## avg_veg_height-Sus_scrofa -0.0915 0.7834 -1.6743 -0.0861
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.9845 1.0677 362
## (Intercept)-Canis_latrans 1.8827 1.0056 1076
## (Intercept)-Sciurus_niger 7.8470 1.0139 306
## (Intercept)-Procyon_lotor 1.9968 1.0078 1611
## (Intercept)-Dasypus_novemcinctus -0.4488 1.0061 906
## (Intercept)-Lynx_rufus 7.6686 1.0950 283
## (Intercept)-Didelphis_virginiana -1.3482 1.0091 1078
## (Intercept)-Sylvilagus_floridanus 0.7673 1.0072 1048
## (Intercept)-Meleagris_gallopavo 4.4393 1.0273 384
## (Intercept)-Sciurus_carolinensis -1.8744 1.0203 639
## (Intercept)-Vulpes_vulpes 1.4991 1.0298 419
## (Intercept)-Sus_scrofa -1.5044 1.0418 583
## Cogon_Patch_Size-Odocoileus_virginianus 3.0226 1.0084 1523
## Cogon_Patch_Size-Canis_latrans 5.1558 1.0065 997
## Cogon_Patch_Size-Sciurus_niger 2.6728 1.0156 585
## Cogon_Patch_Size-Procyon_lotor 1.1379 1.0121 581
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6843 1.0075 1259
## Cogon_Patch_Size-Lynx_rufus 3.2355 1.0139 691
## Cogon_Patch_Size-Didelphis_virginiana 3.9153 1.0175 657
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1348 1.0249 610
## Cogon_Patch_Size-Meleagris_gallopavo 3.8743 1.0236 1037
## Cogon_Patch_Size-Sciurus_carolinensis 1.1562 1.0254 707
## Cogon_Patch_Size-Vulpes_vulpes 2.6423 1.0311 845
## Cogon_Patch_Size-Sus_scrofa 1.7658 1.0160 1031
## Veg_shannon_index-Odocoileus_virginianus 2.5624 1.0118 1161
## Veg_shannon_index-Canis_latrans 2.9926 1.0143 1085
## Veg_shannon_index-Sciurus_niger 3.5998 1.0103 932
## Veg_shannon_index-Procyon_lotor 2.6761 1.0071 752
## Veg_shannon_index-Dasypus_novemcinctus 1.7974 1.0048 1631
## Veg_shannon_index-Lynx_rufus 3.2116 1.0096 1443
## Veg_shannon_index-Didelphis_virginiana 2.8719 1.0071 1184
## Veg_shannon_index-Sylvilagus_floridanus 2.7604 1.0191 1025
## Veg_shannon_index-Meleagris_gallopavo 3.4734 1.0166 1085
## Veg_shannon_index-Sciurus_carolinensis 1.8188 1.0055 1240
## Veg_shannon_index-Vulpes_vulpes 2.4492 1.0010 1077
## Veg_shannon_index-Sus_scrofa 4.1002 1.0466 701
## total_shrub_cover-Odocoileus_virginianus 1.9622 1.0055 2027
## total_shrub_cover-Canis_latrans 2.3474 1.0076 942
## total_shrub_cover-Sciurus_niger 1.3672 1.0042 636
## total_shrub_cover-Procyon_lotor -0.0827 1.0002 861
## total_shrub_cover-Dasypus_novemcinctus 1.1022 1.0010 1211
## total_shrub_cover-Lynx_rufus 1.2086 1.0185 627
## total_shrub_cover-Didelphis_virginiana 0.4694 1.0031 827
## total_shrub_cover-Sylvilagus_floridanus 0.8994 1.0092 854
## total_shrub_cover-Meleagris_gallopavo 0.4066 1.0133 364
## total_shrub_cover-Sciurus_carolinensis 1.2014 1.0086 1017
## total_shrub_cover-Vulpes_vulpes 1.0899 1.0024 834
## total_shrub_cover-Sus_scrofa 1.7963 1.0070 828
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.4204 1.0047 676
## Avg_Cogongrass_Cover-Canis_latrans 2.5322 1.0072 614
## Avg_Cogongrass_Cover-Sciurus_niger 2.0712 1.0048 477
## Avg_Cogongrass_Cover-Procyon_lotor 2.2813 1.0063 511
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.4487 1.0078 569
## Avg_Cogongrass_Cover-Lynx_rufus 2.6040 1.0059 722
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3360 1.0054 655
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5668 1.0075 557
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.4307 1.0027 519
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3018 1.0099 555
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.7604 1.0034 557
## Avg_Cogongrass_Cover-Sus_scrofa 1.8638 1.0012 644
## Tree_Density-Odocoileus_virginianus 2.4229 1.0244 593
## Tree_Density-Canis_latrans -0.6593 1.0074 847
## Tree_Density-Sciurus_niger 1.7662 1.0061 803
## Tree_Density-Procyon_lotor -0.0341 1.0150 881
## Tree_Density-Dasypus_novemcinctus -1.3024 1.0093 483
## Tree_Density-Lynx_rufus 3.0843 1.0399 439
## Tree_Density-Didelphis_virginiana 0.0403 1.0092 1214
## Tree_Density-Sylvilagus_floridanus -0.0019 1.0095 814
## Tree_Density-Meleagris_gallopavo 1.0029 1.0258 690
## Tree_Density-Sciurus_carolinensis -0.1247 1.0238 880
## Tree_Density-Vulpes_vulpes 1.8084 1.0055 686
## Tree_Density-Sus_scrofa 0.3650 1.0157 1009
## Avg_Canopy_Cover-Odocoileus_virginianus 3.9940 1.0054 1072
## Avg_Canopy_Cover-Canis_latrans 1.7824 1.0096 673
## Avg_Canopy_Cover-Sciurus_niger 6.7898 1.0236 538
## Avg_Canopy_Cover-Procyon_lotor 3.4892 1.0115 952
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.1179 1.0085 719
## Avg_Canopy_Cover-Lynx_rufus 5.2709 1.0549 582
## Avg_Canopy_Cover-Didelphis_virginiana 6.5851 1.0305 423
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.3325 1.0115 340
## Avg_Canopy_Cover-Meleagris_gallopavo 6.1732 1.0220 677
## Avg_Canopy_Cover-Sciurus_carolinensis 6.7605 1.0255 422
## Avg_Canopy_Cover-Vulpes_vulpes 6.2526 1.0084 684
## Avg_Canopy_Cover-Sus_scrofa 4.8435 1.0392 1331
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 5.7777 1.0850 337
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.8062 1.0432 383
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.6702 1.0262 243
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.4867 1.0096 781
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1362 1.0042 1111
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.4831 1.0190 333
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6775 1.0066 848
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2867 1.0210 650
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.9971 1.0022 328
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.6920 1.0004 951
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.2853 1.0004 1067
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.2643 1.0076 383
## avg_veg_height-Odocoileus_virginianus 1.5400 1.0081 1113
## avg_veg_height-Canis_latrans 1.1452 1.0264 805
## avg_veg_height-Sciurus_niger 1.4453 1.0112 986
## avg_veg_height-Procyon_lotor 1.4456 1.0097 1069
## avg_veg_height-Dasypus_novemcinctus 1.7152 1.0037 903
## avg_veg_height-Lynx_rufus 1.3053 1.0201 946
## avg_veg_height-Didelphis_virginiana 1.1189 1.0099 1118
## avg_veg_height-Sylvilagus_floridanus 1.2613 1.0089 977
## avg_veg_height-Meleagris_gallopavo 1.7585 1.0259 916
## avg_veg_height-Sciurus_carolinensis 1.9637 1.0065 1024
## avg_veg_height-Vulpes_vulpes 1.5053 1.0173 1003
## avg_veg_height-Sus_scrofa 1.4192 1.0168 1133
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0048 0.0597 -0.1139 0.0052 0.1205
## (Intercept)-Canis_latrans -2.7314 0.1889 -3.1226 -2.7232 -2.3873
## (Intercept)-Sciurus_niger -4.8362 0.5130 -5.8682 -4.8292 -3.8465
## (Intercept)-Procyon_lotor -2.3087 0.1452 -2.6159 -2.3041 -2.0390
## (Intercept)-Dasypus_novemcinctus -1.7661 0.1658 -2.1119 -1.7631 -1.4531
## (Intercept)-Lynx_rufus -3.9304 0.3654 -4.6440 -3.9357 -3.1807
## (Intercept)-Didelphis_virginiana -2.5930 0.3015 -3.2066 -2.5781 -2.0432
## (Intercept)-Sylvilagus_floridanus -3.2302 0.2692 -3.7556 -3.2215 -2.7234
## (Intercept)-Meleagris_gallopavo -4.0887 0.4973 -5.0373 -4.1070 -3.0944
## (Intercept)-Sciurus_carolinensis -2.7069 0.3259 -3.3669 -2.7002 -2.0890
## (Intercept)-Vulpes_vulpes -4.3537 0.6744 -5.7540 -4.3169 -3.1333
## (Intercept)-Sus_scrofa -3.4330 0.6543 -4.7379 -3.4267 -2.1973
## shrub_cover-Odocoileus_virginianus -0.0540 0.0639 -0.1811 -0.0533 0.0707
## shrub_cover-Canis_latrans -0.3108 0.2283 -0.7438 -0.3172 0.1580
## shrub_cover-Sciurus_niger -0.3941 0.4612 -1.3127 -0.3719 0.4818
## shrub_cover-Procyon_lotor 0.2662 0.1649 -0.0721 0.2703 0.5820
## shrub_cover-Dasypus_novemcinctus 0.9202 0.3127 0.3150 0.9131 1.5324
## shrub_cover-Lynx_rufus -0.2451 0.3706 -0.9651 -0.2532 0.4972
## shrub_cover-Didelphis_virginiana 0.9957 0.3879 0.3052 0.9770 1.8259
## shrub_cover-Sylvilagus_floridanus 0.5185 0.4008 -0.2492 0.5126 1.3228
## shrub_cover-Meleagris_gallopavo -0.7081 0.4462 -1.5699 -0.7134 0.1815
## shrub_cover-Sciurus_carolinensis 0.9394 0.4260 0.1270 0.9359 1.7820
## shrub_cover-Vulpes_vulpes 0.1044 0.5824 -1.0597 0.1220 1.2043
## shrub_cover-Sus_scrofa 0.8179 0.8721 -0.8022 0.7776 2.6628
## veg_height-Odocoileus_virginianus -0.2986 0.0642 -0.4211 -0.2996 -0.1731
## veg_height-Canis_latrans -0.5547 0.1774 -0.9058 -0.5521 -0.2276
## veg_height-Sciurus_niger -0.0578 0.3471 -0.7227 -0.0586 0.6555
## veg_height-Procyon_lotor 0.3497 0.1231 0.1020 0.3494 0.5846
## veg_height-Dasypus_novemcinctus 0.2467 0.1368 -0.0116 0.2443 0.5191
## veg_height-Lynx_rufus 0.1281 0.2332 -0.3431 0.1340 0.5689
## veg_height-Didelphis_virginiana 0.4259 0.2360 -0.0192 0.4156 0.9004
## veg_height-Sylvilagus_floridanus 0.1199 0.2425 -0.3514 0.1203 0.5960
## veg_height-Meleagris_gallopavo -0.2429 0.3340 -0.9293 -0.2366 0.4041
## veg_height-Sciurus_carolinensis 0.1017 0.2159 -0.3010 0.0949 0.5392
## veg_height-Vulpes_vulpes -0.1798 0.3166 -0.8435 -0.1726 0.3970
## veg_height-Sus_scrofa -0.1691 0.3270 -0.8426 -0.1607 0.4422
## week-Odocoileus_virginianus 0.2098 0.0616 0.0921 0.2091 0.3347
## week-Canis_latrans 0.0656 0.1335 -0.2067 0.0684 0.3198
## week-Sciurus_niger -0.3125 0.3028 -1.0262 -0.2789 0.1795
## week-Procyon_lotor -0.0501 0.1161 -0.2824 -0.0487 0.1731
## week-Dasypus_novemcinctus -0.1696 0.1359 -0.4432 -0.1649 0.0857
## week-Lynx_rufus -0.0410 0.1949 -0.4453 -0.0343 0.3287
## week-Didelphis_virginiana -0.2142 0.2149 -0.6933 -0.1991 0.1732
## week-Sylvilagus_floridanus -0.1603 0.2085 -0.6195 -0.1485 0.2131
## week-Meleagris_gallopavo -0.2759 0.2409 -0.8108 -0.2580 0.1388
## week-Sciurus_carolinensis 0.1337 0.1791 -0.2242 0.1353 0.4753
## week-Vulpes_vulpes -0.1263 0.2807 -0.7426 -0.1080 0.3842
## week-Sus_scrofa 0.0890 0.2414 -0.3790 0.0840 0.5781
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0021 1917
## (Intercept)-Sciurus_niger 1.0028 487
## (Intercept)-Procyon_lotor 1.0029 2960
## (Intercept)-Dasypus_novemcinctus 0.9998 2451
## (Intercept)-Lynx_rufus 1.0283 541
## (Intercept)-Didelphis_virginiana 1.0019 1535
## (Intercept)-Sylvilagus_floridanus 1.0030 1903
## (Intercept)-Meleagris_gallopavo 1.0141 502
## (Intercept)-Sciurus_carolinensis 1.0082 1354
## (Intercept)-Vulpes_vulpes 1.0343 444
## (Intercept)-Sus_scrofa 1.0019 549
## shrub_cover-Odocoileus_virginianus 1.0020 5835
## shrub_cover-Canis_latrans 1.0101 1717
## shrub_cover-Sciurus_niger 1.0183 892
## shrub_cover-Procyon_lotor 1.0014 3595
## shrub_cover-Dasypus_novemcinctus 1.0002 1512
## shrub_cover-Lynx_rufus 1.0142 695
## shrub_cover-Didelphis_virginiana 1.0014 1382
## shrub_cover-Sylvilagus_floridanus 1.0032 990
## shrub_cover-Meleagris_gallopavo 1.0090 614
## shrub_cover-Sciurus_carolinensis 1.0045 1200
## shrub_cover-Vulpes_vulpes 1.0061 1201
## shrub_cover-Sus_scrofa 1.0003 551
## veg_height-Odocoileus_virginianus 1.0037 5250
## veg_height-Canis_latrans 1.0051 2485
## veg_height-Sciurus_niger 1.0027 1025
## veg_height-Procyon_lotor 1.0018 3523
## veg_height-Dasypus_novemcinctus 1.0025 4008
## veg_height-Lynx_rufus 1.0042 1848
## veg_height-Didelphis_virginiana 1.0027 2543
## veg_height-Sylvilagus_floridanus 1.0078 2127
## veg_height-Meleagris_gallopavo 1.0055 1060
## veg_height-Sciurus_carolinensis 1.0117 2986
## veg_height-Vulpes_vulpes 1.0029 2035
## veg_height-Sus_scrofa 1.0024 2012
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0004 4439
## week-Sciurus_niger 1.0060 1215
## week-Procyon_lotor 1.0064 4282
## week-Dasypus_novemcinctus 1.0003 4587
## week-Lynx_rufus 1.0015 2683
## week-Didelphis_virginiana 1.0004 2571
## week-Sylvilagus_floridanus 1.0019 2712
## week-Meleagris_gallopavo 1.0005 1861
## week-Sciurus_carolinensis 1.0002 4565
## week-Vulpes_vulpes 1.0013 2488
## week-Sus_scrofa 1.0027 4020
# Includes all covariates of occupancy and null for detection
ms_null_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5715
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2574 0.9900 -2.1268 -0.2765 1.7880 1.0067 1086
## Cogon_Patch_Size -0.6936 0.5966 -2.0144 -0.6522 0.3764 1.0053 825
## Veg_shannon_index 0.9445 0.4394 0.1297 0.9166 1.8363 1.0163 527
## total_shrub_cover -0.4904 0.4679 -1.4703 -0.4746 0.4116 1.0013 1498
## Avg_Cogongrass_Cover 1.9475 0.6861 0.6443 1.9264 3.3364 1.0525 403
## Tree_Density -1.8707 0.6476 -3.2101 -1.8386 -0.7110 1.0407 626
## Avg_Canopy_Cover 1.8237 0.5251 0.8643 1.7986 2.9297 1.0283 483
## avg_veg_height -0.5780 0.4499 -1.4370 -0.5847 0.3313 1.0110 705
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.8871 14.1458 3.3604 12.9011 52.8511 1.0440 414
## Cogon_Patch_Size 2.2007 3.1822 0.1071 1.1728 10.2350 1.0718 523
## Veg_shannon_index 0.7498 1.1110 0.0504 0.3848 3.5743 1.0129 902
## total_shrub_cover 1.4691 1.9481 0.0843 0.8806 6.5091 1.0338 647
## Avg_Cogongrass_Cover 1.0815 1.7100 0.0533 0.5124 5.3255 1.0310 731
## Tree_Density 1.9661 3.3834 0.0599 0.8695 10.8160 1.0186 357
## Avg_Canopy_Cover 1.5287 2.1183 0.0809 0.8814 7.2597 1.1004 268
## avg_veg_height 0.3486 0.4827 0.0379 0.2053 1.4776 1.0136 2191
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9748 3.5737 0.0647 0.9628 10.3526 1.1914 80
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.619 0.4043 -3.4126 -2.6268 -1.7703 1.0056 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9877 1.1435 0.7534 1.6997 5.0169 1.0025 2593
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.0167 3.2720 3.2608 7.4506
## (Intercept)-Canis_latrans 0.7424 1.1231 -1.1779 0.6368
## (Intercept)-Sciurus_niger 1.1933 2.3155 -2.5637 0.8939
## (Intercept)-Procyon_lotor 0.8011 1.0226 -1.3209 0.8215
## (Intercept)-Dasypus_novemcinctus -1.5017 1.0431 -3.9226 -1.3877
## (Intercept)-Lynx_rufus 2.2296 3.1744 -2.1685 1.6422
## (Intercept)-Didelphis_virginiana -3.0131 1.1615 -5.6042 -2.9100
## (Intercept)-Sylvilagus_floridanus -1.2632 1.2644 -3.7858 -1.2386
## (Intercept)-Meleagris_gallopavo -1.2740 1.5742 -4.3516 -1.2565
## (Intercept)-Sciurus_carolinensis -3.2898 1.3512 -6.3393 -3.1129
## (Intercept)-Vulpes_vulpes -1.8380 2.2042 -5.4792 -2.0954
## (Intercept)-Sus_scrofa -4.6434 1.6981 -8.5021 -4.4742
## Cogon_Patch_Size-Odocoileus_virginianus -0.6069 1.2057 -2.9842 -0.6235
## Cogon_Patch_Size-Canis_latrans 0.5893 1.0882 -1.0237 0.4000
## Cogon_Patch_Size-Sciurus_niger -1.2841 1.5652 -4.8527 -1.0971
## Cogon_Patch_Size-Procyon_lotor -0.9325 0.7586 -2.4824 -0.8973
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7749 0.6171 -2.0346 -0.7455
## Cogon_Patch_Size-Lynx_rufus -0.5343 1.3171 -2.8404 -0.6045
## Cogon_Patch_Size-Didelphis_virginiana 0.7619 0.8965 -0.7116 0.6478
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7079 1.3377 -4.9821 -1.4681
## Cogon_Patch_Size-Meleagris_gallopavo -0.4472 1.0263 -2.2679 -0.5053
## Cogon_Patch_Size-Sciurus_carolinensis -1.6050 1.2476 -4.7751 -1.3406
## Cogon_Patch_Size-Vulpes_vulpes -1.1671 1.4732 -4.4684 -1.0207
## Cogon_Patch_Size-Sus_scrofa -1.1564 1.2682 -4.1740 -0.9509
## Veg_shannon_index-Odocoileus_virginianus 0.7785 0.8273 -0.8928 0.7942
## Veg_shannon_index-Canis_latrans 1.2658 0.6555 0.1906 1.1965
## Veg_shannon_index-Sciurus_niger 1.0742 0.9615 -0.6744 0.9987
## Veg_shannon_index-Procyon_lotor 1.1854 0.6100 0.1255 1.1368
## Veg_shannon_index-Dasypus_novemcinctus 0.6945 0.5095 -0.3042 0.6846
## Veg_shannon_index-Lynx_rufus 0.8898 0.8829 -0.9450 0.8942
## Veg_shannon_index-Didelphis_virginiana 1.1079 0.6550 -0.0364 1.0515
## Veg_shannon_index-Sylvilagus_floridanus 1.0649 0.6767 -0.1273 1.0246
## Veg_shannon_index-Meleagris_gallopavo 1.2464 0.7706 -0.0539 1.1606
## Veg_shannon_index-Sciurus_carolinensis 0.2716 0.7024 -1.3031 0.3349
## Veg_shannon_index-Vulpes_vulpes 0.4550 0.8201 -1.3461 0.5310
## Veg_shannon_index-Sus_scrofa 1.5672 0.9681 0.1623 1.3984
## total_shrub_cover-Odocoileus_virginianus -0.0644 1.0151 -2.0083 -0.1288
## total_shrub_cover-Canis_latrans 0.2118 0.7227 -0.9977 0.1292
## total_shrub_cover-Sciurus_niger -0.8511 1.0916 -3.2654 -0.7587
## total_shrub_cover-Procyon_lotor -0.9812 0.6276 -2.3108 -0.9337
## total_shrub_cover-Dasypus_novemcinctus 0.0977 0.5552 -0.9210 0.0767
## total_shrub_cover-Lynx_rufus -1.0410 1.1848 -3.8431 -0.8801
## total_shrub_cover-Didelphis_virginiana -0.6224 0.7340 -2.2323 -0.5674
## total_shrub_cover-Sylvilagus_floridanus -0.2496 0.8467 -2.0588 -0.2417
## total_shrub_cover-Meleagris_gallopavo -2.0119 1.3164 -5.1002 -1.8074
## total_shrub_cover-Sciurus_carolinensis -0.0431 0.6981 -1.3855 -0.0779
## total_shrub_cover-Vulpes_vulpes -0.7541 1.1158 -3.3310 -0.6418
## total_shrub_cover-Sus_scrofa 0.0997 0.9158 -1.5872 0.0335
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9186 1.0463 -0.1158 1.9205
## Avg_Cogongrass_Cover-Canis_latrans 2.3268 0.9064 0.7463 2.2575
## Avg_Cogongrass_Cover-Sciurus_niger 1.4795 1.2897 -1.5546 1.5926
## Avg_Cogongrass_Cover-Procyon_lotor 2.2214 0.8769 0.6488 2.1771
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5441 0.9415 0.9794 2.4509
## Avg_Cogongrass_Cover-Lynx_rufus 2.3633 1.0347 0.5380 2.2836
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1455 0.8524 0.5694 2.1044
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4336 0.9722 -0.5717 1.4524
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4641 1.1555 -1.1139 1.5804
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3447 0.9181 0.7825 2.2709
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4127 1.0516 0.5908 2.3392
## Avg_Cogongrass_Cover-Sus_scrofa 1.4553 1.1090 -1.0088 1.5311
## Tree_Density-Odocoileus_virginianus -1.0038 1.1124 -2.9135 -1.0978
## Tree_Density-Canis_latrans -2.3948 1.1417 -5.1639 -2.2015
## Tree_Density-Sciurus_niger -1.9354 1.3565 -4.8586 -1.8821
## Tree_Density-Procyon_lotor -1.5297 0.7702 -3.0825 -1.5191
## Tree_Density-Dasypus_novemcinctus -3.0893 1.5198 -7.0470 -2.7200
## Tree_Density-Lynx_rufus -0.9475 1.2377 -3.0265 -1.0767
## Tree_Density-Didelphis_virginiana -2.2049 1.0695 -4.7776 -2.0643
## Tree_Density-Sylvilagus_floridanus -2.3178 1.2328 -5.2058 -2.1514
## Tree_Density-Meleagris_gallopavo -2.0432 1.1729 -4.5507 -1.9806
## Tree_Density-Sciurus_carolinensis -2.3528 1.2043 -5.4340 -2.1484
## Tree_Density-Vulpes_vulpes -1.8473 1.4586 -4.8152 -1.8268
## Tree_Density-Sus_scrofa -2.2228 1.3346 -5.5041 -2.0347
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3828 1.1132 -1.0213 1.4260
## Avg_Canopy_Cover-Canis_latrans 0.4669 0.7422 -1.0606 0.4703
## Avg_Canopy_Cover-Sciurus_niger 2.0296 1.3630 -0.4412 1.9004
## Avg_Canopy_Cover-Procyon_lotor 1.7388 0.6824 0.5134 1.7040
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9287 0.6517 0.8355 1.8654
## Avg_Canopy_Cover-Lynx_rufus 1.4678 1.2112 -0.9760 1.4734
## Avg_Canopy_Cover-Didelphis_virginiana 2.5098 0.9055 1.1646 2.3669
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9384 1.3624 1.1255 2.6444
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2458 1.0550 0.6589 2.0696
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2279 0.7780 1.0333 2.1285
## Avg_Canopy_Cover-Vulpes_vulpes 2.0387 1.0495 0.3472 1.9227
## Avg_Canopy_Cover-Sus_scrofa 2.0421 0.7978 0.6689 1.9500
## avg_veg_height-Odocoileus_virginianus -0.6006 0.7013 -2.0114 -0.5921
## avg_veg_height-Canis_latrans -0.7170 0.5673 -1.8818 -0.7158
## avg_veg_height-Sciurus_niger -0.7266 0.7397 -2.3098 -0.6950
## avg_veg_height-Procyon_lotor -0.4475 0.5496 -1.5105 -0.4541
## avg_veg_height-Dasypus_novemcinctus -0.3427 0.5458 -1.3872 -0.3564
## avg_veg_height-Lynx_rufus -0.6317 0.7038 -2.0889 -0.6191
## avg_veg_height-Didelphis_virginiana -0.6422 0.6073 -1.8829 -0.6242
## avg_veg_height-Sylvilagus_floridanus -0.7198 0.6246 -1.9803 -0.6932
## avg_veg_height-Meleagris_gallopavo -0.6601 0.6810 -2.0432 -0.6388
## avg_veg_height-Sciurus_carolinensis -0.2663 0.6176 -1.3974 -0.2993
## avg_veg_height-Vulpes_vulpes -0.5851 0.6728 -1.9519 -0.5879
## avg_veg_height-Sus_scrofa -0.6584 0.6420 -1.9643 -0.6392
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.7732 1.0456 361
## (Intercept)-Canis_latrans 3.2849 1.0209 796
## (Intercept)-Sciurus_niger 6.4967 1.0579 375
## (Intercept)-Procyon_lotor 2.7403 1.0105 1171
## (Intercept)-Dasypus_novemcinctus 0.2170 1.0351 639
## (Intercept)-Lynx_rufus 10.8658 1.0309 213
## (Intercept)-Didelphis_virginiana -1.0203 1.0259 1038
## (Intercept)-Sylvilagus_floridanus 1.2680 1.0031 1013
## (Intercept)-Meleagris_gallopavo 1.7951 1.0072 544
## (Intercept)-Sciurus_carolinensis -1.1888 1.0526 366
## (Intercept)-Vulpes_vulpes 3.5920 1.0267 332
## (Intercept)-Sus_scrofa -1.7747 1.0207 520
## Cogon_Patch_Size-Odocoileus_virginianus 1.9980 1.0049 1862
## Cogon_Patch_Size-Canis_latrans 3.3098 1.0048 1369
## Cogon_Patch_Size-Sciurus_niger 1.3456 1.0128 562
## Cogon_Patch_Size-Procyon_lotor 0.4008 1.0374 778
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3779 1.0207 779
## Cogon_Patch_Size-Lynx_rufus 2.4605 1.0142 947
## Cogon_Patch_Size-Didelphis_virginiana 2.8648 1.0174 731
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2433 1.0105 674
## Cogon_Patch_Size-Meleagris_gallopavo 1.8105 1.0035 1512
## Cogon_Patch_Size-Sciurus_carolinensis 0.0716 1.0286 522
## Cogon_Patch_Size-Vulpes_vulpes 1.4706 1.0092 663
## Cogon_Patch_Size-Sus_scrofa 0.7715 1.0156 1476
## Veg_shannon_index-Odocoileus_virginianus 2.3563 1.0089 1760
## Veg_shannon_index-Canis_latrans 2.7841 1.0134 608
## Veg_shannon_index-Sciurus_niger 3.1647 1.0230 938
## Veg_shannon_index-Procyon_lotor 2.5352 1.0355 280
## Veg_shannon_index-Dasypus_novemcinctus 1.7294 1.0114 1331
## Veg_shannon_index-Lynx_rufus 2.6272 1.0131 1177
## Veg_shannon_index-Didelphis_virginiana 2.5839 1.0095 1271
## Veg_shannon_index-Sylvilagus_floridanus 2.5983 1.0196 755
## Veg_shannon_index-Meleagris_gallopavo 2.9902 1.0149 1058
## Veg_shannon_index-Sciurus_carolinensis 1.4908 1.0021 1276
## Veg_shannon_index-Vulpes_vulpes 1.9189 1.0012 1127
## Veg_shannon_index-Sus_scrofa 3.9005 1.0141 1061
## total_shrub_cover-Odocoileus_virginianus 2.1558 1.0011 2314
## total_shrub_cover-Canis_latrans 1.9035 1.0007 1317
## total_shrub_cover-Sciurus_niger 1.2162 1.0104 1074
## total_shrub_cover-Procyon_lotor 0.1181 1.0040 2238
## total_shrub_cover-Dasypus_novemcinctus 1.2220 1.0067 2069
## total_shrub_cover-Lynx_rufus 0.9085 1.0073 586
## total_shrub_cover-Didelphis_virginiana 0.6700 1.0066 980
## total_shrub_cover-Sylvilagus_floridanus 1.4483 1.0077 1659
## total_shrub_cover-Meleagris_gallopavo -0.0508 1.0077 583
## total_shrub_cover-Sciurus_carolinensis 1.4163 1.0010 2865
## total_shrub_cover-Vulpes_vulpes 1.1307 1.0213 1077
## total_shrub_cover-Sus_scrofa 2.0689 1.0011 2438
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.0579 1.0192 742
## Avg_Cogongrass_Cover-Canis_latrans 4.4094 1.0237 652
## Avg_Cogongrass_Cover-Sciurus_niger 3.7130 1.0169 725
## Avg_Cogongrass_Cover-Procyon_lotor 4.0431 1.0398 548
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5881 1.0466 413
## Avg_Cogongrass_Cover-Lynx_rufus 4.6261 1.0330 727
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9484 1.0261 759
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2671 1.0225 802
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4711 1.0117 886
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3832 1.0473 456
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7435 1.0387 612
## Avg_Cogongrass_Cover-Sus_scrofa 3.4034 1.0204 795
## Tree_Density-Odocoileus_virginianus 1.5386 1.0251 947
## Tree_Density-Canis_latrans -0.7143 1.0462 496
## Tree_Density-Sciurus_niger 0.6408 1.0404 999
## Tree_Density-Procyon_lotor -0.0046 1.0353 862
## Tree_Density-Dasypus_novemcinctus -1.1285 1.0280 322
## Tree_Density-Lynx_rufus 1.8349 1.0300 646
## Tree_Density-Didelphis_virginiana -0.5591 1.0302 645
## Tree_Density-Sylvilagus_floridanus -0.3894 1.0271 599
## Tree_Density-Meleagris_gallopavo 0.2149 1.0015 1033
## Tree_Density-Sciurus_carolinensis -0.6010 1.0212 485
## Tree_Density-Vulpes_vulpes 1.0509 1.0227 626
## Tree_Density-Sus_scrofa -0.1181 1.0099 735
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5243 1.0069 1928
## Avg_Canopy_Cover-Canis_latrans 1.8702 1.0126 1089
## Avg_Canopy_Cover-Sciurus_niger 5.0584 1.0271 888
## Avg_Canopy_Cover-Procyon_lotor 3.2011 1.0196 588
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.4087 1.0225 462
## Avg_Canopy_Cover-Lynx_rufus 4.0253 1.0136 704
## Avg_Canopy_Cover-Didelphis_virginiana 4.6886 1.0692 202
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.3564 1.0433 469
## Avg_Canopy_Cover-Meleagris_gallopavo 4.8555 1.0204 875
## Avg_Canopy_Cover-Sciurus_carolinensis 4.1126 1.0437 591
## Avg_Canopy_Cover-Vulpes_vulpes 4.5886 1.0393 788
## Avg_Canopy_Cover-Sus_scrofa 3.8448 1.0185 1062
## avg_veg_height-Odocoileus_virginianus 0.7411 1.0075 1154
## avg_veg_height-Canis_latrans 0.3611 1.0095 1137
## avg_veg_height-Sciurus_niger 0.6558 1.0045 921
## avg_veg_height-Procyon_lotor 0.6815 1.0015 946
## avg_veg_height-Dasypus_novemcinctus 0.7812 1.0055 1188
## avg_veg_height-Lynx_rufus 0.7264 1.0159 1207
## avg_veg_height-Didelphis_virginiana 0.5409 1.0065 1124
## avg_veg_height-Sylvilagus_floridanus 0.4724 1.0058 1057
## avg_veg_height-Meleagris_gallopavo 0.6495 1.0139 1053
## avg_veg_height-Sciurus_carolinensis 1.0778 1.0076 1226
## avg_veg_height-Vulpes_vulpes 0.7367 1.0080 971
## avg_veg_height-Sus_scrofa 0.5925 1.0089 1216
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0048 0.0585 -0.1089 0.0050 0.1219
## (Intercept)-Canis_latrans -2.6504 0.1797 -3.0105 -2.6433 -2.3087
## (Intercept)-Sciurus_niger -4.4905 0.4447 -5.3555 -4.4946 -3.5974
## (Intercept)-Procyon_lotor -2.2664 0.1319 -2.5352 -2.2631 -2.0133
## (Intercept)-Dasypus_novemcinctus -1.5750 0.1335 -1.8448 -1.5744 -1.3149
## (Intercept)-Lynx_rufus -3.8165 0.3415 -4.4851 -3.8201 -3.1544
## (Intercept)-Didelphis_virginiana -2.2859 0.2422 -2.7895 -2.2770 -1.8413
## (Intercept)-Sylvilagus_floridanus -3.1819 0.2949 -3.8004 -3.1667 -2.6431
## (Intercept)-Meleagris_gallopavo -3.4345 0.3190 -4.0861 -3.4186 -2.8239
## (Intercept)-Sciurus_carolinensis -2.4362 0.2652 -2.9909 -2.4180 -1.9453
## (Intercept)-Vulpes_vulpes -4.2441 0.6640 -5.5464 -4.2393 -2.9744
## (Intercept)-Sus_scrofa -2.8630 0.4458 -3.8486 -2.8334 -2.0822
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 4967
## (Intercept)-Canis_latrans 1.0007 2087
## (Intercept)-Sciurus_niger 1.0300 523
## (Intercept)-Procyon_lotor 1.0039 3816
## (Intercept)-Dasypus_novemcinctus 1.0005 5250
## (Intercept)-Lynx_rufus 1.0171 443
## (Intercept)-Didelphis_virginiana 0.9999 4871
## (Intercept)-Sylvilagus_floridanus 1.0001 1445
## (Intercept)-Meleagris_gallopavo 1.0093 1287
## (Intercept)-Sciurus_carolinensis 1.0006 3142
## (Intercept)-Vulpes_vulpes 1.0054 359
## (Intercept)-Sus_scrofa 1.0058 2147
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4408
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3074 0.5608 -1.3873 -0.3242 0.8741 1.0042 2200
## Avg_Cogongrass_Cover 0.0371 0.3135 -0.6155 0.0461 0.6268 1.0097 1503
## total_shrub_cover -0.4438 0.2950 -1.0809 -0.4291 0.1045 1.0049 2822
## avg_veg_height -0.0149 0.2818 -0.5663 -0.0152 0.5350 1.0096 1303
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4036 2.6821 0.6550 2.6965 9.9261 1.0090 1264
## Avg_Cogongrass_Cover 0.3624 0.4203 0.0402 0.2351 1.4247 1.0068 2293
## total_shrub_cover 0.5416 0.5744 0.0582 0.3724 2.0916 1.0096 1315
## avg_veg_height 0.2023 0.2025 0.0340 0.1403 0.7386 1.0113 2683
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0367 0.9881 0.083 0.7405 3.6351 1.0366 443
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5624 0.3882 -3.3063 -2.5691 -1.7562 1.0058 3591
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7048 0.9715 0.6342 1.4662 4.1445 1.0144 2437
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4872 1.4486 0.9682 3.3400
## (Intercept)-Canis_latrans 0.3050 0.7039 -1.0510 0.2850
## (Intercept)-Sciurus_niger -0.6891 1.1733 -2.6427 -0.8138
## (Intercept)-Procyon_lotor 0.6247 0.7189 -0.8131 0.6295
## (Intercept)-Dasypus_novemcinctus -0.7465 0.6216 -2.0248 -0.7443
## (Intercept)-Lynx_rufus -0.0270 0.9577 -1.7131 -0.0905
## (Intercept)-Didelphis_virginiana -1.4241 0.6892 -2.7856 -1.4190
## (Intercept)-Sylvilagus_floridanus -0.2213 0.8640 -1.7167 -0.2846
## (Intercept)-Meleagris_gallopavo -0.7219 0.7989 -2.2889 -0.7292
## (Intercept)-Sciurus_carolinensis -1.5158 0.7074 -2.9599 -1.5006
## (Intercept)-Vulpes_vulpes -1.0015 1.3292 -3.2245 -1.1524
## (Intercept)-Sus_scrofa -1.9925 0.8850 -3.7430 -1.9775
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0389 0.5490 -1.0520 0.0383
## Avg_Cogongrass_Cover-Canis_latrans 0.3566 0.4541 -0.4664 0.3318
## Avg_Cogongrass_Cover-Sciurus_niger -0.3108 0.6117 -1.6694 -0.2551
## Avg_Cogongrass_Cover-Procyon_lotor 0.0308 0.4318 -0.8296 0.0370
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2226 0.3991 -0.5549 0.2224
## Avg_Cogongrass_Cover-Lynx_rufus 0.3847 0.5063 -0.5068 0.3542
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2849 0.4263 -0.5245 0.2743
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2941 0.5124 -1.4141 -0.2638
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4031 0.5749 -1.7324 -0.3446
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1972 0.4276 -0.6289 0.1905
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1776 0.5235 -0.8415 0.1679
## Avg_Cogongrass_Cover-Sus_scrofa -0.2471 0.5876 -1.5902 -0.1903
## total_shrub_cover-Odocoileus_virginianus -0.2756 0.5975 -1.4482 -0.2965
## total_shrub_cover-Canis_latrans 0.0864 0.4589 -0.7142 0.0540
## total_shrub_cover-Sciurus_niger -0.7222 0.6190 -2.1651 -0.6614
## total_shrub_cover-Procyon_lotor -0.9332 0.4995 -2.0533 -0.8850
## total_shrub_cover-Dasypus_novemcinctus -0.0841 0.3755 -0.7970 -0.0960
## total_shrub_cover-Lynx_rufus -0.8986 0.6370 -2.3941 -0.8103
## total_shrub_cover-Didelphis_virginiana -0.2826 0.4191 -1.1074 -0.2799
## total_shrub_cover-Sylvilagus_floridanus -0.4571 0.5610 -1.7267 -0.4162
## total_shrub_cover-Meleagris_gallopavo -1.1969 0.6367 -2.6689 -1.1115
## total_shrub_cover-Sciurus_carolinensis -0.1449 0.4304 -0.9877 -0.1568
## total_shrub_cover-Vulpes_vulpes -0.4663 0.6493 -1.8352 -0.4340
## total_shrub_cover-Sus_scrofa -0.0197 0.5398 -1.0059 -0.0454
## avg_veg_height-Odocoileus_virginianus -0.0177 0.4725 -0.9801 -0.0196
## avg_veg_height-Canis_latrans -0.0733 0.3972 -0.8680 -0.0678
## avg_veg_height-Sciurus_niger -0.1643 0.4780 -1.1755 -0.1349
## avg_veg_height-Procyon_lotor 0.0976 0.4046 -0.6820 0.0882
## avg_veg_height-Dasypus_novemcinctus 0.1772 0.3772 -0.5474 0.1650
## avg_veg_height-Lynx_rufus -0.0045 0.4594 -0.9001 -0.0060
## avg_veg_height-Didelphis_virginiana -0.0315 0.3994 -0.8215 -0.0280
## avg_veg_height-Sylvilagus_floridanus -0.1255 0.4284 -0.9948 -0.1137
## avg_veg_height-Meleagris_gallopavo -0.2033 0.4653 -1.1688 -0.1817
## avg_veg_height-Sciurus_carolinensis 0.2506 0.4165 -0.5069 0.2293
## avg_veg_height-Vulpes_vulpes -0.0522 0.4551 -0.9558 -0.0488
## avg_veg_height-Sus_scrofa -0.0371 0.4371 -0.8952 -0.0289
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.7732 1.0072 1068
## (Intercept)-Canis_latrans 1.7409 1.0046 2170
## (Intercept)-Sciurus_niger 2.0987 1.0189 491
## (Intercept)-Procyon_lotor 2.0054 1.0002 1787
## (Intercept)-Dasypus_novemcinctus 0.4376 1.0027 3200
## (Intercept)-Lynx_rufus 1.9940 1.0122 1405
## (Intercept)-Didelphis_virginiana -0.0911 1.0055 2750
## (Intercept)-Sylvilagus_floridanus 1.6865 1.0097 1213
## (Intercept)-Meleagris_gallopavo 0.8902 1.0016 2369
## (Intercept)-Sciurus_carolinensis -0.1601 1.0014 2958
## (Intercept)-Vulpes_vulpes 2.1660 1.0052 518
## (Intercept)-Sus_scrofa -0.2729 1.0023 1745
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1596 1.0026 2839
## Avg_Cogongrass_Cover-Canis_latrans 1.3106 1.0042 2531
## Avg_Cogongrass_Cover-Sciurus_niger 0.7436 1.0078 1827
## Avg_Cogongrass_Cover-Procyon_lotor 0.8796 1.0036 2712
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0462 1.0040 2606
## Avg_Cogongrass_Cover-Lynx_rufus 1.4905 1.0092 2310
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1658 1.0065 2568
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6309 1.0083 1848
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5499 1.0055 2247
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0518 1.0012 2408
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2823 1.0028 2221
## Avg_Cogongrass_Cover-Sus_scrofa 0.7378 1.0076 2296
## total_shrub_cover-Odocoileus_virginianus 0.9665 1.0016 3673
## total_shrub_cover-Canis_latrans 1.0520 1.0001 3089
## total_shrub_cover-Sciurus_niger 0.3458 1.0035 2408
## total_shrub_cover-Procyon_lotor -0.1043 1.0005 2559
## total_shrub_cover-Dasypus_novemcinctus 0.6838 1.0026 4344
## total_shrub_cover-Lynx_rufus 0.1464 1.0089 1807
## total_shrub_cover-Didelphis_virginiana 0.5413 1.0013 4344
## total_shrub_cover-Sylvilagus_floridanus 0.5459 1.0045 2038
## total_shrub_cover-Meleagris_gallopavo -0.1980 1.0005 1869
## total_shrub_cover-Sciurus_carolinensis 0.7208 1.0030 4666
## total_shrub_cover-Vulpes_vulpes 0.7625 1.0071 2213
## total_shrub_cover-Sus_scrofa 1.1040 1.0068 3393
## avg_veg_height-Odocoileus_virginianus 0.8966 1.0124 2668
## avg_veg_height-Canis_latrans 0.6962 1.0005 2549
## avg_veg_height-Sciurus_niger 0.7085 1.0057 2382
## avg_veg_height-Procyon_lotor 0.9133 1.0047 2235
## avg_veg_height-Dasypus_novemcinctus 0.9511 1.0017 2470
## avg_veg_height-Lynx_rufus 0.9092 1.0074 1974
## avg_veg_height-Didelphis_virginiana 0.7673 1.0039 2551
## avg_veg_height-Sylvilagus_floridanus 0.7212 1.0028 2131
## avg_veg_height-Meleagris_gallopavo 0.6580 1.0085 2310
## avg_veg_height-Sciurus_carolinensis 1.1361 1.0011 2388
## avg_veg_height-Vulpes_vulpes 0.8510 1.0040 2266
## avg_veg_height-Sus_scrofa 0.8133 1.0029 2439
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0041 0.0588 -0.1125 0.0043 0.1212
## (Intercept)-Canis_latrans -2.6336 0.1787 -3.0015 -2.6286 -2.2977
## (Intercept)-Sciurus_niger -3.8997 0.5713 -5.0821 -3.8615 -2.8719
## (Intercept)-Procyon_lotor -2.2755 0.1318 -2.5429 -2.2735 -2.0311
## (Intercept)-Dasypus_novemcinctus -1.5779 0.1341 -1.8478 -1.5782 -1.3193
## (Intercept)-Lynx_rufus -3.5556 0.3119 -4.1977 -3.5468 -2.9672
## (Intercept)-Didelphis_virginiana -2.3163 0.2526 -2.8471 -2.3068 -1.8588
## (Intercept)-Sylvilagus_floridanus -3.2654 0.3276 -3.9558 -3.2492 -2.6797
## (Intercept)-Meleagris_gallopavo -3.3149 0.3231 -3.9897 -3.2959 -2.7395
## (Intercept)-Sciurus_carolinensis -2.4473 0.2689 -3.0120 -2.4327 -1.9552
## (Intercept)-Vulpes_vulpes -4.1212 0.7287 -5.5073 -4.1203 -2.7852
## (Intercept)-Sus_scrofa -2.9439 0.4722 -3.9785 -2.9099 -2.1108
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0010 2773
## (Intercept)-Sciurus_niger 1.0308 467
## (Intercept)-Procyon_lotor 1.0000 4207
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
## (Intercept)-Lynx_rufus 1.0117 1100
## (Intercept)-Didelphis_virginiana 1.0005 3556
## (Intercept)-Sylvilagus_floridanus 1.0157 1034
## (Intercept)-Meleagris_gallopavo 1.0023 1365
## (Intercept)-Sciurus_carolinensis 1.0071 3284
## (Intercept)-Vulpes_vulpes 1.0095 450
## (Intercept)-Sus_scrofa 1.0043 1568
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.481
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2918 0.6753 -1.5767 -0.3184 1.1154 1.0178 1764
## Tree_Density -0.7412 0.3811 -1.5859 -0.7088 -0.0794 1.0120 1647
## Avg_Canopy_Cover 1.0610 0.3208 0.4713 1.0449 1.7444 1.0208 1271
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.3949 4.3383 1.2512 4.2780 16.0095 1.0493 1293
## Tree_Density 0.6761 1.1233 0.0447 0.3123 3.4980 1.0624 852
## Avg_Canopy_Cover 0.5347 0.6354 0.0523 0.3576 2.0268 1.0578 1415
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5607 0.6488 0.0482 0.3502 2.2187 1.0279 453
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5733 0.3944 -3.3197 -2.5832 -1.7779 1.0067 4390
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7895 1.0077 0.6696 1.5568 4.3911 1.009 2882
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.3699 1.6492 1.8724 4.1290 8.2931
## (Intercept)-Canis_latrans 0.3193 0.6816 -0.9108 0.2756 1.7826
## (Intercept)-Sciurus_niger -0.1110 1.4360 -2.3342 -0.3247 3.3918
## (Intercept)-Procyon_lotor 0.7001 0.6417 -0.5825 0.7095 1.9527
## (Intercept)-Dasypus_novemcinctus -1.0187 0.6290 -2.3723 -0.9941 0.1492
## (Intercept)-Lynx_rufus 0.9532 1.5955 -1.3594 0.6501 5.0059
## (Intercept)-Didelphis_virginiana -1.8821 0.7226 -3.3665 -1.8339 -0.5707
## (Intercept)-Sylvilagus_floridanus -0.6601 0.7381 -2.0414 -0.6874 0.8712
## (Intercept)-Meleagris_gallopavo -0.3647 0.8381 -1.8809 -0.4095 1.4426
## (Intercept)-Sciurus_carolinensis -1.9900 0.7414 -3.5243 -1.9572 -0.6642
## (Intercept)-Vulpes_vulpes -1.4734 1.4187 -3.7870 -1.6308 1.8376
## (Intercept)-Sus_scrofa -2.6648 0.9487 -4.6417 -2.6183 -0.9281
## Tree_Density-Odocoileus_virginianus -0.3829 0.6187 -1.4337 -0.4457 1.0388
## Tree_Density-Canis_latrans -0.8627 0.5206 -2.0663 -0.8017 0.0009
## Tree_Density-Sciurus_niger -0.7842 0.7475 -2.4234 -0.7322 0.5519
## Tree_Density-Procyon_lotor -0.4866 0.4021 -1.2943 -0.4857 0.3047
## Tree_Density-Dasypus_novemcinctus -1.2904 0.8689 -3.5745 -1.0964 -0.1726
## Tree_Density-Lynx_rufus -0.0270 0.7453 -1.1607 -0.1399 1.8788
## Tree_Density-Didelphis_virginiana -0.9671 0.7299 -2.7571 -0.8455 0.0960
## Tree_Density-Sylvilagus_floridanus -0.9855 0.7043 -2.7078 -0.8782 0.1043
## Tree_Density-Meleagris_gallopavo -0.8625 0.6749 -2.4433 -0.7962 0.2418
## Tree_Density-Sciurus_carolinensis -0.8970 0.6907 -2.6113 -0.7984 0.2138
## Tree_Density-Vulpes_vulpes -0.6283 0.8330 -2.2225 -0.6284 0.9222
## Tree_Density-Sus_scrofa -0.9319 0.7902 -2.9372 -0.8126 0.2628
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8453 0.6500 -0.4626 0.8421 2.1254
## Avg_Canopy_Cover-Canis_latrans 0.1755 0.4870 -0.7899 0.1728 1.1465
## Avg_Canopy_Cover-Sciurus_niger 1.0611 0.7797 -0.3034 1.0006 2.8114
## Avg_Canopy_Cover-Procyon_lotor 1.0602 0.4464 0.2413 1.0368 1.9994
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0315 0.4145 0.2684 1.0060 1.9150
## Avg_Canopy_Cover-Lynx_rufus 0.9214 0.6764 -0.3341 0.8942 2.3249
## Avg_Canopy_Cover-Didelphis_virginiana 1.2690 0.4796 0.4302 1.2303 2.3062
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6384 0.6974 0.5783 1.5314 3.2794
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3699 0.6301 0.3518 1.2913 2.8279
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2416 0.4739 0.4065 1.2084 2.2515
## Avg_Canopy_Cover-Vulpes_vulpes 1.0826 0.5766 0.0321 1.0529 2.3310
## Avg_Canopy_Cover-Sus_scrofa 1.2297 0.5144 0.3300 1.1817 2.3286
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0446 1056
## (Intercept)-Canis_latrans 1.0022 2418
## (Intercept)-Sciurus_niger 1.0261 458
## (Intercept)-Procyon_lotor 1.0002 2589
## (Intercept)-Dasypus_novemcinctus 1.0049 2735
## (Intercept)-Lynx_rufus 1.0347 402
## (Intercept)-Didelphis_virginiana 1.0010 3114
## (Intercept)-Sylvilagus_floridanus 1.0019 2324
## (Intercept)-Meleagris_gallopavo 1.0009 1938
## (Intercept)-Sciurus_carolinensis 1.0091 2335
## (Intercept)-Vulpes_vulpes 1.1152 405
## (Intercept)-Sus_scrofa 1.0109 1850
## Tree_Density-Odocoileus_virginianus 1.0063 2800
## Tree_Density-Canis_latrans 1.0022 2974
## Tree_Density-Sciurus_niger 1.0205 1548
## Tree_Density-Procyon_lotor 1.0021 3532
## Tree_Density-Dasypus_novemcinctus 1.0199 1045
## Tree_Density-Lynx_rufus 1.0044 938
## Tree_Density-Didelphis_virginiana 1.0090 1895
## Tree_Density-Sylvilagus_floridanus 1.0138 1581
## Tree_Density-Meleagris_gallopavo 1.0177 1903
## Tree_Density-Sciurus_carolinensis 1.0105 2356
## Tree_Density-Vulpes_vulpes 1.0236 1500
## Tree_Density-Sus_scrofa 1.0156 1687
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0071 2919
## Avg_Canopy_Cover-Canis_latrans 1.0069 2294
## Avg_Canopy_Cover-Sciurus_niger 1.0235 1194
## Avg_Canopy_Cover-Procyon_lotor 1.0024 3560
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0041 3623
## Avg_Canopy_Cover-Lynx_rufus 1.0006 1557
## Avg_Canopy_Cover-Didelphis_virginiana 1.0088 2738
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0064 1786
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0125 1717
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0070 2532
## Avg_Canopy_Cover-Vulpes_vulpes 1.0032 2767
## Avg_Canopy_Cover-Sus_scrofa 1.0132 2664
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0048 0.0575 -0.1067 0.0052 0.1172
## (Intercept)-Canis_latrans -2.6462 0.1850 -3.0359 -2.6386 -2.3014
## (Intercept)-Sciurus_niger -4.1434 0.5754 -5.2569 -4.1450 -3.0171
## (Intercept)-Procyon_lotor -2.2663 0.1317 -2.5306 -2.2647 -2.0113
## (Intercept)-Dasypus_novemcinctus -1.5738 0.1327 -1.8439 -1.5694 -1.3189
## (Intercept)-Lynx_rufus -3.7462 0.3529 -4.4118 -3.7474 -3.0557
## (Intercept)-Didelphis_virginiana -2.3011 0.2447 -2.7897 -2.2931 -1.8360
## (Intercept)-Sylvilagus_floridanus -3.1342 0.2847 -3.7179 -3.1215 -2.6130
## (Intercept)-Meleagris_gallopavo -3.4615 0.3388 -4.1605 -3.4563 -2.8343
## (Intercept)-Sciurus_carolinensis -2.4293 0.2610 -2.9610 -2.4200 -1.9463
## (Intercept)-Vulpes_vulpes -4.0208 0.7273 -5.5061 -3.9860 -2.7358
## (Intercept)-Sus_scrofa -2.8743 0.4601 -3.8575 -2.8367 -2.0956
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0101 2437
## (Intercept)-Sciurus_niger 1.0237 431
## (Intercept)-Procyon_lotor 1.0009 4081
## (Intercept)-Dasypus_novemcinctus 0.9998 4957
## (Intercept)-Lynx_rufus 1.0095 535
## (Intercept)-Didelphis_virginiana 1.0019 3886
## (Intercept)-Sylvilagus_floridanus 1.0011 1818
## (Intercept)-Meleagris_gallopavo 1.0064 1204
## (Intercept)-Sciurus_carolinensis 1.0007 3924
## (Intercept)-Vulpes_vulpes 1.0652 442
## (Intercept)-Sus_scrofa 1.0059 1666
# Includes movement covariates of occupancy and null for detection
ms_null_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4333
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3525 0.5915 -1.5340 -0.3601 0.8552 1.0111 1514
## Cogon_Patch_Size -0.1902 0.3673 -0.9578 -0.1747 0.4847 1.0048 1821
## Avg_Cogongrass_Cover 0.1097 0.2808 -0.4563 0.1098 0.6488 1.0053 1998
## total_shrub_cover -0.4225 0.2947 -1.0334 -0.4164 0.1405 1.0019 2423
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6464 2.9110 0.6142 2.9339 11.0142 1.0056 1723
## Cogon_Patch_Size 0.7938 1.0961 0.0642 0.4583 3.5578 1.0120 1397
## Avg_Cogongrass_Cover 0.3609 0.4820 0.0401 0.2270 1.4494 1.0449 1835
## total_shrub_cover 0.5220 0.5625 0.0576 0.3443 2.0712 1.0052 1355
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.368 1.2066 0.1271 1.0294 4.695 1.0427 510
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.565 0.3934 -3.3317 -2.5779 -1.7678 1.0024 4797
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7243 1.046 0.6405 1.461 4.342 1.0123 1720
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5306 1.5383 0.7502 3.4081
## (Intercept)-Canis_latrans 0.3719 0.7437 -1.1062 0.3707
## (Intercept)-Sciurus_niger -0.7120 1.2505 -2.8160 -0.8292
## (Intercept)-Procyon_lotor 0.5668 0.7544 -0.9395 0.5804
## (Intercept)-Dasypus_novemcinctus -0.7639 0.6836 -2.1494 -0.7509
## (Intercept)-Lynx_rufus -0.2162 0.9595 -2.0366 -0.2471
## (Intercept)-Didelphis_virginiana -1.4376 0.7612 -2.9876 -1.4305
## (Intercept)-Sylvilagus_floridanus -0.3797 0.9173 -2.0512 -0.4315
## (Intercept)-Meleagris_gallopavo -0.7077 0.9431 -2.3895 -0.7331
## (Intercept)-Sciurus_carolinensis -1.6448 0.7803 -3.3070 -1.6053
## (Intercept)-Vulpes_vulpes -1.1030 1.3706 -3.4866 -1.2330
## (Intercept)-Sus_scrofa -2.0616 0.9693 -3.9914 -2.0337
## Cogon_Patch_Size-Odocoileus_virginianus -0.0246 0.6839 -1.2451 -0.0626
## Cogon_Patch_Size-Canis_latrans 0.6737 0.6810 -0.3400 0.5593
## Cogon_Patch_Size-Sciurus_niger -0.5757 0.8807 -2.7635 -0.4549
## Cogon_Patch_Size-Procyon_lotor -0.2289 0.4525 -1.1533 -0.2176
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1227 0.4144 -0.9678 -0.1131
## Cogon_Patch_Size-Lynx_rufus -0.1864 0.7456 -1.5573 -0.2369
## Cogon_Patch_Size-Didelphis_virginiana 0.6006 0.5146 -0.2997 0.5702
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7970 0.8160 -2.8660 -0.6536
## Cogon_Patch_Size-Meleagris_gallopavo -0.1154 0.5899 -1.3026 -0.1138
## Cogon_Patch_Size-Sciurus_carolinensis -0.6708 0.6706 -2.2879 -0.5635
## Cogon_Patch_Size-Vulpes_vulpes -0.4835 0.8909 -2.5912 -0.3961
## Cogon_Patch_Size-Sus_scrofa -0.4114 0.7784 -2.3021 -0.3040
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1265 0.5574 -0.9595 0.1171
## Avg_Cogongrass_Cover-Canis_latrans 0.2505 0.4025 -0.5035 0.2338
## Avg_Cogongrass_Cover-Sciurus_niger -0.2200 0.6317 -1.6803 -0.1504
## Avg_Cogongrass_Cover-Procyon_lotor 0.1652 0.4341 -0.6474 0.1521
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3499 0.3753 -0.3398 0.3335
## Avg_Cogongrass_Cover-Lynx_rufus 0.4724 0.4985 -0.3772 0.4297
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1753 0.4086 -0.6306 0.1749
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1709 0.4771 -1.1955 -0.1459
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3579 0.5919 -1.7154 -0.2916
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4209 0.4099 -0.3304 0.4059
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2503 0.4988 -0.7035 0.2382
## Avg_Cogongrass_Cover-Sus_scrofa -0.1659 0.5802 -1.4825 -0.1123
## total_shrub_cover-Odocoileus_virginianus -0.2498 0.5983 -1.3945 -0.2632
## total_shrub_cover-Canis_latrans 0.0471 0.4634 -0.7562 0.0123
## total_shrub_cover-Sciurus_niger -0.6560 0.6080 -2.0259 -0.6108
## total_shrub_cover-Procyon_lotor -0.8869 0.5001 -2.0191 -0.8301
## total_shrub_cover-Dasypus_novemcinctus -0.0948 0.3765 -0.8253 -0.1019
## total_shrub_cover-Lynx_rufus -0.8233 0.6501 -2.3474 -0.7532
## total_shrub_cover-Didelphis_virginiana -0.3780 0.4345 -1.2534 -0.3761
## total_shrub_cover-Sylvilagus_floridanus -0.3911 0.5703 -1.5765 -0.3639
## total_shrub_cover-Meleagris_gallopavo -1.1612 0.6680 -2.7307 -1.0693
## total_shrub_cover-Sciurus_carolinensis -0.1401 0.4297 -0.9924 -0.1518
## total_shrub_cover-Vulpes_vulpes -0.4459 0.6495 -1.8616 -0.4109
## total_shrub_cover-Sus_scrofa 0.0048 0.5695 -1.0000 -0.0271
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9637 1.0065 1014
## (Intercept)-Canis_latrans 1.8809 1.0021 2188
## (Intercept)-Sciurus_niger 2.2502 1.0268 587
## (Intercept)-Procyon_lotor 2.0577 1.0012 2010
## (Intercept)-Dasypus_novemcinctus 0.5687 1.0085 1994
## (Intercept)-Lynx_rufus 1.7305 1.0098 1436
## (Intercept)-Didelphis_virginiana 0.0549 1.0014 2450
## (Intercept)-Sylvilagus_floridanus 1.6182 1.0070 1548
## (Intercept)-Meleagris_gallopavo 1.0955 1.0381 846
## (Intercept)-Sciurus_carolinensis -0.2201 1.0110 2327
## (Intercept)-Vulpes_vulpes 2.0770 1.0387 480
## (Intercept)-Sus_scrofa -0.2112 1.0104 1627
## Cogon_Patch_Size-Odocoileus_virginianus 1.4417 1.0024 3697
## Cogon_Patch_Size-Canis_latrans 2.3363 1.0044 1984
## Cogon_Patch_Size-Sciurus_niger 0.8660 1.0069 1393
## Cogon_Patch_Size-Procyon_lotor 0.6396 1.0004 3471
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6702 1.0057 4563
## Cogon_Patch_Size-Lynx_rufus 1.4655 1.0024 2008
## Cogon_Patch_Size-Didelphis_virginiana 1.6937 1.0036 2724
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3862 1.0113 1638
## Cogon_Patch_Size-Meleagris_gallopavo 1.0654 1.0010 3407
## Cogon_Patch_Size-Sciurus_carolinensis 0.3584 1.0031 2018
## Cogon_Patch_Size-Vulpes_vulpes 1.0568 1.0134 1722
## Cogon_Patch_Size-Sus_scrofa 0.8430 1.0099 2183
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2932 1.0011 3432
## Avg_Cogongrass_Cover-Canis_latrans 1.0903 1.0002 3500
## Avg_Cogongrass_Cover-Sciurus_niger 0.8493 1.0004 1752
## Avg_Cogongrass_Cover-Procyon_lotor 1.0490 1.0032 3453
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1397 1.0002 3395
## Avg_Cogongrass_Cover-Lynx_rufus 1.6016 1.0002 3158
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9926 1.0016 3572
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6990 1.0022 2690
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6139 1.0040 1797
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2774 1.0027 3076
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3175 1.0054 3275
## Avg_Cogongrass_Cover-Sus_scrofa 0.7968 1.0055 2206
## total_shrub_cover-Odocoileus_virginianus 1.0201 1.0006 3551
## total_shrub_cover-Canis_latrans 1.0895 1.0062 3044
## total_shrub_cover-Sciurus_niger 0.4348 1.0017 2379
## total_shrub_cover-Procyon_lotor -0.0558 1.0041 2178
## total_shrub_cover-Dasypus_novemcinctus 0.6509 1.0064 4301
## total_shrub_cover-Lynx_rufus 0.2289 1.0034 1930
## total_shrub_cover-Didelphis_virginiana 0.4680 1.0017 4599
## total_shrub_cover-Sylvilagus_floridanus 0.6653 1.0023 2365
## total_shrub_cover-Meleagris_gallopavo -0.1009 1.0013 1467
## total_shrub_cover-Sciurus_carolinensis 0.7383 1.0027 4481
## total_shrub_cover-Vulpes_vulpes 0.7734 1.0003 2398
## total_shrub_cover-Sus_scrofa 1.2511 1.0007 3009
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0027 0.0601 -0.1151 0.0029 0.1207
## (Intercept)-Canis_latrans -2.6124 0.1731 -2.9693 -2.6053 -2.2900
## (Intercept)-Sciurus_niger -3.9181 0.5535 -5.0429 -3.9003 -2.8932
## (Intercept)-Procyon_lotor -2.2787 0.1322 -2.5396 -2.2777 -2.0260
## (Intercept)-Dasypus_novemcinctus -1.5783 0.1328 -1.8438 -1.5754 -1.3261
## (Intercept)-Lynx_rufus -3.5184 0.3058 -4.1368 -3.5052 -2.9478
## (Intercept)-Didelphis_virginiana -2.3117 0.2472 -2.8164 -2.3026 -1.8594
## (Intercept)-Sylvilagus_floridanus -3.2530 0.3266 -3.9406 -3.2334 -2.6550
## (Intercept)-Meleagris_gallopavo -3.3370 0.3366 -4.0287 -3.3225 -2.7186
## (Intercept)-Sciurus_carolinensis -2.4406 0.2657 -2.9870 -2.4299 -1.9501
## (Intercept)-Vulpes_vulpes -4.1244 0.7566 -5.6426 -4.0930 -2.7769
## (Intercept)-Sus_scrofa -2.9731 0.4953 -4.0306 -2.9338 -2.1004
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0013 2987
## (Intercept)-Sciurus_niger 1.0139 536
## (Intercept)-Procyon_lotor 1.0001 4677
## (Intercept)-Dasypus_novemcinctus 1.0003 5005
## (Intercept)-Lynx_rufus 1.0017 1263
## (Intercept)-Didelphis_virginiana 1.0008 3816
## (Intercept)-Sylvilagus_floridanus 1.0007 1168
## (Intercept)-Meleagris_gallopavo 1.0172 1285
## (Intercept)-Sciurus_carolinensis 1.0010 3573
## (Intercept)-Vulpes_vulpes 1.0257 383
## (Intercept)-Sus_scrofa 1.0184 1368
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4387
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3178 0.5447 -1.3612 -0.3389 0.8382 1.0010 2145
## Veg_shannon_index 0.3824 0.2541 -0.1165 0.3808 0.8992 1.0077 1898
## Avg_Cogongrass_Cover 0.2443 0.2528 -0.2668 0.2433 0.7399 1.0080 2068
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2323 2.4416 0.6392 2.6130 9.6218 1.0127 1378
## Veg_shannon_index 0.2773 0.3354 0.0369 0.1788 1.0577 1.0275 1642
## Avg_Cogongrass_Cover 0.3220 0.3654 0.0392 0.2155 1.2298 1.0002 1995
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9453 0.8826 0.0736 0.692 3.3096 1.0753 460
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5642 0.3837 -3.3251 -2.5629 -1.7959 1.0028 4272
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7145 0.9905 0.6219 1.4748 4.2151 1.0133 2738
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3535 1.3679 0.9828 3.2284
## (Intercept)-Canis_latrans 0.1990 0.6600 -1.1034 0.1938
## (Intercept)-Sciurus_niger -0.5203 1.1954 -2.4608 -0.6572
## (Intercept)-Procyon_lotor 0.4803 0.6654 -0.9025 0.4969
## (Intercept)-Dasypus_novemcinctus -0.7547 0.6140 -2.0293 -0.7401
## (Intercept)-Lynx_rufus -0.0635 1.0106 -1.7909 -0.1500
## (Intercept)-Didelphis_virginiana -1.4972 0.6837 -2.8941 -1.4730
## (Intercept)-Sylvilagus_floridanus -0.3279 0.8193 -1.7840 -0.3841
## (Intercept)-Meleagris_gallopavo -0.4068 0.9007 -1.9942 -0.4676
## (Intercept)-Sciurus_carolinensis -1.4785 0.6939 -2.9019 -1.4534
## (Intercept)-Vulpes_vulpes -1.1027 1.2048 -3.2225 -1.1821
## (Intercept)-Sus_scrofa -2.1368 0.8701 -3.9413 -2.0914
## Veg_shannon_index-Odocoileus_virginianus 0.3177 0.4793 -0.6754 0.3292
## Veg_shannon_index-Canis_latrans 0.6521 0.3824 -0.0453 0.6299
## Veg_shannon_index-Sciurus_niger 0.3846 0.5156 -0.6017 0.3701
## Veg_shannon_index-Procyon_lotor 0.4935 0.3884 -0.2227 0.4745
## Veg_shannon_index-Dasypus_novemcinctus 0.2147 0.3422 -0.4786 0.2249
## Veg_shannon_index-Lynx_rufus 0.2117 0.4898 -0.8532 0.2465
## Veg_shannon_index-Didelphis_virginiana 0.5152 0.3916 -0.2094 0.4975
## Veg_shannon_index-Sylvilagus_floridanus 0.4808 0.4381 -0.3149 0.4600
## Veg_shannon_index-Meleagris_gallopavo 0.4769 0.4590 -0.3399 0.4585
## Veg_shannon_index-Sciurus_carolinensis 0.0345 0.3973 -0.8108 0.0580
## Veg_shannon_index-Vulpes_vulpes 0.1275 0.4703 -0.9026 0.1634
## Veg_shannon_index-Sus_scrofa 0.7089 0.5180 -0.1523 0.6495
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2579 0.5064 -0.7438 0.2510
## Avg_Cogongrass_Cover-Canis_latrans 0.5136 0.3875 -0.1817 0.4892
## Avg_Cogongrass_Cover-Sciurus_niger -0.1013 0.5903 -1.3888 -0.0473
## Avg_Cogongrass_Cover-Procyon_lotor 0.4031 0.4013 -0.3151 0.3709
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4126 0.3324 -0.2058 0.4003
## Avg_Cogongrass_Cover-Lynx_rufus 0.5534 0.4382 -0.2066 0.5157
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4465 0.3757 -0.2629 0.4408
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1022 0.4531 -1.0837 -0.0655
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1217 0.5300 -1.2694 -0.0868
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3943 0.3637 -0.2961 0.3803
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3582 0.4696 -0.5495 0.3449
## Avg_Cogongrass_Cover-Sus_scrofa -0.0494 0.5348 -1.3012 0.0083
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.4885 1.0017 1125
## (Intercept)-Canis_latrans 1.5155 1.0033 2486
## (Intercept)-Sciurus_niger 2.3287 1.0137 578
## (Intercept)-Procyon_lotor 1.7293 1.0110 2302
## (Intercept)-Dasypus_novemcinctus 0.4357 1.0073 3421
## (Intercept)-Lynx_rufus 2.2214 1.0015 999
## (Intercept)-Didelphis_virginiana -0.2101 1.0048 3373
## (Intercept)-Sylvilagus_floridanus 1.4559 1.0051 1438
## (Intercept)-Meleagris_gallopavo 1.6407 1.0033 1273
## (Intercept)-Sciurus_carolinensis -0.1433 1.0040 2598
## (Intercept)-Vulpes_vulpes 1.6469 1.0092 570
## (Intercept)-Sus_scrofa -0.5021 1.0093 1874
## Veg_shannon_index-Odocoileus_virginianus 1.2539 1.0055 2552
## Veg_shannon_index-Canis_latrans 1.4730 1.0050 2791
## Veg_shannon_index-Sciurus_niger 1.4746 1.0021 2106
## Veg_shannon_index-Procyon_lotor 1.3259 1.0109 3072
## Veg_shannon_index-Dasypus_novemcinctus 0.8748 1.0017 4344
## Veg_shannon_index-Lynx_rufus 1.1057 1.0005 2761
## Veg_shannon_index-Didelphis_virginiana 1.3403 1.0063 3730
## Veg_shannon_index-Sylvilagus_floridanus 1.4262 1.0004 2992
## Veg_shannon_index-Meleagris_gallopavo 1.4358 1.0036 2471
## Veg_shannon_index-Sciurus_carolinensis 0.7496 1.0010 3155
## Veg_shannon_index-Vulpes_vulpes 1.0010 1.0011 2785
## Veg_shannon_index-Sus_scrofa 1.9120 1.0066 2193
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3210 1.0035 4154
## Avg_Cogongrass_Cover-Canis_latrans 1.3661 1.0026 3338
## Avg_Cogongrass_Cover-Sciurus_niger 0.9521 1.0081 1819
## Avg_Cogongrass_Cover-Procyon_lotor 1.2942 1.0040 3266
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0933 1.0005 3877
## Avg_Cogongrass_Cover-Lynx_rufus 1.5270 1.0030 3055
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2080 1.0017 4486
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6890 1.0020 2837
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8390 1.0071 2102
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1280 1.0037 4640
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3591 1.0060 3060
## Avg_Cogongrass_Cover-Sus_scrofa 0.8616 1.0027 2682
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0041 0.0587 -0.1126 0.0049 0.1179
## (Intercept)-Canis_latrans -2.6030 0.1739 -2.9626 -2.5990 -2.2700
## (Intercept)-Sciurus_niger -3.9528 0.5827 -5.1061 -3.9232 -2.9091
## (Intercept)-Procyon_lotor -2.2754 0.1319 -2.5407 -2.2716 -2.0316
## (Intercept)-Dasypus_novemcinctus -1.5772 0.1354 -1.8499 -1.5758 -1.3195
## (Intercept)-Lynx_rufus -3.5401 0.3235 -4.1781 -3.5289 -2.9242
## (Intercept)-Didelphis_virginiana -2.3118 0.2510 -2.8373 -2.2985 -1.8565
## (Intercept)-Sylvilagus_floridanus -3.2224 0.3193 -3.8998 -3.2098 -2.6466
## (Intercept)-Meleagris_gallopavo -3.4603 0.3666 -4.2338 -3.4467 -2.8046
## (Intercept)-Sciurus_carolinensis -2.4397 0.2672 -3.0019 -2.4289 -1.9575
## (Intercept)-Vulpes_vulpes -4.0573 0.7243 -5.4565 -4.0527 -2.7521
## (Intercept)-Sus_scrofa -2.9038 0.4645 -3.9205 -2.8712 -2.1006
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5468
## (Intercept)-Canis_latrans 1.0012 2842
## (Intercept)-Sciurus_niger 1.0088 422
## (Intercept)-Procyon_lotor 1.0003 4082
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
## (Intercept)-Lynx_rufus 1.0030 989
## (Intercept)-Didelphis_virginiana 1.0048 3644
## (Intercept)-Sylvilagus_floridanus 1.0092 1074
## (Intercept)-Meleagris_gallopavo 1.0300 916
## (Intercept)-Sciurus_carolinensis 1.0005 3673
## (Intercept)-Vulpes_vulpes 1.0123 413
## (Intercept)-Sus_scrofa 1.0007 1979
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.397
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9372 0.5749 -2.0161 -0.9511 0.2925 1.0035 1586
## Avg_Cogongrass_Cover -0.7338 0.3558 -1.4387 -0.7254 -0.0694 1.0056 1211
## I(Avg_Cogongrass_Cover^2) 0.7420 0.3319 0.1625 0.7208 1.4914 1.0034 1134
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2707 2.7819 0.6629 2.5474 10.5769 1.0314 1635
## Avg_Cogongrass_Cover 0.3412 0.4256 0.0370 0.2050 1.4880 1.0161 1924
## I(Avg_Cogongrass_Cover^2) 0.5234 0.8637 0.0409 0.2670 2.5388 1.0199 720
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6667 0.6349 0.0598 0.4702 2.3732 1.04 548
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5333 0.374 -3.2571 -2.5403 -1.7671 1.0022 4400
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6344 0.9547 0.5963 1.4047 4.1043 1.002 2872
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.6813 1.4301 0.3696 2.4929
## (Intercept)-Canis_latrans -0.5117 0.6785 -1.8517 -0.5144
## (Intercept)-Sciurus_niger -0.9560 1.2298 -2.8908 -1.0920
## (Intercept)-Procyon_lotor -0.2299 0.6650 -1.5852 -0.2221
## (Intercept)-Dasypus_novemcinctus -1.3737 0.6481 -2.7153 -1.3412
## (Intercept)-Lynx_rufus -1.2379 0.8522 -2.9052 -1.2431
## (Intercept)-Didelphis_virginiana -1.9265 0.7026 -3.3822 -1.9126
## (Intercept)-Sylvilagus_floridanus -1.0417 0.7861 -2.5931 -1.0392
## (Intercept)-Meleagris_gallopavo -0.7015 0.9108 -2.3359 -0.7608
## (Intercept)-Sciurus_carolinensis -2.4159 0.7759 -4.0127 -2.3854
## (Intercept)-Vulpes_vulpes -2.1435 1.1637 -4.3046 -2.2013
## (Intercept)-Sus_scrofa -2.4250 0.8920 -4.2710 -2.3684
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7401 0.6113 -1.9943 -0.7193
## Avg_Cogongrass_Cover-Canis_latrans -0.4773 0.5112 -1.4242 -0.4954
## Avg_Cogongrass_Cover-Sciurus_niger -0.9511 0.6341 -2.3927 -0.8986
## Avg_Cogongrass_Cover-Procyon_lotor -0.6063 0.4835 -1.5673 -0.6109
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5357 0.4671 -1.4335 -0.5433
## Avg_Cogongrass_Cover-Lynx_rufus -0.6189 0.5271 -1.6684 -0.6161
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4627 0.5146 -1.4171 -0.4845
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0995 0.5924 -2.4204 -1.0506
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9460 0.5893 -2.2910 -0.8986
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7667 0.5243 -1.8804 -0.7490
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7614 0.5855 -1.9729 -0.7482
## Avg_Cogongrass_Cover-Sus_scrofa -0.9738 0.6100 -2.3015 -0.9266
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1131 0.8050 0.0221 0.9674
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.1804 0.7526 0.1839 1.0041
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2612 0.7166 -1.3667 0.3154
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0338 0.6071 0.1520 0.9294
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6909 0.3486 0.0285 0.6811
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1527 0.5391 0.2969 1.0840
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5445 0.4070 -0.1769 0.5287
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7208 0.4901 -0.1110 0.6751
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2066 0.6328 -1.1620 0.2336
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9435 0.3972 0.2566 0.9125
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8984 0.5041 0.0820 0.8482
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2903 0.5927 -1.1210 0.3661
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.0856 1.0207 1165
## (Intercept)-Canis_latrans 0.8721 1.0000 2547
## (Intercept)-Sciurus_niger 1.8303 1.0383 446
## (Intercept)-Procyon_lotor 1.0513 1.0017 2601
## (Intercept)-Dasypus_novemcinctus -0.1721 1.0057 3319
## (Intercept)-Lynx_rufus 0.5139 1.0087 1943
## (Intercept)-Didelphis_virginiana -0.5867 1.0002 2991
## (Intercept)-Sylvilagus_floridanus 0.4998 1.0098 1776
## (Intercept)-Meleagris_gallopavo 1.2873 1.0076 1280
## (Intercept)-Sciurus_carolinensis -0.9907 1.0002 2510
## (Intercept)-Vulpes_vulpes 0.2614 1.0112 804
## (Intercept)-Sus_scrofa -0.7036 1.0023 2248
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4127 1.0049 2415
## Avg_Cogongrass_Cover-Canis_latrans 0.5711 1.0016 2764
## Avg_Cogongrass_Cover-Sciurus_niger 0.1570 1.0071 1733
## Avg_Cogongrass_Cover-Procyon_lotor 0.3723 1.0000 2426
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4217 1.0018 2208
## Avg_Cogongrass_Cover-Lynx_rufus 0.4475 1.0038 1850
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6217 1.0011 1984
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0807 1.0129 1628
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0786 1.0072 1556
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2023 1.0024 1853
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3536 1.0018 1968
## Avg_Cogongrass_Cover-Sus_scrofa 0.0710 1.0009 1719
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.0641 1.0108 754
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1779 1.0035 803
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5615 1.0205 998
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5551 1.0061 1100
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4050 1.0016 2419
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4140 1.0037 1721
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3371 1.0029 1536
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7821 1.0053 1548
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3771 1.0014 1157
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8062 1.0031 1752
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0249 1.0015 1368
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2421 1.0114 1648
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0037 0.0591 -0.1094 0.0033 0.1188
## (Intercept)-Canis_latrans -2.6384 0.1731 -2.9865 -2.6366 -2.3127
## (Intercept)-Sciurus_niger -3.8914 0.5752 -5.1076 -3.8530 -2.8797
## (Intercept)-Procyon_lotor -2.2739 0.1307 -2.5335 -2.2715 -2.0291
## (Intercept)-Dasypus_novemcinctus -1.5788 0.1316 -1.8488 -1.5752 -1.3333
## (Intercept)-Lynx_rufus -3.4105 0.3105 -4.0355 -3.3967 -2.8403
## (Intercept)-Didelphis_virginiana -2.3299 0.2595 -2.8786 -2.3152 -1.8412
## (Intercept)-Sylvilagus_floridanus -3.1985 0.3061 -3.8570 -3.1763 -2.6467
## (Intercept)-Meleagris_gallopavo -3.4295 0.3638 -4.2053 -3.4109 -2.7614
## (Intercept)-Sciurus_carolinensis -2.4367 0.2597 -2.9779 -2.4266 -1.9559
## (Intercept)-Vulpes_vulpes -3.9443 0.7005 -5.3466 -3.9028 -2.7007
## (Intercept)-Sus_scrofa -2.9095 0.4724 -3.9417 -2.8664 -2.1006
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0004 2898
## (Intercept)-Sciurus_niger 1.0130 503
## (Intercept)-Procyon_lotor 1.0091 4274
## (Intercept)-Dasypus_novemcinctus 0.9997 4952
## (Intercept)-Lynx_rufus 1.0111 1358
## (Intercept)-Didelphis_virginiana 1.0012 3246
## (Intercept)-Sylvilagus_floridanus 1.0112 1320
## (Intercept)-Meleagris_gallopavo 1.0172 974
## (Intercept)-Sciurus_carolinensis 1.0027 3576
## (Intercept)-Vulpes_vulpes 1.0106 567
## (Intercept)-Sus_scrofa 1.0114 1708
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5577
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1086 1.0415 -3.0423 -1.1576 1.0866 1.0051 1159
## Cogon_Patch_Size -0.1612 0.6659 -1.6044 -0.1357 1.0612 1.0198 896
## Veg_shannon_index 0.9802 0.4459 0.1245 0.9614 1.9088 1.0097 758
## total_shrub_cover -0.6417 0.5103 -1.7215 -0.6218 0.2992 1.0044 1769
## Avg_Cogongrass_Cover -0.0316 0.9100 -1.7313 -0.0510 1.7509 1.0048 404
## Tree_Density -1.9916 0.7313 -3.5374 -1.9722 -0.6365 1.0023 633
## Avg_Canopy_Cover 1.8331 0.5855 0.7924 1.7956 3.1104 1.0115 706
## I(Avg_Cogongrass_Cover^2) 1.3341 0.5829 0.2462 1.3244 2.5466 1.0128 752
## avg_veg_height -0.2215 0.4864 -1.1662 -0.2167 0.7383 1.0049 619
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.3272 15.9941 3.5773 13.8585 60.4831 1.0452 398
## Cogon_Patch_Size 2.8518 3.5976 0.1526 1.6678 12.8223 1.0243 614
## Veg_shannon_index 0.6880 0.9376 0.0492 0.3706 3.3216 1.0038 1042
## total_shrub_cover 1.7203 2.1509 0.0901 1.0574 7.4168 1.0100 800
## Avg_Cogongrass_Cover 1.1021 1.6844 0.0520 0.5339 5.4380 1.0018 1369
## Tree_Density 2.9617 6.8621 0.0752 1.1754 16.5663 1.1427 271
## Avg_Canopy_Cover 1.8971 2.7349 0.0891 1.0910 8.4955 1.0348 376
## I(Avg_Cogongrass_Cover^2) 1.7086 2.8088 0.0604 0.7949 8.9129 1.0512 670
## avg_veg_height 0.4404 0.6389 0.0415 0.2505 1.9774 1.0255 1549
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7206 2.4017 0.0687 0.9161 8.3054 1.0776 244
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.615 0.4053 -3.3844 -2.6242 -1.7888 1.0033 4919
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.945 1.0764 0.7534 1.6723 4.6501 0.9997 3236
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.3277 3.4623 2.2573
## (Intercept)-Canis_latrans -1.0404 1.2066 -3.4732
## (Intercept)-Sciurus_niger 0.7258 2.5636 -3.3009
## (Intercept)-Procyon_lotor -0.4728 1.1148 -2.8869
## (Intercept)-Dasypus_novemcinctus -2.7320 1.1832 -5.4290
## (Intercept)-Lynx_rufus -0.3018 2.2889 -4.0400
## (Intercept)-Didelphis_virginiana -4.1482 1.4343 -7.2624
## (Intercept)-Sylvilagus_floridanus -2.2828 1.3930 -5.1555
## (Intercept)-Meleagris_gallopavo -1.8127 1.5939 -5.0172
## (Intercept)-Sciurus_carolinensis -4.9403 1.5598 -8.3993
## (Intercept)-Vulpes_vulpes -4.0400 2.3405 -8.7322
## (Intercept)-Sus_scrofa -5.6852 1.9770 -10.1712
## Cogon_Patch_Size-Odocoileus_virginianus -0.0591 1.3952 -2.7302
## Cogon_Patch_Size-Canis_latrans 1.5463 1.3005 -0.2756
## Cogon_Patch_Size-Sciurus_niger -0.9235 1.8367 -5.3530
## Cogon_Patch_Size-Procyon_lotor -0.4280 0.8154 -2.0680
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2005 0.6968 -1.6233
## Cogon_Patch_Size-Lynx_rufus -0.1669 1.4476 -2.9851
## Cogon_Patch_Size-Didelphis_virginiana 1.5919 1.0244 -0.0868
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3765 1.5171 -5.1945
## Cogon_Patch_Size-Meleagris_gallopavo 0.1918 1.0915 -1.7494
## Cogon_Patch_Size-Sciurus_carolinensis -1.0701 1.2643 -4.2887
## Cogon_Patch_Size-Vulpes_vulpes -0.6589 1.6413 -4.4511
## Cogon_Patch_Size-Sus_scrofa -0.7143 1.4140 -4.1926
## Veg_shannon_index-Odocoileus_virginianus 0.7954 0.8260 -0.9008
## Veg_shannon_index-Canis_latrans 1.3185 0.6850 0.1948
## Veg_shannon_index-Sciurus_niger 1.0612 0.9403 -0.6569
## Veg_shannon_index-Procyon_lotor 1.1585 0.6004 0.1065
## Veg_shannon_index-Dasypus_novemcinctus 0.6602 0.5472 -0.4661
## Veg_shannon_index-Lynx_rufus 1.0237 0.8574 -0.6586
## Veg_shannon_index-Didelphis_virginiana 1.0926 0.6504 -0.1102
## Veg_shannon_index-Sylvilagus_floridanus 1.0031 0.6786 -0.2562
## Veg_shannon_index-Meleagris_gallopavo 1.2318 0.7580 -0.1087
## Veg_shannon_index-Sciurus_carolinensis 0.3800 0.7635 -1.3451
## Veg_shannon_index-Vulpes_vulpes 0.7193 0.7941 -1.0405
## Veg_shannon_index-Sus_scrofa 1.5620 0.9445 0.1969
## total_shrub_cover-Odocoileus_virginianus -0.2294 1.0830 -2.2871
## total_shrub_cover-Canis_latrans 0.0152 0.6996 -1.2523
## total_shrub_cover-Sciurus_niger -1.1321 1.2572 -4.0279
## total_shrub_cover-Procyon_lotor -1.2116 0.6843 -2.7441
## total_shrub_cover-Dasypus_novemcinctus 0.0754 0.5656 -1.0108
## total_shrub_cover-Lynx_rufus -1.3673 1.1919 -4.1173
## total_shrub_cover-Didelphis_virginiana -0.7459 0.7650 -2.4185
## total_shrub_cover-Sylvilagus_floridanus -0.3949 0.8658 -2.2063
## total_shrub_cover-Meleagris_gallopavo -2.2962 1.3966 -5.5913
## total_shrub_cover-Sciurus_carolinensis -0.0553 0.7630 -1.5265
## total_shrub_cover-Vulpes_vulpes -0.8775 1.1283 -3.4785
## total_shrub_cover-Sus_scrofa 0.0722 1.0002 -1.7089
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1091 1.2768 -2.6221
## Avg_Cogongrass_Cover-Canis_latrans 0.0526 1.1374 -2.1906
## Avg_Cogongrass_Cover-Sciurus_niger -0.3872 1.3810 -3.4655
## Avg_Cogongrass_Cover-Procyon_lotor 0.1873 1.1435 -1.9521
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6072 1.2018 -1.5014
## Avg_Cogongrass_Cover-Lynx_rufus 0.1583 1.2495 -2.2609
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1961 1.1762 -2.0217
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5067 1.2786 -3.2428
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2952 1.2816 -2.9866
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0472 1.1925 -2.2943
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1266 1.2622 -2.3041
## Avg_Cogongrass_Cover-Sus_scrofa -0.4405 1.3079 -3.2171
## Tree_Density-Odocoileus_virginianus -1.0313 1.3179 -3.1653
## Tree_Density-Canis_latrans -2.7272 1.3096 -5.9256
## Tree_Density-Sciurus_niger -1.9768 1.5952 -5.2654
## Tree_Density-Procyon_lotor -1.8671 0.9581 -3.8778
## Tree_Density-Dasypus_novemcinctus -3.6172 1.9142 -8.6025
## Tree_Density-Lynx_rufus -0.8757 1.6308 -3.3349
## Tree_Density-Didelphis_virginiana -2.3178 1.2421 -5.2903
## Tree_Density-Sylvilagus_floridanus -2.4462 1.3996 -5.6301
## Tree_Density-Meleagris_gallopavo -2.0380 1.3701 -4.9487
## Tree_Density-Sciurus_carolinensis -2.6985 1.5164 -6.3300
## Tree_Density-Vulpes_vulpes -1.9777 1.5982 -5.2292
## Tree_Density-Sus_scrofa -2.3994 1.6364 -6.2175
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3273 1.2706 -1.3668
## Avg_Canopy_Cover-Canis_latrans 0.3474 0.7645 -1.1836
## Avg_Canopy_Cover-Sciurus_niger 2.0945 1.5457 -0.7586
## Avg_Canopy_Cover-Procyon_lotor 1.6791 0.7291 0.3382
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9477 0.7305 0.7441
## Avg_Canopy_Cover-Lynx_rufus 1.4477 1.2645 -0.9625
## Avg_Canopy_Cover-Didelphis_virginiana 2.5489 0.9613 1.1083
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.1166 1.4844 1.0658
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2835 1.1949 0.6004
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2446 0.8687 0.8873
## Avg_Canopy_Cover-Vulpes_vulpes 2.2582 1.2422 0.4329
## Avg_Canopy_Cover-Sus_scrofa 1.9911 0.8738 0.5753
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8517 1.3053 -0.1462
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1053 1.1012 0.5871
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.8511 1.4039 -2.3205
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8838 0.9866 0.4463
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4023 0.7249 0.1396
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2011 1.2290 0.5221
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0133 0.6951 -0.3704
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1237 0.8917 -0.4174
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2459 1.3373 -2.8844
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6807 0.7604 0.3600
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9107 0.9883 0.4132
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5324 1.1783 -2.3029
## avg_veg_height-Odocoileus_virginianus -0.2188 0.7698 -1.7984
## avg_veg_height-Canis_latrans -0.4211 0.6212 -1.7003
## avg_veg_height-Sciurus_niger -0.3707 0.8107 -2.0846
## avg_veg_height-Procyon_lotor 0.0230 0.6283 -1.1557
## avg_veg_height-Dasypus_novemcinctus 0.0819 0.6071 -1.0508
## avg_veg_height-Lynx_rufus -0.3576 0.7814 -2.0271
## avg_veg_height-Didelphis_virginiana -0.2981 0.6567 -1.6520
## avg_veg_height-Sylvilagus_floridanus -0.3262 0.6701 -1.6702
## avg_veg_height-Meleagris_gallopavo -0.2398 0.7542 -1.7888
## avg_veg_height-Sciurus_carolinensis 0.0524 0.6736 -1.2033
## avg_veg_height-Vulpes_vulpes -0.3443 0.7641 -1.9660
## avg_veg_height-Sus_scrofa -0.2972 0.7013 -1.7271
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.6874 16.0518 1.0410 470
## (Intercept)-Canis_latrans -1.0179 1.2961 1.0254 1381
## (Intercept)-Sciurus_niger 0.4209 6.6966 1.0471 416
## (Intercept)-Procyon_lotor -0.4178 1.5808 1.0144 1263
## (Intercept)-Dasypus_novemcinctus -2.6192 -0.7334 1.0086 697
## (Intercept)-Lynx_rufus -0.5590 5.2865 1.0261 362
## (Intercept)-Didelphis_virginiana -4.0174 -1.6695 1.0005 763
## (Intercept)-Sylvilagus_floridanus -2.2484 0.4415 1.0055 1242
## (Intercept)-Meleagris_gallopavo -1.8146 1.3804 1.0019 870
## (Intercept)-Sciurus_carolinensis -4.7685 -2.3472 1.0152 454
## (Intercept)-Vulpes_vulpes -4.0578 0.8733 1.0299 454
## (Intercept)-Sus_scrofa -5.4890 -2.3647 1.0090 487
## Cogon_Patch_Size-Odocoileus_virginianus -0.0977 3.0626 1.0059 1941
## Cogon_Patch_Size-Canis_latrans 1.3273 4.6624 1.0023 1016
## Cogon_Patch_Size-Sciurus_niger -0.6944 2.1765 1.0283 651
## Cogon_Patch_Size-Procyon_lotor -0.4113 1.0942 1.0075 1222
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1781 1.1341 1.0066 1312
## Cogon_Patch_Size-Lynx_rufus -0.1867 2.9251 1.0063 827
## Cogon_Patch_Size-Didelphis_virginiana 1.4887 3.9355 1.0037 615
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1064 0.7955 1.0185 857
## Cogon_Patch_Size-Meleagris_gallopavo 0.1310 2.5824 1.0019 1578
## Cogon_Patch_Size-Sciurus_carolinensis -0.8366 0.7629 1.0212 1005
## Cogon_Patch_Size-Vulpes_vulpes -0.4646 2.1780 1.0295 850
## Cogon_Patch_Size-Sus_scrofa -0.4859 1.4299 1.0183 1281
## Veg_shannon_index-Odocoileus_virginianus 0.8166 2.4086 1.0059 2237
## Veg_shannon_index-Canis_latrans 1.2370 2.9122 1.0014 1376
## Veg_shannon_index-Sciurus_niger 1.0093 3.1213 1.0038 1299
## Veg_shannon_index-Procyon_lotor 1.1092 2.5113 1.0013 984
## Veg_shannon_index-Dasypus_novemcinctus 0.6726 1.7062 1.0055 1986
## Veg_shannon_index-Lynx_rufus 0.9860 2.7989 1.0080 1451
## Veg_shannon_index-Didelphis_virginiana 1.0595 2.5024 1.0033 1489
## Veg_shannon_index-Sylvilagus_floridanus 0.9673 2.4507 1.0117 1307
## Veg_shannon_index-Meleagris_gallopavo 1.1576 2.9417 1.0039 1113
## Veg_shannon_index-Sciurus_carolinensis 0.4607 1.6477 1.0068 1984
## Veg_shannon_index-Vulpes_vulpes 0.7536 2.2402 1.0053 1714
## Veg_shannon_index-Sus_scrofa 1.4053 3.8794 1.0108 1235
## total_shrub_cover-Odocoileus_virginianus -0.2908 2.1154 1.0074 2662
## total_shrub_cover-Canis_latrans -0.0339 1.5459 1.0080 1707
## total_shrub_cover-Sciurus_niger -0.9849 1.0034 1.0040 967
## total_shrub_cover-Procyon_lotor -1.1468 -0.0539 1.0020 1755
## total_shrub_cover-Dasypus_novemcinctus 0.0547 1.1850 1.0028 2976
## total_shrub_cover-Lynx_rufus -1.2332 0.7022 1.0037 968
## total_shrub_cover-Didelphis_virginiana -0.7106 0.6904 1.0028 2004
## total_shrub_cover-Sylvilagus_floridanus -0.3733 1.3288 1.0012 2284
## total_shrub_cover-Meleagris_gallopavo -2.0464 -0.2025 1.0014 662
## total_shrub_cover-Sciurus_carolinensis -0.0945 1.5298 1.0074 3171
## total_shrub_cover-Vulpes_vulpes -0.7822 1.1230 1.0043 1333
## total_shrub_cover-Sus_scrofa -0.0286 2.2766 1.0044 2056
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1034 2.3493 1.0050 767
## Avg_Cogongrass_Cover-Canis_latrans 0.0358 2.3032 1.0019 682
## Avg_Cogongrass_Cover-Sciurus_niger -0.3153 2.0942 1.0021 617
## Avg_Cogongrass_Cover-Procyon_lotor 0.1712 2.4804 1.0049 667
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5188 3.2692 1.0041 635
## Avg_Cogongrass_Cover-Lynx_rufus 0.1187 2.7262 1.0014 716
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1526 2.6137 1.0034 620
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4421 1.8363 1.0009 548
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2637 2.0850 1.0041 643
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0474 2.4283 1.0036 661
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0959 2.6900 1.0004 804
## Avg_Cogongrass_Cover-Sus_scrofa -0.3549 1.9341 1.0036 712
## Tree_Density-Odocoileus_virginianus -1.1746 1.9296 1.0089 786
## Tree_Density-Canis_latrans -2.5279 -0.7202 1.0214 583
## Tree_Density-Sciurus_niger -1.9374 0.9393 1.0107 672
## Tree_Density-Procyon_lotor -1.8229 -0.0445 1.0058 897
## Tree_Density-Dasypus_novemcinctus -3.1650 -1.2303 1.0281 312
## Tree_Density-Lynx_rufus -1.1060 2.9483 1.0195 516
## Tree_Density-Didelphis_virginiana -2.1449 -0.4264 1.0178 679
## Tree_Density-Sylvilagus_floridanus -2.2643 -0.2023 1.0144 742
## Tree_Density-Meleagris_gallopavo -1.9805 0.5322 1.0048 881
## Tree_Density-Sciurus_carolinensis -2.4270 -0.5949 1.0198 543
## Tree_Density-Vulpes_vulpes -1.9952 1.4471 1.0085 819
## Tree_Density-Sus_scrofa -2.1848 0.0597 1.0094 662
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3885 3.8369 1.0073 1363
## Avg_Canopy_Cover-Canis_latrans 0.3431 1.8207 1.0014 1271
## Avg_Canopy_Cover-Sciurus_niger 1.9543 5.4563 1.0044 769
## Avg_Canopy_Cover-Procyon_lotor 1.6168 3.3038 1.0039 1300
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8691 3.6266 1.0140 574
## Avg_Canopy_Cover-Lynx_rufus 1.4379 4.1243 1.0064 876
## Avg_Canopy_Cover-Didelphis_virginiana 2.3856 4.8228 1.0104 489
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8238 6.8423 1.0060 419
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0745 5.2072 1.0127 605
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1259 4.3133 1.0047 750
## Avg_Canopy_Cover-Vulpes_vulpes 2.0550 5.3139 1.0140 492
## Avg_Canopy_Cover-Sus_scrofa 1.8843 4.0049 1.0147 966
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6292 5.3684 1.0460 849
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8910 4.9360 1.0411 634
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.0005 3.3448 1.0100 459
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7336 4.3160 1.0103 822
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3452 3.0041 1.0158 949
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9597 5.2284 1.0417 725
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0228 2.3906 1.0016 828
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0604 3.0219 1.0231 941
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4445 2.3991 1.0418 517
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6072 3.3955 1.0144 1066
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7599 4.2289 1.0411 791
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.7014 2.3766 1.0013 692
## avg_veg_height-Odocoileus_virginianus -0.2154 1.3164 1.0040 1377
## avg_veg_height-Canis_latrans -0.3969 0.7563 1.0050 1028
## avg_veg_height-Sciurus_niger -0.3183 1.1181 1.0111 1131
## avg_veg_height-Procyon_lotor 0.0053 1.3264 1.0007 1023
## avg_veg_height-Dasypus_novemcinctus 0.0734 1.3308 1.0115 819
## avg_veg_height-Lynx_rufus -0.3269 1.1061 1.0020 1084
## avg_veg_height-Didelphis_virginiana -0.2784 0.9768 1.0008 1095
## avg_veg_height-Sylvilagus_floridanus -0.3142 0.9634 1.0030 1173
## avg_veg_height-Meleagris_gallopavo -0.2210 1.2119 1.0014 1138
## avg_veg_height-Sciurus_carolinensis 0.0201 1.5176 1.0022 1149
## avg_veg_height-Vulpes_vulpes -0.3112 1.0979 1.0025 1212
## avg_veg_height-Sus_scrofa -0.2895 1.0568 1.0030 1106
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0584 -0.1089 0.0047 0.1168
## (Intercept)-Canis_latrans -2.6176 0.1723 -2.9625 -2.6151 -2.2982
## (Intercept)-Sciurus_niger -4.5218 0.4363 -5.3895 -4.5110 -3.6826
## (Intercept)-Procyon_lotor -2.2692 0.1316 -2.5418 -2.2665 -2.0170
## (Intercept)-Dasypus_novemcinctus -1.5700 0.1341 -1.8340 -1.5686 -1.3106
## (Intercept)-Lynx_rufus -3.7281 0.3100 -4.3312 -3.7221 -3.1418
## (Intercept)-Didelphis_virginiana -2.2894 0.2401 -2.7958 -2.2788 -1.8431
## (Intercept)-Sylvilagus_floridanus -3.2012 0.2743 -3.7657 -3.1926 -2.6885
## (Intercept)-Meleagris_gallopavo -3.4049 0.3247 -4.0795 -3.3940 -2.8063
## (Intercept)-Sciurus_carolinensis -2.4332 0.2650 -2.9991 -2.4205 -1.9499
## (Intercept)-Vulpes_vulpes -4.2148 0.6407 -5.5216 -4.1780 -3.0405
## (Intercept)-Sus_scrofa -2.8816 0.4566 -3.8573 -2.8513 -2.0753
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0043 5250
## (Intercept)-Canis_latrans 1.0009 2602
## (Intercept)-Sciurus_niger 1.0136 592
## (Intercept)-Procyon_lotor 1.0056 3747
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## (Intercept)-Lynx_rufus 1.0049 638
## (Intercept)-Didelphis_virginiana 1.0001 4086
## (Intercept)-Sylvilagus_floridanus 1.0033 1958
## (Intercept)-Meleagris_gallopavo 1.0032 847
## (Intercept)-Sciurus_carolinensis 1.0000 3123
## (Intercept)-Vulpes_vulpes 1.0000 524
## (Intercept)-Sus_scrofa 1.0009 2053
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1495
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0800 0.5738 -1.1303 -0.1042 1.1597 1.0004 1787
## Avg_Cogongrass_Cover 0.1333 0.2567 -0.4022 0.1366 0.6245 1.0026 1925
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1642 2.6511 0.5522 2.4240 10.3250 1.0139 744
## Avg_Cogongrass_Cover 0.3275 0.3987 0.0379 0.2047 1.4092 1.0106 1601
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8861 0.8993 0.0635 0.6126 3.2421 1.0026 449
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7462 0.4316 -3.5788 -2.7466 -1.8465 1.0007 3400
## shrub_cover 0.0984 0.2560 -0.3944 0.0994 0.6259 1.0010 3484
## veg_height -0.0419 0.1573 -0.3621 -0.0420 0.2618 1.0016 2982
## week -0.0733 0.1198 -0.3250 -0.0688 0.1533 1.0068 2639
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1360 1.2316 0.7570 1.8441 5.2082 1.0020 2263
## shrub_cover 0.6061 0.4494 0.1435 0.4903 1.7329 1.0084 1907
## veg_height 0.1970 0.1406 0.0559 0.1612 0.5416 1.0014 3226
## week 0.1038 0.0818 0.0263 0.0819 0.3151 1.0012 2208
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3026 1.3470 0.9767 3.1701
## (Intercept)-Canis_latrans 0.4286 0.6536 -0.8035 0.4028
## (Intercept)-Sciurus_niger -0.2161 1.3893 -2.2745 -0.4003
## (Intercept)-Procyon_lotor 0.5071 0.6267 -0.7922 0.5204
## (Intercept)-Dasypus_novemcinctus -0.6103 0.6024 -1.8513 -0.5947
## (Intercept)-Lynx_rufus 0.2572 1.1075 -1.5496 0.1297
## (Intercept)-Didelphis_virginiana -1.1961 0.6617 -2.5236 -1.1833
## (Intercept)-Sylvilagus_floridanus -0.3253 0.7081 -1.6035 -0.3501
## (Intercept)-Meleagris_gallopavo 0.7372 1.3148 -1.2681 0.5446
## (Intercept)-Sciurus_carolinensis -1.2577 0.6948 -2.6847 -1.2347
## (Intercept)-Vulpes_vulpes -0.8954 1.2736 -3.0046 -1.0325
## (Intercept)-Sus_scrofa -1.6616 0.8444 -3.3899 -1.6468
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1398 0.4941 -0.8543 0.1449
## Avg_Cogongrass_Cover-Canis_latrans 0.4095 0.3959 -0.2709 0.3699
## Avg_Cogongrass_Cover-Sciurus_niger -0.2020 0.6009 -1.5810 -0.1378
## Avg_Cogongrass_Cover-Procyon_lotor 0.1999 0.3533 -0.4546 0.1902
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3357 0.3306 -0.2854 0.3216
## Avg_Cogongrass_Cover-Lynx_rufus 0.4025 0.4378 -0.3550 0.3604
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2980 0.3758 -0.4138 0.2886
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2241 0.4508 -1.2149 -0.1850
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2158 0.6486 -1.7015 -0.1393
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3294 0.3583 -0.3517 0.3182
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2614 0.4584 -0.6160 0.2462
## Avg_Cogongrass_Cover-Sus_scrofa -0.1407 0.5638 -1.4425 -0.0710
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.3330 1.0283 869
## (Intercept)-Canis_latrans 1.8082 1.0017 2224
## (Intercept)-Sciurus_niger 3.1286 1.0523 380
## (Intercept)-Procyon_lotor 1.7328 1.0007 2583
## (Intercept)-Dasypus_novemcinctus 0.5694 1.0012 3191
## (Intercept)-Lynx_rufus 2.8678 1.0117 712
## (Intercept)-Didelphis_virginiana 0.0776 1.0019 2986
## (Intercept)-Sylvilagus_floridanus 1.1412 1.0025 1977
## (Intercept)-Meleagris_gallopavo 3.8352 1.0074 637
## (Intercept)-Sciurus_carolinensis 0.0765 1.0059 2390
## (Intercept)-Vulpes_vulpes 2.0709 1.0016 418
## (Intercept)-Sus_scrofa -0.0496 1.0029 1842
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1080 1.0006 3549
## Avg_Cogongrass_Cover-Canis_latrans 1.3060 1.0019 3274
## Avg_Cogongrass_Cover-Sciurus_niger 0.8535 1.0049 1639
## Avg_Cogongrass_Cover-Procyon_lotor 0.9217 1.0013 4154
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0134 1.0010 4129
## Avg_Cogongrass_Cover-Lynx_rufus 1.3649 1.0122 2653
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0815 1.0022 4184
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5761 1.0011 1989
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8705 1.0017 1389
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0950 1.0000 4131
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2365 1.0046 2890
## Avg_Cogongrass_Cover-Sus_scrofa 0.7795 1.0073 2334
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0600 -0.1082 0.0053 0.1198
## (Intercept)-Canis_latrans -2.7685 0.1935 -3.1602 -2.7615 -2.4122
## (Intercept)-Sciurus_niger -4.3054 0.6254 -5.4954 -4.3118 -3.0564
## (Intercept)-Procyon_lotor -2.2960 0.1418 -2.5776 -2.2891 -2.0326
## (Intercept)-Dasypus_novemcinctus -1.7351 0.1581 -2.0521 -1.7326 -1.4301
## (Intercept)-Lynx_rufus -3.7606 0.3837 -4.5301 -3.7501 -3.0202
## (Intercept)-Didelphis_virginiana -2.5556 0.2963 -3.1579 -2.5473 -2.0087
## (Intercept)-Sylvilagus_floridanus -3.2093 0.3085 -3.8648 -3.1924 -2.6487
## (Intercept)-Meleagris_gallopavo -4.3070 0.4876 -5.2995 -4.2988 -3.3627
## (Intercept)-Sciurus_carolinensis -2.6102 0.3132 -3.2590 -2.6043 -2.0389
## (Intercept)-Vulpes_vulpes -4.3042 0.7726 -5.9175 -4.2710 -2.8883
## (Intercept)-Sus_scrofa -3.2780 0.6221 -4.5048 -3.2827 -2.0762
## shrub_cover-Odocoileus_virginianus -0.0562 0.0639 -0.1837 -0.0556 0.0681
## shrub_cover-Canis_latrans -0.3006 0.2124 -0.7269 -0.3004 0.1189
## shrub_cover-Sciurus_niger -0.4732 0.4644 -1.4159 -0.4606 0.4241
## shrub_cover-Procyon_lotor 0.2404 0.1647 -0.0916 0.2424 0.5526
## shrub_cover-Dasypus_novemcinctus 0.8159 0.2974 0.2487 0.8117 1.4059
## shrub_cover-Lynx_rufus -0.3184 0.3456 -1.0179 -0.3138 0.3495
## shrub_cover-Didelphis_virginiana 0.9186 0.3656 0.2598 0.9015 1.6887
## shrub_cover-Sylvilagus_floridanus 0.2365 0.4137 -0.5323 0.2288 1.0808
## shrub_cover-Meleagris_gallopavo -0.8943 0.4077 -1.7473 -0.8773 -0.1367
## shrub_cover-Sciurus_carolinensis 0.7908 0.4108 0.0114 0.7850 1.6168
## shrub_cover-Vulpes_vulpes -0.1935 0.5504 -1.3078 -0.1739 0.8535
## shrub_cover-Sus_scrofa 0.4563 0.7821 -1.1300 0.4607 2.0363
## veg_height-Odocoileus_virginianus -0.2992 0.0650 -0.4241 -0.2986 -0.1677
## veg_height-Canis_latrans -0.6003 0.1840 -0.9692 -0.5954 -0.2577
## veg_height-Sciurus_niger -0.0978 0.3958 -0.8780 -0.1047 0.7003
## veg_height-Procyon_lotor 0.3248 0.1222 0.0861 0.3244 0.5636
## veg_height-Dasypus_novemcinctus 0.2310 0.1330 -0.0246 0.2306 0.4982
## veg_height-Lynx_rufus -0.0159 0.2417 -0.5076 -0.0088 0.4557
## veg_height-Didelphis_virginiana 0.3896 0.2398 -0.0522 0.3809 0.8836
## veg_height-Sylvilagus_floridanus 0.1203 0.2447 -0.3498 0.1194 0.5989
## veg_height-Meleagris_gallopavo -0.3074 0.3536 -1.0126 -0.3072 0.3873
## veg_height-Sciurus_carolinensis 0.0447 0.2072 -0.3497 0.0378 0.4663
## veg_height-Vulpes_vulpes -0.1580 0.3205 -0.8261 -0.1477 0.4469
## veg_height-Sus_scrofa -0.1401 0.3347 -0.8175 -0.1333 0.5052
## week-Odocoileus_virginianus 0.2100 0.0616 0.0895 0.2098 0.3334
## week-Canis_latrans 0.0695 0.1331 -0.2031 0.0745 0.3209
## week-Sciurus_niger -0.3214 0.3055 -1.0513 -0.2896 0.1953
## week-Procyon_lotor -0.0554 0.1179 -0.2955 -0.0522 0.1716
## week-Dasypus_novemcinctus -0.1665 0.1381 -0.4590 -0.1635 0.0958
## week-Lynx_rufus -0.0405 0.1955 -0.4402 -0.0328 0.3230
## week-Didelphis_virginiana -0.2198 0.2175 -0.6873 -0.2020 0.1646
## week-Sylvilagus_floridanus -0.1588 0.2056 -0.6075 -0.1450 0.2108
## week-Meleagris_gallopavo -0.2832 0.2461 -0.8230 -0.2664 0.1425
## week-Sciurus_carolinensis 0.1404 0.1821 -0.2307 0.1414 0.4853
## week-Vulpes_vulpes -0.1386 0.2802 -0.7501 -0.1251 0.3851
## week-Sus_scrofa 0.0870 0.2367 -0.3806 0.0882 0.5438
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0009 2185
## (Intercept)-Sciurus_niger 1.0044 541
## (Intercept)-Procyon_lotor 1.0021 3774
## (Intercept)-Dasypus_novemcinctus 1.0005 4113
## (Intercept)-Lynx_rufus 1.0342 825
## (Intercept)-Didelphis_virginiana 1.0002 2905
## (Intercept)-Sylvilagus_floridanus 1.0026 1536
## (Intercept)-Meleagris_gallopavo 1.0013 472
## (Intercept)-Sciurus_carolinensis 1.0029 2582
## (Intercept)-Vulpes_vulpes 1.0008 352
## (Intercept)-Sus_scrofa 1.0051 1826
## shrub_cover-Odocoileus_virginianus 0.9999 5498
## shrub_cover-Canis_latrans 1.0015 2846
## shrub_cover-Sciurus_niger 1.0033 1120
## shrub_cover-Procyon_lotor 1.0013 4186
## shrub_cover-Dasypus_novemcinctus 1.0001 4018
## shrub_cover-Lynx_rufus 1.0306 1191
## shrub_cover-Didelphis_virginiana 1.0113 2377
## shrub_cover-Sylvilagus_floridanus 1.0001 1736
## shrub_cover-Meleagris_gallopavo 1.0001 604
## shrub_cover-Sciurus_carolinensis 1.0199 2369
## shrub_cover-Vulpes_vulpes 1.0031 1667
## shrub_cover-Sus_scrofa 1.0013 2435
## veg_height-Odocoileus_virginianus 1.0008 4927
## veg_height-Canis_latrans 1.0038 2347
## veg_height-Sciurus_niger 1.0049 1758
## veg_height-Procyon_lotor 1.0014 4084
## veg_height-Dasypus_novemcinctus 1.0024 5439
## veg_height-Lynx_rufus 1.0020 2401
## veg_height-Didelphis_virginiana 1.0006 3369
## veg_height-Sylvilagus_floridanus 1.0010 2719
## veg_height-Meleagris_gallopavo 1.0018 1216
## veg_height-Sciurus_carolinensis 1.0009 3418
## veg_height-Vulpes_vulpes 1.0075 1771
## veg_height-Sus_scrofa 1.0016 3277
## week-Odocoileus_virginianus 1.0018 5250
## week-Canis_latrans 1.0002 4215
## week-Sciurus_niger 1.0148 1595
## week-Procyon_lotor 1.0001 4557
## week-Dasypus_novemcinctus 1.0019 4686
## week-Lynx_rufus 1.0003 2875
## week-Didelphis_virginiana 1.0021 3900
## week-Sylvilagus_floridanus 1.0039 3142
## week-Meleagris_gallopavo 1.0096 1707
## week-Sciurus_carolinensis 1.0010 4825
## week-Vulpes_vulpes 1.0003 2863
## week-Sus_scrofa 1.0021 4484
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.406
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3334 0.5248 -1.3498 -0.3462 0.7682 1.0069 2461
## Avg_Cogongrass_Cover 0.1164 0.2336 -0.3516 0.1195 0.5721 1.0031 2611
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.7861 2.2139 0.5339 2.2412 8.4464 1.0217 1718
## Avg_Cogongrass_Cover 0.2844 0.3002 0.0407 0.1918 1.0578 1.0028 2209
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9556 0.8643 0.0843 0.7238 3.2144 1.0304 515
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5523 0.378 -3.2768 -2.5581 -1.7731 1.002 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7174 0.9914 0.632 1.4664 4.2004 1.0193 2324
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.0534 1.2486 0.8410 2.9522
## (Intercept)-Canis_latrans 0.2307 0.6563 -1.0287 0.2258
## (Intercept)-Sciurus_niger -0.6193 1.0658 -2.3814 -0.7335
## (Intercept)-Procyon_lotor 0.4176 0.6460 -0.9148 0.4353
## (Intercept)-Dasypus_novemcinctus -0.7245 0.6072 -1.9581 -0.7148
## (Intercept)-Lynx_rufus -0.1466 0.8930 -1.7766 -0.1993
## (Intercept)-Didelphis_virginiana -1.3641 0.6452 -2.6779 -1.3460
## (Intercept)-Sylvilagus_floridanus -0.3621 0.7332 -1.7630 -0.3904
## (Intercept)-Meleagris_gallopavo -0.4344 0.8529 -1.9957 -0.4794
## (Intercept)-Sciurus_carolinensis -1.4233 0.6601 -2.7374 -1.4019
## (Intercept)-Vulpes_vulpes -1.0166 1.2043 -3.0505 -1.1317
## (Intercept)-Sus_scrofa -1.8532 0.8096 -3.5118 -1.8233
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1105 0.4650 -0.8269 0.1067
## Avg_Cogongrass_Cover-Canis_latrans 0.3277 0.3487 -0.3018 0.3069
## Avg_Cogongrass_Cover-Sciurus_niger -0.1919 0.5260 -1.3745 -0.1466
## Avg_Cogongrass_Cover-Procyon_lotor 0.2146 0.3519 -0.4384 0.2018
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3155 0.3143 -0.2877 0.3068
## Avg_Cogongrass_Cover-Lynx_rufus 0.4199 0.4109 -0.2818 0.3830
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3083 0.3534 -0.3640 0.2986
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2160 0.4205 -1.1457 -0.1809
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2661 0.4782 -1.3089 -0.2317
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3152 0.3481 -0.3494 0.3133
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2252 0.4293 -0.6151 0.2183
## Avg_Cogongrass_Cover-Sus_scrofa -0.1571 0.4929 -1.2809 -0.1043
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.7970 1.0188 1226
## (Intercept)-Canis_latrans 1.5494 1.0005 2829
## (Intercept)-Sciurus_niger 1.8006 1.0206 754
## (Intercept)-Procyon_lotor 1.6559 1.0015 2238
## (Intercept)-Dasypus_novemcinctus 0.4464 1.0001 3187
## (Intercept)-Lynx_rufus 1.8424 1.0050 1132
## (Intercept)-Didelphis_virginiana -0.1495 1.0008 3509
## (Intercept)-Sylvilagus_floridanus 1.1614 1.0012 1965
## (Intercept)-Meleagris_gallopavo 1.4601 1.0052 1479
## (Intercept)-Sciurus_carolinensis -0.1592 1.0110 3211
## (Intercept)-Vulpes_vulpes 1.6782 1.0073 421
## (Intercept)-Sus_scrofa -0.2945 1.0005 2226
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.0634 1.0019 3821
## Avg_Cogongrass_Cover-Canis_latrans 1.1055 1.0003 4387
## Avg_Cogongrass_Cover-Sciurus_niger 0.7462 1.0008 2335
## Avg_Cogongrass_Cover-Procyon_lotor 0.9438 1.0049 4489
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9659 0.9998 4452
## Avg_Cogongrass_Cover-Lynx_rufus 1.3347 1.0012 3606
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0305 1.0006 4117
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5297 1.0012 3268
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5709 1.0018 2674
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0184 1.0009 4556
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1174 1.0039 3982
## Avg_Cogongrass_Cover-Sus_scrofa 0.6640 1.0011 3052
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0044 0.0581 -0.1085 0.0043 0.1207
## (Intercept)-Canis_latrans -2.6180 0.1736 -2.9696 -2.6108 -2.2984
## (Intercept)-Sciurus_niger -3.8869 0.5597 -5.0011 -3.8553 -2.8736
## (Intercept)-Procyon_lotor -2.2680 0.1307 -2.5327 -2.2656 -2.0215
## (Intercept)-Dasypus_novemcinctus -1.5747 0.1340 -1.8368 -1.5726 -1.3175
## (Intercept)-Lynx_rufus -3.5206 0.3302 -4.2058 -3.5102 -2.8908
## (Intercept)-Didelphis_virginiana -2.3094 0.2514 -2.8413 -2.2978 -1.8487
## (Intercept)-Sylvilagus_floridanus -3.2055 0.3105 -3.8677 -3.1862 -2.6403
## (Intercept)-Meleagris_gallopavo -3.4187 0.3630 -4.1594 -3.3981 -2.7654
## (Intercept)-Sciurus_carolinensis -2.4410 0.2649 -3.0036 -2.4262 -1.9592
## (Intercept)-Vulpes_vulpes -4.0820 0.7391 -5.5733 -4.0439 -2.7802
## (Intercept)-Sus_scrofa -2.9269 0.4756 -3.9867 -2.8855 -2.1019
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 0.9998 2944
## (Intercept)-Sciurus_niger 1.0058 617
## (Intercept)-Procyon_lotor 1.0004 4133
## (Intercept)-Dasypus_novemcinctus 1.0009 5250
## (Intercept)-Lynx_rufus 1.0029 1038
## (Intercept)-Didelphis_virginiana 1.0005 3903
## (Intercept)-Sylvilagus_floridanus 1.0027 1381
## (Intercept)-Meleagris_gallopavo 1.0037 1088
## (Intercept)-Sciurus_carolinensis 1.0005 3738
## (Intercept)-Vulpes_vulpes 1.0038 390
## (Intercept)-Sus_scrofa 1.0008 1946
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.825
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3166 0.5363 -1.3724 -0.3291 0.7914 1.0078 2434
## Avg_Cogongrass_Cover 0.1165 0.2307 -0.3553 0.1181 0.5513 1.0017 2409
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8682 2.2945 0.5305 2.2689 8.4093 1.0046 1799
## Avg_Cogongrass_Cover 0.2899 0.3025 0.0381 0.1944 1.0842 1.0075 2152
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8862 0.7981 0.0873 0.6708 3.0053 1.0152 624
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5748 0.3902 -3.3299 -2.5865 -1.7573 1.0027 4096
## week -0.0671 0.1154 -0.3147 -0.0601 0.1429 1.0018 2966
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7311 1.0096 0.6315 1.4788 4.2826 1.0045 2322
## week 0.0986 0.0729 0.0259 0.0787 0.2924 1.0034 2278
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.1351 1.3158 0.7898 3.0144
## (Intercept)-Canis_latrans 0.2552 0.6474 -1.0279 0.2435
## (Intercept)-Sciurus_niger -0.6331 1.0800 -2.4089 -0.7307
## (Intercept)-Procyon_lotor 0.4470 0.6397 -0.9054 0.4689
## (Intercept)-Dasypus_novemcinctus -0.7218 0.6011 -1.9273 -0.7065
## (Intercept)-Lynx_rufus -0.1269 0.8810 -1.7221 -0.1788
## (Intercept)-Didelphis_virginiana -1.3448 0.6540 -2.6531 -1.3437
## (Intercept)-Sylvilagus_floridanus -0.3697 0.7476 -1.7544 -0.4039
## (Intercept)-Meleagris_gallopavo -0.4089 0.8114 -1.9160 -0.4528
## (Intercept)-Sciurus_carolinensis -1.4146 0.6767 -2.7617 -1.3981
## (Intercept)-Vulpes_vulpes -1.0905 1.1682 -3.0173 -1.2047
## (Intercept)-Sus_scrofa -1.8718 0.8039 -3.5625 -1.8537
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1169 0.4940 -0.8513 0.1112
## Avg_Cogongrass_Cover-Canis_latrans 0.3310 0.3488 -0.3100 0.3055
## Avg_Cogongrass_Cover-Sciurus_niger -0.1956 0.5400 -1.3900 -0.1464
## Avg_Cogongrass_Cover-Procyon_lotor 0.2173 0.3526 -0.4416 0.2033
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3104 0.3195 -0.3090 0.3031
## Avg_Cogongrass_Cover-Lynx_rufus 0.4206 0.4026 -0.2741 0.3844
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3127 0.3548 -0.3629 0.3021
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2144 0.4206 -1.1291 -0.1855
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2555 0.4798 -1.3201 -0.2110
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3140 0.3525 -0.3699 0.3027
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2277 0.4341 -0.5936 0.2177
## Avg_Cogongrass_Cover-Sus_scrofa -0.1555 0.5085 -1.3497 -0.1016
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.0391 1.0156 1198
## (Intercept)-Canis_latrans 1.6031 1.0066 2472
## (Intercept)-Sciurus_niger 1.8506 1.0025 505
## (Intercept)-Procyon_lotor 1.6528 1.0002 2707
## (Intercept)-Dasypus_novemcinctus 0.4310 1.0006 3415
## (Intercept)-Lynx_rufus 1.8339 1.0124 1352
## (Intercept)-Didelphis_virginiana -0.0814 1.0004 3201
## (Intercept)-Sylvilagus_floridanus 1.1749 1.0068 1474
## (Intercept)-Meleagris_gallopavo 1.2698 1.0017 1571
## (Intercept)-Sciurus_carolinensis -0.1029 1.0152 3014
## (Intercept)-Vulpes_vulpes 1.5669 1.0357 624
## (Intercept)-Sus_scrofa -0.3202 1.0010 2227
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1190 1.0012 4631
## Avg_Cogongrass_Cover-Canis_latrans 1.0786 1.0020 4483
## Avg_Cogongrass_Cover-Sciurus_niger 0.7402 1.0013 2009
## Avg_Cogongrass_Cover-Procyon_lotor 0.9595 1.0010 4560
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9481 1.0009 4378
## Avg_Cogongrass_Cover-Lynx_rufus 1.3327 1.0009 3924
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0562 1.0013 4308
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5330 1.0008 3023
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5797 1.0019 2566
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0276 0.9999 4171
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1126 1.0000 3988
## Avg_Cogongrass_Cover-Sus_scrofa 0.6957 1.0045 2796
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0058 0.0593 -0.1115 0.0061 0.1201
## (Intercept)-Canis_latrans -2.6277 0.1769 -2.9859 -2.6201 -2.3018
## (Intercept)-Sciurus_niger -3.9451 0.5797 -5.1570 -3.9116 -2.9244
## (Intercept)-Procyon_lotor -2.2774 0.1315 -2.5527 -2.2744 -2.0251
## (Intercept)-Dasypus_novemcinctus -1.5895 0.1352 -1.8585 -1.5877 -1.3316
## (Intercept)-Lynx_rufus -3.5569 0.3288 -4.2292 -3.5509 -2.9331
## (Intercept)-Didelphis_virginiana -2.3474 0.2568 -2.8900 -2.3335 -1.8778
## (Intercept)-Sylvilagus_floridanus -3.2181 0.3081 -3.8585 -3.2023 -2.6691
## (Intercept)-Meleagris_gallopavo -3.4607 0.3618 -4.2243 -3.4434 -2.8160
## (Intercept)-Sciurus_carolinensis -2.4690 0.2707 -3.0305 -2.4589 -1.9789
## (Intercept)-Vulpes_vulpes -4.0750 0.7235 -5.5652 -4.0359 -2.7623
## (Intercept)-Sus_scrofa -2.9660 0.4705 -3.9829 -2.9314 -2.1448
## week-Odocoileus_virginianus 0.2059 0.0604 0.0874 0.2066 0.3280
## week-Canis_latrans 0.0665 0.1294 -0.1943 0.0685 0.3134
## week-Sciurus_niger -0.3094 0.2974 -0.9912 -0.2802 0.1888
## week-Procyon_lotor -0.0515 0.1156 -0.2866 -0.0466 0.1645
## week-Dasypus_novemcinctus -0.1636 0.1348 -0.4475 -0.1573 0.0857
## week-Lynx_rufus -0.0336 0.1925 -0.4323 -0.0251 0.3311
## week-Didelphis_virginiana -0.2069 0.2121 -0.6634 -0.1911 0.1639
## week-Sylvilagus_floridanus -0.1509 0.2019 -0.5840 -0.1422 0.2149
## week-Meleagris_gallopavo -0.2716 0.2455 -0.8115 -0.2473 0.1544
## week-Sciurus_carolinensis 0.1339 0.1803 -0.2299 0.1367 0.4868
## week-Vulpes_vulpes -0.1194 0.2743 -0.7204 -0.1015 0.3740
## week-Sus_scrofa 0.0859 0.2365 -0.3866 0.0876 0.5396
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0041 2949
## (Intercept)-Sciurus_niger 1.0044 434
## (Intercept)-Procyon_lotor 1.0037 4028
## (Intercept)-Dasypus_novemcinctus 1.0017 5250
## (Intercept)-Lynx_rufus 1.0034 1075
## (Intercept)-Didelphis_virginiana 1.0025 3705
## (Intercept)-Sylvilagus_floridanus 1.0010 1591
## (Intercept)-Meleagris_gallopavo 1.0014 1126
## (Intercept)-Sciurus_carolinensis 1.0015 3415
## (Intercept)-Vulpes_vulpes 1.0283 496
## (Intercept)-Sus_scrofa 1.0045 2129
## week-Odocoileus_virginianus 0.9999 5250
## week-Canis_latrans 1.0005 4482
## week-Sciurus_niger 1.0038 1934
## week-Procyon_lotor 1.0063 4323
## week-Dasypus_novemcinctus 1.0006 4667
## week-Lynx_rufus 1.0044 2965
## week-Didelphis_virginiana 1.0017 3168
## week-Sylvilagus_floridanus 1.0004 3063
## week-Meleagris_gallopavo 1.0074 2333
## week-Sciurus_carolinensis 1.0041 3986
## week-Vulpes_vulpes 1.0010 2787
## week-Sus_scrofa 1.0017 4619
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9805
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2676 0.9631 -2.0523 -0.2899 1.7279 1.0065 1925
## Cogon_Patch_Size -0.6587 0.5715 -1.8889 -0.6235 0.4146 1.0057 1508
## Veg_shannon_index 0.8762 0.4152 0.1080 0.8691 1.7195 1.0015 1125
## total_shrub_cover -0.4752 0.4472 -1.3958 -0.4589 0.3878 1.0032 1853
## Avg_Cogongrass_Cover 1.8283 0.6449 0.5766 1.8020 3.1855 1.0038 674
## Tree_Density -1.7893 0.6044 -3.0772 -1.7590 -0.6951 1.0253 713
## Avg_Canopy_Cover 1.7793 0.4950 0.8899 1.7558 2.8395 1.0047 888
## avg_veg_height -0.5474 0.4106 -1.3702 -0.5442 0.2572 1.0301 835
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.0151 12.0514 3.3674 11.7333 47.2718 1.0810 482
## Cogon_Patch_Size 2.0483 2.8293 0.0980 1.1760 9.5770 1.0210 818
## Veg_shannon_index 0.7465 1.1430 0.0498 0.3916 3.5356 1.0081 1240
## total_shrub_cover 1.2695 1.4582 0.0809 0.8071 5.0078 1.0100 831
## Avg_Cogongrass_Cover 1.0672 1.4952 0.0561 0.5555 5.1370 1.0427 748
## Tree_Density 1.6175 2.6396 0.0635 0.7835 8.1870 1.0222 608
## Avg_Canopy_Cover 1.1998 1.3514 0.0817 0.7981 4.6972 1.0004 1141
## avg_veg_height 0.3686 0.4715 0.0389 0.2174 1.6062 1.0140 1939
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2717 1.4134 0.0717 0.786 5.0024 1.0899 293
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6184 0.4022 -3.3941 -2.6309 -1.7816 1.0014 5250
## week -0.0689 0.1186 -0.3258 -0.0638 0.1537 1.0073 2576
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0181 1.1670 0.7417 1.7346 4.9685 1.0031 2761
## week 0.1001 0.0822 0.0263 0.0790 0.2988 1.0083 2417
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.6995 2.8841 3.3200 7.2586
## (Intercept)-Canis_latrans 0.6407 0.9766 -1.1133 0.5816
## (Intercept)-Sciurus_niger 1.0980 2.3619 -2.5132 0.7460
## (Intercept)-Procyon_lotor 0.8113 0.8777 -0.9863 0.8224
## (Intercept)-Dasypus_novemcinctus -1.4198 0.8362 -3.2173 -1.3869
## (Intercept)-Lynx_rufus 1.9777 2.8576 -1.8550 1.3183
## (Intercept)-Didelphis_virginiana -2.9419 1.0288 -5.1345 -2.8819
## (Intercept)-Sylvilagus_floridanus -1.1985 1.1136 -3.3943 -1.1962
## (Intercept)-Meleagris_gallopavo -1.2304 1.4077 -4.0255 -1.2385
## (Intercept)-Sciurus_carolinensis -3.0595 1.1066 -5.4904 -2.9646
## (Intercept)-Vulpes_vulpes -2.0130 2.1428 -5.5642 -2.2415
## (Intercept)-Sus_scrofa -4.5005 1.5351 -8.0326 -4.3611
## Cogon_Patch_Size-Odocoileus_virginianus -0.5476 1.1830 -2.7529 -0.5948
## Cogon_Patch_Size-Canis_latrans 0.5825 1.0818 -0.9699 0.3874
## Cogon_Patch_Size-Sciurus_niger -1.1444 1.5343 -4.5029 -1.0232
## Cogon_Patch_Size-Procyon_lotor -0.8560 0.6507 -2.1399 -0.8481
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7077 0.5798 -1.9254 -0.6815
## Cogon_Patch_Size-Lynx_rufus -0.5891 1.1996 -2.8562 -0.6291
## Cogon_Patch_Size-Didelphis_virginiana 0.7296 0.8443 -0.6641 0.6445
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7063 1.3135 -5.0211 -1.4548
## Cogon_Patch_Size-Meleagris_gallopavo -0.4307 0.9362 -2.1757 -0.4755
## Cogon_Patch_Size-Sciurus_carolinensis -1.5295 1.1252 -4.2776 -1.3203
## Cogon_Patch_Size-Vulpes_vulpes -1.1107 1.3629 -4.2337 -0.9818
## Cogon_Patch_Size-Sus_scrofa -1.1179 1.2610 -4.1969 -0.9187
## Veg_shannon_index-Odocoileus_virginianus 0.7049 0.8276 -1.1388 0.7451
## Veg_shannon_index-Canis_latrans 1.1761 0.5843 0.1549 1.1284
## Veg_shannon_index-Sciurus_niger 1.0242 0.9319 -0.7120 0.9637
## Veg_shannon_index-Procyon_lotor 1.1129 0.5571 0.1243 1.0821
## Veg_shannon_index-Dasypus_novemcinctus 0.6497 0.4884 -0.3363 0.6498
## Veg_shannon_index-Lynx_rufus 0.7668 0.8602 -1.1158 0.8021
## Veg_shannon_index-Didelphis_virginiana 1.0312 0.6216 -0.1251 0.9994
## Veg_shannon_index-Sylvilagus_floridanus 0.9804 0.6446 -0.2189 0.9433
## Veg_shannon_index-Meleagris_gallopavo 1.1786 0.7141 -0.0146 1.1025
## Veg_shannon_index-Sciurus_carolinensis 0.2118 0.6993 -1.3752 0.2946
## Veg_shannon_index-Vulpes_vulpes 0.3729 0.8377 -1.5517 0.4653
## Veg_shannon_index-Sus_scrofa 1.5096 0.9273 0.1675 1.3415
## total_shrub_cover-Odocoileus_virginianus -0.0520 0.9248 -1.7531 -0.1097
## total_shrub_cover-Canis_latrans 0.1905 0.6707 -0.9380 0.1202
## total_shrub_cover-Sciurus_niger -0.7918 1.0112 -2.9893 -0.7175
## total_shrub_cover-Procyon_lotor -0.9708 0.5978 -2.2902 -0.9152
## total_shrub_cover-Dasypus_novemcinctus 0.0744 0.5351 -0.9486 0.0634
## total_shrub_cover-Lynx_rufus -0.9536 1.0956 -3.4924 -0.8197
## total_shrub_cover-Didelphis_virginiana -0.5857 0.6844 -2.0583 -0.5400
## total_shrub_cover-Sylvilagus_floridanus -0.2789 0.7761 -1.8638 -0.2771
## total_shrub_cover-Meleagris_gallopavo -1.9136 1.1838 -4.6379 -1.7548
## total_shrub_cover-Sciurus_carolinensis -0.0505 0.6772 -1.3172 -0.0855
## total_shrub_cover-Vulpes_vulpes -0.6876 1.0011 -3.0640 -0.6005
## total_shrub_cover-Sus_scrofa 0.1057 0.8517 -1.4153 0.0412
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7757 1.0316 -0.2971 1.7617
## Avg_Cogongrass_Cover-Canis_latrans 2.2260 0.8380 0.7712 2.1725
## Avg_Cogongrass_Cover-Sciurus_niger 1.3554 1.3160 -1.6157 1.4708
## Avg_Cogongrass_Cover-Procyon_lotor 2.0988 0.8273 0.6207 2.0528
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4131 0.8712 0.9411 2.3372
## Avg_Cogongrass_Cover-Lynx_rufus 2.2946 0.9862 0.5861 2.2160
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0482 0.8112 0.5380 2.0018
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3054 0.9272 -0.6292 1.3535
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.3155 1.1350 -1.1698 1.4055
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2048 0.8464 0.7328 2.1487
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.2948 0.9921 0.5280 2.2134
## Avg_Cogongrass_Cover-Sus_scrofa 1.3371 1.0798 -1.1223 1.4205
## Tree_Density-Odocoileus_virginianus -0.9708 1.0604 -2.6970 -1.0871
## Tree_Density-Canis_latrans -2.2200 0.9896 -4.5094 -2.0586
## Tree_Density-Sciurus_niger -1.8874 1.2565 -4.6111 -1.8278
## Tree_Density-Procyon_lotor -1.4681 0.7128 -2.8498 -1.4793
## Tree_Density-Dasypus_novemcinctus -2.8934 1.2971 -6.1158 -2.6215
## Tree_Density-Lynx_rufus -0.8772 1.2033 -2.9232 -1.0112
## Tree_Density-Didelphis_virginiana -2.0842 0.9601 -4.3097 -1.9694
## Tree_Density-Sylvilagus_floridanus -2.1949 1.0593 -4.7421 -2.0579
## Tree_Density-Meleagris_gallopavo -2.0081 1.0681 -4.3556 -1.9332
## Tree_Density-Sciurus_carolinensis -2.1995 1.1067 -4.8969 -2.0385
## Tree_Density-Vulpes_vulpes -1.7723 1.2398 -4.2521 -1.7808
## Tree_Density-Sus_scrofa -2.0674 1.1751 -4.8747 -1.9104
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3900 1.0202 -0.7228 1.4203
## Avg_Canopy_Cover-Canis_latrans 0.5213 0.6725 -0.7637 0.5148
## Avg_Canopy_Cover-Sciurus_niger 1.9603 1.1954 -0.2829 1.8679
## Avg_Canopy_Cover-Procyon_lotor 1.6798 0.6279 0.5373 1.6559
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8620 0.6068 0.7845 1.8206
## Avg_Canopy_Cover-Lynx_rufus 1.3909 1.0903 -0.6990 1.3644
## Avg_Canopy_Cover-Didelphis_virginiana 2.3963 0.7839 1.1004 2.3153
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.7335 1.1483 1.0576 2.5408
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1254 0.9428 0.6214 1.9844
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1231 0.7037 0.9249 2.0664
## Avg_Canopy_Cover-Vulpes_vulpes 2.0022 0.9062 0.4503 1.9167
## Avg_Canopy_Cover-Sus_scrofa 1.9563 0.7365 0.7185 1.8841
## avg_veg_height-Odocoileus_virginianus -0.5804 0.6788 -2.0269 -0.5655
## avg_veg_height-Canis_latrans -0.6954 0.5352 -1.7783 -0.6875
## avg_veg_height-Sciurus_niger -0.7049 0.7189 -2.2416 -0.6605
## avg_veg_height-Procyon_lotor -0.4059 0.5290 -1.4078 -0.4178
## avg_veg_height-Dasypus_novemcinctus -0.3062 0.5307 -1.3035 -0.3269
## avg_veg_height-Lynx_rufus -0.6021 0.6692 -1.9588 -0.6035
## avg_veg_height-Didelphis_virginiana -0.6139 0.5963 -1.8262 -0.5977
## avg_veg_height-Sylvilagus_floridanus -0.6873 0.6050 -1.9642 -0.6687
## avg_veg_height-Meleagris_gallopavo -0.6296 0.6465 -1.9766 -0.6118
## avg_veg_height-Sciurus_carolinensis -0.2315 0.5873 -1.2818 -0.2664
## avg_veg_height-Vulpes_vulpes -0.5473 0.6522 -1.8799 -0.5446
## avg_veg_height-Sus_scrofa -0.6186 0.6198 -1.9485 -0.5981
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.5842 1.1005 462
## (Intercept)-Canis_latrans 2.7157 1.0116 1856
## (Intercept)-Sciurus_niger 6.8966 1.0420 324
## (Intercept)-Procyon_lotor 2.4778 1.0056 2177
## (Intercept)-Dasypus_novemcinctus 0.1108 1.0024 1353
## (Intercept)-Lynx_rufus 9.3059 1.1843 213
## (Intercept)-Didelphis_virginiana -1.0843 1.0056 1655
## (Intercept)-Sylvilagus_floridanus 1.0285 1.0210 1473
## (Intercept)-Meleagris_gallopavo 1.5546 1.0129 904
## (Intercept)-Sciurus_carolinensis -1.1049 1.0018 1252
## (Intercept)-Vulpes_vulpes 3.0286 1.0697 355
## (Intercept)-Sus_scrofa -1.8738 1.0022 915
## Cogon_Patch_Size-Odocoileus_virginianus 2.0182 1.0029 2235
## Cogon_Patch_Size-Canis_latrans 3.2451 1.0125 1625
## Cogon_Patch_Size-Sciurus_niger 1.6848 1.0024 888
## Cogon_Patch_Size-Procyon_lotor 0.4209 1.0034 1868
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3975 1.0001 2339
## Cogon_Patch_Size-Lynx_rufus 2.0436 1.0047 1299
## Cogon_Patch_Size-Didelphis_virginiana 2.6219 1.0041 1465
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1927 1.0290 798
## Cogon_Patch_Size-Meleagris_gallopavo 1.5934 1.0030 1789
## Cogon_Patch_Size-Sciurus_carolinensis 0.0430 1.0118 1159
## Cogon_Patch_Size-Vulpes_vulpes 1.3982 1.0304 1001
## Cogon_Patch_Size-Sus_scrofa 0.8646 1.0139 1505
## Veg_shannon_index-Odocoileus_virginianus 2.2691 1.0022 2332
## Veg_shannon_index-Canis_latrans 2.4839 1.0051 1707
## Veg_shannon_index-Sciurus_niger 3.1717 1.0000 1469
## Veg_shannon_index-Procyon_lotor 2.3001 1.0060 1245
## Veg_shannon_index-Dasypus_novemcinctus 1.6203 1.0007 2282
## Veg_shannon_index-Lynx_rufus 2.3912 1.0040 1439
## Veg_shannon_index-Didelphis_virginiana 2.3945 1.0040 2355
## Veg_shannon_index-Sylvilagus_floridanus 2.3886 1.0019 1611
## Veg_shannon_index-Meleagris_gallopavo 2.7990 1.0014 1607
## Veg_shannon_index-Sciurus_carolinensis 1.3514 1.0002 1610
## Veg_shannon_index-Vulpes_vulpes 1.8035 0.9999 1252
## Veg_shannon_index-Sus_scrofa 3.8794 1.0050 1288
## total_shrub_cover-Odocoileus_virginianus 1.9630 1.0041 2499
## total_shrub_cover-Canis_latrans 1.7222 1.0078 2241
## total_shrub_cover-Sciurus_niger 1.0842 1.0049 1493
## total_shrub_cover-Procyon_lotor 0.0763 1.0015 2589
## total_shrub_cover-Dasypus_novemcinctus 1.1523 1.0004 3075
## total_shrub_cover-Lynx_rufus 0.9529 1.0360 974
## total_shrub_cover-Didelphis_virginiana 0.6468 1.0064 2822
## total_shrub_cover-Sylvilagus_floridanus 1.2241 1.0031 2334
## total_shrub_cover-Meleagris_gallopavo -0.1549 1.0127 705
## total_shrub_cover-Sciurus_carolinensis 1.3684 1.0023 3236
## total_shrub_cover-Vulpes_vulpes 1.0642 1.0206 1328
## total_shrub_cover-Sus_scrofa 1.9886 1.0124 2114
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.8351 1.0032 1143
## Avg_Cogongrass_Cover-Canis_latrans 4.0474 1.0064 1100
## Avg_Cogongrass_Cover-Sciurus_niger 3.5778 1.0073 728
## Avg_Cogongrass_Cover-Procyon_lotor 3.8687 1.0085 1034
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.3904 1.0119 950
## Avg_Cogongrass_Cover-Lynx_rufus 4.4781 1.0115 1279
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7718 1.0032 1133
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.0639 1.0009 1019
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.3595 1.0033 829
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.9824 1.0072 1032
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4879 1.0166 1059
## Avg_Cogongrass_Cover-Sus_scrofa 3.2483 1.0052 890
## Tree_Density-Odocoileus_virginianus 1.4899 1.0277 868
## Tree_Density-Canis_latrans -0.6718 1.0084 972
## Tree_Density-Sciurus_niger 0.4559 1.0029 926
## Tree_Density-Procyon_lotor -0.0222 1.0115 1761
## Tree_Density-Dasypus_novemcinctus -1.1274 1.0028 692
## Tree_Density-Lynx_rufus 1.8605 1.0454 557
## Tree_Density-Didelphis_virginiana -0.4915 1.0001 1131
## Tree_Density-Sylvilagus_floridanus -0.4267 1.0087 1255
## Tree_Density-Meleagris_gallopavo -0.1034 1.0125 1145
## Tree_Density-Sciurus_carolinensis -0.5528 1.0031 1011
## Tree_Density-Vulpes_vulpes 0.8521 1.0058 826
## Tree_Density-Sus_scrofa -0.1833 1.0047 1107
## Avg_Canopy_Cover-Odocoileus_virginianus 3.3700 1.0042 1792
## Avg_Canopy_Cover-Canis_latrans 1.8545 1.0008 1837
## Avg_Canopy_Cover-Sciurus_niger 4.6051 1.0019 1139
## Avg_Canopy_Cover-Procyon_lotor 2.9867 1.0008 2169
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.1686 1.0023 1565
## Avg_Canopy_Cover-Lynx_rufus 3.7338 1.0202 678
## Avg_Canopy_Cover-Didelphis_virginiana 4.2106 1.0012 935
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.4340 1.0011 992
## Avg_Canopy_Cover-Meleagris_gallopavo 4.3453 1.0007 1337
## Avg_Canopy_Cover-Sciurus_carolinensis 3.6759 1.0020 1294
## Avg_Canopy_Cover-Vulpes_vulpes 4.0717 1.0027 1605
## Avg_Canopy_Cover-Sus_scrofa 3.6279 1.0025 1923
## avg_veg_height-Odocoileus_virginianus 0.7520 1.0100 1769
## avg_veg_height-Canis_latrans 0.3238 1.0143 1197
## avg_veg_height-Sciurus_niger 0.6326 1.0157 1372
## avg_veg_height-Procyon_lotor 0.6677 1.0105 1613
## avg_veg_height-Dasypus_novemcinctus 0.8045 1.0178 1525
## avg_veg_height-Lynx_rufus 0.7331 1.0121 1361
## avg_veg_height-Didelphis_virginiana 0.5497 1.0132 1494
## avg_veg_height-Sylvilagus_floridanus 0.4293 1.0155 1291
## avg_veg_height-Meleagris_gallopavo 0.6107 1.0081 1501
## avg_veg_height-Sciurus_carolinensis 1.0307 1.0046 1862
## avg_veg_height-Vulpes_vulpes 0.7316 1.0194 1529
## avg_veg_height-Sus_scrofa 0.5639 1.0194 1312
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0586 -0.1080 0.0056 0.1212
## (Intercept)-Canis_latrans -2.6469 0.1815 -3.0168 -2.6402 -2.3027
## (Intercept)-Sciurus_niger -4.5450 0.4727 -5.4516 -4.5451 -3.5920
## (Intercept)-Procyon_lotor -2.2679 0.1305 -2.5336 -2.2640 -2.0221
## (Intercept)-Dasypus_novemcinctus -1.5876 0.1337 -1.8601 -1.5851 -1.3267
## (Intercept)-Lynx_rufus -3.8014 0.3445 -4.4605 -3.8010 -3.1416
## (Intercept)-Didelphis_virginiana -2.3186 0.2432 -2.8422 -2.3076 -1.8794
## (Intercept)-Sylvilagus_floridanus -3.2062 0.2875 -3.7931 -3.1960 -2.6732
## (Intercept)-Meleagris_gallopavo -3.4641 0.3315 -4.1536 -3.4537 -2.8619
## (Intercept)-Sciurus_carolinensis -2.4562 0.2702 -3.0247 -2.4408 -1.9568
## (Intercept)-Vulpes_vulpes -4.2165 0.6841 -5.5687 -4.1964 -2.9488
## (Intercept)-Sus_scrofa -2.9035 0.4522 -3.8839 -2.8710 -2.1064
## week-Odocoileus_virginianus 0.2063 0.0604 0.0915 0.2063 0.3271
## week-Canis_latrans 0.0690 0.1319 -0.1965 0.0712 0.3187
## week-Sciurus_niger -0.3086 0.2933 -0.9600 -0.2813 0.1906
## week-Procyon_lotor -0.0506 0.1179 -0.2920 -0.0469 0.1699
## week-Dasypus_novemcinctus -0.1609 0.1380 -0.4402 -0.1554 0.0974
## week-Lynx_rufus -0.0437 0.1953 -0.4523 -0.0357 0.3129
## week-Didelphis_virginiana -0.2167 0.2125 -0.6705 -0.2055 0.1711
## week-Sylvilagus_floridanus -0.1552 0.2062 -0.5954 -0.1459 0.2060
## week-Meleagris_gallopavo -0.2759 0.2470 -0.8161 -0.2553 0.1462
## week-Sciurus_carolinensis 0.1331 0.1776 -0.2206 0.1319 0.4777
## week-Vulpes_vulpes -0.1221 0.2686 -0.6900 -0.1050 0.3692
## week-Sus_scrofa 0.0888 0.2339 -0.3883 0.0907 0.5432
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0037 2089
## (Intercept)-Sciurus_niger 1.0197 509
## (Intercept)-Procyon_lotor 1.0079 3813
## (Intercept)-Dasypus_novemcinctus 1.0018 5250
## (Intercept)-Lynx_rufus 1.0399 425
## (Intercept)-Didelphis_virginiana 1.0027 4558
## (Intercept)-Sylvilagus_floridanus 1.0022 1506
## (Intercept)-Meleagris_gallopavo 1.0057 1184
## (Intercept)-Sciurus_carolinensis 1.0016 3694
## (Intercept)-Vulpes_vulpes 1.0669 410
## (Intercept)-Sus_scrofa 1.0083 2255
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0045 4467
## week-Sciurus_niger 1.0072 1384
## week-Procyon_lotor 1.0004 4610
## week-Dasypus_novemcinctus 1.0016 4873
## week-Lynx_rufus 1.0024 2620
## week-Didelphis_virginiana 1.0003 3782
## week-Sylvilagus_floridanus 1.0007 2836
## week-Meleagris_gallopavo 1.0025 2022
## week-Sciurus_carolinensis 1.0002 4834
## week-Vulpes_vulpes 1.0084 2772
## week-Sus_scrofa 1.0007 4855
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8845
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2853 0.5630 -1.3732 -0.2959 0.8781 1.0045 1907
## Avg_Cogongrass_Cover 0.0395 0.3025 -0.5780 0.0452 0.6148 1.0108 1650
## total_shrub_cover -0.4460 0.2938 -1.0474 -0.4418 0.1139 1.0014 2321
## avg_veg_height -0.0122 0.2814 -0.5629 -0.0129 0.5396 1.0084 1327
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4309 2.7400 0.6499 2.7233 10.6682 1.0045 1434
## Avg_Cogongrass_Cover 0.3655 0.4067 0.0410 0.2354 1.4633 1.0013 1998
## total_shrub_cover 0.5353 0.5542 0.0577 0.3696 1.9960 1.0068 1832
## avg_veg_height 0.2128 0.2228 0.0329 0.1464 0.7812 1.0076 2796
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0931 0.9943 0.0868 0.8091 3.7404 1.0421 344
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5850 0.3897 -3.3547 -2.5975 -1.7811 1.0020 4481
## week -0.0743 0.1220 -0.3333 -0.0694 0.1523 1.0006 3097
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7900 1.0307 0.6604 1.5422 4.4890 1.0084 2298
## week 0.1036 0.0775 0.0270 0.0828 0.3081 1.0007 2594
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4737 1.4822 1.0032 3.3249
## (Intercept)-Canis_latrans 0.3186 0.7086 -1.0217 0.3042
## (Intercept)-Sciurus_niger -0.6867 1.0960 -2.5486 -0.7910
## (Intercept)-Procyon_lotor 0.6308 0.7229 -0.8646 0.6515
## (Intercept)-Dasypus_novemcinctus -0.7389 0.6423 -2.0416 -0.7267
## (Intercept)-Lynx_rufus -0.0526 0.9293 -1.7450 -0.1118
## (Intercept)-Didelphis_virginiana -1.4080 0.7073 -2.8658 -1.3823
## (Intercept)-Sylvilagus_floridanus -0.2166 0.8626 -1.7430 -0.2730
## (Intercept)-Meleagris_gallopavo -0.7238 0.8308 -2.3096 -0.7248
## (Intercept)-Sciurus_carolinensis -1.5448 0.7120 -3.0412 -1.5234
## (Intercept)-Vulpes_vulpes -0.9633 1.3084 -3.2084 -1.1044
## (Intercept)-Sus_scrofa -1.9991 0.8855 -3.8343 -1.9764
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0403 0.5493 -1.0360 0.0270
## Avg_Cogongrass_Cover-Canis_latrans 0.3484 0.4465 -0.4504 0.3282
## Avg_Cogongrass_Cover-Sciurus_niger -0.3184 0.6199 -1.7766 -0.2560
## Avg_Cogongrass_Cover-Procyon_lotor 0.0284 0.4351 -0.8374 0.0371
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2213 0.3937 -0.5328 0.2221
## Avg_Cogongrass_Cover-Lynx_rufus 0.3914 0.5022 -0.5051 0.3544
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2829 0.4255 -0.5239 0.2707
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3016 0.5176 -1.4593 -0.2660
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3986 0.5830 -1.7104 -0.3455
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2092 0.4253 -0.6035 0.2041
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1858 0.5230 -0.8170 0.1727
## Avg_Cogongrass_Cover-Sus_scrofa -0.2575 0.5941 -1.6254 -0.1958
## total_shrub_cover-Odocoileus_virginianus -0.2872 0.5939 -1.4629 -0.3004
## total_shrub_cover-Canis_latrans 0.0848 0.4619 -0.7055 0.0484
## total_shrub_cover-Sciurus_niger -0.7081 0.5942 -2.0344 -0.6623
## total_shrub_cover-Procyon_lotor -0.9323 0.5002 -2.0496 -0.8849
## total_shrub_cover-Dasypus_novemcinctus -0.0960 0.3748 -0.7883 -0.1050
## total_shrub_cover-Lynx_rufus -0.8892 0.6325 -2.3564 -0.8241
## total_shrub_cover-Didelphis_virginiana -0.2920 0.4288 -1.1476 -0.2914
## total_shrub_cover-Sylvilagus_floridanus -0.4542 0.5609 -1.6614 -0.4194
## total_shrub_cover-Meleagris_gallopavo -1.2116 0.6426 -2.6984 -1.1195
## total_shrub_cover-Sciurus_carolinensis -0.1607 0.4255 -0.9819 -0.1709
## total_shrub_cover-Vulpes_vulpes -0.4825 0.6351 -1.8666 -0.4439
## total_shrub_cover-Sus_scrofa -0.0172 0.5514 -1.0094 -0.0492
## avg_veg_height-Odocoileus_virginianus -0.0242 0.4674 -0.9739 -0.0198
## avg_veg_height-Canis_latrans -0.0642 0.3924 -0.8562 -0.0620
## avg_veg_height-Sciurus_niger -0.1682 0.4904 -1.1982 -0.1496
## avg_veg_height-Procyon_lotor 0.1036 0.4043 -0.6644 0.1021
## avg_veg_height-Dasypus_novemcinctus 0.1794 0.3781 -0.5324 0.1697
## avg_veg_height-Lynx_rufus 0.0036 0.4704 -0.9041 -0.0074
## avg_veg_height-Didelphis_virginiana -0.0231 0.3978 -0.8346 -0.0143
## avg_veg_height-Sylvilagus_floridanus -0.1238 0.4304 -1.0165 -0.1179
## avg_veg_height-Meleagris_gallopavo -0.2074 0.4697 -1.2022 -0.1822
## avg_veg_height-Sciurus_carolinensis 0.2506 0.4218 -0.5237 0.2273
## avg_veg_height-Vulpes_vulpes -0.0447 0.4489 -0.9535 -0.0309
## avg_veg_height-Sus_scrofa -0.0115 0.4316 -0.9013 -0.0112
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.7507 1.0039 927
## (Intercept)-Canis_latrans 1.7938 1.0017 2293
## (Intercept)-Sciurus_niger 1.7281 1.0049 676
## (Intercept)-Procyon_lotor 2.0321 1.0020 2235
## (Intercept)-Dasypus_novemcinctus 0.5081 1.0070 2680
## (Intercept)-Lynx_rufus 1.9688 1.0016 1391
## (Intercept)-Didelphis_virginiana -0.0992 1.0076 2569
## (Intercept)-Sylvilagus_floridanus 1.6440 1.0028 1428
## (Intercept)-Meleagris_gallopavo 0.9852 1.0107 1790
## (Intercept)-Sciurus_carolinensis -0.2070 1.0013 2876
## (Intercept)-Vulpes_vulpes 2.0689 1.0462 481
## (Intercept)-Sus_scrofa -0.2831 1.0123 2143
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1906 1.0041 3216
## Avg_Cogongrass_Cover-Canis_latrans 1.3057 1.0004 2627
## Avg_Cogongrass_Cover-Sciurus_niger 0.7352 1.0093 1832
## Avg_Cogongrass_Cover-Procyon_lotor 0.8626 1.0096 3440
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0090 1.0050 2971
## Avg_Cogongrass_Cover-Lynx_rufus 1.5035 1.0029 2805
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1586 1.0030 3066
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6199 1.0057 2355
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5759 1.0044 2007
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0619 1.0128 2893
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2564 1.0054 2814
## Avg_Cogongrass_Cover-Sus_scrofa 0.7518 1.0132 2415
## total_shrub_cover-Odocoileus_virginianus 0.9377 1.0028 3647
## total_shrub_cover-Canis_latrans 1.1008 1.0014 3381
## total_shrub_cover-Sciurus_niger 0.3726 1.0048 2623
## total_shrub_cover-Procyon_lotor -0.1047 1.0017 2155
## total_shrub_cover-Dasypus_novemcinctus 0.6462 1.0014 4144
## total_shrub_cover-Lynx_rufus 0.1688 1.0032 1799
## total_shrub_cover-Didelphis_virginiana 0.5699 1.0027 4693
## total_shrub_cover-Sylvilagus_floridanus 0.5525 1.0007 1526
## total_shrub_cover-Meleagris_gallopavo -0.2038 1.0031 1822
## total_shrub_cover-Sciurus_carolinensis 0.6965 1.0008 4449
## total_shrub_cover-Vulpes_vulpes 0.6835 1.0024 1838
## total_shrub_cover-Sus_scrofa 1.1769 1.0020 3741
## avg_veg_height-Odocoileus_virginianus 0.9124 1.0030 2205
## avg_veg_height-Canis_latrans 0.6913 1.0034 2543
## avg_veg_height-Sciurus_niger 0.7011 1.0042 2095
## avg_veg_height-Procyon_lotor 0.9259 1.0041 2501
## avg_veg_height-Dasypus_novemcinctus 0.9517 1.0059 2725
## avg_veg_height-Lynx_rufus 0.9916 1.0011 2351
## avg_veg_height-Didelphis_virginiana 0.7492 1.0045 2699
## avg_veg_height-Sylvilagus_floridanus 0.7211 1.0009 2232
## avg_veg_height-Meleagris_gallopavo 0.6443 1.0047 2083
## avg_veg_height-Sciurus_carolinensis 1.1595 1.0035 2394
## avg_veg_height-Vulpes_vulpes 0.8165 1.0025 2252
## avg_veg_height-Sus_scrofa 0.8538 1.0040 2772
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0067 0.0587 -0.1110 0.0070 0.1220
## (Intercept)-Canis_latrans -2.6477 0.1787 -3.0232 -2.6375 -2.3178
## (Intercept)-Sciurus_niger -3.9809 0.5700 -5.0937 -3.9800 -2.9123
## (Intercept)-Procyon_lotor -2.2842 0.1341 -2.5479 -2.2794 -2.0258
## (Intercept)-Dasypus_novemcinctus -1.5921 0.1350 -1.8584 -1.5914 -1.3319
## (Intercept)-Lynx_rufus -3.5711 0.3103 -4.1965 -3.5666 -2.9717
## (Intercept)-Didelphis_virginiana -2.3514 0.2543 -2.8939 -2.3357 -1.8862
## (Intercept)-Sylvilagus_floridanus -3.3058 0.3321 -3.9997 -3.2903 -2.7037
## (Intercept)-Meleagris_gallopavo -3.3767 0.3291 -4.0780 -3.3656 -2.7677
## (Intercept)-Sciurus_carolinensis -2.4685 0.2746 -3.0392 -2.4548 -1.9637
## (Intercept)-Vulpes_vulpes -4.2238 0.7454 -5.6723 -4.2104 -2.8451
## (Intercept)-Sus_scrofa -2.9848 0.4841 -4.0369 -2.9502 -2.1426
## week-Odocoileus_virginianus 0.2083 0.0607 0.0908 0.2081 0.3273
## week-Canis_latrans 0.0639 0.1292 -0.1954 0.0677 0.3054
## week-Sciurus_niger -0.3258 0.2992 -1.0056 -0.2952 0.1764
## week-Procyon_lotor -0.0533 0.1170 -0.2942 -0.0504 0.1671
## week-Dasypus_novemcinctus -0.1651 0.1346 -0.4400 -0.1616 0.0852
## week-Lynx_rufus -0.0455 0.1960 -0.4541 -0.0361 0.3138
## week-Didelphis_virginiana -0.2177 0.2113 -0.6602 -0.2022 0.1518
## week-Sylvilagus_floridanus -0.1615 0.2084 -0.6040 -0.1521 0.2097
## week-Meleagris_gallopavo -0.2920 0.2461 -0.8332 -0.2678 0.1356
## week-Sciurus_carolinensis 0.1292 0.1792 -0.2262 0.1296 0.4809
## week-Vulpes_vulpes -0.1298 0.2806 -0.7447 -0.1119 0.3749
## week-Sus_scrofa 0.0854 0.2380 -0.3987 0.0896 0.5432
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5104
## (Intercept)-Canis_latrans 1.0051 2892
## (Intercept)-Sciurus_niger 1.0006 558
## (Intercept)-Procyon_lotor 1.0003 4371
## (Intercept)-Dasypus_novemcinctus 1.0006 4973
## (Intercept)-Lynx_rufus 1.0036 1220
## (Intercept)-Didelphis_virginiana 1.0032 3553
## (Intercept)-Sylvilagus_floridanus 1.0021 962
## (Intercept)-Meleagris_gallopavo 1.0042 1514
## (Intercept)-Sciurus_carolinensis 1.0003 3750
## (Intercept)-Vulpes_vulpes 1.0477 397
## (Intercept)-Sus_scrofa 1.0068 1738
## week-Odocoileus_virginianus 1.0005 5250
## week-Canis_latrans 1.0015 4214
## week-Sciurus_niger 1.0005 2013
## week-Procyon_lotor 1.0032 4559
## week-Dasypus_novemcinctus 1.0003 4826
## week-Lynx_rufus 1.0017 3087
## week-Didelphis_virginiana 1.0004 3625
## week-Sylvilagus_floridanus 1.0044 2694
## week-Meleagris_gallopavo 1.0005 2424
## week-Sciurus_carolinensis 1.0007 4552
## week-Vulpes_vulpes 1.0079 2523
## week-Sus_scrofa 1.0016 4632
# Includes week covariate for detection and none for occupancy
ms_week_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8403
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2044 0.4973 -1.1385 -0.2137 0.822 1.0117 2922
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6553 1.8344 0.7827 2.173 7.2738 1.004 2583
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5847 0.3818 -3.3226 -2.5896 -1.8111 1.0004 4222
## week -0.0729 0.1194 -0.3265 -0.0669 0.1498 1.0015 2669
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7343 1.0117 0.6401 1.4982 4.4129 1.0102 2456
## week 0.1029 0.0831 0.0263 0.0817 0.3218 1.0053 2515
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.2472 1.0153 1.6973 3.0966 5.6806
## (Intercept)-Canis_latrans 0.3041 0.4006 -0.4310 0.2921 1.1307
## (Intercept)-Sciurus_niger -0.5972 0.9627 -2.0484 -0.7384 1.7568
## (Intercept)-Procyon_lotor 0.6939 0.3864 -0.0297 0.6863 1.4801
## (Intercept)-Dasypus_novemcinctus -0.6375 0.3692 -1.3635 -0.6338 0.0792
## (Intercept)-Lynx_rufus 0.3289 0.8127 -0.8760 0.1922 2.2337
## (Intercept)-Didelphis_virginiana -1.3547 0.4349 -2.2670 -1.3392 -0.5751
## (Intercept)-Sylvilagus_floridanus -0.2957 0.5585 -1.2243 -0.3354 0.9485
## (Intercept)-Meleagris_gallopavo -0.1853 0.7426 -1.2713 -0.2813 1.5197
## (Intercept)-Sciurus_carolinensis -1.3246 0.4512 -2.2704 -1.3100 -0.4909
## (Intercept)-Vulpes_vulpes -1.1255 1.0060 -2.8096 -1.2426 1.3353
## (Intercept)-Sus_scrofa -1.8282 0.6417 -3.1357 -1.8240 -0.6022
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0074 2003
## (Intercept)-Canis_latrans 1.0006 4845
## (Intercept)-Sciurus_niger 1.0194 607
## (Intercept)-Procyon_lotor 1.0035 5250
## (Intercept)-Dasypus_novemcinctus 1.0058 5250
## (Intercept)-Lynx_rufus 1.0035 1023
## (Intercept)-Didelphis_virginiana 1.0030 4784
## (Intercept)-Sylvilagus_floridanus 1.0014 1870
## (Intercept)-Meleagris_gallopavo 1.0067 874
## (Intercept)-Sciurus_carolinensis 1.0013 5250
## (Intercept)-Vulpes_vulpes 1.0151 527
## (Intercept)-Sus_scrofa 1.0008 2159
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0058 0.0586 -0.1090 0.0054 0.1198
## (Intercept)-Canis_latrans -2.6246 0.1737 -2.9750 -2.6160 -2.3091
## (Intercept)-Sciurus_niger -3.9545 0.5866 -5.1732 -3.9265 -2.8935
## (Intercept)-Procyon_lotor -2.2703 0.1281 -2.5247 -2.2681 -2.0266
## (Intercept)-Dasypus_novemcinctus -1.5906 0.1360 -1.8590 -1.5897 -1.3256
## (Intercept)-Lynx_rufus -3.6106 0.3447 -4.3104 -3.5973 -2.9737
## (Intercept)-Didelphis_virginiana -2.3371 0.2515 -2.8687 -2.3234 -1.8661
## (Intercept)-Sylvilagus_floridanus -3.2260 0.3094 -3.8870 -3.2094 -2.6543
## (Intercept)-Meleagris_gallopavo -3.5139 0.3783 -4.2985 -3.4933 -2.8338
## (Intercept)-Sciurus_carolinensis -2.4619 0.2697 -3.0235 -2.4512 -1.9697
## (Intercept)-Vulpes_vulpes -4.0156 0.7154 -5.5020 -3.9677 -2.7626
## (Intercept)-Sus_scrofa -2.9837 0.4944 -4.0635 -2.9339 -2.1396
## week-Odocoileus_virginianus 0.2051 0.0596 0.0871 0.2051 0.3200
## week-Canis_latrans 0.0663 0.1303 -0.2018 0.0694 0.3065
## week-Sciurus_niger -0.3142 0.3012 -1.0180 -0.2846 0.1837
## week-Procyon_lotor -0.0496 0.1169 -0.2849 -0.0476 0.1709
## week-Dasypus_novemcinctus -0.1624 0.1347 -0.4409 -0.1595 0.0906
## week-Lynx_rufus -0.0401 0.1906 -0.4383 -0.0325 0.3058
## week-Didelphis_virginiana -0.2157 0.2194 -0.6775 -0.2013 0.1797
## week-Sylvilagus_floridanus -0.1636 0.2077 -0.6110 -0.1472 0.2030
## week-Meleagris_gallopavo -0.2817 0.2471 -0.8261 -0.2581 0.1387
## week-Sciurus_carolinensis 0.1322 0.1802 -0.2272 0.1325 0.4798
## week-Vulpes_vulpes -0.1353 0.2806 -0.7498 -0.1169 0.3689
## week-Sus_scrofa 0.0861 0.2405 -0.3938 0.0893 0.5593
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0034 5250
## (Intercept)-Canis_latrans 1.0007 3172
## (Intercept)-Sciurus_niger 1.0170 514
## (Intercept)-Procyon_lotor 1.0026 4154
## (Intercept)-Dasypus_novemcinctus 1.0020 5104
## (Intercept)-Lynx_rufus 1.0071 889
## (Intercept)-Didelphis_virginiana 1.0016 3722
## (Intercept)-Sylvilagus_floridanus 1.0000 1440
## (Intercept)-Meleagris_gallopavo 1.0097 992
## (Intercept)-Sciurus_carolinensis 1.0053 3403
## (Intercept)-Vulpes_vulpes 1.0324 443
## (Intercept)-Sus_scrofa 1.0016 1705
## week-Odocoileus_virginianus 1.0016 5250
## week-Canis_latrans 1.0024 4238
## week-Sciurus_niger 1.0028 1673
## week-Procyon_lotor 1.0028 4901
## week-Dasypus_novemcinctus 1.0014 4691
## week-Lynx_rufus 1.0007 3134
## week-Didelphis_virginiana 1.0009 3430
## week-Sylvilagus_floridanus 1.0015 2997
## week-Meleagris_gallopavo 1.0002 2027
## week-Sciurus_carolinensis 1.0006 4694
## week-Vulpes_vulpes 1.0043 2578
## week-Sus_scrofa 1.0006 4123
#Includes week for detection and only foraging for occupancy
ms_week_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Chain 2
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## Sampling ...
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## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
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## Sampled: 9500 of 10000, 95.00%
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## Sampled: 10000 of 10000, 100.00%
summary(ms_week_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8555
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3413 0.5581 -1.4419 -0.3510 0.8058 1.0009 1917
## Veg_shannon_index 0.3691 0.2467 -0.0954 0.3638 0.8580 1.0033 2167
## Avg_Cogongrass_Cover 0.2334 0.2556 -0.2913 0.2406 0.7232 1.0039 1272
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2385 2.4711 0.6646 2.5805 9.7438 1.0048 1570
## Veg_shannon_index 0.2593 0.2667 0.0383 0.1753 0.9659 1.0190 2260
## Avg_Cogongrass_Cover 0.3138 0.3539 0.0386 0.2003 1.3044 1.0103 1940
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9105 0.8514 0.0781 0.6497 3.192 1.0354 490
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.577 0.3933 -3.3198 -2.5815 -1.7752 1.0004 4488
## week -0.073 0.1198 -0.3239 -0.0676 0.1474 1.0047 2939
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7638 1.0340 0.6469 1.5173 4.2875 1.0026 1999
## week 0.1013 0.0808 0.0266 0.0801 0.3066 1.0021 2700
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3547 1.3809 1.0120 3.2269
## (Intercept)-Canis_latrans 0.2202 0.6629 -1.1345 0.2203
## (Intercept)-Sciurus_niger -0.5089 1.1297 -2.3653 -0.6499
## (Intercept)-Procyon_lotor 0.4707 0.6768 -0.9012 0.5048
## (Intercept)-Dasypus_novemcinctus -0.7625 0.6167 -2.0259 -0.7452
## (Intercept)-Lynx_rufus -0.0220 1.0163 -1.7099 -0.1234
## (Intercept)-Didelphis_virginiana -1.4733 0.6646 -2.8028 -1.4790
## (Intercept)-Sylvilagus_floridanus -0.3331 0.8601 -1.8416 -0.3823
## (Intercept)-Meleagris_gallopavo -0.4735 0.8546 -2.0865 -0.4977
## (Intercept)-Sciurus_carolinensis -1.4853 0.6806 -2.8868 -1.4514
## (Intercept)-Vulpes_vulpes -1.1206 1.2857 -3.2617 -1.2696
## (Intercept)-Sus_scrofa -2.1194 0.8725 -3.9349 -2.1129
## Veg_shannon_index-Odocoileus_virginianus 0.3146 0.4738 -0.6580 0.3207
## Veg_shannon_index-Canis_latrans 0.6340 0.3817 -0.0603 0.6049
## Veg_shannon_index-Sciurus_niger 0.3674 0.5003 -0.6069 0.3485
## Veg_shannon_index-Procyon_lotor 0.4848 0.3786 -0.2217 0.4629
## Veg_shannon_index-Dasypus_novemcinctus 0.2172 0.3304 -0.4500 0.2236
## Veg_shannon_index-Lynx_rufus 0.1936 0.4872 -0.8245 0.2167
## Veg_shannon_index-Didelphis_virginiana 0.4989 0.3785 -0.2017 0.4802
## Veg_shannon_index-Sylvilagus_floridanus 0.4620 0.4272 -0.3279 0.4414
## Veg_shannon_index-Meleagris_gallopavo 0.4713 0.4367 -0.3426 0.4572
## Veg_shannon_index-Sciurus_carolinensis 0.0372 0.3892 -0.7802 0.0523
## Veg_shannon_index-Vulpes_vulpes 0.1280 0.4571 -0.8313 0.1467
## Veg_shannon_index-Sus_scrofa 0.6966 0.4988 -0.1349 0.6411
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2477 0.4928 -0.7302 0.2450
## Avg_Cogongrass_Cover-Canis_latrans 0.4999 0.3821 -0.1601 0.4721
## Avg_Cogongrass_Cover-Sciurus_niger -0.1023 0.5920 -1.4866 -0.0453
## Avg_Cogongrass_Cover-Procyon_lotor 0.3895 0.3983 -0.3192 0.3653
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4067 0.3287 -0.2302 0.4029
## Avg_Cogongrass_Cover-Lynx_rufus 0.5372 0.4453 -0.2442 0.5018
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4294 0.3675 -0.2748 0.4248
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0946 0.4589 -1.0956 -0.0600
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1385 0.5206 -1.2923 -0.0990
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3894 0.3647 -0.3150 0.3790
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3376 0.4703 -0.5398 0.3155
## Avg_Cogongrass_Cover-Sus_scrofa -0.0518 0.5370 -1.3264 0.0129
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.4514 1.0088 1134
## (Intercept)-Canis_latrans 1.5500 1.0023 2465
## (Intercept)-Sciurus_niger 2.1125 1.0078 629
## (Intercept)-Procyon_lotor 1.7650 1.0015 2042
## (Intercept)-Dasypus_novemcinctus 0.3989 1.0017 2617
## (Intercept)-Lynx_rufus 2.2296 1.0123 916
## (Intercept)-Didelphis_virginiana -0.1898 1.0008 3449
## (Intercept)-Sylvilagus_floridanus 1.4467 1.0111 1191
## (Intercept)-Meleagris_gallopavo 1.3537 1.0058 1455
## (Intercept)-Sciurus_carolinensis -0.2048 1.0054 2680
## (Intercept)-Vulpes_vulpes 1.9969 1.0097 481
## (Intercept)-Sus_scrofa -0.4753 1.0001 2083
## Veg_shannon_index-Odocoileus_virginianus 1.2396 1.0011 3168
## Veg_shannon_index-Canis_latrans 1.4727 1.0012 3223
## Veg_shannon_index-Sciurus_niger 1.3951 1.0010 2598
## Veg_shannon_index-Procyon_lotor 1.2772 1.0017 2783
## Veg_shannon_index-Dasypus_novemcinctus 0.8583 1.0018 3591
## Veg_shannon_index-Lynx_rufus 1.0975 1.0016 2878
## Veg_shannon_index-Didelphis_virginiana 1.3312 1.0010 3544
## Veg_shannon_index-Sylvilagus_floridanus 1.3719 1.0023 3179
## Veg_shannon_index-Meleagris_gallopavo 1.3829 1.0044 3779
## Veg_shannon_index-Sciurus_carolinensis 0.7431 1.0012 3569
## Veg_shannon_index-Vulpes_vulpes 0.9741 1.0009 2286
## Veg_shannon_index-Sus_scrofa 1.8243 1.0019 2462
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2617 1.0010 3667
## Avg_Cogongrass_Cover-Canis_latrans 1.3300 0.9999 3543
## Avg_Cogongrass_Cover-Sciurus_niger 0.9186 1.0016 1991
## Avg_Cogongrass_Cover-Procyon_lotor 1.2683 1.0026 3370
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0716 1.0022 4463
## Avg_Cogongrass_Cover-Lynx_rufus 1.5325 1.0039 2657
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1818 1.0006 3620
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7168 1.0048 2132
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7689 1.0011 2042
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1409 1.0027 3855
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3310 1.0029 3084
## Avg_Cogongrass_Cover-Sus_scrofa 0.8275 1.0019 2190
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0063 0.0586 -0.1067 0.0061 0.1225
## (Intercept)-Canis_latrans -2.6145 0.1737 -2.9742 -2.6094 -2.2886
## (Intercept)-Sciurus_niger -4.0395 0.5581 -5.1361 -4.0288 -3.0029
## (Intercept)-Procyon_lotor -2.2850 0.1328 -2.5483 -2.2806 -2.0370
## (Intercept)-Dasypus_novemcinctus -1.5918 0.1353 -1.8600 -1.5885 -1.3289
## (Intercept)-Lynx_rufus -3.5725 0.3387 -4.2793 -3.5566 -2.9381
## (Intercept)-Didelphis_virginiana -2.3360 0.2523 -2.8646 -2.3236 -1.8740
## (Intercept)-Sylvilagus_floridanus -3.2529 0.3344 -3.9585 -3.2289 -2.6659
## (Intercept)-Meleagris_gallopavo -3.4800 0.3647 -4.2329 -3.4627 -2.8203
## (Intercept)-Sciurus_carolinensis -2.4683 0.2681 -3.0283 -2.4524 -1.9824
## (Intercept)-Vulpes_vulpes -4.1053 0.7639 -5.7622 -4.0595 -2.7887
## (Intercept)-Sus_scrofa -2.9362 0.4696 -3.9767 -2.8999 -2.1175
## week-Odocoileus_virginianus 0.2078 0.0596 0.0892 0.2077 0.3254
## week-Canis_latrans 0.0653 0.1310 -0.2034 0.0673 0.3187
## week-Sciurus_niger -0.3245 0.3027 -1.0293 -0.2861 0.1651
## week-Procyon_lotor -0.0522 0.1149 -0.2842 -0.0501 0.1661
## week-Dasypus_novemcinctus -0.1645 0.1403 -0.4513 -0.1600 0.0959
## week-Lynx_rufus -0.0351 0.1935 -0.4384 -0.0283 0.3258
## week-Didelphis_virginiana -0.2171 0.2154 -0.6831 -0.2057 0.1691
## week-Sylvilagus_floridanus -0.1580 0.2078 -0.5932 -0.1496 0.2251
## week-Meleagris_gallopavo -0.2779 0.2420 -0.8243 -0.2558 0.1305
## week-Sciurus_carolinensis 0.1318 0.1773 -0.2170 0.1331 0.4731
## week-Vulpes_vulpes -0.1330 0.2754 -0.7361 -0.1142 0.3478
## week-Sus_scrofa 0.0888 0.2362 -0.3833 0.0899 0.5584
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0046 5250
## (Intercept)-Canis_latrans 1.0015 3190
## (Intercept)-Sciurus_niger 1.0195 469
## (Intercept)-Procyon_lotor 1.0000 4003
## (Intercept)-Dasypus_novemcinctus 1.0016 5031
## (Intercept)-Lynx_rufus 1.0135 998
## (Intercept)-Didelphis_virginiana 1.0000 3956
## (Intercept)-Sylvilagus_floridanus 1.0042 1090
## (Intercept)-Meleagris_gallopavo 1.0180 980
## (Intercept)-Sciurus_carolinensis 1.0043 3600
## (Intercept)-Vulpes_vulpes 1.0124 363
## (Intercept)-Sus_scrofa 1.0049 2136
## week-Odocoileus_virginianus 0.9998 5250
## week-Canis_latrans 1.0020 4147
## week-Sciurus_niger 1.0011 1953
## week-Procyon_lotor 1.0017 4447
## week-Dasypus_novemcinctus 1.0033 4633
## week-Lynx_rufus 1.0009 3227
## week-Didelphis_virginiana 1.0076 3485
## week-Sylvilagus_floridanus 1.0012 2827
## week-Meleagris_gallopavo 1.0029 1757
## week-Sciurus_carolinensis 1.0007 4736
## week-Vulpes_vulpes 1.0014 2600
## week-Sus_scrofa 1.0037 4716
# Includes movement covariates of occupancy and week for detection
ms_week_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
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## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8537
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3511 0.5740 -1.4677 -0.3636 0.8115 1.0009 2530
## Cogon_Patch_Size -0.1810 0.3708 -0.9504 -0.1665 0.5103 1.0246 2011
## Avg_Cogongrass_Cover 0.0896 0.2910 -0.4935 0.0985 0.6398 1.0083 1816
## total_shrub_cover -0.4153 0.2936 -1.0316 -0.4059 0.1243 1.0046 2392
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6975 2.9321 0.6482 2.9362 11.5767 1.0050 1662
## Cogon_Patch_Size 0.7527 1.1106 0.0586 0.4406 3.3694 1.1371 735
## Avg_Cogongrass_Cover 0.3743 0.4288 0.0412 0.2399 1.5255 1.0107 1733
## total_shrub_cover 0.5026 0.5893 0.0517 0.3286 1.9537 1.0277 1545
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3384 1.2317 0.1127 1.0106 4.4882 1.0333 402
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5876 0.3835 -3.3160 -2.5976 -1.7808 1.0024 5250
## week -0.0733 0.1183 -0.3239 -0.0692 0.1451 1.0014 2846
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7654 1.0565 0.6446 1.5080 4.4028 1.0025 2026
## week 0.1032 0.0809 0.0262 0.0797 0.3136 1.0015 2517
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5709 1.5493 0.7644 3.4207
## (Intercept)-Canis_latrans 0.3775 0.7474 -1.0465 0.3647
## (Intercept)-Sciurus_niger -0.7179 1.1696 -2.7071 -0.8193
## (Intercept)-Procyon_lotor 0.5644 0.7571 -0.9777 0.5740
## (Intercept)-Dasypus_novemcinctus -0.7803 0.6696 -2.1819 -0.7587
## (Intercept)-Lynx_rufus -0.1324 0.9940 -1.9759 -0.1766
## (Intercept)-Didelphis_virginiana -1.4343 0.7465 -2.9668 -1.4258
## (Intercept)-Sylvilagus_floridanus -0.3510 0.9188 -2.0154 -0.4011
## (Intercept)-Meleagris_gallopavo -0.7597 0.8448 -2.4446 -0.7560
## (Intercept)-Sciurus_carolinensis -1.6458 0.7620 -3.2035 -1.6353
## (Intercept)-Vulpes_vulpes -1.1584 1.3136 -3.4686 -1.2642
## (Intercept)-Sus_scrofa -2.1094 0.9645 -4.1172 -2.0811
## Cogon_Patch_Size-Odocoileus_virginianus -0.0385 0.6858 -1.2242 -0.0765
## Cogon_Patch_Size-Canis_latrans 0.6604 0.6951 -0.3164 0.5467
## Cogon_Patch_Size-Sciurus_niger -0.5204 0.8582 -2.4199 -0.4097
## Cogon_Patch_Size-Procyon_lotor -0.2069 0.4560 -1.1308 -0.2059
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1200 0.4126 -0.9800 -0.1122
## Cogon_Patch_Size-Lynx_rufus -0.2024 0.7296 -1.5428 -0.2325
## Cogon_Patch_Size-Didelphis_virginiana 0.6001 0.4946 -0.2782 0.5677
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7850 0.8294 -2.7921 -0.6401
## Cogon_Patch_Size-Meleagris_gallopavo -0.0994 0.5708 -1.2689 -0.1018
## Cogon_Patch_Size-Sciurus_carolinensis -0.6534 0.6688 -2.2938 -0.5495
## Cogon_Patch_Size-Vulpes_vulpes -0.4576 0.8293 -2.3364 -0.3704
## Cogon_Patch_Size-Sus_scrofa -0.4024 0.7914 -2.2500 -0.2938
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0909 0.5444 -1.0184 0.0973
## Avg_Cogongrass_Cover-Canis_latrans 0.2414 0.4055 -0.5204 0.2290
## Avg_Cogongrass_Cover-Sciurus_niger -0.2641 0.6409 -1.7409 -0.2029
## Avg_Cogongrass_Cover-Procyon_lotor 0.1647 0.4393 -0.6572 0.1491
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3542 0.3762 -0.3540 0.3418
## Avg_Cogongrass_Cover-Lynx_rufus 0.4667 0.5010 -0.3890 0.4172
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1808 0.4154 -0.6635 0.1767
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1953 0.4894 -1.2327 -0.1664
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3962 0.5995 -1.7643 -0.3203
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4201 0.4185 -0.3571 0.3979
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2398 0.4905 -0.6700 0.2140
## Avg_Cogongrass_Cover-Sus_scrofa -0.1923 0.6008 -1.6078 -0.1158
## total_shrub_cover-Odocoileus_virginianus -0.2549 0.5769 -1.3801 -0.2703
## total_shrub_cover-Canis_latrans 0.0359 0.4529 -0.7565 -0.0036
## total_shrub_cover-Sciurus_niger -0.6475 0.5873 -1.9150 -0.5935
## total_shrub_cover-Procyon_lotor -0.8795 0.5074 -2.0253 -0.8257
## total_shrub_cover-Dasypus_novemcinctus -0.0981 0.3771 -0.8108 -0.1035
## total_shrub_cover-Lynx_rufus -0.8009 0.6355 -2.2295 -0.7231
## total_shrub_cover-Didelphis_virginiana -0.3598 0.4305 -1.2556 -0.3498
## total_shrub_cover-Sylvilagus_floridanus -0.3921 0.5683 -1.6132 -0.3675
## total_shrub_cover-Meleagris_gallopavo -1.1480 0.6585 -2.7175 -1.0495
## total_shrub_cover-Sciurus_carolinensis -0.1389 0.4336 -0.9523 -0.1491
## total_shrub_cover-Vulpes_vulpes -0.4196 0.6439 -1.7497 -0.3967
## total_shrub_cover-Sus_scrofa 0.0027 0.5467 -0.9799 -0.0341
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1051 1.0074 864
## (Intercept)-Canis_latrans 1.9361 1.0026 2023
## (Intercept)-Sciurus_niger 2.1166 1.0561 646
## (Intercept)-Procyon_lotor 2.0311 1.0036 2134
## (Intercept)-Dasypus_novemcinctus 0.5363 1.0004 2742
## (Intercept)-Lynx_rufus 1.9384 1.0157 1312
## (Intercept)-Didelphis_virginiana 0.0246 1.0085 2733
## (Intercept)-Sylvilagus_floridanus 1.6467 1.0127 1366
## (Intercept)-Meleagris_gallopavo 0.9446 1.0100 1829
## (Intercept)-Sciurus_carolinensis -0.2003 1.0006 2162
## (Intercept)-Vulpes_vulpes 1.7836 1.0214 529
## (Intercept)-Sus_scrofa -0.3204 1.0041 1497
## Cogon_Patch_Size-Odocoileus_virginianus 1.4491 1.0048 2385
## Cogon_Patch_Size-Canis_latrans 2.4073 1.0089 1621
## Cogon_Patch_Size-Sciurus_niger 0.8812 1.0275 1217
## Cogon_Patch_Size-Procyon_lotor 0.6862 1.0117 3402
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6653 1.0137 3553
## Cogon_Patch_Size-Lynx_rufus 1.3653 1.0053 2339
## Cogon_Patch_Size-Didelphis_virginiana 1.6589 1.0045 2783
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4569 1.0241 1350
## Cogon_Patch_Size-Meleagris_gallopavo 1.0492 1.0018 3376
## Cogon_Patch_Size-Sciurus_carolinensis 0.3753 1.0182 1541
## Cogon_Patch_Size-Vulpes_vulpes 0.9230 1.0280 1405
## Cogon_Patch_Size-Sus_scrofa 0.8690 1.0199 1978
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1818 1.0047 3361
## Avg_Cogongrass_Cover-Canis_latrans 1.0980 1.0020 3647
## Avg_Cogongrass_Cover-Sciurus_niger 0.8189 1.0044 1803
## Avg_Cogongrass_Cover-Procyon_lotor 1.0921 1.0127 3494
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1375 1.0100 3111
## Avg_Cogongrass_Cover-Lynx_rufus 1.6022 1.0107 2610
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9903 1.0092 3569
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7006 1.0005 2703
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5915 1.0023 2188
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2953 1.0256 2707
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2627 1.0039 3137
## Avg_Cogongrass_Cover-Sus_scrofa 0.8139 1.0050 2136
## total_shrub_cover-Odocoileus_virginianus 0.9489 1.0010 4043
## total_shrub_cover-Canis_latrans 1.0331 1.0010 2826
## total_shrub_cover-Sciurus_niger 0.4050 1.0053 2254
## total_shrub_cover-Procyon_lotor -0.0327 1.0037 2167
## total_shrub_cover-Dasypus_novemcinctus 0.6776 1.0039 3934
## total_shrub_cover-Lynx_rufus 0.2391 1.0100 1695
## total_shrub_cover-Didelphis_virginiana 0.4697 1.0015 4240
## total_shrub_cover-Sylvilagus_floridanus 0.6614 1.0026 1940
## total_shrub_cover-Meleagris_gallopavo -0.1448 1.0018 1503
## total_shrub_cover-Sciurus_carolinensis 0.7515 1.0106 3953
## total_shrub_cover-Vulpes_vulpes 0.7865 1.0057 2073
## total_shrub_cover-Sus_scrofa 1.1859 1.0016 3505
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0061 0.0594 -0.1110 0.0070 0.1205
## (Intercept)-Canis_latrans -2.6276 0.1721 -2.9844 -2.6224 -2.3091
## (Intercept)-Sciurus_niger -3.9940 0.5808 -5.2160 -3.9561 -2.9566
## (Intercept)-Procyon_lotor -2.2838 0.1321 -2.5537 -2.2827 -2.0301
## (Intercept)-Dasypus_novemcinctus -1.5909 0.1332 -1.8586 -1.5903 -1.3344
## (Intercept)-Lynx_rufus -3.5561 0.3105 -4.1746 -3.5527 -2.9684
## (Intercept)-Didelphis_virginiana -2.3399 0.2497 -2.8804 -2.3260 -1.8791
## (Intercept)-Sylvilagus_floridanus -3.2816 0.3195 -3.9378 -3.2693 -2.6887
## (Intercept)-Meleagris_gallopavo -3.3741 0.3329 -4.0734 -3.3623 -2.7693
## (Intercept)-Sciurus_carolinensis -2.4585 0.2708 -3.0313 -2.4463 -1.9702
## (Intercept)-Vulpes_vulpes -4.1486 0.7463 -5.6172 -4.1284 -2.8188
## (Intercept)-Sus_scrofa -2.9712 0.4890 -4.0323 -2.9245 -2.1477
## week-Odocoileus_virginianus 0.2060 0.0608 0.0877 0.2064 0.3247
## week-Canis_latrans 0.0666 0.1302 -0.1928 0.0711 0.3122
## week-Sciurus_niger -0.3226 0.3100 -1.0333 -0.2869 0.1924
## week-Procyon_lotor -0.0503 0.1170 -0.2929 -0.0462 0.1631
## week-Dasypus_novemcinctus -0.1631 0.1380 -0.4458 -0.1595 0.0912
## week-Lynx_rufus -0.0388 0.1927 -0.4371 -0.0323 0.3225
## week-Didelphis_virginiana -0.2095 0.2143 -0.6821 -0.1948 0.1649
## week-Sylvilagus_floridanus -0.1583 0.2085 -0.6073 -0.1443 0.2114
## week-Meleagris_gallopavo -0.2868 0.2453 -0.8263 -0.2646 0.1364
## week-Sciurus_carolinensis 0.1297 0.1764 -0.2223 0.1335 0.4659
## week-Vulpes_vulpes -0.1280 0.2775 -0.7279 -0.1103 0.3735
## week-Sus_scrofa 0.0902 0.2357 -0.3764 0.0911 0.5502
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0003 3111
## (Intercept)-Sciurus_niger 1.0525 513
## (Intercept)-Procyon_lotor 1.0011 4003
## (Intercept)-Dasypus_novemcinctus 0.9999 5250
## (Intercept)-Lynx_rufus 1.0098 1184
## (Intercept)-Didelphis_virginiana 1.0014 3676
## (Intercept)-Sylvilagus_floridanus 1.0049 1223
## (Intercept)-Meleagris_gallopavo 1.0073 1399
## (Intercept)-Sciurus_carolinensis 1.0020 3341
## (Intercept)-Vulpes_vulpes 1.0126 484
## (Intercept)-Sus_scrofa 1.0046 1690
## week-Odocoileus_virginianus 1.0006 4969
## week-Canis_latrans 1.0039 4411
## week-Sciurus_niger 1.0016 1515
## week-Procyon_lotor 1.0011 4001
## week-Dasypus_novemcinctus 1.0003 4857
## week-Lynx_rufus 1.0003 2910
## week-Didelphis_virginiana 1.0008 3592
## week-Sylvilagus_floridanus 1.0021 2939
## week-Meleagris_gallopavo 1.0011 2201
## week-Sciurus_carolinensis 1.0001 4727
## week-Vulpes_vulpes 0.9999 2699
## week-Sus_scrofa 1.0028 4512
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9095
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2660 0.6743 -1.5352 -0.2951 1.1705 1.0007 1650
## Tree_Density -0.7693 0.3713 -1.5990 -0.7406 -0.1304 1.0015 1454
## Avg_Canopy_Cover 1.0672 0.3289 0.4661 1.0469 1.7872 1.0017 1378
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.5095 4.6327 1.2970 4.2636 17.6595 1.0101 877
## Tree_Density 0.6033 0.9898 0.0414 0.2898 3.0545 1.0513 851
## Avg_Canopy_Cover 0.5184 0.5548 0.0552 0.3553 1.9682 1.0044 2055
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4933 0.5327 0.0465 0.3142 2.0093 1.0598 430
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6050 0.3913 -3.3598 -2.6154 -1.7985 1.0038 4466
## week -0.0733 0.1218 -0.3446 -0.0661 0.1488 1.0010 2347
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8874 1.0984 0.6866 1.6018 4.7287 1.0023 1973
## week 0.1031 0.0856 0.0257 0.0796 0.3278 1.0021 1983
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.4270 1.6874 1.9927 4.1676 8.2027
## (Intercept)-Canis_latrans 0.3308 0.6612 -0.8649 0.2891 1.7343
## (Intercept)-Sciurus_niger -0.0560 1.4434 -2.2923 -0.2467 3.3479
## (Intercept)-Procyon_lotor 0.7318 0.6142 -0.4894 0.7233 1.9439
## (Intercept)-Dasypus_novemcinctus -1.0225 0.6127 -2.3199 -1.0074 0.1127
## (Intercept)-Lynx_rufus 1.1981 1.8163 -1.2208 0.7976 5.8222
## (Intercept)-Didelphis_virginiana -1.9226 0.6948 -3.3915 -1.8908 -0.6357
## (Intercept)-Sylvilagus_floridanus -0.6642 0.7291 -2.1024 -0.6659 0.8474
## (Intercept)-Meleagris_gallopavo -0.4153 0.8491 -1.9471 -0.4526 1.4338
## (Intercept)-Sciurus_carolinensis -2.0035 0.7195 -3.5816 -1.9622 -0.7238
## (Intercept)-Vulpes_vulpes -1.3203 1.4513 -3.6153 -1.5141 2.2173
## (Intercept)-Sus_scrofa -2.6643 0.9050 -4.5982 -2.6104 -1.0221
## Tree_Density-Odocoileus_virginianus -0.4161 0.6134 -1.4572 -0.4839 1.0360
## Tree_Density-Canis_latrans -0.8636 0.5044 -2.0127 -0.8097 -0.0265
## Tree_Density-Sciurus_niger -0.7992 0.7172 -2.4440 -0.7482 0.5070
## Tree_Density-Procyon_lotor -0.5102 0.3959 -1.2706 -0.5177 0.2889
## Tree_Density-Dasypus_novemcinctus -1.2672 0.8099 -3.3067 -1.0965 -0.2079
## Tree_Density-Lynx_rufus -0.1313 0.7193 -1.3192 -0.2158 1.5559
## Tree_Density-Didelphis_virginiana -0.9913 0.7096 -2.7200 -0.8799 0.0812
## Tree_Density-Sylvilagus_floridanus -1.0021 0.6870 -2.6663 -0.9050 0.0801
## Tree_Density-Meleagris_gallopavo -0.8919 0.6525 -2.4391 -0.8142 0.1867
## Tree_Density-Sciurus_carolinensis -0.9218 0.6818 -2.5684 -0.8304 0.1336
## Tree_Density-Vulpes_vulpes -0.6830 0.7931 -2.1670 -0.6799 0.7452
## Tree_Density-Sus_scrofa -0.9485 0.7603 -2.8105 -0.8334 0.2147
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8440 0.6343 -0.4559 0.8599 2.1202
## Avg_Canopy_Cover-Canis_latrans 0.1821 0.4721 -0.7712 0.1854 1.0852
## Avg_Canopy_Cover-Sciurus_niger 1.0774 0.7639 -0.2869 1.0194 2.7863
## Avg_Canopy_Cover-Procyon_lotor 1.0466 0.4494 0.2133 1.0183 1.9989
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0307 0.4090 0.2773 1.0132 1.8865
## Avg_Canopy_Cover-Lynx_rufus 0.9632 0.7116 -0.3342 0.9304 2.5033
## Avg_Canopy_Cover-Didelphis_virginiana 1.2803 0.4804 0.4674 1.2289 2.3452
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6418 0.6969 0.5892 1.5239 3.3224
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3825 0.6423 0.3465 1.2941 2.8722
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2640 0.4702 0.4244 1.2300 2.2715
## Avg_Canopy_Cover-Vulpes_vulpes 1.0885 0.5679 0.0703 1.0558 2.3281
## Avg_Canopy_Cover-Sus_scrofa 1.2500 0.5101 0.3406 1.2066 2.3733
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0233 763
## (Intercept)-Canis_latrans 1.0011 2446
## (Intercept)-Sciurus_niger 1.0074 381
## (Intercept)-Procyon_lotor 1.0055 2719
## (Intercept)-Dasypus_novemcinctus 1.0025 2979
## (Intercept)-Lynx_rufus 1.0038 400
## (Intercept)-Didelphis_virginiana 1.0035 3068
## (Intercept)-Sylvilagus_floridanus 1.0014 2534
## (Intercept)-Meleagris_gallopavo 1.0021 1602
## (Intercept)-Sciurus_carolinensis 1.0250 2189
## (Intercept)-Vulpes_vulpes 1.0179 420
## (Intercept)-Sus_scrofa 1.0013 1932
## Tree_Density-Odocoileus_virginianus 1.0044 2122
## Tree_Density-Canis_latrans 1.0008 3059
## Tree_Density-Sciurus_niger 1.0025 1730
## Tree_Density-Procyon_lotor 1.0005 3623
## Tree_Density-Dasypus_novemcinctus 1.0051 1193
## Tree_Density-Lynx_rufus 1.0030 942
## Tree_Density-Didelphis_virginiana 1.0061 1669
## Tree_Density-Sylvilagus_floridanus 1.0061 1682
## Tree_Density-Meleagris_gallopavo 1.0007 2382
## Tree_Density-Sciurus_carolinensis 1.0013 1842
## Tree_Density-Vulpes_vulpes 1.0156 1250
## Tree_Density-Sus_scrofa 1.0050 1787
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0033 2200
## Avg_Canopy_Cover-Canis_latrans 1.0004 2452
## Avg_Canopy_Cover-Sciurus_niger 1.0017 1395
## Avg_Canopy_Cover-Procyon_lotor 1.0011 3450
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0000 3786
## Avg_Canopy_Cover-Lynx_rufus 1.0000 1643
## Avg_Canopy_Cover-Didelphis_virginiana 1.0014 2596
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0046 1727
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0028 1767
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0014 2163
## Avg_Canopy_Cover-Vulpes_vulpes 1.0057 2735
## Avg_Canopy_Cover-Sus_scrofa 0.9998 2714
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0072 0.0588 -0.1091 0.0076 0.1248
## (Intercept)-Canis_latrans -2.6615 0.1854 -3.0477 -2.6527 -2.3227
## (Intercept)-Sciurus_niger -4.2399 0.5555 -5.3049 -4.2428 -3.1503
## (Intercept)-Procyon_lotor -2.2746 0.1305 -2.5321 -2.2729 -2.0237
## (Intercept)-Dasypus_novemcinctus -1.5934 0.1352 -1.8682 -1.5917 -1.3371
## (Intercept)-Lynx_rufus -3.7819 0.3540 -4.4433 -3.7870 -3.0752
## (Intercept)-Didelphis_virginiana -2.3345 0.2477 -2.8365 -2.3298 -1.8731
## (Intercept)-Sylvilagus_floridanus -3.1552 0.2834 -3.7312 -3.1420 -2.6284
## (Intercept)-Meleagris_gallopavo -3.5040 0.3483 -4.2281 -3.4918 -2.8659
## (Intercept)-Sciurus_carolinensis -2.4540 0.2680 -3.0231 -2.4405 -1.9645
## (Intercept)-Vulpes_vulpes -4.1466 0.7528 -5.6854 -4.1124 -2.8019
## (Intercept)-Sus_scrofa -2.8927 0.4478 -3.8760 -2.8604 -2.1072
## week-Odocoileus_virginianus 0.2069 0.0600 0.0912 0.2062 0.3245
## week-Canis_latrans 0.0666 0.1306 -0.1915 0.0697 0.3198
## week-Sciurus_niger -0.3168 0.3115 -1.0362 -0.2843 0.1906
## week-Procyon_lotor -0.0516 0.1192 -0.2930 -0.0459 0.1682
## week-Dasypus_novemcinctus -0.1653 0.1330 -0.4403 -0.1627 0.0862
## week-Lynx_rufus -0.0425 0.1935 -0.4530 -0.0318 0.3142
## week-Didelphis_virginiana -0.2135 0.2127 -0.6630 -0.1986 0.1724
## week-Sylvilagus_floridanus -0.1616 0.2079 -0.6154 -0.1443 0.2115
## week-Meleagris_gallopavo -0.2867 0.2470 -0.8253 -0.2691 0.1442
## week-Sciurus_carolinensis 0.1313 0.1772 -0.2286 0.1350 0.4738
## week-Vulpes_vulpes -0.1336 0.2808 -0.7328 -0.1149 0.3583
## week-Sus_scrofa 0.0912 0.2351 -0.3803 0.0916 0.5548
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0025 2199
## (Intercept)-Sciurus_niger 1.0032 434
## (Intercept)-Procyon_lotor 1.0055 4065
## (Intercept)-Dasypus_novemcinctus 1.0000 5921
## (Intercept)-Lynx_rufus 1.0038 510
## (Intercept)-Didelphis_virginiana 1.0010 3843
## (Intercept)-Sylvilagus_floridanus 1.0014 1612
## (Intercept)-Meleagris_gallopavo 1.0045 1260
## (Intercept)-Sciurus_carolinensis 1.0012 3590
## (Intercept)-Vulpes_vulpes 1.0232 456
## (Intercept)-Sus_scrofa 1.0106 2230
## week-Odocoileus_virginianus 1.0000 5250
## week-Canis_latrans 1.0032 4285
## week-Sciurus_niger 1.0016 1385
## week-Procyon_lotor 1.0002 4456
## week-Dasypus_novemcinctus 1.0008 5250
## week-Lynx_rufus 1.0007 2272
## week-Didelphis_virginiana 1.0016 3469
## week-Sylvilagus_floridanus 1.0003 2977
## week-Meleagris_gallopavo 1.0018 2113
## week-Sciurus_carolinensis 1.0029 4990
## week-Vulpes_vulpes 1.0050 2943
## week-Sus_scrofa 1.0001 4526
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8403
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9350 0.5683 -2.0433 -0.9555 0.2272 1.0041 2656
## Avg_Cogongrass_Cover -0.7358 0.3470 -1.4294 -0.7315 -0.0422 1.0079 1330
## I(Avg_Cogongrass_Cover^2) 0.7450 0.3287 0.1390 0.7307 1.4421 1.0033 1355
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1739 2.5246 0.6291 2.5051 9.8103 1.0033 1784
## Avg_Cogongrass_Cover 0.3440 0.4034 0.0411 0.2139 1.4114 1.0213 1876
## I(Avg_Cogongrass_Cover^2) 0.5905 1.1341 0.0403 0.2629 3.2477 1.0029 640
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6468 0.6109 0.0565 0.4656 2.1854 1.0634 536
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5724 0.3801 -3.3175 -2.5816 -1.8044 1.0022 4052
## week -0.0706 0.1186 -0.3099 -0.0672 0.1563 1.0083 2622
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6946 1.0260 0.6325 1.4530 4.1656 1.0026 2292
## week 0.1001 0.0814 0.0265 0.0776 0.3086 1.0039 2459
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.6275 1.3499 0.4166 2.4801
## (Intercept)-Canis_latrans -0.5143 0.6917 -1.8733 -0.5080
## (Intercept)-Sciurus_niger -0.9763 1.0974 -2.8325 -1.0803
## (Intercept)-Procyon_lotor -0.2281 0.6594 -1.5443 -0.2076
## (Intercept)-Dasypus_novemcinctus -1.3575 0.6363 -2.6147 -1.3541
## (Intercept)-Lynx_rufus -1.2269 0.8925 -2.9439 -1.2371
## (Intercept)-Didelphis_virginiana -1.9287 0.6920 -3.3307 -1.9135
## (Intercept)-Sylvilagus_floridanus -1.0417 0.7847 -2.5400 -1.0675
## (Intercept)-Meleagris_gallopavo -0.6653 0.9280 -2.3015 -0.7300
## (Intercept)-Sciurus_carolinensis -2.3859 0.7599 -3.9344 -2.3447
## (Intercept)-Vulpes_vulpes -2.1529 1.2337 -4.3512 -2.2338
## (Intercept)-Sus_scrofa -2.4205 0.9149 -4.3292 -2.3721
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7369 0.6075 -1.9860 -0.7263
## Avg_Cogongrass_Cover-Canis_latrans -0.4701 0.5030 -1.3929 -0.4931
## Avg_Cogongrass_Cover-Sciurus_niger -0.9536 0.6199 -2.3108 -0.9039
## Avg_Cogongrass_Cover-Procyon_lotor -0.5964 0.4879 -1.5303 -0.6091
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5336 0.4643 -1.4317 -0.5416
## Avg_Cogongrass_Cover-Lynx_rufus -0.6156 0.5302 -1.6330 -0.6213
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4524 0.5130 -1.4157 -0.4926
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0837 0.5744 -2.3905 -1.0336
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9510 0.5828 -2.2663 -0.8990
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7713 0.5180 -1.8394 -0.7542
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7531 0.5719 -1.9361 -0.7349
## Avg_Cogongrass_Cover-Sus_scrofa -0.9822 0.6153 -2.3544 -0.9206
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1434 0.8668 0.0176 0.9707
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2079 0.8145 0.1879 1.0137
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2442 0.7572 -1.4840 0.3189
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0697 0.6870 0.1986 0.9396
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6888 0.3437 0.0442 0.6769
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1826 0.6072 0.3316 1.0818
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5406 0.3871 -0.2105 0.5371
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7283 0.5069 -0.0861 0.6717
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1966 0.6443 -1.2543 0.2460
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9427 0.3862 0.2598 0.9167
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9166 0.5266 0.0789 0.8504
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2793 0.5982 -1.1511 0.3661
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.7453 1.0033 1516
## (Intercept)-Canis_latrans 0.8286 1.0019 1943
## (Intercept)-Sciurus_niger 1.4617 1.0025 551
## (Intercept)-Procyon_lotor 1.0144 1.0006 2454
## (Intercept)-Dasypus_novemcinctus -0.0994 1.0005 3464
## (Intercept)-Lynx_rufus 0.5886 1.0073 1663
## (Intercept)-Didelphis_virginiana -0.6075 1.0022 3249
## (Intercept)-Sylvilagus_floridanus 0.5353 1.0042 2257
## (Intercept)-Meleagris_gallopavo 1.3110 1.0264 841
## (Intercept)-Sciurus_carolinensis -0.9790 1.0035 2743
## (Intercept)-Vulpes_vulpes 0.4655 1.0010 568
## (Intercept)-Sus_scrofa -0.7606 1.0026 2069
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4478 1.0054 2534
## Avg_Cogongrass_Cover-Canis_latrans 0.6093 1.0034 2929
## Avg_Cogongrass_Cover-Sciurus_niger 0.1833 1.0082 2006
## Avg_Cogongrass_Cover-Procyon_lotor 0.4138 1.0047 2638
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4337 1.0029 2661
## Avg_Cogongrass_Cover-Lynx_rufus 0.4530 1.0051 2693
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6565 1.0060 2653
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0912 1.0041 2146
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0831 1.0052 2083
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1917 1.0019 2191
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3365 1.0060 2258
## Avg_Cogongrass_Cover-Sus_scrofa 0.0752 1.0078 2291
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.4204 1.0012 710
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4359 1.0122 785
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5462 1.0130 723
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.9303 1.0024 824
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3936 1.0006 3050
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6765 1.0144 1096
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3161 1.0048 2657
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8469 1.0267 1180
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3420 1.0128 1072
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7815 1.0047 2274
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1967 1.0053 1399
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1913 1.0050 1426
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0599 -0.1139 0.0060 0.1204
## (Intercept)-Canis_latrans -2.6491 0.1765 -3.0104 -2.6400 -2.3269
## (Intercept)-Sciurus_niger -3.9928 0.5784 -5.1571 -3.9734 -2.9249
## (Intercept)-Procyon_lotor -2.2796 0.1312 -2.5425 -2.2762 -2.0311
## (Intercept)-Dasypus_novemcinctus -1.5923 0.1346 -1.8581 -1.5904 -1.3414
## (Intercept)-Lynx_rufus -3.4241 0.3160 -4.0977 -3.4052 -2.8666
## (Intercept)-Didelphis_virginiana -2.3639 0.2612 -2.9142 -2.3534 -1.8891
## (Intercept)-Sylvilagus_floridanus -3.2365 0.3069 -3.8597 -3.2242 -2.6721
## (Intercept)-Meleagris_gallopavo -3.4977 0.3818 -4.2843 -3.4791 -2.8048
## (Intercept)-Sciurus_carolinensis -2.4546 0.2704 -3.0138 -2.4397 -1.9582
## (Intercept)-Vulpes_vulpes -3.9746 0.7128 -5.4738 -3.9294 -2.7402
## (Intercept)-Sus_scrofa -2.9627 0.4796 -4.0106 -2.9249 -2.1355
## week-Odocoileus_virginianus 0.2070 0.0603 0.0900 0.2067 0.3281
## week-Canis_latrans 0.0693 0.1300 -0.1984 0.0737 0.3104
## week-Sciurus_niger -0.3203 0.3079 -1.0299 -0.2833 0.1738
## week-Procyon_lotor -0.0497 0.1175 -0.2909 -0.0467 0.1666
## week-Dasypus_novemcinctus -0.1622 0.1330 -0.4397 -0.1555 0.0811
## week-Lynx_rufus -0.0350 0.1919 -0.4294 -0.0220 0.3114
## week-Didelphis_virginiana -0.2114 0.2131 -0.6698 -0.1994 0.1600
## week-Sylvilagus_floridanus -0.1572 0.2034 -0.5975 -0.1405 0.2006
## week-Meleagris_gallopavo -0.2749 0.2476 -0.8212 -0.2579 0.1493
## week-Sciurus_carolinensis 0.1315 0.1800 -0.2342 0.1357 0.4806
## week-Vulpes_vulpes -0.1238 0.2781 -0.7079 -0.1131 0.3794
## week-Sus_scrofa 0.0855 0.2335 -0.3612 0.0838 0.5582
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5610
## (Intercept)-Canis_latrans 1.0047 2923
## (Intercept)-Sciurus_niger 1.0102 403
## (Intercept)-Procyon_lotor 1.0004 3893
## (Intercept)-Dasypus_novemcinctus 0.9999 4938
## (Intercept)-Lynx_rufus 1.0128 1386
## (Intercept)-Didelphis_virginiana 1.0052 3728
## (Intercept)-Sylvilagus_floridanus 1.0022 1370
## (Intercept)-Meleagris_gallopavo 1.0063 741
## (Intercept)-Sciurus_carolinensis 1.0003 3547
## (Intercept)-Vulpes_vulpes 1.0054 489
## (Intercept)-Sus_scrofa 1.0028 1645
## week-Odocoileus_virginianus 1.0015 4754
## week-Canis_latrans 1.0007 4365
## week-Sciurus_niger 1.0021 1720
## week-Procyon_lotor 1.0025 4458
## week-Dasypus_novemcinctus 1.0005 4766
## week-Lynx_rufus 1.0004 3391
## week-Didelphis_virginiana 1.0016 3285
## week-Sylvilagus_floridanus 1.0008 2956
## week-Meleagris_gallopavo 1.0039 2069
## week-Sciurus_carolinensis 1.0078 4389
## week-Vulpes_vulpes 1.0021 2659
## week-Sus_scrofa 1.0003 4841
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9718
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0602 1.0695 -3.0244 -1.0990 1.2117 1.0027 1668
## Cogon_Patch_Size -0.1277 0.6567 -1.5303 -0.0954 1.0973 1.0244 962
## Veg_shannon_index 0.9489 0.4418 0.1239 0.9378 1.8664 1.0170 1033
## total_shrub_cover -0.6091 0.4767 -1.6055 -0.5864 0.2809 1.0158 1523
## Avg_Cogongrass_Cover -0.0219 0.8707 -1.6848 -0.0378 1.7206 1.0356 428
## Tree_Density -1.9510 0.7130 -3.4129 -1.9074 -0.5839 1.0176 793
## Avg_Canopy_Cover 1.7970 0.5756 0.7608 1.7565 3.0495 1.0212 482
## I(Avg_Cogongrass_Cover^2) 1.3042 0.5499 0.2600 1.2802 2.4326 1.0126 790
## avg_veg_height -0.1988 0.4656 -1.1447 -0.1926 0.7340 1.0094 670
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.9069 15.6502 3.4198 13.6488 58.9859 1.0236 500
## Cogon_Patch_Size 2.7005 3.9856 0.1250 1.5115 12.3346 1.0780 525
## Veg_shannon_index 0.7245 1.1026 0.0487 0.3680 3.7272 1.0216 934
## total_shrub_cover 1.4818 1.8328 0.0810 0.9258 6.2288 1.0358 801
## Avg_Cogongrass_Cover 0.9997 1.6774 0.0509 0.4604 5.4028 1.0010 1173
## Tree_Density 2.5583 4.6302 0.0617 1.0111 15.5192 1.0943 351
## Avg_Canopy_Cover 1.7111 2.1460 0.1034 1.0535 7.0677 1.0067 702
## I(Avg_Cogongrass_Cover^2) 1.7102 4.0386 0.0538 0.6416 9.9303 1.2102 327
## avg_veg_height 0.4037 0.5040 0.0424 0.2412 1.7199 1.0223 1875
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5238 1.9107 0.0597 0.8532 7.2036 1.2013 158
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6182 0.4113 -3.4092 -2.6324 -1.7708 1.0023 4903
## week -0.0728 0.1178 -0.3220 -0.0685 0.1440 1.0045 2436
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9968 1.1522 0.7534 1.7172 5.1118 1.0130 3090
## week 0.1000 0.0745 0.0264 0.0790 0.2955 1.0117 2534
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.2126 3.4599 2.1749 6.5884
## (Intercept)-Canis_latrans -0.9787 1.2270 -3.5452 -0.9580
## (Intercept)-Sciurus_niger 0.6579 2.6409 -3.2027 0.2294
## (Intercept)-Procyon_lotor -0.4026 1.0930 -2.7560 -0.3608
## (Intercept)-Dasypus_novemcinctus -2.6304 1.1801 -5.2828 -2.5216
## (Intercept)-Lynx_rufus 0.0627 2.5388 -3.7447 -0.2967
## (Intercept)-Didelphis_virginiana -4.0850 1.3587 -6.9763 -4.0188
## (Intercept)-Sylvilagus_floridanus -2.1341 1.4270 -5.0863 -2.0908
## (Intercept)-Meleagris_gallopavo -1.6665 1.5773 -4.6577 -1.6990
## (Intercept)-Sciurus_carolinensis -4.8332 1.5416 -8.3524 -4.6827
## (Intercept)-Vulpes_vulpes -4.0908 2.4190 -8.6044 -4.1797
## (Intercept)-Sus_scrofa -5.6097 1.9266 -9.9094 -5.4601
## Cogon_Patch_Size-Odocoileus_virginianus -0.0136 1.3330 -2.5457 -0.0525
## Cogon_Patch_Size-Canis_latrans 1.4333 1.2153 -0.3511 1.2318
## Cogon_Patch_Size-Sciurus_niger -0.6848 1.7039 -4.5382 -0.5123
## Cogon_Patch_Size-Procyon_lotor -0.3814 0.8177 -1.8788 -0.3841
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1701 0.6658 -1.6393 -0.1332
## Cogon_Patch_Size-Lynx_rufus -0.1103 1.4626 -2.9753 -0.1276
## Cogon_Patch_Size-Didelphis_virginiana 1.5279 0.9501 -0.0490 1.4346
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2396 1.4327 -4.7368 -1.0047
## Cogon_Patch_Size-Meleagris_gallopavo 0.2376 1.1639 -1.7731 0.1427
## Cogon_Patch_Size-Sciurus_carolinensis -1.0153 1.2895 -4.2010 -0.7695
## Cogon_Patch_Size-Vulpes_vulpes -0.5979 1.6551 -4.2027 -0.4486
## Cogon_Patch_Size-Sus_scrofa -0.6995 1.4677 -4.4152 -0.4296
## Veg_shannon_index-Odocoileus_virginianus 0.7747 0.8359 -0.9708 0.7899
## Veg_shannon_index-Canis_latrans 1.3000 0.6839 0.1900 1.2188
## Veg_shannon_index-Sciurus_niger 1.0008 0.9101 -0.7886 0.9844
## Veg_shannon_index-Procyon_lotor 1.1191 0.5943 0.0509 1.0757
## Veg_shannon_index-Dasypus_novemcinctus 0.6598 0.5448 -0.4307 0.6653
## Veg_shannon_index-Lynx_rufus 1.0006 0.8920 -0.7457 0.9680
## Veg_shannon_index-Didelphis_virginiana 1.0761 0.6451 -0.0919 1.0361
## Veg_shannon_index-Sylvilagus_floridanus 0.9894 0.6939 -0.2969 0.9443
## Veg_shannon_index-Meleagris_gallopavo 1.2052 0.7747 -0.1454 1.1301
## Veg_shannon_index-Sciurus_carolinensis 0.3473 0.7812 -1.4245 0.4402
## Veg_shannon_index-Vulpes_vulpes 0.6737 0.8264 -1.1728 0.7167
## Veg_shannon_index-Sus_scrofa 1.5433 0.9370 0.1664 1.3684
## total_shrub_cover-Odocoileus_virginianus -0.2253 1.0057 -2.1250 -0.2562
## total_shrub_cover-Canis_latrans -0.0224 0.6555 -1.2288 -0.0450
## total_shrub_cover-Sciurus_niger -1.0733 1.1203 -3.6080 -0.9548
## total_shrub_cover-Procyon_lotor -1.1435 0.6479 -2.5952 -1.0942
## total_shrub_cover-Dasypus_novemcinctus 0.0408 0.5591 -1.0290 0.0326
## total_shrub_cover-Lynx_rufus -1.2110 1.0883 -3.6085 -1.0905
## total_shrub_cover-Didelphis_virginiana -0.7204 0.7293 -2.2836 -0.6867
## total_shrub_cover-Sylvilagus_floridanus -0.3792 0.8549 -2.0908 -0.3733
## total_shrub_cover-Meleagris_gallopavo -2.1347 1.3097 -5.2629 -1.9286
## total_shrub_cover-Sciurus_carolinensis -0.0550 0.7417 -1.4564 -0.0775
## total_shrub_cover-Vulpes_vulpes -0.8134 1.0567 -3.2131 -0.7108
## total_shrub_cover-Sus_scrofa 0.0184 0.9360 -1.6496 -0.0482
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0758 1.2235 -2.5170 -0.0848
## Avg_Cogongrass_Cover-Canis_latrans 0.0539 1.0767 -2.0547 0.0409
## Avg_Cogongrass_Cover-Sciurus_niger -0.3277 1.3391 -3.3170 -0.2683
## Avg_Cogongrass_Cover-Procyon_lotor 0.1766 1.0889 -1.9105 0.1427
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5571 1.1723 -1.5108 0.4779
## Avg_Cogongrass_Cover-Lynx_rufus 0.1461 1.1874 -2.0794 0.1051
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2043 1.1067 -1.9296 0.1630
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4719 1.1867 -2.9398 -0.4457
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2368 1.2039 -2.7268 -0.2020
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0442 1.1285 -2.1020 0.0085
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1251 1.2258 -2.1275 0.0666
## Avg_Cogongrass_Cover-Sus_scrofa -0.4235 1.2641 -3.1927 -0.3619
## Tree_Density-Odocoileus_virginianus -1.0220 1.3195 -3.0848 -1.2089
## Tree_Density-Canis_latrans -2.6453 1.3521 -5.9506 -2.4092
## Tree_Density-Sciurus_niger -1.9500 1.5127 -5.3430 -1.8788
## Tree_Density-Procyon_lotor -1.8011 0.9202 -3.7630 -1.7566
## Tree_Density-Dasypus_novemcinctus -3.4154 1.8056 -8.1357 -2.9584
## Tree_Density-Lynx_rufus -0.9795 1.4781 -3.3602 -1.1749
## Tree_Density-Didelphis_virginiana -2.1920 1.1111 -4.8619 -2.0635
## Tree_Density-Sylvilagus_floridanus -2.4086 1.3653 -5.7110 -2.2347
## Tree_Density-Meleagris_gallopavo -1.9492 1.2550 -4.6812 -1.8967
## Tree_Density-Sciurus_carolinensis -2.5995 1.4176 -6.1870 -2.3122
## Tree_Density-Vulpes_vulpes -1.9590 1.4802 -5.0770 -1.9022
## Tree_Density-Sus_scrofa -2.2916 1.5501 -5.9423 -2.0957
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3058 1.2305 -1.2582 1.3329
## Avg_Canopy_Cover-Canis_latrans 0.3502 0.7610 -1.1046 0.3369
## Avg_Canopy_Cover-Sciurus_niger 1.9543 1.4344 -0.9527 1.8865
## Avg_Canopy_Cover-Procyon_lotor 1.6409 0.7235 0.3323 1.5941
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9104 0.7177 0.7322 1.8250
## Avg_Canopy_Cover-Lynx_rufus 1.4702 1.2698 -0.9673 1.4464
## Avg_Canopy_Cover-Didelphis_virginiana 2.5023 0.8993 1.0978 2.3945
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0532 1.4019 1.0343 2.7919
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2138 1.1203 0.5744 2.0480
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1978 0.8546 0.8518 2.0852
## Avg_Canopy_Cover-Vulpes_vulpes 2.1608 1.1310 0.3681 2.0160
## Avg_Canopy_Cover-Sus_scrofa 1.9867 0.8331 0.5582 1.8912
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8268 1.3947 -0.0301 1.5854
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0245 1.1026 0.5924 1.7973
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.9052 1.4017 -1.9861 1.0060
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7999 1.0080 0.4047 1.6155
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3608 0.7063 0.1446 1.3100
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1328 1.2415 0.4902 1.8972
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9992 0.6570 -0.3226 0.9890
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0842 0.8159 -0.3223 1.0347
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3191 1.4222 -2.8966 0.5219
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6224 0.7769 0.3831 1.5342
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8312 1.0204 0.3861 1.6716
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5324 1.1517 -2.3710 0.7069
## avg_veg_height-Odocoileus_virginianus -0.2058 0.7376 -1.6670 -0.1981
## avg_veg_height-Canis_latrans -0.3905 0.5851 -1.6000 -0.3761
## avg_veg_height-Sciurus_niger -0.3422 0.7649 -1.9772 -0.3143
## avg_veg_height-Procyon_lotor 0.0447 0.6030 -1.0939 0.0336
## avg_veg_height-Dasypus_novemcinctus 0.0966 0.5939 -0.9918 0.0745
## avg_veg_height-Lynx_rufus -0.2964 0.7432 -1.8232 -0.2614
## avg_veg_height-Didelphis_virginiana -0.2858 0.6406 -1.6063 -0.2645
## avg_veg_height-Sylvilagus_floridanus -0.3096 0.6513 -1.6646 -0.2975
## avg_veg_height-Meleagris_gallopavo -0.2269 0.7227 -1.7307 -0.2058
## avg_veg_height-Sciurus_carolinensis 0.0501 0.6482 -1.1563 0.0219
## avg_veg_height-Vulpes_vulpes -0.3147 0.7252 -1.8021 -0.2963
## avg_veg_height-Sus_scrofa -0.2647 0.6774 -1.6678 -0.2627
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8186 1.0054 361
## (Intercept)-Canis_latrans 1.4147 1.0134 882
## (Intercept)-Sciurus_niger 7.1429 1.0936 335
## (Intercept)-Procyon_lotor 1.5910 1.0162 983
## (Intercept)-Dasypus_novemcinctus -0.6010 1.0401 797
## (Intercept)-Lynx_rufus 6.2058 1.0152 273
## (Intercept)-Didelphis_virginiana -1.6453 1.0235 1171
## (Intercept)-Sylvilagus_floridanus 0.6211 1.0093 1253
## (Intercept)-Meleagris_gallopavo 1.6294 1.0179 855
## (Intercept)-Sciurus_carolinensis -2.2914 1.0240 551
## (Intercept)-Vulpes_vulpes 1.3588 1.1298 345
## (Intercept)-Sus_scrofa -2.2412 1.0267 576
## Cogon_Patch_Size-Odocoileus_virginianus 2.8369 1.0103 1602
## Cogon_Patch_Size-Canis_latrans 4.4206 1.0312 1183
## Cogon_Patch_Size-Sciurus_niger 2.3941 1.0289 677
## Cogon_Patch_Size-Procyon_lotor 1.1466 1.0200 785
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0612 1.0111 1061
## Cogon_Patch_Size-Lynx_rufus 3.0335 1.0055 950
## Cogon_Patch_Size-Didelphis_virginiana 3.6329 1.0301 883
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9448 1.0324 783
## Cogon_Patch_Size-Meleagris_gallopavo 2.8690 1.0143 1089
## Cogon_Patch_Size-Sciurus_carolinensis 0.7876 1.0440 609
## Cogon_Patch_Size-Vulpes_vulpes 2.2306 1.0540 677
## Cogon_Patch_Size-Sus_scrofa 1.3942 1.0237 993
## Veg_shannon_index-Odocoileus_virginianus 2.4246 1.0042 1236
## Veg_shannon_index-Canis_latrans 2.9272 1.0301 1072
## Veg_shannon_index-Sciurus_niger 2.7854 1.0093 904
## Veg_shannon_index-Procyon_lotor 2.3979 1.0316 849
## Veg_shannon_index-Dasypus_novemcinctus 1.7295 1.0019 1977
## Veg_shannon_index-Lynx_rufus 2.9084 1.0102 1196
## Veg_shannon_index-Didelphis_virginiana 2.4832 1.0112 1831
## Veg_shannon_index-Sylvilagus_floridanus 2.4628 1.0052 1456
## Veg_shannon_index-Meleagris_gallopavo 2.9628 1.0099 1132
## Veg_shannon_index-Sciurus_carolinensis 1.6749 1.0031 1763
## Veg_shannon_index-Vulpes_vulpes 2.2153 1.0070 1560
## Veg_shannon_index-Sus_scrofa 3.8449 1.0252 1012
## total_shrub_cover-Odocoileus_virginianus 1.9260 1.0092 2595
## total_shrub_cover-Canis_latrans 1.4189 1.0007 2367
## total_shrub_cover-Sciurus_niger 0.8516 1.0265 961
## total_shrub_cover-Procyon_lotor -0.0217 1.0004 1794
## total_shrub_cover-Dasypus_novemcinctus 1.1891 1.0083 3107
## total_shrub_cover-Lynx_rufus 0.6564 1.0300 945
## total_shrub_cover-Didelphis_virginiana 0.6352 1.0158 2580
## total_shrub_cover-Sylvilagus_floridanus 1.3434 1.0007 2249
## total_shrub_cover-Meleagris_gallopavo -0.1769 1.0297 686
## total_shrub_cover-Sciurus_carolinensis 1.4360 1.0037 2751
## total_shrub_cover-Vulpes_vulpes 1.0501 1.0180 1561
## total_shrub_cover-Sus_scrofa 2.0789 1.0094 2524
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.2269 1.0129 769
## Avg_Cogongrass_Cover-Canis_latrans 2.1901 1.0132 697
## Avg_Cogongrass_Cover-Sciurus_niger 2.0235 1.0292 713
## Avg_Cogongrass_Cover-Procyon_lotor 2.4151 1.0283 670
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.1659 1.0253 622
## Avg_Cogongrass_Cover-Lynx_rufus 2.5519 1.0254 647
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3683 1.0190 718
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7388 1.0307 735
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.9766 1.0205 669
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3238 1.0271 662
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6328 1.0113 782
## Avg_Cogongrass_Cover-Sus_scrofa 1.8709 1.0256 720
## Tree_Density-Odocoileus_virginianus 2.1867 1.0196 916
## Tree_Density-Canis_latrans -0.7359 1.0327 573
## Tree_Density-Sciurus_niger 0.8387 1.0457 715
## Tree_Density-Procyon_lotor -0.0613 1.0143 1166
## Tree_Density-Dasypus_novemcinctus -1.2035 1.1032 365
## Tree_Density-Lynx_rufus 2.5744 1.0129 689
## Tree_Density-Didelphis_virginiana -0.3839 1.0293 754
## Tree_Density-Sylvilagus_floridanus -0.2326 1.0285 645
## Tree_Density-Meleagris_gallopavo 0.4618 1.0157 1134
## Tree_Density-Sciurus_carolinensis -0.5899 1.0427 557
## Tree_Density-Vulpes_vulpes 0.9513 1.0158 784
## Tree_Density-Sus_scrofa 0.1717 1.0446 709
## Avg_Canopy_Cover-Odocoileus_virginianus 3.7631 1.0112 1031
## Avg_Canopy_Cover-Canis_latrans 1.8796 1.0055 923
## Avg_Canopy_Cover-Sciurus_niger 5.1414 1.0001 788
## Avg_Canopy_Cover-Procyon_lotor 3.2025 1.0223 861
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.5469 1.0351 675
## Avg_Canopy_Cover-Lynx_rufus 4.1218 1.0091 831
## Avg_Canopy_Cover-Didelphis_virginiana 4.6143 1.0305 627
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.5209 1.0057 597
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9533 1.0188 738
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2764 1.0154 719
## Avg_Canopy_Cover-Vulpes_vulpes 4.9112 1.0080 765
## Avg_Canopy_Cover-Sus_scrofa 3.8798 1.0163 1171
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 5.4629 1.0723 397
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.9246 1.0232 404
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.3882 1.0168 436
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.3479 1.0328 562
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9564 1.0184 1105
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2297 1.0410 489
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.2969 1.0075 1105
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9148 1.0044 1071
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.4970 1.0202 324
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.4189 1.0186 946
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.2948 1.0171 524
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.3042 1.0113 640
## avg_veg_height-Odocoileus_virginianus 1.2281 1.0004 1240
## avg_veg_height-Canis_latrans 0.6978 1.0072 1128
## avg_veg_height-Sciurus_niger 1.0903 1.0051 1275
## avg_veg_height-Procyon_lotor 1.2854 1.0032 999
## avg_veg_height-Dasypus_novemcinctus 1.3352 1.0075 1142
## avg_veg_height-Lynx_rufus 1.1150 1.0064 1126
## avg_veg_height-Didelphis_virginiana 0.9486 1.0094 1155
## avg_veg_height-Sylvilagus_floridanus 0.9472 1.0067 1271
## avg_veg_height-Meleagris_gallopavo 1.1816 1.0011 1197
## avg_veg_height-Sciurus_carolinensis 1.3803 1.0067 1409
## avg_veg_height-Vulpes_vulpes 1.0792 1.0135 1286
## avg_veg_height-Sus_scrofa 1.0510 1.0034 1280
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0590 -0.1112 0.0058 0.1215
## (Intercept)-Canis_latrans -2.6295 0.1746 -2.9956 -2.6258 -2.2957
## (Intercept)-Sciurus_niger -4.5534 0.4545 -5.4179 -4.5705 -3.6191
## (Intercept)-Procyon_lotor -2.2778 0.1308 -2.5447 -2.2718 -2.0355
## (Intercept)-Dasypus_novemcinctus -1.5927 0.1352 -1.8601 -1.5910 -1.3362
## (Intercept)-Lynx_rufus -3.7861 0.3119 -4.4025 -3.7799 -3.1967
## (Intercept)-Didelphis_virginiana -2.3225 0.2452 -2.8333 -2.3108 -1.8793
## (Intercept)-Sylvilagus_floridanus -3.2356 0.2793 -3.7914 -3.2276 -2.7221
## (Intercept)-Meleagris_gallopavo -3.4803 0.3459 -4.2158 -3.4601 -2.8355
## (Intercept)-Sciurus_carolinensis -2.4634 0.2663 -3.0310 -2.4537 -1.9719
## (Intercept)-Vulpes_vulpes -4.1949 0.6575 -5.5922 -4.1475 -3.0157
## (Intercept)-Sus_scrofa -2.8817 0.4411 -3.8252 -2.8475 -2.1059
## week-Odocoileus_virginianus 0.2067 0.0589 0.0965 0.2053 0.3260
## week-Canis_latrans 0.0644 0.1299 -0.1992 0.0673 0.3101
## week-Sciurus_niger -0.3217 0.2975 -1.0076 -0.2904 0.1695
## week-Procyon_lotor -0.0533 0.1173 -0.2919 -0.0517 0.1695
## week-Dasypus_novemcinctus -0.1659 0.1372 -0.4450 -0.1609 0.0926
## week-Lynx_rufus -0.0430 0.1908 -0.4359 -0.0365 0.3136
## week-Didelphis_virginiana -0.2118 0.2113 -0.6615 -0.2006 0.1696
## week-Sylvilagus_floridanus -0.1522 0.2036 -0.5740 -0.1383 0.2089
## week-Meleagris_gallopavo -0.2836 0.2472 -0.8289 -0.2607 0.1459
## week-Sciurus_carolinensis 0.1319 0.1817 -0.2320 0.1293 0.4818
## week-Vulpes_vulpes -0.1341 0.2744 -0.7273 -0.1174 0.3580
## week-Sus_scrofa 0.0825 0.2348 -0.3823 0.0865 0.5358
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0005 1988
## (Intercept)-Sciurus_niger 1.0119 561
## (Intercept)-Procyon_lotor 0.9999 3804
## (Intercept)-Dasypus_novemcinctus 1.0017 5032
## (Intercept)-Lynx_rufus 1.0188 732
## (Intercept)-Didelphis_virginiana 1.0011 4063
## (Intercept)-Sylvilagus_floridanus 1.0019 1578
## (Intercept)-Meleagris_gallopavo 1.0139 623
## (Intercept)-Sciurus_carolinensis 1.0024 3562
## (Intercept)-Vulpes_vulpes 1.0643 491
## (Intercept)-Sus_scrofa 1.0019 2252
## week-Odocoileus_virginianus 1.0012 5689
## week-Canis_latrans 1.0029 4007
## week-Sciurus_niger 1.0234 1406
## week-Procyon_lotor 1.0007 4298
## week-Dasypus_novemcinctus 1.0006 4817
## week-Lynx_rufus 1.0074 2704
## week-Didelphis_virginiana 1.0031 3003
## week-Sylvilagus_floridanus 1.0062 3127
## week-Meleagris_gallopavo 1.0032 2048
## week-Sciurus_carolinensis 1.0005 4767
## week-Vulpes_vulpes 1.0023 2771
## week-Sus_scrofa 1.0039 4718
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5878
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0848 0.5531 -1.1439 -0.0999 1.0375 1.0031 2162
## Avg_Cogongrass_Cover 0.1370 0.2570 -0.3945 0.1462 0.6172 1.0102 1732
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1209 2.5879 0.6013 2.4513 9.9359 1.0007 1236
## Avg_Cogongrass_Cover 0.3236 0.3995 0.0379 0.2038 1.3421 1.0193 1844
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.889 0.9113 0.0663 0.6039 3.3316 1.008 403
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7342 0.4212 -3.5455 -2.7445 -1.8946 1.0016 5001
## shrub_cover 0.0919 0.2570 -0.4377 0.0959 0.5984 1.0134 3169
## veg_height -0.0428 0.1562 -0.3533 -0.0425 0.2660 1.0020 2678
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0686 1.1863 0.7557 1.7698 5.0977 1.0035 1843
## shrub_cover 0.6118 0.4840 0.1439 0.4971 1.8117 1.0190 1880
## veg_height 0.1936 0.1275 0.0555 0.1608 0.5254 1.0003 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3387 1.3713 1.0612 3.1887
## (Intercept)-Canis_latrans 0.4341 0.6598 -0.8588 0.4162
## (Intercept)-Sciurus_niger -0.3601 1.0968 -2.2420 -0.4589
## (Intercept)-Procyon_lotor 0.5285 0.6393 -0.8030 0.5428
## (Intercept)-Dasypus_novemcinctus -0.6185 0.5973 -1.8103 -0.6017
## (Intercept)-Lynx_rufus 0.2824 1.1024 -1.5298 0.1434
## (Intercept)-Didelphis_virginiana -1.1997 0.6675 -2.5191 -1.1930
## (Intercept)-Sylvilagus_floridanus -0.3239 0.7366 -1.6913 -0.3439
## (Intercept)-Meleagris_gallopavo 0.7110 1.2341 -1.2291 0.5545
## (Intercept)-Sciurus_carolinensis -1.2812 0.6769 -2.6816 -1.2616
## (Intercept)-Vulpes_vulpes -0.9520 1.1895 -3.0834 -1.0653
## (Intercept)-Sus_scrofa -1.6660 0.8429 -3.4336 -1.6455
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1502 0.5079 -0.8458 0.1474
## Avg_Cogongrass_Cover-Canis_latrans 0.4198 0.3929 -0.2488 0.3839
## Avg_Cogongrass_Cover-Sciurus_niger -0.2037 0.6239 -1.6302 -0.1359
## Avg_Cogongrass_Cover-Procyon_lotor 0.1949 0.3496 -0.4755 0.1849
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3286 0.3303 -0.3031 0.3217
## Avg_Cogongrass_Cover-Lynx_rufus 0.3950 0.4360 -0.3814 0.3596
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3084 0.3717 -0.4213 0.3016
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2191 0.4460 -1.2171 -0.1875
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1994 0.6376 -1.6216 -0.1344
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3256 0.3545 -0.3555 0.3199
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2641 0.4516 -0.5983 0.2475
## Avg_Cogongrass_Cover-Sus_scrofa -0.1308 0.5485 -1.4184 -0.0713
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.6044 1.0042 979
## (Intercept)-Canis_latrans 1.7834 1.0007 2521
## (Intercept)-Sciurus_niger 2.1291 1.0054 775
## (Intercept)-Procyon_lotor 1.7846 1.0021 1965
## (Intercept)-Dasypus_novemcinctus 0.5573 1.0033 2910
## (Intercept)-Lynx_rufus 2.8668 1.0091 779
## (Intercept)-Didelphis_virginiana 0.1179 1.0014 2946
## (Intercept)-Sylvilagus_floridanus 1.2078 1.0052 1715
## (Intercept)-Meleagris_gallopavo 3.5765 1.0074 567
## (Intercept)-Sciurus_carolinensis -0.0289 1.0021 2741
## (Intercept)-Vulpes_vulpes 1.7399 1.0026 529
## (Intercept)-Sus_scrofa -0.0516 1.0039 1692
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1913 1.0005 3831
## Avg_Cogongrass_Cover-Canis_latrans 1.3028 1.0031 3667
## Avg_Cogongrass_Cover-Sciurus_niger 0.8360 1.0213 1364
## Avg_Cogongrass_Cover-Procyon_lotor 0.9139 1.0022 4509
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0226 1.0012 3707
## Avg_Cogongrass_Cover-Lynx_rufus 1.3322 1.0064 2884
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0706 1.0008 4162
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5684 1.0118 2067
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9281 1.0188 1360
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0275 0.9998 4439
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1996 1.0019 3064
## Avg_Cogongrass_Cover-Sus_scrofa 0.7697 1.0157 1844
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0038 0.0599 -0.1182 0.0038 0.1185
## (Intercept)-Canis_latrans -2.7652 0.1872 -3.1436 -2.7582 -2.4092
## (Intercept)-Sciurus_niger -4.1765 0.6086 -5.3978 -4.1780 -2.9889
## (Intercept)-Procyon_lotor -2.2935 0.1437 -2.5893 -2.2915 -2.0277
## (Intercept)-Dasypus_novemcinctus -1.7149 0.1565 -2.0393 -1.7116 -1.4219
## (Intercept)-Lynx_rufus -3.7521 0.3703 -4.4734 -3.7395 -3.0460
## (Intercept)-Didelphis_virginiana -2.5190 0.2825 -3.1119 -2.5079 -2.0066
## (Intercept)-Sylvilagus_floridanus -3.1884 0.3061 -3.8409 -3.1659 -2.6416
## (Intercept)-Meleagris_gallopavo -4.2592 0.4627 -5.1762 -4.2390 -3.3643
## (Intercept)-Sciurus_carolinensis -2.5829 0.3157 -3.2610 -2.5637 -2.0130
## (Intercept)-Vulpes_vulpes -4.2844 0.7664 -5.8468 -4.2435 -2.9059
## (Intercept)-Sus_scrofa -3.2427 0.6123 -4.4692 -3.2384 -2.0803
## shrub_cover-Odocoileus_virginianus -0.0550 0.0645 -0.1804 -0.0538 0.0693
## shrub_cover-Canis_latrans -0.2960 0.2174 -0.7160 -0.2959 0.1203
## shrub_cover-Sciurus_niger -0.4463 0.4653 -1.3586 -0.4445 0.4864
## shrub_cover-Procyon_lotor 0.2385 0.1678 -0.0975 0.2423 0.5584
## shrub_cover-Dasypus_novemcinctus 0.8071 0.2991 0.2400 0.8007 1.4088
## shrub_cover-Lynx_rufus -0.3164 0.3396 -1.0054 -0.3089 0.3388
## shrub_cover-Didelphis_virginiana 0.9052 0.3591 0.2415 0.8925 1.6514
## shrub_cover-Sylvilagus_floridanus 0.2372 0.4184 -0.5373 0.2226 1.0950
## shrub_cover-Meleagris_gallopavo -0.8968 0.3979 -1.7074 -0.8843 -0.1352
## shrub_cover-Sciurus_carolinensis 0.7679 0.4014 0.0006 0.7642 1.5837
## shrub_cover-Vulpes_vulpes -0.2309 0.5673 -1.3981 -0.2214 0.8735
## shrub_cover-Sus_scrofa 0.4637 0.7895 -1.0823 0.4571 2.0761
## veg_height-Odocoileus_virginianus -0.2954 0.0645 -0.4235 -0.2954 -0.1715
## veg_height-Canis_latrans -0.6072 0.1836 -0.9776 -0.6013 -0.2605
## veg_height-Sciurus_niger -0.0909 0.3956 -0.8615 -0.0944 0.7254
## veg_height-Procyon_lotor 0.3266 0.1231 0.0852 0.3272 0.5634
## veg_height-Dasypus_novemcinctus 0.2249 0.1314 -0.0291 0.2222 0.4895
## veg_height-Lynx_rufus -0.0043 0.2448 -0.5021 0.0021 0.4668
## veg_height-Didelphis_virginiana 0.3964 0.2383 -0.0454 0.3889 0.8935
## veg_height-Sylvilagus_floridanus 0.1125 0.2436 -0.3647 0.1122 0.5940
## veg_height-Meleagris_gallopavo -0.3108 0.3506 -1.0158 -0.3056 0.3689
## veg_height-Sciurus_carolinensis 0.0461 0.2108 -0.3468 0.0383 0.4811
## veg_height-Vulpes_vulpes -0.1556 0.3160 -0.8185 -0.1457 0.4316
## veg_height-Sus_scrofa -0.1468 0.3308 -0.8125 -0.1403 0.4909
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0029 2174
## (Intercept)-Sciurus_niger 1.0044 606
## (Intercept)-Procyon_lotor 1.0019 3685
## (Intercept)-Dasypus_novemcinctus 1.0014 4323
## (Intercept)-Lynx_rufus 1.0032 788
## (Intercept)-Didelphis_virginiana 1.0047 2722
## (Intercept)-Sylvilagus_floridanus 1.0007 1440
## (Intercept)-Meleagris_gallopavo 1.0071 535
## (Intercept)-Sciurus_carolinensis 1.0010 2593
## (Intercept)-Vulpes_vulpes 1.0103 478
## (Intercept)-Sus_scrofa 1.0057 1465
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0030 2783
## shrub_cover-Sciurus_niger 1.0018 1417
## shrub_cover-Procyon_lotor 1.0060 3954
## shrub_cover-Dasypus_novemcinctus 1.0008 3861
## shrub_cover-Lynx_rufus 1.0064 1372
## shrub_cover-Didelphis_virginiana 1.0081 2080
## shrub_cover-Sylvilagus_floridanus 1.0235 1472
## shrub_cover-Meleagris_gallopavo 1.0001 695
## shrub_cover-Sciurus_carolinensis 1.0012 2630
## shrub_cover-Vulpes_vulpes 1.0110 1472
## shrub_cover-Sus_scrofa 1.0007 2226
## veg_height-Odocoileus_virginianus 0.9999 4790
## veg_height-Canis_latrans 1.0014 2114
## veg_height-Sciurus_niger 1.0042 1997
## veg_height-Procyon_lotor 1.0009 4398
## veg_height-Dasypus_novemcinctus 1.0023 5048
## veg_height-Lynx_rufus 1.0004 2134
## veg_height-Didelphis_virginiana 1.0013 3565
## veg_height-Sylvilagus_floridanus 1.0091 2199
## veg_height-Meleagris_gallopavo 1.0121 1290
## veg_height-Sciurus_carolinensis 1.0030 3228
## veg_height-Vulpes_vulpes 1.0018 2038
## veg_height-Sus_scrofa 1.0015 3260
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6992
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0737 0.9932 -2.0253 -0.0760 1.9527 1.0042 1408
## Cogon_Patch_Size -0.6374 0.6074 -1.9294 -0.6332 0.5099 1.0174 891
## Veg_shannon_index 0.9718 0.4751 0.0858 0.9501 1.9395 1.0271 802
## total_shrub_cover -0.5488 0.5341 -1.6942 -0.5200 0.4403 1.0185 780
## Avg_Cogongrass_Cover 1.8916 0.7305 0.4687 1.8811 3.3526 1.0131 469
## Tree_Density -1.8414 0.6903 -3.2536 -1.8169 -0.5168 1.0125 765
## Avg_Canopy_Cover 1.9582 0.5794 0.9029 1.9239 3.1841 1.0004 1080
## avg_veg_height -0.4417 0.4775 -1.3741 -0.4383 0.5107 1.0006 553
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.1791 12.3280 3.0418 11.6921 46.9761 1.0033 734
## Cogon_Patch_Size 2.0414 3.1154 0.0884 1.1103 9.6905 1.0230 710
## Veg_shannon_index 0.8649 1.2764 0.0480 0.4409 4.2810 1.0379 619
## total_shrub_cover 1.4314 2.0536 0.0645 0.7627 6.9409 1.0222 511
## Avg_Cogongrass_Cover 1.2336 2.0497 0.0528 0.5609 6.3079 1.0401 754
## Tree_Density 2.6935 4.6871 0.0768 1.2129 14.3049 1.1345 495
## Avg_Canopy_Cover 2.1286 2.6085 0.1149 1.3258 8.9394 1.0521 491
## avg_veg_height 0.3888 0.5077 0.0410 0.2260 1.7803 1.0242 1647
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9013 2.4123 0.0666 1.0093 9.0293 1.041 168
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7641 0.4373 -3.5883 -2.7779 -1.8551 1.0021 5250
## shrub_cover 0.1975 0.2557 -0.3159 0.1971 0.7013 1.0047 1635
## veg_height -0.0262 0.1575 -0.3472 -0.0269 0.2892 1.0080 2363
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2120 1.2241 0.8119 1.9156 5.3492 1.0010 1959
## shrub_cover 0.5832 0.4290 0.1398 0.4687 1.6951 1.0019 1498
## veg_height 0.1972 0.1390 0.0576 0.1627 0.5327 1.0016 2542
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.6762 2.9876 2.9873 7.2687
## (Intercept)-Canis_latrans 0.9091 1.1078 -1.0922 0.8272
## (Intercept)-Sciurus_niger 1.4197 2.3526 -2.3197 1.1256
## (Intercept)-Procyon_lotor 0.9401 1.0594 -1.2062 0.9328
## (Intercept)-Dasypus_novemcinctus -1.3692 1.0193 -3.5881 -1.3002
## (Intercept)-Lynx_rufus 2.2936 2.7721 -1.8888 1.8813
## (Intercept)-Didelphis_virginiana -2.7078 1.1960 -5.2087 -2.6455
## (Intercept)-Sylvilagus_floridanus -0.9659 1.2951 -3.5695 -0.9836
## (Intercept)-Meleagris_gallopavo -0.1180 2.0378 -3.6190 -0.2768
## (Intercept)-Sciurus_carolinensis -2.8389 1.2759 -5.6892 -2.7575
## (Intercept)-Vulpes_vulpes -1.7822 2.1993 -5.4603 -2.0426
## (Intercept)-Sus_scrofa -4.1234 1.7972 -7.9264 -4.0288
## Cogon_Patch_Size-Odocoileus_virginianus -0.5423 1.1728 -2.7583 -0.5863
## Cogon_Patch_Size-Canis_latrans 0.5109 1.1537 -1.1667 0.3244
## Cogon_Patch_Size-Sciurus_niger -1.1677 1.5102 -4.5818 -0.9877
## Cogon_Patch_Size-Procyon_lotor -0.9702 0.7141 -2.4516 -0.9496
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5702 0.7381 -2.0209 -0.5950
## Cogon_Patch_Size-Lynx_rufus -0.6926 1.2617 -3.1521 -0.6952
## Cogon_Patch_Size-Didelphis_virginiana 0.6729 0.8816 -0.7747 0.5778
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6009 1.3199 -4.8181 -1.3823
## Cogon_Patch_Size-Meleagris_gallopavo -0.2754 1.1901 -2.2526 -0.3760
## Cogon_Patch_Size-Sciurus_carolinensis -1.4356 1.1850 -4.3350 -1.2599
## Cogon_Patch_Size-Vulpes_vulpes -1.0329 1.3541 -4.0909 -0.9261
## Cogon_Patch_Size-Sus_scrofa -1.0881 1.2524 -4.1975 -0.9238
## Veg_shannon_index-Odocoileus_virginianus 0.7957 0.8883 -1.1564 0.8276
## Veg_shannon_index-Canis_latrans 1.3098 0.6856 0.1062 1.2465
## Veg_shannon_index-Sciurus_niger 1.1109 1.0188 -0.9193 1.0718
## Veg_shannon_index-Procyon_lotor 1.2329 0.6216 0.1017 1.2086
## Veg_shannon_index-Dasypus_novemcinctus 0.6465 0.5697 -0.5529 0.6574
## Veg_shannon_index-Lynx_rufus 0.9256 0.9375 -1.1215 0.9373
## Veg_shannon_index-Didelphis_virginiana 1.1793 0.7118 -0.0839 1.1292
## Veg_shannon_index-Sylvilagus_floridanus 1.0802 0.7044 -0.1999 1.0406
## Veg_shannon_index-Meleagris_gallopavo 1.2961 0.8335 -0.1098 1.2053
## Veg_shannon_index-Sciurus_carolinensis 0.2609 0.8051 -1.5977 0.3450
## Veg_shannon_index-Vulpes_vulpes 0.4303 0.9303 -1.7321 0.5300
## Veg_shannon_index-Sus_scrofa 1.6152 0.9717 0.1831 1.4639
## total_shrub_cover-Odocoileus_virginianus -0.1249 0.9783 -1.9913 -0.1847
## total_shrub_cover-Canis_latrans 0.4921 0.9038 -0.8855 0.3622
## total_shrub_cover-Sciurus_niger -0.6534 1.0909 -2.9422 -0.6108
## total_shrub_cover-Procyon_lotor -1.0371 0.6791 -2.5457 -0.9829
## total_shrub_cover-Dasypus_novemcinctus -0.1600 0.6542 -1.5201 -0.1555
## total_shrub_cover-Lynx_rufus -0.7818 1.2610 -3.5887 -0.6714
## total_shrub_cover-Didelphis_virginiana -0.8166 0.8451 -2.7230 -0.7229
## total_shrub_cover-Sylvilagus_floridanus -0.6610 1.0669 -3.0694 -0.5503
## total_shrub_cover-Meleagris_gallopavo -1.5594 1.3351 -4.7438 -1.3461
## total_shrub_cover-Sciurus_carolinensis -0.4914 0.9117 -2.5277 -0.4280
## total_shrub_cover-Vulpes_vulpes -0.8855 1.2369 -3.7277 -0.7287
## total_shrub_cover-Sus_scrofa -0.1802 1.0141 -2.1911 -0.2269
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8569 1.1286 -0.3067 1.8365
## Avg_Cogongrass_Cover-Canis_latrans 2.3846 0.9877 0.7350 2.2919
## Avg_Cogongrass_Cover-Sciurus_niger 1.3402 1.4769 -2.2524 1.5636
## Avg_Cogongrass_Cover-Procyon_lotor 2.1011 0.8840 0.4844 2.0588
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5157 0.9919 0.8476 2.4281
## Avg_Cogongrass_Cover-Lynx_rufus 2.2628 1.0364 0.4471 2.1924
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0298 0.9163 0.3007 1.9838
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3150 1.0374 -0.8875 1.3479
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5329 1.3157 -1.4232 1.6464
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2497 0.9690 0.4965 2.1975
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4031 1.0861 0.4537 2.3215
## Avg_Cogongrass_Cover-Sus_scrofa 1.4877 1.2156 -1.3602 1.5849
## Tree_Density-Odocoileus_virginianus -0.7515 1.2599 -2.7349 -0.9131
## Tree_Density-Canis_latrans -2.5824 1.2827 -5.7655 -2.3812
## Tree_Density-Sciurus_niger -1.9532 1.5027 -5.2026 -1.8946
## Tree_Density-Procyon_lotor -1.5011 0.7711 -3.0424 -1.5193
## Tree_Density-Dasypus_novemcinctus -3.3818 1.7506 -8.0603 -2.9531
## Tree_Density-Lynx_rufus -0.7530 1.4142 -2.9993 -0.9353
## Tree_Density-Didelphis_virginiana -2.1279 1.1540 -4.8166 -2.0196
## Tree_Density-Sylvilagus_floridanus -2.3066 1.3069 -5.4152 -2.1579
## Tree_Density-Meleagris_gallopavo -2.1768 1.3774 -5.1891 -2.0734
## Tree_Density-Sciurus_carolinensis -2.2984 1.3082 -5.4676 -2.1345
## Tree_Density-Vulpes_vulpes -1.7435 1.5515 -4.7979 -1.7813
## Tree_Density-Sus_scrofa -2.2410 1.5268 -5.9659 -2.0246
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3606 1.1917 -1.0655 1.4026
## Avg_Canopy_Cover-Canis_latrans 0.3481 0.7141 -1.0053 0.3223
## Avg_Canopy_Cover-Sciurus_niger 2.1743 1.5314 -0.6432 2.0688
## Avg_Canopy_Cover-Procyon_lotor 1.8117 0.7424 0.4472 1.7669
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1238 0.7311 0.8900 2.0489
## Avg_Canopy_Cover-Lynx_rufus 1.5496 1.3881 -1.0647 1.5203
## Avg_Canopy_Cover-Didelphis_virginiana 2.8776 1.0805 1.2736 2.7085
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3255 1.5108 1.2443 3.0601
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4631 1.2672 0.5303 2.2531
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6430 1.0684 1.0816 2.4719
## Avg_Canopy_Cover-Vulpes_vulpes 2.2583 1.1498 0.3521 2.1123
## Avg_Canopy_Cover-Sus_scrofa 2.1644 0.9152 0.5938 2.0653
## avg_veg_height-Odocoileus_virginianus -0.4668 0.7281 -1.9577 -0.4595
## avg_veg_height-Canis_latrans -0.4468 0.6101 -1.6369 -0.4513
## avg_veg_height-Sciurus_niger -0.5873 0.7907 -2.3406 -0.5407
## avg_veg_height-Procyon_lotor -0.3924 0.5894 -1.5566 -0.3835
## avg_veg_height-Dasypus_novemcinctus -0.2423 0.5876 -1.3892 -0.2547
## avg_veg_height-Lynx_rufus -0.5331 0.7471 -2.0960 -0.5156
## avg_veg_height-Didelphis_virginiana -0.5723 0.6550 -1.9464 -0.5427
## avg_veg_height-Sylvilagus_floridanus -0.6135 0.6655 -1.9969 -0.5903
## avg_veg_height-Meleagris_gallopavo -0.4453 0.7730 -2.0126 -0.4425
## avg_veg_height-Sciurus_carolinensis -0.1366 0.6616 -1.3181 -0.1632
## avg_veg_height-Vulpes_vulpes -0.4277 0.7212 -1.8454 -0.4298
## avg_veg_height-Sus_scrofa -0.4971 0.6771 -1.8697 -0.4808
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.7244 1.0088 385
## (Intercept)-Canis_latrans 3.3769 1.0040 1450
## (Intercept)-Sciurus_niger 7.0108 1.0279 388
## (Intercept)-Procyon_lotor 3.0646 1.0041 1371
## (Intercept)-Dasypus_novemcinctus 0.4745 1.0103 1194
## (Intercept)-Lynx_rufus 8.9694 1.0341 283
## (Intercept)-Didelphis_virginiana -0.4888 1.0046 1258
## (Intercept)-Sylvilagus_floridanus 1.7707 1.0165 1212
## (Intercept)-Meleagris_gallopavo 4.4832 1.0173 435
## (Intercept)-Sciurus_carolinensis -0.5147 1.0147 1086
## (Intercept)-Vulpes_vulpes 3.7033 1.0084 285
## (Intercept)-Sus_scrofa -0.7919 1.0088 689
## Cogon_Patch_Size-Odocoileus_virginianus 2.0338 1.0040 1869
## Cogon_Patch_Size-Canis_latrans 3.2337 1.0126 1193
## Cogon_Patch_Size-Sciurus_niger 1.5014 1.0064 680
## Cogon_Patch_Size-Procyon_lotor 0.3773 1.0144 885
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0288 1.0068 1469
## Cogon_Patch_Size-Lynx_rufus 1.9049 1.0209 1136
## Cogon_Patch_Size-Didelphis_virginiana 2.7378 1.0042 1154
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4318 1.0113 835
## Cogon_Patch_Size-Meleagris_gallopavo 2.2762 1.0042 1119
## Cogon_Patch_Size-Sciurus_carolinensis 0.3724 1.0229 642
## Cogon_Patch_Size-Vulpes_vulpes 1.4681 1.0059 982
## Cogon_Patch_Size-Sus_scrofa 0.8636 1.0011 1200
## Veg_shannon_index-Odocoileus_virginianus 2.5043 1.0075 1242
## Veg_shannon_index-Canis_latrans 2.8477 1.0136 926
## Veg_shannon_index-Sciurus_niger 3.3317 1.0079 947
## Veg_shannon_index-Procyon_lotor 2.5268 1.0206 733
## Veg_shannon_index-Dasypus_novemcinctus 1.7297 1.0182 1496
## Veg_shannon_index-Lynx_rufus 2.7274 1.0091 806
## Veg_shannon_index-Didelphis_virginiana 2.7141 1.0236 863
## Veg_shannon_index-Sylvilagus_floridanus 2.6413 1.0194 1303
## Veg_shannon_index-Meleagris_gallopavo 3.2891 1.0101 1225
## Veg_shannon_index-Sciurus_carolinensis 1.5846 1.0042 741
## Veg_shannon_index-Vulpes_vulpes 1.9982 1.0060 735
## Veg_shannon_index-Sus_scrofa 3.9924 1.0381 799
## total_shrub_cover-Odocoileus_virginianus 2.0140 1.0027 1494
## total_shrub_cover-Canis_latrans 2.5963 1.0023 811
## total_shrub_cover-Sciurus_niger 1.5644 1.0089 1046
## total_shrub_cover-Procyon_lotor 0.1056 1.0096 1164
## total_shrub_cover-Dasypus_novemcinctus 1.0992 1.0029 1757
## total_shrub_cover-Lynx_rufus 1.5833 1.0363 597
## total_shrub_cover-Didelphis_virginiana 0.6196 1.0083 958
## total_shrub_cover-Sylvilagus_floridanus 1.1218 1.0100 874
## total_shrub_cover-Meleagris_gallopavo 0.4072 1.0207 541
## total_shrub_cover-Sciurus_carolinensis 1.1192 1.0283 1048
## total_shrub_cover-Vulpes_vulpes 1.1537 1.0255 616
## total_shrub_cover-Sus_scrofa 1.9543 1.0073 1196
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.2007 1.0169 900
## Avg_Cogongrass_Cover-Canis_latrans 4.6153 1.0174 701
## Avg_Cogongrass_Cover-Sciurus_niger 3.7084 1.0161 523
## Avg_Cogongrass_Cover-Procyon_lotor 3.9711 1.0198 765
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.7591 1.0385 610
## Avg_Cogongrass_Cover-Lynx_rufus 4.5406 1.0077 922
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9475 1.0222 890
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2595 1.0052 776
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.8042 1.0172 617
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3630 1.0132 713
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7685 1.0177 755
## Avg_Cogongrass_Cover-Sus_scrofa 3.6914 1.0064 1088
## Tree_Density-Odocoileus_virginianus 2.1936 1.0343 816
## Tree_Density-Canis_latrans -0.6775 1.0287 693
## Tree_Density-Sciurus_niger 0.9793 1.0046 894
## Tree_Density-Procyon_lotor 0.0519 1.0091 1283
## Tree_Density-Dasypus_novemcinctus -1.1748 1.0463 450
## Tree_Density-Lynx_rufus 2.5728 1.0997 550
## Tree_Density-Didelphis_virginiana -0.1063 1.0045 1181
## Tree_Density-Sylvilagus_floridanus -0.0857 1.0084 1147
## Tree_Density-Meleagris_gallopavo 0.3216 1.0147 848
## Tree_Density-Sciurus_carolinensis -0.0763 1.0049 988
## Tree_Density-Vulpes_vulpes 1.5211 1.0347 849
## Tree_Density-Sus_scrofa 0.2656 1.0114 841
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6849 1.0020 1994
## Avg_Canopy_Cover-Canis_latrans 1.7917 1.0028 1253
## Avg_Canopy_Cover-Sciurus_niger 5.4887 1.0060 797
## Avg_Canopy_Cover-Procyon_lotor 3.4318 1.0085 1204
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7726 1.0194 1127
## Avg_Canopy_Cover-Lynx_rufus 4.5987 1.0130 535
## Avg_Canopy_Cover-Didelphis_virginiana 5.4170 1.0082 677
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1130 1.0137 615
## Avg_Canopy_Cover-Meleagris_gallopavo 5.5040 1.0253 1082
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2041 1.0232 939
## Avg_Canopy_Cover-Vulpes_vulpes 5.0072 1.0089 976
## Avg_Canopy_Cover-Sus_scrofa 4.2343 1.0093 1399
## avg_veg_height-Odocoileus_virginianus 0.9569 1.0008 1059
## avg_veg_height-Canis_latrans 0.7616 1.0014 859
## avg_veg_height-Sciurus_niger 0.8537 1.0008 984
## avg_veg_height-Procyon_lotor 0.7512 1.0023 909
## avg_veg_height-Dasypus_novemcinctus 0.9479 1.0001 978
## avg_veg_height-Lynx_rufus 0.8808 1.0021 941
## avg_veg_height-Didelphis_virginiana 0.6515 1.0053 1059
## avg_veg_height-Sylvilagus_floridanus 0.6327 1.0012 1030
## avg_veg_height-Meleagris_gallopavo 1.1107 1.0026 978
## avg_veg_height-Sciurus_carolinensis 1.2783 1.0006 959
## avg_veg_height-Vulpes_vulpes 1.0691 1.0011 898
## avg_veg_height-Sus_scrofa 0.8674 1.0006 1051
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0598 -0.1122 0.0054 0.1243
## (Intercept)-Canis_latrans -2.7636 0.1869 -3.1451 -2.7567 -2.4168
## (Intercept)-Sciurus_niger -4.6722 0.4936 -5.6550 -4.6674 -3.7137
## (Intercept)-Procyon_lotor -2.2937 0.1430 -2.5856 -2.2879 -2.0263
## (Intercept)-Dasypus_novemcinctus -1.7385 0.1627 -2.0656 -1.7325 -1.4297
## (Intercept)-Lynx_rufus -3.9401 0.3865 -4.6427 -3.9589 -3.1622
## (Intercept)-Didelphis_virginiana -2.5293 0.2910 -3.1183 -2.5206 -1.9879
## (Intercept)-Sylvilagus_floridanus -3.2068 0.2733 -3.7672 -3.1929 -2.7076
## (Intercept)-Meleagris_gallopavo -3.9931 0.4825 -4.9503 -3.9849 -3.0377
## (Intercept)-Sciurus_carolinensis -2.6628 0.3253 -3.3179 -2.6534 -2.0522
## (Intercept)-Vulpes_vulpes -4.3495 0.7029 -5.7758 -4.3253 -3.0735
## (Intercept)-Sus_scrofa -3.2692 0.5909 -4.4175 -3.2765 -2.1161
## shrub_cover-Odocoileus_virginianus -0.0543 0.0644 -0.1792 -0.0557 0.0731
## shrub_cover-Canis_latrans -0.3586 0.2265 -0.7995 -0.3614 0.0890
## shrub_cover-Sciurus_niger -0.3918 0.4498 -1.3457 -0.3738 0.4415
## shrub_cover-Procyon_lotor 0.2603 0.1617 -0.0639 0.2642 0.5697
## shrub_cover-Dasypus_novemcinctus 0.8706 0.3028 0.2960 0.8620 1.4614
## shrub_cover-Lynx_rufus -0.2390 0.3764 -0.9843 -0.2412 0.4932
## shrub_cover-Didelphis_virginiana 0.9496 0.3671 0.2783 0.9316 1.7146
## shrub_cover-Sylvilagus_floridanus 0.4606 0.4224 -0.3647 0.4623 1.2788
## shrub_cover-Meleagris_gallopavo -0.6547 0.4361 -1.5317 -0.6559 0.1991
## shrub_cover-Sciurus_carolinensis 0.8950 0.4123 0.1225 0.8899 1.7190
## shrub_cover-Vulpes_vulpes 0.1091 0.5852 -1.0488 0.1101 1.2397
## shrub_cover-Sus_scrofa 0.5890 0.7812 -0.9498 0.5784 2.1307
## veg_height-Odocoileus_virginianus -0.2950 0.0643 -0.4207 -0.2948 -0.1704
## veg_height-Canis_latrans -0.5985 0.1792 -0.9590 -0.5931 -0.2635
## veg_height-Sciurus_niger -0.0824 0.3472 -0.7443 -0.0824 0.5979
## veg_height-Procyon_lotor 0.3368 0.1213 0.1062 0.3351 0.5795
## veg_height-Dasypus_novemcinctus 0.2371 0.1340 -0.0213 0.2352 0.5001
## veg_height-Lynx_rufus 0.0729 0.2437 -0.4137 0.0785 0.5383
## veg_height-Didelphis_virginiana 0.4124 0.2353 -0.0274 0.4044 0.8957
## veg_height-Sylvilagus_floridanus 0.1306 0.2465 -0.3564 0.1305 0.6076
## veg_height-Meleagris_gallopavo -0.2736 0.3406 -0.9675 -0.2667 0.3648
## veg_height-Sciurus_carolinensis 0.0849 0.2147 -0.3191 0.0790 0.5219
## veg_height-Vulpes_vulpes -0.2194 0.3334 -0.9239 -0.2020 0.3965
## veg_height-Sus_scrofa -0.1661 0.3213 -0.8168 -0.1547 0.4473
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0013 2071
## (Intercept)-Sciurus_niger 1.0040 531
## (Intercept)-Procyon_lotor 1.0020 3635
## (Intercept)-Dasypus_novemcinctus 1.0063 3390
## (Intercept)-Lynx_rufus 1.0316 396
## (Intercept)-Didelphis_virginiana 1.0009 1748
## (Intercept)-Sylvilagus_floridanus 1.0036 1788
## (Intercept)-Meleagris_gallopavo 1.0007 519
## (Intercept)-Sciurus_carolinensis 1.0015 1476
## (Intercept)-Vulpes_vulpes 1.0265 435
## (Intercept)-Sus_scrofa 1.0053 1273
## shrub_cover-Odocoileus_virginianus 1.0013 5250
## shrub_cover-Canis_latrans 1.0034 1861
## shrub_cover-Sciurus_niger 1.0049 926
## shrub_cover-Procyon_lotor 1.0035 4187
## shrub_cover-Dasypus_novemcinctus 1.0062 2510
## shrub_cover-Lynx_rufus 1.0174 731
## shrub_cover-Didelphis_virginiana 1.0006 1368
## shrub_cover-Sylvilagus_floridanus 1.0121 1258
## shrub_cover-Meleagris_gallopavo 1.0020 721
## shrub_cover-Sciurus_carolinensis 1.0062 1420
## shrub_cover-Vulpes_vulpes 1.0218 1261
## shrub_cover-Sus_scrofa 1.0090 1307
## veg_height-Odocoileus_virginianus 1.0015 4902
## veg_height-Canis_latrans 1.0002 2235
## veg_height-Sciurus_niger 1.0133 987
## veg_height-Procyon_lotor 1.0012 3925
## veg_height-Dasypus_novemcinctus 1.0006 4597
## veg_height-Lynx_rufus 1.0046 1658
## veg_height-Didelphis_virginiana 1.0032 3237
## veg_height-Sylvilagus_floridanus 1.0009 2616
## veg_height-Meleagris_gallopavo 1.0051 1173
## veg_height-Sciurus_carolinensis 1.0000 2895
## veg_height-Vulpes_vulpes 1.0070 1379
## veg_height-Sus_scrofa 1.0043 3187
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7047
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1189 0.6299 -1.0881 0.1038 1.4223 1.0212 884
## Avg_Cogongrass_Cover -0.0635 0.3612 -0.7800 -0.0534 0.6055 1.0065 1117
## total_shrub_cover -0.8656 0.4793 -1.9349 -0.8141 -0.0463 1.0083 596
## avg_veg_height 0.1614 0.3604 -0.5442 0.1556 0.8740 1.0001 805
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3953 3.2921 0.2835 2.5244 11.5375 1.0199 795
## Avg_Cogongrass_Cover 0.4768 0.7409 0.0438 0.2608 2.1770 1.1084 502
## total_shrub_cover 1.1150 1.4678 0.0780 0.6919 4.6413 1.0206 831
## avg_veg_height 0.3043 0.3673 0.0360 0.1867 1.2708 1.0155 1703
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9511 1.9092 0.112 1.3938 6.9399 1.0142 298
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7801 0.4237 -3.5849 -2.7874 -1.9265 1.0083 2875
## shrub_cover 0.3741 0.2899 -0.1838 0.3657 0.9553 1.0003 1383
## veg_height -0.0367 0.1641 -0.3649 -0.0358 0.2858 1.0007 1571
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0537 1.2256 0.7481 1.7506 5.1603 1.0466 1525
## shrub_cover 0.7093 0.5131 0.1514 0.5837 2.0165 1.0097 1058
## veg_height 0.2016 0.1332 0.0584 0.1667 0.5445 1.0060 3069
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5145 1.7383 0.5728 3.3513
## (Intercept)-Canis_latrans 0.6844 0.8491 -0.8852 0.6293
## (Intercept)-Sciurus_niger -0.0803 1.4939 -2.3760 -0.2719
## (Intercept)-Procyon_lotor 0.8258 0.8436 -0.7864 0.7921
## (Intercept)-Dasypus_novemcinctus -0.3907 0.8494 -1.9966 -0.4337
## (Intercept)-Lynx_rufus 0.2237 1.1273 -1.7520 0.1388
## (Intercept)-Didelphis_virginiana -0.8045 0.9227 -2.5745 -0.8433
## (Intercept)-Sylvilagus_floridanus 0.3720 1.0158 -1.4376 0.3061
## (Intercept)-Meleagris_gallopavo -0.1065 1.2365 -2.2407 -0.1927
## (Intercept)-Sciurus_carolinensis -0.9173 0.9760 -2.8479 -0.9258
## (Intercept)-Vulpes_vulpes -0.2640 1.6518 -2.9928 -0.4002
## (Intercept)-Sus_scrofa -1.2245 1.1930 -3.6489 -1.2191
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0601 0.6213 -1.3046 -0.0637
## Avg_Cogongrass_Cover-Canis_latrans 0.3297 0.5359 -0.6023 0.2910
## Avg_Cogongrass_Cover-Sciurus_niger -0.4196 0.7823 -2.1520 -0.3466
## Avg_Cogongrass_Cover-Procyon_lotor -0.1431 0.4971 -1.1597 -0.1243
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1274 0.4563 -0.7898 0.1264
## Avg_Cogongrass_Cover-Lynx_rufus 0.3168 0.5972 -0.7121 0.2736
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1414 0.5033 -0.8251 0.1269
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4388 0.6280 -1.8692 -0.3799
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4467 0.7350 -2.0848 -0.3585
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0188 0.4941 -0.9660 0.0156
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0740 0.6153 -1.1862 0.0705
## Avg_Cogongrass_Cover-Sus_scrofa -0.3075 0.7081 -1.9447 -0.2414
## total_shrub_cover-Odocoileus_virginianus -0.4135 0.7752 -1.8514 -0.4322
## total_shrub_cover-Canis_latrans 0.2426 0.7512 -1.0761 0.1586
## total_shrub_cover-Sciurus_niger -0.9955 0.9865 -3.1465 -0.9203
## total_shrub_cover-Procyon_lotor -1.3568 0.6869 -2.9272 -1.2614
## total_shrub_cover-Dasypus_novemcinctus -0.4844 0.7016 -2.2421 -0.3923
## total_shrub_cover-Lynx_rufus -1.3209 0.9691 -3.5276 -1.2234
## total_shrub_cover-Didelphis_virginiana -0.8600 0.7218 -2.5148 -0.7768
## total_shrub_cover-Sylvilagus_floridanus -1.4739 1.0363 -3.9066 -1.3032
## total_shrub_cover-Meleagris_gallopavo -1.4490 0.9061 -3.4932 -1.3369
## total_shrub_cover-Sciurus_carolinensis -0.8972 0.8049 -2.7687 -0.7959
## total_shrub_cover-Vulpes_vulpes -1.0431 1.1518 -3.6448 -0.9297
## total_shrub_cover-Sus_scrofa -0.6594 0.9482 -2.7293 -0.6004
## avg_veg_height-Odocoileus_virginianus 0.1215 0.5639 -0.9846 0.1201
## avg_veg_height-Canis_latrans 0.2003 0.4855 -0.7303 0.1932
## avg_veg_height-Sciurus_niger -0.0485 0.6461 -1.4877 -0.0095
## avg_veg_height-Procyon_lotor 0.1975 0.4682 -0.6921 0.1835
## avg_veg_height-Dasypus_novemcinctus 0.3813 0.4663 -0.4862 0.3635
## avg_veg_height-Lynx_rufus 0.1481 0.5927 -1.0259 0.1422
## avg_veg_height-Didelphis_virginiana 0.0610 0.4965 -0.9545 0.0656
## avg_veg_height-Sylvilagus_floridanus 0.1193 0.5373 -0.9238 0.1076
## avg_veg_height-Meleagris_gallopavo -0.0286 0.6930 -1.5074 0.0082
## avg_veg_height-Sciurus_carolinensis 0.5024 0.5107 -0.3982 0.4586
## avg_veg_height-Vulpes_vulpes 0.1418 0.5580 -0.9836 0.1355
## avg_veg_height-Sus_scrofa 0.2078 0.5467 -0.8283 0.1979
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4582 1.0123 707
## (Intercept)-Canis_latrans 2.5208 1.0007 1133
## (Intercept)-Sciurus_niger 3.6195 1.1819 334
## (Intercept)-Procyon_lotor 2.5645 1.0040 2110
## (Intercept)-Dasypus_novemcinctus 1.3751 1.0027 1128
## (Intercept)-Lynx_rufus 2.7629 1.0399 865
## (Intercept)-Didelphis_virginiana 1.1304 1.0023 1046
## (Intercept)-Sylvilagus_floridanus 2.5805 1.0016 1107
## (Intercept)-Meleagris_gallopavo 2.6227 1.0075 672
## (Intercept)-Sciurus_carolinensis 0.9967 1.0073 923
## (Intercept)-Vulpes_vulpes 3.7817 1.0042 324
## (Intercept)-Sus_scrofa 1.1232 1.0109 608
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1486 1.0050 2621
## Avg_Cogongrass_Cover-Canis_latrans 1.4941 1.0055 1786
## Avg_Cogongrass_Cover-Sciurus_niger 0.8374 1.0326 919
## Avg_Cogongrass_Cover-Procyon_lotor 0.7615 1.0029 1502
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0342 1.0024 1774
## Avg_Cogongrass_Cover-Lynx_rufus 1.6279 1.0124 1769
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1993 1.0054 2270
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6144 1.0019 1386
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7701 1.0096 888
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9749 1.0026 1500
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2951 1.0016 1878
## Avg_Cogongrass_Cover-Sus_scrofa 0.8885 1.0032 1679
## total_shrub_cover-Odocoileus_virginianus 1.2438 1.0024 2482
## total_shrub_cover-Canis_latrans 2.0418 1.0065 1021
## total_shrub_cover-Sciurus_niger 0.7684 1.0015 800
## total_shrub_cover-Procyon_lotor -0.2749 1.0053 844
## total_shrub_cover-Dasypus_novemcinctus 0.6343 1.0105 849
## total_shrub_cover-Lynx_rufus 0.3534 1.0027 761
## total_shrub_cover-Didelphis_virginiana 0.2956 1.0004 1069
## total_shrub_cover-Sylvilagus_floridanus 0.0488 1.0132 563
## total_shrub_cover-Meleagris_gallopavo 0.0660 1.0012 1012
## total_shrub_cover-Sciurus_carolinensis 0.3972 1.0043 861
## total_shrub_cover-Vulpes_vulpes 1.0644 1.0028 690
## total_shrub_cover-Sus_scrofa 1.0427 1.0070 602
## avg_veg_height-Odocoileus_virginianus 1.2520 1.0016 1633
## avg_veg_height-Canis_latrans 1.1719 1.0049 1471
## avg_veg_height-Sciurus_niger 1.1412 1.0099 1274
## avg_veg_height-Procyon_lotor 1.1594 1.0030 1735
## avg_veg_height-Dasypus_novemcinctus 1.3435 1.0078 1571
## avg_veg_height-Lynx_rufus 1.3054 1.0008 1563
## avg_veg_height-Didelphis_virginiana 1.0336 1.0057 1350
## avg_veg_height-Sylvilagus_floridanus 1.1884 0.9999 1287
## avg_veg_height-Meleagris_gallopavo 1.2564 1.0015 1089
## avg_veg_height-Sciurus_carolinensis 1.6056 1.0028 1223
## avg_veg_height-Vulpes_vulpes 1.2429 1.0012 1390
## avg_veg_height-Sus_scrofa 1.3162 1.0002 1197
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0036 0.0601 -0.1154 0.0027 0.1197
## (Intercept)-Canis_latrans -2.8041 0.1989 -3.2119 -2.7969 -2.4376
## (Intercept)-Sciurus_niger -4.2078 0.6774 -5.5187 -4.2065 -2.8699
## (Intercept)-Procyon_lotor -2.2995 0.1391 -2.5825 -2.2941 -2.0412
## (Intercept)-Dasypus_novemcinctus -1.7944 0.1835 -2.1665 -1.7863 -1.4502
## (Intercept)-Lynx_rufus -3.6303 0.3575 -4.3532 -3.6176 -2.9647
## (Intercept)-Didelphis_virginiana -2.7083 0.3236 -3.3875 -2.6952 -2.1167
## (Intercept)-Sylvilagus_floridanus -3.3381 0.2902 -3.9332 -3.3269 -2.7876
## (Intercept)-Meleagris_gallopavo -3.8494 0.5559 -4.9426 -3.8407 -2.7753
## (Intercept)-Sciurus_carolinensis -2.7832 0.3485 -3.4988 -2.7660 -2.1389
## (Intercept)-Vulpes_vulpes -4.5082 0.7704 -6.0188 -4.5166 -3.0518
## (Intercept)-Sus_scrofa -3.6022 0.6128 -4.7512 -3.6129 -2.3302
## shrub_cover-Odocoileus_virginianus -0.0528 0.0654 -0.1815 -0.0531 0.0732
## shrub_cover-Canis_latrans -0.3057 0.2456 -0.7896 -0.3044 0.1757
## shrub_cover-Sciurus_niger -0.1798 0.5528 -1.2816 -0.1865 0.8977
## shrub_cover-Procyon_lotor 0.3202 0.1601 0.0012 0.3212 0.6292
## shrub_cover-Dasypus_novemcinctus 1.0395 0.3749 0.3683 1.0223 1.8077
## shrub_cover-Lynx_rufus 0.0419 0.3797 -0.7158 0.0469 0.7503
## shrub_cover-Didelphis_virginiana 1.2096 0.4299 0.4344 1.1913 2.1142
## shrub_cover-Sylvilagus_floridanus 0.7417 0.4278 -0.1669 0.7604 1.5544
## shrub_cover-Meleagris_gallopavo -0.5303 0.4885 -1.4913 -0.5351 0.4118
## shrub_cover-Sciurus_carolinensis 1.1385 0.4372 0.3000 1.1375 2.0076
## shrub_cover-Vulpes_vulpes 0.1858 0.6516 -1.1283 0.1864 1.4922
## shrub_cover-Sus_scrofa 1.0144 0.8154 -0.6792 1.0223 2.5592
## veg_height-Odocoileus_virginianus -0.2961 0.0647 -0.4251 -0.2946 -0.1735
## veg_height-Canis_latrans -0.6070 0.1888 -0.9919 -0.6001 -0.2538
## veg_height-Sciurus_niger -0.0302 0.4274 -0.8228 -0.0449 0.8628
## veg_height-Procyon_lotor 0.3323 0.1216 0.0940 0.3333 0.5721
## veg_height-Dasypus_novemcinctus 0.2471 0.1381 -0.0139 0.2425 0.5285
## veg_height-Lynx_rufus 0.0154 0.2459 -0.4727 0.0180 0.4964
## veg_height-Didelphis_virginiana 0.4054 0.2508 -0.0634 0.3960 0.9311
## veg_height-Sylvilagus_floridanus 0.0445 0.2451 -0.4308 0.0415 0.5398
## veg_height-Meleagris_gallopavo -0.2403 0.4175 -1.0163 -0.2609 0.6591
## veg_height-Sciurus_carolinensis 0.0848 0.2210 -0.3320 0.0807 0.5383
## veg_height-Vulpes_vulpes -0.1708 0.3354 -0.8610 -0.1559 0.4426
## veg_height-Sus_scrofa -0.1986 0.3323 -0.8911 -0.1903 0.4332
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0028 5250
## (Intercept)-Canis_latrans 1.0001 1819
## (Intercept)-Sciurus_niger 1.1048 393
## (Intercept)-Procyon_lotor 1.0014 3534
## (Intercept)-Dasypus_novemcinctus 1.0065 1427
## (Intercept)-Lynx_rufus 1.0285 1039
## (Intercept)-Didelphis_virginiana 1.0031 1183
## (Intercept)-Sylvilagus_floridanus 1.0040 1148
## (Intercept)-Meleagris_gallopavo 1.0037 627
## (Intercept)-Sciurus_carolinensis 1.0068 1146
## (Intercept)-Vulpes_vulpes 1.0153 313
## (Intercept)-Sus_scrofa 1.0052 721
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0014 1300
## shrub_cover-Sciurus_niger 1.0154 873
## shrub_cover-Procyon_lotor 1.0000 4080
## shrub_cover-Dasypus_novemcinctus 1.0100 835
## shrub_cover-Lynx_rufus 1.0149 1146
## shrub_cover-Didelphis_virginiana 1.0041 904
## shrub_cover-Sylvilagus_floridanus 1.0074 791
## shrub_cover-Meleagris_gallopavo 1.0030 768
## shrub_cover-Sciurus_carolinensis 1.0022 894
## shrub_cover-Vulpes_vulpes 1.0097 768
## shrub_cover-Sus_scrofa 1.0035 751
## veg_height-Odocoileus_virginianus 1.0009 5250
## veg_height-Canis_latrans 1.0022 1973
## veg_height-Sciurus_niger 1.0162 1084
## veg_height-Procyon_lotor 1.0003 3928
## veg_height-Dasypus_novemcinctus 1.0010 3948
## veg_height-Lynx_rufus 1.0002 2257
## veg_height-Didelphis_virginiana 1.0003 2515
## veg_height-Sylvilagus_floridanus 1.0001 1320
## veg_height-Meleagris_gallopavo 1.0048 1030
## veg_height-Sciurus_carolinensis 1.0027 2335
## veg_height-Vulpes_vulpes 1.0062 1370
## veg_height-Sus_scrofa 1.0009 2459
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5892
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0197 0.4945 -0.9624 -0.024 0.9831 1.0021 2924
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8328 2.1395 0.7058 2.2643 8.5771 1.005 1982
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7338 0.4240 -3.5489 -2.7438 -1.8720 1.0017 4209
## shrub_cover 0.0798 0.2606 -0.4432 0.0785 0.6013 1.0007 2999
## veg_height -0.0461 0.1510 -0.3509 -0.0415 0.2454 1.0001 3243
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0916 1.2290 0.7773 1.7796 5.1249 1.0061 2658
## shrub_cover 0.6304 0.4543 0.1484 0.5088 1.8102 1.0017 1656
## veg_height 0.1929 0.1284 0.0557 0.1591 0.5307 1.0051 2719
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.3674 1.1413 1.7158 3.1739 6.2211
## (Intercept)-Canis_latrans 0.4114 0.4296 -0.3750 0.3978 1.3061
## (Intercept)-Sciurus_niger -0.3403 1.0113 -1.8721 -0.4852 2.1784
## (Intercept)-Procyon_lotor 0.7457 0.4043 -0.0038 0.7277 1.5714
## (Intercept)-Dasypus_novemcinctus -0.5623 0.3781 -1.3129 -0.5545 0.1631
## (Intercept)-Lynx_rufus 0.6182 0.9441 -0.7193 0.4391 3.0625
## (Intercept)-Didelphis_virginiana -1.1916 0.4591 -2.1422 -1.1764 -0.3173
## (Intercept)-Sylvilagus_floridanus -0.3080 0.4996 -1.2008 -0.3327 0.7568
## (Intercept)-Meleagris_gallopavo 0.7962 1.0124 -0.7447 0.6546 3.1409
## (Intercept)-Sciurus_carolinensis -1.1928 0.4818 -2.1796 -1.1849 -0.2775
## (Intercept)-Vulpes_vulpes -0.9988 1.0212 -2.6669 -1.1221 1.3535
## (Intercept)-Sus_scrofa -1.6201 0.6625 -2.9860 -1.6021 -0.2975
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0123 1610
## (Intercept)-Canis_latrans 1.0009 4526
## (Intercept)-Sciurus_niger 1.0126 628
## (Intercept)-Procyon_lotor 1.0008 4680
## (Intercept)-Dasypus_novemcinctus 1.0034 4598
## (Intercept)-Lynx_rufus 1.0035 761
## (Intercept)-Didelphis_virginiana 1.0002 4269
## (Intercept)-Sylvilagus_floridanus 1.0047 2677
## (Intercept)-Meleagris_gallopavo 1.0061 998
## (Intercept)-Sciurus_carolinensis 1.0015 4458
## (Intercept)-Vulpes_vulpes 1.0267 496
## (Intercept)-Sus_scrofa 1.0065 2222
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0047 0.0590 -0.1101 0.0050 0.1201
## (Intercept)-Canis_latrans -2.7487 0.1914 -3.1533 -2.7433 -2.3917
## (Intercept)-Sciurus_niger -4.2070 0.6151 -5.3882 -4.1993 -3.0156
## (Intercept)-Procyon_lotor -2.2959 0.1447 -2.5853 -2.2947 -2.0243
## (Intercept)-Dasypus_novemcinctus -1.7176 0.1600 -2.0464 -1.7125 -1.4191
## (Intercept)-Lynx_rufus -3.8124 0.3688 -4.5402 -3.8116 -3.0930
## (Intercept)-Didelphis_virginiana -2.5213 0.2895 -3.1228 -2.5092 -1.9837
## (Intercept)-Sylvilagus_floridanus -3.1667 0.3083 -3.8206 -3.1475 -2.6190
## (Intercept)-Meleagris_gallopavo -4.2848 0.4537 -5.1745 -4.2849 -3.3889
## (Intercept)-Sciurus_carolinensis -2.5837 0.3098 -3.2444 -2.5662 -2.0292
## (Intercept)-Vulpes_vulpes -4.2026 0.7570 -5.7918 -4.1504 -2.8740
## (Intercept)-Sus_scrofa -3.2896 0.6242 -4.4991 -3.2961 -2.0722
## shrub_cover-Odocoileus_virginianus -0.0553 0.0644 -0.1844 -0.0552 0.0692
## shrub_cover-Canis_latrans -0.3191 0.2190 -0.7373 -0.3208 0.1136
## shrub_cover-Sciurus_niger -0.4937 0.4737 -1.4334 -0.4938 0.4316
## shrub_cover-Procyon_lotor 0.2390 0.1653 -0.1010 0.2420 0.5595
## shrub_cover-Dasypus_novemcinctus 0.7972 0.2950 0.2422 0.7863 1.3778
## shrub_cover-Lynx_rufus -0.3946 0.3477 -1.0887 -0.3914 0.2781
## shrub_cover-Didelphis_virginiana 0.9071 0.3656 0.2409 0.8877 1.6820
## shrub_cover-Sylvilagus_floridanus 0.2076 0.4232 -0.5770 0.1867 1.0891
## shrub_cover-Meleagris_gallopavo -0.9135 0.3982 -1.7172 -0.9050 -0.1509
## shrub_cover-Sciurus_carolinensis 0.7853 0.4179 0.0115 0.7786 1.6601
## shrub_cover-Vulpes_vulpes -0.2465 0.5792 -1.4452 -0.2290 0.8653
## shrub_cover-Sus_scrofa 0.5070 0.8300 -1.1156 0.4910 2.2280
## veg_height-Odocoileus_virginianus -0.2974 0.0621 -0.4201 -0.2970 -0.1780
## veg_height-Canis_latrans -0.5913 0.1836 -0.9568 -0.5830 -0.2433
## veg_height-Sciurus_niger -0.1275 0.3768 -0.8708 -0.1281 0.6247
## veg_height-Procyon_lotor 0.3301 0.1228 0.0887 0.3301 0.5708
## veg_height-Dasypus_novemcinctus 0.2274 0.1322 -0.0280 0.2271 0.4881
## veg_height-Lynx_rufus 0.0134 0.2441 -0.4857 0.0113 0.4782
## veg_height-Didelphis_virginiana 0.4016 0.2379 -0.0424 0.3927 0.8881
## veg_height-Sylvilagus_floridanus 0.0945 0.2378 -0.3722 0.0915 0.5677
## veg_height-Meleagris_gallopavo -0.3515 0.3308 -1.0340 -0.3409 0.2741
## veg_height-Sciurus_carolinensis 0.0460 0.2056 -0.3454 0.0396 0.4601
## veg_height-Vulpes_vulpes -0.1567 0.3143 -0.8278 -0.1413 0.4182
## veg_height-Sus_scrofa -0.1706 0.3266 -0.8477 -0.1647 0.4466
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 4613
## (Intercept)-Canis_latrans 1.0017 2260
## (Intercept)-Sciurus_niger 1.0077 585
## (Intercept)-Procyon_lotor 1.0015 3572
## (Intercept)-Dasypus_novemcinctus 1.0001 4210
## (Intercept)-Lynx_rufus 1.0086 705
## (Intercept)-Didelphis_virginiana 1.0019 2782
## (Intercept)-Sylvilagus_floridanus 1.0025 1562
## (Intercept)-Meleagris_gallopavo 1.0014 552
## (Intercept)-Sciurus_carolinensis 1.0078 2644
## (Intercept)-Vulpes_vulpes 1.0268 493
## (Intercept)-Sus_scrofa 1.0022 1583
## shrub_cover-Odocoileus_virginianus 1.0007 4731
## shrub_cover-Canis_latrans 1.0011 2348
## shrub_cover-Sciurus_niger 0.9999 1324
## shrub_cover-Procyon_lotor 1.0022 4038
## shrub_cover-Dasypus_novemcinctus 1.0004 3659
## shrub_cover-Lynx_rufus 1.0040 1271
## shrub_cover-Didelphis_virginiana 1.0017 2382
## shrub_cover-Sylvilagus_floridanus 1.0178 1604
## shrub_cover-Meleagris_gallopavo 1.0090 752
## shrub_cover-Sciurus_carolinensis 1.0071 2302
## shrub_cover-Vulpes_vulpes 1.0010 1710
## shrub_cover-Sus_scrofa 0.9999 1857
## veg_height-Odocoileus_virginianus 1.0005 5250
## veg_height-Canis_latrans 1.0016 2328
## veg_height-Sciurus_niger 1.0027 1957
## veg_height-Procyon_lotor 1.0017 3955
## veg_height-Dasypus_novemcinctus 1.0019 4545
## veg_height-Lynx_rufus 1.0064 2232
## veg_height-Didelphis_virginiana 0.9999 3624
## veg_height-Sylvilagus_floridanus 1.0004 2772
## veg_height-Meleagris_gallopavo 1.0038 1405
## veg_height-Sciurus_carolinensis 1.0008 3668
## veg_height-Vulpes_vulpes 1.0083 1974
## veg_height-Sus_scrofa 1.0048 2934
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0796 0.5798 -1.1814 -0.0998 1.1252 1.0013 1670
## Veg_shannon_index 0.4077 0.2664 -0.0979 0.3974 0.9626 1.0048 1522
## Avg_Cogongrass_Cover 0.2696 0.2814 -0.2965 0.2725 0.8177 1.0045 1299
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5017 2.8612 0.6531 2.7377 10.3246 1.0093 1184
## Veg_shannon_index 0.2913 0.3534 0.0389 0.1861 1.1135 1.0073 1935
## Avg_Cogongrass_Cover 0.3340 0.3735 0.0393 0.2128 1.3450 1.0146 1717
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9092 0.9715 0.0579 0.6173 3.5361 1.0053 441
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7474 0.4337 -3.5725 -2.7413 -1.8558 0.9999 3878
## shrub_cover 0.0805 0.2623 -0.4282 0.0780 0.6002 1.0059 3140
## veg_height -0.0456 0.1558 -0.3590 -0.0440 0.2563 0.9999 2146
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1591 1.3298 0.7826 1.8370 5.4190 1.0067 1480
## shrub_cover 0.6119 0.4506 0.1476 0.4970 1.7084 1.0084 1632
## veg_height 0.1964 0.1383 0.0564 0.1608 0.5560 1.0110 2756
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5594 1.4068 1.1866 3.4360
## (Intercept)-Canis_latrans 0.4134 0.6654 -0.8785 0.3985
## (Intercept)-Sciurus_niger -0.0829 1.2682 -2.0821 -0.2447
## (Intercept)-Procyon_lotor 0.5424 0.6546 -0.7887 0.5562
## (Intercept)-Dasypus_novemcinctus -0.6330 0.6162 -1.8767 -0.6178
## (Intercept)-Lynx_rufus 0.2921 1.1101 -1.6068 0.1589
## (Intercept)-Didelphis_virginiana -1.3013 0.6865 -2.7132 -1.2806
## (Intercept)-Sylvilagus_floridanus -0.3092 0.7773 -1.7640 -0.3519
## (Intercept)-Meleagris_gallopavo 0.6681 1.2474 -1.3693 0.5377
## (Intercept)-Sciurus_carolinensis -1.2969 0.7119 -2.7720 -1.2885
## (Intercept)-Vulpes_vulpes -0.7872 1.3528 -3.0114 -0.9238
## (Intercept)-Sus_scrofa -1.9620 0.9092 -3.8280 -1.9255
## Veg_shannon_index-Odocoileus_virginianus 0.3313 0.4933 -0.6688 0.3334
## Veg_shannon_index-Canis_latrans 0.6535 0.4002 -0.0542 0.6300
## Veg_shannon_index-Sciurus_niger 0.4586 0.5432 -0.5641 0.4422
## Veg_shannon_index-Procyon_lotor 0.4846 0.3790 -0.2244 0.4760
## Veg_shannon_index-Dasypus_novemcinctus 0.2107 0.3434 -0.4885 0.2197
## Veg_shannon_index-Lynx_rufus 0.2921 0.5141 -0.8048 0.3086
## Veg_shannon_index-Didelphis_virginiana 0.5308 0.3972 -0.1886 0.5092
## Veg_shannon_index-Sylvilagus_floridanus 0.4830 0.4358 -0.3094 0.4492
## Veg_shannon_index-Meleagris_gallopavo 0.5656 0.5408 -0.3789 0.5200
## Veg_shannon_index-Sciurus_carolinensis 0.0142 0.4086 -0.8526 0.0372
## Veg_shannon_index-Vulpes_vulpes 0.1714 0.4958 -0.8565 0.1994
## Veg_shannon_index-Sus_scrofa 0.7482 0.5484 -0.1426 0.6739
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2770 0.5145 -0.7175 0.2688
## Avg_Cogongrass_Cover-Canis_latrans 0.6067 0.4279 -0.1150 0.5639
## Avg_Cogongrass_Cover-Sciurus_niger -0.0753 0.6488 -1.5219 -0.0099
## Avg_Cogongrass_Cover-Procyon_lotor 0.3629 0.3895 -0.3451 0.3464
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4312 0.3477 -0.2237 0.4218
## Avg_Cogongrass_Cover-Lynx_rufus 0.5403 0.4504 -0.2579 0.5116
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4342 0.3892 -0.3388 0.4271
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1042 0.4728 -1.1304 -0.0660
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0076 0.6558 -1.5127 0.0518
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3959 0.3772 -0.3261 0.3896
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3902 0.4891 -0.5467 0.3755
## Avg_Cogongrass_Cover-Sus_scrofa 0.0013 0.5567 -1.3036 0.0700
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.7397 1.0078 869
## (Intercept)-Canis_latrans 1.7555 1.0034 2850
## (Intercept)-Sciurus_niger 2.8075 1.0126 502
## (Intercept)-Procyon_lotor 1.8295 1.0011 1575
## (Intercept)-Dasypus_novemcinctus 0.6018 1.0007 3185
## (Intercept)-Lynx_rufus 2.9571 1.0054 880
## (Intercept)-Didelphis_virginiana 0.0243 1.0008 2679
## (Intercept)-Sylvilagus_floridanus 1.3867 1.0035 1676
## (Intercept)-Meleagris_gallopavo 3.5894 1.0166 627
## (Intercept)-Sciurus_carolinensis 0.0912 1.0017 2801
## (Intercept)-Vulpes_vulpes 2.3662 1.0046 422
## (Intercept)-Sus_scrofa -0.2656 1.0009 1629
## Veg_shannon_index-Odocoileus_virginianus 1.2793 1.0022 2975
## Veg_shannon_index-Canis_latrans 1.5019 1.0019 2994
## Veg_shannon_index-Sciurus_niger 1.6509 1.0024 2040
## Veg_shannon_index-Procyon_lotor 1.2724 1.0020 2568
## Veg_shannon_index-Dasypus_novemcinctus 0.8635 1.0004 3889
## Veg_shannon_index-Lynx_rufus 1.2544 1.0123 2498
## Veg_shannon_index-Didelphis_virginiana 1.3567 1.0026 3285
## Veg_shannon_index-Sylvilagus_floridanus 1.4262 1.0028 2748
## Veg_shannon_index-Meleagris_gallopavo 1.7805 1.0038 2332
## Veg_shannon_index-Sciurus_carolinensis 0.7547 1.0008 3124
## Veg_shannon_index-Vulpes_vulpes 1.1265 1.0031 1904
## Veg_shannon_index-Sus_scrofa 2.0705 1.0003 1898
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3201 1.0042 3082
## Avg_Cogongrass_Cover-Canis_latrans 1.5628 1.0083 2934
## Avg_Cogongrass_Cover-Sciurus_niger 1.0381 1.0047 1401
## Avg_Cogongrass_Cover-Procyon_lotor 1.2100 1.0016 3243
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1442 1.0035 3583
## Avg_Cogongrass_Cover-Lynx_rufus 1.5415 1.0082 2686
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2451 1.0055 3637
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7313 1.0032 1619
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1615 1.0122 1313
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1776 1.0016 3725
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4128 1.0008 2703
## Avg_Cogongrass_Cover-Sus_scrofa 0.9750 1.0006 1899
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0599 -0.1110 0.0062 0.1222
## (Intercept)-Canis_latrans -2.7439 0.1916 -3.1317 -2.7409 -2.3914
## (Intercept)-Sciurus_niger -4.3371 0.6085 -5.5411 -4.3545 -3.1422
## (Intercept)-Procyon_lotor -2.2941 0.1463 -2.5939 -2.2891 -2.0213
## (Intercept)-Dasypus_novemcinctus -1.7193 0.1535 -2.0214 -1.7172 -1.4266
## (Intercept)-Lynx_rufus -3.7349 0.3634 -4.4733 -3.7337 -3.0378
## (Intercept)-Didelphis_virginiana -2.5169 0.2851 -3.1057 -2.5032 -1.9962
## (Intercept)-Sylvilagus_floridanus -3.2298 0.3243 -3.9295 -3.2097 -2.6530
## (Intercept)-Meleagris_gallopavo -4.2649 0.4867 -5.2539 -4.2465 -3.3482
## (Intercept)-Sciurus_carolinensis -2.5894 0.3131 -3.2568 -2.5720 -2.0306
## (Intercept)-Vulpes_vulpes -4.3705 0.7638 -5.9025 -4.3386 -2.9970
## (Intercept)-Sus_scrofa -3.1999 0.6048 -4.4148 -3.2068 -2.0427
## shrub_cover-Odocoileus_virginianus -0.0549 0.0636 -0.1804 -0.0546 0.0674
## shrub_cover-Canis_latrans -0.2967 0.2121 -0.7163 -0.2972 0.1161
## shrub_cover-Sciurus_niger -0.5338 0.4621 -1.4983 -0.5253 0.3478
## shrub_cover-Procyon_lotor 0.2258 0.1759 -0.1337 0.2328 0.5545
## shrub_cover-Dasypus_novemcinctus 0.8075 0.2935 0.2390 0.8055 1.3799
## shrub_cover-Lynx_rufus -0.3358 0.3473 -1.0302 -0.3280 0.3485
## shrub_cover-Didelphis_virginiana 0.9098 0.3640 0.2610 0.8896 1.6618
## shrub_cover-Sylvilagus_floridanus 0.1648 0.4100 -0.5798 0.1362 1.0100
## shrub_cover-Meleagris_gallopavo -0.9152 0.4084 -1.7760 -0.8978 -0.1450
## shrub_cover-Sciurus_carolinensis 0.7791 0.4093 0.0098 0.7662 1.6086
## shrub_cover-Vulpes_vulpes -0.1957 0.5409 -1.2827 -0.1832 0.8628
## shrub_cover-Sus_scrofa 0.4321 0.7945 -1.1260 0.4282 2.0777
## veg_height-Odocoileus_virginianus -0.2961 0.0645 -0.4243 -0.2962 -0.1701
## veg_height-Canis_latrans -0.5892 0.1823 -0.9596 -0.5823 -0.2578
## veg_height-Sciurus_niger -0.1145 0.3958 -0.9092 -0.1122 0.6895
## veg_height-Procyon_lotor 0.3237 0.1228 0.0845 0.3234 0.5620
## veg_height-Dasypus_novemcinctus 0.2262 0.1316 -0.0293 0.2250 0.4809
## veg_height-Lynx_rufus -0.0126 0.2432 -0.5106 -0.0071 0.4441
## veg_height-Didelphis_virginiana 0.3922 0.2378 -0.0564 0.3824 0.8766
## veg_height-Sylvilagus_floridanus 0.1310 0.2412 -0.3362 0.1320 0.5947
## veg_height-Meleagris_gallopavo -0.3095 0.3522 -1.0312 -0.3061 0.3649
## veg_height-Sciurus_carolinensis 0.0400 0.2099 -0.3615 0.0365 0.4665
## veg_height-Vulpes_vulpes -0.1883 0.3317 -0.8774 -0.1673 0.4091
## veg_height-Sus_scrofa -0.1450 0.3276 -0.8279 -0.1382 0.4713
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0025 2211
## (Intercept)-Sciurus_niger 1.0064 459
## (Intercept)-Procyon_lotor 1.0018 3534
## (Intercept)-Dasypus_novemcinctus 1.0012 4599
## (Intercept)-Lynx_rufus 1.0207 918
## (Intercept)-Didelphis_virginiana 1.0073 2229
## (Intercept)-Sylvilagus_floridanus 1.0044 1364
## (Intercept)-Meleagris_gallopavo 1.0183 504
## (Intercept)-Sciurus_carolinensis 1.0052 2365
## (Intercept)-Vulpes_vulpes 1.0016 404
## (Intercept)-Sus_scrofa 1.0099 1840
## shrub_cover-Odocoileus_virginianus 1.0031 5342
## shrub_cover-Canis_latrans 1.0037 3016
## shrub_cover-Sciurus_niger 1.0244 1058
## shrub_cover-Procyon_lotor 1.0069 3184
## shrub_cover-Dasypus_novemcinctus 1.0030 3654
## shrub_cover-Lynx_rufus 1.0165 1399
## shrub_cover-Didelphis_virginiana 1.0013 2132
## shrub_cover-Sylvilagus_floridanus 1.0032 1797
## shrub_cover-Meleagris_gallopavo 1.0249 692
## shrub_cover-Sciurus_carolinensis 1.0031 2195
## shrub_cover-Vulpes_vulpes 1.0003 1795
## shrub_cover-Sus_scrofa 1.0104 2178
## veg_height-Odocoileus_virginianus 1.0021 4990
## veg_height-Canis_latrans 1.0041 1947
## veg_height-Sciurus_niger 1.0023 1491
## veg_height-Procyon_lotor 1.0032 4071
## veg_height-Dasypus_novemcinctus 1.0001 5250
## veg_height-Lynx_rufus 1.0012 2455
## veg_height-Didelphis_virginiana 1.0112 3462
## veg_height-Sylvilagus_floridanus 1.0061 2426
## veg_height-Meleagris_gallopavo 1.0055 1204
## veg_height-Sciurus_carolinensis 1.0013 3246
## veg_height-Vulpes_vulpes 1.0012 1618
## veg_height-Sus_scrofa 1.0016 3330
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6348
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0290 0.6066 -1.1846 -0.0354 1.2671 1.0013 1314
## Cogon_Patch_Size -0.1472 0.3786 -0.9927 -0.1246 0.5426 1.0026 1773
## Avg_Cogongrass_Cover 0.1130 0.3387 -0.5616 0.1166 0.7801 1.0009 1165
## total_shrub_cover -0.7353 0.4315 -1.6885 -0.7042 0.0243 1.0046 642
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3172 2.9857 0.3170 2.5353 11.0104 1.0053 1040
## Cogon_Patch_Size 0.7495 1.1213 0.0550 0.4224 3.3495 1.0248 905
## Avg_Cogongrass_Cover 0.4354 0.5982 0.0404 0.2498 1.9482 1.0050 1212
## total_shrub_cover 0.7977 1.0543 0.0541 0.4532 3.7255 1.0081 506
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0424 1.8746 0.1361 1.5345 7.1288 1.0131 313
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7603 0.4144 -3.5891 -2.7597 -1.9271 1.0036 3014
## shrub_cover 0.3371 0.2777 -0.2168 0.3399 0.9039 1.0002 1449
## veg_height -0.0289 0.1571 -0.3389 -0.0266 0.2813 1.0020 2646
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9801 1.1962 0.7165 1.6840 4.8587 1.0172 2085
## shrub_cover 0.6371 0.4769 0.1305 0.5143 1.8686 1.0037 785
## veg_height 0.1945 0.1352 0.0550 0.1585 0.5569 1.0027 2663
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4209 1.6481 0.4643 3.2776
## (Intercept)-Canis_latrans 0.5863 0.8545 -0.9982 0.5409
## (Intercept)-Sciurus_niger -0.3705 1.3170 -2.6132 -0.4972
## (Intercept)-Procyon_lotor 0.7207 0.8453 -0.9329 0.7071
## (Intercept)-Dasypus_novemcinctus -0.5167 0.7881 -2.1077 -0.5290
## (Intercept)-Lynx_rufus 0.0192 1.0955 -1.8551 -0.0589
## (Intercept)-Didelphis_virginiana -0.9965 0.8819 -2.7708 -1.0008
## (Intercept)-Sylvilagus_floridanus 0.0713 0.9918 -1.7901 0.0253
## (Intercept)-Meleagris_gallopavo -0.1321 1.1919 -2.1387 -0.2458
## (Intercept)-Sciurus_carolinensis -1.1346 0.9344 -2.9730 -1.1262
## (Intercept)-Vulpes_vulpes -0.6407 1.4449 -3.2556 -0.7146
## (Intercept)-Sus_scrofa -1.4358 1.1867 -3.8080 -1.4251
## Cogon_Patch_Size-Odocoileus_virginianus -0.0262 0.6720 -1.2732 -0.0621
## Cogon_Patch_Size-Canis_latrans 0.6181 0.6919 -0.4141 0.5131
## Cogon_Patch_Size-Sciurus_niger -0.4566 0.8735 -2.4763 -0.3586
## Cogon_Patch_Size-Procyon_lotor -0.2344 0.4647 -1.1726 -0.2145
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0640 0.4402 -0.9487 -0.0599
## Cogon_Patch_Size-Lynx_rufus -0.1283 0.7402 -1.5574 -0.1570
## Cogon_Patch_Size-Didelphis_virginiana 0.5714 0.5130 -0.3301 0.5345
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7207 0.8176 -2.6719 -0.5735
## Cogon_Patch_Size-Meleagris_gallopavo -0.0217 0.7078 -1.3582 -0.0603
## Cogon_Patch_Size-Sciurus_carolinensis -0.6104 0.6813 -2.2301 -0.5146
## Cogon_Patch_Size-Vulpes_vulpes -0.3801 0.8412 -2.3014 -0.3031
## Cogon_Patch_Size-Sus_scrofa -0.3472 0.7703 -2.1110 -0.2668
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1159 0.5989 -1.1033 0.1258
## Avg_Cogongrass_Cover-Canis_latrans 0.3577 0.4703 -0.4596 0.3181
## Avg_Cogongrass_Cover-Sciurus_niger -0.2819 0.7696 -2.0920 -0.1973
## Avg_Cogongrass_Cover-Procyon_lotor 0.1099 0.4741 -0.8097 0.1043
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3547 0.4064 -0.4238 0.3400
## Avg_Cogongrass_Cover-Lynx_rufus 0.4895 0.5475 -0.4228 0.4403
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1564 0.4600 -0.7690 0.1614
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1816 0.5816 -1.4715 -0.1412
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2988 0.7647 -2.0940 -0.2178
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3798 0.4670 -0.4994 0.3640
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2813 0.5636 -0.8009 0.2634
## Avg_Cogongrass_Cover-Sus_scrofa -0.1183 0.6813 -1.6364 -0.0567
## total_shrub_cover-Odocoileus_virginianus -0.3968 0.6904 -1.7429 -0.4250
## total_shrub_cover-Canis_latrans 0.0845 0.6682 -0.9998 0.0012
## total_shrub_cover-Sciurus_niger -0.8395 0.8021 -2.6211 -0.7651
## total_shrub_cover-Procyon_lotor -1.1785 0.6294 -2.6453 -1.0941
## total_shrub_cover-Dasypus_novemcinctus -0.3753 0.5661 -1.6567 -0.3319
## total_shrub_cover-Lynx_rufus -1.1576 0.8783 -3.2749 -1.0355
## total_shrub_cover-Didelphis_virginiana -0.7766 0.6040 -2.1839 -0.7072
## total_shrub_cover-Sylvilagus_floridanus -1.1572 0.9314 -3.4830 -0.9960
## total_shrub_cover-Meleagris_gallopavo -1.2752 0.8641 -3.3187 -1.1588
## total_shrub_cover-Sciurus_carolinensis -0.6610 0.6800 -2.2172 -0.5971
## total_shrub_cover-Vulpes_vulpes -0.8470 0.9317 -3.0278 -0.7624
## total_shrub_cover-Sus_scrofa -0.5214 0.8297 -2.2474 -0.5024
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1499 1.0009 859
## (Intercept)-Canis_latrans 2.4175 1.0120 1474
## (Intercept)-Sciurus_niger 2.6001 1.0176 554
## (Intercept)-Procyon_lotor 2.4395 1.0086 1601
## (Intercept)-Dasypus_novemcinctus 1.0481 1.0049 1388
## (Intercept)-Lynx_rufus 2.4848 1.0039 1235
## (Intercept)-Didelphis_virginiana 0.7956 1.0030 1407
## (Intercept)-Sylvilagus_floridanus 2.1236 1.0014 1095
## (Intercept)-Meleagris_gallopavo 2.5973 1.0119 685
## (Intercept)-Sciurus_carolinensis 0.6447 1.0029 1097
## (Intercept)-Vulpes_vulpes 2.5153 1.0431 548
## (Intercept)-Sus_scrofa 0.8242 1.0007 748
## Cogon_Patch_Size-Odocoileus_virginianus 1.3820 1.0067 3134
## Cogon_Patch_Size-Canis_latrans 2.3047 1.0008 1992
## Cogon_Patch_Size-Sciurus_niger 0.9814 1.0040 1436
## Cogon_Patch_Size-Procyon_lotor 0.6317 1.0076 2357
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7969 1.0015 2764
## Cogon_Patch_Size-Lynx_rufus 1.4480 1.0031 1779
## Cogon_Patch_Size-Didelphis_virginiana 1.6924 1.0013 2159
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4471 1.0113 1274
## Cogon_Patch_Size-Meleagris_gallopavo 1.5533 1.0043 2138
## Cogon_Patch_Size-Sciurus_carolinensis 0.4797 1.0039 1772
## Cogon_Patch_Size-Vulpes_vulpes 1.1133 1.0002 1388
## Cogon_Patch_Size-Sus_scrofa 0.9002 1.0014 1824
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2974 1.0043 2725
## Avg_Cogongrass_Cover-Canis_latrans 1.3991 1.0008 2427
## Avg_Cogongrass_Cover-Sciurus_niger 0.9828 1.0008 1281
## Avg_Cogongrass_Cover-Procyon_lotor 1.0319 1.0010 2338
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2094 1.0002 2203
## Avg_Cogongrass_Cover-Lynx_rufus 1.7760 1.0017 2177
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0605 1.0064 2543
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8794 1.0025 1607
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0383 1.0023 1089
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3324 1.0078 2254
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4568 1.0020 2237
## Avg_Cogongrass_Cover-Sus_scrofa 1.0549 1.0003 1480
## total_shrub_cover-Odocoileus_virginianus 1.0768 1.0008 2661
## total_shrub_cover-Canis_latrans 1.7082 1.0031 1049
## total_shrub_cover-Sciurus_niger 0.5577 1.0033 1041
## total_shrub_cover-Procyon_lotor -0.1793 1.0087 759
## total_shrub_cover-Dasypus_novemcinctus 0.5845 1.0026 1180
## total_shrub_cover-Lynx_rufus 0.2486 1.0024 704
## total_shrub_cover-Didelphis_virginiana 0.2238 1.0044 1329
## total_shrub_cover-Sylvilagus_floridanus 0.2044 1.0099 479
## total_shrub_cover-Meleagris_gallopavo 0.0713 1.0029 665
## total_shrub_cover-Sciurus_carolinensis 0.4829 1.0080 797
## total_shrub_cover-Vulpes_vulpes 0.7373 1.0062 880
## total_shrub_cover-Sus_scrofa 1.0708 1.0022 936
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0040 0.0599 -0.1113 0.0033 0.1213
## (Intercept)-Canis_latrans -2.7672 0.1982 -3.1747 -2.7569 -2.4021
## (Intercept)-Sciurus_niger -4.1391 0.6553 -5.4504 -4.1397 -2.8724
## (Intercept)-Procyon_lotor -2.2993 0.1419 -2.5849 -2.2983 -2.0226
## (Intercept)-Dasypus_novemcinctus -1.7639 0.1714 -2.1171 -1.7569 -1.4514
## (Intercept)-Lynx_rufus -3.5987 0.3486 -4.3100 -3.5881 -2.9468
## (Intercept)-Didelphis_virginiana -2.6040 0.3050 -3.2455 -2.5939 -2.0509
## (Intercept)-Sylvilagus_floridanus -3.3117 0.2872 -3.8888 -3.3031 -2.7722
## (Intercept)-Meleagris_gallopavo -3.8632 0.5432 -4.9719 -3.8497 -2.8307
## (Intercept)-Sciurus_carolinensis -2.7511 0.3681 -3.5336 -2.7263 -2.1041
## (Intercept)-Vulpes_vulpes -4.3757 0.7392 -5.8482 -4.3681 -3.0008
## (Intercept)-Sus_scrofa -3.5296 0.6325 -4.7528 -3.5395 -2.3033
## shrub_cover-Odocoileus_virginianus -0.0528 0.0646 -0.1807 -0.0530 0.0740
## shrub_cover-Canis_latrans -0.2827 0.2433 -0.7601 -0.2787 0.1959
## shrub_cover-Sciurus_niger -0.1867 0.5184 -1.2341 -0.1782 0.8356
## shrub_cover-Procyon_lotor 0.3091 0.1619 -0.0115 0.3099 0.6169
## shrub_cover-Dasypus_novemcinctus 0.9571 0.3390 0.3303 0.9426 1.6537
## shrub_cover-Lynx_rufus 0.0272 0.3750 -0.7357 0.0393 0.7309
## shrub_cover-Didelphis_virginiana 1.0703 0.4034 0.3508 1.0466 1.9218
## shrub_cover-Sylvilagus_floridanus 0.6974 0.4313 -0.1960 0.7159 1.5108
## shrub_cover-Meleagris_gallopavo -0.5478 0.4723 -1.5072 -0.5405 0.3524
## shrub_cover-Sciurus_carolinensis 1.0304 0.4533 0.1628 1.0232 1.9417
## shrub_cover-Vulpes_vulpes 0.1643 0.5936 -0.9968 0.1579 1.3489
## shrub_cover-Sus_scrofa 0.9237 0.8245 -0.7359 0.9104 2.5823
## veg_height-Odocoileus_virginianus -0.2943 0.0650 -0.4243 -0.2945 -0.1657
## veg_height-Canis_latrans -0.5867 0.1860 -0.9733 -0.5800 -0.2364
## veg_height-Sciurus_niger -0.0423 0.3920 -0.8052 -0.0532 0.7730
## veg_height-Procyon_lotor 0.3307 0.1219 0.0861 0.3323 0.5650
## veg_height-Dasypus_novemcinctus 0.2417 0.1364 -0.0217 0.2383 0.5114
## veg_height-Lynx_rufus 0.0164 0.2383 -0.4622 0.0202 0.4818
## veg_height-Didelphis_virginiana 0.3976 0.2381 -0.0498 0.3899 0.8912
## veg_height-Sylvilagus_floridanus 0.0396 0.2448 -0.4528 0.0400 0.5284
## veg_height-Meleagris_gallopavo -0.2716 0.3855 -1.0572 -0.2601 0.4875
## veg_height-Sciurus_carolinensis 0.0928 0.2300 -0.3357 0.0854 0.5702
## veg_height-Vulpes_vulpes -0.1595 0.3182 -0.8170 -0.1458 0.4308
## veg_height-Sus_scrofa -0.1676 0.3268 -0.8490 -0.1572 0.4473
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5250
## (Intercept)-Canis_latrans 1.0035 1858
## (Intercept)-Sciurus_niger 1.0103 534
## (Intercept)-Procyon_lotor 1.0059 3612
## (Intercept)-Dasypus_novemcinctus 1.0025 2195
## (Intercept)-Lynx_rufus 1.0075 1094
## (Intercept)-Didelphis_virginiana 1.0023 1547
## (Intercept)-Sylvilagus_floridanus 1.0029 1439
## (Intercept)-Meleagris_gallopavo 1.0084 542
## (Intercept)-Sciurus_carolinensis 1.0031 809
## (Intercept)-Vulpes_vulpes 1.0567 435
## (Intercept)-Sus_scrofa 1.0071 708
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0021 1618
## shrub_cover-Sciurus_niger 1.0033 1012
## shrub_cover-Procyon_lotor 1.0004 3766
## shrub_cover-Dasypus_novemcinctus 1.0010 1190
## shrub_cover-Lynx_rufus 1.0041 1155
## shrub_cover-Didelphis_virginiana 1.0019 1085
## shrub_cover-Sylvilagus_floridanus 1.0048 1064
## shrub_cover-Meleagris_gallopavo 1.0085 770
## shrub_cover-Sciurus_carolinensis 1.0012 773
## shrub_cover-Vulpes_vulpes 1.0031 1253
## shrub_cover-Sus_scrofa 1.0030 627
## veg_height-Odocoileus_virginianus 1.0009 5250
## veg_height-Canis_latrans 1.0037 2259
## veg_height-Sciurus_niger 1.0015 1615
## veg_height-Procyon_lotor 1.0003 3957
## veg_height-Dasypus_novemcinctus 1.0009 4722
## veg_height-Lynx_rufus 1.0004 2404
## veg_height-Didelphis_virginiana 1.0012 3037
## veg_height-Sylvilagus_floridanus 1.0039 1837
## veg_height-Meleagris_gallopavo 1.0007 1118
## veg_height-Sciurus_carolinensis 1.0008 1833
## veg_height-Vulpes_vulpes 1.0003 1878
## veg_height-Sus_scrofa 1.0011 2656
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6375
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0040 0.7228 -1.3524 -0.0282 1.5808 1.0095 1360
## Tree_Density -0.8278 0.3993 -1.6983 -0.8023 -0.1220 1.0041 998
## Avg_Canopy_Cover 1.1661 0.3828 0.4885 1.1400 1.9888 1.0044 1263
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.3226 5.7338 1.3005 4.6863 22.0796 1.0379 472
## Tree_Density 0.6492 0.9926 0.0445 0.3410 3.0797 1.0202 802
## Avg_Canopy_Cover 0.8224 0.9584 0.0802 0.5408 3.1493 1.0093 1261
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5635 0.7649 0.046 0.306 2.712 1.0329 326
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7622 0.4361 -3.6051 -2.7749 -1.8936 1.0023 4904
## shrub_cover 0.1152 0.2564 -0.4063 0.1129 0.6238 1.0013 3460
## veg_height -0.0195 0.1499 -0.3286 -0.0146 0.2706 1.0001 3460
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2381 1.3175 0.8147 1.9156 5.3919 1.0227 2008
## shrub_cover 0.6273 0.4619 0.1535 0.5061 1.8078 1.0029 1944
## veg_height 0.1930 0.1311 0.0579 0.1594 0.5375 1.0048 3423
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7158 1.9017 2.0651 4.3824 9.4627
## (Intercept)-Canis_latrans 0.4477 0.7008 -0.8519 0.4190 1.9151
## (Intercept)-Sciurus_niger 0.4950 1.9385 -2.0949 0.1574 5.3956
## (Intercept)-Procyon_lotor 0.8255 0.6609 -0.4363 0.8094 2.1996
## (Intercept)-Dasypus_novemcinctus -0.9413 0.6445 -2.3247 -0.9153 0.2467
## (Intercept)-Lynx_rufus 1.7015 1.9013 -0.9679 1.3260 6.4077
## (Intercept)-Didelphis_virginiana -1.7071 0.7631 -3.2952 -1.6708 -0.2790
## (Intercept)-Sylvilagus_floridanus -0.6243 0.7559 -2.1132 -0.6347 0.9082
## (Intercept)-Meleagris_gallopavo 0.6787 1.4854 -1.3674 0.4357 4.2365
## (Intercept)-Sciurus_carolinensis -1.7856 0.7842 -3.4593 -1.7476 -0.3234
## (Intercept)-Vulpes_vulpes -1.0278 1.7543 -3.6568 -1.3234 3.2972
## (Intercept)-Sus_scrofa -2.4992 1.0394 -4.6858 -2.4455 -0.5316
## Tree_Density-Odocoileus_virginianus -0.4498 0.6540 -1.5907 -0.5097 1.0312
## Tree_Density-Canis_latrans -0.9591 0.5523 -2.2384 -0.9011 -0.0525
## Tree_Density-Sciurus_niger -0.8821 0.7979 -2.6093 -0.8373 0.6087
## Tree_Density-Procyon_lotor -0.5423 0.4160 -1.3616 -0.5469 0.3001
## Tree_Density-Dasypus_novemcinctus -1.3542 0.8313 -3.4999 -1.1929 -0.1990
## Tree_Density-Lynx_rufus -0.1571 0.7763 -1.4310 -0.2549 1.6444
## Tree_Density-Didelphis_virginiana -1.0264 0.7129 -2.7556 -0.9359 0.1000
## Tree_Density-Sylvilagus_floridanus -1.0571 0.7178 -2.7989 -0.9619 0.1067
## Tree_Density-Meleagris_gallopavo -1.0231 0.7368 -2.7125 -0.9442 0.2421
## Tree_Density-Sciurus_carolinensis -0.9364 0.6896 -2.5356 -0.8631 0.2051
## Tree_Density-Vulpes_vulpes -0.7668 0.7679 -2.3929 -0.7391 0.7537
## Tree_Density-Sus_scrofa -1.0158 0.8046 -2.9506 -0.9100 0.2418
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8683 0.7704 -0.7110 0.8833 2.4228
## Avg_Canopy_Cover-Canis_latrans 0.0234 0.5061 -0.9982 0.0232 0.9946
## Avg_Canopy_Cover-Sciurus_niger 1.1431 0.9473 -0.6266 1.0883 3.2116
## Avg_Canopy_Cover-Procyon_lotor 1.1107 0.4903 0.2373 1.0817 2.1850
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1077 0.4610 0.2863 1.0733 2.0903
## Avg_Canopy_Cover-Lynx_rufus 1.1181 0.8514 -0.4139 1.0616 2.9894
## Avg_Canopy_Cover-Didelphis_virginiana 1.5125 0.6308 0.4711 1.4419 2.9768
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9855 0.8983 0.6986 1.8408 4.1470
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5000 0.7800 0.1903 1.4126 3.2898
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4515 0.6036 0.4718 1.3822 2.8524
## Avg_Canopy_Cover-Vulpes_vulpes 1.1915 0.7418 -0.0940 1.1241 2.8677
## Avg_Canopy_Cover-Sus_scrofa 1.3691 0.6131 0.3243 1.3095 2.7715
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0304 598
## (Intercept)-Canis_latrans 1.0216 2068
## (Intercept)-Sciurus_niger 1.0787 282
## (Intercept)-Procyon_lotor 1.0005 2772
## (Intercept)-Dasypus_novemcinctus 1.0017 1855
## (Intercept)-Lynx_rufus 1.0451 389
## (Intercept)-Didelphis_virginiana 1.0004 2474
## (Intercept)-Sylvilagus_floridanus 1.0004 2475
## (Intercept)-Meleagris_gallopavo 1.0205 429
## (Intercept)-Sciurus_carolinensis 1.0004 2124
## (Intercept)-Vulpes_vulpes 1.0180 273
## (Intercept)-Sus_scrofa 1.0056 1529
## Tree_Density-Odocoileus_virginianus 1.0088 1828
## Tree_Density-Canis_latrans 1.0034 1987
## Tree_Density-Sciurus_niger 1.0041 1328
## Tree_Density-Procyon_lotor 1.0034 2229
## Tree_Density-Dasypus_novemcinctus 1.0007 1008
## Tree_Density-Lynx_rufus 1.0047 953
## Tree_Density-Didelphis_virginiana 1.0007 1616
## Tree_Density-Sylvilagus_floridanus 1.0020 1742
## Tree_Density-Meleagris_gallopavo 1.0059 1360
## Tree_Density-Sciurus_carolinensis 1.0021 1749
## Tree_Density-Vulpes_vulpes 1.0054 1811
## Tree_Density-Sus_scrofa 1.0011 1298
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0017 2392
## Avg_Canopy_Cover-Canis_latrans 1.0002 2166
## Avg_Canopy_Cover-Sciurus_niger 1.0026 952
## Avg_Canopy_Cover-Procyon_lotor 1.0006 2648
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0021 2977
## Avg_Canopy_Cover-Lynx_rufus 1.0040 1443
## Avg_Canopy_Cover-Didelphis_virginiana 1.0014 1813
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0067 1032
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0025 1369
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0004 1859
## Avg_Canopy_Cover-Vulpes_vulpes 1.0024 1947
## Avg_Canopy_Cover-Sus_scrofa 1.0008 2481
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0033 0.0587 -0.1088 0.0035 0.1211
## (Intercept)-Canis_latrans -2.7705 0.1933 -3.1631 -2.7657 -2.4058
## (Intercept)-Sciurus_niger -4.5367 0.5930 -5.6158 -4.5578 -3.3326
## (Intercept)-Procyon_lotor -2.2965 0.1447 -2.5961 -2.2902 -2.0213
## (Intercept)-Dasypus_novemcinctus -1.7274 0.1570 -2.0468 -1.7240 -1.4296
## (Intercept)-Lynx_rufus -3.9896 0.3655 -4.6571 -4.0101 -3.2407
## (Intercept)-Didelphis_virginiana -2.5622 0.2932 -3.1732 -2.5452 -2.0128
## (Intercept)-Sylvilagus_floridanus -3.1354 0.2736 -3.7025 -3.1231 -2.6314
## (Intercept)-Meleagris_gallopavo -4.1711 0.4672 -5.1063 -4.1592 -3.3058
## (Intercept)-Sciurus_carolinensis -2.6245 0.3200 -3.3100 -2.6092 -2.0544
## (Intercept)-Vulpes_vulpes -4.3638 0.7935 -5.9935 -4.3052 -2.9534
## (Intercept)-Sus_scrofa -3.1845 0.6158 -4.4339 -3.1748 -2.0212
## shrub_cover-Odocoileus_virginianus -0.0546 0.0638 -0.1817 -0.0542 0.0713
## shrub_cover-Canis_latrans -0.3159 0.2195 -0.7540 -0.3144 0.1083
## shrub_cover-Sciurus_niger -0.4999 0.4528 -1.4294 -0.4888 0.3635
## shrub_cover-Procyon_lotor 0.2401 0.1617 -0.0886 0.2419 0.5489
## shrub_cover-Dasypus_novemcinctus 0.8317 0.2904 0.2685 0.8235 1.4218
## shrub_cover-Lynx_rufus -0.3845 0.3139 -1.0422 -0.3743 0.1988
## shrub_cover-Didelphis_virginiana 0.9413 0.3526 0.2812 0.9265 1.6731
## shrub_cover-Sylvilagus_floridanus 0.3650 0.3872 -0.3778 0.3617 1.1318
## shrub_cover-Meleagris_gallopavo -0.8574 0.4001 -1.6941 -0.8388 -0.1140
## shrub_cover-Sciurus_carolinensis 0.8381 0.4075 0.0644 0.8309 1.6640
## shrub_cover-Vulpes_vulpes -0.1821 0.5681 -1.3744 -0.1674 0.9339
## shrub_cover-Sus_scrofa 0.4747 0.7998 -1.0736 0.4523 2.1126
## veg_height-Odocoileus_virginianus -0.2951 0.0652 -0.4239 -0.2945 -0.1683
## veg_height-Canis_latrans -0.5951 0.1860 -0.9751 -0.5891 -0.2442
## veg_height-Sciurus_niger -0.1102 0.3497 -0.8191 -0.1047 0.5633
## veg_height-Procyon_lotor 0.3343 0.1203 0.0988 0.3342 0.5780
## veg_height-Dasypus_novemcinctus 0.2369 0.1311 -0.0159 0.2363 0.4984
## veg_height-Lynx_rufus 0.0769 0.2356 -0.3964 0.0838 0.5227
## veg_height-Didelphis_virginiana 0.4390 0.2385 -0.0075 0.4324 0.9460
## veg_height-Sylvilagus_floridanus 0.1311 0.2320 -0.3183 0.1304 0.5929
## veg_height-Meleagris_gallopavo -0.2518 0.3179 -0.9105 -0.2449 0.3473
## veg_height-Sciurus_carolinensis 0.0808 0.2120 -0.3206 0.0758 0.5263
## veg_height-Vulpes_vulpes -0.1423 0.3142 -0.7939 -0.1230 0.4398
## veg_height-Sus_scrofa -0.1252 0.3158 -0.7655 -0.1203 0.4735
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0050 2123
## (Intercept)-Sciurus_niger 1.0712 340
## (Intercept)-Procyon_lotor 1.0022 4257
## (Intercept)-Dasypus_novemcinctus 1.0011 4252
## (Intercept)-Lynx_rufus 1.0037 470
## (Intercept)-Didelphis_virginiana 1.0006 2547
## (Intercept)-Sylvilagus_floridanus 1.0021 2173
## (Intercept)-Meleagris_gallopavo 1.0017 534
## (Intercept)-Sciurus_carolinensis 1.0008 2130
## (Intercept)-Vulpes_vulpes 1.0079 317
## (Intercept)-Sus_scrofa 1.0063 2045
## shrub_cover-Odocoileus_virginianus 0.9999 5250
## shrub_cover-Canis_latrans 1.0027 2487
## shrub_cover-Sciurus_niger 1.0115 846
## shrub_cover-Procyon_lotor 1.0063 3584
## shrub_cover-Dasypus_novemcinctus 1.0012 3720
## shrub_cover-Lynx_rufus 1.0007 1359
## shrub_cover-Didelphis_virginiana 1.0012 2398
## shrub_cover-Sylvilagus_floridanus 0.9999 2045
## shrub_cover-Meleagris_gallopavo 1.0017 695
## shrub_cover-Sciurus_carolinensis 1.0010 2071
## shrub_cover-Vulpes_vulpes 1.0158 1715
## shrub_cover-Sus_scrofa 1.0086 2090
## veg_height-Odocoileus_virginianus 1.0019 5015
## veg_height-Canis_latrans 1.0025 2218
## veg_height-Sciurus_niger 1.0078 1880
## veg_height-Procyon_lotor 1.0007 4422
## veg_height-Dasypus_novemcinctus 1.0049 4461
## veg_height-Lynx_rufus 1.0001 1997
## veg_height-Didelphis_virginiana 1.0000 3803
## veg_height-Sylvilagus_floridanus 1.0059 3231
## veg_height-Meleagris_gallopavo 1.0050 1792
## veg_height-Sciurus_carolinensis 1.0052 3148
## veg_height-Vulpes_vulpes 1.0031 2005
## veg_height-Sus_scrofa 1.0001 4136
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5912
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7279 0.6004 -1.8563 -0.7477 0.5453 1.0038 2436
## Avg_Cogongrass_Cover -0.7840 0.3907 -1.5942 -0.7641 -0.0371 1.0161 1255
## I(Avg_Cogongrass_Cover^2) 0.8143 0.3377 0.2057 0.7975 1.5362 1.0176 1030
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4836 2.7913 0.6167 2.7204 10.9367 1.0196 1203
## Avg_Cogongrass_Cover 0.4440 0.5621 0.0421 0.2612 1.9353 1.0342 1130
## I(Avg_Cogongrass_Cover^2) 0.4678 0.7802 0.0388 0.2385 2.4163 1.0131 847
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6339 0.669 0.054 0.4262 2.4932 1.0899 312
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7258 0.4197 -3.5542 -2.7330 -1.8635 1.0069 2899
## shrub_cover 0.1016 0.2593 -0.4133 0.1022 0.6356 1.0014 3225
## veg_height -0.0203 0.1518 -0.3221 -0.0201 0.2767 1.0028 2676
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0793 1.2216 0.7540 1.7631 5.3042 1.0067 2466
## shrub_cover 0.5913 0.4200 0.1358 0.4858 1.6692 1.0013 1750
## veg_height 0.1855 0.1235 0.0549 0.1549 0.5087 1.0031 2780
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7932 1.4511 0.3820 2.6365
## (Intercept)-Canis_latrans -0.4201 0.6841 -1.7761 -0.4205
## (Intercept)-Sciurus_niger -0.6267 1.2477 -2.7179 -0.7448
## (Intercept)-Procyon_lotor -0.1570 0.6669 -1.5427 -0.1482
## (Intercept)-Dasypus_novemcinctus -1.3047 0.6585 -2.6630 -1.2824
## (Intercept)-Lynx_rufus -0.8845 1.0097 -2.6446 -0.9491
## (Intercept)-Didelphis_virginiana -1.7947 0.7294 -3.3040 -1.7791
## (Intercept)-Sylvilagus_floridanus -1.0070 0.7649 -2.5099 -1.0087
## (Intercept)-Meleagris_gallopavo 0.3548 1.2473 -1.6756 0.2283
## (Intercept)-Sciurus_carolinensis -2.2969 0.7848 -3.9410 -2.2607
## (Intercept)-Vulpes_vulpes -1.9415 1.2835 -4.2412 -2.0357
## (Intercept)-Sus_scrofa -2.3210 0.9317 -4.2784 -2.2859
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7668 0.6632 -2.1502 -0.7498
## Avg_Cogongrass_Cover-Canis_latrans -0.3697 0.5477 -1.3605 -0.4083
## Avg_Cogongrass_Cover-Sciurus_niger -1.0596 0.7341 -2.7353 -0.9854
## Avg_Cogongrass_Cover-Procyon_lotor -0.6887 0.5085 -1.7227 -0.6894
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5447 0.4920 -1.4943 -0.5503
## Avg_Cogongrass_Cover-Lynx_rufus -0.7145 0.6005 -1.9579 -0.6996
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4905 0.5435 -1.4885 -0.5110
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2353 0.6731 -2.7746 -1.1702
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0478 0.7507 -2.7618 -0.9676
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8168 0.5610 -1.9856 -0.8019
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8133 0.6600 -2.2088 -0.7860
## Avg_Cogongrass_Cover-Sus_scrofa -1.0290 0.6740 -2.5242 -0.9726
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1337 0.7589 0.0553 1.0025
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2149 0.7026 0.2164 1.0763
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4067 0.7284 -1.1875 0.4291
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0428 0.6015 0.2280 0.9502
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7434 0.3705 0.0669 0.7292
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1637 0.5501 0.2876 1.0995
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6074 0.4293 -0.1741 0.5862
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7424 0.4612 -0.0805 0.7121
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4848 0.7238 -0.9382 0.4920
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9876 0.4153 0.2507 0.9592
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9676 0.5543 0.1163 0.9009
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4469 0.6471 -1.0451 0.5119
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.1258 1.0149 1003
## (Intercept)-Canis_latrans 0.9258 1.0089 2325
## (Intercept)-Sciurus_niger 2.3083 1.0171 472
## (Intercept)-Procyon_lotor 1.1115 1.0043 1960
## (Intercept)-Dasypus_novemcinctus -0.0563 1.0040 2899
## (Intercept)-Lynx_rufus 1.2680 1.0111 1197
## (Intercept)-Didelphis_virginiana -0.4278 1.0016 3166
## (Intercept)-Sylvilagus_floridanus 0.4996 1.0077 2390
## (Intercept)-Meleagris_gallopavo 3.0888 1.0243 768
## (Intercept)-Sciurus_carolinensis -0.8310 1.0147 2376
## (Intercept)-Vulpes_vulpes 0.9744 1.0839 523
## (Intercept)-Sus_scrofa -0.5879 1.0071 1559
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5023 1.0036 2394
## Avg_Cogongrass_Cover-Canis_latrans 0.8349 1.0040 2763
## Avg_Cogongrass_Cover-Sciurus_niger 0.1825 1.0191 1206
## Avg_Cogongrass_Cover-Procyon_lotor 0.3459 1.0011 2769
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4680 1.0059 2657
## Avg_Cogongrass_Cover-Lynx_rufus 0.4321 1.0039 1828
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6618 1.0090 2514
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1291 1.0103 1187
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2197 1.0079 1311
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2407 1.0053 1926
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4050 1.0050 1617
## Avg_Cogongrass_Cover-Sus_scrofa 0.1666 1.0157 1591
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.0210 1.0070 1044
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9931 1.0120 1272
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.8116 1.0066 698
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5586 1.0218 953
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5281 1.0077 2529
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3992 1.0090 1442
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5135 1.0079 1468
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7213 1.0040 1732
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.0088 1.0096 839
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9111 1.0069 2030
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2807 1.0055 1473
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5599 1.0125 947
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0024 0.0589 -0.1110 0.0025 0.1181
## (Intercept)-Canis_latrans -2.7466 0.1829 -3.1146 -2.7426 -2.4063
## (Intercept)-Sciurus_niger -4.2578 0.6381 -5.4926 -4.2574 -3.0132
## (Intercept)-Procyon_lotor -2.3081 0.1495 -2.6169 -2.3034 -2.0299
## (Intercept)-Dasypus_novemcinctus -1.7208 0.1553 -2.0361 -1.7169 -1.4250
## (Intercept)-Lynx_rufus -3.6527 0.3641 -4.3829 -3.6434 -2.9792
## (Intercept)-Didelphis_virginiana -2.5488 0.2923 -3.1454 -2.5396 -2.0006
## (Intercept)-Sylvilagus_floridanus -3.2115 0.3040 -3.8394 -3.1970 -2.6649
## (Intercept)-Meleagris_gallopavo -4.2475 0.4736 -5.1519 -4.2622 -3.2959
## (Intercept)-Sciurus_carolinensis -2.5768 0.3112 -3.2422 -2.5591 -2.0205
## (Intercept)-Vulpes_vulpes -4.1896 0.7290 -5.6706 -4.1565 -2.8601
## (Intercept)-Sus_scrofa -3.2505 0.6289 -4.5139 -3.2444 -2.0483
## shrub_cover-Odocoileus_virginianus -0.0549 0.0638 -0.1769 -0.0559 0.0700
## shrub_cover-Canis_latrans -0.2753 0.2162 -0.7022 -0.2760 0.1384
## shrub_cover-Sciurus_niger -0.4518 0.4444 -1.3500 -0.4461 0.4077
## shrub_cover-Procyon_lotor 0.2206 0.1688 -0.1302 0.2282 0.5403
## shrub_cover-Dasypus_novemcinctus 0.8031 0.2947 0.2388 0.7946 1.3870
## shrub_cover-Lynx_rufus -0.3039 0.3579 -1.0261 -0.2954 0.4064
## shrub_cover-Didelphis_virginiana 0.9484 0.3699 0.2739 0.9296 1.7196
## shrub_cover-Sylvilagus_floridanus 0.1854 0.4084 -0.5606 0.1635 1.0300
## shrub_cover-Meleagris_gallopavo -0.8823 0.3920 -1.6588 -0.8780 -0.1328
## shrub_cover-Sciurus_carolinensis 0.7535 0.4019 0.0034 0.7404 1.5698
## shrub_cover-Vulpes_vulpes -0.1639 0.5767 -1.3382 -0.1573 0.9503
## shrub_cover-Sus_scrofa 0.4810 0.7933 -1.0582 0.4735 2.1031
## veg_height-Odocoileus_virginianus -0.2948 0.0643 -0.4216 -0.2948 -0.1670
## veg_height-Canis_latrans -0.5816 0.1793 -0.9461 -0.5771 -0.2436
## veg_height-Sciurus_niger -0.0362 0.3819 -0.7677 -0.0403 0.7467
## veg_height-Procyon_lotor 0.3354 0.1245 0.0882 0.3361 0.5838
## veg_height-Dasypus_novemcinctus 0.2263 0.1315 -0.0345 0.2244 0.4879
## veg_height-Lynx_rufus 0.0573 0.2391 -0.4214 0.0582 0.5152
## veg_height-Didelphis_virginiana 0.3737 0.2415 -0.0699 0.3636 0.8741
## veg_height-Sylvilagus_floridanus 0.1442 0.2480 -0.3374 0.1423 0.6470
## veg_height-Meleagris_gallopavo -0.2269 0.3392 -0.8870 -0.2324 0.4426
## veg_height-Sciurus_carolinensis 0.0567 0.2031 -0.3425 0.0549 0.4641
## veg_height-Vulpes_vulpes -0.1283 0.3060 -0.7486 -0.1195 0.4534
## veg_height-Sus_scrofa -0.1307 0.3240 -0.7778 -0.1239 0.4803
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0014 2531
## (Intercept)-Sciurus_niger 1.0142 423
## (Intercept)-Procyon_lotor 1.0015 3219
## (Intercept)-Dasypus_novemcinctus 1.0026 4197
## (Intercept)-Lynx_rufus 1.0022 929
## (Intercept)-Didelphis_virginiana 1.0050 2713
## (Intercept)-Sylvilagus_floridanus 1.0073 1603
## (Intercept)-Meleagris_gallopavo 1.0329 538
## (Intercept)-Sciurus_carolinensis 1.0002 2729
## (Intercept)-Vulpes_vulpes 1.0621 533
## (Intercept)-Sus_scrofa 1.0015 1429
## shrub_cover-Odocoileus_virginianus 1.0005 5250
## shrub_cover-Canis_latrans 1.0013 2816
## shrub_cover-Sciurus_niger 1.0003 1302
## shrub_cover-Procyon_lotor 1.0020 3563
## shrub_cover-Dasypus_novemcinctus 1.0000 3737
## shrub_cover-Lynx_rufus 1.0011 1461
## shrub_cover-Didelphis_virginiana 1.0024 1950
## shrub_cover-Sylvilagus_floridanus 1.0002 1711
## shrub_cover-Meleagris_gallopavo 1.0201 665
## shrub_cover-Sciurus_carolinensis 1.0016 2863
## shrub_cover-Vulpes_vulpes 1.0062 1822
## shrub_cover-Sus_scrofa 1.0044 1996
## veg_height-Odocoileus_virginianus 1.0066 5250
## veg_height-Canis_latrans 1.0010 2437
## veg_height-Sciurus_niger 1.0033 1590
## veg_height-Procyon_lotor 1.0014 4021
## veg_height-Dasypus_novemcinctus 1.0027 4911
## veg_height-Lynx_rufus 1.0037 2532
## veg_height-Didelphis_virginiana 1.0015 2676
## veg_height-Sylvilagus_floridanus 1.0019 2190
## veg_height-Meleagris_gallopavo 1.0008 1154
## veg_height-Sciurus_carolinensis 1.0011 3663
## veg_height-Vulpes_vulpes 1.0021 1937
## veg_height-Sus_scrofa 1.0012 2690
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8454 1.0932 -2.8764 -0.8732 1.4632 1.0079 1445
## Cogon_Patch_Size -0.0815 0.7131 -1.6240 -0.0386 1.2177 1.0057 669
## Veg_shannon_index 1.0018 0.4889 0.0896 0.9856 2.0083 1.0152 820
## total_shrub_cover -0.7594 0.5707 -1.9647 -0.7387 0.2781 1.0157 802
## Avg_Cogongrass_Cover -0.2179 0.9861 -2.1298 -0.2264 1.7455 1.0060 422
## Tree_Density -2.0411 0.8168 -3.7146 -2.0208 -0.4716 1.0025 507
## Avg_Canopy_Cover 2.0393 0.6945 0.7813 1.9916 3.5596 1.0061 472
## I(Avg_Cogongrass_Cover^2) 1.5183 0.6002 0.3622 1.4915 2.7696 1.0094 275
## avg_veg_height -0.0772 0.5507 -1.2080 -0.0632 0.9965 1.0359 482
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.5951 16.6518 3.3288 14.6906 62.5159 1.0042 447
## Cogon_Patch_Size 2.9801 4.3423 0.1131 1.5986 13.9155 1.0314 513
## Veg_shannon_index 0.8517 1.3140 0.0508 0.4307 4.1055 1.0017 797
## total_shrub_cover 1.4730 1.9455 0.0671 0.8163 6.8844 1.0084 584
## Avg_Cogongrass_Cover 1.5029 2.5739 0.0554 0.6229 8.5233 1.0071 584
## Tree_Density 3.5212 6.8815 0.0720 1.5380 20.2494 1.1068 474
## Avg_Canopy_Cover 3.1924 3.9128 0.1649 1.9963 13.1647 1.0104 478
## I(Avg_Cogongrass_Cover^2) 1.4983 3.7257 0.0521 0.5251 8.5568 1.2308 234
## avg_veg_height 0.5169 0.6844 0.0417 0.2883 2.3542 1.0282 1276
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8035 2.5872 0.0593 0.8953 9.4246 1.0419 166
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7760 0.4273 -3.5956 -2.7919 -1.9122 1.0050 4694
## shrub_cover 0.2270 0.2678 -0.2990 0.2230 0.7557 1.0062 1668
## veg_height -0.0123 0.1510 -0.3182 -0.0082 0.2817 1.0041 1548
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2223 1.2503 0.8580 1.9170 5.3817 1.0133 1401
## shrub_cover 0.6459 0.4656 0.1407 0.5264 1.9031 1.0024 1381
## veg_height 0.1889 0.1246 0.0566 0.1551 0.5306 1.0174 3202
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.6418 3.6875 2.3409
## (Intercept)-Canis_latrans -0.7951 1.3003 -3.3380
## (Intercept)-Sciurus_niger 1.1750 2.7117 -2.8724
## (Intercept)-Procyon_lotor -0.2696 1.1521 -2.6316
## (Intercept)-Dasypus_novemcinctus -2.6018 1.2296 -5.3404
## (Intercept)-Lynx_rufus 0.4915 2.7098 -3.6120
## (Intercept)-Didelphis_virginiana -4.0382 1.5818 -7.7108
## (Intercept)-Sylvilagus_floridanus -2.1498 1.5324 -5.3211
## (Intercept)-Meleagris_gallopavo -0.5463 2.2711 -4.3602
## (Intercept)-Sciurus_carolinensis -4.6628 1.7105 -8.5926
## (Intercept)-Vulpes_vulpes -3.9794 2.6080 -8.9315
## (Intercept)-Sus_scrofa -5.4156 2.2568 -10.4164
## Cogon_Patch_Size-Odocoileus_virginianus 0.0476 1.4496 -2.7923
## Cogon_Patch_Size-Canis_latrans 1.5367 1.4420 -0.4217
## Cogon_Patch_Size-Sciurus_niger -0.6072 1.8238 -4.6220
## Cogon_Patch_Size-Procyon_lotor -0.4723 0.8122 -2.1299
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0075 0.8252 -1.6470
## Cogon_Patch_Size-Lynx_rufus -0.1670 1.4905 -3.1616
## Cogon_Patch_Size-Didelphis_virginiana 1.5542 1.0737 -0.2008
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2694 1.6471 -5.5507
## Cogon_Patch_Size-Meleagris_gallopavo 0.4221 1.4541 -2.0227
## Cogon_Patch_Size-Sciurus_carolinensis -1.0030 1.4011 -4.3529
## Cogon_Patch_Size-Vulpes_vulpes -0.5060 1.6064 -4.2652
## Cogon_Patch_Size-Sus_scrofa -0.6332 1.4825 -4.2440
## Veg_shannon_index-Odocoileus_virginianus 0.8190 0.9147 -1.1779
## Veg_shannon_index-Canis_latrans 1.3260 0.6953 0.1206
## Veg_shannon_index-Sciurus_niger 1.1313 0.9837 -0.6926
## Veg_shannon_index-Procyon_lotor 1.2112 0.6419 0.0810
## Veg_shannon_index-Dasypus_novemcinctus 0.6226 0.6171 -0.6606
## Veg_shannon_index-Lynx_rufus 1.1283 0.9900 -0.7159
## Veg_shannon_index-Didelphis_virginiana 1.1945 0.7441 -0.1546
## Veg_shannon_index-Sylvilagus_floridanus 1.0541 0.7363 -0.3259
## Veg_shannon_index-Meleagris_gallopavo 1.2405 0.8619 -0.2784
## Veg_shannon_index-Sciurus_carolinensis 0.3678 0.8683 -1.6093
## Veg_shannon_index-Vulpes_vulpes 0.6702 0.9085 -1.3776
## Veg_shannon_index-Sus_scrofa 1.6017 1.0504 0.0667
## total_shrub_cover-Odocoileus_virginianus -0.3867 1.0651 -2.4124
## total_shrub_cover-Canis_latrans 0.2195 0.8687 -1.1996
## total_shrub_cover-Sciurus_niger -0.9621 1.2076 -3.7369
## total_shrub_cover-Procyon_lotor -1.3122 0.7225 -2.9363
## total_shrub_cover-Dasypus_novemcinctus -0.2790 0.7368 -1.8270
## total_shrub_cover-Lynx_rufus -1.0941 1.2766 -3.9497
## total_shrub_cover-Didelphis_virginiana -1.0445 0.9070 -3.1647
## total_shrub_cover-Sylvilagus_floridanus -0.8772 1.0794 -3.2459
## total_shrub_cover-Meleagris_gallopavo -1.7385 1.4262 -5.0949
## total_shrub_cover-Sciurus_carolinensis -0.6138 0.9490 -2.7043
## total_shrub_cover-Vulpes_vulpes -1.0176 1.2127 -4.0143
## total_shrub_cover-Sus_scrofa -0.4010 1.0767 -2.6246
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2995 1.4440 -3.2549
## Avg_Cogongrass_Cover-Canis_latrans 0.0729 1.2838 -2.3195
## Avg_Cogongrass_Cover-Sciurus_niger -0.6944 1.6877 -4.7946
## Avg_Cogongrass_Cover-Procyon_lotor -0.1010 1.2022 -2.3897
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4979 1.3668 -1.8802
## Avg_Cogongrass_Cover-Lynx_rufus -0.1001 1.3443 -2.6057
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1084 1.3062 -2.5772
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8491 1.3994 -3.8929
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4211 1.5009 -3.5030
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1990 1.2934 -2.7570
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0110 1.3836 -2.5063
## Avg_Cogongrass_Cover-Sus_scrofa -0.5983 1.4950 -3.8891
## Tree_Density-Odocoileus_virginianus -0.9435 1.4931 -3.2461
## Tree_Density-Canis_latrans -2.8794 1.4991 -6.4006
## Tree_Density-Sciurus_niger -2.0053 1.8437 -5.6463
## Tree_Density-Procyon_lotor -1.9656 1.0135 -4.1060
## Tree_Density-Dasypus_novemcinctus -3.9197 2.0881 -9.3806
## Tree_Density-Lynx_rufus -0.8117 1.7702 -3.5183
## Tree_Density-Didelphis_virginiana -2.2629 1.3013 -5.1778
## Tree_Density-Sylvilagus_floridanus -2.5421 1.5229 -6.0772
## Tree_Density-Meleagris_gallopavo -2.2740 1.5476 -5.6196
## Tree_Density-Sciurus_carolinensis -2.6421 1.6361 -6.5534
## Tree_Density-Vulpes_vulpes -2.0416 1.7730 -5.6811
## Tree_Density-Sus_scrofa -2.5365 1.7878 -6.8840
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2817 1.4520 -1.7633
## Avg_Canopy_Cover-Canis_latrans 0.1301 0.7295 -1.3043
## Avg_Canopy_Cover-Sciurus_niger 2.3691 1.9187 -1.1845
## Avg_Canopy_Cover-Procyon_lotor 1.7082 0.8361 0.1953
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1867 0.8630 0.7554
## Avg_Canopy_Cover-Lynx_rufus 1.7445 1.6270 -1.1865
## Avg_Canopy_Cover-Didelphis_virginiana 3.1571 1.3922 1.2034
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7727 1.7720 1.2051
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6832 1.5595 0.4053
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9837 1.3860 1.0685
## Avg_Canopy_Cover-Vulpes_vulpes 2.6485 1.5410 0.3563
## Avg_Canopy_Cover-Sus_scrofa 2.2711 1.1203 0.4867
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9984 1.5020 0.0244
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0428 1.0162 0.5478
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.0766 1.3454 -2.3280
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9461 0.9055 0.5328
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5130 0.7401 0.1716
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.2358 1.2203 0.6029
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1869 0.7428 -0.3403
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3057 0.8706 -0.3180
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.8904 1.2973 -2.1659
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7878 0.8280 0.4632
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9948 1.0615 0.4605
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0219 1.3166 -1.9183
## avg_veg_height-Odocoileus_virginianus -0.0836 0.8297 -1.7868
## avg_veg_height-Canis_latrans -0.1678 0.6678 -1.5342
## avg_veg_height-Sciurus_niger -0.2120 0.9255 -2.2208
## avg_veg_height-Procyon_lotor 0.0687 0.6717 -1.2708
## avg_veg_height-Dasypus_novemcinctus 0.2467 0.6589 -1.0019
## avg_veg_height-Lynx_rufus -0.2976 0.9049 -2.2882
## avg_veg_height-Didelphis_virginiana -0.2426 0.7663 -1.9269
## avg_veg_height-Sylvilagus_floridanus -0.1732 0.7662 -1.8099
## avg_veg_height-Meleagris_gallopavo -0.0449 0.8834 -1.9039
## avg_veg_height-Sciurus_carolinensis 0.2247 0.7530 -1.2002
## avg_veg_height-Vulpes_vulpes -0.1855 0.8468 -2.0133
## avg_veg_height-Sus_scrofa -0.1144 0.7889 -1.7475
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0140 16.5179 1.0034 331
## (Intercept)-Canis_latrans -0.8084 1.8487 1.0018 1166
## (Intercept)-Sciurus_niger 0.8252 7.6745 1.0735 227
## (Intercept)-Procyon_lotor -0.2471 2.0029 1.0088 1335
## (Intercept)-Dasypus_novemcinctus -2.5129 -0.3882 1.0167 821
## (Intercept)-Lynx_rufus 0.0476 7.1913 1.0527 275
## (Intercept)-Didelphis_virginiana -3.8578 -1.3825 1.0021 594
## (Intercept)-Sylvilagus_floridanus -2.1097 0.8181 1.0059 1029
## (Intercept)-Meleagris_gallopavo -0.7947 4.5989 1.0011 346
## (Intercept)-Sciurus_carolinensis -4.4819 -1.6979 1.0058 591
## (Intercept)-Vulpes_vulpes -4.0666 1.6856 1.0790 395
## (Intercept)-Sus_scrofa -5.2125 -1.5600 1.0046 389
## Cogon_Patch_Size-Odocoileus_virginianus 0.0081 3.2474 1.0083 1779
## Cogon_Patch_Size-Canis_latrans 1.2593 5.2326 1.0144 735
## Cogon_Patch_Size-Sciurus_niger -0.4719 2.7516 1.0102 632
## Cogon_Patch_Size-Procyon_lotor -0.4388 1.0423 1.0085 826
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0017 1.6638 1.0036 1312
## Cogon_Patch_Size-Lynx_rufus -0.1653 2.8877 1.0032 741
## Cogon_Patch_Size-Didelphis_virginiana 1.4350 3.9983 1.0110 944
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9347 1.0952 1.0197 718
## Cogon_Patch_Size-Meleagris_gallopavo 0.2832 3.7242 1.0311 988
## Cogon_Patch_Size-Sciurus_carolinensis -0.7528 1.0376 1.0038 817
## Cogon_Patch_Size-Vulpes_vulpes -0.3338 2.3237 1.0066 601
## Cogon_Patch_Size-Sus_scrofa -0.4323 1.7313 1.0147 1020
## Veg_shannon_index-Odocoileus_virginianus 0.8514 2.5549 1.0076 1832
## Veg_shannon_index-Canis_latrans 1.2802 2.8874 1.0093 1558
## Veg_shannon_index-Sciurus_niger 1.0673 3.3312 1.0071 1047
## Veg_shannon_index-Procyon_lotor 1.1643 2.6876 1.0346 897
## Veg_shannon_index-Dasypus_novemcinctus 0.6445 1.7724 1.0083 1481
## Veg_shannon_index-Lynx_rufus 1.0775 3.2226 1.0170 862
## Veg_shannon_index-Didelphis_virginiana 1.1457 2.8635 1.0134 1435
## Veg_shannon_index-Sylvilagus_floridanus 1.0240 2.6231 1.0115 1387
## Veg_shannon_index-Meleagris_gallopavo 1.1735 3.1765 1.0102 1316
## Veg_shannon_index-Sciurus_carolinensis 0.4557 1.8012 1.0031 1338
## Veg_shannon_index-Vulpes_vulpes 0.7311 2.2736 1.0022 1355
## Veg_shannon_index-Sus_scrofa 1.4208 4.1982 1.0020 776
## total_shrub_cover-Odocoileus_virginianus -0.4342 1.9177 1.0020 1735
## total_shrub_cover-Canis_latrans 0.1099 2.3306 1.0004 1043
## total_shrub_cover-Sciurus_niger -0.8719 1.1874 1.0129 1075
## total_shrub_cover-Procyon_lotor -1.2367 -0.0833 1.0021 927
## total_shrub_cover-Dasypus_novemcinctus -0.2577 1.1025 1.0022 1456
## total_shrub_cover-Lynx_rufus -0.9768 1.1974 1.0340 590
## total_shrub_cover-Didelphis_virginiana -0.9210 0.4507 1.0111 1296
## total_shrub_cover-Sylvilagus_floridanus -0.7771 0.9952 1.0032 1002
## total_shrub_cover-Meleagris_gallopavo -1.4755 0.4080 1.0034 404
## total_shrub_cover-Sciurus_carolinensis -0.5670 1.1169 1.0187 1401
## total_shrub_cover-Vulpes_vulpes -0.8728 1.0838 1.0108 780
## total_shrub_cover-Sus_scrofa -0.4114 1.8196 1.0120 993
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2712 2.5703 1.0024 744
## Avg_Cogongrass_Cover-Canis_latrans 0.0300 2.8226 1.0049 611
## Avg_Cogongrass_Cover-Sciurus_niger -0.5402 2.2245 1.0016 532
## Avg_Cogongrass_Cover-Procyon_lotor -0.1172 2.3754 1.0060 650
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3901 3.5915 1.0111 563
## Avg_Cogongrass_Cover-Lynx_rufus -0.1204 2.6653 1.0085 716
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1296 2.5985 1.0157 649
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7609 1.6861 1.0027 632
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3565 2.3721 1.0110 677
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1879 2.3867 1.0028 676
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0463 2.9981 1.0122 710
## Avg_Cogongrass_Cover-Sus_scrofa -0.4942 2.0946 1.0075 634
## Tree_Density-Odocoileus_virginianus -1.1236 2.4524 1.0298 573
## Tree_Density-Canis_latrans -2.6442 -0.6865 1.0058 625
## Tree_Density-Sciurus_niger -2.0049 1.6368 1.0135 492
## Tree_Density-Procyon_lotor -1.9149 -0.0996 1.0008 775
## Tree_Density-Dasypus_novemcinctus -3.4248 -1.2441 1.0282 403
## Tree_Density-Lynx_rufus -1.0611 3.4718 1.0068 427
## Tree_Density-Didelphis_virginiana -2.1542 0.1457 1.0030 625
## Tree_Density-Sylvilagus_floridanus -2.3550 -0.0155 1.0068 674
## Tree_Density-Meleagris_gallopavo -2.1751 0.6191 1.0015 869
## Tree_Density-Sciurus_carolinensis -2.3922 -0.0840 1.0092 685
## Tree_Density-Vulpes_vulpes -2.0141 1.6230 1.0224 655
## Tree_Density-Sus_scrofa -2.2684 0.2608 1.0139 788
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3274 4.1867 1.0019 1529
## Avg_Canopy_Cover-Canis_latrans 0.1262 1.5856 1.0032 1250
## Avg_Canopy_Cover-Sciurus_niger 2.2311 6.6012 1.0170 672
## Avg_Canopy_Cover-Procyon_lotor 1.6648 3.5233 1.0112 992
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0857 4.1431 1.0154 692
## Avg_Canopy_Cover-Lynx_rufus 1.6726 5.2650 1.0465 542
## Avg_Canopy_Cover-Didelphis_virginiana 2.8694 6.5379 1.0049 679
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4891 8.1502 1.0115 501
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4279 6.4165 1.0072 476
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7095 6.3892 1.0256 633
## Avg_Canopy_Cover-Vulpes_vulpes 2.3791 6.4096 1.0140 645
## Avg_Canopy_Cover-Sus_scrofa 2.1169 4.8413 1.0010 1276
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7488 5.5861 1.0553 380
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8849 4.5490 1.0056 883
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2570 3.3041 1.0644 198
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8294 3.9823 1.0105 673
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4660 3.0836 1.0045 833
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0020 5.3022 1.0251 405
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1901 2.6962 1.0181 567
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2716 3.2497 1.0030 778
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0683 3.0981 1.0211 339
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6842 3.6738 1.0061 751
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8394 4.4659 1.0328 489
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1567 3.0732 1.0863 264
## avg_veg_height-Odocoileus_virginianus -0.0808 1.5306 1.0353 961
## avg_veg_height-Canis_latrans -0.1432 1.1115 1.0144 747
## avg_veg_height-Sciurus_niger -0.1566 1.5234 1.0302 752
## avg_veg_height-Procyon_lotor 0.0652 1.3957 1.0206 879
## avg_veg_height-Dasypus_novemcinctus 0.2272 1.5777 1.0142 979
## avg_veg_height-Lynx_rufus -0.2365 1.3292 1.0152 796
## avg_veg_height-Didelphis_virginiana -0.2116 1.1718 1.0111 947
## avg_veg_height-Sylvilagus_floridanus -0.1332 1.3193 1.0168 993
## avg_veg_height-Meleagris_gallopavo -0.0286 1.6336 1.0216 803
## avg_veg_height-Sciurus_carolinensis 0.1866 1.8055 1.0149 1031
## avg_veg_height-Vulpes_vulpes -0.1404 1.4063 1.0292 576
## avg_veg_height-Sus_scrofa -0.0903 1.3819 1.0152 882
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0047 0.0598 -0.1132 0.0046 0.1274
## (Intercept)-Canis_latrans -2.7216 0.1854 -3.0962 -2.7181 -2.3751
## (Intercept)-Sciurus_niger -4.7619 0.5069 -5.7390 -4.7590 -3.7355
## (Intercept)-Procyon_lotor -2.2999 0.1448 -2.6009 -2.2944 -2.0370
## (Intercept)-Dasypus_novemcinctus -1.7460 0.1631 -2.0794 -1.7411 -1.4447
## (Intercept)-Lynx_rufus -3.8773 0.3544 -4.5497 -3.8792 -3.1800
## (Intercept)-Didelphis_virginiana -2.5673 0.2945 -3.1623 -2.5575 -2.0154
## (Intercept)-Sylvilagus_floridanus -3.2164 0.2722 -3.7705 -3.2088 -2.7130
## (Intercept)-Meleagris_gallopavo -4.0620 0.5236 -5.0769 -4.0485 -3.0590
## (Intercept)-Sciurus_carolinensis -2.6973 0.3308 -3.3632 -2.6894 -2.0806
## (Intercept)-Vulpes_vulpes -4.3210 0.6900 -5.7532 -4.2891 -3.0638
## (Intercept)-Sus_scrofa -3.3536 0.5999 -4.4968 -3.3689 -2.1641
## shrub_cover-Odocoileus_virginianus -0.0535 0.0639 -0.1773 -0.0543 0.0713
## shrub_cover-Canis_latrans -0.3100 0.2293 -0.7478 -0.3131 0.1548
## shrub_cover-Sciurus_niger -0.4052 0.4772 -1.4099 -0.3809 0.4799
## shrub_cover-Procyon_lotor 0.2655 0.1605 -0.0515 0.2685 0.5723
## shrub_cover-Dasypus_novemcinctus 0.9075 0.3075 0.3379 0.9019 1.5144
## shrub_cover-Lynx_rufus -0.2164 0.3666 -0.9499 -0.2236 0.5072
## shrub_cover-Didelphis_virginiana 0.9992 0.3795 0.2928 0.9863 1.7761
## shrub_cover-Sylvilagus_floridanus 0.5222 0.3942 -0.2501 0.5324 1.2785
## shrub_cover-Meleagris_gallopavo -0.7183 0.4640 -1.6275 -0.7137 0.1668
## shrub_cover-Sciurus_carolinensis 0.9394 0.4202 0.1571 0.9275 1.8005
## shrub_cover-Vulpes_vulpes 0.1021 0.5858 -1.0762 0.1200 1.2436
## shrub_cover-Sus_scrofa 0.7590 0.8027 -0.8197 0.7398 2.3472
## veg_height-Odocoileus_virginianus -0.2951 0.0650 -0.4218 -0.2951 -0.1683
## veg_height-Canis_latrans -0.5582 0.1761 -0.9184 -0.5533 -0.2315
## veg_height-Sciurus_niger -0.0661 0.3387 -0.7384 -0.0647 0.6208
## veg_height-Procyon_lotor 0.3499 0.1218 0.1070 0.3509 0.5883
## veg_height-Dasypus_novemcinctus 0.2436 0.1347 -0.0129 0.2430 0.5153
## veg_height-Lynx_rufus 0.1170 0.2333 -0.3396 0.1179 0.5621
## veg_height-Didelphis_virginiana 0.4152 0.2337 -0.0210 0.4075 0.8834
## veg_height-Sylvilagus_floridanus 0.1268 0.2401 -0.3444 0.1266 0.5989
## veg_height-Meleagris_gallopavo -0.2395 0.3400 -0.9430 -0.2316 0.4200
## veg_height-Sciurus_carolinensis 0.1050 0.2146 -0.3054 0.0978 0.5379
## veg_height-Vulpes_vulpes -0.1838 0.3187 -0.8639 -0.1759 0.4224
## veg_height-Sus_scrofa -0.1601 0.3196 -0.8152 -0.1480 0.4526
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0015 2550
## (Intercept)-Sciurus_niger 1.0551 417
## (Intercept)-Procyon_lotor 1.0006 3166
## (Intercept)-Dasypus_novemcinctus 1.0037 3098
## (Intercept)-Lynx_rufus 1.0325 473
## (Intercept)-Didelphis_virginiana 1.0016 1191
## (Intercept)-Sylvilagus_floridanus 1.0050 1590
## (Intercept)-Meleagris_gallopavo 1.0096 353
## (Intercept)-Sciurus_carolinensis 1.0112 1510
## (Intercept)-Vulpes_vulpes 1.0152 460
## (Intercept)-Sus_scrofa 1.0162 674
## shrub_cover-Odocoileus_virginianus 1.0004 5250
## shrub_cover-Canis_latrans 1.0011 1839
## shrub_cover-Sciurus_niger 1.0056 733
## shrub_cover-Procyon_lotor 1.0016 3669
## shrub_cover-Dasypus_novemcinctus 1.0011 1820
## shrub_cover-Lynx_rufus 1.0399 824
## shrub_cover-Didelphis_virginiana 1.0075 1271
## shrub_cover-Sylvilagus_floridanus 1.0007 1464
## shrub_cover-Meleagris_gallopavo 1.0039 434
## shrub_cover-Sciurus_carolinensis 1.0064 1405
## shrub_cover-Vulpes_vulpes 1.0054 1321
## shrub_cover-Sus_scrofa 1.0158 916
## veg_height-Odocoileus_virginianus 1.0009 5485
## veg_height-Canis_latrans 1.0060 2266
## veg_height-Sciurus_niger 1.0107 1230
## veg_height-Procyon_lotor 1.0012 3693
## veg_height-Dasypus_novemcinctus 1.0002 4035
## veg_height-Lynx_rufus 1.0000 1678
## veg_height-Didelphis_virginiana 1.0010 3067
## veg_height-Sylvilagus_floridanus 1.0003 1862
## veg_height-Meleagris_gallopavo 1.0139 1090
## veg_height-Sciurus_carolinensis 1.0013 2760
## veg_height-Vulpes_vulpes 1.0057 1822
## veg_height-Sus_scrofa 1.0050 2964
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8927
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2015 0.4909 -1.155 -0.2068 0.8038 1.0029 3076
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6967 1.8428 0.788 2.2114 7.8275 1.0008 2475
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3901 0.4141 -3.2366 -2.3892 -1.5416 1.0025 3983
## week 0.2890 0.2343 -0.1907 0.2960 0.7345 1.0019 3272
## I(week^2) -0.2932 0.0979 -0.4958 -0.2909 -0.1058 1.0026 2292
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0191 1.1653 0.7381 1.7264 5.0022 1.0043 1700
## week 0.4555 0.3327 0.1198 0.3663 1.3227 1.0004 2174
## I(week^2) 0.0698 0.0476 0.0216 0.0573 0.1910 1.0017 2457
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.2927 1.0307 1.7112 3.1247 5.8758
## (Intercept)-Canis_latrans 0.3132 0.4004 -0.4293 0.2990 1.1732
## (Intercept)-Sciurus_niger -0.6221 0.9272 -2.0934 -0.7526 1.6262
## (Intercept)-Procyon_lotor 0.7017 0.3878 -0.0303 0.6901 1.4982
## (Intercept)-Dasypus_novemcinctus -0.6385 0.3635 -1.3726 -0.6309 0.0517
## (Intercept)-Lynx_rufus 0.3497 0.8460 -0.8766 0.2185 2.4433
## (Intercept)-Didelphis_virginiana -1.3567 0.4370 -2.2387 -1.3425 -0.5381
## (Intercept)-Sylvilagus_floridanus -0.3005 0.5489 -1.2643 -0.3473 0.9297
## (Intercept)-Meleagris_gallopavo -0.1988 0.7198 -1.2901 -0.2844 1.4320
## (Intercept)-Sciurus_carolinensis -1.3359 0.4529 -2.2559 -1.3203 -0.4916
## (Intercept)-Vulpes_vulpes -1.0215 1.1034 -2.7915 -1.1578 1.6890
## (Intercept)-Sus_scrofa -1.8399 0.6254 -3.1011 -1.8317 -0.6714
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0022 1891
## (Intercept)-Canis_latrans 1.0033 5250
## (Intercept)-Sciurus_niger 1.0089 599
## (Intercept)-Procyon_lotor 1.0019 5048
## (Intercept)-Dasypus_novemcinctus 1.0005 5250
## (Intercept)-Lynx_rufus 1.0311 947
## (Intercept)-Didelphis_virginiana 1.0023 5030
## (Intercept)-Sylvilagus_floridanus 1.0015 2032
## (Intercept)-Meleagris_gallopavo 1.0047 1089
## (Intercept)-Sciurus_carolinensis 1.0000 5015
## (Intercept)-Vulpes_vulpes 1.0076 477
## (Intercept)-Sus_scrofa 1.0010 2606
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5243 0.0786 0.3693 0.5236 0.6754
## (Intercept)-Canis_latrans -2.4330 0.1918 -2.8238 -2.4291 -2.0738
## (Intercept)-Sciurus_niger -3.9053 0.6338 -5.2516 -3.8566 -2.8089
## (Intercept)-Procyon_lotor -2.1531 0.1504 -2.4588 -2.1492 -1.8679
## (Intercept)-Dasypus_novemcinctus -1.4440 0.1570 -1.7576 -1.4431 -1.1455
## (Intercept)-Lynx_rufus -3.4398 0.3652 -4.1868 -3.4342 -2.7612
## (Intercept)-Didelphis_virginiana -2.1157 0.2731 -2.6756 -2.1021 -1.6002
## (Intercept)-Sylvilagus_floridanus -3.0920 0.3390 -3.8179 -3.0742 -2.4805
## (Intercept)-Meleagris_gallopavo -3.3541 0.4061 -4.2310 -3.3318 -2.6312
## (Intercept)-Sciurus_carolinensis -2.2610 0.2870 -2.8439 -2.2456 -1.7260
## (Intercept)-Vulpes_vulpes -3.8987 0.7580 -5.5058 -3.8578 -2.5837
## (Intercept)-Sus_scrofa -2.8499 0.5223 -3.9810 -2.8060 -1.9315
## week-Odocoileus_virginianus 1.2787 0.1222 1.0400 1.2752 1.5205
## week-Canis_latrans 0.5749 0.2594 0.0662 0.5732 1.1072
## week-Sciurus_niger -0.4732 0.5576 -1.6901 -0.4216 0.4828
## week-Procyon_lotor 0.2030 0.2131 -0.2210 0.2014 0.6267
## week-Dasypus_novemcinctus 0.0927 0.2262 -0.3607 0.0892 0.5366
## week-Lynx_rufus 0.3626 0.3546 -0.3320 0.3612 1.0562
## week-Didelphis_virginiana 0.0319 0.3716 -0.7259 0.0363 0.7352
## week-Sylvilagus_floridanus 0.0387 0.3444 -0.6546 0.0476 0.7035
## week-Meleagris_gallopavo -0.2451 0.4232 -1.1123 -0.2376 0.5452
## week-Sciurus_carolinensis 0.7805 0.3717 0.0798 0.7681 1.5548
## week-Vulpes_vulpes 0.1503 0.5275 -0.9732 0.1664 1.1238
## week-Sus_scrofa 0.6638 0.4554 -0.1738 0.6508 1.6371
## I(week^2)-Odocoileus_virginianus -0.5272 0.0505 -0.6299 -0.5271 -0.4266
## I(week^2)-Canis_latrans -0.2404 0.1074 -0.4553 -0.2380 -0.0338
## I(week^2)-Sciurus_niger -0.2914 0.2328 -0.7848 -0.2838 0.1506
## I(week^2)-Procyon_lotor -0.1330 0.0926 -0.3192 -0.1329 0.0463
## I(week^2)-Dasypus_novemcinctus -0.1764 0.1040 -0.3859 -0.1740 0.0153
## I(week^2)-Lynx_rufus -0.2377 0.1543 -0.5466 -0.2328 0.0584
## I(week^2)-Didelphis_virginiana -0.4035 0.2089 -0.8597 -0.3847 -0.0485
## I(week^2)-Sylvilagus_floridanus -0.1828 0.1604 -0.5102 -0.1787 0.1242
## I(week^2)-Meleagris_gallopavo -0.3993 0.2282 -0.9101 -0.3802 -0.0209
## I(week^2)-Sciurus_carolinensis -0.2772 0.1430 -0.5702 -0.2727 -0.0050
## I(week^2)-Vulpes_vulpes -0.3981 0.2310 -0.8924 -0.3823 0.0189
## I(week^2)-Sus_scrofa -0.2403 0.1800 -0.6163 -0.2346 0.0945
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0006 3522
## (Intercept)-Sciurus_niger 1.0110 449
## (Intercept)-Procyon_lotor 1.0009 3821
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## (Intercept)-Lynx_rufus 1.0367 953
## (Intercept)-Didelphis_virginiana 1.0011 4339
## (Intercept)-Sylvilagus_floridanus 1.0035 1510
## (Intercept)-Meleagris_gallopavo 1.0057 850
## (Intercept)-Sciurus_carolinensis 1.0015 3984
## (Intercept)-Vulpes_vulpes 1.0194 421
## (Intercept)-Sus_scrofa 1.0103 1498
## week-Odocoileus_virginianus 1.0006 5009
## week-Canis_latrans 1.0007 3979
## week-Sciurus_niger 1.0011 1159
## week-Procyon_lotor 1.0007 4120
## week-Dasypus_novemcinctus 0.9999 4905
## week-Lynx_rufus 1.0110 2194
## week-Didelphis_virginiana 1.0003 3034
## week-Sylvilagus_floridanus 1.0040 2883
## week-Meleagris_gallopavo 1.0017 1301
## week-Sciurus_carolinensis 1.0008 4297
## week-Vulpes_vulpes 1.0042 1881
## week-Sus_scrofa 1.0007 4020
## I(week^2)-Odocoileus_virginianus 1.0019 5250
## I(week^2)-Canis_latrans 1.0004 4019
## I(week^2)-Sciurus_niger 1.0006 1388
## I(week^2)-Procyon_lotor 1.0006 4093
## I(week^2)-Dasypus_novemcinctus 1.0022 4548
## I(week^2)-Lynx_rufus 1.0011 1964
## I(week^2)-Didelphis_virginiana 1.0022 1899
## I(week^2)-Sylvilagus_floridanus 1.0081 2029
## I(week^2)-Meleagris_gallopavo 1.0073 919
## I(week^2)-Sciurus_carolinensis 1.0007 4302
## I(week^2)-Vulpes_vulpes 1.0016 1685
## I(week^2)-Sus_scrofa 1.0008 4422
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0547
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1893 1.0104 -2.1017 -0.2146 1.8913 1.0006 1319
## Cogon_Patch_Size -0.6893 0.6051 -2.0090 -0.6613 0.4232 1.0122 1209
## Veg_shannon_index 0.9315 0.4321 0.0918 0.9255 1.7904 1.0069 931
## total_shrub_cover -0.4809 0.4710 -1.4646 -0.4722 0.4389 1.0000 1568
## Avg_Cogongrass_Cover 1.9192 0.6650 0.6471 1.9126 3.2743 1.0262 588
## Tree_Density -1.8590 0.6404 -3.2083 -1.8204 -0.6914 1.0021 782
## Avg_Canopy_Cover 1.8183 0.5174 0.8518 1.7944 2.9106 1.0068 958
## avg_veg_height -0.5243 0.4231 -1.3547 -0.5234 0.3055 1.0165 749
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.1945 14.4741 3.4048 13.1037 55.0934 1.0090 432
## Cogon_Patch_Size 2.3837 3.4466 0.1128 1.3263 11.0394 1.0010 386
## Veg_shannon_index 0.7840 1.1265 0.0502 0.4167 3.6613 1.0365 827
## total_shrub_cover 1.4022 1.9580 0.0746 0.8043 6.1534 1.0238 555
## Avg_Cogongrass_Cover 1.0782 1.6619 0.0511 0.5183 5.5234 1.0310 724
## Tree_Density 2.0886 3.7584 0.0656 0.8508 11.1890 1.0265 578
## Avg_Canopy_Cover 1.4370 1.7897 0.0846 0.8877 6.3837 1.0375 727
## avg_veg_height 0.3457 0.4362 0.0392 0.2106 1.5105 1.0045 1851
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6018 2.0644 0.0673 0.9028 7.4161 1.0462 223
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4544 0.4490 -3.2964 -2.4662 -1.5386 1.0002 5250
## week 0.2887 0.2301 -0.1876 0.2932 0.7170 1.0011 2421
## I(week^2) -0.2893 0.1010 -0.5056 -0.2873 -0.1000 1.0104 2019
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3867 1.3905 0.9239 2.0591 5.8212 1.0093 2478
## week 0.4388 0.3136 0.1134 0.3541 1.2879 1.0047 1702
## I(week^2) 0.0716 0.0502 0.0215 0.0587 0.2031 1.0053 1591
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1822 3.4064 3.3300 7.5497
## (Intercept)-Canis_latrans 0.8227 1.5653 -1.1852 0.6326
## (Intercept)-Sciurus_niger 1.4758 2.6350 -2.3845 1.0656
## (Intercept)-Procyon_lotor 0.8261 0.9276 -1.1196 0.8294
## (Intercept)-Dasypus_novemcinctus -1.5034 0.9329 -3.6108 -1.4399
## (Intercept)-Lynx_rufus 1.9005 2.6504 -2.2445 1.4955
## (Intercept)-Didelphis_virginiana -2.9875 1.0981 -5.3219 -2.9270
## (Intercept)-Sylvilagus_floridanus -1.1983 1.2843 -3.6655 -1.2283
## (Intercept)-Meleagris_gallopavo -1.0981 1.6690 -3.9694 -1.1766
## (Intercept)-Sciurus_carolinensis -3.2383 1.2011 -5.9567 -3.1018
## (Intercept)-Vulpes_vulpes -1.3343 2.6741 -5.2741 -1.7689
## (Intercept)-Sus_scrofa -4.6784 1.7191 -8.4796 -4.5199
## Cogon_Patch_Size-Odocoileus_virginianus -0.5686 1.2504 -2.9345 -0.6189
## Cogon_Patch_Size-Canis_latrans 0.6414 1.1830 -1.0109 0.4287
## Cogon_Patch_Size-Sciurus_niger -1.2865 1.6502 -5.0549 -1.0892
## Cogon_Patch_Size-Procyon_lotor -0.9131 0.7267 -2.3683 -0.9014
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7736 0.6095 -2.0517 -0.7449
## Cogon_Patch_Size-Lynx_rufus -0.6339 1.2905 -3.0756 -0.6432
## Cogon_Patch_Size-Didelphis_virginiana 0.7809 0.8663 -0.6735 0.7028
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8188 1.4679 -5.5787 -1.5258
## Cogon_Patch_Size-Meleagris_gallopavo -0.3929 1.0279 -2.1596 -0.4852
## Cogon_Patch_Size-Sciurus_carolinensis -1.6431 1.1973 -4.5819 -1.4038
## Cogon_Patch_Size-Vulpes_vulpes -1.0850 1.6076 -4.7118 -0.9718
## Cogon_Patch_Size-Sus_scrofa -1.1964 1.2771 -4.3129 -0.9858
## Veg_shannon_index-Odocoileus_virginianus 0.7559 0.8987 -1.2214 0.7990
## Veg_shannon_index-Canis_latrans 1.2632 0.6181 0.1591 1.2058
## Veg_shannon_index-Sciurus_niger 1.0510 0.9505 -0.9276 1.0325
## Veg_shannon_index-Procyon_lotor 1.1770 0.5867 0.1367 1.1459
## Veg_shannon_index-Dasypus_novemcinctus 0.6812 0.5144 -0.3424 0.6936
## Veg_shannon_index-Lynx_rufus 0.8267 0.8995 -1.2120 0.8684
## Veg_shannon_index-Didelphis_virginiana 1.0920 0.6445 -0.0507 1.0552
## Veg_shannon_index-Sylvilagus_floridanus 1.0521 0.6648 -0.1882 1.0257
## Veg_shannon_index-Meleagris_gallopavo 1.2542 0.7441 -0.0363 1.1776
## Veg_shannon_index-Sciurus_carolinensis 0.2538 0.7336 -1.3952 0.3231
## Veg_shannon_index-Vulpes_vulpes 0.4637 0.8629 -1.5819 0.5513
## Veg_shannon_index-Sus_scrofa 1.6038 0.9715 0.1880 1.4240
## total_shrub_cover-Odocoileus_virginianus -0.0717 0.9755 -1.9183 -0.1348
## total_shrub_cover-Canis_latrans 0.2060 0.7388 -1.0404 0.1405
## total_shrub_cover-Sciurus_niger -0.8217 1.0939 -3.2208 -0.7242
## total_shrub_cover-Procyon_lotor -0.9634 0.6137 -2.2913 -0.9132
## total_shrub_cover-Dasypus_novemcinctus 0.0807 0.5503 -0.9397 0.0584
## total_shrub_cover-Lynx_rufus -1.0220 1.1381 -3.7500 -0.8627
## total_shrub_cover-Didelphis_virginiana -0.5800 0.6977 -2.0799 -0.5421
## total_shrub_cover-Sylvilagus_floridanus -0.2584 0.7863 -1.8691 -0.2555
## total_shrub_cover-Meleagris_gallopavo -1.9422 1.3024 -4.9696 -1.7311
## total_shrub_cover-Sciurus_carolinensis -0.0465 0.6910 -1.3585 -0.0670
## total_shrub_cover-Vulpes_vulpes -0.7005 1.0668 -3.1926 -0.6080
## total_shrub_cover-Sus_scrofa 0.1114 0.9092 -1.4613 0.0225
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8934 1.0487 -0.1717 1.8756
## Avg_Cogongrass_Cover-Canis_latrans 2.2884 0.8816 0.7599 2.2250
## Avg_Cogongrass_Cover-Sciurus_niger 1.5060 1.2993 -1.5309 1.6350
## Avg_Cogongrass_Cover-Procyon_lotor 2.1752 0.8526 0.6159 2.1336
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5073 0.8948 0.9599 2.4414
## Avg_Cogongrass_Cover-Lynx_rufus 2.3889 1.0099 0.7169 2.2748
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1058 0.8046 0.5857 2.0885
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4057 0.9267 -0.5345 1.4392
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4328 1.1722 -1.2851 1.5545
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3045 0.8686 0.8037 2.2439
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3677 1.0546 0.5123 2.3032
## Avg_Cogongrass_Cover-Sus_scrofa 1.4018 1.1159 -1.2113 1.5034
## Tree_Density-Odocoileus_virginianus -0.9514 1.1668 -2.8496 -1.0877
## Tree_Density-Canis_latrans -2.3598 1.0729 -4.9851 -2.1885
## Tree_Density-Sciurus_niger -1.9607 1.4091 -4.9540 -1.8837
## Tree_Density-Procyon_lotor -1.4950 0.7400 -2.9324 -1.5066
## Tree_Density-Dasypus_novemcinctus -3.1001 1.5216 -7.1988 -2.7228
## Tree_Density-Lynx_rufus -0.8743 1.3476 -2.9050 -1.0766
## Tree_Density-Didelphis_virginiana -2.1688 1.0241 -4.6390 -2.0119
## Tree_Density-Sylvilagus_floridanus -2.3082 1.2057 -5.2922 -2.1224
## Tree_Density-Meleagris_gallopavo -2.0343 1.1821 -4.7297 -1.9617
## Tree_Density-Sciurus_carolinensis -2.3498 1.2074 -5.4483 -2.1379
## Tree_Density-Vulpes_vulpes -1.8875 1.3818 -4.9200 -1.8550
## Tree_Density-Sus_scrofa -2.1802 1.3711 -5.6075 -1.9773
## Avg_Canopy_Cover-Odocoileus_virginianus 1.4018 1.1448 -1.0147 1.4471
## Avg_Canopy_Cover-Canis_latrans 0.5138 0.7310 -0.9361 0.4981
## Avg_Canopy_Cover-Sciurus_niger 1.9761 1.2990 -0.4071 1.9042
## Avg_Canopy_Cover-Procyon_lotor 1.7260 0.6696 0.5416 1.6795
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9102 0.6312 0.7985 1.8572
## Avg_Canopy_Cover-Lynx_rufus 1.3656 1.1663 -1.0119 1.3977
## Avg_Canopy_Cover-Didelphis_virginiana 2.5012 0.8276 1.1737 2.4018
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8741 1.2751 1.0631 2.6367
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2181 1.0210 0.6481 2.0524
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1847 0.7335 0.9328 2.1015
## Avg_Canopy_Cover-Vulpes_vulpes 2.0998 1.0843 0.3404 1.9730
## Avg_Canopy_Cover-Sus_scrofa 2.0044 0.7836 0.6858 1.9265
## avg_veg_height-Odocoileus_virginianus -0.5457 0.6731 -1.9014 -0.5465
## avg_veg_height-Canis_latrans -0.6737 0.5578 -1.7751 -0.6554
## avg_veg_height-Sciurus_niger -0.6652 0.7364 -2.2435 -0.6376
## avg_veg_height-Procyon_lotor -0.4099 0.5377 -1.4468 -0.4127
## avg_veg_height-Dasypus_novemcinctus -0.3127 0.5326 -1.3058 -0.3262
## avg_veg_height-Lynx_rufus -0.5735 0.6710 -1.9228 -0.5623
## avg_veg_height-Didelphis_virginiana -0.5909 0.5796 -1.7704 -0.5785
## avg_veg_height-Sylvilagus_floridanus -0.6801 0.5906 -1.9166 -0.6611
## avg_veg_height-Meleagris_gallopavo -0.6017 0.6594 -1.9379 -0.5907
## avg_veg_height-Sciurus_carolinensis -0.2401 0.5954 -1.3099 -0.2616
## avg_veg_height-Vulpes_vulpes -0.5174 0.6620 -1.8189 -0.5166
## avg_veg_height-Sus_scrofa -0.6178 0.6182 -1.8967 -0.6118
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 17.2415 1.0044 351
## (Intercept)-Canis_latrans 3.6440 1.2004 184
## (Intercept)-Sciurus_niger 8.0758 1.0154 293
## (Intercept)-Procyon_lotor 2.6208 1.0160 2079
## (Intercept)-Dasypus_novemcinctus 0.1452 1.0149 1215
## (Intercept)-Lynx_rufus 8.3748 1.0175 264
## (Intercept)-Didelphis_virginiana -0.9885 1.0029 1583
## (Intercept)-Sylvilagus_floridanus 1.3804 1.0025 804
## (Intercept)-Meleagris_gallopavo 2.1932 1.0255 533
## (Intercept)-Sciurus_carolinensis -1.2032 1.0039 738
## (Intercept)-Vulpes_vulpes 5.4547 1.0068 170
## (Intercept)-Sus_scrofa -1.8131 1.0183 683
## Cogon_Patch_Size-Odocoileus_virginianus 2.2221 1.0041 2158
## Cogon_Patch_Size-Canis_latrans 3.6126 1.0069 791
## Cogon_Patch_Size-Sciurus_niger 1.5594 1.0106 673
## Cogon_Patch_Size-Procyon_lotor 0.4866 1.0203 1001
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3739 1.0165 1522
## Cogon_Patch_Size-Lynx_rufus 2.1117 1.0027 1060
## Cogon_Patch_Size-Didelphis_virginiana 2.7461 1.0055 810
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2173 1.0093 563
## Cogon_Patch_Size-Meleagris_gallopavo 1.8821 1.0163 1403
## Cogon_Patch_Size-Sciurus_carolinensis 0.0146 1.0015 861
## Cogon_Patch_Size-Vulpes_vulpes 1.8024 1.0029 786
## Cogon_Patch_Size-Sus_scrofa 0.7737 1.0052 1562
## Veg_shannon_index-Odocoileus_virginianus 2.4014 1.0056 1840
## Veg_shannon_index-Canis_latrans 2.6675 1.0074 1322
## Veg_shannon_index-Sciurus_niger 3.0747 1.0017 1219
## Veg_shannon_index-Procyon_lotor 2.4260 1.0079 980
## Veg_shannon_index-Dasypus_novemcinctus 1.6672 1.0080 1475
## Veg_shannon_index-Lynx_rufus 2.5267 1.0050 1326
## Veg_shannon_index-Didelphis_virginiana 2.5203 1.0019 2130
## Veg_shannon_index-Sylvilagus_floridanus 2.4456 1.0049 1598
## Veg_shannon_index-Meleagris_gallopavo 2.9121 1.0054 1664
## Veg_shannon_index-Sciurus_carolinensis 1.5187 1.0140 1031
## Veg_shannon_index-Vulpes_vulpes 1.9324 1.0088 1077
## Veg_shannon_index-Sus_scrofa 3.9827 1.0292 883
## total_shrub_cover-Odocoileus_virginianus 2.0782 1.0002 2541
## total_shrub_cover-Canis_latrans 1.9610 1.0137 1658
## total_shrub_cover-Sciurus_niger 1.2557 1.0065 1412
## total_shrub_cover-Procyon_lotor 0.0944 1.0030 1875
## total_shrub_cover-Dasypus_novemcinctus 1.2148 1.0002 3048
## total_shrub_cover-Lynx_rufus 0.8257 1.0026 777
## total_shrub_cover-Didelphis_virginiana 0.6758 1.0017 2105
## total_shrub_cover-Sylvilagus_floridanus 1.3440 1.0075 2142
## total_shrub_cover-Meleagris_gallopavo -0.0062 1.0126 589
## total_shrub_cover-Sciurus_carolinensis 1.4498 1.0007 3077
## total_shrub_cover-Vulpes_vulpes 1.1256 1.0040 1323
## total_shrub_cover-Sus_scrofa 2.1351 1.0084 2209
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.0502 1.0103 1167
## Avg_Cogongrass_Cover-Canis_latrans 4.2002 1.0257 911
## Avg_Cogongrass_Cover-Sciurus_niger 3.6510 1.0167 822
## Avg_Cogongrass_Cover-Procyon_lotor 3.9796 1.0148 750
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.4645 1.0204 733
## Avg_Cogongrass_Cover-Lynx_rufus 4.7379 1.0142 879
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7808 1.0114 1059
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1303 1.0181 979
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4033 1.0234 731
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2188 1.0126 702
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.6604 1.0073 683
## Avg_Cogongrass_Cover-Sus_scrofa 3.2248 1.0140 965
## Tree_Density-Odocoileus_virginianus 1.7994 1.0065 732
## Tree_Density-Canis_latrans -0.7462 1.0116 726
## Tree_Density-Sciurus_niger 0.8055 1.0000 1017
## Tree_Density-Procyon_lotor 0.0160 1.0011 1177
## Tree_Density-Dasypus_novemcinctus -1.1547 1.0518 571
## Tree_Density-Lynx_rufus 2.2323 1.0113 538
## Tree_Density-Didelphis_virginiana -0.6162 1.0264 1114
## Tree_Density-Sylvilagus_floridanus -0.4864 1.0204 833
## Tree_Density-Meleagris_gallopavo 0.1838 1.0025 1132
## Tree_Density-Sciurus_carolinensis -0.6372 1.0090 783
## Tree_Density-Vulpes_vulpes 0.7732 1.0101 853
## Tree_Density-Sus_scrofa -0.1168 1.0057 1076
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6129 1.0006 1850
## Avg_Canopy_Cover-Canis_latrans 1.9666 1.0058 1134
## Avg_Canopy_Cover-Sciurus_niger 4.6480 1.0227 973
## Avg_Canopy_Cover-Procyon_lotor 3.1174 1.0056 1609
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3243 1.0066 1120
## Avg_Canopy_Cover-Lynx_rufus 3.7243 1.0016 879
## Avg_Canopy_Cover-Didelphis_virginiana 4.3782 1.0109 823
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.8683 1.0185 890
## Avg_Canopy_Cover-Meleagris_gallopavo 4.7324 1.0141 1150
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8745 1.0140 1386
## Avg_Canopy_Cover-Vulpes_vulpes 4.6639 1.0172 1089
## Avg_Canopy_Cover-Sus_scrofa 3.7439 1.0021 1686
## avg_veg_height-Odocoileus_virginianus 0.7785 1.0079 1326
## avg_veg_height-Canis_latrans 0.3918 1.0113 1379
## avg_veg_height-Sciurus_niger 0.6887 1.0063 1217
## avg_veg_height-Procyon_lotor 0.6553 1.0098 1302
## avg_veg_height-Dasypus_novemcinctus 0.8199 1.0068 1051
## avg_veg_height-Lynx_rufus 0.7509 1.0044 1370
## avg_veg_height-Didelphis_virginiana 0.5092 1.0066 1345
## avg_veg_height-Sylvilagus_floridanus 0.4338 1.0073 1324
## avg_veg_height-Meleagris_gallopavo 0.6564 1.0075 1176
## avg_veg_height-Sciurus_carolinensis 1.0331 1.0064 1423
## avg_veg_height-Vulpes_vulpes 0.8046 1.0102 1082
## avg_veg_height-Sus_scrofa 0.5663 1.0074 1318
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5244 0.0800 0.3682 0.5237 0.6834
## (Intercept)-Canis_latrans -2.4779 0.2067 -2.9061 -2.4693 -2.0953
## (Intercept)-Sciurus_niger -4.5416 0.4763 -5.4602 -4.5425 -3.6132
## (Intercept)-Procyon_lotor -2.1583 0.1494 -2.4564 -2.1509 -1.8820
## (Intercept)-Dasypus_novemcinctus -1.4438 0.1580 -1.7581 -1.4431 -1.1350
## (Intercept)-Lynx_rufus -3.6416 0.3524 -4.3212 -3.6359 -2.9442
## (Intercept)-Didelphis_virginiana -2.0999 0.2630 -2.6527 -2.0911 -1.6161
## (Intercept)-Sylvilagus_floridanus -3.0841 0.3087 -3.7239 -3.0711 -2.5145
## (Intercept)-Meleagris_gallopavo -3.3381 0.3537 -4.0811 -3.3232 -2.6942
## (Intercept)-Sciurus_carolinensis -2.2499 0.2817 -2.8385 -2.2332 -1.7326
## (Intercept)-Vulpes_vulpes -4.2536 0.7205 -5.6776 -4.2246 -2.9592
## (Intercept)-Sus_scrofa -2.7560 0.4711 -3.7424 -2.7227 -1.9212
## week-Odocoileus_virginianus 1.2801 0.1193 1.0383 1.2786 1.5181
## week-Canis_latrans 0.5722 0.2600 0.0695 0.5722 1.0989
## week-Sciurus_niger -0.4334 0.5421 -1.6062 -0.3851 0.5253
## week-Procyon_lotor 0.1925 0.2112 -0.2095 0.1827 0.6111
## week-Dasypus_novemcinctus 0.0953 0.2266 -0.3482 0.0933 0.5305
## week-Lynx_rufus 0.3551 0.3507 -0.3263 0.3570 1.0247
## week-Didelphis_virginiana 0.0286 0.3764 -0.7352 0.0383 0.7467
## week-Sylvilagus_floridanus 0.0348 0.3423 -0.6580 0.0391 0.7077
## week-Meleagris_gallopavo -0.2289 0.4426 -1.1597 -0.2060 0.5756
## week-Sciurus_carolinensis 0.7863 0.3755 0.0813 0.7698 1.5542
## week-Vulpes_vulpes 0.1547 0.5253 -0.9331 0.1738 1.1081
## week-Sus_scrofa 0.6598 0.4462 -0.1897 0.6496 1.5791
## I(week^2)-Odocoileus_virginianus -0.5278 0.0496 -0.6269 -0.5276 -0.4302
## I(week^2)-Canis_latrans -0.2371 0.1084 -0.4494 -0.2356 -0.0255
## I(week^2)-Sciurus_niger -0.2801 0.2349 -0.8017 -0.2651 0.1471
## I(week^2)-Procyon_lotor -0.1288 0.0909 -0.3130 -0.1274 0.0507
## I(week^2)-Dasypus_novemcinctus -0.1759 0.1035 -0.3869 -0.1743 0.0248
## I(week^2)-Lynx_rufus -0.2374 0.1547 -0.5579 -0.2337 0.0598
## I(week^2)-Didelphis_virginiana -0.4156 0.2208 -0.9396 -0.3905 -0.0521
## I(week^2)-Sylvilagus_floridanus -0.1759 0.1584 -0.4934 -0.1723 0.1235
## I(week^2)-Meleagris_gallopavo -0.3912 0.2437 -0.9304 -0.3639 0.0161
## I(week^2)-Sciurus_carolinensis -0.2793 0.1458 -0.5708 -0.2769 -0.0023
## I(week^2)-Vulpes_vulpes -0.4031 0.2454 -0.9466 -0.3844 0.0217
## I(week^2)-Sus_scrofa -0.2288 0.1796 -0.5942 -0.2252 0.1197
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0113 1175
## (Intercept)-Sciurus_niger 1.0270 590
## (Intercept)-Procyon_lotor 1.0004 4909
## (Intercept)-Dasypus_novemcinctus 1.0017 5250
## (Intercept)-Lynx_rufus 1.0016 478
## (Intercept)-Didelphis_virginiana 1.0011 4100
## (Intercept)-Sylvilagus_floridanus 1.0002 1374
## (Intercept)-Meleagris_gallopavo 1.0068 1115
## (Intercept)-Sciurus_carolinensis 1.0008 3586
## (Intercept)-Vulpes_vulpes 1.0071 299
## (Intercept)-Sus_scrofa 1.0037 2489
## week-Odocoileus_virginianus 1.0002 4952
## week-Canis_latrans 1.0014 3760
## week-Sciurus_niger 1.0059 839
## week-Procyon_lotor 1.0014 4374
## week-Dasypus_novemcinctus 1.0036 4595
## week-Lynx_rufus 1.0000 2271
## week-Didelphis_virginiana 1.0012 2780
## week-Sylvilagus_floridanus 1.0015 2664
## week-Meleagris_gallopavo 1.0003 1175
## week-Sciurus_carolinensis 1.0014 3788
## week-Vulpes_vulpes 1.0132 1200
## week-Sus_scrofa 1.0019 4063
## I(week^2)-Odocoileus_virginianus 1.0000 4951
## I(week^2)-Canis_latrans 1.0056 4157
## I(week^2)-Sciurus_niger 1.0263 738
## I(week^2)-Procyon_lotor 1.0012 3940
## I(week^2)-Dasypus_novemcinctus 1.0032 4581
## I(week^2)-Lynx_rufus 1.0029 2232
## I(week^2)-Didelphis_virginiana 1.0082 1591
## I(week^2)-Sylvilagus_floridanus 1.0024 2513
## I(week^2)-Meleagris_gallopavo 1.0244 805
## I(week^2)-Sciurus_carolinensis 1.0007 4139
## I(week^2)-Vulpes_vulpes 1.0056 938
## I(week^2)-Sus_scrofa 1.0001 4368
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.925
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2701 0.5545 -1.3397 -0.2805 0.8858 1.0053 2200
## Avg_Cogongrass_Cover 0.0456 0.3079 -0.5911 0.0532 0.6225 1.0008 1584
## total_shrub_cover -0.4406 0.3029 -1.0855 -0.4318 0.1193 1.0020 2660
## avg_veg_height -0.0238 0.2818 -0.5815 -0.0266 0.5243 1.0058 1357
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4024 2.4853 0.6777 2.7324 9.8510 1.0268 1584
## Avg_Cogongrass_Cover 0.3793 0.4405 0.0437 0.2469 1.4978 1.0123 2270
## total_shrub_cover 0.5323 0.5697 0.0575 0.3606 2.0081 1.0097 1661
## avg_veg_height 0.2065 0.2011 0.0321 0.1452 0.7438 1.0049 3006
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0606 1.0168 0.0806 0.7687 3.8391 1.0063 426
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3977 0.4173 -3.2149 -2.4014 -1.5418 1.0051 3004
## week 0.2832 0.2298 -0.1859 0.2879 0.7287 1.0046 3111
## I(week^2) -0.2920 0.1030 -0.5078 -0.2883 -0.0995 1.0105 2206
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0785 1.1355 0.7839 1.7959 5.0209 1.0083 1913
## week 0.4583 0.3164 0.1206 0.3720 1.2941 1.0071 1841
## I(week^2) 0.0734 0.0535 0.0218 0.0587 0.2102 1.0247 1551
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4907 1.3886 1.0435 3.3697
## (Intercept)-Canis_latrans 0.3090 0.6989 -1.0332 0.3049
## (Intercept)-Sciurus_niger -0.5944 1.2008 -2.5060 -0.7298
## (Intercept)-Procyon_lotor 0.6264 0.7140 -0.8141 0.6251
## (Intercept)-Dasypus_novemcinctus -0.7363 0.6328 -2.0228 -0.7341
## (Intercept)-Lynx_rufus 0.0187 0.9679 -1.6944 -0.0479
## (Intercept)-Didelphis_virginiana -1.4243 0.7166 -2.8706 -1.4227
## (Intercept)-Sylvilagus_floridanus -0.2372 0.8523 -1.8049 -0.2827
## (Intercept)-Meleagris_gallopavo -0.7115 0.8361 -2.3379 -0.7196
## (Intercept)-Sciurus_carolinensis -1.5365 0.7275 -3.0229 -1.5150
## (Intercept)-Vulpes_vulpes -0.8626 1.3899 -3.2268 -1.0060
## (Intercept)-Sus_scrofa -1.9690 0.8939 -3.7647 -1.9631
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0475 0.5559 -1.0861 0.0444
## Avg_Cogongrass_Cover-Canis_latrans 0.3779 0.4478 -0.4647 0.3605
## Avg_Cogongrass_Cover-Sciurus_niger -0.3369 0.6417 -1.8243 -0.2680
## Avg_Cogongrass_Cover-Procyon_lotor 0.0428 0.4371 -0.8131 0.0398
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2360 0.3969 -0.5209 0.2285
## Avg_Cogongrass_Cover-Lynx_rufus 0.4056 0.5138 -0.5065 0.3670
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2905 0.4359 -0.5325 0.2794
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2989 0.5121 -1.4372 -0.2707
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4100 0.5769 -1.6860 -0.3605
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2139 0.4276 -0.6170 0.2017
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2215 0.5463 -0.8109 0.2013
## Avg_Cogongrass_Cover-Sus_scrofa -0.2480 0.5864 -1.5777 -0.1872
## total_shrub_cover-Odocoileus_virginianus -0.2728 0.5957 -1.4551 -0.2916
## total_shrub_cover-Canis_latrans 0.0789 0.4504 -0.7369 0.0554
## total_shrub_cover-Sciurus_niger -0.6825 0.5967 -2.0214 -0.6329
## total_shrub_cover-Procyon_lotor -0.9075 0.5103 -2.0366 -0.8504
## total_shrub_cover-Dasypus_novemcinctus -0.0881 0.3719 -0.7979 -0.0955
## total_shrub_cover-Lynx_rufus -0.8819 0.6318 -2.3457 -0.8090
## total_shrub_cover-Didelphis_virginiana -0.2856 0.4245 -1.1246 -0.2854
## total_shrub_cover-Sylvilagus_floridanus -0.4476 0.5403 -1.6169 -0.4146
## total_shrub_cover-Meleagris_gallopavo -1.1943 0.6558 -2.7294 -1.1084
## total_shrub_cover-Sciurus_carolinensis -0.1654 0.4245 -0.9923 -0.1663
## total_shrub_cover-Vulpes_vulpes -0.4917 0.6781 -1.9661 -0.4422
## total_shrub_cover-Sus_scrofa 0.0023 0.5475 -1.0210 -0.0285
## avg_veg_height-Odocoileus_virginianus -0.0291 0.4717 -0.9914 -0.0234
## avg_veg_height-Canis_latrans -0.0784 0.3913 -0.8691 -0.0732
## avg_veg_height-Sciurus_niger -0.1811 0.4913 -1.2138 -0.1583
## avg_veg_height-Procyon_lotor 0.0804 0.4012 -0.7013 0.0701
## avg_veg_height-Dasypus_novemcinctus 0.1621 0.3755 -0.5556 0.1585
## avg_veg_height-Lynx_rufus -0.0139 0.4738 -0.9413 -0.0207
## avg_veg_height-Didelphis_virginiana -0.0373 0.3937 -0.8156 -0.0336
## avg_veg_height-Sylvilagus_floridanus -0.1241 0.4190 -0.9936 -0.1069
## avg_veg_height-Meleagris_gallopavo -0.2102 0.4712 -1.2051 -0.1927
## avg_veg_height-Sciurus_carolinensis 0.2427 0.4233 -0.5397 0.2197
## avg_veg_height-Vulpes_vulpes -0.0701 0.4661 -1.0282 -0.0612
## avg_veg_height-Sus_scrofa -0.0368 0.4415 -0.9427 -0.0329
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5910 1.0213 1252
## (Intercept)-Canis_latrans 1.7499 1.0028 2196
## (Intercept)-Sciurus_niger 2.2199 1.0135 611
## (Intercept)-Procyon_lotor 2.0444 1.0006 2003
## (Intercept)-Dasypus_novemcinctus 0.4825 1.0012 3011
## (Intercept)-Lynx_rufus 2.1450 1.0131 1134
## (Intercept)-Didelphis_virginiana -0.0015 1.0028 2704
## (Intercept)-Sylvilagus_floridanus 1.6395 1.0039 1430
## (Intercept)-Meleagris_gallopavo 0.9701 1.0045 1994
## (Intercept)-Sciurus_carolinensis -0.1375 1.0021 2815
## (Intercept)-Vulpes_vulpes 2.2418 1.0102 401
## (Intercept)-Sus_scrofa -0.2156 1.0006 1820
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1569 1.0076 3478
## Avg_Cogongrass_Cover-Canis_latrans 1.3555 1.0088 2674
## Avg_Cogongrass_Cover-Sciurus_niger 0.7309 1.0064 1946
## Avg_Cogongrass_Cover-Procyon_lotor 0.9144 1.0017 3209
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0198 1.0008 2884
## Avg_Cogongrass_Cover-Lynx_rufus 1.5111 1.0020 2965
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1803 1.0066 3012
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6103 1.0005 1973
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5797 1.0055 2140
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0973 1.0014 2814
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3775 1.0038 2573
## Avg_Cogongrass_Cover-Sus_scrofa 0.7500 1.0017 2017
## total_shrub_cover-Odocoileus_virginianus 0.9693 1.0002 3606
## total_shrub_cover-Canis_latrans 1.0687 1.0051 3742
## total_shrub_cover-Sciurus_niger 0.4015 1.0016 2786
## total_shrub_cover-Procyon_lotor -0.0701 1.0060 2744
## total_shrub_cover-Dasypus_novemcinctus 0.6464 1.0019 4213
## total_shrub_cover-Lynx_rufus 0.1718 1.0011 2009
## total_shrub_cover-Didelphis_virginiana 0.5349 0.9999 4878
## total_shrub_cover-Sylvilagus_floridanus 0.5151 1.0063 2128
## total_shrub_cover-Meleagris_gallopavo -0.1453 1.0059 1837
## total_shrub_cover-Sciurus_carolinensis 0.6861 1.0004 4254
## total_shrub_cover-Vulpes_vulpes 0.7100 1.0044 1691
## total_shrub_cover-Sus_scrofa 1.1815 1.0012 3686
## avg_veg_height-Odocoileus_virginianus 0.8912 1.0056 2430
## avg_veg_height-Canis_latrans 0.6869 1.0027 2342
## avg_veg_height-Sciurus_niger 0.7259 1.0008 2311
## avg_veg_height-Procyon_lotor 0.9146 1.0005 2738
## avg_veg_height-Dasypus_novemcinctus 0.9544 1.0033 2747
## avg_veg_height-Lynx_rufus 0.9526 1.0009 2202
## avg_veg_height-Didelphis_virginiana 0.7253 1.0076 2732
## avg_veg_height-Sylvilagus_floridanus 0.6857 1.0064 2349
## avg_veg_height-Meleagris_gallopavo 0.6676 1.0021 2185
## avg_veg_height-Sciurus_carolinensis 1.1555 1.0025 2485
## avg_veg_height-Vulpes_vulpes 0.8496 1.0055 2145
## avg_veg_height-Sus_scrofa 0.8452 1.0005 2698
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5240 0.0787 0.3692 0.5237 0.6759
## (Intercept)-Canis_latrans -2.4550 0.1965 -2.8617 -2.4512 -2.0870
## (Intercept)-Sciurus_niger -3.9713 0.6000 -5.1858 -3.9601 -2.8701
## (Intercept)-Procyon_lotor -2.1694 0.1519 -2.4825 -2.1629 -1.8818
## (Intercept)-Dasypus_novemcinctus -1.4464 0.1605 -1.7753 -1.4445 -1.1425
## (Intercept)-Lynx_rufus -3.4259 0.3306 -4.0933 -3.4166 -2.8036
## (Intercept)-Didelphis_virginiana -2.1272 0.2733 -2.7008 -2.1199 -1.6164
## (Intercept)-Sylvilagus_floridanus -3.1546 0.3435 -3.8644 -3.1456 -2.5274
## (Intercept)-Meleagris_gallopavo -3.2140 0.3452 -3.9367 -3.2016 -2.5765
## (Intercept)-Sciurus_carolinensis -2.2729 0.2915 -2.8909 -2.2644 -1.7457
## (Intercept)-Vulpes_vulpes -4.0954 0.7796 -5.5636 -4.1048 -2.6314
## (Intercept)-Sus_scrofa -2.8491 0.5255 -4.0128 -2.8099 -1.9501
## week-Odocoileus_virginianus 1.2819 0.1220 1.0471 1.2804 1.5240
## week-Canis_latrans 0.5790 0.2621 0.0801 0.5697 1.1129
## week-Sciurus_niger -0.4966 0.5696 -1.7277 -0.4560 0.4909
## week-Procyon_lotor 0.1935 0.2115 -0.2247 0.1892 0.6240
## week-Dasypus_novemcinctus 0.0967 0.2268 -0.3564 0.0988 0.5307
## week-Lynx_rufus 0.3576 0.3472 -0.3409 0.3593 1.0384
## week-Didelphis_virginiana 0.0221 0.3762 -0.7547 0.0325 0.7328
## week-Sylvilagus_floridanus 0.0425 0.3400 -0.6235 0.0455 0.7016
## week-Meleagris_gallopavo -0.2470 0.4339 -1.1518 -0.2244 0.5518
## week-Sciurus_carolinensis 0.7880 0.3636 0.1027 0.7775 1.5417
## week-Vulpes_vulpes 0.1323 0.5202 -0.9497 0.1542 1.1273
## week-Sus_scrofa 0.6657 0.4609 -0.1962 0.6516 1.6323
## I(week^2)-Odocoileus_virginianus -0.5285 0.0505 -0.6295 -0.5282 -0.4323
## I(week^2)-Canis_latrans -0.2412 0.1073 -0.4589 -0.2408 -0.0341
## I(week^2)-Sciurus_niger -0.2974 0.2396 -0.7988 -0.2903 0.1565
## I(week^2)-Procyon_lotor -0.1307 0.0896 -0.3068 -0.1300 0.0465
## I(week^2)-Dasypus_novemcinctus -0.1779 0.1046 -0.3864 -0.1792 0.0283
## I(week^2)-Lynx_rufus -0.2348 0.1530 -0.5383 -0.2327 0.0545
## I(week^2)-Didelphis_virginiana -0.4094 0.2204 -0.9204 -0.3827 -0.0488
## I(week^2)-Sylvilagus_floridanus -0.1734 0.1563 -0.4909 -0.1701 0.1223
## I(week^2)-Meleagris_gallopavo -0.3962 0.2391 -0.9479 -0.3699 0.0020
## I(week^2)-Sciurus_carolinensis -0.2796 0.1452 -0.5749 -0.2729 -0.0097
## I(week^2)-Vulpes_vulpes -0.4143 0.2538 -0.9783 -0.3917 0.0205
## I(week^2)-Sus_scrofa -0.2373 0.1775 -0.5937 -0.2344 0.1066
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0017 2900
## (Intercept)-Sciurus_niger 1.0109 506
## (Intercept)-Procyon_lotor 1.0027 4063
## (Intercept)-Dasypus_novemcinctus 1.0023 5250
## (Intercept)-Lynx_rufus 1.0223 1097
## (Intercept)-Didelphis_virginiana 1.0015 4268
## (Intercept)-Sylvilagus_floridanus 1.0062 1329
## (Intercept)-Meleagris_gallopavo 1.0065 1559
## (Intercept)-Sciurus_carolinensis 1.0014 3693
## (Intercept)-Vulpes_vulpes 1.0057 385
## (Intercept)-Sus_scrofa 1.0054 1489
## week-Odocoileus_virginianus 1.0051 4801
## week-Canis_latrans 1.0001 3662
## week-Sciurus_niger 1.0155 990
## week-Procyon_lotor 1.0018 3982
## week-Dasypus_novemcinctus 0.9998 4601
## week-Lynx_rufus 1.0006 2772
## week-Didelphis_virginiana 1.0020 2613
## week-Sylvilagus_floridanus 1.0010 2930
## week-Meleagris_gallopavo 1.0086 1285
## week-Sciurus_carolinensis 1.0011 3834
## week-Vulpes_vulpes 1.0109 1717
## week-Sus_scrofa 1.0031 3972
## I(week^2)-Odocoileus_virginianus 1.0032 4892
## I(week^2)-Canis_latrans 1.0021 3732
## I(week^2)-Sciurus_niger 1.0096 1168
## I(week^2)-Procyon_lotor 1.0010 4192
## I(week^2)-Dasypus_novemcinctus 0.9998 4518
## I(week^2)-Lynx_rufus 1.0045 2090
## I(week^2)-Didelphis_virginiana 1.0109 1551
## I(week^2)-Sylvilagus_floridanus 1.0000 2600
## I(week^2)-Meleagris_gallopavo 1.0184 956
## I(week^2)-Sciurus_carolinensis 1.0020 4756
## I(week^2)-Vulpes_vulpes 1.0027 1143
## I(week^2)-Sus_scrofa 1.0001 4393
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9537
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2601 0.6704 -1.5515 -0.2925 1.1863 1.0059 2043
## Tree_Density -0.7398 0.3554 -1.5508 -0.7116 -0.1088 1.0040 1304
## Avg_Canopy_Cover 1.0516 0.3144 0.4668 1.0412 1.7253 1.0039 1799
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.3267 4.1863 1.1964 4.2300 16.1098 1.0044 880
## Tree_Density 0.5023 0.6937 0.0426 0.2675 2.4308 1.0162 1186
## Avg_Canopy_Cover 0.5106 0.5607 0.0547 0.3455 2.0293 1.0118 1831
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5132 0.6087 0.0471 0.3154 2.1692 1.0317 436
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3941 0.4128 -3.1943 -2.3983 -1.5342 1.0004 4453
## week 0.2764 0.2391 -0.2181 0.2868 0.7176 1.0050 2776
## I(week^2) -0.2900 0.0998 -0.4987 -0.2855 -0.1024 1.0023 1945
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1162 1.1978 0.7957 1.8252 5.1374 1.0078 1740
## week 0.4567 0.3448 0.1178 0.3639 1.3044 1.0133 1782
## I(week^2) 0.0714 0.0509 0.0226 0.0582 0.1963 1.0137 1688
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.3771 1.6264 1.8868 4.1268 8.2715
## (Intercept)-Canis_latrans 0.3280 0.6639 -0.9000 0.2919 1.7404
## (Intercept)-Sciurus_niger -0.1384 1.3651 -2.2760 -0.3102 3.1264
## (Intercept)-Procyon_lotor 0.7160 0.6314 -0.5541 0.7093 1.9389
## (Intercept)-Dasypus_novemcinctus -0.9813 0.6059 -2.1972 -0.9540 0.1659
## (Intercept)-Lynx_rufus 1.1235 1.7591 -1.2818 0.7317 5.5399
## (Intercept)-Didelphis_virginiana -1.8991 0.6809 -3.3123 -1.8799 -0.6513
## (Intercept)-Sylvilagus_floridanus -0.6432 0.7567 -2.0268 -0.6614 0.8372
## (Intercept)-Meleagris_gallopavo -0.4396 0.8075 -1.8776 -0.4899 1.2862
## (Intercept)-Sciurus_carolinensis -1.9502 0.7103 -3.4317 -1.9042 -0.6559
## (Intercept)-Vulpes_vulpes -1.3686 1.5012 -3.7011 -1.5964 2.4954
## (Intercept)-Sus_scrofa -2.6464 0.9301 -4.5566 -2.6056 -0.9367
## Tree_Density-Odocoileus_virginianus -0.4172 0.5721 -1.4487 -0.4644 0.9066
## Tree_Density-Canis_latrans -0.8382 0.4950 -1.9566 -0.7907 -0.0021
## Tree_Density-Sciurus_niger -0.7476 0.6591 -2.1950 -0.7144 0.4886
## Tree_Density-Procyon_lotor -0.4894 0.3862 -1.2782 -0.4859 0.2735
## Tree_Density-Dasypus_novemcinctus -1.1823 0.7361 -3.0517 -1.0374 -0.1505
## Tree_Density-Lynx_rufus -0.1383 0.6508 -1.2450 -0.2035 1.4029
## Tree_Density-Didelphis_virginiana -0.9326 0.6391 -2.4914 -0.8436 0.0608
## Tree_Density-Sylvilagus_floridanus -0.9555 0.6450 -2.4646 -0.8598 0.0183
## Tree_Density-Meleagris_gallopavo -0.8290 0.6098 -2.2395 -0.7643 0.1777
## Tree_Density-Sciurus_carolinensis -0.8727 0.6225 -2.3362 -0.7948 0.1530
## Tree_Density-Vulpes_vulpes -0.6750 0.6688 -2.1047 -0.6383 0.5574
## Tree_Density-Sus_scrofa -0.8861 0.7011 -2.5916 -0.7964 0.2305
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8336 0.6360 -0.4221 0.8403 2.1160
## Avg_Canopy_Cover-Canis_latrans 0.1868 0.4746 -0.7527 0.1879 1.0987
## Avg_Canopy_Cover-Sciurus_niger 1.0269 0.7360 -0.2673 0.9754 2.6190
## Avg_Canopy_Cover-Procyon_lotor 1.0451 0.4474 0.2524 1.0120 2.0079
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0244 0.4046 0.2774 0.9998 1.9123
## Avg_Canopy_Cover-Lynx_rufus 0.9658 0.6893 -0.2843 0.9342 2.4390
## Avg_Canopy_Cover-Didelphis_virginiana 1.2653 0.4761 0.4111 1.2309 2.3204
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6291 0.7028 0.6074 1.5122 3.2832
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3535 0.5952 0.3680 1.2855 2.7139
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2348 0.4697 0.4225 1.1951 2.2563
## Avg_Canopy_Cover-Vulpes_vulpes 1.0791 0.5707 0.0321 1.0490 2.3086
## Avg_Canopy_Cover-Sus_scrofa 1.2485 0.5154 0.3406 1.2131 2.3797
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0069 981
## (Intercept)-Canis_latrans 1.0019 2326
## (Intercept)-Sciurus_niger 1.0085 401
## (Intercept)-Procyon_lotor 1.0062 2778
## (Intercept)-Dasypus_novemcinctus 1.0003 3071
## (Intercept)-Lynx_rufus 1.0236 358
## (Intercept)-Didelphis_virginiana 1.0022 2691
## (Intercept)-Sylvilagus_floridanus 1.0050 2182
## (Intercept)-Meleagris_gallopavo 1.0000 1880
## (Intercept)-Sciurus_carolinensis 1.0080 2813
## (Intercept)-Vulpes_vulpes 1.0023 296
## (Intercept)-Sus_scrofa 1.0049 1964
## Tree_Density-Odocoileus_virginianus 1.0090 2260
## Tree_Density-Canis_latrans 1.0002 2713
## Tree_Density-Sciurus_niger 1.0006 1885
## Tree_Density-Procyon_lotor 1.0012 4016
## Tree_Density-Dasypus_novemcinctus 1.0049 1412
## Tree_Density-Lynx_rufus 1.0237 740
## Tree_Density-Didelphis_virginiana 1.0001 1807
## Tree_Density-Sylvilagus_floridanus 1.0024 2154
## Tree_Density-Meleagris_gallopavo 1.0035 1998
## Tree_Density-Sciurus_carolinensis 1.0006 2084
## Tree_Density-Vulpes_vulpes 1.0046 2112
## Tree_Density-Sus_scrofa 1.0010 2074
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0048 3047
## Avg_Canopy_Cover-Canis_latrans 1.0073 2820
## Avg_Canopy_Cover-Sciurus_niger 1.0061 1610
## Avg_Canopy_Cover-Procyon_lotor 1.0015 3083
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0007 3499
## Avg_Canopy_Cover-Lynx_rufus 1.0004 1662
## Avg_Canopy_Cover-Didelphis_virginiana 1.0025 3036
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0066 1702
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0152 1678
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0072 3566
## Avg_Canopy_Cover-Vulpes_vulpes 1.0025 2801
## Avg_Canopy_Cover-Sus_scrofa 1.0054 3305
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5255 0.0793 0.3745 0.5249 0.6857
## (Intercept)-Canis_latrans -2.4642 0.1993 -2.8781 -2.4588 -2.0936
## (Intercept)-Sciurus_niger -4.1707 0.6258 -5.3743 -4.1761 -2.9769
## (Intercept)-Procyon_lotor -2.1606 0.1517 -2.4640 -2.1575 -1.8664
## (Intercept)-Dasypus_novemcinctus -1.4422 0.1574 -1.7559 -1.4403 -1.1386
## (Intercept)-Lynx_rufus -3.6080 0.3676 -4.3124 -3.6151 -2.8928
## (Intercept)-Didelphis_virginiana -2.1156 0.2701 -2.6734 -2.1041 -1.6142
## (Intercept)-Sylvilagus_floridanus -3.0353 0.3053 -3.6618 -3.0257 -2.4745
## (Intercept)-Meleagris_gallopavo -3.3222 0.3545 -4.0574 -3.3042 -2.6774
## (Intercept)-Sciurus_carolinensis -2.2575 0.2805 -2.8519 -2.2440 -1.7303
## (Intercept)-Vulpes_vulpes -3.8940 0.7381 -5.4729 -3.8431 -2.6125
## (Intercept)-Sus_scrofa -2.7582 0.4856 -3.7944 -2.7292 -1.9074
## week-Odocoileus_virginianus 1.2819 0.1232 1.0402 1.2821 1.5252
## week-Canis_latrans 0.5754 0.2636 0.0729 0.5732 1.1032
## week-Sciurus_niger -0.4852 0.5577 -1.6825 -0.4449 0.5016
## week-Procyon_lotor 0.1885 0.2102 -0.2159 0.1847 0.5976
## week-Dasypus_novemcinctus 0.0951 0.2237 -0.3358 0.0902 0.5411
## week-Lynx_rufus 0.3629 0.3512 -0.3325 0.3674 1.0447
## week-Didelphis_virginiana 0.0363 0.3729 -0.7295 0.0463 0.7312
## week-Sylvilagus_floridanus 0.0407 0.3398 -0.6331 0.0404 0.6998
## week-Meleagris_gallopavo -0.2606 0.4375 -1.1968 -0.2373 0.5382
## week-Sciurus_carolinensis 0.7785 0.3669 0.0701 0.7658 1.5350
## week-Vulpes_vulpes 0.1418 0.5174 -0.9220 0.1587 1.1256
## week-Sus_scrofa 0.6589 0.4586 -0.2040 0.6514 1.6134
## I(week^2)-Odocoileus_virginianus -0.5284 0.0505 -0.6280 -0.5284 -0.4302
## I(week^2)-Canis_latrans -0.2396 0.1080 -0.4552 -0.2404 -0.0293
## I(week^2)-Sciurus_niger -0.2899 0.2342 -0.8012 -0.2753 0.1468
## I(week^2)-Procyon_lotor -0.1265 0.0907 -0.3041 -0.1266 0.0481
## I(week^2)-Dasypus_novemcinctus -0.1749 0.1029 -0.3844 -0.1733 0.0176
## I(week^2)-Lynx_rufus -0.2372 0.1518 -0.5509 -0.2343 0.0443
## I(week^2)-Didelphis_virginiana -0.4077 0.2101 -0.8926 -0.3851 -0.0500
## I(week^2)-Sylvilagus_floridanus -0.1717 0.1599 -0.4965 -0.1692 0.1361
## I(week^2)-Meleagris_gallopavo -0.3971 0.2431 -0.9499 -0.3736 0.0106
## I(week^2)-Sciurus_carolinensis -0.2776 0.1440 -0.5672 -0.2743 -0.0039
## I(week^2)-Vulpes_vulpes -0.4064 0.2462 -0.9513 -0.3878 0.0199
## I(week^2)-Sus_scrofa -0.2333 0.1801 -0.6017 -0.2293 0.1182
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5154
## (Intercept)-Canis_latrans 1.0006 2956
## (Intercept)-Sciurus_niger 1.0013 451
## (Intercept)-Procyon_lotor 1.0016 4351
## (Intercept)-Dasypus_novemcinctus 1.0033 5250
## (Intercept)-Lynx_rufus 1.0106 671
## (Intercept)-Didelphis_virginiana 1.0029 4171
## (Intercept)-Sylvilagus_floridanus 1.0033 2374
## (Intercept)-Meleagris_gallopavo 1.0008 1574
## (Intercept)-Sciurus_carolinensis 1.0008 4020
## (Intercept)-Vulpes_vulpes 1.0007 417
## (Intercept)-Sus_scrofa 1.0020 2176
## week-Odocoileus_virginianus 1.0008 5250
## week-Canis_latrans 1.0028 3685
## week-Sciurus_niger 1.0143 888
## week-Procyon_lotor 1.0005 4356
## week-Dasypus_novemcinctus 1.0004 4667
## week-Lynx_rufus 1.0002 2515
## week-Didelphis_virginiana 1.0003 2878
## week-Sylvilagus_floridanus 1.0060 3146
## week-Meleagris_gallopavo 1.0102 1123
## week-Sciurus_carolinensis 1.0042 3890
## week-Vulpes_vulpes 1.0054 1591
## week-Sus_scrofa 1.0026 3933
## I(week^2)-Odocoileus_virginianus 1.0004 4809
## I(week^2)-Canis_latrans 1.0072 3626
## I(week^2)-Sciurus_niger 1.0056 1269
## I(week^2)-Procyon_lotor 1.0001 4249
## I(week^2)-Dasypus_novemcinctus 1.0006 4506
## I(week^2)-Lynx_rufus 1.0022 2204
## I(week^2)-Didelphis_virginiana 1.0033 1870
## I(week^2)-Sylvilagus_floridanus 1.0027 2586
## I(week^2)-Meleagris_gallopavo 1.0039 814
## I(week^2)-Sciurus_carolinensis 1.0037 3917
## I(week^2)-Vulpes_vulpes 1.0087 1291
## I(week^2)-Sus_scrofa 1.0020 4288
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Sampled: 1000 of 10000, 10.00%
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## Sampled: 4000 of 10000, 40.00%
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## Sampled: 4500 of 10000, 45.00%
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## Sampled: 5000 of 10000, 50.00%
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## Sampled: 5500 of 10000, 55.00%
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## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7000 of 10000, 70.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
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## Sampled: 8500 of 10000, 85.00%
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## Sampled: 9000 of 10000, 90.00%
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## Sampled: 9500 of 10000, 95.00%
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## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
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## Sampling ...
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## Sampled: 4000 of 10000, 40.00%
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## Sampled: 4500 of 10000, 45.00%
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## Sampled: 5000 of 10000, 50.00%
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## Sampled: 5500 of 10000, 55.00%
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## Sampled: 6000 of 10000, 60.00%
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## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7000 of 10000, 70.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
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## Sampled: 8500 of 10000, 85.00%
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## Sampled: 9000 of 10000, 90.00%
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## Sampled: 9500 of 10000, 95.00%
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## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Sampled: 1000 of 10000, 10.00%
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## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
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## Sampled: 3500 of 10000, 35.00%
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## Sampled: 4000 of 10000, 40.00%
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## Sampled: 4500 of 10000, 45.00%
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## Sampled: 5000 of 10000, 50.00%
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## Sampled: 5500 of 10000, 55.00%
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## Sampled: 6000 of 10000, 60.00%
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## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7000 of 10000, 70.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
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## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9335
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3037 0.6088 -1.4630 -0.3223 0.9581 1.0100 1285
## Cogon_Patch_Size -0.1655 0.3588 -0.9327 -0.1500 0.5106 1.0091 2068
## Avg_Cogongrass_Cover 0.0992 0.2861 -0.4785 0.1047 0.6571 1.0056 1790
## total_shrub_cover -0.4267 0.3088 -1.1040 -0.4161 0.1415 1.0041 2289
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8978 3.7989 0.7283 2.9628 12.2111 1.0435 720
## Cogon_Patch_Size 0.7063 0.8782 0.0589 0.4428 2.9726 1.0089 1289
## Avg_Cogongrass_Cover 0.3666 0.4440 0.0424 0.2329 1.4719 1.0122 2253
## total_shrub_cover 0.5368 0.5999 0.0581 0.3575 2.2095 1.0026 1401
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2988 1.1773 0.0908 0.9835 4.4176 1.0019 397
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3971 0.4238 -3.2154 -2.4032 -1.5481 1.0024 4561
## week 0.2906 0.2354 -0.2026 0.2964 0.7421 1.0010 3300
## I(week^2) -0.2934 0.1006 -0.5007 -0.2916 -0.1033 1.0007 2018
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1011 1.2174 0.7744 1.8020 5.1120 1.0031 2498
## week 0.4632 0.3465 0.1176 0.3707 1.3265 1.0055 2094
## I(week^2) 0.0724 0.0633 0.0225 0.0572 0.2093 1.0524 1039
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6423 1.6154 0.9451 3.4537
## (Intercept)-Canis_latrans 0.3739 0.7277 -1.0092 0.3579
## (Intercept)-Sciurus_niger -0.6787 1.2080 -2.7169 -0.7759
## (Intercept)-Procyon_lotor 0.5944 0.7579 -0.9783 0.6047
## (Intercept)-Dasypus_novemcinctus -0.7829 0.6673 -2.1924 -0.7545
## (Intercept)-Lynx_rufus -0.0089 1.2735 -1.8765 -0.1190
## (Intercept)-Didelphis_virginiana -1.4322 0.7435 -2.9731 -1.4236
## (Intercept)-Sylvilagus_floridanus -0.3323 0.9339 -2.0341 -0.3952
## (Intercept)-Meleagris_gallopavo -0.7262 0.8665 -2.4680 -0.7422
## (Intercept)-Sciurus_carolinensis -1.6186 0.7817 -3.2854 -1.5865
## (Intercept)-Vulpes_vulpes -0.8859 1.5960 -3.4510 -1.0790
## (Intercept)-Sus_scrofa -2.1020 0.9640 -4.1200 -2.0841
## Cogon_Patch_Size-Odocoileus_virginianus -0.0339 0.6591 -1.2676 -0.0720
## Cogon_Patch_Size-Canis_latrans 0.6513 0.6472 -0.3116 0.5494
## Cogon_Patch_Size-Sciurus_niger -0.4894 0.8363 -2.3681 -0.4014
## Cogon_Patch_Size-Procyon_lotor -0.1943 0.4610 -1.0773 -0.1988
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1005 0.4108 -0.9621 -0.0867
## Cogon_Patch_Size-Lynx_rufus -0.1735 0.7233 -1.5872 -0.1899
## Cogon_Patch_Size-Didelphis_virginiana 0.6029 0.4901 -0.2798 0.5711
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7396 0.7827 -2.6070 -0.6136
## Cogon_Patch_Size-Meleagris_gallopavo -0.0859 0.5921 -1.2878 -0.0867
## Cogon_Patch_Size-Sciurus_carolinensis -0.6488 0.6704 -2.3294 -0.5440
## Cogon_Patch_Size-Vulpes_vulpes -0.4519 0.8366 -2.3445 -0.3696
## Cogon_Patch_Size-Sus_scrofa -0.3884 0.7237 -2.0812 -0.2969
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1006 0.5616 -0.9811 0.0987
## Avg_Cogongrass_Cover-Canis_latrans 0.2501 0.4011 -0.5043 0.2319
## Avg_Cogongrass_Cover-Sciurus_niger -0.2331 0.6176 -1.6559 -0.1800
## Avg_Cogongrass_Cover-Procyon_lotor 0.1610 0.4291 -0.6507 0.1502
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3446 0.3705 -0.3694 0.3277
## Avg_Cogongrass_Cover-Lynx_rufus 0.4587 0.5173 -0.4123 0.4063
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1835 0.4058 -0.6546 0.1873
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1837 0.4827 -1.1939 -0.1593
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3849 0.5888 -1.7154 -0.3306
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4229 0.4136 -0.3363 0.3981
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2407 0.4968 -0.6902 0.2246
## Avg_Cogongrass_Cover-Sus_scrofa -0.1911 0.5796 -1.5022 -0.1211
## total_shrub_cover-Odocoileus_virginianus -0.2578 0.5964 -1.4127 -0.2818
## total_shrub_cover-Canis_latrans 0.0454 0.4710 -0.8013 0.0099
## total_shrub_cover-Sciurus_niger -0.6545 0.6003 -2.0280 -0.6040
## total_shrub_cover-Procyon_lotor -0.8925 0.5057 -2.0124 -0.8398
## total_shrub_cover-Dasypus_novemcinctus -0.0990 0.3807 -0.8213 -0.1033
## total_shrub_cover-Lynx_rufus -0.8285 0.6472 -2.3076 -0.7584
## total_shrub_cover-Didelphis_virginiana -0.3786 0.4390 -1.2639 -0.3724
## total_shrub_cover-Sylvilagus_floridanus -0.4026 0.5747 -1.6366 -0.3822
## total_shrub_cover-Meleagris_gallopavo -1.1693 0.6615 -2.7130 -1.0804
## total_shrub_cover-Sciurus_carolinensis -0.1413 0.4489 -1.0103 -0.1507
## total_shrub_cover-Vulpes_vulpes -0.4845 0.6904 -2.0001 -0.4354
## total_shrub_cover-Sus_scrofa 0.0116 0.5705 -1.0162 -0.0347
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4487 1.0140 852
## (Intercept)-Canis_latrans 1.8902 1.0000 2498
## (Intercept)-Sciurus_niger 2.0169 1.0067 648
## (Intercept)-Procyon_lotor 2.0630 1.0014 2206
## (Intercept)-Dasypus_novemcinctus 0.4670 1.0021 2930
## (Intercept)-Lynx_rufus 2.3888 1.1470 324
## (Intercept)-Didelphis_virginiana 0.0243 1.0182 2749
## (Intercept)-Sylvilagus_floridanus 1.7555 1.0056 1251
## (Intercept)-Meleagris_gallopavo 1.0169 1.0015 1868
## (Intercept)-Sciurus_carolinensis -0.1540 1.0001 1762
## (Intercept)-Vulpes_vulpes 2.8852 1.0624 314
## (Intercept)-Sus_scrofa -0.2726 1.0020 1497
## Cogon_Patch_Size-Odocoileus_virginianus 1.3797 1.0040 3431
## Cogon_Patch_Size-Canis_latrans 2.1862 1.0072 2633
## Cogon_Patch_Size-Sciurus_niger 0.9586 1.0026 1741
## Cogon_Patch_Size-Procyon_lotor 0.7104 1.0051 3282
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6480 1.0052 4345
## Cogon_Patch_Size-Lynx_rufus 1.3328 1.0070 2009
## Cogon_Patch_Size-Didelphis_virginiana 1.6812 1.0012 2540
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4346 1.0227 1571
## Cogon_Patch_Size-Meleagris_gallopavo 1.1481 1.0013 3131
## Cogon_Patch_Size-Sciurus_carolinensis 0.3443 1.0044 1991
## Cogon_Patch_Size-Vulpes_vulpes 0.9838 1.0087 1783
## Cogon_Patch_Size-Sus_scrofa 0.7758 1.0054 2374
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2586 1.0079 3113
## Avg_Cogongrass_Cover-Canis_latrans 1.0955 1.0048 3574
## Avg_Cogongrass_Cover-Sciurus_niger 0.8349 1.0016 2129
## Avg_Cogongrass_Cover-Procyon_lotor 1.0482 1.0044 3421
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1251 0.9998 3894
## Avg_Cogongrass_Cover-Lynx_rufus 1.6525 1.0082 2306
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9756 1.0059 3281
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6987 1.0042 2951
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6168 1.0014 2101
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2963 1.0035 2772
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2663 1.0053 2829
## Avg_Cogongrass_Cover-Sus_scrofa 0.7798 1.0015 2507
## total_shrub_cover-Odocoileus_virginianus 1.0168 1.0013 3502
## total_shrub_cover-Canis_latrans 1.0844 1.0007 3150
## total_shrub_cover-Sciurus_niger 0.4024 1.0002 2669
## total_shrub_cover-Procyon_lotor -0.0458 1.0018 2062
## total_shrub_cover-Dasypus_novemcinctus 0.6761 1.0010 4254
## total_shrub_cover-Lynx_rufus 0.2610 1.0032 1768
## total_shrub_cover-Didelphis_virginiana 0.4872 1.0004 4116
## total_shrub_cover-Sylvilagus_floridanus 0.6662 1.0040 2387
## total_shrub_cover-Meleagris_gallopavo -0.1236 1.0020 1739
## total_shrub_cover-Sciurus_carolinensis 0.7753 1.0039 3465
## total_shrub_cover-Vulpes_vulpes 0.7752 1.0079 1988
## total_shrub_cover-Sus_scrofa 1.2632 1.0033 2914
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5249 0.0802 0.3669 0.5249 0.6790
## (Intercept)-Canis_latrans -2.4402 0.1939 -2.8270 -2.4327 -2.0858
## (Intercept)-Sciurus_niger -3.9648 0.5903 -5.1300 -3.9570 -2.8469
## (Intercept)-Procyon_lotor -2.1678 0.1502 -2.4624 -2.1646 -1.8806
## (Intercept)-Dasypus_novemcinctus -1.4472 0.1583 -1.7619 -1.4463 -1.1465
## (Intercept)-Lynx_rufus -3.4020 0.3323 -4.0796 -3.3913 -2.7723
## (Intercept)-Didelphis_virginiana -2.1189 0.2770 -2.6950 -2.1046 -1.6174
## (Intercept)-Sylvilagus_floridanus -3.1841 0.3447 -3.9110 -3.1672 -2.5574
## (Intercept)-Meleagris_gallopavo -3.2291 0.3617 -3.9853 -3.2054 -2.5722
## (Intercept)-Sciurus_carolinensis -2.2659 0.2908 -2.8516 -2.2542 -1.7270
## (Intercept)-Vulpes_vulpes -4.0802 0.7776 -5.5700 -4.0660 -2.6489
## (Intercept)-Sus_scrofa -2.8382 0.5271 -4.0061 -2.7935 -1.9216
## week-Odocoileus_virginianus 1.2817 0.1229 1.0439 1.2801 1.5197
## week-Canis_latrans 0.5775 0.2696 0.0696 0.5727 1.1384
## week-Sciurus_niger -0.4826 0.5333 -1.6074 -0.4503 0.4694
## week-Procyon_lotor 0.1958 0.2086 -0.2046 0.1951 0.6082
## week-Dasypus_novemcinctus 0.0902 0.2249 -0.3508 0.0904 0.5326
## week-Lynx_rufus 0.3708 0.3589 -0.3386 0.3704 1.0885
## week-Didelphis_virginiana 0.0345 0.3784 -0.7206 0.0417 0.7555
## week-Sylvilagus_floridanus 0.0307 0.3504 -0.6602 0.0314 0.7091
## week-Meleagris_gallopavo -0.2648 0.4322 -1.1575 -0.2416 0.5254
## week-Sciurus_carolinensis 0.7964 0.3722 0.1035 0.7823 1.5783
## week-Vulpes_vulpes 0.1728 0.5251 -0.9155 0.1925 1.1658
## week-Sus_scrofa 0.6719 0.4481 -0.1728 0.6633 1.5908
## I(week^2)-Odocoileus_virginianus -0.5283 0.0506 -0.6282 -0.5279 -0.4312
## I(week^2)-Canis_latrans -0.2415 0.1084 -0.4593 -0.2373 -0.0392
## I(week^2)-Sciurus_niger -0.2913 0.2359 -0.8023 -0.2796 0.1282
## I(week^2)-Procyon_lotor -0.1302 0.0897 -0.3088 -0.1284 0.0451
## I(week^2)-Dasypus_novemcinctus -0.1728 0.1051 -0.3847 -0.1722 0.0293
## I(week^2)-Lynx_rufus -0.2414 0.1566 -0.5603 -0.2356 0.0513
## I(week^2)-Didelphis_virginiana -0.4112 0.2112 -0.8834 -0.3921 -0.0503
## I(week^2)-Sylvilagus_floridanus -0.1808 0.1589 -0.5073 -0.1780 0.1263
## I(week^2)-Meleagris_gallopavo -0.4089 0.2674 -0.9896 -0.3781 0.0152
## I(week^2)-Sciurus_carolinensis -0.2830 0.1464 -0.5745 -0.2790 -0.0122
## I(week^2)-Vulpes_vulpes -0.4099 0.2500 -0.9801 -0.3870 0.0245
## I(week^2)-Sus_scrofa -0.2423 0.1791 -0.5975 -0.2368 0.1043
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0002 3242
## (Intercept)-Sciurus_niger 1.0049 609
## (Intercept)-Procyon_lotor 1.0028 4222
## (Intercept)-Dasypus_novemcinctus 1.0022 4930
## (Intercept)-Lynx_rufus 1.0330 1334
## (Intercept)-Didelphis_virginiana 1.0019 4153
## (Intercept)-Sylvilagus_floridanus 1.0095 1135
## (Intercept)-Meleagris_gallopavo 1.0029 1455
## (Intercept)-Sciurus_carolinensis 1.0005 3545
## (Intercept)-Vulpes_vulpes 1.0287 303
## (Intercept)-Sus_scrofa 1.0042 1790
## week-Odocoileus_virginianus 1.0027 4996
## week-Canis_latrans 1.0001 3875
## week-Sciurus_niger 1.0042 1074
## week-Procyon_lotor 1.0002 4273
## week-Dasypus_novemcinctus 1.0013 5250
## week-Lynx_rufus 1.0031 2924
## week-Didelphis_virginiana 1.0012 3052
## week-Sylvilagus_floridanus 1.0005 2847
## week-Meleagris_gallopavo 1.0027 1081
## week-Sciurus_carolinensis 1.0007 3714
## week-Vulpes_vulpes 1.0033 1809
## week-Sus_scrofa 1.0014 3811
## I(week^2)-Odocoileus_virginianus 1.0038 5250
## I(week^2)-Canis_latrans 1.0021 3815
## I(week^2)-Sciurus_niger 1.0071 1364
## I(week^2)-Procyon_lotor 1.0030 4160
## I(week^2)-Dasypus_novemcinctus 1.0015 4489
## I(week^2)-Lynx_rufus 1.0049 2405
## I(week^2)-Didelphis_virginiana 1.0033 1614
## I(week^2)-Sylvilagus_floridanus 1.0011 2265
## I(week^2)-Meleagris_gallopavo 1.0133 648
## I(week^2)-Sciurus_carolinensis 1.0002 3631
## I(week^2)-Vulpes_vulpes 1.0079 1087
## I(week^2)-Sus_scrofa 1.0011 4133
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9255
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2775 0.5663 -1.3774 -0.2901 0.8907 1.0034 2146
## Veg_shannon_index 0.3898 0.2508 -0.0663 0.3756 0.9107 1.0073 1848
## Avg_Cogongrass_Cover 0.2403 0.2598 -0.2720 0.2401 0.7492 1.0010 1840
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3288 2.6981 0.6782 2.6266 10.4274 1.0155 1296
## Veg_shannon_index 0.2635 0.2874 0.0383 0.1797 0.9897 1.0225 2179
## Avg_Cogongrass_Cover 0.3399 0.3961 0.0404 0.2184 1.2528 1.0060 2039
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9056 0.8882 0.0671 0.6453 3.2511 1.0212 397
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4003 0.4278 -3.2199 -2.4045 -1.5387 1.0021 4656
## week 0.2899 0.2314 -0.1886 0.2935 0.7233 1.0014 3484
## I(week^2) -0.2901 0.1005 -0.4992 -0.2861 -0.0936 1.0070 1947
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1009 1.1956 0.7756 1.8101 5.1973 1.0062 2255
## week 0.4591 0.3428 0.1170 0.3643 1.3575 1.0068 1430
## I(week^2) 0.0711 0.0520 0.0215 0.0579 0.1998 1.0230 2056
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4037 1.3892 1.0201 3.2564
## (Intercept)-Canis_latrans 0.2283 0.6513 -1.0574 0.2359
## (Intercept)-Sciurus_niger -0.5186 1.2133 -2.3764 -0.6825
## (Intercept)-Procyon_lotor 0.5011 0.6503 -0.8375 0.5218
## (Intercept)-Dasypus_novemcinctus -0.7395 0.6016 -1.9868 -0.7198
## (Intercept)-Lynx_rufus -0.0030 1.0026 -1.7435 -0.0863
## (Intercept)-Didelphis_virginiana -1.4547 0.6677 -2.8010 -1.4325
## (Intercept)-Sylvilagus_floridanus -0.3042 0.8906 -1.8509 -0.3643
## (Intercept)-Meleagris_gallopavo -0.3968 0.8878 -2.0078 -0.4512
## (Intercept)-Sciurus_carolinensis -1.4976 0.6942 -2.9417 -1.4667
## (Intercept)-Vulpes_vulpes -0.7874 1.3996 -3.1316 -0.9575
## (Intercept)-Sus_scrofa -2.1364 0.8656 -4.0019 -2.1086
## Veg_shannon_index-Odocoileus_virginianus 0.3186 0.4618 -0.6292 0.3259
## Veg_shannon_index-Canis_latrans 0.6504 0.3799 -0.0253 0.6241
## Veg_shannon_index-Sciurus_niger 0.3815 0.4918 -0.5285 0.3671
## Veg_shannon_index-Procyon_lotor 0.4977 0.3845 -0.1890 0.4742
## Veg_shannon_index-Dasypus_novemcinctus 0.2222 0.3326 -0.4497 0.2291
## Veg_shannon_index-Lynx_rufus 0.2276 0.4872 -0.7913 0.2420
## Veg_shannon_index-Didelphis_virginiana 0.5212 0.3889 -0.1895 0.5031
## Veg_shannon_index-Sylvilagus_floridanus 0.4895 0.4381 -0.2814 0.4642
## Veg_shannon_index-Meleagris_gallopavo 0.4883 0.4570 -0.3559 0.4537
## Veg_shannon_index-Sciurus_carolinensis 0.0520 0.3878 -0.7455 0.0722
## Veg_shannon_index-Vulpes_vulpes 0.1617 0.4673 -0.8157 0.1711
## Veg_shannon_index-Sus_scrofa 0.7128 0.5172 -0.1275 0.6554
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2455 0.5098 -0.7463 0.2433
## Avg_Cogongrass_Cover-Canis_latrans 0.5196 0.3912 -0.1547 0.4860
## Avg_Cogongrass_Cover-Sciurus_niger -0.1179 0.5935 -1.4741 -0.0524
## Avg_Cogongrass_Cover-Procyon_lotor 0.3832 0.4032 -0.3648 0.3620
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4153 0.3361 -0.2159 0.4121
## Avg_Cogongrass_Cover-Lynx_rufus 0.5682 0.4640 -0.2173 0.5188
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4401 0.3833 -0.2780 0.4227
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1148 0.4592 -1.1149 -0.0781
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1369 0.5317 -1.3241 -0.0888
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3914 0.3734 -0.3228 0.3816
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3649 0.4928 -0.5571 0.3414
## Avg_Cogongrass_Cover-Sus_scrofa -0.0512 0.5502 -1.3056 0.0077
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5575 1.0010 1095
## (Intercept)-Canis_latrans 1.5477 1.0026 2584
## (Intercept)-Sciurus_niger 2.3435 1.0207 539
## (Intercept)-Procyon_lotor 1.7294 1.0019 1912
## (Intercept)-Dasypus_novemcinctus 0.4371 1.0002 3209
## (Intercept)-Lynx_rufus 2.2565 1.0224 879
## (Intercept)-Didelphis_virginiana -0.1551 1.0006 3084
## (Intercept)-Sylvilagus_floridanus 1.5979 1.0034 978
## (Intercept)-Meleagris_gallopavo 1.5905 1.0081 1306
## (Intercept)-Sciurus_carolinensis -0.1750 1.0065 2873
## (Intercept)-Vulpes_vulpes 2.5413 1.0198 451
## (Intercept)-Sus_scrofa -0.5225 1.0021 2005
## Veg_shannon_index-Odocoileus_virginianus 1.2233 1.0051 3171
## Veg_shannon_index-Canis_latrans 1.4539 1.0023 3224
## Veg_shannon_index-Sciurus_niger 1.4325 1.0007 2331
## Veg_shannon_index-Procyon_lotor 1.3222 1.0051 2699
## Veg_shannon_index-Dasypus_novemcinctus 0.8733 1.0043 4133
## Veg_shannon_index-Lynx_rufus 1.1580 1.0107 2423
## Veg_shannon_index-Didelphis_virginiana 1.3531 1.0020 3673
## Veg_shannon_index-Sylvilagus_floridanus 1.4151 1.0046 2635
## Veg_shannon_index-Meleagris_gallopavo 1.4709 1.0075 2543
## Veg_shannon_index-Sciurus_carolinensis 0.7619 1.0016 2941
## Veg_shannon_index-Vulpes_vulpes 1.0483 1.0005 1620
## Veg_shannon_index-Sus_scrofa 1.8882 1.0024 2677
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3037 1.0029 3501
## Avg_Cogongrass_Cover-Canis_latrans 1.3727 1.0031 3376
## Avg_Cogongrass_Cover-Sciurus_niger 0.9034 1.0032 1619
## Avg_Cogongrass_Cover-Procyon_lotor 1.2594 1.0047 3619
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1049 1.0009 3822
## Avg_Cogongrass_Cover-Lynx_rufus 1.6412 1.0009 2856
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2476 1.0018 3256
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7153 1.0004 1955
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8239 1.0020 2001
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1586 1.0003 3911
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3810 1.0018 2654
## Avg_Cogongrass_Cover-Sus_scrofa 0.8818 0.9999 2612
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5264 0.0798 0.3697 0.5260 0.6884
## (Intercept)-Canis_latrans -2.4302 0.1869 -2.8089 -2.4242 -2.0799
## (Intercept)-Sciurus_niger -3.9666 0.6089 -5.1857 -3.9500 -2.8596
## (Intercept)-Procyon_lotor -2.1702 0.1536 -2.4744 -2.1677 -1.8811
## (Intercept)-Dasypus_novemcinctus -1.4408 0.1561 -1.7455 -1.4397 -1.1335
## (Intercept)-Lynx_rufus -3.4164 0.3531 -4.1335 -3.4023 -2.7487
## (Intercept)-Didelphis_virginiana -2.1218 0.2756 -2.6815 -2.1101 -1.6061
## (Intercept)-Sylvilagus_floridanus -3.1484 0.3543 -3.9016 -3.1290 -2.5057
## (Intercept)-Meleagris_gallopavo -3.3687 0.3910 -4.1850 -3.3544 -2.6333
## (Intercept)-Sciurus_carolinensis -2.2666 0.2898 -2.8812 -2.2506 -1.7319
## (Intercept)-Vulpes_vulpes -4.1079 0.8003 -5.6341 -4.0944 -2.6194
## (Intercept)-Sus_scrofa -2.7934 0.4952 -3.8759 -2.7561 -1.9074
## week-Odocoileus_virginianus 1.2822 0.1238 1.0360 1.2821 1.5249
## week-Canis_latrans 0.5850 0.2655 0.0766 0.5828 1.1052
## week-Sciurus_niger -0.4749 0.5562 -1.6736 -0.4287 0.4820
## week-Procyon_lotor 0.1892 0.2078 -0.2235 0.1926 0.5973
## week-Dasypus_novemcinctus 0.0979 0.2212 -0.3328 0.0992 0.5297
## week-Lynx_rufus 0.3602 0.3504 -0.3421 0.3558 1.0507
## week-Didelphis_virginiana 0.0406 0.3681 -0.7044 0.0499 0.7341
## week-Sylvilagus_floridanus 0.0505 0.3478 -0.6543 0.0557 0.7192
## week-Meleagris_gallopavo -0.2500 0.4413 -1.2088 -0.2301 0.5651
## week-Sciurus_carolinensis 0.7867 0.3718 0.0811 0.7729 1.5502
## week-Vulpes_vulpes 0.1466 0.5349 -0.9637 0.1638 1.1772
## week-Sus_scrofa 0.6693 0.4546 -0.1973 0.6522 1.6017
## I(week^2)-Odocoileus_virginianus -0.5284 0.0506 -0.6288 -0.5289 -0.4313
## I(week^2)-Canis_latrans -0.2443 0.1074 -0.4546 -0.2452 -0.0314
## I(week^2)-Sciurus_niger -0.2952 0.2423 -0.8092 -0.2805 0.1395
## I(week^2)-Procyon_lotor -0.1278 0.0906 -0.3088 -0.1267 0.0475
## I(week^2)-Dasypus_novemcinctus -0.1759 0.1017 -0.3820 -0.1751 0.0208
## I(week^2)-Lynx_rufus -0.2378 0.1524 -0.5551 -0.2323 0.0473
## I(week^2)-Didelphis_virginiana -0.4064 0.2052 -0.8703 -0.3927 -0.0563
## I(week^2)-Sylvilagus_floridanus -0.1815 0.1568 -0.5001 -0.1803 0.1213
## I(week^2)-Meleagris_gallopavo -0.4022 0.2405 -0.9590 -0.3773 0.0060
## I(week^2)-Sciurus_carolinensis -0.2802 0.1449 -0.5773 -0.2766 -0.0015
## I(week^2)-Vulpes_vulpes -0.4019 0.2425 -0.9370 -0.3848 0.0201
## I(week^2)-Sus_scrofa -0.2381 0.1809 -0.6128 -0.2325 0.1096
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 4256
## (Intercept)-Canis_latrans 1.0005 3467
## (Intercept)-Sciurus_niger 1.0289 480
## (Intercept)-Procyon_lotor 1.0008 4253
## (Intercept)-Dasypus_novemcinctus 1.0021 5033
## (Intercept)-Lynx_rufus 1.0227 1035
## (Intercept)-Didelphis_virginiana 1.0013 4010
## (Intercept)-Sylvilagus_floridanus 1.0014 1200
## (Intercept)-Meleagris_gallopavo 1.0095 1067
## (Intercept)-Sciurus_carolinensis 1.0030 3453
## (Intercept)-Vulpes_vulpes 1.0124 386
## (Intercept)-Sus_scrofa 1.0009 2199
## week-Odocoileus_virginianus 1.0007 5250
## week-Canis_latrans 1.0001 3735
## week-Sciurus_niger 1.0035 865
## week-Procyon_lotor 1.0076 4482
## week-Dasypus_novemcinctus 1.0003 4862
## week-Lynx_rufus 1.0007 2753
## week-Didelphis_virginiana 1.0008 2985
## week-Sylvilagus_floridanus 1.0037 3039
## week-Meleagris_gallopavo 1.0006 1115
## week-Sciurus_carolinensis 1.0012 3747
## week-Vulpes_vulpes 1.0011 1672
## week-Sus_scrofa 1.0017 4315
## I(week^2)-Odocoileus_virginianus 1.0026 5250
## I(week^2)-Canis_latrans 1.0004 4000
## I(week^2)-Sciurus_niger 1.0094 916
## I(week^2)-Procyon_lotor 1.0016 4568
## I(week^2)-Dasypus_novemcinctus 1.0004 4478
## I(week^2)-Lynx_rufus 0.9999 2454
## I(week^2)-Didelphis_virginiana 1.0012 2005
## I(week^2)-Sylvilagus_floridanus 1.0009 2634
## I(week^2)-Meleagris_gallopavo 1.0364 735
## I(week^2)-Sciurus_carolinensis 1.0005 3835
## I(week^2)-Vulpes_vulpes 1.0131 1151
## I(week^2)-Sus_scrofa 1.0007 4105
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9067
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2974 0.5310 -1.3263 -0.3085 0.7894 1.0025 1935
## Avg_Cogongrass_Cover 0.1162 0.2369 -0.3663 0.1184 0.5718 1.0048 2066
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8634 2.1571 0.6006 2.3033 8.6809 1.0079 1967
## Avg_Cogongrass_Cover 0.2933 0.3429 0.0407 0.1937 1.1445 1.0429 1661
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9294 0.9091 0.0689 0.6736 3.3077 1.0063 443
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3861 0.4125 -3.1898 -2.3861 -1.5699 1.0004 4589
## week 0.2921 0.2339 -0.1837 0.2967 0.7315 1.0010 2772
## I(week^2) -0.2948 0.0991 -0.4999 -0.2910 -0.1137 1.0006 2131
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0284 1.2197 0.7612 1.7283 5.0920 1.0054 1946
## week 0.4576 0.3432 0.1135 0.3671 1.3404 1.0041 1732
## I(week^2) 0.0723 0.0522 0.0219 0.0583 0.2086 1.0043 1739
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.1387 1.2792 0.8773 3.0406
## (Intercept)-Canis_latrans 0.2506 0.6511 -1.0589 0.2407
## (Intercept)-Sciurus_niger -0.5835 1.1082 -2.4140 -0.7049
## (Intercept)-Procyon_lotor 0.4438 0.6395 -0.8695 0.4647
## (Intercept)-Dasypus_novemcinctus -0.6964 0.6039 -1.9240 -0.6837
## (Intercept)-Lynx_rufus -0.0708 0.9596 -1.6599 -0.1573
## (Intercept)-Didelphis_virginiana -1.3627 0.6676 -2.7167 -1.3508
## (Intercept)-Sylvilagus_floridanus -0.3677 0.7565 -1.7014 -0.4059
## (Intercept)-Meleagris_gallopavo -0.4289 0.8216 -1.9666 -0.4567
## (Intercept)-Sciurus_carolinensis -1.4158 0.6492 -2.7614 -1.4019
## (Intercept)-Vulpes_vulpes -0.9411 1.3217 -3.1180 -1.1165
## (Intercept)-Sus_scrofa -1.8594 0.8284 -3.5261 -1.8414
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1130 0.4719 -0.7853 0.1140
## Avg_Cogongrass_Cover-Canis_latrans 0.3368 0.3627 -0.3282 0.3116
## Avg_Cogongrass_Cover-Sciurus_niger -0.2165 0.5351 -1.4349 -0.1657
## Avg_Cogongrass_Cover-Procyon_lotor 0.2185 0.3492 -0.4458 0.2114
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3189 0.3203 -0.2839 0.3111
## Avg_Cogongrass_Cover-Lynx_rufus 0.4165 0.4240 -0.3099 0.3860
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3127 0.3612 -0.3903 0.3001
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2174 0.4203 -1.1200 -0.1800
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2678 0.4834 -1.3718 -0.2215
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3128 0.3537 -0.3726 0.3054
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2366 0.4455 -0.5997 0.2178
## Avg_Cogongrass_Cover-Sus_scrofa -0.1686 0.5200 -1.4095 -0.1101
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.9889 1.0239 1334
## (Intercept)-Canis_latrans 1.5389 0.9999 2277
## (Intercept)-Sciurus_niger 1.9853 1.0057 469
## (Intercept)-Procyon_lotor 1.6608 1.0018 2085
## (Intercept)-Dasypus_novemcinctus 0.4812 1.0005 3268
## (Intercept)-Lynx_rufus 2.0631 1.0037 879
## (Intercept)-Didelphis_virginiana -0.0597 1.0041 2545
## (Intercept)-Sylvilagus_floridanus 1.2542 1.0005 1457
## (Intercept)-Meleagris_gallopavo 1.3811 1.0041 1372
## (Intercept)-Sciurus_carolinensis -0.2061 1.0044 2993
## (Intercept)-Vulpes_vulpes 2.3745 1.0135 407
## (Intercept)-Sus_scrofa -0.2834 1.0072 2112
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.0593 1.0023 3935
## Avg_Cogongrass_Cover-Canis_latrans 1.1113 1.0000 3820
## Avg_Cogongrass_Cover-Sciurus_niger 0.6856 1.0016 1964
## Avg_Cogongrass_Cover-Procyon_lotor 0.9378 0.9999 4760
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9664 1.0040 4538
## Avg_Cogongrass_Cover-Lynx_rufus 1.2962 1.0029 3001
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0522 1.0085 4472
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5359 1.0027 2979
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5574 1.0036 1877
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0465 1.0006 4046
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1804 1.0051 3695
## Avg_Cogongrass_Cover-Sus_scrofa 0.7067 1.0029 2056
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5255 0.0795 0.3729 0.5250 0.6809
## (Intercept)-Canis_latrans -2.4379 0.1881 -2.8309 -2.4332 -2.0768
## (Intercept)-Sciurus_niger -3.9093 0.6082 -5.1480 -3.8802 -2.8046
## (Intercept)-Procyon_lotor -2.1584 0.1504 -2.4510 -2.1562 -1.8693
## (Intercept)-Dasypus_novemcinctus -1.4465 0.1559 -1.7545 -1.4455 -1.1485
## (Intercept)-Lynx_rufus -3.4020 0.3536 -4.1276 -3.3939 -2.7477
## (Intercept)-Didelphis_virginiana -2.1245 0.2745 -2.6846 -2.1163 -1.6175
## (Intercept)-Sylvilagus_floridanus -3.0939 0.3299 -3.7652 -3.0763 -2.4794
## (Intercept)-Meleagris_gallopavo -3.3039 0.3831 -4.0942 -3.2833 -2.5873
## (Intercept)-Sciurus_carolinensis -2.2559 0.2901 -2.8627 -2.2384 -1.7427
## (Intercept)-Vulpes_vulpes -3.9685 0.7724 -5.5240 -3.9206 -2.5845
## (Intercept)-Sus_scrofa -2.8110 0.5006 -3.9084 -2.7646 -1.9481
## week-Odocoileus_virginianus 1.2811 0.1233 1.0403 1.2816 1.5263
## week-Canis_latrans 0.5809 0.2639 0.0687 0.5797 1.1147
## week-Sciurus_niger -0.4703 0.5534 -1.6552 -0.4223 0.4862
## week-Procyon_lotor 0.1961 0.2152 -0.2182 0.1913 0.6307
## week-Dasypus_novemcinctus 0.0942 0.2242 -0.3469 0.0943 0.5309
## week-Lynx_rufus 0.3659 0.3510 -0.3070 0.3640 1.0572
## week-Didelphis_virginiana 0.0337 0.3740 -0.7154 0.0372 0.7513
## week-Sylvilagus_floridanus 0.0490 0.3426 -0.6434 0.0565 0.6979
## week-Meleagris_gallopavo -0.2489 0.4326 -1.1889 -0.2239 0.5449
## week-Sciurus_carolinensis 0.7950 0.3733 0.1028 0.7780 1.5774
## week-Vulpes_vulpes 0.1572 0.5281 -0.9497 0.1699 1.1559
## week-Sus_scrofa 0.6778 0.4657 -0.1903 0.6571 1.6397
## I(week^2)-Odocoileus_virginianus -0.5283 0.0511 -0.6272 -0.5282 -0.4288
## I(week^2)-Canis_latrans -0.2421 0.1081 -0.4494 -0.2435 -0.0310
## I(week^2)-Sciurus_niger -0.2952 0.2451 -0.8158 -0.2823 0.1493
## I(week^2)-Procyon_lotor -0.1324 0.0918 -0.3177 -0.1304 0.0411
## I(week^2)-Dasypus_novemcinctus -0.1756 0.1032 -0.3841 -0.1736 0.0221
## I(week^2)-Lynx_rufus -0.2427 0.1548 -0.5586 -0.2378 0.0511
## I(week^2)-Didelphis_virginiana -0.4122 0.2114 -0.8796 -0.3920 -0.0585
## I(week^2)-Sylvilagus_floridanus -0.1814 0.1570 -0.5068 -0.1772 0.1255
## I(week^2)-Meleagris_gallopavo -0.4103 0.2420 -0.9539 -0.3834 -0.0120
## I(week^2)-Sciurus_carolinensis -0.2827 0.1443 -0.5737 -0.2801 -0.0083
## I(week^2)-Vulpes_vulpes -0.4098 0.2486 -0.9820 -0.3859 0.0225
## I(week^2)-Sus_scrofa -0.2428 0.1788 -0.6113 -0.2399 0.0997
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 4835
## (Intercept)-Canis_latrans 1.0052 3382
## (Intercept)-Sciurus_niger 1.0063 471
## (Intercept)-Procyon_lotor 1.0020 4348
## (Intercept)-Dasypus_novemcinctus 1.0057 5250
## (Intercept)-Lynx_rufus 1.0032 1030
## (Intercept)-Didelphis_virginiana 1.0031 4101
## (Intercept)-Sylvilagus_floridanus 1.0086 1546
## (Intercept)-Meleagris_gallopavo 0.9999 1190
## (Intercept)-Sciurus_carolinensis 1.0007 4308
## (Intercept)-Vulpes_vulpes 1.0121 387
## (Intercept)-Sus_scrofa 1.0074 1837
## week-Odocoileus_virginianus 1.0001 4315
## week-Canis_latrans 1.0007 3790
## week-Sciurus_niger 1.0047 1092
## week-Procyon_lotor 1.0014 4433
## week-Dasypus_novemcinctus 1.0023 4868
## week-Lynx_rufus 1.0004 2982
## week-Didelphis_virginiana 1.0018 2928
## week-Sylvilagus_floridanus 1.0006 2690
## week-Meleagris_gallopavo 1.0005 1226
## week-Sciurus_carolinensis 1.0013 3320
## week-Vulpes_vulpes 1.0040 1613
## week-Sus_scrofa 1.0024 3595
## I(week^2)-Odocoileus_virginianus 1.0000 4343
## I(week^2)-Canis_latrans 1.0019 3745
## I(week^2)-Sciurus_niger 1.0110 1186
## I(week^2)-Procyon_lotor 1.0018 4450
## I(week^2)-Dasypus_novemcinctus 1.0022 4257
## I(week^2)-Lynx_rufus 1.0004 2391
## I(week^2)-Didelphis_virginiana 1.0185 1540
## I(week^2)-Sylvilagus_floridanus 1.0020 2549
## I(week^2)-Meleagris_gallopavo 1.0086 704
## I(week^2)-Sciurus_carolinensis 1.0001 4438
## I(week^2)-Vulpes_vulpes 1.0048 1470
## I(week^2)-Sus_scrofa 1.0037 3932
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9059 0.5631 -2.0059 -0.9154 0.2653 1.0083 2099
## Avg_Cogongrass_Cover -0.7271 0.3522 -1.4353 -0.7189 -0.0595 1.0033 1236
## I(Avg_Cogongrass_Cover^2) 0.7385 0.3382 0.1442 0.7157 1.4422 1.0069 1423
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1536 2.5498 0.6124 2.4972 9.5633 1.0156 1290
## Avg_Cogongrass_Cover 0.3664 0.4736 0.0411 0.2210 1.5004 1.0069 2055
## I(Avg_Cogongrass_Cover^2) 0.6086 1.1218 0.0429 0.2687 3.5346 1.0860 477
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6391 0.6702 0.0524 0.4181 2.4679 1.0291 384
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3924 0.4070 -3.1857 -2.4020 -1.5578 1.0025 3666
## week 0.2855 0.2321 -0.1873 0.2920 0.7195 1.0056 3255
## I(week^2) -0.2912 0.1020 -0.5003 -0.2871 -0.1004 1.0051 2000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0101 1.1245 0.7499 1.7254 4.9267 1.0094 2047
## week 0.4459 0.3108 0.1132 0.3650 1.2962 1.0053 2272
## I(week^2) 0.0724 0.0531 0.0221 0.0577 0.2056 1.0025 2211
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.6430 1.3528 0.3477 2.5271
## (Intercept)-Canis_latrans -0.5139 0.6932 -1.8888 -0.5103
## (Intercept)-Sciurus_niger -0.8737 1.2374 -2.8496 -1.0296
## (Intercept)-Procyon_lotor -0.2312 0.6645 -1.6124 -0.2122
## (Intercept)-Dasypus_novemcinctus -1.3321 0.6317 -2.5985 -1.3125
## (Intercept)-Lynx_rufus -1.1566 0.8977 -2.8314 -1.1926
## (Intercept)-Didelphis_virginiana -1.9419 0.7210 -3.3866 -1.9157
## (Intercept)-Sylvilagus_floridanus -1.0203 0.7658 -2.5670 -1.0209
## (Intercept)-Meleagris_gallopavo -0.6245 0.9281 -2.2498 -0.6799
## (Intercept)-Sciurus_carolinensis -2.3703 0.7773 -3.9848 -2.3376
## (Intercept)-Vulpes_vulpes -1.9654 1.3042 -4.3021 -2.0364
## (Intercept)-Sus_scrofa -2.3934 0.8823 -4.2375 -2.3624
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7203 0.6048 -1.9305 -0.7166
## Avg_Cogongrass_Cover-Canis_latrans -0.4565 0.5074 -1.4175 -0.4745
## Avg_Cogongrass_Cover-Sciurus_niger -0.9642 0.6344 -2.3699 -0.9043
## Avg_Cogongrass_Cover-Procyon_lotor -0.5971 0.4877 -1.5269 -0.6103
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5265 0.4685 -1.4295 -0.5321
## Avg_Cogongrass_Cover-Lynx_rufus -0.6087 0.5461 -1.6953 -0.6130
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4433 0.5210 -1.4122 -0.4646
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1014 0.5905 -2.4369 -1.0463
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9465 0.5838 -2.2679 -0.9075
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7697 0.5249 -1.8704 -0.7466
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7578 0.5995 -2.0195 -0.7343
## Avg_Cogongrass_Cover-Sus_scrofa -0.9948 0.6326 -2.4551 -0.9375
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1423 0.8764 0.0274 0.9655
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2170 0.8236 0.1944 1.0176
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2605 0.7422 -1.4679 0.3206
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0795 0.7198 0.1790 0.9328
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6830 0.3467 0.0207 0.6770
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1642 0.5705 0.3092 1.0752
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5237 0.3930 -0.2467 0.5227
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7242 0.5255 -0.1259 0.6774
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1877 0.6427 -1.2447 0.2311
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9394 0.3895 0.2475 0.9186
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9446 0.6337 0.0709 0.8557
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2553 0.6324 -1.2946 0.3456
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.6164 1.0052 1114
## (Intercept)-Canis_latrans 0.8571 1.0066 2674
## (Intercept)-Sciurus_niger 2.1470 1.0333 448
## (Intercept)-Procyon_lotor 1.0103 1.0051 2125
## (Intercept)-Dasypus_novemcinctus -0.1280 1.0011 2928
## (Intercept)-Lynx_rufus 0.7214 1.0066 1580
## (Intercept)-Didelphis_virginiana -0.5718 1.0032 3141
## (Intercept)-Sylvilagus_floridanus 0.4946 1.0087 2109
## (Intercept)-Meleagris_gallopavo 1.4200 1.0039 1364
## (Intercept)-Sciurus_carolinensis -0.9432 1.0036 2061
## (Intercept)-Vulpes_vulpes 0.9576 1.0208 496
## (Intercept)-Sus_scrofa -0.7459 1.0032 2157
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4884 1.0012 2353
## Avg_Cogongrass_Cover-Canis_latrans 0.6150 1.0011 2762
## Avg_Cogongrass_Cover-Sciurus_niger 0.1147 1.0015 1842
## Avg_Cogongrass_Cover-Procyon_lotor 0.4049 1.0008 2575
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4472 1.0005 2938
## Avg_Cogongrass_Cover-Lynx_rufus 0.4855 1.0036 2373
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6739 1.0025 2432
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1169 1.0040 1978
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0822 1.0051 2272
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2377 1.0042 2374
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4048 1.0065 1885
## Avg_Cogongrass_Cover-Sus_scrofa 0.1120 1.0031 1697
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.4344 1.0230 687
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4723 1.0149 639
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5867 1.0260 696
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.9807 1.0234 854
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3909 1.0005 3120
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4917 1.0200 1286
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2972 1.0047 2566
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8624 1.0122 1314
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3301 1.0155 1107
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7892 1.0023 2398
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.3405 1.0375 682
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2331 1.0094 1377
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5228 0.0784 0.3722 0.5226 0.6779
## (Intercept)-Canis_latrans -2.4598 0.1900 -2.8482 -2.4587 -2.0922
## (Intercept)-Sciurus_niger -3.9699 0.6376 -5.2043 -3.9498 -2.7758
## (Intercept)-Procyon_lotor -2.1670 0.1502 -2.4703 -2.1625 -1.8860
## (Intercept)-Dasypus_novemcinctus -1.4513 0.1582 -1.7726 -1.4477 -1.1538
## (Intercept)-Lynx_rufus -3.2957 0.3507 -4.0314 -3.2755 -2.6667
## (Intercept)-Didelphis_virginiana -2.1427 0.2796 -2.7355 -2.1326 -1.6306
## (Intercept)-Sylvilagus_floridanus -3.1097 0.3303 -3.7852 -3.0912 -2.5213
## (Intercept)-Meleagris_gallopavo -3.3584 0.4030 -4.1823 -3.3345 -2.6364
## (Intercept)-Sciurus_carolinensis -2.2534 0.2843 -2.8405 -2.2395 -1.7217
## (Intercept)-Vulpes_vulpes -3.9200 0.7645 -5.4448 -3.8952 -2.5724
## (Intercept)-Sus_scrofa -2.8128 0.4908 -3.8369 -2.7837 -1.9269
## week-Odocoileus_virginianus 1.2791 0.1237 1.0398 1.2803 1.5216
## week-Canis_latrans 0.5699 0.2668 0.0631 0.5660 1.1033
## week-Sciurus_niger -0.4454 0.5350 -1.5765 -0.4038 0.4951
## week-Procyon_lotor 0.1925 0.2090 -0.2145 0.1908 0.6027
## week-Dasypus_novemcinctus 0.0898 0.2294 -0.3734 0.0918 0.5331
## week-Lynx_rufus 0.3746 0.3528 -0.3105 0.3699 1.0921
## week-Didelphis_virginiana 0.0238 0.3812 -0.7511 0.0302 0.7434
## week-Sylvilagus_floridanus 0.0356 0.3413 -0.6460 0.0483 0.6856
## week-Meleagris_gallopavo -0.2334 0.4309 -1.1407 -0.2107 0.5391
## week-Sciurus_carolinensis 0.7745 0.3666 0.0712 0.7676 1.5274
## week-Vulpes_vulpes 0.1632 0.5214 -0.9134 0.1803 1.1505
## week-Sus_scrofa 0.6536 0.4443 -0.1734 0.6371 1.5748
## I(week^2)-Odocoileus_virginianus -0.5266 0.0509 -0.6270 -0.5265 -0.4273
## I(week^2)-Canis_latrans -0.2392 0.1096 -0.4580 -0.2376 -0.0229
## I(week^2)-Sciurus_niger -0.2696 0.2344 -0.7669 -0.2607 0.1663
## I(week^2)-Procyon_lotor -0.1297 0.0903 -0.3160 -0.1275 0.0472
## I(week^2)-Dasypus_novemcinctus -0.1747 0.1058 -0.3862 -0.1754 0.0312
## I(week^2)-Lynx_rufus -0.2415 0.1598 -0.5748 -0.2340 0.0556
## I(week^2)-Didelphis_virginiana -0.4157 0.2129 -0.8887 -0.3943 -0.0522
## I(week^2)-Sylvilagus_floridanus -0.1812 0.1584 -0.5008 -0.1757 0.1261
## I(week^2)-Meleagris_gallopavo -0.3885 0.2293 -0.9165 -0.3691 0.0021
## I(week^2)-Sciurus_carolinensis -0.2770 0.1428 -0.5689 -0.2766 0.0011
## I(week^2)-Vulpes_vulpes -0.4169 0.2623 -1.0059 -0.3902 0.0234
## I(week^2)-Sus_scrofa -0.2369 0.1765 -0.5961 -0.2310 0.1066
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0025 5582
## (Intercept)-Canis_latrans 1.0028 3068
## (Intercept)-Sciurus_niger 1.0213 456
## (Intercept)-Procyon_lotor 1.0032 4151
## (Intercept)-Dasypus_novemcinctus 1.0027 4992
## (Intercept)-Lynx_rufus 1.0046 1177
## (Intercept)-Didelphis_virginiana 1.0031 3885
## (Intercept)-Sylvilagus_floridanus 1.0184 1518
## (Intercept)-Meleagris_gallopavo 1.0013 1014
## (Intercept)-Sciurus_carolinensis 1.0035 3888
## (Intercept)-Vulpes_vulpes 1.0531 430
## (Intercept)-Sus_scrofa 1.0033 2039
## week-Odocoileus_virginianus 1.0020 5250
## week-Canis_latrans 1.0063 3790
## week-Sciurus_niger 1.0057 1207
## week-Procyon_lotor 1.0020 4244
## week-Dasypus_novemcinctus 1.0003 4367
## week-Lynx_rufus 1.0015 2944
## week-Didelphis_virginiana 1.0030 2887
## week-Sylvilagus_floridanus 1.0016 3097
## week-Meleagris_gallopavo 1.0061 1373
## week-Sciurus_carolinensis 1.0071 3495
## week-Vulpes_vulpes 1.0103 1766
## week-Sus_scrofa 1.0031 4003
## I(week^2)-Odocoileus_virginianus 1.0035 5250
## I(week^2)-Canis_latrans 1.0023 4167
## I(week^2)-Sciurus_niger 1.0024 1241
## I(week^2)-Procyon_lotor 1.0000 4158
## I(week^2)-Dasypus_novemcinctus 1.0012 4717
## I(week^2)-Lynx_rufus 1.0056 2463
## I(week^2)-Didelphis_virginiana 1.0021 1822
## I(week^2)-Sylvilagus_floridanus 1.0004 2427
## I(week^2)-Meleagris_gallopavo 1.0237 854
## I(week^2)-Sciurus_carolinensis 1.0135 3661
## I(week^2)-Vulpes_vulpes 1.0062 1062
## I(week^2)-Sus_scrofa 1.0027 3981
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0405
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0039 1.0626 -2.9863 -1.0642 1.2115 1.0098 1398
## Cogon_Patch_Size -0.1246 0.6448 -1.5033 -0.1031 1.1155 1.0066 973
## Veg_shannon_index 0.9533 0.4368 0.1541 0.9330 1.8954 1.0237 720
## total_shrub_cover -0.6066 0.4814 -1.6318 -0.5874 0.2996 1.0013 1389
## Avg_Cogongrass_Cover 0.0057 0.8762 -1.6978 -0.0284 1.7820 1.0539 475
## Tree_Density -1.9993 0.7129 -3.4997 -1.9565 -0.7367 1.0404 502
## Avg_Canopy_Cover 1.8120 0.5521 0.8114 1.7800 3.0218 1.0054 697
## I(Avg_Cogongrass_Cover^2) 1.2824 0.5734 0.1825 1.2668 2.4660 1.0048 831
## avg_veg_height -0.1978 0.4647 -1.1399 -0.1831 0.7079 1.0467 586
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.7471 14.0865 3.5083 13.7109 54.3947 1.0322 570
## Cogon_Patch_Size 2.5321 3.3573 0.1093 1.5013 11.3743 1.0431 833
## Veg_shannon_index 0.6980 1.0091 0.0498 0.3633 3.3377 1.0097 939
## total_shrub_cover 1.4971 1.8427 0.0840 0.8992 6.3506 1.0091 918
## Avg_Cogongrass_Cover 1.1058 1.9149 0.0528 0.4916 6.0524 1.0719 720
## Tree_Density 2.2876 4.0409 0.0673 0.9296 12.7243 1.0533 415
## Avg_Canopy_Cover 1.6905 2.2700 0.0884 0.9473 7.9381 1.0139 674
## I(Avg_Cogongrass_Cover^2) 1.7646 3.2798 0.0571 0.7171 9.7550 1.0410 387
## avg_veg_height 0.4044 0.5506 0.0394 0.2411 1.8411 1.0302 1786
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4243 1.823 0.0582 0.7598 6.4278 1.0212 197
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4376 0.4343 -3.2650 -2.4509 -1.5367 1.0009 4601
## week 0.2808 0.2296 -0.1893 0.2891 0.7270 1.0026 2837
## I(week^2) -0.2880 0.1002 -0.4926 -0.2859 -0.0886 1.0027 2011
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3028 1.2924 0.8756 1.9661 5.5807 1.0058 3423
## week 0.4330 0.3047 0.1160 0.3546 1.2096 1.0020 1514
## I(week^2) 0.0713 0.0492 0.0221 0.0578 0.2044 1.0036 2087
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.2499 3.1997 2.4442 6.7447
## (Intercept)-Canis_latrans -0.9656 1.2329 -3.3894 -0.9750
## (Intercept)-Sciurus_niger 1.0206 2.8959 -3.2792 0.6205
## (Intercept)-Procyon_lotor -0.3893 1.0717 -2.5818 -0.3551
## (Intercept)-Dasypus_novemcinctus -2.6269 1.1135 -5.1432 -2.5326
## (Intercept)-Lynx_rufus 0.0634 2.5429 -3.8338 -0.3407
## (Intercept)-Didelphis_virginiana -4.0591 1.3757 -6.9910 -3.9475
## (Intercept)-Sylvilagus_floridanus -2.1460 1.4027 -5.0577 -2.0998
## (Intercept)-Meleagris_gallopavo -1.7404 1.5307 -4.8054 -1.7571
## (Intercept)-Sciurus_carolinensis -4.7734 1.4640 -7.9293 -4.6419
## (Intercept)-Vulpes_vulpes -3.8999 2.2875 -8.2849 -3.9726
## (Intercept)-Sus_scrofa -5.5422 1.9019 -9.7199 -5.3907
## Cogon_Patch_Size-Odocoileus_virginianus -0.0420 1.3097 -2.6375 -0.0888
## Cogon_Patch_Size-Canis_latrans 1.4160 1.2044 -0.3390 1.2202
## Cogon_Patch_Size-Sciurus_niger -0.6514 1.6527 -4.4059 -0.5092
## Cogon_Patch_Size-Procyon_lotor -0.3896 0.7611 -1.8968 -0.3723
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1846 0.6796 -1.6217 -0.1623
## Cogon_Patch_Size-Lynx_rufus -0.1759 1.4147 -2.9779 -0.1693
## Cogon_Patch_Size-Didelphis_virginiana 1.4729 0.9539 -0.0986 1.3658
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2361 1.4193 -4.7141 -1.0154
## Cogon_Patch_Size-Meleagris_gallopavo 0.2124 1.0721 -1.7357 0.1396
## Cogon_Patch_Size-Sciurus_carolinensis -0.9718 1.2025 -3.9391 -0.7589
## Cogon_Patch_Size-Vulpes_vulpes -0.5535 1.5213 -4.0505 -0.4128
## Cogon_Patch_Size-Sus_scrofa -0.6201 1.3810 -4.0084 -0.4254
## Veg_shannon_index-Odocoileus_virginianus 0.7359 0.8417 -1.1718 0.7684
## Veg_shannon_index-Canis_latrans 1.3044 0.6773 0.2066 1.2272
## Veg_shannon_index-Sciurus_niger 1.0317 0.9112 -0.6842 0.9730
## Veg_shannon_index-Procyon_lotor 1.1092 0.5923 0.0547 1.0645
## Veg_shannon_index-Dasypus_novemcinctus 0.6506 0.5255 -0.4275 0.6666
## Veg_shannon_index-Lynx_rufus 1.0023 0.8708 -0.7228 0.9712
## Veg_shannon_index-Didelphis_virginiana 1.0772 0.6562 -0.0783 1.0252
## Veg_shannon_index-Sylvilagus_floridanus 0.9880 0.6932 -0.3176 0.9544
## Veg_shannon_index-Meleagris_gallopavo 1.1853 0.7281 -0.0887 1.1322
## Veg_shannon_index-Sciurus_carolinensis 0.3502 0.7396 -1.3163 0.4349
## Veg_shannon_index-Vulpes_vulpes 0.6708 0.8274 -1.1291 0.7150
## Veg_shannon_index-Sus_scrofa 1.5422 0.9597 0.1811 1.3635
## total_shrub_cover-Odocoileus_virginianus -0.2251 1.0120 -2.0876 -0.2881
## total_shrub_cover-Canis_latrans -0.0271 0.6718 -1.2145 -0.0596
## total_shrub_cover-Sciurus_niger -0.9997 1.1700 -3.6956 -0.8913
## total_shrub_cover-Procyon_lotor -1.1625 0.6594 -2.6027 -1.0987
## total_shrub_cover-Dasypus_novemcinctus 0.0581 0.5699 -1.0089 0.0434
## total_shrub_cover-Lynx_rufus -1.2239 1.1592 -3.9881 -1.0699
## total_shrub_cover-Didelphis_virginiana -0.7166 0.7447 -2.3092 -0.6714
## total_shrub_cover-Sylvilagus_floridanus -0.3948 0.8361 -2.1001 -0.3886
## total_shrub_cover-Meleagris_gallopavo -2.1571 1.3395 -5.2480 -1.9121
## total_shrub_cover-Sciurus_carolinensis -0.0601 0.7358 -1.4461 -0.0969
## total_shrub_cover-Vulpes_vulpes -0.8184 1.0712 -3.1972 -0.7347
## total_shrub_cover-Sus_scrofa 0.0222 0.9292 -1.6650 -0.0647
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0541 1.2396 -2.4679 -0.0639
## Avg_Cogongrass_Cover-Canis_latrans 0.1024 1.1258 -2.1271 0.0767
## Avg_Cogongrass_Cover-Sciurus_niger -0.3027 1.3800 -3.2928 -0.2191
## Avg_Cogongrass_Cover-Procyon_lotor 0.1897 1.1139 -1.8891 0.1425
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5969 1.1728 -1.4684 0.5158
## Avg_Cogongrass_Cover-Lynx_rufus 0.1821 1.2255 -2.0902 0.1381
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2372 1.1414 -1.8864 0.1850
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4993 1.2022 -3.0793 -0.4425
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2637 1.2343 -2.7868 -0.2035
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0723 1.1281 -2.1300 0.0588
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1521 1.2516 -2.2303 0.1025
## Avg_Cogongrass_Cover-Sus_scrofa -0.4180 1.2812 -3.1936 -0.3476
## Tree_Density-Odocoileus_virginianus -1.1095 1.2108 -3.1412 -1.2399
## Tree_Density-Canis_latrans -2.6559 1.2667 -5.8207 -2.4305
## Tree_Density-Sciurus_niger -2.0263 1.4405 -5.2575 -1.9335
## Tree_Density-Procyon_lotor -1.8421 0.8996 -3.7330 -1.8099
## Tree_Density-Dasypus_novemcinctus -3.3976 1.7392 -7.9279 -2.9909
## Tree_Density-Lynx_rufus -1.0791 1.4483 -3.4357 -1.2431
## Tree_Density-Didelphis_virginiana -2.2337 1.1042 -4.8346 -2.0902
## Tree_Density-Sylvilagus_floridanus -2.4152 1.3252 -5.7702 -2.2339
## Tree_Density-Meleagris_gallopavo -2.0463 1.2125 -4.5876 -1.9917
## Tree_Density-Sciurus_carolinensis -2.5814 1.4007 -6.0917 -2.3242
## Tree_Density-Vulpes_vulpes -1.9454 1.4524 -4.7102 -1.9491
## Tree_Density-Sus_scrofa -2.3163 1.4193 -5.6855 -2.1387
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2795 1.1768 -1.3398 1.3574
## Avg_Canopy_Cover-Canis_latrans 0.3993 0.7628 -1.0681 0.3822
## Avg_Canopy_Cover-Sciurus_niger 1.9931 1.4228 -0.5893 1.8958
## Avg_Canopy_Cover-Procyon_lotor 1.6432 0.6995 0.3454 1.6041
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9009 0.6759 0.7294 1.8397
## Avg_Canopy_Cover-Lynx_rufus 1.5411 1.2418 -0.8736 1.5384
## Avg_Canopy_Cover-Didelphis_virginiana 2.4764 0.9050 1.0684 2.3380
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0270 1.4545 1.0709 2.7072
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2079 1.1132 0.5738 2.0116
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1884 0.8249 0.8872 2.0865
## Avg_Canopy_Cover-Vulpes_vulpes 2.2276 1.1898 0.4493 2.0317
## Avg_Canopy_Cover-Sus_scrofa 1.9741 0.8070 0.6135 1.8957
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8671 1.4329 -0.0807 1.5880
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0333 1.0658 0.5732 1.8427
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.8175 1.5238 -2.7872 0.9763
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8024 0.9814 0.3859 1.6450
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3580 0.7015 0.1206 1.2989
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1853 1.2685 0.4699 1.9289
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9708 0.6868 -0.3969 0.9796
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0598 0.8263 -0.4620 1.0095
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2131 1.3376 -2.9217 0.4241
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6159 0.7739 0.3506 1.5308
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8248 0.9387 0.3337 1.7075
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4439 1.2171 -2.6035 0.6397
## avg_veg_height-Odocoileus_virginianus -0.2046 0.7406 -1.7323 -0.1932
## avg_veg_height-Canis_latrans -0.3972 0.5942 -1.6738 -0.3763
## avg_veg_height-Sciurus_niger -0.3259 0.7633 -1.9532 -0.2928
## avg_veg_height-Procyon_lotor 0.0440 0.5837 -1.0941 0.0463
## avg_veg_height-Dasypus_novemcinctus 0.0904 0.5822 -1.0095 0.0782
## avg_veg_height-Lynx_rufus -0.3034 0.7536 -1.8336 -0.2740
## avg_veg_height-Didelphis_virginiana -0.2703 0.6416 -1.5783 -0.2460
## avg_veg_height-Sylvilagus_floridanus -0.3027 0.6402 -1.6385 -0.2862
## avg_veg_height-Meleagris_gallopavo -0.2247 0.7066 -1.6144 -0.2083
## avg_veg_height-Sciurus_carolinensis 0.0591 0.6372 -1.1454 0.0330
## avg_veg_height-Vulpes_vulpes -0.2928 0.7448 -1.8640 -0.2736
## avg_veg_height-Sus_scrofa -0.2828 0.6733 -1.6550 -0.2756
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.8204 1.0386 509
## (Intercept)-Canis_latrans 1.3714 1.0152 1195
## (Intercept)-Sciurus_niger 7.8087 1.0752 289
## (Intercept)-Procyon_lotor 1.5997 1.0008 1214
## (Intercept)-Dasypus_novemcinctus -0.7264 1.0071 1057
## (Intercept)-Lynx_rufus 6.5500 1.0532 314
## (Intercept)-Didelphis_virginiana -1.5711 1.0044 1452
## (Intercept)-Sylvilagus_floridanus 0.5195 1.0006 1197
## (Intercept)-Meleagris_gallopavo 1.3077 1.0018 1032
## (Intercept)-Sciurus_carolinensis -2.2588 1.0042 869
## (Intercept)-Vulpes_vulpes 1.0718 1.0012 366
## (Intercept)-Sus_scrofa -2.2769 1.0022 696
## Cogon_Patch_Size-Odocoileus_virginianus 2.7601 1.0031 1886
## Cogon_Patch_Size-Canis_latrans 4.2614 1.0137 1241
## Cogon_Patch_Size-Sciurus_niger 2.3062 1.0082 729
## Cogon_Patch_Size-Procyon_lotor 1.0682 1.0060 1058
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1032 1.0204 1125
## Cogon_Patch_Size-Lynx_rufus 2.6814 1.0079 913
## Cogon_Patch_Size-Didelphis_virginiana 3.6030 1.0117 1040
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9348 1.0189 814
## Cogon_Patch_Size-Meleagris_gallopavo 2.5527 1.0084 1440
## Cogon_Patch_Size-Sciurus_carolinensis 0.8096 1.0271 1099
## Cogon_Patch_Size-Vulpes_vulpes 2.2053 1.0067 907
## Cogon_Patch_Size-Sus_scrofa 1.5783 1.0053 912
## Veg_shannon_index-Odocoileus_virginianus 2.2805 1.0158 1825
## Veg_shannon_index-Canis_latrans 2.8466 1.0068 940
## Veg_shannon_index-Sciurus_niger 3.0860 1.0113 1174
## Veg_shannon_index-Procyon_lotor 2.3757 1.0166 812
## Veg_shannon_index-Dasypus_novemcinctus 1.6653 1.0177 1844
## Veg_shannon_index-Lynx_rufus 2.7777 1.0131 1366
## Veg_shannon_index-Didelphis_virginiana 2.5437 1.0035 1341
## Veg_shannon_index-Sylvilagus_floridanus 2.5268 1.0044 1547
## Veg_shannon_index-Meleagris_gallopavo 2.9083 1.0120 1221
## Veg_shannon_index-Sciurus_carolinensis 1.6234 1.0079 1711
## Veg_shannon_index-Vulpes_vulpes 2.2045 1.0129 1457
## Veg_shannon_index-Sus_scrofa 3.8647 1.0099 980
## total_shrub_cover-Odocoileus_virginianus 1.9346 1.0020 2281
## total_shrub_cover-Canis_latrans 1.3849 1.0036 2039
## total_shrub_cover-Sciurus_niger 1.1052 1.0124 990
## total_shrub_cover-Procyon_lotor -0.0107 1.0015 1799
## total_shrub_cover-Dasypus_novemcinctus 1.2355 1.0016 2888
## total_shrub_cover-Lynx_rufus 0.6752 1.0045 843
## total_shrub_cover-Didelphis_virginiana 0.6547 1.0006 2065
## total_shrub_cover-Sylvilagus_floridanus 1.2561 1.0019 2269
## total_shrub_cover-Meleagris_gallopavo -0.2335 1.0052 797
## total_shrub_cover-Sciurus_carolinensis 1.4811 1.0017 2677
## total_shrub_cover-Vulpes_vulpes 1.1016 1.0037 1236
## total_shrub_cover-Sus_scrofa 2.1251 1.0092 2180
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.4331 1.0216 916
## Avg_Cogongrass_Cover-Canis_latrans 2.3632 1.0378 648
## Avg_Cogongrass_Cover-Sciurus_niger 2.1705 1.0234 686
## Avg_Cogongrass_Cover-Procyon_lotor 2.4669 1.0512 642
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.2382 1.0489 548
## Avg_Cogongrass_Cover-Lynx_rufus 2.7537 1.0533 783
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.6151 1.0527 478
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6678 1.0289 824
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0628 1.0404 703
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3263 1.0333 628
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.7355 1.0458 752
## Avg_Cogongrass_Cover-Sus_scrofa 1.9356 1.0199 703
## Tree_Density-Odocoileus_virginianus 1.8022 1.0294 958
## Tree_Density-Canis_latrans -0.7934 1.0100 666
## Tree_Density-Sciurus_niger 0.7765 1.0185 879
## Tree_Density-Procyon_lotor -0.1272 1.0086 963
## Tree_Density-Dasypus_novemcinctus -1.2189 1.0067 406
## Tree_Density-Lynx_rufus 2.4198 1.0753 613
## Tree_Density-Didelphis_virginiana -0.5144 1.0153 895
## Tree_Density-Sylvilagus_floridanus -0.3868 1.0034 730
## Tree_Density-Meleagris_gallopavo 0.3532 1.0156 1198
## Tree_Density-Sciurus_carolinensis -0.6378 1.0081 700
## Tree_Density-Vulpes_vulpes 1.1515 1.0483 773
## Tree_Density-Sus_scrofa -0.0208 1.0087 877
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5309 1.0018 1570
## Avg_Canopy_Cover-Canis_latrans 1.9056 1.0068 1109
## Avg_Canopy_Cover-Sciurus_niger 5.2544 1.0118 710
## Avg_Canopy_Cover-Procyon_lotor 3.1382 1.0069 1429
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.4416 1.0028 781
## Avg_Canopy_Cover-Lynx_rufus 4.2012 1.0064 793
## Avg_Canopy_Cover-Didelphis_virginiana 4.6460 1.0171 665
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.6890 1.0048 554
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9991 1.0011 832
## Avg_Canopy_Cover-Sciurus_carolinensis 4.1063 1.0056 991
## Avg_Canopy_Cover-Vulpes_vulpes 5.1738 1.0022 675
## Avg_Canopy_Cover-Sus_scrofa 3.8111 1.0042 1035
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 5.5407 1.0108 571
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7903 1.0019 594
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.3707 1.0357 340
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.1897 1.0318 739
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9170 1.0057 1181
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.4281 1.0240 576
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.3224 1.0143 1098
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8536 1.0055 1095
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.3768 1.0227 379
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3959 1.0063 1090
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.0878 1.0038 744
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.2798 1.0154 563
## avg_veg_height-Odocoileus_virginianus 1.2131 1.0310 1210
## avg_veg_height-Canis_latrans 0.7307 1.0360 1059
## avg_veg_height-Sciurus_niger 1.0647 1.0248 1173
## avg_veg_height-Procyon_lotor 1.2249 1.0246 1087
## avg_veg_height-Dasypus_novemcinctus 1.2564 1.0108 1145
## avg_veg_height-Lynx_rufus 1.0803 1.0128 1309
## avg_veg_height-Didelphis_virginiana 0.9348 1.0308 1053
## avg_veg_height-Sylvilagus_floridanus 0.8885 1.0204 1262
## avg_veg_height-Meleagris_gallopavo 1.0983 1.0255 1232
## avg_veg_height-Sciurus_carolinensis 1.4253 1.0121 1050
## avg_veg_height-Vulpes_vulpes 1.0959 1.0342 961
## avg_veg_height-Sus_scrofa 1.0138 1.0253 1139
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5254 0.0803 0.3691 0.5256 0.6853
## (Intercept)-Canis_latrans -2.4422 0.1912 -2.8335 -2.4361 -2.0903
## (Intercept)-Sciurus_niger -4.5605 0.5168 -5.5641 -4.5623 -3.4877
## (Intercept)-Procyon_lotor -2.1601 0.1517 -2.4704 -2.1573 -1.8640
## (Intercept)-Dasypus_novemcinctus -1.4440 0.1579 -1.7601 -1.4460 -1.1450
## (Intercept)-Lynx_rufus -3.6081 0.3336 -4.2772 -3.6038 -2.9719
## (Intercept)-Didelphis_virginiana -2.1061 0.2635 -2.6536 -2.0944 -1.6220
## (Intercept)-Sylvilagus_floridanus -3.1087 0.3031 -3.7172 -3.0984 -2.5551
## (Intercept)-Meleagris_gallopavo -3.2922 0.3525 -4.0197 -3.2857 -2.6389
## (Intercept)-Sciurus_carolinensis -2.2579 0.2842 -2.8496 -2.2486 -1.7265
## (Intercept)-Vulpes_vulpes -4.0592 0.6451 -5.3808 -4.0479 -2.8402
## (Intercept)-Sus_scrofa -2.7475 0.4735 -3.7563 -2.7234 -1.9127
## week-Odocoileus_virginianus 1.2799 0.1241 1.0395 1.2798 1.5273
## week-Canis_latrans 0.5807 0.2646 0.0659 0.5832 1.1026
## week-Sciurus_niger -0.4206 0.5289 -1.5695 -0.3781 0.5174
## week-Procyon_lotor 0.1952 0.2110 -0.2203 0.1934 0.6135
## week-Dasypus_novemcinctus 0.0962 0.2268 -0.3438 0.0959 0.5382
## week-Lynx_rufus 0.3542 0.3453 -0.3222 0.3540 1.0499
## week-Didelphis_virginiana 0.0307 0.3725 -0.7260 0.0385 0.7453
## week-Sylvilagus_floridanus 0.0361 0.3450 -0.6534 0.0473 0.6973
## week-Meleagris_gallopavo -0.2461 0.4264 -1.1332 -0.2229 0.5222
## week-Sciurus_carolinensis 0.7723 0.3636 0.0650 0.7639 1.5133
## week-Vulpes_vulpes 0.1291 0.5211 -0.9506 0.1534 1.1063
## week-Sus_scrofa 0.6588 0.4555 -0.1974 0.6437 1.6282
## I(week^2)-Odocoileus_virginianus -0.5274 0.0509 -0.6262 -0.5269 -0.4281
## I(week^2)-Canis_latrans -0.2426 0.1082 -0.4570 -0.2419 -0.0371
## I(week^2)-Sciurus_niger -0.2825 0.2256 -0.7580 -0.2759 0.1407
## I(week^2)-Procyon_lotor -0.1311 0.0907 -0.3081 -0.1286 0.0470
## I(week^2)-Dasypus_novemcinctus -0.1744 0.1026 -0.3814 -0.1727 0.0208
## I(week^2)-Lynx_rufus -0.2386 0.1503 -0.5436 -0.2359 0.0525
## I(week^2)-Didelphis_virginiana -0.4053 0.2094 -0.8781 -0.3817 -0.0536
## I(week^2)-Sylvilagus_floridanus -0.1730 0.1581 -0.5026 -0.1692 0.1250
## I(week^2)-Meleagris_gallopavo -0.4017 0.2405 -0.9634 -0.3730 -0.0090
## I(week^2)-Sciurus_carolinensis -0.2726 0.1430 -0.5609 -0.2699 0.0061
## I(week^2)-Vulpes_vulpes -0.3981 0.2456 -0.9483 -0.3768 0.0279
## I(week^2)-Sus_scrofa -0.2305 0.1794 -0.5859 -0.2290 0.1269
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0127 2366
## (Intercept)-Sciurus_niger 1.0157 303
## (Intercept)-Procyon_lotor 1.0012 4584
## (Intercept)-Dasypus_novemcinctus 1.0005 5250
## (Intercept)-Lynx_rufus 1.0327 743
## (Intercept)-Didelphis_virginiana 1.0015 4093
## (Intercept)-Sylvilagus_floridanus 1.0025 1779
## (Intercept)-Meleagris_gallopavo 1.0109 1292
## (Intercept)-Sciurus_carolinensis 1.0002 3794
## (Intercept)-Vulpes_vulpes 1.0028 584
## (Intercept)-Sus_scrofa 1.0042 2322
## week-Odocoileus_virginianus 1.0013 5250
## week-Canis_latrans 1.0031 3960
## week-Sciurus_niger 1.0007 809
## week-Procyon_lotor 1.0052 4679
## week-Dasypus_novemcinctus 1.0002 5025
## week-Lynx_rufus 0.9999 2579
## week-Didelphis_virginiana 1.0024 3514
## week-Sylvilagus_floridanus 1.0028 3243
## week-Meleagris_gallopavo 1.0098 1127
## week-Sciurus_carolinensis 1.0025 3631
## week-Vulpes_vulpes 1.0067 1242
## week-Sus_scrofa 1.0006 3871
## I(week^2)-Odocoileus_virginianus 1.0007 5250
## I(week^2)-Canis_latrans 1.0034 4104
## I(week^2)-Sciurus_niger 1.0034 945
## I(week^2)-Procyon_lotor 1.0012 4540
## I(week^2)-Dasypus_novemcinctus 1.0007 4436
## I(week^2)-Lynx_rufus 1.0000 2067
## I(week^2)-Didelphis_virginiana 1.0027 1533
## I(week^2)-Sylvilagus_floridanus 1.0026 2410
## I(week^2)-Meleagris_gallopavo 1.0071 833
## I(week^2)-Sciurus_carolinensis 1.0025 3968
## I(week^2)-Vulpes_vulpes 1.0021 1063
## I(week^2)-Sus_scrofa 1.0004 4097
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2183
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0268 0.5448 -0.9698 -0.001 1.207 1.0117 1451
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.082 2.4259 0.7568 2.4388 9.3652 1.055 1076
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5744 0.4533 -3.4320 -2.5827 -1.6436 1.0022 3441
## shrub_cover 0.0733 0.2742 -0.4708 0.0717 0.6147 1.0037 3118
## veg_height -0.0597 0.1560 -0.3729 -0.0597 0.2447 1.0018 3064
## week 0.2907 0.2373 -0.1968 0.2960 0.7471 1.0029 2951
## I(week^2) -0.2946 0.0995 -0.4955 -0.2923 -0.1038 1.0006 2095
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5074 1.4966 0.9494 2.1417 6.1898 1.0023 2464
## shrub_cover 0.6791 0.5006 0.1600 0.5451 2.0147 1.0007 1588
## veg_height 0.2052 0.1366 0.0593 0.1700 0.5652 0.9999 2761
## week 0.4736 0.3412 0.1212 0.3823 1.4028 1.0005 1731
## I(week^2) 0.0707 0.0474 0.0227 0.0582 0.1937 1.0109 2199
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4804 1.1619 1.7939 3.2861 6.5354
## (Intercept)-Canis_latrans 0.4180 0.4245 -0.3743 0.3973 1.3237
## (Intercept)-Sciurus_niger -0.2776 1.0800 -1.8683 -0.4599 2.4250
## (Intercept)-Procyon_lotor 0.7532 0.4099 -0.0014 0.7339 1.5978
## (Intercept)-Dasypus_novemcinctus -0.5629 0.3765 -1.3007 -0.5569 0.1986
## (Intercept)-Lynx_rufus 0.6847 1.0033 -0.7025 0.5094 3.3395
## (Intercept)-Didelphis_virginiana -1.1939 0.4700 -2.1553 -1.1809 -0.3050
## (Intercept)-Sylvilagus_floridanus -0.2765 0.5128 -1.2070 -0.3093 0.8502
## (Intercept)-Meleagris_gallopavo 1.0585 1.2734 -0.6767 0.8428 4.0132
## (Intercept)-Sciurus_carolinensis -1.1986 0.4712 -2.1701 -1.1788 -0.3021
## (Intercept)-Vulpes_vulpes -0.8433 1.1883 -2.6506 -1.0284 2.0377
## (Intercept)-Sus_scrofa -1.6251 0.6612 -2.9332 -1.6236 -0.3082
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0180 1261
## (Intercept)-Canis_latrans 1.0012 4456
## (Intercept)-Sciurus_niger 1.0262 502
## (Intercept)-Procyon_lotor 1.0015 5250
## (Intercept)-Dasypus_novemcinctus 1.0045 5041
## (Intercept)-Lynx_rufus 1.0203 647
## (Intercept)-Didelphis_virginiana 1.0002 4529
## (Intercept)-Sylvilagus_floridanus 1.0000 2423
## (Intercept)-Meleagris_gallopavo 1.0748 516
## (Intercept)-Sciurus_carolinensis 1.0049 4291
## (Intercept)-Vulpes_vulpes 1.0313 487
## (Intercept)-Sus_scrofa 1.0111 2358
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5374 0.0802 0.3826 0.5363 0.6934
## (Intercept)-Canis_latrans -2.5711 0.2043 -2.9878 -2.5635 -2.1902
## (Intercept)-Sciurus_niger -4.2804 0.7044 -5.7248 -4.2674 -2.9525
## (Intercept)-Procyon_lotor -2.1812 0.1609 -2.5075 -2.1762 -1.8753
## (Intercept)-Dasypus_novemcinctus -1.5783 0.1764 -1.9379 -1.5754 -1.2385
## (Intercept)-Lynx_rufus -3.6791 0.3940 -4.4773 -3.6708 -2.9237
## (Intercept)-Didelphis_virginiana -2.3401 0.3110 -2.9693 -2.3298 -1.7654
## (Intercept)-Sylvilagus_floridanus -3.1147 0.3337 -3.8296 -3.0962 -2.5178
## (Intercept)-Meleagris_gallopavo -4.2723 0.4990 -5.2270 -4.2771 -3.2762
## (Intercept)-Sciurus_carolinensis -2.3935 0.3338 -3.0857 -2.3791 -1.7798
## (Intercept)-Vulpes_vulpes -4.1463 0.7853 -5.7762 -4.1108 -2.7434
## (Intercept)-Sus_scrofa -3.1969 0.6580 -4.5092 -3.1920 -1.9102
## shrub_cover-Odocoileus_virginianus -0.0631 0.0692 -0.1979 -0.0632 0.0733
## shrub_cover-Canis_latrans -0.3296 0.2191 -0.7566 -0.3343 0.1009
## shrub_cover-Sciurus_niger -0.5228 0.4807 -1.5125 -0.5162 0.4185
## shrub_cover-Procyon_lotor 0.2355 0.1643 -0.0996 0.2395 0.5466
## shrub_cover-Dasypus_novemcinctus 0.8038 0.2945 0.2498 0.8024 1.4072
## shrub_cover-Lynx_rufus -0.4111 0.3438 -1.0979 -0.4060 0.2696
## shrub_cover-Didelphis_virginiana 0.9369 0.3669 0.2542 0.9246 1.7043
## shrub_cover-Sylvilagus_floridanus 0.2003 0.4253 -0.5793 0.1833 1.0904
## shrub_cover-Meleagris_gallopavo -0.9885 0.4051 -1.8150 -0.9718 -0.2481
## shrub_cover-Sciurus_carolinensis 0.7690 0.4125 0.0056 0.7563 1.6089
## shrub_cover-Vulpes_vulpes -0.2842 0.5817 -1.4919 -0.2770 0.8503
## shrub_cover-Sus_scrofa 0.5281 0.8579 -1.1935 0.5105 2.3042
## veg_height-Odocoileus_virginianus -0.3336 0.0686 -0.4707 -0.3319 -0.2003
## veg_height-Canis_latrans -0.6073 0.1882 -0.9868 -0.6018 -0.2588
## veg_height-Sciurus_niger -0.1562 0.3925 -0.9578 -0.1486 0.6369
## veg_height-Procyon_lotor 0.3297 0.1210 0.0895 0.3284 0.5713
## veg_height-Dasypus_novemcinctus 0.2268 0.1331 -0.0354 0.2258 0.4899
## veg_height-Lynx_rufus 0.0032 0.2394 -0.4722 0.0065 0.4653
## veg_height-Didelphis_virginiana 0.4087 0.2378 -0.0312 0.4007 0.8906
## veg_height-Sylvilagus_floridanus 0.0972 0.2412 -0.3607 0.0931 0.5808
## veg_height-Meleagris_gallopavo -0.3785 0.3273 -1.0431 -0.3716 0.2504
## veg_height-Sciurus_carolinensis 0.0469 0.2128 -0.3495 0.0423 0.4900
## veg_height-Vulpes_vulpes -0.1605 0.3207 -0.8455 -0.1401 0.4184
## veg_height-Sus_scrofa -0.1706 0.3343 -0.8552 -0.1610 0.4668
## week-Odocoileus_virginianus 1.3121 0.1232 1.0729 1.3117 1.5510
## week-Canis_latrans 0.5831 0.2633 0.0747 0.5782 1.0965
## week-Sciurus_niger -0.4697 0.5628 -1.7603 -0.4161 0.5013
## week-Procyon_lotor 0.1956 0.2113 -0.2203 0.1922 0.6209
## week-Dasypus_novemcinctus 0.1021 0.2285 -0.3472 0.0994 0.5494
## week-Lynx_rufus 0.3797 0.3604 -0.3188 0.3710 1.1116
## week-Didelphis_virginiana 0.0258 0.3795 -0.7528 0.0284 0.7655
## week-Sylvilagus_floridanus 0.0205 0.3499 -0.6818 0.0198 0.6917
## week-Meleagris_gallopavo -0.2398 0.4324 -1.1301 -0.2193 0.5609
## week-Sciurus_carolinensis 0.8015 0.3689 0.1060 0.7930 1.5467
## week-Vulpes_vulpes 0.1374 0.5270 -0.9541 0.1594 1.1616
## week-Sus_scrofa 0.6911 0.4576 -0.1893 0.6798 1.6306
## I(week^2)-Odocoileus_virginianus -0.5406 0.0511 -0.6398 -0.5404 -0.4425
## I(week^2)-Canis_latrans -0.2445 0.1079 -0.4556 -0.2450 -0.0291
## I(week^2)-Sciurus_niger -0.2818 0.2315 -0.7636 -0.2702 0.1447
## I(week^2)-Procyon_lotor -0.1322 0.0912 -0.3131 -0.1304 0.0460
## I(week^2)-Dasypus_novemcinctus -0.1784 0.1040 -0.3841 -0.1766 0.0225
## I(week^2)-Lynx_rufus -0.2401 0.1553 -0.5587 -0.2372 0.0531
## I(week^2)-Didelphis_virginiana -0.4141 0.2107 -0.8818 -0.3940 -0.0511
## I(week^2)-Sylvilagus_floridanus -0.1772 0.1594 -0.4966 -0.1740 0.1258
## I(week^2)-Meleagris_gallopavo -0.3965 0.2250 -0.8954 -0.3805 0.0056
## I(week^2)-Sciurus_carolinensis -0.2836 0.1429 -0.5739 -0.2806 -0.0077
## I(week^2)-Vulpes_vulpes -0.4027 0.2499 -0.9808 -0.3800 0.0291
## I(week^2)-Sus_scrofa -0.2467 0.1802 -0.6043 -0.2463 0.0967
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0032 4909
## (Intercept)-Canis_latrans 1.0014 2601
## (Intercept)-Sciurus_niger 1.0055 510
## (Intercept)-Procyon_lotor 1.0036 3807
## (Intercept)-Dasypus_novemcinctus 1.0013 4461
## (Intercept)-Lynx_rufus 1.0062 820
## (Intercept)-Didelphis_virginiana 1.0000 2959
## (Intercept)-Sylvilagus_floridanus 1.0024 1588
## (Intercept)-Meleagris_gallopavo 1.0213 560
## (Intercept)-Sciurus_carolinensis 1.0010 2801
## (Intercept)-Vulpes_vulpes 1.0147 409
## (Intercept)-Sus_scrofa 1.0087 1799
## shrub_cover-Odocoileus_virginianus 1.0017 5250
## shrub_cover-Canis_latrans 1.0031 2887
## shrub_cover-Sciurus_niger 1.0014 1040
## shrub_cover-Procyon_lotor 1.0004 3957
## shrub_cover-Dasypus_novemcinctus 1.0004 3974
## shrub_cover-Lynx_rufus 1.0022 1459
## shrub_cover-Didelphis_virginiana 1.0032 2134
## shrub_cover-Sylvilagus_floridanus 1.0049 1763
## shrub_cover-Meleagris_gallopavo 1.0153 610
## shrub_cover-Sciurus_carolinensis 1.0022 2057
## shrub_cover-Vulpes_vulpes 1.0004 1488
## shrub_cover-Sus_scrofa 1.0002 1879
## veg_height-Odocoileus_virginianus 0.9998 5250
## veg_height-Canis_latrans 1.0007 2210
## veg_height-Sciurus_niger 1.0036 1889
## veg_height-Procyon_lotor 1.0039 4182
## veg_height-Dasypus_novemcinctus 1.0014 4825
## veg_height-Lynx_rufus 1.0025 2335
## veg_height-Didelphis_virginiana 1.0016 3524
## veg_height-Sylvilagus_floridanus 1.0007 2599
## veg_height-Meleagris_gallopavo 1.0027 1253
## veg_height-Sciurus_carolinensis 1.0008 3605
## veg_height-Vulpes_vulpes 1.0022 1871
## veg_height-Sus_scrofa 1.0027 3314
## week-Odocoileus_virginianus 1.0000 4652
## week-Canis_latrans 1.0023 3944
## week-Sciurus_niger 1.0234 847
## week-Procyon_lotor 1.0001 4136
## week-Dasypus_novemcinctus 1.0003 4512
## week-Lynx_rufus 1.0011 2423
## week-Didelphis_virginiana 1.0000 2913
## week-Sylvilagus_floridanus 1.0018 2811
## week-Meleagris_gallopavo 1.0063 989
## week-Sciurus_carolinensis 1.0023 3436
## week-Vulpes_vulpes 1.0017 1589
## week-Sus_scrofa 1.0024 3688
## I(week^2)-Odocoileus_virginianus 1.0003 4717
## I(week^2)-Canis_latrans 1.0043 4130
## I(week^2)-Sciurus_niger 1.0092 1090
## I(week^2)-Procyon_lotor 0.9998 4323
## I(week^2)-Dasypus_novemcinctus 1.0013 4231
## I(week^2)-Lynx_rufus 1.0002 2408
## I(week^2)-Didelphis_virginiana 1.0041 1837
## I(week^2)-Sylvilagus_floridanus 1.0018 2313
## I(week^2)-Meleagris_gallopavo 1.0070 816
## I(week^2)-Sciurus_carolinensis 1.0012 4284
## I(week^2)-Vulpes_vulpes 1.0010 1295
## I(week^2)-Sus_scrofa 1.0007 4257
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.3368
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0603 1.0082 -2.0298 -0.0761 1.9695 1.0010 1691
## Cogon_Patch_Size -0.5951 0.6410 -1.9188 -0.5856 0.6875 1.0044 1093
## Veg_shannon_index 0.9770 0.4879 0.0941 0.9519 2.0473 1.0137 553
## total_shrub_cover -0.5629 0.5752 -1.8487 -0.5174 0.4483 1.0032 698
## Avg_Cogongrass_Cover 1.8376 0.7390 0.4436 1.8312 3.3133 1.0199 543
## Tree_Density -1.8801 0.7220 -3.3917 -1.8410 -0.5698 1.0107 729
## Avg_Canopy_Cover 2.0022 0.6293 0.8905 1.9507 3.4052 1.0061 712
## avg_veg_height -0.4065 0.4877 -1.3552 -0.4167 0.5743 1.0019 606
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.6378 15.2228 3.2853 12.5195 53.7798 1.0168 420
## Cogon_Patch_Size 2.5376 3.9687 0.0878 1.2908 12.6062 1.0117 571
## Veg_shannon_index 0.9111 1.3407 0.0517 0.4658 4.4361 1.0553 617
## total_shrub_cover 1.5480 2.3487 0.0649 0.7840 7.2540 1.0328 282
## Avg_Cogongrass_Cover 1.4750 2.5837 0.0573 0.6149 8.1700 1.0009 448
## Tree_Density 3.2223 4.9925 0.0834 1.5596 15.7782 1.0176 571
## Avg_Canopy_Cover 2.5083 3.5651 0.1457 1.4526 11.6484 1.0166 479
## avg_veg_height 0.4716 0.7455 0.0385 0.2498 2.2147 1.0221 1280
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4261 3.996 0.0811 1.1123 13.9144 1.0269 99
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6087 0.4629 -3.5112 -2.6204 -1.6566 1.0033 5250
## shrub_cover 0.2074 0.2728 -0.3178 0.2040 0.7556 1.0010 1579
## veg_height -0.0314 0.1595 -0.3504 -0.0268 0.2764 1.0051 1888
## week 0.2964 0.2379 -0.1905 0.3045 0.7508 1.0061 2762
## I(week^2) -0.2963 0.1015 -0.5026 -0.2932 -0.1043 1.0088 1521
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6346 1.4935 0.9742 2.2873 6.4638 1.0032 3039
## shrub_cover 0.6514 0.4766 0.1548 0.5283 1.8857 1.0020 1095
## veg_height 0.2079 0.1446 0.0612 0.1709 0.5584 1.0043 3131
## week 0.4649 0.3294 0.1182 0.3797 1.3308 1.0027 1708
## I(week^2) 0.0734 0.0558 0.0221 0.0589 0.2078 1.0157 1307
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.9789 3.2815 3.2814 7.4414
## (Intercept)-Canis_latrans 0.9290 1.1520 -1.1885 0.8541
## (Intercept)-Sciurus_niger 1.7175 2.9611 -2.7329 1.2636
## (Intercept)-Procyon_lotor 0.9368 1.0931 -1.3871 0.9882
## (Intercept)-Dasypus_novemcinctus -1.4466 1.1057 -3.9868 -1.3434
## (Intercept)-Lynx_rufus 2.2910 2.8385 -1.9226 1.8226
## (Intercept)-Didelphis_virginiana -2.7603 1.2589 -5.4645 -2.6962
## (Intercept)-Sylvilagus_floridanus -0.9524 1.3840 -3.6196 -0.9869
## (Intercept)-Meleagris_gallopavo 0.0548 2.1202 -3.7975 -0.1494
## (Intercept)-Sciurus_carolinensis -2.9404 1.4097 -6.1480 -2.8130
## (Intercept)-Vulpes_vulpes -1.5799 2.2623 -5.5455 -1.7765
## (Intercept)-Sus_scrofa -4.2861 2.0237 -8.7105 -4.1237
## Cogon_Patch_Size-Odocoileus_virginianus -0.4911 1.2876 -2.8541 -0.5551
## Cogon_Patch_Size-Canis_latrans 0.6466 1.2236 -1.0513 0.4106
## Cogon_Patch_Size-Sciurus_niger -1.1011 1.6163 -4.7417 -0.9427
## Cogon_Patch_Size-Procyon_lotor -1.0027 0.7542 -2.6212 -0.9443
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5788 0.7571 -2.1017 -0.5788
## Cogon_Patch_Size-Lynx_rufus -0.6826 1.3960 -3.4137 -0.6919
## Cogon_Patch_Size-Didelphis_virginiana 0.7950 0.9841 -0.7546 0.6657
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7078 1.5571 -5.6869 -1.4068
## Cogon_Patch_Size-Meleagris_gallopavo -0.0923 1.3660 -2.2754 -0.2633
## Cogon_Patch_Size-Sciurus_carolinensis -1.5099 1.3527 -4.8406 -1.2641
## Cogon_Patch_Size-Vulpes_vulpes -0.9450 1.5879 -4.3720 -0.8671
## Cogon_Patch_Size-Sus_scrofa -1.1050 1.3792 -4.5167 -0.9151
## Veg_shannon_index-Odocoileus_virginianus 0.8021 0.9524 -1.2939 0.8387
## Veg_shannon_index-Canis_latrans 1.3553 0.7025 0.1415 1.2872
## Veg_shannon_index-Sciurus_niger 1.1206 1.0190 -0.8508 1.0729
## Veg_shannon_index-Procyon_lotor 1.2782 0.6426 0.1480 1.2237
## Veg_shannon_index-Dasypus_novemcinctus 0.6570 0.5687 -0.5176 0.6604
## Veg_shannon_index-Lynx_rufus 0.9005 0.9593 -1.0484 0.9156
## Veg_shannon_index-Didelphis_virginiana 1.1959 0.7299 -0.0603 1.1184
## Veg_shannon_index-Sylvilagus_floridanus 1.0853 0.7295 -0.2352 1.0355
## Veg_shannon_index-Meleagris_gallopavo 1.3377 0.9210 -0.2011 1.2379
## Veg_shannon_index-Sciurus_carolinensis 0.2251 0.8223 -1.5831 0.3106
## Veg_shannon_index-Vulpes_vulpes 0.4674 0.9480 -1.6724 0.5628
## Veg_shannon_index-Sus_scrofa 1.6191 1.0078 0.1749 1.4254
## total_shrub_cover-Odocoileus_virginianus -0.1513 1.0096 -2.0062 -0.1902
## total_shrub_cover-Canis_latrans 0.5397 0.9486 -0.8297 0.3646
## total_shrub_cover-Sciurus_niger -0.7095 1.1696 -3.3836 -0.6177
## total_shrub_cover-Procyon_lotor -1.0461 0.7197 -2.6370 -0.9705
## total_shrub_cover-Dasypus_novemcinctus -0.1693 0.6839 -1.5273 -0.1602
## total_shrub_cover-Lynx_rufus -0.8002 1.3799 -4.0852 -0.6759
## total_shrub_cover-Didelphis_virginiana -0.8569 0.9023 -3.0028 -0.7290
## total_shrub_cover-Sylvilagus_floridanus -0.7574 1.1178 -3.5120 -0.6151
## total_shrub_cover-Meleagris_gallopavo -1.5394 1.4054 -4.8559 -1.2948
## total_shrub_cover-Sciurus_carolinensis -0.5484 1.0086 -2.9329 -0.4233
## total_shrub_cover-Vulpes_vulpes -0.9045 1.3297 -4.3342 -0.7077
## total_shrub_cover-Sus_scrofa -0.2450 1.1140 -2.4347 -0.2466
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8104 1.1838 -0.5167 1.8050
## Avg_Cogongrass_Cover-Canis_latrans 2.3933 1.0476 0.6396 2.2851
## Avg_Cogongrass_Cover-Sciurus_niger 1.2446 1.5650 -2.6009 1.4825
## Avg_Cogongrass_Cover-Procyon_lotor 2.0824 0.9165 0.4031 2.0451
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5231 1.0366 0.7723 2.4235
## Avg_Cogongrass_Cover-Lynx_rufus 2.2681 1.1290 0.3824 2.1618
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0158 0.9330 0.3355 1.9840
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2270 1.0740 -0.9587 1.2660
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4225 1.4403 -2.0632 1.5872
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2069 0.9947 0.4512 2.1366
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4320 1.1591 0.5048 2.3128
## Avg_Cogongrass_Cover-Sus_scrofa 1.3778 1.3720 -1.7676 1.5157
## Tree_Density-Odocoileus_virginianus -0.7575 1.2645 -2.9034 -0.9010
## Tree_Density-Canis_latrans -2.6805 1.3672 -5.8360 -2.4546
## Tree_Density-Sciurus_niger -2.0768 1.6453 -5.7672 -1.9542
## Tree_Density-Procyon_lotor -1.4902 0.7947 -3.0807 -1.4844
## Tree_Density-Dasypus_novemcinctus -3.6227 1.8563 -8.3470 -3.2106
## Tree_Density-Lynx_rufus -0.5905 1.4896 -2.9975 -0.8051
## Tree_Density-Didelphis_virginiana -2.1890 1.2458 -5.0090 -2.0433
## Tree_Density-Sylvilagus_floridanus -2.4172 1.4955 -6.1210 -2.2056
## Tree_Density-Meleagris_gallopavo -2.2953 1.4909 -5.6966 -2.1258
## Tree_Density-Sciurus_carolinensis -2.4510 1.5004 -6.1394 -2.2378
## Tree_Density-Vulpes_vulpes -1.8480 1.6828 -5.3123 -1.8211
## Tree_Density-Sus_scrofa -2.3545 1.6623 -6.4528 -2.0967
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3237 1.3451 -1.4473 1.3620
## Avg_Canopy_Cover-Canis_latrans 0.2939 0.6971 -1.0842 0.2905
## Avg_Canopy_Cover-Sciurus_niger 2.2898 1.7046 -0.7054 2.1115
## Avg_Canopy_Cover-Procyon_lotor 1.8271 0.7797 0.4703 1.7773
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1674 0.7765 0.8567 2.0872
## Avg_Canopy_Cover-Lynx_rufus 1.5559 1.4479 -1.1001 1.5199
## Avg_Canopy_Cover-Didelphis_virginiana 3.0125 1.2646 1.2924 2.7776
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4846 1.6786 1.2316 3.1287
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5977 1.4207 0.5658 2.3443
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7611 1.2409 1.1292 2.5170
## Avg_Canopy_Cover-Vulpes_vulpes 2.4030 1.2693 0.4261 2.2225
## Avg_Canopy_Cover-Sus_scrofa 2.2055 0.9796 0.5908 2.0865
## avg_veg_height-Odocoileus_virginianus -0.4582 0.7870 -1.9418 -0.4642
## avg_veg_height-Canis_latrans -0.4507 0.6165 -1.6902 -0.4509
## avg_veg_height-Sciurus_niger -0.5779 0.8483 -2.3692 -0.5417
## avg_veg_height-Procyon_lotor -0.3482 0.6009 -1.5056 -0.3606
## avg_veg_height-Dasypus_novemcinctus -0.1946 0.5945 -1.3172 -0.2166
## avg_veg_height-Lynx_rufus -0.5068 0.8274 -2.1951 -0.4858
## avg_veg_height-Didelphis_virginiana -0.5673 0.6825 -2.0248 -0.5398
## avg_veg_height-Sylvilagus_floridanus -0.5801 0.6782 -2.0114 -0.5612
## avg_veg_height-Meleagris_gallopavo -0.3797 0.8627 -2.0057 -0.3936
## avg_veg_height-Sciurus_carolinensis -0.0699 0.7073 -1.3107 -0.1251
## avg_veg_height-Vulpes_vulpes -0.3595 0.7691 -1.8123 -0.3671
## avg_veg_height-Sus_scrofa -0.4721 0.7091 -1.9159 -0.4579
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.9323 1.0105 438
## (Intercept)-Canis_latrans 3.4421 1.0067 1227
## (Intercept)-Sciurus_niger 9.1798 1.0827 264
## (Intercept)-Procyon_lotor 2.9737 1.0294 1154
## (Intercept)-Dasypus_novemcinctus 0.4491 0.9999 965
## (Intercept)-Lynx_rufus 9.2875 1.0110 293
## (Intercept)-Didelphis_virginiana -0.4615 1.0092 1256
## (Intercept)-Sylvilagus_floridanus 1.9829 1.0234 968
## (Intercept)-Meleagris_gallopavo 4.8701 1.0205 348
## (Intercept)-Sciurus_carolinensis -0.4335 1.0039 895
## (Intercept)-Vulpes_vulpes 3.8310 1.0041 376
## (Intercept)-Sus_scrofa -0.7739 1.0109 501
## Cogon_Patch_Size-Odocoileus_virginianus 2.4133 1.0089 1994
## Cogon_Patch_Size-Canis_latrans 3.6865 1.0018 1306
## Cogon_Patch_Size-Sciurus_niger 1.8190 1.0192 746
## Cogon_Patch_Size-Procyon_lotor 0.3011 1.0059 425
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9655 1.0002 1442
## Cogon_Patch_Size-Lynx_rufus 2.2292 1.0053 843
## Cogon_Patch_Size-Didelphis_virginiana 3.1145 1.0060 837
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4965 1.0154 683
## Cogon_Patch_Size-Meleagris_gallopavo 3.3826 1.0033 830
## Cogon_Patch_Size-Sciurus_carolinensis 0.3885 1.0030 843
## Cogon_Patch_Size-Vulpes_vulpes 2.1667 1.0086 700
## Cogon_Patch_Size-Sus_scrofa 1.1310 1.0045 1228
## Veg_shannon_index-Odocoileus_virginianus 2.6425 1.0028 1416
## Veg_shannon_index-Canis_latrans 2.9619 1.0056 843
## Veg_shannon_index-Sciurus_niger 3.3348 1.0057 924
## Veg_shannon_index-Procyon_lotor 2.7032 1.0141 516
## Veg_shannon_index-Dasypus_novemcinctus 1.7793 1.0046 1426
## Veg_shannon_index-Lynx_rufus 2.7627 1.0285 1169
## Veg_shannon_index-Didelphis_virginiana 2.8393 1.0071 889
## Veg_shannon_index-Sylvilagus_floridanus 2.7557 1.0133 990
## Veg_shannon_index-Meleagris_gallopavo 3.5711 1.0258 815
## Veg_shannon_index-Sciurus_carolinensis 1.5978 1.0049 935
## Veg_shannon_index-Vulpes_vulpes 2.1294 1.0152 836
## Veg_shannon_index-Sus_scrofa 4.1731 1.0283 853
## total_shrub_cover-Odocoileus_virginianus 1.9880 1.0023 2079
## total_shrub_cover-Canis_latrans 2.9050 1.0039 730
## total_shrub_cover-Sciurus_niger 1.4800 1.0213 884
## total_shrub_cover-Procyon_lotor 0.1168 1.0036 989
## total_shrub_cover-Dasypus_novemcinctus 1.0965 1.0007 1982
## total_shrub_cover-Lynx_rufus 1.7385 1.0142 213
## total_shrub_cover-Didelphis_virginiana 0.5731 1.0050 742
## total_shrub_cover-Sylvilagus_floridanus 1.0969 1.0090 688
## total_shrub_cover-Meleagris_gallopavo 0.5519 1.0082 407
## total_shrub_cover-Sciurus_carolinensis 1.0739 1.0012 662
## total_shrub_cover-Vulpes_vulpes 1.1414 1.0044 519
## total_shrub_cover-Sus_scrofa 1.9183 1.0047 783
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.2343 1.0155 849
## Avg_Cogongrass_Cover-Canis_latrans 4.8403 1.0063 606
## Avg_Cogongrass_Cover-Sciurus_niger 3.7633 1.0270 504
## Avg_Cogongrass_Cover-Procyon_lotor 3.9888 1.0090 731
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8455 1.0044 536
## Avg_Cogongrass_Cover-Lynx_rufus 4.8660 1.0212 773
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9688 1.0023 920
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2441 1.0094 653
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.9119 1.0086 575
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3629 1.0058 786
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.0725 1.0065 667
## Avg_Cogongrass_Cover-Sus_scrofa 3.6329 1.0077 659
## Tree_Density-Odocoileus_virginianus 2.1837 1.0028 1030
## Tree_Density-Canis_latrans -0.7207 1.0029 507
## Tree_Density-Sciurus_niger 1.0110 1.0028 817
## Tree_Density-Procyon_lotor 0.0814 1.0045 1185
## Tree_Density-Dasypus_novemcinctus -1.1867 1.0029 361
## Tree_Density-Lynx_rufus 3.1181 1.0142 669
## Tree_Density-Didelphis_virginiana -0.0308 1.0052 1165
## Tree_Density-Sylvilagus_floridanus 0.0340 1.0014 932
## Tree_Density-Meleagris_gallopavo 0.4297 1.0071 975
## Tree_Density-Sciurus_carolinensis 0.0013 1.0054 1021
## Tree_Density-Vulpes_vulpes 1.4288 1.0189 945
## Tree_Density-Sus_scrofa 0.3131 1.0061 889
## Avg_Canopy_Cover-Odocoileus_virginianus 3.9190 1.0027 1390
## Avg_Canopy_Cover-Canis_latrans 1.6893 1.0016 1644
## Avg_Canopy_Cover-Sciurus_niger 6.2801 1.0064 551
## Avg_Canopy_Cover-Procyon_lotor 3.5484 1.0062 732
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9209 1.0105 734
## Avg_Canopy_Cover-Lynx_rufus 4.6152 1.0047 619
## Avg_Canopy_Cover-Didelphis_virginiana 6.1835 1.0111 366
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.8783 1.0033 620
## Avg_Canopy_Cover-Meleagris_gallopavo 6.0974 1.0212 617
## Avg_Canopy_Cover-Sciurus_carolinensis 5.7074 1.0043 355
## Avg_Canopy_Cover-Vulpes_vulpes 5.3767 1.0195 810
## Avg_Canopy_Cover-Sus_scrofa 4.4080 1.0075 1413
## avg_veg_height-Odocoileus_virginianus 1.0698 1.0028 1333
## avg_veg_height-Canis_latrans 0.7712 1.0000 864
## avg_veg_height-Sciurus_niger 0.9727 1.0022 915
## avg_veg_height-Procyon_lotor 0.8557 1.0026 1121
## avg_veg_height-Dasypus_novemcinctus 1.0169 1.0010 1251
## avg_veg_height-Lynx_rufus 1.0411 1.0150 946
## avg_veg_height-Didelphis_virginiana 0.7072 1.0011 1144
## avg_veg_height-Sylvilagus_floridanus 0.6903 1.0009 1194
## avg_veg_height-Meleagris_gallopavo 1.2952 1.0081 892
## avg_veg_height-Sciurus_carolinensis 1.4784 1.0017 1173
## avg_veg_height-Vulpes_vulpes 1.2145 1.0022 1014
## avg_veg_height-Sus_scrofa 0.8988 1.0010 1317
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5399 0.0802 0.3809 0.5399 0.6975
## (Intercept)-Canis_latrans -2.5790 0.1987 -2.9838 -2.5746 -2.2026
## (Intercept)-Sciurus_niger -4.7596 0.5547 -5.8451 -4.7581 -3.6661
## (Intercept)-Procyon_lotor -2.1875 0.1646 -2.5193 -2.1822 -1.8765
## (Intercept)-Dasypus_novemcinctus -1.6043 0.1807 -1.9681 -1.6020 -1.2507
## (Intercept)-Lynx_rufus -3.7689 0.3869 -4.5054 -3.7766 -3.0036
## (Intercept)-Didelphis_virginiana -2.3562 0.3131 -2.9990 -2.3493 -1.7716
## (Intercept)-Sylvilagus_floridanus -3.1205 0.3019 -3.7311 -3.1101 -2.5515
## (Intercept)-Meleagris_gallopavo -3.9296 0.5144 -4.9303 -3.9185 -2.9342
## (Intercept)-Sciurus_carolinensis -2.4940 0.3492 -3.2243 -2.4787 -1.8656
## (Intercept)-Vulpes_vulpes -4.2925 0.7067 -5.6816 -4.2647 -2.9526
## (Intercept)-Sus_scrofa -3.1652 0.6484 -4.4496 -3.1599 -1.9330
## shrub_cover-Odocoileus_virginianus -0.0612 0.0679 -0.1934 -0.0594 0.0701
## shrub_cover-Canis_latrans -0.3768 0.2253 -0.8202 -0.3795 0.0683
## shrub_cover-Sciurus_niger -0.4281 0.4820 -1.4164 -0.4099 0.4913
## shrub_cover-Procyon_lotor 0.2655 0.1592 -0.0593 0.2682 0.5669
## shrub_cover-Dasypus_novemcinctus 0.8997 0.3108 0.2996 0.8947 1.5299
## shrub_cover-Lynx_rufus -0.2404 0.3797 -0.9639 -0.2509 0.5280
## shrub_cover-Didelphis_virginiana 0.9733 0.3715 0.2992 0.9541 1.7479
## shrub_cover-Sylvilagus_floridanus 0.4983 0.4220 -0.3354 0.4988 1.3419
## shrub_cover-Meleagris_gallopavo -0.7227 0.4535 -1.6021 -0.7186 0.1867
## shrub_cover-Sciurus_carolinensis 0.9250 0.4298 0.1009 0.9172 1.7800
## shrub_cover-Vulpes_vulpes 0.0847 0.5827 -1.1070 0.1005 1.2540
## shrub_cover-Sus_scrofa 0.6284 0.8500 -1.0418 0.6016 2.3497
## veg_height-Odocoileus_virginianus -0.3329 0.0681 -0.4690 -0.3319 -0.1986
## veg_height-Canis_latrans -0.6094 0.1835 -0.9911 -0.6046 -0.2651
## veg_height-Sciurus_niger -0.0791 0.3737 -0.8482 -0.0812 0.6687
## veg_height-Procyon_lotor 0.3419 0.1216 0.1077 0.3402 0.5863
## veg_height-Dasypus_novemcinctus 0.2422 0.1355 -0.0202 0.2418 0.5084
## veg_height-Lynx_rufus 0.0680 0.2373 -0.4112 0.0714 0.5241
## veg_height-Didelphis_virginiana 0.4280 0.2347 -0.0134 0.4163 0.9165
## veg_height-Sylvilagus_floridanus 0.1317 0.2448 -0.3438 0.1282 0.6189
## veg_height-Meleagris_gallopavo -0.2904 0.3547 -1.0119 -0.2784 0.3852
## veg_height-Sciurus_carolinensis 0.0921 0.2156 -0.3251 0.0889 0.5248
## veg_height-Vulpes_vulpes -0.2298 0.3366 -0.9501 -0.2170 0.3934
## veg_height-Sus_scrofa -0.1715 0.3288 -0.8487 -0.1582 0.4429
## week-Odocoileus_virginianus 1.3121 0.1254 1.0732 1.3096 1.5652
## week-Canis_latrans 0.5815 0.2662 0.0638 0.5729 1.1081
## week-Sciurus_niger -0.4569 0.5476 -1.6345 -0.4151 0.4920
## week-Procyon_lotor 0.1952 0.2091 -0.2170 0.1972 0.6073
## week-Dasypus_novemcinctus 0.1000 0.2277 -0.3585 0.0989 0.5469
## week-Lynx_rufus 0.3669 0.3574 -0.3282 0.3637 1.0771
## week-Didelphis_virginiana 0.0361 0.3808 -0.7282 0.0437 0.7731
## week-Sylvilagus_floridanus 0.0305 0.3556 -0.6869 0.0368 0.7049
## week-Meleagris_gallopavo -0.2257 0.4332 -1.1221 -0.2107 0.5773
## week-Sciurus_carolinensis 0.8061 0.3749 0.1024 0.7928 1.6093
## week-Vulpes_vulpes 0.1463 0.5376 -0.9660 0.1721 1.1591
## week-Sus_scrofa 0.6665 0.4537 -0.1921 0.6484 1.5871
## I(week^2)-Odocoileus_virginianus -0.5418 0.0514 -0.6445 -0.5409 -0.4424
## I(week^2)-Canis_latrans -0.2422 0.1068 -0.4549 -0.2405 -0.0348
## I(week^2)-Sciurus_niger -0.2998 0.2436 -0.8146 -0.2883 0.1402
## I(week^2)-Procyon_lotor -0.1301 0.0900 -0.3046 -0.1286 0.0429
## I(week^2)-Dasypus_novemcinctus -0.1798 0.1055 -0.3949 -0.1805 0.0262
## I(week^2)-Lynx_rufus -0.2368 0.1510 -0.5419 -0.2335 0.0607
## I(week^2)-Didelphis_virginiana -0.4146 0.2146 -0.8945 -0.3978 -0.0515
## I(week^2)-Sylvilagus_floridanus -0.1767 0.1612 -0.4996 -0.1735 0.1297
## I(week^2)-Meleagris_gallopavo -0.3962 0.2342 -0.9195 -0.3706 0.0080
## I(week^2)-Sciurus_carolinensis -0.2868 0.1461 -0.5864 -0.2841 -0.0102
## I(week^2)-Vulpes_vulpes -0.4061 0.2541 -0.9668 -0.3839 0.0320
## I(week^2)-Sus_scrofa -0.2413 0.1800 -0.6031 -0.2380 0.1070
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5504
## (Intercept)-Canis_latrans 1.0033 2575
## (Intercept)-Sciurus_niger 1.0160 483
## (Intercept)-Procyon_lotor 1.0005 3510
## (Intercept)-Dasypus_novemcinctus 1.0023 3758
## (Intercept)-Lynx_rufus 1.0049 448
## (Intercept)-Didelphis_virginiana 1.0066 2381
## (Intercept)-Sylvilagus_floridanus 1.0070 2035
## (Intercept)-Meleagris_gallopavo 1.0081 536
## (Intercept)-Sciurus_carolinensis 1.0032 1161
## (Intercept)-Vulpes_vulpes 1.0059 400
## (Intercept)-Sus_scrofa 1.0047 1021
## shrub_cover-Odocoileus_virginianus 1.0006 5045
## shrub_cover-Canis_latrans 1.0009 1837
## shrub_cover-Sciurus_niger 1.0037 857
## shrub_cover-Procyon_lotor 1.0000 4034
## shrub_cover-Dasypus_novemcinctus 1.0009 2576
## shrub_cover-Lynx_rufus 0.9999 671
## shrub_cover-Didelphis_virginiana 1.0062 1887
## shrub_cover-Sylvilagus_floridanus 1.0077 1103
## shrub_cover-Meleagris_gallopavo 1.0029 588
## shrub_cover-Sciurus_carolinensis 1.0017 1225
## shrub_cover-Vulpes_vulpes 1.0006 1104
## shrub_cover-Sus_scrofa 1.0090 1031
## veg_height-Odocoileus_virginianus 0.9998 5250
## veg_height-Canis_latrans 1.0048 2223
## veg_height-Sciurus_niger 1.0177 1008
## veg_height-Procyon_lotor 1.0037 4226
## veg_height-Dasypus_novemcinctus 1.0010 4812
## veg_height-Lynx_rufus 1.0157 1630
## veg_height-Didelphis_virginiana 1.0073 3184
## veg_height-Sylvilagus_floridanus 1.0043 2008
## veg_height-Meleagris_gallopavo 1.0027 1165
## veg_height-Sciurus_carolinensis 1.0004 2366
## veg_height-Vulpes_vulpes 1.0028 1639
## veg_height-Sus_scrofa 1.0019 2969
## week-Odocoileus_virginianus 0.9998 5250
## week-Canis_latrans 1.0013 4052
## week-Sciurus_niger 1.0130 680
## week-Procyon_lotor 1.0003 4342
## week-Dasypus_novemcinctus 1.0001 4365
## week-Lynx_rufus 1.0014 2307
## week-Didelphis_virginiana 1.0029 2450
## week-Sylvilagus_floridanus 1.0002 2788
## week-Meleagris_gallopavo 1.0006 1160
## week-Sciurus_carolinensis 1.0033 3142
## week-Vulpes_vulpes 1.0084 1608
## week-Sus_scrofa 1.0000 3644
## I(week^2)-Odocoileus_virginianus 1.0010 5250
## I(week^2)-Canis_latrans 1.0026 3668
## I(week^2)-Sciurus_niger 1.0088 619
## I(week^2)-Procyon_lotor 1.0022 4359
## I(week^2)-Dasypus_novemcinctus 1.0050 4040
## I(week^2)-Lynx_rufus 1.0020 2204
## I(week^2)-Didelphis_virginiana 1.0101 1438
## I(week^2)-Sylvilagus_floridanus 1.0018 1994
## I(week^2)-Meleagris_gallopavo 1.0111 746
## I(week^2)-Sciurus_carolinensis 1.0051 3386
## I(week^2)-Vulpes_vulpes 1.0101 1140
## I(week^2)-Sus_scrofa 1.0001 3776
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.3375
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1202 0.6093 -1.0857 0.0997 1.4050 1.0082 895
## Avg_Cogongrass_Cover -0.0683 0.3565 -0.8189 -0.0613 0.5966 1.0073 1323
## total_shrub_cover -0.8321 0.4841 -1.8929 -0.7943 -0.0044 1.0078 511
## avg_veg_height 0.1551 0.3475 -0.5082 0.1461 0.8664 1.0032 758
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0810 2.7699 0.2483 2.3577 10.6583 1.0053 966
## Avg_Cogongrass_Cover 0.4775 0.6947 0.0421 0.2650 2.0892 1.0244 927
## total_shrub_cover 1.0360 1.3051 0.0652 0.6401 4.3642 1.0311 618
## avg_veg_height 0.3017 0.3561 0.0377 0.1914 1.2666 1.0321 1040
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7219 1.7646 0.083 1.2014 6.2965 1.0604 304
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6052 0.4625 -3.4918 -2.6094 -1.6329 1.0017 3233
## shrub_cover 0.3794 0.2993 -0.2039 0.3700 0.9887 1.0033 1006
## veg_height -0.0408 0.1647 -0.3696 -0.0404 0.2825 1.0029 2057
## week 0.2883 0.2364 -0.1913 0.2979 0.7308 1.0021 2777
## I(week^2) -0.2966 0.1012 -0.5098 -0.2947 -0.1072 1.0100 1654
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4135 1.3829 0.8767 2.0605 6.0447 1.0010 1900
## shrub_cover 0.7509 0.5651 0.1573 0.6061 2.1507 1.0068 628
## veg_height 0.2113 0.1472 0.0608 0.1755 0.5848 1.0031 2758
## week 0.4718 0.3485 0.1219 0.3816 1.3930 1.0141 1667
## I(week^2) 0.0724 0.0513 0.0225 0.0584 0.2044 1.0041 1731
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3895 1.5419 0.5741 3.2985
## (Intercept)-Canis_latrans 0.6372 0.8084 -0.8705 0.5863
## (Intercept)-Sciurus_niger -0.1614 1.3015 -2.3973 -0.2688
## (Intercept)-Procyon_lotor 0.8237 0.8294 -0.7885 0.8059
## (Intercept)-Dasypus_novemcinctus -0.3537 0.8132 -1.8397 -0.4040
## (Intercept)-Lynx_rufus 0.2027 1.0988 -1.6408 0.0970
## (Intercept)-Didelphis_virginiana -0.8323 0.8706 -2.4979 -0.8461
## (Intercept)-Sylvilagus_floridanus 0.3317 0.9699 -1.4690 0.2615
## (Intercept)-Meleagris_gallopavo -0.0539 1.1725 -2.0483 -0.1468
## (Intercept)-Sciurus_carolinensis -0.8861 0.9295 -2.7004 -0.9044
## (Intercept)-Vulpes_vulpes -0.3147 1.4355 -2.8368 -0.4161
## (Intercept)-Sus_scrofa -1.2160 1.1497 -3.4737 -1.2202
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0613 0.5976 -1.2733 -0.0555
## Avg_Cogongrass_Cover-Canis_latrans 0.3223 0.5333 -0.6401 0.2855
## Avg_Cogongrass_Cover-Sciurus_niger -0.4507 0.7694 -2.2823 -0.3569
## Avg_Cogongrass_Cover-Procyon_lotor -0.1227 0.4872 -1.1329 -0.1107
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1237 0.4514 -0.7562 0.1221
## Avg_Cogongrass_Cover-Lynx_rufus 0.3229 0.5862 -0.7318 0.2787
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1375 0.5064 -0.8443 0.1275
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4409 0.5980 -1.7912 -0.3935
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4475 0.7315 -2.1744 -0.3742
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0291 0.4929 -0.9726 0.0369
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1021 0.6031 -1.0543 0.0840
## Avg_Cogongrass_Cover-Sus_scrofa -0.3335 0.7196 -1.9721 -0.2596
## total_shrub_cover-Odocoileus_virginianus -0.4033 0.7313 -1.7966 -0.4285
## total_shrub_cover-Canis_latrans 0.2237 0.7113 -0.9865 0.1521
## total_shrub_cover-Sciurus_niger -0.9156 0.8956 -2.8922 -0.8564
## total_shrub_cover-Procyon_lotor -1.3164 0.6819 -2.9324 -1.2224
## total_shrub_cover-Dasypus_novemcinctus -0.4722 0.7098 -2.3338 -0.3551
## total_shrub_cover-Lynx_rufus -1.2527 0.9153 -3.3654 -1.1547
## total_shrub_cover-Didelphis_virginiana -0.8405 0.7201 -2.6302 -0.7500
## total_shrub_cover-Sylvilagus_floridanus -1.4424 1.0202 -3.9455 -1.2785
## total_shrub_cover-Meleagris_gallopavo -1.4185 0.9208 -3.5428 -1.2940
## total_shrub_cover-Sciurus_carolinensis -0.8342 0.7697 -2.5778 -0.7433
## total_shrub_cover-Vulpes_vulpes -1.0197 1.1581 -3.6059 -0.8959
## total_shrub_cover-Sus_scrofa -0.5986 0.9097 -2.6141 -0.5673
## avg_veg_height-Odocoileus_virginianus 0.1351 0.5417 -0.9325 0.1306
## avg_veg_height-Canis_latrans 0.1822 0.4875 -0.7469 0.1571
## avg_veg_height-Sciurus_niger -0.0941 0.6746 -1.6200 -0.0490
## avg_veg_height-Procyon_lotor 0.1857 0.4594 -0.6786 0.1758
## avg_veg_height-Dasypus_novemcinctus 0.3739 0.4650 -0.4523 0.3449
## avg_veg_height-Lynx_rufus 0.1444 0.5824 -0.9783 0.1279
## avg_veg_height-Didelphis_virginiana 0.0617 0.4902 -0.9464 0.0657
## avg_veg_height-Sylvilagus_floridanus 0.1284 0.5292 -0.9020 0.1101
## avg_veg_height-Meleagris_gallopavo -0.0369 0.6603 -1.4585 -0.0011
## avg_veg_height-Sciurus_carolinensis 0.4841 0.5077 -0.4058 0.4420
## avg_veg_height-Vulpes_vulpes 0.1240 0.5559 -0.9816 0.1158
## avg_veg_height-Sus_scrofa 0.1778 0.5365 -0.8957 0.1711
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.7380 1.0312 682
## (Intercept)-Canis_latrans 2.4212 1.0037 1704
## (Intercept)-Sciurus_niger 2.6833 1.0077 508
## (Intercept)-Procyon_lotor 2.5584 1.0100 1342
## (Intercept)-Dasypus_novemcinctus 1.3563 1.0120 951
## (Intercept)-Lynx_rufus 2.7611 1.0419 669
## (Intercept)-Didelphis_virginiana 0.9657 1.0070 739
## (Intercept)-Sylvilagus_floridanus 2.4094 1.0016 989
## (Intercept)-Meleagris_gallopavo 2.5547 1.0130 654
## (Intercept)-Sciurus_carolinensis 0.9790 1.0024 1121
## (Intercept)-Vulpes_vulpes 3.0284 1.0055 415
## (Intercept)-Sus_scrofa 0.9976 1.0045 692
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1320 1.0010 2652
## Avg_Cogongrass_Cover-Canis_latrans 1.4870 1.0038 1772
## Avg_Cogongrass_Cover-Sciurus_niger 0.7928 1.0106 966
## Avg_Cogongrass_Cover-Procyon_lotor 0.8071 1.0032 1933
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0496 1.0120 2757
## Avg_Cogongrass_Cover-Lynx_rufus 1.6629 1.0051 1894
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1640 1.0029 2258
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5896 1.0014 1690
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7651 1.0069 1177
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9982 1.0023 1877
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3717 1.0076 2001
## Avg_Cogongrass_Cover-Sus_scrofa 0.8333 1.0045 1453
## total_shrub_cover-Odocoileus_virginianus 1.1612 1.0053 2483
## total_shrub_cover-Canis_latrans 1.8709 1.0051 974
## total_shrub_cover-Sciurus_niger 0.7550 1.0024 770
## total_shrub_cover-Procyon_lotor -0.2617 1.0044 689
## total_shrub_cover-Dasypus_novemcinctus 0.5601 1.0314 698
## total_shrub_cover-Lynx_rufus 0.3501 1.0119 780
## total_shrub_cover-Didelphis_virginiana 0.2974 1.0065 631
## total_shrub_cover-Sylvilagus_floridanus 0.0940 1.0094 408
## total_shrub_cover-Meleagris_gallopavo 0.0669 1.0076 671
## total_shrub_cover-Sciurus_carolinensis 0.4441 1.0032 761
## total_shrub_cover-Vulpes_vulpes 0.9532 1.0114 491
## total_shrub_cover-Sus_scrofa 1.1228 1.0035 620
## avg_veg_height-Odocoileus_virginianus 1.2193 1.0043 1636
## avg_veg_height-Canis_latrans 1.2026 1.0037 1375
## avg_veg_height-Sciurus_niger 1.0882 1.0080 1081
## avg_veg_height-Procyon_lotor 1.1433 1.0039 1147
## avg_veg_height-Dasypus_novemcinctus 1.4135 1.0161 1013
## avg_veg_height-Lynx_rufus 1.3709 1.0028 1411
## avg_veg_height-Didelphis_virginiana 1.0184 1.0002 1476
## avg_veg_height-Sylvilagus_floridanus 1.1947 1.0023 1167
## avg_veg_height-Meleagris_gallopavo 1.1931 1.0022 1085
## avg_veg_height-Sciurus_carolinensis 1.5940 1.0050 1250
## avg_veg_height-Vulpes_vulpes 1.2465 1.0046 1695
## avg_veg_height-Sus_scrofa 1.2423 1.0033 1862
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5351 0.0814 0.3744 0.5338 0.6945
## (Intercept)-Canis_latrans -2.6195 0.2109 -3.0452 -2.6111 -2.2138
## (Intercept)-Sciurus_niger -4.2503 0.7166 -5.6286 -4.2612 -2.8455
## (Intercept)-Procyon_lotor -2.1884 0.1616 -2.5170 -2.1853 -1.8755
## (Intercept)-Dasypus_novemcinctus -1.6576 0.1979 -2.0584 -1.6506 -1.2906
## (Intercept)-Lynx_rufus -3.4729 0.3795 -4.2692 -3.4591 -2.7690
## (Intercept)-Didelphis_virginiana -2.5099 0.3496 -3.2381 -2.4967 -1.8765
## (Intercept)-Sylvilagus_floridanus -3.2349 0.3111 -3.8610 -3.2296 -2.6610
## (Intercept)-Meleagris_gallopavo -3.7637 0.5796 -4.9028 -3.7559 -2.6653
## (Intercept)-Sciurus_carolinensis -2.5956 0.3662 -3.3417 -2.5791 -1.9347
## (Intercept)-Vulpes_vulpes -4.3428 0.7525 -5.8165 -4.3366 -2.9141
## (Intercept)-Sus_scrofa -3.4840 0.6524 -4.7489 -3.5004 -2.1332
## shrub_cover-Odocoileus_virginianus -0.0595 0.0681 -0.1932 -0.0589 0.0712
## shrub_cover-Canis_latrans -0.3039 0.2510 -0.7944 -0.3062 0.1885
## shrub_cover-Sciurus_niger -0.2167 0.5496 -1.2794 -0.2201 0.8619
## shrub_cover-Procyon_lotor 0.3176 0.1625 -0.0076 0.3205 0.6264
## shrub_cover-Dasypus_novemcinctus 1.0508 0.3753 0.3601 1.0327 1.8082
## shrub_cover-Lynx_rufus 0.0251 0.3881 -0.7661 0.0394 0.7387
## shrub_cover-Didelphis_virginiana 1.2206 0.4343 0.4246 1.1955 2.1249
## shrub_cover-Sylvilagus_floridanus 0.7515 0.4484 -0.1855 0.7642 1.6034
## shrub_cover-Meleagris_gallopavo -0.5597 0.4911 -1.5329 -0.5562 0.3916
## shrub_cover-Sciurus_carolinensis 1.1426 0.4535 0.2599 1.1405 2.0430
## shrub_cover-Vulpes_vulpes 0.1927 0.6616 -1.1502 0.2019 1.5090
## shrub_cover-Sus_scrofa 1.0360 0.8952 -0.8226 1.0395 2.7946
## veg_height-Odocoileus_virginianus -0.3321 0.0686 -0.4657 -0.3319 -0.1984
## veg_height-Canis_latrans -0.6192 0.1829 -0.9811 -0.6133 -0.2673
## veg_height-Sciurus_niger -0.0280 0.4387 -0.8629 -0.0409 0.8924
## veg_height-Procyon_lotor 0.3355 0.1225 0.0957 0.3338 0.5813
## veg_height-Dasypus_novemcinctus 0.2494 0.1401 -0.0216 0.2478 0.5305
## veg_height-Lynx_rufus 0.0098 0.2522 -0.4944 0.0134 0.4949
## veg_height-Didelphis_virginiana 0.4037 0.2541 -0.0637 0.3940 0.9378
## veg_height-Sylvilagus_floridanus 0.0323 0.2459 -0.4319 0.0307 0.5310
## veg_height-Meleagris_gallopavo -0.2535 0.4096 -1.0334 -0.2697 0.5827
## veg_height-Sciurus_carolinensis 0.0803 0.2242 -0.3498 0.0752 0.5338
## veg_height-Vulpes_vulpes -0.1770 0.3429 -0.8921 -0.1659 0.4720
## veg_height-Sus_scrofa -0.1935 0.3358 -0.8795 -0.1840 0.4595
## week-Odocoileus_virginianus 1.3113 0.1269 1.0654 1.3082 1.5593
## week-Canis_latrans 0.5938 0.2699 0.0678 0.5910 1.1226
## week-Sciurus_niger -0.4900 0.5751 -1.7471 -0.4385 0.4762
## week-Procyon_lotor 0.1978 0.2075 -0.2114 0.1958 0.6089
## week-Dasypus_novemcinctus 0.0974 0.2290 -0.3497 0.0970 0.5456
## week-Lynx_rufus 0.3748 0.3554 -0.3263 0.3741 1.0706
## week-Didelphis_virginiana 0.0173 0.3783 -0.7404 0.0268 0.7321
## week-Sylvilagus_floridanus 0.0346 0.3530 -0.6772 0.0400 0.7219
## week-Meleagris_gallopavo -0.2453 0.4442 -1.1933 -0.2226 0.5643
## week-Sciurus_carolinensis 0.8038 0.3756 0.0867 0.7934 1.5632
## week-Vulpes_vulpes 0.1653 0.5455 -1.0286 0.1873 1.1713
## week-Sus_scrofa 0.6837 0.4627 -0.2055 0.6675 1.6519
## I(week^2)-Odocoileus_virginianus -0.5406 0.0521 -0.6425 -0.5401 -0.4392
## I(week^2)-Canis_latrans -0.2477 0.1102 -0.4637 -0.2460 -0.0385
## I(week^2)-Sciurus_niger -0.2805 0.2426 -0.8019 -0.2635 0.1584
## I(week^2)-Procyon_lotor -0.1324 0.0897 -0.3137 -0.1309 0.0418
## I(week^2)-Dasypus_novemcinctus -0.1791 0.1052 -0.3909 -0.1780 0.0210
## I(week^2)-Lynx_rufus -0.2367 0.1514 -0.5468 -0.2335 0.0527
## I(week^2)-Didelphis_virginiana -0.4208 0.2149 -0.8981 -0.4009 -0.0624
## I(week^2)-Sylvilagus_floridanus -0.1828 0.1645 -0.5149 -0.1784 0.1216
## I(week^2)-Meleagris_gallopavo -0.3972 0.2377 -0.9295 -0.3732 0.0113
## I(week^2)-Sciurus_carolinensis -0.2840 0.1450 -0.5811 -0.2853 -0.0019
## I(week^2)-Vulpes_vulpes -0.4020 0.2550 -0.9651 -0.3771 0.0299
## I(week^2)-Sus_scrofa -0.2461 0.1816 -0.6193 -0.2416 0.0957
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0032 5250
## (Intercept)-Canis_latrans 1.0007 1944
## (Intercept)-Sciurus_niger 1.0024 467
## (Intercept)-Procyon_lotor 1.0057 3476
## (Intercept)-Dasypus_novemcinctus 1.0050 1495
## (Intercept)-Lynx_rufus 1.0170 918
## (Intercept)-Didelphis_virginiana 1.0023 989
## (Intercept)-Sylvilagus_floridanus 1.0000 1555
## (Intercept)-Meleagris_gallopavo 1.0037 623
## (Intercept)-Sciurus_carolinensis 1.0122 1341
## (Intercept)-Vulpes_vulpes 1.0074 446
## (Intercept)-Sus_scrofa 1.0066 787
## shrub_cover-Odocoileus_virginianus 1.0005 5250
## shrub_cover-Canis_latrans 1.0139 1402
## shrub_cover-Sciurus_niger 1.0023 786
## shrub_cover-Procyon_lotor 1.0012 3287
## shrub_cover-Dasypus_novemcinctus 1.0138 764
## shrub_cover-Lynx_rufus 1.0008 1172
## shrub_cover-Didelphis_virginiana 1.0029 684
## shrub_cover-Sylvilagus_floridanus 1.0116 721
## shrub_cover-Meleagris_gallopavo 1.0048 717
## shrub_cover-Sciurus_carolinensis 1.0103 864
## shrub_cover-Vulpes_vulpes 1.0028 571
## shrub_cover-Sus_scrofa 1.0088 475
## veg_height-Odocoileus_virginianus 1.0008 5250
## veg_height-Canis_latrans 1.0030 2102
## veg_height-Sciurus_niger 1.0005 1332
## veg_height-Procyon_lotor 1.0007 4176
## veg_height-Dasypus_novemcinctus 0.9998 4892
## veg_height-Lynx_rufus 1.0112 2097
## veg_height-Didelphis_virginiana 1.0022 2364
## veg_height-Sylvilagus_floridanus 1.0044 1532
## veg_height-Meleagris_gallopavo 1.0041 1130
## veg_height-Sciurus_carolinensis 1.0033 2697
## veg_height-Vulpes_vulpes 1.0149 1677
## veg_height-Sus_scrofa 1.0008 2470
## week-Odocoileus_virginianus 1.0021 3929
## week-Canis_latrans 1.0012 3164
## week-Sciurus_niger 1.0141 932
## week-Procyon_lotor 0.9999 4420
## week-Dasypus_novemcinctus 1.0016 4467
## week-Lynx_rufus 1.0010 2375
## week-Didelphis_virginiana 1.0046 2297
## week-Sylvilagus_floridanus 1.0016 2389
## week-Meleagris_gallopavo 1.0114 1121
## week-Sciurus_carolinensis 1.0041 3304
## week-Vulpes_vulpes 1.0012 1168
## week-Sus_scrofa 1.0044 2976
## I(week^2)-Odocoileus_virginianus 1.0013 3777
## I(week^2)-Canis_latrans 1.0019 3423
## I(week^2)-Sciurus_niger 1.0171 898
## I(week^2)-Procyon_lotor 0.9999 4302
## I(week^2)-Dasypus_novemcinctus 0.9999 4139
## I(week^2)-Lynx_rufus 1.0001 2656
## I(week^2)-Didelphis_virginiana 1.0065 1409
## I(week^2)-Sylvilagus_floridanus 1.0021 2026
## I(week^2)-Meleagris_gallopavo 1.0278 784
## I(week^2)-Sciurus_carolinensis 1.0066 3940
## I(week^2)-Vulpes_vulpes 1.0146 992
## I(week^2)-Sus_scrofa 1.0005 3165
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T<- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2655
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0265 0.7020 -1.3789 -0.0489 1.4453 1.0060 1778
## Tree_Density -0.7943 0.3821 -1.6355 -0.7635 -0.1074 1.0039 1435
## Avg_Canopy_Cover 1.1587 0.3817 0.4812 1.1313 2.0099 1.0090 1205
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.7455 4.6014 1.3510 4.5361 17.0262 1.0239 1116
## Tree_Density 0.6396 1.0283 0.0413 0.3085 3.3166 1.0226 763
## Avg_Canopy_Cover 0.7535 0.8306 0.0749 0.5097 2.9220 1.0082 1247
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5213 0.6962 0.039 0.2979 2.4301 1.0456 433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5887 0.4662 -3.4884 -2.5947 -1.6164 1.0007 3913
## shrub_cover 0.1214 0.2645 -0.4094 0.1184 0.6538 1.0019 3495
## veg_height -0.0204 0.1519 -0.3193 -0.0197 0.2758 1.0062 3537
## week 0.2863 0.2430 -0.2401 0.2970 0.7299 1.0039 2443
## I(week^2) -0.2969 0.1024 -0.5088 -0.2956 -0.0973 1.0025 1917
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6056 1.5099 0.9720 2.2326 6.5419 1.0042 1914
## shrub_cover 0.6480 0.4639 0.1569 0.5307 1.8831 1.0061 2047
## veg_height 0.2013 0.1353 0.0582 0.1673 0.5550 1.0068 2874
## week 0.4740 0.3498 0.1197 0.3796 1.3795 1.0040 1665
## I(week^2) 0.0722 0.0522 0.0229 0.0587 0.2023 1.0078 1959
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.5995 1.6531 2.0599 4.3674 8.5500
## (Intercept)-Canis_latrans 0.4297 0.6465 -0.7644 0.4069 1.7455
## (Intercept)-Sciurus_niger 0.2862 1.4542 -1.9806 0.0876 3.6856
## (Intercept)-Procyon_lotor 0.8132 0.6591 -0.4832 0.8072 2.1603
## (Intercept)-Dasypus_novemcinctus -0.9063 0.6397 -2.2491 -0.8774 0.2791
## (Intercept)-Lynx_rufus 1.6382 1.6672 -0.8704 1.3669 5.6380
## (Intercept)-Didelphis_virginiana -1.6681 0.7340 -3.2134 -1.6456 -0.3114
## (Intercept)-Sylvilagus_floridanus -0.6024 0.7301 -2.0246 -0.6089 0.8404
## (Intercept)-Meleagris_gallopavo 0.4700 1.1698 -1.5143 0.3512 3.1322
## (Intercept)-Sciurus_carolinensis -1.7822 0.7810 -3.4616 -1.7454 -0.3655
## (Intercept)-Vulpes_vulpes -0.9911 1.5974 -3.5989 -1.2110 2.8504
## (Intercept)-Sus_scrofa -2.4743 0.9982 -4.6032 -2.4207 -0.6496
## Tree_Density-Odocoileus_virginianus -0.4328 0.6208 -1.4880 -0.4928 1.0294
## Tree_Density-Canis_latrans -0.9256 0.5395 -2.1877 -0.8712 -0.0233
## Tree_Density-Sciurus_niger -0.8253 0.7514 -2.4990 -0.7898 0.6112
## Tree_Density-Procyon_lotor -0.5205 0.4074 -1.2945 -0.5256 0.3190
## Tree_Density-Dasypus_novemcinctus -1.3143 0.8647 -3.5255 -1.1181 -0.2101
## Tree_Density-Lynx_rufus -0.1566 0.7405 -1.3328 -0.2589 1.5570
## Tree_Density-Didelphis_virginiana -0.9750 0.6721 -2.6105 -0.8798 0.1117
## Tree_Density-Sylvilagus_floridanus -1.0225 0.6902 -2.6676 -0.9208 0.0904
## Tree_Density-Meleagris_gallopavo -0.9888 0.7517 -2.7523 -0.9022 0.2660
## Tree_Density-Sciurus_carolinensis -0.9169 0.6916 -2.5512 -0.8317 0.2114
## Tree_Density-Vulpes_vulpes -0.7402 0.7730 -2.3360 -0.7192 0.6683
## Tree_Density-Sus_scrofa -0.9542 0.8146 -2.9530 -0.8502 0.3266
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8806 0.7345 -0.5622 0.8805 2.3327
## Avg_Canopy_Cover-Canis_latrans 0.0550 0.4969 -0.9337 0.0560 1.0341
## Avg_Canopy_Cover-Sciurus_niger 1.1657 0.9242 -0.4451 1.0962 3.2466
## Avg_Canopy_Cover-Procyon_lotor 1.1155 0.4948 0.2333 1.0841 2.1753
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1103 0.4520 0.3022 1.0837 2.0590
## Avg_Canopy_Cover-Lynx_rufus 1.1018 0.8383 -0.4345 1.0557 2.9739
## Avg_Canopy_Cover-Didelphis_virginiana 1.5075 0.6286 0.5124 1.4221 2.9992
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9166 0.8320 0.6801 1.7764 3.8664
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4822 0.7766 0.2147 1.3810 3.3189
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4407 0.5959 0.4938 1.3469 2.8164
## Avg_Canopy_Cover-Vulpes_vulpes 1.1968 0.6990 -0.0124 1.1377 2.7426
## Avg_Canopy_Cover-Sus_scrofa 1.3548 0.6037 0.3564 1.2955 2.7513
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0053 959
## (Intercept)-Canis_latrans 1.0041 2745
## (Intercept)-Sciurus_niger 1.0069 453
## (Intercept)-Procyon_lotor 1.0067 2608
## (Intercept)-Dasypus_novemcinctus 1.0042 2694
## (Intercept)-Lynx_rufus 1.0049 473
## (Intercept)-Didelphis_virginiana 1.0013 2526
## (Intercept)-Sylvilagus_floridanus 1.0005 2901
## (Intercept)-Meleagris_gallopavo 1.0023 908
## (Intercept)-Sciurus_carolinensis 1.0047 1937
## (Intercept)-Vulpes_vulpes 1.0261 349
## (Intercept)-Sus_scrofa 1.0026 1262
## Tree_Density-Odocoileus_virginianus 1.0028 1964
## Tree_Density-Canis_latrans 1.0000 2759
## Tree_Density-Sciurus_niger 1.0020 1507
## Tree_Density-Procyon_lotor 1.0014 2945
## Tree_Density-Dasypus_novemcinctus 1.0175 1082
## Tree_Density-Lynx_rufus 1.0094 1114
## Tree_Density-Didelphis_virginiana 1.0027 1628
## Tree_Density-Sylvilagus_floridanus 1.0062 2012
## Tree_Density-Meleagris_gallopavo 1.0035 1553
## Tree_Density-Sciurus_carolinensis 1.0063 1589
## Tree_Density-Vulpes_vulpes 1.0038 1677
## Tree_Density-Sus_scrofa 1.0145 1730
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0034 2021
## Avg_Canopy_Cover-Canis_latrans 1.0040 2412
## Avg_Canopy_Cover-Sciurus_niger 1.0177 976
## Avg_Canopy_Cover-Procyon_lotor 1.0040 3239
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0026 3754
## Avg_Canopy_Cover-Lynx_rufus 1.0088 1629
## Avg_Canopy_Cover-Didelphis_virginiana 1.0075 1785
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0033 1320
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0191 1201
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0096 1644
## Avg_Canopy_Cover-Vulpes_vulpes 1.0024 1975
## Avg_Canopy_Cover-Sus_scrofa 1.0037 2154
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5387 0.0813 0.3780 0.5392 0.6991
## (Intercept)-Canis_latrans -2.5789 0.2108 -3.0163 -2.5681 -2.1955
## (Intercept)-Sciurus_niger -4.5394 0.6345 -5.8046 -4.5552 -3.2739
## (Intercept)-Procyon_lotor -2.1880 0.1669 -2.5201 -2.1849 -1.8773
## (Intercept)-Dasypus_novemcinctus -1.5914 0.1793 -1.9507 -1.5881 -1.2586
## (Intercept)-Lynx_rufus -3.8442 0.3638 -4.5460 -3.8475 -3.1266
## (Intercept)-Didelphis_virginiana -2.3797 0.3173 -3.0178 -2.3746 -1.7816
## (Intercept)-Sylvilagus_floridanus -3.0415 0.2987 -3.6710 -3.0320 -2.4966
## (Intercept)-Meleagris_gallopavo -4.0612 0.4801 -5.0504 -4.0501 -3.1558
## (Intercept)-Sciurus_carolinensis -2.4520 0.3363 -3.1544 -2.4410 -1.8298
## (Intercept)-Vulpes_vulpes -4.3048 0.8092 -5.9444 -4.2772 -2.8431
## (Intercept)-Sus_scrofa -3.1029 0.6373 -4.3003 -3.1111 -1.8428
## shrub_cover-Odocoileus_virginianus -0.0605 0.0678 -0.1905 -0.0602 0.0694
## shrub_cover-Canis_latrans -0.3201 0.2238 -0.7547 -0.3202 0.1079
## shrub_cover-Sciurus_niger -0.5007 0.4558 -1.4348 -0.4882 0.3501
## shrub_cover-Procyon_lotor 0.2413 0.1622 -0.0820 0.2446 0.5443
## shrub_cover-Dasypus_novemcinctus 0.8417 0.2999 0.2648 0.8351 1.4344
## shrub_cover-Lynx_rufus -0.3952 0.3090 -1.0244 -0.3856 0.1953
## shrub_cover-Didelphis_virginiana 0.9746 0.3673 0.3022 0.9648 1.7282
## shrub_cover-Sylvilagus_floridanus 0.3663 0.3901 -0.3842 0.3680 1.1408
## shrub_cover-Meleagris_gallopavo -0.8566 0.3982 -1.6866 -0.8445 -0.1170
## shrub_cover-Sciurus_carolinensis 0.8481 0.4032 0.0687 0.8417 1.6709
## shrub_cover-Vulpes_vulpes -0.1754 0.5635 -1.3112 -0.1589 0.9225
## shrub_cover-Sus_scrofa 0.5067 0.8239 -1.1272 0.4949 2.1955
## veg_height-Odocoileus_virginianus -0.3320 0.0680 -0.4681 -0.3315 -0.1994
## veg_height-Canis_latrans -0.6013 0.1867 -0.9919 -0.5952 -0.2535
## veg_height-Sciurus_niger -0.0908 0.3630 -0.8265 -0.0869 0.6280
## veg_height-Procyon_lotor 0.3369 0.1227 0.0984 0.3356 0.5792
## veg_height-Dasypus_novemcinctus 0.2365 0.1333 -0.0221 0.2365 0.5057
## veg_height-Lynx_rufus 0.0761 0.2381 -0.4040 0.0791 0.5412
## veg_height-Didelphis_virginiana 0.4469 0.2422 -0.0148 0.4421 0.9428
## veg_height-Sylvilagus_floridanus 0.1364 0.2401 -0.3390 0.1413 0.5961
## veg_height-Meleagris_gallopavo -0.2613 0.3312 -0.9576 -0.2544 0.3655
## veg_height-Sciurus_carolinensis 0.0827 0.2142 -0.3279 0.0794 0.5134
## veg_height-Vulpes_vulpes -0.1628 0.3246 -0.8461 -0.1502 0.4320
## veg_height-Sus_scrofa -0.1215 0.3188 -0.7823 -0.1171 0.4904
## week-Odocoileus_virginianus 1.3134 0.1241 1.0754 1.3130 1.5540
## week-Canis_latrans 0.5858 0.2642 0.0844 0.5814 1.1198
## week-Sciurus_niger -0.5105 0.5680 -1.7485 -0.4680 0.4756
## week-Procyon_lotor 0.2046 0.2155 -0.2165 0.2022 0.6301
## week-Dasypus_novemcinctus 0.0974 0.2267 -0.3439 0.0983 0.5432
## week-Lynx_rufus 0.3731 0.3480 -0.3030 0.3700 1.0679
## week-Didelphis_virginiana 0.0276 0.3833 -0.7589 0.0418 0.7620
## week-Sylvilagus_floridanus 0.0302 0.3514 -0.6796 0.0316 0.7063
## week-Meleagris_gallopavo -0.2538 0.4441 -1.1907 -0.2321 0.5586
## week-Sciurus_carolinensis 0.8038 0.3737 0.1162 0.7952 1.5804
## week-Vulpes_vulpes 0.1465 0.5278 -0.9448 0.1636 1.1506
## week-Sus_scrofa 0.6913 0.4675 -0.1767 0.6800 1.6529
## I(week^2)-Odocoileus_virginianus -0.5414 0.0512 -0.6432 -0.5417 -0.4416
## I(week^2)-Canis_latrans -0.2467 0.1080 -0.4666 -0.2441 -0.0414
## I(week^2)-Sciurus_niger -0.2940 0.2378 -0.8063 -0.2858 0.1465
## I(week^2)-Procyon_lotor -0.1328 0.0942 -0.3188 -0.1322 0.0515
## I(week^2)-Dasypus_novemcinctus -0.1786 0.1046 -0.3883 -0.1760 0.0218
## I(week^2)-Lynx_rufus -0.2422 0.1553 -0.5594 -0.2378 0.0580
## I(week^2)-Didelphis_virginiana -0.4199 0.2113 -0.8928 -0.4020 -0.0594
## I(week^2)-Sylvilagus_floridanus -0.1779 0.1615 -0.5063 -0.1740 0.1241
## I(week^2)-Meleagris_gallopavo -0.4078 0.2423 -0.9651 -0.3838 -0.0023
## I(week^2)-Sciurus_carolinensis -0.2844 0.1473 -0.5915 -0.2790 -0.0128
## I(week^2)-Vulpes_vulpes -0.4073 0.2431 -0.9523 -0.3880 0.0128
## I(week^2)-Sus_scrofa -0.2422 0.1807 -0.6009 -0.2401 0.1137
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0039 5250
## (Intercept)-Canis_latrans 1.0032 2252
## (Intercept)-Sciurus_niger 1.0030 442
## (Intercept)-Procyon_lotor 1.0040 3870
## (Intercept)-Dasypus_novemcinctus 1.0002 4116
## (Intercept)-Lynx_rufus 1.0010 667
## (Intercept)-Didelphis_virginiana 1.0022 2353
## (Intercept)-Sylvilagus_floridanus 1.0069 2441
## (Intercept)-Meleagris_gallopavo 1.0223 740
## (Intercept)-Sciurus_carolinensis 1.0026 2450
## (Intercept)-Vulpes_vulpes 1.0210 327
## (Intercept)-Sus_scrofa 1.0040 2011
## shrub_cover-Odocoileus_virginianus 1.0025 5769
## shrub_cover-Canis_latrans 1.0023 2647
## shrub_cover-Sciurus_niger 1.0073 1050
## shrub_cover-Procyon_lotor 1.0012 4116
## shrub_cover-Dasypus_novemcinctus 1.0054 3601
## shrub_cover-Lynx_rufus 1.0017 1265
## shrub_cover-Didelphis_virginiana 1.0011 2265
## shrub_cover-Sylvilagus_floridanus 0.9999 1886
## shrub_cover-Meleagris_gallopavo 1.0148 931
## shrub_cover-Sciurus_carolinensis 1.0144 2233
## shrub_cover-Vulpes_vulpes 1.0045 1781
## shrub_cover-Sus_scrofa 1.0042 2438
## veg_height-Odocoileus_virginianus 1.0018 5250
## veg_height-Canis_latrans 1.0044 2347
## veg_height-Sciurus_niger 1.0011 1876
## veg_height-Procyon_lotor 1.0021 4129
## veg_height-Dasypus_novemcinctus 1.0020 4758
## veg_height-Lynx_rufus 1.0046 2129
## veg_height-Didelphis_virginiana 1.0021 3153
## veg_height-Sylvilagus_floridanus 1.0006 3240
## veg_height-Meleagris_gallopavo 1.0007 1990
## veg_height-Sciurus_carolinensis 1.0012 3286
## veg_height-Vulpes_vulpes 1.0065 1844
## veg_height-Sus_scrofa 1.0025 4279
## week-Odocoileus_virginianus 1.0033 4663
## week-Canis_latrans 1.0004 3869
## week-Sciurus_niger 1.0128 856
## week-Procyon_lotor 0.9998 4456
## week-Dasypus_novemcinctus 0.9999 4604
## week-Lynx_rufus 1.0021 2338
## week-Didelphis_virginiana 1.0003 2539
## week-Sylvilagus_floridanus 1.0105 2916
## week-Meleagris_gallopavo 1.0006 861
## week-Sciurus_carolinensis 1.0015 3494
## week-Vulpes_vulpes 1.0042 1462
## week-Sus_scrofa 1.0060 3849
## I(week^2)-Odocoileus_virginianus 1.0048 4875
## I(week^2)-Canis_latrans 1.0002 3917
## I(week^2)-Sciurus_niger 1.0028 1121
## I(week^2)-Procyon_lotor 0.9998 4325
## I(week^2)-Dasypus_novemcinctus 1.0024 4031
## I(week^2)-Lynx_rufus 1.0014 1897
## I(week^2)-Didelphis_virginiana 1.0105 1751
## I(week^2)-Sylvilagus_floridanus 1.0024 2403
## I(week^2)-Meleagris_gallopavo 1.0058 613
## I(week^2)-Sciurus_carolinensis 1.0025 3858
## I(week^2)-Vulpes_vulpes 1.0115 1286
## I(week^2)-Sus_scrofa 1.0067 3903
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2653
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0064 0.6281 -1.2369 -0.0106 1.2671 1.0197 1190
## Cogon_Patch_Size -0.1516 0.3788 -0.9254 -0.1372 0.5645 1.0022 1806
## Avg_Cogongrass_Cover 0.1242 0.3293 -0.5313 0.1291 0.7715 1.0097 1032
## total_shrub_cover -0.7191 0.4414 -1.6960 -0.6872 0.0556 1.0236 544
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3376 2.9079 0.3694 2.5691 11.1687 1.0067 849
## Cogon_Patch_Size 0.7490 1.1749 0.0573 0.4101 3.5055 1.0309 1076
## Avg_Cogongrass_Cover 0.4063 0.6204 0.0382 0.2303 1.7283 1.0808 1102
## total_shrub_cover 0.8149 1.1350 0.0565 0.4496 3.6744 1.0179 486
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9099 1.8478 0.1352 1.3974 6.7681 1.0157 259
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5939 0.4502 -3.4478 -2.6040 -1.6772 1.0012 3588
## shrub_cover 0.3308 0.2830 -0.2221 0.3266 0.9032 1.0010 1563
## veg_height -0.0469 0.1579 -0.3656 -0.0467 0.2610 1.0011 2695
## week 0.2891 0.2326 -0.1889 0.2970 0.7256 1.0031 2868
## I(week^2) -0.2914 0.0982 -0.4893 -0.2895 -0.1071 1.0007 1830
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3758 1.3662 0.8787 2.0346 6.0119 1.0016 2074
## shrub_cover 0.6797 0.4921 0.1455 0.5523 1.9978 1.0233 1088
## veg_height 0.2032 0.1363 0.0571 0.1688 0.5500 1.0017 2784
## week 0.4634 0.3278 0.1244 0.3795 1.2946 1.0033 1873
## I(week^2) 0.0707 0.0481 0.0223 0.0578 0.1999 1.0079 2235
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4539 1.6207 0.6696 3.3464
## (Intercept)-Canis_latrans 0.6221 0.8384 -0.9206 0.5880
## (Intercept)-Sciurus_niger -0.3025 1.3302 -2.5677 -0.4223
## (Intercept)-Procyon_lotor 0.7411 0.8384 -0.8794 0.7223
## (Intercept)-Dasypus_novemcinctus -0.5248 0.8087 -2.1825 -0.5347
## (Intercept)-Lynx_rufus 0.0681 1.1693 -1.8683 -0.0295
## (Intercept)-Didelphis_virginiana -0.9827 0.8740 -2.6841 -0.9844
## (Intercept)-Sylvilagus_floridanus 0.0531 0.9835 -1.7390 0.0001
## (Intercept)-Meleagris_gallopavo -0.0012 1.2659 -2.2465 -0.1047
## (Intercept)-Sciurus_carolinensis -1.1428 0.9027 -3.0340 -1.1239
## (Intercept)-Vulpes_vulpes -0.5980 1.4574 -3.1914 -0.6894
## (Intercept)-Sus_scrofa -1.4496 1.1881 -3.7948 -1.4173
## Cogon_Patch_Size-Odocoileus_virginianus -0.0141 0.6761 -1.2582 -0.0532
## Cogon_Patch_Size-Canis_latrans 0.6011 0.7051 -0.4235 0.4802
## Cogon_Patch_Size-Sciurus_niger -0.4433 0.8424 -2.4243 -0.3624
## Cogon_Patch_Size-Procyon_lotor -0.2367 0.4557 -1.2030 -0.2200
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0642 0.4373 -0.9528 -0.0609
## Cogon_Patch_Size-Lynx_rufus -0.1193 0.7569 -1.5446 -0.1498
## Cogon_Patch_Size-Didelphis_virginiana 0.5434 0.5169 -0.3698 0.5067
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7256 0.8040 -2.7035 -0.6056
## Cogon_Patch_Size-Meleagris_gallopavo -0.0196 0.6978 -1.2932 -0.0504
## Cogon_Patch_Size-Sciurus_carolinensis -0.6193 0.7073 -2.2977 -0.5001
## Cogon_Patch_Size-Vulpes_vulpes -0.3879 0.8280 -2.2130 -0.3223
## Cogon_Patch_Size-Sus_scrofa -0.3624 0.7861 -2.1630 -0.2678
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1155 0.5706 -1.0349 0.1279
## Avg_Cogongrass_Cover-Canis_latrans 0.3452 0.4676 -0.4622 0.3107
## Avg_Cogongrass_Cover-Sciurus_niger -0.2215 0.7420 -1.9207 -0.1327
## Avg_Cogongrass_Cover-Procyon_lotor 0.1187 0.4469 -0.7833 0.1135
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3579 0.4163 -0.3878 0.3357
## Avg_Cogongrass_Cover-Lynx_rufus 0.4635 0.5343 -0.4645 0.4167
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1444 0.4546 -0.7872 0.1446
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1661 0.5488 -1.3838 -0.1313
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2293 0.7379 -1.9155 -0.1675
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3727 0.4523 -0.4820 0.3647
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2879 0.5391 -0.6961 0.2690
## Avg_Cogongrass_Cover-Sus_scrofa -0.0985 0.6575 -1.6065 -0.0385
## total_shrub_cover-Odocoileus_virginianus -0.3777 0.6987 -1.7231 -0.4059
## total_shrub_cover-Canis_latrans 0.0963 0.6663 -1.0186 0.0307
## total_shrub_cover-Sciurus_niger -0.8352 0.8494 -2.7138 -0.7605
## total_shrub_cover-Procyon_lotor -1.1662 0.6193 -2.6006 -1.0789
## total_shrub_cover-Dasypus_novemcinctus -0.3905 0.5924 -1.7088 -0.3357
## total_shrub_cover-Lynx_rufus -1.0990 0.8816 -3.0578 -0.9902
## total_shrub_cover-Didelphis_virginiana -0.7851 0.6413 -2.3054 -0.7092
## total_shrub_cover-Sylvilagus_floridanus -1.1199 0.9194 -3.3215 -0.9616
## total_shrub_cover-Meleagris_gallopavo -1.2359 0.8539 -3.2182 -1.1282
## total_shrub_cover-Sciurus_carolinensis -0.6828 0.6937 -2.2881 -0.6117
## total_shrub_cover-Vulpes_vulpes -0.8156 0.9903 -3.1010 -0.7253
## total_shrub_cover-Sus_scrofa -0.4865 0.8230 -2.1856 -0.4584
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0428 1.0125 686
## (Intercept)-Canis_latrans 2.3865 1.0151 1328
## (Intercept)-Sciurus_niger 2.6970 1.0133 510
## (Intercept)-Procyon_lotor 2.4473 1.0140 1810
## (Intercept)-Dasypus_novemcinctus 1.0956 1.0217 1724
## (Intercept)-Lynx_rufus 2.6386 1.0204 818
## (Intercept)-Didelphis_virginiana 0.7891 1.0096 1123
## (Intercept)-Sylvilagus_floridanus 2.2083 1.0078 1065
## (Intercept)-Meleagris_gallopavo 2.8565 1.0229 746
## (Intercept)-Sciurus_carolinensis 0.5641 1.0015 1311
## (Intercept)-Vulpes_vulpes 2.7137 1.0058 465
## (Intercept)-Sus_scrofa 0.8028 1.0015 685
## Cogon_Patch_Size-Odocoileus_virginianus 1.4389 1.0010 2712
## Cogon_Patch_Size-Canis_latrans 2.3392 1.0062 1689
## Cogon_Patch_Size-Sciurus_niger 1.0142 1.0046 1361
## Cogon_Patch_Size-Procyon_lotor 0.6216 1.0012 2750
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7900 1.0009 3315
## Cogon_Patch_Size-Lynx_rufus 1.5570 1.0022 1859
## Cogon_Patch_Size-Didelphis_virginiana 1.6434 1.0080 1983
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4284 1.0067 1332
## Cogon_Patch_Size-Meleagris_gallopavo 1.4961 1.0014 2048
## Cogon_Patch_Size-Sciurus_carolinensis 0.4435 1.0028 1546
## Cogon_Patch_Size-Vulpes_vulpes 1.1254 1.0041 1662
## Cogon_Patch_Size-Sus_scrofa 0.8530 1.0085 1619
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2478 1.0050 2454
## Avg_Cogongrass_Cover-Canis_latrans 1.3711 1.0013 2185
## Avg_Cogongrass_Cover-Sciurus_niger 1.0080 1.0270 1066
## Avg_Cogongrass_Cover-Procyon_lotor 1.0102 1.0015 2924
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2880 1.0054 2656
## Avg_Cogongrass_Cover-Lynx_rufus 1.6321 1.0006 2258
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0669 1.0021 2226
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8435 1.0033 1791
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0460 1.0129 1264
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3138 1.0015 2035
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4498 1.0047 2282
## Avg_Cogongrass_Cover-Sus_scrofa 1.0397 1.0029 1357
## total_shrub_cover-Odocoileus_virginianus 1.1076 1.0097 2373
## total_shrub_cover-Canis_latrans 1.6341 1.0009 1205
## total_shrub_cover-Sciurus_niger 0.6501 1.0040 968
## total_shrub_cover-Procyon_lotor -0.1731 1.0072 918
## total_shrub_cover-Dasypus_novemcinctus 0.5858 1.0102 1109
## total_shrub_cover-Lynx_rufus 0.3687 1.0125 647
## total_shrub_cover-Didelphis_virginiana 0.1904 1.0047 937
## total_shrub_cover-Sylvilagus_floridanus 0.2384 1.0366 480
## total_shrub_cover-Meleagris_gallopavo 0.1844 1.0195 784
## total_shrub_cover-Sciurus_carolinensis 0.4733 1.0165 983
## total_shrub_cover-Vulpes_vulpes 0.9238 1.0308 572
## total_shrub_cover-Sus_scrofa 1.1250 1.0026 843
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5367 0.0813 0.3785 0.5363 0.6952
## (Intercept)-Canis_latrans -2.5844 0.2117 -3.0213 -2.5744 -2.1898
## (Intercept)-Sciurus_niger -4.2025 0.6993 -5.5657 -4.2017 -2.8482
## (Intercept)-Procyon_lotor -2.1936 0.1584 -2.5113 -2.1898 -1.8926
## (Intercept)-Dasypus_novemcinctus -1.6385 0.1917 -2.0378 -1.6317 -1.2814
## (Intercept)-Lynx_rufus -3.4441 0.3739 -4.2099 -3.4309 -2.7559
## (Intercept)-Didelphis_virginiana -2.4239 0.3339 -3.1106 -2.4082 -1.8167
## (Intercept)-Sylvilagus_floridanus -3.2075 0.3093 -3.8327 -3.2016 -2.6315
## (Intercept)-Meleagris_gallopavo -3.8165 0.5556 -4.8989 -3.8147 -2.7194
## (Intercept)-Sciurus_carolinensis -2.5722 0.3784 -3.3523 -2.5483 -1.8917
## (Intercept)-Vulpes_vulpes -4.2730 0.7514 -5.7577 -4.2813 -2.8690
## (Intercept)-Sus_scrofa -3.4015 0.6416 -4.6744 -3.4033 -2.0909
## shrub_cover-Odocoileus_virginianus -0.0599 0.0681 -0.1946 -0.0610 0.0723
## shrub_cover-Canis_latrans -0.3059 0.2411 -0.7663 -0.3096 0.1760
## shrub_cover-Sciurus_niger -0.1994 0.5440 -1.2991 -0.1987 0.8505
## shrub_cover-Procyon_lotor 0.3098 0.1624 -0.0154 0.3138 0.6146
## shrub_cover-Dasypus_novemcinctus 0.9810 0.3453 0.3536 0.9694 1.6765
## shrub_cover-Lynx_rufus 0.0106 0.3724 -0.7425 0.0247 0.7024
## shrub_cover-Didelphis_virginiana 1.1106 0.4097 0.3900 1.0851 1.9672
## shrub_cover-Sylvilagus_floridanus 0.6900 0.4351 -0.2157 0.7042 1.4933
## shrub_cover-Meleagris_gallopavo -0.5851 0.4747 -1.4919 -0.5890 0.3492
## shrub_cover-Sciurus_carolinensis 1.0563 0.4409 0.2029 1.0583 1.9111
## shrub_cover-Vulpes_vulpes 0.1405 0.6260 -1.0973 0.1303 1.4058
## shrub_cover-Sus_scrofa 0.9241 0.8288 -0.6712 0.9218 2.5757
## veg_height-Odocoileus_virginianus -0.3323 0.0689 -0.4681 -0.3320 -0.1977
## veg_height-Canis_latrans -0.5979 0.1892 -0.9897 -0.5916 -0.2456
## veg_height-Sciurus_niger -0.0754 0.3955 -0.8316 -0.0829 0.7341
## veg_height-Procyon_lotor 0.3363 0.1240 0.0975 0.3353 0.5921
## veg_height-Dasypus_novemcinctus 0.2445 0.1377 -0.0228 0.2413 0.5215
## veg_height-Lynx_rufus 0.0024 0.2353 -0.4732 0.0052 0.4513
## veg_height-Didelphis_virginiana 0.3958 0.2464 -0.0627 0.3904 0.8978
## veg_height-Sylvilagus_floridanus 0.0370 0.2442 -0.4383 0.0356 0.5215
## veg_height-Meleagris_gallopavo -0.2946 0.3687 -1.0355 -0.2940 0.4334
## veg_height-Sciurus_carolinensis 0.0976 0.2361 -0.3370 0.0910 0.6023
## veg_height-Vulpes_vulpes -0.1912 0.3319 -0.8768 -0.1729 0.4055
## veg_height-Sus_scrofa -0.1806 0.3261 -0.8353 -0.1712 0.4570
## week-Odocoileus_virginianus 1.3092 0.1243 1.0664 1.3093 1.5565
## week-Canis_latrans 0.5868 0.2634 0.0851 0.5824 1.1162
## week-Sciurus_niger -0.4975 0.5758 -1.7887 -0.4331 0.4663
## week-Procyon_lotor 0.1929 0.2123 -0.2190 0.1935 0.6087
## week-Dasypus_novemcinctus 0.0928 0.2265 -0.3581 0.0939 0.5410
## week-Lynx_rufus 0.3640 0.3483 -0.3149 0.3620 1.0476
## week-Didelphis_virginiana 0.0319 0.3779 -0.7408 0.0408 0.7594
## week-Sylvilagus_floridanus 0.0307 0.3472 -0.6755 0.0351 0.6919
## week-Meleagris_gallopavo -0.2261 0.4392 -1.1547 -0.1967 0.5595
## week-Sciurus_carolinensis 0.7909 0.3730 0.0873 0.7794 1.5531
## week-Vulpes_vulpes 0.1554 0.5366 -0.9926 0.1799 1.1369
## week-Sus_scrofa 0.6760 0.4505 -0.1838 0.6655 1.5854
## I(week^2)-Odocoileus_virginianus -0.5397 0.0509 -0.6412 -0.5388 -0.4432
## I(week^2)-Canis_latrans -0.2440 0.1076 -0.4604 -0.2419 -0.0372
## I(week^2)-Sciurus_niger -0.2929 0.2403 -0.8169 -0.2777 0.1439
## I(week^2)-Procyon_lotor -0.1302 0.0924 -0.3131 -0.1305 0.0462
## I(week^2)-Dasypus_novemcinctus -0.1795 0.1043 -0.3850 -0.1775 0.0225
## I(week^2)-Lynx_rufus -0.2321 0.1512 -0.5383 -0.2306 0.0578
## I(week^2)-Didelphis_virginiana -0.4147 0.2129 -0.8952 -0.3967 -0.0493
## I(week^2)-Sylvilagus_floridanus -0.1737 0.1570 -0.4893 -0.1698 0.1292
## I(week^2)-Meleagris_gallopavo -0.3903 0.2281 -0.9076 -0.3695 -0.0121
## I(week^2)-Sciurus_carolinensis -0.2815 0.1443 -0.5687 -0.2806 -0.0056
## I(week^2)-Vulpes_vulpes -0.4003 0.2405 -0.9405 -0.3793 0.0252
## I(week^2)-Sus_scrofa -0.2461 0.1744 -0.6011 -0.2424 0.0992
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 4992
## (Intercept)-Canis_latrans 1.0022 2124
## (Intercept)-Sciurus_niger 1.0007 413
## (Intercept)-Procyon_lotor 1.0003 4061
## (Intercept)-Dasypus_novemcinctus 1.0046 2415
## (Intercept)-Lynx_rufus 1.0009 1008
## (Intercept)-Didelphis_virginiana 1.0064 1480
## (Intercept)-Sylvilagus_floridanus 1.0084 1723
## (Intercept)-Meleagris_gallopavo 1.0274 717
## (Intercept)-Sciurus_carolinensis 1.0064 964
## (Intercept)-Vulpes_vulpes 1.0030 428
## (Intercept)-Sus_scrofa 1.0058 880
## shrub_cover-Odocoileus_virginianus 1.0005 5250
## shrub_cover-Canis_latrans 1.0002 1687
## shrub_cover-Sciurus_niger 1.0016 955
## shrub_cover-Procyon_lotor 1.0003 3783
## shrub_cover-Dasypus_novemcinctus 1.0264 1233
## shrub_cover-Lynx_rufus 1.0063 1227
## shrub_cover-Didelphis_virginiana 1.0212 1117
## shrub_cover-Sylvilagus_floridanus 1.0190 770
## shrub_cover-Meleagris_gallopavo 1.0207 723
## shrub_cover-Sciurus_carolinensis 1.0020 1097
## shrub_cover-Vulpes_vulpes 1.0014 858
## shrub_cover-Sus_scrofa 1.0024 870
## veg_height-Odocoileus_virginianus 1.0000 4962
## veg_height-Canis_latrans 1.0078 1982
## veg_height-Sciurus_niger 1.0015 1971
## veg_height-Procyon_lotor 1.0049 4215
## veg_height-Dasypus_novemcinctus 0.9998 4335
## veg_height-Lynx_rufus 1.0018 2468
## veg_height-Didelphis_virginiana 1.0022 3189
## veg_height-Sylvilagus_floridanus 1.0077 1803
## veg_height-Meleagris_gallopavo 1.0001 1507
## veg_height-Sciurus_carolinensis 1.0082 2131
## veg_height-Vulpes_vulpes 1.0046 1411
## veg_height-Sus_scrofa 1.0007 2948
## week-Odocoileus_virginianus 1.0011 4769
## week-Canis_latrans 1.0025 3600
## week-Sciurus_niger 1.0074 807
## week-Procyon_lotor 1.0018 4387
## week-Dasypus_novemcinctus 1.0006 4903
## week-Lynx_rufus 1.0021 3003
## week-Didelphis_virginiana 1.0014 2765
## week-Sylvilagus_floridanus 1.0010 2728
## week-Meleagris_gallopavo 1.0064 1172
## week-Sciurus_carolinensis 1.0031 3297
## week-Vulpes_vulpes 1.0024 1241
## week-Sus_scrofa 1.0005 3190
## I(week^2)-Odocoileus_virginianus 1.0006 4637
## I(week^2)-Canis_latrans 1.0000 3764
## I(week^2)-Sciurus_niger 1.0033 1078
## I(week^2)-Procyon_lotor 1.0030 4177
## I(week^2)-Dasypus_novemcinctus 1.0031 4473
## I(week^2)-Lynx_rufus 1.0005 2299
## I(week^2)-Didelphis_virginiana 1.0071 1626
## I(week^2)-Sylvilagus_floridanus 1.0012 2382
## I(week^2)-Meleagris_gallopavo 1.0061 1000
## I(week^2)-Sciurus_carolinensis 1.0023 3697
## I(week^2)-Vulpes_vulpes 1.0079 1140
## I(week^2)-Sus_scrofa 1.0001 3704
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2432
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0297 0.5994 -1.1765 -0.0470 1.2296 1.0014 1132
## Veg_shannon_index 0.4040 0.2642 -0.1078 0.3964 0.9555 1.0011 1789
## Avg_Cogongrass_Cover 0.2746 0.2822 -0.2972 0.2808 0.8199 1.0141 1351
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7215 3.5774 0.5954 2.8245 12.4790 1.0170 442
## Veg_shannon_index 0.2973 0.3209 0.0394 0.1998 1.1607 1.0111 1959
## Avg_Cogongrass_Cover 0.3692 0.5530 0.0379 0.2107 1.5996 1.0582 941
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8605 1.0374 0.0577 0.5562 3.3905 1.0716 394
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5703 0.4725 -3.4594 -2.5819 -1.6087 1.0002 3348
## shrub_cover 0.0789 0.2655 -0.4535 0.0771 0.6099 1.0018 2618
## veg_height -0.0540 0.1546 -0.3669 -0.0491 0.2466 1.0041 2316
## week 0.2893 0.2395 -0.2150 0.2947 0.7401 1.0014 2973
## I(week^2) -0.2971 0.1013 -0.5059 -0.2956 -0.1057 1.0011 1894
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5846 1.5951 0.9521 2.2121 6.4983 1.0073 1930
## shrub_cover 0.6579 0.4823 0.1588 0.5330 1.8526 1.0020 1806
## veg_height 0.1971 0.1366 0.0593 0.1638 0.5377 1.0074 3502
## week 0.4766 0.3658 0.1255 0.3785 1.4093 1.0003 1719
## I(week^2) 0.0735 0.0529 0.0230 0.0589 0.2117 1.0042 2001
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5788 1.4578 1.0386 3.4182
## (Intercept)-Canis_latrans 0.4116 0.6702 -0.8559 0.3935
## (Intercept)-Sciurus_niger 0.0665 1.3902 -2.1007 -0.1364
## (Intercept)-Procyon_lotor 0.5715 0.6558 -0.7718 0.5877
## (Intercept)-Dasypus_novemcinctus -0.6302 0.6026 -1.8482 -0.6193
## (Intercept)-Lynx_rufus 0.4190 1.2003 -1.4233 0.2517
## (Intercept)-Didelphis_virginiana -1.3170 0.6881 -2.6720 -1.3087
## (Intercept)-Sylvilagus_floridanus -0.3182 0.7531 -1.6903 -0.3568
## (Intercept)-Meleagris_gallopavo 0.8796 1.3946 -1.2221 0.6631
## (Intercept)-Sciurus_carolinensis -1.3089 0.6845 -2.7302 -1.2753
## (Intercept)-Vulpes_vulpes -0.6534 1.6483 -2.9749 -0.8898
## (Intercept)-Sus_scrofa -1.9205 0.9562 -3.9232 -1.8900
## Veg_shannon_index-Odocoileus_virginianus 0.3323 0.5060 -0.7307 0.3448
## Veg_shannon_index-Canis_latrans 0.6652 0.4096 -0.0819 0.6385
## Veg_shannon_index-Sciurus_niger 0.4535 0.5721 -0.6240 0.4385
## Veg_shannon_index-Procyon_lotor 0.4745 0.3787 -0.2405 0.4634
## Veg_shannon_index-Dasypus_novemcinctus 0.2041 0.3530 -0.5137 0.2171
## Veg_shannon_index-Lynx_rufus 0.2832 0.5201 -0.8102 0.2975
## Veg_shannon_index-Didelphis_virginiana 0.5321 0.4008 -0.2092 0.5085
## Veg_shannon_index-Sylvilagus_floridanus 0.4755 0.4391 -0.3346 0.4502
## Veg_shannon_index-Meleagris_gallopavo 0.5448 0.5187 -0.4207 0.5185
## Veg_shannon_index-Sciurus_carolinensis 0.0125 0.4110 -0.8623 0.0411
## Veg_shannon_index-Vulpes_vulpes 0.1649 0.5066 -0.9073 0.1838
## Veg_shannon_index-Sus_scrofa 0.7533 0.5584 -0.1500 0.6780
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2731 0.5390 -0.7890 0.2598
## Avg_Cogongrass_Cover-Canis_latrans 0.6126 0.4331 -0.1241 0.5654
## Avg_Cogongrass_Cover-Sciurus_niger -0.0714 0.7073 -1.6697 -0.0039
## Avg_Cogongrass_Cover-Procyon_lotor 0.3580 0.3880 -0.3594 0.3365
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4331 0.3434 -0.2366 0.4254
## Avg_Cogongrass_Cover-Lynx_rufus 0.5485 0.4805 -0.2739 0.5140
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4380 0.3872 -0.3191 0.4279
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1248 0.4898 -1.2349 -0.0785
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0084 0.6819 -1.5266 0.0605
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4122 0.3768 -0.2980 0.4066
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4090 0.5083 -0.4955 0.3815
## Avg_Cogongrass_Cover-Sus_scrofa -0.0087 0.5905 -1.3774 0.0528
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8861 1.0049 741
## (Intercept)-Canis_latrans 1.7731 1.0041 2261
## (Intercept)-Sciurus_niger 3.5379 1.0012 398
## (Intercept)-Procyon_lotor 1.8232 1.0023 2486
## (Intercept)-Dasypus_novemcinctus 0.5483 1.0006 3295
## (Intercept)-Lynx_rufus 3.2042 1.0068 582
## (Intercept)-Didelphis_virginiana 0.0110 1.0004 2475
## (Intercept)-Sylvilagus_floridanus 1.3014 1.0039 1865
## (Intercept)-Meleagris_gallopavo 4.2072 1.0000 586
## (Intercept)-Sciurus_carolinensis 0.0198 1.0031 2667
## (Intercept)-Vulpes_vulpes 3.2688 1.0493 250
## (Intercept)-Sus_scrofa -0.1162 1.0020 1556
## Veg_shannon_index-Odocoileus_virginianus 1.3162 1.0016 2926
## Veg_shannon_index-Canis_latrans 1.5475 1.0024 2806
## Veg_shannon_index-Sciurus_niger 1.6412 1.0085 2346
## Veg_shannon_index-Procyon_lotor 1.2648 1.0025 3012
## Veg_shannon_index-Dasypus_novemcinctus 0.8887 1.0010 3512
## Veg_shannon_index-Lynx_rufus 1.3097 1.0012 2464
## Veg_shannon_index-Didelphis_virginiana 1.3749 1.0030 3283
## Veg_shannon_index-Sylvilagus_floridanus 1.4222 1.0061 2875
## Veg_shannon_index-Meleagris_gallopavo 1.6557 1.0219 2598
## Veg_shannon_index-Sciurus_carolinensis 0.7496 1.0019 3317
## Veg_shannon_index-Vulpes_vulpes 1.1381 1.0031 2075
## Veg_shannon_index-Sus_scrofa 2.0569 1.0025 2039
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3504 1.0064 3120
## Avg_Cogongrass_Cover-Canis_latrans 1.6034 1.0010 3213
## Avg_Cogongrass_Cover-Sciurus_niger 1.1095 1.0274 1026
## Avg_Cogongrass_Cover-Procyon_lotor 1.1662 1.0106 3454
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1180 1.0009 4075
## Avg_Cogongrass_Cover-Lynx_rufus 1.6054 1.0033 2610
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2297 1.0023 3100
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7248 1.0120 1804
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2150 1.0186 1141
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1843 1.0001 3632
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4914 1.0024 2518
## Avg_Cogongrass_Cover-Sus_scrofa 0.9572 1.0082 1353
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5380 0.0806 0.3810 0.5374 0.6957
## (Intercept)-Canis_latrans -2.5508 0.2046 -2.9680 -2.5407 -2.1695
## (Intercept)-Sciurus_niger -4.4439 0.6607 -5.7370 -4.4587 -3.1137
## (Intercept)-Procyon_lotor -2.1880 0.1684 -2.5333 -2.1853 -1.8660
## (Intercept)-Dasypus_novemcinctus -1.5836 0.1792 -1.9473 -1.5807 -1.2398
## (Intercept)-Lynx_rufus -3.6318 0.3880 -4.4074 -3.6312 -2.9044
## (Intercept)-Didelphis_virginiana -2.3303 0.3093 -2.9697 -2.3145 -1.7460
## (Intercept)-Sylvilagus_floridanus -3.1306 0.3417 -3.8551 -3.1180 -2.5117
## (Intercept)-Meleagris_gallopavo -4.1971 0.4907 -5.1681 -4.1973 -3.2413
## (Intercept)-Sciurus_carolinensis -2.4094 0.3307 -3.0898 -2.3995 -1.8083
## (Intercept)-Vulpes_vulpes -4.2643 0.8136 -5.9247 -4.2389 -2.7786
## (Intercept)-Sus_scrofa -3.1152 0.6394 -4.3782 -3.1090 -1.8725
## shrub_cover-Odocoileus_virginianus -0.0622 0.0679 -0.1984 -0.0627 0.0696
## shrub_cover-Canis_latrans -0.3043 0.2158 -0.7325 -0.3061 0.1156
## shrub_cover-Sciurus_niger -0.5754 0.4690 -1.5536 -0.5541 0.3056
## shrub_cover-Procyon_lotor 0.2287 0.1729 -0.1267 0.2363 0.5534
## shrub_cover-Dasypus_novemcinctus 0.8174 0.3018 0.2358 0.8103 1.4212
## shrub_cover-Lynx_rufus -0.3564 0.3442 -1.0230 -0.3627 0.3473
## shrub_cover-Didelphis_virginiana 0.9242 0.3703 0.2644 0.9030 1.7212
## shrub_cover-Sylvilagus_floridanus 0.1798 0.4155 -0.5926 0.1555 1.0576
## shrub_cover-Meleagris_gallopavo -0.9404 0.3960 -1.7786 -0.9211 -0.2008
## shrub_cover-Sciurus_carolinensis 0.8009 0.4142 -0.0053 0.7951 1.6290
## shrub_cover-Vulpes_vulpes -0.2359 0.5610 -1.4108 -0.2189 0.8640
## shrub_cover-Sus_scrofa 0.4625 0.8282 -1.1794 0.4410 2.1639
## veg_height-Odocoileus_virginianus -0.3325 0.0684 -0.4680 -0.3320 -0.1987
## veg_height-Canis_latrans -0.5981 0.1785 -0.9653 -0.5942 -0.2604
## veg_height-Sciurus_niger -0.1408 0.3809 -0.9284 -0.1369 0.6226
## veg_height-Procyon_lotor 0.3262 0.1230 0.0815 0.3269 0.5639
## veg_height-Dasypus_novemcinctus 0.2253 0.1337 -0.0284 0.2256 0.4970
## veg_height-Lynx_rufus -0.0132 0.2449 -0.5060 -0.0088 0.4646
## veg_height-Didelphis_virginiana 0.3940 0.2368 -0.0577 0.3869 0.8623
## veg_height-Sylvilagus_floridanus 0.1054 0.2426 -0.3795 0.0994 0.5797
## veg_height-Meleagris_gallopavo -0.3309 0.3383 -1.0132 -0.3280 0.3313
## veg_height-Sciurus_carolinensis 0.0417 0.2120 -0.3674 0.0357 0.4778
## veg_height-Vulpes_vulpes -0.1823 0.3236 -0.8516 -0.1621 0.4058
## veg_height-Sus_scrofa -0.1600 0.3312 -0.8438 -0.1551 0.4688
## week-Odocoileus_virginianus 1.3093 0.1239 1.0682 1.3068 1.5549
## week-Canis_latrans 0.5923 0.2671 0.0904 0.5898 1.1250
## week-Sciurus_niger -0.4867 0.5677 -1.7470 -0.4365 0.4584
## week-Procyon_lotor 0.2010 0.2128 -0.2076 0.1972 0.6355
## week-Dasypus_novemcinctus 0.1009 0.2281 -0.3296 0.1010 0.5586
## week-Lynx_rufus 0.3642 0.3532 -0.3007 0.3654 1.0670
## week-Didelphis_virginiana 0.0360 0.3843 -0.7509 0.0427 0.7604
## week-Sylvilagus_floridanus 0.0317 0.3427 -0.6533 0.0334 0.6850
## week-Meleagris_gallopavo -0.2382 0.4223 -1.1354 -0.2154 0.5283
## week-Sciurus_carolinensis 0.8080 0.3695 0.1361 0.7920 1.5588
## week-Vulpes_vulpes 0.1324 0.5304 -0.9988 0.1578 1.1284
## week-Sus_scrofa 0.6742 0.4603 -0.2155 0.6643 1.6204
## I(week^2)-Odocoileus_virginianus -0.5396 0.0512 -0.6393 -0.5387 -0.4419
## I(week^2)-Canis_latrans -0.2458 0.1088 -0.4659 -0.2439 -0.0375
## I(week^2)-Sciurus_niger -0.2986 0.2477 -0.8260 -0.2870 0.1669
## I(week^2)-Procyon_lotor -0.1341 0.0921 -0.3201 -0.1327 0.0447
## I(week^2)-Dasypus_novemcinctus -0.1808 0.1056 -0.3927 -0.1785 0.0233
## I(week^2)-Lynx_rufus -0.2363 0.1551 -0.5521 -0.2333 0.0579
## I(week^2)-Didelphis_virginiana -0.4142 0.2112 -0.8911 -0.3984 -0.0584
## I(week^2)-Sylvilagus_floridanus -0.1736 0.1606 -0.5044 -0.1720 0.1344
## I(week^2)-Meleagris_gallopavo -0.4017 0.2309 -0.9261 -0.3787 -0.0042
## I(week^2)-Sciurus_carolinensis -0.2850 0.1445 -0.5720 -0.2813 -0.0097
## I(week^2)-Vulpes_vulpes -0.4176 0.2508 -0.9758 -0.3932 0.0108
## I(week^2)-Sus_scrofa -0.2430 0.1811 -0.6154 -0.2425 0.1125
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0021 5012
## (Intercept)-Canis_latrans 1.0026 2509
## (Intercept)-Sciurus_niger 1.0041 352
## (Intercept)-Procyon_lotor 1.0028 3358
## (Intercept)-Dasypus_novemcinctus 1.0002 4180
## (Intercept)-Lynx_rufus 1.0060 859
## (Intercept)-Didelphis_virginiana 1.0002 3270
## (Intercept)-Sylvilagus_floridanus 1.0057 1510
## (Intercept)-Meleagris_gallopavo 1.0017 494
## (Intercept)-Sciurus_carolinensis 1.0011 2340
## (Intercept)-Vulpes_vulpes 1.0227 347
## (Intercept)-Sus_scrofa 1.0009 1804
## shrub_cover-Odocoileus_virginianus 1.0004 5250
## shrub_cover-Canis_latrans 1.0069 2946
## shrub_cover-Sciurus_niger 1.0072 1031
## shrub_cover-Procyon_lotor 1.0005 3245
## shrub_cover-Dasypus_novemcinctus 1.0070 3380
## shrub_cover-Lynx_rufus 1.0016 1492
## shrub_cover-Didelphis_virginiana 1.0018 1938
## shrub_cover-Sylvilagus_floridanus 1.0014 1539
## shrub_cover-Meleagris_gallopavo 1.0023 652
## shrub_cover-Sciurus_carolinensis 1.0049 2389
## shrub_cover-Vulpes_vulpes 1.0035 1744
## shrub_cover-Sus_scrofa 1.0015 2368
## veg_height-Odocoileus_virginianus 0.9999 4230
## veg_height-Canis_latrans 1.0045 2300
## veg_height-Sciurus_niger 1.0089 1753
## veg_height-Procyon_lotor 1.0009 4434
## veg_height-Dasypus_novemcinctus 1.0033 4855
## veg_height-Lynx_rufus 1.0023 2266
## veg_height-Didelphis_virginiana 1.0005 3252
## veg_height-Sylvilagus_floridanus 1.0019 2621
## veg_height-Meleagris_gallopavo 1.0053 1175
## veg_height-Sciurus_carolinensis 1.0036 3629
## veg_height-Vulpes_vulpes 1.0006 1847
## veg_height-Sus_scrofa 1.0017 3030
## week-Odocoileus_virginianus 1.0030 4618
## week-Canis_latrans 1.0016 3951
## week-Sciurus_niger 1.0018 655
## week-Procyon_lotor 1.0055 4486
## week-Dasypus_novemcinctus 1.0005 5250
## week-Lynx_rufus 1.0018 2532
## week-Didelphis_virginiana 1.0002 2907
## week-Sylvilagus_floridanus 1.0049 2886
## week-Meleagris_gallopavo 1.0006 1081
## week-Sciurus_carolinensis 1.0075 3499
## week-Vulpes_vulpes 1.0094 1606
## week-Sus_scrofa 1.0023 4111
## I(week^2)-Odocoileus_virginianus 1.0048 5250
## I(week^2)-Canis_latrans 1.0015 3908
## I(week^2)-Sciurus_niger 1.0035 815
## I(week^2)-Procyon_lotor 1.0024 4479
## I(week^2)-Dasypus_novemcinctus 1.0032 4523
## I(week^2)-Lynx_rufus 1.0004 2096
## I(week^2)-Didelphis_virginiana 1.0054 1955
## I(week^2)-Sylvilagus_floridanus 1.0042 2449
## I(week^2)-Meleagris_gallopavo 1.0037 661
## I(week^2)-Sciurus_carolinensis 1.0042 4007
## I(week^2)-Vulpes_vulpes 1.0069 1215
## I(week^2)-Sus_scrofa 1.0008 4410
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.2017
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0815 0.5723 -1.1447 -0.0993 1.0895 1.0052 1498
## Avg_Cogongrass_Cover 0.1327 0.2524 -0.3940 0.1435 0.6146 1.0018 1503
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2491 2.9263 0.5884 2.4785 10.3828 1.0784 742
## Avg_Cogongrass_Cover 0.3277 0.3954 0.0377 0.2020 1.4188 1.0126 1552
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8912 0.8822 0.0634 0.6256 3.3168 1.0074 484
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5457 0.4496 -3.4033 -2.5521 -1.6252 1.0027 3762
## shrub_cover 0.1019 0.2581 -0.4215 0.1009 0.6088 1.0008 2689
## veg_height -0.0538 0.1577 -0.3791 -0.0515 0.2498 1.0022 3071
## week 0.2934 0.2354 -0.1879 0.2995 0.7408 1.0033 3698
## I(week^2) -0.2958 0.1012 -0.5014 -0.2932 -0.0995 1.0053 1966
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4231 1.3440 0.8740 2.0977 5.9238 1.0041 1832
## shrub_cover 0.6368 0.4854 0.1457 0.5128 1.8422 1.0035 1454
## veg_height 0.2022 0.1398 0.0568 0.1694 0.5381 1.0030 2962
## week 0.4674 0.3359 0.1226 0.3810 1.3139 1.0113 1594
## I(week^2) 0.0737 0.0553 0.0226 0.0592 0.2153 1.0025 2411
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3879 1.4777 0.9991 3.1977
## (Intercept)-Canis_latrans 0.4122 0.6569 -0.8590 0.4085
## (Intercept)-Sciurus_niger -0.2448 1.2448 -2.1390 -0.4290
## (Intercept)-Procyon_lotor 0.5208 0.6337 -0.7491 0.5438
## (Intercept)-Dasypus_novemcinctus -0.6098 0.5820 -1.8045 -0.6005
## (Intercept)-Lynx_rufus 0.1787 1.0567 -1.5403 0.0674
## (Intercept)-Didelphis_virginiana -1.2059 0.6719 -2.6007 -1.1858
## (Intercept)-Sylvilagus_floridanus -0.3277 0.7184 -1.6702 -0.3468
## (Intercept)-Meleagris_gallopavo 0.7079 1.2978 -1.3576 0.5383
## (Intercept)-Sciurus_carolinensis -1.3199 0.6950 -2.7298 -1.2964
## (Intercept)-Vulpes_vulpes -0.8437 1.3324 -3.0119 -0.9789
## (Intercept)-Sus_scrofa -1.6670 0.8615 -3.4670 -1.6339
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1217 0.5100 -0.8739 0.1216
## Avg_Cogongrass_Cover-Canis_latrans 0.4128 0.3989 -0.2689 0.3773
## Avg_Cogongrass_Cover-Sciurus_niger -0.2043 0.6299 -1.6532 -0.1348
## Avg_Cogongrass_Cover-Procyon_lotor 0.2081 0.3462 -0.4527 0.1974
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3288 0.3298 -0.2961 0.3176
## Avg_Cogongrass_Cover-Lynx_rufus 0.4116 0.4353 -0.3740 0.3800
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3090 0.3677 -0.3872 0.3008
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2279 0.4552 -1.2382 -0.1817
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1977 0.6500 -1.7028 -0.1298
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3198 0.3610 -0.3639 0.3064
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2588 0.4478 -0.5979 0.2451
## Avg_Cogongrass_Cover-Sus_scrofa -0.1328 0.5729 -1.5440 -0.0531
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.6495 1.0635 609
## (Intercept)-Canis_latrans 1.7449 1.0031 2605
## (Intercept)-Sciurus_niger 2.8536 1.0645 523
## (Intercept)-Procyon_lotor 1.7850 1.0033 2361
## (Intercept)-Dasypus_novemcinctus 0.5211 1.0079 3182
## (Intercept)-Lynx_rufus 2.6671 1.0360 807
## (Intercept)-Didelphis_virginiana 0.0798 1.0000 2474
## (Intercept)-Sylvilagus_floridanus 1.1764 1.0047 1896
## (Intercept)-Meleagris_gallopavo 3.8684 1.0247 542
## (Intercept)-Sciurus_carolinensis -0.0205 1.0061 2811
## (Intercept)-Vulpes_vulpes 2.2810 1.0288 347
## (Intercept)-Sus_scrofa -0.0505 1.0014 1703
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1543 1.0073 3616
## Avg_Cogongrass_Cover-Canis_latrans 1.3029 1.0061 3417
## Avg_Cogongrass_Cover-Sciurus_niger 0.8083 1.0048 1122
## Avg_Cogongrass_Cover-Procyon_lotor 0.9331 1.0001 4639
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0126 1.0020 4215
## Avg_Cogongrass_Cover-Lynx_rufus 1.3680 1.0020 3045
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0617 1.0009 3886
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5653 1.0024 1844
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8961 1.0042 1437
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0723 1.0012 3905
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2249 1.0010 2864
## Avg_Cogongrass_Cover-Sus_scrofa 0.7969 1.0048 1703
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5356 0.0803 0.3781 0.5367 0.6928
## (Intercept)-Canis_latrans -2.5766 0.2097 -3.0151 -2.5703 -2.1829
## (Intercept)-Sciurus_niger -4.2538 0.6614 -5.5372 -4.2454 -2.9593
## (Intercept)-Procyon_lotor -2.1834 0.1649 -2.5264 -2.1774 -1.8772
## (Intercept)-Dasypus_novemcinctus -1.5829 0.1761 -1.9423 -1.5774 -1.2482
## (Intercept)-Lynx_rufus -3.5752 0.3786 -4.3573 -3.5621 -2.8817
## (Intercept)-Didelphis_virginiana -2.3376 0.3082 -2.9635 -2.3285 -1.7694
## (Intercept)-Sylvilagus_floridanus -3.1034 0.3218 -3.7699 -3.0818 -2.5251
## (Intercept)-Meleagris_gallopavo -4.1602 0.5195 -5.1865 -4.1697 -3.1233
## (Intercept)-Sciurus_carolinensis -2.3959 0.3266 -3.0830 -2.3822 -1.7986
## (Intercept)-Vulpes_vulpes -4.1507 0.7807 -5.7103 -4.1220 -2.7266
## (Intercept)-Sus_scrofa -3.1427 0.6525 -4.4692 -3.1234 -1.8940
## shrub_cover-Odocoileus_virginianus -0.0626 0.0686 -0.1949 -0.0630 0.0695
## shrub_cover-Canis_latrans -0.2970 0.2206 -0.7264 -0.2994 0.1324
## shrub_cover-Sciurus_niger -0.4932 0.4585 -1.4466 -0.4885 0.3942
## shrub_cover-Procyon_lotor 0.2408 0.1656 -0.0883 0.2451 0.5555
## shrub_cover-Dasypus_novemcinctus 0.8153 0.2981 0.2465 0.8117 1.4096
## shrub_cover-Lynx_rufus -0.3202 0.3398 -1.0246 -0.3162 0.3420
## shrub_cover-Didelphis_virginiana 0.9378 0.3647 0.2598 0.9273 1.7065
## shrub_cover-Sylvilagus_floridanus 0.2490 0.4186 -0.5411 0.2422 1.1131
## shrub_cover-Meleagris_gallopavo -0.9086 0.4226 -1.7813 -0.8993 -0.0985
## shrub_cover-Sciurus_carolinensis 0.7816 0.4009 0.0359 0.7693 1.5942
## shrub_cover-Vulpes_vulpes -0.2071 0.5665 -1.3912 -0.1935 0.8739
## shrub_cover-Sus_scrofa 0.5019 0.8357 -1.1108 0.4912 2.2418
## veg_height-Odocoileus_virginianus -0.3341 0.0695 -0.4704 -0.3344 -0.2007
## veg_height-Canis_latrans -0.6120 0.1893 -1.0034 -0.6069 -0.2589
## veg_height-Sciurus_niger -0.1159 0.4063 -0.9139 -0.1135 0.7363
## veg_height-Procyon_lotor 0.3286 0.1232 0.0855 0.3290 0.5707
## veg_height-Dasypus_novemcinctus 0.2242 0.1331 -0.0370 0.2238 0.4829
## veg_height-Lynx_rufus -0.0300 0.2508 -0.5263 -0.0203 0.4454
## veg_height-Didelphis_virginiana 0.3951 0.2407 -0.0518 0.3896 0.8962
## veg_height-Sylvilagus_floridanus 0.1107 0.2520 -0.3792 0.1093 0.6030
## veg_height-Meleagris_gallopavo -0.3220 0.3506 -1.0329 -0.3222 0.3691
## veg_height-Sciurus_carolinensis 0.0394 0.2078 -0.3681 0.0342 0.4642
## veg_height-Vulpes_vulpes -0.1777 0.3205 -0.8429 -0.1690 0.4260
## veg_height-Sus_scrofa -0.1615 0.3308 -0.8372 -0.1547 0.4539
## week-Odocoileus_virginianus 1.3126 0.1236 1.0774 1.3123 1.5665
## week-Canis_latrans 0.5858 0.2688 0.0686 0.5793 1.1071
## week-Sciurus_niger -0.4893 0.5600 -1.7172 -0.4488 0.4987
## week-Procyon_lotor 0.1978 0.2107 -0.2152 0.2000 0.6130
## week-Dasypus_novemcinctus 0.0979 0.2277 -0.3526 0.0936 0.5342
## week-Lynx_rufus 0.3729 0.3536 -0.3193 0.3713 1.0713
## week-Didelphis_virginiana 0.0369 0.3810 -0.7261 0.0430 0.7662
## week-Sylvilagus_floridanus 0.0362 0.3482 -0.6516 0.0396 0.7047
## week-Meleagris_gallopavo -0.2465 0.4371 -1.1983 -0.2171 0.5445
## week-Sciurus_carolinensis 0.8032 0.3748 0.1138 0.7888 1.5803
## week-Vulpes_vulpes 0.1630 0.5269 -0.9513 0.1890 1.1443
## week-Sus_scrofa 0.6782 0.4609 -0.1986 0.6589 1.6333
## I(week^2)-Odocoileus_virginianus -0.5407 0.0508 -0.6446 -0.5402 -0.4416
## I(week^2)-Canis_latrans -0.2433 0.1101 -0.4645 -0.2424 -0.0236
## I(week^2)-Sciurus_niger -0.2904 0.2418 -0.7925 -0.2826 0.1550
## I(week^2)-Procyon_lotor -0.1301 0.0910 -0.3106 -0.1303 0.0493
## I(week^2)-Dasypus_novemcinctus -0.1799 0.1056 -0.3936 -0.1787 0.0262
## I(week^2)-Lynx_rufus -0.2400 0.1561 -0.5532 -0.2343 0.0544
## I(week^2)-Didelphis_virginiana -0.4154 0.2090 -0.8859 -0.3962 -0.0594
## I(week^2)-Sylvilagus_floridanus -0.1782 0.1630 -0.5227 -0.1735 0.1342
## I(week^2)-Meleagris_gallopavo -0.4055 0.2349 -0.9295 -0.3870 -0.0018
## I(week^2)-Sciurus_carolinensis -0.2845 0.1464 -0.5846 -0.2824 -0.0048
## I(week^2)-Vulpes_vulpes -0.3967 0.2456 -0.9296 -0.3780 0.0270
## I(week^2)-Sus_scrofa -0.2476 0.1804 -0.6200 -0.2435 0.0931
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 4819
## (Intercept)-Canis_latrans 1.0068 2362
## (Intercept)-Sciurus_niger 1.0135 600
## (Intercept)-Procyon_lotor 1.0014 3904
## (Intercept)-Dasypus_novemcinctus 1.0054 4547
## (Intercept)-Lynx_rufus 1.0088 891
## (Intercept)-Didelphis_virginiana 1.0024 3069
## (Intercept)-Sylvilagus_floridanus 1.0095 1537
## (Intercept)-Meleagris_gallopavo 1.0011 501
## (Intercept)-Sciurus_carolinensis 1.0011 3078
## (Intercept)-Vulpes_vulpes 1.0104 478
## (Intercept)-Sus_scrofa 1.0095 1763
## shrub_cover-Odocoileus_virginianus 1.0011 5250
## shrub_cover-Canis_latrans 1.0014 2606
## shrub_cover-Sciurus_niger 1.0024 1160
## shrub_cover-Procyon_lotor 1.0019 3992
## shrub_cover-Dasypus_novemcinctus 1.0062 3265
## shrub_cover-Lynx_rufus 1.0034 1507
## shrub_cover-Didelphis_virginiana 1.0004 2085
## shrub_cover-Sylvilagus_floridanus 1.0056 1629
## shrub_cover-Meleagris_gallopavo 1.0033 593
## shrub_cover-Sciurus_carolinensis 1.0046 2576
## shrub_cover-Vulpes_vulpes 1.0024 1464
## shrub_cover-Sus_scrofa 1.0044 1792
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0181 1962
## veg_height-Sciurus_niger 1.0031 1690
## veg_height-Procyon_lotor 1.0008 4180
## veg_height-Dasypus_novemcinctus 1.0001 4927
## veg_height-Lynx_rufus 1.0023 2429
## veg_height-Didelphis_virginiana 1.0049 3313
## veg_height-Sylvilagus_floridanus 1.0006 2269
## veg_height-Meleagris_gallopavo 1.0074 1453
## veg_height-Sciurus_carolinensis 0.9999 3776
## veg_height-Vulpes_vulpes 1.0018 1786
## veg_height-Sus_scrofa 1.0103 3218
## week-Odocoileus_virginianus 0.9998 4942
## week-Canis_latrans 1.0003 3802
## week-Sciurus_niger 1.0122 946
## week-Procyon_lotor 1.0020 4377
## week-Dasypus_novemcinctus 1.0034 4829
## week-Lynx_rufus 1.0015 2390
## week-Didelphis_virginiana 1.0008 2653
## week-Sylvilagus_floridanus 1.0000 3091
## week-Meleagris_gallopavo 1.0117 995
## week-Sciurus_carolinensis 1.0013 3717
## week-Vulpes_vulpes 1.0044 1833
## week-Sus_scrofa 1.0008 3938
## I(week^2)-Odocoileus_virginianus 1.0011 4914
## I(week^2)-Canis_latrans 1.0008 3966
## I(week^2)-Sciurus_niger 1.0136 1173
## I(week^2)-Procyon_lotor 1.0012 4126
## I(week^2)-Dasypus_novemcinctus 1.0087 4481
## I(week^2)-Lynx_rufus 1.0002 2457
## I(week^2)-Didelphis_virginiana 1.0001 1579
## I(week^2)-Sylvilagus_floridanus 1.0028 3153
## I(week^2)-Meleagris_gallopavo 1.0067 764
## I(week^2)-Sciurus_carolinensis 1.0000 3955
## I(week^2)-Vulpes_vulpes 1.0102 1379
## I(week^2)-Sus_scrofa 1.0014 4289
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1865
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7729 0.5950 -1.9092 -0.7874 0.4671 1.0193 2290
## Avg_Cogongrass_Cover -0.7934 0.3930 -1.5739 -0.7882 -0.0687 1.0219 1227
## I(Avg_Cogongrass_Cover^2) 0.8192 0.3306 0.2301 0.8035 1.5309 1.0064 1182
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4462 2.7712 0.6188 2.7053 10.4493 1.0178 986
## Avg_Cogongrass_Cover 0.4713 0.6137 0.0403 0.2759 2.1850 1.0259 1321
## I(Avg_Cogongrass_Cover^2) 0.4927 0.7569 0.0399 0.2444 2.4845 1.0788 473
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6432 0.7987 0.0541 0.4064 2.5997 1.0864 371
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5334 0.4531 -3.3891 -2.5470 -1.6106 1.0037 4086
## shrub_cover 0.1048 0.2574 -0.4179 0.1039 0.6150 1.0015 3060
## veg_height -0.0156 0.1540 -0.3211 -0.0139 0.2917 1.0024 2346
## week 0.2923 0.2417 -0.2034 0.2967 0.7458 1.0011 3102
## I(week^2) -0.2951 0.0984 -0.4958 -0.2946 -0.1079 1.0002 1782
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4233 1.4536 0.8857 2.0481 6.0044 1.0063 2726
## shrub_cover 0.6123 0.4840 0.1368 0.4944 1.7470 1.0382 1592
## veg_height 0.1926 0.1320 0.0551 0.1582 0.5430 0.9999 3121
## week 0.4675 0.3418 0.1227 0.3769 1.3798 1.0066 1858
## I(week^2) 0.0709 0.0467 0.0220 0.0584 0.1925 1.0079 2091
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7773 1.3794 0.4589 2.6543
## (Intercept)-Canis_latrans -0.4403 0.6984 -1.8315 -0.4385
## (Intercept)-Sciurus_niger -0.7324 1.2814 -2.7487 -0.8723
## (Intercept)-Procyon_lotor -0.1408 0.6795 -1.4834 -0.1411
## (Intercept)-Dasypus_novemcinctus -1.2974 0.6426 -2.5695 -1.2949
## (Intercept)-Lynx_rufus -0.9084 1.0014 -2.7766 -0.9662
## (Intercept)-Didelphis_virginiana -1.8203 0.7282 -3.3302 -1.8059
## (Intercept)-Sylvilagus_floridanus -1.0470 0.7504 -2.5299 -1.0346
## (Intercept)-Meleagris_gallopavo 0.3166 1.3160 -1.7683 0.1354
## (Intercept)-Sciurus_carolinensis -2.3325 0.7799 -3.9406 -2.3049
## (Intercept)-Vulpes_vulpes -1.9782 1.2319 -4.2577 -2.0418
## (Intercept)-Sus_scrofa -2.3514 0.9316 -4.2976 -2.3191
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7840 0.6628 -2.1534 -0.7708
## Avg_Cogongrass_Cover-Canis_latrans -0.3745 0.5597 -1.3542 -0.4190
## Avg_Cogongrass_Cover-Sciurus_niger -1.0963 0.7596 -2.8721 -1.0142
## Avg_Cogongrass_Cover-Procyon_lotor -0.7058 0.5159 -1.7195 -0.7073
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5525 0.4930 -1.4896 -0.5671
## Avg_Cogongrass_Cover-Lynx_rufus -0.7100 0.6220 -1.9798 -0.7097
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4919 0.5540 -1.4926 -0.5163
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2306 0.6716 -2.7662 -1.1597
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0832 0.7812 -2.9191 -0.9941
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8301 0.5706 -2.0374 -0.8152
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8082 0.6524 -2.1954 -0.7899
## Avg_Cogongrass_Cover-Sus_scrofa -1.0479 0.7048 -2.7386 -0.9670
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1522 0.7713 0.0650 1.0087
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2428 0.7366 0.2333 1.0926
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3595 0.6953 -1.1495 0.4106
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0363 0.5592 0.1911 0.9583
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7379 0.3682 0.0502 0.7307
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1923 0.5643 0.3116 1.1187
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6113 0.4297 -0.1848 0.5995
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7380 0.4565 -0.1027 0.7066
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4631 0.7408 -1.0543 0.4690
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0089 0.4259 0.2438 0.9772
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0010 0.5736 0.1421 0.9149
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4368 0.6161 -1.0424 0.5087
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.8041 1.0286 979
## (Intercept)-Canis_latrans 0.8819 1.0090 2596
## (Intercept)-Sciurus_niger 2.2885 1.0453 541
## (Intercept)-Procyon_lotor 1.2355 1.0121 2590
## (Intercept)-Dasypus_novemcinctus -0.0558 1.0008 3247
## (Intercept)-Lynx_rufus 1.2507 1.0106 956
## (Intercept)-Didelphis_virginiana -0.4296 1.0003 2852
## (Intercept)-Sylvilagus_floridanus 0.3892 1.0026 2333
## (Intercept)-Meleagris_gallopavo 3.4368 1.0175 554
## (Intercept)-Sciurus_carolinensis -0.8921 1.0014 2421
## (Intercept)-Vulpes_vulpes 0.6097 1.0009 768
## (Intercept)-Sus_scrofa -0.6248 1.0066 1602
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5409 1.0054 2512
## Avg_Cogongrass_Cover-Canis_latrans 0.8657 1.0011 2854
## Avg_Cogongrass_Cover-Sciurus_niger 0.1680 1.0183 1221
## Avg_Cogongrass_Cover-Procyon_lotor 0.3495 1.0005 2753
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4739 1.0001 2683
## Avg_Cogongrass_Cover-Lynx_rufus 0.5205 1.0038 1877
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6842 1.0005 2939
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1193 1.0090 1434
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2230 1.0200 1184
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2510 1.0011 1726
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4272 1.0024 1697
## Avg_Cogongrass_Cover-Sus_scrofa 0.1491 1.0131 1665
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.1939 1.0480 1035
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1336 1.0171 998
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.6160 1.0270 924
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3762 1.0217 1422
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4980 1.0022 2729
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5328 1.0192 1074
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5444 1.0041 2020
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7421 1.0022 1909
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.9667 1.0076 799
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9447 1.0028 1905
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.4422 1.0037 857
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.4584 1.0035 1360
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5389 0.0813 0.3803 0.5390 0.6972
## (Intercept)-Canis_latrans -2.5664 0.2039 -2.9780 -2.5633 -2.1814
## (Intercept)-Sciurus_niger -4.2450 0.6850 -5.5963 -4.2329 -2.9157
## (Intercept)-Procyon_lotor -2.2006 0.1666 -2.5441 -2.1945 -1.8941
## (Intercept)-Dasypus_novemcinctus -1.5858 0.1752 -1.9349 -1.5834 -1.2439
## (Intercept)-Lynx_rufus -3.5031 0.3863 -4.2717 -3.4919 -2.7747
## (Intercept)-Didelphis_virginiana -2.3483 0.3084 -2.9742 -2.3396 -1.7798
## (Intercept)-Sylvilagus_floridanus -3.0947 0.3213 -3.7443 -3.0802 -2.5030
## (Intercept)-Meleagris_gallopavo -4.1197 0.4819 -5.0842 -4.1160 -3.1789
## (Intercept)-Sciurus_carolinensis -2.3867 0.3301 -3.0743 -2.3657 -1.7849
## (Intercept)-Vulpes_vulpes -4.0907 0.7572 -5.6263 -4.0604 -2.7104
## (Intercept)-Sus_scrofa -3.1163 0.6423 -4.4181 -3.1094 -1.8645
## shrub_cover-Odocoileus_virginianus -0.0606 0.0674 -0.1940 -0.0609 0.0705
## shrub_cover-Canis_latrans -0.2758 0.2163 -0.7048 -0.2731 0.1441
## shrub_cover-Sciurus_niger -0.4629 0.4744 -1.4268 -0.4541 0.4650
## shrub_cover-Procyon_lotor 0.2246 0.1714 -0.1197 0.2290 0.5437
## shrub_cover-Dasypus_novemcinctus 0.8207 0.2966 0.2677 0.8115 1.4271
## shrub_cover-Lynx_rufus -0.3098 0.3538 -1.0054 -0.3122 0.4047
## shrub_cover-Didelphis_virginiana 0.9425 0.3818 0.2649 0.9278 1.7600
## shrub_cover-Sylvilagus_floridanus 0.1970 0.4041 -0.5293 0.1800 1.0424
## shrub_cover-Meleagris_gallopavo -0.8837 0.3987 -1.7252 -0.8783 -0.1360
## shrub_cover-Sciurus_carolinensis 0.7529 0.4047 -0.0193 0.7458 1.5598
## shrub_cover-Vulpes_vulpes -0.2015 0.5913 -1.4457 -0.1874 0.9700
## shrub_cover-Sus_scrofa 0.4831 0.8068 -1.1125 0.4728 2.0958
## veg_height-Odocoileus_virginianus -0.3319 0.0686 -0.4644 -0.3312 -0.1995
## veg_height-Canis_latrans -0.5827 0.1819 -0.9501 -0.5774 -0.2438
## veg_height-Sciurus_niger -0.0204 0.4001 -0.7686 -0.0402 0.8281
## veg_height-Procyon_lotor 0.3381 0.1239 0.0989 0.3395 0.5910
## veg_height-Dasypus_novemcinctus 0.2323 0.1306 -0.0174 0.2305 0.4950
## veg_height-Lynx_rufus 0.0538 0.2406 -0.4416 0.0578 0.5045
## veg_height-Didelphis_virginiana 0.3776 0.2465 -0.0788 0.3722 0.8843
## veg_height-Sylvilagus_floridanus 0.1438 0.2464 -0.3309 0.1367 0.6320
## veg_height-Meleagris_gallopavo -0.2304 0.3494 -0.9160 -0.2252 0.4538
## veg_height-Sciurus_carolinensis 0.0537 0.2060 -0.3322 0.0485 0.4828
## veg_height-Vulpes_vulpes -0.1356 0.3136 -0.7832 -0.1280 0.4713
## veg_height-Sus_scrofa -0.1223 0.3336 -0.7926 -0.1150 0.5137
## week-Odocoileus_virginianus 1.3147 0.1252 1.0638 1.3152 1.5642
## week-Canis_latrans 0.5841 0.2644 0.0707 0.5784 1.1141
## week-Sciurus_niger -0.4880 0.5646 -1.6968 -0.4460 0.4813
## week-Procyon_lotor 0.1987 0.2125 -0.2063 0.1958 0.6177
## week-Dasypus_novemcinctus 0.0962 0.2275 -0.3559 0.0989 0.5430
## week-Lynx_rufus 0.3683 0.3578 -0.3246 0.3655 1.0762
## week-Didelphis_virginiana 0.0378 0.3741 -0.7230 0.0484 0.7384
## week-Sylvilagus_floridanus 0.0252 0.3530 -0.6897 0.0274 0.6954
## week-Meleagris_gallopavo -0.2165 0.4298 -1.1160 -0.1919 0.5433
## week-Sciurus_carolinensis 0.7996 0.3700 0.1145 0.7899 1.5695
## week-Vulpes_vulpes 0.1458 0.5373 -0.9891 0.1690 1.1458
## week-Sus_scrofa 0.6794 0.4534 -0.1931 0.6735 1.5976
## I(week^2)-Odocoileus_virginianus -0.5425 0.0516 -0.6438 -0.5431 -0.4431
## I(week^2)-Canis_latrans -0.2440 0.1094 -0.4632 -0.2423 -0.0336
## I(week^2)-Sciurus_niger -0.2841 0.2331 -0.7503 -0.2756 0.1543
## I(week^2)-Procyon_lotor -0.1326 0.0932 -0.3178 -0.1314 0.0494
## I(week^2)-Dasypus_novemcinctus -0.1784 0.1027 -0.3829 -0.1786 0.0193
## I(week^2)-Lynx_rufus -0.2377 0.1529 -0.5559 -0.2365 0.0563
## I(week^2)-Didelphis_virginiana -0.4060 0.2021 -0.8616 -0.3891 -0.0618
## I(week^2)-Sylvilagus_floridanus -0.1804 0.1637 -0.5079 -0.1775 0.1309
## I(week^2)-Meleagris_gallopavo -0.4086 0.2330 -0.9289 -0.3885 -0.0129
## I(week^2)-Sciurus_carolinensis -0.2821 0.1460 -0.5805 -0.2782 -0.0083
## I(week^2)-Vulpes_vulpes -0.4080 0.2490 -0.9629 -0.3843 0.0268
## I(week^2)-Sus_scrofa -0.2410 0.1794 -0.5941 -0.2370 0.1088
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0002 2911
## (Intercept)-Sciurus_niger 1.0360 516
## (Intercept)-Procyon_lotor 1.0020 3285
## (Intercept)-Dasypus_novemcinctus 1.0018 4535
## (Intercept)-Lynx_rufus 1.0042 947
## (Intercept)-Didelphis_virginiana 1.0029 2941
## (Intercept)-Sylvilagus_floridanus 1.0031 1824
## (Intercept)-Meleagris_gallopavo 1.0093 597
## (Intercept)-Sciurus_carolinensis 1.0015 2986
## (Intercept)-Vulpes_vulpes 1.0024 522
## (Intercept)-Sus_scrofa 1.0036 1623
## shrub_cover-Odocoileus_virginianus 1.0002 5250
## shrub_cover-Canis_latrans 0.9999 2889
## shrub_cover-Sciurus_niger 1.0051 1033
## shrub_cover-Procyon_lotor 1.0001 3880
## shrub_cover-Dasypus_novemcinctus 1.0005 3605
## shrub_cover-Lynx_rufus 1.0115 1549
## shrub_cover-Didelphis_virginiana 1.0126 2050
## shrub_cover-Sylvilagus_floridanus 1.0016 1595
## shrub_cover-Meleagris_gallopavo 1.0063 770
## shrub_cover-Sciurus_carolinensis 1.0000 2601
## shrub_cover-Vulpes_vulpes 1.0005 1736
## shrub_cover-Sus_scrofa 1.0010 2111
## veg_height-Odocoileus_virginianus 1.0007 5250
## veg_height-Canis_latrans 1.0026 2477
## veg_height-Sciurus_niger 1.0126 1694
## veg_height-Procyon_lotor 1.0016 3811
## veg_height-Dasypus_novemcinctus 1.0001 5060
## veg_height-Lynx_rufus 1.0003 2774
## veg_height-Didelphis_virginiana 1.0027 2955
## veg_height-Sylvilagus_floridanus 1.0022 2174
## veg_height-Meleagris_gallopavo 1.0123 1109
## veg_height-Sciurus_carolinensis 1.0003 3428
## veg_height-Vulpes_vulpes 1.0037 1947
## veg_height-Sus_scrofa 1.0120 3247
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0014 3951
## week-Sciurus_niger 1.0030 964
## week-Procyon_lotor 1.0022 3710
## week-Dasypus_novemcinctus 1.0001 4034
## week-Lynx_rufus 1.0003 2846
## week-Didelphis_virginiana 1.0003 3675
## week-Sylvilagus_floridanus 1.0009 2804
## week-Meleagris_gallopavo 1.0024 1158
## week-Sciurus_carolinensis 1.0022 3951
## week-Vulpes_vulpes 1.0020 1754
## week-Sus_scrofa 1.0012 3681
## I(week^2)-Odocoileus_virginianus 0.9999 4927
## I(week^2)-Canis_latrans 1.0023 3782
## I(week^2)-Sciurus_niger 1.0003 1148
## I(week^2)-Procyon_lotor 1.0082 3602
## I(week^2)-Dasypus_novemcinctus 1.0013 4837
## I(week^2)-Lynx_rufus 1.0014 2597
## I(week^2)-Didelphis_virginiana 1.0015 1579
## I(week^2)-Sylvilagus_floridanus 1.0055 1647
## I(week^2)-Meleagris_gallopavo 1.0036 664
## I(week^2)-Sciurus_carolinensis 1.0019 4175
## I(week^2)-Vulpes_vulpes 1.0051 1326
## I(week^2)-Sus_scrofa 1.0003 3839
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 12 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.3202
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8462 1.0954 -2.8673 -0.8956 1.4404 1.0045 1245
## Cogon_Patch_Size -0.0493 0.7178 -1.6123 -0.0015 1.2605 1.0074 947
## Veg_shannon_index 0.9876 0.5061 0.0508 0.9610 2.0559 1.0125 748
## total_shrub_cover -0.7315 0.5736 -1.9484 -0.6965 0.2866 1.0358 701
## Avg_Cogongrass_Cover -0.2185 0.9852 -2.1774 -0.2214 1.7942 1.0153 344
## Tree_Density -2.0335 0.8294 -3.6931 -2.0209 -0.4890 1.0376 584
## Avg_Canopy_Cover 2.0730 0.7081 0.8184 2.0124 3.6234 1.0089 412
## I(Avg_Cogongrass_Cover^2) 1.5215 0.5902 0.4584 1.4850 2.7796 1.0101 402
## avg_veg_height -0.0872 0.5643 -1.2415 -0.0699 1.0121 1.0017 461
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.1143 19.8462 3.5414 14.7120 69.0966 1.0015 300
## Cogon_Patch_Size 3.3778 5.3842 0.1092 1.6861 17.4457 1.0812 401
## Veg_shannon_index 0.9272 1.4233 0.0488 0.4557 4.7192 1.0215 885
## total_shrub_cover 1.4536 2.3293 0.0718 0.7610 6.9820 1.0227 454
## Avg_Cogongrass_Cover 1.2922 2.2011 0.0482 0.5605 7.4318 1.0136 471
## Tree_Density 3.8852 6.9589 0.0750 1.4699 22.4615 1.0391 280
## Avg_Canopy_Cover 3.2724 4.5174 0.1984 1.9711 14.5034 1.0407 311
## I(Avg_Cogongrass_Cover^2) 1.1429 2.3732 0.0504 0.4742 6.2425 1.1914 471
## avg_veg_height 0.5422 0.8644 0.0425 0.2860 2.5085 1.0427 1544
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8346 3.1263 0.0535 0.8578 8.3787 1.1986 106
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6098 0.4574 -3.4719 -2.6291 -1.6778 1.0001 3113
## shrub_cover 0.2263 0.2805 -0.3346 0.2255 0.7936 1.0055 1749
## veg_height -0.0185 0.1549 -0.3364 -0.0151 0.2891 1.0079 2470
## week 0.2879 0.2349 -0.1893 0.2940 0.7336 1.0034 3088
## I(week^2) -0.2972 0.1010 -0.5049 -0.2959 -0.0994 1.0006 1993
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5908 1.4617 0.9728 2.2519 6.2005 1.0027 3667
## shrub_cover 0.6735 0.5004 0.1457 0.5548 2.0035 1.0023 1486
## veg_height 0.1972 0.1337 0.0583 0.1648 0.5280 1.0092 2667
## week 0.4598 0.3352 0.1215 0.3717 1.3247 1.0073 1757
## I(week^2) 0.0733 0.0541 0.0221 0.0592 0.2039 1.0032 1965
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.5350 3.7173 2.2598
## (Intercept)-Canis_latrans -0.7870 1.3840 -3.3110
## (Intercept)-Sciurus_niger 1.1693 2.9489 -3.2823
## (Intercept)-Procyon_lotor -0.3015 1.1408 -2.6636
## (Intercept)-Dasypus_novemcinctus -2.6097 1.2789 -5.5736
## (Intercept)-Lynx_rufus 0.6698 3.2521 -4.0177
## (Intercept)-Didelphis_virginiana -4.0372 1.5581 -7.4287
## (Intercept)-Sylvilagus_floridanus -2.2170 1.6431 -5.5561
## (Intercept)-Meleagris_gallopavo -0.2496 2.6807 -4.2673
## (Intercept)-Sciurus_carolinensis -4.6324 1.6723 -8.2912
## (Intercept)-Vulpes_vulpes -3.8682 2.4878 -8.5542
## (Intercept)-Sus_scrofa -5.4408 2.2226 -10.4785
## Cogon_Patch_Size-Odocoileus_virginianus 0.0672 1.4573 -2.7526
## Cogon_Patch_Size-Canis_latrans 1.5898 1.4373 -0.3666
## Cogon_Patch_Size-Sciurus_niger -0.7048 1.9952 -5.4218
## Cogon_Patch_Size-Procyon_lotor -0.4667 0.8024 -2.1414
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0510 0.8329 -1.6092
## Cogon_Patch_Size-Lynx_rufus -0.1777 1.5897 -3.2855
## Cogon_Patch_Size-Didelphis_virginiana 1.6057 1.1300 -0.1709
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2669 1.8395 -5.7669
## Cogon_Patch_Size-Meleagris_gallopavo 0.5837 1.5818 -1.9461
## Cogon_Patch_Size-Sciurus_carolinensis -0.9586 1.4158 -4.5312
## Cogon_Patch_Size-Vulpes_vulpes -0.4160 1.7090 -4.0144
## Cogon_Patch_Size-Sus_scrofa -0.6673 1.6259 -4.5173
## Veg_shannon_index-Odocoileus_virginianus 0.7793 0.9291 -1.3353
## Veg_shannon_index-Canis_latrans 1.3355 0.7300 0.1166
## Veg_shannon_index-Sciurus_niger 1.1237 1.0599 -0.9069
## Veg_shannon_index-Procyon_lotor 1.2118 0.6488 0.0737
## Veg_shannon_index-Dasypus_novemcinctus 0.6144 0.6281 -0.7007
## Veg_shannon_index-Lynx_rufus 1.0778 0.9653 -0.7229
## Veg_shannon_index-Didelphis_virginiana 1.1761 0.7440 -0.1496
## Veg_shannon_index-Sylvilagus_floridanus 1.0605 0.7776 -0.3521
## Veg_shannon_index-Meleagris_gallopavo 1.2737 0.9168 -0.3019
## Veg_shannon_index-Sciurus_carolinensis 0.3274 0.8619 -1.6504
## Veg_shannon_index-Vulpes_vulpes 0.6330 0.9939 -1.4299
## Veg_shannon_index-Sus_scrofa 1.6151 1.0565 0.0757
## total_shrub_cover-Odocoileus_virginianus -0.3787 1.0008 -2.3224
## total_shrub_cover-Canis_latrans 0.2058 0.8611 -1.1871
## total_shrub_cover-Sciurus_niger -0.9450 1.2600 -3.7707
## total_shrub_cover-Procyon_lotor -1.2978 0.7259 -2.9984
## total_shrub_cover-Dasypus_novemcinctus -0.3286 0.7724 -1.9152
## total_shrub_cover-Lynx_rufus -1.0468 1.2711 -4.0496
## total_shrub_cover-Didelphis_virginiana -1.0451 0.9342 -3.1144
## total_shrub_cover-Sylvilagus_floridanus -0.8643 1.0983 -3.3676
## total_shrub_cover-Meleagris_gallopavo -1.6466 1.4633 -5.1491
## total_shrub_cover-Sciurus_carolinensis -0.5998 0.9626 -2.7328
## total_shrub_cover-Vulpes_vulpes -0.9396 1.2004 -3.7043
## total_shrub_cover-Sus_scrofa -0.3709 1.0463 -2.4469
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2663 1.3895 -3.0953
## Avg_Cogongrass_Cover-Canis_latrans 0.0450 1.2714 -2.3663
## Avg_Cogongrass_Cover-Sciurus_niger -0.5982 1.5462 -4.0462
## Avg_Cogongrass_Cover-Procyon_lotor -0.1066 1.2207 -2.4824
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4374 1.3398 -1.8682
## Avg_Cogongrass_Cover-Lynx_rufus -0.0940 1.3239 -2.6326
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1262 1.2446 -2.5268
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7743 1.3736 -3.8714
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4025 1.4916 -3.5358
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2204 1.2760 -2.7235
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0207 1.3568 -2.7066
## Avg_Cogongrass_Cover-Sus_scrofa -0.5421 1.4285 -3.5427
## Tree_Density-Odocoileus_virginianus -0.8763 1.5327 -3.2326
## Tree_Density-Canis_latrans -2.9136 1.4563 -6.4082
## Tree_Density-Sciurus_niger -1.9652 1.8395 -5.5800
## Tree_Density-Procyon_lotor -1.9741 1.0116 -4.1176
## Tree_Density-Dasypus_novemcinctus -4.0224 2.2856 -10.0822
## Tree_Density-Lynx_rufus -0.7992 1.8091 -3.5973
## Tree_Density-Didelphis_virginiana -2.3042 1.3397 -5.4097
## Tree_Density-Sylvilagus_floridanus -2.6417 1.5738 -6.5303
## Tree_Density-Meleagris_gallopavo -2.3228 1.5812 -5.9117
## Tree_Density-Sciurus_carolinensis -2.6824 1.5890 -6.6399
## Tree_Density-Vulpes_vulpes -1.9320 1.9038 -5.6641
## Tree_Density-Sus_scrofa -2.5493 1.7561 -7.1718
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3270 1.5542 -1.7449
## Avg_Canopy_Cover-Canis_latrans 0.1437 0.7234 -1.2642
## Avg_Canopy_Cover-Sciurus_niger 2.4921 1.8422 -0.8226
## Avg_Canopy_Cover-Procyon_lotor 1.6995 0.8363 0.2115
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2439 0.9129 0.7878
## Avg_Canopy_Cover-Lynx_rufus 1.7778 1.6205 -1.1702
## Avg_Canopy_Cover-Didelphis_virginiana 3.1889 1.4158 1.2531
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8908 1.8622 1.2282
## Avg_Canopy_Cover-Meleagris_gallopavo 2.7365 1.6507 0.3373
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0435 1.4855 1.0556
## Avg_Canopy_Cover-Vulpes_vulpes 2.6285 1.4750 0.4260
## Avg_Canopy_Cover-Sus_scrofa 2.2841 1.1295 0.5106
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8855 1.2918 0.0507
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9948 1.0075 0.5426
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2817 1.2111 -1.2082
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8998 0.8771 0.5322
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5395 0.7679 0.2472
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1065 1.0370 0.5427
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2050 0.7384 -0.1853
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2614 0.8714 -0.3456
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.9349 1.2678 -1.9265
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7674 0.8081 0.4295
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8711 0.8874 0.4660
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1159 1.1154 -1.2989
## avg_veg_height-Odocoileus_virginianus -0.1050 0.8771 -1.9350
## avg_veg_height-Canis_latrans -0.1820 0.6734 -1.5398
## avg_veg_height-Sciurus_niger -0.2349 0.9069 -2.1126
## avg_veg_height-Procyon_lotor 0.0747 0.6876 -1.3084
## avg_veg_height-Dasypus_novemcinctus 0.2663 0.6798 -1.0423
## avg_veg_height-Lynx_rufus -0.2847 0.9204 -2.2893
## avg_veg_height-Didelphis_virginiana -0.2643 0.7800 -1.9386
## avg_veg_height-Sylvilagus_floridanus -0.1900 0.7663 -1.8402
## avg_veg_height-Meleagris_gallopavo -0.0554 0.9207 -1.9718
## avg_veg_height-Sciurus_carolinensis 0.2265 0.7473 -1.1447
## avg_veg_height-Vulpes_vulpes -0.1961 0.8742 -2.0334
## avg_veg_height-Sus_scrofa -0.1413 0.7923 -1.7620
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8660 16.2677 1.0021 292
## (Intercept)-Canis_latrans -0.8192 2.0131 1.0208 804
## (Intercept)-Sciurus_niger 0.7620 8.4011 1.0412 326
## (Intercept)-Procyon_lotor -0.2588 1.8621 1.0035 1062
## (Intercept)-Dasypus_novemcinctus -2.5049 -0.4540 1.0077 706
## (Intercept)-Lynx_rufus 0.1653 8.2416 1.1454 205
## (Intercept)-Didelphis_virginiana -3.9096 -1.3594 1.0049 515
## (Intercept)-Sylvilagus_floridanus -2.1701 0.8447 1.0030 728
## (Intercept)-Meleagris_gallopavo -0.6735 6.1543 1.0294 228
## (Intercept)-Sciurus_carolinensis -4.5146 -1.7696 1.0033 694
## (Intercept)-Vulpes_vulpes -3.9680 1.5521 1.0068 332
## (Intercept)-Sus_scrofa -5.2068 -1.6343 1.0093 454
## Cogon_Patch_Size-Odocoileus_virginianus 0.0344 3.2153 1.0083 1488
## Cogon_Patch_Size-Canis_latrans 1.2980 5.1903 1.0125 864
## Cogon_Patch_Size-Sciurus_niger -0.4661 2.8013 1.0676 476
## Cogon_Patch_Size-Procyon_lotor -0.4352 0.9833 1.0079 683
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0391 1.7933 1.0021 1284
## Cogon_Patch_Size-Lynx_rufus -0.1844 3.1725 1.0133 635
## Cogon_Patch_Size-Didelphis_virginiana 1.4632 4.1191 1.0233 406
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9057 1.1307 1.0311 599
## Cogon_Patch_Size-Meleagris_gallopavo 0.3641 4.6150 1.0164 709
## Cogon_Patch_Size-Sciurus_carolinensis -0.7036 1.0671 1.0125 694
## Cogon_Patch_Size-Vulpes_vulpes -0.3323 2.9525 1.0062 731
## Cogon_Patch_Size-Sus_scrofa -0.4089 1.7399 1.0009 861
## Veg_shannon_index-Odocoileus_virginianus 0.8145 2.5691 1.0015 1390
## Veg_shannon_index-Canis_latrans 1.2524 3.0146 1.0073 680
## Veg_shannon_index-Sciurus_niger 1.0634 3.4761 1.0068 1110
## Veg_shannon_index-Procyon_lotor 1.1498 2.6724 1.0156 762
## Veg_shannon_index-Dasypus_novemcinctus 0.6414 1.7921 1.0022 1636
## Veg_shannon_index-Lynx_rufus 1.0253 3.2059 1.0153 1247
## Veg_shannon_index-Didelphis_virginiana 1.1021 2.8392 1.0092 995
## Veg_shannon_index-Sylvilagus_floridanus 1.0122 2.7759 1.0046 1040
## Veg_shannon_index-Meleagris_gallopavo 1.1838 3.4078 1.0304 1029
## Veg_shannon_index-Sciurus_carolinensis 0.4283 1.8205 1.0023 1363
## Veg_shannon_index-Vulpes_vulpes 0.7075 2.3710 1.0095 1069
## Veg_shannon_index-Sus_scrofa 1.4252 4.3039 1.0047 933
## total_shrub_cover-Odocoileus_virginianus -0.4063 1.7231 1.0124 1960
## total_shrub_cover-Canis_latrans 0.0998 2.2035 1.0094 789
## total_shrub_cover-Sciurus_niger -0.8416 1.2877 1.0627 553
## total_shrub_cover-Procyon_lotor -1.2345 -0.0597 1.0066 1071
## total_shrub_cover-Dasypus_novemcinctus -0.2812 1.0697 1.0186 1111
## total_shrub_cover-Lynx_rufus -0.9213 1.2265 1.0239 459
## total_shrub_cover-Didelphis_virginiana -0.9420 0.4384 1.0097 1001
## total_shrub_cover-Sylvilagus_floridanus -0.7645 0.9536 1.0136 703
## total_shrub_cover-Meleagris_gallopavo -1.3941 0.5844 1.0299 368
## total_shrub_cover-Sciurus_carolinensis -0.5388 1.1663 1.0080 1388
## total_shrub_cover-Vulpes_vulpes -0.8246 1.1436 1.0083 611
## total_shrub_cover-Sus_scrofa -0.3926 1.8167 1.0238 1132
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2734 2.4957 1.0044 662
## Avg_Cogongrass_Cover-Canis_latrans 0.0093 2.7551 1.0102 521
## Avg_Cogongrass_Cover-Sciurus_niger -0.4993 2.2167 1.0272 550
## Avg_Cogongrass_Cover-Procyon_lotor -0.1201 2.3961 1.0062 488
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2961 3.4594 1.0032 550
## Avg_Cogongrass_Cover-Lynx_rufus -0.1206 2.6189 1.0071 692
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1248 2.4444 1.0088 583
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6841 1.6488 1.0124 526
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3344 2.3754 1.0156 554
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2111 2.3180 1.0057 508
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0541 2.9050 1.0124 592
## Avg_Cogongrass_Cover-Sus_scrofa -0.4791 2.1714 1.0164 633
## Tree_Density-Odocoileus_virginianus -1.0909 2.6035 1.0141 487
## Tree_Density-Canis_latrans -2.6880 -0.7683 1.0178 539
## Tree_Density-Sciurus_niger -1.9716 2.1369 1.0048 682
## Tree_Density-Procyon_lotor -1.9292 -0.1461 1.0106 774
## Tree_Density-Dasypus_novemcinctus -3.4214 -1.2942 1.0202 306
## Tree_Density-Lynx_rufus -1.0443 3.6464 1.0271 365
## Tree_Density-Didelphis_virginiana -2.2052 0.0468 1.0053 699
## Tree_Density-Sylvilagus_floridanus -2.4209 -0.1595 1.0242 762
## Tree_Density-Meleagris_gallopavo -2.2262 0.5742 1.0064 1047
## Tree_Density-Sciurus_carolinensis -2.4685 -0.1241 1.0103 693
## Tree_Density-Vulpes_vulpes -1.9887 1.9857 1.0841 501
## Tree_Density-Sus_scrofa -2.3054 0.2577 1.0074 787
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3467 4.4045 1.0111 827
## Avg_Canopy_Cover-Canis_latrans 0.1339 1.5850 1.0045 1276
## Avg_Canopy_Cover-Sciurus_niger 2.3308 6.6142 1.0325 491
## Avg_Canopy_Cover-Procyon_lotor 1.6354 3.5179 1.0081 717
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1266 4.3479 1.0141 417
## Avg_Canopy_Cover-Lynx_rufus 1.7014 5.2741 1.0156 464
## Avg_Canopy_Cover-Didelphis_virginiana 2.9240 6.6956 1.0127 272
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5308 8.5119 1.0122 310
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4268 6.8102 1.0262 582
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7447 6.8215 1.0010 383
## Avg_Canopy_Cover-Vulpes_vulpes 2.3738 6.3925 1.0159 526
## Avg_Canopy_Cover-Sus_scrofa 2.1358 4.9822 1.0200 1212
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7015 4.8901 1.0469 706
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8431 4.4441 1.0563 471
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2943 3.6855 1.0197 403
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7974 3.9312 1.0061 689
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4608 3.2585 1.0009 696
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9467 4.6905 1.0212 653
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1828 2.7432 1.0025 631
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2255 3.1410 1.0034 723
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0827 3.0838 1.0550 272
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6767 3.6105 1.0022 766
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7583 3.8781 1.0085 795
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1748 3.1524 1.0065 472
## avg_veg_height-Odocoileus_virginianus -0.0897 1.6336 1.0017 779
## avg_veg_height-Canis_latrans -0.1794 1.1284 1.0015 779
## avg_veg_height-Sciurus_niger -0.1897 1.4425 1.0076 857
## avg_veg_height-Procyon_lotor 0.0614 1.4621 1.0041 628
## avg_veg_height-Dasypus_novemcinctus 0.2471 1.6726 1.0050 789
## avg_veg_height-Lynx_rufus -0.2037 1.3385 1.0085 917
## avg_veg_height-Didelphis_virginiana -0.2230 1.1623 1.0005 689
## avg_veg_height-Sylvilagus_floridanus -0.1513 1.2410 1.0009 725
## avg_veg_height-Meleagris_gallopavo -0.0334 1.7304 1.0015 736
## avg_veg_height-Sciurus_carolinensis 0.1864 1.7874 1.0002 824
## avg_veg_height-Vulpes_vulpes -0.1602 1.4691 1.0007 792
## avg_veg_height-Sus_scrofa -0.1282 1.4004 1.0037 726
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5393 0.0795 0.3890 0.5364 0.7022
## (Intercept)-Canis_latrans -2.5391 0.2027 -2.9599 -2.5324 -2.1603
## (Intercept)-Sciurus_niger -4.7532 0.5354 -5.8179 -4.7588 -3.7224
## (Intercept)-Procyon_lotor -2.1874 0.1622 -2.5204 -2.1796 -1.8866
## (Intercept)-Dasypus_novemcinctus -1.6219 0.1833 -1.9881 -1.6162 -1.2725
## (Intercept)-Lynx_rufus -3.7521 0.3790 -4.4870 -3.7619 -2.9744
## (Intercept)-Didelphis_virginiana -2.3724 0.3189 -3.0386 -2.3601 -1.7921
## (Intercept)-Sylvilagus_floridanus -3.1076 0.2947 -3.6991 -3.0983 -2.5509
## (Intercept)-Meleagris_gallopavo -3.9849 0.5595 -5.1152 -3.9715 -2.9520
## (Intercept)-Sciurus_carolinensis -2.5200 0.3383 -3.2087 -2.5074 -1.8820
## (Intercept)-Vulpes_vulpes -4.1746 0.6929 -5.6800 -4.1316 -2.9433
## (Intercept)-Sus_scrofa -3.2478 0.6526 -4.5208 -3.2488 -1.9493
## shrub_cover-Odocoileus_virginianus -0.0608 0.0674 -0.1920 -0.0614 0.0730
## shrub_cover-Canis_latrans -0.3196 0.2293 -0.7539 -0.3195 0.1444
## shrub_cover-Sciurus_niger -0.4014 0.4557 -1.3669 -0.3891 0.4570
## shrub_cover-Procyon_lotor 0.2663 0.1650 -0.0707 0.2694 0.5846
## shrub_cover-Dasypus_novemcinctus 0.9361 0.3193 0.3294 0.9355 1.5634
## shrub_cover-Lynx_rufus -0.2401 0.3777 -0.9783 -0.2438 0.4890
## shrub_cover-Didelphis_virginiana 1.0180 0.3807 0.3204 0.9967 1.8171
## shrub_cover-Sylvilagus_floridanus 0.5141 0.3959 -0.2638 0.5130 1.2992
## shrub_cover-Meleagris_gallopavo -0.7502 0.4790 -1.7240 -0.7391 0.1577
## shrub_cover-Sciurus_carolinensis 0.9539 0.4187 0.1407 0.9509 1.8013
## shrub_cover-Vulpes_vulpes 0.0791 0.5781 -1.0892 0.0829 1.2346
## shrub_cover-Sus_scrofa 0.7573 0.8369 -0.8885 0.7469 2.4681
## veg_height-Odocoileus_virginianus -0.3320 0.0693 -0.4667 -0.3304 -0.1972
## veg_height-Canis_latrans -0.5651 0.1827 -0.9259 -0.5597 -0.2215
## veg_height-Sciurus_niger -0.0778 0.3386 -0.7324 -0.0780 0.6208
## veg_height-Procyon_lotor 0.3516 0.1236 0.1095 0.3523 0.5992
## veg_height-Dasypus_novemcinctus 0.2543 0.1367 -0.0122 0.2513 0.5268
## veg_height-Lynx_rufus 0.1182 0.2392 -0.3739 0.1211 0.5813
## veg_height-Didelphis_virginiana 0.4257 0.2401 -0.0222 0.4212 0.9102
## veg_height-Sylvilagus_floridanus 0.1163 0.2397 -0.3363 0.1140 0.5852
## veg_height-Meleagris_gallopavo -0.2566 0.3454 -0.9593 -0.2552 0.4073
## veg_height-Sciurus_carolinensis 0.1103 0.2180 -0.3151 0.1066 0.5502
## veg_height-Vulpes_vulpes -0.1939 0.3255 -0.9008 -0.1835 0.4161
## veg_height-Sus_scrofa -0.1789 0.3275 -0.8599 -0.1698 0.4369
## week-Odocoileus_virginianus 1.3150 0.1240 1.0726 1.3149 1.5605
## week-Canis_latrans 0.5867 0.2620 0.0707 0.5916 1.0801
## week-Sciurus_niger -0.4693 0.5476 -1.6616 -0.4365 0.4830
## week-Procyon_lotor 0.2029 0.2114 -0.2061 0.2035 0.6101
## week-Dasypus_novemcinctus 0.0964 0.2275 -0.3547 0.0961 0.5381
## week-Lynx_rufus 0.3681 0.3555 -0.3276 0.3702 1.0710
## week-Didelphis_virginiana 0.0360 0.3770 -0.7346 0.0434 0.7535
## week-Sylvilagus_floridanus 0.0317 0.3484 -0.6659 0.0336 0.7173
## week-Meleagris_gallopavo -0.2477 0.4323 -1.1386 -0.2356 0.5568
## week-Sciurus_carolinensis 0.7932 0.3689 0.0817 0.7897 1.5503
## week-Vulpes_vulpes 0.1444 0.5163 -0.9484 0.1685 1.1145
## week-Sus_scrofa 0.6741 0.4579 -0.1935 0.6537 1.6389
## I(week^2)-Odocoileus_virginianus -0.5418 0.0509 -0.6417 -0.5416 -0.4446
## I(week^2)-Canis_latrans -0.2429 0.1085 -0.4635 -0.2419 -0.0260
## I(week^2)-Sciurus_niger -0.3110 0.2422 -0.8389 -0.2985 0.1284
## I(week^2)-Procyon_lotor -0.1336 0.0918 -0.3184 -0.1346 0.0444
## I(week^2)-Dasypus_novemcinctus -0.1787 0.1025 -0.3863 -0.1774 0.0184
## I(week^2)-Lynx_rufus -0.2387 0.1547 -0.5516 -0.2353 0.0578
## I(week^2)-Didelphis_virginiana -0.4147 0.2091 -0.8884 -0.3990 -0.0560
## I(week^2)-Sylvilagus_floridanus -0.1747 0.1579 -0.4909 -0.1712 0.1280
## I(week^2)-Meleagris_gallopavo -0.4201 0.2364 -0.9587 -0.3998 -0.0213
## I(week^2)-Sciurus_carolinensis -0.2813 0.1445 -0.5722 -0.2793 -0.0002
## I(week^2)-Vulpes_vulpes -0.4079 0.2501 -0.9616 -0.3891 0.0200
## I(week^2)-Sus_scrofa -0.2441 0.1812 -0.6143 -0.2396 0.1006
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5026
## (Intercept)-Canis_latrans 1.0004 2602
## (Intercept)-Sciurus_niger 1.0117 422
## (Intercept)-Procyon_lotor 1.0011 3376
## (Intercept)-Dasypus_novemcinctus 1.0038 2802
## (Intercept)-Lynx_rufus 1.0133 533
## (Intercept)-Didelphis_virginiana 1.0018 2132
## (Intercept)-Sylvilagus_floridanus 1.0013 1874
## (Intercept)-Meleagris_gallopavo 1.0192 263
## (Intercept)-Sciurus_carolinensis 1.0046 1709
## (Intercept)-Vulpes_vulpes 1.0307 436
## (Intercept)-Sus_scrofa 1.0116 865
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0011 1328
## shrub_cover-Sciurus_niger 1.0074 688
## shrub_cover-Procyon_lotor 1.0018 3716
## shrub_cover-Dasypus_novemcinctus 1.0062 1872
## shrub_cover-Lynx_rufus 1.0026 821
## shrub_cover-Didelphis_virginiana 1.0020 1382
## shrub_cover-Sylvilagus_floridanus 1.0014 1486
## shrub_cover-Meleagris_gallopavo 1.0128 388
## shrub_cover-Sciurus_carolinensis 1.0044 1326
## shrub_cover-Vulpes_vulpes 1.0010 1233
## shrub_cover-Sus_scrofa 1.0049 987
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0012 2138
## veg_height-Sciurus_niger 1.0117 1060
## veg_height-Procyon_lotor 1.0012 3637
## veg_height-Dasypus_novemcinctus 1.0015 3637
## veg_height-Lynx_rufus 1.0063 1786
## veg_height-Didelphis_virginiana 1.0031 2912
## veg_height-Sylvilagus_floridanus 1.0015 2349
## veg_height-Meleagris_gallopavo 1.0173 925
## veg_height-Sciurus_carolinensis 1.0039 2843
## veg_height-Vulpes_vulpes 1.0026 1729
## veg_height-Sus_scrofa 1.0054 2687
## week-Odocoileus_virginianus 1.0066 4196
## week-Canis_latrans 1.0002 3912
## week-Sciurus_niger 1.0035 641
## week-Procyon_lotor 1.0012 4511
## week-Dasypus_novemcinctus 1.0009 5195
## week-Lynx_rufus 1.0060 1950
## week-Didelphis_virginiana 1.0018 2540
## week-Sylvilagus_floridanus 1.0039 2764
## week-Meleagris_gallopavo 1.0047 1040
## week-Sciurus_carolinensis 1.0016 3751
## week-Vulpes_vulpes 1.0016 1913
## week-Sus_scrofa 1.0001 3276
## I(week^2)-Odocoileus_virginianus 1.0061 4312
## I(week^2)-Canis_latrans 1.0008 3887
## I(week^2)-Sciurus_niger 1.0047 694
## I(week^2)-Procyon_lotor 1.0002 4315
## I(week^2)-Dasypus_novemcinctus 1.0015 4507
## I(week^2)-Lynx_rufus 1.0012 2170
## I(week^2)-Didelphis_virginiana 1.0008 1540
## I(week^2)-Sylvilagus_floridanus 1.0020 2281
## I(week^2)-Meleagris_gallopavo 1.0022 659
## I(week^2)-Sciurus_carolinensis 0.9998 4064
## I(week^2)-Vulpes_vulpes 1.0029 1146
## I(week^2)-Sus_scrofa 1.0007 3987
waicOcc(ms_full_full_T, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1833.2234 126.9899 3920.4266
waicOcc(ms_full_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1872.0107 128.1648 4000.3509
waicOcc(ms_full_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1866.8409 108.6426 3950.9669
waicOcc(ms_full_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1870.7047 123.5901 3988.5897
waicOcc(ms_full_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1881.5458 114.3743 3991.8402
waicOcc(ms_full_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1885.2806 109.5527 3989.6665
waicOcc(ms_full_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1899.72202 94.31029 3988.06463
waicOcc(ms_full_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1879.5528 110.8898 3980.8851
waicOcc(ms_full_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1826.7987 129.7719 3913.1413
waicOcc(ms_null_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1965.94480 40.73707 4013.36375
waicOcc(ms_null_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1895.33579 77.36108 3945.39374
waicOcc(ms_null_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1939.46212 67.05615 4013.03654
waicOcc(ms_null_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1932.50093 56.67791 3978.35768
waicOcc(ms_null_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1934.7632 68.0258 4005.5781
waicOcc(ms_null_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1944.73374 62.15736 4013.78221
waicOcc(ms_null_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1949.09375 56.83624 4011.85997
waicOcc(ms_null_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1942.58464 59.22382 4003.61692
waicOcc(ms_null_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1887.51245 77.99672 3931.01835
waicOcc(ms_week_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1884.54846 84.67637 3938.44966
waicOcc(ms_week_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1927.13371 76.34858 4006.96458
waicOcc(ms_week_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1953.83918 49.91656 4007.51146
waicOcc(ms_week_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1933.29051 70.24008 4007.06117
waicOcc(ms_week_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1922.69327 76.74106 3998.86866
waicOcc(ms_week_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1920.64995 65.06362 3971.42714
waicOcc(ms_week_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1937.42464 65.92721 4006.70369
waicOcc(ms_week_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1930.12976 68.40982 3997.07917
waicOcc(ms_week_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1877.33625 86.32096 3927.31441
waicOcc(ms_cover_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1845.4084 118.5037 3927.8242
waicOcc(ms_cover_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1883.8044 119.6919 4006.9925
waicOcc(ms_cover_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1912.43311 84.93742 3994.74107
waicOcc(ms_cover_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1893.3484 104.7341 3996.1650
waicOcc(ms_cover_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1882.2947 115.5589 3995.7073
waicOcc(ms_cover_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1879.06114 98.67941 3955.48112
waicOcc(ms_cover_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1897.7662 100.1562 3995.8448
waicOcc(ms_cover_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1891.5166 101.8754 3986.7840
waicOcc(ms_cover_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1839.4884 119.8258 3918.6283
waicOcc(ms_weekQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1809.10860 99.79554 3817.80828
waicOcc(ms_weekQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1852.42071 90.46295 3885.76733
waicOcc(ms_weekQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1879.09046 64.30093 3886.78277
waicOcc(ms_weekQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1858.34082 84.68814 3886.05791
waicOcc(ms_weekQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1848.20036 91.74966 3879.90003
waicOcc(ms_weekQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1845.90826 79.03646 3849.88944
waicOcc(ms_weekQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1862.21589 80.32867 3885.08910
waicOcc(ms_weekQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1856.26703 82.50434 3877.54274
waicOcc(ms_weekQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1802.4392 100.4557 3805.7899
waicOcc(ms_fullQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1754.756 144.197 3797.906
waicOcc(ms_fullQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1795.0670 144.4641 3879.0622
waicOcc(ms_fullQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1822.3472 110.6006 3865.8956
waicOcc(ms_fullQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1803.7557 130.8491 3869.2097
waicOcc(ms_fullQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1793.3526 140.1497 3867.0045
waicOcc(ms_fullQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1789.7760 123.7473 3827.0466
waicOcc(ms_fullQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1807.8815 125.5436 3866.8502
waicOcc(ms_fullQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1801.8532 127.7958 3859.2980
waicOcc(ms_fullQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1749.7813 144.8286 3789.2198
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T <- ppcOcc(ms_fullQ_fullQ_T, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 12
## Currently on species 2 out of 12
## Currently on species 3 out of 12
## Currently on species 4 out of 12
## Currently on species 5 out of 12
## Currently on species 6 out of 12
## Currently on species 7 out of 12
## Currently on species 8 out of 12
## Currently on species 9 out of 12
## Currently on species 10 out of 12
## Currently on species 11 out of 12
## Currently on species 12 out of 12
summary(ppc.ms_fullQ_fullQ_T)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.3159
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Odocoileus_virginianus Bayesian p-value: 0
## Canis_latrans Bayesian p-value: 0.6436
## Sciurus_niger Bayesian p-value: 0.1291
## Procyon_lotor Bayesian p-value: 0.0653
## Dasypus_novemcinctus Bayesian p-value: 0
## Lynx_rufus Bayesian p-value: 0.289
## Didelphis_virginiana Bayesian p-value: 0.4309
## Sylvilagus_floridanus Bayesian p-value: 0.4107
## Meleagris_gallopavo Bayesian p-value: 0.588
## Sciurus_carolinensis Bayesian p-value: 0.3893
## Vulpes_vulpes Bayesian p-value: 0.2764
## Sus_scrofa Bayesian p-value: 0.568
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.3202
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8462 1.0954 -2.8673 -0.8956 1.4404 1.0045 1245
## Cogon_Patch_Size -0.0493 0.7178 -1.6123 -0.0015 1.2605 1.0074 947
## Veg_shannon_index 0.9876 0.5061 0.0508 0.9610 2.0559 1.0125 748
## total_shrub_cover -0.7315 0.5736 -1.9484 -0.6965 0.2866 1.0358 701
## Avg_Cogongrass_Cover -0.2185 0.9852 -2.1774 -0.2214 1.7942 1.0153 344
## Tree_Density -2.0335 0.8294 -3.6931 -2.0209 -0.4890 1.0376 584
## Avg_Canopy_Cover 2.0730 0.7081 0.8184 2.0124 3.6234 1.0089 412
## I(Avg_Cogongrass_Cover^2) 1.5215 0.5902 0.4584 1.4850 2.7796 1.0101 402
## avg_veg_height -0.0872 0.5643 -1.2415 -0.0699 1.0121 1.0017 461
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.1143 19.8462 3.5414 14.7120 69.0966 1.0015 300
## Cogon_Patch_Size 3.3778 5.3842 0.1092 1.6861 17.4457 1.0812 401
## Veg_shannon_index 0.9272 1.4233 0.0488 0.4557 4.7192 1.0215 885
## total_shrub_cover 1.4536 2.3293 0.0718 0.7610 6.9820 1.0227 454
## Avg_Cogongrass_Cover 1.2922 2.2011 0.0482 0.5605 7.4318 1.0136 471
## Tree_Density 3.8852 6.9589 0.0750 1.4699 22.4615 1.0391 280
## Avg_Canopy_Cover 3.2724 4.5174 0.1984 1.9711 14.5034 1.0407 311
## I(Avg_Cogongrass_Cover^2) 1.1429 2.3732 0.0504 0.4742 6.2425 1.1914 471
## avg_veg_height 0.5422 0.8644 0.0425 0.2860 2.5085 1.0427 1544
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8346 3.1263 0.0535 0.8578 8.3787 1.1986 106
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6098 0.4574 -3.4719 -2.6291 -1.6778 1.0001 3113
## shrub_cover 0.2263 0.2805 -0.3346 0.2255 0.7936 1.0055 1749
## veg_height -0.0185 0.1549 -0.3364 -0.0151 0.2891 1.0079 2470
## week 0.2879 0.2349 -0.1893 0.2940 0.7336 1.0034 3088
## I(week^2) -0.2972 0.1010 -0.5049 -0.2959 -0.0994 1.0006 1993
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5908 1.4617 0.9728 2.2519 6.2005 1.0027 3667
## shrub_cover 0.6735 0.5004 0.1457 0.5548 2.0035 1.0023 1486
## veg_height 0.1972 0.1337 0.0583 0.1648 0.5280 1.0092 2667
## week 0.4598 0.3352 0.1215 0.3717 1.3247 1.0073 1757
## I(week^2) 0.0733 0.0541 0.0221 0.0592 0.2039 1.0032 1965
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.5350 3.7173 2.2598
## (Intercept)-Canis_latrans -0.7870 1.3840 -3.3110
## (Intercept)-Sciurus_niger 1.1693 2.9489 -3.2823
## (Intercept)-Procyon_lotor -0.3015 1.1408 -2.6636
## (Intercept)-Dasypus_novemcinctus -2.6097 1.2789 -5.5736
## (Intercept)-Lynx_rufus 0.6698 3.2521 -4.0177
## (Intercept)-Didelphis_virginiana -4.0372 1.5581 -7.4287
## (Intercept)-Sylvilagus_floridanus -2.2170 1.6431 -5.5561
## (Intercept)-Meleagris_gallopavo -0.2496 2.6807 -4.2673
## (Intercept)-Sciurus_carolinensis -4.6324 1.6723 -8.2912
## (Intercept)-Vulpes_vulpes -3.8682 2.4878 -8.5542
## (Intercept)-Sus_scrofa -5.4408 2.2226 -10.4785
## Cogon_Patch_Size-Odocoileus_virginianus 0.0672 1.4573 -2.7526
## Cogon_Patch_Size-Canis_latrans 1.5898 1.4373 -0.3666
## Cogon_Patch_Size-Sciurus_niger -0.7048 1.9952 -5.4218
## Cogon_Patch_Size-Procyon_lotor -0.4667 0.8024 -2.1414
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0510 0.8329 -1.6092
## Cogon_Patch_Size-Lynx_rufus -0.1777 1.5897 -3.2855
## Cogon_Patch_Size-Didelphis_virginiana 1.6057 1.1300 -0.1709
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2669 1.8395 -5.7669
## Cogon_Patch_Size-Meleagris_gallopavo 0.5837 1.5818 -1.9461
## Cogon_Patch_Size-Sciurus_carolinensis -0.9586 1.4158 -4.5312
## Cogon_Patch_Size-Vulpes_vulpes -0.4160 1.7090 -4.0144
## Cogon_Patch_Size-Sus_scrofa -0.6673 1.6259 -4.5173
## Veg_shannon_index-Odocoileus_virginianus 0.7793 0.9291 -1.3353
## Veg_shannon_index-Canis_latrans 1.3355 0.7300 0.1166
## Veg_shannon_index-Sciurus_niger 1.1237 1.0599 -0.9069
## Veg_shannon_index-Procyon_lotor 1.2118 0.6488 0.0737
## Veg_shannon_index-Dasypus_novemcinctus 0.6144 0.6281 -0.7007
## Veg_shannon_index-Lynx_rufus 1.0778 0.9653 -0.7229
## Veg_shannon_index-Didelphis_virginiana 1.1761 0.7440 -0.1496
## Veg_shannon_index-Sylvilagus_floridanus 1.0605 0.7776 -0.3521
## Veg_shannon_index-Meleagris_gallopavo 1.2737 0.9168 -0.3019
## Veg_shannon_index-Sciurus_carolinensis 0.3274 0.8619 -1.6504
## Veg_shannon_index-Vulpes_vulpes 0.6330 0.9939 -1.4299
## Veg_shannon_index-Sus_scrofa 1.6151 1.0565 0.0757
## total_shrub_cover-Odocoileus_virginianus -0.3787 1.0008 -2.3224
## total_shrub_cover-Canis_latrans 0.2058 0.8611 -1.1871
## total_shrub_cover-Sciurus_niger -0.9450 1.2600 -3.7707
## total_shrub_cover-Procyon_lotor -1.2978 0.7259 -2.9984
## total_shrub_cover-Dasypus_novemcinctus -0.3286 0.7724 -1.9152
## total_shrub_cover-Lynx_rufus -1.0468 1.2711 -4.0496
## total_shrub_cover-Didelphis_virginiana -1.0451 0.9342 -3.1144
## total_shrub_cover-Sylvilagus_floridanus -0.8643 1.0983 -3.3676
## total_shrub_cover-Meleagris_gallopavo -1.6466 1.4633 -5.1491
## total_shrub_cover-Sciurus_carolinensis -0.5998 0.9626 -2.7328
## total_shrub_cover-Vulpes_vulpes -0.9396 1.2004 -3.7043
## total_shrub_cover-Sus_scrofa -0.3709 1.0463 -2.4469
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2663 1.3895 -3.0953
## Avg_Cogongrass_Cover-Canis_latrans 0.0450 1.2714 -2.3663
## Avg_Cogongrass_Cover-Sciurus_niger -0.5982 1.5462 -4.0462
## Avg_Cogongrass_Cover-Procyon_lotor -0.1066 1.2207 -2.4824
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4374 1.3398 -1.8682
## Avg_Cogongrass_Cover-Lynx_rufus -0.0940 1.3239 -2.6326
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1262 1.2446 -2.5268
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7743 1.3736 -3.8714
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4025 1.4916 -3.5358
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2204 1.2760 -2.7235
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0207 1.3568 -2.7066
## Avg_Cogongrass_Cover-Sus_scrofa -0.5421 1.4285 -3.5427
## Tree_Density-Odocoileus_virginianus -0.8763 1.5327 -3.2326
## Tree_Density-Canis_latrans -2.9136 1.4563 -6.4082
## Tree_Density-Sciurus_niger -1.9652 1.8395 -5.5800
## Tree_Density-Procyon_lotor -1.9741 1.0116 -4.1176
## Tree_Density-Dasypus_novemcinctus -4.0224 2.2856 -10.0822
## Tree_Density-Lynx_rufus -0.7992 1.8091 -3.5973
## Tree_Density-Didelphis_virginiana -2.3042 1.3397 -5.4097
## Tree_Density-Sylvilagus_floridanus -2.6417 1.5738 -6.5303
## Tree_Density-Meleagris_gallopavo -2.3228 1.5812 -5.9117
## Tree_Density-Sciurus_carolinensis -2.6824 1.5890 -6.6399
## Tree_Density-Vulpes_vulpes -1.9320 1.9038 -5.6641
## Tree_Density-Sus_scrofa -2.5493 1.7561 -7.1718
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3270 1.5542 -1.7449
## Avg_Canopy_Cover-Canis_latrans 0.1437 0.7234 -1.2642
## Avg_Canopy_Cover-Sciurus_niger 2.4921 1.8422 -0.8226
## Avg_Canopy_Cover-Procyon_lotor 1.6995 0.8363 0.2115
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2439 0.9129 0.7878
## Avg_Canopy_Cover-Lynx_rufus 1.7778 1.6205 -1.1702
## Avg_Canopy_Cover-Didelphis_virginiana 3.1889 1.4158 1.2531
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8908 1.8622 1.2282
## Avg_Canopy_Cover-Meleagris_gallopavo 2.7365 1.6507 0.3373
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0435 1.4855 1.0556
## Avg_Canopy_Cover-Vulpes_vulpes 2.6285 1.4750 0.4260
## Avg_Canopy_Cover-Sus_scrofa 2.2841 1.1295 0.5106
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8855 1.2918 0.0507
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9948 1.0075 0.5426
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2817 1.2111 -1.2082
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8998 0.8771 0.5322
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5395 0.7679 0.2472
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1065 1.0370 0.5427
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2050 0.7384 -0.1853
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2614 0.8714 -0.3456
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.9349 1.2678 -1.9265
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7674 0.8081 0.4295
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8711 0.8874 0.4660
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1159 1.1154 -1.2989
## avg_veg_height-Odocoileus_virginianus -0.1050 0.8771 -1.9350
## avg_veg_height-Canis_latrans -0.1820 0.6734 -1.5398
## avg_veg_height-Sciurus_niger -0.2349 0.9069 -2.1126
## avg_veg_height-Procyon_lotor 0.0747 0.6876 -1.3084
## avg_veg_height-Dasypus_novemcinctus 0.2663 0.6798 -1.0423
## avg_veg_height-Lynx_rufus -0.2847 0.9204 -2.2893
## avg_veg_height-Didelphis_virginiana -0.2643 0.7800 -1.9386
## avg_veg_height-Sylvilagus_floridanus -0.1900 0.7663 -1.8402
## avg_veg_height-Meleagris_gallopavo -0.0554 0.9207 -1.9718
## avg_veg_height-Sciurus_carolinensis 0.2265 0.7473 -1.1447
## avg_veg_height-Vulpes_vulpes -0.1961 0.8742 -2.0334
## avg_veg_height-Sus_scrofa -0.1413 0.7923 -1.7620
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8660 16.2677 1.0021 292
## (Intercept)-Canis_latrans -0.8192 2.0131 1.0208 804
## (Intercept)-Sciurus_niger 0.7620 8.4011 1.0412 326
## (Intercept)-Procyon_lotor -0.2588 1.8621 1.0035 1062
## (Intercept)-Dasypus_novemcinctus -2.5049 -0.4540 1.0077 706
## (Intercept)-Lynx_rufus 0.1653 8.2416 1.1454 205
## (Intercept)-Didelphis_virginiana -3.9096 -1.3594 1.0049 515
## (Intercept)-Sylvilagus_floridanus -2.1701 0.8447 1.0030 728
## (Intercept)-Meleagris_gallopavo -0.6735 6.1543 1.0294 228
## (Intercept)-Sciurus_carolinensis -4.5146 -1.7696 1.0033 694
## (Intercept)-Vulpes_vulpes -3.9680 1.5521 1.0068 332
## (Intercept)-Sus_scrofa -5.2068 -1.6343 1.0093 454
## Cogon_Patch_Size-Odocoileus_virginianus 0.0344 3.2153 1.0083 1488
## Cogon_Patch_Size-Canis_latrans 1.2980 5.1903 1.0125 864
## Cogon_Patch_Size-Sciurus_niger -0.4661 2.8013 1.0676 476
## Cogon_Patch_Size-Procyon_lotor -0.4352 0.9833 1.0079 683
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0391 1.7933 1.0021 1284
## Cogon_Patch_Size-Lynx_rufus -0.1844 3.1725 1.0133 635
## Cogon_Patch_Size-Didelphis_virginiana 1.4632 4.1191 1.0233 406
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9057 1.1307 1.0311 599
## Cogon_Patch_Size-Meleagris_gallopavo 0.3641 4.6150 1.0164 709
## Cogon_Patch_Size-Sciurus_carolinensis -0.7036 1.0671 1.0125 694
## Cogon_Patch_Size-Vulpes_vulpes -0.3323 2.9525 1.0062 731
## Cogon_Patch_Size-Sus_scrofa -0.4089 1.7399 1.0009 861
## Veg_shannon_index-Odocoileus_virginianus 0.8145 2.5691 1.0015 1390
## Veg_shannon_index-Canis_latrans 1.2524 3.0146 1.0073 680
## Veg_shannon_index-Sciurus_niger 1.0634 3.4761 1.0068 1110
## Veg_shannon_index-Procyon_lotor 1.1498 2.6724 1.0156 762
## Veg_shannon_index-Dasypus_novemcinctus 0.6414 1.7921 1.0022 1636
## Veg_shannon_index-Lynx_rufus 1.0253 3.2059 1.0153 1247
## Veg_shannon_index-Didelphis_virginiana 1.1021 2.8392 1.0092 995
## Veg_shannon_index-Sylvilagus_floridanus 1.0122 2.7759 1.0046 1040
## Veg_shannon_index-Meleagris_gallopavo 1.1838 3.4078 1.0304 1029
## Veg_shannon_index-Sciurus_carolinensis 0.4283 1.8205 1.0023 1363
## Veg_shannon_index-Vulpes_vulpes 0.7075 2.3710 1.0095 1069
## Veg_shannon_index-Sus_scrofa 1.4252 4.3039 1.0047 933
## total_shrub_cover-Odocoileus_virginianus -0.4063 1.7231 1.0124 1960
## total_shrub_cover-Canis_latrans 0.0998 2.2035 1.0094 789
## total_shrub_cover-Sciurus_niger -0.8416 1.2877 1.0627 553
## total_shrub_cover-Procyon_lotor -1.2345 -0.0597 1.0066 1071
## total_shrub_cover-Dasypus_novemcinctus -0.2812 1.0697 1.0186 1111
## total_shrub_cover-Lynx_rufus -0.9213 1.2265 1.0239 459
## total_shrub_cover-Didelphis_virginiana -0.9420 0.4384 1.0097 1001
## total_shrub_cover-Sylvilagus_floridanus -0.7645 0.9536 1.0136 703
## total_shrub_cover-Meleagris_gallopavo -1.3941 0.5844 1.0299 368
## total_shrub_cover-Sciurus_carolinensis -0.5388 1.1663 1.0080 1388
## total_shrub_cover-Vulpes_vulpes -0.8246 1.1436 1.0083 611
## total_shrub_cover-Sus_scrofa -0.3926 1.8167 1.0238 1132
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2734 2.4957 1.0044 662
## Avg_Cogongrass_Cover-Canis_latrans 0.0093 2.7551 1.0102 521
## Avg_Cogongrass_Cover-Sciurus_niger -0.4993 2.2167 1.0272 550
## Avg_Cogongrass_Cover-Procyon_lotor -0.1201 2.3961 1.0062 488
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2961 3.4594 1.0032 550
## Avg_Cogongrass_Cover-Lynx_rufus -0.1206 2.6189 1.0071 692
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1248 2.4444 1.0088 583
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6841 1.6488 1.0124 526
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3344 2.3754 1.0156 554
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2111 2.3180 1.0057 508
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0541 2.9050 1.0124 592
## Avg_Cogongrass_Cover-Sus_scrofa -0.4791 2.1714 1.0164 633
## Tree_Density-Odocoileus_virginianus -1.0909 2.6035 1.0141 487
## Tree_Density-Canis_latrans -2.6880 -0.7683 1.0178 539
## Tree_Density-Sciurus_niger -1.9716 2.1369 1.0048 682
## Tree_Density-Procyon_lotor -1.9292 -0.1461 1.0106 774
## Tree_Density-Dasypus_novemcinctus -3.4214 -1.2942 1.0202 306
## Tree_Density-Lynx_rufus -1.0443 3.6464 1.0271 365
## Tree_Density-Didelphis_virginiana -2.2052 0.0468 1.0053 699
## Tree_Density-Sylvilagus_floridanus -2.4209 -0.1595 1.0242 762
## Tree_Density-Meleagris_gallopavo -2.2262 0.5742 1.0064 1047
## Tree_Density-Sciurus_carolinensis -2.4685 -0.1241 1.0103 693
## Tree_Density-Vulpes_vulpes -1.9887 1.9857 1.0841 501
## Tree_Density-Sus_scrofa -2.3054 0.2577 1.0074 787
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3467 4.4045 1.0111 827
## Avg_Canopy_Cover-Canis_latrans 0.1339 1.5850 1.0045 1276
## Avg_Canopy_Cover-Sciurus_niger 2.3308 6.6142 1.0325 491
## Avg_Canopy_Cover-Procyon_lotor 1.6354 3.5179 1.0081 717
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1266 4.3479 1.0141 417
## Avg_Canopy_Cover-Lynx_rufus 1.7014 5.2741 1.0156 464
## Avg_Canopy_Cover-Didelphis_virginiana 2.9240 6.6956 1.0127 272
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5308 8.5119 1.0122 310
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4268 6.8102 1.0262 582
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7447 6.8215 1.0010 383
## Avg_Canopy_Cover-Vulpes_vulpes 2.3738 6.3925 1.0159 526
## Avg_Canopy_Cover-Sus_scrofa 2.1358 4.9822 1.0200 1212
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7015 4.8901 1.0469 706
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8431 4.4441 1.0563 471
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2943 3.6855 1.0197 403
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7974 3.9312 1.0061 689
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4608 3.2585 1.0009 696
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9467 4.6905 1.0212 653
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1828 2.7432 1.0025 631
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2255 3.1410 1.0034 723
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0827 3.0838 1.0550 272
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6767 3.6105 1.0022 766
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7583 3.8781 1.0085 795
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1748 3.1524 1.0065 472
## avg_veg_height-Odocoileus_virginianus -0.0897 1.6336 1.0017 779
## avg_veg_height-Canis_latrans -0.1794 1.1284 1.0015 779
## avg_veg_height-Sciurus_niger -0.1897 1.4425 1.0076 857
## avg_veg_height-Procyon_lotor 0.0614 1.4621 1.0041 628
## avg_veg_height-Dasypus_novemcinctus 0.2471 1.6726 1.0050 789
## avg_veg_height-Lynx_rufus -0.2037 1.3385 1.0085 917
## avg_veg_height-Didelphis_virginiana -0.2230 1.1623 1.0005 689
## avg_veg_height-Sylvilagus_floridanus -0.1513 1.2410 1.0009 725
## avg_veg_height-Meleagris_gallopavo -0.0334 1.7304 1.0015 736
## avg_veg_height-Sciurus_carolinensis 0.1864 1.7874 1.0002 824
## avg_veg_height-Vulpes_vulpes -0.1602 1.4691 1.0007 792
## avg_veg_height-Sus_scrofa -0.1282 1.4004 1.0037 726
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5393 0.0795 0.3890 0.5364 0.7022
## (Intercept)-Canis_latrans -2.5391 0.2027 -2.9599 -2.5324 -2.1603
## (Intercept)-Sciurus_niger -4.7532 0.5354 -5.8179 -4.7588 -3.7224
## (Intercept)-Procyon_lotor -2.1874 0.1622 -2.5204 -2.1796 -1.8866
## (Intercept)-Dasypus_novemcinctus -1.6219 0.1833 -1.9881 -1.6162 -1.2725
## (Intercept)-Lynx_rufus -3.7521 0.3790 -4.4870 -3.7619 -2.9744
## (Intercept)-Didelphis_virginiana -2.3724 0.3189 -3.0386 -2.3601 -1.7921
## (Intercept)-Sylvilagus_floridanus -3.1076 0.2947 -3.6991 -3.0983 -2.5509
## (Intercept)-Meleagris_gallopavo -3.9849 0.5595 -5.1152 -3.9715 -2.9520
## (Intercept)-Sciurus_carolinensis -2.5200 0.3383 -3.2087 -2.5074 -1.8820
## (Intercept)-Vulpes_vulpes -4.1746 0.6929 -5.6800 -4.1316 -2.9433
## (Intercept)-Sus_scrofa -3.2478 0.6526 -4.5208 -3.2488 -1.9493
## shrub_cover-Odocoileus_virginianus -0.0608 0.0674 -0.1920 -0.0614 0.0730
## shrub_cover-Canis_latrans -0.3196 0.2293 -0.7539 -0.3195 0.1444
## shrub_cover-Sciurus_niger -0.4014 0.4557 -1.3669 -0.3891 0.4570
## shrub_cover-Procyon_lotor 0.2663 0.1650 -0.0707 0.2694 0.5846
## shrub_cover-Dasypus_novemcinctus 0.9361 0.3193 0.3294 0.9355 1.5634
## shrub_cover-Lynx_rufus -0.2401 0.3777 -0.9783 -0.2438 0.4890
## shrub_cover-Didelphis_virginiana 1.0180 0.3807 0.3204 0.9967 1.8171
## shrub_cover-Sylvilagus_floridanus 0.5141 0.3959 -0.2638 0.5130 1.2992
## shrub_cover-Meleagris_gallopavo -0.7502 0.4790 -1.7240 -0.7391 0.1577
## shrub_cover-Sciurus_carolinensis 0.9539 0.4187 0.1407 0.9509 1.8013
## shrub_cover-Vulpes_vulpes 0.0791 0.5781 -1.0892 0.0829 1.2346
## shrub_cover-Sus_scrofa 0.7573 0.8369 -0.8885 0.7469 2.4681
## veg_height-Odocoileus_virginianus -0.3320 0.0693 -0.4667 -0.3304 -0.1972
## veg_height-Canis_latrans -0.5651 0.1827 -0.9259 -0.5597 -0.2215
## veg_height-Sciurus_niger -0.0778 0.3386 -0.7324 -0.0780 0.6208
## veg_height-Procyon_lotor 0.3516 0.1236 0.1095 0.3523 0.5992
## veg_height-Dasypus_novemcinctus 0.2543 0.1367 -0.0122 0.2513 0.5268
## veg_height-Lynx_rufus 0.1182 0.2392 -0.3739 0.1211 0.5813
## veg_height-Didelphis_virginiana 0.4257 0.2401 -0.0222 0.4212 0.9102
## veg_height-Sylvilagus_floridanus 0.1163 0.2397 -0.3363 0.1140 0.5852
## veg_height-Meleagris_gallopavo -0.2566 0.3454 -0.9593 -0.2552 0.4073
## veg_height-Sciurus_carolinensis 0.1103 0.2180 -0.3151 0.1066 0.5502
## veg_height-Vulpes_vulpes -0.1939 0.3255 -0.9008 -0.1835 0.4161
## veg_height-Sus_scrofa -0.1789 0.3275 -0.8599 -0.1698 0.4369
## week-Odocoileus_virginianus 1.3150 0.1240 1.0726 1.3149 1.5605
## week-Canis_latrans 0.5867 0.2620 0.0707 0.5916 1.0801
## week-Sciurus_niger -0.4693 0.5476 -1.6616 -0.4365 0.4830
## week-Procyon_lotor 0.2029 0.2114 -0.2061 0.2035 0.6101
## week-Dasypus_novemcinctus 0.0964 0.2275 -0.3547 0.0961 0.5381
## week-Lynx_rufus 0.3681 0.3555 -0.3276 0.3702 1.0710
## week-Didelphis_virginiana 0.0360 0.3770 -0.7346 0.0434 0.7535
## week-Sylvilagus_floridanus 0.0317 0.3484 -0.6659 0.0336 0.7173
## week-Meleagris_gallopavo -0.2477 0.4323 -1.1386 -0.2356 0.5568
## week-Sciurus_carolinensis 0.7932 0.3689 0.0817 0.7897 1.5503
## week-Vulpes_vulpes 0.1444 0.5163 -0.9484 0.1685 1.1145
## week-Sus_scrofa 0.6741 0.4579 -0.1935 0.6537 1.6389
## I(week^2)-Odocoileus_virginianus -0.5418 0.0509 -0.6417 -0.5416 -0.4446
## I(week^2)-Canis_latrans -0.2429 0.1085 -0.4635 -0.2419 -0.0260
## I(week^2)-Sciurus_niger -0.3110 0.2422 -0.8389 -0.2985 0.1284
## I(week^2)-Procyon_lotor -0.1336 0.0918 -0.3184 -0.1346 0.0444
## I(week^2)-Dasypus_novemcinctus -0.1787 0.1025 -0.3863 -0.1774 0.0184
## I(week^2)-Lynx_rufus -0.2387 0.1547 -0.5516 -0.2353 0.0578
## I(week^2)-Didelphis_virginiana -0.4147 0.2091 -0.8884 -0.3990 -0.0560
## I(week^2)-Sylvilagus_floridanus -0.1747 0.1579 -0.4909 -0.1712 0.1280
## I(week^2)-Meleagris_gallopavo -0.4201 0.2364 -0.9587 -0.3998 -0.0213
## I(week^2)-Sciurus_carolinensis -0.2813 0.1445 -0.5722 -0.2793 -0.0002
## I(week^2)-Vulpes_vulpes -0.4079 0.2501 -0.9616 -0.3891 0.0200
## I(week^2)-Sus_scrofa -0.2441 0.1812 -0.6143 -0.2396 0.1006
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5026
## (Intercept)-Canis_latrans 1.0004 2602
## (Intercept)-Sciurus_niger 1.0117 422
## (Intercept)-Procyon_lotor 1.0011 3376
## (Intercept)-Dasypus_novemcinctus 1.0038 2802
## (Intercept)-Lynx_rufus 1.0133 533
## (Intercept)-Didelphis_virginiana 1.0018 2132
## (Intercept)-Sylvilagus_floridanus 1.0013 1874
## (Intercept)-Meleagris_gallopavo 1.0192 263
## (Intercept)-Sciurus_carolinensis 1.0046 1709
## (Intercept)-Vulpes_vulpes 1.0307 436
## (Intercept)-Sus_scrofa 1.0116 865
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0011 1328
## shrub_cover-Sciurus_niger 1.0074 688
## shrub_cover-Procyon_lotor 1.0018 3716
## shrub_cover-Dasypus_novemcinctus 1.0062 1872
## shrub_cover-Lynx_rufus 1.0026 821
## shrub_cover-Didelphis_virginiana 1.0020 1382
## shrub_cover-Sylvilagus_floridanus 1.0014 1486
## shrub_cover-Meleagris_gallopavo 1.0128 388
## shrub_cover-Sciurus_carolinensis 1.0044 1326
## shrub_cover-Vulpes_vulpes 1.0010 1233
## shrub_cover-Sus_scrofa 1.0049 987
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0012 2138
## veg_height-Sciurus_niger 1.0117 1060
## veg_height-Procyon_lotor 1.0012 3637
## veg_height-Dasypus_novemcinctus 1.0015 3637
## veg_height-Lynx_rufus 1.0063 1786
## veg_height-Didelphis_virginiana 1.0031 2912
## veg_height-Sylvilagus_floridanus 1.0015 2349
## veg_height-Meleagris_gallopavo 1.0173 925
## veg_height-Sciurus_carolinensis 1.0039 2843
## veg_height-Vulpes_vulpes 1.0026 1729
## veg_height-Sus_scrofa 1.0054 2687
## week-Odocoileus_virginianus 1.0066 4196
## week-Canis_latrans 1.0002 3912
## week-Sciurus_niger 1.0035 641
## week-Procyon_lotor 1.0012 4511
## week-Dasypus_novemcinctus 1.0009 5195
## week-Lynx_rufus 1.0060 1950
## week-Didelphis_virginiana 1.0018 2540
## week-Sylvilagus_floridanus 1.0039 2764
## week-Meleagris_gallopavo 1.0047 1040
## week-Sciurus_carolinensis 1.0016 3751
## week-Vulpes_vulpes 1.0016 1913
## week-Sus_scrofa 1.0001 3276
## I(week^2)-Odocoileus_virginianus 1.0061 4312
## I(week^2)-Canis_latrans 1.0008 3887
## I(week^2)-Sciurus_niger 1.0047 694
## I(week^2)-Procyon_lotor 1.0002 4315
## I(week^2)-Dasypus_novemcinctus 1.0015 4507
## I(week^2)-Lynx_rufus 1.0012 2170
## I(week^2)-Didelphis_virginiana 1.0008 1540
## I(week^2)-Sylvilagus_floridanus 1.0020 2281
## I(week^2)-Meleagris_gallopavo 1.0022 659
## I(week^2)-Sciurus_carolinensis 0.9998 4064
## I(week^2)-Vulpes_vulpes 1.0029 1146
## I(week^2)-Sus_scrofa 1.0007 3987
names(ms_fullQ_fullQ_T)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T$beta.samples)
## 'mcmc' num [1:5250, 1:108] 6.77 10.62 6.59 8.87 5.87 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:108] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.006857143
MCMCplot(ms_fullQ_fullQ_T$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:5250, 1:12, 1:100] 0.998 1 0.999 1 0.998 ...
## $ z.0.samples : int [1:5250, 1:12, 1:100] 1 1 1 1 1 1 1 1 1 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:5250, 1:12, 1:100] 0.998 1 0.999 1 0.998 ...
psi.0.quants <- apply(out.pred$psi.0.samples, c(3), function(x) quantile(x, prob = c(0.025, 0.5, 0.975)))
str(psi.0.quants)
## num [1:3, 1:100] 0.000269 0.149748 0.999938 0.000278 0.150773 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:3] "2.5%" "50%" "97.5%"
## ..$ : NULL
psi.plot.dat <- data.frame(
psi.med = psi.0.quants[2, ],
psi.low = psi.0.quants[1, ],
psi.high = psi.0.quants[3, ],
Avg_Cogongrass_Cover = cogon.pred.vals
)
str(psi.plot.dat)
## 'data.frame': 100 obs. of 4 variables:
## $ psi.med : num 0.15 0.151 0.154 0.151 0.151 ...
## $ psi.low : num 0.000269 0.000278 0.000293 0.000299 0.000296 ...
## $ psi.high : num 1 1 1 1 1 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.3202
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8462 1.0954 -2.8673 -0.8956 1.4404 1.0045 1245
## Cogon_Patch_Size -0.0493 0.7178 -1.6123 -0.0015 1.2605 1.0074 947
## Veg_shannon_index 0.9876 0.5061 0.0508 0.9610 2.0559 1.0125 748
## total_shrub_cover -0.7315 0.5736 -1.9484 -0.6965 0.2866 1.0358 701
## Avg_Cogongrass_Cover -0.2185 0.9852 -2.1774 -0.2214 1.7942 1.0153 344
## Tree_Density -2.0335 0.8294 -3.6931 -2.0209 -0.4890 1.0376 584
## Avg_Canopy_Cover 2.0730 0.7081 0.8184 2.0124 3.6234 1.0089 412
## I(Avg_Cogongrass_Cover^2) 1.5215 0.5902 0.4584 1.4850 2.7796 1.0101 402
## avg_veg_height -0.0872 0.5643 -1.2415 -0.0699 1.0121 1.0017 461
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.1143 19.8462 3.5414 14.7120 69.0966 1.0015 300
## Cogon_Patch_Size 3.3778 5.3842 0.1092 1.6861 17.4457 1.0812 401
## Veg_shannon_index 0.9272 1.4233 0.0488 0.4557 4.7192 1.0215 885
## total_shrub_cover 1.4536 2.3293 0.0718 0.7610 6.9820 1.0227 454
## Avg_Cogongrass_Cover 1.2922 2.2011 0.0482 0.5605 7.4318 1.0136 471
## Tree_Density 3.8852 6.9589 0.0750 1.4699 22.4615 1.0391 280
## Avg_Canopy_Cover 3.2724 4.5174 0.1984 1.9711 14.5034 1.0407 311
## I(Avg_Cogongrass_Cover^2) 1.1429 2.3732 0.0504 0.4742 6.2425 1.1914 471
## avg_veg_height 0.5422 0.8644 0.0425 0.2860 2.5085 1.0427 1544
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8346 3.1263 0.0535 0.8578 8.3787 1.1986 106
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6098 0.4574 -3.4719 -2.6291 -1.6778 1.0001 3113
## shrub_cover 0.2263 0.2805 -0.3346 0.2255 0.7936 1.0055 1749
## veg_height -0.0185 0.1549 -0.3364 -0.0151 0.2891 1.0079 2470
## week 0.2879 0.2349 -0.1893 0.2940 0.7336 1.0034 3088
## I(week^2) -0.2972 0.1010 -0.5049 -0.2959 -0.0994 1.0006 1993
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5908 1.4617 0.9728 2.2519 6.2005 1.0027 3667
## shrub_cover 0.6735 0.5004 0.1457 0.5548 2.0035 1.0023 1486
## veg_height 0.1972 0.1337 0.0583 0.1648 0.5280 1.0092 2667
## week 0.4598 0.3352 0.1215 0.3717 1.3247 1.0073 1757
## I(week^2) 0.0733 0.0541 0.0221 0.0592 0.2039 1.0032 1965
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.5350 3.7173 2.2598
## (Intercept)-Canis_latrans -0.7870 1.3840 -3.3110
## (Intercept)-Sciurus_niger 1.1693 2.9489 -3.2823
## (Intercept)-Procyon_lotor -0.3015 1.1408 -2.6636
## (Intercept)-Dasypus_novemcinctus -2.6097 1.2789 -5.5736
## (Intercept)-Lynx_rufus 0.6698 3.2521 -4.0177
## (Intercept)-Didelphis_virginiana -4.0372 1.5581 -7.4287
## (Intercept)-Sylvilagus_floridanus -2.2170 1.6431 -5.5561
## (Intercept)-Meleagris_gallopavo -0.2496 2.6807 -4.2673
## (Intercept)-Sciurus_carolinensis -4.6324 1.6723 -8.2912
## (Intercept)-Vulpes_vulpes -3.8682 2.4878 -8.5542
## (Intercept)-Sus_scrofa -5.4408 2.2226 -10.4785
## Cogon_Patch_Size-Odocoileus_virginianus 0.0672 1.4573 -2.7526
## Cogon_Patch_Size-Canis_latrans 1.5898 1.4373 -0.3666
## Cogon_Patch_Size-Sciurus_niger -0.7048 1.9952 -5.4218
## Cogon_Patch_Size-Procyon_lotor -0.4667 0.8024 -2.1414
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0510 0.8329 -1.6092
## Cogon_Patch_Size-Lynx_rufus -0.1777 1.5897 -3.2855
## Cogon_Patch_Size-Didelphis_virginiana 1.6057 1.1300 -0.1709
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2669 1.8395 -5.7669
## Cogon_Patch_Size-Meleagris_gallopavo 0.5837 1.5818 -1.9461
## Cogon_Patch_Size-Sciurus_carolinensis -0.9586 1.4158 -4.5312
## Cogon_Patch_Size-Vulpes_vulpes -0.4160 1.7090 -4.0144
## Cogon_Patch_Size-Sus_scrofa -0.6673 1.6259 -4.5173
## Veg_shannon_index-Odocoileus_virginianus 0.7793 0.9291 -1.3353
## Veg_shannon_index-Canis_latrans 1.3355 0.7300 0.1166
## Veg_shannon_index-Sciurus_niger 1.1237 1.0599 -0.9069
## Veg_shannon_index-Procyon_lotor 1.2118 0.6488 0.0737
## Veg_shannon_index-Dasypus_novemcinctus 0.6144 0.6281 -0.7007
## Veg_shannon_index-Lynx_rufus 1.0778 0.9653 -0.7229
## Veg_shannon_index-Didelphis_virginiana 1.1761 0.7440 -0.1496
## Veg_shannon_index-Sylvilagus_floridanus 1.0605 0.7776 -0.3521
## Veg_shannon_index-Meleagris_gallopavo 1.2737 0.9168 -0.3019
## Veg_shannon_index-Sciurus_carolinensis 0.3274 0.8619 -1.6504
## Veg_shannon_index-Vulpes_vulpes 0.6330 0.9939 -1.4299
## Veg_shannon_index-Sus_scrofa 1.6151 1.0565 0.0757
## total_shrub_cover-Odocoileus_virginianus -0.3787 1.0008 -2.3224
## total_shrub_cover-Canis_latrans 0.2058 0.8611 -1.1871
## total_shrub_cover-Sciurus_niger -0.9450 1.2600 -3.7707
## total_shrub_cover-Procyon_lotor -1.2978 0.7259 -2.9984
## total_shrub_cover-Dasypus_novemcinctus -0.3286 0.7724 -1.9152
## total_shrub_cover-Lynx_rufus -1.0468 1.2711 -4.0496
## total_shrub_cover-Didelphis_virginiana -1.0451 0.9342 -3.1144
## total_shrub_cover-Sylvilagus_floridanus -0.8643 1.0983 -3.3676
## total_shrub_cover-Meleagris_gallopavo -1.6466 1.4633 -5.1491
## total_shrub_cover-Sciurus_carolinensis -0.5998 0.9626 -2.7328
## total_shrub_cover-Vulpes_vulpes -0.9396 1.2004 -3.7043
## total_shrub_cover-Sus_scrofa -0.3709 1.0463 -2.4469
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2663 1.3895 -3.0953
## Avg_Cogongrass_Cover-Canis_latrans 0.0450 1.2714 -2.3663
## Avg_Cogongrass_Cover-Sciurus_niger -0.5982 1.5462 -4.0462
## Avg_Cogongrass_Cover-Procyon_lotor -0.1066 1.2207 -2.4824
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4374 1.3398 -1.8682
## Avg_Cogongrass_Cover-Lynx_rufus -0.0940 1.3239 -2.6326
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1262 1.2446 -2.5268
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7743 1.3736 -3.8714
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4025 1.4916 -3.5358
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2204 1.2760 -2.7235
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0207 1.3568 -2.7066
## Avg_Cogongrass_Cover-Sus_scrofa -0.5421 1.4285 -3.5427
## Tree_Density-Odocoileus_virginianus -0.8763 1.5327 -3.2326
## Tree_Density-Canis_latrans -2.9136 1.4563 -6.4082
## Tree_Density-Sciurus_niger -1.9652 1.8395 -5.5800
## Tree_Density-Procyon_lotor -1.9741 1.0116 -4.1176
## Tree_Density-Dasypus_novemcinctus -4.0224 2.2856 -10.0822
## Tree_Density-Lynx_rufus -0.7992 1.8091 -3.5973
## Tree_Density-Didelphis_virginiana -2.3042 1.3397 -5.4097
## Tree_Density-Sylvilagus_floridanus -2.6417 1.5738 -6.5303
## Tree_Density-Meleagris_gallopavo -2.3228 1.5812 -5.9117
## Tree_Density-Sciurus_carolinensis -2.6824 1.5890 -6.6399
## Tree_Density-Vulpes_vulpes -1.9320 1.9038 -5.6641
## Tree_Density-Sus_scrofa -2.5493 1.7561 -7.1718
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3270 1.5542 -1.7449
## Avg_Canopy_Cover-Canis_latrans 0.1437 0.7234 -1.2642
## Avg_Canopy_Cover-Sciurus_niger 2.4921 1.8422 -0.8226
## Avg_Canopy_Cover-Procyon_lotor 1.6995 0.8363 0.2115
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2439 0.9129 0.7878
## Avg_Canopy_Cover-Lynx_rufus 1.7778 1.6205 -1.1702
## Avg_Canopy_Cover-Didelphis_virginiana 3.1889 1.4158 1.2531
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8908 1.8622 1.2282
## Avg_Canopy_Cover-Meleagris_gallopavo 2.7365 1.6507 0.3373
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0435 1.4855 1.0556
## Avg_Canopy_Cover-Vulpes_vulpes 2.6285 1.4750 0.4260
## Avg_Canopy_Cover-Sus_scrofa 2.2841 1.1295 0.5106
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8855 1.2918 0.0507
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9948 1.0075 0.5426
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2817 1.2111 -1.2082
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8998 0.8771 0.5322
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5395 0.7679 0.2472
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1065 1.0370 0.5427
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2050 0.7384 -0.1853
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2614 0.8714 -0.3456
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.9349 1.2678 -1.9265
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7674 0.8081 0.4295
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8711 0.8874 0.4660
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1159 1.1154 -1.2989
## avg_veg_height-Odocoileus_virginianus -0.1050 0.8771 -1.9350
## avg_veg_height-Canis_latrans -0.1820 0.6734 -1.5398
## avg_veg_height-Sciurus_niger -0.2349 0.9069 -2.1126
## avg_veg_height-Procyon_lotor 0.0747 0.6876 -1.3084
## avg_veg_height-Dasypus_novemcinctus 0.2663 0.6798 -1.0423
## avg_veg_height-Lynx_rufus -0.2847 0.9204 -2.2893
## avg_veg_height-Didelphis_virginiana -0.2643 0.7800 -1.9386
## avg_veg_height-Sylvilagus_floridanus -0.1900 0.7663 -1.8402
## avg_veg_height-Meleagris_gallopavo -0.0554 0.9207 -1.9718
## avg_veg_height-Sciurus_carolinensis 0.2265 0.7473 -1.1447
## avg_veg_height-Vulpes_vulpes -0.1961 0.8742 -2.0334
## avg_veg_height-Sus_scrofa -0.1413 0.7923 -1.7620
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8660 16.2677 1.0021 292
## (Intercept)-Canis_latrans -0.8192 2.0131 1.0208 804
## (Intercept)-Sciurus_niger 0.7620 8.4011 1.0412 326
## (Intercept)-Procyon_lotor -0.2588 1.8621 1.0035 1062
## (Intercept)-Dasypus_novemcinctus -2.5049 -0.4540 1.0077 706
## (Intercept)-Lynx_rufus 0.1653 8.2416 1.1454 205
## (Intercept)-Didelphis_virginiana -3.9096 -1.3594 1.0049 515
## (Intercept)-Sylvilagus_floridanus -2.1701 0.8447 1.0030 728
## (Intercept)-Meleagris_gallopavo -0.6735 6.1543 1.0294 228
## (Intercept)-Sciurus_carolinensis -4.5146 -1.7696 1.0033 694
## (Intercept)-Vulpes_vulpes -3.9680 1.5521 1.0068 332
## (Intercept)-Sus_scrofa -5.2068 -1.6343 1.0093 454
## Cogon_Patch_Size-Odocoileus_virginianus 0.0344 3.2153 1.0083 1488
## Cogon_Patch_Size-Canis_latrans 1.2980 5.1903 1.0125 864
## Cogon_Patch_Size-Sciurus_niger -0.4661 2.8013 1.0676 476
## Cogon_Patch_Size-Procyon_lotor -0.4352 0.9833 1.0079 683
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0391 1.7933 1.0021 1284
## Cogon_Patch_Size-Lynx_rufus -0.1844 3.1725 1.0133 635
## Cogon_Patch_Size-Didelphis_virginiana 1.4632 4.1191 1.0233 406
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9057 1.1307 1.0311 599
## Cogon_Patch_Size-Meleagris_gallopavo 0.3641 4.6150 1.0164 709
## Cogon_Patch_Size-Sciurus_carolinensis -0.7036 1.0671 1.0125 694
## Cogon_Patch_Size-Vulpes_vulpes -0.3323 2.9525 1.0062 731
## Cogon_Patch_Size-Sus_scrofa -0.4089 1.7399 1.0009 861
## Veg_shannon_index-Odocoileus_virginianus 0.8145 2.5691 1.0015 1390
## Veg_shannon_index-Canis_latrans 1.2524 3.0146 1.0073 680
## Veg_shannon_index-Sciurus_niger 1.0634 3.4761 1.0068 1110
## Veg_shannon_index-Procyon_lotor 1.1498 2.6724 1.0156 762
## Veg_shannon_index-Dasypus_novemcinctus 0.6414 1.7921 1.0022 1636
## Veg_shannon_index-Lynx_rufus 1.0253 3.2059 1.0153 1247
## Veg_shannon_index-Didelphis_virginiana 1.1021 2.8392 1.0092 995
## Veg_shannon_index-Sylvilagus_floridanus 1.0122 2.7759 1.0046 1040
## Veg_shannon_index-Meleagris_gallopavo 1.1838 3.4078 1.0304 1029
## Veg_shannon_index-Sciurus_carolinensis 0.4283 1.8205 1.0023 1363
## Veg_shannon_index-Vulpes_vulpes 0.7075 2.3710 1.0095 1069
## Veg_shannon_index-Sus_scrofa 1.4252 4.3039 1.0047 933
## total_shrub_cover-Odocoileus_virginianus -0.4063 1.7231 1.0124 1960
## total_shrub_cover-Canis_latrans 0.0998 2.2035 1.0094 789
## total_shrub_cover-Sciurus_niger -0.8416 1.2877 1.0627 553
## total_shrub_cover-Procyon_lotor -1.2345 -0.0597 1.0066 1071
## total_shrub_cover-Dasypus_novemcinctus -0.2812 1.0697 1.0186 1111
## total_shrub_cover-Lynx_rufus -0.9213 1.2265 1.0239 459
## total_shrub_cover-Didelphis_virginiana -0.9420 0.4384 1.0097 1001
## total_shrub_cover-Sylvilagus_floridanus -0.7645 0.9536 1.0136 703
## total_shrub_cover-Meleagris_gallopavo -1.3941 0.5844 1.0299 368
## total_shrub_cover-Sciurus_carolinensis -0.5388 1.1663 1.0080 1388
## total_shrub_cover-Vulpes_vulpes -0.8246 1.1436 1.0083 611
## total_shrub_cover-Sus_scrofa -0.3926 1.8167 1.0238 1132
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2734 2.4957 1.0044 662
## Avg_Cogongrass_Cover-Canis_latrans 0.0093 2.7551 1.0102 521
## Avg_Cogongrass_Cover-Sciurus_niger -0.4993 2.2167 1.0272 550
## Avg_Cogongrass_Cover-Procyon_lotor -0.1201 2.3961 1.0062 488
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2961 3.4594 1.0032 550
## Avg_Cogongrass_Cover-Lynx_rufus -0.1206 2.6189 1.0071 692
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1248 2.4444 1.0088 583
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6841 1.6488 1.0124 526
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3344 2.3754 1.0156 554
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2111 2.3180 1.0057 508
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.0541 2.9050 1.0124 592
## Avg_Cogongrass_Cover-Sus_scrofa -0.4791 2.1714 1.0164 633
## Tree_Density-Odocoileus_virginianus -1.0909 2.6035 1.0141 487
## Tree_Density-Canis_latrans -2.6880 -0.7683 1.0178 539
## Tree_Density-Sciurus_niger -1.9716 2.1369 1.0048 682
## Tree_Density-Procyon_lotor -1.9292 -0.1461 1.0106 774
## Tree_Density-Dasypus_novemcinctus -3.4214 -1.2942 1.0202 306
## Tree_Density-Lynx_rufus -1.0443 3.6464 1.0271 365
## Tree_Density-Didelphis_virginiana -2.2052 0.0468 1.0053 699
## Tree_Density-Sylvilagus_floridanus -2.4209 -0.1595 1.0242 762
## Tree_Density-Meleagris_gallopavo -2.2262 0.5742 1.0064 1047
## Tree_Density-Sciurus_carolinensis -2.4685 -0.1241 1.0103 693
## Tree_Density-Vulpes_vulpes -1.9887 1.9857 1.0841 501
## Tree_Density-Sus_scrofa -2.3054 0.2577 1.0074 787
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3467 4.4045 1.0111 827
## Avg_Canopy_Cover-Canis_latrans 0.1339 1.5850 1.0045 1276
## Avg_Canopy_Cover-Sciurus_niger 2.3308 6.6142 1.0325 491
## Avg_Canopy_Cover-Procyon_lotor 1.6354 3.5179 1.0081 717
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1266 4.3479 1.0141 417
## Avg_Canopy_Cover-Lynx_rufus 1.7014 5.2741 1.0156 464
## Avg_Canopy_Cover-Didelphis_virginiana 2.9240 6.6956 1.0127 272
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5308 8.5119 1.0122 310
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4268 6.8102 1.0262 582
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7447 6.8215 1.0010 383
## Avg_Canopy_Cover-Vulpes_vulpes 2.3738 6.3925 1.0159 526
## Avg_Canopy_Cover-Sus_scrofa 2.1358 4.9822 1.0200 1212
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7015 4.8901 1.0469 706
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8431 4.4441 1.0563 471
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2943 3.6855 1.0197 403
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7974 3.9312 1.0061 689
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4608 3.2585 1.0009 696
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9467 4.6905 1.0212 653
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1828 2.7432 1.0025 631
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2255 3.1410 1.0034 723
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0827 3.0838 1.0550 272
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6767 3.6105 1.0022 766
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7583 3.8781 1.0085 795
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1748 3.1524 1.0065 472
## avg_veg_height-Odocoileus_virginianus -0.0897 1.6336 1.0017 779
## avg_veg_height-Canis_latrans -0.1794 1.1284 1.0015 779
## avg_veg_height-Sciurus_niger -0.1897 1.4425 1.0076 857
## avg_veg_height-Procyon_lotor 0.0614 1.4621 1.0041 628
## avg_veg_height-Dasypus_novemcinctus 0.2471 1.6726 1.0050 789
## avg_veg_height-Lynx_rufus -0.2037 1.3385 1.0085 917
## avg_veg_height-Didelphis_virginiana -0.2230 1.1623 1.0005 689
## avg_veg_height-Sylvilagus_floridanus -0.1513 1.2410 1.0009 725
## avg_veg_height-Meleagris_gallopavo -0.0334 1.7304 1.0015 736
## avg_veg_height-Sciurus_carolinensis 0.1864 1.7874 1.0002 824
## avg_veg_height-Vulpes_vulpes -0.1602 1.4691 1.0007 792
## avg_veg_height-Sus_scrofa -0.1282 1.4004 1.0037 726
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5393 0.0795 0.3890 0.5364 0.7022
## (Intercept)-Canis_latrans -2.5391 0.2027 -2.9599 -2.5324 -2.1603
## (Intercept)-Sciurus_niger -4.7532 0.5354 -5.8179 -4.7588 -3.7224
## (Intercept)-Procyon_lotor -2.1874 0.1622 -2.5204 -2.1796 -1.8866
## (Intercept)-Dasypus_novemcinctus -1.6219 0.1833 -1.9881 -1.6162 -1.2725
## (Intercept)-Lynx_rufus -3.7521 0.3790 -4.4870 -3.7619 -2.9744
## (Intercept)-Didelphis_virginiana -2.3724 0.3189 -3.0386 -2.3601 -1.7921
## (Intercept)-Sylvilagus_floridanus -3.1076 0.2947 -3.6991 -3.0983 -2.5509
## (Intercept)-Meleagris_gallopavo -3.9849 0.5595 -5.1152 -3.9715 -2.9520
## (Intercept)-Sciurus_carolinensis -2.5200 0.3383 -3.2087 -2.5074 -1.8820
## (Intercept)-Vulpes_vulpes -4.1746 0.6929 -5.6800 -4.1316 -2.9433
## (Intercept)-Sus_scrofa -3.2478 0.6526 -4.5208 -3.2488 -1.9493
## shrub_cover-Odocoileus_virginianus -0.0608 0.0674 -0.1920 -0.0614 0.0730
## shrub_cover-Canis_latrans -0.3196 0.2293 -0.7539 -0.3195 0.1444
## shrub_cover-Sciurus_niger -0.4014 0.4557 -1.3669 -0.3891 0.4570
## shrub_cover-Procyon_lotor 0.2663 0.1650 -0.0707 0.2694 0.5846
## shrub_cover-Dasypus_novemcinctus 0.9361 0.3193 0.3294 0.9355 1.5634
## shrub_cover-Lynx_rufus -0.2401 0.3777 -0.9783 -0.2438 0.4890
## shrub_cover-Didelphis_virginiana 1.0180 0.3807 0.3204 0.9967 1.8171
## shrub_cover-Sylvilagus_floridanus 0.5141 0.3959 -0.2638 0.5130 1.2992
## shrub_cover-Meleagris_gallopavo -0.7502 0.4790 -1.7240 -0.7391 0.1577
## shrub_cover-Sciurus_carolinensis 0.9539 0.4187 0.1407 0.9509 1.8013
## shrub_cover-Vulpes_vulpes 0.0791 0.5781 -1.0892 0.0829 1.2346
## shrub_cover-Sus_scrofa 0.7573 0.8369 -0.8885 0.7469 2.4681
## veg_height-Odocoileus_virginianus -0.3320 0.0693 -0.4667 -0.3304 -0.1972
## veg_height-Canis_latrans -0.5651 0.1827 -0.9259 -0.5597 -0.2215
## veg_height-Sciurus_niger -0.0778 0.3386 -0.7324 -0.0780 0.6208
## veg_height-Procyon_lotor 0.3516 0.1236 0.1095 0.3523 0.5992
## veg_height-Dasypus_novemcinctus 0.2543 0.1367 -0.0122 0.2513 0.5268
## veg_height-Lynx_rufus 0.1182 0.2392 -0.3739 0.1211 0.5813
## veg_height-Didelphis_virginiana 0.4257 0.2401 -0.0222 0.4212 0.9102
## veg_height-Sylvilagus_floridanus 0.1163 0.2397 -0.3363 0.1140 0.5852
## veg_height-Meleagris_gallopavo -0.2566 0.3454 -0.9593 -0.2552 0.4073
## veg_height-Sciurus_carolinensis 0.1103 0.2180 -0.3151 0.1066 0.5502
## veg_height-Vulpes_vulpes -0.1939 0.3255 -0.9008 -0.1835 0.4161
## veg_height-Sus_scrofa -0.1789 0.3275 -0.8599 -0.1698 0.4369
## week-Odocoileus_virginianus 1.3150 0.1240 1.0726 1.3149 1.5605
## week-Canis_latrans 0.5867 0.2620 0.0707 0.5916 1.0801
## week-Sciurus_niger -0.4693 0.5476 -1.6616 -0.4365 0.4830
## week-Procyon_lotor 0.2029 0.2114 -0.2061 0.2035 0.6101
## week-Dasypus_novemcinctus 0.0964 0.2275 -0.3547 0.0961 0.5381
## week-Lynx_rufus 0.3681 0.3555 -0.3276 0.3702 1.0710
## week-Didelphis_virginiana 0.0360 0.3770 -0.7346 0.0434 0.7535
## week-Sylvilagus_floridanus 0.0317 0.3484 -0.6659 0.0336 0.7173
## week-Meleagris_gallopavo -0.2477 0.4323 -1.1386 -0.2356 0.5568
## week-Sciurus_carolinensis 0.7932 0.3689 0.0817 0.7897 1.5503
## week-Vulpes_vulpes 0.1444 0.5163 -0.9484 0.1685 1.1145
## week-Sus_scrofa 0.6741 0.4579 -0.1935 0.6537 1.6389
## I(week^2)-Odocoileus_virginianus -0.5418 0.0509 -0.6417 -0.5416 -0.4446
## I(week^2)-Canis_latrans -0.2429 0.1085 -0.4635 -0.2419 -0.0260
## I(week^2)-Sciurus_niger -0.3110 0.2422 -0.8389 -0.2985 0.1284
## I(week^2)-Procyon_lotor -0.1336 0.0918 -0.3184 -0.1346 0.0444
## I(week^2)-Dasypus_novemcinctus -0.1787 0.1025 -0.3863 -0.1774 0.0184
## I(week^2)-Lynx_rufus -0.2387 0.1547 -0.5516 -0.2353 0.0578
## I(week^2)-Didelphis_virginiana -0.4147 0.2091 -0.8884 -0.3990 -0.0560
## I(week^2)-Sylvilagus_floridanus -0.1747 0.1579 -0.4909 -0.1712 0.1280
## I(week^2)-Meleagris_gallopavo -0.4201 0.2364 -0.9587 -0.3998 -0.0213
## I(week^2)-Sciurus_carolinensis -0.2813 0.1445 -0.5722 -0.2793 -0.0002
## I(week^2)-Vulpes_vulpes -0.4079 0.2501 -0.9616 -0.3891 0.0200
## I(week^2)-Sus_scrofa -0.2441 0.1812 -0.6143 -0.2396 0.1006
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5026
## (Intercept)-Canis_latrans 1.0004 2602
## (Intercept)-Sciurus_niger 1.0117 422
## (Intercept)-Procyon_lotor 1.0011 3376
## (Intercept)-Dasypus_novemcinctus 1.0038 2802
## (Intercept)-Lynx_rufus 1.0133 533
## (Intercept)-Didelphis_virginiana 1.0018 2132
## (Intercept)-Sylvilagus_floridanus 1.0013 1874
## (Intercept)-Meleagris_gallopavo 1.0192 263
## (Intercept)-Sciurus_carolinensis 1.0046 1709
## (Intercept)-Vulpes_vulpes 1.0307 436
## (Intercept)-Sus_scrofa 1.0116 865
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0011 1328
## shrub_cover-Sciurus_niger 1.0074 688
## shrub_cover-Procyon_lotor 1.0018 3716
## shrub_cover-Dasypus_novemcinctus 1.0062 1872
## shrub_cover-Lynx_rufus 1.0026 821
## shrub_cover-Didelphis_virginiana 1.0020 1382
## shrub_cover-Sylvilagus_floridanus 1.0014 1486
## shrub_cover-Meleagris_gallopavo 1.0128 388
## shrub_cover-Sciurus_carolinensis 1.0044 1326
## shrub_cover-Vulpes_vulpes 1.0010 1233
## shrub_cover-Sus_scrofa 1.0049 987
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0012 2138
## veg_height-Sciurus_niger 1.0117 1060
## veg_height-Procyon_lotor 1.0012 3637
## veg_height-Dasypus_novemcinctus 1.0015 3637
## veg_height-Lynx_rufus 1.0063 1786
## veg_height-Didelphis_virginiana 1.0031 2912
## veg_height-Sylvilagus_floridanus 1.0015 2349
## veg_height-Meleagris_gallopavo 1.0173 925
## veg_height-Sciurus_carolinensis 1.0039 2843
## veg_height-Vulpes_vulpes 1.0026 1729
## veg_height-Sus_scrofa 1.0054 2687
## week-Odocoileus_virginianus 1.0066 4196
## week-Canis_latrans 1.0002 3912
## week-Sciurus_niger 1.0035 641
## week-Procyon_lotor 1.0012 4511
## week-Dasypus_novemcinctus 1.0009 5195
## week-Lynx_rufus 1.0060 1950
## week-Didelphis_virginiana 1.0018 2540
## week-Sylvilagus_floridanus 1.0039 2764
## week-Meleagris_gallopavo 1.0047 1040
## week-Sciurus_carolinensis 1.0016 3751
## week-Vulpes_vulpes 1.0016 1913
## week-Sus_scrofa 1.0001 3276
## I(week^2)-Odocoileus_virginianus 1.0061 4312
## I(week^2)-Canis_latrans 1.0008 3887
## I(week^2)-Sciurus_niger 1.0047 694
## I(week^2)-Procyon_lotor 1.0002 4315
## I(week^2)-Dasypus_novemcinctus 1.0015 4507
## I(week^2)-Lynx_rufus 1.0012 2170
## I(week^2)-Didelphis_virginiana 1.0008 1540
## I(week^2)-Sylvilagus_floridanus 1.0020 2281
## I(week^2)-Meleagris_gallopavo 1.0022 659
## I(week^2)-Sciurus_carolinensis 0.9998 4064
## I(week^2)-Vulpes_vulpes 1.0029 1146
## I(week^2)-Sus_scrofa 1.0007 3987
names(ms_fullQ_fullQ_T)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T$beta.samples)
## 'mcmc' num [1:5250, 1:108] 6.77 10.62 6.59 8.87 5.87 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:108] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.006857143
MCMCplot(ms_fullQ_fullQ_T$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 174 × 2
## Plot Veg_shannon_index
## <chr> <dbl>
## 1 BI200 2.75
## 2 BI201 2.70
## 3 BI202 2.59
## 4 BI97 1.61
## 5 BI99 2.97
## 6 BN210 2.97
## 7 BN211 2.43
## 8 BN212 2.22
## 9 BN96 3.05
## 10 BN98 2.79
## # ℹ 164 more rows
if (!is.numeric(fuel_data$Height)) {
fuel_data$Height <- as.numeric(as.character(fuel_data$Height))
}
## Warning: NAs introduced by coercion
# Calculate average vegetation height per plot
veg_height <- fuel_data %>%
group_by(Plot) %>%
summarize(avg_veg_height = mean(Height, na.rm = TRUE), .groups = "drop")
# Tree density from point-centered quarter data
if (!is.numeric(tree_data$Distance)) {
tree_data$Distance <- as.numeric(as.character(tree_data$Distance))
}
tree_density_data <- tree_data %>%
group_by(Plot) %>%
summarize(Average_Distance = mean(Distance) / 100, # Convert to meters
Tree_Density = 10000 / (Average_Distance^2)) # Convert to trees per hectare
# Average canopy cover from vegetation quadrats
tree_canopy_data <- Veg_Cover %>%
distinct(Plot, Quadrat, .keep_all = TRUE) %>% # Ensure each quadrat counts once per plot
group_by(Plot) %>%
summarize(Avg_Canopy_Cover = mean(Canopy_Cover, na.rm = TRUE), .groups = "drop") # Calculate the average canopy cover per plot
cor(tree_density_data$Tree_Density, tree_canopy_data$Avg_Canopy_Cover)
## [1] 0.2836106
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter(Class == "Mammalia" | Name == "Meleagris_gallopavo") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 10) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 9
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$week)
week_matrix <- matrix(rep(week_vals, each = num_sites), nrow = num_sites, ncol = num_weeks, byrow = FALSE)
# Create detection covariate list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_matrix
)
# Remove dash and convert to numeric
week_numeric <- as.numeric(gsub("-", "", det.covs$week))
## Scale and center week_numeric
week_numeric <- scale(week_numeric)
# Reshape into the original 32x36 matrix
det.covs$week <- matrix(week_numeric, nrow = 32, ncol = 36)
str(det.covs)
## List of 3
## $ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
This requires combining the presence data and the site covariate data into a single list. This also means that the presence data is in a 3-d format.
# Combine into a named list
data_list <- list(
y = detection_array,
occ.covs = covariates_matrix,
det.covs = det.covs
)
str(data_list)
## List of 3
## $ y : num [1:32, 1:36, 1:9] 1 1 0 1 0 0 0 1 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## .. ..$ : chr [1:9] "Odocoileus_virginianus" "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" ...
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:9, 1:32, 1:36] 1 0 0 0 0 0 0 0 0 1 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:9] "Odocoileus_virginianus" "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
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## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
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## Sampled: 10000 of 10000, 100.00%
summary(ms_null_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1992
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1147 0.5798 -0.9859 0.1063 1.3321 1.0017 3860
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2634 3.0197 0.7108 2.369 11.6404 1.0094 1545
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2262 0.4212 -3.0159 -2.2369 -1.3541 1.0039 4956
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.682 1.2057 0.5372 1.3735 4.5464 1.0066 4334
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4643 1.2495 1.7381 3.2538 6.4540
## (Intercept)-Canis_latrans 0.3221 0.4073 -0.4501 0.3083 1.1556
## (Intercept)-Procyon_lotor 0.7303 0.4049 -0.0367 0.7216 1.5482
## (Intercept)-Dasypus_novemcinctus -0.6137 0.3634 -1.3335 -0.6152 0.0948
## (Intercept)-Lynx_rufus 0.4292 0.9629 -0.8351 0.2508 2.9674
## (Intercept)-Didelphis_virginiana -1.3400 0.4387 -2.2519 -1.3230 -0.5294
## (Intercept)-Sylvilagus_floridanus -0.2576 0.5791 -1.2211 -0.3048 0.9673
## (Intercept)-Meleagris_gallopavo -0.1731 0.7715 -1.2951 -0.2852 1.6968
## (Intercept)-Sciurus_carolinensis -1.3078 0.4378 -2.2030 -1.2858 -0.5025
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0215 997
## (Intercept)-Canis_latrans 1.0020 4751
## (Intercept)-Procyon_lotor 1.0031 5250
## (Intercept)-Dasypus_novemcinctus 1.0021 5250
## (Intercept)-Lynx_rufus 1.0125 473
## (Intercept)-Didelphis_virginiana 1.0030 5047
## (Intercept)-Sylvilagus_floridanus 1.0198 1836
## (Intercept)-Meleagris_gallopavo 1.0033 723
## (Intercept)-Sciurus_carolinensis 1.0001 4989
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0042 0.0593 -0.1132 0.0040 0.1219
## (Intercept)-Canis_latrans -2.6021 0.1745 -2.9543 -2.5975 -2.2734
## (Intercept)-Procyon_lotor -2.2571 0.1287 -2.5192 -2.2545 -2.0094
## (Intercept)-Dasypus_novemcinctus -1.5743 0.1363 -1.8528 -1.5730 -1.3190
## (Intercept)-Lynx_rufus -3.5803 0.3539 -4.3168 -3.5627 -2.9275
## (Intercept)-Didelphis_virginiana -2.2957 0.2438 -2.7991 -2.2874 -1.8520
## (Intercept)-Sylvilagus_floridanus -3.1784 0.3094 -3.8308 -3.1595 -2.6106
## (Intercept)-Meleagris_gallopavo -3.4397 0.3807 -4.2685 -3.4113 -2.7546
## (Intercept)-Sciurus_carolinensis -2.4164 0.2655 -2.9812 -2.4064 -1.9305
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0025 3078
## (Intercept)-Procyon_lotor 1.0011 4168
## (Intercept)-Dasypus_novemcinctus 1.0023 5250
## (Intercept)-Lynx_rufus 1.0025 660
## (Intercept)-Didelphis_virginiana 1.0008 4028
## (Intercept)-Sylvilagus_floridanus 1.0171 1401
## (Intercept)-Meleagris_gallopavo 1.0100 781
## (Intercept)-Sciurus_carolinensis 1.0004 3654
# Includes all covariates of detection and occupancy
ms_full_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
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## Sampled: 3500 of 10000, 35.00%
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## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7592
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3167 1.0744 -1.8053 0.2976 2.5373 1.0004 2209
## Cogon_Patch_Size -0.3447 0.6814 -1.6998 -0.3466 1.0651 1.0027 1521
## Veg_shannon_index 0.9119 0.5130 -0.0465 0.8853 1.9907 1.0057 659
## total_shrub_cover -0.6031 0.6120 -1.8994 -0.5675 0.5391 1.0141 1214
## Avg_Cogongrass_Cover 1.8533 0.7514 0.4771 1.8007 3.4250 1.0248 547
## Tree_Density -1.7756 0.8197 -3.4145 -1.7672 -0.1076 1.0178 929
## Avg_Canopy_Cover 1.8526 0.7603 0.3697 1.8199 3.4694 1.0018 994
## avg_veg_height -0.3090 0.5107 -1.3305 -0.3110 0.6976 1.0251 872
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.4512 38.4045 2.8020 13.2548 93.0426 1.1283 257
## Cogon_Patch_Size 2.7627 4.6435 0.0940 1.3197 14.6508 1.0184 367
## Veg_shannon_index 0.7851 1.2544 0.0461 0.4090 3.6473 1.0335 1001
## total_shrub_cover 2.0733 4.0038 0.0819 1.0121 10.1139 1.0985 747
## Avg_Cogongrass_Cover 1.0359 1.8848 0.0486 0.4520 5.4408 1.0054 1521
## Tree_Density 4.2309 7.1573 0.0872 1.8897 22.5492 1.0158 401
## Avg_Canopy_Cover 4.5454 7.8852 0.2107 2.3271 22.7167 1.0491 451
## avg_veg_height 0.5058 0.7908 0.0407 0.2693 2.5000 1.0133 2229
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7365 4.8913 0.0693 1.0519 16.1995 1.023 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3852 0.4627 -3.2634 -2.3935 -1.3971 1.0022 4367
## shrub_cover 0.2561 0.2882 -0.3141 0.2525 0.8597 1.0024 2972
## veg_height 0.0170 0.1819 -0.3479 0.0169 0.3752 1.0013 3400
## week -0.0468 0.1275 -0.3047 -0.0445 0.1927 1.0055 3473
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0288 1.4588 0.6591 1.6507 5.6291 1.0042 4301
## shrub_cover 0.6446 0.5273 0.1425 0.5097 1.9258 1.0141 2916
## veg_height 0.2459 0.2006 0.0626 0.1911 0.7667 1.0050 3854
## week 0.1042 0.0990 0.0261 0.0790 0.3331 1.0185 3334
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.6178 4.8110 3.1912 7.7023
## (Intercept)-Canis_latrans 1.1027 1.5057 -1.0612 0.9197
## (Intercept)-Procyon_lotor 1.0101 1.1923 -1.3273 1.0126
## (Intercept)-Dasypus_novemcinctus -1.4388 1.1976 -4.1743 -1.3227
## (Intercept)-Lynx_rufus 2.8194 3.8004 -2.4613 1.9455
## (Intercept)-Didelphis_virginiana -2.8488 1.3990 -5.8303 -2.7291
## (Intercept)-Sylvilagus_floridanus -0.7797 1.4480 -3.5600 -0.8051
## (Intercept)-Meleagris_gallopavo 0.0648 2.4937 -3.8220 -0.2105
## (Intercept)-Sciurus_carolinensis -2.9270 1.5683 -6.5760 -2.7422
## Cogon_Patch_Size-Odocoileus_virginianus -0.2747 1.3532 -2.8083 -0.3356
## Cogon_Patch_Size-Canis_latrans 0.8038 1.3122 -0.9833 0.5375
## Cogon_Patch_Size-Procyon_lotor -0.8790 0.8037 -2.6436 -0.8153
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4268 0.8031 -2.0024 -0.4524
## Cogon_Patch_Size-Lynx_rufus -0.4775 1.4874 -3.4333 -0.4895
## Cogon_Patch_Size-Didelphis_virginiana 0.9350 1.0355 -0.6370 0.7751
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5599 1.5594 -5.4600 -1.2782
## Cogon_Patch_Size-Meleagris_gallopavo -0.0891 1.3069 -2.2783 -0.2085
## Cogon_Patch_Size-Sciurus_carolinensis -1.3803 1.3561 -4.8549 -1.1140
## Veg_shannon_index-Odocoileus_virginianus 0.7812 0.8867 -1.0426 0.8029
## Veg_shannon_index-Canis_latrans 1.2590 0.7196 0.0191 1.1990
## Veg_shannon_index-Procyon_lotor 1.2288 0.6789 0.0892 1.1591
## Veg_shannon_index-Dasypus_novemcinctus 0.6038 0.5760 -0.5094 0.6017
## Veg_shannon_index-Lynx_rufus 0.8564 0.9362 -1.2102 0.8748
## Veg_shannon_index-Didelphis_virginiana 1.1456 0.7529 -0.1303 1.0795
## Veg_shannon_index-Sylvilagus_floridanus 1.0628 0.7262 -0.2436 1.0076
## Veg_shannon_index-Meleagris_gallopavo 1.2408 0.8822 -0.2637 1.1501
## Veg_shannon_index-Sciurus_carolinensis 0.2276 0.8308 -1.5940 0.3114
## total_shrub_cover-Odocoileus_virginianus -0.1178 1.1461 -2.2965 -0.1803
## total_shrub_cover-Canis_latrans 0.6696 1.0665 -0.9037 0.4747
## total_shrub_cover-Procyon_lotor -1.1205 0.7142 -2.7316 -1.0463
## total_shrub_cover-Dasypus_novemcinctus -0.2385 0.7458 -1.8985 -0.1936
## total_shrub_cover-Lynx_rufus -0.9792 1.5539 -4.4206 -0.8093
## total_shrub_cover-Didelphis_virginiana -0.9019 0.9390 -3.1099 -0.7808
## total_shrub_cover-Sylvilagus_floridanus -0.8228 1.2026 -3.8297 -0.6756
## total_shrub_cover-Meleagris_gallopavo -1.7170 1.4658 -5.1962 -1.4613
## total_shrub_cover-Sciurus_carolinensis -0.5970 1.0093 -3.0073 -0.4994
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8226 1.1042 -0.3067 1.7846
## Avg_Cogongrass_Cover-Canis_latrans 2.2204 0.9841 0.5830 2.1096
## Avg_Cogongrass_Cover-Procyon_lotor 1.9713 0.9142 0.2922 1.9279
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3685 1.0014 0.7163 2.2516
## Avg_Cogongrass_Cover-Lynx_rufus 2.1354 1.0340 0.3104 2.0564
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9484 0.9257 0.2467 1.9016
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2945 1.0819 -1.0145 1.3337
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5395 1.2651 -1.3893 1.6130
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1021 0.9771 0.4042 2.0256
## Tree_Density-Odocoileus_virginianus -0.6282 1.5247 -3.0674 -0.8261
## Tree_Density-Canis_latrans -2.6978 1.4863 -6.3308 -2.4175
## Tree_Density-Procyon_lotor -1.4491 0.8736 -3.1874 -1.4401
## Tree_Density-Dasypus_novemcinctus -3.8273 2.0970 -9.1119 -3.3466
## Tree_Density-Lynx_rufus -0.4989 1.6430 -3.1730 -0.7074
## Tree_Density-Didelphis_virginiana -2.1944 1.3286 -5.1619 -2.0734
## Tree_Density-Sylvilagus_floridanus -2.4368 1.6318 -6.1572 -2.2289
## Tree_Density-Meleagris_gallopavo -2.2689 1.6113 -6.0423 -2.1149
## Tree_Density-Sciurus_carolinensis -2.4237 1.6259 -6.2331 -2.1943
## Avg_Canopy_Cover-Odocoileus_virginianus 1.1597 1.5898 -2.1095 1.2164
## Avg_Canopy_Cover-Canis_latrans 0.1193 0.7556 -1.3243 0.1105
## Avg_Canopy_Cover-Procyon_lotor 1.7892 0.8745 0.3188 1.6947
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2372 0.8537 0.8784 2.1236
## Avg_Canopy_Cover-Lynx_rufus 1.4110 1.7188 -1.6301 1.3104
## Avg_Canopy_Cover-Didelphis_virginiana 3.2338 1.5023 1.3191 2.9250
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9877 2.1331 1.2880 3.5273
## Avg_Canopy_Cover-Meleagris_gallopavo 2.7628 1.7150 0.4254 2.4445
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9222 1.3733 1.0700 2.6492
## avg_veg_height-Odocoileus_virginianus -0.3443 0.8168 -1.9566 -0.3371
## avg_veg_height-Canis_latrans -0.3694 0.6351 -1.6032 -0.3721
## avg_veg_height-Procyon_lotor -0.2986 0.6082 -1.5020 -0.2911
## avg_veg_height-Dasypus_novemcinctus -0.1012 0.6170 -1.2645 -0.1356
## avg_veg_height-Lynx_rufus -0.4196 0.8171 -2.1466 -0.3992
## avg_veg_height-Didelphis_virginiana -0.4900 0.6992 -1.9547 -0.4711
## avg_veg_height-Sylvilagus_floridanus -0.5222 0.7127 -2.0368 -0.4927
## avg_veg_height-Meleagris_gallopavo -0.3168 0.8700 -2.0748 -0.3183
## avg_veg_height-Sciurus_carolinensis 0.0306 0.7180 -1.2419 -0.0120
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 18.0285 1.0704 198
## (Intercept)-Canis_latrans 4.1653 1.0485 397
## (Intercept)-Procyon_lotor 3.3624 1.0034 923
## (Intercept)-Dasypus_novemcinctus 0.6620 1.0192 570
## (Intercept)-Lynx_rufus 12.8690 1.0509 134
## (Intercept)-Didelphis_virginiana -0.4815 1.0016 834
## (Intercept)-Sylvilagus_floridanus 2.1815 1.0107 1051
## (Intercept)-Meleagris_gallopavo 5.3859 1.0896 265
## (Intercept)-Sciurus_carolinensis -0.3813 1.0125 458
## Cogon_Patch_Size-Odocoileus_virginianus 2.8244 1.0071 2140
## Cogon_Patch_Size-Canis_latrans 4.1991 1.0026 858
## Cogon_Patch_Size-Procyon_lotor 0.5260 1.0067 626
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2500 1.0036 1573
## Cogon_Patch_Size-Lynx_rufus 2.7269 1.0074 933
## Cogon_Patch_Size-Didelphis_virginiana 3.4343 1.0049 376
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7152 1.0171 789
## Cogon_Patch_Size-Meleagris_gallopavo 2.9920 1.0061 932
## Cogon_Patch_Size-Sciurus_carolinensis 0.5488 1.0177 614
## Veg_shannon_index-Odocoileus_virginianus 2.5247 1.0061 1270
## Veg_shannon_index-Canis_latrans 2.8598 1.0007 877
## Veg_shannon_index-Procyon_lotor 2.8088 1.0023 544
## Veg_shannon_index-Dasypus_novemcinctus 1.7498 1.0027 1080
## Veg_shannon_index-Lynx_rufus 2.6776 1.0115 1000
## Veg_shannon_index-Didelphis_virginiana 2.8352 1.0020 1005
## Veg_shannon_index-Sylvilagus_floridanus 2.6668 1.0024 781
## Veg_shannon_index-Meleagris_gallopavo 3.1996 1.0031 1311
## Veg_shannon_index-Sciurus_carolinensis 1.6455 1.0079 1165
## total_shrub_cover-Odocoileus_virginianus 2.4165 1.0046 1983
## total_shrub_cover-Canis_latrans 3.3308 1.0133 499
## total_shrub_cover-Procyon_lotor 0.0919 1.0049 1798
## total_shrub_cover-Dasypus_novemcinctus 1.1553 1.0061 1770
## total_shrub_cover-Lynx_rufus 1.9270 1.0244 382
## total_shrub_cover-Didelphis_virginiana 0.6246 1.0017 1228
## total_shrub_cover-Sylvilagus_floridanus 1.1041 1.0067 804
## total_shrub_cover-Meleagris_gallopavo 0.4948 1.0122 529
## total_shrub_cover-Sciurus_carolinensis 1.1351 1.0105 966
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.1426 1.0186 989
## Avg_Cogongrass_Cover-Canis_latrans 4.4967 1.0236 866
## Avg_Cogongrass_Cover-Procyon_lotor 3.9833 1.0175 732
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6951 1.0336 661
## Avg_Cogongrass_Cover-Lynx_rufus 4.4235 1.0163 946
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9488 1.0165 733
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3582 1.0083 970
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.8812 1.0097 832
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2974 1.0158 763
## Tree_Density-Odocoileus_virginianus 3.0487 1.0061 761
## Tree_Density-Canis_latrans -0.6001 1.0249 850
## Tree_Density-Procyon_lotor 0.2307 1.0060 1001
## Tree_Density-Dasypus_novemcinctus -1.2029 1.0226 376
## Tree_Density-Lynx_rufus 3.3164 1.0170 509
## Tree_Density-Didelphis_virginiana 0.2467 1.0098 805
## Tree_Density-Sylvilagus_floridanus 0.3653 1.0229 706
## Tree_Density-Meleagris_gallopavo 0.6681 1.0472 911
## Tree_Density-Sciurus_carolinensis 0.2251 1.0196 489
## Avg_Canopy_Cover-Odocoileus_virginianus 4.2483 1.0048 1606
## Avg_Canopy_Cover-Canis_latrans 1.5698 1.0062 857
## Avg_Canopy_Cover-Procyon_lotor 3.8007 1.0021 751
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2349 1.0073 441
## Avg_Canopy_Cover-Lynx_rufus 5.1968 1.0134 527
## Avg_Canopy_Cover-Didelphis_virginiana 7.2027 1.0115 347
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.1914 1.0071 349
## Avg_Canopy_Cover-Meleagris_gallopavo 7.1491 1.0112 490
## Avg_Canopy_Cover-Sciurus_carolinensis 6.4713 1.0012 578
## avg_veg_height-Odocoileus_virginianus 1.2650 1.0074 1316
## avg_veg_height-Canis_latrans 0.8925 1.0172 1284
## avg_veg_height-Procyon_lotor 0.9015 1.0136 1237
## avg_veg_height-Dasypus_novemcinctus 1.2108 1.0227 1202
## avg_veg_height-Lynx_rufus 1.1703 1.0032 1195
## avg_veg_height-Didelphis_virginiana 0.8066 1.0087 1421
## avg_veg_height-Sylvilagus_floridanus 0.7952 1.0082 1245
## avg_veg_height-Meleagris_gallopavo 1.3589 1.0182 1179
## avg_veg_height-Sciurus_carolinensis 1.5852 1.0093 1319
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0060 0.0607 -0.1158 0.0059 0.1227
## (Intercept)-Canis_latrans -2.7673 0.1924 -3.1663 -2.7611 -2.4123
## (Intercept)-Procyon_lotor -2.2981 0.1440 -2.6049 -2.2887 -2.0341
## (Intercept)-Dasypus_novemcinctus -1.7656 0.1605 -2.0882 -1.7624 -1.4630
## (Intercept)-Lynx_rufus -3.9224 0.3976 -4.6685 -3.9409 -3.1070
## (Intercept)-Didelphis_virginiana -2.5648 0.2849 -3.1590 -2.5522 -2.0426
## (Intercept)-Sylvilagus_floridanus -3.2143 0.2703 -3.7599 -3.2094 -2.7012
## (Intercept)-Meleagris_gallopavo -4.0013 0.5038 -4.9991 -3.9960 -3.0325
## (Intercept)-Sciurus_carolinensis -2.7084 0.3256 -3.3619 -2.7052 -2.0942
## shrub_cover-Odocoileus_virginianus -0.0552 0.0649 -0.1805 -0.0558 0.0740
## shrub_cover-Canis_latrans -0.3834 0.2234 -0.8110 -0.3914 0.0674
## shrub_cover-Procyon_lotor 0.2668 0.1617 -0.0615 0.2736 0.5694
## shrub_cover-Dasypus_novemcinctus 0.9201 0.3168 0.3039 0.9163 1.5473
## shrub_cover-Lynx_rufus -0.2039 0.3809 -0.9312 -0.2068 0.5598
## shrub_cover-Didelphis_virginiana 0.9746 0.3600 0.3074 0.9580 1.7214
## shrub_cover-Sylvilagus_floridanus 0.5146 0.4130 -0.2933 0.5175 1.3296
## shrub_cover-Meleagris_gallopavo -0.6393 0.4563 -1.5412 -0.6304 0.2237
## shrub_cover-Sciurus_carolinensis 0.9489 0.4153 0.1497 0.9458 1.7810
## veg_height-Odocoileus_virginianus -0.2988 0.0656 -0.4300 -0.2979 -0.1734
## veg_height-Canis_latrans -0.6025 0.1859 -0.9849 -0.5990 -0.2624
## veg_height-Procyon_lotor 0.3466 0.1221 0.1088 0.3478 0.5840
## veg_height-Dasypus_novemcinctus 0.2484 0.1354 -0.0049 0.2478 0.5164
## veg_height-Lynx_rufus 0.0866 0.2389 -0.3879 0.0899 0.5482
## veg_height-Didelphis_virginiana 0.4494 0.2346 0.0016 0.4398 0.9223
## veg_height-Sylvilagus_floridanus 0.1409 0.2479 -0.3468 0.1463 0.6239
## veg_height-Meleagris_gallopavo -0.2877 0.3643 -1.0335 -0.2784 0.4225
## veg_height-Sciurus_carolinensis 0.1039 0.2182 -0.3147 0.1017 0.5488
## week-Odocoileus_virginianus 0.2110 0.0607 0.0935 0.2104 0.3344
## week-Canis_latrans 0.0680 0.1316 -0.1991 0.0719 0.3153
## week-Procyon_lotor -0.0473 0.1145 -0.2810 -0.0449 0.1679
## week-Dasypus_novemcinctus -0.1625 0.1367 -0.4409 -0.1551 0.0884
## week-Lynx_rufus -0.0294 0.1919 -0.4246 -0.0203 0.3239
## week-Didelphis_virginiana -0.2058 0.2140 -0.6812 -0.1865 0.1709
## week-Sylvilagus_floridanus -0.1459 0.2055 -0.5813 -0.1322 0.2227
## week-Meleagris_gallopavo -0.2662 0.2500 -0.8209 -0.2442 0.1670
## week-Sciurus_carolinensis 0.1418 0.1799 -0.2169 0.1464 0.4891
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0097 1580
## (Intercept)-Procyon_lotor 1.0008 3018
## (Intercept)-Dasypus_novemcinctus 1.0012 3023
## (Intercept)-Lynx_rufus 1.0571 376
## (Intercept)-Didelphis_virginiana 1.0007 2167
## (Intercept)-Sylvilagus_floridanus 1.0005 1596
## (Intercept)-Meleagris_gallopavo 1.0075 498
## (Intercept)-Sciurus_carolinensis 1.0159 1622
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0057 1894
## shrub_cover-Procyon_lotor 1.0017 3271
## shrub_cover-Dasypus_novemcinctus 1.0027 2617
## shrub_cover-Lynx_rufus 1.0506 569
## shrub_cover-Didelphis_virginiana 1.0011 1972
## shrub_cover-Sylvilagus_floridanus 1.0025 1216
## shrub_cover-Meleagris_gallopavo 1.0141 657
## shrub_cover-Sciurus_carolinensis 1.0078 1397
## veg_height-Odocoileus_virginianus 1.0016 5250
## veg_height-Canis_latrans 1.0080 2177
## veg_height-Procyon_lotor 1.0018 3920
## veg_height-Dasypus_novemcinctus 1.0020 4115
## veg_height-Lynx_rufus 1.0052 1883
## veg_height-Didelphis_virginiana 1.0028 3359
## veg_height-Sylvilagus_floridanus 1.0000 2071
## veg_height-Meleagris_gallopavo 1.0054 1269
## veg_height-Sciurus_carolinensis 1.0044 3063
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0047 4389
## week-Procyon_lotor 1.0004 4351
## week-Dasypus_novemcinctus 1.0011 4786
## week-Lynx_rufus 1.0041 2481
## week-Didelphis_virginiana 1.0027 3522
## week-Sylvilagus_floridanus 1.0067 3069
## week-Meleagris_gallopavo 1.0154 2209
## week-Sciurus_carolinensis 1.0019 4923
#Includes all covariates of detection and only null for occupancy
ms_full_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7242
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3237 0.596 -0.838 0.3022 1.5304 1.0011 2586
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4712 3.7109 0.6857 2.5344 11.8986 1.0081 1622
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3803 0.4858 -3.2907 -2.4002 -1.3546 1.0002 5464
## shrub_cover 0.1461 0.2864 -0.4298 0.1458 0.7328 1.0009 3933
## veg_height -0.0072 0.1817 -0.3655 -0.0066 0.3529 1.0011 3752
## week -0.0441 0.1248 -0.3020 -0.0375 0.1809 1.0019 3773
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2320 1.7342 0.6984 1.8088 6.3784 1.0094 3246
## shrub_cover 0.6550 0.5301 0.1367 0.5139 2.0465 1.0032 2353
## veg_height 0.2414 0.1973 0.0647 0.1878 0.7521 1.0089 3555
## week 0.1015 0.0825 0.0259 0.0774 0.3128 1.0055 3701
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.5901 1.2795 1.8095 3.3506 6.7544
## (Intercept)-Canis_latrans 0.4396 0.4263 -0.3317 0.4271 1.3447
## (Intercept)-Procyon_lotor 0.7723 0.4148 -0.0105 0.7579 1.6090
## (Intercept)-Dasypus_novemcinctus -0.5471 0.3718 -1.2925 -0.5434 0.1674
## (Intercept)-Lynx_rufus 0.7815 1.1161 -0.6683 0.5669 3.6095
## (Intercept)-Didelphis_virginiana -1.1590 0.4703 -2.1434 -1.1355 -0.2957
## (Intercept)-Sylvilagus_floridanus -0.2646 0.5313 -1.1825 -0.2952 0.8183
## (Intercept)-Meleagris_gallopavo 1.0337 1.2775 -0.7650 0.7880 4.2242
## (Intercept)-Sciurus_carolinensis -1.1666 0.4715 -2.1198 -1.1540 -0.2519
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0079 1264
## (Intercept)-Canis_latrans 1.0003 3685
## (Intercept)-Procyon_lotor 1.0007 5250
## (Intercept)-Dasypus_novemcinctus 1.0004 5250
## (Intercept)-Lynx_rufus 1.0165 647
## (Intercept)-Didelphis_virginiana 0.9998 4369
## (Intercept)-Sylvilagus_floridanus 1.0048 2041
## (Intercept)-Meleagris_gallopavo 1.0010 550
## (Intercept)-Sciurus_carolinensis 1.0018 3347
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0062 0.0594 -0.1108 0.0068 0.1231
## (Intercept)-Canis_latrans -2.7607 0.1912 -3.1467 -2.7536 -2.4060
## (Intercept)-Procyon_lotor -2.2995 0.1441 -2.5939 -2.2943 -2.0283
## (Intercept)-Dasypus_novemcinctus -1.7268 0.1563 -2.0502 -1.7232 -1.4287
## (Intercept)-Lynx_rufus -3.8416 0.3715 -4.5585 -3.8453 -3.1067
## (Intercept)-Didelphis_virginiana -2.5482 0.2915 -3.1707 -2.5357 -2.0049
## (Intercept)-Sylvilagus_floridanus -3.1987 0.3056 -3.8537 -3.1814 -2.6463
## (Intercept)-Meleagris_gallopavo -4.3277 0.4994 -5.3087 -4.3361 -3.2815
## (Intercept)-Sciurus_carolinensis -2.5882 0.3048 -3.2232 -2.5698 -2.0358
## shrub_cover-Odocoileus_virginianus -0.0551 0.0642 -0.1801 -0.0549 0.0680
## shrub_cover-Canis_latrans -0.3202 0.2220 -0.7603 -0.3192 0.1074
## shrub_cover-Procyon_lotor 0.2485 0.1655 -0.0823 0.2532 0.5588
## shrub_cover-Dasypus_novemcinctus 0.8163 0.2922 0.2646 0.8086 1.3936
## shrub_cover-Lynx_rufus -0.3786 0.3403 -1.0413 -0.3831 0.3096
## shrub_cover-Didelphis_virginiana 0.9232 0.3651 0.2468 0.9035 1.6935
## shrub_cover-Sylvilagus_floridanus 0.2080 0.4150 -0.5564 0.1886 1.0631
## shrub_cover-Meleagris_gallopavo -0.8977 0.4189 -1.7467 -0.8867 -0.0872
## shrub_cover-Sciurus_carolinensis 0.7765 0.4014 0.0014 0.7727 1.5954
## veg_height-Odocoileus_virginianus -0.2986 0.0635 -0.4247 -0.2985 -0.1741
## veg_height-Canis_latrans -0.6012 0.1876 -0.9858 -0.5974 -0.2460
## veg_height-Procyon_lotor 0.3385 0.1221 0.1015 0.3382 0.5781
## veg_height-Dasypus_novemcinctus 0.2325 0.1334 -0.0319 0.2338 0.4955
## veg_height-Lynx_rufus 0.0272 0.2414 -0.4511 0.0338 0.4890
## veg_height-Didelphis_virginiana 0.4292 0.2395 -0.0143 0.4227 0.9130
## veg_height-Sylvilagus_floridanus 0.1256 0.2436 -0.3559 0.1265 0.6075
## veg_height-Meleagris_gallopavo -0.3679 0.3378 -1.0492 -0.3576 0.2603
## veg_height-Sciurus_carolinensis 0.0521 0.2054 -0.3344 0.0446 0.4650
## week-Odocoileus_virginianus 0.2119 0.0599 0.0974 0.2109 0.3297
## week-Canis_latrans 0.0686 0.1318 -0.1991 0.0726 0.3151
## week-Procyon_lotor -0.0482 0.1173 -0.2813 -0.0453 0.1719
## week-Dasypus_novemcinctus -0.1600 0.1389 -0.4563 -0.1541 0.0967
## week-Lynx_rufus -0.0224 0.1903 -0.4127 -0.0130 0.3265
## week-Didelphis_virginiana -0.2019 0.2155 -0.6733 -0.1841 0.1815
## week-Sylvilagus_floridanus -0.1464 0.2069 -0.5792 -0.1357 0.2297
## week-Meleagris_gallopavo -0.2536 0.2412 -0.7948 -0.2349 0.1520
## week-Sciurus_carolinensis 0.1435 0.1781 -0.2082 0.1435 0.4904
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0004 2146
## (Intercept)-Procyon_lotor 1.0002 4056
## (Intercept)-Dasypus_novemcinctus 1.0007 4244
## (Intercept)-Lynx_rufus 1.0023 720
## (Intercept)-Didelphis_virginiana 1.0001 2733
## (Intercept)-Sylvilagus_floridanus 1.0041 1380
## (Intercept)-Meleagris_gallopavo 1.0105 495
## (Intercept)-Sciurus_carolinensis 1.0009 2748
## shrub_cover-Odocoileus_virginianus 1.0014 5250
## shrub_cover-Canis_latrans 1.0027 2379
## shrub_cover-Procyon_lotor 1.0016 3778
## shrub_cover-Dasypus_novemcinctus 1.0017 3711
## shrub_cover-Lynx_rufus 1.0050 1453
## shrub_cover-Didelphis_virginiana 1.0000 2345
## shrub_cover-Sylvilagus_floridanus 1.0035 1665
## shrub_cover-Meleagris_gallopavo 1.0059 605
## shrub_cover-Sciurus_carolinensis 1.0006 2333
## veg_height-Odocoileus_virginianus 1.0003 5250
## veg_height-Canis_latrans 1.0005 2181
## veg_height-Procyon_lotor 1.0008 4097
## veg_height-Dasypus_novemcinctus 1.0017 4726
## veg_height-Lynx_rufus 1.0017 2266
## veg_height-Didelphis_virginiana 1.0009 3420
## veg_height-Sylvilagus_floridanus 1.0061 2699
## veg_height-Meleagris_gallopavo 1.0137 1490
## veg_height-Sciurus_carolinensis 1.0000 3546
## week-Odocoileus_virginianus 1.0018 4788
## week-Canis_latrans 0.9999 4201
## week-Procyon_lotor 1.0000 3860
## week-Dasypus_novemcinctus 1.0015 4695
## week-Lynx_rufus 1.0008 2711
## week-Didelphis_virginiana 1.0055 3912
## week-Sylvilagus_floridanus 1.0085 2860
## week-Meleagris_gallopavo 1.0253 1909
## week-Sciurus_carolinensis 1.0012 3745
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.768
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4255 0.6738 -0.9001 0.4305 1.8136 1.0118 1586
## Avg_Cogongrass_Cover 0.0216 0.3723 -0.7249 0.0256 0.7318 1.0089 1289
## total_shrub_cover -0.8107 0.5045 -1.9436 -0.7742 0.0886 1.0099 970
## avg_veg_height 0.2046 0.3981 -0.5636 0.1942 1.0237 1.0284 881
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8703 3.9043 0.4886 2.7622 13.3287 1.0148 1622
## Avg_Cogongrass_Cover 0.4540 0.6274 0.0410 0.2587 2.0223 1.0012 2040
## total_shrub_cover 1.1683 1.6091 0.0756 0.6712 5.1578 1.0549 1032
## avg_veg_height 0.3726 0.5135 0.0390 0.2161 1.7281 1.0065 1894
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0889 1.2285 0.059 0.6856 4.4101 1.0468 361
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3913 0.4608 -3.2325 -2.4157 -1.4200 1.0038 4282
## shrub_cover 0.3830 0.3049 -0.2088 0.3837 0.9918 1.0021 2069
## veg_height -0.0035 0.1859 -0.3754 -0.0030 0.3624 1.0047 3087
## week -0.0486 0.1242 -0.3054 -0.0461 0.1805 1.0079 3615
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9265 1.5081 0.6179 1.5429 5.4705 1.0157 4550
## shrub_cover 0.7241 0.6042 0.1475 0.5635 2.3405 1.0066 1608
## veg_height 0.2511 0.2137 0.0619 0.1944 0.7870 1.0007 2860
## week 0.1026 0.0838 0.0261 0.0798 0.3207 1.0053 3590
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9331 1.6623 1.1462 3.7285
## (Intercept)-Canis_latrans 0.7324 0.7875 -0.6892 0.6720
## (Intercept)-Procyon_lotor 0.9941 0.7733 -0.4841 0.9632
## (Intercept)-Dasypus_novemcinctus -0.3395 0.7723 -1.7863 -0.3822
## (Intercept)-Lynx_rufus 0.3431 1.0627 -1.4966 0.2604
## (Intercept)-Didelphis_virginiana -0.8364 0.8633 -2.4145 -0.8660
## (Intercept)-Sylvilagus_floridanus 0.4343 0.9673 -1.2716 0.3670
## (Intercept)-Meleagris_gallopavo 0.0994 1.2913 -1.9656 -0.0672
## (Intercept)-Sciurus_carolinensis -0.8973 0.8787 -2.5475 -0.9020
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0138 0.6375 -1.3131 -0.0180
## Avg_Cogongrass_Cover-Canis_latrans 0.3526 0.5423 -0.6145 0.3165
## Avg_Cogongrass_Cover-Procyon_lotor -0.0966 0.4876 -1.1266 -0.0821
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1336 0.4586 -0.7575 0.1310
## Avg_Cogongrass_Cover-Lynx_rufus 0.3680 0.5952 -0.6669 0.3223
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1616 0.5158 -0.8471 0.1538
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3867 0.6060 -1.6744 -0.3414
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3890 0.7299 -2.0716 -0.3213
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0278 0.4980 -1.0108 0.0429
## total_shrub_cover-Odocoileus_virginianus -0.4229 0.7683 -1.8533 -0.4454
## total_shrub_cover-Canis_latrans 0.2224 0.7440 -1.1289 0.1613
## total_shrub_cover-Procyon_lotor -1.3377 0.6522 -2.8212 -1.2486
## total_shrub_cover-Dasypus_novemcinctus -0.4435 0.6895 -2.1742 -0.3352
## total_shrub_cover-Lynx_rufus -1.2634 0.9104 -3.2303 -1.1806
## total_shrub_cover-Didelphis_virginiana -0.8250 0.7140 -2.4342 -0.7378
## total_shrub_cover-Sylvilagus_floridanus -1.4070 0.9937 -3.7014 -1.2772
## total_shrub_cover-Meleagris_gallopavo -1.3791 0.9201 -3.4957 -1.2843
## total_shrub_cover-Sciurus_carolinensis -0.8417 0.7853 -2.7161 -0.7354
## avg_veg_height-Odocoileus_virginianus 0.1515 0.6031 -1.0529 0.1647
## avg_veg_height-Canis_latrans 0.2080 0.5101 -0.7614 0.1915
## avg_veg_height-Procyon_lotor 0.2226 0.4918 -0.7591 0.2234
## avg_veg_height-Dasypus_novemcinctus 0.4049 0.4948 -0.4537 0.3763
## avg_veg_height-Lynx_rufus 0.1537 0.6274 -1.1113 0.1487
## avg_veg_height-Didelphis_virginiana 0.0776 0.5157 -0.9666 0.0782
## avg_veg_height-Sylvilagus_floridanus 0.1501 0.5627 -0.9419 0.1367
## avg_veg_height-Meleagris_gallopavo -0.0205 0.7723 -1.6838 0.0214
## avg_veg_height-Sciurus_carolinensis 0.5460 0.5438 -0.4002 0.4956
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.7323 1.0059 938
## (Intercept)-Canis_latrans 2.4389 1.0133 1801
## (Intercept)-Procyon_lotor 2.6575 1.0142 1980
## (Intercept)-Dasypus_novemcinctus 1.3739 1.0130 1089
## (Intercept)-Lynx_rufus 2.6405 1.0032 1084
## (Intercept)-Didelphis_virginiana 0.9854 1.0032 949
## (Intercept)-Sylvilagus_floridanus 2.4937 1.0248 1143
## (Intercept)-Meleagris_gallopavo 3.1253 1.0240 549
## (Intercept)-Sciurus_carolinensis 0.8501 1.0068 1353
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2665 1.0025 2998
## Avg_Cogongrass_Cover-Canis_latrans 1.5320 1.0095 2097
## Avg_Cogongrass_Cover-Procyon_lotor 0.7857 1.0039 1744
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0117 1.0079 2104
## Avg_Cogongrass_Cover-Lynx_rufus 1.6878 1.0071 2336
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2053 1.0134 2028
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6874 1.0065 1651
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8912 1.0006 1298
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9762 1.0033 1806
## total_shrub_cover-Odocoileus_virginianus 1.2199 1.0012 2581
## total_shrub_cover-Canis_latrans 1.8722 1.0177 944
## total_shrub_cover-Procyon_lotor -0.3044 1.0059 1083
## total_shrub_cover-Dasypus_novemcinctus 0.6096 1.0130 896
## total_shrub_cover-Lynx_rufus 0.3382 1.0196 814
## total_shrub_cover-Didelphis_virginiana 0.3470 1.0081 970
## total_shrub_cover-Sylvilagus_floridanus 0.1656 1.0173 593
## total_shrub_cover-Meleagris_gallopavo 0.2390 1.0101 1011
## total_shrub_cover-Sciurus_carolinensis 0.4203 1.0199 857
## avg_veg_height-Odocoileus_virginianus 1.3165 1.0089 1756
## avg_veg_height-Canis_latrans 1.2553 1.0217 1362
## avg_veg_height-Procyon_lotor 1.2204 1.0063 1731
## avg_veg_height-Dasypus_novemcinctus 1.4575 1.0071 1378
## avg_veg_height-Lynx_rufus 1.4482 1.0188 1599
## avg_veg_height-Didelphis_virginiana 1.0816 1.0083 1674
## avg_veg_height-Sylvilagus_floridanus 1.3040 1.0319 1169
## avg_veg_height-Meleagris_gallopavo 1.4128 1.0240 879
## avg_veg_height-Sciurus_carolinensis 1.7549 1.0245 1417
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0064 0.0613 -0.1144 0.0052 0.1287
## (Intercept)-Canis_latrans -2.8123 0.2039 -3.2459 -2.8029 -2.4442
## (Intercept)-Procyon_lotor -2.3002 0.1386 -2.5779 -2.2980 -2.0340
## (Intercept)-Dasypus_novemcinctus -1.7998 0.1808 -2.1700 -1.7958 -1.4621
## (Intercept)-Lynx_rufus -3.6111 0.3678 -4.3641 -3.5934 -2.9363
## (Intercept)-Didelphis_virginiana -2.7163 0.3305 -3.4161 -2.7086 -2.1001
## (Intercept)-Sylvilagus_floridanus -3.3332 0.2828 -3.9053 -3.3257 -2.7916
## (Intercept)-Meleagris_gallopavo -3.8769 0.5819 -5.0441 -3.8661 -2.7533
## (Intercept)-Sciurus_carolinensis -2.7809 0.3458 -3.4885 -2.7701 -2.1466
## shrub_cover-Odocoileus_virginianus -0.0545 0.0629 -0.1793 -0.0547 0.0671
## shrub_cover-Canis_latrans -0.2924 0.2522 -0.7800 -0.2978 0.2003
## shrub_cover-Procyon_lotor 0.3234 0.1598 0.0067 0.3276 0.6328
## shrub_cover-Dasypus_novemcinctus 1.0338 0.3683 0.3614 1.0134 1.7732
## shrub_cover-Lynx_rufus 0.0471 0.3856 -0.7306 0.0567 0.7490
## shrub_cover-Didelphis_virginiana 1.2104 0.4338 0.4293 1.1850 2.1169
## shrub_cover-Sylvilagus_floridanus 0.7360 0.4332 -0.1922 0.7518 1.5393
## shrub_cover-Meleagris_gallopavo -0.5223 0.5064 -1.5162 -0.5153 0.4518
## shrub_cover-Sciurus_carolinensis 1.1236 0.4382 0.2594 1.1218 1.9792
## veg_height-Odocoileus_virginianus -0.2978 0.0642 -0.4230 -0.2993 -0.1722
## veg_height-Canis_latrans -0.6230 0.1905 -1.0097 -0.6186 -0.2655
## veg_height-Procyon_lotor 0.3376 0.1236 0.0939 0.3346 0.5826
## veg_height-Dasypus_novemcinctus 0.2467 0.1404 -0.0242 0.2452 0.5236
## veg_height-Lynx_rufus 0.0112 0.2534 -0.5010 0.0133 0.5071
## veg_height-Didelphis_virginiana 0.4155 0.2586 -0.0545 0.4053 0.9511
## veg_height-Sylvilagus_floridanus 0.0265 0.2569 -0.4755 0.0223 0.5406
## veg_height-Meleagris_gallopavo -0.2400 0.4496 -1.1218 -0.2508 0.6580
## veg_height-Sciurus_carolinensis 0.0754 0.2245 -0.3471 0.0682 0.5221
## week-Odocoileus_virginianus 0.2117 0.0615 0.0920 0.2107 0.3339
## week-Canis_latrans 0.0737 0.1318 -0.1960 0.0799 0.3193
## week-Procyon_lotor -0.0452 0.1169 -0.2883 -0.0431 0.1735
## week-Dasypus_novemcinctus -0.1647 0.1357 -0.4525 -0.1575 0.0886
## week-Lynx_rufus -0.0341 0.1966 -0.4368 -0.0286 0.3254
## week-Didelphis_virginiana -0.2007 0.2131 -0.6495 -0.1871 0.1840
## week-Sylvilagus_floridanus -0.1481 0.2057 -0.5970 -0.1363 0.2123
## week-Meleagris_gallopavo -0.2703 0.2438 -0.8311 -0.2470 0.1474
## week-Sciurus_carolinensis 0.1453 0.1797 -0.2127 0.1464 0.4896
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0049 1585
## (Intercept)-Procyon_lotor 1.0031 4035
## (Intercept)-Dasypus_novemcinctus 1.0070 1500
## (Intercept)-Lynx_rufus 1.0014 1077
## (Intercept)-Didelphis_virginiana 1.0021 1118
## (Intercept)-Sylvilagus_floridanus 1.0304 1301
## (Intercept)-Meleagris_gallopavo 1.0199 507
## (Intercept)-Sciurus_carolinensis 1.0035 1064
## shrub_cover-Odocoileus_virginianus 1.0017 5126
## shrub_cover-Canis_latrans 1.0046 1297
## shrub_cover-Procyon_lotor 1.0003 4102
## shrub_cover-Dasypus_novemcinctus 1.0079 953
## shrub_cover-Lynx_rufus 1.0156 1086
## shrub_cover-Didelphis_virginiana 1.0082 904
## shrub_cover-Sylvilagus_floridanus 1.0202 676
## shrub_cover-Meleagris_gallopavo 1.0024 702
## shrub_cover-Sciurus_carolinensis 1.0115 1041
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0027 1705
## veg_height-Procyon_lotor 1.0008 4050
## veg_height-Dasypus_novemcinctus 1.0026 4419
## veg_height-Lynx_rufus 1.0188 1880
## veg_height-Didelphis_virginiana 1.0033 2378
## veg_height-Sylvilagus_floridanus 1.0056 1769
## veg_height-Meleagris_gallopavo 1.0093 746
## veg_height-Sciurus_carolinensis 1.0020 3127
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0029 4343
## week-Procyon_lotor 0.9999 4354
## week-Dasypus_novemcinctus 1.0050 4687
## week-Lynx_rufus 1.0041 2739
## week-Didelphis_virginiana 1.0002 3296
## week-Sylvilagus_floridanus 1.0052 2716
## week-Meleagris_gallopavo 1.0020 2358
## week-Sciurus_carolinensis 0.9999 4291
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7435
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3195 0.8050 -1.2118 0.2894 1.9990 1.0013 2229
## Tree_Density -0.7969 0.4594 -1.8192 -0.7677 0.0132 1.0036 1923
## Avg_Canopy_Cover 1.1496 0.4527 0.3168 1.1310 2.1106 1.0013 2116
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.9811 7.3442 1.1910 4.9457 25.1265 1.0088 1070
## Tree_Density 0.9871 1.8969 0.0468 0.4244 5.1335 1.0074 1144
## Avg_Canopy_Cover 1.2136 1.4874 0.1154 0.7758 4.8662 1.0024 1505
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4771 0.6456 0.0438 0.2716 2.1767 1.0247 400
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3892 0.4948 -3.3061 -2.4142 -1.3522 0.9998 5250
## shrub_cover 0.1914 0.2909 -0.3935 0.1872 0.7814 1.0029 4256
## veg_height 0.0257 0.1791 -0.3250 0.0259 0.3904 1.0010 3533
## week -0.0472 0.1279 -0.3091 -0.0436 0.2002 1.0010 3439
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1932 1.5840 0.6959 1.7856 6.2449 1.0020 4230
## shrub_cover 0.6805 0.6090 0.1510 0.5290 2.0698 1.0306 2356
## veg_height 0.2389 0.1940 0.0619 0.1869 0.7250 1.0073 3536
## week 0.1033 0.0876 0.0249 0.0788 0.3226 1.0089 3478
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8760 1.9291 2.0370 4.5554 9.6446
## (Intercept)-Canis_latrans 0.4405 0.6225 -0.7368 0.4274 1.7250
## (Intercept)-Procyon_lotor 0.8828 0.6604 -0.3641 0.8625 2.2603
## (Intercept)-Dasypus_novemcinctus -0.9424 0.6383 -2.2855 -0.8970 0.2314
## (Intercept)-Lynx_rufus 1.7563 1.9312 -0.8318 1.3725 6.6677
## (Intercept)-Didelphis_virginiana -1.6892 0.7509 -3.2553 -1.6700 -0.2763
## (Intercept)-Sylvilagus_floridanus -0.5480 0.7632 -2.0381 -0.5578 1.0047
## (Intercept)-Meleagris_gallopavo 0.7242 1.3833 -1.3849 0.5098 4.1019
## (Intercept)-Sciurus_carolinensis -1.7499 0.7791 -3.3779 -1.7167 -0.3047
## Tree_Density-Odocoileus_virginianus -0.3707 0.7495 -1.5509 -0.4507 1.4338
## Tree_Density-Canis_latrans -0.9698 0.5758 -2.3086 -0.8987 -0.0324
## Tree_Density-Procyon_lotor -0.5056 0.4225 -1.3542 -0.5021 0.3242
## Tree_Density-Dasypus_novemcinctus -1.4482 0.9845 -3.9732 -1.2196 -0.1927
## Tree_Density-Lynx_rufus -0.0143 0.9114 -1.3748 -0.1527 2.1985
## Tree_Density-Didelphis_virginiana -1.0604 0.8095 -3.0627 -0.9254 0.1267
## Tree_Density-Sylvilagus_floridanus -1.0890 0.7935 -3.0736 -0.9596 0.1230
## Tree_Density-Meleagris_gallopavo -1.0675 0.8729 -3.1185 -0.9645 0.4180
## Tree_Density-Sciurus_carolinensis -0.9522 0.7610 -2.7478 -0.8559 0.3023
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7909 0.8563 -0.9751 0.7982 2.5158
## Avg_Canopy_Cover-Canis_latrans -0.0910 0.4823 -1.0527 -0.0916 0.8513
## Avg_Canopy_Cover-Procyon_lotor 1.1141 0.5157 0.1826 1.0765 2.2011
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1071 0.4900 0.2245 1.0745 2.1773
## Avg_Canopy_Cover-Lynx_rufus 1.1026 0.9663 -0.6511 1.0374 3.2656
## Avg_Canopy_Cover-Didelphis_virginiana 1.5750 0.6912 0.4517 1.4823 3.1989
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.1639 0.9789 0.7025 2.0062 4.4674
## Avg_Canopy_Cover-Meleagris_gallopavo 1.6067 0.9223 0.1105 1.5037 3.7580
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4995 0.6595 0.4378 1.4205 3.0354
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0075 798
## (Intercept)-Canis_latrans 1.0005 3500
## (Intercept)-Procyon_lotor 1.0011 2968
## (Intercept)-Dasypus_novemcinctus 1.0015 2507
## (Intercept)-Lynx_rufus 1.0330 372
## (Intercept)-Didelphis_virginiana 1.0022 2333
## (Intercept)-Sylvilagus_floridanus 1.0060 2443
## (Intercept)-Meleagris_gallopavo 1.0303 620
## (Intercept)-Sciurus_carolinensis 1.0015 2398
## Tree_Density-Odocoileus_virginianus 1.0049 1709
## Tree_Density-Canis_latrans 1.0004 2592
## Tree_Density-Procyon_lotor 1.0009 2761
## Tree_Density-Dasypus_novemcinctus 1.0007 1269
## Tree_Density-Lynx_rufus 1.0045 985
## Tree_Density-Didelphis_virginiana 1.0028 1915
## Tree_Density-Sylvilagus_floridanus 1.0004 1588
## Tree_Density-Meleagris_gallopavo 1.0081 1536
## Tree_Density-Sciurus_carolinensis 1.0032 2344
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0001 2847
## Avg_Canopy_Cover-Canis_latrans 1.0014 3405
## Avg_Canopy_Cover-Procyon_lotor 1.0002 3798
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0026 3932
## Avg_Canopy_Cover-Lynx_rufus 1.0075 1103
## Avg_Canopy_Cover-Didelphis_virginiana 1.0024 2064
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0018 1426
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0001 1529
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0001 1901
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0594 -0.1094 0.0051 0.1221
## (Intercept)-Canis_latrans -2.7689 0.1907 -3.1579 -2.7635 -2.4116
## (Intercept)-Procyon_lotor -2.3021 0.1460 -2.6027 -2.2987 -2.0280
## (Intercept)-Dasypus_novemcinctus -1.7430 0.1597 -2.0517 -1.7402 -1.4420
## (Intercept)-Lynx_rufus -3.9867 0.3602 -4.6686 -3.9961 -3.2621
## (Intercept)-Didelphis_virginiana -2.5994 0.2910 -3.1936 -2.5943 -2.0456
## (Intercept)-Sylvilagus_floridanus -3.1550 0.2754 -3.7298 -3.1439 -2.6505
## (Intercept)-Meleagris_gallopavo -4.2127 0.4909 -5.2128 -4.2014 -3.2848
## (Intercept)-Sciurus_carolinensis -2.6549 0.3295 -3.3518 -2.6410 -2.0413
## shrub_cover-Odocoileus_virginianus -0.0557 0.0661 -0.1875 -0.0561 0.0728
## shrub_cover-Canis_latrans -0.3099 0.2217 -0.7489 -0.3104 0.1214
## shrub_cover-Procyon_lotor 0.2481 0.1627 -0.0734 0.2496 0.5585
## shrub_cover-Dasypus_novemcinctus 0.8509 0.2952 0.2881 0.8453 1.4340
## shrub_cover-Lynx_rufus -0.3919 0.3232 -1.0786 -0.3786 0.2115
## shrub_cover-Didelphis_virginiana 0.9747 0.3559 0.3256 0.9575 1.7134
## shrub_cover-Sylvilagus_floridanus 0.4131 0.3935 -0.3677 0.4114 1.1857
## shrub_cover-Meleagris_gallopavo -0.8518 0.4134 -1.7088 -0.8384 -0.0858
## shrub_cover-Sciurus_carolinensis 0.8658 0.4163 0.0484 0.8568 1.7070
## veg_height-Odocoileus_virginianus -0.2988 0.0654 -0.4302 -0.2983 -0.1688
## veg_height-Canis_latrans -0.5974 0.1880 -0.9775 -0.5916 -0.2454
## veg_height-Procyon_lotor 0.3434 0.1243 0.0989 0.3425 0.5896
## veg_height-Dasypus_novemcinctus 0.2454 0.1362 -0.0164 0.2452 0.5123
## veg_height-Lynx_rufus 0.0920 0.2373 -0.3838 0.0935 0.5548
## veg_height-Didelphis_virginiana 0.4734 0.2425 0.0231 0.4638 0.9673
## veg_height-Sylvilagus_floridanus 0.1578 0.2362 -0.3063 0.1594 0.6116
## veg_height-Meleagris_gallopavo -0.2479 0.3326 -0.9509 -0.2308 0.3802
## veg_height-Sciurus_carolinensis 0.0948 0.2194 -0.3154 0.0915 0.5462
## week-Odocoileus_virginianus 0.2133 0.0620 0.0931 0.2133 0.3383
## week-Canis_latrans 0.0715 0.1311 -0.1952 0.0736 0.3184
## week-Procyon_lotor -0.0451 0.1181 -0.2849 -0.0421 0.1762
## week-Dasypus_novemcinctus -0.1636 0.1380 -0.4414 -0.1598 0.0908
## week-Lynx_rufus -0.0291 0.1938 -0.4339 -0.0224 0.3336
## week-Didelphis_virginiana -0.2047 0.2176 -0.6789 -0.1916 0.1802
## week-Sylvilagus_floridanus -0.1427 0.2049 -0.5724 -0.1271 0.2239
## week-Meleagris_gallopavo -0.2647 0.2394 -0.7994 -0.2454 0.1552
## week-Sciurus_carolinensis 0.1443 0.1829 -0.2190 0.1456 0.4977
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0079 4509
## (Intercept)-Canis_latrans 1.0002 1900
## (Intercept)-Procyon_lotor 1.0005 3152
## (Intercept)-Dasypus_novemcinctus 1.0038 4320
## (Intercept)-Lynx_rufus 1.0050 540
## (Intercept)-Didelphis_virginiana 1.0012 2118
## (Intercept)-Sylvilagus_floridanus 1.0007 1884
## (Intercept)-Meleagris_gallopavo 1.0053 534
## (Intercept)-Sciurus_carolinensis 1.0014 1806
## shrub_cover-Odocoileus_virginianus 1.0016 5510
## shrub_cover-Canis_latrans 1.0023 2784
## shrub_cover-Procyon_lotor 1.0022 4065
## shrub_cover-Dasypus_novemcinctus 1.0033 3767
## shrub_cover-Lynx_rufus 1.0058 1319
## shrub_cover-Didelphis_virginiana 1.0061 2358
## shrub_cover-Sylvilagus_floridanus 1.0039 2015
## shrub_cover-Meleagris_gallopavo 1.0025 724
## shrub_cover-Sciurus_carolinensis 1.0013 1861
## veg_height-Odocoileus_virginianus 1.0003 5250
## veg_height-Canis_latrans 1.0026 2200
## veg_height-Procyon_lotor 1.0013 4132
## veg_height-Dasypus_novemcinctus 1.0021 4553
## veg_height-Lynx_rufus 1.0006 1944
## veg_height-Didelphis_virginiana 1.0000 3169
## veg_height-Sylvilagus_floridanus 1.0006 3199
## veg_height-Meleagris_gallopavo 1.0022 2471
## veg_height-Sciurus_carolinensis 1.0020 2707
## week-Odocoileus_virginianus 1.0021 5250
## week-Canis_latrans 1.0038 4016
## week-Procyon_lotor 1.0015 4070
## week-Dasypus_novemcinctus 1.0011 4604
## week-Lynx_rufus 1.0005 2547
## week-Didelphis_virginiana 1.0011 3902
## week-Sylvilagus_floridanus 1.0016 3057
## week-Meleagris_gallopavo 1.0012 2228
## week-Sciurus_carolinensis 1.0006 4401
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7332
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3319 0.6994 -1.0633 0.3301 1.7439 1.0042 1990
## Cogon_Patch_Size 0.0273 0.4207 -0.8322 0.0281 0.8907 1.0027 2131
## Avg_Cogongrass_Cover 0.1748 0.3456 -0.5103 0.1678 0.8695 1.0047 1415
## total_shrub_cover -0.7699 0.4628 -1.7823 -0.7301 0.0630 1.0214 1047
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2832 4.3464 0.3754 3.0446 15.4328 1.0092 999
## Cogon_Patch_Size 0.8666 1.4055 0.0581 0.4603 4.1789 1.0429 1191
## Avg_Cogongrass_Cover 0.4047 0.5591 0.0409 0.2325 1.8241 1.0172 2138
## total_shrub_cover 1.0437 1.7904 0.0641 0.5774 4.5968 1.0575 949
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4905 1.9179 0.0708 0.9481 6.2465 1.0725 306
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3857 0.4602 -3.2619 -2.3996 -1.4464 1.0004 3937
## shrub_cover 0.3610 0.2960 -0.2450 0.3578 0.9653 1.0002 2323
## veg_height 0.0002 0.1823 -0.3601 -0.0017 0.3702 1.0049 3747
## week -0.0465 0.1269 -0.3062 -0.0416 0.1955 0.9998 3605
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9389 1.4591 0.6070 1.5619 5.5564 1.0137 3558
## shrub_cover 0.6706 0.5537 0.1326 0.5161 2.0803 1.0008 1631
## veg_height 0.2401 0.2058 0.0593 0.1828 0.7880 1.0043 3876
## week 0.1036 0.0871 0.0261 0.0795 0.3363 1.0026 3675
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9399 1.7783 0.8272 3.7503
## (Intercept)-Canis_latrans 0.7206 0.7932 -0.7693 0.6845
## (Intercept)-Procyon_lotor 0.9338 0.8094 -0.5946 0.8972
## (Intercept)-Dasypus_novemcinctus -0.4081 0.7682 -1.8948 -0.4376
## (Intercept)-Lynx_rufus 0.2591 1.1652 -1.7157 0.1644
## (Intercept)-Didelphis_virginiana -1.0176 0.8606 -2.6522 -1.0408
## (Intercept)-Sylvilagus_floridanus 0.2338 1.0150 -1.6058 0.1789
## (Intercept)-Meleagris_gallopavo 0.0428 1.3550 -2.1666 -0.1189
## (Intercept)-Sciurus_carolinensis -1.0646 0.9453 -2.9023 -1.0678
## Cogon_Patch_Size-Odocoileus_virginianus 0.1114 0.7347 -1.2403 0.0819
## Cogon_Patch_Size-Canis_latrans 0.7649 0.7895 -0.3259 0.6086
## Cogon_Patch_Size-Procyon_lotor -0.1498 0.4857 -1.1274 -0.1428
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0003 0.4578 -0.9083 0.0048
## Cogon_Patch_Size-Lynx_rufus 0.0427 0.8352 -1.4813 -0.0099
## Cogon_Patch_Size-Didelphis_virginiana 0.6286 0.5078 -0.2340 0.5806
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6562 0.8618 -2.7297 -0.5074
## Cogon_Patch_Size-Meleagris_gallopavo 0.0897 0.7757 -1.2878 0.0365
## Cogon_Patch_Size-Sciurus_carolinensis -0.5496 0.7279 -2.2927 -0.4363
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1418 0.5921 -1.0211 0.1362
## Avg_Cogongrass_Cover-Canis_latrans 0.3599 0.4700 -0.4562 0.3241
## Avg_Cogongrass_Cover-Procyon_lotor 0.1146 0.4789 -0.8601 0.1224
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3526 0.4161 -0.4234 0.3364
## Avg_Cogongrass_Cover-Lynx_rufus 0.4775 0.5607 -0.4588 0.4165
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1424 0.4601 -0.7711 0.1438
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1361 0.5784 -1.3878 -0.1128
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2249 0.7464 -1.8562 -0.1626
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3781 0.4667 -0.5071 0.3581
## total_shrub_cover-Odocoileus_virginianus -0.4179 0.7504 -1.8407 -0.4576
## total_shrub_cover-Canis_latrans 0.1286 0.6982 -1.0255 0.0460
## total_shrub_cover-Procyon_lotor -1.2738 0.6685 -2.8807 -1.1670
## total_shrub_cover-Dasypus_novemcinctus -0.4152 0.6094 -1.9128 -0.3424
## total_shrub_cover-Lynx_rufus -1.2154 0.9074 -3.1604 -1.1080
## total_shrub_cover-Didelphis_virginiana -0.7864 0.6663 -2.3312 -0.7141
## total_shrub_cover-Sylvilagus_floridanus -1.2693 0.9709 -3.6067 -1.1141
## total_shrub_cover-Meleagris_gallopavo -1.3444 0.9278 -3.5655 -1.2433
## total_shrub_cover-Sciurus_carolinensis -0.6967 0.7400 -2.4361 -0.6234
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.0397 1.0051 714
## (Intercept)-Canis_latrans 2.3975 1.0064 1782
## (Intercept)-Procyon_lotor 2.6722 1.0067 1786
## (Intercept)-Dasypus_novemcinctus 1.2395 1.0096 1238
## (Intercept)-Lynx_rufus 2.9537 1.0082 875
## (Intercept)-Didelphis_virginiana 0.7428 1.0190 1042
## (Intercept)-Sylvilagus_floridanus 2.4462 1.0188 1023
## (Intercept)-Meleagris_gallopavo 3.1183 1.0204 532
## (Intercept)-Sciurus_carolinensis 0.8077 1.0051 1041
## Cogon_Patch_Size-Odocoileus_virginianus 1.7382 1.0021 2919
## Cogon_Patch_Size-Canis_latrans 2.7890 1.0021 1701
## Cogon_Patch_Size-Procyon_lotor 0.7761 1.0079 2286
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9075 1.0011 3190
## Cogon_Patch_Size-Lynx_rufus 1.8127 1.0051 1586
## Cogon_Patch_Size-Didelphis_virginiana 1.7416 1.0031 2398
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5872 1.0048 1491
## Cogon_Patch_Size-Meleagris_gallopavo 1.7513 1.0015 2125
## Cogon_Patch_Size-Sciurus_carolinensis 0.5342 1.0072 1786
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3467 1.0044 2716
## Avg_Cogongrass_Cover-Canis_latrans 1.3974 1.0018 2478
## Avg_Cogongrass_Cover-Procyon_lotor 1.0630 1.0010 2217
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2262 1.0018 2829
## Avg_Cogongrass_Cover-Lynx_rufus 1.7605 1.0006 2390
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0536 0.9999 2678
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9711 1.0149 1665
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1516 1.0059 1205
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3855 1.0037 2282
## total_shrub_cover-Odocoileus_virginianus 1.2117 1.0038 2787
## total_shrub_cover-Canis_latrans 1.7580 1.0014 1319
## total_shrub_cover-Procyon_lotor -0.2593 1.0101 1094
## total_shrub_cover-Dasypus_novemcinctus 0.5724 1.0300 984
## total_shrub_cover-Lynx_rufus 0.2947 1.0219 942
## total_shrub_cover-Didelphis_virginiana 0.2703 1.0064 1062
## total_shrub_cover-Sylvilagus_floridanus 0.1481 1.0068 635
## total_shrub_cover-Meleagris_gallopavo 0.1079 1.0228 637
## total_shrub_cover-Sciurus_carolinensis 0.5359 1.0295 624
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0061 0.0599 -0.1105 0.0056 0.1229
## (Intercept)-Canis_latrans -2.7664 0.1969 -3.1709 -2.7621 -2.3976
## (Intercept)-Procyon_lotor -2.3051 0.1403 -2.5871 -2.3019 -2.0355
## (Intercept)-Dasypus_novemcinctus -1.7847 0.1782 -2.1549 -1.7793 -1.4589
## (Intercept)-Lynx_rufus -3.6086 0.3674 -4.3603 -3.5933 -2.9332
## (Intercept)-Didelphis_virginiana -2.6272 0.3060 -3.2438 -2.6110 -2.0625
## (Intercept)-Sylvilagus_floridanus -3.3288 0.2861 -3.8912 -3.3223 -2.7917
## (Intercept)-Meleagris_gallopavo -3.8877 0.5557 -4.9400 -3.8998 -2.7786
## (Intercept)-Sciurus_carolinensis -2.7796 0.3730 -3.5750 -2.7565 -2.1218
## shrub_cover-Odocoileus_virginianus -0.0527 0.0640 -0.1784 -0.0533 0.0720
## shrub_cover-Canis_latrans -0.2926 0.2383 -0.7487 -0.2927 0.1891
## shrub_cover-Procyon_lotor 0.3197 0.1627 -0.0077 0.3226 0.6271
## shrub_cover-Dasypus_novemcinctus 0.9904 0.3528 0.3429 0.9749 1.7173
## shrub_cover-Lynx_rufus 0.0462 0.3768 -0.7036 0.0591 0.7481
## shrub_cover-Didelphis_virginiana 1.0703 0.4018 0.3628 1.0464 1.9036
## shrub_cover-Sylvilagus_floridanus 0.7462 0.4179 -0.1099 0.7537 1.5415
## shrub_cover-Meleagris_gallopavo -0.5230 0.4726 -1.4444 -0.5253 0.4140
## shrub_cover-Sciurus_carolinensis 1.0573 0.4388 0.2119 1.0555 1.9197
## veg_height-Odocoileus_virginianus -0.2984 0.0645 -0.4273 -0.2976 -0.1715
## veg_height-Canis_latrans -0.5917 0.1866 -0.9660 -0.5876 -0.2413
## veg_height-Procyon_lotor 0.3426 0.1211 0.1085 0.3411 0.5803
## veg_height-Dasypus_novemcinctus 0.2492 0.1377 -0.0207 0.2451 0.5288
## veg_height-Lynx_rufus 0.0248 0.2375 -0.4477 0.0251 0.4871
## veg_height-Didelphis_virginiana 0.4104 0.2412 -0.0364 0.4053 0.9049
## veg_height-Sylvilagus_floridanus 0.0391 0.2481 -0.4460 0.0383 0.5281
## veg_height-Meleagris_gallopavo -0.2760 0.3994 -1.0372 -0.2819 0.5363
## veg_height-Sciurus_carolinensis 0.1058 0.2358 -0.3333 0.0950 0.5928
## week-Odocoileus_virginianus 0.2125 0.0608 0.0959 0.2116 0.3313
## week-Canis_latrans 0.0754 0.1323 -0.1926 0.0783 0.3231
## week-Procyon_lotor -0.0471 0.1185 -0.2850 -0.0438 0.1768
## week-Dasypus_novemcinctus -0.1591 0.1341 -0.4287 -0.1562 0.0913
## week-Lynx_rufus -0.0281 0.1953 -0.4345 -0.0213 0.3373
## week-Didelphis_virginiana -0.2067 0.2183 -0.6839 -0.1911 0.1797
## week-Sylvilagus_floridanus -0.1450 0.2020 -0.5768 -0.1317 0.2281
## week-Meleagris_gallopavo -0.2654 0.2448 -0.8079 -0.2416 0.1461
## week-Sciurus_carolinensis 0.1437 0.1812 -0.2145 0.1458 0.4970
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0059 5706
## (Intercept)-Canis_latrans 1.0000 2324
## (Intercept)-Procyon_lotor 1.0000 3955
## (Intercept)-Dasypus_novemcinctus 1.0110 1955
## (Intercept)-Lynx_rufus 1.0000 834
## (Intercept)-Didelphis_virginiana 1.0049 1532
## (Intercept)-Sylvilagus_floridanus 1.0231 1379
## (Intercept)-Meleagris_gallopavo 1.0171 549
## (Intercept)-Sciurus_carolinensis 1.0043 783
## shrub_cover-Odocoileus_virginianus 1.0023 5106
## shrub_cover-Canis_latrans 1.0011 1637
## shrub_cover-Procyon_lotor 1.0033 3584
## shrub_cover-Dasypus_novemcinctus 1.0182 1340
## shrub_cover-Lynx_rufus 1.0013 1134
## shrub_cover-Didelphis_virginiana 1.0015 1100
## shrub_cover-Sylvilagus_floridanus 1.0008 876
## shrub_cover-Meleagris_gallopavo 1.0041 707
## shrub_cover-Sciurus_carolinensis 1.0055 947
## veg_height-Odocoileus_virginianus 1.0032 4635
## veg_height-Canis_latrans 1.0010 2342
## veg_height-Procyon_lotor 1.0001 4150
## veg_height-Dasypus_novemcinctus 1.0027 4154
## veg_height-Lynx_rufus 1.0044 2458
## veg_height-Didelphis_virginiana 1.0026 3195
## veg_height-Sylvilagus_floridanus 1.0063 1905
## veg_height-Meleagris_gallopavo 1.0035 1318
## veg_height-Sciurus_carolinensis 1.0009 1484
## week-Odocoileus_virginianus 1.0008 5158
## week-Canis_latrans 1.0004 4433
## week-Procyon_lotor 1.0010 4487
## week-Dasypus_novemcinctus 1.0003 4701
## week-Lynx_rufus 0.9998 2881
## week-Didelphis_virginiana 1.0022 3370
## week-Sylvilagus_floridanus 1.0010 2813
## week-Meleagris_gallopavo 1.0004 2250
## week-Sciurus_carolinensis 1.0061 4230
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7222
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2795 0.6800 -1.0338 0.2485 1.7433 1.0017 2750
## Veg_shannon_index 0.3863 0.2831 -0.1383 0.3757 0.9714 1.0011 2179
## Avg_Cogongrass_Cover 0.3832 0.2968 -0.1984 0.3853 0.9634 1.0041 2155
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0804 4.2498 0.5653 2.8953 14.8432 1.0297 1351
## Veg_shannon_index 0.2850 0.3271 0.0384 0.1828 1.1105 1.0050 2882
## Avg_Cogongrass_Cover 0.3396 0.5077 0.0382 0.1953 1.4482 1.0375 1765
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6719 0.8172 0.0527 0.3922 3.0676 1.0225 504
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3812 0.4868 -3.2824 -2.4005 -1.3659 1.0007 4987
## shrub_cover 0.1530 0.2874 -0.4172 0.1542 0.7471 1.0005 3985
## veg_height -0.0105 0.1732 -0.3634 -0.0092 0.3228 1.0013 3578
## week -0.0448 0.1264 -0.3012 -0.0428 0.1953 1.0020 3558
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1477 1.4974 0.6981 1.7641 5.9552 1.0054 4089
## shrub_cover 0.6533 0.5682 0.1461 0.4945 2.1055 1.0031 3177
## veg_height 0.2342 0.1862 0.0624 0.1842 0.7236 1.0033 3923
## week 0.1022 0.0855 0.0262 0.0792 0.3177 1.0006 3653
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7631 1.4651 1.3317 3.5872
## (Intercept)-Canis_latrans 0.4675 0.6350 -0.7675 0.4578
## (Intercept)-Procyon_lotor 0.6448 0.6254 -0.6430 0.6536
## (Intercept)-Dasypus_novemcinctus -0.5927 0.5860 -1.7979 -0.5797
## (Intercept)-Lynx_rufus 0.5098 1.2432 -1.3947 0.3371
## (Intercept)-Didelphis_virginiana -1.2622 0.6854 -2.5996 -1.2711
## (Intercept)-Sylvilagus_floridanus -0.1969 0.8427 -1.6050 -0.2671
## (Intercept)-Meleagris_gallopavo 0.9227 1.3489 -1.1239 0.7356
## (Intercept)-Sciurus_carolinensis -1.2782 0.6708 -2.6471 -1.2689
## Veg_shannon_index-Odocoileus_virginianus 0.3368 0.4987 -0.6544 0.3388
## Veg_shannon_index-Canis_latrans 0.6397 0.4018 -0.0851 0.6145
## Veg_shannon_index-Procyon_lotor 0.4652 0.3821 -0.2531 0.4555
## Veg_shannon_index-Dasypus_novemcinctus 0.2044 0.3501 -0.5058 0.2142
## Veg_shannon_index-Lynx_rufus 0.2838 0.5151 -0.7740 0.2924
## Veg_shannon_index-Didelphis_virginiana 0.5298 0.3995 -0.2082 0.5036
## Veg_shannon_index-Sylvilagus_floridanus 0.4670 0.4511 -0.3510 0.4422
## Veg_shannon_index-Meleagris_gallopavo 0.5391 0.5359 -0.4376 0.5058
## Veg_shannon_index-Sciurus_carolinensis 0.0238 0.4091 -0.8544 0.0500
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3861 0.5429 -0.6649 0.3708
## Avg_Cogongrass_Cover-Canis_latrans 0.6609 0.4312 -0.0777 0.6241
## Avg_Cogongrass_Cover-Procyon_lotor 0.4162 0.3948 -0.3500 0.4066
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4647 0.3432 -0.1834 0.4561
## Avg_Cogongrass_Cover-Lynx_rufus 0.5970 0.4742 -0.2472 0.5687
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4851 0.3828 -0.2718 0.4819
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0322 0.4875 -1.0919 0.0075
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1026 0.7111 -1.4160 0.1656
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4579 0.3765 -0.2877 0.4483
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1914 1.0138 844
## (Intercept)-Canis_latrans 1.7403 1.0001 2988
## (Intercept)-Procyon_lotor 1.8759 1.0015 2416
## (Intercept)-Dasypus_novemcinctus 0.5171 1.0025 3113
## (Intercept)-Lynx_rufus 3.4843 1.0035 588
## (Intercept)-Didelphis_virginiana 0.1545 1.0068 2520
## (Intercept)-Sylvilagus_floridanus 1.6068 1.0176 729
## (Intercept)-Meleagris_gallopavo 4.1314 1.0183 637
## (Intercept)-Sciurus_carolinensis 0.0184 1.0014 2625
## Veg_shannon_index-Odocoileus_virginianus 1.3442 1.0013 3549
## Veg_shannon_index-Canis_latrans 1.4885 1.0030 2985
## Veg_shannon_index-Procyon_lotor 1.2625 1.0012 2754
## Veg_shannon_index-Dasypus_novemcinctus 0.8703 1.0013 4041
## Veg_shannon_index-Lynx_rufus 1.2991 1.0041 2275
## Veg_shannon_index-Didelphis_virginiana 1.3736 1.0025 3590
## Veg_shannon_index-Sylvilagus_floridanus 1.4257 1.0053 3019
## Veg_shannon_index-Meleagris_gallopavo 1.7156 1.0040 2410
## Veg_shannon_index-Sciurus_carolinensis 0.7716 1.0017 3192
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5288 1.0009 3454
## Avg_Cogongrass_Cover-Canis_latrans 1.6374 1.0005 2985
## Avg_Cogongrass_Cover-Procyon_lotor 1.2417 1.0072 3213
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1673 1.0030 4090
## Avg_Cogongrass_Cover-Lynx_rufus 1.6064 1.0003 2771
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2503 1.0014 2857
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8137 1.0048 2286
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.2829 1.0158 1317
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2096 1.0011 3949
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0060 0.0595 -0.1137 0.0073 0.1207
## (Intercept)-Canis_latrans -2.7498 0.1913 -3.1407 -2.7440 -2.3890
## (Intercept)-Procyon_lotor -2.2997 0.1457 -2.5941 -2.2963 -2.0213
## (Intercept)-Dasypus_novemcinctus -1.7319 0.1590 -2.0471 -1.7302 -1.4274
## (Intercept)-Lynx_rufus -3.7987 0.3799 -4.5778 -3.7898 -3.0713
## (Intercept)-Didelphis_virginiana -2.5509 0.2902 -3.1416 -2.5410 -2.0043
## (Intercept)-Sylvilagus_floridanus -3.2508 0.3217 -3.9404 -3.2353 -2.6809
## (Intercept)-Meleagris_gallopavo -4.2820 0.4647 -5.2011 -4.2870 -3.3622
## (Intercept)-Sciurus_carolinensis -2.5926 0.3131 -3.2585 -2.5751 -2.0206
## shrub_cover-Odocoileus_virginianus -0.0566 0.0653 -0.1864 -0.0564 0.0701
## shrub_cover-Canis_latrans -0.3013 0.2121 -0.7178 -0.3003 0.1128
## shrub_cover-Procyon_lotor 0.2352 0.1694 -0.1163 0.2393 0.5554
## shrub_cover-Dasypus_novemcinctus 0.8286 0.2952 0.2723 0.8222 1.4261
## shrub_cover-Lynx_rufus -0.3519 0.3506 -1.0634 -0.3412 0.3181
## shrub_cover-Didelphis_virginiana 0.9288 0.3579 0.2508 0.9193 1.6649
## shrub_cover-Sylvilagus_floridanus 0.2035 0.4039 -0.5629 0.1850 1.0242
## shrub_cover-Meleagris_gallopavo -0.8806 0.3875 -1.6669 -0.8743 -0.1278
## shrub_cover-Sciurus_carolinensis 0.7850 0.4039 0.0277 0.7783 1.5985
## veg_height-Odocoileus_virginianus -0.2999 0.0649 -0.4291 -0.2998 -0.1726
## veg_height-Canis_latrans -0.6040 0.1862 -0.9890 -0.5971 -0.2532
## veg_height-Procyon_lotor 0.3307 0.1243 0.0828 0.3305 0.5744
## veg_height-Dasypus_novemcinctus 0.2315 0.1335 -0.0321 0.2305 0.4929
## veg_height-Lynx_rufus -0.0074 0.2409 -0.4823 -0.0016 0.4471
## veg_height-Didelphis_virginiana 0.4125 0.2423 -0.0338 0.4037 0.9184
## veg_height-Sylvilagus_floridanus 0.1129 0.2524 -0.3845 0.1084 0.6089
## veg_height-Meleagris_gallopavo -0.3222 0.3523 -1.0463 -0.3133 0.3577
## veg_height-Sciurus_carolinensis 0.0445 0.2057 -0.3489 0.0434 0.4536
## week-Odocoileus_virginianus 0.2123 0.0606 0.0934 0.2123 0.3303
## week-Canis_latrans 0.0730 0.1324 -0.2022 0.0740 0.3248
## week-Procyon_lotor -0.0488 0.1204 -0.2903 -0.0450 0.1774
## week-Dasypus_novemcinctus -0.1599 0.1369 -0.4517 -0.1550 0.0924
## week-Lynx_rufus -0.0338 0.1967 -0.4407 -0.0270 0.3272
## week-Didelphis_virginiana -0.2030 0.2184 -0.6750 -0.1904 0.1884
## week-Sylvilagus_floridanus -0.1477 0.2073 -0.6070 -0.1324 0.2172
## week-Meleagris_gallopavo -0.2630 0.2438 -0.8034 -0.2427 0.1566
## week-Sciurus_carolinensis 0.1426 0.1786 -0.2122 0.1453 0.4889
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0066 2401
## (Intercept)-Procyon_lotor 1.0019 3579
## (Intercept)-Dasypus_novemcinctus 1.0009 4386
## (Intercept)-Lynx_rufus 1.0001 724
## (Intercept)-Didelphis_virginiana 1.0026 2638
## (Intercept)-Sylvilagus_floridanus 1.0103 1152
## (Intercept)-Meleagris_gallopavo 1.0015 563
## (Intercept)-Sciurus_carolinensis 1.0010 2809
## shrub_cover-Odocoileus_virginianus 1.0002 5250
## shrub_cover-Canis_latrans 1.0004 2626
## shrub_cover-Procyon_lotor 1.0006 3515
## shrub_cover-Dasypus_novemcinctus 1.0012 4203
## shrub_cover-Lynx_rufus 1.0015 1385
## shrub_cover-Didelphis_virginiana 1.0087 2540
## shrub_cover-Sylvilagus_floridanus 1.0067 1585
## shrub_cover-Meleagris_gallopavo 1.0055 675
## shrub_cover-Sciurus_carolinensis 1.0000 2246
## veg_height-Odocoileus_virginianus 1.0023 5250
## veg_height-Canis_latrans 1.0000 2385
## veg_height-Procyon_lotor 1.0008 4008
## veg_height-Dasypus_novemcinctus 1.0018 4937
## veg_height-Lynx_rufus 1.0007 2307
## veg_height-Didelphis_virginiana 1.0015 3339
## veg_height-Sylvilagus_floridanus 1.0061 2164
## veg_height-Meleagris_gallopavo 1.0005 1331
## veg_height-Sciurus_carolinensis 1.0018 3488
## week-Odocoileus_virginianus 1.0010 5250
## week-Canis_latrans 1.0002 3973
## week-Procyon_lotor 0.9999 4700
## week-Dasypus_novemcinctus 0.9999 4896
## week-Lynx_rufus 1.0016 2811
## week-Didelphis_virginiana 1.0016 3964
## week-Sylvilagus_floridanus 1.0003 2852
## week-Meleagris_gallopavo 1.0017 1942
## week-Sciurus_carolinensis 1.0002 4586
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.721
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3778 0.6879 -1.6612 -0.4031 1.0843 1.0012 2383
## Avg_Cogongrass_Cover -0.6434 0.4156 -1.4744 -0.6374 0.1401 1.0021 1601
## I(Avg_Cogongrass_Cover^2) 0.9232 0.4179 0.2353 0.8824 1.8757 1.0253 709
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2830 4.1030 0.6037 3.0211 15.5474 1.0031 1465
## Avg_Cogongrass_Cover 0.5495 0.7609 0.0458 0.3017 2.7600 1.0062 2036
## I(Avg_Cogongrass_Cover^2) 0.6735 2.3309 0.0403 0.2588 3.5191 1.2364 638
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4436 0.5074 0.0432 0.2704 1.873 1.0091 663
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3719 0.4822 -3.2540 -2.3961 -1.3429 1.0023 5250
## shrub_cover 0.1595 0.2857 -0.4175 0.1605 0.7194 1.0001 4225
## veg_height 0.0113 0.1778 -0.3423 0.0136 0.3647 1.0038 3677
## week -0.0512 0.1289 -0.3166 -0.0449 0.1917 1.0003 3167
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1165 1.6945 0.6625 1.7155 6.1258 1.0426 4747
## shrub_cover 0.6421 0.5186 0.1347 0.5030 1.9618 1.0197 2219
## veg_height 0.2326 0.1814 0.0570 0.1824 0.7075 1.0098 3612
## week 0.1046 0.0887 0.0257 0.0801 0.3369 1.0011 3488
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.1079 1.6374 0.5645 2.8717
## (Intercept)-Canis_latrans -0.3663 0.6858 -1.7420 -0.3681
## (Intercept)-Procyon_lotor -0.0334 0.6423 -1.3559 -0.0310
## (Intercept)-Dasypus_novemcinctus -1.2592 0.6274 -2.5609 -1.2419
## (Intercept)-Lynx_rufus -0.8289 0.9994 -2.6091 -0.8924
## (Intercept)-Didelphis_virginiana -1.7620 0.7223 -3.1986 -1.7509
## (Intercept)-Sylvilagus_floridanus -0.9634 0.7412 -2.4173 -0.9589
## (Intercept)-Meleagris_gallopavo 0.6164 1.3400 -1.5052 0.4243
## (Intercept)-Sciurus_carolinensis -2.2500 0.7723 -3.8368 -2.2172
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.6471 0.7436 -2.2158 -0.6304
## Avg_Cogongrass_Cover-Canis_latrans -0.2463 0.5817 -1.2879 -0.2874
## Avg_Cogongrass_Cover-Procyon_lotor -0.5948 0.5364 -1.6501 -0.6097
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4733 0.5039 -1.4598 -0.4766
## Avg_Cogongrass_Cover-Lynx_rufus -0.6263 0.6431 -1.9677 -0.6094
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4028 0.5760 -1.4705 -0.4225
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1797 0.7170 -2.8320 -1.1054
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9403 0.8295 -2.9119 -0.8791
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7624 0.5756 -2.0117 -0.7309
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.2885 1.0944 0.0158 1.0732
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3057 0.8209 0.2360 1.1372
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1156 0.8192 0.1622 0.9606
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7226 0.3827 0.0210 0.7093
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2315 0.6581 0.3006 1.1332
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5841 0.4563 -0.2600 0.5681
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7587 0.5154 -0.1103 0.7098
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.5773 0.8799 -1.0685 0.5537
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9921 0.4356 0.2134 0.9570
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0247 1.0087 892
## (Intercept)-Canis_latrans 0.9788 1.0000 1864
## (Intercept)-Procyon_lotor 1.2311 1.0056 2979
## (Intercept)-Dasypus_novemcinctus -0.0834 1.0033 3305
## (Intercept)-Lynx_rufus 1.3791 1.0050 1063
## (Intercept)-Didelphis_virginiana -0.3851 1.0024 3254
## (Intercept)-Sylvilagus_floridanus 0.5067 1.0025 2515
## (Intercept)-Meleagris_gallopavo 3.7684 1.0487 834
## (Intercept)-Sciurus_carolinensis -0.8380 1.0005 2405
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.8343 1.0000 2728
## Avg_Cogongrass_Cover-Canis_latrans 1.0461 1.0007 2851
## Avg_Cogongrass_Cover-Procyon_lotor 0.4933 1.0024 2842
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5537 1.0055 2521
## Avg_Cogongrass_Cover-Lynx_rufus 0.6318 1.0062 2062
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8088 1.0002 2972
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0286 1.0087 1600
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4875 1.0163 1421
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2805 1.0061 2112
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.7258 1.0696 485
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4618 1.0079 572
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.1427 1.0640 411
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5396 1.0027 2429
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7239 1.0401 781
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5584 1.0150 1992
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8938 1.0050 1890
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.3956 1.0875 489
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9328 1.0044 2252
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0070 0.0600 -0.1099 0.0064 0.1258
## (Intercept)-Canis_latrans -2.7588 0.1845 -3.1365 -2.7504 -2.4205
## (Intercept)-Procyon_lotor -2.3179 0.1449 -2.6143 -2.3120 -2.0475
## (Intercept)-Dasypus_novemcinctus -1.7329 0.1575 -2.0525 -1.7291 -1.4368
## (Intercept)-Lynx_rufus -3.6318 0.3740 -4.3892 -3.6265 -2.9289
## (Intercept)-Didelphis_virginiana -2.5789 0.3000 -3.2053 -2.5626 -2.0409
## (Intercept)-Sylvilagus_floridanus -3.2303 0.3052 -3.8629 -3.2229 -2.6735
## (Intercept)-Meleagris_gallopavo -4.3236 0.4890 -5.2752 -4.3251 -3.3407
## (Intercept)-Sciurus_carolinensis -2.5964 0.3083 -3.2311 -2.5803 -2.0477
## shrub_cover-Odocoileus_virginianus -0.0571 0.0647 -0.1879 -0.0551 0.0686
## shrub_cover-Canis_latrans -0.2711 0.2153 -0.6932 -0.2748 0.1568
## shrub_cover-Procyon_lotor 0.2239 0.1761 -0.1358 0.2285 0.5560
## shrub_cover-Dasypus_novemcinctus 0.8190 0.2975 0.2395 0.8142 1.4173
## shrub_cover-Lynx_rufus -0.2798 0.3686 -1.0113 -0.2737 0.4200
## shrub_cover-Didelphis_virginiana 0.9593 0.3867 0.2701 0.9381 1.7791
## shrub_cover-Sylvilagus_floridanus 0.2036 0.4080 -0.5514 0.1837 1.0384
## shrub_cover-Meleagris_gallopavo -0.8990 0.4120 -1.7585 -0.8845 -0.0997
## shrub_cover-Sciurus_carolinensis 0.7817 0.4031 0.0299 0.7744 1.5997
## veg_height-Odocoileus_virginianus -0.2982 0.0645 -0.4262 -0.2986 -0.1719
## veg_height-Canis_latrans -0.5920 0.1863 -0.9623 -0.5854 -0.2420
## veg_height-Procyon_lotor 0.3415 0.1246 0.0994 0.3396 0.5915
## veg_height-Dasypus_novemcinctus 0.2351 0.1336 -0.0211 0.2348 0.4991
## veg_height-Lynx_rufus 0.0649 0.2424 -0.4283 0.0709 0.5276
## veg_height-Didelphis_virginiana 0.3972 0.2552 -0.0837 0.3918 0.9291
## veg_height-Sylvilagus_floridanus 0.1496 0.2506 -0.3329 0.1498 0.6484
## veg_height-Meleagris_gallopavo -0.2548 0.3628 -0.9628 -0.2489 0.4779
## veg_height-Sciurus_carolinensis 0.0590 0.2114 -0.3468 0.0539 0.4856
## week-Odocoileus_virginianus 0.2111 0.0605 0.0938 0.2108 0.3308
## week-Canis_latrans 0.0689 0.1308 -0.1955 0.0720 0.3219
## week-Procyon_lotor -0.0475 0.1181 -0.2913 -0.0429 0.1731
## week-Dasypus_novemcinctus -0.1660 0.1375 -0.4440 -0.1620 0.0890
## week-Lynx_rufus -0.0371 0.1916 -0.4474 -0.0252 0.3131
## week-Didelphis_virginiana -0.2075 0.2127 -0.6749 -0.1939 0.1668
## week-Sylvilagus_floridanus -0.1449 0.2101 -0.6067 -0.1261 0.2250
## week-Meleagris_gallopavo -0.2729 0.2480 -0.8331 -0.2473 0.1409
## week-Sciurus_carolinensis 0.1366 0.1818 -0.2292 0.1341 0.4937
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0027 2506
## (Intercept)-Procyon_lotor 1.0062 2764
## (Intercept)-Dasypus_novemcinctus 1.0010 4156
## (Intercept)-Lynx_rufus 1.0060 926
## (Intercept)-Didelphis_virginiana 1.0078 2154
## (Intercept)-Sylvilagus_floridanus 1.0005 1428
## (Intercept)-Meleagris_gallopavo 1.1581 490
## (Intercept)-Sciurus_carolinensis 1.0026 2501
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0048 3472
## shrub_cover-Procyon_lotor 1.0096 2921
## shrub_cover-Dasypus_novemcinctus 1.0096 3599
## shrub_cover-Lynx_rufus 1.0001 1469
## shrub_cover-Didelphis_virginiana 1.0226 1937
## shrub_cover-Sylvilagus_floridanus 1.0026 1679
## shrub_cover-Meleagris_gallopavo 1.0889 617
## shrub_cover-Sciurus_carolinensis 1.0102 2689
## veg_height-Odocoileus_virginianus 1.0002 5250
## veg_height-Canis_latrans 1.0052 2134
## veg_height-Procyon_lotor 1.0026 4177
## veg_height-Dasypus_novemcinctus 1.0007 4775
## veg_height-Lynx_rufus 0.9998 2180
## veg_height-Didelphis_virginiana 1.0105 2672
## veg_height-Sylvilagus_floridanus 1.0088 2013
## veg_height-Meleagris_gallopavo 1.0402 840
## veg_height-Sciurus_carolinensis 1.0001 3384
## week-Odocoileus_virginianus 1.0026 5039
## week-Canis_latrans 0.9999 3300
## week-Procyon_lotor 1.0060 4612
## week-Dasypus_novemcinctus 1.0020 4787
## week-Lynx_rufus 1.0043 3134
## week-Didelphis_virginiana 1.0001 3899
## week-Sylvilagus_floridanus 1.0009 2814
## week-Meleagris_gallopavo 1.0023 1572
## week-Sciurus_carolinensis 1.0089 4810
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.751
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4787 1.1802 -2.7151 -0.5300 1.9791 1.0018 1978
## Cogon_Patch_Size 0.1855 0.7802 -1.4039 0.1896 1.7826 1.0068 1367
## Veg_shannon_index 0.9727 0.5682 -0.0151 0.9219 2.2824 1.0274 266
## total_shrub_cover -0.7935 0.6551 -2.2450 -0.7571 0.4087 1.0107 463
## Avg_Cogongrass_Cover -0.2083 0.9989 -2.1877 -0.2028 1.7250 1.0071 527
## Tree_Density -1.8706 0.9514 -3.6477 -1.8913 0.1912 1.0051 1288
## Avg_Canopy_Cover 1.8833 0.8370 0.2796 1.8541 3.6302 1.0118 1231
## I(Avg_Cogongrass_Cover^2) 1.6158 0.6579 0.4622 1.5764 3.0648 1.0378 529
## avg_veg_height 0.0212 0.5886 -1.1297 0.0069 1.2105 1.0031 714
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.9027 30.1352 3.0244 15.0515 85.7186 1.1926 291
## Cogon_Patch_Size 4.6505 9.0266 0.1231 2.0600 25.1084 1.1011 546
## Veg_shannon_index 0.8275 1.3657 0.0480 0.4036 4.2367 1.0349 1327
## total_shrub_cover 1.9158 3.1064 0.0809 0.9144 9.7857 1.0238 458
## Avg_Cogongrass_Cover 1.5752 3.0428 0.0541 0.6077 9.4334 1.0179 960
## Tree_Density 8.5295 18.3713 0.0918 2.7687 53.8360 1.1572 269
## Avg_Canopy_Cover 6.9814 14.5309 0.2660 2.9626 39.1697 1.3103 213
## I(Avg_Cogongrass_Cover^2) 1.3870 3.1713 0.0512 0.4913 8.6266 1.1778 322
## avg_veg_height 0.6909 1.1105 0.0457 0.3341 3.6096 1.0186 1285
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.6499 4.414 0.0629 0.9772 15.105 1.3516 122
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3980 0.4683 -3.2925 -2.4139 -1.4432 1.0015 4766
## shrub_cover 0.2807 0.2969 -0.3012 0.2742 0.8878 1.0027 2411
## veg_height 0.0370 0.1750 -0.3169 0.0390 0.3886 1.0036 2922
## week -0.0500 0.1262 -0.3158 -0.0453 0.1873 1.0020 3361
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0720 1.4819 0.6547 1.6775 5.7845 1.0024 3566
## shrub_cover 0.6947 0.5886 0.1517 0.5310 2.1796 1.0092 2245
## veg_height 0.2413 0.1998 0.0630 0.1861 0.7520 1.0008 4112
## week 0.1044 0.0910 0.0261 0.0808 0.3203 1.0138 3945
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.8627 4.3911 2.3209
## (Intercept)-Canis_latrans -0.6805 1.4018 -3.3350
## (Intercept)-Procyon_lotor -0.3219 1.2838 -3.1087
## (Intercept)-Dasypus_novemcinctus -2.8247 1.5723 -6.6049
## (Intercept)-Lynx_rufus 0.4110 2.7880 -3.9804
## (Intercept)-Didelphis_virginiana -4.1128 1.7609 -8.1908
## (Intercept)-Sylvilagus_floridanus -2.2582 1.7397 -6.0735
## (Intercept)-Meleagris_gallopavo -0.3457 2.7264 -4.7981
## (Intercept)-Sciurus_carolinensis -4.6671 1.9787 -9.3887
## Cogon_Patch_Size-Odocoileus_virginianus 0.3159 1.6320 -2.6584
## Cogon_Patch_Size-Canis_latrans 1.9240 1.8194 -0.2869
## Cogon_Patch_Size-Procyon_lotor -0.4699 0.8949 -2.4270
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1225 0.9215 -1.6019
## Cogon_Patch_Size-Lynx_rufus -0.0918 1.8884 -3.7528
## Cogon_Patch_Size-Didelphis_virginiana 1.8035 1.2320 -0.0614
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3457 2.0629 -6.8163
## Cogon_Patch_Size-Meleagris_gallopavo 0.7231 1.7937 -2.1307
## Cogon_Patch_Size-Sciurus_carolinensis -1.0341 1.7429 -5.3659
## Veg_shannon_index-Odocoileus_virginianus 0.8298 0.9445 -1.1480
## Veg_shannon_index-Canis_latrans 1.3499 0.8307 0.0735
## Veg_shannon_index-Procyon_lotor 1.2387 0.7244 0.0609
## Veg_shannon_index-Dasypus_novemcinctus 0.6415 0.6639 -0.6909
## Veg_shannon_index-Lynx_rufus 1.0502 0.9582 -0.8319
## Veg_shannon_index-Didelphis_virginiana 1.2085 0.7999 -0.1785
## Veg_shannon_index-Sylvilagus_floridanus 1.0825 0.8074 -0.2941
## Veg_shannon_index-Meleagris_gallopavo 1.2753 0.9428 -0.2559
## Veg_shannon_index-Sciurus_carolinensis 0.3798 0.9120 -1.6435
## total_shrub_cover-Odocoileus_virginianus -0.4003 1.1283 -2.6195
## total_shrub_cover-Canis_latrans 0.2706 1.0374 -1.4640
## total_shrub_cover-Procyon_lotor -1.3603 0.7745 -3.1459
## total_shrub_cover-Dasypus_novemcinctus -0.4015 0.9063 -2.4489
## total_shrub_cover-Lynx_rufus -1.2220 1.3663 -4.4065
## total_shrub_cover-Didelphis_virginiana -1.1461 1.0496 -3.7627
## total_shrub_cover-Sylvilagus_floridanus -0.9699 1.2147 -3.7624
## total_shrub_cover-Meleagris_gallopavo -1.8111 1.5699 -5.5589
## total_shrub_cover-Sciurus_carolinensis -0.7394 1.1268 -3.4233
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2575 1.4674 -3.2988
## Avg_Cogongrass_Cover-Canis_latrans -0.0387 1.2804 -2.5904
## Avg_Cogongrass_Cover-Procyon_lotor -0.1905 1.2117 -2.6235
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4543 1.3879 -1.9829
## Avg_Cogongrass_Cover-Lynx_rufus -0.2094 1.3826 -3.0949
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2179 1.3236 -2.8874
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8631 1.4664 -4.0952
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4949 1.6014 -3.9765
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2172 1.3524 -2.9041
## Tree_Density-Odocoileus_virginianus -0.5641 1.9178 -3.2849
## Tree_Density-Canis_latrans -3.1693 1.9480 -8.1367
## Tree_Density-Procyon_lotor -1.9724 1.1194 -4.4815
## Tree_Density-Dasypus_novemcinctus -4.8511 3.2174 -13.5898
## Tree_Density-Lynx_rufus -0.1457 2.3585 -3.3152
## Tree_Density-Didelphis_virginiana -2.4225 1.6893 -6.3896
## Tree_Density-Sylvilagus_floridanus -2.8229 1.9878 -7.9100
## Tree_Density-Meleagris_gallopavo -2.2165 2.0948 -6.7277
## Tree_Density-Sciurus_carolinensis -2.9524 2.1907 -8.6669
## Avg_Canopy_Cover-Odocoileus_virginianus 0.9735 1.8354 -2.8538
## Avg_Canopy_Cover-Canis_latrans -0.0493 0.8241 -1.9394
## Avg_Canopy_Cover-Procyon_lotor 1.7193 0.9486 0.1058
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.4682 1.2001 0.7865
## Avg_Canopy_Cover-Lynx_rufus 1.5055 1.9466 -2.0264
## Avg_Canopy_Cover-Didelphis_virginiana 3.6077 1.9387 1.2407
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.5229 2.5297 1.2899
## Avg_Canopy_Cover-Meleagris_gallopavo 3.0253 2.1887 0.2932
## Avg_Canopy_Cover-Sciurus_carolinensis 3.4296 1.9850 1.0261
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9992 1.3787 0.0724
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1620 1.1863 0.5907
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9711 0.9273 0.5076
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6077 0.8478 0.2299
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1622 1.0812 0.5012
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2703 0.7800 -0.1828
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3800 0.9174 -0.3014
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.9511 1.4463 -2.5656
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8441 0.8741 0.4131
## avg_veg_height-Odocoileus_virginianus 0.0158 0.9244 -1.9393
## avg_veg_height-Canis_latrans -0.1128 0.7019 -1.5521
## avg_veg_height-Procyon_lotor 0.1617 0.7034 -1.2480
## avg_veg_height-Dasypus_novemcinctus 0.3638 0.7164 -0.9600
## avg_veg_height-Lynx_rufus -0.2207 1.0006 -2.4363
## avg_veg_height-Didelphis_virginiana -0.1972 0.8106 -1.9777
## avg_veg_height-Sylvilagus_floridanus -0.1191 0.8237 -1.8580
## avg_veg_height-Meleagris_gallopavo 0.0457 0.9661 -1.9401
## avg_veg_height-Sciurus_carolinensis 0.3429 0.7905 -1.0659
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0633 17.7715 1.0566 333
## (Intercept)-Canis_latrans -0.7384 2.4048 1.0117 1344
## (Intercept)-Procyon_lotor -0.2647 2.0321 1.0225 1116
## (Intercept)-Dasypus_novemcinctus -2.6095 -0.4290 1.0691 383
## (Intercept)-Lynx_rufus 0.0640 7.0269 1.0347 336
## (Intercept)-Didelphis_virginiana -3.9574 -1.1705 1.0350 494
## (Intercept)-Sylvilagus_floridanus -2.1456 0.9629 1.0165 605
## (Intercept)-Meleagris_gallopavo -0.6587 6.2889 1.0036 272
## (Intercept)-Sciurus_carolinensis -4.4054 -1.5405 1.0506 301
## Cogon_Patch_Size-Odocoileus_virginianus 0.2233 3.9412 1.0059 1700
## Cogon_Patch_Size-Canis_latrans 1.5186 6.7093 1.0317 828
## Cogon_Patch_Size-Procyon_lotor -0.4009 1.1271 1.0390 299
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0996 2.0422 1.0068 1507
## Cogon_Patch_Size-Lynx_rufus -0.1275 3.9004 1.0080 731
## Cogon_Patch_Size-Didelphis_virginiana 1.6280 4.6888 1.0598 584
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9360 1.4534 1.0576 585
## Cogon_Patch_Size-Meleagris_gallopavo 0.4890 4.9830 1.0084 747
## Cogon_Patch_Size-Sciurus_carolinensis -0.7001 1.3849 1.0553 662
## Veg_shannon_index-Odocoileus_virginianus 0.8269 2.7708 1.0105 1023
## Veg_shannon_index-Canis_latrans 1.2314 3.3869 1.0378 378
## Veg_shannon_index-Procyon_lotor 1.1443 2.9227 1.0406 336
## Veg_shannon_index-Dasypus_novemcinctus 0.6441 1.9783 1.0162 1004
## Veg_shannon_index-Lynx_rufus 0.9976 3.0941 1.0102 1283
## Veg_shannon_index-Didelphis_virginiana 1.1315 3.0255 1.0173 686
## Veg_shannon_index-Sylvilagus_floridanus 1.0090 2.9318 1.0225 453
## Veg_shannon_index-Meleagris_gallopavo 1.1557 3.5195 1.0299 361
## Veg_shannon_index-Sciurus_carolinensis 0.4588 2.0078 1.0080 1165
## total_shrub_cover-Odocoileus_virginianus -0.4580 2.0514 1.0087 1687
## total_shrub_cover-Canis_latrans 0.1325 2.7764 1.0207 678
## total_shrub_cover-Procyon_lotor -1.2818 -0.0613 1.0041 787
## total_shrub_cover-Dasypus_novemcinctus -0.3114 1.1263 1.0201 757
## total_shrub_cover-Lynx_rufus -1.0809 1.2088 1.0017 545
## total_shrub_cover-Didelphis_virginiana -1.0018 0.5108 1.0128 603
## total_shrub_cover-Sylvilagus_floridanus -0.8181 1.0235 1.0332 595
## total_shrub_cover-Meleagris_gallopavo -1.5347 0.5239 1.0090 396
## total_shrub_cover-Sciurus_carolinensis -0.6333 1.1905 1.0249 774
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2669 2.6714 1.0118 971
## Avg_Cogongrass_Cover-Canis_latrans -0.0442 2.4719 1.0027 883
## Avg_Cogongrass_Cover-Procyon_lotor -0.1865 2.2156 1.0062 747
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3548 3.5113 1.0063 677
## Avg_Cogongrass_Cover-Lynx_rufus -0.1890 2.4458 1.0043 757
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2051 2.3343 1.0061 898
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7546 1.6497 1.0112 852
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4173 2.3499 1.0200 698
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2009 2.4436 1.0075 849
## Tree_Density-Odocoileus_virginianus -0.8546 4.1737 1.0321 699
## Tree_Density-Canis_latrans -2.7668 -0.5437 1.0239 305
## Tree_Density-Procyon_lotor -1.8828 -0.0192 1.0341 557
## Tree_Density-Dasypus_novemcinctus -3.9650 -1.2954 1.1480 217
## Tree_Density-Lynx_rufus -0.6520 6.2362 1.0282 465
## Tree_Density-Didelphis_virginiana -2.2090 0.3784 1.0698 379
## Tree_Density-Sylvilagus_floridanus -2.4788 0.2069 1.0321 458
## Tree_Density-Meleagris_gallopavo -2.1356 1.9530 1.0164 715
## Tree_Density-Sciurus_carolinensis -2.5160 0.2292 1.0537 330
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0871 4.3735 1.0428 1174
## Avg_Canopy_Cover-Canis_latrans -0.0043 1.4118 1.0578 403
## Avg_Canopy_Cover-Procyon_lotor 1.6262 3.8320 1.0373 501
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2393 5.5596 1.0812 237
## Avg_Canopy_Cover-Lynx_rufus 1.4104 5.6358 1.0267 369
## Avg_Canopy_Cover-Didelphis_virginiana 3.1700 8.5788 1.0841 241
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9344 11.2295 1.1216 181
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5444 8.7103 1.1526 260
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9306 8.7360 1.1719 142
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7697 5.2168 1.0332 537
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9295 5.3597 1.0121 527
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8404 4.1586 1.0080 719
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5163 3.4942 1.0264 606
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9976 4.7858 1.0063 804
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2349 2.9339 1.0435 630
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3118 3.4465 1.0488 790
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.1124 3.4008 1.0895 327
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7427 3.9255 1.0172 708
## avg_veg_height-Odocoileus_virginianus 0.0322 1.8292 1.0045 1145
## avg_veg_height-Canis_latrans -0.0912 1.2357 1.0043 1185
## avg_veg_height-Procyon_lotor 0.1682 1.5452 1.0012 1248
## avg_veg_height-Dasypus_novemcinctus 0.3389 1.9015 1.0033 1074
## avg_veg_height-Lynx_rufus -0.1656 1.5446 1.0062 1008
## avg_veg_height-Didelphis_virginiana -0.1596 1.3124 1.0006 1290
## avg_veg_height-Sylvilagus_floridanus -0.1010 1.4425 1.0024 1286
## avg_veg_height-Meleagris_gallopavo 0.0598 1.9662 1.0058 934
## avg_veg_height-Sciurus_carolinensis 0.2952 2.0700 1.0015 1369
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0061 0.0602 -0.1108 0.0068 0.1219
## (Intercept)-Canis_latrans -2.7358 0.1867 -3.1167 -2.7291 -2.3791
## (Intercept)-Procyon_lotor -2.3004 0.1439 -2.5951 -2.2981 -2.0241
## (Intercept)-Dasypus_novemcinctus -1.7773 0.1683 -2.1208 -1.7729 -1.4612
## (Intercept)-Lynx_rufus -3.8554 0.3682 -4.5568 -3.8649 -3.1202
## (Intercept)-Didelphis_virginiana -2.6072 0.2993 -3.2168 -2.5981 -2.0502
## (Intercept)-Sylvilagus_floridanus -3.2160 0.2738 -3.7725 -3.2024 -2.7094
## (Intercept)-Meleagris_gallopavo -4.0636 0.5325 -5.1299 -4.0436 -3.0546
## (Intercept)-Sciurus_carolinensis -2.7384 0.3381 -3.4378 -2.7277 -2.1025
## shrub_cover-Odocoileus_virginianus -0.0542 0.0640 -0.1829 -0.0531 0.0684
## shrub_cover-Canis_latrans -0.3186 0.2313 -0.7536 -0.3220 0.1421
## shrub_cover-Procyon_lotor 0.2767 0.1566 -0.0432 0.2778 0.5847
## shrub_cover-Dasypus_novemcinctus 0.9584 0.3239 0.3116 0.9572 1.5942
## shrub_cover-Lynx_rufus -0.1761 0.3786 -0.8883 -0.1869 0.6113
## shrub_cover-Didelphis_virginiana 1.0304 0.3777 0.3292 1.0212 1.8233
## shrub_cover-Sylvilagus_floridanus 0.5396 0.3940 -0.2156 0.5439 1.3230
## shrub_cover-Meleagris_gallopavo -0.6845 0.4723 -1.6274 -0.6741 0.2122
## shrub_cover-Sciurus_carolinensis 0.9898 0.4247 0.1766 0.9822 1.8502
## veg_height-Odocoileus_virginianus -0.2989 0.0644 -0.4250 -0.2984 -0.1711
## veg_height-Canis_latrans -0.5583 0.1862 -0.9311 -0.5566 -0.2002
## veg_height-Procyon_lotor 0.3570 0.1236 0.1197 0.3571 0.6019
## veg_height-Dasypus_novemcinctus 0.2601 0.1383 -0.0073 0.2572 0.5400
## veg_height-Lynx_rufus 0.1327 0.2424 -0.3618 0.1368 0.5926
## veg_height-Didelphis_virginiana 0.4491 0.2415 -0.0174 0.4428 0.9414
## veg_height-Sylvilagus_floridanus 0.1368 0.2554 -0.3593 0.1361 0.6423
## veg_height-Meleagris_gallopavo -0.2413 0.3618 -0.9542 -0.2383 0.4873
## veg_height-Sciurus_carolinensis 0.1259 0.2193 -0.2921 0.1192 0.5703
## week-Odocoileus_virginianus 0.2126 0.0603 0.0972 0.2111 0.3360
## week-Canis_latrans 0.0761 0.1315 -0.1939 0.0802 0.3226
## week-Procyon_lotor -0.0504 0.1180 -0.2875 -0.0482 0.1682
## week-Dasypus_novemcinctus -0.1623 0.1372 -0.4446 -0.1541 0.0884
## week-Lynx_rufus -0.0353 0.1895 -0.4403 -0.0249 0.3145
## week-Didelphis_virginiana -0.2091 0.2160 -0.6832 -0.1928 0.1711
## week-Sylvilagus_floridanus -0.1452 0.2053 -0.5855 -0.1303 0.2309
## week-Meleagris_gallopavo -0.2673 0.2415 -0.7972 -0.2487 0.1568
## week-Sciurus_carolinensis 0.1370 0.1804 -0.2221 0.1424 0.4835
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0007 2207
## (Intercept)-Procyon_lotor 1.0002 2871
## (Intercept)-Dasypus_novemcinctus 1.0022 2740
## (Intercept)-Lynx_rufus 1.0030 519
## (Intercept)-Didelphis_virginiana 1.0076 1513
## (Intercept)-Sylvilagus_floridanus 1.0093 1570
## (Intercept)-Meleagris_gallopavo 1.0050 332
## (Intercept)-Sciurus_carolinensis 1.0101 1176
## shrub_cover-Odocoileus_virginianus 1.0013 5250
## shrub_cover-Canis_latrans 1.0002 1728
## shrub_cover-Procyon_lotor 1.0014 3871
## shrub_cover-Dasypus_novemcinctus 1.0035 1787
## shrub_cover-Lynx_rufus 1.0009 738
## shrub_cover-Didelphis_virginiana 1.0066 1641
## shrub_cover-Sylvilagus_floridanus 1.0155 1634
## shrub_cover-Meleagris_gallopavo 1.0030 426
## shrub_cover-Sciurus_carolinensis 1.0175 1230
## veg_height-Odocoileus_virginianus 1.0004 4714
## veg_height-Canis_latrans 1.0020 2405
## veg_height-Procyon_lotor 1.0025 4534
## veg_height-Dasypus_novemcinctus 1.0010 4036
## veg_height-Lynx_rufus 1.0155 1656
## veg_height-Didelphis_virginiana 1.0020 2791
## veg_height-Sylvilagus_floridanus 1.0001 1980
## veg_height-Meleagris_gallopavo 1.0068 816
## veg_height-Sciurus_carolinensis 1.0030 2501
## week-Odocoileus_virginianus 1.0001 5250
## week-Canis_latrans 1.0005 4504
## week-Procyon_lotor 1.0015 4543
## week-Dasypus_novemcinctus 1.0013 4882
## week-Lynx_rufus 1.0023 2423
## week-Didelphis_virginiana 1.0036 3841
## week-Sylvilagus_floridanus 1.0019 2805
## week-Meleagris_gallopavo 1.0007 2081
## week-Sciurus_carolinensis 1.0052 4612
# Includes all covariates of occupancy and null for detection
ms_null_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2458
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0323 1.0132 -1.9104 -0.0023 2.0973 1.0040 2341
## Cogon_Patch_Size -0.4156 0.5949 -1.6205 -0.4122 0.7628 1.0023 1774
## Veg_shannon_index 0.8564 0.4281 0.0440 0.8482 1.7290 1.0102 1087
## total_shrub_cover -0.4879 0.5313 -1.5597 -0.4696 0.5602 1.0005 2359
## Avg_Cogongrass_Cover 1.9095 0.6516 0.6275 1.8902 3.2252 1.0136 613
## Tree_Density -1.7267 0.6717 -3.0784 -1.7223 -0.4457 1.0176 1133
## Avg_Canopy_Cover 1.6728 0.5743 0.5716 1.6512 2.8782 1.0012 1748
## avg_veg_height -0.4602 0.4530 -1.3555 -0.4605 0.4263 1.0306 1057
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.0070 14.5855 2.7116 11.8244 54.7876 1.0046 969
## Cogon_Patch_Size 2.0486 3.5137 0.1032 1.1346 9.1878 1.0659 1249
## Veg_shannon_index 0.5824 0.8937 0.0451 0.3245 2.7051 1.0097 1996
## total_shrub_cover 1.6844 2.3416 0.0788 0.9537 7.7331 1.0132 1083
## Avg_Cogongrass_Cover 0.8501 1.3415 0.0505 0.4097 4.3910 1.0023 1374
## Tree_Density 2.4520 4.0129 0.0719 1.0839 13.0437 1.0132 958
## Avg_Canopy_Cover 2.0314 3.0877 0.1047 1.1924 9.1122 1.0773 1067
## avg_veg_height 0.4194 0.6543 0.0410 0.2349 1.8251 1.0420 2707
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6712 2.3977 0.0643 0.8623 8.279 1.0906 279
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2398 0.4258 -3.0123 -2.262 -1.3445 1.0011 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7132 1.2328 0.5719 1.3931 4.7919 1.0077 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.6591 3.0319 2.9028 7.1863
## (Intercept)-Canis_latrans 0.7117 1.0862 -1.2037 0.6187
## (Intercept)-Procyon_lotor 0.8068 0.9880 -1.2832 0.8569
## (Intercept)-Dasypus_novemcinctus -1.4381 0.9290 -3.4448 -1.3761
## (Intercept)-Lynx_rufus 1.7792 2.5225 -1.9150 1.2751
## (Intercept)-Didelphis_virginiana -2.9411 1.1314 -5.3375 -2.8847
## (Intercept)-Sylvilagus_floridanus -1.0577 1.2241 -3.4470 -1.0617
## (Intercept)-Meleagris_gallopavo -1.2415 1.4364 -4.1110 -1.2515
## (Intercept)-Sciurus_carolinensis -3.0659 1.1821 -5.7337 -2.9650
## Cogon_Patch_Size-Odocoileus_virginianus -0.3877 1.1659 -2.6246 -0.4338
## Cogon_Patch_Size-Canis_latrans 0.6665 1.1194 -0.8965 0.4658
## Cogon_Patch_Size-Procyon_lotor -0.7881 0.7225 -2.1693 -0.7732
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6720 0.5858 -1.9292 -0.6449
## Cogon_Patch_Size-Lynx_rufus -0.4216 1.3296 -2.9193 -0.4732
## Cogon_Patch_Size-Didelphis_virginiana 0.8130 0.8454 -0.5734 0.7295
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5205 1.2656 -4.6406 -1.3137
## Cogon_Patch_Size-Meleagris_gallopavo -0.3112 0.9987 -2.1316 -0.3718
## Cogon_Patch_Size-Sciurus_carolinensis -1.3864 1.0488 -3.9846 -1.1971
## Veg_shannon_index-Odocoileus_virginianus 0.7247 0.7957 -0.9593 0.7463
## Veg_shannon_index-Canis_latrans 1.1672 0.6068 0.1221 1.1115
## Veg_shannon_index-Procyon_lotor 1.1045 0.5591 0.0680 1.0742
## Veg_shannon_index-Dasypus_novemcinctus 0.6443 0.4903 -0.3274 0.6349
## Veg_shannon_index-Lynx_rufus 0.7898 0.7939 -0.9374 0.8063
## Veg_shannon_index-Didelphis_virginiana 1.0005 0.6111 -0.1163 0.9691
## Veg_shannon_index-Sylvilagus_floridanus 0.9957 0.6470 -0.1998 0.9565
## Veg_shannon_index-Meleagris_gallopavo 1.1711 0.6950 -0.0321 1.1111
## Veg_shannon_index-Sciurus_carolinensis 0.2736 0.6750 -1.2253 0.3398
## total_shrub_cover-Odocoileus_virginianus -0.0611 1.0430 -2.0466 -0.1268
## total_shrub_cover-Canis_latrans 0.2540 0.7502 -0.9660 0.1779
## total_shrub_cover-Procyon_lotor -0.9896 0.6365 -2.3458 -0.9324
## total_shrub_cover-Dasypus_novemcinctus 0.0854 0.5579 -0.9492 0.0753
## total_shrub_cover-Lynx_rufus -1.1431 1.2541 -4.1496 -0.9759
## total_shrub_cover-Didelphis_virginiana -0.5778 0.7425 -2.2042 -0.5255
## total_shrub_cover-Sylvilagus_floridanus -0.2592 0.8442 -2.0474 -0.2353
## total_shrub_cover-Meleagris_gallopavo -2.0461 1.3153 -5.1200 -1.8552
## total_shrub_cover-Sciurus_carolinensis -0.0317 0.7013 -1.4217 -0.0507
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8611 0.9934 -0.1459 1.8533
## Avg_Cogongrass_Cover-Canis_latrans 2.1633 0.8245 0.7339 2.1099
## Avg_Cogongrass_Cover-Procyon_lotor 2.0568 0.8121 0.5712 2.0001
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3665 0.8492 0.9278 2.2853
## Avg_Cogongrass_Cover-Lynx_rufus 2.2355 0.9479 0.6309 2.1470
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0273 0.7816 0.5831 1.9861
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4127 0.9093 -0.4883 1.4488
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4523 1.0765 -0.9853 1.5477
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1981 0.8356 0.7176 2.1409
## Tree_Density-Odocoileus_virginianus -0.7895 1.2044 -2.6583 -0.9426
## Tree_Density-Canis_latrans -2.2673 1.1016 -4.9452 -2.1074
## Tree_Density-Procyon_lotor -1.4088 0.7447 -2.8402 -1.4221
## Tree_Density-Dasypus_novemcinctus -3.1017 1.5336 -7.0412 -2.7479
## Tree_Density-Lynx_rufus -0.6969 1.3112 -2.7748 -0.8714
## Tree_Density-Didelphis_virginiana -2.1297 1.0628 -4.7342 -1.9972
## Tree_Density-Sylvilagus_floridanus -2.2928 1.2486 -5.4367 -2.0773
## Tree_Density-Meleagris_gallopavo -2.0052 1.2208 -4.6717 -1.9464
## Tree_Density-Sciurus_carolinensis -2.2755 1.2242 -5.3217 -2.0773
## Avg_Canopy_Cover-Odocoileus_virginianus 1.1900 1.1932 -1.3870 1.2403
## Avg_Canopy_Cover-Canis_latrans 0.3553 0.6997 -1.0218 0.3471
## Avg_Canopy_Cover-Procyon_lotor 1.6852 0.6804 0.4454 1.6395
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9045 0.6546 0.8174 1.8452
## Avg_Canopy_Cover-Lynx_rufus 1.1864 1.3038 -1.2792 1.1453
## Avg_Canopy_Cover-Didelphis_virginiana 2.5209 0.8955 1.1059 2.3973
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0035 1.4355 1.0887 2.6985
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2109 1.1145 0.5172 2.0301
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1732 0.7600 0.9223 2.0891
## avg_veg_height-Odocoileus_virginianus -0.4948 0.7290 -1.9636 -0.4799
## avg_veg_height-Canis_latrans -0.6427 0.5726 -1.7953 -0.6278
## avg_veg_height-Procyon_lotor -0.3476 0.5605 -1.4313 -0.3487
## avg_veg_height-Dasypus_novemcinctus -0.2433 0.5511 -1.2780 -0.2591
## avg_veg_height-Lynx_rufus -0.5264 0.7356 -1.9912 -0.5323
## avg_veg_height-Didelphis_virginiana -0.5575 0.6085 -1.8031 -0.5418
## avg_veg_height-Sylvilagus_floridanus -0.6520 0.6281 -1.9481 -0.6252
## avg_veg_height-Meleagris_gallopavo -0.5846 0.6970 -2.0556 -0.5563
## avg_veg_height-Sciurus_carolinensis -0.1346 0.6225 -1.2590 -0.1657
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.7533 1.0087 561
## (Intercept)-Canis_latrans 3.1818 1.0028 1344
## (Intercept)-Procyon_lotor 2.6803 1.0107 1122
## (Intercept)-Dasypus_novemcinctus 0.2712 1.0249 1490
## (Intercept)-Lynx_rufus 7.9676 1.0104 279
## (Intercept)-Didelphis_virginiana -0.8557 1.0024 1717
## (Intercept)-Sylvilagus_floridanus 1.5594 1.0010 1286
## (Intercept)-Meleagris_gallopavo 1.5736 1.0071 930
## (Intercept)-Sciurus_carolinensis -1.0419 1.0078 1200
## Cogon_Patch_Size-Odocoileus_virginianus 2.2207 1.0010 2661
## Cogon_Patch_Size-Canis_latrans 3.5007 1.0004 1585
## Cogon_Patch_Size-Procyon_lotor 0.5586 1.0053 1173
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4054 1.0043 1734
## Cogon_Patch_Size-Lynx_rufus 2.5417 1.0228 1053
## Cogon_Patch_Size-Didelphis_virginiana 2.7033 1.0034 1337
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4087 1.0062 1360
## Cogon_Patch_Size-Meleagris_gallopavo 1.8536 1.0158 1644
## Cogon_Patch_Size-Sciurus_carolinensis 0.1364 1.0005 1231
## Veg_shannon_index-Odocoileus_virginianus 2.2620 1.0031 2281
## Veg_shannon_index-Canis_latrans 2.5584 1.0154 1202
## Veg_shannon_index-Procyon_lotor 2.2946 1.0072 947
## Veg_shannon_index-Dasypus_novemcinctus 1.5883 1.0086 2438
## Veg_shannon_index-Lynx_rufus 2.3181 1.0119 1891
## Veg_shannon_index-Didelphis_virginiana 2.3084 1.0066 1911
## Veg_shannon_index-Sylvilagus_floridanus 2.3661 1.0082 1708
## Veg_shannon_index-Meleagris_gallopavo 2.7908 1.0024 1541
## Veg_shannon_index-Sciurus_carolinensis 1.4120 1.0062 2293
## total_shrub_cover-Odocoileus_virginianus 2.2610 1.0004 2293
## total_shrub_cover-Canis_latrans 2.0636 1.0011 1716
## total_shrub_cover-Procyon_lotor 0.1064 1.0010 2612
## total_shrub_cover-Dasypus_novemcinctus 1.2041 1.0056 2816
## total_shrub_cover-Lynx_rufus 0.8942 1.0151 920
## total_shrub_cover-Didelphis_virginiana 0.7809 1.0017 2349
## total_shrub_cover-Sylvilagus_floridanus 1.4039 1.0005 1832
## total_shrub_cover-Meleagris_gallopavo -0.0555 1.0080 786
## total_shrub_cover-Sciurus_carolinensis 1.4245 1.0013 2527
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.8924 1.0071 1218
## Avg_Cogongrass_Cover-Canis_latrans 3.9611 1.0138 947
## Avg_Cogongrass_Cover-Procyon_lotor 3.8053 1.0145 890
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.2946 1.0115 765
## Avg_Cogongrass_Cover-Lynx_rufus 4.3107 1.0050 836
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.6933 1.0088 1055
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1029 1.0022 1133
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.3415 1.0114 1129
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0377 1.0024 823
## Tree_Density-Odocoileus_virginianus 2.0195 1.0054 1117
## Tree_Density-Canis_latrans -0.5252 1.0236 1207
## Tree_Density-Procyon_lotor 0.1429 1.0143 1700
## Tree_Density-Dasypus_novemcinctus -1.1061 1.0043 723
## Tree_Density-Lynx_rufus 2.4189 1.0230 720
## Tree_Density-Didelphis_virginiana -0.4646 1.0047 1317
## Tree_Density-Sylvilagus_floridanus -0.3666 1.0017 1112
## Tree_Density-Meleagris_gallopavo 0.3418 1.0108 1499
## Tree_Density-Sciurus_carolinensis -0.4903 1.0030 1238
## Avg_Canopy_Cover-Odocoileus_virginianus 3.4485 1.0001 2048
## Avg_Canopy_Cover-Canis_latrans 1.7534 1.0009 1951
## Avg_Canopy_Cover-Procyon_lotor 3.1666 1.0058 1847
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3822 1.0005 1461
## Avg_Canopy_Cover-Lynx_rufus 3.9845 1.0049 972
## Avg_Canopy_Cover-Didelphis_virginiana 4.6506 1.0108 1052
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.5508 1.0040 872
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9509 1.0147 906
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9670 1.0013 1742
## avg_veg_height-Odocoileus_virginianus 0.9413 1.0113 2014
## avg_veg_height-Canis_latrans 0.4409 1.0153 1256
## avg_veg_height-Procyon_lotor 0.7853 1.0169 1238
## avg_veg_height-Dasypus_novemcinctus 0.9043 1.0172 1840
## avg_veg_height-Lynx_rufus 0.9713 1.0121 1555
## avg_veg_height-Didelphis_virginiana 0.5904 1.0081 1710
## avg_veg_height-Sylvilagus_floridanus 0.5346 1.0123 1510
## avg_veg_height-Meleagris_gallopavo 0.7177 1.0148 1399
## avg_veg_height-Sciurus_carolinensis 1.2243 1.0140 1452
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0037 0.0580 -0.1105 0.0029 0.1149
## (Intercept)-Canis_latrans -2.6370 0.1836 -3.0191 -2.6317 -2.3016
## (Intercept)-Procyon_lotor -2.2627 0.1308 -2.5285 -2.2594 -2.0135
## (Intercept)-Dasypus_novemcinctus -1.5750 0.1320 -1.8364 -1.5738 -1.3203
## (Intercept)-Lynx_rufus -3.7510 0.3349 -4.4003 -3.7477 -3.1211
## (Intercept)-Didelphis_virginiana -2.2766 0.2405 -2.7752 -2.2682 -1.8363
## (Intercept)-Sylvilagus_floridanus -3.1652 0.2776 -3.7450 -3.1586 -2.6489
## (Intercept)-Meleagris_gallopavo -3.4028 0.3098 -4.0482 -3.3921 -2.8265
## (Intercept)-Sciurus_carolinensis -2.4116 0.2574 -2.9455 -2.3969 -1.9366
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5114
## (Intercept)-Canis_latrans 1.0014 1913
## (Intercept)-Procyon_lotor 1.0026 4120
## (Intercept)-Dasypus_novemcinctus 1.0022 5250
## (Intercept)-Lynx_rufus 1.0065 431
## (Intercept)-Didelphis_virginiana 1.0012 4453
## (Intercept)-Sylvilagus_floridanus 1.0029 1906
## (Intercept)-Meleagris_gallopavo 1.0127 1336
## (Intercept)-Sciurus_carolinensis 1.0000 3686
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2138
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0748 0.6718 -1.2379 0.0549 1.4995 1.0005 3367
## Avg_Cogongrass_Cover 0.1544 0.3226 -0.4815 0.1553 0.7989 1.0023 2109
## total_shrub_cover -0.4720 0.3512 -1.2214 -0.4600 0.1942 1.0005 2881
## avg_veg_height -0.0003 0.3133 -0.6033 -0.0029 0.6262 1.0002 1809
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2303 4.2695 0.7058 3.1265 14.4506 1.0034 1916
## Avg_Cogongrass_Cover 0.3623 0.4505 0.0402 0.2191 1.5916 1.0049 2831
## total_shrub_cover 0.6749 0.7680 0.0625 0.4381 2.6763 1.0072 2074
## avg_veg_height 0.2621 0.3057 0.0351 0.1707 1.0185 1.0011 3255
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7634 0.9492 0.0587 0.4701 3.1126 1.0849 480
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2257 0.4281 -3.023 -2.247 -1.2826 1.0022 4976
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6388 1.1446 0.5515 1.3335 4.5534 1.018 4702
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8824 1.5574 1.3540 3.6828
## (Intercept)-Canis_latrans 0.4068 0.6346 -0.7898 0.3836
## (Intercept)-Procyon_lotor 0.7666 0.6626 -0.5210 0.7465
## (Intercept)-Dasypus_novemcinctus -0.6737 0.5874 -1.8379 -0.6629
## (Intercept)-Lynx_rufus 0.1540 1.0350 -1.4537 0.0374
## (Intercept)-Didelphis_virginiana -1.3931 0.6537 -2.7252 -1.3975
## (Intercept)-Sylvilagus_floridanus -0.1338 0.8972 -1.6015 -0.2337
## (Intercept)-Meleagris_gallopavo -0.6595 0.8053 -2.2286 -0.6759
## (Intercept)-Sciurus_carolinensis -1.5137 0.6863 -2.9323 -1.4949
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1498 0.5750 -0.9907 0.1415
## Avg_Cogongrass_Cover-Canis_latrans 0.4090 0.4592 -0.4103 0.3823
## Avg_Cogongrass_Cover-Procyon_lotor 0.1003 0.4353 -0.7630 0.1068
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2489 0.3939 -0.5105 0.2439
## Avg_Cogongrass_Cover-Lynx_rufus 0.4448 0.5051 -0.4433 0.4133
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3276 0.4304 -0.4672 0.3053
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2133 0.5165 -1.3164 -0.1718
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3245 0.5919 -1.6537 -0.2656
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2372 0.4270 -0.6010 0.2415
## total_shrub_cover-Odocoileus_virginianus -0.2740 0.6623 -1.5615 -0.2965
## total_shrub_cover-Canis_latrans 0.1041 0.4755 -0.7405 0.0733
## total_shrub_cover-Procyon_lotor -0.9715 0.5342 -2.1813 -0.9076
## total_shrub_cover-Dasypus_novemcinctus -0.0848 0.3795 -0.8158 -0.0872
## total_shrub_cover-Lynx_rufus -0.9572 0.6997 -2.6164 -0.8707
## total_shrub_cover-Didelphis_virginiana -0.2921 0.4441 -1.1995 -0.2905
## total_shrub_cover-Sylvilagus_floridanus -0.4772 0.6043 -1.8259 -0.4361
## total_shrub_cover-Meleagris_gallopavo -1.2975 0.6999 -2.8947 -1.2041
## total_shrub_cover-Sciurus_carolinensis -0.1438 0.4423 -0.9955 -0.1516
## avg_veg_height-Odocoileus_virginianus -0.0196 0.5165 -1.0400 -0.0248
## avg_veg_height-Canis_latrans -0.0813 0.3994 -0.8698 -0.0811
## avg_veg_height-Procyon_lotor 0.0889 0.4169 -0.7084 0.0780
## avg_veg_height-Dasypus_novemcinctus 0.1822 0.3828 -0.5570 0.1760
## avg_veg_height-Lynx_rufus -0.0071 0.5106 -1.0073 -0.0136
## avg_veg_height-Didelphis_virginiana -0.0476 0.4197 -0.9071 -0.0407
## avg_veg_height-Sylvilagus_floridanus -0.1426 0.4521 -1.0626 -0.1269
## avg_veg_height-Meleagris_gallopavo -0.2585 0.5206 -1.4153 -0.2151
## avg_veg_height-Sciurus_carolinensis 0.2752 0.4266 -0.5029 0.2500
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.6320 1.0015 1124
## (Intercept)-Canis_latrans 1.7842 1.0008 2761
## (Intercept)-Procyon_lotor 2.1238 1.0040 2975
## (Intercept)-Dasypus_novemcinctus 0.4707 1.0171 3477
## (Intercept)-Lynx_rufus 2.4036 1.0110 860
## (Intercept)-Didelphis_virginiana -0.1126 1.0064 2947
## (Intercept)-Sylvilagus_floridanus 1.9074 1.0071 1090
## (Intercept)-Meleagris_gallopavo 0.9921 1.0004 2105
## (Intercept)-Sciurus_carolinensis -0.1818 1.0033 3061
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3649 1.0000 3307
## Avg_Cogongrass_Cover-Canis_latrans 1.3751 1.0022 2890
## Avg_Cogongrass_Cover-Procyon_lotor 0.9638 1.0016 3408
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0323 1.0003 3073
## Avg_Cogongrass_Cover-Lynx_rufus 1.5520 1.0028 2962
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2030 1.0023 2574
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7137 1.0003 2803
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6669 1.0009 2134
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1023 1.0032 2851
## total_shrub_cover-Odocoileus_virginianus 1.0968 1.0000 4381
## total_shrub_cover-Canis_latrans 1.1136 1.0013 3390
## total_shrub_cover-Procyon_lotor -0.0886 1.0009 3060
## total_shrub_cover-Dasypus_novemcinctus 0.6836 1.0022 4691
## total_shrub_cover-Lynx_rufus 0.1616 1.0004 2068
## total_shrub_cover-Didelphis_virginiana 0.5724 1.0010 4686
## total_shrub_cover-Sylvilagus_floridanus 0.5922 1.0032 1765
## total_shrub_cover-Meleagris_gallopavo -0.2214 1.0046 1751
## total_shrub_cover-Sciurus_carolinensis 0.7484 1.0004 4501
## avg_veg_height-Odocoileus_virginianus 1.0101 1.0004 2720
## avg_veg_height-Canis_latrans 0.7087 1.0013 2631
## avg_veg_height-Procyon_lotor 0.9197 1.0006 2961
## avg_veg_height-Dasypus_novemcinctus 0.9627 1.0016 2926
## avg_veg_height-Lynx_rufus 1.0324 1.0011 2599
## avg_veg_height-Didelphis_virginiana 0.7559 1.0015 3135
## avg_veg_height-Sylvilagus_floridanus 0.7456 1.0000 2874
## avg_veg_height-Meleagris_gallopavo 0.6508 1.0022 2964
## avg_veg_height-Sciurus_carolinensis 1.1712 1.0007 3134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0042 0.0586 -0.1086 0.0040 0.1208
## (Intercept)-Canis_latrans -2.6300 0.1802 -2.9962 -2.6253 -2.2852
## (Intercept)-Procyon_lotor -2.2696 0.1290 -2.5339 -2.2685 -2.0219
## (Intercept)-Dasypus_novemcinctus -1.5729 0.1324 -1.8371 -1.5665 -1.3193
## (Intercept)-Lynx_rufus -3.5479 0.3097 -4.1741 -3.5384 -2.9635
## (Intercept)-Didelphis_virginiana -2.3078 0.2498 -2.8343 -2.2959 -1.8599
## (Intercept)-Sylvilagus_floridanus -3.2526 0.3312 -3.9455 -3.2306 -2.6615
## (Intercept)-Meleagris_gallopavo -3.3143 0.3232 -3.9888 -3.3054 -2.7188
## (Intercept)-Sciurus_carolinensis -2.4302 0.2648 -2.9916 -2.4186 -1.9479
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0023 5021
## (Intercept)-Canis_latrans 1.0054 2837
## (Intercept)-Procyon_lotor 1.0002 3866
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## (Intercept)-Lynx_rufus 1.0059 1198
## (Intercept)-Didelphis_virginiana 1.0006 3773
## (Intercept)-Sylvilagus_floridanus 1.0054 995
## (Intercept)-Meleagris_gallopavo 1.0009 1480
## (Intercept)-Sciurus_carolinensis 1.0025 3493
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2805
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0873 0.7910 -1.4535 0.0649 1.7545 1.0149 1581
## Tree_Density -0.7558 0.4345 -1.7166 -0.7217 0.0035 1.0552 993
## Avg_Canopy_Cover 1.0282 0.3757 0.3319 1.0112 1.8144 1.0078 2744
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.8923 7.5222 1.1947 4.7396 26.3334 1.0823 672
## Tree_Density 0.8521 1.4245 0.0483 0.3772 4.6449 1.0055 1131
## Avg_Canopy_Cover 0.7488 0.9589 0.0671 0.4793 2.9844 1.0116 1910
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4662 0.6149 0.0407 0.2635 2.1525 1.089 352
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2322 0.4205 -3.0349 -2.2473 -1.3554 0.9999 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7017 1.1594 0.5664 1.4023 4.5613 1.0072 4072
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7336 1.9296 1.9930 4.3854 9.6248
## (Intercept)-Canis_latrans 0.3251 0.6284 -0.8198 0.2848 1.6719
## (Intercept)-Procyon_lotor 0.7564 0.6110 -0.4366 0.7537 1.9789
## (Intercept)-Dasypus_novemcinctus -1.0194 0.6163 -2.2857 -0.9829 0.0881
## (Intercept)-Lynx_rufus 1.3921 2.1991 -1.2660 0.8399 7.2191
## (Intercept)-Didelphis_virginiana -1.8873 0.6940 -3.3465 -1.8585 -0.5914
## (Intercept)-Sylvilagus_floridanus -0.6774 0.7339 -2.1290 -0.6916 0.8209
## (Intercept)-Meleagris_gallopavo -0.3935 0.8336 -1.8852 -0.4436 1.3928
## (Intercept)-Sciurus_carolinensis -1.9504 0.7200 -3.4530 -1.9154 -0.6492
## Tree_Density-Odocoileus_virginianus -0.3657 0.7130 -1.5290 -0.4532 1.3640
## Tree_Density-Canis_latrans -0.8882 0.5398 -2.1058 -0.8406 0.0315
## Tree_Density-Procyon_lotor -0.4831 0.4159 -1.2886 -0.4886 0.3702
## Tree_Density-Dasypus_novemcinctus -1.3585 0.8908 -3.6711 -1.1525 -0.1671
## Tree_Density-Lynx_rufus 0.0061 0.8747 -1.3871 -0.1134 2.0762
## Tree_Density-Didelphis_virginiana -1.0396 0.7808 -3.0072 -0.8933 0.0879
## Tree_Density-Sylvilagus_floridanus -1.0860 0.8076 -3.0360 -0.9532 0.0825
## Tree_Density-Meleagris_gallopavo -0.8976 0.7171 -2.5192 -0.8238 0.2993
## Tree_Density-Sciurus_carolinensis -0.9663 0.7425 -2.7727 -0.8539 0.1813
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8080 0.7470 -0.7182 0.8117 2.3338
## Avg_Canopy_Cover-Canis_latrans 0.0863 0.4846 -0.8904 0.0846 1.0073
## Avg_Canopy_Cover-Procyon_lotor 1.0350 0.4693 0.1666 1.0148 2.0288
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0179 0.4283 0.2260 0.9931 1.9165
## Avg_Canopy_Cover-Lynx_rufus 0.9203 0.7950 -0.5898 0.8726 2.6596
## Avg_Canopy_Cover-Didelphis_virginiana 1.2715 0.5126 0.4119 1.2249 2.4235
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7351 0.7840 0.5992 1.5864 3.5920
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4280 0.6855 0.3391 1.3306 3.0428
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2557 0.4948 0.4087 1.2062 2.3473
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0745 722
## (Intercept)-Canis_latrans 1.0043 2895
## (Intercept)-Procyon_lotor 1.0019 2809
## (Intercept)-Dasypus_novemcinctus 1.0159 2690
## (Intercept)-Lynx_rufus 1.1586 211
## (Intercept)-Didelphis_virginiana 1.0023 2758
## (Intercept)-Sylvilagus_floridanus 1.0023 2601
## (Intercept)-Meleagris_gallopavo 1.0048 1563
## (Intercept)-Sciurus_carolinensis 1.0156 2849
## Tree_Density-Odocoileus_virginianus 1.0112 1674
## Tree_Density-Canis_latrans 1.0196 3198
## Tree_Density-Procyon_lotor 1.0061 2823
## Tree_Density-Dasypus_novemcinctus 1.0128 1372
## Tree_Density-Lynx_rufus 1.0184 674
## Tree_Density-Didelphis_virginiana 1.0133 1834
## Tree_Density-Sylvilagus_floridanus 1.0164 1690
## Tree_Density-Meleagris_gallopavo 1.0043 2197
## Tree_Density-Sciurus_carolinensis 1.0163 2416
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0077 2672
## Avg_Canopy_Cover-Canis_latrans 1.0019 2952
## Avg_Canopy_Cover-Procyon_lotor 1.0024 3717
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0063 4350
## Avg_Canopy_Cover-Lynx_rufus 1.0124 1369
## Avg_Canopy_Cover-Didelphis_virginiana 1.0055 3400
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0028 1731
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0040 1989
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0021 3572
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0595 -0.1123 0.0051 0.1210
## (Intercept)-Canis_latrans -2.6341 0.1795 -3.0017 -2.6291 -2.3036
## (Intercept)-Procyon_lotor -2.2647 0.1283 -2.5236 -2.2634 -2.0246
## (Intercept)-Dasypus_novemcinctus -1.5753 0.1353 -1.8530 -1.5701 -1.3186
## (Intercept)-Lynx_rufus -3.7417 0.3659 -4.4496 -3.7460 -3.0246
## (Intercept)-Didelphis_virginiana -2.2888 0.2448 -2.7875 -2.2838 -1.8328
## (Intercept)-Sylvilagus_floridanus -3.1084 0.2764 -3.6801 -3.0949 -2.5961
## (Intercept)-Meleagris_gallopavo -3.4245 0.3315 -4.1095 -3.4098 -2.8105
## (Intercept)-Sciurus_carolinensis -2.4239 0.2594 -2.9706 -2.4088 -1.9482
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5158
## (Intercept)-Canis_latrans 1.0031 2716
## (Intercept)-Procyon_lotor 1.0003 4356
## (Intercept)-Dasypus_novemcinctus 1.0000 5250
## (Intercept)-Lynx_rufus 1.0640 531
## (Intercept)-Didelphis_virginiana 1.0014 4284
## (Intercept)-Sylvilagus_floridanus 1.0011 2036
## (Intercept)-Meleagris_gallopavo 1.0034 1246
## (Intercept)-Sciurus_carolinensis 1.0064 3927
# Includes movement covariates of occupancy and null for detection
ms_null_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2495
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0786 0.6799 -1.1982 0.0566 1.4841 1.0007 3782
## Cogon_Patch_Size -0.0021 0.3922 -0.7943 -0.0002 0.7672 1.0103 2737
## Avg_Cogongrass_Cover 0.1647 0.3175 -0.4783 0.1720 0.7809 1.0088 2585
## total_shrub_cover -0.4800 0.3618 -1.2290 -0.4620 0.1942 1.0022 2604
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.6349 4.6473 0.7794 3.3312 16.0279 1.0438 1214
## Cogon_Patch_Size 0.7757 1.0586 0.0586 0.4449 3.6253 1.0003 2102
## Avg_Cogongrass_Cover 0.3941 0.5195 0.0435 0.2410 1.7211 1.0009 3028
## total_shrub_cover 0.6675 0.8967 0.0610 0.4057 2.7355 1.0057 2160
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9651 0.9702 0.0687 0.6685 3.5145 1.0061 598
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2195 0.4167 -3.0085 -2.2297 -1.3747 1.0007 4997
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6488 1.1668 0.5365 1.3435 4.6934 1.0099 3912
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.0053 1.6714 1.4121 3.7845
## (Intercept)-Canis_latrans 0.4846 0.6983 -0.8734 0.4642
## (Intercept)-Procyon_lotor 0.7596 0.7119 -0.6283 0.7447
## (Intercept)-Dasypus_novemcinctus -0.7054 0.6248 -1.9854 -0.6890
## (Intercept)-Lynx_rufus 0.1031 1.0889 -1.6961 -0.0060
## (Intercept)-Didelphis_virginiana -1.4314 0.7178 -2.9266 -1.4278
## (Intercept)-Sylvilagus_floridanus -0.2121 0.9272 -1.8881 -0.2618
## (Intercept)-Meleagris_gallopavo -0.6240 0.9170 -2.3093 -0.6547
## (Intercept)-Sciurus_carolinensis -1.5570 0.7361 -3.1171 -1.5308
## Cogon_Patch_Size-Odocoileus_virginianus 0.0851 0.7103 -1.1960 0.0470
## Cogon_Patch_Size-Canis_latrans 0.7382 0.7132 -0.2926 0.6119
## Cogon_Patch_Size-Procyon_lotor -0.1444 0.4524 -1.0713 -0.1389
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0768 0.4123 -0.9436 -0.0570
## Cogon_Patch_Size-Lynx_rufus -0.0271 0.7757 -1.3899 -0.0863
## Cogon_Patch_Size-Didelphis_virginiana 0.6311 0.4979 -0.2393 0.5991
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6716 0.8038 -2.6039 -0.5573
## Cogon_Patch_Size-Meleagris_gallopavo -0.0337 0.5948 -1.1972 -0.0397
## Cogon_Patch_Size-Sciurus_carolinensis -0.5845 0.6642 -2.1728 -0.4831
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1495 0.5734 -0.9742 0.1441
## Avg_Cogongrass_Cover-Canis_latrans 0.2676 0.4195 -0.5097 0.2494
## Avg_Cogongrass_Cover-Procyon_lotor 0.1864 0.4412 -0.6359 0.1705
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3577 0.3835 -0.3471 0.3430
## Avg_Cogongrass_Cover-Lynx_rufus 0.5024 0.5065 -0.3744 0.4635
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1835 0.4176 -0.6597 0.1886
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1788 0.5153 -1.2774 -0.1451
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3742 0.6201 -1.8117 -0.3126
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4278 0.4086 -0.3328 0.4091
## total_shrub_cover-Odocoileus_virginianus -0.2881 0.6438 -1.5508 -0.3024
## total_shrub_cover-Canis_latrans 0.0560 0.4878 -0.8291 0.0278
## total_shrub_cover-Procyon_lotor -0.9564 0.5498 -2.2338 -0.8939
## total_shrub_cover-Dasypus_novemcinctus -0.0996 0.3892 -0.8298 -0.1025
## total_shrub_cover-Lynx_rufus -0.9007 0.7126 -2.5459 -0.8106
## total_shrub_cover-Didelphis_virginiana -0.3880 0.4458 -1.3129 -0.3670
## total_shrub_cover-Sylvilagus_floridanus -0.4475 0.6274 -1.9389 -0.3965
## total_shrub_cover-Meleagris_gallopavo -1.2526 0.7100 -2.9558 -1.1545
## total_shrub_cover-Sciurus_carolinensis -0.1477 0.4530 -1.0127 -0.1619
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.8287 1.0277 989
## (Intercept)-Canis_latrans 1.9334 1.0001 2548
## (Intercept)-Procyon_lotor 2.2317 1.0027 2346
## (Intercept)-Dasypus_novemcinctus 0.5004 1.0038 2994
## (Intercept)-Lynx_rufus 2.5278 1.0083 848
## (Intercept)-Didelphis_virginiana -0.0324 1.0017 3157
## (Intercept)-Sylvilagus_floridanus 1.8561 1.0119 1136
## (Intercept)-Meleagris_gallopavo 1.2640 1.0120 1183
## (Intercept)-Sciurus_carolinensis -0.1808 1.0011 2285
## Cogon_Patch_Size-Odocoileus_virginianus 1.6466 1.0032 3724
## Cogon_Patch_Size-Canis_latrans 2.4418 1.0025 2197
## Cogon_Patch_Size-Procyon_lotor 0.7440 1.0019 3694
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7192 1.0005 4148
## Cogon_Patch_Size-Lynx_rufus 1.6894 1.0013 2127
## Cogon_Patch_Size-Didelphis_virginiana 1.6816 1.0002 3059
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5565 1.0006 2154
## Cogon_Patch_Size-Meleagris_gallopavo 1.1287 1.0115 2821
## Cogon_Patch_Size-Sciurus_carolinensis 0.4494 1.0014 2605
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3291 1.0056 3335
## Avg_Cogongrass_Cover-Canis_latrans 1.1438 1.0014 3581
## Avg_Cogongrass_Cover-Procyon_lotor 1.0752 1.0008 3555
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1580 1.0099 3567
## Avg_Cogongrass_Cover-Lynx_rufus 1.6182 1.0034 3278
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0116 1.0091 3741
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7503 1.0032 2886
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7164 1.0017 2186
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2648 1.0034 3544
## total_shrub_cover-Odocoileus_virginianus 1.0488 1.0000 3674
## total_shrub_cover-Canis_latrans 1.1133 1.0041 2305
## total_shrub_cover-Procyon_lotor -0.0675 1.0011 2276
## total_shrub_cover-Dasypus_novemcinctus 0.7102 1.0007 3654
## total_shrub_cover-Lynx_rufus 0.2384 1.0086 1597
## total_shrub_cover-Didelphis_virginiana 0.4493 1.0017 3850
## total_shrub_cover-Sylvilagus_floridanus 0.6331 1.0067 1862
## total_shrub_cover-Meleagris_gallopavo -0.1706 1.0088 1643
## total_shrub_cover-Sciurus_carolinensis 0.7879 1.0003 4054
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0044 0.0598 -0.1108 0.0045 0.1215
## (Intercept)-Canis_latrans -2.6149 0.1734 -2.9722 -2.6098 -2.2904
## (Intercept)-Procyon_lotor -2.2750 0.1311 -2.5435 -2.2723 -2.0194
## (Intercept)-Dasypus_novemcinctus -1.5757 0.1323 -1.8380 -1.5738 -1.3236
## (Intercept)-Lynx_rufus -3.5381 0.3238 -4.2130 -3.5258 -2.9441
## (Intercept)-Didelphis_virginiana -2.3070 0.2496 -2.8213 -2.2944 -1.8528
## (Intercept)-Sylvilagus_floridanus -3.2661 0.3332 -3.9564 -3.2438 -2.6489
## (Intercept)-Meleagris_gallopavo -3.3252 0.3479 -4.0945 -3.2934 -2.7206
## (Intercept)-Sciurus_carolinensis -2.4299 0.2655 -2.9831 -2.4133 -1.9542
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5524
## (Intercept)-Canis_latrans 1.0005 3235
## (Intercept)-Procyon_lotor 1.0003 4026
## (Intercept)-Dasypus_novemcinctus 1.0022 5029
## (Intercept)-Lynx_rufus 1.0042 923
## (Intercept)-Didelphis_virginiana 1.0006 3879
## (Intercept)-Sylvilagus_floridanus 1.0154 1013
## (Intercept)-Meleagris_gallopavo 1.0068 1013
## (Intercept)-Sciurus_carolinensis 1.0012 2873
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0225 0.6377 -1.2144 0.0022 1.3578 1.0010 2949
## Veg_shannon_index 0.3809 0.2770 -0.1661 0.3763 0.9446 1.0037 2336
## Avg_Cogongrass_Cover 0.3683 0.2697 -0.1685 0.3674 0.9009 1.0028 2198
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8348 3.5654 0.5854 2.8049 13.4482 1.0006 2105
## Veg_shannon_index 0.2719 0.3784 0.0358 0.1665 1.1186 1.0295 2754
## Avg_Cogongrass_Cover 0.2888 0.3585 0.0360 0.1905 1.1374 1.0211 3307
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7434 0.7748 0.0598 0.5026 2.8773 1.0277 579
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2329 0.4384 -3.0827 -2.2488 -1.3443 1.003 4879
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6796 1.1879 0.5618 1.3796 4.6145 1.005 3979
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5933 1.4806 1.0896 3.4412
## (Intercept)-Canis_latrans 0.2808 0.6127 -0.8828 0.2697
## (Intercept)-Procyon_lotor 0.5910 0.6263 -0.6635 0.6068
## (Intercept)-Dasypus_novemcinctus -0.6934 0.5712 -1.8675 -0.6778
## (Intercept)-Lynx_rufus 0.0864 1.0553 -1.6096 -0.0236
## (Intercept)-Didelphis_virginiana -1.4337 0.6567 -2.7843 -1.4143
## (Intercept)-Sylvilagus_floridanus -0.2551 0.8208 -1.6732 -0.3282
## (Intercept)-Meleagris_gallopavo -0.3171 0.8806 -1.8482 -0.3743
## (Intercept)-Sciurus_carolinensis -1.4543 0.6735 -2.8449 -1.4332
## Veg_shannon_index-Odocoileus_virginianus 0.3137 0.4834 -0.7271 0.3279
## Veg_shannon_index-Canis_latrans 0.6443 0.3756 -0.0479 0.6174
## Veg_shannon_index-Procyon_lotor 0.4899 0.3863 -0.2333 0.4742
## Veg_shannon_index-Dasypus_novemcinctus 0.2281 0.3307 -0.4420 0.2315
## Veg_shannon_index-Lynx_rufus 0.2252 0.4925 -0.8247 0.2492
## Veg_shannon_index-Didelphis_virginiana 0.5116 0.3889 -0.1874 0.4908
## Veg_shannon_index-Sylvilagus_floridanus 0.4862 0.4491 -0.3036 0.4592
## Veg_shannon_index-Meleagris_gallopavo 0.4869 0.4395 -0.3532 0.4682
## Veg_shannon_index-Sciurus_carolinensis 0.0617 0.3972 -0.8164 0.0789
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3516 0.4987 -0.6456 0.3446
## Avg_Cogongrass_Cover-Canis_latrans 0.5675 0.3847 -0.1115 0.5385
## Avg_Cogongrass_Cover-Procyon_lotor 0.4559 0.4019 -0.2732 0.4316
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4521 0.3293 -0.1755 0.4414
## Avg_Cogongrass_Cover-Lynx_rufus 0.6092 0.4408 -0.1682 0.5730
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4996 0.3733 -0.2167 0.4931
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0014 0.4517 -0.9783 0.0336
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0179 0.5223 -1.1406 0.0099
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4463 0.3641 -0.2790 0.4413
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0653 1.0028 1192
## (Intercept)-Canis_latrans 1.5112 1.0027 3273
## (Intercept)-Procyon_lotor 1.8074 1.0034 2621
## (Intercept)-Dasypus_novemcinctus 0.3984 1.0026 3473
## (Intercept)-Lynx_rufus 2.5735 1.0080 568
## (Intercept)-Didelphis_virginiana -0.1926 1.0083 2440
## (Intercept)-Sylvilagus_floridanus 1.4982 1.0099 1214
## (Intercept)-Meleagris_gallopavo 1.6693 1.0048 1225
## (Intercept)-Sciurus_carolinensis -0.2011 1.0095 2679
## Veg_shannon_index-Odocoileus_virginianus 1.2473 1.0010 3410
## Veg_shannon_index-Canis_latrans 1.4589 0.9999 3833
## Veg_shannon_index-Procyon_lotor 1.3030 1.0005 3029
## Veg_shannon_index-Dasypus_novemcinctus 0.8693 1.0028 4419
## Veg_shannon_index-Lynx_rufus 1.1277 1.0025 2482
## Veg_shannon_index-Didelphis_virginiana 1.3426 1.0011 3932
## Veg_shannon_index-Sylvilagus_floridanus 1.4073 1.0106 2294
## Veg_shannon_index-Meleagris_gallopavo 1.4060 1.0053 3232
## Veg_shannon_index-Sciurus_carolinensis 0.7867 1.0010 3414
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3876 1.0096 4041
## Avg_Cogongrass_Cover-Canis_latrans 1.4112 1.0019 3928
## Avg_Cogongrass_Cover-Procyon_lotor 1.3364 1.0009 3335
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1102 1.0002 3786
## Avg_Cogongrass_Cover-Lynx_rufus 1.5860 1.0017 3481
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2649 1.0026 3622
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8180 1.0068 2563
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9509 1.0013 2427
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1851 1.0060 3960
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0045 0.0590 -0.1109 0.0044 0.1182
## (Intercept)-Canis_latrans -2.6066 0.1709 -2.9593 -2.6020 -2.2851
## (Intercept)-Procyon_lotor -2.2723 0.1335 -2.5483 -2.2692 -2.0200
## (Intercept)-Dasypus_novemcinctus -1.5725 0.1335 -1.8346 -1.5710 -1.3131
## (Intercept)-Lynx_rufus -3.5371 0.3346 -4.2527 -3.5146 -2.9245
## (Intercept)-Didelphis_virginiana -2.2991 0.2509 -2.8253 -2.2838 -1.8398
## (Intercept)-Sylvilagus_floridanus -3.2175 0.3250 -3.9166 -3.1947 -2.6370
## (Intercept)-Meleagris_gallopavo -3.4430 0.3718 -4.2216 -3.4284 -2.7646
## (Intercept)-Sciurus_carolinensis -2.4212 0.2637 -2.9588 -2.4071 -1.9366
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5250
## (Intercept)-Canis_latrans 1.0040 3285
## (Intercept)-Procyon_lotor 1.0041 3987
## (Intercept)-Dasypus_novemcinctus 1.0003 4894
## (Intercept)-Lynx_rufus 1.0011 934
## (Intercept)-Didelphis_virginiana 1.0025 3525
## (Intercept)-Sylvilagus_floridanus 1.0025 1019
## (Intercept)-Meleagris_gallopavo 1.0127 956
## (Intercept)-Sciurus_carolinensis 1.0013 3388
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1873
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5569 0.6530 -1.8102 -0.5743 0.8169 1.0006 3607
## Avg_Cogongrass_Cover -0.5971 0.3811 -1.3566 -0.5928 0.1450 1.0001 1567
## I(Avg_Cogongrass_Cover^2) 0.8518 0.3969 0.2049 0.8132 1.7727 1.0086 1199
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6415 3.2435 0.5756 2.6881 12.5348 1.0007 2457
## Avg_Cogongrass_Cover 0.3798 0.5196 0.0398 0.2274 1.6517 1.0079 2608
## I(Avg_Cogongrass_Cover^2) 0.6848 1.7406 0.0409 0.2769 3.5941 1.1373 550
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4812 0.5821 0.043 0.2903 1.9908 1.0195 520
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2173 0.4198 -3.0193 -2.2325 -1.3406 1.0016 5445
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6185 1.1149 0.5287 1.33 4.399 1.0053 4588
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8445 1.4366 0.4256 2.6783
## (Intercept)-Canis_latrans -0.4663 0.6794 -1.8119 -0.4737
## (Intercept)-Procyon_lotor -0.1367 0.6394 -1.4454 -0.1255
## (Intercept)-Dasypus_novemcinctus -1.2994 0.6148 -2.5922 -1.2819
## (Intercept)-Lynx_rufus -1.1194 0.9022 -2.8051 -1.1424
## (Intercept)-Didelphis_virginiana -1.8766 0.6892 -3.2865 -1.8655
## (Intercept)-Sylvilagus_floridanus -0.9295 0.7812 -2.4061 -0.9492
## (Intercept)-Meleagris_gallopavo -0.5309 0.8805 -2.1320 -0.5872
## (Intercept)-Sciurus_carolinensis -2.3184 0.7588 -3.8886 -2.3059
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.6059 0.6380 -1.8571 -0.5955
## Avg_Cogongrass_Cover-Canis_latrans -0.3713 0.5145 -1.3164 -0.3938
## Avg_Cogongrass_Cover-Procyon_lotor -0.5145 0.5087 -1.4856 -0.5164
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4628 0.4749 -1.3762 -0.4629
## Avg_Cogongrass_Cover-Lynx_rufus -0.5381 0.5582 -1.6443 -0.5334
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3720 0.5291 -1.3661 -0.3839
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0225 0.6210 -2.4197 -0.9617
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8524 0.6062 -2.1691 -0.8050
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7006 0.5328 -1.8111 -0.6800
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.2446 0.9940 0.0561 1.0419
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2465 0.8135 0.1924 1.0489
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1103 0.7413 0.1600 0.9563
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6776 0.3554 0.0010 0.6644
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1954 0.6404 0.2998 1.0969
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5222 0.4073 -0.2676 0.5169
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7569 0.6058 -0.1217 0.6781
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2097 0.7010 -1.2917 0.2380
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9226 0.4024 0.1971 0.8961
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.0597 1.0034 1379
## (Intercept)-Canis_latrans 0.8347 1.0038 2715
## (Intercept)-Procyon_lotor 1.0999 1.0056 2540
## (Intercept)-Dasypus_novemcinctus -0.1433 1.0002 3256
## (Intercept)-Lynx_rufus 0.7095 1.0078 1064
## (Intercept)-Didelphis_virginiana -0.5654 1.0006 3378
## (Intercept)-Sylvilagus_floridanus 0.6829 1.0041 2106
## (Intercept)-Meleagris_gallopavo 1.4637 1.0137 1376
## (Intercept)-Sciurus_carolinensis -0.9034 1.0029 2394
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6763 1.0011 2861
## Avg_Cogongrass_Cover-Canis_latrans 0.6964 1.0021 2635
## Avg_Cogongrass_Cover-Procyon_lotor 0.5011 1.0002 2583
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4832 1.0002 2778
## Avg_Cogongrass_Cover-Lynx_rufus 0.5631 1.0027 2448
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7611 1.0000 2223
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0125 1.0005 2143
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2239 1.0010 2023
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2962 1.0054 2271
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.5863 1.0382 594
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4003 1.0088 1000
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.1674 1.0190 776
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4073 0.9999 2793
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7297 1.0343 1005
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3531 1.0012 2590
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.0914 1.0407 754
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5426 1.0065 790
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7984 1.0047 2347
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0041 0.0594 -0.1101 0.0037 0.1200
## (Intercept)-Canis_latrans -2.6287 0.1705 -2.9735 -2.6251 -2.3104
## (Intercept)-Procyon_lotor -2.2721 0.1312 -2.5331 -2.2710 -2.0180
## (Intercept)-Dasypus_novemcinctus -1.5749 0.1330 -1.8377 -1.5734 -1.3214
## (Intercept)-Lynx_rufus -3.4086 0.3223 -4.0693 -3.3899 -2.8232
## (Intercept)-Didelphis_virginiana -2.3195 0.2545 -2.8647 -2.3071 -1.8594
## (Intercept)-Sylvilagus_floridanus -3.2138 0.3196 -3.8875 -3.1929 -2.6387
## (Intercept)-Meleagris_gallopavo -3.4423 0.3785 -4.2274 -3.4275 -2.7485
## (Intercept)-Sciurus_carolinensis -2.4255 0.2641 -2.9963 -2.4118 -1.9416
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0031 5138
## (Intercept)-Canis_latrans 1.0010 2847
## (Intercept)-Procyon_lotor 1.0015 4073
## (Intercept)-Dasypus_novemcinctus 1.0010 5250
## (Intercept)-Lynx_rufus 1.0032 1232
## (Intercept)-Didelphis_virginiana 1.0004 3316
## (Intercept)-Sylvilagus_floridanus 1.0037 1308
## (Intercept)-Meleagris_gallopavo 1.0146 794
## (Intercept)-Sciurus_carolinensis 1.0007 3548
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2918
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7420 1.1155 -2.8504 -0.7946 1.6237 1.0021 2423
## Cogon_Patch_Size 0.1004 0.6957 -1.3100 0.1080 1.4971 1.0048 1326
## Veg_shannon_index 0.8503 0.4608 -0.0142 0.8464 1.8022 1.0001 1003
## total_shrub_cover -0.6110 0.5581 -1.7966 -0.5915 0.4340 1.0060 2716
## Avg_Cogongrass_Cover 0.0234 0.9340 -1.8380 0.0143 1.9045 1.0281 590
## Tree_Density -1.9081 0.7911 -3.5011 -1.9008 -0.3388 1.0038 1021
## Avg_Canopy_Cover 1.6555 0.6436 0.4254 1.6428 2.9492 1.0012 1999
## I(Avg_Cogongrass_Cover^2) 1.4205 0.6059 0.3177 1.3871 2.6943 1.0116 1191
## avg_veg_height -0.1031 0.4962 -1.0975 -0.1078 0.8459 1.0228 820
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.9395 19.4275 2.9366 13.4128 69.0445 1.0054 778
## Cogon_Patch_Size 3.0416 4.6689 0.1253 1.6704 14.5673 1.0297 889
## Veg_shannon_index 0.6118 0.9370 0.0426 0.3096 3.0719 1.0048 1418
## total_shrub_cover 1.9894 2.9224 0.0985 1.1232 9.3067 1.0181 1203
## Avg_Cogongrass_Cover 1.1220 2.0149 0.0473 0.4921 5.8539 1.0287 877
## Tree_Density 3.8958 7.5567 0.0674 1.4379 23.1936 1.0163 480
## Avg_Canopy_Cover 2.8282 4.3173 0.1461 1.5635 13.4171 1.0034 579
## I(Avg_Cogongrass_Cover^2) 1.9944 6.2402 0.0547 0.6557 10.8221 1.2831 270
## avg_veg_height 0.4710 0.6697 0.0419 0.2613 2.1533 1.0114 2497
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7391 2.5838 0.0562 0.8573 9.1804 1.0233 197
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2286 0.4313 -3.037 -2.2491 -1.3459 1.0017 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7164 1.2657 0.5685 1.4131 4.6759 1.004 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.1179 3.3737 2.0776 6.5427
## (Intercept)-Canis_latrans -0.9638 1.2622 -3.5424 -0.9601
## (Intercept)-Procyon_lotor -0.4095 1.1100 -2.7978 -0.3479
## (Intercept)-Dasypus_novemcinctus -2.7237 1.2942 -5.5446 -2.5855
## (Intercept)-Lynx_rufus -0.1243 2.7928 -4.1108 -0.5503
## (Intercept)-Didelphis_virginiana -4.1369 1.4107 -7.2353 -4.0504
## (Intercept)-Sylvilagus_floridanus -2.1701 1.4629 -5.2004 -2.1227
## (Intercept)-Meleagris_gallopavo -1.7803 1.5330 -4.9666 -1.7674
## (Intercept)-Sciurus_carolinensis -4.8679 1.6162 -8.5154 -4.6692
## Cogon_Patch_Size-Odocoileus_virginianus 0.1564 1.3757 -2.3558 0.0636
## Cogon_Patch_Size-Canis_latrans 1.5986 1.3399 -0.2944 1.3663
## Cogon_Patch_Size-Procyon_lotor -0.3223 0.8601 -1.8827 -0.3454
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1748 0.7199 -1.6462 -0.1366
## Cogon_Patch_Size-Lynx_rufus -0.0954 1.5015 -3.1023 -0.1050
## Cogon_Patch_Size-Didelphis_virginiana 1.6643 1.0235 -0.0151 1.5532
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1868 1.5594 -4.9157 -0.9151
## Cogon_Patch_Size-Meleagris_gallopavo 0.3495 1.2451 -1.7152 0.2207
## Cogon_Patch_Size-Sciurus_carolinensis -0.9943 1.3219 -4.3918 -0.7579
## Veg_shannon_index-Odocoileus_virginianus 0.6962 0.8264 -1.0733 0.7188
## Veg_shannon_index-Canis_latrans 1.2076 0.6659 0.1085 1.1286
## Veg_shannon_index-Procyon_lotor 1.0515 0.5959 -0.0118 1.0131
## Veg_shannon_index-Dasypus_novemcinctus 0.6274 0.5457 -0.4580 0.6271
## Veg_shannon_index-Lynx_rufus 0.8544 0.8563 -0.7831 0.8504
## Veg_shannon_index-Didelphis_virginiana 0.9931 0.6413 -0.2149 0.9635
## Veg_shannon_index-Sylvilagus_floridanus 0.9449 0.6746 -0.3243 0.9169
## Veg_shannon_index-Meleagris_gallopavo 1.1140 0.7343 -0.1866 1.0436
## Veg_shannon_index-Sciurus_carolinensis 0.3638 0.7447 -1.3122 0.4233
## total_shrub_cover-Odocoileus_virginianus -0.1826 1.1004 -2.3701 -0.2292
## total_shrub_cover-Canis_latrans 0.0345 0.6963 -1.2038 -0.0147
## total_shrub_cover-Procyon_lotor -1.1806 0.6787 -2.6682 -1.1280
## total_shrub_cover-Dasypus_novemcinctus 0.0821 0.5767 -1.0022 0.0675
## total_shrub_cover-Lynx_rufus -1.4299 1.2715 -4.4652 -1.2311
## total_shrub_cover-Didelphis_virginiana -0.6991 0.7718 -2.3299 -0.6571
## total_shrub_cover-Sylvilagus_floridanus -0.3531 0.9305 -2.3131 -0.3400
## total_shrub_cover-Meleagris_gallopavo -2.2892 1.3975 -5.5318 -2.0828
## total_shrub_cover-Sciurus_carolinensis -0.0087 0.7816 -1.4653 -0.0449
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0156 1.3169 -2.6942 0.0106
## Avg_Cogongrass_Cover-Canis_latrans 0.0035 1.1523 -2.2757 -0.0007
## Avg_Cogongrass_Cover-Procyon_lotor 0.1503 1.1355 -2.0419 0.1227
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5784 1.2277 -1.6273 0.4867
## Avg_Cogongrass_Cover-Lynx_rufus 0.1427 1.2052 -2.2337 0.1134
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1579 1.1712 -2.1250 0.1475
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5336 1.3212 -3.4022 -0.4282
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2910 1.3106 -3.0512 -0.2258
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0285 1.1812 -2.2779 0.0268
## Tree_Density-Odocoileus_virginianus -0.8359 1.5014 -2.9809 -1.0827
## Tree_Density-Canis_latrans -2.7970 1.4504 -6.4512 -2.5240
## Tree_Density-Procyon_lotor -1.8276 0.9802 -3.8588 -1.7947
## Tree_Density-Dasypus_novemcinctus -3.7306 2.0654 -8.9626 -3.2281
## Tree_Density-Lynx_rufus -0.7386 1.6933 -3.3100 -1.0054
## Tree_Density-Didelphis_virginiana -2.3362 1.2208 -5.3779 -2.1538
## Tree_Density-Sylvilagus_floridanus -2.5739 1.5416 -6.4033 -2.3326
## Tree_Density-Meleagris_gallopavo -2.0821 1.3429 -4.9755 -2.0288
## Tree_Density-Sciurus_carolinensis -2.7178 1.5801 -6.6130 -2.4193
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0982 1.3950 -1.8965 1.1656
## Avg_Canopy_Cover-Canis_latrans 0.1847 0.7410 -1.3435 0.1945
## Avg_Canopy_Cover-Procyon_lotor 1.6130 0.7474 0.2481 1.5732
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9482 0.7925 0.6599 1.8591
## Avg_Canopy_Cover-Lynx_rufus 1.1711 1.3828 -1.3820 1.1138
## Avg_Canopy_Cover-Didelphis_virginiana 2.6112 1.0170 1.0596 2.4690
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3583 1.7338 0.9833 3.0189
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2982 1.2978 0.4767 2.0361
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2533 0.9795 0.8246 2.1082
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9781 1.8404 -0.1011 1.6690
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1084 1.1659 0.5845 1.8684
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9021 1.0358 0.4230 1.7332
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4411 0.7715 0.1647 1.3697
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1649 1.2724 0.4522 1.9275
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0160 0.6878 -0.4023 1.0140
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1325 0.8638 -0.4574 1.0937
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3154 1.3870 -2.8251 0.5354
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6719 0.7745 0.3892 1.5808
## avg_veg_height-Odocoileus_virginianus -0.1164 0.7927 -1.7170 -0.1128
## avg_veg_height-Canis_latrans -0.3648 0.6345 -1.6809 -0.3311
## avg_veg_height-Procyon_lotor 0.1045 0.6048 -1.0616 0.0966
## avg_veg_height-Dasypus_novemcinctus 0.1483 0.6084 -1.0049 0.1489
## avg_veg_height-Lynx_rufus -0.2108 0.7868 -1.8876 -0.1999
## avg_veg_height-Didelphis_virginiana -0.2259 0.6810 -1.6393 -0.2114
## avg_veg_height-Sylvilagus_floridanus -0.2626 0.7023 -1.7214 -0.2367
## avg_veg_height-Meleagris_gallopavo -0.1895 0.7628 -1.8061 -0.1679
## avg_veg_height-Sciurus_carolinensis 0.1325 0.6594 -1.1118 0.1078
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.3206 1.0099 524
## (Intercept)-Canis_latrans 1.5151 1.0348 1284
## (Intercept)-Procyon_lotor 1.6137 1.0020 1435
## (Intercept)-Dasypus_novemcinctus -0.6950 1.0068 636
## (Intercept)-Lynx_rufus 7.2667 1.0115 275
## (Intercept)-Didelphis_virginiana -1.6769 1.0001 1134
## (Intercept)-Sylvilagus_floridanus 0.5590 1.0081 933
## (Intercept)-Meleagris_gallopavo 1.2729 1.0001 859
## (Intercept)-Sciurus_carolinensis -2.3371 1.0014 703
## Cogon_Patch_Size-Odocoileus_virginianus 3.0574 1.0095 2140
## Cogon_Patch_Size-Canis_latrans 4.9070 1.0033 1234
## Cogon_Patch_Size-Procyon_lotor 1.3248 1.0121 1140
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1291 1.0077 1144
## Cogon_Patch_Size-Lynx_rufus 3.1163 1.0004 949
## Cogon_Patch_Size-Didelphis_virginiana 3.9644 1.0085 789
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1295 1.0015 797
## Cogon_Patch_Size-Meleagris_gallopavo 3.2912 1.0027 1272
## Cogon_Patch_Size-Sciurus_carolinensis 0.8580 1.0019 978
## Veg_shannon_index-Odocoileus_virginianus 2.2490 1.0003 1861
## Veg_shannon_index-Canis_latrans 2.7267 1.0040 1539
## Veg_shannon_index-Procyon_lotor 2.3659 1.0016 1002
## Veg_shannon_index-Dasypus_novemcinctus 1.6867 1.0018 1965
## Veg_shannon_index-Lynx_rufus 2.5600 1.0019 1451
## Veg_shannon_index-Didelphis_virginiana 2.3559 0.9999 2035
## Veg_shannon_index-Sylvilagus_floridanus 2.3954 1.0041 1576
## Veg_shannon_index-Meleagris_gallopavo 2.7203 1.0031 1636
## Veg_shannon_index-Sciurus_carolinensis 1.6534 1.0021 1540
## total_shrub_cover-Odocoileus_virginianus 2.2732 1.0028 2569
## total_shrub_cover-Canis_latrans 1.5448 1.0072 2519
## total_shrub_cover-Procyon_lotor 0.0292 1.0078 2141
## total_shrub_cover-Dasypus_novemcinctus 1.2350 1.0053 3215
## total_shrub_cover-Lynx_rufus 0.5035 1.0064 908
## total_shrub_cover-Didelphis_virginiana 0.7403 1.0012 3070
## total_shrub_cover-Sylvilagus_floridanus 1.4652 1.0050 1796
## total_shrub_cover-Meleagris_gallopavo -0.2388 1.0052 819
## total_shrub_cover-Sciurus_carolinensis 1.5882 1.0009 2933
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.5658 1.0130 933
## Avg_Cogongrass_Cover-Canis_latrans 2.2293 1.0235 872
## Avg_Cogongrass_Cover-Procyon_lotor 2.3990 1.0119 802
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.1440 1.0183 609
## Avg_Cogongrass_Cover-Lynx_rufus 2.6615 1.0111 943
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.5841 1.0133 882
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7887 1.0240 959
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0962 1.0159 909
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4121 1.0132 836
## Tree_Density-Odocoileus_virginianus 2.7076 1.0042 849
## Tree_Density-Canis_latrans -0.6733 1.0168 780
## Tree_Density-Procyon_lotor 0.0043 1.0016 1067
## Tree_Density-Dasypus_novemcinctus -1.1913 1.0026 374
## Tree_Density-Lynx_rufus 3.4519 1.0073 641
## Tree_Density-Didelphis_virginiana -0.4321 1.0042 841
## Tree_Density-Sylvilagus_floridanus -0.2857 1.0044 736
## Tree_Density-Meleagris_gallopavo 0.4584 1.0098 1347
## Tree_Density-Sciurus_carolinensis -0.5043 1.0047 698
## Avg_Canopy_Cover-Odocoileus_virginianus 3.8605 1.0055 1901
## Avg_Canopy_Cover-Canis_latrans 1.6243 1.0118 1654
## Avg_Canopy_Cover-Procyon_lotor 3.2024 1.0005 1085
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7901 1.0017 447
## Avg_Canopy_Cover-Lynx_rufus 4.0075 1.0319 880
## Avg_Canopy_Cover-Didelphis_virginiana 5.0425 1.0041 594
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.7544 1.0046 524
## Avg_Canopy_Cover-Meleagris_gallopavo 5.5527 1.0067 707
## Avg_Canopy_Cover-Sciurus_carolinensis 4.5810 1.0028 758
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 5.9213 1.1263 245
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.0850 1.0157 542
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.5168 1.0017 923
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1767 1.0017 1060
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2889 1.0307 491
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.3938 1.0062 1139
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9872 1.0172 935
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.4489 1.0291 493
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5029 1.0041 1227
## avg_veg_height-Odocoileus_virginianus 1.4651 1.0097 1534
## avg_veg_height-Canis_latrans 0.8216 1.0159 1223
## avg_veg_height-Procyon_lotor 1.2982 1.0236 1431
## avg_veg_height-Dasypus_novemcinctus 1.3774 1.0129 1444
## avg_veg_height-Lynx_rufus 1.2741 1.0130 1431
## avg_veg_height-Didelphis_virginiana 1.0471 1.0138 1400
## avg_veg_height-Sylvilagus_floridanus 1.0565 1.0167 1298
## avg_veg_height-Meleagris_gallopavo 1.2476 1.0093 1324
## avg_veg_height-Sciurus_carolinensis 1.5109 1.0073 1618
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0030 0.0591 -0.1107 0.0020 0.1195
## (Intercept)-Canis_latrans -2.6069 0.1722 -2.9589 -2.6040 -2.2768
## (Intercept)-Procyon_lotor -2.2668 0.1292 -2.5241 -2.2637 -2.0205
## (Intercept)-Dasypus_novemcinctus -1.5703 0.1333 -1.8352 -1.5682 -1.3144
## (Intercept)-Lynx_rufus -3.6867 0.3285 -4.3491 -3.6779 -3.0432
## (Intercept)-Didelphis_virginiana -2.2804 0.2338 -2.7538 -2.2708 -1.8434
## (Intercept)-Sylvilagus_floridanus -3.1921 0.2828 -3.7802 -3.1805 -2.6620
## (Intercept)-Meleagris_gallopavo -3.3898 0.3157 -4.0434 -3.3808 -2.7938
## (Intercept)-Sciurus_carolinensis -2.4249 0.2616 -2.9815 -2.4126 -1.9448
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0022 2428
## (Intercept)-Procyon_lotor 1.0006 2896
## (Intercept)-Dasypus_novemcinctus 1.0043 5250
## (Intercept)-Lynx_rufus 1.0071 693
## (Intercept)-Didelphis_virginiana 1.0014 4358
## (Intercept)-Sylvilagus_floridanus 1.0013 1452
## (Intercept)-Meleagris_gallopavo 1.0139 1141
## (Intercept)-Sciurus_carolinensis 1.0057 3770
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7815
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2761 0.6444 -1.0009 0.2638 1.6303 1.0029 2911
## Avg_Cogongrass_Cover 0.2552 0.2735 -0.3033 0.2612 0.7768 1.0028 2271
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8823 4.2017 0.6233 2.7469 14.1415 1.0042 1327
## Avg_Cogongrass_Cover 0.3101 0.3886 0.0353 0.1893 1.3009 1.0313 2442
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5482 0.6495 0.0441 0.3326 2.2911 1.0137 438
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3666 0.4811 -3.2247 -2.3946 -1.3108 1.0003 5250
## shrub_cover 0.1586 0.2872 -0.4021 0.1533 0.7366 1.0047 3434
## veg_height -0.0093 0.1749 -0.3710 -0.0075 0.3315 1.0009 3198
## week -0.0456 0.1256 -0.3046 -0.0414 0.1899 1.0016 3755
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1660 1.5445 0.6933 1.7378 6.1613 1.0092 3236
## shrub_cover 0.6549 0.5484 0.1439 0.5075 2.0554 1.0047 1524
## veg_height 0.2367 0.1923 0.0606 0.1848 0.7280 1.0047 3601
## week 0.1029 0.0820 0.0260 0.0793 0.3309 1.0162 3867
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6850 1.4785 1.4350 3.4461
## (Intercept)-Canis_latrans 0.5226 0.6014 -0.6308 0.5000
## (Intercept)-Procyon_lotor 0.6286 0.5902 -0.5697 0.6389
## (Intercept)-Dasypus_novemcinctus -0.5623 0.5509 -1.6719 -0.5573
## (Intercept)-Lynx_rufus 0.3616 1.1571 -1.3442 0.1873
## (Intercept)-Didelphis_virginiana -1.1902 0.6233 -2.4820 -1.1741
## (Intercept)-Sylvilagus_floridanus -0.2511 0.6893 -1.5100 -0.2784
## (Intercept)-Meleagris_gallopavo 1.0053 1.3984 -1.0629 0.7712
## (Intercept)-Sciurus_carolinensis -1.2400 0.6343 -2.5444 -1.2305
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2308 0.4988 -0.7856 0.2257
## Avg_Cogongrass_Cover-Canis_latrans 0.4780 0.4046 -0.2059 0.4443
## Avg_Cogongrass_Cover-Procyon_lotor 0.2554 0.3488 -0.4299 0.2529
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3728 0.3224 -0.2473 0.3631
## Avg_Cogongrass_Cover-Lynx_rufus 0.4587 0.4456 -0.3333 0.4274
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3463 0.3572 -0.3562 0.3380
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1546 0.4575 -1.1517 -0.1189
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0775 0.6526 -1.5895 -0.0149
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3752 0.3608 -0.3117 0.3737
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3519 1.0011 927
## (Intercept)-Canis_latrans 1.8069 1.0011 3367
## (Intercept)-Procyon_lotor 1.7827 1.0008 2625
## (Intercept)-Dasypus_novemcinctus 0.5080 1.0046 3449
## (Intercept)-Lynx_rufus 3.3353 1.0508 470
## (Intercept)-Didelphis_virginiana 0.0054 1.0000 3437
## (Intercept)-Sylvilagus_floridanus 1.1663 1.0030 1721
## (Intercept)-Meleagris_gallopavo 4.5147 1.0101 473
## (Intercept)-Sciurus_carolinensis -0.0245 1.0028 3387
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2446 1.0045 3818
## Avg_Cogongrass_Cover-Canis_latrans 1.4105 0.9999 3535
## Avg_Cogongrass_Cover-Procyon_lotor 0.9593 1.0027 4438
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0368 1.0063 4073
## Avg_Cogongrass_Cover-Lynx_rufus 1.4180 1.0092 2310
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0574 1.0019 3821
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6384 1.0027 2189
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0614 1.0018 1381
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1164 1.0005 3725
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0063 0.0591 -0.1118 0.0066 0.1185
## (Intercept)-Canis_latrans -2.7654 0.1955 -3.1720 -2.7614 -2.4072
## (Intercept)-Procyon_lotor -2.2953 0.1461 -2.5913 -2.2929 -2.0293
## (Intercept)-Dasypus_novemcinctus -1.7284 0.1602 -2.0480 -1.7259 -1.4249
## (Intercept)-Lynx_rufus -3.7112 0.3819 -4.4647 -3.7114 -2.9974
## (Intercept)-Didelphis_virginiana -2.5483 0.2815 -3.1294 -2.5372 -2.0328
## (Intercept)-Sylvilagus_floridanus -3.2111 0.3156 -3.8927 -3.1954 -2.6342
## (Intercept)-Meleagris_gallopavo -4.3159 0.4957 -5.2971 -4.3095 -3.3288
## (Intercept)-Sciurus_carolinensis -2.5934 0.3134 -3.2376 -2.5768 -2.0271
## shrub_cover-Odocoileus_virginianus -0.0551 0.0646 -0.1800 -0.0551 0.0754
## shrub_cover-Canis_latrans -0.2924 0.2181 -0.7271 -0.2915 0.1295
## shrub_cover-Procyon_lotor 0.2469 0.1624 -0.0827 0.2503 0.5535
## shrub_cover-Dasypus_novemcinctus 0.8328 0.2993 0.2633 0.8225 1.4464
## shrub_cover-Lynx_rufus -0.2931 0.3550 -1.0086 -0.2914 0.4042
## shrub_cover-Didelphis_virginiana 0.9319 0.3633 0.2760 0.9159 1.6892
## shrub_cover-Sylvilagus_floridanus 0.2326 0.4222 -0.5599 0.2117 1.0836
## shrub_cover-Meleagris_gallopavo -0.9002 0.4181 -1.7467 -0.8936 -0.1020
## shrub_cover-Sciurus_carolinensis 0.8021 0.4025 0.0167 0.7943 1.5924
## veg_height-Odocoileus_virginianus -0.2977 0.0649 -0.4273 -0.2966 -0.1700
## veg_height-Canis_latrans -0.6090 0.1910 -1.0019 -0.6037 -0.2468
## veg_height-Procyon_lotor 0.3337 0.1235 0.0899 0.3337 0.5777
## veg_height-Dasypus_novemcinctus 0.2307 0.1348 -0.0351 0.2285 0.4957
## veg_height-Lynx_rufus -0.0003 0.2462 -0.4898 0.0028 0.4697
## veg_height-Didelphis_virginiana 0.4044 0.2362 -0.0375 0.3971 0.8738
## veg_height-Sylvilagus_floridanus 0.1217 0.2465 -0.3653 0.1186 0.6072
## veg_height-Meleagris_gallopavo -0.3149 0.3640 -1.0206 -0.3185 0.4100
## veg_height-Sciurus_carolinensis 0.0489 0.2106 -0.3414 0.0420 0.4721
## week-Odocoileus_virginianus 0.2115 0.0615 0.0911 0.2118 0.3327
## week-Canis_latrans 0.0741 0.1318 -0.1946 0.0786 0.3185
## week-Procyon_lotor -0.0480 0.1173 -0.2835 -0.0461 0.1797
## week-Dasypus_novemcinctus -0.1601 0.1369 -0.4390 -0.1535 0.0912
## week-Lynx_rufus -0.0267 0.1939 -0.4327 -0.0193 0.3216
## week-Didelphis_virginiana -0.2046 0.2190 -0.6645 -0.1890 0.1826
## week-Sylvilagus_floridanus -0.1458 0.2026 -0.5799 -0.1316 0.2089
## week-Meleagris_gallopavo -0.2661 0.2422 -0.7918 -0.2477 0.1558
## week-Sciurus_carolinensis 0.1413 0.1810 -0.2188 0.1420 0.4934
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5553
## (Intercept)-Canis_latrans 1.0001 2168
## (Intercept)-Procyon_lotor 1.0050 3619
## (Intercept)-Dasypus_novemcinctus 1.0011 4184
## (Intercept)-Lynx_rufus 1.0034 813
## (Intercept)-Didelphis_virginiana 1.0000 2591
## (Intercept)-Sylvilagus_floridanus 1.0029 1373
## (Intercept)-Meleagris_gallopavo 1.0088 459
## (Intercept)-Sciurus_carolinensis 1.0007 3106
## shrub_cover-Odocoileus_virginianus 1.0024 4991
## shrub_cover-Canis_latrans 1.0026 2764
## shrub_cover-Procyon_lotor 1.0024 3753
## shrub_cover-Dasypus_novemcinctus 1.0025 3391
## shrub_cover-Lynx_rufus 1.0025 1279
## shrub_cover-Didelphis_virginiana 1.0043 2013
## shrub_cover-Sylvilagus_floridanus 1.0017 1573
## shrub_cover-Meleagris_gallopavo 1.0112 509
## shrub_cover-Sciurus_carolinensis 1.0003 2672
## veg_height-Odocoileus_virginianus 1.0029 4281
## veg_height-Canis_latrans 1.0039 2285
## veg_height-Procyon_lotor 1.0004 4221
## veg_height-Dasypus_novemcinctus 1.0044 4953
## veg_height-Lynx_rufus 1.0025 2455
## veg_height-Didelphis_virginiana 1.0017 3784
## veg_height-Sylvilagus_floridanus 1.0066 2295
## veg_height-Meleagris_gallopavo 1.0051 1210
## veg_height-Sciurus_carolinensis 1.0018 3617
## week-Odocoileus_virginianus 1.0012 5808
## week-Canis_latrans 1.0023 3510
## week-Procyon_lotor 1.0054 4474
## week-Dasypus_novemcinctus 1.0029 4830
## week-Lynx_rufus 1.0007 2712
## week-Didelphis_virginiana 1.0011 4088
## week-Sylvilagus_floridanus 1.0018 2899
## week-Meleagris_gallopavo 1.0025 1847
## week-Sciurus_carolinensis 1.0012 4641
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.188
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0246 0.5994 -1.1441 0.0065 1.3262 1.0021 3671
## Avg_Cogongrass_Cover 0.2218 0.2495 -0.2886 0.2203 0.7182 0.9999 2926
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3580 3.1008 0.5670 2.4649 11.6374 1.0024 2204
## Avg_Cogongrass_Cover 0.2801 0.3381 0.0365 0.1793 1.1835 1.0094 2814
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6716 0.7365 0.0535 0.4423 2.6774 1.0164 511
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2249 0.4181 -3.0154 -2.2392 -1.3238 1.0011 4943
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6222 1.1591 0.5396 1.3243 4.5778 1.019 4606
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3956 1.3301 1.1413 3.2419
## (Intercept)-Canis_latrans 0.3189 0.5845 -0.7886 0.3045
## (Intercept)-Procyon_lotor 0.5571 0.5966 -0.6697 0.5796
## (Intercept)-Dasypus_novemcinctus -0.6525 0.5575 -1.7629 -0.6461
## (Intercept)-Lynx_rufus 0.0126 0.9529 -1.5254 -0.0894
## (Intercept)-Didelphis_virginiana -1.3251 0.6299 -2.6097 -1.3063
## (Intercept)-Sylvilagus_floridanus -0.2989 0.7100 -1.5718 -0.3369
## (Intercept)-Meleagris_gallopavo -0.2541 0.9402 -1.7289 -0.3395
## (Intercept)-Sciurus_carolinensis -1.3775 0.6394 -2.6647 -1.3664
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1987 0.4816 -0.7477 0.1897
## Avg_Cogongrass_Cover-Canis_latrans 0.3734 0.3550 -0.2668 0.3542
## Avg_Cogongrass_Cover-Procyon_lotor 0.2778 0.3534 -0.3887 0.2604
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3416 0.3163 -0.2647 0.3363
## Avg_Cogongrass_Cover-Lynx_rufus 0.4712 0.4029 -0.2428 0.4385
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3420 0.3457 -0.3218 0.3369
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1387 0.4144 -1.0393 -0.1101
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1777 0.4999 -1.2541 -0.1398
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3572 0.3428 -0.2896 0.3440
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.4422 1.0014 1192
## (Intercept)-Canis_latrans 1.4738 1.0013 3566
## (Intercept)-Procyon_lotor 1.6843 1.0016 2446
## (Intercept)-Dasypus_novemcinctus 0.4338 1.0027 3539
## (Intercept)-Lynx_rufus 2.2075 1.0112 910
## (Intercept)-Didelphis_virginiana -0.1121 1.0001 3219
## (Intercept)-Sylvilagus_floridanus 1.1854 1.0131 1657
## (Intercept)-Meleagris_gallopavo 1.7616 1.0049 737
## (Intercept)-Sciurus_carolinensis -0.1747 1.0061 2810
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2080 1.0008 3585
## Avg_Cogongrass_Cover-Canis_latrans 1.1414 1.0022 4218
## Avg_Cogongrass_Cover-Procyon_lotor 1.0313 1.0013 4162
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9947 1.0049 4785
## Avg_Cogongrass_Cover-Lynx_rufus 1.3497 1.0000 3590
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0479 1.0040 4201
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5886 1.0013 2734
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7073 1.0042 2580
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0721 1.0006 4091
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0044 0.0593 -0.1135 0.0044 0.1190
## (Intercept)-Canis_latrans -2.6133 0.1781 -2.9762 -2.6074 -2.2813
## (Intercept)-Procyon_lotor -2.2688 0.1320 -2.5318 -2.2643 -2.0222
## (Intercept)-Dasypus_novemcinctus -1.5729 0.1317 -1.8388 -1.5715 -1.3221
## (Intercept)-Lynx_rufus -3.5049 0.3362 -4.1843 -3.4922 -2.8779
## (Intercept)-Didelphis_virginiana -2.2998 0.2484 -2.8122 -2.2905 -1.8438
## (Intercept)-Sylvilagus_floridanus -3.1634 0.3090 -3.8286 -3.1442 -2.6298
## (Intercept)-Meleagris_gallopavo -3.4288 0.3737 -4.2119 -3.4021 -2.7643
## (Intercept)-Sciurus_carolinensis -2.4276 0.2677 -2.9930 -2.4152 -1.9396
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0056 2924
## (Intercept)-Procyon_lotor 1.0008 4040
## (Intercept)-Dasypus_novemcinctus 1.0038 5250
## (Intercept)-Lynx_rufus 1.0048 961
## (Intercept)-Didelphis_virginiana 1.0022 4291
## (Intercept)-Sylvilagus_floridanus 1.0101 1326
## (Intercept)-Meleagris_gallopavo 1.0043 969
## (Intercept)-Sciurus_carolinensis 1.0001 3445
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5625
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0392 0.6146 -1.1319 0.0152 1.3306 1.0061 3074
## Avg_Cogongrass_Cover 0.2208 0.2581 -0.2923 0.2234 0.7265 1.0003 3137
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0512 11.0865 0.5509 2.5630 13.2442 1.4755 341
## Avg_Cogongrass_Cover 0.2898 0.3614 0.0388 0.1876 1.1884 1.0021 3490
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6156 0.6317 0.0533 0.4142 2.36 1.0033 546
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2245 0.4181 -3.0056 -2.2384 -1.3564 1.0015 5250
## week -0.0483 0.1251 -0.3163 -0.0434 0.1807 0.9999 3905
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6577 1.1103 0.5375 1.3593 4.6882 1.0001 4263
## week 0.0993 0.0804 0.0258 0.0769 0.3125 1.0027 4382
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5496 1.6732 1.2188 3.3190
## (Intercept)-Canis_latrans 0.3129 0.5971 -0.8452 0.2997
## (Intercept)-Procyon_lotor 0.5698 0.5753 -0.5705 0.5687
## (Intercept)-Dasypus_novemcinctus -0.6440 0.5541 -1.7735 -0.6357
## (Intercept)-Lynx_rufus 0.1366 1.6699 -1.4901 -0.0520
## (Intercept)-Didelphis_virginiana -1.3329 0.6235 -2.6043 -1.3115
## (Intercept)-Sylvilagus_floridanus -0.2793 0.7355 -1.5800 -0.3180
## (Intercept)-Meleagris_gallopavo -0.2904 0.8963 -1.7089 -0.3546
## (Intercept)-Sciurus_carolinensis -1.3795 0.6368 -2.6786 -1.3624
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1929 0.4801 -0.7662 0.1947
## Avg_Cogongrass_Cover-Canis_latrans 0.3883 0.3711 -0.2728 0.3624
## Avg_Cogongrass_Cover-Procyon_lotor 0.2676 0.3584 -0.3850 0.2512
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3561 0.3229 -0.2548 0.3437
## Avg_Cogongrass_Cover-Lynx_rufus 0.4673 0.4129 -0.2769 0.4386
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3520 0.3521 -0.3420 0.3389
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1546 0.4287 -1.0839 -0.1223
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1967 0.5014 -1.3045 -0.1467
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3565 0.3504 -0.3197 0.3462
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9046 1.0995 389
## (Intercept)-Canis_latrans 1.5125 1.0020 3452
## (Intercept)-Procyon_lotor 1.7098 1.0012 3221
## (Intercept)-Dasypus_novemcinctus 0.4364 1.0044 3701
## (Intercept)-Lynx_rufus 2.3530 1.3530 203
## (Intercept)-Didelphis_virginiana -0.1409 1.0030 2821
## (Intercept)-Sylvilagus_floridanus 1.3372 1.0099 1440
## (Intercept)-Meleagris_gallopavo 1.4737 1.0316 989
## (Intercept)-Sciurus_carolinensis -0.1572 1.0018 3217
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1535 1.0003 4157
## Avg_Cogongrass_Cover-Canis_latrans 1.1937 1.0010 4482
## Avg_Cogongrass_Cover-Procyon_lotor 1.0074 1.0005 3779
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0406 1.0006 4663
## Avg_Cogongrass_Cover-Lynx_rufus 1.3927 1.0019 3495
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0714 1.0009 3988
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6078 1.0081 2810
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7043 1.0029 2410
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0740 1.0000 4121
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0063 0.0596 -0.1133 0.0059 0.1201
## (Intercept)-Canis_latrans -2.6170 0.1731 -2.9665 -2.6139 -2.2936
## (Intercept)-Procyon_lotor -2.2728 0.1308 -2.5350 -2.2672 -2.0262
## (Intercept)-Dasypus_novemcinctus -1.5880 0.1354 -1.8554 -1.5855 -1.3322
## (Intercept)-Lynx_rufus -3.5362 0.3332 -4.2442 -3.5234 -2.9229
## (Intercept)-Didelphis_virginiana -2.3272 0.2513 -2.8483 -2.3179 -1.8714
## (Intercept)-Sylvilagus_floridanus -3.2184 0.3177 -3.8765 -3.1946 -2.6295
## (Intercept)-Meleagris_gallopavo -3.4543 0.3694 -4.2120 -3.4423 -2.7830
## (Intercept)-Sciurus_carolinensis -2.4485 0.2681 -3.0155 -2.4323 -1.9747
## week-Odocoileus_virginianus 0.2068 0.0607 0.0901 0.2059 0.3257
## week-Canis_latrans 0.0706 0.1325 -0.1972 0.0746 0.3270
## week-Procyon_lotor -0.0499 0.1179 -0.2935 -0.0472 0.1765
## week-Dasypus_novemcinctus -0.1611 0.1340 -0.4394 -0.1533 0.0829
## week-Lynx_rufus -0.0282 0.1924 -0.4236 -0.0227 0.3349
## week-Didelphis_virginiana -0.1992 0.2073 -0.6494 -0.1878 0.1744
## week-Sylvilagus_floridanus -0.1447 0.2010 -0.5736 -0.1303 0.2110
## week-Meleagris_gallopavo -0.2566 0.2406 -0.7939 -0.2361 0.1609
## week-Sciurus_carolinensis 0.1385 0.1780 -0.2243 0.1370 0.4825
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0008 3236
## (Intercept)-Procyon_lotor 1.0003 4437
## (Intercept)-Dasypus_novemcinctus 1.0022 5250
## (Intercept)-Lynx_rufus 1.0081 1017
## (Intercept)-Didelphis_virginiana 1.0002 3734
## (Intercept)-Sylvilagus_floridanus 1.0129 1227
## (Intercept)-Meleagris_gallopavo 1.0046 911
## (Intercept)-Sciurus_carolinensis 1.0003 3462
## week-Odocoileus_virginianus 1.0012 4693
## week-Canis_latrans 1.0001 5595
## week-Procyon_lotor 1.0001 4348
## week-Dasypus_novemcinctus 1.0004 4657
## week-Lynx_rufus 1.0006 3128
## week-Didelphis_virginiana 1.0003 4387
## week-Sylvilagus_floridanus 1.0009 3257
## week-Meleagris_gallopavo 1.0005 2415
## week-Sciurus_carolinensis 1.0007 4293
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.551
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0957 1.0499 -1.9972 0.0628 2.2552 1.0019 2636
## Cogon_Patch_Size -0.4062 0.5872 -1.5764 -0.4067 0.7752 1.0018 2153
## Veg_shannon_index 0.8396 0.4209 0.0526 0.8280 1.7085 1.0014 1133
## total_shrub_cover -0.4832 0.5073 -1.5400 -0.4650 0.4920 1.0006 2033
## Avg_Cogongrass_Cover 1.9178 0.6655 0.6320 1.9163 3.2437 1.0054 654
## Tree_Density -1.7431 0.6620 -3.0834 -1.7409 -0.4420 1.0110 1003
## Avg_Canopy_Cover 1.6453 0.5819 0.5270 1.6363 2.8416 1.0007 1228
## avg_veg_height -0.4722 0.4535 -1.3723 -0.4694 0.4211 1.0059 972
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.8963 20.7806 2.8215 12.2824 63.7564 1.0563 458
## Cogon_Patch_Size 2.1585 3.1151 0.0973 1.1750 9.6035 1.0128 606
## Veg_shannon_index 0.5600 0.7986 0.0448 0.3119 2.6633 1.0108 1890
## total_shrub_cover 1.5869 2.2292 0.0762 0.8904 7.4047 1.0166 1066
## Avg_Cogongrass_Cover 0.7509 1.1570 0.0488 0.3908 3.6390 1.0167 1602
## Tree_Density 2.3285 4.5516 0.0611 0.8966 13.5231 1.0904 615
## Avg_Canopy_Cover 1.9527 2.6195 0.1089 1.1281 8.7922 1.0060 860
## avg_veg_height 0.4112 0.5193 0.0402 0.2411 1.8333 1.0247 2492
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2846 1.8223 0.0576 0.6786 5.9797 1.0222 271
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2489 0.4349 -3.0733 -2.2655 -1.3373 1.0011 5250
## week -0.0487 0.1275 -0.3112 -0.0440 0.1874 1.0010 3775
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7549 1.1417 0.5880 1.4398 4.8051 1.0123 4205
## week 0.1006 0.0829 0.0257 0.0774 0.3112 1.0075 3939
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.0235 3.6862 3.3023 7.3324
## (Intercept)-Canis_latrans 0.6520 0.9806 -1.1412 0.5914
## (Intercept)-Procyon_lotor 0.8937 0.9113 -0.8840 0.8757
## (Intercept)-Dasypus_novemcinctus -1.4663 0.8907 -3.3749 -1.3878
## (Intercept)-Lynx_rufus 2.0869 2.8164 -1.7127 1.4798
## (Intercept)-Didelphis_virginiana -2.9532 1.0796 -5.3819 -2.8680
## (Intercept)-Sylvilagus_floridanus -1.1575 1.2121 -3.4707 -1.1567
## (Intercept)-Meleagris_gallopavo -1.1643 1.3258 -3.7809 -1.1770
## (Intercept)-Sciurus_carolinensis -3.0767 1.1430 -5.6985 -2.9548
## Cogon_Patch_Size-Odocoileus_virginianus -0.3631 1.2168 -2.7261 -0.4257
## Cogon_Patch_Size-Canis_latrans 0.6886 1.0801 -0.8905 0.5034
## Cogon_Patch_Size-Procyon_lotor -0.7879 0.6847 -2.1833 -0.7735
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6625 0.5978 -1.9247 -0.6303
## Cogon_Patch_Size-Lynx_rufus -0.3950 1.2872 -2.6986 -0.4705
## Cogon_Patch_Size-Didelphis_virginiana 0.8159 0.8508 -0.5907 0.7195
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6064 1.3566 -4.8476 -1.3596
## Cogon_Patch_Size-Meleagris_gallopavo -0.3206 1.0065 -2.1825 -0.3756
## Cogon_Patch_Size-Sciurus_carolinensis -1.4165 1.0885 -4.1183 -1.2206
## Veg_shannon_index-Odocoileus_virginianus 0.7174 0.7716 -0.9481 0.7400
## Veg_shannon_index-Canis_latrans 1.1430 0.5829 0.1547 1.0866
## Veg_shannon_index-Procyon_lotor 1.0620 0.5625 0.0720 1.0242
## Veg_shannon_index-Dasypus_novemcinctus 0.6275 0.4795 -0.3221 0.6309
## Veg_shannon_index-Lynx_rufus 0.7994 0.7727 -0.8435 0.7950
## Veg_shannon_index-Didelphis_virginiana 0.9963 0.6027 -0.1137 0.9639
## Veg_shannon_index-Sylvilagus_floridanus 0.9695 0.6245 -0.1350 0.9316
## Veg_shannon_index-Meleagris_gallopavo 1.1606 0.6803 0.0027 1.0798
## Veg_shannon_index-Sciurus_carolinensis 0.2870 0.6408 -1.1513 0.3439
## total_shrub_cover-Odocoileus_virginianus -0.0325 1.0396 -1.9690 -0.1193
## total_shrub_cover-Canis_latrans 0.2149 0.7091 -0.9503 0.1291
## total_shrub_cover-Procyon_lotor -0.9921 0.6441 -2.3872 -0.9332
## total_shrub_cover-Dasypus_novemcinctus 0.0811 0.5397 -0.9258 0.0677
## total_shrub_cover-Lynx_rufus -1.1069 1.1627 -3.8408 -0.9417
## total_shrub_cover-Didelphis_virginiana -0.5664 0.7106 -2.0650 -0.5247
## total_shrub_cover-Sylvilagus_floridanus -0.2180 0.8291 -1.8175 -0.2293
## total_shrub_cover-Meleagris_gallopavo -1.9844 1.2818 -4.9635 -1.7712
## total_shrub_cover-Sciurus_carolinensis -0.0576 0.6931 -1.3700 -0.0768
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8537 0.9631 -0.0418 1.8383
## Avg_Cogongrass_Cover-Canis_latrans 2.1416 0.8191 0.6356 2.0991
## Avg_Cogongrass_Cover-Procyon_lotor 2.0501 0.8159 0.5185 2.0119
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3374 0.8566 0.8589 2.2648
## Avg_Cogongrass_Cover-Lynx_rufus 2.2093 0.9320 0.5516 2.1419
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0327 0.8016 0.5495 1.9953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4481 0.9000 -0.3830 1.4857
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4765 1.0684 -0.9013 1.5491
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1909 0.8219 0.7110 2.1410
## Tree_Density-Odocoileus_virginianus -0.8779 1.1897 -2.7676 -1.0336
## Tree_Density-Canis_latrans -2.2684 1.0830 -4.8966 -2.1006
## Tree_Density-Procyon_lotor -1.4230 0.7399 -2.8565 -1.4448
## Tree_Density-Dasypus_novemcinctus -3.0188 1.5325 -6.9917 -2.6565
## Tree_Density-Lynx_rufus -0.7715 1.4195 -2.9546 -0.9550
## Tree_Density-Didelphis_virginiana -2.1130 1.0219 -4.5687 -1.9959
## Tree_Density-Sylvilagus_floridanus -2.3078 1.2643 -5.4429 -2.1147
## Tree_Density-Meleagris_gallopavo -2.0672 1.1456 -4.8076 -1.9629
## Tree_Density-Sciurus_carolinensis -2.2535 1.2175 -5.1995 -2.0525
## Avg_Canopy_Cover-Odocoileus_virginianus 1.1667 1.2318 -1.4613 1.2322
## Avg_Canopy_Cover-Canis_latrans 0.3563 0.6691 -0.9778 0.3567
## Avg_Canopy_Cover-Procyon_lotor 1.6287 0.6796 0.4168 1.5869
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8691 0.6550 0.7718 1.8023
## Avg_Canopy_Cover-Lynx_rufus 1.1794 1.2511 -1.2391 1.1558
## Avg_Canopy_Cover-Didelphis_virginiana 2.4859 0.8948 1.1013 2.3673
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9075 1.3381 0.9761 2.6492
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1901 1.0990 0.5848 1.9950
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1401 0.7644 0.8700 2.0591
## avg_veg_height-Odocoileus_virginianus -0.5198 0.7230 -1.9903 -0.5067
## avg_veg_height-Canis_latrans -0.6531 0.5662 -1.8342 -0.6455
## avg_veg_height-Procyon_lotor -0.3696 0.5496 -1.4390 -0.3798
## avg_veg_height-Dasypus_novemcinctus -0.2483 0.5514 -1.2761 -0.2644
## avg_veg_height-Lynx_rufus -0.5421 0.7484 -2.1094 -0.5290
## avg_veg_height-Didelphis_virginiana -0.5651 0.6176 -1.8372 -0.5444
## avg_veg_height-Sylvilagus_floridanus -0.6820 0.6332 -2.0264 -0.6569
## avg_veg_height-Meleagris_gallopavo -0.6168 0.6918 -2.0753 -0.5913
## avg_veg_height-Sciurus_carolinensis -0.1534 0.6237 -1.2804 -0.1801
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.5914 1.0285 290
## (Intercept)-Canis_latrans 2.8351 1.0032 1724
## (Intercept)-Procyon_lotor 2.7490 1.0043 1757
## (Intercept)-Dasypus_novemcinctus 0.0813 1.0138 983
## (Intercept)-Lynx_rufus 9.0910 1.0380 196
## (Intercept)-Didelphis_virginiana -1.0386 1.0014 1436
## (Intercept)-Sylvilagus_floridanus 1.3433 1.0109 1171
## (Intercept)-Meleagris_gallopavo 1.5208 1.0001 1096
## (Intercept)-Sciurus_carolinensis -1.1556 1.0019 1080
## Cogon_Patch_Size-Odocoileus_virginianus 2.2850 1.0029 2316
## Cogon_Patch_Size-Canis_latrans 3.3406 0.9999 1837
## Cogon_Patch_Size-Procyon_lotor 0.5048 1.0033 962
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4226 1.0035 1421
## Cogon_Patch_Size-Lynx_rufus 2.4940 1.0029 1378
## Cogon_Patch_Size-Didelphis_virginiana 2.7049 1.0015 1529
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3136 1.0113 974
## Cogon_Patch_Size-Meleagris_gallopavo 1.9283 1.0018 1789
## Cogon_Patch_Size-Sciurus_carolinensis 0.1274 1.0012 975
## Veg_shannon_index-Odocoileus_virginianus 2.1990 1.0029 2178
## Veg_shannon_index-Canis_latrans 2.4553 1.0029 913
## Veg_shannon_index-Procyon_lotor 2.2424 1.0044 868
## Veg_shannon_index-Dasypus_novemcinctus 1.5496 1.0008 1552
## Veg_shannon_index-Lynx_rufus 2.3459 1.0008 1620
## Veg_shannon_index-Didelphis_virginiana 2.3209 1.0037 2151
## Veg_shannon_index-Sylvilagus_floridanus 2.3145 1.0025 1273
## Veg_shannon_index-Meleagris_gallopavo 2.7199 1.0038 1487
## Veg_shannon_index-Sciurus_carolinensis 1.4026 1.0073 2164
## total_shrub_cover-Odocoileus_virginianus 2.3288 1.0053 2704
## total_shrub_cover-Canis_latrans 1.9017 1.0041 1612
## total_shrub_cover-Procyon_lotor 0.0998 1.0051 2345
## total_shrub_cover-Dasypus_novemcinctus 1.2090 1.0016 2503
## total_shrub_cover-Lynx_rufus 0.8039 1.0071 951
## total_shrub_cover-Didelphis_virginiana 0.7792 1.0006 3257
## total_shrub_cover-Sylvilagus_floridanus 1.4938 1.0025 2175
## total_shrub_cover-Meleagris_gallopavo -0.0837 1.0044 814
## total_shrub_cover-Sciurus_carolinensis 1.3777 1.0027 3205
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.7540 1.0061 993
## Avg_Cogongrass_Cover-Canis_latrans 3.9346 1.0033 925
## Avg_Cogongrass_Cover-Procyon_lotor 3.8135 1.0100 837
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.2391 1.0058 746
## Avg_Cogongrass_Cover-Lynx_rufus 4.2304 1.0022 985
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7400 1.0074 999
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1352 1.0036 1204
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.3619 1.0014 1042
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0038 1.0021 834
## Tree_Density-Odocoileus_virginianus 1.9886 1.0375 825
## Tree_Density-Canis_latrans -0.6208 1.0080 993
## Tree_Density-Procyon_lotor 0.0469 1.0112 1240
## Tree_Density-Dasypus_novemcinctus -1.0652 1.0396 562
## Tree_Density-Lynx_rufus 2.5238 1.0568 538
## Tree_Density-Didelphis_virginiana -0.4642 1.0026 1209
## Tree_Density-Sylvilagus_floridanus -0.3862 1.0083 1101
## Tree_Density-Meleagris_gallopavo -0.0800 1.0132 1232
## Tree_Density-Sciurus_carolinensis -0.5393 1.0182 1028
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5601 1.0116 1801
## Avg_Canopy_Cover-Canis_latrans 1.6507 1.0026 1604
## Avg_Canopy_Cover-Procyon_lotor 3.0802 1.0047 1782
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3377 1.0011 1222
## Avg_Canopy_Cover-Lynx_rufus 3.8181 1.0049 596
## Avg_Canopy_Cover-Didelphis_virginiana 4.5362 1.0024 742
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.1987 1.0045 881
## Avg_Canopy_Cover-Meleagris_gallopavo 4.8794 1.0042 809
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8510 1.0005 1247
## avg_veg_height-Odocoileus_virginianus 0.9418 1.0039 1615
## avg_veg_height-Canis_latrans 0.4458 1.0045 1432
## avg_veg_height-Procyon_lotor 0.7080 1.0026 1378
## avg_veg_height-Dasypus_novemcinctus 0.8706 1.0005 1529
## avg_veg_height-Lynx_rufus 0.9296 1.0075 1420
## avg_veg_height-Didelphis_virginiana 0.6099 1.0022 1480
## avg_veg_height-Sylvilagus_floridanus 0.4963 1.0035 1584
## avg_veg_height-Meleagris_gallopavo 0.6685 1.0032 1757
## avg_veg_height-Sciurus_carolinensis 1.1768 1.0015 1657
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0060 0.0588 -0.1077 0.0063 0.1227
## (Intercept)-Canis_latrans -2.6439 0.1800 -3.0151 -2.6404 -2.3013
## (Intercept)-Procyon_lotor -2.2719 0.1297 -2.5356 -2.2706 -2.0201
## (Intercept)-Dasypus_novemcinctus -1.5874 0.1332 -1.8536 -1.5864 -1.3307
## (Intercept)-Lynx_rufus -3.7892 0.3440 -4.4520 -3.7903 -3.1289
## (Intercept)-Didelphis_virginiana -2.3029 0.2359 -2.7961 -2.2944 -1.8699
## (Intercept)-Sylvilagus_floridanus -3.1828 0.2853 -3.7759 -3.1695 -2.6592
## (Intercept)-Meleagris_gallopavo -3.4295 0.3160 -4.0714 -3.4230 -2.8370
## (Intercept)-Sciurus_carolinensis -2.4278 0.2600 -2.9699 -2.4115 -1.9539
## week-Odocoileus_virginianus 0.2061 0.0605 0.0868 0.2065 0.3242
## week-Canis_latrans 0.0669 0.1326 -0.2018 0.0689 0.3134
## week-Procyon_lotor -0.0468 0.1174 -0.2899 -0.0461 0.1786
## week-Dasypus_novemcinctus -0.1602 0.1366 -0.4396 -0.1538 0.0941
## week-Lynx_rufus -0.0295 0.1906 -0.4204 -0.0195 0.3215
## week-Didelphis_virginiana -0.1932 0.2124 -0.6565 -0.1821 0.1919
## week-Sylvilagus_floridanus -0.1446 0.2010 -0.5717 -0.1341 0.2125
## week-Meleagris_gallopavo -0.2609 0.2355 -0.7706 -0.2440 0.1473
## week-Sciurus_carolinensis 0.1362 0.1770 -0.2155 0.1393 0.4856
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0017 2502
## (Intercept)-Procyon_lotor 1.0009 3831
## (Intercept)-Dasypus_novemcinctus 1.0008 5250
## (Intercept)-Lynx_rufus 1.0402 513
## (Intercept)-Didelphis_virginiana 1.0008 4224
## (Intercept)-Sylvilagus_floridanus 1.0112 1590
## (Intercept)-Meleagris_gallopavo 1.0024 1337
## (Intercept)-Sciurus_carolinensis 1.0017 3243
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0040 4477
## week-Procyon_lotor 1.0052 4311
## week-Dasypus_novemcinctus 0.9998 4695
## week-Lynx_rufus 1.0036 2561
## week-Didelphis_virginiana 1.0028 3953
## week-Sylvilagus_floridanus 1.0022 3280
## week-Meleagris_gallopavo 1.0020 2490
## week-Sciurus_carolinensis 1.0003 5385
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.543
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0532 0.6541 -1.1932 0.0317 1.4176 1.0034 2923
## Avg_Cogongrass_Cover 0.1498 0.3233 -0.5167 0.1512 0.7670 1.0002 2311
## total_shrub_cover -0.4734 0.3401 -1.1773 -0.4575 0.1786 1.0010 3231
## avg_veg_height -0.0012 0.3120 -0.6193 0.0020 0.6144 1.0001 1784
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.1486 3.8697 0.6805 3.0433 14.0422 1.0098 1881
## Avg_Cogongrass_Cover 0.3665 0.4441 0.0402 0.2264 1.5182 1.0092 2700
## total_shrub_cover 0.6980 0.8261 0.0666 0.4515 2.7926 1.0015 2673
## avg_veg_height 0.2658 0.3422 0.0358 0.1670 1.0597 1.0089 3008
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7629 0.8968 0.0568 0.4943 3.1106 1.0108 541
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2351 0.4220 -3.0371 -2.2494 -1.3658 1.0021 5250
## week -0.0445 0.1253 -0.3032 -0.0420 0.1903 1.0001 3652
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6665 1.2327 0.5299 1.3358 4.7171 1.0009 4688
## week 0.1016 0.0864 0.0256 0.0776 0.3258 1.0020 4361
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8215 1.5284 1.2701 3.6520
## (Intercept)-Canis_latrans 0.3993 0.6500 -0.8197 0.3809
## (Intercept)-Procyon_lotor 0.7745 0.6819 -0.5866 0.7780
## (Intercept)-Dasypus_novemcinctus -0.6964 0.5718 -1.8534 -0.6947
## (Intercept)-Lynx_rufus 0.0326 0.9396 -1.6564 -0.0381
## (Intercept)-Didelphis_virginiana -1.4068 0.6625 -2.7396 -1.3955
## (Intercept)-Sylvilagus_floridanus -0.1335 0.9197 -1.6670 -0.2417
## (Intercept)-Meleagris_gallopavo -0.6508 0.8138 -2.2010 -0.6668
## (Intercept)-Sciurus_carolinensis -1.4789 0.6688 -2.8574 -1.4560
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1402 0.5781 -0.9969 0.1301
## Avg_Cogongrass_Cover-Canis_latrans 0.4149 0.4551 -0.4155 0.3858
## Avg_Cogongrass_Cover-Procyon_lotor 0.0895 0.4398 -0.7780 0.0880
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2528 0.3920 -0.4945 0.2447
## Avg_Cogongrass_Cover-Lynx_rufus 0.4652 0.5122 -0.4371 0.4227
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3333 0.4262 -0.4734 0.3214
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2378 0.5410 -1.4091 -0.1992
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3380 0.5813 -1.6644 -0.2782
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2355 0.4302 -0.6237 0.2349
## total_shrub_cover-Odocoileus_virginianus -0.2629 0.6319 -1.4684 -0.2838
## total_shrub_cover-Canis_latrans 0.1030 0.4696 -0.7435 0.0756
## total_shrub_cover-Procyon_lotor -0.9896 0.5337 -2.2013 -0.9252
## total_shrub_cover-Dasypus_novemcinctus -0.0673 0.3847 -0.8090 -0.0754
## total_shrub_cover-Lynx_rufus -0.9544 0.6729 -2.4802 -0.8712
## total_shrub_cover-Didelphis_virginiana -0.2770 0.4371 -1.1474 -0.2731
## total_shrub_cover-Sylvilagus_floridanus -0.4811 0.6073 -1.8313 -0.4378
## total_shrub_cover-Meleagris_gallopavo -1.3027 0.6976 -2.8950 -1.2097
## total_shrub_cover-Sciurus_carolinensis -0.1510 0.4452 -0.9849 -0.1572
## avg_veg_height-Odocoileus_virginianus -0.0040 0.5197 -1.0299 -0.0086
## avg_veg_height-Canis_latrans -0.0803 0.4154 -0.9222 -0.0813
## avg_veg_height-Procyon_lotor 0.0912 0.4247 -0.7102 0.0739
## avg_veg_height-Dasypus_novemcinctus 0.1864 0.3927 -0.5500 0.1701
## avg_veg_height-Lynx_rufus -0.0117 0.5003 -1.0043 -0.0162
## avg_veg_height-Didelphis_virginiana -0.0457 0.4129 -0.8559 -0.0434
## avg_veg_height-Sylvilagus_floridanus -0.1213 0.4556 -1.0152 -0.1168
## avg_veg_height-Meleagris_gallopavo -0.2505 0.5182 -1.3527 -0.2176
## avg_veg_height-Sciurus_carolinensis 0.2706 0.4407 -0.5403 0.2528
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3835 1.0101 1016
## (Intercept)-Canis_latrans 1.7470 1.0004 2430
## (Intercept)-Procyon_lotor 2.1457 1.0044 2192
## (Intercept)-Dasypus_novemcinctus 0.3889 1.0009 3066
## (Intercept)-Lynx_rufus 2.0928 1.0031 1343
## (Intercept)-Didelphis_virginiana -0.1338 1.0012 2552
## (Intercept)-Sylvilagus_floridanus 2.0638 1.0158 1091
## (Intercept)-Meleagris_gallopavo 1.0014 1.0143 1832
## (Intercept)-Sciurus_carolinensis -0.1815 1.0045 2540
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3198 1.0004 3098
## Avg_Cogongrass_Cover-Canis_latrans 1.3887 1.0002 3297
## Avg_Cogongrass_Cover-Procyon_lotor 0.9749 1.0018 3112
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0572 1.0034 2770
## Avg_Cogongrass_Cover-Lynx_rufus 1.5828 1.0027 3341
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2126 0.9999 3063
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7165 1.0045 2128
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6420 1.0023 2380
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0906 1.0011 3181
## total_shrub_cover-Odocoileus_virginianus 1.1223 1.0040 4007
## total_shrub_cover-Canis_latrans 1.1154 1.0011 3522
## total_shrub_cover-Procyon_lotor -0.1204 1.0014 2824
## total_shrub_cover-Dasypus_novemcinctus 0.7198 1.0014 4969
## total_shrub_cover-Lynx_rufus 0.1392 1.0012 2253
## total_shrub_cover-Didelphis_virginiana 0.5708 1.0003 4940
## total_shrub_cover-Sylvilagus_floridanus 0.6224 1.0036 1972
## total_shrub_cover-Meleagris_gallopavo -0.1866 1.0021 2109
## total_shrub_cover-Sciurus_carolinensis 0.7735 0.9998 4406
## avg_veg_height-Odocoileus_virginianus 1.0582 0.9999 2683
## avg_veg_height-Canis_latrans 0.7204 1.0007 3059
## avg_veg_height-Procyon_lotor 0.9626 1.0011 2902
## avg_veg_height-Dasypus_novemcinctus 1.0096 1.0007 2262
## avg_veg_height-Lynx_rufus 0.9796 1.0035 2467
## avg_veg_height-Didelphis_virginiana 0.7697 1.0003 2942
## avg_veg_height-Sylvilagus_floridanus 0.7614 1.0004 2820
## avg_veg_height-Meleagris_gallopavo 0.6908 1.0008 2746
## avg_veg_height-Sciurus_carolinensis 1.2055 0.9998 2734
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0040 0.0597 -0.1126 0.0038 0.1211
## (Intercept)-Canis_latrans -2.6446 0.1808 -3.0173 -2.6363 -2.3147
## (Intercept)-Procyon_lotor -2.2839 0.1335 -2.5514 -2.2799 -2.0278
## (Intercept)-Dasypus_novemcinctus -1.5861 0.1337 -1.8547 -1.5847 -1.3277
## (Intercept)-Lynx_rufus -3.5451 0.3168 -4.1984 -3.5388 -2.9492
## (Intercept)-Didelphis_virginiana -2.3383 0.2533 -2.8740 -2.3278 -1.8730
## (Intercept)-Sylvilagus_floridanus -3.2789 0.3363 -3.9926 -3.2651 -2.6728
## (Intercept)-Meleagris_gallopavo -3.3562 0.3424 -4.0896 -3.3386 -2.7403
## (Intercept)-Sciurus_carolinensis -2.4552 0.2667 -3.0135 -2.4410 -1.9774
## week-Odocoileus_virginianus 0.2082 0.0617 0.0860 0.2084 0.3319
## week-Canis_latrans 0.0708 0.1296 -0.1934 0.0746 0.3174
## week-Procyon_lotor -0.0480 0.1199 -0.2865 -0.0454 0.1748
## week-Dasypus_novemcinctus -0.1611 0.1361 -0.4363 -0.1562 0.0938
## week-Lynx_rufus -0.0309 0.1948 -0.4284 -0.0215 0.3362
## week-Didelphis_virginiana -0.1995 0.2120 -0.6645 -0.1843 0.1721
## week-Sylvilagus_floridanus -0.1409 0.2056 -0.5694 -0.1295 0.2300
## week-Meleagris_gallopavo -0.2574 0.2457 -0.8073 -0.2355 0.1759
## week-Sciurus_carolinensis 0.1399 0.1794 -0.2231 0.1438 0.4893
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5555
## (Intercept)-Canis_latrans 1.0027 3088
## (Intercept)-Procyon_lotor 1.0015 3794
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
## (Intercept)-Lynx_rufus 1.0054 1181
## (Intercept)-Didelphis_virginiana 1.0009 3713
## (Intercept)-Sylvilagus_floridanus 1.0168 906
## (Intercept)-Meleagris_gallopavo 1.0047 1162
## (Intercept)-Sciurus_carolinensis 1.0086 3338
## week-Odocoileus_virginianus 1.0012 4892
## week-Canis_latrans 1.0001 4473
## week-Procyon_lotor 1.0078 4039
## week-Dasypus_novemcinctus 1.0035 4479
## week-Lynx_rufus 1.0100 3059
## week-Didelphis_virginiana 1.0001 4070
## week-Sylvilagus_floridanus 1.0017 2914
## week-Meleagris_gallopavo 1.0024 2662
## week-Sciurus_carolinensis 1.0005 4849
# Includes week covariate for detection and none for occupancy
ms_week_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
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## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
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## Sampled: 3500 of 10000, 35.00%
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## Sampled: 4000 of 10000, 40.00%
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## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
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## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5463
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1154 0.5756 -0.9407 0.0959 1.3566 1.0005 4512
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.107 2.7598 0.7007 2.3619 10.2426 1.0003 2814
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2405 0.4317 -3.0586 -2.2462 -1.3485 0.9999 5250
## week -0.0453 0.1236 -0.3004 -0.0415 0.1863 1.0015 4228
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7062 1.2336 0.5499 1.3841 4.6580 1.0093 3903
## week 0.0997 0.0835 0.0249 0.0771 0.2964 1.0020 3870
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4121 1.1249 1.7562 3.2230 6.0879
## (Intercept)-Canis_latrans 0.3253 0.4047 -0.4301 0.3062 1.1739
## (Intercept)-Procyon_lotor 0.7218 0.3955 -0.0254 0.7066 1.5379
## (Intercept)-Dasypus_novemcinctus -0.6145 0.3634 -1.3495 -0.6118 0.0812
## (Intercept)-Lynx_rufus 0.3704 0.8931 -0.8806 0.2182 2.6399
## (Intercept)-Didelphis_virginiana -1.3318 0.4435 -2.2352 -1.3112 -0.4963
## (Intercept)-Sylvilagus_floridanus -0.2914 0.5416 -1.2114 -0.3356 0.9054
## (Intercept)-Meleagris_gallopavo -0.1959 0.7003 -1.2889 -0.2848 1.3627
## (Intercept)-Sciurus_carolinensis -1.3084 0.4587 -2.2611 -1.2856 -0.4542
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0021 1899
## (Intercept)-Canis_latrans 1.0029 4931
## (Intercept)-Procyon_lotor 1.0016 5093
## (Intercept)-Dasypus_novemcinctus 1.0002 5830
## (Intercept)-Lynx_rufus 1.0092 669
## (Intercept)-Didelphis_virginiana 1.0005 4398
## (Intercept)-Sylvilagus_floridanus 1.0013 1886
## (Intercept)-Meleagris_gallopavo 1.0286 1007
## (Intercept)-Sciurus_carolinensis 1.0018 4977
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0047 0.0592 -0.1116 0.0042 0.1203
## (Intercept)-Canis_latrans -2.6133 0.1711 -2.9573 -2.6074 -2.2902
## (Intercept)-Procyon_lotor -2.2670 0.1284 -2.5271 -2.2661 -2.0288
## (Intercept)-Dasypus_novemcinctus -1.5865 0.1347 -1.8581 -1.5850 -1.3256
## (Intercept)-Lynx_rufus -3.5856 0.3543 -4.3000 -3.5678 -2.9333
## (Intercept)-Didelphis_virginiana -2.3265 0.2470 -2.8305 -2.3169 -1.8715
## (Intercept)-Sylvilagus_floridanus -3.1905 0.3163 -3.8443 -3.1690 -2.6173
## (Intercept)-Meleagris_gallopavo -3.4685 0.3802 -4.2475 -3.4508 -2.7831
## (Intercept)-Sciurus_carolinensis -2.4498 0.2644 -3.0102 -2.4375 -1.9740
## week-Odocoileus_virginianus 0.2072 0.0606 0.0881 0.2065 0.3283
## week-Canis_latrans 0.0684 0.1289 -0.1991 0.0713 0.3085
## week-Procyon_lotor -0.0474 0.1162 -0.2838 -0.0448 0.1733
## week-Dasypus_novemcinctus -0.1595 0.1372 -0.4575 -0.1525 0.0879
## week-Lynx_rufus -0.0313 0.1923 -0.4320 -0.0234 0.3298
## week-Didelphis_virginiana -0.2024 0.2149 -0.6628 -0.1888 0.1799
## week-Sylvilagus_floridanus -0.1408 0.2026 -0.5676 -0.1302 0.2210
## week-Meleagris_gallopavo -0.2540 0.2354 -0.7623 -0.2352 0.1517
## week-Sciurus_carolinensis 0.1372 0.1733 -0.1967 0.1399 0.4678
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0045 5250
## (Intercept)-Canis_latrans 1.0008 2906
## (Intercept)-Procyon_lotor 1.0014 4499
## (Intercept)-Dasypus_novemcinctus 1.0034 5250
## (Intercept)-Lynx_rufus 1.0052 664
## (Intercept)-Didelphis_virginiana 1.0009 4008
## (Intercept)-Sylvilagus_floridanus 1.0016 1222
## (Intercept)-Meleagris_gallopavo 1.0426 899
## (Intercept)-Sciurus_carolinensis 1.0004 3790
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0001 4413
## week-Procyon_lotor 1.0011 4513
## week-Dasypus_novemcinctus 1.0003 4683
## week-Lynx_rufus 1.0062 2948
## week-Didelphis_virginiana 1.0015 4019
## week-Sylvilagus_floridanus 1.0014 3237
## week-Meleagris_gallopavo 1.0050 2410
## week-Sciurus_carolinensis 1.0006 5253
#Includes week for detection and only foraging for occupancy
ms_week_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
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## Sampled: 1500 of 10000, 15.00%
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## Sampled: 4500 of 10000, 45.00%
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## Sampled: 6500 of 10000, 65.00%
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
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## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.486
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0544 0.6461 -1.1766 0.0392 1.4106 1.0086 2912
## Veg_shannon_index 0.3880 0.2717 -0.1416 0.3833 0.9279 1.0023 2507
## Avg_Cogongrass_Cover 0.3703 0.2706 -0.1535 0.3664 0.9087 1.0003 1981
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8223 3.7361 0.6384 2.7487 13.6822 1.0015 1459
## Veg_shannon_index 0.2637 0.3210 0.0355 0.1681 1.0556 1.0084 2762
## Avg_Cogongrass_Cover 0.3014 0.3967 0.0374 0.1911 1.1881 1.0298 3396
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6731 0.7496 0.058 0.4472 2.6748 1.0127 677
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2504 0.4317 -3.0661 -2.2650 -1.3470 1.0002 5250
## week -0.0481 0.1231 -0.3046 -0.0429 0.1848 0.9998 3957
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7105 1.2488 0.5587 1.3946 4.7903 1.0099 4532
## week 0.0998 0.0823 0.0254 0.0773 0.3107 1.0086 3656
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6549 1.5152 1.2806 3.4483
## (Intercept)-Canis_latrans 0.2983 0.6020 -0.8703 0.2909
## (Intercept)-Procyon_lotor 0.5982 0.6095 -0.6106 0.5998
## (Intercept)-Dasypus_novemcinctus -0.6951 0.5755 -1.8843 -0.6786
## (Intercept)-Lynx_rufus 0.1587 1.1172 -1.4937 0.0257
## (Intercept)-Didelphis_virginiana -1.4373 0.6365 -2.7585 -1.4260
## (Intercept)-Sylvilagus_floridanus -0.2490 0.8167 -1.6270 -0.3136
## (Intercept)-Meleagris_gallopavo -0.2496 0.9229 -1.8208 -0.3457
## (Intercept)-Sciurus_carolinensis -1.4435 0.6630 -2.8454 -1.4178
## Veg_shannon_index-Odocoileus_virginianus 0.3153 0.4901 -0.7359 0.3291
## Veg_shannon_index-Canis_latrans 0.6408 0.3766 -0.0177 0.6115
## Veg_shannon_index-Procyon_lotor 0.4930 0.3829 -0.2043 0.4676
## Veg_shannon_index-Dasypus_novemcinctus 0.2391 0.3330 -0.4314 0.2396
## Veg_shannon_index-Lynx_rufus 0.2179 0.4945 -0.8247 0.2460
## Veg_shannon_index-Didelphis_virginiana 0.5147 0.3832 -0.1803 0.4939
## Veg_shannon_index-Sylvilagus_floridanus 0.4900 0.4386 -0.2906 0.4728
## Veg_shannon_index-Meleagris_gallopavo 0.5080 0.4566 -0.3357 0.4858
## Veg_shannon_index-Sciurus_carolinensis 0.0744 0.3819 -0.7426 0.0995
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3511 0.5120 -0.6144 0.3408
## Avg_Cogongrass_Cover-Canis_latrans 0.5692 0.3855 -0.1122 0.5365
## Avg_Cogongrass_Cover-Procyon_lotor 0.4579 0.3985 -0.2570 0.4345
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4521 0.3203 -0.1645 0.4447
## Avg_Cogongrass_Cover-Lynx_rufus 0.6028 0.4428 -0.1736 0.5671
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4932 0.3680 -0.2068 0.4840
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0088 0.4552 -0.9979 0.0231
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0274 0.5490 -1.1926 0.0129
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4435 0.3563 -0.2561 0.4379
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.2360 1.0066 975
## (Intercept)-Canis_latrans 1.5049 1.0011 3132
## (Intercept)-Procyon_lotor 1.8042 1.0011 2818
## (Intercept)-Dasypus_novemcinctus 0.3906 1.0020 3275
## (Intercept)-Lynx_rufus 2.7495 1.0292 667
## (Intercept)-Didelphis_virginiana -0.2303 1.0035 3176
## (Intercept)-Sylvilagus_floridanus 1.5747 1.0069 1075
## (Intercept)-Meleagris_gallopavo 1.9005 1.0015 1024
## (Intercept)-Sciurus_carolinensis -0.2130 1.0010 2718
## Veg_shannon_index-Odocoileus_virginianus 1.2500 1.0020 3187
## Veg_shannon_index-Canis_latrans 1.4611 1.0011 3604
## Veg_shannon_index-Procyon_lotor 1.3013 1.0015 2976
## Veg_shannon_index-Dasypus_novemcinctus 0.8960 1.0037 4511
## Veg_shannon_index-Lynx_rufus 1.1043 1.0014 2811
## Veg_shannon_index-Didelphis_virginiana 1.3391 1.0007 3715
## Veg_shannon_index-Sylvilagus_floridanus 1.4512 1.0002 2904
## Veg_shannon_index-Meleagris_gallopavo 1.4954 1.0017 3082
## Veg_shannon_index-Sciurus_carolinensis 0.7808 1.0019 3762
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4402 1.0004 3576
## Avg_Cogongrass_Cover-Canis_latrans 1.3940 1.0025 3657
## Avg_Cogongrass_Cover-Procyon_lotor 1.3207 1.0013 3286
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1007 1.0026 3426
## Avg_Cogongrass_Cover-Lynx_rufus 1.5435 1.0076 3242
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2404 1.0063 4051
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8039 1.0001 2599
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9863 0.9999 2144
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1588 1.0002 4123
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0594 -0.1101 0.0048 0.1213
## (Intercept)-Canis_latrans -2.6119 0.1727 -2.9677 -2.6075 -2.2832
## (Intercept)-Procyon_lotor -2.2813 0.1332 -2.5471 -2.2790 -2.0282
## (Intercept)-Dasypus_novemcinctus -1.5858 0.1352 -1.8598 -1.5820 -1.3240
## (Intercept)-Lynx_rufus -3.5719 0.3363 -4.2698 -3.5589 -2.9480
## (Intercept)-Didelphis_virginiana -2.3314 0.2539 -2.8606 -2.3201 -1.8628
## (Intercept)-Sylvilagus_floridanus -3.2350 0.3216 -3.9340 -3.2189 -2.6528
## (Intercept)-Meleagris_gallopavo -3.4970 0.3662 -4.2463 -3.4877 -2.8181
## (Intercept)-Sciurus_carolinensis -2.4527 0.2676 -3.0190 -2.4335 -1.9778
## week-Odocoileus_virginianus 0.2078 0.0605 0.0917 0.2069 0.3305
## week-Canis_latrans 0.0694 0.1267 -0.1933 0.0726 0.3085
## week-Procyon_lotor -0.0492 0.1169 -0.2851 -0.0466 0.1701
## week-Dasypus_novemcinctus -0.1580 0.1363 -0.4372 -0.1548 0.0973
## week-Lynx_rufus -0.0247 0.1918 -0.4205 -0.0189 0.3347
## week-Didelphis_virginiana -0.1986 0.2117 -0.6628 -0.1860 0.1822
## week-Sylvilagus_floridanus -0.1384 0.2006 -0.5669 -0.1270 0.2207
## week-Meleagris_gallopavo -0.2711 0.2465 -0.8254 -0.2478 0.1442
## week-Sciurus_carolinensis 0.1343 0.1767 -0.2144 0.1352 0.4798
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 3860
## (Intercept)-Canis_latrans 1.0015 3182
## (Intercept)-Procyon_lotor 1.0021 4004
## (Intercept)-Dasypus_novemcinctus 1.0003 5091
## (Intercept)-Lynx_rufus 1.0119 892
## (Intercept)-Didelphis_virginiana 1.0014 3625
## (Intercept)-Sylvilagus_floridanus 1.0026 1067
## (Intercept)-Meleagris_gallopavo 1.0023 1013
## (Intercept)-Sciurus_carolinensis 1.0037 3392
## week-Odocoileus_virginianus 0.9998 5250
## week-Canis_latrans 1.0025 4384
## week-Procyon_lotor 1.0004 4238
## week-Dasypus_novemcinctus 1.0019 4598
## week-Lynx_rufus 1.0004 2709
## week-Didelphis_virginiana 1.0000 4387
## week-Sylvilagus_floridanus 1.0015 3244
## week-Meleagris_gallopavo 1.0026 2225
## week-Sciurus_carolinensis 1.0019 4694
# Includes movement covariates of occupancy and week for detection
ms_week_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.493
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0485 0.6855 -1.2377 0.0285 1.4957 1.0017 3399
## Cogon_Patch_Size -0.0158 0.3913 -0.8437 -0.0131 0.7678 1.0011 2755
## Avg_Cogongrass_Cover 0.1715 0.3020 -0.4201 0.1684 0.7700 1.0028 2265
## total_shrub_cover -0.4788 0.3451 -1.1941 -0.4673 0.1719 1.0027 3003
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.5396 4.8760 0.7506 3.2344 16.5799 1.0216 864
## Cogon_Patch_Size 0.7986 1.1567 0.0586 0.4483 3.6358 1.0129 2063
## Avg_Cogongrass_Cover 0.3532 0.4629 0.0397 0.2151 1.5046 1.0075 2666
## total_shrub_cover 0.6294 0.8353 0.0604 0.3971 2.5446 1.0604 1340
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8915 0.9553 0.0596 0.5853 3.3899 1.0259 520
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2361 0.4233 -3.0453 -2.2498 -1.3393 1.0011 5030
## week -0.0481 0.1253 -0.3068 -0.0449 0.1882 1.0012 3805
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6877 1.2405 0.5546 1.3564 4.8873 1.015 3561
## week 0.1017 0.0874 0.0259 0.0788 0.3160 1.003 4184
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9414 1.7208 1.3076 3.6918
## (Intercept)-Canis_latrans 0.4490 0.6736 -0.8259 0.4249
## (Intercept)-Procyon_lotor 0.7319 0.6989 -0.6487 0.7315
## (Intercept)-Dasypus_novemcinctus -0.7058 0.6118 -1.9782 -0.6984
## (Intercept)-Lynx_rufus 0.0879 1.0883 -1.7048 -0.0186
## (Intercept)-Didelphis_virginiana -1.4416 0.7207 -2.9213 -1.4307
## (Intercept)-Sylvilagus_floridanus -0.2488 0.9868 -1.8759 -0.3163
## (Intercept)-Meleagris_gallopavo -0.6110 0.8989 -2.2152 -0.6513
## (Intercept)-Sciurus_carolinensis -1.5563 0.7337 -3.0603 -1.5364
## Cogon_Patch_Size-Odocoileus_virginianus 0.0879 0.7046 -1.1721 0.0409
## Cogon_Patch_Size-Canis_latrans 0.7442 0.7088 -0.2610 0.6235
## Cogon_Patch_Size-Procyon_lotor -0.1292 0.4518 -1.0277 -0.1294
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0678 0.4108 -0.9257 -0.0607
## Cogon_Patch_Size-Lynx_rufus -0.0393 0.7569 -1.4247 -0.0864
## Cogon_Patch_Size-Didelphis_virginiana 0.6339 0.5064 -0.2409 0.5957
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6903 0.8290 -2.7437 -0.5551
## Cogon_Patch_Size-Meleagris_gallopavo -0.0465 0.5756 -1.2652 -0.0323
## Cogon_Patch_Size-Sciurus_carolinensis -0.5897 0.6850 -2.2590 -0.4785
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1640 0.5521 -0.9414 0.1553
## Avg_Cogongrass_Cover-Canis_latrans 0.2549 0.4029 -0.5113 0.2461
## Avg_Cogongrass_Cover-Procyon_lotor 0.1864 0.4309 -0.6220 0.1704
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3501 0.3634 -0.3224 0.3355
## Avg_Cogongrass_Cover-Lynx_rufus 0.4614 0.4963 -0.4018 0.4222
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1868 0.3988 -0.6193 0.1881
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1458 0.4852 -1.1681 -0.1224
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3218 0.6133 -1.6871 -0.2621
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4086 0.3990 -0.3463 0.3943
## total_shrub_cover-Odocoileus_virginianus -0.2851 0.6380 -1.4724 -0.3174
## total_shrub_cover-Canis_latrans 0.0315 0.4674 -0.8021 0.0097
## total_shrub_cover-Procyon_lotor -0.9534 0.5193 -2.1306 -0.9029
## total_shrub_cover-Dasypus_novemcinctus -0.1079 0.3813 -0.8311 -0.1135
## total_shrub_cover-Lynx_rufus -0.8987 0.6870 -2.5702 -0.8216
## total_shrub_cover-Didelphis_virginiana -0.3949 0.4394 -1.2964 -0.3937
## total_shrub_cover-Sylvilagus_floridanus -0.4263 0.6336 -1.7289 -0.3898
## total_shrub_cover-Meleagris_gallopavo -1.2352 0.6913 -2.8492 -1.1492
## total_shrub_cover-Sciurus_carolinensis -0.1538 0.4475 -1.0109 -0.1635
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.1618 1.0104 834
## (Intercept)-Canis_latrans 1.8724 1.0008 2748
## (Intercept)-Procyon_lotor 2.1474 1.0035 2561
## (Intercept)-Dasypus_novemcinctus 0.4659 1.0072 3185
## (Intercept)-Lynx_rufus 2.4233 1.0075 704
## (Intercept)-Didelphis_virginiana -0.0561 1.0029 2590
## (Intercept)-Sylvilagus_floridanus 1.7231 1.0252 1033
## (Intercept)-Meleagris_gallopavo 1.1706 1.0083 680
## (Intercept)-Sciurus_carolinensis -0.1401 1.0026 2310
## Cogon_Patch_Size-Odocoileus_virginianus 1.6321 1.0011 2973
## Cogon_Patch_Size-Canis_latrans 2.4597 1.0093 2350
## Cogon_Patch_Size-Procyon_lotor 0.7568 1.0001 3243
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7122 1.0008 4023
## Cogon_Patch_Size-Lynx_rufus 1.6790 1.0010 2134
## Cogon_Patch_Size-Didelphis_virginiana 1.7216 1.0072 2934
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5537 1.0013 1960
## Cogon_Patch_Size-Meleagris_gallopavo 1.0567 1.0015 3582
## Cogon_Patch_Size-Sciurus_carolinensis 0.4133 1.0031 2476
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2905 1.0046 3431
## Avg_Cogongrass_Cover-Canis_latrans 1.0984 1.0043 3700
## Avg_Cogongrass_Cover-Procyon_lotor 1.0811 1.0013 3164
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1135 1.0018 3434
## Avg_Cogongrass_Cover-Lynx_rufus 1.5747 1.0006 2935
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9658 1.0006 3728
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7431 1.0005 2693
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7115 1.0010 2299
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2381 0.9998 2980
## total_shrub_cover-Odocoileus_virginianus 1.1107 1.0023 3447
## total_shrub_cover-Canis_latrans 1.0103 1.0045 3335
## total_shrub_cover-Procyon_lotor -0.0718 1.0002 2884
## total_shrub_cover-Dasypus_novemcinctus 0.6657 1.0021 4625
## total_shrub_cover-Lynx_rufus 0.2092 1.0008 1770
## total_shrub_cover-Didelphis_virginiana 0.4502 1.0017 4203
## total_shrub_cover-Sylvilagus_floridanus 0.6816 1.0055 1358
## total_shrub_cover-Meleagris_gallopavo -0.1435 1.0032 1638
## total_shrub_cover-Sciurus_carolinensis 0.7663 1.0030 4185
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0065 0.0598 -0.1103 0.0060 0.1224
## (Intercept)-Canis_latrans -2.6181 0.1750 -2.9756 -2.6118 -2.2903
## (Intercept)-Procyon_lotor -2.2804 0.1297 -2.5380 -2.2776 -2.0307
## (Intercept)-Dasypus_novemcinctus -1.5860 0.1330 -1.8537 -1.5847 -1.3341
## (Intercept)-Lynx_rufus -3.5411 0.3129 -4.1982 -3.5239 -2.9692
## (Intercept)-Didelphis_virginiana -2.3275 0.2455 -2.8254 -2.3235 -1.8590
## (Intercept)-Sylvilagus_floridanus -3.2708 0.3237 -3.9438 -3.2541 -2.6804
## (Intercept)-Meleagris_gallopavo -3.3796 0.3494 -4.1111 -3.3557 -2.7619
## (Intercept)-Sciurus_carolinensis -2.4505 0.2652 -3.0028 -2.4378 -1.9810
## week-Odocoileus_virginianus 0.2072 0.0604 0.0899 0.2061 0.3276
## week-Canis_latrans 0.0684 0.1264 -0.1783 0.0663 0.3114
## week-Procyon_lotor -0.0472 0.1177 -0.2920 -0.0437 0.1704
## week-Dasypus_novemcinctus -0.1574 0.1347 -0.4357 -0.1524 0.0952
## week-Lynx_rufus -0.0335 0.1891 -0.4306 -0.0286 0.3238
## week-Didelphis_virginiana -0.1997 0.2119 -0.6680 -0.1813 0.1688
## week-Sylvilagus_floridanus -0.1501 0.2048 -0.5864 -0.1398 0.2121
## week-Meleagris_gallopavo -0.2692 0.2469 -0.8194 -0.2467 0.1505
## week-Sciurus_carolinensis 0.1341 0.1810 -0.2252 0.1357 0.4772
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5043
## (Intercept)-Canis_latrans 1.0017 2984
## (Intercept)-Procyon_lotor 1.0002 3863
## (Intercept)-Dasypus_novemcinctus 1.0019 4931
## (Intercept)-Lynx_rufus 1.0075 1177
## (Intercept)-Didelphis_virginiana 1.0008 4029
## (Intercept)-Sylvilagus_floridanus 1.0020 1015
## (Intercept)-Meleagris_gallopavo 1.0022 1105
## (Intercept)-Sciurus_carolinensis 1.0008 3404
## week-Odocoileus_virginianus 1.0004 5044
## week-Canis_latrans 1.0015 4550
## week-Procyon_lotor 1.0004 4414
## week-Dasypus_novemcinctus 1.0032 4911
## week-Lynx_rufus 1.0008 3043
## week-Didelphis_virginiana 1.0024 4005
## week-Sylvilagus_floridanus 1.0030 2882
## week-Meleagris_gallopavo 1.0059 2651
## week-Sciurus_carolinensis 1.0014 4812
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4843
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1040 0.7912 -1.3743 0.0682 1.7795 1.0009 1900
## Tree_Density -0.7640 0.4288 -1.7041 -0.7365 0.0213 1.0025 2030
## Avg_Canopy_Cover 1.0377 0.3833 0.3181 1.0165 1.8373 1.0002 2400
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.0096 9.1189 1.2055 4.8788 25.3672 1.1076 699
## Tree_Density 0.9658 2.1549 0.0488 0.3998 5.1062 1.0327 1495
## Avg_Canopy_Cover 0.7806 0.8954 0.0699 0.4954 3.2463 1.0027 2251
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4597 0.5712 0.0441 0.2794 2.009 1.0173 693
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2386 0.4329 -3.0697 -2.2594 -1.2992 1.0013 5250
## week -0.0475 0.1254 -0.3039 -0.0443 0.1899 1.0022 3764
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7899 1.3875 0.5694 1.4244 4.9423 1.0128 4449
## week 0.1013 0.0839 0.0263 0.0779 0.3032 1.0030 3725
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7895 2.0682 2.0390 4.4343 9.7301
## (Intercept)-Canis_latrans 0.3313 0.6327 -0.8362 0.3032 1.6568
## (Intercept)-Procyon_lotor 0.7654 0.6047 -0.4037 0.7450 1.9863
## (Intercept)-Dasypus_novemcinctus -1.0392 0.6331 -2.4211 -1.0054 0.1352
## (Intercept)-Lynx_rufus 1.2798 1.9803 -1.2203 0.7832 6.5098
## (Intercept)-Didelphis_virginiana -1.9283 0.7175 -3.4974 -1.8812 -0.6242
## (Intercept)-Sylvilagus_floridanus -0.6928 0.7157 -2.1135 -0.6993 0.7818
## (Intercept)-Meleagris_gallopavo -0.3645 0.9049 -1.9829 -0.4302 1.5876
## (Intercept)-Sciurus_carolinensis -1.9789 0.7293 -3.5491 -1.9446 -0.6614
## Tree_Density-Odocoileus_virginianus -0.3548 0.7200 -1.4993 -0.4394 1.3782
## Tree_Density-Canis_latrans -0.8952 0.5655 -2.1918 -0.8324 0.0261
## Tree_Density-Procyon_lotor -0.4799 0.4237 -1.2835 -0.4847 0.3765
## Tree_Density-Dasypus_novemcinctus -1.4032 0.9489 -3.8288 -1.1816 -0.1740
## Tree_Density-Lynx_rufus 0.0369 0.8999 -1.3435 -0.0912 2.1954
## Tree_Density-Didelphis_virginiana -1.0646 0.8110 -3.0744 -0.9236 0.1032
## Tree_Density-Sylvilagus_floridanus -1.0931 0.7797 -2.9946 -0.9757 0.0861
## Tree_Density-Meleagris_gallopavo -0.9281 0.7491 -2.6690 -0.8335 0.2998
## Tree_Density-Sciurus_carolinensis -0.9846 0.7595 -2.8987 -0.8827 0.1688
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7985 0.7305 -0.6637 0.7940 2.2531
## Avg_Canopy_Cover-Canis_latrans 0.0754 0.4865 -0.8689 0.0755 1.0262
## Avg_Canopy_Cover-Procyon_lotor 1.0354 0.4732 0.1868 1.0046 2.0444
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0312 0.4366 0.2298 1.0071 1.9334
## Avg_Canopy_Cover-Lynx_rufus 0.9098 0.8073 -0.6400 0.8789 2.6192
## Avg_Canopy_Cover-Didelphis_virginiana 1.2952 0.5098 0.4128 1.2485 2.4274
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7872 0.7993 0.5803 1.6482 3.7079
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4730 0.7144 0.3249 1.3801 3.1675
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2753 0.5207 0.3705 1.2235 2.4556
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0175 485
## (Intercept)-Canis_latrans 1.0076 2552
## (Intercept)-Procyon_lotor 1.0016 3197
## (Intercept)-Dasypus_novemcinctus 1.0035 2443
## (Intercept)-Lynx_rufus 1.0095 331
## (Intercept)-Didelphis_virginiana 1.0001 2629
## (Intercept)-Sylvilagus_floridanus 1.0018 3085
## (Intercept)-Meleagris_gallopavo 1.0120 1434
## (Intercept)-Sciurus_carolinensis 1.0009 2551
## Tree_Density-Odocoileus_virginianus 1.0020 1814
## Tree_Density-Canis_latrans 1.0050 3363
## Tree_Density-Procyon_lotor 1.0005 3426
## Tree_Density-Dasypus_novemcinctus 1.0082 1202
## Tree_Density-Lynx_rufus 1.0060 831
## Tree_Density-Didelphis_virginiana 1.0089 1554
## Tree_Density-Sylvilagus_floridanus 1.0031 1900
## Tree_Density-Meleagris_gallopavo 1.0015 2217
## Tree_Density-Sciurus_carolinensis 1.0015 2013
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0015 3191
## Avg_Canopy_Cover-Canis_latrans 1.0033 2668
## Avg_Canopy_Cover-Procyon_lotor 0.9999 3895
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0003 4208
## Avg_Canopy_Cover-Lynx_rufus 1.0023 1663
## Avg_Canopy_Cover-Didelphis_virginiana 1.0007 2833
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0036 1624
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0012 2189
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0031 3281
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0590 -0.1084 0.0052 0.1235
## (Intercept)-Canis_latrans -2.6447 0.1795 -3.0222 -2.6399 -2.3136
## (Intercept)-Procyon_lotor -2.2694 0.1301 -2.5325 -2.2687 -2.0189
## (Intercept)-Dasypus_novemcinctus -1.5861 0.1342 -1.8534 -1.5838 -1.3317
## (Intercept)-Lynx_rufus -3.7593 0.3557 -4.4239 -3.7664 -3.0643
## (Intercept)-Didelphis_virginiana -2.3191 0.2440 -2.8249 -2.3128 -1.8731
## (Intercept)-Sylvilagus_floridanus -3.1206 0.2665 -3.6719 -3.1140 -2.6152
## (Intercept)-Meleagris_gallopavo -3.4753 0.3462 -4.1865 -3.4658 -2.8310
## (Intercept)-Sciurus_carolinensis -2.4485 0.2640 -3.0010 -2.4360 -1.9694
## week-Odocoileus_virginianus 0.2085 0.0607 0.0924 0.2074 0.3306
## week-Canis_latrans 0.0675 0.1308 -0.1898 0.0682 0.3159
## week-Procyon_lotor -0.0462 0.1195 -0.2908 -0.0447 0.1744
## week-Dasypus_novemcinctus -0.1575 0.1350 -0.4339 -0.1553 0.0992
## week-Lynx_rufus -0.0342 0.1922 -0.4264 -0.0266 0.3133
## week-Didelphis_virginiana -0.1950 0.2116 -0.6546 -0.1807 0.1837
## week-Sylvilagus_floridanus -0.1438 0.2026 -0.5631 -0.1296 0.2276
## week-Meleagris_gallopavo -0.2597 0.2409 -0.8110 -0.2391 0.1505
## week-Sciurus_carolinensis 0.1422 0.1764 -0.2084 0.1463 0.4842
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5250
## (Intercept)-Canis_latrans 1.0055 2697
## (Intercept)-Procyon_lotor 1.0010 4244
## (Intercept)-Dasypus_novemcinctus 1.0007 4839
## (Intercept)-Lynx_rufus 1.0014 524
## (Intercept)-Didelphis_virginiana 1.0001 5127
## (Intercept)-Sylvilagus_floridanus 1.0015 2014
## (Intercept)-Meleagris_gallopavo 1.0040 1162
## (Intercept)-Sciurus_carolinensis 1.0014 3529
## week-Odocoileus_virginianus 1.0013 5297
## week-Canis_latrans 1.0001 4193
## week-Procyon_lotor 1.0016 4457
## week-Dasypus_novemcinctus 1.0004 4188
## week-Lynx_rufus 1.0010 2606
## week-Didelphis_virginiana 1.0002 4036
## week-Sylvilagus_floridanus 1.0044 3018
## week-Meleagris_gallopavo 1.0030 2168
## week-Sciurus_carolinensis 1.0008 5010
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.4602
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5737 0.6492 -1.7509 -0.6010 0.7968 1.0048 3169
## Avg_Cogongrass_Cover -0.5841 0.3772 -1.3584 -0.5752 0.1589 1.0036 1734
## I(Avg_Cogongrass_Cover^2) 0.8302 0.4003 0.1599 0.7942 1.7013 1.0091 1077
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6945 3.7324 0.6094 2.6899 12.9926 1.0405 1779
## Avg_Cogongrass_Cover 0.3897 0.5069 0.0396 0.2286 1.6756 1.0008 2923
## I(Avg_Cogongrass_Cover^2) 0.6633 1.4815 0.0399 0.2768 3.6103 1.0310 770
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4967 0.5559 0.0478 0.3261 2.0881 1.0065 648
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2409 0.4147 -3.0135 -2.2582 -1.3668 1.0050 5250
## week -0.0451 0.1225 -0.2951 -0.0401 0.1863 1.0044 3594
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6405 1.1167 0.5644 1.3352 4.5491 1.0056 4810
## week 0.1019 0.0876 0.0259 0.0775 0.3247 1.0057 3949
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8633 1.5133 0.5395 2.6647
## (Intercept)-Canis_latrans -0.4571 0.6638 -1.7894 -0.4530
## (Intercept)-Procyon_lotor -0.1461 0.6462 -1.4820 -0.1364
## (Intercept)-Dasypus_novemcinctus -1.2762 0.6251 -2.5420 -1.2490
## (Intercept)-Lynx_rufus -1.1359 0.9036 -2.7858 -1.1641
## (Intercept)-Didelphis_virginiana -1.8517 0.6936 -3.2981 -1.8247
## (Intercept)-Sylvilagus_floridanus -0.9482 0.8023 -2.4535 -0.9756
## (Intercept)-Meleagris_gallopavo -0.5172 0.9287 -2.0832 -0.6051
## (Intercept)-Sciurus_carolinensis -2.3274 0.7649 -3.9411 -2.2980
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.6142 0.6264 -1.8870 -0.6122
## Avg_Cogongrass_Cover-Canis_latrans -0.3585 0.5206 -1.3254 -0.3785
## Avg_Cogongrass_Cover-Procyon_lotor -0.4964 0.5075 -1.4555 -0.5127
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4394 0.4756 -1.3619 -0.4382
## Avg_Cogongrass_Cover-Lynx_rufus -0.5003 0.5564 -1.5807 -0.5069
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3517 0.5280 -1.3628 -0.3732
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0076 0.6207 -2.3784 -0.9483
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8385 0.6047 -2.2132 -0.8028
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6886 0.5394 -1.8172 -0.6593
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.2047 0.9677 -0.0058 1.0091
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2761 0.8535 0.1924 1.0701
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1029 0.7225 0.1511 0.9554
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6559 0.3558 -0.0190 0.6493
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1802 0.6354 0.2728 1.0862
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5048 0.4178 -0.2763 0.4979
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7222 0.5246 -0.1491 0.6690
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2091 0.7404 -1.3589 0.2450
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9138 0.4129 0.2017 0.8784
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.4696 1.0075 1195
## (Intercept)-Canis_latrans 0.8561 1.0012 2665
## (Intercept)-Procyon_lotor 1.0938 1.0000 2695
## (Intercept)-Dasypus_novemcinctus -0.0944 1.0004 3665
## (Intercept)-Lynx_rufus 0.7047 1.0081 1340
## (Intercept)-Didelphis_virginiana -0.5749 1.0046 3091
## (Intercept)-Sylvilagus_floridanus 0.6052 1.0083 1462
## (Intercept)-Meleagris_gallopavo 1.5508 1.0005 1047
## (Intercept)-Sciurus_carolinensis -0.9351 1.0009 2565
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6591 1.0001 3047
## Avg_Cogongrass_Cover-Canis_latrans 0.7371 1.0087 2775
## Avg_Cogongrass_Cover-Procyon_lotor 0.5473 1.0006 2882
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5043 1.0006 3051
## Avg_Cogongrass_Cover-Lynx_rufus 0.6477 1.0011 2818
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7586 1.0021 2586
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0540 1.0011 2367
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2774 1.0046 2357
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3055 1.0047 2400
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.6309 1.0018 799
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5668 1.0048 879
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0067 1.0085 888
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3884 0.9999 3142
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6517 1.0100 920
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3319 1.0085 2354
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9180 1.0192 1228
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.4889 1.0332 572
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8095 1.0010 2136
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0045 0.0587 -0.1095 0.0051 0.1182
## (Intercept)-Canis_latrans -2.6378 0.1751 -2.9874 -2.6318 -2.3113
## (Intercept)-Procyon_lotor -2.2804 0.1295 -2.5437 -2.2776 -2.0338
## (Intercept)-Dasypus_novemcinctus -1.5860 0.1353 -1.8517 -1.5825 -1.3230
## (Intercept)-Lynx_rufus -3.4156 0.3192 -4.0914 -3.4011 -2.8381
## (Intercept)-Didelphis_virginiana -2.3478 0.2668 -2.9098 -2.3289 -1.8621
## (Intercept)-Sylvilagus_floridanus -3.2295 0.3187 -3.9021 -3.2115 -2.6558
## (Intercept)-Meleagris_gallopavo -3.5041 0.3940 -4.3397 -3.4810 -2.8170
## (Intercept)-Sciurus_carolinensis -2.4434 0.2638 -2.9973 -2.4323 -1.9709
## week-Odocoileus_virginianus 0.2088 0.0599 0.0932 0.2091 0.3241
## week-Canis_latrans 0.0703 0.1288 -0.1934 0.0731 0.3151
## week-Procyon_lotor -0.0489 0.1181 -0.2939 -0.0475 0.1754
## week-Dasypus_novemcinctus -0.1567 0.1349 -0.4329 -0.1532 0.0908
## week-Lynx_rufus -0.0382 0.1947 -0.4494 -0.0286 0.3135
## week-Didelphis_virginiana -0.1993 0.2122 -0.6642 -0.1820 0.1771
## week-Sylvilagus_floridanus -0.1464 0.2029 -0.5674 -0.1370 0.2150
## week-Meleagris_gallopavo -0.2666 0.2455 -0.8296 -0.2412 0.1435
## week-Sciurus_carolinensis 0.1396 0.1756 -0.2133 0.1422 0.4748
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0036 3032
## (Intercept)-Procyon_lotor 1.0003 4146
## (Intercept)-Dasypus_novemcinctus 1.0003 5202
## (Intercept)-Lynx_rufus 1.0159 1221
## (Intercept)-Didelphis_virginiana 1.0027 3267
## (Intercept)-Sylvilagus_floridanus 1.0098 963
## (Intercept)-Meleagris_gallopavo 1.0068 692
## (Intercept)-Sciurus_carolinensis 1.0030 3737
## week-Odocoileus_virginianus 1.0018 5250
## week-Canis_latrans 1.0019 4549
## week-Procyon_lotor 1.0010 4409
## week-Dasypus_novemcinctus 1.0005 4982
## week-Lynx_rufus 1.0013 3153
## week-Didelphis_virginiana 1.0018 3412
## week-Sylvilagus_floridanus 1.0029 2926
## week-Meleagris_gallopavo 1.0050 2258
## week-Sciurus_carolinensis 1.0001 4471
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5433
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7368 1.1105 -2.8126 -0.7722 1.5914 1.0002 2138
## Cogon_Patch_Size 0.1140 0.6929 -1.2924 0.1198 1.4623 1.0200 1125
## Veg_shannon_index 0.8553 0.4510 0.0069 0.8466 1.7570 1.0076 1176
## total_shrub_cover -0.6288 0.5247 -1.7234 -0.6076 0.3619 1.0022 2269
## Avg_Cogongrass_Cover 0.0269 0.9190 -1.7253 0.0348 1.8376 1.0328 503
## Tree_Density -1.8773 0.7921 -3.4334 -1.8677 -0.3037 1.0182 1119
## Avg_Canopy_Cover 1.6420 0.6364 0.4178 1.6146 2.9491 1.0087 1329
## I(Avg_Cogongrass_Cover^2) 1.4152 0.6093 0.3126 1.3885 2.7125 1.0071 785
## avg_veg_height -0.0988 0.5016 -1.1073 -0.0892 0.8529 1.0117 933
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.2587 18.5746 3.0208 13.1095 63.9611 1.0808 459
## Cogon_Patch_Size 3.0919 5.3291 0.1131 1.6436 14.7971 1.1224 461
## Veg_shannon_index 0.6332 1.5175 0.0443 0.3079 3.1922 1.1069 1498
## total_shrub_cover 1.8575 2.9364 0.0931 1.0341 7.9721 1.0794 575
## Avg_Cogongrass_Cover 0.9915 1.5613 0.0507 0.4523 5.0730 1.0108 1750
## Tree_Density 3.8511 7.6817 0.0719 1.3951 24.5015 1.1358 264
## Avg_Canopy_Cover 2.6450 3.6999 0.1261 1.4966 12.8651 1.0843 666
## I(Avg_Cogongrass_Cover^2) 1.5973 4.2318 0.0491 0.5487 9.1305 1.0829 476
## avg_veg_height 0.4961 0.6924 0.0416 0.2749 2.3922 1.0042 2613
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5014 2.0231 0.0543 0.7648 7.1857 1.1606 246
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2414 0.4396 -3.0619 -2.2581 -1.3247 1.0005 6911
## week -0.0430 0.1267 -0.3062 -0.0424 0.1963 1.0004 3557
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7433 1.2139 0.5676 1.4198 4.8927 1.0039 4173
## week 0.1016 0.0943 0.0262 0.0766 0.3223 1.0124 3945
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.0078 3.4373 2.2650 6.4315
## (Intercept)-Canis_latrans -0.8950 1.2238 -3.3466 -0.9013
## (Intercept)-Procyon_lotor -0.3707 1.0515 -2.5638 -0.3382
## (Intercept)-Dasypus_novemcinctus -2.7072 1.1815 -5.3439 -2.5828
## (Intercept)-Lynx_rufus -0.1456 2.3104 -3.9705 -0.4283
## (Intercept)-Didelphis_virginiana -4.1236 1.3895 -7.1327 -4.0142
## (Intercept)-Sylvilagus_floridanus -2.1449 1.3974 -5.0633 -2.1210
## (Intercept)-Meleagris_gallopavo -1.7240 1.5422 -4.7588 -1.7123
## (Intercept)-Sciurus_carolinensis -4.7720 1.5106 -8.1602 -4.6170
## Cogon_Patch_Size-Odocoileus_virginianus 0.1399 1.3868 -2.4976 0.1006
## Cogon_Patch_Size-Canis_latrans 1.6064 1.4244 -0.2471 1.3344
## Cogon_Patch_Size-Procyon_lotor -0.3240 0.8040 -1.8760 -0.3241
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1489 0.6932 -1.5896 -0.1272
## Cogon_Patch_Size-Lynx_rufus -0.0443 1.6291 -3.0299 -0.0737
## Cogon_Patch_Size-Didelphis_virginiana 1.6594 1.0487 -0.0611 1.5383
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1674 1.5390 -4.9025 -0.9198
## Cogon_Patch_Size-Meleagris_gallopavo 0.3286 1.1851 -1.7410 0.2304
## Cogon_Patch_Size-Sciurus_carolinensis -0.9518 1.2848 -3.9876 -0.7026
## Veg_shannon_index-Odocoileus_virginianus 0.6977 0.7898 -1.0086 0.7190
## Veg_shannon_index-Canis_latrans 1.1935 0.6616 0.1121 1.1245
## Veg_shannon_index-Procyon_lotor 1.0513 0.5895 -0.0151 1.0066
## Veg_shannon_index-Dasypus_novemcinctus 0.6192 0.5298 -0.4212 0.6227
## Veg_shannon_index-Lynx_rufus 0.8821 0.8380 -0.7434 0.8557
## Veg_shannon_index-Didelphis_virginiana 1.0088 0.6546 -0.1663 0.9724
## Veg_shannon_index-Sylvilagus_floridanus 0.9496 0.6914 -0.3232 0.9146
## Veg_shannon_index-Meleagris_gallopavo 1.1233 0.7574 -0.1260 1.0385
## Veg_shannon_index-Sciurus_carolinensis 0.3464 0.7375 -1.3198 0.4114
## total_shrub_cover-Odocoileus_virginianus -0.2041 1.0968 -2.2223 -0.2691
## total_shrub_cover-Canis_latrans 0.0011 0.7424 -1.3204 -0.0511
## total_shrub_cover-Procyon_lotor -1.1568 0.6698 -2.6138 -1.1009
## total_shrub_cover-Dasypus_novemcinctus 0.0670 0.5661 -1.0107 0.0538
## total_shrub_cover-Lynx_rufus -1.4037 1.2781 -4.5084 -1.2153
## total_shrub_cover-Didelphis_virginiana -0.7193 0.7756 -2.3429 -0.6821
## total_shrub_cover-Sylvilagus_floridanus -0.3942 0.8956 -2.2227 -0.3790
## total_shrub_cover-Meleagris_gallopavo -2.2486 1.4496 -5.6354 -2.0020
## total_shrub_cover-Sciurus_carolinensis -0.0516 0.7504 -1.4503 -0.0775
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0457 1.2449 -2.4621 -0.0463
## Avg_Cogongrass_Cover-Canis_latrans 0.0027 1.1213 -2.1542 -0.0046
## Avg_Cogongrass_Cover-Procyon_lotor 0.1458 1.1259 -2.0080 0.1121
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5120 1.1703 -1.5805 0.4575
## Avg_Cogongrass_Cover-Lynx_rufus 0.1337 1.2141 -2.1720 0.1151
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1321 1.1342 -2.0038 0.1055
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5065 1.2511 -3.1733 -0.4393
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2883 1.2706 -2.9798 -0.2398
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0418 1.1542 -2.1863 0.0149
## Tree_Density-Odocoileus_virginianus -0.8524 1.4514 -3.0611 -1.0586
## Tree_Density-Canis_latrans -2.7447 1.4650 -6.4463 -2.4634
## Tree_Density-Procyon_lotor -1.7687 0.9829 -3.7873 -1.7497
## Tree_Density-Dasypus_novemcinctus -3.7061 2.0848 -9.2786 -3.1955
## Tree_Density-Lynx_rufus -0.6791 1.7807 -3.3601 -0.9585
## Tree_Density-Didelphis_virginiana -2.2329 1.1948 -5.0391 -2.0920
## Tree_Density-Sylvilagus_floridanus -2.4647 1.4377 -5.9592 -2.2583
## Tree_Density-Meleagris_gallopavo -1.9748 1.3457 -4.8535 -1.9500
## Tree_Density-Sciurus_carolinensis -2.6943 1.5752 -6.5956 -2.4132
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0545 1.3267 -1.7778 1.1205
## Avg_Canopy_Cover-Canis_latrans 0.1951 0.7129 -1.2559 0.1944
## Avg_Canopy_Cover-Procyon_lotor 1.5975 0.7443 0.2608 1.5577
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9284 0.7518 0.6782 1.8351
## Avg_Canopy_Cover-Lynx_rufus 1.1851 1.3695 -1.4598 1.1677
## Avg_Canopy_Cover-Didelphis_virginiana 2.6032 1.0245 1.0522 2.4656
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2804 1.6294 1.0047 2.9702
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2670 1.2363 0.4872 2.0420
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2452 0.9338 0.8442 2.1190
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8624 1.4175 -0.1070 1.6309
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0880 1.3662 0.5493 1.8139
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8371 0.9826 0.3673 1.6885
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4371 0.7305 0.1926 1.3686
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1440 1.2598 0.4885 1.9048
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0236 0.6744 -0.3312 1.0247
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1599 0.8885 -0.3796 1.1155
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4329 1.2981 -2.6702 0.6189
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6247 0.7776 0.3730 1.5429
## avg_veg_height-Odocoileus_virginianus -0.1007 0.7962 -1.7489 -0.0765
## avg_veg_height-Canis_latrans -0.3612 0.6346 -1.6480 -0.3307
## avg_veg_height-Procyon_lotor 0.1051 0.6153 -1.0694 0.0955
## avg_veg_height-Dasypus_novemcinctus 0.1689 0.6019 -0.9802 0.1565
## avg_veg_height-Lynx_rufus -0.2414 0.7949 -1.9651 -0.2004
## avg_veg_height-Didelphis_virginiana -0.2315 0.7003 -1.6951 -0.2054
## avg_veg_height-Sylvilagus_floridanus -0.2562 0.6889 -1.7042 -0.2326
## avg_veg_height-Meleagris_gallopavo -0.1675 0.7555 -1.7418 -0.1369
## avg_veg_height-Sciurus_carolinensis 0.1520 0.6766 -1.0902 0.1268
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.1644 1.0509 387
## (Intercept)-Canis_latrans 1.6227 1.0111 1154
## (Intercept)-Procyon_lotor 1.6121 1.0110 1354
## (Intercept)-Dasypus_novemcinctus -0.7592 1.0263 573
## (Intercept)-Lynx_rufus 5.4569 1.0119 412
## (Intercept)-Didelphis_virginiana -1.6705 1.0146 1107
## (Intercept)-Sylvilagus_floridanus 0.6113 1.0117 1369
## (Intercept)-Meleagris_gallopavo 1.4881 1.0066 891
## (Intercept)-Sciurus_carolinensis -2.2266 1.0271 710
## Cogon_Patch_Size-Odocoileus_virginianus 3.1579 1.0117 1584
## Cogon_Patch_Size-Canis_latrans 5.0503 1.0369 743
## Cogon_Patch_Size-Procyon_lotor 1.2247 1.0319 1512
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1331 1.0271 1381
## Cogon_Patch_Size-Lynx_rufus 3.4991 1.0333 684
## Cogon_Patch_Size-Didelphis_virginiana 4.0895 1.0277 801
## Cogon_Patch_Size-Sylvilagus_floridanus 1.0528 1.0455 537
## Cogon_Patch_Size-Meleagris_gallopavo 3.0068 1.0158 1104
## Cogon_Patch_Size-Sciurus_carolinensis 0.8410 1.0608 817
## Veg_shannon_index-Odocoileus_virginianus 2.2060 1.0001 2451
## Veg_shannon_index-Canis_latrans 2.7107 1.0112 1005
## Veg_shannon_index-Procyon_lotor 2.3680 1.0141 974
## Veg_shannon_index-Dasypus_novemcinctus 1.6696 1.0005 2066
## Veg_shannon_index-Lynx_rufus 2.6680 1.0037 1423
## Veg_shannon_index-Didelphis_virginiana 2.4546 1.0014 1707
## Veg_shannon_index-Sylvilagus_floridanus 2.3849 1.0039 1593
## Veg_shannon_index-Meleagris_gallopavo 2.9579 1.0043 1411
## Veg_shannon_index-Sciurus_carolinensis 1.6006 1.0031 1875
## total_shrub_cover-Odocoileus_virginianus 2.2128 1.0026 2154
## total_shrub_cover-Canis_latrans 1.6105 1.0043 1764
## total_shrub_cover-Procyon_lotor 0.0366 1.0048 1942
## total_shrub_cover-Dasypus_novemcinctus 1.2384 1.0002 2843
## total_shrub_cover-Lynx_rufus 0.5626 1.0077 728
## total_shrub_cover-Didelphis_virginiana 0.7018 1.0021 2986
## total_shrub_cover-Sylvilagus_floridanus 1.3031 1.0002 1943
## total_shrub_cover-Meleagris_gallopavo -0.2277 1.0121 629
## total_shrub_cover-Sciurus_carolinensis 1.5038 1.0006 3049
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.4347 1.0216 792
## Avg_Cogongrass_Cover-Canis_latrans 2.2735 1.0173 722
## Avg_Cogongrass_Cover-Procyon_lotor 2.4676 1.0451 722
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.0511 1.0319 719
## Avg_Cogongrass_Cover-Lynx_rufus 2.5825 1.0196 891
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.4753 1.0132 779
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8265 1.0189 790
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0794 1.0241 788
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3818 1.0243 752
## Tree_Density-Odocoileus_virginianus 2.7185 1.0402 784
## Tree_Density-Canis_latrans -0.6903 1.0535 428
## Tree_Density-Procyon_lotor 0.1319 1.0098 1597
## Tree_Density-Dasypus_novemcinctus -1.2193 1.1080 243
## Tree_Density-Lynx_rufus 3.8043 1.0566 503
## Tree_Density-Didelphis_virginiana -0.3368 1.0410 772
## Tree_Density-Sylvilagus_floridanus -0.2107 1.0547 715
## Tree_Density-Meleagris_gallopavo 0.6671 1.0104 1422
## Tree_Density-Sciurus_carolinensis -0.5595 1.0855 611
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6506 1.0007 1693
## Avg_Canopy_Cover-Canis_latrans 1.6017 1.0067 1598
## Avg_Canopy_Cover-Procyon_lotor 3.1868 1.0110 1094
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.6362 1.0293 666
## Avg_Canopy_Cover-Lynx_rufus 4.0730 1.0069 912
## Avg_Canopy_Cover-Didelphis_virginiana 5.0139 1.0466 619
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.2023 1.0590 516
## Avg_Canopy_Cover-Meleagris_gallopavo 5.2883 1.0188 783
## Avg_Canopy_Cover-Sciurus_carolinensis 4.5000 1.0101 1065
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 5.3901 1.0050 447
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.0543 1.0559 291
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.1312 1.0159 607
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1023 1.0051 916
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2497 1.0159 563
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.3555 1.0042 1005
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1482 1.0032 888
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5051 1.0132 511
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.4501 1.0103 974
## avg_veg_height-Odocoileus_virginianus 1.4879 1.0054 1813
## avg_veg_height-Canis_latrans 0.8045 1.0127 1339
## avg_veg_height-Procyon_lotor 1.3216 1.0062 1368
## avg_veg_height-Dasypus_novemcinctus 1.4159 1.0112 1422
## avg_veg_height-Lynx_rufus 1.1793 1.0131 1397
## avg_veg_height-Didelphis_virginiana 1.0515 1.0187 1473
## avg_veg_height-Sylvilagus_floridanus 1.0141 1.0042 1526
## avg_veg_height-Meleagris_gallopavo 1.2132 1.0011 1320
## avg_veg_height-Sciurus_carolinensis 1.5505 1.0058 1554
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0064 0.0587 -0.1110 0.0066 0.1192
## (Intercept)-Canis_latrans -2.6219 0.1726 -2.9808 -2.6150 -2.3019
## (Intercept)-Procyon_lotor -2.2729 0.1285 -2.5322 -2.2715 -2.0263
## (Intercept)-Dasypus_novemcinctus -1.5841 0.1337 -1.8541 -1.5838 -1.3250
## (Intercept)-Lynx_rufus -3.7288 0.3224 -4.3818 -3.7248 -3.1044
## (Intercept)-Didelphis_virginiana -2.3072 0.2468 -2.8205 -2.2959 -1.8454
## (Intercept)-Sylvilagus_floridanus -3.2190 0.2772 -3.7926 -3.2101 -2.7084
## (Intercept)-Meleagris_gallopavo -3.4326 0.3217 -4.0839 -3.4229 -2.8334
## (Intercept)-Sciurus_carolinensis -2.4322 0.2634 -2.9847 -2.4184 -1.9507
## week-Odocoileus_virginianus 0.2075 0.0605 0.0885 0.2069 0.3281
## week-Canis_latrans 0.0717 0.1297 -0.1968 0.0778 0.3229
## week-Procyon_lotor -0.0472 0.1181 -0.2869 -0.0441 0.1807
## week-Dasypus_novemcinctus -0.1587 0.1363 -0.4441 -0.1544 0.0986
## week-Lynx_rufus -0.0234 0.1933 -0.4258 -0.0194 0.3362
## week-Didelphis_virginiana -0.1972 0.2115 -0.6528 -0.1819 0.1755
## week-Sylvilagus_floridanus -0.1438 0.2000 -0.5715 -0.1357 0.2276
## week-Meleagris_gallopavo -0.2633 0.2412 -0.8003 -0.2410 0.1486
## week-Sciurus_carolinensis 0.1420 0.1760 -0.2150 0.1424 0.4824
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0013 2650
## (Intercept)-Procyon_lotor 1.0009 3956
## (Intercept)-Dasypus_novemcinctus 1.0009 4663
## (Intercept)-Lynx_rufus 1.0088 641
## (Intercept)-Didelphis_virginiana 1.0023 4001
## (Intercept)-Sylvilagus_floridanus 1.0024 1719
## (Intercept)-Meleagris_gallopavo 1.0081 751
## (Intercept)-Sciurus_carolinensis 1.0012 3588
## week-Odocoileus_virginianus 1.0039 5250
## week-Canis_latrans 1.0008 4269
## week-Procyon_lotor 1.0021 3972
## week-Dasypus_novemcinctus 1.0002 5250
## week-Lynx_rufus 1.0134 2465
## week-Didelphis_virginiana 1.0015 4178
## week-Sylvilagus_floridanus 1.0018 2492
## week-Meleagris_gallopavo 1.0057 2640
## week-Sciurus_carolinensis 1.0005 4981
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2452
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2423 0.6422 -0.9844 0.2239 1.5796 1.0053 2729
## Avg_Cogongrass_Cover 0.2311 0.2739 -0.3096 0.2308 0.7584 1.0145 2456
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6002 3.5796 0.5744 2.5943 13.0038 1.0120 1453
## Avg_Cogongrass_Cover 0.3173 0.3979 0.0370 0.1951 1.3671 1.0064 2002
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6052 0.7212 0.0511 0.3623 2.509 1.0312 591
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3487 0.4797 -3.2481 -2.3645 -1.3169 1.0007 5065
## shrub_cover 0.1728 0.2799 -0.3998 0.1759 0.7347 1.0022 3816
## veg_height -0.0040 0.1805 -0.3577 -0.0055 0.3621 1.0000 3814
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0537 1.4547 0.6571 1.6791 5.7910 1.0017 4539
## shrub_cover 0.6182 0.5092 0.1300 0.4831 1.9292 1.0051 2059
## veg_height 0.2364 0.2100 0.0598 0.1858 0.7009 1.0184 3852
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5629 1.4171 1.3146 3.3509
## (Intercept)-Canis_latrans 0.4984 0.6152 -0.6741 0.4737
## (Intercept)-Procyon_lotor 0.6338 0.5843 -0.5338 0.6339
## (Intercept)-Dasypus_novemcinctus -0.5576 0.5587 -1.7084 -0.5378
## (Intercept)-Lynx_rufus 0.2847 1.0632 -1.3597 0.1656
## (Intercept)-Didelphis_virginiana -1.1803 0.6371 -2.4214 -1.1763
## (Intercept)-Sylvilagus_floridanus -0.2791 0.6812 -1.5566 -0.3007
## (Intercept)-Meleagris_gallopavo 0.7845 1.3660 -1.2139 0.5710
## (Intercept)-Sciurus_carolinensis -1.2431 0.6495 -2.5811 -1.2268
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2269 0.5004 -0.7557 0.2146
## Avg_Cogongrass_Cover-Canis_latrans 0.4590 0.3985 -0.2291 0.4307
## Avg_Cogongrass_Cover-Procyon_lotor 0.2420 0.3513 -0.4299 0.2310
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3576 0.3257 -0.2481 0.3464
## Avg_Cogongrass_Cover-Lynx_rufus 0.4513 0.4320 -0.3267 0.4232
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3343 0.3641 -0.3742 0.3290
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1607 0.4522 -1.1521 -0.1217
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1127 0.6502 -1.5890 -0.0562
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3621 0.3622 -0.3308 0.3553
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9335 1.0059 1068
## (Intercept)-Canis_latrans 1.7450 1.0018 3066
## (Intercept)-Procyon_lotor 1.7630 1.0022 3297
## (Intercept)-Dasypus_novemcinctus 0.5214 1.0021 3511
## (Intercept)-Lynx_rufus 2.8151 1.0086 780
## (Intercept)-Didelphis_virginiana 0.0605 1.0049 2837
## (Intercept)-Sylvilagus_floridanus 1.1202 1.0097 1983
## (Intercept)-Meleagris_gallopavo 4.2367 1.0370 474
## (Intercept)-Sciurus_carolinensis -0.0070 1.0015 2797
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2552 1.0068 3706
## Avg_Cogongrass_Cover-Canis_latrans 1.3644 1.0007 3526
## Avg_Cogongrass_Cover-Procyon_lotor 0.9769 1.0027 4335
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0371 1.0002 4889
## Avg_Cogongrass_Cover-Lynx_rufus 1.3781 1.0022 3152
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0788 1.0051 4257
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5998 1.0099 2387
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0214 1.0302 1306
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1217 1.0009 3971
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0040 0.0596 -0.1147 0.0041 0.1214
## (Intercept)-Canis_latrans -2.7498 0.1940 -3.1527 -2.7410 -2.3920
## (Intercept)-Procyon_lotor -2.2850 0.1467 -2.5836 -2.2819 -2.0034
## (Intercept)-Dasypus_novemcinctus -1.7199 0.1551 -2.0405 -1.7184 -1.4278
## (Intercept)-Lynx_rufus -3.7017 0.3683 -4.4380 -3.7024 -2.9989
## (Intercept)-Didelphis_virginiana -2.5243 0.2888 -3.1329 -2.5073 -2.0001
## (Intercept)-Sylvilagus_floridanus -3.1808 0.3083 -3.8328 -3.1611 -2.6297
## (Intercept)-Meleagris_gallopavo -4.1669 0.5045 -5.2012 -4.1608 -3.1799
## (Intercept)-Sciurus_carolinensis -2.5775 0.3046 -3.2121 -2.5689 -2.0094
## shrub_cover-Odocoileus_virginianus -0.0545 0.0632 -0.1779 -0.0537 0.0654
## shrub_cover-Canis_latrans -0.2955 0.2156 -0.7234 -0.2947 0.1187
## shrub_cover-Procyon_lotor 0.2423 0.1632 -0.0926 0.2476 0.5509
## shrub_cover-Dasypus_novemcinctus 0.8217 0.2955 0.2615 0.8140 1.4189
## shrub_cover-Lynx_rufus -0.2857 0.3522 -0.9659 -0.2864 0.4131
## shrub_cover-Didelphis_virginiana 0.9231 0.3647 0.2595 0.9072 1.6806
## shrub_cover-Sylvilagus_floridanus 0.2395 0.4146 -0.5236 0.2228 1.0817
## shrub_cover-Meleagris_gallopavo -0.8255 0.4190 -1.6998 -0.8146 -0.0406
## shrub_cover-Sciurus_carolinensis 0.7952 0.3930 0.0598 0.7739 1.6060
## veg_height-Odocoileus_virginianus -0.2961 0.0639 -0.4178 -0.2968 -0.1717
## veg_height-Canis_latrans -0.6061 0.1865 -0.9830 -0.6027 -0.2616
## veg_height-Procyon_lotor 0.3295 0.1220 0.0875 0.3294 0.5737
## veg_height-Dasypus_novemcinctus 0.2313 0.1315 -0.0204 0.2293 0.5013
## veg_height-Lynx_rufus -0.0003 0.2517 -0.5084 0.0035 0.4807
## veg_height-Didelphis_virginiana 0.4167 0.2412 -0.0310 0.4098 0.9221
## veg_height-Sylvilagus_floridanus 0.1300 0.2500 -0.3565 0.1267 0.6133
## veg_height-Meleagris_gallopavo -0.2993 0.3725 -1.0484 -0.2907 0.4441
## veg_height-Sciurus_carolinensis 0.0540 0.2082 -0.3410 0.0517 0.4804
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0065 2234
## (Intercept)-Procyon_lotor 1.0012 3723
## (Intercept)-Dasypus_novemcinctus 1.0026 4217
## (Intercept)-Lynx_rufus 1.0023 783
## (Intercept)-Didelphis_virginiana 1.0066 2323
## (Intercept)-Sylvilagus_floridanus 1.0170 1564
## (Intercept)-Meleagris_gallopavo 1.0441 455
## (Intercept)-Sciurus_carolinensis 1.0016 2686
## shrub_cover-Odocoileus_virginianus 1.0018 5250
## shrub_cover-Canis_latrans 1.0006 2871
## shrub_cover-Procyon_lotor 1.0002 4432
## shrub_cover-Dasypus_novemcinctus 1.0129 3766
## shrub_cover-Lynx_rufus 1.0032 1537
## shrub_cover-Didelphis_virginiana 1.0017 2044
## shrub_cover-Sylvilagus_floridanus 1.0010 1650
## shrub_cover-Meleagris_gallopavo 1.0185 550
## shrub_cover-Sciurus_carolinensis 1.0005 2822
## veg_height-Odocoileus_virginianus 1.0016 5250
## veg_height-Canis_latrans 1.0016 2331
## veg_height-Procyon_lotor 0.9999 4274
## veg_height-Dasypus_novemcinctus 1.0017 4766
## veg_height-Lynx_rufus 1.0051 2310
## veg_height-Didelphis_virginiana 1.0009 3354
## veg_height-Sylvilagus_floridanus 1.0159 2222
## veg_height-Meleagris_gallopavo 1.0156 1060
## veg_height-Sciurus_carolinensis 1.0005 3406
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3025
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2848 1.0628 -1.8013 0.2383 2.4598 1.0012 2013
## Cogon_Patch_Size -0.3463 0.6511 -1.6555 -0.3488 0.9391 1.0013 1729
## Veg_shannon_index 0.8997 0.4860 0.0163 0.8812 1.9012 1.0035 604
## total_shrub_cover -0.5927 0.5896 -1.8457 -0.5644 0.5350 1.0105 1160
## Avg_Cogongrass_Cover 1.8268 0.7107 0.4969 1.8205 3.2556 1.0110 627
## Tree_Density -1.7116 0.7977 -3.2807 -1.7198 -0.1064 1.0036 1155
## Avg_Canopy_Cover 1.8145 0.7100 0.4687 1.7749 3.2978 1.0033 939
## avg_veg_height -0.3036 0.5077 -1.2987 -0.3096 0.6652 1.0101 797
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.4767 22.2376 2.4973 12.1762 79.8297 1.0724 487
## Cogon_Patch_Size 2.5965 4.1721 0.0863 1.3173 12.9454 1.0288 328
## Veg_shannon_index 0.6950 1.0917 0.0481 0.3638 3.3707 1.0142 1674
## total_shrub_cover 1.7450 2.8919 0.0714 0.8587 8.7961 1.0068 552
## Avg_Cogongrass_Cover 1.0004 1.7047 0.0471 0.4353 5.3387 1.0288 1162
## Tree_Density 4.1246 7.9108 0.0767 1.6885 23.4149 1.0197 532
## Avg_Canopy_Cover 3.6442 5.3377 0.1825 2.0499 16.5236 1.0276 544
## avg_veg_height 0.4880 0.8187 0.0415 0.2643 2.3062 1.0259 1801
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.105 3.2987 0.0597 0.9667 11.079 1.0357 136
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3702 0.4714 -3.2631 -2.3890 -1.3570 1.0054 4580
## shrub_cover 0.2527 0.2858 -0.2989 0.2512 0.8438 1.0021 2265
## veg_height 0.0145 0.1777 -0.3457 0.0128 0.3551 1.0009 3498
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0279 1.5152 0.6642 1.6329 5.7453 1.0063 3970
## shrub_cover 0.6367 0.6398 0.1279 0.4860 1.9757 1.0261 3005
## veg_height 0.2481 0.2024 0.0631 0.1926 0.7682 1.0031 3430
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.9821 3.4363 3.0630 7.4138
## (Intercept)-Canis_latrans 1.0250 1.1438 -0.9532 0.9363
## (Intercept)-Procyon_lotor 1.0072 1.0478 -1.1126 1.0186
## (Intercept)-Dasypus_novemcinctus -1.3793 1.0885 -3.8687 -1.3047
## (Intercept)-Lynx_rufus 2.5297 3.2487 -1.7778 1.8091
## (Intercept)-Didelphis_virginiana -2.7528 1.2950 -5.6327 -2.6669
## (Intercept)-Sylvilagus_floridanus -0.9588 1.3620 -3.7559 -0.9557
## (Intercept)-Meleagris_gallopavo -0.0131 2.3289 -3.6777 -0.2588
## (Intercept)-Sciurus_carolinensis -2.7579 1.4068 -5.8384 -2.6828
## Cogon_Patch_Size-Odocoileus_virginianus -0.2778 1.3063 -2.7810 -0.3330
## Cogon_Patch_Size-Canis_latrans 0.7907 1.2523 -0.9341 0.5467
## Cogon_Patch_Size-Procyon_lotor -0.8628 0.7891 -2.4721 -0.8314
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4439 0.7729 -1.9411 -0.4691
## Cogon_Patch_Size-Lynx_rufus -0.5114 1.4342 -3.3468 -0.5309
## Cogon_Patch_Size-Didelphis_virginiana 0.8770 0.9742 -0.6764 0.7530
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5510 1.5145 -5.2933 -1.2955
## Cogon_Patch_Size-Meleagris_gallopavo -0.0449 1.3039 -2.2030 -0.1551
## Cogon_Patch_Size-Sciurus_carolinensis -1.3450 1.2888 -4.5559 -1.1096
## Veg_shannon_index-Odocoileus_virginianus 0.7678 0.8765 -1.0749 0.7797
## Veg_shannon_index-Canis_latrans 1.2452 0.6895 0.0590 1.1933
## Veg_shannon_index-Procyon_lotor 1.1888 0.6343 0.0837 1.1390
## Veg_shannon_index-Dasypus_novemcinctus 0.6176 0.5618 -0.5183 0.6227
## Veg_shannon_index-Lynx_rufus 0.8635 0.8545 -0.9439 0.8744
## Veg_shannon_index-Didelphis_virginiana 1.1099 0.6865 -0.1103 1.0407
## Veg_shannon_index-Sylvilagus_floridanus 1.0310 0.7044 -0.2684 0.9879
## Veg_shannon_index-Meleagris_gallopavo 1.2152 0.8195 -0.1780 1.1150
## Veg_shannon_index-Sciurus_carolinensis 0.2827 0.7811 -1.4802 0.3624
## total_shrub_cover-Odocoileus_virginianus -0.1571 1.0464 -2.0311 -0.2192
## total_shrub_cover-Canis_latrans 0.5222 0.9720 -0.9263 0.3563
## total_shrub_cover-Procyon_lotor -1.0794 0.6924 -2.6203 -1.0131
## total_shrub_cover-Dasypus_novemcinctus -0.2250 0.7059 -1.6897 -0.2005
## total_shrub_cover-Lynx_rufus -0.9312 1.3528 -4.0393 -0.7853
## total_shrub_cover-Didelphis_virginiana -0.9010 0.9521 -3.2081 -0.7649
## total_shrub_cover-Sylvilagus_floridanus -0.7177 1.0662 -3.1675 -0.6146
## total_shrub_cover-Meleagris_gallopavo -1.6347 1.4341 -5.1499 -1.3865
## total_shrub_cover-Sciurus_carolinensis -0.5636 0.9794 -2.8070 -0.4698
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7468 1.0641 -0.3897 1.7206
## Avg_Cogongrass_Cover-Canis_latrans 2.1623 0.9501 0.5200 2.1013
## Avg_Cogongrass_Cover-Procyon_lotor 1.9521 0.8800 0.3728 1.9068
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3353 0.9864 0.6684 2.2369
## Avg_Cogongrass_Cover-Lynx_rufus 2.0787 1.0093 0.3145 2.0235
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9111 0.8935 0.2483 1.8715
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2496 1.0163 -0.9053 1.3095
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5106 1.2495 -1.3165 1.5788
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0862 0.9379 0.4416 2.0228
## Tree_Density-Odocoileus_virginianus -0.6028 1.4770 -2.8007 -0.8266
## Tree_Density-Canis_latrans -2.6316 1.4170 -6.0615 -2.3767
## Tree_Density-Procyon_lotor -1.3986 0.8101 -2.8723 -1.4288
## Tree_Density-Dasypus_novemcinctus -3.6249 2.0185 -9.0503 -3.1277
## Tree_Density-Lynx_rufus -0.3938 1.7748 -2.9239 -0.6779
## Tree_Density-Didelphis_virginiana -2.1178 1.2397 -4.8656 -2.0058
## Tree_Density-Sylvilagus_floridanus -2.3922 1.5772 -6.3125 -2.1635
## Tree_Density-Meleagris_gallopavo -2.3303 1.6010 -6.2111 -2.1082
## Tree_Density-Sciurus_carolinensis -2.3060 1.5106 -5.9939 -2.1274
## Avg_Canopy_Cover-Odocoileus_virginianus 1.1397 1.4651 -1.8359 1.1761
## Avg_Canopy_Cover-Canis_latrans 0.1460 0.6994 -1.2512 0.1566
## Avg_Canopy_Cover-Procyon_lotor 1.7222 0.7802 0.3575 1.6576
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1623 0.8015 0.8383 2.0655
## Avg_Canopy_Cover-Lynx_rufus 1.3281 1.6384 -1.6571 1.2419
## Avg_Canopy_Cover-Didelphis_virginiana 3.0826 1.3411 1.2609 2.8252
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6888 1.8096 1.2446 3.3601
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6559 1.5519 0.5022 2.3703
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7571 1.2793 1.0028 2.5024
## avg_veg_height-Odocoileus_virginianus -0.3582 0.7875 -1.9656 -0.3485
## avg_veg_height-Canis_latrans -0.3570 0.6175 -1.5927 -0.3595
## avg_veg_height-Procyon_lotor -0.2883 0.5950 -1.4688 -0.2845
## avg_veg_height-Dasypus_novemcinctus -0.0883 0.6133 -1.2626 -0.0984
## avg_veg_height-Lynx_rufus -0.4189 0.8351 -2.1637 -0.4040
## avg_veg_height-Didelphis_virginiana -0.4828 0.6917 -1.9379 -0.4492
## avg_veg_height-Sylvilagus_floridanus -0.5263 0.7151 -2.0682 -0.4900
## avg_veg_height-Meleagris_gallopavo -0.3088 0.8647 -2.0678 -0.3014
## avg_veg_height-Sciurus_carolinensis 0.0232 0.6798 -1.2149 -0.0066
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.3092 1.0270 448
## (Intercept)-Canis_latrans 3.6686 1.0109 1215
## (Intercept)-Procyon_lotor 3.0462 1.0137 1328
## (Intercept)-Dasypus_novemcinctus 0.5546 1.0347 711
## (Intercept)-Lynx_rufus 11.0277 1.0182 189
## (Intercept)-Didelphis_virginiana -0.4103 1.0039 942
## (Intercept)-Sylvilagus_floridanus 1.7648 1.0062 873
## (Intercept)-Meleagris_gallopavo 4.9715 1.0860 205
## (Intercept)-Sciurus_carolinensis -0.0999 1.0105 748
## Cogon_Patch_Size-Odocoileus_virginianus 2.5966 1.0042 1854
## Cogon_Patch_Size-Canis_latrans 3.8487 1.0089 1065
## Cogon_Patch_Size-Procyon_lotor 0.5302 1.0062 827
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1878 1.0018 1368
## Cogon_Patch_Size-Lynx_rufus 2.5473 1.0087 787
## Cogon_Patch_Size-Didelphis_virginiana 3.1421 1.0035 919
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6866 1.0073 757
## Cogon_Patch_Size-Meleagris_gallopavo 2.8890 1.0029 822
## Cogon_Patch_Size-Sciurus_carolinensis 0.5327 1.0146 663
## Veg_shannon_index-Odocoileus_virginianus 2.4367 1.0033 1562
## Veg_shannon_index-Canis_latrans 2.7761 1.0023 775
## Veg_shannon_index-Procyon_lotor 2.5605 1.0052 539
## Veg_shannon_index-Dasypus_novemcinctus 1.7357 1.0012 1288
## Veg_shannon_index-Lynx_rufus 2.5552 1.0030 1128
## Veg_shannon_index-Didelphis_virginiana 2.6403 1.0060 695
## Veg_shannon_index-Sylvilagus_floridanus 2.5694 1.0035 951
## Veg_shannon_index-Meleagris_gallopavo 3.0718 1.0044 1361
## Veg_shannon_index-Sciurus_carolinensis 1.6205 1.0003 1589
## total_shrub_cover-Odocoileus_virginianus 2.1201 1.0077 1971
## total_shrub_cover-Canis_latrans 2.9164 1.0015 770
## total_shrub_cover-Procyon_lotor 0.1171 1.0005 1648
## total_shrub_cover-Dasypus_novemcinctus 1.1180 1.0058 1562
## total_shrub_cover-Lynx_rufus 1.4542 1.0095 613
## total_shrub_cover-Didelphis_virginiana 0.5826 1.0054 905
## total_shrub_cover-Sylvilagus_floridanus 1.1475 1.0020 1018
## total_shrub_cover-Meleagris_gallopavo 0.4808 1.0218 492
## total_shrub_cover-Sciurus_carolinensis 1.1277 1.0078 1135
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.9048 1.0157 1142
## Avg_Cogongrass_Cover-Canis_latrans 4.2947 1.0026 735
## Avg_Cogongrass_Cover-Procyon_lotor 3.8617 1.0060 889
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5879 1.0034 641
## Avg_Cogongrass_Cover-Lynx_rufus 4.2608 1.0014 921
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8033 1.0073 983
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.0585 1.0194 1148
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.7868 1.0050 884
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1201 1.0072 855
## Tree_Density-Odocoileus_virginianus 3.1066 1.0012 751
## Tree_Density-Canis_latrans -0.5848 1.0183 728
## Tree_Density-Procyon_lotor 0.2501 1.0040 1594
## Tree_Density-Dasypus_novemcinctus -1.1509 1.0233 426
## Tree_Density-Lynx_rufus 3.9281 1.0213 458
## Tree_Density-Didelphis_virginiana 0.1218 1.0089 1237
## Tree_Density-Sylvilagus_floridanus 0.1689 1.0157 776
## Tree_Density-Meleagris_gallopavo 0.3632 1.0023 865
## Tree_Density-Sciurus_carolinensis 0.2717 1.0062 949
## Avg_Canopy_Cover-Odocoileus_virginianus 4.0890 1.0074 1655
## Avg_Canopy_Cover-Canis_latrans 1.4849 1.0022 1760
## Avg_Canopy_Cover-Procyon_lotor 3.4232 1.0023 1149
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0192 1.0021 665
## Avg_Canopy_Cover-Lynx_rufus 4.9312 1.0055 590
## Avg_Canopy_Cover-Didelphis_virginiana 6.3649 1.0061 414
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1925 1.0012 540
## Avg_Canopy_Cover-Meleagris_gallopavo 6.4431 1.0051 481
## Avg_Canopy_Cover-Sciurus_carolinensis 5.9963 1.0027 531
## avg_veg_height-Odocoileus_virginianus 1.1841 1.0082 1535
## avg_veg_height-Canis_latrans 0.8648 1.0044 1328
## avg_veg_height-Procyon_lotor 0.8705 1.0097 1449
## avg_veg_height-Dasypus_novemcinctus 1.1732 1.0014 1416
## avg_veg_height-Lynx_rufus 1.1612 1.0039 1064
## avg_veg_height-Didelphis_virginiana 0.8140 1.0009 1250
## avg_veg_height-Sylvilagus_floridanus 0.7787 1.0132 1134
## avg_veg_height-Meleagris_gallopavo 1.3456 1.0018 997
## avg_veg_height-Sciurus_carolinensis 1.4553 1.0021 1523
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0048 0.0604 -0.1142 0.0049 0.1237
## (Intercept)-Canis_latrans -2.7526 0.1869 -3.1390 -2.7447 -2.4121
## (Intercept)-Procyon_lotor -2.2892 0.1419 -2.5782 -2.2866 -2.0215
## (Intercept)-Dasypus_novemcinctus -1.7440 0.1628 -2.0741 -1.7394 -1.4436
## (Intercept)-Lynx_rufus -3.8812 0.3867 -4.6013 -3.8919 -3.1019
## (Intercept)-Didelphis_virginiana -2.5364 0.2865 -3.1242 -2.5247 -1.9985
## (Intercept)-Sylvilagus_floridanus -3.1712 0.2683 -3.7083 -3.1664 -2.6679
## (Intercept)-Meleagris_gallopavo -3.9509 0.5091 -4.9943 -3.9442 -2.9995
## (Intercept)-Sciurus_carolinensis -2.6701 0.3343 -3.3601 -2.6563 -2.0506
## shrub_cover-Odocoileus_virginianus -0.0533 0.0639 -0.1796 -0.0529 0.0701
## shrub_cover-Canis_latrans -0.3613 0.2234 -0.7938 -0.3659 0.0803
## shrub_cover-Procyon_lotor 0.2707 0.1624 -0.0628 0.2738 0.5816
## shrub_cover-Dasypus_novemcinctus 0.9030 0.3089 0.3128 0.9010 1.5251
## shrub_cover-Lynx_rufus -0.1779 0.3789 -0.8841 -0.1916 0.5977
## shrub_cover-Didelphis_virginiana 0.9709 0.3665 0.3103 0.9557 1.7489
## shrub_cover-Sylvilagus_floridanus 0.4864 0.4049 -0.3033 0.4824 1.3011
## shrub_cover-Meleagris_gallopavo -0.6367 0.4616 -1.5535 -0.6197 0.2375
## shrub_cover-Sciurus_carolinensis 0.9249 0.4276 0.1289 0.9118 1.7804
## veg_height-Odocoileus_virginianus -0.2955 0.0647 -0.4224 -0.2951 -0.1703
## veg_height-Canis_latrans -0.6016 0.1834 -0.9849 -0.5965 -0.2626
## veg_height-Procyon_lotor 0.3442 0.1221 0.1022 0.3449 0.5843
## veg_height-Dasypus_novemcinctus 0.2476 0.1339 -0.0083 0.2462 0.5122
## veg_height-Lynx_rufus 0.0849 0.2461 -0.4158 0.0886 0.5550
## veg_height-Didelphis_virginiana 0.4400 0.2373 -0.0044 0.4353 0.9226
## veg_height-Sylvilagus_floridanus 0.1428 0.2432 -0.3372 0.1442 0.6221
## veg_height-Meleagris_gallopavo -0.2879 0.3672 -1.0615 -0.2740 0.4145
## veg_height-Sciurus_carolinensis 0.0950 0.2172 -0.3074 0.0908 0.5350
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5389
## (Intercept)-Canis_latrans 1.0029 2138
## (Intercept)-Procyon_lotor 1.0021 3047
## (Intercept)-Dasypus_novemcinctus 1.0000 2948
## (Intercept)-Lynx_rufus 1.0023 369
## (Intercept)-Didelphis_virginiana 1.0053 1939
## (Intercept)-Sylvilagus_floridanus 1.0059 2033
## (Intercept)-Meleagris_gallopavo 1.0109 529
## (Intercept)-Sciurus_carolinensis 1.0071 1223
## shrub_cover-Odocoileus_virginianus 1.0028 5489
## shrub_cover-Canis_latrans 1.0024 2223
## shrub_cover-Procyon_lotor 1.0089 3586
## shrub_cover-Dasypus_novemcinctus 1.0010 2400
## shrub_cover-Lynx_rufus 1.0020 577
## shrub_cover-Didelphis_virginiana 1.0019 1601
## shrub_cover-Sylvilagus_floridanus 1.0065 1254
## shrub_cover-Meleagris_gallopavo 1.0123 674
## shrub_cover-Sciurus_carolinensis 1.0020 1098
## veg_height-Odocoileus_virginianus 1.0019 5250
## veg_height-Canis_latrans 1.0017 2191
## veg_height-Procyon_lotor 1.0075 3639
## veg_height-Dasypus_novemcinctus 1.0023 4797
## veg_height-Lynx_rufus 1.0003 1911
## veg_height-Didelphis_virginiana 1.0066 3233
## veg_height-Sylvilagus_floridanus 1.0018 2603
## veg_height-Meleagris_gallopavo 1.0090 1014
## veg_height-Sciurus_carolinensis 1.0032 2368
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3635
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4293 0.7012 -0.9496 0.3967 1.9046 1.0009 1491
## Avg_Cogongrass_Cover 0.0187 0.3672 -0.7165 0.0228 0.7243 1.0116 1720
## total_shrub_cover -0.8109 0.4883 -1.8764 -0.7783 0.0700 1.0133 1120
## avg_veg_height 0.1941 0.3874 -0.5323 0.1860 0.9960 1.0099 1194
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.1149 4.4817 0.4658 2.8838 15.2718 1.0058 1537
## Avg_Cogongrass_Cover 0.4584 0.5859 0.0409 0.2637 2.0859 1.0051 2054
## total_shrub_cover 1.2195 1.6014 0.0715 0.7335 5.1587 1.0357 1027
## avg_veg_height 0.3744 0.6097 0.0386 0.2102 1.6990 1.0361 1751
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1408 1.1965 0.071 0.7645 4.386 1.0364 522
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3721 0.4620 -3.2301 -2.3932 -1.4007 1.0013 4618
## shrub_cover 0.3868 0.3043 -0.2212 0.3846 1.0087 1.0087 2469
## veg_height 0.0028 0.1844 -0.3737 0.0053 0.3624 1.0021 3457
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9123 1.4263 0.6057 1.5507 5.2704 1.0085 3496
## shrub_cover 0.7017 0.5952 0.1347 0.5489 2.1498 1.0028 1248
## veg_height 0.2413 0.1954 0.0615 0.1885 0.7281 1.0084 2917
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9802 1.7438 1.2067 3.7702
## (Intercept)-Canis_latrans 0.7405 0.7952 -0.6532 0.6866
## (Intercept)-Procyon_lotor 0.9798 0.7715 -0.4736 0.9495
## (Intercept)-Dasypus_novemcinctus -0.3634 0.7650 -1.8059 -0.3930
## (Intercept)-Lynx_rufus 0.3580 1.1234 -1.5097 0.2381
## (Intercept)-Didelphis_virginiana -0.9160 0.8491 -2.5201 -0.9502
## (Intercept)-Sylvilagus_floridanus 0.4752 1.0012 -1.2592 0.3835
## (Intercept)-Meleagris_gallopavo 0.0319 1.2941 -2.1024 -0.1305
## (Intercept)-Sciurus_carolinensis -0.9053 0.8930 -2.6832 -0.9218
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0057 0.6124 -1.2025 0.0092
## Avg_Cogongrass_Cover-Canis_latrans 0.3538 0.5395 -0.5967 0.3130
## Avg_Cogongrass_Cover-Procyon_lotor -0.0964 0.4990 -1.1541 -0.0703
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1492 0.4498 -0.7325 0.1419
## Avg_Cogongrass_Cover-Lynx_rufus 0.3604 0.5735 -0.6449 0.3136
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1660 0.5060 -0.8239 0.1533
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3908 0.5961 -1.7355 -0.3367
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4083 0.7299 -2.1014 -0.3431
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0346 0.4909 -0.9777 0.0438
## total_shrub_cover-Odocoileus_virginianus -0.3948 0.7884 -1.9247 -0.4326
## total_shrub_cover-Canis_latrans 0.2874 0.7810 -1.0221 0.1949
## total_shrub_cover-Procyon_lotor -1.3511 0.6759 -2.9589 -1.2569
## total_shrub_cover-Dasypus_novemcinctus -0.4041 0.6481 -2.0390 -0.3218
## total_shrub_cover-Lynx_rufus -1.2561 0.9167 -3.2821 -1.1778
## total_shrub_cover-Didelphis_virginiana -0.7811 0.6843 -2.3606 -0.7059
## total_shrub_cover-Sylvilagus_floridanus -1.4793 1.0107 -3.8454 -1.3262
## total_shrub_cover-Meleagris_gallopavo -1.4309 0.9110 -3.5480 -1.3270
## total_shrub_cover-Sciurus_carolinensis -0.8334 0.8075 -2.8071 -0.7174
## avg_veg_height-Odocoileus_virginianus 0.1549 0.5945 -1.0280 0.1482
## avg_veg_height-Canis_latrans 0.2045 0.5010 -0.7593 0.1976
## avg_veg_height-Procyon_lotor 0.2189 0.4891 -0.7173 0.2068
## avg_veg_height-Dasypus_novemcinctus 0.3864 0.4866 -0.4641 0.3568
## avg_veg_height-Lynx_rufus 0.1430 0.6238 -1.1149 0.1329
## avg_veg_height-Didelphis_virginiana 0.0601 0.5063 -0.9529 0.0665
## avg_veg_height-Sylvilagus_floridanus 0.1390 0.5630 -0.9759 0.1341
## avg_veg_height-Meleagris_gallopavo -0.0475 0.7544 -1.7293 -0.0016
## avg_veg_height-Sciurus_carolinensis 0.5401 0.5429 -0.4185 0.5059
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.1695 1.0024 933
## (Intercept)-Canis_latrans 2.4433 1.0010 1561
## (Intercept)-Procyon_lotor 2.5811 1.0047 2214
## (Intercept)-Dasypus_novemcinctus 1.2765 1.0072 1588
## (Intercept)-Lynx_rufus 3.0298 1.0090 820
## (Intercept)-Didelphis_virginiana 0.8169 1.0056 1111
## (Intercept)-Sylvilagus_floridanus 2.7144 1.0113 834
## (Intercept)-Meleagris_gallopavo 3.1298 1.0076 540
## (Intercept)-Sciurus_carolinensis 0.9394 1.0218 1242
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2856 1.0116 2946
## Avg_Cogongrass_Cover-Canis_latrans 1.5693 1.0028 2753
## Avg_Cogongrass_Cover-Procyon_lotor 0.8563 1.0121 2084
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0618 1.0103 2526
## Avg_Cogongrass_Cover-Lynx_rufus 1.6100 1.0072 2884
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2031 1.0084 2448
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6325 1.0055 1719
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8785 1.0009 1499
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9971 1.0108 2385
## total_shrub_cover-Odocoileus_virginianus 1.3189 1.0039 2870
## total_shrub_cover-Canis_latrans 2.1204 1.0169 1068
## total_shrub_cover-Procyon_lotor -0.2808 1.0216 1340
## total_shrub_cover-Dasypus_novemcinctus 0.6209 1.0004 1085
## total_shrub_cover-Lynx_rufus 0.3951 1.0222 997
## total_shrub_cover-Didelphis_virginiana 0.3465 1.0068 1240
## total_shrub_cover-Sylvilagus_floridanus 0.0550 1.0304 647
## total_shrub_cover-Meleagris_gallopavo 0.1146 1.0300 1010
## total_shrub_cover-Sciurus_carolinensis 0.4271 1.0316 816
## avg_veg_height-Odocoileus_virginianus 1.4012 1.0068 2321
## avg_veg_height-Canis_latrans 1.2380 1.0114 1877
## avg_veg_height-Procyon_lotor 1.2320 1.0068 2204
## avg_veg_height-Dasypus_novemcinctus 1.4479 1.0132 1632
## avg_veg_height-Lynx_rufus 1.4107 1.0076 1778
## avg_veg_height-Didelphis_virginiana 1.0333 1.0067 1884
## avg_veg_height-Sylvilagus_floridanus 1.2774 1.0187 1618
## avg_veg_height-Meleagris_gallopavo 1.3120 1.0016 1114
## avg_veg_height-Sciurus_carolinensis 1.7246 1.0127 1498
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0039 0.0595 -0.1140 0.0039 0.1204
## (Intercept)-Canis_latrans -2.7933 0.2002 -3.2069 -2.7886 -2.4207
## (Intercept)-Procyon_lotor -2.2936 0.1376 -2.5724 -2.2914 -2.0311
## (Intercept)-Dasypus_novemcinctus -1.7778 0.1774 -2.1419 -1.7693 -1.4443
## (Intercept)-Lynx_rufus -3.6003 0.3655 -4.3642 -3.5778 -2.9318
## (Intercept)-Didelphis_virginiana -2.6559 0.3226 -3.3342 -2.6389 -2.0737
## (Intercept)-Sylvilagus_floridanus -3.3258 0.2821 -3.8964 -3.3236 -2.7986
## (Intercept)-Meleagris_gallopavo -3.8019 0.5978 -5.0203 -3.7863 -2.6787
## (Intercept)-Sciurus_carolinensis -2.7488 0.3542 -3.4973 -2.7340 -2.1088
## shrub_cover-Odocoileus_virginianus -0.0537 0.0643 -0.1788 -0.0543 0.0691
## shrub_cover-Canis_latrans -0.2972 0.2467 -0.7557 -0.3015 0.1901
## shrub_cover-Procyon_lotor 0.3252 0.1592 0.0059 0.3265 0.6312
## shrub_cover-Dasypus_novemcinctus 1.0086 0.3628 0.3403 0.9909 1.7317
## shrub_cover-Lynx_rufus 0.0474 0.3929 -0.7675 0.0575 0.7751
## shrub_cover-Didelphis_virginiana 1.1451 0.4223 0.3729 1.1136 2.0192
## shrub_cover-Sylvilagus_floridanus 0.7608 0.4220 -0.1070 0.7744 1.5414
## shrub_cover-Meleagris_gallopavo -0.4936 0.5181 -1.5469 -0.4837 0.4653
## shrub_cover-Sciurus_carolinensis 1.1152 0.4439 0.2597 1.1185 2.0043
## veg_height-Odocoileus_virginianus -0.2958 0.0644 -0.4202 -0.2962 -0.1698
## veg_height-Canis_latrans -0.6109 0.1945 -1.0030 -0.6081 -0.2430
## veg_height-Procyon_lotor 0.3376 0.1226 0.1013 0.3367 0.5800
## veg_height-Dasypus_novemcinctus 0.2465 0.1361 -0.0118 0.2429 0.5197
## veg_height-Lynx_rufus 0.0192 0.2558 -0.4917 0.0210 0.5076
## veg_height-Didelphis_virginiana 0.4122 0.2513 -0.0650 0.4046 0.9358
## veg_height-Sylvilagus_floridanus 0.0357 0.2538 -0.4448 0.0322 0.5574
## veg_height-Meleagris_gallopavo -0.2126 0.4455 -1.0745 -0.2246 0.6577
## veg_height-Sciurus_carolinensis 0.0804 0.2263 -0.3504 0.0732 0.5420
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 4302
## (Intercept)-Canis_latrans 1.0128 1543
## (Intercept)-Procyon_lotor 1.0007 3993
## (Intercept)-Dasypus_novemcinctus 1.0027 1694
## (Intercept)-Lynx_rufus 1.0176 867
## (Intercept)-Didelphis_virginiana 1.0086 1124
## (Intercept)-Sylvilagus_floridanus 1.0240 1434
## (Intercept)-Meleagris_gallopavo 1.0083 496
## (Intercept)-Sciurus_carolinensis 1.0227 1096
## shrub_cover-Odocoileus_virginianus 1.0011 5543
## shrub_cover-Canis_latrans 1.0021 1572
## shrub_cover-Procyon_lotor 1.0008 4123
## shrub_cover-Dasypus_novemcinctus 1.0025 944
## shrub_cover-Lynx_rufus 1.0038 1033
## shrub_cover-Didelphis_virginiana 1.0086 819
## shrub_cover-Sylvilagus_floridanus 1.0179 906
## shrub_cover-Meleagris_gallopavo 1.0081 660
## shrub_cover-Sciurus_carolinensis 1.0252 817
## veg_height-Odocoileus_virginianus 1.0010 5250
## veg_height-Canis_latrans 1.0142 2128
## veg_height-Procyon_lotor 1.0017 3596
## veg_height-Dasypus_novemcinctus 1.0017 3914
## veg_height-Lynx_rufus 1.0008 1906
## veg_height-Didelphis_virginiana 1.0025 2374
## veg_height-Sylvilagus_floridanus 1.0069 1317
## veg_height-Meleagris_gallopavo 1.0034 820
## veg_height-Sciurus_carolinensis 1.0086 2525
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3517
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3444 0.6126 -0.8153 0.3164 1.6451 1.0077 3819
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5647 3.6362 0.6678 2.5727 12.8187 1.0174 1400
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3701 0.4835 -3.2926 -2.3858 -1.3873 1.0001 4373
## shrub_cover 0.1423 0.2881 -0.4533 0.1482 0.7075 1.0008 3854
## veg_height -0.0040 0.1761 -0.3594 -0.0052 0.3522 1.0010 4562
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1760 1.6423 0.6911 1.7483 6.1695 1.0061 4609
## shrub_cover 0.6674 0.5602 0.1380 0.5175 2.0835 1.0034 3078
## veg_height 0.2350 0.1957 0.0594 0.1879 0.6766 1.0288 3817
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.5992 1.2541 1.8326 3.3772 6.7329
## (Intercept)-Canis_latrans 0.4448 0.4258 -0.3393 0.4327 1.3235
## (Intercept)-Procyon_lotor 0.7784 0.4130 0.0193 0.7678 1.6197
## (Intercept)-Dasypus_novemcinctus -0.5396 0.3773 -1.2882 -0.5321 0.1904
## (Intercept)-Lynx_rufus 0.8067 1.1195 -0.6626 0.5810 3.6821
## (Intercept)-Didelphis_virginiana -1.1741 0.4721 -2.1459 -1.1550 -0.2946
## (Intercept)-Sylvilagus_floridanus -0.2525 0.5154 -1.1883 -0.2855 0.8753
## (Intercept)-Meleagris_gallopavo 1.1352 1.4182 -0.7258 0.8701 4.6307
## (Intercept)-Sciurus_carolinensis -1.1683 0.4779 -2.1670 -1.1498 -0.2758
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0028 1295
## (Intercept)-Canis_latrans 1.0024 4610
## (Intercept)-Procyon_lotor 1.0003 5533
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## (Intercept)-Lynx_rufus 1.0136 599
## (Intercept)-Didelphis_virginiana 0.9999 4769
## (Intercept)-Sylvilagus_floridanus 1.0073 2567
## (Intercept)-Meleagris_gallopavo 1.0430 527
## (Intercept)-Sciurus_carolinensis 1.0002 4015
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0035 0.0598 -0.1104 0.0035 0.1207
## (Intercept)-Canis_latrans -2.7427 0.1893 -3.1300 -2.7363 -2.3940
## (Intercept)-Procyon_lotor -2.2915 0.1459 -2.5963 -2.2876 -2.0227
## (Intercept)-Dasypus_novemcinctus -1.7161 0.1576 -2.0369 -1.7095 -1.4289
## (Intercept)-Lynx_rufus -3.8321 0.3729 -4.5730 -3.8372 -3.0916
## (Intercept)-Didelphis_virginiana -2.5216 0.2845 -3.1159 -2.5124 -1.9925
## (Intercept)-Sylvilagus_floridanus -3.1840 0.3035 -3.8176 -3.1693 -2.6355
## (Intercept)-Meleagris_gallopavo -4.3036 0.4918 -5.2151 -4.3270 -3.2449
## (Intercept)-Sciurus_carolinensis -2.5770 0.3102 -3.2529 -2.5562 -2.0207
## shrub_cover-Odocoileus_virginianus -0.0543 0.0642 -0.1832 -0.0530 0.0687
## shrub_cover-Canis_latrans -0.3095 0.2201 -0.7464 -0.3099 0.1221
## shrub_cover-Procyon_lotor 0.2459 0.1655 -0.1013 0.2502 0.5517
## shrub_cover-Dasypus_novemcinctus 0.8125 0.2923 0.2463 0.8041 1.3896
## shrub_cover-Lynx_rufus -0.3947 0.3451 -1.0836 -0.3961 0.2925
## shrub_cover-Didelphis_virginiana 0.9214 0.3608 0.2517 0.9021 1.6573
## shrub_cover-Sylvilagus_floridanus 0.2186 0.4174 -0.5492 0.2050 1.0933
## shrub_cover-Meleagris_gallopavo -0.9080 0.4134 -1.7125 -0.9103 -0.0821
## shrub_cover-Sciurus_carolinensis 0.7881 0.4054 0.0353 0.7710 1.6076
## veg_height-Odocoileus_virginianus -0.2956 0.0654 -0.4241 -0.2940 -0.1696
## veg_height-Canis_latrans -0.5912 0.1840 -0.9607 -0.5881 -0.2358
## veg_height-Procyon_lotor 0.3371 0.1216 0.0991 0.3365 0.5760
## veg_height-Dasypus_novemcinctus 0.2329 0.1329 -0.0215 0.2293 0.4964
## veg_height-Lynx_rufus 0.0300 0.2474 -0.4826 0.0386 0.4851
## veg_height-Didelphis_virginiana 0.4245 0.2366 -0.0207 0.4217 0.9049
## veg_height-Sylvilagus_floridanus 0.1146 0.2563 -0.3935 0.1129 0.6282
## veg_height-Meleagris_gallopavo -0.3474 0.3333 -1.0104 -0.3411 0.3004
## veg_height-Sciurus_carolinensis 0.0549 0.2074 -0.3454 0.0539 0.4768
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0005 2309
## (Intercept)-Procyon_lotor 1.0003 3264
## (Intercept)-Dasypus_novemcinctus 1.0012 4228
## (Intercept)-Lynx_rufus 1.0102 728
## (Intercept)-Didelphis_virginiana 1.0006 2743
## (Intercept)-Sylvilagus_floridanus 1.0052 1577
## (Intercept)-Meleagris_gallopavo 1.0249 521
## (Intercept)-Sciurus_carolinensis 1.0002 2551
## shrub_cover-Odocoileus_virginianus 0.9998 5749
## shrub_cover-Canis_latrans 1.0010 2814
## shrub_cover-Procyon_lotor 1.0005 4004
## shrub_cover-Dasypus_novemcinctus 1.0011 3930
## shrub_cover-Lynx_rufus 1.0058 1252
## shrub_cover-Didelphis_virginiana 1.0003 2417
## shrub_cover-Sylvilagus_floridanus 1.0030 1773
## shrub_cover-Meleagris_gallopavo 1.0175 679
## shrub_cover-Sciurus_carolinensis 1.0008 2640
## veg_height-Odocoileus_virginianus 1.0023 5250
## veg_height-Canis_latrans 1.0010 2140
## veg_height-Procyon_lotor 1.0015 4260
## veg_height-Dasypus_novemcinctus 1.0041 4655
## veg_height-Lynx_rufus 1.0007 2004
## veg_height-Didelphis_virginiana 1.0006 3522
## veg_height-Sylvilagus_floridanus 1.0000 2695
## veg_height-Meleagris_gallopavo 1.0047 1397
## veg_height-Sciurus_carolinensis 1.0000 3387
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3778
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2806 0.6603 -0.9685 0.2485 1.6748 1.0156 2220
## Veg_shannon_index 0.3828 0.2844 -0.1713 0.3805 0.9793 0.9998 2400
## Avg_Cogongrass_Cover 0.4004 0.2979 -0.1850 0.3957 0.9988 1.0014 1725
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0728 4.0109 0.7178 2.9540 13.9005 1.0527 1241
## Veg_shannon_index 0.2913 0.3893 0.0353 0.1819 1.1411 1.0043 2391
## Avg_Cogongrass_Cover 0.3193 0.4377 0.0366 0.1925 1.3222 1.0094 2101
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6661 0.7608 0.0555 0.416 2.6184 1.0118 579
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3586 0.4761 -3.2466 -2.3711 -1.3473 1.0011 5250
## shrub_cover 0.1534 0.2798 -0.4225 0.1530 0.7088 1.0011 3696
## veg_height -0.0148 0.1802 -0.3608 -0.0157 0.3363 1.0020 3952
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1005 1.5114 0.6744 1.6887 5.7439 1.0239 3194
## shrub_cover 0.6374 0.5167 0.1333 0.5048 1.9363 1.0172 2124
## veg_height 0.2329 0.1841 0.0607 0.1856 0.6859 1.0028 3790
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8420 1.5853 1.4760 3.5720
## (Intercept)-Canis_latrans 0.4711 0.6314 -0.7607 0.4627
## (Intercept)-Procyon_lotor 0.6415 0.6113 -0.5575 0.6438
## (Intercept)-Dasypus_novemcinctus -0.6024 0.5711 -1.7464 -0.5918
## (Intercept)-Lynx_rufus 0.4552 1.1491 -1.3739 0.2911
## (Intercept)-Didelphis_virginiana -1.2925 0.6800 -2.6401 -1.2811
## (Intercept)-Sylvilagus_floridanus -0.2435 0.7760 -1.6254 -0.2942
## (Intercept)-Meleagris_gallopavo 0.9485 1.3447 -1.1919 0.7850
## (Intercept)-Sciurus_carolinensis -1.2696 0.6806 -2.6312 -1.2631
## Veg_shannon_index-Odocoileus_virginianus 0.3221 0.5054 -0.7436 0.3397
## Veg_shannon_index-Canis_latrans 0.6430 0.4016 -0.0803 0.6167
## Veg_shannon_index-Procyon_lotor 0.4662 0.3740 -0.2390 0.4415
## Veg_shannon_index-Dasypus_novemcinctus 0.2090 0.3518 -0.5231 0.2138
## Veg_shannon_index-Lynx_rufus 0.2584 0.5366 -0.8752 0.2802
## Veg_shannon_index-Didelphis_virginiana 0.5167 0.3935 -0.1876 0.4838
## Veg_shannon_index-Sylvilagus_floridanus 0.4653 0.4369 -0.3632 0.4553
## Veg_shannon_index-Meleagris_gallopavo 0.5444 0.5334 -0.4795 0.5153
## Veg_shannon_index-Sciurus_carolinensis 0.0308 0.4135 -0.8713 0.0601
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3791 0.5200 -0.6419 0.3709
## Avg_Cogongrass_Cover-Canis_latrans 0.6645 0.4342 -0.0750 0.6256
## Avg_Cogongrass_Cover-Procyon_lotor 0.4288 0.3862 -0.2923 0.4034
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4749 0.3433 -0.1713 0.4590
## Avg_Cogongrass_Cover-Lynx_rufus 0.6085 0.4715 -0.2361 0.5765
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4831 0.3827 -0.2734 0.4729
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0102 0.4835 -1.0976 0.0244
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1236 0.6775 -1.3800 0.1742
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4567 0.3792 -0.2786 0.4549
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3471 1.0302 987
## (Intercept)-Canis_latrans 1.7314 1.0080 3145
## (Intercept)-Procyon_lotor 1.8297 1.0062 2166
## (Intercept)-Dasypus_novemcinctus 0.4882 1.0064 3400
## (Intercept)-Lynx_rufus 3.2984 1.0138 718
## (Intercept)-Didelphis_virginiana 0.0080 1.0004 2789
## (Intercept)-Sylvilagus_floridanus 1.4702 1.0094 1250
## (Intercept)-Meleagris_gallopavo 4.1273 1.0443 514
## (Intercept)-Sciurus_carolinensis 0.0469 1.0013 2805
## Veg_shannon_index-Odocoileus_virginianus 1.3241 1.0033 3371
## Veg_shannon_index-Canis_latrans 1.5155 1.0011 3330
## Veg_shannon_index-Procyon_lotor 1.2699 1.0010 3151
## Veg_shannon_index-Dasypus_novemcinctus 0.8844 1.0017 3672
## Veg_shannon_index-Lynx_rufus 1.2439 1.0018 2166
## Veg_shannon_index-Didelphis_virginiana 1.3953 1.0005 3912
## Veg_shannon_index-Sylvilagus_floridanus 1.3590 1.0002 3404
## Veg_shannon_index-Meleagris_gallopavo 1.7520 1.0002 2692
## Veg_shannon_index-Sciurus_carolinensis 0.7595 1.0001 3519
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4085 1.0004 3048
## Avg_Cogongrass_Cover-Canis_latrans 1.6598 1.0008 3351
## Avg_Cogongrass_Cover-Procyon_lotor 1.2702 1.0005 3967
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1821 1.0001 3687
## Avg_Cogongrass_Cover-Lynx_rufus 1.6399 1.0029 2485
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2697 1.0013 2832
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8327 0.9999 2171
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.3141 1.0010 1485
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2025 1.0011 3448
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0602 -0.1122 0.0045 0.1247
## (Intercept)-Canis_latrans -2.7421 0.1930 -3.1463 -2.7341 -2.3877
## (Intercept)-Procyon_lotor -2.2939 0.1455 -2.5950 -2.2910 -2.0246
## (Intercept)-Dasypus_novemcinctus -1.7167 0.1555 -2.0336 -1.7127 -1.4224
## (Intercept)-Lynx_rufus -3.7373 0.3821 -4.4832 -3.7269 -3.0184
## (Intercept)-Didelphis_virginiana -2.5311 0.2880 -3.1358 -2.5213 -1.9992
## (Intercept)-Sylvilagus_floridanus -3.2240 0.3256 -3.9324 -3.2087 -2.6413
## (Intercept)-Meleagris_gallopavo -4.2530 0.4780 -5.1979 -4.2652 -3.3000
## (Intercept)-Sciurus_carolinensis -2.5775 0.3148 -3.2472 -2.5633 -2.0209
## shrub_cover-Odocoileus_virginianus -0.0544 0.0642 -0.1836 -0.0536 0.0690
## shrub_cover-Canis_latrans -0.2992 0.2122 -0.7189 -0.2975 0.1198
## shrub_cover-Procyon_lotor 0.2321 0.1730 -0.1236 0.2390 0.5560
## shrub_cover-Dasypus_novemcinctus 0.8180 0.2926 0.2714 0.8154 1.4116
## shrub_cover-Lynx_rufus -0.3103 0.3548 -1.0342 -0.2972 0.3696
## shrub_cover-Didelphis_virginiana 0.9240 0.3677 0.2602 0.9044 1.6942
## shrub_cover-Sylvilagus_floridanus 0.2194 0.4153 -0.5267 0.1991 1.0797
## shrub_cover-Meleagris_gallopavo -0.8903 0.4094 -1.7069 -0.8809 -0.1004
## shrub_cover-Sciurus_carolinensis 0.7908 0.4022 0.0210 0.7878 1.5950
## veg_height-Odocoileus_virginianus -0.2953 0.0646 -0.4203 -0.2945 -0.1688
## veg_height-Canis_latrans -0.6002 0.1788 -0.9608 -0.5992 -0.2527
## veg_height-Procyon_lotor 0.3318 0.1232 0.0915 0.3307 0.5730
## veg_height-Dasypus_novemcinctus 0.2273 0.1332 -0.0308 0.2272 0.4883
## veg_height-Lynx_rufus 0.0002 0.2441 -0.4874 0.0009 0.4716
## veg_height-Didelphis_virginiana 0.4168 0.2399 -0.0361 0.4065 0.9105
## veg_height-Sylvilagus_floridanus 0.1098 0.2495 -0.3730 0.1095 0.5941
## veg_height-Meleagris_gallopavo -0.3208 0.3590 -1.0719 -0.3161 0.3877
## veg_height-Sciurus_carolinensis 0.0487 0.2108 -0.3555 0.0435 0.4726
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0041 5250
## (Intercept)-Canis_latrans 0.9999 2171
## (Intercept)-Procyon_lotor 1.0025 3739
## (Intercept)-Dasypus_novemcinctus 1.0016 3817
## (Intercept)-Lynx_rufus 1.0130 631
## (Intercept)-Didelphis_virginiana 1.0023 2599
## (Intercept)-Sylvilagus_floridanus 1.0174 1186
## (Intercept)-Meleagris_gallopavo 1.0702 455
## (Intercept)-Sciurus_carolinensis 1.0037 2354
## shrub_cover-Odocoileus_virginianus 1.0009 5235
## shrub_cover-Canis_latrans 0.9998 2927
## shrub_cover-Procyon_lotor 1.0031 3691
## shrub_cover-Dasypus_novemcinctus 1.0040 3651
## shrub_cover-Lynx_rufus 1.0009 1020
## shrub_cover-Didelphis_virginiana 1.0035 2391
## shrub_cover-Sylvilagus_floridanus 1.0010 1569
## shrub_cover-Meleagris_gallopavo 1.0702 569
## shrub_cover-Sciurus_carolinensis 1.0020 2350
## veg_height-Odocoileus_virginianus 1.0022 5250
## veg_height-Canis_latrans 0.9999 1996
## veg_height-Procyon_lotor 1.0054 4201
## veg_height-Dasypus_novemcinctus 1.0009 4688
## veg_height-Lynx_rufus 1.0036 1855
## veg_height-Didelphis_virginiana 1.0033 3661
## veg_height-Sylvilagus_floridanus 1.0004 2396
## veg_height-Meleagris_gallopavo 1.0023 1307
## veg_height-Sciurus_carolinensis 1.0006 3619
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3425
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3361 0.7129 -1.0138 0.3202 1.7669 1.0029 2063
## Cogon_Patch_Size 0.0093 0.4071 -0.8274 0.0127 0.8170 1.0010 2381
## Avg_Cogongrass_Cover 0.1641 0.3493 -0.5208 0.1586 0.8705 1.0020 1231
## total_shrub_cover -0.7695 0.4725 -1.7969 -0.7328 0.0745 1.0050 1050
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.3297 5.0537 0.4290 3.0138 15.7007 1.0250 1039
## Cogon_Patch_Size 0.8047 1.1819 0.0542 0.4289 3.7886 1.0024 1462
## Avg_Cogongrass_Cover 0.3939 0.5556 0.0396 0.2251 1.7216 1.0035 1883
## total_shrub_cover 0.9968 1.9250 0.0619 0.5337 4.6470 1.1003 1423
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3422 1.5333 0.0813 0.8496 5.3366 1.0161 477
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3723 0.4512 -3.2344 -2.3812 -1.4152 1.0019 5250
## shrub_cover 0.3484 0.2925 -0.2487 0.3455 0.9291 1.0024 2015
## veg_height 0.0043 0.1763 -0.3508 0.0041 0.3488 1.0010 3674
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8888 1.4251 0.6032 1.5266 5.3205 1.0154 3379
## shrub_cover 0.6566 0.5717 0.1297 0.5094 2.0591 1.0291 2216
## veg_height 0.2341 0.2114 0.0620 0.1796 0.6977 1.0020 3790
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9333 1.7571 0.9552 3.7163
## (Intercept)-Canis_latrans 0.7110 0.7908 -0.7042 0.6692
## (Intercept)-Procyon_lotor 0.8961 0.7835 -0.6490 0.8844
## (Intercept)-Dasypus_novemcinctus -0.4237 0.7330 -1.8055 -0.4445
## (Intercept)-Lynx_rufus 0.2689 1.1286 -1.7410 0.1823
## (Intercept)-Didelphis_virginiana -1.0233 0.8429 -2.7064 -1.0317
## (Intercept)-Sylvilagus_floridanus 0.2067 0.9987 -1.5924 0.1425
## (Intercept)-Meleagris_gallopavo 0.1178 1.5217 -2.1323 -0.0589
## (Intercept)-Sciurus_carolinensis -1.0353 0.9026 -2.7548 -1.0570
## Cogon_Patch_Size-Odocoileus_virginianus 0.1092 0.7205 -1.1707 0.0496
## Cogon_Patch_Size-Canis_latrans 0.7202 0.7559 -0.3240 0.5807
## Cogon_Patch_Size-Procyon_lotor -0.1460 0.4692 -1.1033 -0.1423
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0019 0.4501 -0.9197 -0.0038
## Cogon_Patch_Size-Lynx_rufus 0.0188 0.7745 -1.3865 -0.0292
## Cogon_Patch_Size-Didelphis_virginiana 0.5911 0.5061 -0.2905 0.5465
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6327 0.8336 -2.7494 -0.4898
## Cogon_Patch_Size-Meleagris_gallopavo 0.0793 0.7181 -1.2790 0.0459
## Cogon_Patch_Size-Sciurus_carolinensis -0.5323 0.7019 -2.2643 -0.4254
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1412 0.5987 -1.0468 0.1250
## Avg_Cogongrass_Cover-Canis_latrans 0.3395 0.4526 -0.4485 0.3051
## Avg_Cogongrass_Cover-Procyon_lotor 0.1118 0.4634 -0.7766 0.1091
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3392 0.4129 -0.4406 0.3224
## Avg_Cogongrass_Cover-Lynx_rufus 0.4625 0.5379 -0.4610 0.4136
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1382 0.4697 -0.8029 0.1361
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1470 0.5711 -1.3715 -0.1131
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2387 0.7721 -2.0142 -0.1651
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3667 0.4498 -0.4946 0.3486
## total_shrub_cover-Odocoileus_virginianus -0.4421 0.7299 -1.8630 -0.4551
## total_shrub_cover-Canis_latrans 0.1118 0.6850 -1.0047 0.0410
## total_shrub_cover-Procyon_lotor -1.2390 0.6413 -2.7081 -1.1482
## total_shrub_cover-Dasypus_novemcinctus -0.4198 0.6171 -1.9368 -0.3562
## total_shrub_cover-Lynx_rufus -1.1841 0.9257 -3.2810 -1.0732
## total_shrub_cover-Didelphis_virginiana -0.7791 0.6183 -2.1711 -0.7157
## total_shrub_cover-Sylvilagus_floridanus -1.2259 0.9723 -3.5785 -1.0640
## total_shrub_cover-Meleagris_gallopavo -1.3345 0.8900 -3.4531 -1.2049
## total_shrub_cover-Sciurus_carolinensis -0.6843 0.7090 -2.3849 -0.6057
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.0581 1.0050 739
## (Intercept)-Canis_latrans 2.4207 1.0012 1906
## (Intercept)-Procyon_lotor 2.5087 1.0008 1875
## (Intercept)-Dasypus_novemcinctus 1.1407 1.0210 1415
## (Intercept)-Lynx_rufus 2.7746 1.0052 835
## (Intercept)-Didelphis_virginiana 0.7168 1.0049 1271
## (Intercept)-Sylvilagus_floridanus 2.4174 1.0123 942
## (Intercept)-Meleagris_gallopavo 3.2443 1.1251 368
## (Intercept)-Sciurus_carolinensis 0.8531 1.0076 1220
## Cogon_Patch_Size-Odocoileus_virginianus 1.7897 1.0001 3291
## Cogon_Patch_Size-Canis_latrans 2.5012 1.0040 1734
## Cogon_Patch_Size-Procyon_lotor 0.7812 1.0032 3247
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9235 1.0055 3542
## Cogon_Patch_Size-Lynx_rufus 1.7394 1.0030 1812
## Cogon_Patch_Size-Didelphis_virginiana 1.7071 1.0006 2407
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5930 1.0020 1586
## Cogon_Patch_Size-Meleagris_gallopavo 1.6477 1.0036 2253
## Cogon_Patch_Size-Sciurus_carolinensis 0.4944 1.0064 2217
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3678 1.0002 2443
## Avg_Cogongrass_Cover-Canis_latrans 1.3156 1.0018 2501
## Avg_Cogongrass_Cover-Procyon_lotor 1.0280 1.0013 2946
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2093 1.0043 3323
## Avg_Cogongrass_Cover-Lynx_rufus 1.6725 1.0028 2454
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0787 1.0015 2480
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8821 1.0012 1570
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0909 1.0022 877
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3030 1.0009 2539
## total_shrub_cover-Odocoileus_virginianus 1.1156 1.0023 2514
## total_shrub_cover-Canis_latrans 1.7713 1.0013 1293
## total_shrub_cover-Procyon_lotor -0.2417 1.0097 1368
## total_shrub_cover-Dasypus_novemcinctus 0.5532 1.0126 1121
## total_shrub_cover-Lynx_rufus 0.3679 1.0023 885
## total_shrub_cover-Didelphis_virginiana 0.2512 1.0089 1168
## total_shrub_cover-Sylvilagus_floridanus 0.2631 1.0213 675
## total_shrub_cover-Meleagris_gallopavo 0.0849 1.0096 838
## total_shrub_cover-Sciurus_carolinensis 0.4915 1.0116 1031
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0042 0.0600 -0.1126 0.0027 0.1237
## (Intercept)-Canis_latrans -2.7611 0.1933 -3.1530 -2.7568 -2.4000
## (Intercept)-Procyon_lotor -2.2967 0.1385 -2.5752 -2.2949 -2.0302
## (Intercept)-Dasypus_novemcinctus -1.7749 0.1738 -2.1368 -1.7652 -1.4581
## (Intercept)-Lynx_rufus -3.5999 0.3590 -4.3439 -3.5798 -2.9401
## (Intercept)-Didelphis_virginiana -2.5872 0.3060 -3.2271 -2.5747 -2.0264
## (Intercept)-Sylvilagus_floridanus -3.2962 0.2966 -3.9063 -3.2925 -2.7337
## (Intercept)-Meleagris_gallopavo -3.8389 0.5743 -4.9631 -3.8309 -2.7293
## (Intercept)-Sciurus_carolinensis -2.7463 0.3601 -3.4973 -2.7267 -2.0949
## shrub_cover-Odocoileus_virginianus -0.0535 0.0640 -0.1782 -0.0539 0.0731
## shrub_cover-Canis_latrans -0.2993 0.2378 -0.7720 -0.2992 0.1692
## shrub_cover-Procyon_lotor 0.3161 0.1607 -0.0043 0.3160 0.6310
## shrub_cover-Dasypus_novemcinctus 0.9758 0.3524 0.3299 0.9581 1.6932
## shrub_cover-Lynx_rufus 0.0182 0.3762 -0.7630 0.0314 0.7215
## shrub_cover-Didelphis_virginiana 1.0610 0.4025 0.3350 1.0391 1.9132
## shrub_cover-Sylvilagus_floridanus 0.7117 0.4342 -0.1986 0.7261 1.5319
## shrub_cover-Meleagris_gallopavo -0.5107 0.4972 -1.4897 -0.5161 0.4358
## shrub_cover-Sciurus_carolinensis 1.0435 0.4363 0.2306 1.0345 1.9045
## veg_height-Odocoileus_virginianus -0.2961 0.0656 -0.4233 -0.2965 -0.1682
## veg_height-Canis_latrans -0.5966 0.1878 -0.9872 -0.5901 -0.2506
## veg_height-Procyon_lotor 0.3378 0.1238 0.0920 0.3402 0.5827
## veg_height-Dasypus_novemcinctus 0.2487 0.1380 -0.0231 0.2453 0.5286
## veg_height-Lynx_rufus 0.0270 0.2401 -0.4454 0.0312 0.4883
## veg_height-Didelphis_virginiana 0.4035 0.2444 -0.0532 0.3978 0.9029
## veg_height-Sylvilagus_floridanus 0.0420 0.2430 -0.4370 0.0400 0.5164
## veg_height-Meleagris_gallopavo -0.2636 0.3842 -1.0124 -0.2673 0.5229
## veg_height-Sciurus_carolinensis 0.1064 0.2261 -0.3161 0.0952 0.5722
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5153
## (Intercept)-Canis_latrans 1.0017 2031
## (Intercept)-Procyon_lotor 1.0041 3605
## (Intercept)-Dasypus_novemcinctus 1.0039 2084
## (Intercept)-Lynx_rufus 1.0016 998
## (Intercept)-Didelphis_virginiana 1.0079 1498
## (Intercept)-Sylvilagus_floridanus 1.0128 1425
## (Intercept)-Meleagris_gallopavo 1.0199 545
## (Intercept)-Sciurus_carolinensis 1.0076 1044
## shrub_cover-Odocoileus_virginianus 1.0014 5250
## shrub_cover-Canis_latrans 1.0079 1889
## shrub_cover-Procyon_lotor 1.0007 3413
## shrub_cover-Dasypus_novemcinctus 1.0032 1261
## shrub_cover-Lynx_rufus 1.0020 1216
## shrub_cover-Didelphis_virginiana 1.0051 1173
## shrub_cover-Sylvilagus_floridanus 1.0067 1011
## shrub_cover-Meleagris_gallopavo 1.0197 775
## shrub_cover-Sciurus_carolinensis 1.0114 884
## veg_height-Odocoileus_virginianus 1.0012 5250
## veg_height-Canis_latrans 1.0012 1948
## veg_height-Procyon_lotor 0.9999 4180
## veg_height-Dasypus_novemcinctus 0.9998 3132
## veg_height-Lynx_rufus 1.0064 2267
## veg_height-Didelphis_virginiana 0.9998 3450
## veg_height-Sylvilagus_floridanus 1.0045 1791
## veg_height-Meleagris_gallopavo 1.0018 1376
## veg_height-Sciurus_carolinensis 1.0037 1856
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3185
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3072 0.7878 -1.1670 0.2799 1.9346 1.0009 2385
## Tree_Density -0.7808 0.4426 -1.7582 -0.7557 0.0371 1.0005 1487
## Avg_Canopy_Cover 1.1334 0.4532 0.2917 1.1019 2.1031 1.0047 1943
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.7784 7.1866 1.1001 4.7313 25.3310 1.0146 504
## Tree_Density 0.9192 1.6370 0.0457 0.4046 4.8563 1.0206 1528
## Avg_Canopy_Cover 1.1716 1.4184 0.1005 0.7632 4.6710 1.0088 1483
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5141 0.6962 0.0446 0.2778 2.4547 1.1075 436
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3744 0.4820 -3.2617 -2.3950 -1.3462 1.0005 5048
## shrub_cover 0.1940 0.2881 -0.3911 0.1928 0.7565 1.0026 4080
## veg_height 0.0276 0.1789 -0.3309 0.0259 0.3745 1.0004 4015
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1052 1.5785 0.6564 1.7059 5.8033 1.0247 4549
## shrub_cover 0.6737 0.5729 0.1469 0.5254 2.1309 1.0067 2139
## veg_height 0.2414 0.2033 0.0630 0.1906 0.7466 1.0034 4239
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8138 1.9547 2.0472 4.4669 9.8121
## (Intercept)-Canis_latrans 0.4354 0.6372 -0.7339 0.4104 1.7439
## (Intercept)-Procyon_lotor 0.8702 0.6684 -0.3977 0.8500 2.2191
## (Intercept)-Dasypus_novemcinctus -0.9122 0.6522 -2.2747 -0.8971 0.2904
## (Intercept)-Lynx_rufus 1.6428 2.0359 -1.0071 1.2254 6.8231
## (Intercept)-Didelphis_virginiana -1.6686 0.7452 -3.2261 -1.6485 -0.2423
## (Intercept)-Sylvilagus_floridanus -0.5509 0.7469 -2.0322 -0.5464 0.9408
## (Intercept)-Meleagris_gallopavo 0.6712 1.4006 -1.4855 0.4776 4.1622
## (Intercept)-Sciurus_carolinensis -1.7586 0.7728 -3.3932 -1.7135 -0.3053
## Tree_Density-Odocoileus_virginianus -0.3821 0.7063 -1.5510 -0.4561 1.2795
## Tree_Density-Canis_latrans -0.9523 0.5817 -2.2788 -0.8894 0.0020
## Tree_Density-Procyon_lotor -0.4986 0.4255 -1.3738 -0.4975 0.3252
## Tree_Density-Dasypus_novemcinctus -1.4048 0.9288 -3.7776 -1.2045 -0.1807
## Tree_Density-Lynx_rufus -0.0167 0.8874 -1.3913 -0.1505 2.1990
## Tree_Density-Didelphis_virginiana -1.0268 0.7832 -3.0003 -0.9078 0.1922
## Tree_Density-Sylvilagus_floridanus -1.0653 0.7779 -2.9461 -0.9417 0.1131
## Tree_Density-Meleagris_gallopavo -1.0298 0.8688 -3.0343 -0.9320 0.4319
## Tree_Density-Sciurus_carolinensis -0.9317 0.7603 -2.6377 -0.8355 0.2931
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8040 0.8408 -0.9115 0.8062 2.5091
## Avg_Canopy_Cover-Canis_latrans -0.0749 0.4894 -1.0389 -0.0761 0.8931
## Avg_Canopy_Cover-Procyon_lotor 1.1096 0.5228 0.1998 1.0661 2.2669
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1122 0.4801 0.2595 1.0785 2.1564
## Avg_Canopy_Cover-Lynx_rufus 1.0638 0.9577 -0.6728 0.9969 3.1543
## Avg_Canopy_Cover-Didelphis_virginiana 1.5764 0.7233 0.4585 1.4733 3.2627
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.1441 0.9684 0.7279 1.9901 4.4228
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5764 0.9069 0.1204 1.4531 3.6444
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5237 0.6620 0.4727 1.4439 3.0881
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0042 706
## (Intercept)-Canis_latrans 1.0038 3291
## (Intercept)-Procyon_lotor 1.0094 3217
## (Intercept)-Dasypus_novemcinctus 1.0000 3208
## (Intercept)-Lynx_rufus 1.0199 260
## (Intercept)-Didelphis_virginiana 1.0040 2599
## (Intercept)-Sylvilagus_floridanus 1.0002 2946
## (Intercept)-Meleagris_gallopavo 1.0369 538
## (Intercept)-Sciurus_carolinensis 1.0093 2101
## Tree_Density-Odocoileus_virginianus 1.0119 2353
## Tree_Density-Canis_latrans 1.0016 2901
## Tree_Density-Procyon_lotor 1.0016 3390
## Tree_Density-Dasypus_novemcinctus 1.0015 1439
## Tree_Density-Lynx_rufus 1.0026 852
## Tree_Density-Didelphis_virginiana 1.0039 2010
## Tree_Density-Sylvilagus_floridanus 1.0076 1933
## Tree_Density-Meleagris_gallopavo 1.0044 1613
## Tree_Density-Sciurus_carolinensis 1.0004 2225
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0019 2927
## Avg_Canopy_Cover-Canis_latrans 1.0021 3167
## Avg_Canopy_Cover-Procyon_lotor 1.0049 3637
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0015 4067
## Avg_Canopy_Cover-Lynx_rufus 1.0097 1211
## Avg_Canopy_Cover-Didelphis_virginiana 1.0165 1995
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0110 1145
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0033 1364
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0111 2046
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0599 -0.1126 0.0048 0.1237
## (Intercept)-Canis_latrans -2.7527 0.1899 -3.1411 -2.7466 -2.3918
## (Intercept)-Procyon_lotor -2.2975 0.1454 -2.5969 -2.2945 -2.0263
## (Intercept)-Dasypus_novemcinctus -1.7304 0.1607 -2.0580 -1.7274 -1.4302
## (Intercept)-Lynx_rufus -3.9306 0.3726 -4.6248 -3.9469 -3.1763
## (Intercept)-Didelphis_virginiana -2.5738 0.2904 -3.1625 -2.5661 -2.0318
## (Intercept)-Sylvilagus_floridanus -3.1292 0.2686 -3.6835 -3.1232 -2.6116
## (Intercept)-Meleagris_gallopavo -4.1277 0.4755 -5.1025 -4.1216 -3.2240
## (Intercept)-Sciurus_carolinensis -2.6255 0.3192 -3.2917 -2.6095 -2.0499
## shrub_cover-Odocoileus_virginianus -0.0530 0.0646 -0.1797 -0.0518 0.0710
## shrub_cover-Canis_latrans -0.3239 0.2251 -0.7624 -0.3233 0.1227
## shrub_cover-Procyon_lotor 0.2514 0.1642 -0.0825 0.2540 0.5692
## shrub_cover-Dasypus_novemcinctus 0.8473 0.2990 0.2578 0.8442 1.4395
## shrub_cover-Lynx_rufus -0.3617 0.3305 -1.0380 -0.3533 0.2717
## shrub_cover-Didelphis_virginiana 0.9615 0.3569 0.2904 0.9515 1.7089
## shrub_cover-Sylvilagus_floridanus 0.4085 0.3888 -0.3285 0.3996 1.1877
## shrub_cover-Meleagris_gallopavo -0.8096 0.4156 -1.6791 -0.7934 -0.0314
## shrub_cover-Sciurus_carolinensis 0.8571 0.4064 0.0900 0.8470 1.6702
## veg_height-Odocoileus_virginianus -0.2945 0.0643 -0.4240 -0.2931 -0.1701
## veg_height-Canis_latrans -0.5991 0.1848 -0.9768 -0.5954 -0.2532
## veg_height-Procyon_lotor 0.3440 0.1238 0.1029 0.3454 0.5862
## veg_height-Dasypus_novemcinctus 0.2446 0.1353 -0.0170 0.2422 0.5108
## veg_height-Lynx_rufus 0.0759 0.2456 -0.4118 0.0814 0.5549
## veg_height-Didelphis_virginiana 0.4763 0.2442 0.0377 0.4616 0.9854
## veg_height-Sylvilagus_floridanus 0.1557 0.2395 -0.3082 0.1579 0.6204
## veg_height-Meleagris_gallopavo -0.2326 0.3318 -0.8979 -0.2271 0.4064
## veg_height-Sciurus_carolinensis 0.0921 0.2134 -0.3132 0.0876 0.5293
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 5250
## (Intercept)-Canis_latrans 1.0053 2348
## (Intercept)-Procyon_lotor 1.0028 3436
## (Intercept)-Dasypus_novemcinctus 1.0026 4798
## (Intercept)-Lynx_rufus 1.0132 514
## (Intercept)-Didelphis_virginiana 1.0002 2265
## (Intercept)-Sylvilagus_floridanus 1.0030 2084
## (Intercept)-Meleagris_gallopavo 1.0324 543
## (Intercept)-Sciurus_carolinensis 1.0077 2243
## shrub_cover-Odocoileus_virginianus 1.0017 5250
## shrub_cover-Canis_latrans 1.0004 2351
## shrub_cover-Procyon_lotor 1.0012 4094
## shrub_cover-Dasypus_novemcinctus 1.0010 3624
## shrub_cover-Lynx_rufus 1.0099 1073
## shrub_cover-Didelphis_virginiana 1.0048 2414
## shrub_cover-Sylvilagus_floridanus 1.0061 1936
## shrub_cover-Meleagris_gallopavo 1.0351 730
## shrub_cover-Sciurus_carolinensis 1.0015 2400
## veg_height-Odocoileus_virginianus 1.0015 5250
## veg_height-Canis_latrans 1.0005 2429
## veg_height-Procyon_lotor 1.0007 4276
## veg_height-Dasypus_novemcinctus 1.0001 4373
## veg_height-Lynx_rufus 1.0186 2135
## veg_height-Didelphis_virginiana 1.0010 2992
## veg_height-Sylvilagus_floridanus 1.0067 3256
## veg_height-Meleagris_gallopavo 1.0041 1724
## veg_height-Sciurus_carolinensis 1.0008 3198
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3015
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3766 0.6897 -1.6275 -0.4086 1.0929 1.0042 2515
## Avg_Cogongrass_Cover -0.6375 0.4265 -1.5202 -0.6320 0.1802 1.0082 1773
## I(Avg_Cogongrass_Cover^2) 0.9270 0.4093 0.2413 0.8891 1.8663 1.0097 974
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2723 4.2716 0.6214 3.0904 14.5754 1.0135 1298
## Avg_Cogongrass_Cover 0.5429 0.7818 0.0437 0.2890 2.5466 1.0347 1795
## I(Avg_Cogongrass_Cover^2) 0.6231 1.3768 0.0383 0.2618 3.4196 1.0084 838
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4552 0.5597 0.0409 0.2751 1.9485 1.0119 719
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3580 0.4744 -3.2380 -2.3789 -1.3569 1.0031 5250
## shrub_cover 0.1593 0.2860 -0.4122 0.1489 0.7338 1.0011 2966
## veg_height 0.0100 0.1751 -0.3437 0.0090 0.3559 1.0020 3408
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1144 1.6938 0.6657 1.6875 6.0901 1.0167 3895
## shrub_cover 0.6542 0.5747 0.1407 0.5067 2.0143 1.0218 2215
## veg_height 0.2281 0.1719 0.0587 0.1807 0.6672 1.0023 4127
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.1372 1.5739 0.6699 2.9134
## (Intercept)-Canis_latrans -0.3775 0.6880 -1.7491 -0.3819
## (Intercept)-Procyon_lotor -0.0668 0.6467 -1.3559 -0.0688
## (Intercept)-Dasypus_novemcinctus -1.2451 0.6298 -2.5186 -1.2294
## (Intercept)-Lynx_rufus -0.8049 1.0313 -2.6100 -0.8818
## (Intercept)-Didelphis_virginiana -1.7422 0.7278 -3.2047 -1.7208
## (Intercept)-Sylvilagus_floridanus -0.9884 0.7425 -2.4581 -1.0050
## (Intercept)-Meleagris_gallopavo 0.6632 1.4460 -1.4747 0.4423
## (Intercept)-Sciurus_carolinensis -2.2718 0.7903 -3.9462 -2.2276
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.6413 0.7230 -2.0973 -0.6420
## Avg_Cogongrass_Cover-Canis_latrans -0.2605 0.5976 -1.3020 -0.2988
## Avg_Cogongrass_Cover-Procyon_lotor -0.6031 0.5465 -1.6411 -0.6129
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4708 0.5088 -1.4351 -0.4746
## Avg_Cogongrass_Cover-Lynx_rufus -0.6097 0.6449 -1.9173 -0.5956
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3996 0.5766 -1.4653 -0.4246
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1843 0.7011 -2.8314 -1.1033
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9186 0.8333 -2.8534 -0.8380
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7672 0.5884 -1.9952 -0.7407
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.2733 0.9560 0.0730 1.0927
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3187 0.8247 0.2421 1.1423
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1210 0.7041 0.2062 0.9898
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7216 0.3821 0.0031 0.7088
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2229 0.6161 0.3072 1.1347
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5892 0.4681 -0.2886 0.5757
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7688 0.5017 -0.0697 0.7268
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.5755 0.9009 -1.1902 0.5620
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9986 0.4367 0.2473 0.9627
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8469 1.0081 1143
## (Intercept)-Canis_latrans 0.9974 1.0101 2687
## (Intercept)-Procyon_lotor 1.1902 1.0014 2828
## (Intercept)-Dasypus_novemcinctus -0.0581 1.0008 3492
## (Intercept)-Lynx_rufus 1.4342 1.0007 975
## (Intercept)-Didelphis_virginiana -0.4055 1.0002 2950
## (Intercept)-Sylvilagus_floridanus 0.5224 1.0027 2622
## (Intercept)-Meleagris_gallopavo 4.1708 1.0249 498
## (Intercept)-Sciurus_carolinensis -0.7969 1.0009 2342
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.8511 1.0006 2620
## Avg_Cogongrass_Cover-Canis_latrans 1.0664 1.0031 2607
## Avg_Cogongrass_Cover-Procyon_lotor 0.4738 1.0012 2491
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5449 1.0033 2748
## Avg_Cogongrass_Cover-Lynx_rufus 0.6554 1.0094 2218
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8422 1.0003 2333
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0339 1.0181 1464
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5375 1.0222 1392
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3290 1.0041 2202
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.6406 1.0124 708
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4454 1.0284 840
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0196 1.0217 953
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5037 1.0059 2336
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6461 1.0100 1249
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.6211 1.0015 1427
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8785 1.0062 1615
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.3661 1.0212 577
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9503 1.0081 1905
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0031 0.0600 -0.1165 0.0031 0.1199
## (Intercept)-Canis_latrans -2.7452 0.1854 -3.1211 -2.7398 -2.3892
## (Intercept)-Procyon_lotor -2.3114 0.1512 -2.6194 -2.3069 -2.0271
## (Intercept)-Dasypus_novemcinctus -1.7200 0.1554 -2.0304 -1.7187 -1.4222
## (Intercept)-Lynx_rufus -3.6381 0.3721 -4.3831 -3.6241 -2.9551
## (Intercept)-Didelphis_virginiana -2.5540 0.2916 -3.1559 -2.5409 -2.0122
## (Intercept)-Sylvilagus_floridanus -3.1879 0.3070 -3.8244 -3.1750 -2.6218
## (Intercept)-Meleagris_gallopavo -4.2727 0.5281 -5.3274 -4.2802 -3.1911
## (Intercept)-Sciurus_carolinensis -2.5719 0.3149 -3.2351 -2.5519 -1.9952
## shrub_cover-Odocoileus_virginianus -0.0554 0.0643 -0.1815 -0.0555 0.0719
## shrub_cover-Canis_latrans -0.2691 0.2209 -0.7078 -0.2642 0.1462
## shrub_cover-Procyon_lotor 0.2204 0.1720 -0.1199 0.2231 0.5520
## shrub_cover-Dasypus_novemcinctus 0.8254 0.2924 0.2737 0.8188 1.4166
## shrub_cover-Lynx_rufus -0.3025 0.3660 -1.0263 -0.2957 0.4025
## shrub_cover-Didelphis_virginiana 0.9687 0.3817 0.2827 0.9512 1.7810
## shrub_cover-Sylvilagus_floridanus 0.2052 0.4059 -0.5374 0.1839 1.0533
## shrub_cover-Meleagris_gallopavo -0.8861 0.4329 -1.7646 -0.8794 -0.0785
## shrub_cover-Sciurus_carolinensis 0.7831 0.4046 0.0173 0.7718 1.6100
## veg_height-Odocoileus_virginianus -0.2970 0.0654 -0.4275 -0.2966 -0.1682
## veg_height-Canis_latrans -0.5884 0.1849 -0.9725 -0.5862 -0.2351
## veg_height-Procyon_lotor 0.3448 0.1244 0.1008 0.3452 0.5929
## veg_height-Dasypus_novemcinctus 0.2343 0.1355 -0.0289 0.2320 0.5049
## veg_height-Lynx_rufus 0.0535 0.2451 -0.4415 0.0583 0.5205
## veg_height-Didelphis_virginiana 0.3874 0.2502 -0.0704 0.3783 0.9092
## veg_height-Sylvilagus_floridanus 0.1449 0.2492 -0.3425 0.1439 0.6391
## veg_height-Meleagris_gallopavo -0.2535 0.3551 -0.9629 -0.2508 0.4596
## veg_height-Sciurus_carolinensis 0.0629 0.2143 -0.3382 0.0571 0.4973
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0031 2463
## (Intercept)-Procyon_lotor 1.0056 2893
## (Intercept)-Dasypus_novemcinctus 1.0001 4391
## (Intercept)-Lynx_rufus 1.0037 880
## (Intercept)-Didelphis_virginiana 1.0063 2566
## (Intercept)-Sylvilagus_floridanus 1.0021 1347
## (Intercept)-Meleagris_gallopavo 1.0271 382
## (Intercept)-Sciurus_carolinensis 0.9999 2556
## shrub_cover-Odocoileus_virginianus 1.0027 5250
## shrub_cover-Canis_latrans 1.0006 2759
## shrub_cover-Procyon_lotor 1.0015 3228
## shrub_cover-Dasypus_novemcinctus 1.0009 3388
## shrub_cover-Lynx_rufus 1.0059 1227
## shrub_cover-Didelphis_virginiana 1.0007 1840
## shrub_cover-Sylvilagus_floridanus 1.0032 1752
## shrub_cover-Meleagris_gallopavo 1.0127 467
## shrub_cover-Sciurus_carolinensis 1.0033 2476
## veg_height-Odocoileus_virginianus 1.0027 5250
## veg_height-Canis_latrans 1.0025 2409
## veg_height-Procyon_lotor 1.0019 4148
## veg_height-Dasypus_novemcinctus 1.0012 4396
## veg_height-Lynx_rufus 1.0017 2408
## veg_height-Didelphis_virginiana 1.0010 2924
## veg_height-Sylvilagus_floridanus 1.0006 2105
## veg_height-Meleagris_gallopavo 1.0240 818
## veg_height-Sciurus_carolinensis 1.0012 3314
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.3328
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4626 1.1665 -2.6218 -0.5135 1.9883 1.0078 2135
## Cogon_Patch_Size 0.1821 0.7768 -1.3910 0.1900 1.7464 1.0037 1384
## Veg_shannon_index 0.9627 0.5449 -0.0307 0.9321 2.1525 1.0093 689
## total_shrub_cover -0.7814 0.6683 -2.1186 -0.7594 0.4799 1.0154 853
## Avg_Cogongrass_Cover -0.1707 1.0580 -2.1849 -0.2004 2.0207 1.0192 484
## Tree_Density -1.9135 0.8962 -3.6793 -1.9174 -0.1080 1.0059 922
## Avg_Canopy_Cover 1.8695 0.8400 0.1948 1.8403 3.5858 1.0051 996
## I(Avg_Cogongrass_Cover^2) 1.5751 0.6283 0.4262 1.5471 2.8986 1.0221 793
## avg_veg_height 0.0085 0.6019 -1.1811 0.0120 1.2181 1.0172 579
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.5367 23.3500 3.0560 14.5327 81.6585 1.0005 449
## Cogon_Patch_Size 3.8183 6.5807 0.1125 1.8325 19.6179 1.0894 575
## Veg_shannon_index 0.8528 1.4114 0.0485 0.3895 4.4830 1.0034 1358
## total_shrub_cover 2.2417 3.4982 0.0783 1.1154 11.6009 1.0947 430
## Avg_Cogongrass_Cover 1.6682 3.4078 0.0534 0.6118 9.7909 1.0454 493
## Tree_Density 6.6365 14.6737 0.0801 2.0774 44.3943 1.3790 270
## Avg_Canopy_Cover 6.1387 9.0429 0.2370 3.2515 31.2522 1.0795 362
## I(Avg_Cogongrass_Cover^2) 1.2627 2.3600 0.0550 0.5185 7.2515 1.1431 473
## avg_veg_height 0.6655 1.1846 0.0442 0.3277 3.3821 1.0580 1422
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4586 4.9757 0.0565 0.914 14.2512 1.2065 139
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3814 0.4668 -3.2480 -2.4005 -1.3842 1.0010 4926
## shrub_cover 0.2832 0.2897 -0.2996 0.2807 0.8694 0.9999 2430
## veg_height 0.0405 0.1796 -0.3169 0.0407 0.4019 1.0025 3617
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0212 1.6278 0.6300 1.6250 5.8338 1.0168 4076
## shrub_cover 0.6589 0.5493 0.1362 0.5130 1.9941 1.0070 2118
## veg_height 0.2361 0.2052 0.0599 0.1847 0.6979 1.0364 3957
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.7108 4.1822 2.0867
## (Intercept)-Canis_latrans -0.6051 1.3758 -3.1275
## (Intercept)-Procyon_lotor -0.2380 1.2180 -2.6939
## (Intercept)-Dasypus_novemcinctus -2.6696 1.4020 -5.8674
## (Intercept)-Lynx_rufus 0.5579 2.8429 -3.8473
## (Intercept)-Didelphis_virginiana -3.9867 1.6099 -7.4593
## (Intercept)-Sylvilagus_floridanus -2.1295 1.7480 -5.7961
## (Intercept)-Meleagris_gallopavo -0.6343 2.4435 -4.6832
## (Intercept)-Sciurus_carolinensis -4.6471 1.9242 -8.7884
## Cogon_Patch_Size-Odocoileus_virginianus 0.3684 1.6626 -2.3708
## Cogon_Patch_Size-Canis_latrans 1.7570 1.5183 -0.3323
## Cogon_Patch_Size-Procyon_lotor -0.4456 0.8787 -2.3086
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0810 0.9048 -1.6843
## Cogon_Patch_Size-Lynx_rufus -0.1256 1.7230 -3.6861
## Cogon_Patch_Size-Didelphis_virginiana 1.7445 1.2246 -0.1118
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1678 1.7399 -5.6294
## Cogon_Patch_Size-Meleagris_gallopavo 0.6239 1.6380 -2.1093
## Cogon_Patch_Size-Sciurus_carolinensis -0.9647 1.5291 -4.6890
## Veg_shannon_index-Odocoileus_virginianus 0.8070 0.9212 -1.1693
## Veg_shannon_index-Canis_latrans 1.3326 0.7677 0.0723
## Veg_shannon_index-Procyon_lotor 1.2126 0.6892 0.0541
## Veg_shannon_index-Dasypus_novemcinctus 0.6270 0.6508 -0.6524
## Veg_shannon_index-Lynx_rufus 1.0373 1.0515 -0.8784
## Veg_shannon_index-Didelphis_virginiana 1.2043 0.7719 -0.1336
## Veg_shannon_index-Sylvilagus_floridanus 1.0936 0.7992 -0.3151
## Veg_shannon_index-Meleagris_gallopavo 1.2551 0.9274 -0.3321
## Veg_shannon_index-Sciurus_carolinensis 0.3619 0.9077 -1.6681
## total_shrub_cover-Odocoileus_virginianus -0.3273 1.1825 -2.5491
## total_shrub_cover-Canis_latrans 0.3394 1.0304 -1.2843
## total_shrub_cover-Procyon_lotor -1.3975 0.7823 -3.1547
## total_shrub_cover-Dasypus_novemcinctus -0.3221 0.8307 -2.1287
## total_shrub_cover-Lynx_rufus -1.3229 1.5098 -4.7831
## total_shrub_cover-Didelphis_virginiana -1.1919 1.1580 -3.9785
## total_shrub_cover-Sylvilagus_floridanus -0.9282 1.3062 -3.9247
## total_shrub_cover-Meleagris_gallopavo -1.9640 1.6153 -5.8539
## total_shrub_cover-Sciurus_carolinensis -0.6648 1.1228 -3.2396
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2718 1.4959 -3.2362
## Avg_Cogongrass_Cover-Canis_latrans 0.0257 1.3058 -2.4582
## Avg_Cogongrass_Cover-Procyon_lotor -0.1567 1.2953 -2.6957
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5363 1.4996 -1.9671
## Avg_Cogongrass_Cover-Lynx_rufus -0.0863 1.4560 -2.8035
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1552 1.3532 -2.8384
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8476 1.5228 -4.1578
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5119 1.7187 -4.4512
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1661 1.3910 -2.8305
## Tree_Density-Odocoileus_virginianus -0.7023 1.7924 -3.2600
## Tree_Density-Canis_latrans -3.0449 1.6516 -7.1710
## Tree_Density-Procyon_lotor -1.9462 1.0820 -4.2877
## Tree_Density-Dasypus_novemcinctus -4.4521 2.9205 -12.3232
## Tree_Density-Lynx_rufus -0.4792 2.1943 -3.4807
## Tree_Density-Didelphis_virginiana -2.3288 1.5392 -6.0413
## Tree_Density-Sylvilagus_floridanus -2.8013 1.9898 -7.9989
## Tree_Density-Meleagris_gallopavo -2.3646 1.8241 -6.5710
## Tree_Density-Sciurus_carolinensis -2.8051 2.0102 -8.1389
## Avg_Canopy_Cover-Odocoileus_virginianus 0.9695 1.7270 -2.6236
## Avg_Canopy_Cover-Canis_latrans -0.0027 0.7719 -1.5595
## Avg_Canopy_Cover-Procyon_lotor 1.6920 0.9220 0.1058
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3905 1.0567 0.7927
## Avg_Canopy_Cover-Lynx_rufus 1.4413 1.8621 -1.9035
## Avg_Canopy_Cover-Didelphis_virginiana 3.5896 1.7848 1.2364
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.5904 2.4794 1.3250
## Avg_Canopy_Cover-Meleagris_gallopavo 3.0105 1.9312 0.4610
## Avg_Canopy_Cover-Sciurus_carolinensis 3.3298 1.7693 0.9795
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9397 1.2429 0.0124
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0394 1.0389 0.5322
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9330 0.9342 0.4848
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5454 0.7854 0.2008
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1766 1.1817 0.4885
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2335 0.7885 -0.2875
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3269 0.9087 -0.3330
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.8918 1.3351 -2.2812
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8096 0.8694 0.4061
## avg_veg_height-Odocoileus_virginianus -0.0197 0.9171 -1.9576
## avg_veg_height-Canis_latrans -0.1287 0.7183 -1.5739
## avg_veg_height-Procyon_lotor 0.1126 0.7178 -1.3127
## avg_veg_height-Dasypus_novemcinctus 0.3192 0.7025 -1.0271
## avg_veg_height-Lynx_rufus -0.2564 1.0073 -2.4879
## avg_veg_height-Didelphis_virginiana -0.2222 0.8214 -1.9822
## avg_veg_height-Sylvilagus_floridanus -0.1492 0.8314 -1.8647
## avg_veg_height-Meleagris_gallopavo 0.0185 0.9855 -1.9700
## avg_veg_height-Sciurus_carolinensis 0.3155 0.7919 -1.1139
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9320 17.9307 1.0057 310
## (Intercept)-Canis_latrans -0.6472 2.3616 1.0061 1144
## (Intercept)-Procyon_lotor -0.2053 2.1656 1.0052 1401
## (Intercept)-Dasypus_novemcinctus -2.5388 -0.3247 1.0605 551
## (Intercept)-Lynx_rufus 0.1019 7.6573 1.0423 273
## (Intercept)-Didelphis_virginiana -3.8521 -1.1727 1.0079 835
## (Intercept)-Sylvilagus_floridanus -2.0590 1.2548 1.0189 826
## (Intercept)-Meleagris_gallopavo -0.9078 5.0088 1.0169 238
## (Intercept)-Sciurus_carolinensis -4.4572 -1.4184 1.0175 638
## Cogon_Patch_Size-Odocoileus_virginianus 0.2213 4.4527 1.0165 1465
## Cogon_Patch_Size-Canis_latrans 1.4622 5.5026 1.0191 680
## Cogon_Patch_Size-Procyon_lotor -0.3917 1.1142 1.0184 672
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0610 1.9700 1.0109 1102
## Cogon_Patch_Size-Lynx_rufus -0.0883 3.3038 1.0246 528
## Cogon_Patch_Size-Didelphis_virginiana 1.5574 4.7339 1.0272 600
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8705 1.3963 1.0641 657
## Cogon_Patch_Size-Meleagris_gallopavo 0.4365 4.5661 1.0193 753
## Cogon_Patch_Size-Sciurus_carolinensis -0.6784 1.2108 1.0367 854
## Veg_shannon_index-Odocoileus_virginianus 0.8245 2.6468 1.0019 1353
## Veg_shannon_index-Canis_latrans 1.2413 3.1010 1.0098 840
## Veg_shannon_index-Procyon_lotor 1.1325 2.7875 1.0097 480
## Veg_shannon_index-Dasypus_novemcinctus 0.6245 1.9480 1.0060 1451
## Veg_shannon_index-Lynx_rufus 0.9964 3.2531 1.0032 1096
## Veg_shannon_index-Didelphis_virginiana 1.1277 2.9245 1.0096 1022
## Veg_shannon_index-Sylvilagus_floridanus 1.0162 2.8793 1.0052 738
## Veg_shannon_index-Meleagris_gallopavo 1.1583 3.4201 1.0050 791
## Veg_shannon_index-Sciurus_carolinensis 0.4462 1.9757 1.0096 1278
## total_shrub_cover-Odocoileus_virginianus -0.3772 2.2858 1.0052 1727
## total_shrub_cover-Canis_latrans 0.1977 2.8129 1.0174 759
## total_shrub_cover-Procyon_lotor -1.3120 -0.0809 1.0220 1075
## total_shrub_cover-Dasypus_novemcinctus -0.2707 1.1912 1.0050 1043
## total_shrub_cover-Lynx_rufus -1.1642 1.3572 1.0427 487
## total_shrub_cover-Didelphis_virginiana -1.0258 0.5600 1.0421 716
## total_shrub_cover-Sylvilagus_floridanus -0.7833 1.2301 1.0185 768
## total_shrub_cover-Meleagris_gallopavo -1.7127 0.4542 1.0590 361
## total_shrub_cover-Sciurus_carolinensis -0.5925 1.3016 1.0081 844
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.3112 2.7429 1.0108 721
## Avg_Cogongrass_Cover-Canis_latrans -0.0082 2.8010 1.0188 740
## Avg_Cogongrass_Cover-Procyon_lotor -0.1630 2.4427 1.0100 626
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3691 4.0569 1.0359 418
## Avg_Cogongrass_Cover-Lynx_rufus -0.1440 2.9621 1.0187 761
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1478 2.5779 1.0110 780
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7503 1.9368 1.0041 718
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4058 2.4677 1.0085 511
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1798 2.6560 1.0167 675
## Tree_Density-Odocoileus_virginianus -0.9941 3.5368 1.0425 567
## Tree_Density-Canis_latrans -2.7564 -0.7649 1.1282 512
## Tree_Density-Procyon_lotor -1.8927 0.0192 1.0291 949
## Tree_Density-Dasypus_novemcinctus -3.6692 -1.2203 1.1772 261
## Tree_Density-Lynx_rufus -0.8947 5.0946 1.1877 325
## Tree_Density-Didelphis_virginiana -2.1684 0.2728 1.0245 697
## Tree_Density-Sylvilagus_floridanus -2.4382 0.0448 1.1173 468
## Tree_Density-Meleagris_gallopavo -2.1882 0.9114 1.0526 525
## Tree_Density-Sciurus_carolinensis -2.4321 0.2035 1.0851 565
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0041 4.4247 1.0160 1291
## Avg_Canopy_Cover-Canis_latrans 0.0165 1.5306 1.0057 1311
## Avg_Canopy_Cover-Procyon_lotor 1.6312 3.7295 1.0187 575
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2114 4.9575 1.0665 371
## Avg_Canopy_Cover-Lynx_rufus 1.3290 5.5462 1.0331 524
## Avg_Canopy_Cover-Didelphis_virginiana 3.2411 8.1994 1.0866 294
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1072 10.6715 1.0640 239
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5899 8.0818 1.0099 418
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9529 7.8885 1.0108 303
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7676 5.0669 1.0384 605
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8860 4.5648 1.0446 828
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8096 4.1015 1.0288 861
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4760 3.3403 1.0344 1042
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9827 5.1129 1.0507 654
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2125 2.8793 1.0173 855
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2920 3.2696 1.0151 895
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0829 3.0476 1.0317 387
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7144 3.8157 1.0142 1234
## avg_veg_height-Odocoileus_virginianus -0.0137 1.7251 1.0109 1167
## avg_veg_height-Canis_latrans -0.1202 1.2526 1.0207 915
## avg_veg_height-Procyon_lotor 0.1133 1.5150 1.0220 964
## avg_veg_height-Dasypus_novemcinctus 0.2967 1.7946 1.0124 977
## avg_veg_height-Lynx_rufus -0.1872 1.5294 1.0224 798
## avg_veg_height-Didelphis_virginiana -0.1799 1.3010 1.0132 859
## avg_veg_height-Sylvilagus_floridanus -0.1265 1.4353 1.0253 835
## avg_veg_height-Meleagris_gallopavo 0.0399 1.9611 1.0058 868
## avg_veg_height-Sciurus_carolinensis 0.2782 1.9934 1.0063 1086
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0031 0.0591 -0.1118 0.0028 0.1171
## (Intercept)-Canis_latrans -2.7195 0.1874 -3.1055 -2.7154 -2.3650
## (Intercept)-Procyon_lotor -2.3044 0.1443 -2.6004 -2.2999 -2.0385
## (Intercept)-Dasypus_novemcinctus -1.7605 0.1634 -2.0912 -1.7575 -1.4522
## (Intercept)-Lynx_rufus -3.8343 0.3607 -4.5339 -3.8384 -3.1197
## (Intercept)-Didelphis_virginiana -2.5878 0.3027 -3.2083 -2.5788 -2.0164
## (Intercept)-Sylvilagus_floridanus -3.1873 0.2655 -3.7255 -3.1795 -2.6944
## (Intercept)-Meleagris_gallopavo -3.9535 0.5240 -4.9659 -3.9497 -2.9219
## (Intercept)-Sciurus_carolinensis -2.7013 0.3294 -3.3632 -2.6931 -2.0839
## shrub_cover-Odocoileus_virginianus -0.0542 0.0631 -0.1795 -0.0530 0.0668
## shrub_cover-Canis_latrans -0.3152 0.2331 -0.7551 -0.3177 0.1490
## shrub_cover-Procyon_lotor 0.2771 0.1626 -0.0539 0.2765 0.5966
## shrub_cover-Dasypus_novemcinctus 0.9403 0.3145 0.3371 0.9392 1.5560
## shrub_cover-Lynx_rufus -0.1540 0.3733 -0.8830 -0.1632 0.5899
## shrub_cover-Didelphis_virginiana 1.0370 0.3830 0.3668 1.0140 1.8301
## shrub_cover-Sylvilagus_floridanus 0.5404 0.4059 -0.2578 0.5367 1.3591
## shrub_cover-Meleagris_gallopavo -0.6286 0.4616 -1.5446 -0.6290 0.2766
## shrub_cover-Sciurus_carolinensis 0.9712 0.4128 0.1690 0.9678 1.8034
## veg_height-Odocoileus_virginianus -0.2956 0.0642 -0.4211 -0.2949 -0.1713
## veg_height-Canis_latrans -0.5559 0.1815 -0.9277 -0.5507 -0.2145
## veg_height-Procyon_lotor 0.3626 0.1217 0.1268 0.3631 0.6039
## veg_height-Dasypus_novemcinctus 0.2580 0.1357 -0.0035 0.2554 0.5302
## veg_height-Lynx_rufus 0.1280 0.2443 -0.3625 0.1282 0.6057
## veg_height-Didelphis_virginiana 0.4511 0.2430 -0.0137 0.4444 0.9556
## veg_height-Sylvilagus_floridanus 0.1452 0.2435 -0.3266 0.1413 0.6352
## veg_height-Meleagris_gallopavo -0.2324 0.3737 -0.9897 -0.2281 0.4825
## veg_height-Sciurus_carolinensis 0.1156 0.2185 -0.3003 0.1079 0.5525
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 4882
## (Intercept)-Canis_latrans 1.0028 2041
## (Intercept)-Procyon_lotor 1.0131 3361
## (Intercept)-Dasypus_novemcinctus 1.0004 2273
## (Intercept)-Lynx_rufus 1.0425 539
## (Intercept)-Didelphis_virginiana 1.0089 1183
## (Intercept)-Sylvilagus_floridanus 1.0014 1621
## (Intercept)-Meleagris_gallopavo 1.0057 345
## (Intercept)-Sciurus_carolinensis 1.0020 1284
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0046 1456
## shrub_cover-Procyon_lotor 1.0003 3177
## shrub_cover-Dasypus_novemcinctus 1.0023 1641
## shrub_cover-Lynx_rufus 1.0164 619
## shrub_cover-Didelphis_virginiana 1.0079 1009
## shrub_cover-Sylvilagus_floridanus 1.0010 950
## shrub_cover-Meleagris_gallopavo 1.0031 614
## shrub_cover-Sciurus_carolinensis 1.0013 1284
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0005 2478
## veg_height-Procyon_lotor 1.0088 3785
## veg_height-Dasypus_novemcinctus 1.0003 4080
## veg_height-Lynx_rufus 1.0025 1791
## veg_height-Didelphis_virginiana 1.0002 2879
## veg_height-Sylvilagus_floridanus 1.0056 2385
## veg_height-Meleagris_gallopavo 1.0069 901
## veg_height-Sciurus_carolinensis 1.0003 2593
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T10<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.5477
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0975 0.5589 -1.0122 0.0897 1.2187 1.0001 4330
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1356 2.7195 0.6877 2.3633 9.9317 1.0074 2036
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0198 0.4672 -2.9161 -2.0275 -1.0475 1.0048 5250
## week 0.3683 0.2376 -0.1299 0.3749 0.8138 1.0054 3686
## I(week^2) -0.2964 0.1154 -0.5335 -0.2932 -0.0729 1.0019 2525
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0209 1.3772 0.6677 1.6459 5.6189 1.0006 4622
## week 0.4009 0.3530 0.0989 0.3139 1.2017 1.0261 3130
## I(week^2) 0.0842 0.0787 0.0236 0.0644 0.2581 1.0097 2727
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4161 1.1541 1.7731 3.2245 6.2859
## (Intercept)-Canis_latrans 0.3267 0.4164 -0.4515 0.3132 1.1832
## (Intercept)-Procyon_lotor 0.7172 0.3963 -0.0067 0.7016 1.5263
## (Intercept)-Dasypus_novemcinctus -0.6149 0.3671 -1.3558 -0.6066 0.0961
## (Intercept)-Lynx_rufus 0.4315 0.9339 -0.8142 0.2569 2.7419
## (Intercept)-Didelphis_virginiana -1.3340 0.4496 -2.2794 -1.3092 -0.5286
## (Intercept)-Sylvilagus_floridanus -0.2744 0.5666 -1.2124 -0.3145 0.9859
## (Intercept)-Meleagris_gallopavo -0.1806 0.7373 -1.2800 -0.2825 1.5830
## (Intercept)-Sciurus_carolinensis -1.3059 0.4473 -2.2132 -1.2929 -0.4706
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 1532
## (Intercept)-Canis_latrans 1.0005 4924
## (Intercept)-Procyon_lotor 1.0005 5250
## (Intercept)-Dasypus_novemcinctus 1.0007 5250
## (Intercept)-Lynx_rufus 1.0075 794
## (Intercept)-Didelphis_virginiana 1.0024 5250
## (Intercept)-Sylvilagus_floridanus 1.0097 2006
## (Intercept)-Meleagris_gallopavo 1.0090 894
## (Intercept)-Sciurus_carolinensis 1.0009 5250
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5253 0.0808 0.3697 0.5252 0.6891
## (Intercept)-Canis_latrans -2.4267 0.1906 -2.8156 -2.4195 -2.0596
## (Intercept)-Procyon_lotor -2.1507 0.1500 -2.4523 -2.1484 -1.8620
## (Intercept)-Dasypus_novemcinctus -1.4359 0.1568 -1.7463 -1.4335 -1.1306
## (Intercept)-Lynx_rufus -3.4324 0.3712 -4.1727 -3.4203 -2.7424
## (Intercept)-Didelphis_virginiana -2.0932 0.2672 -2.6361 -2.0823 -1.5970
## (Intercept)-Sylvilagus_floridanus -3.0688 0.3371 -3.7691 -3.0524 -2.4446
## (Intercept)-Meleagris_gallopavo -3.3061 0.4072 -4.1886 -3.2812 -2.5851
## (Intercept)-Sciurus_carolinensis -2.2398 0.2822 -2.8261 -2.2330 -1.7075
## week-Odocoileus_virginianus 1.2802 0.1228 1.0438 1.2789 1.5191
## week-Canis_latrans 0.5914 0.2611 0.0941 0.5856 1.1122
## week-Procyon_lotor 0.2069 0.2101 -0.2019 0.2082 0.6146
## week-Dasypus_novemcinctus 0.1158 0.2243 -0.3256 0.1169 0.5552
## week-Lynx_rufus 0.3864 0.3469 -0.2962 0.3939 1.0788
## week-Didelphis_virginiana 0.0844 0.3675 -0.6713 0.0889 0.7836
## week-Sylvilagus_floridanus 0.0894 0.3405 -0.5876 0.0965 0.7245
## week-Meleagris_gallopavo -0.1528 0.4256 -1.0736 -0.1227 0.6026
## week-Sciurus_carolinensis 0.7994 0.3582 0.1325 0.7866 1.5406
## I(week^2)-Odocoileus_virginianus -0.5282 0.0507 -0.6292 -0.5279 -0.4313
## I(week^2)-Canis_latrans -0.2458 0.1083 -0.4633 -0.2460 -0.0357
## I(week^2)-Procyon_lotor -0.1362 0.0912 -0.3153 -0.1359 0.0405
## I(week^2)-Dasypus_novemcinctus -0.1820 0.1042 -0.3943 -0.1809 0.0171
## I(week^2)-Lynx_rufus -0.2438 0.1518 -0.5588 -0.2418 0.0513
## I(week^2)-Didelphis_virginiana -0.4297 0.2148 -0.9117 -0.4064 -0.0665
## I(week^2)-Sylvilagus_floridanus -0.1919 0.1604 -0.5194 -0.1883 0.1124
## I(week^2)-Meleagris_gallopavo -0.4127 0.2422 -0.9444 -0.3914 -0.0029
## I(week^2)-Sciurus_carolinensis -0.2858 0.1411 -0.5756 -0.2829 -0.0203
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 4997
## (Intercept)-Canis_latrans 1.0008 3465
## (Intercept)-Procyon_lotor 1.0060 4141
## (Intercept)-Dasypus_novemcinctus 1.0001 5250
## (Intercept)-Lynx_rufus 1.0011 762
## (Intercept)-Didelphis_virginiana 1.0033 4397
## (Intercept)-Sylvilagus_floridanus 1.0050 1566
## (Intercept)-Meleagris_gallopavo 1.0024 1007
## (Intercept)-Sciurus_carolinensis 1.0007 3636
## week-Odocoileus_virginianus 1.0000 4744
## week-Canis_latrans 1.0003 4026
## week-Procyon_lotor 1.0026 4531
## week-Dasypus_novemcinctus 1.0000 4889
## week-Lynx_rufus 1.0041 2510
## week-Didelphis_virginiana 1.0011 3305
## week-Sylvilagus_floridanus 1.0017 3082
## week-Meleagris_gallopavo 1.0169 1265
## week-Sciurus_carolinensis 1.0012 4066
## I(week^2)-Odocoileus_virginianus 1.0008 5250
## I(week^2)-Canis_latrans 1.0011 4268
## I(week^2)-Procyon_lotor 1.0067 4279
## I(week^2)-Dasypus_novemcinctus 1.0017 4386
## I(week^2)-Lynx_rufus 1.0005 2614
## I(week^2)-Didelphis_virginiana 1.0006 1765
## I(week^2)-Sylvilagus_floridanus 1.0006 2423
## I(week^2)-Meleagris_gallopavo 1.0156 770
## I(week^2)-Sciurus_carolinensis 1.0010 4407
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6898
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0750 1.0306 -1.9337 0.0505 2.1372 1.0085 3073
## Cogon_Patch_Size -0.4112 0.5973 -1.5989 -0.4067 0.8080 1.0035 1753
## Veg_shannon_index 0.8492 0.4290 0.0188 0.8418 1.7024 1.0016 898
## total_shrub_cover -0.5084 0.5197 -1.6015 -0.4875 0.4681 1.0044 2224
## Avg_Cogongrass_Cover 1.9139 0.6791 0.5935 1.9129 3.2788 1.0027 720
## Tree_Density -1.7570 0.6575 -3.0649 -1.7534 -0.4530 1.0162 1336
## Avg_Canopy_Cover 1.6339 0.5777 0.5290 1.6192 2.8404 1.0084 1332
## avg_veg_height -0.4837 0.4687 -1.4058 -0.4939 0.4661 1.0132 886
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.7807 16.2997 2.9199 12.0383 58.6519 1.0218 874
## Cogon_Patch_Size 2.2382 3.7035 0.0950 1.2032 10.4014 1.0613 1106
## Veg_shannon_index 0.5629 0.8583 0.0461 0.2980 2.6131 1.0139 1474
## total_shrub_cover 1.7790 2.6948 0.0828 0.9525 8.1143 1.0376 904
## Avg_Cogongrass_Cover 0.8034 1.2368 0.0478 0.3939 3.7624 1.0113 1715
## Tree_Density 2.2635 4.4244 0.0722 1.0122 11.6085 1.0527 1227
## Avg_Canopy_Cover 1.9836 3.1150 0.1005 1.1370 8.9119 1.1341 639
## avg_veg_height 0.4349 0.6377 0.0416 0.2461 1.9931 1.0374 2274
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4444 2.4742 0.065 0.758 6.4791 1.2495 240
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0237 0.4716 -2.8975 -2.0454 -1.0034 1.0039 5250
## week 0.3653 0.2444 -0.1258 0.3721 0.8273 1.0023 4203
## I(week^2) -0.2924 0.1095 -0.5183 -0.2899 -0.0782 1.0002 3278
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0778 1.5364 0.6860 1.6784 5.8355 1.0192 4616
## week 0.4103 0.3637 0.1037 0.3238 1.1827 1.0168 4559
## I(week^2) 0.0838 0.0683 0.0235 0.0647 0.2597 1.0058 2643
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.8822 3.2069 3.2789 7.3212
## (Intercept)-Canis_latrans 0.6849 0.9996 -1.0936 0.6191
## (Intercept)-Procyon_lotor 0.8694 0.8943 -0.9658 0.8696
## (Intercept)-Dasypus_novemcinctus -1.4373 0.8710 -3.3595 -1.3729
## (Intercept)-Lynx_rufus 1.9512 2.7448 -1.8018 1.3648
## (Intercept)-Didelphis_virginiana -2.9474 1.0977 -5.3441 -2.8501
## (Intercept)-Sylvilagus_floridanus -1.1447 1.1778 -3.5876 -1.1453
## (Intercept)-Meleagris_gallopavo -1.1648 1.4340 -3.9629 -1.1667
## (Intercept)-Sciurus_carolinensis -3.0814 1.1395 -5.6113 -3.0019
## Cogon_Patch_Size-Odocoileus_virginianus -0.3848 1.1930 -2.5689 -0.4277
## Cogon_Patch_Size-Canis_latrans 0.7112 1.1511 -0.8842 0.5042
## Cogon_Patch_Size-Procyon_lotor -0.7958 0.6974 -2.2265 -0.7818
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6762 0.5958 -1.9328 -0.6551
## Cogon_Patch_Size-Lynx_rufus -0.3880 1.3499 -2.8140 -0.4627
## Cogon_Patch_Size-Didelphis_virginiana 0.8278 0.8706 -0.5691 0.7327
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6021 1.3446 -4.9169 -1.3598
## Cogon_Patch_Size-Meleagris_gallopavo -0.3381 1.0079 -2.1533 -0.3962
## Cogon_Patch_Size-Sciurus_carolinensis -1.4168 1.1047 -4.1983 -1.2187
## Veg_shannon_index-Odocoileus_virginianus 0.6972 0.7599 -0.9302 0.7242
## Veg_shannon_index-Canis_latrans 1.1495 0.5855 0.1466 1.0950
## Veg_shannon_index-Procyon_lotor 1.0834 0.5643 0.0652 1.0457
## Veg_shannon_index-Dasypus_novemcinctus 0.6421 0.4839 -0.3094 0.6538
## Veg_shannon_index-Lynx_rufus 0.7935 0.8057 -0.8955 0.8187
## Veg_shannon_index-Didelphis_virginiana 1.0141 0.6052 -0.0632 0.9780
## Veg_shannon_index-Sylvilagus_floridanus 0.9816 0.6333 -0.1269 0.9399
## Veg_shannon_index-Meleagris_gallopavo 1.1600 0.6969 -0.0203 1.0970
## Veg_shannon_index-Sciurus_carolinensis 0.2901 0.6635 -1.2161 0.3510
## total_shrub_cover-Odocoileus_virginianus -0.0283 1.0231 -1.9024 -0.1079
## total_shrub_cover-Canis_latrans 0.2488 0.7650 -0.9616 0.1644
## total_shrub_cover-Procyon_lotor -1.0108 0.6177 -2.3367 -0.9692
## total_shrub_cover-Dasypus_novemcinctus 0.0954 0.5590 -0.9563 0.0802
## total_shrub_cover-Lynx_rufus -1.1704 1.2700 -4.1772 -0.9749
## total_shrub_cover-Didelphis_virginiana -0.6168 0.7086 -2.1555 -0.5750
## total_shrub_cover-Sylvilagus_floridanus -0.2224 0.8438 -1.8336 -0.2246
## total_shrub_cover-Meleagris_gallopavo -2.0917 1.3285 -5.1582 -1.8841
## total_shrub_cover-Sciurus_carolinensis -0.0431 0.6949 -1.3732 -0.0652
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8560 0.9996 -0.1904 1.8655
## Avg_Cogongrass_Cover-Canis_latrans 2.1669 0.8410 0.6517 2.1090
## Avg_Cogongrass_Cover-Procyon_lotor 2.0688 0.8216 0.5531 2.0395
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3681 0.8718 0.8798 2.2851
## Avg_Cogongrass_Cover-Lynx_rufus 2.2190 0.9423 0.5492 2.1583
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0434 0.8013 0.6005 2.0121
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4298 0.9171 -0.5006 1.4642
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4919 1.1094 -1.0067 1.5775
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1916 0.8443 0.7131 2.1344
## Tree_Density-Odocoileus_virginianus -0.8831 1.1268 -2.7125 -1.0157
## Tree_Density-Canis_latrans -2.2769 1.0449 -4.7280 -2.1505
## Tree_Density-Procyon_lotor -1.4089 0.7461 -2.7972 -1.4392
## Tree_Density-Dasypus_novemcinctus -3.0321 1.3942 -6.5009 -2.7357
## Tree_Density-Lynx_rufus -0.7903 1.2433 -2.8267 -0.9292
## Tree_Density-Didelphis_virginiana -2.1247 1.0321 -4.5505 -2.0038
## Tree_Density-Sylvilagus_floridanus -2.2852 1.2429 -5.2101 -2.1067
## Tree_Density-Meleagris_gallopavo -2.0988 1.1857 -4.8072 -1.9806
## Tree_Density-Sciurus_carolinensis -2.2895 1.1524 -5.0932 -2.1137
## Avg_Canopy_Cover-Odocoileus_virginianus 1.1748 1.1931 -1.3064 1.2388
## Avg_Canopy_Cover-Canis_latrans 0.3618 0.6922 -0.9989 0.3687
## Avg_Canopy_Cover-Procyon_lotor 1.6357 0.6771 0.4029 1.5972
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8619 0.6342 0.7822 1.8197
## Avg_Canopy_Cover-Lynx_rufus 1.2058 1.2273 -1.1507 1.2017
## Avg_Canopy_Cover-Didelphis_virginiana 2.4934 0.8924 1.1147 2.3584
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9110 1.4451 1.0081 2.6072
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1802 1.0785 0.6220 2.0046
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1560 0.7689 0.9091 2.0555
## avg_veg_height-Odocoileus_virginianus -0.5378 0.7624 -2.1608 -0.5130
## avg_veg_height-Canis_latrans -0.6696 0.5845 -1.8471 -0.6469
## avg_veg_height-Procyon_lotor -0.3882 0.5540 -1.4558 -0.3972
## avg_veg_height-Dasypus_novemcinctus -0.2521 0.5628 -1.3240 -0.2880
## avg_veg_height-Lynx_rufus -0.5578 0.7606 -2.1020 -0.5629
## avg_veg_height-Didelphis_virginiana -0.5821 0.6187 -1.8328 -0.5809
## avg_veg_height-Sylvilagus_floridanus -0.6698 0.6533 -1.9764 -0.6531
## avg_veg_height-Meleagris_gallopavo -0.6361 0.7151 -2.1617 -0.6186
## avg_veg_height-Sciurus_carolinensis -0.1575 0.6375 -1.3123 -0.2043
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.4978 1.0314 481
## (Intercept)-Canis_latrans 2.9583 1.0174 1515
## (Intercept)-Procyon_lotor 2.6351 1.0045 1756
## (Intercept)-Dasypus_novemcinctus 0.1298 1.0019 1206
## (Intercept)-Lynx_rufus 9.0970 1.0693 238
## (Intercept)-Didelphis_virginiana -1.0444 1.0165 1394
## (Intercept)-Sylvilagus_floridanus 1.2115 1.0012 1171
## (Intercept)-Meleagris_gallopavo 1.8195 1.0141 723
## (Intercept)-Sciurus_carolinensis -1.0681 1.0005 1451
## Cogon_Patch_Size-Odocoileus_virginianus 2.2935 1.0088 2303
## Cogon_Patch_Size-Canis_latrans 3.4775 1.0133 1722
## Cogon_Patch_Size-Procyon_lotor 0.5381 1.0064 1235
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4614 1.0042 1683
## Cogon_Patch_Size-Lynx_rufus 2.6149 1.0108 1202
## Cogon_Patch_Size-Didelphis_virginiana 2.7678 1.0171 1087
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3347 1.0134 1123
## Cogon_Patch_Size-Meleagris_gallopavo 1.9547 1.0086 2021
## Cogon_Patch_Size-Sciurus_carolinensis 0.1147 1.0084 1404
## Veg_shannon_index-Odocoileus_virginianus 2.1516 1.0073 1929
## Veg_shannon_index-Canis_latrans 2.4502 1.0041 1290
## Veg_shannon_index-Procyon_lotor 2.3219 1.0079 847
## Veg_shannon_index-Dasypus_novemcinctus 1.5886 1.0015 1898
## Veg_shannon_index-Lynx_rufus 2.2737 1.0014 1511
## Veg_shannon_index-Didelphis_virginiana 2.3642 1.0004 1736
## Veg_shannon_index-Sylvilagus_floridanus 2.3673 1.0051 737
## Veg_shannon_index-Meleagris_gallopavo 2.7618 1.0043 1329
## Veg_shannon_index-Sciurus_carolinensis 1.4105 1.0018 1808
## total_shrub_cover-Odocoileus_virginianus 2.2778 1.0053 2362
## total_shrub_cover-Canis_latrans 2.0370 1.0171 1562
## total_shrub_cover-Procyon_lotor 0.0853 1.0142 2651
## total_shrub_cover-Dasypus_novemcinctus 1.2327 1.0082 2570
## total_shrub_cover-Lynx_rufus 0.8494 1.0185 893
## total_shrub_cover-Didelphis_virginiana 0.6521 1.0044 1972
## total_shrub_cover-Sylvilagus_floridanus 1.5747 1.0035 2143
## total_shrub_cover-Meleagris_gallopavo -0.1167 1.0271 708
## total_shrub_cover-Sciurus_carolinensis 1.3794 1.0042 3351
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.8318 1.0014 1240
## Avg_Cogongrass_Cover-Canis_latrans 3.9700 1.0022 1012
## Avg_Cogongrass_Cover-Procyon_lotor 3.7567 1.0051 984
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.2937 1.0088 897
## Avg_Cogongrass_Cover-Lynx_rufus 4.2588 1.0039 1128
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7395 1.0034 1189
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1501 1.0024 1157
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4187 1.0051 957
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0085 1.0020 922
## Tree_Density-Odocoileus_virginianus 1.7821 1.0123 1149
## Tree_Density-Canis_latrans -0.5929 1.0053 1362
## Tree_Density-Procyon_lotor 0.1826 1.0092 1710
## Tree_Density-Dasypus_novemcinctus -1.1849 1.0032 781
## Tree_Density-Lynx_rufus 1.9825 1.0230 735
## Tree_Density-Didelphis_virginiana -0.4772 1.0054 1481
## Tree_Density-Sylvilagus_floridanus -0.2856 1.0075 1176
## Tree_Density-Meleagris_gallopavo -0.0101 1.0027 1233
## Tree_Density-Sciurus_carolinensis -0.5630 1.0034 1258
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5101 1.0031 2108
## Avg_Canopy_Cover-Canis_latrans 1.6969 1.0059 2103
## Avg_Canopy_Cover-Procyon_lotor 3.1333 1.0114 1411
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2569 1.0085 1399
## Avg_Canopy_Cover-Lynx_rufus 3.7386 1.0226 813
## Avg_Canopy_Cover-Didelphis_virginiana 4.5576 1.0206 747
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.6247 1.0544 552
## Avg_Canopy_Cover-Meleagris_gallopavo 4.8689 1.0293 1054
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9060 1.0075 1546
## avg_veg_height-Odocoileus_virginianus 0.9389 1.0029 1671
## avg_veg_height-Canis_latrans 0.4598 1.0051 1519
## avg_veg_height-Procyon_lotor 0.7261 1.0038 1639
## avg_veg_height-Dasypus_novemcinctus 0.9295 1.0029 1469
## avg_veg_height-Lynx_rufus 0.9405 1.0198 1162
## avg_veg_height-Didelphis_virginiana 0.6421 1.0122 1492
## avg_veg_height-Sylvilagus_floridanus 0.5516 1.0048 1366
## avg_veg_height-Meleagris_gallopavo 0.7406 1.0131 1161
## avg_veg_height-Sciurus_carolinensis 1.2297 1.0049 1738
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5259 0.0795 0.3700 0.5235 0.6791
## (Intercept)-Canis_latrans -2.4453 0.1954 -2.8427 -2.4426 -2.0796
## (Intercept)-Procyon_lotor -2.1545 0.1490 -2.4565 -2.1500 -1.8745
## (Intercept)-Dasypus_novemcinctus -1.4372 0.1595 -1.7619 -1.4321 -1.1337
## (Intercept)-Lynx_rufus -3.6119 0.3475 -4.2868 -3.6083 -2.9494
## (Intercept)-Didelphis_virginiana -2.0734 0.2631 -2.6149 -2.0640 -1.5919
## (Intercept)-Sylvilagus_floridanus -3.0632 0.3018 -3.6865 -3.0551 -2.5043
## (Intercept)-Meleagris_gallopavo -3.2764 0.3408 -3.9722 -3.2714 -2.6186
## (Intercept)-Sciurus_carolinensis -2.2440 0.2831 -2.8427 -2.2313 -1.7261
## week-Odocoileus_virginianus 1.2827 0.1222 1.0443 1.2806 1.5233
## week-Canis_latrans 0.5944 0.2647 0.0956 0.5903 1.1227
## week-Procyon_lotor 0.2025 0.2114 -0.2083 0.2028 0.6213
## week-Dasypus_novemcinctus 0.1064 0.2222 -0.3283 0.1079 0.5311
## week-Lynx_rufus 0.3964 0.3481 -0.2847 0.3951 1.0750
## week-Didelphis_virginiana 0.0783 0.3706 -0.6590 0.0846 0.7935
## week-Sylvilagus_floridanus 0.0760 0.3424 -0.5953 0.0771 0.7215
## week-Meleagris_gallopavo -0.1601 0.4088 -1.0174 -0.1416 0.5886
## week-Sciurus_carolinensis 0.8040 0.3751 0.1094 0.7968 1.5894
## I(week^2)-Odocoileus_virginianus -0.5287 0.0509 -0.6282 -0.5288 -0.4307
## I(week^2)-Canis_latrans -0.2461 0.1085 -0.4671 -0.2447 -0.0380
## I(week^2)-Procyon_lotor -0.1307 0.0918 -0.3130 -0.1307 0.0495
## I(week^2)-Dasypus_novemcinctus -0.1758 0.1021 -0.3791 -0.1731 0.0200
## I(week^2)-Lynx_rufus -0.2503 0.1543 -0.5643 -0.2475 0.0435
## I(week^2)-Didelphis_virginiana -0.4272 0.2227 -0.9263 -0.3997 -0.0596
## I(week^2)-Sylvilagus_floridanus -0.1837 0.1612 -0.5139 -0.1811 0.1193
## I(week^2)-Meleagris_gallopavo -0.4067 0.2392 -0.9386 -0.3822 -0.0044
## I(week^2)-Sciurus_carolinensis -0.2846 0.1438 -0.5756 -0.2841 -0.0053
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0013 2549
## (Intercept)-Procyon_lotor 1.0064 4194
## (Intercept)-Dasypus_novemcinctus 1.0009 5250
## (Intercept)-Lynx_rufus 1.0201 456
## (Intercept)-Didelphis_virginiana 1.0017 5184
## (Intercept)-Sylvilagus_floridanus 1.0010 1930
## (Intercept)-Meleagris_gallopavo 1.0034 1222
## (Intercept)-Sciurus_carolinensis 1.0002 4058
## week-Odocoileus_virginianus 1.0015 5250
## week-Canis_latrans 1.0007 3749
## week-Procyon_lotor 1.0007 4350
## week-Dasypus_novemcinctus 1.0001 5250
## week-Lynx_rufus 1.0015 2636
## week-Didelphis_virginiana 0.9999 2941
## week-Sylvilagus_floridanus 1.0003 3081
## week-Meleagris_gallopavo 1.0005 1528
## week-Sciurus_carolinensis 1.0041 4098
## I(week^2)-Odocoileus_virginianus 1.0011 5250
## I(week^2)-Canis_latrans 1.0002 3997
## I(week^2)-Procyon_lotor 1.0025 4212
## I(week^2)-Dasypus_novemcinctus 1.0006 4138
## I(week^2)-Lynx_rufus 1.0000 1993
## I(week^2)-Didelphis_virginiana 1.0002 1762
## I(week^2)-Sylvilagus_floridanus 1.0052 2493
## I(week^2)-Meleagris_gallopavo 1.0150 876
## I(week^2)-Sciurus_carolinensis 1.0043 4515
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7337
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0685 0.6630 -1.2101 0.0548 1.4427 1.0054 4169
## Avg_Cogongrass_Cover 0.1373 0.3285 -0.4956 0.1299 0.7740 1.0123 2260
## total_shrub_cover -0.4796 0.3529 -1.2499 -0.4635 0.1770 1.0002 2329
## avg_veg_height 0.0009 0.3160 -0.6141 -0.0037 0.6248 1.0190 1778
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.3870 4.0205 0.7581 3.3027 14.3141 1.0049 1715
## Avg_Cogongrass_Cover 0.3731 0.5143 0.0395 0.2267 1.5389 1.0136 2800
## total_shrub_cover 0.6976 0.8776 0.0645 0.4510 2.7934 1.0167 2133
## avg_veg_height 0.2662 0.3255 0.0365 0.1674 1.0754 1.0043 3419
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6987 0.7638 0.057 0.446 2.7379 1.0219 617
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0149 0.4727 -2.9021 -2.0395 -1.0163 0.9998 5250
## week 0.3599 0.2403 -0.1301 0.3713 0.7996 1.0027 3905
## I(week^2) -0.2860 0.1128 -0.5102 -0.2875 -0.0684 1.0022 3687
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9908 1.3560 0.6656 1.6339 5.5156 1.0040 5026
## week 0.4077 0.3266 0.0997 0.3193 1.2866 1.0105 3382
## I(week^2) 0.0829 0.0676 0.0237 0.0646 0.2526 1.0014 2811
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9686 1.5172 1.4889 3.7713
## (Intercept)-Canis_latrans 0.4110 0.7249 -0.8016 0.3629
## (Intercept)-Procyon_lotor 0.7916 0.6642 -0.5177 0.7867
## (Intercept)-Dasypus_novemcinctus -0.6839 0.5771 -1.8382 -0.6776
## (Intercept)-Lynx_rufus 0.1537 0.9921 -1.5419 0.0696
## (Intercept)-Didelphis_virginiana -1.4381 0.6578 -2.7816 -1.4243
## (Intercept)-Sylvilagus_floridanus -0.0845 0.9121 -1.5702 -0.1863
## (Intercept)-Meleagris_gallopavo -0.7093 0.7775 -2.2544 -0.7062
## (Intercept)-Sciurus_carolinensis -1.4978 0.6759 -2.8982 -1.4894
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1151 0.5591 -0.9841 0.1124
## Avg_Cogongrass_Cover-Canis_latrans 0.4056 0.4598 -0.4050 0.3754
## Avg_Cogongrass_Cover-Procyon_lotor 0.0812 0.4486 -0.8257 0.0845
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2547 0.3887 -0.4938 0.2489
## Avg_Cogongrass_Cover-Lynx_rufus 0.4465 0.5126 -0.4615 0.4148
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3239 0.4288 -0.5031 0.3166
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2436 0.5344 -1.3860 -0.2056
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3412 0.6028 -1.7454 -0.2873
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2210 0.4156 -0.6055 0.2133
## total_shrub_cover-Odocoileus_virginianus -0.3025 0.6461 -1.5767 -0.3161
## total_shrub_cover-Canis_latrans 0.0942 0.4858 -0.7613 0.0719
## total_shrub_cover-Procyon_lotor -0.9909 0.5239 -2.1537 -0.9406
## total_shrub_cover-Dasypus_novemcinctus -0.0749 0.3800 -0.8013 -0.0887
## total_shrub_cover-Lynx_rufus -0.9483 0.6827 -2.5413 -0.8590
## total_shrub_cover-Didelphis_virginiana -0.2886 0.4452 -1.1852 -0.2882
## total_shrub_cover-Sylvilagus_floridanus -0.4839 0.6060 -1.9041 -0.4394
## total_shrub_cover-Meleagris_gallopavo -1.3015 0.6864 -2.8910 -1.2169
## total_shrub_cover-Sciurus_carolinensis -0.1391 0.4422 -0.9895 -0.1484
## avg_veg_height-Odocoileus_virginianus -0.0219 0.5265 -1.0524 -0.0192
## avg_veg_height-Canis_latrans -0.0760 0.4184 -0.9499 -0.0640
## avg_veg_height-Procyon_lotor 0.0940 0.4238 -0.7026 0.0857
## avg_veg_height-Dasypus_novemcinctus 0.1782 0.3941 -0.5766 0.1619
## avg_veg_height-Lynx_rufus -0.0024 0.5140 -0.9864 -0.0024
## avg_veg_height-Didelphis_virginiana -0.0467 0.4213 -0.8843 -0.0419
## avg_veg_height-Sylvilagus_floridanus -0.1278 0.4549 -1.0658 -0.1149
## avg_veg_height-Meleagris_gallopavo -0.2521 0.5233 -1.3770 -0.2188
## avg_veg_height-Sciurus_carolinensis 0.2799 0.4382 -0.5196 0.2607
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4697 1.0113 1206
## (Intercept)-Canis_latrans 1.8747 1.0008 1693
## (Intercept)-Procyon_lotor 2.1561 1.0067 2301
## (Intercept)-Dasypus_novemcinctus 0.4596 1.0012 3569
## (Intercept)-Lynx_rufus 2.2497 1.0247 1087
## (Intercept)-Didelphis_virginiana -0.1888 1.0007 2836
## (Intercept)-Sylvilagus_floridanus 2.0937 1.0087 1110
## (Intercept)-Meleagris_gallopavo 0.8795 1.0007 2218
## (Intercept)-Sciurus_carolinensis -0.1843 0.9999 2213
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2825 1.0039 3513
## Avg_Cogongrass_Cover-Canis_latrans 1.4154 1.0074 3161
## Avg_Cogongrass_Cover-Procyon_lotor 0.9428 1.0107 3149
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0325 1.0031 3357
## Avg_Cogongrass_Cover-Lynx_rufus 1.5976 1.0036 2581
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2065 1.0081 3360
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7056 1.0077 2291
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7121 1.0101 2017
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0584 1.0022 3425
## total_shrub_cover-Odocoileus_virginianus 1.0475 0.9998 3815
## total_shrub_cover-Canis_latrans 1.1272 1.0005 2663
## total_shrub_cover-Procyon_lotor -0.1248 1.0000 2371
## total_shrub_cover-Dasypus_novemcinctus 0.6902 1.0009 4605
## total_shrub_cover-Lynx_rufus 0.1512 1.0004 1904
## total_shrub_cover-Didelphis_virginiana 0.5884 1.0011 4560
## total_shrub_cover-Sylvilagus_floridanus 0.5751 1.0133 1635
## total_shrub_cover-Meleagris_gallopavo -0.2260 1.0016 2143
## total_shrub_cover-Sciurus_carolinensis 0.7567 1.0005 4633
## avg_veg_height-Odocoileus_virginianus 1.0107 1.0082 2850
## avg_veg_height-Canis_latrans 0.7278 1.0069 3016
## avg_veg_height-Procyon_lotor 0.9712 1.0082 2694
## avg_veg_height-Dasypus_novemcinctus 0.9806 1.0017 2981
## avg_veg_height-Lynx_rufus 1.0376 1.0068 2590
## avg_veg_height-Didelphis_virginiana 0.7752 1.0116 3099
## avg_veg_height-Sylvilagus_floridanus 0.7348 1.0083 2670
## avg_veg_height-Meleagris_gallopavo 0.6685 1.0075 2532
## avg_veg_height-Sciurus_carolinensis 1.2128 1.0091 3007
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5241 0.0797 0.3679 0.5238 0.6793
## (Intercept)-Canis_latrans -2.4496 0.1997 -2.8648 -2.4409 -2.0855
## (Intercept)-Procyon_lotor -2.1642 0.1499 -2.4676 -2.1598 -1.8763
## (Intercept)-Dasypus_novemcinctus -1.4365 0.1563 -1.7519 -1.4330 -1.1471
## (Intercept)-Lynx_rufus -3.3867 0.3324 -4.0628 -3.3797 -2.7576
## (Intercept)-Didelphis_virginiana -2.1042 0.2715 -2.6566 -2.0949 -1.5945
## (Intercept)-Sylvilagus_floridanus -3.1643 0.3607 -3.9321 -3.1484 -2.4998
## (Intercept)-Meleagris_gallopavo -3.1671 0.3527 -3.9199 -3.1501 -2.5362
## (Intercept)-Sciurus_carolinensis -2.2563 0.2927 -2.8706 -2.2381 -1.7156
## week-Odocoileus_virginianus 1.2785 0.1208 1.0474 1.2772 1.5211
## week-Canis_latrans 0.5856 0.2647 0.0909 0.5806 1.1160
## week-Procyon_lotor 0.2072 0.2111 -0.2082 0.2075 0.6213
## week-Dasypus_novemcinctus 0.1125 0.2234 -0.3215 0.1128 0.5587
## week-Lynx_rufus 0.3970 0.3493 -0.2902 0.4037 1.0724
## week-Didelphis_virginiana 0.0779 0.3673 -0.6619 0.0945 0.7728
## week-Sylvilagus_floridanus 0.0738 0.3506 -0.6492 0.0831 0.7305
## week-Meleagris_gallopavo -0.1608 0.4115 -1.0049 -0.1414 0.5872
## week-Sciurus_carolinensis 0.7875 0.3599 0.1091 0.7761 1.5145
## I(week^2)-Odocoileus_virginianus -0.5275 0.0503 -0.6278 -0.5275 -0.4313
## I(week^2)-Canis_latrans -0.2440 0.1087 -0.4613 -0.2418 -0.0366
## I(week^2)-Procyon_lotor -0.1345 0.0912 -0.3160 -0.1350 0.0409
## I(week^2)-Dasypus_novemcinctus -0.1792 0.1027 -0.3822 -0.1772 0.0215
## I(week^2)-Lynx_rufus -0.2458 0.1546 -0.5586 -0.2406 0.0537
## I(week^2)-Didelphis_virginiana -0.4199 0.2136 -0.8990 -0.3990 -0.0509
## I(week^2)-Sylvilagus_floridanus -0.1857 0.1638 -0.5270 -0.1833 0.1198
## I(week^2)-Meleagris_gallopavo -0.3986 0.2431 -0.9570 -0.3725 0.0065
## I(week^2)-Sciurus_carolinensis -0.2820 0.1454 -0.5747 -0.2795 -0.0105
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0017 2897
## (Intercept)-Procyon_lotor 1.0004 3872
## (Intercept)-Dasypus_novemcinctus 1.0028 4748
## (Intercept)-Lynx_rufus 1.0252 1321
## (Intercept)-Didelphis_virginiana 1.0005 4130
## (Intercept)-Sylvilagus_floridanus 1.0079 1073
## (Intercept)-Meleagris_gallopavo 1.0012 1582
## (Intercept)-Sciurus_carolinensis 1.0106 3675
## week-Odocoileus_virginianus 1.0019 4773
## week-Canis_latrans 1.0020 3852
## week-Procyon_lotor 0.9999 4774
## week-Dasypus_novemcinctus 1.0002 4552
## week-Lynx_rufus 1.0007 2897
## week-Didelphis_virginiana 1.0019 3161
## week-Sylvilagus_floridanus 1.0010 2741
## week-Meleagris_gallopavo 1.0059 1654
## week-Sciurus_carolinensis 1.0004 4242
## I(week^2)-Odocoileus_virginianus 1.0004 4549
## I(week^2)-Canis_latrans 1.0009 3759
## I(week^2)-Procyon_lotor 1.0007 4727
## I(week^2)-Dasypus_novemcinctus 1.0007 3846
## I(week^2)-Lynx_rufus 1.0056 2410
## I(week^2)-Didelphis_virginiana 1.0028 1953
## I(week^2)-Sylvilagus_floridanus 1.0007 2150
## I(week^2)-Meleagris_gallopavo 1.0018 977
## I(week^2)-Sciurus_carolinensis 1.0016 4601
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.7255
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1303 0.8014 -1.4002 0.1019 1.8319 1.0062 2181
## Tree_Density -0.7570 0.4133 -1.6488 -0.7305 0.0015 1.0051 1684
## Avg_Canopy_Cover 1.0492 0.3895 0.3242 1.0348 1.8758 1.0022 2094
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.6849 6.4948 1.2257 4.8301 22.9317 1.0040 1149
## Tree_Density 0.8928 1.4587 0.0464 0.3950 4.8153 1.0164 1287
## Avg_Canopy_Cover 0.7971 1.0117 0.0691 0.5111 3.2105 1.0089 2198
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4468 0.5284 0.0417 0.2751 1.8285 1.0742 534
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0240 0.4710 -2.8994 -2.0379 -1.0259 1.0012 5038
## week 0.3703 0.2448 -0.1185 0.3749 0.8411 1.0000 4005
## I(week^2) -0.2894 0.1150 -0.5197 -0.2868 -0.0642 1.0009 2528
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0424 1.4184 0.7022 1.6654 5.5467 1.0007 5250
## week 0.4065 0.3238 0.0955 0.3157 1.2032 1.0008 2972
## I(week^2) 0.0839 0.0712 0.0240 0.0645 0.2647 1.0195 2423
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7974 1.8633 2.0751 4.5016 9.3473
## (Intercept)-Canis_latrans 0.3579 0.6980 -0.8247 0.3040 1.9121
## (Intercept)-Procyon_lotor 0.7805 0.6211 -0.3853 0.7636 2.0647
## (Intercept)-Dasypus_novemcinctus -1.0330 0.6274 -2.4063 -0.9989 0.1025
## (Intercept)-Lynx_rufus 1.2231 1.7929 -1.2888 0.8555 5.6196
## (Intercept)-Didelphis_virginiana -1.9075 0.6900 -3.3675 -1.8822 -0.6268
## (Intercept)-Sylvilagus_floridanus -0.6447 0.7602 -2.0552 -0.6554 0.8702
## (Intercept)-Meleagris_gallopavo -0.2624 1.0377 -1.8296 -0.3644 1.7919
## (Intercept)-Sciurus_carolinensis -1.9762 0.7240 -3.5337 -1.9251 -0.6524
## Tree_Density-Odocoileus_virginianus -0.3516 0.6998 -1.4821 -0.4381 1.3174
## Tree_Density-Canis_latrans -0.9000 0.5636 -2.1825 -0.8405 0.0191
## Tree_Density-Procyon_lotor -0.4722 0.4117 -1.2813 -0.4722 0.3489
## Tree_Density-Dasypus_novemcinctus -1.3881 0.9494 -3.7690 -1.1722 -0.1923
## Tree_Density-Lynx_rufus 0.0087 0.8403 -1.3029 -0.1128 2.0432
## Tree_Density-Didelphis_virginiana -1.0367 0.7836 -2.9394 -0.9112 0.1321
## Tree_Density-Sylvilagus_floridanus -1.0686 0.7649 -2.8983 -0.9595 0.0900
## Tree_Density-Meleagris_gallopavo -0.9172 0.7360 -2.6544 -0.8345 0.2953
## Tree_Density-Sciurus_carolinensis -0.9592 0.7371 -2.7767 -0.8489 0.1852
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8077 0.7275 -0.6857 0.8086 2.2802
## Avg_Canopy_Cover-Canis_latrans 0.0835 0.4858 -0.8632 0.0791 1.0668
## Avg_Canopy_Cover-Procyon_lotor 1.0593 0.4771 0.1912 1.0224 2.0779
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0434 0.4321 0.2402 1.0254 1.9421
## Avg_Canopy_Cover-Lynx_rufus 0.9535 0.8046 -0.4956 0.9044 2.6311
## Avg_Canopy_Cover-Didelphis_virginiana 1.3028 0.5139 0.4317 1.2574 2.4545
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7928 0.8456 0.5837 1.6301 3.9063
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4686 0.7472 0.2972 1.3677 3.1834
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2608 0.4952 0.3786 1.2173 2.3459
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0073 778
## (Intercept)-Canis_latrans 1.0025 2003
## (Intercept)-Procyon_lotor 1.0037 2830
## (Intercept)-Dasypus_novemcinctus 1.0040 2612
## (Intercept)-Lynx_rufus 1.0104 408
## (Intercept)-Didelphis_virginiana 1.0000 2929
## (Intercept)-Sylvilagus_floridanus 1.0080 2442
## (Intercept)-Meleagris_gallopavo 1.0639 412
## (Intercept)-Sciurus_carolinensis 1.0036 3041
## Tree_Density-Odocoileus_virginianus 1.0016 2064
## Tree_Density-Canis_latrans 1.0133 2813
## Tree_Density-Procyon_lotor 1.0010 3118
## Tree_Density-Dasypus_novemcinctus 1.0165 1465
## Tree_Density-Lynx_rufus 1.0093 826
## Tree_Density-Didelphis_virginiana 1.0069 1906
## Tree_Density-Sylvilagus_floridanus 1.0168 1646
## Tree_Density-Meleagris_gallopavo 1.0066 2122
## Tree_Density-Sciurus_carolinensis 1.0047 2859
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0021 2715
## Avg_Canopy_Cover-Canis_latrans 1.0156 2847
## Avg_Canopy_Cover-Procyon_lotor 1.0013 3168
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0000 3964
## Avg_Canopy_Cover-Lynx_rufus 1.0039 1328
## Avg_Canopy_Cover-Didelphis_virginiana 1.0008 2955
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0017 1618
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0011 1681
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0013 2881
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5253 0.0807 0.3727 0.5233 0.6832
## (Intercept)-Canis_latrans -2.4592 0.2003 -2.8762 -2.4494 -2.0860
## (Intercept)-Procyon_lotor -2.1576 0.1481 -2.4653 -2.1542 -1.8783
## (Intercept)-Dasypus_novemcinctus -1.4325 0.1569 -1.7465 -1.4326 -1.1268
## (Intercept)-Lynx_rufus -3.5843 0.3727 -4.2982 -3.5883 -2.8422
## (Intercept)-Didelphis_virginiana -2.0871 0.2684 -2.6465 -2.0767 -1.5869
## (Intercept)-Sylvilagus_floridanus -3.0156 0.3040 -3.6398 -3.0037 -2.4568
## (Intercept)-Meleagris_gallopavo -3.3269 0.3814 -4.1338 -3.3070 -2.6406
## (Intercept)-Sciurus_carolinensis -2.2364 0.2788 -2.8122 -2.2288 -1.7222
## week-Odocoileus_virginianus 1.2823 0.1228 1.0494 1.2790 1.5273
## week-Canis_latrans 0.5804 0.2655 0.0680 0.5743 1.1098
## week-Procyon_lotor 0.2026 0.2078 -0.2012 0.1982 0.6197
## week-Dasypus_novemcinctus 0.1111 0.2263 -0.3422 0.1156 0.5456
## week-Lynx_rufus 0.3988 0.3415 -0.2715 0.4018 1.0698
## week-Didelphis_virginiana 0.0760 0.3686 -0.6771 0.0774 0.7803
## week-Sylvilagus_floridanus 0.0870 0.3400 -0.5988 0.0927 0.7372
## week-Meleagris_gallopavo -0.1653 0.4242 -1.0810 -0.1366 0.6000
## week-Sciurus_carolinensis 0.8108 0.3619 0.1351 0.7985 1.5363
## I(week^2)-Odocoileus_virginianus -0.5285 0.0510 -0.6302 -0.5275 -0.4315
## I(week^2)-Canis_latrans -0.2449 0.1079 -0.4602 -0.2439 -0.0374
## I(week^2)-Procyon_lotor -0.1311 0.0909 -0.3110 -0.1296 0.0461
## I(week^2)-Dasypus_novemcinctus -0.1808 0.1048 -0.3940 -0.1791 0.0194
## I(week^2)-Lynx_rufus -0.2397 0.1499 -0.5445 -0.2361 0.0443
## I(week^2)-Didelphis_virginiana -0.4278 0.2205 -0.9326 -0.4054 -0.0514
## I(week^2)-Sylvilagus_floridanus -0.1794 0.1587 -0.5030 -0.1755 0.1242
## I(week^2)-Meleagris_gallopavo -0.4159 0.2485 -0.9789 -0.3897 -0.0039
## I(week^2)-Sciurus_carolinensis -0.2873 0.1454 -0.5870 -0.2853 -0.0059
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0020 2518
## (Intercept)-Procyon_lotor 1.0024 4333
## (Intercept)-Dasypus_novemcinctus 1.0021 5250
## (Intercept)-Lynx_rufus 1.0128 513
## (Intercept)-Didelphis_virginiana 1.0014 4158
## (Intercept)-Sylvilagus_floridanus 1.0013 2263
## (Intercept)-Meleagris_gallopavo 1.0044 1209
## (Intercept)-Sciurus_carolinensis 1.0002 4045
## week-Odocoileus_virginianus 1.0006 5015
## week-Canis_latrans 1.0015 3196
## week-Procyon_lotor 1.0040 4596
## week-Dasypus_novemcinctus 1.0011 4960
## week-Lynx_rufus 1.0009 2449
## week-Didelphis_virginiana 0.9999 2786
## week-Sylvilagus_floridanus 1.0012 3266
## week-Meleagris_gallopavo 1.0037 1435
## week-Sciurus_carolinensis 1.0048 3868
## I(week^2)-Odocoileus_virginianus 1.0020 5250
## I(week^2)-Canis_latrans 1.0010 3514
## I(week^2)-Procyon_lotor 1.0027 4208
## I(week^2)-Dasypus_novemcinctus 1.0028 4566
## I(week^2)-Lynx_rufus 1.0020 2132
## I(week^2)-Didelphis_virginiana 1.0115 1568
## I(week^2)-Sylvilagus_floridanus 1.0011 2490
## I(week^2)-Meleagris_gallopavo 1.0149 712
## I(week^2)-Sciurus_carolinensis 1.0010 4296
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6155
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0479 0.6713 -1.2129 0.0301 1.4148 1.0007 3530
## Cogon_Patch_Size 0.0010 0.3895 -0.7950 0.0137 0.7319 1.0007 2951
## Avg_Cogongrass_Cover 0.1503 0.3084 -0.4595 0.1494 0.7675 1.0007 2158
## total_shrub_cover -0.4915 0.3510 -1.2249 -0.4790 0.1739 1.0053 2769
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.6134 4.4553 0.7804 3.3158 16.2373 1.0049 1488
## Cogon_Patch_Size 0.8220 1.3017 0.0561 0.4323 3.9743 1.0022 1925
## Avg_Cogongrass_Cover 0.3856 0.5137 0.0416 0.2345 1.6260 1.0056 2839
## total_shrub_cover 0.6425 0.7913 0.0559 0.3952 2.7069 1.0042 1841
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9132 0.9676 0.0585 0.6349 3.576 1.018 628
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0219 0.4543 -2.9098 -2.0317 -1.0570 1.0017 5250
## week 0.3616 0.2445 -0.1285 0.3663 0.8293 1.0053 3857
## I(week^2) -0.2958 0.1144 -0.5328 -0.2933 -0.0754 1.0115 2767
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9640 1.3847 0.6641 1.5875 5.5158 1.0077 4167
## week 0.4087 0.3263 0.1022 0.3186 1.2038 1.0009 3758
## I(week^2) 0.0850 0.0750 0.0233 0.0639 0.2683 1.0068 2667
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9692 1.6185 1.4023 3.7556
## (Intercept)-Canis_latrans 0.4717 0.6728 -0.8218 0.4495
## (Intercept)-Procyon_lotor 0.7504 0.7069 -0.6280 0.7324
## (Intercept)-Dasypus_novemcinctus -0.6971 0.6305 -1.9656 -0.6807
## (Intercept)-Lynx_rufus 0.1338 1.0578 -1.6774 0.0330
## (Intercept)-Didelphis_virginiana -1.4368 0.7112 -2.8788 -1.4245
## (Intercept)-Sylvilagus_floridanus -0.2240 0.9686 -1.8968 -0.3083
## (Intercept)-Meleagris_gallopavo -0.6442 0.8451 -2.2664 -0.6708
## (Intercept)-Sciurus_carolinensis -1.5799 0.7364 -3.1144 -1.5498
## Cogon_Patch_Size-Odocoileus_virginianus 0.0950 0.7028 -1.1462 0.0448
## Cogon_Patch_Size-Canis_latrans 0.7491 0.7134 -0.2802 0.6294
## Cogon_Patch_Size-Procyon_lotor -0.1234 0.4635 -1.0437 -0.1192
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0734 0.4100 -0.9417 -0.0573
## Cogon_Patch_Size-Lynx_rufus -0.0263 0.7740 -1.4374 -0.0778
## Cogon_Patch_Size-Didelphis_virginiana 0.6368 0.4936 -0.2425 0.6065
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6724 0.8733 -2.8493 -0.5298
## Cogon_Patch_Size-Meleagris_gallopavo -0.0261 0.5904 -1.2068 -0.0241
## Cogon_Patch_Size-Sciurus_carolinensis -0.5724 0.6850 -2.2288 -0.4496
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1337 0.5730 -0.9774 0.1286
## Avg_Cogongrass_Cover-Canis_latrans 0.2383 0.4054 -0.5084 0.2240
## Avg_Cogongrass_Cover-Procyon_lotor 0.1698 0.4381 -0.6608 0.1589
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3445 0.3815 -0.3990 0.3232
## Avg_Cogongrass_Cover-Lynx_rufus 0.4735 0.5018 -0.3652 0.4301
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1763 0.4130 -0.6351 0.1844
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1814 0.5078 -1.3072 -0.1371
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3917 0.6244 -1.8050 -0.3248
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4144 0.4169 -0.3396 0.3895
## total_shrub_cover-Odocoileus_virginianus -0.3010 0.6456 -1.5109 -0.3113
## total_shrub_cover-Canis_latrans 0.0151 0.4851 -0.8432 -0.0159
## total_shrub_cover-Procyon_lotor -0.9610 0.5412 -2.1973 -0.9020
## total_shrub_cover-Dasypus_novemcinctus -0.1151 0.3906 -0.8340 -0.1222
## total_shrub_cover-Lynx_rufus -0.9199 0.7055 -2.6265 -0.8161
## total_shrub_cover-Didelphis_virginiana -0.3938 0.4507 -1.3136 -0.3975
## total_shrub_cover-Sylvilagus_floridanus -0.4511 0.6218 -1.8398 -0.4092
## total_shrub_cover-Meleagris_gallopavo -1.2493 0.7013 -2.9220 -1.1472
## total_shrub_cover-Sciurus_carolinensis -0.1711 0.4478 -1.0411 -0.1819
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.8414 1.0013 1039
## (Intercept)-Canis_latrans 1.8810 1.0057 2750
## (Intercept)-Procyon_lotor 2.1781 1.0139 2527
## (Intercept)-Dasypus_novemcinctus 0.5403 1.0064 2675
## (Intercept)-Lynx_rufus 2.5245 1.0120 894
## (Intercept)-Didelphis_virginiana -0.0630 1.0008 2924
## (Intercept)-Sylvilagus_floridanus 1.9000 1.0143 1039
## (Intercept)-Meleagris_gallopavo 1.0830 1.0007 1812
## (Intercept)-Sciurus_carolinensis -0.1712 1.0015 2567
## Cogon_Patch_Size-Odocoileus_virginianus 1.6562 1.0065 3324
## Cogon_Patch_Size-Canis_latrans 2.5055 1.0009 2330
## Cogon_Patch_Size-Procyon_lotor 0.7726 1.0006 2839
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7114 1.0008 4087
## Cogon_Patch_Size-Lynx_rufus 1.7343 1.0003 1823
## Cogon_Patch_Size-Didelphis_virginiana 1.7242 1.0048 3188
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5802 1.0064 1635
## Cogon_Patch_Size-Meleagris_gallopavo 1.1437 1.0010 3534
## Cogon_Patch_Size-Sciurus_carolinensis 0.4267 1.0010 2020
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2973 1.0003 3482
## Avg_Cogongrass_Cover-Canis_latrans 1.0949 1.0008 2743
## Avg_Cogongrass_Cover-Procyon_lotor 1.1131 1.0009 3649
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1309 1.0002 2981
## Avg_Cogongrass_Cover-Lynx_rufus 1.6046 1.0015 3285
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9741 1.0022 4127
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7152 1.0029 2203
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6527 1.0025 2059
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3248 1.0009 2887
## total_shrub_cover-Odocoileus_virginianus 1.0370 1.0003 3545
## total_shrub_cover-Canis_latrans 1.0671 1.0029 2914
## total_shrub_cover-Procyon_lotor -0.0815 1.0000 2203
## total_shrub_cover-Dasypus_novemcinctus 0.6911 1.0029 4143
## total_shrub_cover-Lynx_rufus 0.2056 1.0033 1818
## total_shrub_cover-Didelphis_virginiana 0.4879 1.0001 4298
## total_shrub_cover-Sylvilagus_floridanus 0.6457 1.0085 1688
## total_shrub_cover-Meleagris_gallopavo -0.1570 1.0026 1730
## total_shrub_cover-Sciurus_carolinensis 0.7530 1.0005 3767
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5239 0.0798 0.3678 0.5230 0.6849
## (Intercept)-Canis_latrans -2.4325 0.1901 -2.8152 -2.4286 -2.0701
## (Intercept)-Procyon_lotor -2.1617 0.1532 -2.4724 -2.1600 -1.8717
## (Intercept)-Dasypus_novemcinctus -1.4345 0.1591 -1.7549 -1.4335 -1.1377
## (Intercept)-Lynx_rufus -3.3905 0.3327 -4.0532 -3.3818 -2.7675
## (Intercept)-Didelphis_virginiana -2.0848 0.2689 -2.6367 -2.0678 -1.5844
## (Intercept)-Sylvilagus_floridanus -3.1632 0.3551 -3.9067 -3.1497 -2.5215
## (Intercept)-Meleagris_gallopavo -3.1810 0.3572 -3.9287 -3.1654 -2.5300
## (Intercept)-Sciurus_carolinensis -2.2499 0.2926 -2.8709 -2.2346 -1.7329
## week-Odocoileus_virginianus 1.2767 0.1221 1.0419 1.2782 1.5168
## week-Canis_latrans 0.5838 0.2612 0.0843 0.5859 1.1150
## week-Procyon_lotor 0.2051 0.2119 -0.2099 0.2089 0.6249
## week-Dasypus_novemcinctus 0.1153 0.2237 -0.3282 0.1157 0.5445
## week-Lynx_rufus 0.3935 0.3491 -0.3073 0.3978 1.0875
## week-Didelphis_virginiana 0.0701 0.3763 -0.7098 0.0878 0.7672
## week-Sylvilagus_floridanus 0.0725 0.3396 -0.5999 0.0769 0.7332
## week-Meleagris_gallopavo -0.1761 0.4174 -1.0496 -0.1493 0.5831
## week-Sciurus_carolinensis 0.7970 0.3697 0.1008 0.7856 1.5497
## I(week^2)-Odocoileus_virginianus -0.5267 0.0500 -0.6252 -0.5269 -0.4288
## I(week^2)-Canis_latrans -0.2420 0.1065 -0.4503 -0.2421 -0.0323
## I(week^2)-Procyon_lotor -0.1343 0.0926 -0.3185 -0.1351 0.0502
## I(week^2)-Dasypus_novemcinctus -0.1808 0.1043 -0.3879 -0.1773 0.0197
## I(week^2)-Lynx_rufus -0.2466 0.1544 -0.5629 -0.2436 0.0529
## I(week^2)-Didelphis_virginiana -0.4311 0.2211 -0.9325 -0.4089 -0.0579
## I(week^2)-Sylvilagus_floridanus -0.1860 0.1596 -0.5066 -0.1831 0.1228
## I(week^2)-Meleagris_gallopavo -0.4233 0.2415 -0.9788 -0.3939 -0.0195
## I(week^2)-Sciurus_carolinensis -0.2843 0.1462 -0.5857 -0.2819 -0.0110
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0060 3091
## (Intercept)-Procyon_lotor 1.0025 4075
## (Intercept)-Dasypus_novemcinctus 1.0032 5250
## (Intercept)-Lynx_rufus 1.0009 1233
## (Intercept)-Didelphis_virginiana 1.0019 4086
## (Intercept)-Sylvilagus_floridanus 1.0041 1106
## (Intercept)-Meleagris_gallopavo 1.0005 1390
## (Intercept)-Sciurus_carolinensis 1.0028 3599
## week-Odocoileus_virginianus 0.9998 5013
## week-Canis_latrans 1.0012 3675
## week-Procyon_lotor 1.0014 4147
## week-Dasypus_novemcinctus 1.0003 4985
## week-Lynx_rufus 1.0015 2828
## week-Didelphis_virginiana 1.0009 3007
## week-Sylvilagus_floridanus 1.0028 2893
## week-Meleagris_gallopavo 1.0038 1600
## week-Sciurus_carolinensis 1.0019 4075
## I(week^2)-Odocoileus_virginianus 0.9998 5250
## I(week^2)-Canis_latrans 1.0015 3905
## I(week^2)-Procyon_lotor 1.0064 4247
## I(week^2)-Dasypus_novemcinctus 1.0045 3894
## I(week^2)-Lynx_rufus 1.0008 2353
## I(week^2)-Didelphis_virginiana 1.0068 1577
## I(week^2)-Sylvilagus_floridanus 1.0108 2186
## I(week^2)-Meleagris_gallopavo 1.0162 778
## I(week^2)-Sciurus_carolinensis 1.0034 4305
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.649
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0448 0.6466 -1.1802 0.0274 1.3897 1.0028 2820
## Veg_shannon_index 0.3826 0.2741 -0.1365 0.3791 0.9300 1.0010 2505
## Avg_Cogongrass_Cover 0.3775 0.2768 -0.1568 0.3703 0.9277 1.0026 2457
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9102 3.9010 0.6318 2.8129 13.5484 1.0041 1765
## Veg_shannon_index 0.2652 0.3206 0.0351 0.1686 1.0521 1.0007 2609
## Avg_Cogongrass_Cover 0.2945 0.4860 0.0364 0.1849 1.1997 1.1143 3791
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.739 0.7977 0.0589 0.4824 2.8611 1.0382 579
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0256 0.4648 -2.8933 -2.0417 -1.0431 1.0002 5250
## week 0.3716 0.2465 -0.1136 0.3761 0.8432 1.0000 4109
## I(week^2) -0.2912 0.1120 -0.5193 -0.2886 -0.0743 1.0003 2995
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0321 1.4659 0.6701 1.6395 5.8825 1.0016 4839
## week 0.4162 0.3469 0.0999 0.3203 1.2838 1.0048 3979
## I(week^2) 0.0846 0.0724 0.0233 0.0655 0.2616 1.0016 3537
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6411 1.5328 1.1791 3.4628
## (Intercept)-Canis_latrans 0.2764 0.6165 -0.9396 0.2651
## (Intercept)-Procyon_lotor 0.5995 0.6296 -0.6408 0.5933
## (Intercept)-Dasypus_novemcinctus -0.7164 0.5822 -1.9112 -0.7065
## (Intercept)-Lynx_rufus 0.1050 0.9948 -1.5853 0.0189
## (Intercept)-Didelphis_virginiana -1.4549 0.6547 -2.7537 -1.4432
## (Intercept)-Sylvilagus_floridanus -0.2415 0.8540 -1.7252 -0.3256
## (Intercept)-Meleagris_gallopavo -0.2407 1.0146 -1.8968 -0.3379
## (Intercept)-Sciurus_carolinensis -1.4359 0.6826 -2.8604 -1.4220
## Veg_shannon_index-Odocoileus_virginianus 0.3300 0.4813 -0.6786 0.3364
## Veg_shannon_index-Canis_latrans 0.6422 0.3855 -0.0481 0.6192
## Veg_shannon_index-Procyon_lotor 0.4941 0.3836 -0.2149 0.4753
## Veg_shannon_index-Dasypus_novemcinctus 0.2351 0.3395 -0.4615 0.2461
## Veg_shannon_index-Lynx_rufus 0.2173 0.5044 -0.8863 0.2421
## Veg_shannon_index-Didelphis_virginiana 0.5217 0.3780 -0.1682 0.4976
## Veg_shannon_index-Sylvilagus_floridanus 0.4874 0.4438 -0.3102 0.4585
## Veg_shannon_index-Meleagris_gallopavo 0.5149 0.4585 -0.3301 0.4913
## Veg_shannon_index-Sciurus_carolinensis 0.0605 0.3940 -0.7774 0.0793
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3743 0.5080 -0.6227 0.3628
## Avg_Cogongrass_Cover-Canis_latrans 0.5756 0.3831 -0.0974 0.5493
## Avg_Cogongrass_Cover-Procyon_lotor 0.4609 0.4014 -0.2580 0.4377
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4561 0.3334 -0.1999 0.4488
## Avg_Cogongrass_Cover-Lynx_rufus 0.6174 0.4368 -0.1524 0.5825
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5002 0.3659 -0.2123 0.4969
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0029 0.4614 -1.0073 0.0262
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0021 0.5381 -1.1622 0.0306
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4403 0.3639 -0.2820 0.4364
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4980 1.0167 1090
## (Intercept)-Canis_latrans 1.5322 1.0074 2678
## (Intercept)-Procyon_lotor 1.8012 1.0033 2738
## (Intercept)-Dasypus_novemcinctus 0.4400 0.9998 3602
## (Intercept)-Lynx_rufus 2.4004 1.0050 947
## (Intercept)-Didelphis_virginiana -0.1922 1.0013 3395
## (Intercept)-Sylvilagus_floridanus 1.6760 1.0072 1199
## (Intercept)-Meleagris_gallopavo 1.9722 1.0030 829
## (Intercept)-Sciurus_carolinensis -0.0950 1.0048 2656
## Veg_shannon_index-Odocoileus_virginianus 1.2588 1.0022 4009
## Veg_shannon_index-Canis_latrans 1.4706 1.0005 3453
## Veg_shannon_index-Procyon_lotor 1.3177 1.0016 2865
## Veg_shannon_index-Dasypus_novemcinctus 0.8954 0.9998 4144
## Veg_shannon_index-Lynx_rufus 1.1393 1.0001 2708
## Veg_shannon_index-Didelphis_virginiana 1.3346 1.0014 4121
## Veg_shannon_index-Sylvilagus_floridanus 1.4480 1.0036 3076
## Veg_shannon_index-Meleagris_gallopavo 1.5003 1.0060 2659
## Veg_shannon_index-Sciurus_carolinensis 0.7786 1.0068 3566
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4415 1.0035 3746
## Avg_Cogongrass_Cover-Canis_latrans 1.4037 0.9998 3412
## Avg_Cogongrass_Cover-Procyon_lotor 1.3221 0.9999 3614
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1349 1.0021 3463
## Avg_Cogongrass_Cover-Lynx_rufus 1.5769 1.0003 3297
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2554 1.0018 3987
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8439 1.0053 2566
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9621 1.0055 2142
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1613 1.0003 3564
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5247 0.0791 0.3708 0.5257 0.6778
## (Intercept)-Canis_latrans -2.4215 0.1845 -2.7925 -2.4171 -2.0680
## (Intercept)-Procyon_lotor -2.1653 0.1520 -2.4720 -2.1613 -1.8752
## (Intercept)-Dasypus_novemcinctus -1.4325 0.1553 -1.7346 -1.4293 -1.1344
## (Intercept)-Lynx_rufus -3.3965 0.3497 -4.1043 -3.3833 -2.7517
## (Intercept)-Didelphis_virginiana -2.0981 0.2703 -2.6627 -2.0862 -1.6086
## (Intercept)-Sylvilagus_floridanus -3.1222 0.3605 -3.8802 -3.0986 -2.4638
## (Intercept)-Meleagris_gallopavo -3.3270 0.4063 -4.1817 -3.3075 -2.6100
## (Intercept)-Sciurus_carolinensis -2.2491 0.2908 -2.8602 -2.2336 -1.7193
## week-Odocoileus_virginianus 1.2807 0.1235 1.0404 1.2817 1.5250
## week-Canis_latrans 0.5869 0.2612 0.0882 0.5853 1.1129
## week-Procyon_lotor 0.2086 0.2102 -0.1935 0.2056 0.6272
## week-Dasypus_novemcinctus 0.1136 0.2237 -0.3172 0.1146 0.5669
## week-Lynx_rufus 0.3979 0.3527 -0.2763 0.3964 1.1112
## week-Didelphis_virginiana 0.0722 0.3743 -0.6849 0.0815 0.7753
## week-Sylvilagus_floridanus 0.0720 0.3444 -0.6076 0.0751 0.7372
## week-Meleagris_gallopavo -0.1618 0.4235 -1.0526 -0.1414 0.6097
## week-Sciurus_carolinensis 0.7952 0.3729 0.1099 0.7792 1.5795
## I(week^2)-Odocoileus_virginianus -0.5278 0.0507 -0.6243 -0.5273 -0.4284
## I(week^2)-Canis_latrans -0.2471 0.1095 -0.4679 -0.2467 -0.0401
## I(week^2)-Procyon_lotor -0.1348 0.0913 -0.3144 -0.1340 0.0435
## I(week^2)-Dasypus_novemcinctus -0.1805 0.1039 -0.3889 -0.1801 0.0223
## I(week^2)-Lynx_rufus -0.2470 0.1552 -0.5613 -0.2431 0.0512
## I(week^2)-Didelphis_virginiana -0.4253 0.2137 -0.9171 -0.4029 -0.0618
## I(week^2)-Sylvilagus_floridanus -0.1884 0.1609 -0.5125 -0.1814 0.1168
## I(week^2)-Meleagris_gallopavo -0.4171 0.2422 -0.9763 -0.3910 -0.0069
## I(week^2)-Sciurus_carolinensis -0.2831 0.1473 -0.5883 -0.2780 -0.0107
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5250
## (Intercept)-Canis_latrans 1.0037 3560
## (Intercept)-Procyon_lotor 1.0024 4195
## (Intercept)-Dasypus_novemcinctus 1.0019 4801
## (Intercept)-Lynx_rufus 1.0013 1083
## (Intercept)-Didelphis_virginiana 1.0026 4324
## (Intercept)-Sylvilagus_floridanus 1.0020 993
## (Intercept)-Meleagris_gallopavo 1.0052 876
## (Intercept)-Sciurus_carolinensis 1.0004 3619
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0010 4097
## week-Procyon_lotor 1.0010 4379
## week-Dasypus_novemcinctus 1.0011 4730
## week-Lynx_rufus 1.0031 2757
## week-Didelphis_virginiana 1.0022 3082
## week-Sylvilagus_floridanus 1.0020 3418
## week-Meleagris_gallopavo 1.0025 1422
## week-Sciurus_carolinensis 1.0005 4086
## I(week^2)-Odocoileus_virginianus 1.0001 4964
## I(week^2)-Canis_latrans 1.0024 4012
## I(week^2)-Procyon_lotor 1.0007 4652
## I(week^2)-Dasypus_novemcinctus 1.0010 4982
## I(week^2)-Lynx_rufus 1.0049 2384
## I(week^2)-Didelphis_virginiana 1.0009 1972
## I(week^2)-Sylvilagus_floridanus 1.0003 2029
## I(week^2)-Meleagris_gallopavo 1.0221 830
## I(week^2)-Sciurus_carolinensis 1.0004 4330
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6163
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.033 0.6015 -1.1251 0.0168 1.2669 1.0003 3803
## Avg_Cogongrass_Cover 0.228 0.2490 -0.2697 0.2284 0.7187 1.0045 2758
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4389 3.3040 0.5848 2.4870 11.6630 1.0135 2211
## Avg_Cogongrass_Cover 0.2735 0.3108 0.0392 0.1791 1.0676 1.0086 3207
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.673 0.6949 0.0602 0.4524 2.5268 1.0115 593
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0199 0.4644 -2.9133 -2.0324 -1.0751 1.0001 5250
## week 0.3692 0.2412 -0.1352 0.3738 0.8266 1.0027 4156
## I(week^2) -0.2962 0.1164 -0.5399 -0.2919 -0.0826 1.0080 2056
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9979 1.5198 0.6451 1.6105 5.6603 1.0125 4648
## week 0.4049 0.3296 0.0991 0.3145 1.2246 1.0026 3610
## I(week^2) 0.0852 0.0822 0.0230 0.0649 0.2657 1.0191 2044
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4258 1.3696 1.2307 3.2660
## (Intercept)-Canis_latrans 0.3116 0.5974 -0.8502 0.3050
## (Intercept)-Procyon_lotor 0.5782 0.5968 -0.6079 0.5819
## (Intercept)-Dasypus_novemcinctus -0.6515 0.5663 -1.8210 -0.6361
## (Intercept)-Lynx_rufus -0.0174 0.8815 -1.6113 -0.0818
## (Intercept)-Didelphis_virginiana -1.3330 0.6254 -2.6184 -1.3263
## (Intercept)-Sylvilagus_floridanus -0.2579 0.7433 -1.6493 -0.3076
## (Intercept)-Meleagris_gallopavo -0.3076 0.8754 -1.7837 -0.3724
## (Intercept)-Sciurus_carolinensis -1.4002 0.6477 -2.7497 -1.3836
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2089 0.4734 -0.7149 0.2075
## Avg_Cogongrass_Cover-Canis_latrans 0.3777 0.3551 -0.2642 0.3566
## Avg_Cogongrass_Cover-Procyon_lotor 0.2743 0.3610 -0.3796 0.2507
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3503 0.3165 -0.2482 0.3387
## Avg_Cogongrass_Cover-Lynx_rufus 0.4671 0.4028 -0.2433 0.4390
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3612 0.3495 -0.3353 0.3582
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1362 0.4192 -1.0577 -0.1176
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1722 0.4855 -1.2117 -0.1379
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3638 0.3389 -0.2771 0.3555
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.5685 1.0123 983
## (Intercept)-Canis_latrans 1.5028 1.0022 3360
## (Intercept)-Procyon_lotor 1.7369 1.0089 3244
## (Intercept)-Dasypus_novemcinctus 0.4374 1.0051 3335
## (Intercept)-Lynx_rufus 1.9305 1.0040 1175
## (Intercept)-Didelphis_virginiana -0.1307 1.0032 3504
## (Intercept)-Sylvilagus_floridanus 1.3261 1.0061 1454
## (Intercept)-Meleagris_gallopavo 1.5664 1.0454 1074
## (Intercept)-Sciurus_carolinensis -0.2027 1.0045 2814
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1748 1.0005 3295
## Avg_Cogongrass_Cover-Canis_latrans 1.1475 1.0035 4082
## Avg_Cogongrass_Cover-Procyon_lotor 1.0414 1.0010 4528
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0220 1.0007 4126
## Avg_Cogongrass_Cover-Lynx_rufus 1.3483 1.0017 4021
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0901 1.0057 4162
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6466 1.0016 3077
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7121 1.0048 2581
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0597 1.0000 4360
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5260 0.0783 0.3711 0.5271 0.6793
## (Intercept)-Canis_latrans -2.4314 0.1907 -2.8178 -2.4277 -2.0733
## (Intercept)-Procyon_lotor -2.1585 0.1502 -2.4690 -2.1537 -1.8794
## (Intercept)-Dasypus_novemcinctus -1.4336 0.1553 -1.7466 -1.4304 -1.1348
## (Intercept)-Lynx_rufus -3.3479 0.3430 -4.0491 -3.3308 -2.7035
## (Intercept)-Didelphis_virginiana -2.0982 0.2665 -2.6420 -2.0924 -1.5980
## (Intercept)-Sylvilagus_floridanus -3.0879 0.3436 -3.8157 -3.0695 -2.4671
## (Intercept)-Meleagris_gallopavo -3.2902 0.3877 -4.1051 -3.2674 -2.5824
## (Intercept)-Sciurus_carolinensis -2.2493 0.2816 -2.8265 -2.2389 -1.7250
## week-Odocoileus_virginianus 1.2815 0.1224 1.0505 1.2803 1.5307
## week-Canis_latrans 0.5897 0.2569 0.1084 0.5791 1.1179
## week-Procyon_lotor 0.2085 0.2074 -0.2061 0.2095 0.6275
## week-Dasypus_novemcinctus 0.1109 0.2242 -0.3309 0.1104 0.5440
## week-Lynx_rufus 0.4039 0.3436 -0.2562 0.4045 1.0896
## week-Didelphis_virginiana 0.0800 0.3684 -0.6908 0.0904 0.7570
## week-Sylvilagus_floridanus 0.0776 0.3401 -0.5918 0.0854 0.7220
## week-Meleagris_gallopavo -0.1735 0.4381 -1.1121 -0.1420 0.6170
## week-Sciurus_carolinensis 0.8025 0.3700 0.1025 0.7927 1.5730
## I(week^2)-Odocoileus_virginianus -0.5285 0.0502 -0.6295 -0.5282 -0.4326
## I(week^2)-Canis_latrans -0.2460 0.1065 -0.4621 -0.2430 -0.0416
## I(week^2)-Procyon_lotor -0.1346 0.0909 -0.3144 -0.1335 0.0414
## I(week^2)-Dasypus_novemcinctus -0.1796 0.1024 -0.3841 -0.1775 0.0187
## I(week^2)-Lynx_rufus -0.2473 0.1524 -0.5628 -0.2469 0.0430
## I(week^2)-Didelphis_virginiana -0.4329 0.2217 -0.9624 -0.4074 -0.0758
## I(week^2)-Sylvilagus_floridanus -0.1909 0.1598 -0.5170 -0.1863 0.1126
## I(week^2)-Meleagris_gallopavo -0.4233 0.2527 -1.0077 -0.3955 -0.0130
## I(week^2)-Sciurus_carolinensis -0.2863 0.1472 -0.5865 -0.2836 -0.0071
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0026 5250
## (Intercept)-Canis_latrans 1.0037 3471
## (Intercept)-Procyon_lotor 1.0017 4315
## (Intercept)-Dasypus_novemcinctus 1.0004 4829
## (Intercept)-Lynx_rufus 1.0022 1203
## (Intercept)-Didelphis_virginiana 1.0013 4123
## (Intercept)-Sylvilagus_floridanus 1.0041 1310
## (Intercept)-Meleagris_gallopavo 1.0204 1109
## (Intercept)-Sciurus_carolinensis 1.0002 3870
## week-Odocoileus_virginianus 1.0018 5250
## week-Canis_latrans 1.0006 4034
## week-Procyon_lotor 0.9999 4394
## week-Dasypus_novemcinctus 1.0004 4858
## week-Lynx_rufus 1.0009 2932
## week-Didelphis_virginiana 1.0000 2545
## week-Sylvilagus_floridanus 1.0003 2976
## week-Meleagris_gallopavo 1.0079 1326
## week-Sciurus_carolinensis 1.0001 4054
## I(week^2)-Odocoileus_virginianus 1.0009 5250
## I(week^2)-Canis_latrans 0.9998 3990
## I(week^2)-Procyon_lotor 1.0001 4203
## I(week^2)-Dasypus_novemcinctus 0.9998 4606
## I(week^2)-Lynx_rufus 1.0021 2493
## I(week^2)-Didelphis_virginiana 1.0027 1627
## I(week^2)-Sylvilagus_floridanus 1.0015 2458
## I(week^2)-Meleagris_gallopavo 1.0330 804
## I(week^2)-Sciurus_carolinensis 1.0007 4325
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
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## ----------------------------------------
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## ----------------------------------------
## Sampling ...
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## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.6862
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5737 0.6510 -1.7960 -0.6018 0.8159 1.0020 2769
## Avg_Cogongrass_Cover -0.5883 0.3852 -1.3508 -0.5913 0.1602 1.0011 1704
## I(Avg_Cogongrass_Cover^2) 0.8351 0.3910 0.1698 0.7980 1.7093 1.0043 1181
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0066 4.6421 0.6390 2.8183 14.3605 1.0479 613
## Avg_Cogongrass_Cover 0.3965 0.5513 0.0401 0.2347 1.7670 1.0119 1992
## I(Avg_Cogongrass_Cover^2) 0.6144 1.0976 0.0415 0.2754 3.3745 1.0462 650
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4786 0.5057 0.0487 0.3095 1.8493 1.014 634
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0197 0.4549 -2.8860 -2.0344 -1.0666 1.0010 5250
## week 0.3750 0.2482 -0.1336 0.3859 0.8454 1.0024 3262
## I(week^2) -0.2913 0.1132 -0.5191 -0.2905 -0.0725 1.0015 2867
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9470 1.4420 0.6369 1.5670 5.5376 1.0135 3859
## week 0.4096 0.3435 0.1009 0.3202 1.2291 1.0254 3356
## I(week^2) 0.0836 0.0717 0.0235 0.0642 0.2589 1.0072 1988
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.9831 1.5936 0.6393 2.7279
## (Intercept)-Canis_latrans -0.4471 0.6765 -1.8424 -0.4364
## (Intercept)-Procyon_lotor -0.1387 0.6455 -1.4623 -0.1386
## (Intercept)-Dasypus_novemcinctus -1.2951 0.6090 -2.5074 -1.2878
## (Intercept)-Lynx_rufus -1.1190 0.9543 -2.8513 -1.1644
## (Intercept)-Didelphis_virginiana -1.8818 0.7028 -3.2750 -1.8621
## (Intercept)-Sylvilagus_floridanus -0.9588 0.7821 -2.4911 -0.9816
## (Intercept)-Meleagris_gallopavo -0.4545 1.3649 -2.1419 -0.5865
## (Intercept)-Sciurus_carolinensis -2.3527 0.7649 -3.9818 -2.3064
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.6110 0.6426 -1.8751 -0.5995
## Avg_Cogongrass_Cover-Canis_latrans -0.3596 0.5119 -1.3277 -0.3739
## Avg_Cogongrass_Cover-Procyon_lotor -0.5013 0.5187 -1.4702 -0.5098
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4590 0.4731 -1.3782 -0.4624
## Avg_Cogongrass_Cover-Lynx_rufus -0.5276 0.5622 -1.6559 -0.5333
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3586 0.5252 -1.3429 -0.3801
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0283 0.6139 -2.4114 -0.9673
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8452 0.6123 -2.2388 -0.8017
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7110 0.5540 -1.8731 -0.6788
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1818 0.8716 0.0109 1.0110
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2541 0.8418 0.1936 1.0546
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1105 0.7411 0.1819 0.9576
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6670 0.3509 0.0032 0.6563
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1809 0.6080 0.2548 1.0994
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5149 0.4269 -0.2683 0.4997
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7186 0.5155 -0.1557 0.6700
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2123 0.6965 -1.3522 0.2514
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9249 0.4224 0.1894 0.8856
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8829 1.0059 1024
## (Intercept)-Canis_latrans 0.8816 1.0020 2912
## (Intercept)-Procyon_lotor 1.1163 1.0072 2639
## (Intercept)-Dasypus_novemcinctus -0.1009 1.0023 3046
## (Intercept)-Lynx_rufus 0.8733 1.0006 1073
## (Intercept)-Didelphis_virginiana -0.5598 1.0043 3434
## (Intercept)-Sylvilagus_floridanus 0.6613 1.0009 1910
## (Intercept)-Meleagris_gallopavo 1.5846 1.2072 219
## (Intercept)-Sciurus_carolinensis -0.9586 1.0052 2636
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6372 1.0015 2296
## Avg_Cogongrass_Cover-Canis_latrans 0.7270 1.0044 3064
## Avg_Cogongrass_Cover-Procyon_lotor 0.5811 1.0016 2618
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4956 1.0020 2728
## Avg_Cogongrass_Cover-Lynx_rufus 0.5775 1.0013 2630
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7348 1.0042 2677
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0050 1.0062 1858
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2284 1.0044 2060
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3183 1.0037 2161
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2964 1.0194 817
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4812 1.0388 723
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.1484 1.0318 914
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4029 1.0006 2818
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6365 1.0079 1513
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3491 1.0105 1821
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9311 1.0072 1483
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5262 1.0151 879
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8618 1.0033 1993
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5227 0.0802 0.3669 0.5215 0.6808
## (Intercept)-Canis_latrans -2.4453 0.1890 -2.8336 -2.4392 -2.0893
## (Intercept)-Procyon_lotor -2.1633 0.1536 -2.4700 -2.1604 -1.8732
## (Intercept)-Dasypus_novemcinctus -1.4324 0.1563 -1.7495 -1.4295 -1.1383
## (Intercept)-Lynx_rufus -3.2497 0.3422 -3.9716 -3.2312 -2.6274
## (Intercept)-Didelphis_virginiana -2.1142 0.2800 -2.7020 -2.1018 -1.6127
## (Intercept)-Sylvilagus_floridanus -3.0896 0.3398 -3.8071 -3.0718 -2.4723
## (Intercept)-Meleagris_gallopavo -3.3286 0.3968 -4.1539 -3.3094 -2.6056
## (Intercept)-Sciurus_carolinensis -2.2481 0.2852 -2.8565 -2.2360 -1.7167
## week-Odocoileus_virginianus 1.2805 0.1214 1.0526 1.2786 1.5214
## week-Canis_latrans 0.5949 0.2595 0.0869 0.5923 1.1162
## week-Procyon_lotor 0.2110 0.2098 -0.1980 0.2097 0.6237
## week-Dasypus_novemcinctus 0.1161 0.2271 -0.3432 0.1193 0.5617
## week-Lynx_rufus 0.3981 0.3505 -0.2931 0.3906 1.1090
## week-Didelphis_virginiana 0.0840 0.3607 -0.6589 0.1025 0.7633
## week-Sylvilagus_floridanus 0.0820 0.3461 -0.5910 0.0817 0.7445
## week-Meleagris_gallopavo -0.1533 0.4405 -1.0728 -0.1300 0.6497
## week-Sciurus_carolinensis 0.7960 0.3642 0.1188 0.7901 1.5383
## I(week^2)-Odocoileus_virginianus -0.5283 0.0499 -0.6281 -0.5277 -0.4323
## I(week^2)-Canis_latrans -0.2468 0.1073 -0.4638 -0.2462 -0.0434
## I(week^2)-Procyon_lotor -0.1343 0.0914 -0.3138 -0.1337 0.0450
## I(week^2)-Dasypus_novemcinctus -0.1827 0.1034 -0.3901 -0.1814 0.0212
## I(week^2)-Lynx_rufus -0.2439 0.1528 -0.5573 -0.2401 0.0426
## I(week^2)-Didelphis_virginiana -0.4242 0.2218 -0.9121 -0.3990 -0.0587
## I(week^2)-Sylvilagus_floridanus -0.1872 0.1579 -0.5095 -0.1850 0.1122
## I(week^2)-Meleagris_gallopavo -0.4218 0.2409 -0.9534 -0.3983 -0.0185
## I(week^2)-Sciurus_carolinensis -0.2835 0.1474 -0.5822 -0.2789 -0.0080
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0028 3333
## (Intercept)-Procyon_lotor 1.0008 4080
## (Intercept)-Dasypus_novemcinctus 1.0005 5250
## (Intercept)-Lynx_rufus 1.0029 1184
## (Intercept)-Didelphis_virginiana 1.0086 3180
## (Intercept)-Sylvilagus_floridanus 1.0062 1389
## (Intercept)-Meleagris_gallopavo 1.0542 771
## (Intercept)-Sciurus_carolinensis 1.0063 3727
## week-Odocoileus_virginianus 1.0010 5250
## week-Canis_latrans 1.0000 4036
## week-Procyon_lotor 1.0022 4392
## week-Dasypus_novemcinctus 1.0011 4573
## week-Lynx_rufus 0.9999 2566
## week-Didelphis_virginiana 1.0060 3179
## week-Sylvilagus_floridanus 1.0006 2937
## week-Meleagris_gallopavo 1.0009 1407
## week-Sciurus_carolinensis 1.0027 3909
## I(week^2)-Odocoileus_virginianus 1.0005 5250
## I(week^2)-Canis_latrans 1.0010 3939
## I(week^2)-Procyon_lotor 1.0001 4303
## I(week^2)-Dasypus_novemcinctus 1.0012 4573
## I(week^2)-Lynx_rufus 1.0050 3092
## I(week^2)-Didelphis_virginiana 1.0045 1574
## I(week^2)-Sylvilagus_floridanus 1.0014 2863
## I(week^2)-Meleagris_gallopavo 1.0063 730
## I(week^2)-Sciurus_carolinensis 1.0006 4577
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
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## Sampled: 3500 of 10000, 35.00%
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## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
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## Sampled: 5500 of 10000, 55.00%
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## Sampled: 6000 of 10000, 60.00%
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## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7000 of 10000, 70.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
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## Sampled: 8500 of 10000, 85.00%
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## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Sampled: 1000 of 10000, 10.00%
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## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3500 of 10000, 35.00%
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## Sampled: 4000 of 10000, 40.00%
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## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.73
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7014 1.1013 -2.7954 -0.7362 1.5843 1.0008 2373
## Cogon_Patch_Size 0.0952 0.7032 -1.3195 0.0918 1.5581 1.0056 1367
## Veg_shannon_index 0.8652 0.4527 0.0076 0.8565 1.8011 1.0018 1039
## total_shrub_cover -0.6131 0.5353 -1.7366 -0.5872 0.3808 1.0046 2144
## Avg_Cogongrass_Cover 0.0579 0.9289 -1.8021 0.0337 1.9201 1.0019 478
## Tree_Density -1.8945 0.7750 -3.3714 -1.8877 -0.2947 1.0058 952
## Avg_Canopy_Cover 1.6654 0.6243 0.4534 1.6455 2.9123 1.0027 1534
## I(Avg_Cogongrass_Cover^2) 1.4185 0.6046 0.3333 1.3802 2.7334 1.0132 681
## avg_veg_height -0.1266 0.5019 -1.1528 -0.1106 0.8384 1.0115 845
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.5690 17.0374 2.8674 12.7748 61.3256 1.0217 824
## Cogon_Patch_Size 2.9490 4.2917 0.1263 1.6203 13.3307 1.0127 965
## Veg_shannon_index 0.6163 0.9450 0.0463 0.3311 2.8614 1.0105 2173
## total_shrub_cover 1.8570 2.7191 0.0835 1.0785 8.3179 1.0291 1227
## Avg_Cogongrass_Cover 1.0611 1.8714 0.0484 0.4423 5.9224 1.0836 1467
## Tree_Density 3.6349 7.2159 0.0683 1.3246 21.3947 1.0942 561
## Avg_Canopy_Cover 2.5018 3.3825 0.1284 1.5261 10.9346 1.0228 1362
## I(Avg_Cogongrass_Cover^2) 1.4721 3.1030 0.0519 0.5539 8.8268 1.1391 273
## avg_veg_height 0.4869 0.6815 0.0428 0.2686 2.2459 1.0099 2275
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4658 1.8311 0.0625 0.7981 6.6743 1.0213 365
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0282 0.4668 -2.9169 -2.0474 -1.0748 1.0006 4968
## week 0.3687 0.2445 -0.1379 0.3780 0.8384 1.0013 4105
## I(week^2) -0.2963 0.1119 -0.5244 -0.2945 -0.0804 1.0020 3177
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0397 1.4676 0.6696 1.6845 5.6433 1.0034 4802
## week 0.4186 0.3397 0.1005 0.3233 1.2717 1.0047 3407
## I(week^2) 0.0848 0.0704 0.0237 0.0655 0.2563 1.0120 2591
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 6.9149 3.3693 2.0485 6.3706
## (Intercept)-Canis_latrans -0.9001 1.1502 -3.1646 -0.9071
## (Intercept)-Procyon_lotor -0.3840 1.0250 -2.4973 -0.3756
## (Intercept)-Dasypus_novemcinctus -2.6922 1.1614 -5.3868 -2.5859
## (Intercept)-Lynx_rufus 0.0152 2.3136 -3.6892 -0.2950
## (Intercept)-Didelphis_virginiana -4.1239 1.3826 -7.0924 -4.0332
## (Intercept)-Sylvilagus_floridanus -2.1060 1.4026 -4.9895 -2.0797
## (Intercept)-Meleagris_gallopavo -1.7031 1.5159 -4.6757 -1.6919
## (Intercept)-Sciurus_carolinensis -4.7817 1.4814 -8.1123 -4.6372
## Cogon_Patch_Size-Odocoileus_virginianus 0.1558 1.4369 -2.4504 0.0745
## Cogon_Patch_Size-Canis_latrans 1.5883 1.4077 -0.3325 1.3317
## Cogon_Patch_Size-Procyon_lotor -0.3473 0.7959 -1.9134 -0.3433
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1787 0.6941 -1.5954 -0.1419
## Cogon_Patch_Size-Lynx_rufus 0.0125 1.6035 -2.8974 -0.0661
## Cogon_Patch_Size-Didelphis_virginiana 1.6269 1.0246 -0.0677 1.5103
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1490 1.5152 -4.9113 -0.8996
## Cogon_Patch_Size-Meleagris_gallopavo 0.3002 1.1717 -1.7124 0.2037
## Cogon_Patch_Size-Sciurus_carolinensis -0.9789 1.2540 -4.1038 -0.7679
## Veg_shannon_index-Odocoileus_virginianus 0.7162 0.8139 -1.0098 0.7341
## Veg_shannon_index-Canis_latrans 1.2024 0.6477 0.1224 1.1402
## Veg_shannon_index-Procyon_lotor 1.0658 0.5934 -0.0019 1.0278
## Veg_shannon_index-Dasypus_novemcinctus 0.6217 0.5305 -0.4436 0.6309
## Veg_shannon_index-Lynx_rufus 0.8994 0.8452 -0.7125 0.8714
## Veg_shannon_index-Didelphis_virginiana 1.0230 0.6586 -0.1738 0.9793
## Veg_shannon_index-Sylvilagus_floridanus 0.9430 0.6809 -0.3692 0.9072
## Veg_shannon_index-Meleagris_gallopavo 1.1278 0.7416 -0.1773 1.0582
## Veg_shannon_index-Sciurus_carolinensis 0.3598 0.7237 -1.2781 0.4184
## total_shrub_cover-Odocoileus_virginianus -0.1944 1.0679 -2.2205 -0.2538
## total_shrub_cover-Canis_latrans 0.0338 0.7424 -1.2647 -0.0016
## total_shrub_cover-Procyon_lotor -1.1753 0.6682 -2.6460 -1.1210
## total_shrub_cover-Dasypus_novemcinctus 0.0837 0.5766 -0.9888 0.0651
## total_shrub_cover-Lynx_rufus -1.3589 1.2428 -4.1835 -1.1705
## total_shrub_cover-Didelphis_virginiana -0.7117 0.7675 -2.3799 -0.6717
## total_shrub_cover-Sylvilagus_floridanus -0.3950 0.9197 -2.2470 -0.3793
## total_shrub_cover-Meleagris_gallopavo -2.2237 1.4000 -5.5306 -1.9952
## total_shrub_cover-Sciurus_carolinensis -0.0286 0.7660 -1.5013 -0.0519
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0102 1.2696 -2.5587 -0.0012
## Avg_Cogongrass_Cover-Canis_latrans 0.0744 1.1371 -2.1761 0.0612
## Avg_Cogongrass_Cover-Procyon_lotor 0.1651 1.1293 -1.9847 0.1233
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5938 1.2081 -1.5959 0.5023
## Avg_Cogongrass_Cover-Lynx_rufus 0.1606 1.2353 -2.2810 0.1313
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1881 1.1540 -2.0788 0.1616
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4486 1.2584 -3.1663 -0.3723
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2554 1.2937 -2.9947 -0.2129
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0761 1.1671 -2.1308 0.0494
## Tree_Density-Odocoileus_virginianus -0.8464 1.4299 -2.9955 -1.0686
## Tree_Density-Canis_latrans -2.7305 1.3095 -5.8134 -2.5185
## Tree_Density-Procyon_lotor -1.7827 0.9505 -3.7345 -1.7663
## Tree_Density-Dasypus_novemcinctus -3.6624 1.9955 -9.0975 -3.1533
## Tree_Density-Lynx_rufus -0.7983 1.6466 -3.2745 -1.0620
## Tree_Density-Didelphis_virginiana -2.2857 1.1624 -5.1132 -2.1242
## Tree_Density-Sylvilagus_floridanus -2.4746 1.4486 -6.0455 -2.2835
## Tree_Density-Meleagris_gallopavo -2.0180 1.3545 -4.8582 -1.9771
## Tree_Density-Sciurus_carolinensis -2.6517 1.4497 -6.4422 -2.3635
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0503 1.3419 -1.8203 1.1319
## Avg_Canopy_Cover-Canis_latrans 0.2032 0.7052 -1.1716 0.2066
## Avg_Canopy_Cover-Procyon_lotor 1.6083 0.7177 0.2807 1.5717
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9383 0.7512 0.6853 1.8656
## Avg_Canopy_Cover-Lynx_rufus 1.2531 1.3638 -1.4195 1.2447
## Avg_Canopy_Cover-Didelphis_virginiana 2.5884 0.9584 1.0831 2.4538
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2615 1.5835 1.0231 2.9816
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2832 1.2084 0.4467 2.0787
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2506 0.8996 0.8507 2.1331
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8830 1.5206 -0.0303 1.6341
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0093 1.0363 0.5769 1.8163
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8307 0.9872 0.4148 1.6884
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3937 0.7094 0.1646 1.3358
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1052 1.1849 0.4626 1.8906
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0378 0.6837 -0.3188 1.0333
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1406 0.8262 -0.3662 1.1038
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.5038 1.2964 -2.5503 0.6754
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6230 0.7525 0.3590 1.5679
## avg_veg_height-Odocoileus_virginianus -0.1434 0.7886 -1.8277 -0.1159
## avg_veg_height-Canis_latrans -0.3794 0.6361 -1.7158 -0.3558
## avg_veg_height-Procyon_lotor 0.0971 0.6193 -1.0332 0.0791
## avg_veg_height-Dasypus_novemcinctus 0.1569 0.6099 -1.0070 0.1398
## avg_veg_height-Lynx_rufus -0.2628 0.7996 -1.9824 -0.2306
## avg_veg_height-Didelphis_virginiana -0.2590 0.6866 -1.6932 -0.2361
## avg_veg_height-Sylvilagus_floridanus -0.2837 0.6885 -1.7495 -0.2479
## avg_veg_height-Meleagris_gallopavo -0.1605 0.7771 -1.7388 -0.1400
## avg_veg_height-Sciurus_carolinensis 0.1211 0.6780 -1.1262 0.0947
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.0514 1.0331 501
## (Intercept)-Canis_latrans 1.3755 1.0108 1385
## (Intercept)-Procyon_lotor 1.5397 1.0008 1616
## (Intercept)-Dasypus_novemcinctus -0.7424 1.0199 799
## (Intercept)-Lynx_rufus 5.5405 1.0459 395
## (Intercept)-Didelphis_virginiana -1.6473 1.0175 948
## (Intercept)-Sylvilagus_floridanus 0.7055 1.0168 1107
## (Intercept)-Meleagris_gallopavo 1.3647 1.0055 988
## (Intercept)-Sciurus_carolinensis -2.2700 1.0229 972
## Cogon_Patch_Size-Odocoileus_virginianus 3.3510 1.0039 1564
## Cogon_Patch_Size-Canis_latrans 5.1140 1.0115 913
## Cogon_Patch_Size-Procyon_lotor 1.1719 1.0092 1407
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1206 1.0091 1356
## Cogon_Patch_Size-Lynx_rufus 3.5293 1.0141 825
## Cogon_Patch_Size-Didelphis_virginiana 3.9612 1.0027 828
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2030 1.0087 1057
## Cogon_Patch_Size-Meleagris_gallopavo 2.9196 1.0001 1405
## Cogon_Patch_Size-Sciurus_carolinensis 0.8384 1.0119 1254
## Veg_shannon_index-Odocoileus_virginianus 2.2689 1.0022 1618
## Veg_shannon_index-Canis_latrans 2.6558 1.0017 1638
## Veg_shannon_index-Procyon_lotor 2.3721 1.0047 1082
## Veg_shannon_index-Dasypus_novemcinctus 1.6344 1.0029 2625
## Veg_shannon_index-Lynx_rufus 2.6556 1.0035 1546
## Veg_shannon_index-Didelphis_virginiana 2.5181 1.0015 2315
## Veg_shannon_index-Sylvilagus_floridanus 2.3884 1.0057 1725
## Veg_shannon_index-Meleagris_gallopavo 2.7942 1.0077 1379
## Veg_shannon_index-Sciurus_carolinensis 1.6269 1.0033 2080
## total_shrub_cover-Odocoileus_virginianus 2.1359 1.0037 2086
## total_shrub_cover-Canis_latrans 1.6378 1.0100 1986
## total_shrub_cover-Procyon_lotor -0.0360 1.0025 2365
## total_shrub_cover-Dasypus_novemcinctus 1.2676 1.0036 2858
## total_shrub_cover-Lynx_rufus 0.5911 1.0095 1032
## total_shrub_cover-Didelphis_virginiana 0.6740 1.0108 2734
## total_shrub_cover-Sylvilagus_floridanus 1.3320 1.0079 1776
## total_shrub_cover-Meleagris_gallopavo -0.1377 1.0368 830
## total_shrub_cover-Sciurus_carolinensis 1.5488 1.0025 3030
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.4594 1.0040 696
## Avg_Cogongrass_Cover-Canis_latrans 2.3186 1.0051 627
## Avg_Cogongrass_Cover-Procyon_lotor 2.5007 1.0040 720
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.2168 1.0244 657
## Avg_Cogongrass_Cover-Lynx_rufus 2.6690 1.0041 750
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.6051 1.0061 662
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7897 1.0053 806
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1158 1.0042 903
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4811 1.0025 693
## Tree_Density-Odocoileus_virginianus 2.7402 1.0033 933
## Tree_Density-Canis_latrans -0.7795 1.0289 676
## Tree_Density-Procyon_lotor 0.0441 1.0093 1342
## Tree_Density-Dasypus_novemcinctus -1.2440 1.0295 428
## Tree_Density-Lynx_rufus 3.1915 1.0243 561
## Tree_Density-Didelphis_virginiana -0.4590 1.0104 876
## Tree_Density-Sylvilagus_floridanus -0.2498 1.0222 842
## Tree_Density-Meleagris_gallopavo 0.6563 1.0043 1225
## Tree_Density-Sciurus_carolinensis -0.6308 1.0217 708
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6508 1.0031 2049
## Avg_Canopy_Cover-Canis_latrans 1.5977 1.0043 1921
## Avg_Canopy_Cover-Procyon_lotor 3.0954 1.0036 2072
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.6245 1.0258 990
## Avg_Canopy_Cover-Lynx_rufus 4.0875 1.0095 821
## Avg_Canopy_Cover-Didelphis_virginiana 4.8690 1.0216 761
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.0230 1.0301 845
## Avg_Canopy_Cover-Meleagris_gallopavo 5.2551 1.0087 1171
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2935 1.0127 1222
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 5.2826 1.0525 471
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.5198 1.0631 359
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.0864 1.0242 936
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9563 1.0077 1023
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.9324 1.0332 560
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4193 1.0085 994
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8705 1.0086 1107
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.6952 1.0112 526
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3141 1.0149 1171
## avg_veg_height-Odocoileus_virginianus 1.3660 1.0064 1558
## avg_veg_height-Canis_latrans 0.8484 1.0098 1211
## avg_veg_height-Procyon_lotor 1.3937 1.0076 1263
## avg_veg_height-Dasypus_novemcinctus 1.3856 1.0022 1317
## avg_veg_height-Lynx_rufus 1.2151 1.0072 1251
## avg_veg_height-Didelphis_virginiana 1.0507 1.0062 1304
## avg_veg_height-Sylvilagus_floridanus 1.0174 1.0021 1431
## avg_veg_height-Meleagris_gallopavo 1.3334 1.0041 963
## avg_veg_height-Sciurus_carolinensis 1.5261 1.0017 1624
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5253 0.0798 0.3750 0.5242 0.6857
## (Intercept)-Canis_latrans -2.4279 0.1891 -2.8174 -2.4235 -2.0684
## (Intercept)-Procyon_lotor -2.1560 0.1504 -2.4577 -2.1534 -1.8697
## (Intercept)-Dasypus_novemcinctus -1.4371 0.1604 -1.7611 -1.4323 -1.1373
## (Intercept)-Lynx_rufus -3.5732 0.3328 -4.2271 -3.5751 -2.9273
## (Intercept)-Didelphis_virginiana -2.0787 0.2670 -2.6312 -2.0702 -1.5894
## (Intercept)-Sylvilagus_floridanus -3.1118 0.3078 -3.7396 -3.1003 -2.5493
## (Intercept)-Meleagris_gallopavo -3.2876 0.3496 -4.0281 -3.2748 -2.6537
## (Intercept)-Sciurus_carolinensis -2.2303 0.2892 -2.8402 -2.2181 -1.6946
## week-Odocoileus_virginianus 1.2811 0.1231 1.0445 1.2794 1.5243
## week-Canis_latrans 0.5927 0.2634 0.0863 0.5903 1.1385
## week-Procyon_lotor 0.2040 0.2104 -0.1987 0.2048 0.6167
## week-Dasypus_novemcinctus 0.1089 0.2235 -0.3363 0.1132 0.5383
## week-Lynx_rufus 0.3997 0.3493 -0.2698 0.3982 1.0980
## week-Didelphis_virginiana 0.0617 0.3713 -0.7122 0.0711 0.7593
## week-Sylvilagus_floridanus 0.0709 0.3423 -0.6138 0.0752 0.7174
## week-Meleagris_gallopavo -0.1860 0.4262 -1.0726 -0.1652 0.6066
## week-Sciurus_carolinensis 0.8044 0.3731 0.0963 0.7958 1.5832
## I(week^2)-Odocoileus_virginianus -0.5284 0.0504 -0.6282 -0.5282 -0.4291
## I(week^2)-Canis_latrans -0.2471 0.1082 -0.4681 -0.2440 -0.0434
## I(week^2)-Procyon_lotor -0.1315 0.0912 -0.3091 -0.1325 0.0450
## I(week^2)-Dasypus_novemcinctus -0.1790 0.1043 -0.3902 -0.1787 0.0251
## I(week^2)-Lynx_rufus -0.2469 0.1516 -0.5500 -0.2441 0.0412
## I(week^2)-Didelphis_virginiana -0.4345 0.2209 -0.9197 -0.4149 -0.0653
## I(week^2)-Sylvilagus_floridanus -0.1907 0.1633 -0.5332 -0.1855 0.1168
## I(week^2)-Meleagris_gallopavo -0.4269 0.2508 -0.9903 -0.3977 -0.0157
## I(week^2)-Sciurus_carolinensis -0.2879 0.1452 -0.5852 -0.2835 -0.0103
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0018 5250
## (Intercept)-Canis_latrans 1.0007 3177
## (Intercept)-Procyon_lotor 1.0143 3860
## (Intercept)-Dasypus_novemcinctus 1.0001 5109
## (Intercept)-Lynx_rufus 1.0188 596
## (Intercept)-Didelphis_virginiana 1.0007 4407
## (Intercept)-Sylvilagus_floridanus 1.0010 1732
## (Intercept)-Meleagris_gallopavo 1.0007 974
## (Intercept)-Sciurus_carolinensis 1.0011 3845
## week-Odocoileus_virginianus 1.0008 5250
## week-Canis_latrans 1.0000 3873
## week-Procyon_lotor 1.0019 3752
## week-Dasypus_novemcinctus 1.0000 4907
## week-Lynx_rufus 1.0008 2558
## week-Didelphis_virginiana 1.0007 3183
## week-Sylvilagus_floridanus 1.0031 2771
## week-Meleagris_gallopavo 1.0024 1243
## week-Sciurus_carolinensis 1.0011 4318
## I(week^2)-Odocoileus_virginianus 1.0006 5250
## I(week^2)-Canis_latrans 1.0010 3981
## I(week^2)-Procyon_lotor 1.0013 3661
## I(week^2)-Dasypus_novemcinctus 1.0002 4472
## I(week^2)-Lynx_rufus 1.0015 2490
## I(week^2)-Didelphis_virginiana 1.0038 1848
## I(week^2)-Sylvilagus_floridanus 1.0115 2042
## I(week^2)-Meleagris_gallopavo 1.0181 698
## I(week^2)-Sciurus_carolinensis 0.9999 4623
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.974
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3489 0.6131 -0.8193 0.3189 1.6618 1.0018 2355
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5666 3.5266 0.7049 2.6228 11.9206 1.026 1535
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1644 0.5085 -3.1442 -2.1827 -1.1244 1.0017 5250
## shrub_cover 0.1460 0.2862 -0.4220 0.1411 0.7290 1.0020 4282
## veg_height -0.0107 0.1841 -0.3856 -0.0095 0.3477 1.0024 3966
## week 0.3784 0.2451 -0.1248 0.3819 0.8503 1.0020 3411
## I(week^2) -0.2964 0.1178 -0.5358 -0.2934 -0.0753 1.0021 2770
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5792 1.8253 0.8298 2.1151 7.1807 1.0010 4405
## shrub_cover 0.6911 0.5622 0.1512 0.5443 2.2161 1.0008 2146
## veg_height 0.2548 0.2332 0.0646 0.1969 0.8015 1.0416 4622
## week 0.4273 0.3435 0.1034 0.3294 1.3285 1.0020 3821
## I(week^2) 0.0860 0.0679 0.0245 0.0669 0.2647 1.0016 2835
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.6087 1.1918 1.8552 3.4122 6.4861
## (Intercept)-Canis_latrans 0.4464 0.4242 -0.3250 0.4343 1.3417
## (Intercept)-Procyon_lotor 0.7734 0.4127 0.0356 0.7465 1.6478
## (Intercept)-Dasypus_novemcinctus -0.5407 0.3814 -1.2953 -0.5353 0.2038
## (Intercept)-Lynx_rufus 0.7873 1.1346 -0.6786 0.5619 3.6951
## (Intercept)-Didelphis_virginiana -1.1730 0.4762 -2.1565 -1.1572 -0.2840
## (Intercept)-Sylvilagus_floridanus -0.2434 0.5487 -1.1729 -0.2853 0.9167
## (Intercept)-Meleagris_gallopavo 1.1527 1.3228 -0.6579 0.9034 4.4597
## (Intercept)-Sciurus_carolinensis -1.1678 0.4705 -2.1464 -1.1500 -0.2648
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0159 1103
## (Intercept)-Canis_latrans 1.0049 4798
## (Intercept)-Procyon_lotor 1.0014 4580
## (Intercept)-Dasypus_novemcinctus 1.0014 5028
## (Intercept)-Lynx_rufus 1.0272 625
## (Intercept)-Didelphis_virginiana 1.0005 4301
## (Intercept)-Sylvilagus_floridanus 1.0012 1671
## (Intercept)-Meleagris_gallopavo 1.0393 578
## (Intercept)-Sciurus_carolinensis 1.0002 3833
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5396 0.0816 0.3808 0.5391 0.6994
## (Intercept)-Canis_latrans -2.5627 0.2009 -2.9690 -2.5575 -2.1901
## (Intercept)-Procyon_lotor -2.1812 0.1615 -2.5085 -2.1756 -1.8734
## (Intercept)-Dasypus_novemcinctus -1.5741 0.1789 -1.9440 -1.5721 -1.2377
## (Intercept)-Lynx_rufus -3.6567 0.3900 -4.4240 -3.6561 -2.8957
## (Intercept)-Didelphis_virginiana -2.3231 0.3089 -2.9657 -2.3139 -1.7496
## (Intercept)-Sylvilagus_floridanus -3.0847 0.3324 -3.7924 -3.0676 -2.4866
## (Intercept)-Meleagris_gallopavo -4.2250 0.5153 -5.2055 -4.2298 -3.1708
## (Intercept)-Sciurus_carolinensis -2.4035 0.3321 -3.1054 -2.3916 -1.8060
## shrub_cover-Odocoileus_virginianus -0.0615 0.0674 -0.1936 -0.0618 0.0698
## shrub_cover-Canis_latrans -0.3205 0.2206 -0.7597 -0.3219 0.1135
## shrub_cover-Procyon_lotor 0.2492 0.1659 -0.0845 0.2560 0.5635
## shrub_cover-Dasypus_novemcinctus 0.8259 0.2946 0.2494 0.8263 1.4354
## shrub_cover-Lynx_rufus -0.3764 0.3557 -1.0877 -0.3694 0.3257
## shrub_cover-Didelphis_virginiana 0.9409 0.3593 0.2944 0.9264 1.6942
## shrub_cover-Sylvilagus_floridanus 0.2205 0.4141 -0.5453 0.2092 1.0600
## shrub_cover-Meleagris_gallopavo -0.9505 0.4210 -1.8037 -0.9479 -0.1554
## shrub_cover-Sciurus_carolinensis 0.8065 0.4178 0.0182 0.7977 1.6551
## veg_height-Odocoileus_virginianus -0.3334 0.0684 -0.4690 -0.3336 -0.2026
## veg_height-Canis_latrans -0.6086 0.1879 -0.9889 -0.6017 -0.2597
## veg_height-Procyon_lotor 0.3393 0.1250 0.0958 0.3382 0.5877
## veg_height-Dasypus_novemcinctus 0.2354 0.1363 -0.0349 0.2344 0.5004
## veg_height-Lynx_rufus 0.0303 0.2457 -0.4701 0.0336 0.5035
## veg_height-Didelphis_virginiana 0.4350 0.2451 -0.0218 0.4288 0.9397
## veg_height-Sylvilagus_floridanus 0.1141 0.2424 -0.3555 0.1136 0.5771
## veg_height-Meleagris_gallopavo -0.3796 0.3399 -1.0718 -0.3749 0.2827
## veg_height-Sciurus_carolinensis 0.0565 0.2149 -0.3447 0.0487 0.5032
## week-Odocoileus_virginianus 1.3171 0.1253 1.0745 1.3158 1.5639
## week-Canis_latrans 0.6061 0.2673 0.0865 0.6057 1.1518
## week-Procyon_lotor 0.2060 0.2132 -0.2060 0.2064 0.6287
## week-Dasypus_novemcinctus 0.1218 0.2279 -0.3303 0.1248 0.5632
## week-Lynx_rufus 0.4023 0.3558 -0.2943 0.4008 1.1000
## week-Didelphis_virginiana 0.0727 0.3831 -0.7082 0.0877 0.7780
## week-Sylvilagus_floridanus 0.0733 0.3473 -0.6204 0.0860 0.7244
## week-Meleagris_gallopavo -0.1615 0.4265 -1.0315 -0.1438 0.6165
## week-Sciurus_carolinensis 0.8206 0.3764 0.1165 0.8023 1.6098
## I(week^2)-Odocoileus_virginianus -0.5431 0.0520 -0.6463 -0.5425 -0.4399
## I(week^2)-Canis_latrans -0.2505 0.1094 -0.4665 -0.2486 -0.0415
## I(week^2)-Procyon_lotor -0.1341 0.0934 -0.3156 -0.1329 0.0448
## I(week^2)-Dasypus_novemcinctus -0.1862 0.1054 -0.3973 -0.1847 0.0143
## I(week^2)-Lynx_rufus -0.2473 0.1544 -0.5579 -0.2453 0.0515
## I(week^2)-Didelphis_virginiana -0.4403 0.2270 -0.9596 -0.4169 -0.0740
## I(week^2)-Sylvilagus_floridanus -0.1859 0.1651 -0.5292 -0.1812 0.1282
## I(week^2)-Meleagris_gallopavo -0.4265 0.2429 -0.9570 -0.3988 -0.0090
## I(week^2)-Sciurus_carolinensis -0.2901 0.1457 -0.5863 -0.2879 -0.0136
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0066 2562
## (Intercept)-Procyon_lotor 1.0002 3900
## (Intercept)-Dasypus_novemcinctus 1.0003 4601
## (Intercept)-Lynx_rufus 1.0088 842
## (Intercept)-Didelphis_virginiana 1.0016 2659
## (Intercept)-Sylvilagus_floridanus 1.0070 1580
## (Intercept)-Meleagris_gallopavo 1.0334 497
## (Intercept)-Sciurus_carolinensis 1.0002 2704
## shrub_cover-Odocoileus_virginianus 1.0014 5250
## shrub_cover-Canis_latrans 1.0012 2871
## shrub_cover-Procyon_lotor 1.0061 4094
## shrub_cover-Dasypus_novemcinctus 1.0044 3549
## shrub_cover-Lynx_rufus 1.0011 1370
## shrub_cover-Didelphis_virginiana 1.0009 2353
## shrub_cover-Sylvilagus_floridanus 1.0114 1676
## shrub_cover-Meleagris_gallopavo 1.0142 589
## shrub_cover-Sciurus_carolinensis 0.9999 2314
## veg_height-Odocoileus_virginianus 1.0015 5250
## veg_height-Canis_latrans 1.0005 2355
## veg_height-Procyon_lotor 1.0016 4299
## veg_height-Dasypus_novemcinctus 1.0018 4843
## veg_height-Lynx_rufus 1.0039 2225
## veg_height-Didelphis_virginiana 1.0008 3443
## veg_height-Sylvilagus_floridanus 1.0010 2764
## veg_height-Meleagris_gallopavo 1.0018 1460
## veg_height-Sciurus_carolinensis 1.0015 2859
## week-Odocoileus_virginianus 1.0012 5250
## week-Canis_latrans 1.0009 3468
## week-Procyon_lotor 1.0005 4410
## week-Dasypus_novemcinctus 1.0010 5007
## week-Lynx_rufus 1.0016 2740
## week-Didelphis_virginiana 1.0019 2755
## week-Sylvilagus_floridanus 1.0021 3100
## week-Meleagris_gallopavo 1.0011 1070
## week-Sciurus_carolinensis 1.0072 4111
## I(week^2)-Odocoileus_virginianus 1.0011 5250
## I(week^2)-Canis_latrans 1.0009 3747
## I(week^2)-Procyon_lotor 1.0017 3949
## I(week^2)-Dasypus_novemcinctus 1.0017 4277
## I(week^2)-Lynx_rufus 1.0020 2256
## I(week^2)-Didelphis_virginiana 1.0044 1308
## I(week^2)-Sylvilagus_floridanus 1.0026 2071
## I(week^2)-Meleagris_gallopavo 1.0031 684
## I(week^2)-Sciurus_carolinensis 1.0037 4331
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.981
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2791 1.0787 -1.8496 0.2669 2.4591 1.0027 2967
## Cogon_Patch_Size -0.3285 0.6474 -1.6041 -0.3433 1.0171 1.0119 1522
## Veg_shannon_index 0.8825 0.4805 -0.0408 0.8750 1.8670 1.0038 532
## total_shrub_cover -0.5931 0.6312 -1.9860 -0.5609 0.5748 1.0008 997
## Avg_Cogongrass_Cover 1.8117 0.7241 0.4082 1.7991 3.2705 1.0127 647
## Tree_Density -1.7292 0.7629 -3.2560 -1.7258 -0.2034 1.0058 1031
## Avg_Canopy_Cover 1.8413 0.7061 0.4797 1.8237 3.2685 1.0016 1089
## avg_veg_height -0.3011 0.5088 -1.3211 -0.2964 0.7188 1.0084 983
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.5265 17.9169 2.5449 12.4061 61.4726 1.0071 619
## Cogon_Patch_Size 2.6804 4.6815 0.0907 1.2335 14.5699 1.0158 829
## Veg_shannon_index 0.7599 1.2536 0.0496 0.3875 3.8162 1.0188 1700
## total_shrub_cover 1.9150 3.2395 0.0734 0.9684 9.5180 1.0251 777
## Avg_Cogongrass_Cover 0.9867 1.7595 0.0485 0.4196 5.2384 1.0160 1310
## Tree_Density 3.7806 6.7102 0.0730 1.6922 19.6514 1.0046 692
## Avg_Canopy_Cover 3.7443 5.5844 0.1824 2.0791 17.3841 1.0215 601
## avg_veg_height 0.4985 0.7238 0.0413 0.2700 2.2827 1.0066 2109
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0608 2.8824 0.0553 0.9652 10.6196 1.0904 127
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1538 0.5004 -3.1031 -2.1715 -1.1040 1.0000 5250
## shrub_cover 0.2525 0.2929 -0.3341 0.2537 0.8189 1.0007 2777
## veg_height 0.0178 0.1830 -0.3513 0.0190 0.3770 1.0004 3467
## week 0.3704 0.2498 -0.1397 0.3763 0.8578 1.0007 4026
## I(week^2) -0.2957 0.1145 -0.5324 -0.2930 -0.0721 1.0026 2938
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3954 1.7200 0.7759 1.9280 6.8677 1.0007 3758
## shrub_cover 0.6558 0.5570 0.1414 0.5136 1.9752 1.0049 2659
## veg_height 0.2503 0.2371 0.0651 0.1941 0.7664 1.0576 4546
## week 0.4220 0.3465 0.1054 0.3270 1.2672 1.0017 3347
## I(week^2) 0.0871 0.0756 0.0237 0.0672 0.2706 1.0314 1981
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.9721 3.3859 2.9518 7.4100
## (Intercept)-Canis_latrans 1.0128 1.1547 -0.9719 0.9081
## (Intercept)-Procyon_lotor 1.0275 1.0738 -1.1408 1.0123
## (Intercept)-Dasypus_novemcinctus -1.3820 1.0543 -3.7028 -1.3118
## (Intercept)-Lynx_rufus 2.3410 2.8035 -1.9297 1.9031
## (Intercept)-Didelphis_virginiana -2.7751 1.2656 -5.5063 -2.6597
## (Intercept)-Sylvilagus_floridanus -0.9343 1.3533 -3.7364 -0.9329
## (Intercept)-Meleagris_gallopavo 0.0515 2.1095 -3.5958 -0.1414
## (Intercept)-Sciurus_carolinensis -2.7634 1.4213 -5.7695 -2.6492
## Cogon_Patch_Size-Odocoileus_virginianus -0.2852 1.2633 -2.6809 -0.3535
## Cogon_Patch_Size-Canis_latrans 0.7803 1.2235 -0.9564 0.5688
## Cogon_Patch_Size-Procyon_lotor -0.8378 0.7597 -2.4843 -0.8006
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4176 0.7556 -1.8425 -0.4437
## Cogon_Patch_Size-Lynx_rufus -0.3692 1.4846 -2.9984 -0.4539
## Cogon_Patch_Size-Didelphis_virginiana 0.8707 0.9642 -0.6197 0.7325
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5515 1.6416 -5.6399 -1.2434
## Cogon_Patch_Size-Meleagris_gallopavo -0.0385 1.4710 -2.3799 -0.1761
## Cogon_Patch_Size-Sciurus_carolinensis -1.2829 1.2716 -4.4382 -1.0779
## Veg_shannon_index-Odocoileus_virginianus 0.7487 0.8737 -1.1006 0.7691
## Veg_shannon_index-Canis_latrans 1.2430 0.6691 0.0930 1.1829
## Veg_shannon_index-Procyon_lotor 1.1750 0.6319 0.0796 1.1187
## Veg_shannon_index-Dasypus_novemcinctus 0.6073 0.5519 -0.5145 0.6268
## Veg_shannon_index-Lynx_rufus 0.8238 0.8793 -1.0439 0.8236
## Veg_shannon_index-Didelphis_virginiana 1.1230 0.7081 -0.0794 1.0527
## Veg_shannon_index-Sylvilagus_floridanus 1.0410 0.7092 -0.2232 0.9761
## Veg_shannon_index-Meleagris_gallopavo 1.2540 0.8533 -0.1328 1.1636
## Veg_shannon_index-Sciurus_carolinensis 0.2334 0.7917 -1.5683 0.3141
## total_shrub_cover-Odocoileus_virginianus -0.1409 1.0853 -2.2464 -0.1978
## total_shrub_cover-Canis_latrans 0.5920 1.0460 -0.8834 0.3921
## total_shrub_cover-Procyon_lotor -1.1073 0.7364 -2.7242 -1.0190
## total_shrub_cover-Dasypus_novemcinctus -0.2193 0.7179 -1.7430 -0.1931
## total_shrub_cover-Lynx_rufus -1.0047 1.5094 -4.4288 -0.8200
## total_shrub_cover-Didelphis_virginiana -0.8963 0.9172 -3.0737 -0.7873
## total_shrub_cover-Sylvilagus_floridanus -0.7171 1.1811 -3.3984 -0.6175
## total_shrub_cover-Meleagris_gallopavo -1.6367 1.4354 -5.0295 -1.3843
## total_shrub_cover-Sciurus_carolinensis -0.6335 1.0555 -3.1436 -0.4989
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7497 1.0617 -0.2902 1.7351
## Avg_Cogongrass_Cover-Canis_latrans 2.1664 0.9215 0.5206 2.1067
## Avg_Cogongrass_Cover-Procyon_lotor 1.9415 0.8611 0.3306 1.9091
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3192 0.9640 0.6673 2.2363
## Avg_Cogongrass_Cover-Lynx_rufus 2.0912 1.0200 0.3125 2.0225
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.8905 0.8871 0.2423 1.8477
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2576 1.0395 -0.9769 1.3148
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4708 1.2717 -1.4926 1.5824
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0657 0.9142 0.4067 2.0163
## Tree_Density-Odocoileus_virginianus -0.6319 1.3723 -2.8150 -0.8099
## Tree_Density-Canis_latrans -2.5831 1.3689 -6.0252 -2.3220
## Tree_Density-Procyon_lotor -1.4004 0.7964 -2.9509 -1.4052
## Tree_Density-Dasypus_novemcinctus -3.5733 1.9484 -8.6848 -3.1069
## Tree_Density-Lynx_rufus -0.4792 1.5786 -2.9220 -0.6875
## Tree_Density-Didelphis_virginiana -2.1020 1.2477 -4.8910 -2.0161
## Tree_Density-Sylvilagus_floridanus -2.4218 1.5177 -6.2846 -2.1788
## Tree_Density-Meleagris_gallopavo -2.2863 1.5094 -5.6634 -2.1210
## Tree_Density-Sciurus_carolinensis -2.3168 1.5436 -5.9954 -2.1016
## Avg_Canopy_Cover-Odocoileus_virginianus 1.1886 1.4195 -1.6854 1.1968
## Avg_Canopy_Cover-Canis_latrans 0.1618 0.7084 -1.2388 0.1643
## Avg_Canopy_Cover-Procyon_lotor 1.7360 0.7906 0.3339 1.6875
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1650 0.7909 0.8837 2.0714
## Avg_Canopy_Cover-Lynx_rufus 1.4010 1.6454 -1.6156 1.3245
## Avg_Canopy_Cover-Didelphis_virginiana 3.0803 1.2974 1.2637 2.8623
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7047 1.8121 1.2341 3.3702
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6757 1.5738 0.4497 2.4000
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8105 1.3015 1.0096 2.5721
## avg_veg_height-Odocoileus_virginianus -0.3265 0.7885 -1.9209 -0.3091
## avg_veg_height-Canis_latrans -0.3535 0.6332 -1.6018 -0.3549
## avg_veg_height-Procyon_lotor -0.3015 0.6023 -1.4946 -0.2960
## avg_veg_height-Dasypus_novemcinctus -0.1021 0.6078 -1.2661 -0.1107
## avg_veg_height-Lynx_rufus -0.4230 0.7967 -2.0716 -0.4012
## avg_veg_height-Didelphis_virginiana -0.4881 0.6920 -1.9620 -0.4448
## avg_veg_height-Sylvilagus_floridanus -0.5229 0.6958 -2.0350 -0.4945
## avg_veg_height-Meleagris_gallopavo -0.3241 0.8426 -2.0367 -0.3074
## avg_veg_height-Sciurus_carolinensis 0.0362 0.7042 -1.2206 -0.0107
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8069 1.0158 425
## (Intercept)-Canis_latrans 3.6325 1.0264 1017
## (Intercept)-Procyon_lotor 3.1914 1.0089 1288
## (Intercept)-Dasypus_novemcinctus 0.5138 1.0062 1276
## (Intercept)-Lynx_rufus 8.9764 1.0196 266
## (Intercept)-Didelphis_virginiana -0.5699 1.0018 1149
## (Intercept)-Sylvilagus_floridanus 1.7715 1.0053 1151
## (Intercept)-Meleagris_gallopavo 4.6097 1.0220 393
## (Intercept)-Sciurus_carolinensis -0.2472 1.0023 769
## Cogon_Patch_Size-Odocoileus_virginianus 2.4935 1.0037 2266
## Cogon_Patch_Size-Canis_latrans 3.9523 1.0100 1310
## Cogon_Patch_Size-Procyon_lotor 0.5309 1.0056 628
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1641 1.0011 2431
## Cogon_Patch_Size-Lynx_rufus 3.2027 1.0121 866
## Cogon_Patch_Size-Didelphis_virginiana 3.1312 1.0228 776
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7022 1.0071 646
## Cogon_Patch_Size-Meleagris_gallopavo 3.2449 1.0151 782
## Cogon_Patch_Size-Sciurus_carolinensis 0.5987 1.0047 872
## Veg_shannon_index-Odocoileus_virginianus 2.4082 1.0071 1615
## Veg_shannon_index-Canis_latrans 2.7191 1.0005 1082
## Veg_shannon_index-Procyon_lotor 2.6051 1.0014 529
## Veg_shannon_index-Dasypus_novemcinctus 1.6813 1.0004 1830
## Veg_shannon_index-Lynx_rufus 2.5790 1.0064 1609
## Veg_shannon_index-Didelphis_virginiana 2.7428 1.0007 883
## Veg_shannon_index-Sylvilagus_floridanus 2.6028 1.0003 1062
## Veg_shannon_index-Meleagris_gallopavo 3.2170 1.0033 1258
## Veg_shannon_index-Sciurus_carolinensis 1.5893 1.0077 1518
## total_shrub_cover-Odocoileus_virginianus 2.2153 1.0062 1920
## total_shrub_cover-Canis_latrans 3.2353 1.0128 572
## total_shrub_cover-Procyon_lotor 0.0707 1.0025 1309
## total_shrub_cover-Dasypus_novemcinctus 1.1022 1.0007 2009
## total_shrub_cover-Lynx_rufus 1.6548 1.0391 412
## total_shrub_cover-Didelphis_virginiana 0.6107 1.0016 1140
## total_shrub_cover-Sylvilagus_floridanus 1.2717 1.0183 655
## total_shrub_cover-Meleagris_gallopavo 0.5936 1.0123 528
## total_shrub_cover-Sciurus_carolinensis 1.0696 1.0214 906
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.8542 1.0156 1110
## Avg_Cogongrass_Cover-Canis_latrans 4.2021 1.0039 918
## Avg_Cogongrass_Cover-Procyon_lotor 3.7200 1.0043 970
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.4811 1.0001 955
## Avg_Cogongrass_Cover-Lynx_rufus 4.3812 1.0020 859
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7616 1.0042 977
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1691 1.0176 811
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.7164 1.0258 854
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.9817 1.0049 849
## Tree_Density-Odocoileus_virginianus 2.5938 1.0004 684
## Tree_Density-Canis_latrans -0.5933 1.0023 779
## Tree_Density-Procyon_lotor 0.1819 1.0028 1213
## Tree_Density-Dasypus_novemcinctus -1.0855 1.0046 403
## Tree_Density-Lynx_rufus 3.3755 1.0056 499
## Tree_Density-Didelphis_virginiana 0.1858 1.0036 1725
## Tree_Density-Sylvilagus_floridanus 0.0315 1.0090 857
## Tree_Density-Meleagris_gallopavo 0.3689 1.0249 1112
## Tree_Density-Sciurus_carolinensis 0.2476 1.0056 965
## Avg_Canopy_Cover-Odocoileus_virginianus 4.0644 1.0033 1418
## Avg_Canopy_Cover-Canis_latrans 1.5841 1.0027 1434
## Avg_Canopy_Cover-Procyon_lotor 3.4364 1.0014 1198
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9056 1.0087 833
## Avg_Canopy_Cover-Lynx_rufus 4.9394 1.0506 606
## Avg_Canopy_Cover-Didelphis_virginiana 6.3869 1.0008 458
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.0896 1.0080 525
## Avg_Canopy_Cover-Meleagris_gallopavo 6.6218 1.0058 516
## Avg_Canopy_Cover-Sciurus_carolinensis 5.9435 1.0055 586
## avg_veg_height-Odocoileus_virginianus 1.2058 1.0042 1486
## avg_veg_height-Canis_latrans 0.9040 1.0066 1479
## avg_veg_height-Procyon_lotor 0.8976 1.0066 1418
## avg_veg_height-Dasypus_novemcinctus 1.2037 1.0068 1615
## avg_veg_height-Lynx_rufus 1.1162 1.0029 1402
## avg_veg_height-Didelphis_virginiana 0.7775 1.0019 1505
## avg_veg_height-Sylvilagus_floridanus 0.7636 1.0013 1399
## avg_veg_height-Meleagris_gallopavo 1.3359 1.0034 1061
## avg_veg_height-Sciurus_carolinensis 1.5316 1.0119 1472
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5389 0.0804 0.3839 0.5377 0.7005
## (Intercept)-Canis_latrans -2.5689 0.2018 -2.9792 -2.5623 -2.1978
## (Intercept)-Procyon_lotor -2.1795 0.1634 -2.5028 -2.1800 -1.8674
## (Intercept)-Dasypus_novemcinctus -1.6051 0.1836 -1.9723 -1.6004 -1.2638
## (Intercept)-Lynx_rufus -3.7341 0.4043 -4.4720 -3.7584 -2.9145
## (Intercept)-Didelphis_virginiana -2.3374 0.3097 -2.9485 -2.3254 -1.7526
## (Intercept)-Sylvilagus_floridanus -3.0758 0.2975 -3.6948 -3.0639 -2.5351
## (Intercept)-Meleagris_gallopavo -3.8500 0.5193 -4.9223 -3.8404 -2.8666
## (Intercept)-Sciurus_carolinensis -2.5078 0.3473 -3.2221 -2.4991 -1.8540
## shrub_cover-Odocoileus_virginianus -0.0612 0.0676 -0.1957 -0.0612 0.0690
## shrub_cover-Canis_latrans -0.3654 0.2278 -0.8106 -0.3647 0.0855
## shrub_cover-Procyon_lotor 0.2735 0.1621 -0.0566 0.2784 0.5759
## shrub_cover-Dasypus_novemcinctus 0.9151 0.3179 0.3072 0.9138 1.5459
## shrub_cover-Lynx_rufus -0.1952 0.3860 -0.9256 -0.2119 0.5913
## shrub_cover-Didelphis_virginiana 0.9815 0.3709 0.3055 0.9622 1.7581
## shrub_cover-Sylvilagus_floridanus 0.4796 0.4084 -0.3410 0.4883 1.2434
## shrub_cover-Meleagris_gallopavo -0.6631 0.4463 -1.5422 -0.6538 0.1707
## shrub_cover-Sciurus_carolinensis 0.9583 0.4224 0.1466 0.9591 1.7899
## veg_height-Odocoileus_virginianus -0.3340 0.0688 -0.4660 -0.3344 -0.2006
## veg_height-Canis_latrans -0.6057 0.1843 -0.9875 -0.5970 -0.2522
## veg_height-Procyon_lotor 0.3473 0.1235 0.1035 0.3466 0.5931
## veg_height-Dasypus_novemcinctus 0.2474 0.1375 -0.0227 0.2460 0.5138
## veg_height-Lynx_rufus 0.0769 0.2392 -0.4163 0.0827 0.5368
## veg_height-Didelphis_virginiana 0.4536 0.2437 -0.0065 0.4470 0.9488
## veg_height-Sylvilagus_floridanus 0.1466 0.2455 -0.3367 0.1462 0.6192
## veg_height-Meleagris_gallopavo -0.2908 0.3645 -1.0725 -0.2776 0.4029
## veg_height-Sciurus_carolinensis 0.0990 0.2215 -0.3219 0.0955 0.5555
## week-Odocoileus_virginianus 1.3119 0.1228 1.0714 1.3104 1.5571
## week-Canis_latrans 0.5946 0.2587 0.0946 0.5926 1.1132
## week-Procyon_lotor 0.2076 0.2106 -0.2069 0.2050 0.6221
## week-Dasypus_novemcinctus 0.1144 0.2268 -0.3327 0.1139 0.5553
## week-Lynx_rufus 0.3849 0.3501 -0.2785 0.3835 1.1036
## week-Didelphis_virginiana 0.0739 0.3792 -0.7070 0.0850 0.7666
## week-Sylvilagus_floridanus 0.0776 0.3503 -0.6484 0.0869 0.7327
## week-Meleagris_gallopavo -0.1726 0.4321 -1.0849 -0.1526 0.6091
## week-Sciurus_carolinensis 0.8115 0.3781 0.1111 0.7932 1.5920
## I(week^2)-Odocoileus_virginianus -0.5410 0.0512 -0.6438 -0.5403 -0.4412
## I(week^2)-Canis_latrans -0.2473 0.1073 -0.4614 -0.2456 -0.0391
## I(week^2)-Procyon_lotor -0.1342 0.0929 -0.3208 -0.1331 0.0416
## I(week^2)-Dasypus_novemcinctus -0.1814 0.1041 -0.3906 -0.1802 0.0180
## I(week^2)-Lynx_rufus -0.2419 0.1595 -0.5741 -0.2359 0.0568
## I(week^2)-Didelphis_virginiana -0.4409 0.2267 -0.9470 -0.4175 -0.0674
## I(week^2)-Sylvilagus_floridanus -0.1843 0.1599 -0.5065 -0.1802 0.1263
## I(week^2)-Meleagris_gallopavo -0.4182 0.2526 -1.0021 -0.3944 0.0084
## I(week^2)-Sciurus_carolinensis -0.2890 0.1490 -0.5937 -0.2842 -0.0025
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 4817
## (Intercept)-Canis_latrans 1.0035 1999
## (Intercept)-Procyon_lotor 1.0029 2859
## (Intercept)-Dasypus_novemcinctus 1.0015 3565
## (Intercept)-Lynx_rufus 1.0274 340
## (Intercept)-Didelphis_virginiana 1.0067 2325
## (Intercept)-Sylvilagus_floridanus 1.0025 2133
## (Intercept)-Meleagris_gallopavo 1.0347 606
## (Intercept)-Sciurus_carolinensis 1.0045 1405
## shrub_cover-Odocoileus_virginianus 1.0015 5008
## shrub_cover-Canis_latrans 1.0008 1831
## shrub_cover-Procyon_lotor 1.0023 3269
## shrub_cover-Dasypus_novemcinctus 1.0008 2374
## shrub_cover-Lynx_rufus 1.0296 536
## shrub_cover-Didelphis_virginiana 1.0047 1935
## shrub_cover-Sylvilagus_floridanus 1.0045 1182
## shrub_cover-Meleagris_gallopavo 1.0386 661
## shrub_cover-Sciurus_carolinensis 1.0019 1137
## veg_height-Odocoileus_virginianus 1.0007 4712
## veg_height-Canis_latrans 1.0038 2778
## veg_height-Procyon_lotor 1.0022 3647
## veg_height-Dasypus_novemcinctus 0.9998 4012
## veg_height-Lynx_rufus 1.0005 1945
## veg_height-Didelphis_virginiana 1.0018 3303
## veg_height-Sylvilagus_floridanus 1.0005 2312
## veg_height-Meleagris_gallopavo 1.0087 1241
## veg_height-Sciurus_carolinensis 1.0021 3046
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0005 3772
## week-Procyon_lotor 1.0005 4260
## week-Dasypus_novemcinctus 1.0006 4956
## week-Lynx_rufus 1.0003 2404
## week-Didelphis_virginiana 1.0037 2721
## week-Sylvilagus_floridanus 1.0005 2471
## week-Meleagris_gallopavo 1.0011 1179
## week-Sciurus_carolinensis 1.0095 3169
## I(week^2)-Odocoileus_virginianus 1.0001 4609
## I(week^2)-Canis_latrans 1.0012 3509
## I(week^2)-Procyon_lotor 1.0006 4353
## I(week^2)-Dasypus_novemcinctus 1.0032 4697
## I(week^2)-Lynx_rufus 1.0015 2107
## I(week^2)-Didelphis_virginiana 1.0048 1649
## I(week^2)-Sylvilagus_floridanus 1.0008 2257
## I(week^2)-Meleagris_gallopavo 1.0112 708
## I(week^2)-Sciurus_carolinensis 1.0041 4162
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9833
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4112 0.6804 -0.9084 0.4019 1.8117 1.0038 1773
## Avg_Cogongrass_Cover 0.0279 0.3675 -0.7076 0.0352 0.7378 1.0090 1442
## total_shrub_cover -0.7794 0.4807 -1.8042 -0.7540 0.1132 1.0038 947
## avg_veg_height 0.2037 0.3771 -0.5157 0.1919 0.9628 1.0295 1194
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9482 4.1881 0.4575 2.8024 14.8568 1.0027 1151
## Avg_Cogongrass_Cover 0.4267 0.5762 0.0404 0.2474 1.9015 1.0086 2339
## total_shrub_cover 1.1117 1.4876 0.0724 0.6603 4.9004 1.0472 1366
## avg_veg_height 0.3448 0.4473 0.0376 0.2050 1.4904 1.0065 1879
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1201 1.3635 0.0637 0.665 4.9758 1.0068 439
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1691 0.5055 -3.1262 -2.1845 -1.0758 1.0001 4066
## shrub_cover 0.3872 0.3161 -0.2416 0.3830 1.0436 1.0024 2406
## veg_height -0.0086 0.1928 -0.3948 -0.0069 0.3791 1.0036 3179
## week 0.3722 0.2483 -0.1584 0.3825 0.8439 1.0026 3944
## I(week^2) -0.3028 0.1172 -0.5435 -0.3010 -0.0733 1.0024 2270
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2603 1.7097 0.7043 1.8390 6.2769 1.0124 4388
## shrub_cover 0.7291 0.6027 0.1502 0.5676 2.2860 1.0117 1483
## veg_height 0.2526 0.1963 0.0654 0.1993 0.7839 1.0050 3176
## week 0.4295 0.3503 0.1056 0.3374 1.3519 1.0165 2831
## I(week^2) 0.0873 0.0769 0.0243 0.0664 0.2797 1.0069 2176
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8564 1.6422 1.1108 3.6505
## (Intercept)-Canis_latrans 0.7155 0.7748 -0.7383 0.6801
## (Intercept)-Procyon_lotor 0.9797 0.7577 -0.4721 0.9522
## (Intercept)-Dasypus_novemcinctus -0.3758 0.7534 -1.8045 -0.4294
## (Intercept)-Lynx_rufus 0.3684 1.0865 -1.4966 0.2510
## (Intercept)-Didelphis_virginiana -0.8814 0.8401 -2.4207 -0.9312
## (Intercept)-Sylvilagus_floridanus 0.4268 0.9561 -1.2757 0.3595
## (Intercept)-Meleagris_gallopavo 0.1213 1.4812 -2.0967 -0.0460
## (Intercept)-Sciurus_carolinensis -0.8936 0.8852 -2.5840 -0.9270
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0036 0.5985 -1.2071 -0.0112
## Avg_Cogongrass_Cover-Canis_latrans 0.3457 0.5196 -0.5774 0.3164
## Avg_Cogongrass_Cover-Procyon_lotor -0.0820 0.4845 -1.0948 -0.0633
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1412 0.4506 -0.7421 0.1425
## Avg_Cogongrass_Cover-Lynx_rufus 0.3514 0.5616 -0.6318 0.3117
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1560 0.4990 -0.8092 0.1531
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3710 0.6051 -1.7410 -0.3101
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3487 0.7342 -2.0039 -0.2868
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0508 0.4986 -0.9632 0.0607
## total_shrub_cover-Odocoileus_virginianus -0.4036 0.7507 -1.8543 -0.4309
## total_shrub_cover-Canis_latrans 0.2458 0.7373 -0.9778 0.1590
## total_shrub_cover-Procyon_lotor -1.3053 0.6534 -2.8165 -1.2179
## total_shrub_cover-Dasypus_novemcinctus -0.4022 0.6549 -2.0481 -0.3200
## total_shrub_cover-Lynx_rufus -1.1909 0.9008 -3.1394 -1.1280
## total_shrub_cover-Didelphis_virginiana -0.7754 0.6802 -2.4131 -0.6913
## total_shrub_cover-Sylvilagus_floridanus -1.3658 1.0123 -3.6442 -1.2394
## total_shrub_cover-Meleagris_gallopavo -1.3790 0.8916 -3.3380 -1.2950
## total_shrub_cover-Sciurus_carolinensis -0.8110 0.7744 -2.6290 -0.7048
## avg_veg_height-Odocoileus_virginianus 0.1585 0.5846 -1.0403 0.1595
## avg_veg_height-Canis_latrans 0.2154 0.4890 -0.7089 0.1977
## avg_veg_height-Procyon_lotor 0.2115 0.4833 -0.7160 0.1987
## avg_veg_height-Dasypus_novemcinctus 0.3947 0.4770 -0.4791 0.3665
## avg_veg_height-Lynx_rufus 0.1480 0.6076 -1.0291 0.1392
## avg_veg_height-Didelphis_virginiana 0.0733 0.5056 -0.9468 0.0837
## avg_veg_height-Sylvilagus_floridanus 0.1334 0.5403 -0.9303 0.1339
## avg_veg_height-Meleagris_gallopavo -0.0168 0.7285 -1.6085 0.0270
## avg_veg_height-Sciurus_carolinensis 0.5297 0.5363 -0.3839 0.4934
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.7079 1.0052 784
## (Intercept)-Canis_latrans 2.4107 1.0175 1717
## (Intercept)-Procyon_lotor 2.5364 1.0050 2498
## (Intercept)-Dasypus_novemcinctus 1.2779 1.0113 1113
## (Intercept)-Lynx_rufus 2.8727 1.0288 910
## (Intercept)-Didelphis_virginiana 0.9177 1.0125 955
## (Intercept)-Sylvilagus_floridanus 2.4623 1.0266 1041
## (Intercept)-Meleagris_gallopavo 3.4268 1.0295 477
## (Intercept)-Sciurus_carolinensis 0.9346 1.0179 739
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2306 1.0148 2510
## Avg_Cogongrass_Cover-Canis_latrans 1.4954 1.0106 2246
## Avg_Cogongrass_Cover-Procyon_lotor 0.8203 1.0059 2508
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0820 1.0039 2682
## Avg_Cogongrass_Cover-Lynx_rufus 1.5915 1.0053 2380
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1390 1.0121 2353
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6636 1.0077 1837
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9531 1.0014 1421
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0022 1.0178 1841
## total_shrub_cover-Odocoileus_virginianus 1.1828 1.0042 2938
## total_shrub_cover-Canis_latrans 2.0781 1.0251 1263
## total_shrub_cover-Procyon_lotor -0.2584 1.0137 1175
## total_shrub_cover-Dasypus_novemcinctus 0.6390 1.0071 968
## total_shrub_cover-Lynx_rufus 0.4398 1.0080 859
## total_shrub_cover-Didelphis_virginiana 0.3466 1.0167 910
## total_shrub_cover-Sylvilagus_floridanus 0.2072 1.0422 603
## total_shrub_cover-Meleagris_gallopavo 0.1537 1.0163 984
## total_shrub_cover-Sciurus_carolinensis 0.4140 1.0156 832
## avg_veg_height-Odocoileus_virginianus 1.3289 1.0177 2175
## avg_veg_height-Canis_latrans 1.2406 1.0192 1942
## avg_veg_height-Procyon_lotor 1.1974 1.0059 2168
## avg_veg_height-Dasypus_novemcinctus 1.4181 1.0095 1440
## avg_veg_height-Lynx_rufus 1.4272 1.0249 1794
## avg_veg_height-Didelphis_virginiana 1.0455 1.0095 1912
## avg_veg_height-Sylvilagus_floridanus 1.2685 1.0391 1322
## avg_veg_height-Meleagris_gallopavo 1.2672 1.0229 1130
## avg_veg_height-Sciurus_carolinensis 1.7274 1.0239 1901
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5380 0.0804 0.3802 0.5375 0.6975
## (Intercept)-Canis_latrans -2.6155 0.2156 -3.0564 -2.6065 -2.2076
## (Intercept)-Procyon_lotor -2.1844 0.1598 -2.4995 -2.1823 -1.8805
## (Intercept)-Dasypus_novemcinctus -1.6446 0.2002 -2.0632 -1.6362 -1.2814
## (Intercept)-Lynx_rufus -3.4486 0.3836 -4.2527 -3.4309 -2.7452
## (Intercept)-Didelphis_virginiana -2.4629 0.3582 -3.2380 -2.4410 -1.8253
## (Intercept)-Sylvilagus_floridanus -3.2083 0.3099 -3.8312 -3.2016 -2.6222
## (Intercept)-Meleagris_gallopavo -3.7136 0.6088 -4.9468 -3.7028 -2.5518
## (Intercept)-Sciurus_carolinensis -2.5697 0.3752 -3.3672 -2.5547 -1.8899
## shrub_cover-Odocoileus_virginianus -0.0596 0.0681 -0.1960 -0.0581 0.0726
## shrub_cover-Canis_latrans -0.2998 0.2508 -0.7802 -0.3032 0.1936
## shrub_cover-Procyon_lotor 0.3215 0.1616 -0.0020 0.3249 0.6262
## shrub_cover-Dasypus_novemcinctus 1.0229 0.3716 0.3583 1.0053 1.7911
## shrub_cover-Lynx_rufus 0.0321 0.4018 -0.8227 0.0551 0.7555
## shrub_cover-Didelphis_virginiana 1.1937 0.4361 0.4166 1.1633 2.1393
## shrub_cover-Sylvilagus_floridanus 0.7438 0.4504 -0.2676 0.7748 1.5726
## shrub_cover-Meleagris_gallopavo -0.5331 0.5042 -1.5120 -0.5343 0.4297
## shrub_cover-Sciurus_carolinensis 1.1365 0.4502 0.2537 1.1327 2.0259
## veg_height-Odocoileus_virginianus -0.3343 0.0692 -0.4697 -0.3332 -0.2007
## veg_height-Canis_latrans -0.6312 0.1892 -1.0158 -0.6253 -0.2786
## veg_height-Procyon_lotor 0.3389 0.1244 0.1011 0.3394 0.5790
## veg_height-Dasypus_novemcinctus 0.2508 0.1397 -0.0225 0.2470 0.5339
## veg_height-Lynx_rufus 0.0220 0.2530 -0.4926 0.0263 0.4958
## veg_height-Didelphis_virginiana 0.4255 0.2609 -0.0706 0.4139 0.9615
## veg_height-Sylvilagus_floridanus 0.0350 0.2574 -0.4584 0.0292 0.5443
## veg_height-Meleagris_gallopavo -0.2502 0.4405 -1.0607 -0.2640 0.6564
## veg_height-Sciurus_carolinensis 0.0768 0.2322 -0.3584 0.0694 0.5507
## week-Odocoileus_virginianus 1.3132 0.1221 1.0768 1.3141 1.5515
## week-Canis_latrans 0.6013 0.2628 0.0938 0.5978 1.1222
## week-Procyon_lotor 0.2088 0.2094 -0.2025 0.2078 0.6109
## week-Dasypus_novemcinctus 0.1153 0.2269 -0.3159 0.1130 0.5652
## week-Lynx_rufus 0.4024 0.3510 -0.2735 0.4002 1.1246
## week-Didelphis_virginiana 0.0711 0.3774 -0.7152 0.0821 0.7676
## week-Sylvilagus_floridanus 0.0720 0.3512 -0.6394 0.0767 0.7361
## week-Meleagris_gallopavo -0.1848 0.4325 -1.0747 -0.1725 0.6139
## week-Sciurus_carolinensis 0.8216 0.3762 0.1242 0.8060 1.6084
## I(week^2)-Odocoileus_virginianus -0.5414 0.0503 -0.6423 -0.5410 -0.4444
## I(week^2)-Canis_latrans -0.2519 0.1098 -0.4739 -0.2487 -0.0427
## I(week^2)-Procyon_lotor -0.1360 0.0913 -0.3149 -0.1371 0.0438
## I(week^2)-Dasypus_novemcinctus -0.1843 0.1043 -0.3931 -0.1818 0.0153
## I(week^2)-Lynx_rufus -0.2529 0.1601 -0.5848 -0.2472 0.0484
## I(week^2)-Didelphis_virginiana -0.4443 0.2313 -0.9715 -0.4201 -0.0711
## I(week^2)-Sylvilagus_floridanus -0.1887 0.1617 -0.5087 -0.1862 0.1242
## I(week^2)-Meleagris_gallopavo -0.4377 0.2550 -1.0353 -0.4119 -0.0131
## I(week^2)-Sciurus_carolinensis -0.2964 0.1484 -0.6039 -0.2910 -0.0217
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0090 1869
## (Intercept)-Procyon_lotor 1.0001 3761
## (Intercept)-Dasypus_novemcinctus 1.0036 1723
## (Intercept)-Lynx_rufus 1.0088 1025
## (Intercept)-Didelphis_virginiana 1.0212 918
## (Intercept)-Sylvilagus_floridanus 1.0235 1622
## (Intercept)-Meleagris_gallopavo 1.0229 609
## (Intercept)-Sciurus_carolinensis 1.0051 1109
## shrub_cover-Odocoileus_virginianus 0.9998 4645
## shrub_cover-Canis_latrans 1.0046 1322
## shrub_cover-Procyon_lotor 1.0076 3501
## shrub_cover-Dasypus_novemcinctus 1.0038 977
## shrub_cover-Lynx_rufus 1.0166 987
## shrub_cover-Didelphis_virginiana 1.0145 889
## shrub_cover-Sylvilagus_floridanus 1.0086 788
## shrub_cover-Meleagris_gallopavo 1.0187 764
## shrub_cover-Sciurus_carolinensis 1.0212 839
## veg_height-Odocoileus_virginianus 1.0003 5250
## veg_height-Canis_latrans 1.0027 2197
## veg_height-Procyon_lotor 1.0014 4160
## veg_height-Dasypus_novemcinctus 1.0003 3564
## veg_height-Lynx_rufus 1.0053 1961
## veg_height-Didelphis_virginiana 1.0013 2633
## veg_height-Sylvilagus_floridanus 1.0129 1721
## veg_height-Meleagris_gallopavo 1.0245 994
## veg_height-Sciurus_carolinensis 1.0057 2203
## week-Odocoileus_virginianus 1.0006 5027
## week-Canis_latrans 1.0001 3885
## week-Procyon_lotor 1.0007 4419
## week-Dasypus_novemcinctus 1.0005 4785
## week-Lynx_rufus 1.0011 2936
## week-Didelphis_virginiana 1.0023 2601
## week-Sylvilagus_floridanus 1.0035 2507
## week-Meleagris_gallopavo 1.0031 1136
## week-Sciurus_carolinensis 1.0007 3382
## I(week^2)-Odocoileus_virginianus 1.0022 4843
## I(week^2)-Canis_latrans 1.0012 4093
## I(week^2)-Procyon_lotor 1.0015 4065
## I(week^2)-Dasypus_novemcinctus 1.0006 4360
## I(week^2)-Lynx_rufus 1.0097 2146
## I(week^2)-Didelphis_virginiana 1.0037 1295
## I(week^2)-Sylvilagus_floridanus 0.9998 2406
## I(week^2)-Meleagris_gallopavo 1.0039 569
## I(week^2)-Sciurus_carolinensis 1.0000 3713
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T10<- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.8255
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2953 0.7984 -1.2710 0.2783 1.9237 1.0017 2177
## Tree_Density -0.8092 0.4537 -1.8283 -0.7763 0.0110 1.0026 1512
## Avg_Canopy_Cover 1.1760 0.4731 0.3055 1.1395 2.2371 1.0041 1874
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.2702 8.0196 1.1418 4.9154 27.6757 1.0231 340
## Tree_Density 0.9555 1.8325 0.0494 0.4211 5.0623 1.0117 1561
## Avg_Canopy_Cover 1.2632 1.5382 0.0975 0.8251 5.1081 1.0124 1603
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5087 0.7174 0.043 0.2872 2.2579 1.0393 488
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1683 0.5149 -3.1379 -2.1855 -1.0657 1.0008 5250
## shrub_cover 0.1958 0.2903 -0.3732 0.1922 0.7886 1.0031 4184
## veg_height 0.0244 0.1830 -0.3331 0.0250 0.3861 1.0000 4264
## week 0.3791 0.2479 -0.1191 0.3852 0.8520 1.0009 3677
## I(week^2) -0.3003 0.1142 -0.5399 -0.2972 -0.0867 1.0057 2315
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4830 1.7598 0.7951 2.0162 7.0769 1.0061 4818
## shrub_cover 0.6854 0.5447 0.1556 0.5392 2.1518 1.0087 2736
## veg_height 0.2461 0.1854 0.0664 0.1951 0.7393 1.0023 3245
## week 0.4143 0.3301 0.1055 0.3263 1.2687 1.0033 2958
## I(week^2) 0.0842 0.0663 0.0237 0.0656 0.2553 1.0168 1942
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.9296 2.0752 2.0748 4.5112 10.2351
## (Intercept)-Canis_latrans 0.4515 0.6389 -0.7538 0.4258 1.8109
## (Intercept)-Procyon_lotor 0.8566 0.6707 -0.4110 0.8310 2.2302
## (Intercept)-Dasypus_novemcinctus -0.9091 0.6446 -2.2511 -0.8771 0.2627
## (Intercept)-Lynx_rufus 1.9264 2.2150 -0.9234 1.4432 7.9863
## (Intercept)-Didelphis_virginiana -1.6608 0.7672 -3.2520 -1.6329 -0.2177
## (Intercept)-Sylvilagus_floridanus -0.5662 0.7703 -2.0723 -0.5770 0.9935
## (Intercept)-Meleagris_gallopavo 0.6567 1.3680 -1.3919 0.4695 3.8049
## (Intercept)-Sciurus_carolinensis -1.7534 0.8014 -3.4519 -1.7172 -0.2976
## Tree_Density-Odocoileus_virginianus -0.3999 0.7305 -1.6395 -0.4749 1.2681
## Tree_Density-Canis_latrans -0.9712 0.5889 -2.3588 -0.8995 0.0302
## Tree_Density-Procyon_lotor -0.5020 0.4376 -1.3590 -0.5040 0.3647
## Tree_Density-Dasypus_novemcinctus -1.4242 0.9376 -3.7955 -1.2118 -0.1785
## Tree_Density-Lynx_rufus -0.0554 0.9234 -1.5328 -0.1754 2.1183
## Tree_Density-Didelphis_virginiana -1.0682 0.8039 -3.0196 -0.9313 0.1416
## Tree_Density-Sylvilagus_floridanus -1.0954 0.8036 -3.0778 -0.9638 0.1609
## Tree_Density-Meleagris_gallopavo -1.0784 0.8592 -3.1743 -0.9714 0.3232
## Tree_Density-Sciurus_carolinensis -0.9496 0.7555 -2.7956 -0.8501 0.3030
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8126 0.8656 -0.9039 0.8161 2.6319
## Avg_Canopy_Cover-Canis_latrans -0.0809 0.5010 -1.0975 -0.0743 0.9004
## Avg_Canopy_Cover-Procyon_lotor 1.1241 0.5335 0.1713 1.0841 2.2976
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1164 0.4920 0.2589 1.0854 2.2059
## Avg_Canopy_Cover-Lynx_rufus 1.1162 1.0263 -0.7344 1.0261 3.3464
## Avg_Canopy_Cover-Didelphis_virginiana 1.6112 0.7097 0.5097 1.5137 3.2820
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.2239 1.0316 0.7152 2.0572 4.6827
## Avg_Canopy_Cover-Meleagris_gallopavo 1.6474 0.9397 0.1595 1.5206 3.9387
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5439 0.7150 0.4350 1.4430 3.2238
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0049 318
## (Intercept)-Canis_latrans 1.0004 3320
## (Intercept)-Procyon_lotor 1.0039 2542
## (Intercept)-Dasypus_novemcinctus 1.0010 2696
## (Intercept)-Lynx_rufus 1.0479 233
## (Intercept)-Didelphis_virginiana 1.0028 2394
## (Intercept)-Sylvilagus_floridanus 1.0036 2620
## (Intercept)-Meleagris_gallopavo 1.0034 611
## (Intercept)-Sciurus_carolinensis 1.0010 1941
## Tree_Density-Odocoileus_virginianus 1.0040 1772
## Tree_Density-Canis_latrans 1.0004 2431
## Tree_Density-Procyon_lotor 1.0024 3338
## Tree_Density-Dasypus_novemcinctus 1.0008 1238
## Tree_Density-Lynx_rufus 1.0255 851
## Tree_Density-Didelphis_virginiana 1.0036 1897
## Tree_Density-Sylvilagus_floridanus 1.0052 1851
## Tree_Density-Meleagris_gallopavo 1.0031 1454
## Tree_Density-Sciurus_carolinensis 1.0032 1786
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0067 3069
## Avg_Canopy_Cover-Canis_latrans 1.0020 2331
## Avg_Canopy_Cover-Procyon_lotor 1.0018 3427
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0017 3979
## Avg_Canopy_Cover-Lynx_rufus 1.0070 862
## Avg_Canopy_Cover-Didelphis_virginiana 1.0028 2135
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0090 1312
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0037 1383
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0053 1892
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5376 0.0803 0.3807 0.5388 0.6979
## (Intercept)-Canis_latrans -2.5751 0.2086 -3.0080 -2.5705 -2.1860
## (Intercept)-Procyon_lotor -2.1863 0.1650 -2.5175 -2.1807 -1.8771
## (Intercept)-Dasypus_novemcinctus -1.5925 0.1767 -1.9457 -1.5896 -1.2574
## (Intercept)-Lynx_rufus -3.8061 0.3741 -4.5289 -3.8096 -3.0601
## (Intercept)-Didelphis_virginiana -2.3822 0.3167 -3.0374 -2.3654 -1.8037
## (Intercept)-Sylvilagus_floridanus -3.0364 0.2957 -3.6561 -3.0254 -2.4890
## (Intercept)-Meleagris_gallopavo -4.0210 0.4799 -4.9783 -4.0169 -3.1024
## (Intercept)-Sciurus_carolinensis -2.4659 0.3379 -3.1577 -2.4563 -1.8185
## shrub_cover-Odocoileus_virginianus -0.0626 0.0674 -0.1959 -0.0638 0.0664
## shrub_cover-Canis_latrans -0.3253 0.2263 -0.7562 -0.3285 0.1223
## shrub_cover-Procyon_lotor 0.2464 0.1638 -0.0849 0.2519 0.5548
## shrub_cover-Dasypus_novemcinctus 0.8713 0.2976 0.2995 0.8667 1.4674
## shrub_cover-Lynx_rufus -0.3658 0.3193 -1.0274 -0.3548 0.2373
## shrub_cover-Didelphis_virginiana 0.9912 0.3667 0.3147 0.9720 1.7541
## shrub_cover-Sylvilagus_floridanus 0.4286 0.3934 -0.3357 0.4259 1.2282
## shrub_cover-Meleagris_gallopavo -0.8400 0.4113 -1.6586 -0.8284 -0.0600
## shrub_cover-Sciurus_carolinensis 0.8851 0.4091 0.1112 0.8764 1.7036
## veg_height-Odocoileus_virginianus -0.3332 0.0682 -0.4647 -0.3333 -0.1991
## veg_height-Canis_latrans -0.6083 0.1862 -0.9906 -0.6019 -0.2606
## veg_height-Procyon_lotor 0.3477 0.1234 0.1063 0.3475 0.5896
## veg_height-Dasypus_novemcinctus 0.2472 0.1364 -0.0215 0.2458 0.5161
## veg_height-Lynx_rufus 0.0925 0.2472 -0.4077 0.0946 0.5783
## veg_height-Didelphis_virginiana 0.4907 0.2471 0.0269 0.4797 0.9892
## veg_height-Sylvilagus_floridanus 0.1560 0.2403 -0.3272 0.1638 0.6249
## veg_height-Meleagris_gallopavo -0.2390 0.3441 -0.9424 -0.2354 0.4289
## veg_height-Sciurus_carolinensis 0.0925 0.2172 -0.3218 0.0893 0.5302
## week-Odocoileus_virginianus 1.3103 0.1254 1.0682 1.3090 1.5591
## week-Canis_latrans 0.6008 0.2667 0.0833 0.5954 1.1457
## week-Procyon_lotor 0.2078 0.2117 -0.2021 0.2047 0.6285
## week-Dasypus_novemcinctus 0.1151 0.2254 -0.3273 0.1176 0.5534
## week-Lynx_rufus 0.4091 0.3476 -0.2681 0.4070 1.0966
## week-Didelphis_virginiana 0.0877 0.3767 -0.6782 0.1010 0.8037
## week-Sylvilagus_floridanus 0.0660 0.3500 -0.6340 0.0686 0.7347
## week-Meleagris_gallopavo -0.1599 0.4241 -1.0582 -0.1431 0.6276
## week-Sciurus_carolinensis 0.8074 0.3639 0.1294 0.7982 1.5410
## I(week^2)-Odocoileus_virginianus -0.5407 0.0518 -0.6449 -0.5404 -0.4402
## I(week^2)-Canis_latrans -0.2492 0.1103 -0.4714 -0.2473 -0.0429
## I(week^2)-Procyon_lotor -0.1338 0.0927 -0.3192 -0.1316 0.0492
## I(week^2)-Dasypus_novemcinctus -0.1831 0.1050 -0.3941 -0.1817 0.0191
## I(week^2)-Lynx_rufus -0.2520 0.1543 -0.5665 -0.2470 0.0364
## I(week^2)-Didelphis_virginiana -0.4373 0.2298 -0.9516 -0.4135 -0.0638
## I(week^2)-Sylvilagus_floridanus -0.1797 0.1624 -0.5126 -0.1751 0.1260
## I(week^2)-Meleagris_gallopavo -0.4149 0.2435 -0.9590 -0.3902 -0.0137
## I(week^2)-Sciurus_carolinensis -0.2859 0.1450 -0.5742 -0.2823 -0.0125
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 4556
## (Intercept)-Canis_latrans 1.0071 2406
## (Intercept)-Procyon_lotor 1.0019 3699
## (Intercept)-Dasypus_novemcinctus 1.0008 4442
## (Intercept)-Lynx_rufus 1.0246 585
## (Intercept)-Didelphis_virginiana 1.0012 2430
## (Intercept)-Sylvilagus_floridanus 1.0018 2161
## (Intercept)-Meleagris_gallopavo 1.0009 639
## (Intercept)-Sciurus_carolinensis 1.0059 2202
## shrub_cover-Odocoileus_virginianus 0.9999 4834
## shrub_cover-Canis_latrans 1.0059 2680
## shrub_cover-Procyon_lotor 1.0018 3925
## shrub_cover-Dasypus_novemcinctus 1.0011 3652
## shrub_cover-Lynx_rufus 1.0020 1424
## shrub_cover-Didelphis_virginiana 1.0045 2107
## shrub_cover-Sylvilagus_floridanus 1.0020 2009
## shrub_cover-Meleagris_gallopavo 1.0002 777
## shrub_cover-Sciurus_carolinensis 1.0002 2103
## veg_height-Odocoileus_virginianus 1.0010 5250
## veg_height-Canis_latrans 1.0113 2448
## veg_height-Procyon_lotor 1.0024 4218
## veg_height-Dasypus_novemcinctus 1.0005 4782
## veg_height-Lynx_rufus 1.0038 1916
## veg_height-Didelphis_virginiana 1.0000 3218
## veg_height-Sylvilagus_floridanus 1.0014 2856
## veg_height-Meleagris_gallopavo 1.0012 1803
## veg_height-Sciurus_carolinensis 1.0001 2765
## week-Odocoileus_virginianus 0.9998 4578
## week-Canis_latrans 1.0020 3735
## week-Procyon_lotor 1.0021 3977
## week-Dasypus_novemcinctus 0.9999 4966
## week-Lynx_rufus 0.9999 2475
## week-Didelphis_virginiana 1.0003 2754
## week-Sylvilagus_floridanus 1.0006 3019
## week-Meleagris_gallopavo 1.0024 1199
## week-Sciurus_carolinensis 1.0005 4034
## I(week^2)-Odocoileus_virginianus 0.9998 4544
## I(week^2)-Canis_latrans 1.0010 3966
## I(week^2)-Procyon_lotor 1.0010 4306
## I(week^2)-Dasypus_novemcinctus 1.0002 4650
## I(week^2)-Lynx_rufus 1.0018 2139
## I(week^2)-Didelphis_virginiana 1.0046 1203
## I(week^2)-Sylvilagus_floridanus 1.0002 2477
## I(week^2)-Meleagris_gallopavo 1.0401 684
## I(week^2)-Sciurus_carolinensis 1.0008 4035
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.1182
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3570 0.6793 -0.9563 0.3510 1.7282 1.0045 2190
## Cogon_Patch_Size 0.0337 0.4046 -0.7748 0.0348 0.8282 1.0007 2274
## Avg_Cogongrass_Cover 0.1597 0.3500 -0.5224 0.1533 0.8550 1.0035 1189
## total_shrub_cover -0.7646 0.4720 -1.8010 -0.7293 0.0979 1.0016 1160
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2257 4.5529 0.4448 2.8881 15.9025 1.0374 1199
## Cogon_Patch_Size 0.7916 1.2990 0.0554 0.4170 3.7795 1.0099 1632
## Avg_Cogongrass_Cover 0.3917 0.5785 0.0400 0.2227 1.7509 1.0198 2342
## total_shrub_cover 0.9791 1.2999 0.0672 0.5585 4.3426 1.0081 1058
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2282 1.3354 0.0777 0.8246 4.6452 1.0245 439
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1523 0.4983 -3.1076 -2.1743 -1.1058 1.0013 5250
## shrub_cover 0.3651 0.2908 -0.2081 0.3581 0.9617 1.0066 2654
## veg_height -0.0060 0.1858 -0.3844 -0.0063 0.3603 1.0014 3547
## week 0.3722 0.2499 -0.1413 0.3816 0.8460 1.0004 3779
## I(week^2) -0.3013 0.1157 -0.5439 -0.2977 -0.0841 1.0046 2308
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3098 1.7187 0.7229 1.8605 6.5892 1.0022 3618
## shrub_cover 0.6800 0.5510 0.1347 0.5278 2.1152 1.0006 1824
## veg_height 0.2496 0.1966 0.0636 0.1961 0.7585 1.0041 3237
## week 0.4361 0.3684 0.1056 0.3449 1.3290 1.0037 3575
## I(week^2) 0.0882 0.0722 0.0237 0.0671 0.2807 1.0095 1777
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9128 1.7186 1.1031 3.7190
## (Intercept)-Canis_latrans 0.7195 0.7599 -0.6581 0.6795
## (Intercept)-Procyon_lotor 0.9352 0.7826 -0.5553 0.9173
## (Intercept)-Dasypus_novemcinctus -0.4342 0.7248 -1.9576 -0.4188
## (Intercept)-Lynx_rufus 0.3755 1.2124 -1.6306 0.2327
## (Intercept)-Didelphis_virginiana -0.9989 0.8127 -2.5517 -1.0243
## (Intercept)-Sylvilagus_floridanus 0.2667 0.9984 -1.5293 0.2126
## (Intercept)-Meleagris_gallopavo 0.0853 1.3815 -2.1244 -0.0707
## (Intercept)-Sciurus_carolinensis -1.0385 0.9030 -2.8169 -1.0428
## Cogon_Patch_Size-Odocoileus_virginianus 0.1313 0.7247 -1.1612 0.0865
## Cogon_Patch_Size-Canis_latrans 0.7273 0.7368 -0.3529 0.6003
## Cogon_Patch_Size-Procyon_lotor -0.1338 0.4580 -1.0738 -0.1251
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0005 0.4369 -0.8964 0.0036
## Cogon_Patch_Size-Lynx_rufus 0.0477 0.7735 -1.3908 0.0152
## Cogon_Patch_Size-Didelphis_virginiana 0.5815 0.4999 -0.2994 0.5444
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6170 0.8506 -2.6747 -0.4684
## Cogon_Patch_Size-Meleagris_gallopavo 0.0871 0.7453 -1.3063 0.0505
## Cogon_Patch_Size-Sciurus_carolinensis -0.5243 0.6877 -2.1647 -0.4204
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1553 0.5705 -0.9397 0.1478
## Avg_Cogongrass_Cover-Canis_latrans 0.3384 0.4661 -0.4850 0.3038
## Avg_Cogongrass_Cover-Procyon_lotor 0.0973 0.4671 -0.8291 0.0931
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3326 0.4076 -0.4369 0.3264
## Avg_Cogongrass_Cover-Lynx_rufus 0.4408 0.5559 -0.4916 0.3940
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1158 0.4610 -0.8096 0.1215
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1474 0.5679 -1.3464 -0.1150
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2288 0.7320 -1.8481 -0.1740
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3625 0.4455 -0.4729 0.3492
## total_shrub_cover-Odocoileus_virginianus -0.4082 0.7299 -1.8177 -0.4204
## total_shrub_cover-Canis_latrans 0.1154 0.7040 -1.0217 0.0192
## total_shrub_cover-Procyon_lotor -1.2538 0.6538 -2.7925 -1.1601
## total_shrub_cover-Dasypus_novemcinctus -0.4010 0.5660 -1.6665 -0.3577
## total_shrub_cover-Lynx_rufus -1.1875 0.9390 -3.2823 -1.0919
## total_shrub_cover-Didelphis_virginiana -0.7939 0.6298 -2.2579 -0.7279
## total_shrub_cover-Sylvilagus_floridanus -1.2576 0.9547 -3.6263 -1.1097
## total_shrub_cover-Meleagris_gallopavo -1.3252 0.8535 -3.3183 -1.2285
## total_shrub_cover-Sciurus_carolinensis -0.6946 0.6987 -2.3564 -0.6313
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.9493 1.0131 765
## (Intercept)-Canis_latrans 2.3525 1.0116 1944
## (Intercept)-Procyon_lotor 2.5865 1.0030 1927
## (Intercept)-Dasypus_novemcinctus 1.0043 1.0029 1569
## (Intercept)-Lynx_rufus 3.2629 1.0019 634
## (Intercept)-Didelphis_virginiana 0.7043 1.0064 1374
## (Intercept)-Sylvilagus_floridanus 2.4532 1.0078 976
## (Intercept)-Meleagris_gallopavo 3.2498 1.0357 534
## (Intercept)-Sciurus_carolinensis 0.7692 1.0086 1089
## Cogon_Patch_Size-Odocoileus_virginianus 1.7389 1.0062 2590
## Cogon_Patch_Size-Canis_latrans 2.6415 1.0034 2220
## Cogon_Patch_Size-Procyon_lotor 0.7483 1.0026 3189
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8551 1.0009 3491
## Cogon_Patch_Size-Lynx_rufus 1.7379 1.0011 1854
## Cogon_Patch_Size-Didelphis_virginiana 1.6415 0.9999 2602
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5964 1.0051 1723
## Cogon_Patch_Size-Meleagris_gallopavo 1.7069 1.0022 2206
## Cogon_Patch_Size-Sciurus_carolinensis 0.5282 1.0077 2231
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3305 1.0001 2664
## Avg_Cogongrass_Cover-Canis_latrans 1.3545 1.0080 2838
## Avg_Cogongrass_Cover-Procyon_lotor 1.0117 1.0028 2860
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1817 1.0066 3162
## Avg_Cogongrass_Cover-Lynx_rufus 1.6495 1.0092 2354
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9956 1.0007 2486
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8894 1.0011 1612
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0899 1.0013 1101
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2654 1.0072 2299
## total_shrub_cover-Odocoileus_virginianus 1.1320 1.0018 3009
## total_shrub_cover-Canis_latrans 1.8042 1.0067 1386
## total_shrub_cover-Procyon_lotor -0.2093 1.0011 1457
## total_shrub_cover-Dasypus_novemcinctus 0.5493 1.0015 1480
## total_shrub_cover-Lynx_rufus 0.4576 1.0004 933
## total_shrub_cover-Didelphis_virginiana 0.2666 1.0058 1251
## total_shrub_cover-Sylvilagus_floridanus 0.2079 1.0012 719
## total_shrub_cover-Meleagris_gallopavo 0.1114 1.0017 1055
## total_shrub_cover-Sciurus_carolinensis 0.5000 1.0193 968
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5391 0.0807 0.3782 0.5375 0.6972
## (Intercept)-Canis_latrans -2.5805 0.2112 -3.0099 -2.5727 -2.1913
## (Intercept)-Procyon_lotor -2.1873 0.1598 -2.5087 -2.1836 -1.8690
## (Intercept)-Dasypus_novemcinctus -1.6301 0.1913 -2.0167 -1.6249 -1.2779
## (Intercept)-Lynx_rufus -3.4521 0.3921 -4.2505 -3.4373 -2.7192
## (Intercept)-Didelphis_virginiana -2.3939 0.3287 -3.0810 -2.3810 -1.7988
## (Intercept)-Sylvilagus_floridanus -3.2134 0.3120 -3.8519 -3.2052 -2.6209
## (Intercept)-Meleagris_gallopavo -3.7322 0.6024 -4.9217 -3.7224 -2.5855
## (Intercept)-Sciurus_carolinensis -2.5641 0.3778 -3.3625 -2.5406 -1.8899
## shrub_cover-Odocoileus_virginianus -0.0580 0.0686 -0.1942 -0.0587 0.0788
## shrub_cover-Canis_latrans -0.2954 0.2454 -0.7802 -0.2943 0.1720
## shrub_cover-Procyon_lotor 0.3171 0.1614 -0.0077 0.3181 0.6316
## shrub_cover-Dasypus_novemcinctus 0.9727 0.3430 0.3528 0.9581 1.6929
## shrub_cover-Lynx_rufus 0.0289 0.3859 -0.7554 0.0394 0.7548
## shrub_cover-Didelphis_virginiana 1.0956 0.4022 0.3660 1.0816 1.9327
## shrub_cover-Sylvilagus_floridanus 0.7282 0.4235 -0.1438 0.7377 1.5409
## shrub_cover-Meleagris_gallopavo -0.5371 0.5021 -1.5353 -0.5344 0.4171
## shrub_cover-Sciurus_carolinensis 1.0646 0.4390 0.2047 1.0631 1.9158
## veg_height-Odocoileus_virginianus -0.3314 0.0687 -0.4670 -0.3312 -0.2006
## veg_height-Canis_latrans -0.6049 0.1870 -0.9798 -0.5997 -0.2488
## veg_height-Procyon_lotor 0.3408 0.1238 0.1007 0.3411 0.5851
## veg_height-Dasypus_novemcinctus 0.2511 0.1384 -0.0163 0.2502 0.5235
## veg_height-Lynx_rufus 0.0287 0.2431 -0.4514 0.0297 0.5037
## veg_height-Didelphis_virginiana 0.4258 0.2474 -0.0338 0.4187 0.9329
## veg_height-Sylvilagus_floridanus 0.0411 0.2505 -0.4421 0.0380 0.5414
## veg_height-Meleagris_gallopavo -0.2945 0.4035 -1.0841 -0.2997 0.5361
## veg_height-Sciurus_carolinensis 0.0976 0.2339 -0.3421 0.0895 0.5889
## week-Odocoileus_virginianus 1.3148 0.1271 1.0617 1.3153 1.5625
## week-Canis_latrans 0.6036 0.2672 0.0988 0.5958 1.1472
## week-Procyon_lotor 0.2074 0.2153 -0.2122 0.2092 0.6326
## week-Dasypus_novemcinctus 0.1128 0.2281 -0.3242 0.1123 0.5514
## week-Lynx_rufus 0.3941 0.3520 -0.2978 0.3912 1.0854
## week-Didelphis_virginiana 0.0793 0.3918 -0.7328 0.0913 0.8312
## week-Sylvilagus_floridanus 0.0794 0.3437 -0.5975 0.0907 0.7399
## week-Meleagris_gallopavo -0.2053 0.4383 -1.1266 -0.1820 0.6067
## week-Sciurus_carolinensis 0.8184 0.3735 0.1396 0.8044 1.6080
## I(week^2)-Odocoileus_virginianus -0.5421 0.0526 -0.6458 -0.5420 -0.4410
## I(week^2)-Canis_latrans -0.2493 0.1086 -0.4669 -0.2470 -0.0404
## I(week^2)-Procyon_lotor -0.1335 0.0923 -0.3132 -0.1341 0.0484
## I(week^2)-Dasypus_novemcinctus -0.1852 0.1063 -0.3972 -0.1862 0.0233
## I(week^2)-Lynx_rufus -0.2454 0.1551 -0.5685 -0.2413 0.0506
## I(week^2)-Didelphis_virginiana -0.4513 0.2328 -0.9987 -0.4232 -0.0648
## I(week^2)-Sylvilagus_floridanus -0.1896 0.1635 -0.5296 -0.1848 0.1121
## I(week^2)-Meleagris_gallopavo -0.4392 0.2572 -1.0308 -0.4095 -0.0257
## I(week^2)-Sciurus_carolinensis -0.2942 0.1465 -0.5883 -0.2906 -0.0164
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0040 2213
## (Intercept)-Procyon_lotor 1.0000 4040
## (Intercept)-Dasypus_novemcinctus 0.9998 2679
## (Intercept)-Lynx_rufus 1.0018 806
## (Intercept)-Didelphis_virginiana 1.0086 1572
## (Intercept)-Sylvilagus_floridanus 1.0034 1446
## (Intercept)-Meleagris_gallopavo 1.0049 541
## (Intercept)-Sciurus_carolinensis 1.0108 1096
## shrub_cover-Odocoileus_virginianus 1.0044 5250
## shrub_cover-Canis_latrans 1.0000 1788
## shrub_cover-Procyon_lotor 1.0000 4041
## shrub_cover-Dasypus_novemcinctus 1.0016 1369
## shrub_cover-Lynx_rufus 1.0006 1002
## shrub_cover-Didelphis_virginiana 1.0062 1194
## shrub_cover-Sylvilagus_floridanus 1.0055 770
## shrub_cover-Meleagris_gallopavo 1.0045 676
## shrub_cover-Sciurus_carolinensis 1.0120 1133
## veg_height-Odocoileus_virginianus 1.0021 5250
## veg_height-Canis_latrans 1.0111 2123
## veg_height-Procyon_lotor 1.0049 4303
## veg_height-Dasypus_novemcinctus 0.9998 4317
## veg_height-Lynx_rufus 1.0007 2326
## veg_height-Didelphis_virginiana 1.0020 3383
## veg_height-Sylvilagus_floridanus 1.0049 1855
## veg_height-Meleagris_gallopavo 1.0012 1137
## veg_height-Sciurus_carolinensis 1.0070 2027
## week-Odocoileus_virginianus 1.0032 5000
## week-Canis_latrans 1.0018 4027
## week-Procyon_lotor 1.0012 4309
## week-Dasypus_novemcinctus 1.0001 4751
## week-Lynx_rufus 1.0047 2682
## week-Didelphis_virginiana 1.0011 2618
## week-Sylvilagus_floridanus 1.0011 2562
## week-Meleagris_gallopavo 1.0023 1219
## week-Sciurus_carolinensis 1.0014 3543
## I(week^2)-Odocoileus_virginianus 1.0008 4657
## I(week^2)-Canis_latrans 1.0000 3799
## I(week^2)-Procyon_lotor 1.0004 4078
## I(week^2)-Dasypus_novemcinctus 1.0001 4234
## I(week^2)-Lynx_rufus 1.0027 2285
## I(week^2)-Didelphis_virginiana 1.0036 1113
## I(week^2)-Sylvilagus_floridanus 1.0062 1932
## I(week^2)-Meleagris_gallopavo 1.0073 618
## I(week^2)-Sciurus_carolinensis 1.0016 3807
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0112
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2918 0.6761 -0.9860 0.2612 1.6955 1.0076 1869
## Veg_shannon_index 0.3893 0.2822 -0.1673 0.3861 0.9540 1.0009 2437
## Avg_Cogongrass_Cover 0.3977 0.2957 -0.1747 0.4011 0.9770 1.0008 2015
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.4101 5.4296 0.6222 2.9639 17.4823 1.1073 357
## Veg_shannon_index 0.2935 0.4138 0.0367 0.1825 1.2200 1.0492 2954
## Avg_Cogongrass_Cover 0.3228 0.4358 0.0373 0.1913 1.4243 1.0038 2318
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6362 0.7332 0.0487 0.3967 2.6564 1.0021 629
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1528 0.5166 -3.1059 -2.1741 -1.0576 1.0007 5250
## shrub_cover 0.1510 0.2852 -0.4196 0.1481 0.7307 1.0011 3930
## veg_height -0.0159 0.1797 -0.3619 -0.0156 0.3468 1.0022 3865
## week 0.3703 0.2438 -0.1312 0.3736 0.8369 1.0009 3436
## I(week^2) -0.3009 0.1155 -0.5354 -0.2989 -0.0828 1.0011 2515
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5115 1.7611 0.7950 2.0332 7.0312 1.0040 3557
## shrub_cover 0.6576 0.5245 0.1434 0.5154 2.0254 1.0042 2410
## veg_height 0.2433 0.1978 0.0629 0.1900 0.7314 1.0117 3893
## week 0.4264 0.3637 0.1023 0.3328 1.2817 1.0195 3544
## I(week^2) 0.0892 0.0753 0.0240 0.0679 0.2854 0.9999 1304
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9001 1.7431 1.4048 3.6267
## (Intercept)-Canis_latrans 0.4744 0.6195 -0.6978 0.4532
## (Intercept)-Procyon_lotor 0.6499 0.6089 -0.5961 0.6614
## (Intercept)-Dasypus_novemcinctus -0.5906 0.5619 -1.7359 -0.5823
## (Intercept)-Lynx_rufus 0.6037 1.5566 -1.2769 0.3680
## (Intercept)-Didelphis_virginiana -1.2716 0.6589 -2.6214 -1.2653
## (Intercept)-Sylvilagus_floridanus -0.2716 0.7333 -1.6065 -0.2971
## (Intercept)-Meleagris_gallopavo 1.0452 1.4112 -1.0631 0.8383
## (Intercept)-Sciurus_carolinensis -1.2777 0.6765 -2.6762 -1.2677
## Veg_shannon_index-Odocoileus_virginianus 0.3241 0.5070 -0.7177 0.3435
## Veg_shannon_index-Canis_latrans 0.6563 0.4007 -0.0509 0.6258
## Veg_shannon_index-Procyon_lotor 0.4682 0.3715 -0.2124 0.4540
## Veg_shannon_index-Dasypus_novemcinctus 0.2184 0.3440 -0.4938 0.2244
## Veg_shannon_index-Lynx_rufus 0.2774 0.5301 -0.8137 0.3033
## Veg_shannon_index-Didelphis_virginiana 0.5138 0.3943 -0.2192 0.4881
## Veg_shannon_index-Sylvilagus_floridanus 0.4840 0.4379 -0.2934 0.4512
## Veg_shannon_index-Meleagris_gallopavo 0.5474 0.5274 -0.4309 0.4996
## Veg_shannon_index-Sciurus_carolinensis 0.0196 0.4105 -0.8832 0.0583
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3861 0.5369 -0.7042 0.3776
## Avg_Cogongrass_Cover-Canis_latrans 0.6640 0.4238 -0.0376 0.6240
## Avg_Cogongrass_Cover-Procyon_lotor 0.4291 0.3932 -0.3015 0.4161
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4740 0.3452 -0.1939 0.4660
## Avg_Cogongrass_Cover-Lynx_rufus 0.5908 0.4538 -0.2229 0.5561
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4893 0.3772 -0.2069 0.4808
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0243 0.4708 -1.0364 0.0138
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1419 0.6676 -1.4136 0.1924
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4569 0.3686 -0.2479 0.4516
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.9164 1.0514 370
## (Intercept)-Canis_latrans 1.7627 1.0011 3280
## (Intercept)-Procyon_lotor 1.8329 1.0011 2832
## (Intercept)-Dasypus_novemcinctus 0.5061 1.0029 4006
## (Intercept)-Lynx_rufus 3.8048 1.0681 313
## (Intercept)-Didelphis_virginiana 0.0183 1.0028 2684
## (Intercept)-Sylvilagus_floridanus 1.3117 1.0043 2055
## (Intercept)-Meleagris_gallopavo 4.6508 1.0107 575
## (Intercept)-Sciurus_carolinensis 0.0304 1.0043 2885
## Veg_shannon_index-Odocoileus_virginianus 1.3047 1.0009 3478
## Veg_shannon_index-Canis_latrans 1.5506 1.0012 2920
## Veg_shannon_index-Procyon_lotor 1.2404 1.0017 2991
## Veg_shannon_index-Dasypus_novemcinctus 0.8764 1.0008 4240
## Veg_shannon_index-Lynx_rufus 1.2708 1.0080 2614
## Veg_shannon_index-Didelphis_virginiana 1.3711 1.0022 3981
## Veg_shannon_index-Sylvilagus_floridanus 1.4684 1.0073 2900
## Veg_shannon_index-Meleagris_gallopavo 1.6915 1.0014 2460
## Veg_shannon_index-Sciurus_carolinensis 0.7398 1.0009 3563
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4662 1.0013 3144
## Avg_Cogongrass_Cover-Canis_latrans 1.6160 1.0010 3441
## Avg_Cogongrass_Cover-Procyon_lotor 1.2732 0.9998 3455
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1754 1.0002 3760
## Avg_Cogongrass_Cover-Lynx_rufus 1.5807 1.0056 3061
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2338 1.0004 3544
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8142 1.0026 2456
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.3362 1.0036 1428
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2002 1.0004 3806
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5392 0.0799 0.3848 0.5383 0.7017
## (Intercept)-Canis_latrans -2.5527 0.2012 -2.9642 -2.5460 -2.1797
## (Intercept)-Procyon_lotor -2.1860 0.1670 -2.5369 -2.1769 -1.8775
## (Intercept)-Dasypus_novemcinctus -1.5791 0.1787 -1.9426 -1.5722 -1.2487
## (Intercept)-Lynx_rufus -3.6040 0.3980 -4.3948 -3.6028 -2.8341
## (Intercept)-Didelphis_virginiana -2.3028 0.3101 -2.9443 -2.2933 -1.7410
## (Intercept)-Sylvilagus_floridanus -3.0997 0.3389 -3.8112 -3.0855 -2.4773
## (Intercept)-Meleagris_gallopavo -4.1555 0.4855 -5.1025 -4.1541 -3.2026
## (Intercept)-Sciurus_carolinensis -2.3971 0.3370 -3.1080 -2.3856 -1.7815
## shrub_cover-Odocoileus_virginianus -0.0600 0.0676 -0.1955 -0.0604 0.0713
## shrub_cover-Canis_latrans -0.3010 0.2134 -0.7204 -0.3006 0.1187
## shrub_cover-Procyon_lotor 0.2345 0.1766 -0.1400 0.2418 0.5632
## shrub_cover-Dasypus_novemcinctus 0.8297 0.2958 0.2697 0.8257 1.4339
## shrub_cover-Lynx_rufus -0.3322 0.3539 -1.0587 -0.3298 0.3706
## shrub_cover-Didelphis_virginiana 0.9450 0.3663 0.2772 0.9261 1.7000
## shrub_cover-Sylvilagus_floridanus 0.2304 0.4108 -0.5179 0.2183 1.0771
## shrub_cover-Meleagris_gallopavo -0.9157 0.4032 -1.7156 -0.9047 -0.1499
## shrub_cover-Sciurus_carolinensis 0.8168 0.4184 0.0061 0.8041 1.6726
## veg_height-Odocoileus_virginianus -0.3322 0.0678 -0.4684 -0.3315 -0.2001
## veg_height-Canis_latrans -0.6096 0.1836 -0.9738 -0.6040 -0.2582
## veg_height-Procyon_lotor 0.3363 0.1231 0.0998 0.3353 0.5760
## veg_height-Dasypus_novemcinctus 0.2356 0.1355 -0.0273 0.2357 0.5040
## veg_height-Lynx_rufus -0.0074 0.2541 -0.5213 -0.0030 0.4829
## veg_height-Didelphis_virginiana 0.4129 0.2354 -0.0429 0.4080 0.8814
## veg_height-Sylvilagus_floridanus 0.1217 0.2496 -0.3735 0.1232 0.6029
## veg_height-Meleagris_gallopavo -0.3457 0.3540 -1.0325 -0.3486 0.3697
## veg_height-Sciurus_carolinensis 0.0453 0.2140 -0.3539 0.0393 0.4825
## week-Odocoileus_virginianus 1.3106 0.1250 1.0651 1.3099 1.5624
## week-Canis_latrans 0.6003 0.2675 0.0789 0.5974 1.1474
## week-Procyon_lotor 0.2089 0.2110 -0.2023 0.2090 0.6217
## week-Dasypus_novemcinctus 0.1167 0.2264 -0.3189 0.1190 0.5652
## week-Lynx_rufus 0.3984 0.3504 -0.2992 0.4072 1.0744
## week-Didelphis_virginiana 0.0889 0.3768 -0.6892 0.0985 0.7921
## week-Sylvilagus_floridanus 0.0758 0.3497 -0.6232 0.0750 0.7582
## week-Meleagris_gallopavo -0.1779 0.4269 -1.0979 -0.1528 0.6068
## week-Sciurus_carolinensis 0.8185 0.3706 0.1300 0.8129 1.5773
## I(week^2)-Odocoileus_virginianus -0.5413 0.0510 -0.6425 -0.5407 -0.4434
## I(week^2)-Canis_latrans -0.2496 0.1101 -0.4678 -0.2488 -0.0332
## I(week^2)-Procyon_lotor -0.1352 0.0917 -0.3140 -0.1347 0.0438
## I(week^2)-Dasypus_novemcinctus -0.1858 0.1039 -0.3976 -0.1837 0.0110
## I(week^2)-Lynx_rufus -0.2512 0.1603 -0.5824 -0.2448 0.0506
## I(week^2)-Didelphis_virginiana -0.4493 0.2297 -0.9754 -0.4252 -0.0767
## I(week^2)-Sylvilagus_floridanus -0.1866 0.1665 -0.5194 -0.1820 0.1168
## I(week^2)-Meleagris_gallopavo -0.4415 0.2663 -1.0483 -0.4083 -0.0055
## I(week^2)-Sciurus_carolinensis -0.2903 0.1464 -0.5966 -0.2862 -0.0184
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0067 2740
## (Intercept)-Procyon_lotor 1.0011 3838
## (Intercept)-Dasypus_novemcinctus 1.0015 4236
## (Intercept)-Lynx_rufus 1.0091 718
## (Intercept)-Didelphis_virginiana 1.0024 2909
## (Intercept)-Sylvilagus_floridanus 1.0022 1507
## (Intercept)-Meleagris_gallopavo 1.0023 601
## (Intercept)-Sciurus_carolinensis 1.0051 2758
## shrub_cover-Odocoileus_virginianus 1.0012 5250
## shrub_cover-Canis_latrans 1.0035 2989
## shrub_cover-Procyon_lotor 1.0017 3147
## shrub_cover-Dasypus_novemcinctus 1.0008 3881
## shrub_cover-Lynx_rufus 1.0017 1292
## shrub_cover-Didelphis_virginiana 1.0005 2374
## shrub_cover-Sylvilagus_floridanus 1.0014 1720
## shrub_cover-Meleagris_gallopavo 1.0032 681
## shrub_cover-Sciurus_carolinensis 1.0017 2333
## veg_height-Odocoileus_virginianus 0.9998 5250
## veg_height-Canis_latrans 1.0032 2377
## veg_height-Procyon_lotor 1.0025 4230
## veg_height-Dasypus_novemcinctus 1.0001 4664
## veg_height-Lynx_rufus 1.0022 2204
## veg_height-Didelphis_virginiana 1.0052 3641
## veg_height-Sylvilagus_floridanus 1.0044 2506
## veg_height-Meleagris_gallopavo 1.0047 1093
## veg_height-Sciurus_carolinensis 1.0009 3781
## week-Odocoileus_virginianus 1.0006 4637
## week-Canis_latrans 1.0019 3246
## week-Procyon_lotor 1.0000 4692
## week-Dasypus_novemcinctus 1.0026 5250
## week-Lynx_rufus 1.0028 2626
## week-Didelphis_virginiana 1.0013 2867
## week-Sylvilagus_floridanus 1.0008 3199
## week-Meleagris_gallopavo 1.0204 1031
## week-Sciurus_carolinensis 1.0004 4041
## I(week^2)-Odocoileus_virginianus 1.0023 4732
## I(week^2)-Canis_latrans 1.0037 3745
## I(week^2)-Procyon_lotor 1.0008 4716
## I(week^2)-Dasypus_novemcinctus 1.0005 4302
## I(week^2)-Lynx_rufus 1.0048 1853
## I(week^2)-Didelphis_virginiana 1.0232 1419
## I(week^2)-Sylvilagus_floridanus 1.0000 2416
## I(week^2)-Meleagris_gallopavo 1.0072 507
## I(week^2)-Sciurus_carolinensis 1.0007 4321
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.9775
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2663 0.6477 -0.9816 0.2560 1.6079 0.9998 2585
## Avg_Cogongrass_Cover 0.2542 0.2809 -0.2886 0.2571 0.7915 1.0027 2317
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7915 4.6544 0.5741 2.6781 13.1025 1.0575 1730
## Avg_Cogongrass_Cover 0.3114 0.3800 0.0359 0.1883 1.3986 1.0039 2252
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5792 0.7269 0.0464 0.3379 2.6584 1.0646 444
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1598 0.5131 -3.1239 -2.1826 -1.1077 1.0004 5513
## shrub_cover 0.1693 0.2799 -0.3904 0.1674 0.7460 1.0016 3676
## veg_height -0.0178 0.1806 -0.3762 -0.0165 0.3355 1.0002 3940
## week 0.3777 0.2439 -0.1169 0.3820 0.8644 1.0013 3753
## I(week^2) -0.2986 0.1140 -0.5345 -0.2982 -0.0781 1.0013 2961
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4898 1.9677 0.8139 2.0375 6.8087 1.0273 3493
## shrub_cover 0.6680 0.5347 0.1473 0.5152 2.1219 1.0018 2733
## veg_height 0.2521 0.1948 0.0636 0.2004 0.7599 1.0091 3039
## week 0.4224 0.3497 0.1043 0.3284 1.2644 1.0175 3432
## I(week^2) 0.0881 0.0767 0.0241 0.0673 0.2786 1.0215 1567
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6290 1.4500 1.2925 3.4301
## (Intercept)-Canis_latrans 0.4989 0.6158 -0.7115 0.4908
## (Intercept)-Procyon_lotor 0.6438 0.5763 -0.5331 0.6524
## (Intercept)-Dasypus_novemcinctus -0.5711 0.5622 -1.6846 -0.5623
## (Intercept)-Lynx_rufus 0.4403 1.1743 -1.2540 0.2546
## (Intercept)-Didelphis_virginiana -1.1574 0.6309 -2.4155 -1.1594
## (Intercept)-Sylvilagus_floridanus -0.2542 0.6836 -1.5308 -0.2666
## (Intercept)-Meleagris_gallopavo 1.0379 1.4788 -1.0945 0.7827
## (Intercept)-Sciurus_carolinensis -1.2372 0.6436 -2.5717 -1.2309
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2304 0.5044 -0.8043 0.2334
## Avg_Cogongrass_Cover-Canis_latrans 0.4800 0.4007 -0.2223 0.4499
## Avg_Cogongrass_Cover-Procyon_lotor 0.2558 0.3595 -0.4180 0.2487
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3710 0.3223 -0.2617 0.3652
## Avg_Cogongrass_Cover-Lynx_rufus 0.4637 0.4367 -0.3043 0.4306
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3461 0.3665 -0.3556 0.3437
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1477 0.4569 -1.1348 -0.1190
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0522 0.6485 -1.5478 0.0108
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3736 0.3526 -0.3048 0.3706
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0570 1.0099 1040
## (Intercept)-Canis_latrans 1.7376 1.0021 2479
## (Intercept)-Procyon_lotor 1.7303 1.0159 2723
## (Intercept)-Dasypus_novemcinctus 0.5104 1.0027 3558
## (Intercept)-Lynx_rufus 3.4001 1.0027 628
## (Intercept)-Didelphis_virginiana 0.0995 1.0017 2579
## (Intercept)-Sylvilagus_floridanus 1.1876 1.0077 2169
## (Intercept)-Meleagris_gallopavo 4.6072 1.0075 420
## (Intercept)-Sciurus_carolinensis 0.0174 1.0006 2711
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2409 1.0013 3604
## Avg_Cogongrass_Cover-Canis_latrans 1.3620 1.0010 3672
## Avg_Cogongrass_Cover-Procyon_lotor 0.9855 1.0002 3888
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0183 1.0010 4444
## Avg_Cogongrass_Cover-Lynx_rufus 1.4231 1.0016 3013
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0931 1.0008 4163
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6602 1.0047 2411
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.1002 1.0073 1373
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0706 1.0018 4192
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5392 0.0814 0.3756 0.5397 0.6952
## (Intercept)-Canis_latrans -2.5782 0.2070 -3.0005 -2.5709 -2.1899
## (Intercept)-Procyon_lotor -2.1773 0.1648 -2.5063 -2.1729 -1.8704
## (Intercept)-Dasypus_novemcinctus -1.5814 0.1796 -1.9473 -1.5788 -1.2379
## (Intercept)-Lynx_rufus -3.5854 0.3894 -4.3625 -3.5821 -2.8461
## (Intercept)-Didelphis_virginiana -2.3209 0.3082 -2.9628 -2.3116 -1.7338
## (Intercept)-Sylvilagus_floridanus -3.0904 0.3262 -3.7725 -3.0729 -2.4990
## (Intercept)-Meleagris_gallopavo -4.1483 0.5140 -5.1847 -4.1513 -3.1480
## (Intercept)-Sciurus_carolinensis -2.4021 0.3271 -3.0679 -2.3945 -1.7782
## shrub_cover-Odocoileus_virginianus -0.0616 0.0679 -0.1949 -0.0614 0.0720
## shrub_cover-Canis_latrans -0.3021 0.2171 -0.7405 -0.3004 0.1210
## shrub_cover-Procyon_lotor 0.2444 0.1687 -0.0990 0.2494 0.5631
## shrub_cover-Dasypus_novemcinctus 0.8349 0.2949 0.2821 0.8256 1.4285
## shrub_cover-Lynx_rufus -0.3100 0.3569 -1.0411 -0.3060 0.3870
## shrub_cover-Didelphis_virginiana 0.9501 0.3611 0.3090 0.9375 1.7248
## shrub_cover-Sylvilagus_floridanus 0.2527 0.4263 -0.5330 0.2337 1.1252
## shrub_cover-Meleagris_gallopavo -0.9026 0.4182 -1.7580 -0.8832 -0.1118
## shrub_cover-Sciurus_carolinensis 0.8254 0.4071 0.0477 0.8185 1.6455
## veg_height-Odocoileus_virginianus -0.3341 0.0676 -0.4639 -0.3340 -0.2021
## veg_height-Canis_latrans -0.6210 0.1901 -1.0155 -0.6150 -0.2648
## veg_height-Procyon_lotor 0.3378 0.1220 0.1044 0.3373 0.5799
## veg_height-Dasypus_novemcinctus 0.2291 0.1348 -0.0319 0.2280 0.4988
## veg_height-Lynx_rufus -0.0050 0.2473 -0.5062 0.0008 0.4655
## veg_height-Didelphis_virginiana 0.4209 0.2459 -0.0450 0.4097 0.9262
## veg_height-Sylvilagus_floridanus 0.1249 0.2527 -0.3756 0.1235 0.6253
## veg_height-Meleagris_gallopavo -0.3482 0.3723 -1.0813 -0.3517 0.3723
## veg_height-Sciurus_carolinensis 0.0540 0.2145 -0.3514 0.0495 0.4973
## week-Odocoileus_virginianus 1.3129 0.1263 1.0699 1.3108 1.5623
## week-Canis_latrans 0.5979 0.2658 0.0854 0.5954 1.1301
## week-Procyon_lotor 0.2124 0.2132 -0.1982 0.2116 0.6328
## week-Dasypus_novemcinctus 0.1177 0.2291 -0.3456 0.1141 0.5630
## week-Lynx_rufus 0.3947 0.3550 -0.2965 0.3943 1.0933
## week-Didelphis_virginiana 0.0902 0.3809 -0.6966 0.1031 0.7857
## week-Sylvilagus_floridanus 0.0747 0.3422 -0.5975 0.0743 0.7566
## week-Meleagris_gallopavo -0.1857 0.4304 -1.0809 -0.1617 0.5955
## week-Sciurus_carolinensis 0.8151 0.3706 0.1281 0.8040 1.5852
## I(week^2)-Odocoileus_virginianus -0.5418 0.0518 -0.6434 -0.5412 -0.4423
## I(week^2)-Canis_latrans -0.2483 0.1079 -0.4624 -0.2471 -0.0381
## I(week^2)-Procyon_lotor -0.1360 0.0915 -0.3155 -0.1356 0.0380
## I(week^2)-Dasypus_novemcinctus -0.1836 0.1059 -0.3963 -0.1822 0.0210
## I(week^2)-Lynx_rufus -0.2404 0.1563 -0.5601 -0.2407 0.0553
## I(week^2)-Didelphis_virginiana -0.4423 0.2316 -0.9588 -0.4170 -0.0567
## I(week^2)-Sylvilagus_floridanus -0.1781 0.1611 -0.4991 -0.1763 0.1274
## I(week^2)-Meleagris_gallopavo -0.4448 0.2620 -1.0730 -0.4163 -0.0205
## I(week^2)-Sciurus_carolinensis -0.2882 0.1465 -0.5873 -0.2845 -0.0086
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0022 2651
## (Intercept)-Procyon_lotor 1.0031 3865
## (Intercept)-Dasypus_novemcinctus 1.0002 4494
## (Intercept)-Lynx_rufus 1.0025 919
## (Intercept)-Didelphis_virginiana 1.0023 2439
## (Intercept)-Sylvilagus_floridanus 1.0064 1802
## (Intercept)-Meleagris_gallopavo 1.0072 471
## (Intercept)-Sciurus_carolinensis 1.0005 2580
## shrub_cover-Odocoileus_virginianus 1.0002 5250
## shrub_cover-Canis_latrans 1.0008 2866
## shrub_cover-Procyon_lotor 1.0045 4010
## shrub_cover-Dasypus_novemcinctus 1.0000 3518
## shrub_cover-Lynx_rufus 1.0046 1377
## shrub_cover-Didelphis_virginiana 0.9999 2063
## shrub_cover-Sylvilagus_floridanus 1.0056 1669
## shrub_cover-Meleagris_gallopavo 1.0064 604
## shrub_cover-Sciurus_carolinensis 1.0014 2566
## veg_height-Odocoileus_virginianus 0.9999 5250
## veg_height-Canis_latrans 1.0060 2454
## veg_height-Procyon_lotor 1.0006 4649
## veg_height-Dasypus_novemcinctus 1.0008 4428
## veg_height-Lynx_rufus 1.0051 2301
## veg_height-Didelphis_virginiana 1.0017 3510
## veg_height-Sylvilagus_floridanus 1.0018 2474
## veg_height-Meleagris_gallopavo 1.0035 1192
## veg_height-Sciurus_carolinensis 1.0020 3307
## week-Odocoileus_virginianus 1.0008 5002
## week-Canis_latrans 1.0010 3770
## week-Procyon_lotor 1.0026 5157
## week-Dasypus_novemcinctus 1.0015 4819
## week-Lynx_rufus 1.0017 2883
## week-Didelphis_virginiana 1.0113 2531
## week-Sylvilagus_floridanus 1.0065 3149
## week-Meleagris_gallopavo 1.0116 1206
## week-Sciurus_carolinensis 1.0019 4058
## I(week^2)-Odocoileus_virginianus 1.0006 4722
## I(week^2)-Canis_latrans 1.0010 4073
## I(week^2)-Procyon_lotor 1.0014 4587
## I(week^2)-Dasypus_novemcinctus 1.0005 4180
## I(week^2)-Lynx_rufus 1.0019 2242
## I(week^2)-Didelphis_virginiana 1.0087 1406
## I(week^2)-Sylvilagus_floridanus 1.0043 2700
## I(week^2)-Meleagris_gallopavo 1.0344 476
## I(week^2)-Sciurus_carolinensis 1.0030 4629
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.962
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3699 0.6792 -1.6431 -0.3940 1.0706 1.0011 2236
## Avg_Cogongrass_Cover -0.6302 0.4207 -1.4886 -0.6232 0.1765 1.0191 1486
## I(Avg_Cogongrass_Cover^2) 0.8897 0.3820 0.2236 0.8606 1.7074 1.0171 1201
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0465 4.0055 0.6309 2.9495 14.0541 1.0157 1667
## Avg_Cogongrass_Cover 0.4932 0.6755 0.0422 0.2706 2.3711 1.0184 1852
## I(Avg_Cogongrass_Cover^2) 0.5040 0.9175 0.0379 0.2286 2.8632 1.0721 806
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4595 0.6269 0.0431 0.2715 1.9864 1.029 567
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1701 0.5147 -3.1439 -2.1845 -1.0749 1.0006 4848
## shrub_cover 0.1568 0.2954 -0.4360 0.1557 0.7530 1.0045 4088
## veg_height 0.0034 0.1815 -0.3642 0.0046 0.3681 1.0054 3385
## week 0.3785 0.2458 -0.1240 0.3864 0.8451 1.0068 3475
## I(week^2) -0.3007 0.1148 -0.5345 -0.2993 -0.0719 1.0040 3218
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4900 1.8613 0.7997 2.0176 6.7816 1.0062 4329
## shrub_cover 0.6817 0.6316 0.1418 0.5242 2.1607 1.0297 2525
## veg_height 0.2389 0.1791 0.0631 0.1898 0.7037 1.0004 3856
## week 0.4262 0.3533 0.1032 0.3299 1.3932 1.0067 2871
## I(week^2) 0.0860 0.0772 0.0241 0.0664 0.2642 1.0361 2153
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.9649 1.4513 0.6613 2.7881
## (Intercept)-Canis_latrans -0.3597 0.6650 -1.6963 -0.3595
## (Intercept)-Procyon_lotor -0.0659 0.6261 -1.3085 -0.0609
## (Intercept)-Dasypus_novemcinctus -1.2446 0.6110 -2.5201 -1.2213
## (Intercept)-Lynx_rufus -0.7982 1.0101 -2.5285 -0.8872
## (Intercept)-Didelphis_virginiana -1.7322 0.7203 -3.1678 -1.7235
## (Intercept)-Sylvilagus_floridanus -0.9602 0.7551 -2.4816 -0.9602
## (Intercept)-Meleagris_gallopavo 0.6765 1.4833 -1.4797 0.4333
## (Intercept)-Sciurus_carolinensis -2.2293 0.7989 -3.9263 -2.1942
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.6559 0.6949 -2.1088 -0.6382
## Avg_Cogongrass_Cover-Canis_latrans -0.2536 0.5710 -1.2655 -0.2954
## Avg_Cogongrass_Cover-Procyon_lotor -0.6119 0.5310 -1.6565 -0.6175
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4642 0.4969 -1.4090 -0.4784
## Avg_Cogongrass_Cover-Lynx_rufus -0.5986 0.6213 -1.8456 -0.5878
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3965 0.5582 -1.4360 -0.4195
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1421 0.6896 -2.7372 -1.0688
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8713 0.8006 -2.7011 -0.7996
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7375 0.5791 -1.9631 -0.7238
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1699 0.7765 0.0438 1.0359
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2407 0.7525 0.2120 1.0992
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0396 0.5757 0.1780 0.9568
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7310 0.3821 0.0248 0.7211
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1845 0.6022 0.2576 1.0975
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5934 0.4515 -0.2391 0.5680
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7295 0.4796 -0.1131 0.7002
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.5793 0.8122 -1.0175 0.5636
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9752 0.4324 0.2058 0.9502
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.3665 1.0058 1055
## (Intercept)-Canis_latrans 0.9357 1.0033 3163
## (Intercept)-Procyon_lotor 1.1635 1.0015 2601
## (Intercept)-Dasypus_novemcinctus -0.0732 1.0021 3602
## (Intercept)-Lynx_rufus 1.5106 1.0209 899
## (Intercept)-Didelphis_virginiana -0.3346 1.0070 3106
## (Intercept)-Sylvilagus_floridanus 0.5257 1.0002 2063
## (Intercept)-Meleagris_gallopavo 4.4811 1.0088 453
## (Intercept)-Sciurus_carolinensis -0.7386 1.0146 2197
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.7038 1.0038 2444
## Avg_Cogongrass_Cover-Canis_latrans 1.0027 1.0123 2812
## Avg_Cogongrass_Cover-Procyon_lotor 0.4130 1.0060 2584
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5365 1.0111 2664
## Avg_Cogongrass_Cover-Lynx_rufus 0.6201 1.0037 2103
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7660 1.0045 2495
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0019 1.0068 1637
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5307 1.0300 1075
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3326 1.0132 2106
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.1017 1.0181 1025
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1708 1.0129 980
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4445 1.0284 1471
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5269 1.0072 2699
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6100 1.0411 1213
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5723 1.0091 1955
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7923 1.0145 1830
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.1942 1.0418 699
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9241 1.0200 1871
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5373 0.0785 0.3873 0.5359 0.6885
## (Intercept)-Canis_latrans -2.5623 0.2035 -2.9731 -2.5574 -2.1843
## (Intercept)-Procyon_lotor -2.1895 0.1676 -2.5284 -2.1835 -1.8811
## (Intercept)-Dasypus_novemcinctus -1.5790 0.1796 -1.9418 -1.5747 -1.2366
## (Intercept)-Lynx_rufus -3.4698 0.3953 -4.2853 -3.4510 -2.7401
## (Intercept)-Didelphis_virginiana -2.3483 0.3154 -3.0027 -2.3398 -1.7627
## (Intercept)-Sylvilagus_floridanus -3.0842 0.3304 -3.7938 -3.0722 -2.4916
## (Intercept)-Meleagris_gallopavo -4.1814 0.5235 -5.1839 -4.1835 -3.1393
## (Intercept)-Sciurus_carolinensis -2.3834 0.3263 -3.0731 -2.3716 -1.7855
## shrub_cover-Odocoileus_virginianus -0.0612 0.0680 -0.1942 -0.0628 0.0733
## shrub_cover-Canis_latrans -0.2713 0.2164 -0.7014 -0.2669 0.1385
## shrub_cover-Procyon_lotor 0.2310 0.1708 -0.1121 0.2315 0.5539
## shrub_cover-Dasypus_novemcinctus 0.8400 0.2957 0.2760 0.8416 1.4318
## shrub_cover-Lynx_rufus -0.2908 0.3636 -1.0253 -0.2863 0.3974
## shrub_cover-Didelphis_virginiana 0.9891 0.3835 0.3006 0.9730 1.8064
## shrub_cover-Sylvilagus_floridanus 0.2240 0.4132 -0.5084 0.1985 1.0770
## shrub_cover-Meleagris_gallopavo -0.9153 0.4193 -1.7639 -0.9008 -0.1211
## shrub_cover-Sciurus_carolinensis 0.8024 0.4065 0.0386 0.7918 1.6462
## veg_height-Odocoileus_virginianus -0.3327 0.0689 -0.4683 -0.3315 -0.1981
## veg_height-Canis_latrans -0.6039 0.1836 -0.9727 -0.5984 -0.2660
## veg_height-Procyon_lotor 0.3401 0.1239 0.1021 0.3403 0.5817
## veg_height-Dasypus_novemcinctus 0.2375 0.1319 -0.0206 0.2345 0.5000
## veg_height-Lynx_rufus 0.0561 0.2474 -0.4392 0.0635 0.5277
## veg_height-Didelphis_virginiana 0.3972 0.2565 -0.0829 0.3925 0.9203
## veg_height-Sylvilagus_floridanus 0.1451 0.2560 -0.3505 0.1425 0.6582
## veg_height-Meleagris_gallopavo -0.2758 0.3647 -0.9827 -0.2866 0.4680
## veg_height-Sciurus_carolinensis 0.0561 0.2111 -0.3434 0.0508 0.4847
## week-Odocoileus_virginianus 1.3113 0.1253 1.0683 1.3123 1.5564
## week-Canis_latrans 0.6033 0.2626 0.0989 0.6021 1.1148
## week-Procyon_lotor 0.2078 0.2117 -0.2072 0.2072 0.6286
## week-Dasypus_novemcinctus 0.1201 0.2279 -0.3316 0.1225 0.5722
## week-Lynx_rufus 0.4122 0.3528 -0.2643 0.4040 1.1321
## week-Didelphis_virginiana 0.0783 0.3763 -0.6695 0.0854 0.7992
## week-Sylvilagus_floridanus 0.0840 0.3476 -0.6120 0.0861 0.7652
## week-Meleagris_gallopavo -0.1736 0.4336 -1.0973 -0.1463 0.6227
## week-Sciurus_carolinensis 0.8196 0.3729 0.1289 0.8027 1.5874
## I(week^2)-Odocoileus_virginianus -0.5405 0.0512 -0.6382 -0.5400 -0.4402
## I(week^2)-Canis_latrans -0.2526 0.1084 -0.4636 -0.2510 -0.0400
## I(week^2)-Procyon_lotor -0.1335 0.0920 -0.3174 -0.1347 0.0445
## I(week^2)-Dasypus_novemcinctus -0.1876 0.1051 -0.3993 -0.1857 0.0137
## I(week^2)-Lynx_rufus -0.2546 0.1577 -0.5782 -0.2498 0.0415
## I(week^2)-Didelphis_virginiana -0.4332 0.2216 -0.9300 -0.4107 -0.0601
## I(week^2)-Sylvilagus_floridanus -0.1890 0.1623 -0.5238 -0.1859 0.1145
## I(week^2)-Meleagris_gallopavo -0.4302 0.2500 -1.0005 -0.4024 -0.0162
## I(week^2)-Sciurus_carolinensis -0.2940 0.1480 -0.5945 -0.2910 -0.0148
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0027 5250
## (Intercept)-Canis_latrans 1.0040 2672
## (Intercept)-Procyon_lotor 1.0040 3474
## (Intercept)-Dasypus_novemcinctus 1.0037 4125
## (Intercept)-Lynx_rufus 1.0263 803
## (Intercept)-Didelphis_virginiana 1.0013 2875
## (Intercept)-Sylvilagus_floridanus 1.0010 1625
## (Intercept)-Meleagris_gallopavo 1.0048 450
## (Intercept)-Sciurus_carolinensis 1.0005 2860
## shrub_cover-Odocoileus_virginianus 1.0007 5250
## shrub_cover-Canis_latrans 1.0019 2774
## shrub_cover-Procyon_lotor 1.0027 3794
## shrub_cover-Dasypus_novemcinctus 1.0004 3654
## shrub_cover-Lynx_rufus 1.0135 1351
## shrub_cover-Didelphis_virginiana 1.0006 1816
## shrub_cover-Sylvilagus_floridanus 1.0039 1604
## shrub_cover-Meleagris_gallopavo 1.0063 476
## shrub_cover-Sciurus_carolinensis 1.0002 2422
## veg_height-Odocoileus_virginianus 1.0026 5250
## veg_height-Canis_latrans 0.9999 2498
## veg_height-Procyon_lotor 1.0021 4746
## veg_height-Dasypus_novemcinctus 1.0031 4283
## veg_height-Lynx_rufus 1.0005 2446
## veg_height-Didelphis_virginiana 1.0069 2992
## veg_height-Sylvilagus_floridanus 1.0037 2049
## veg_height-Meleagris_gallopavo 1.0184 1035
## veg_height-Sciurus_carolinensis 1.0045 3733
## week-Odocoileus_virginianus 1.0017 4869
## week-Canis_latrans 1.0001 3745
## week-Procyon_lotor 1.0000 4375
## week-Dasypus_novemcinctus 1.0005 4804
## week-Lynx_rufus 1.0005 3001
## week-Didelphis_virginiana 1.0006 2745
## week-Sylvilagus_floridanus 1.0009 3323
## week-Meleagris_gallopavo 1.0316 935
## week-Sciurus_carolinensis 1.0116 4016
## I(week^2)-Odocoileus_virginianus 1.0019 5001
## I(week^2)-Canis_latrans 1.0007 3872
## I(week^2)-Procyon_lotor 1.0004 4312
## I(week^2)-Dasypus_novemcinctus 1.0006 4454
## I(week^2)-Lynx_rufus 1.0011 2335
## I(week^2)-Didelphis_virginiana 1.0097 1498
## I(week^2)-Sylvilagus_floridanus 1.0027 2311
## I(week^2)-Meleagris_gallopavo 1.0545 583
## I(week^2)-Sciurus_carolinensis 1.0115 4041
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 9 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0147
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4685 1.1492 -2.6444 -0.5057 1.9260 1.0056 2316
## Cogon_Patch_Size 0.2170 0.7677 -1.3115 0.2195 1.7107 1.0022 1179
## Veg_shannon_index 0.9341 0.5274 -0.0460 0.9076 2.0911 1.0172 571
## total_shrub_cover -0.8225 0.6568 -2.2526 -0.7720 0.3816 1.0011 729
## Avg_Cogongrass_Cover -0.2221 1.0220 -2.2627 -0.2099 1.7694 1.0234 496
## Tree_Density -1.8806 0.8858 -3.5837 -1.8757 -0.0590 1.0019 1012
## Avg_Canopy_Cover 1.8777 0.8160 0.2651 1.8509 3.5457 1.0133 1569
## I(Avg_Cogongrass_Cover^2) 1.5605 0.6469 0.3802 1.5231 2.9740 1.0337 495
## avg_veg_height 0.0369 0.5736 -1.0939 0.0397 1.1481 1.0064 683
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.9328 21.4222 3.0883 14.5553 79.6968 1.0770 368
## Cogon_Patch_Size 3.5410 5.3737 0.1106 1.7800 17.3044 1.0497 573
## Veg_shannon_index 0.8238 1.3533 0.0470 0.3930 4.2940 1.0433 1041
## total_shrub_cover 2.0399 3.1436 0.0713 1.0325 9.8879 1.0531 416
## Avg_Cogongrass_Cover 1.5275 3.2366 0.0513 0.5775 8.9153 1.0873 788
## Tree_Density 5.8015 12.9544 0.0789 1.8440 37.6491 1.0215 331
## Avg_Canopy_Cover 5.8910 10.0076 0.2600 3.0048 29.5992 1.1322 254
## I(Avg_Cogongrass_Cover^2) 1.4018 3.4337 0.0506 0.4822 8.4980 1.0277 514
## avg_veg_height 0.6908 1.0564 0.0464 0.3454 3.4751 1.0033 1392
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2539 4.5759 0.062 0.8239 13.6201 1.1308 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1866 0.5120 -3.1492 -2.2082 -1.1172 1.0003 5250
## shrub_cover 0.2819 0.3007 -0.3226 0.2813 0.8749 1.0003 2969
## veg_height 0.0300 0.1801 -0.3216 0.0277 0.3948 1.0042 2663
## week 0.3664 0.2482 -0.1390 0.3746 0.8434 1.0012 3661
## I(week^2) -0.2994 0.1189 -0.5507 -0.2972 -0.0718 1.0050 2656
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4224 1.8321 0.7876 1.9647 6.6811 1.0091 3977
## shrub_cover 0.6929 0.5631 0.1485 0.5466 2.0894 1.0000 2468
## veg_height 0.2463 0.1953 0.0660 0.1938 0.7316 1.0080 3224
## week 0.4332 0.3546 0.1081 0.3315 1.3003 1.0021 3581
## I(week^2) 0.0902 0.0783 0.0240 0.0677 0.2832 1.0252 1770
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.6227 3.8664 2.3169
## (Intercept)-Canis_latrans -0.6588 1.3992 -3.2514
## (Intercept)-Procyon_lotor -0.2902 1.2173 -2.9598
## (Intercept)-Dasypus_novemcinctus -2.5801 1.3717 -5.8454
## (Intercept)-Lynx_rufus 0.5404 2.8045 -4.0082
## (Intercept)-Didelphis_virginiana -4.0073 1.6130 -7.5568
## (Intercept)-Sylvilagus_floridanus -2.0590 1.6541 -5.4247
## (Intercept)-Meleagris_gallopavo -0.4203 2.6778 -4.6688
## (Intercept)-Sciurus_carolinensis -4.4807 1.8373 -8.7532
## Cogon_Patch_Size-Odocoileus_virginianus 0.2494 1.4852 -2.6412
## Cogon_Patch_Size-Canis_latrans 1.7573 1.5015 -0.3421
## Cogon_Patch_Size-Procyon_lotor -0.3562 0.9083 -2.1609
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1241 0.8823 -1.6156
## Cogon_Patch_Size-Lynx_rufus 0.0012 1.7061 -3.3176
## Cogon_Patch_Size-Didelphis_virginiana 1.7268 1.1546 -0.0800
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1283 1.7372 -5.4239
## Cogon_Patch_Size-Meleagris_gallopavo 0.6717 1.6034 -2.0234
## Cogon_Patch_Size-Sciurus_carolinensis -0.8700 1.4707 -4.5148
## Veg_shannon_index-Odocoileus_virginianus 0.7742 0.9098 -1.2088
## Veg_shannon_index-Canis_latrans 1.2983 0.7890 0.0192
## Veg_shannon_index-Procyon_lotor 1.1727 0.6665 0.0369
## Veg_shannon_index-Dasypus_novemcinctus 0.5959 0.6396 -0.7365
## Veg_shannon_index-Lynx_rufus 1.0155 0.9471 -0.8270
## Veg_shannon_index-Didelphis_virginiana 1.1774 0.8023 -0.1793
## Veg_shannon_index-Sylvilagus_floridanus 1.0384 0.7700 -0.3792
## Veg_shannon_index-Meleagris_gallopavo 1.2493 0.9243 -0.3146
## Veg_shannon_index-Sciurus_carolinensis 0.3477 0.8710 -1.6172
## total_shrub_cover-Odocoileus_virginianus -0.3919 1.1887 -2.7521
## total_shrub_cover-Canis_latrans 0.3109 0.9654 -1.2767
## total_shrub_cover-Procyon_lotor -1.3626 0.7565 -3.0452
## total_shrub_cover-Dasypus_novemcinctus -0.3657 0.8205 -2.1194
## total_shrub_cover-Lynx_rufus -1.3490 1.4959 -4.8860
## total_shrub_cover-Didelphis_virginiana -1.1822 1.0940 -3.8041
## total_shrub_cover-Sylvilagus_floridanus -1.0379 1.2667 -4.1776
## total_shrub_cover-Meleagris_gallopavo -1.9096 1.5642 -5.6124
## total_shrub_cover-Sciurus_carolinensis -0.7857 1.1760 -3.5491
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.3026 1.4744 -3.2424
## Avg_Cogongrass_Cover-Canis_latrans -0.0122 1.3439 -2.4952
## Avg_Cogongrass_Cover-Procyon_lotor -0.1991 1.2319 -2.6176
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4281 1.3888 -2.0121
## Avg_Cogongrass_Cover-Lynx_rufus -0.1572 1.4136 -2.9190
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1948 1.3124 -2.8237
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8885 1.4413 -4.0473
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5304 1.6299 -3.9856
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2393 1.3444 -2.9021
## Tree_Density-Odocoileus_virginianus -0.7294 1.6557 -3.1624
## Tree_Density-Canis_latrans -2.9190 1.6097 -6.9499
## Tree_Density-Procyon_lotor -1.9020 1.0697 -4.1154
## Tree_Density-Dasypus_novemcinctus -4.2246 2.6157 -11.2857
## Tree_Density-Lynx_rufus -0.5469 2.0239 -3.4383
## Tree_Density-Didelphis_virginiana -2.2968 1.4789 -5.7370
## Tree_Density-Sylvilagus_floridanus -2.6574 1.8401 -7.3130
## Tree_Density-Meleagris_gallopavo -2.2915 1.8256 -6.3880
## Tree_Density-Sciurus_carolinensis -2.6128 1.7995 -7.0294
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0041 1.6767 -2.4123
## Avg_Canopy_Cover-Canis_latrans -0.0137 0.7497 -1.5632
## Avg_Canopy_Cover-Procyon_lotor 1.6597 0.8788 0.1202
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3088 1.0104 0.7535
## Avg_Canopy_Cover-Lynx_rufus 1.4998 1.9961 -2.0886
## Avg_Canopy_Cover-Didelphis_virginiana 3.5809 1.8118 1.3105
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.3662 2.3438 1.3196
## Avg_Canopy_Cover-Meleagris_gallopavo 3.0171 2.1643 0.3456
## Avg_Canopy_Cover-Sciurus_carolinensis 3.3279 1.8440 1.0590
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9667 1.3266 0.0154
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0299 1.0740 0.5157
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9384 1.0077 0.4529
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5126 0.7731 0.1514
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1813 1.1624 0.4996
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2094 0.7910 -0.3288
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3076 0.8994 -0.3705
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.8736 1.4001 -2.4276
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7979 0.8643 0.3744
## avg_veg_height-Odocoileus_virginianus 0.0151 0.9078 -1.8399
## avg_veg_height-Canis_latrans -0.1053 0.7041 -1.5471
## avg_veg_height-Procyon_lotor 0.1460 0.7066 -1.2349
## avg_veg_height-Dasypus_novemcinctus 0.3570 0.7079 -0.9344
## avg_veg_height-Lynx_rufus -0.2309 0.9855 -2.5796
## avg_veg_height-Didelphis_virginiana -0.2186 0.8274 -2.0787
## avg_veg_height-Sylvilagus_floridanus -0.1288 0.8202 -1.8849
## avg_veg_height-Meleagris_gallopavo 0.0474 1.0536 -2.2106
## avg_veg_height-Sciurus_carolinensis 0.3956 0.8151 -1.0327
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8118 17.8014 1.0501 321
## (Intercept)-Canis_latrans -0.7198 2.3278 1.0184 1318
## (Intercept)-Procyon_lotor -0.2274 1.9758 1.0551 924
## (Intercept)-Dasypus_novemcinctus -2.4033 -0.3426 1.0233 486
## (Intercept)-Lynx_rufus 0.1578 7.2435 1.0074 322
## (Intercept)-Didelphis_virginiana -3.8885 -1.1380 1.0324 659
## (Intercept)-Sylvilagus_floridanus -2.0422 1.2569 1.0335 635
## (Intercept)-Meleagris_gallopavo -0.7929 5.9661 1.0236 273
## (Intercept)-Sciurus_carolinensis -4.3334 -1.3232 1.0509 515
## Cogon_Patch_Size-Odocoileus_virginianus 0.2062 3.4699 1.0107 1732
## Cogon_Patch_Size-Canis_latrans 1.4810 5.5647 1.0469 800
## Cogon_Patch_Size-Procyon_lotor -0.3301 1.2369 1.0420 523
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1012 1.9375 1.0172 1197
## Cogon_Patch_Size-Lynx_rufus 0.0058 3.6013 1.0244 648
## Cogon_Patch_Size-Didelphis_virginiana 1.5795 4.3607 1.0481 600
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8141 1.4486 1.0095 756
## Cogon_Patch_Size-Meleagris_gallopavo 0.4832 4.4692 1.0056 791
## Cogon_Patch_Size-Sciurus_carolinensis -0.6163 1.2742 1.0016 715
## Veg_shannon_index-Odocoileus_virginianus 0.7744 2.5623 1.0107 1127
## Veg_shannon_index-Canis_latrans 1.2188 3.0920 1.0260 719
## Veg_shannon_index-Procyon_lotor 1.1035 2.7100 1.0365 548
## Veg_shannon_index-Dasypus_novemcinctus 0.6112 1.8552 1.0041 1401
## Veg_shannon_index-Lynx_rufus 0.9808 3.0524 1.0092 1431
## Veg_shannon_index-Didelphis_virginiana 1.0909 3.0592 1.0176 1120
## Veg_shannon_index-Sylvilagus_floridanus 0.9994 2.7665 1.0174 1061
## Veg_shannon_index-Meleagris_gallopavo 1.1542 3.4356 1.0171 1238
## Veg_shannon_index-Sciurus_carolinensis 0.4369 1.8767 1.0149 1376
## total_shrub_cover-Odocoileus_virginianus -0.4331 2.1547 1.0049 1889
## total_shrub_cover-Canis_latrans 0.1760 2.6154 1.0281 675
## total_shrub_cover-Procyon_lotor -1.2972 -0.1094 1.0035 1207
## total_shrub_cover-Dasypus_novemcinctus -0.3241 1.1487 1.0030 1160
## total_shrub_cover-Lynx_rufus -1.1403 1.0864 1.0121 499
## total_shrub_cover-Didelphis_virginiana -1.0168 0.4969 1.0080 762
## total_shrub_cover-Sylvilagus_floridanus -0.8720 0.9988 1.0028 868
## total_shrub_cover-Meleagris_gallopavo -1.6191 0.4955 1.0086 373
## total_shrub_cover-Sciurus_carolinensis -0.6509 1.1818 1.0053 679
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2803 2.6526 1.0137 782
## Avg_Cogongrass_Cover-Canis_latrans -0.0537 2.8023 1.0166 722
## Avg_Cogongrass_Cover-Procyon_lotor -0.2179 2.2627 1.0098 529
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3099 3.5648 1.0141 499
## Avg_Cogongrass_Cover-Lynx_rufus -0.1607 2.7044 1.0100 840
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1991 2.4719 1.0153 724
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7765 1.6424 1.0098 763
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4160 2.3273 1.0381 568
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2388 2.4608 1.0134 502
## Tree_Density-Odocoileus_virginianus -0.9822 3.3315 1.0110 855
## Tree_Density-Canis_latrans -2.6004 -0.6830 1.0067 610
## Tree_Density-Procyon_lotor -1.8685 0.0674 1.0126 998
## Tree_Density-Dasypus_novemcinctus -3.4941 -1.2537 1.0099 231
## Tree_Density-Lynx_rufus -0.9077 4.7375 1.0002 359
## Tree_Density-Didelphis_virginiana -2.1450 0.3379 1.0126 770
## Tree_Density-Sylvilagus_floridanus -2.3674 0.1853 1.0076 515
## Tree_Density-Meleagris_gallopavo -2.1516 1.0364 1.0194 672
## Tree_Density-Sciurus_carolinensis -2.3458 0.2664 1.0040 505
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0668 4.3848 1.0067 1445
## Avg_Canopy_Cover-Canis_latrans 0.0045 1.4495 1.0416 1131
## Avg_Canopy_Cover-Procyon_lotor 1.6118 3.6320 1.0204 1023
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1704 4.7503 1.0338 368
## Avg_Canopy_Cover-Lynx_rufus 1.3951 5.8272 1.0201 461
## Avg_Canopy_Cover-Didelphis_virginiana 3.1906 8.0834 1.0451 326
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9274 10.2811 1.1012 262
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5259 8.4893 1.0693 270
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8963 8.1245 1.1237 258
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7467 5.3038 1.0099 688
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8363 4.6500 1.0256 663
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8090 4.1853 1.0337 512
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4525 3.2285 1.0365 627
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9860 5.1125 1.0341 612
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1975 2.8483 1.0343 700
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2734 3.2320 1.0163 864
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0472 3.1947 1.0173 389
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7061 3.7417 1.0395 711
## avg_veg_height-Odocoileus_virginianus 0.0500 1.7318 1.0015 1186
## avg_veg_height-Canis_latrans -0.0980 1.2260 1.0032 1225
## avg_veg_height-Procyon_lotor 0.1415 1.5324 1.0095 906
## avg_veg_height-Dasypus_novemcinctus 0.3271 1.8627 1.0050 1003
## avg_veg_height-Lynx_rufus -0.1441 1.4868 1.0074 1005
## avg_veg_height-Didelphis_virginiana -0.1765 1.2715 1.0058 889
## avg_veg_height-Sylvilagus_floridanus -0.0928 1.4132 1.0031 1127
## avg_veg_height-Meleagris_gallopavo 0.0833 2.0650 1.0054 781
## avg_veg_height-Sciurus_carolinensis 0.3205 2.2413 1.0026 1244
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5377 0.0805 0.3813 0.5372 0.7026
## (Intercept)-Canis_latrans -2.5346 0.1988 -2.9398 -2.5267 -2.1655
## (Intercept)-Procyon_lotor -2.1863 0.1647 -2.5318 -2.1826 -1.8704
## (Intercept)-Dasypus_novemcinctus -1.6273 0.1849 -2.0057 -1.6234 -1.2719
## (Intercept)-Lynx_rufus -3.7105 0.3815 -4.4674 -3.7106 -2.9710
## (Intercept)-Didelphis_virginiana -2.3838 0.3208 -3.0394 -2.3726 -1.7908
## (Intercept)-Sylvilagus_floridanus -3.0882 0.2923 -3.6821 -3.0800 -2.5411
## (Intercept)-Meleagris_gallopavo -3.8862 0.5393 -4.9580 -3.8769 -2.8587
## (Intercept)-Sciurus_carolinensis -2.5396 0.3494 -3.2219 -2.5331 -1.8752
## shrub_cover-Odocoileus_virginianus -0.0600 0.0682 -0.1921 -0.0602 0.0727
## shrub_cover-Canis_latrans -0.3258 0.2331 -0.7777 -0.3275 0.1381
## shrub_cover-Procyon_lotor 0.2735 0.1628 -0.0613 0.2783 0.5858
## shrub_cover-Dasypus_novemcinctus 0.9642 0.3224 0.3396 0.9569 1.5972
## shrub_cover-Lynx_rufus -0.1800 0.3746 -0.8841 -0.1922 0.5700
## shrub_cover-Didelphis_virginiana 1.0448 0.3884 0.3430 1.0233 1.8684
## shrub_cover-Sylvilagus_floridanus 0.5432 0.3936 -0.2070 0.5352 1.3301
## shrub_cover-Meleagris_gallopavo -0.6871 0.4697 -1.6291 -0.6778 0.2224
## shrub_cover-Sciurus_carolinensis 1.0121 0.4374 0.1691 1.0140 1.8448
## veg_height-Odocoileus_virginianus -0.3332 0.0694 -0.4687 -0.3332 -0.1976
## veg_height-Canis_latrans -0.5741 0.1822 -0.9405 -0.5697 -0.2302
## veg_height-Procyon_lotor 0.3600 0.1236 0.1251 0.3583 0.6026
## veg_height-Dasypus_novemcinctus 0.2590 0.1377 -0.0014 0.2562 0.5333
## veg_height-Lynx_rufus 0.1273 0.2428 -0.3586 0.1275 0.5923
## veg_height-Didelphis_virginiana 0.4627 0.2445 0.0036 0.4535 0.9714
## veg_height-Sylvilagus_floridanus 0.1277 0.2475 -0.3503 0.1296 0.6128
## veg_height-Meleagris_gallopavo -0.2475 0.3736 -1.0238 -0.2397 0.4831
## veg_height-Sciurus_carolinensis 0.1186 0.2252 -0.3260 0.1155 0.5671
## week-Odocoileus_virginianus 1.3141 0.1243 1.0718 1.3135 1.5653
## week-Canis_latrans 0.5942 0.2613 0.0884 0.5932 1.1158
## week-Procyon_lotor 0.2076 0.2139 -0.2121 0.2114 0.6187
## week-Dasypus_novemcinctus 0.1122 0.2292 -0.3313 0.1136 0.5612
## week-Lynx_rufus 0.3854 0.3562 -0.3146 0.3864 1.0720
## week-Didelphis_virginiana 0.0663 0.3752 -0.7211 0.0793 0.7583
## week-Sylvilagus_floridanus 0.0770 0.3479 -0.6428 0.0850 0.7347
## week-Meleagris_gallopavo -0.1840 0.4330 -1.0609 -0.1673 0.6371
## week-Sciurus_carolinensis 0.8146 0.3716 0.1176 0.8060 1.5785
## I(week^2)-Odocoileus_virginianus -0.5418 0.0513 -0.6418 -0.5416 -0.4414
## I(week^2)-Canis_latrans -0.2468 0.1085 -0.4619 -0.2461 -0.0393
## I(week^2)-Procyon_lotor -0.1329 0.0917 -0.3157 -0.1315 0.0452
## I(week^2)-Dasypus_novemcinctus -0.1817 0.1048 -0.3924 -0.1812 0.0177
## I(week^2)-Lynx_rufus -0.2484 0.1593 -0.5789 -0.2435 0.0490
## I(week^2)-Didelphis_virginiana -0.4526 0.2342 -0.9894 -0.4260 -0.0737
## I(week^2)-Sylvilagus_floridanus -0.1862 0.1633 -0.5125 -0.1817 0.1259
## I(week^2)-Meleagris_gallopavo -0.4237 0.2608 -1.0267 -0.3939 0.0095
## I(week^2)-Sciurus_carolinensis -0.2887 0.1459 -0.5805 -0.2855 -0.0124
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0077 2696
## (Intercept)-Procyon_lotor 1.0002 3268
## (Intercept)-Dasypus_novemcinctus 1.0011 2931
## (Intercept)-Lynx_rufus 1.0102 496
## (Intercept)-Didelphis_virginiana 1.0012 1554
## (Intercept)-Sylvilagus_floridanus 1.0104 1862
## (Intercept)-Meleagris_gallopavo 1.0047 456
## (Intercept)-Sciurus_carolinensis 0.9998 1439
## shrub_cover-Odocoileus_virginianus 1.0022 5250
## shrub_cover-Canis_latrans 1.0137 1600
## shrub_cover-Procyon_lotor 1.0017 3059
## shrub_cover-Dasypus_novemcinctus 1.0009 1722
## shrub_cover-Lynx_rufus 1.0044 687
## shrub_cover-Didelphis_virginiana 1.0001 1332
## shrub_cover-Sylvilagus_floridanus 1.0018 1373
## shrub_cover-Meleagris_gallopavo 1.0037 452
## shrub_cover-Sciurus_carolinensis 1.0025 1154
## veg_height-Odocoileus_virginianus 1.0016 5250
## veg_height-Canis_latrans 1.0031 2363
## veg_height-Procyon_lotor 1.0019 3447
## veg_height-Dasypus_novemcinctus 1.0008 3511
## veg_height-Lynx_rufus 1.0025 1641
## veg_height-Didelphis_virginiana 1.0005 2347
## veg_height-Sylvilagus_floridanus 1.0043 1988
## veg_height-Meleagris_gallopavo 1.0037 876
## veg_height-Sciurus_carolinensis 1.0011 2509
## week-Odocoileus_virginianus 1.0000 5250
## week-Canis_latrans 1.0001 3823
## week-Procyon_lotor 1.0007 4394
## week-Dasypus_novemcinctus 1.0006 4782
## week-Lynx_rufus 1.0027 2199
## week-Didelphis_virginiana 1.0019 2589
## week-Sylvilagus_floridanus 1.0005 2899
## week-Meleagris_gallopavo 1.0017 1364
## week-Sciurus_carolinensis 1.0028 3706
## I(week^2)-Odocoileus_virginianus 1.0002 5250
## I(week^2)-Canis_latrans 1.0000 3938
## I(week^2)-Procyon_lotor 1.0009 4259
## I(week^2)-Dasypus_novemcinctus 1.0002 4411
## I(week^2)-Lynx_rufus 1.0004 2054
## I(week^2)-Didelphis_virginiana 1.0138 1292
## I(week^2)-Sylvilagus_floridanus 1.0008 2327
## I(week^2)-Meleagris_gallopavo 1.0267 647
## I(week^2)-Sciurus_carolinensis 1.0023 3993
waicOcc(ms_full_full_T10, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1740.3345 119.1109 3718.8908
waicOcc(ms_full_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1778.4027 115.9515 3788.7084
waicOcc(ms_full_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1770.83771 99.38701 3740.44945
waicOcc(ms_full_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1775.8817 113.8257 3779.4149
waicOcc(ms_full_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1786.3616 103.3889 3779.5010
waicOcc(ms_full_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1790.42297 98.65178 3778.14950
waicOcc(ms_full_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1799.57954 86.54299 3772.24506
waicOcc(ms_full_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1783.8114 103.0531 3773.7289
waicOcc(ms_full_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1734.7742 121.7806 3713.1096
waicOcc(ms_null_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1862.34320 36.75272 3798.19182
waicOcc(ms_null_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1802.18611 68.61513 3741.60247
waicOcc(ms_null_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1842.40971 57.10052 3799.02047
waicOcc(ms_null_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1834.77017 49.63279 3768.80592
waicOcc(ms_null_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1838.34058 58.77373 3794.22861
waicOcc(ms_null_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1847.37129 53.14712 3801.03683
waicOcc(ms_null_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1851.33361 48.50854 3799.68431
waicOcc(ms_null_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1845.50584 51.59207 3794.19581
waicOcc(ms_null_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1795.34851 70.25541 3731.20783
waicOcc(ms_week_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1792.35446 75.80369 3736.31631
waicOcc(ms_week_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1831.25530 66.65421 3795.81902
waicOcc(ms_week_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1851.86485 44.48855 3792.70679
waicOcc(ms_week_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1837.09029 60.83221 3795.84500
waicOcc(ms_week_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1828.22339 65.98015 3788.40707
waicOcc(ms_week_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1823.75231 57.88378 3763.27217
waicOcc(ms_week_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1840.55191 56.99945 3795.10273
waicOcc(ms_week_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1834.580 60.088 3789.336
waicOcc(ms_week_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1785.30539 78.32676 3727.26428
waicOcc(ms_cover_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1752.2554 109.4251 3723.3611
waicOcc(ms_cover_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1789.1634 105.4922 3789.3111
waicOcc(ms_cover_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1810.38390 79.05639 3778.88058
waicOcc(ms_cover_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1797.34135 94.89678 3784.47625
waicOcc(ms_cover_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1787.4258 105.5868 3786.0252
waicOcc(ms_cover_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1782.2972 91.2308 3747.0561
waicOcc(ms_cover_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1801.48005 88.98014 3780.92038
waicOcc(ms_cover_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1794.77700 94.60528 3778.76454
waicOcc(ms_cover_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1746.2922 111.1849 3714.9541
waicOcc(ms_weekQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1718.51706 88.22992 3613.49396
waicOcc(ms_weekQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1758.34023 77.96383 3672.60813
waicOcc(ms_weekQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1778.5390 56.8746 3670.8273
waicOcc(ms_weekQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1763.49026 72.93721 3672.85493
waicOcc(ms_weekQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1754.55347 79.11156 3667.33006
waicOcc(ms_weekQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1750.51268 69.75112 3640.52760
waicOcc(ms_weekQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1767.38704 68.20139 3671.17686
waicOcc(ms_weekQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1760.99786 72.26364 3666.52300
waicOcc(ms_weekQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1712.05339 90.72057 3605.54791
waicOcc(ms_fullQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1665.3572 132.9789 3596.6723
waicOcc(ms_fullQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1702.362 130.384 3665.491
waicOcc(ms_fullQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1723.5365 101.3532 3649.7792
waicOcc(ms_fullQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1710.5985 117.1126 3655.4221
waicOcc(ms_fullQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1700.9806 126.9158 3655.7928
waicOcc(ms_fullQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1695.4815 112.5686 3616.1003
waicOcc(ms_fullQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1714.3883 111.8632 3652.5029
waicOcc(ms_fullQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1708.569 115.318 3647.774
waicOcc(ms_fullQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1660.0564 133.8827 3587.8782
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T10 <- ppcOcc(ms_fullQ_fullQ_T10, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 9
## Currently on species 2 out of 9
## Currently on species 3 out of 9
## Currently on species 4 out of 9
## Currently on species 5 out of 9
## Currently on species 6 out of 9
## Currently on species 7 out of 9
## Currently on species 8 out of 9
## Currently on species 9 out of 9
summary(ppc.ms_fullQ_fullQ_T10)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T10, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.3005
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Odocoileus_virginianus Bayesian p-value: 0
## Canis_latrans Bayesian p-value: 0.609
## Procyon_lotor Bayesian p-value: 0.0693
## Dasypus_novemcinctus Bayesian p-value: 0
## Lynx_rufus Bayesian p-value: 0.2893
## Didelphis_virginiana Bayesian p-value: 0.4156
## Sylvilagus_floridanus Bayesian p-value: 0.3926
## Meleagris_gallopavo Bayesian p-value: 0.5728
## Sciurus_carolinensis Bayesian p-value: 0.3562
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T10) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0147
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4685 1.1492 -2.6444 -0.5057 1.9260 1.0056 2316
## Cogon_Patch_Size 0.2170 0.7677 -1.3115 0.2195 1.7107 1.0022 1179
## Veg_shannon_index 0.9341 0.5274 -0.0460 0.9076 2.0911 1.0172 571
## total_shrub_cover -0.8225 0.6568 -2.2526 -0.7720 0.3816 1.0011 729
## Avg_Cogongrass_Cover -0.2221 1.0220 -2.2627 -0.2099 1.7694 1.0234 496
## Tree_Density -1.8806 0.8858 -3.5837 -1.8757 -0.0590 1.0019 1012
## Avg_Canopy_Cover 1.8777 0.8160 0.2651 1.8509 3.5457 1.0133 1569
## I(Avg_Cogongrass_Cover^2) 1.5605 0.6469 0.3802 1.5231 2.9740 1.0337 495
## avg_veg_height 0.0369 0.5736 -1.0939 0.0397 1.1481 1.0064 683
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.9328 21.4222 3.0883 14.5553 79.6968 1.0770 368
## Cogon_Patch_Size 3.5410 5.3737 0.1106 1.7800 17.3044 1.0497 573
## Veg_shannon_index 0.8238 1.3533 0.0470 0.3930 4.2940 1.0433 1041
## total_shrub_cover 2.0399 3.1436 0.0713 1.0325 9.8879 1.0531 416
## Avg_Cogongrass_Cover 1.5275 3.2366 0.0513 0.5775 8.9153 1.0873 788
## Tree_Density 5.8015 12.9544 0.0789 1.8440 37.6491 1.0215 331
## Avg_Canopy_Cover 5.8910 10.0076 0.2600 3.0048 29.5992 1.1322 254
## I(Avg_Cogongrass_Cover^2) 1.4018 3.4337 0.0506 0.4822 8.4980 1.0277 514
## avg_veg_height 0.6908 1.0564 0.0464 0.3454 3.4751 1.0033 1392
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2539 4.5759 0.062 0.8239 13.6201 1.1308 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1866 0.5120 -3.1492 -2.2082 -1.1172 1.0003 5250
## shrub_cover 0.2819 0.3007 -0.3226 0.2813 0.8749 1.0003 2969
## veg_height 0.0300 0.1801 -0.3216 0.0277 0.3948 1.0042 2663
## week 0.3664 0.2482 -0.1390 0.3746 0.8434 1.0012 3661
## I(week^2) -0.2994 0.1189 -0.5507 -0.2972 -0.0718 1.0050 2656
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4224 1.8321 0.7876 1.9647 6.6811 1.0091 3977
## shrub_cover 0.6929 0.5631 0.1485 0.5466 2.0894 1.0000 2468
## veg_height 0.2463 0.1953 0.0660 0.1938 0.7316 1.0080 3224
## week 0.4332 0.3546 0.1081 0.3315 1.3003 1.0021 3581
## I(week^2) 0.0902 0.0783 0.0240 0.0677 0.2832 1.0252 1770
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.6227 3.8664 2.3169
## (Intercept)-Canis_latrans -0.6588 1.3992 -3.2514
## (Intercept)-Procyon_lotor -0.2902 1.2173 -2.9598
## (Intercept)-Dasypus_novemcinctus -2.5801 1.3717 -5.8454
## (Intercept)-Lynx_rufus 0.5404 2.8045 -4.0082
## (Intercept)-Didelphis_virginiana -4.0073 1.6130 -7.5568
## (Intercept)-Sylvilagus_floridanus -2.0590 1.6541 -5.4247
## (Intercept)-Meleagris_gallopavo -0.4203 2.6778 -4.6688
## (Intercept)-Sciurus_carolinensis -4.4807 1.8373 -8.7532
## Cogon_Patch_Size-Odocoileus_virginianus 0.2494 1.4852 -2.6412
## Cogon_Patch_Size-Canis_latrans 1.7573 1.5015 -0.3421
## Cogon_Patch_Size-Procyon_lotor -0.3562 0.9083 -2.1609
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1241 0.8823 -1.6156
## Cogon_Patch_Size-Lynx_rufus 0.0012 1.7061 -3.3176
## Cogon_Patch_Size-Didelphis_virginiana 1.7268 1.1546 -0.0800
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1283 1.7372 -5.4239
## Cogon_Patch_Size-Meleagris_gallopavo 0.6717 1.6034 -2.0234
## Cogon_Patch_Size-Sciurus_carolinensis -0.8700 1.4707 -4.5148
## Veg_shannon_index-Odocoileus_virginianus 0.7742 0.9098 -1.2088
## Veg_shannon_index-Canis_latrans 1.2983 0.7890 0.0192
## Veg_shannon_index-Procyon_lotor 1.1727 0.6665 0.0369
## Veg_shannon_index-Dasypus_novemcinctus 0.5959 0.6396 -0.7365
## Veg_shannon_index-Lynx_rufus 1.0155 0.9471 -0.8270
## Veg_shannon_index-Didelphis_virginiana 1.1774 0.8023 -0.1793
## Veg_shannon_index-Sylvilagus_floridanus 1.0384 0.7700 -0.3792
## Veg_shannon_index-Meleagris_gallopavo 1.2493 0.9243 -0.3146
## Veg_shannon_index-Sciurus_carolinensis 0.3477 0.8710 -1.6172
## total_shrub_cover-Odocoileus_virginianus -0.3919 1.1887 -2.7521
## total_shrub_cover-Canis_latrans 0.3109 0.9654 -1.2767
## total_shrub_cover-Procyon_lotor -1.3626 0.7565 -3.0452
## total_shrub_cover-Dasypus_novemcinctus -0.3657 0.8205 -2.1194
## total_shrub_cover-Lynx_rufus -1.3490 1.4959 -4.8860
## total_shrub_cover-Didelphis_virginiana -1.1822 1.0940 -3.8041
## total_shrub_cover-Sylvilagus_floridanus -1.0379 1.2667 -4.1776
## total_shrub_cover-Meleagris_gallopavo -1.9096 1.5642 -5.6124
## total_shrub_cover-Sciurus_carolinensis -0.7857 1.1760 -3.5491
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.3026 1.4744 -3.2424
## Avg_Cogongrass_Cover-Canis_latrans -0.0122 1.3439 -2.4952
## Avg_Cogongrass_Cover-Procyon_lotor -0.1991 1.2319 -2.6176
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4281 1.3888 -2.0121
## Avg_Cogongrass_Cover-Lynx_rufus -0.1572 1.4136 -2.9190
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1948 1.3124 -2.8237
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8885 1.4413 -4.0473
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5304 1.6299 -3.9856
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2393 1.3444 -2.9021
## Tree_Density-Odocoileus_virginianus -0.7294 1.6557 -3.1624
## Tree_Density-Canis_latrans -2.9190 1.6097 -6.9499
## Tree_Density-Procyon_lotor -1.9020 1.0697 -4.1154
## Tree_Density-Dasypus_novemcinctus -4.2246 2.6157 -11.2857
## Tree_Density-Lynx_rufus -0.5469 2.0239 -3.4383
## Tree_Density-Didelphis_virginiana -2.2968 1.4789 -5.7370
## Tree_Density-Sylvilagus_floridanus -2.6574 1.8401 -7.3130
## Tree_Density-Meleagris_gallopavo -2.2915 1.8256 -6.3880
## Tree_Density-Sciurus_carolinensis -2.6128 1.7995 -7.0294
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0041 1.6767 -2.4123
## Avg_Canopy_Cover-Canis_latrans -0.0137 0.7497 -1.5632
## Avg_Canopy_Cover-Procyon_lotor 1.6597 0.8788 0.1202
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3088 1.0104 0.7535
## Avg_Canopy_Cover-Lynx_rufus 1.4998 1.9961 -2.0886
## Avg_Canopy_Cover-Didelphis_virginiana 3.5809 1.8118 1.3105
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.3662 2.3438 1.3196
## Avg_Canopy_Cover-Meleagris_gallopavo 3.0171 2.1643 0.3456
## Avg_Canopy_Cover-Sciurus_carolinensis 3.3279 1.8440 1.0590
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9667 1.3266 0.0154
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0299 1.0740 0.5157
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9384 1.0077 0.4529
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5126 0.7731 0.1514
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1813 1.1624 0.4996
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2094 0.7910 -0.3288
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3076 0.8994 -0.3705
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.8736 1.4001 -2.4276
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7979 0.8643 0.3744
## avg_veg_height-Odocoileus_virginianus 0.0151 0.9078 -1.8399
## avg_veg_height-Canis_latrans -0.1053 0.7041 -1.5471
## avg_veg_height-Procyon_lotor 0.1460 0.7066 -1.2349
## avg_veg_height-Dasypus_novemcinctus 0.3570 0.7079 -0.9344
## avg_veg_height-Lynx_rufus -0.2309 0.9855 -2.5796
## avg_veg_height-Didelphis_virginiana -0.2186 0.8274 -2.0787
## avg_veg_height-Sylvilagus_floridanus -0.1288 0.8202 -1.8849
## avg_veg_height-Meleagris_gallopavo 0.0474 1.0536 -2.2106
## avg_veg_height-Sciurus_carolinensis 0.3956 0.8151 -1.0327
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8118 17.8014 1.0501 321
## (Intercept)-Canis_latrans -0.7198 2.3278 1.0184 1318
## (Intercept)-Procyon_lotor -0.2274 1.9758 1.0551 924
## (Intercept)-Dasypus_novemcinctus -2.4033 -0.3426 1.0233 486
## (Intercept)-Lynx_rufus 0.1578 7.2435 1.0074 322
## (Intercept)-Didelphis_virginiana -3.8885 -1.1380 1.0324 659
## (Intercept)-Sylvilagus_floridanus -2.0422 1.2569 1.0335 635
## (Intercept)-Meleagris_gallopavo -0.7929 5.9661 1.0236 273
## (Intercept)-Sciurus_carolinensis -4.3334 -1.3232 1.0509 515
## Cogon_Patch_Size-Odocoileus_virginianus 0.2062 3.4699 1.0107 1732
## Cogon_Patch_Size-Canis_latrans 1.4810 5.5647 1.0469 800
## Cogon_Patch_Size-Procyon_lotor -0.3301 1.2369 1.0420 523
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1012 1.9375 1.0172 1197
## Cogon_Patch_Size-Lynx_rufus 0.0058 3.6013 1.0244 648
## Cogon_Patch_Size-Didelphis_virginiana 1.5795 4.3607 1.0481 600
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8141 1.4486 1.0095 756
## Cogon_Patch_Size-Meleagris_gallopavo 0.4832 4.4692 1.0056 791
## Cogon_Patch_Size-Sciurus_carolinensis -0.6163 1.2742 1.0016 715
## Veg_shannon_index-Odocoileus_virginianus 0.7744 2.5623 1.0107 1127
## Veg_shannon_index-Canis_latrans 1.2188 3.0920 1.0260 719
## Veg_shannon_index-Procyon_lotor 1.1035 2.7100 1.0365 548
## Veg_shannon_index-Dasypus_novemcinctus 0.6112 1.8552 1.0041 1401
## Veg_shannon_index-Lynx_rufus 0.9808 3.0524 1.0092 1431
## Veg_shannon_index-Didelphis_virginiana 1.0909 3.0592 1.0176 1120
## Veg_shannon_index-Sylvilagus_floridanus 0.9994 2.7665 1.0174 1061
## Veg_shannon_index-Meleagris_gallopavo 1.1542 3.4356 1.0171 1238
## Veg_shannon_index-Sciurus_carolinensis 0.4369 1.8767 1.0149 1376
## total_shrub_cover-Odocoileus_virginianus -0.4331 2.1547 1.0049 1889
## total_shrub_cover-Canis_latrans 0.1760 2.6154 1.0281 675
## total_shrub_cover-Procyon_lotor -1.2972 -0.1094 1.0035 1207
## total_shrub_cover-Dasypus_novemcinctus -0.3241 1.1487 1.0030 1160
## total_shrub_cover-Lynx_rufus -1.1403 1.0864 1.0121 499
## total_shrub_cover-Didelphis_virginiana -1.0168 0.4969 1.0080 762
## total_shrub_cover-Sylvilagus_floridanus -0.8720 0.9988 1.0028 868
## total_shrub_cover-Meleagris_gallopavo -1.6191 0.4955 1.0086 373
## total_shrub_cover-Sciurus_carolinensis -0.6509 1.1818 1.0053 679
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2803 2.6526 1.0137 782
## Avg_Cogongrass_Cover-Canis_latrans -0.0537 2.8023 1.0166 722
## Avg_Cogongrass_Cover-Procyon_lotor -0.2179 2.2627 1.0098 529
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3099 3.5648 1.0141 499
## Avg_Cogongrass_Cover-Lynx_rufus -0.1607 2.7044 1.0100 840
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1991 2.4719 1.0153 724
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7765 1.6424 1.0098 763
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4160 2.3273 1.0381 568
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2388 2.4608 1.0134 502
## Tree_Density-Odocoileus_virginianus -0.9822 3.3315 1.0110 855
## Tree_Density-Canis_latrans -2.6004 -0.6830 1.0067 610
## Tree_Density-Procyon_lotor -1.8685 0.0674 1.0126 998
## Tree_Density-Dasypus_novemcinctus -3.4941 -1.2537 1.0099 231
## Tree_Density-Lynx_rufus -0.9077 4.7375 1.0002 359
## Tree_Density-Didelphis_virginiana -2.1450 0.3379 1.0126 770
## Tree_Density-Sylvilagus_floridanus -2.3674 0.1853 1.0076 515
## Tree_Density-Meleagris_gallopavo -2.1516 1.0364 1.0194 672
## Tree_Density-Sciurus_carolinensis -2.3458 0.2664 1.0040 505
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0668 4.3848 1.0067 1445
## Avg_Canopy_Cover-Canis_latrans 0.0045 1.4495 1.0416 1131
## Avg_Canopy_Cover-Procyon_lotor 1.6118 3.6320 1.0204 1023
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1704 4.7503 1.0338 368
## Avg_Canopy_Cover-Lynx_rufus 1.3951 5.8272 1.0201 461
## Avg_Canopy_Cover-Didelphis_virginiana 3.1906 8.0834 1.0451 326
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9274 10.2811 1.1012 262
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5259 8.4893 1.0693 270
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8963 8.1245 1.1237 258
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7467 5.3038 1.0099 688
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8363 4.6500 1.0256 663
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8090 4.1853 1.0337 512
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4525 3.2285 1.0365 627
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9860 5.1125 1.0341 612
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1975 2.8483 1.0343 700
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2734 3.2320 1.0163 864
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0472 3.1947 1.0173 389
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7061 3.7417 1.0395 711
## avg_veg_height-Odocoileus_virginianus 0.0500 1.7318 1.0015 1186
## avg_veg_height-Canis_latrans -0.0980 1.2260 1.0032 1225
## avg_veg_height-Procyon_lotor 0.1415 1.5324 1.0095 906
## avg_veg_height-Dasypus_novemcinctus 0.3271 1.8627 1.0050 1003
## avg_veg_height-Lynx_rufus -0.1441 1.4868 1.0074 1005
## avg_veg_height-Didelphis_virginiana -0.1765 1.2715 1.0058 889
## avg_veg_height-Sylvilagus_floridanus -0.0928 1.4132 1.0031 1127
## avg_veg_height-Meleagris_gallopavo 0.0833 2.0650 1.0054 781
## avg_veg_height-Sciurus_carolinensis 0.3205 2.2413 1.0026 1244
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5377 0.0805 0.3813 0.5372 0.7026
## (Intercept)-Canis_latrans -2.5346 0.1988 -2.9398 -2.5267 -2.1655
## (Intercept)-Procyon_lotor -2.1863 0.1647 -2.5318 -2.1826 -1.8704
## (Intercept)-Dasypus_novemcinctus -1.6273 0.1849 -2.0057 -1.6234 -1.2719
## (Intercept)-Lynx_rufus -3.7105 0.3815 -4.4674 -3.7106 -2.9710
## (Intercept)-Didelphis_virginiana -2.3838 0.3208 -3.0394 -2.3726 -1.7908
## (Intercept)-Sylvilagus_floridanus -3.0882 0.2923 -3.6821 -3.0800 -2.5411
## (Intercept)-Meleagris_gallopavo -3.8862 0.5393 -4.9580 -3.8769 -2.8587
## (Intercept)-Sciurus_carolinensis -2.5396 0.3494 -3.2219 -2.5331 -1.8752
## shrub_cover-Odocoileus_virginianus -0.0600 0.0682 -0.1921 -0.0602 0.0727
## shrub_cover-Canis_latrans -0.3258 0.2331 -0.7777 -0.3275 0.1381
## shrub_cover-Procyon_lotor 0.2735 0.1628 -0.0613 0.2783 0.5858
## shrub_cover-Dasypus_novemcinctus 0.9642 0.3224 0.3396 0.9569 1.5972
## shrub_cover-Lynx_rufus -0.1800 0.3746 -0.8841 -0.1922 0.5700
## shrub_cover-Didelphis_virginiana 1.0448 0.3884 0.3430 1.0233 1.8684
## shrub_cover-Sylvilagus_floridanus 0.5432 0.3936 -0.2070 0.5352 1.3301
## shrub_cover-Meleagris_gallopavo -0.6871 0.4697 -1.6291 -0.6778 0.2224
## shrub_cover-Sciurus_carolinensis 1.0121 0.4374 0.1691 1.0140 1.8448
## veg_height-Odocoileus_virginianus -0.3332 0.0694 -0.4687 -0.3332 -0.1976
## veg_height-Canis_latrans -0.5741 0.1822 -0.9405 -0.5697 -0.2302
## veg_height-Procyon_lotor 0.3600 0.1236 0.1251 0.3583 0.6026
## veg_height-Dasypus_novemcinctus 0.2590 0.1377 -0.0014 0.2562 0.5333
## veg_height-Lynx_rufus 0.1273 0.2428 -0.3586 0.1275 0.5923
## veg_height-Didelphis_virginiana 0.4627 0.2445 0.0036 0.4535 0.9714
## veg_height-Sylvilagus_floridanus 0.1277 0.2475 -0.3503 0.1296 0.6128
## veg_height-Meleagris_gallopavo -0.2475 0.3736 -1.0238 -0.2397 0.4831
## veg_height-Sciurus_carolinensis 0.1186 0.2252 -0.3260 0.1155 0.5671
## week-Odocoileus_virginianus 1.3141 0.1243 1.0718 1.3135 1.5653
## week-Canis_latrans 0.5942 0.2613 0.0884 0.5932 1.1158
## week-Procyon_lotor 0.2076 0.2139 -0.2121 0.2114 0.6187
## week-Dasypus_novemcinctus 0.1122 0.2292 -0.3313 0.1136 0.5612
## week-Lynx_rufus 0.3854 0.3562 -0.3146 0.3864 1.0720
## week-Didelphis_virginiana 0.0663 0.3752 -0.7211 0.0793 0.7583
## week-Sylvilagus_floridanus 0.0770 0.3479 -0.6428 0.0850 0.7347
## week-Meleagris_gallopavo -0.1840 0.4330 -1.0609 -0.1673 0.6371
## week-Sciurus_carolinensis 0.8146 0.3716 0.1176 0.8060 1.5785
## I(week^2)-Odocoileus_virginianus -0.5418 0.0513 -0.6418 -0.5416 -0.4414
## I(week^2)-Canis_latrans -0.2468 0.1085 -0.4619 -0.2461 -0.0393
## I(week^2)-Procyon_lotor -0.1329 0.0917 -0.3157 -0.1315 0.0452
## I(week^2)-Dasypus_novemcinctus -0.1817 0.1048 -0.3924 -0.1812 0.0177
## I(week^2)-Lynx_rufus -0.2484 0.1593 -0.5789 -0.2435 0.0490
## I(week^2)-Didelphis_virginiana -0.4526 0.2342 -0.9894 -0.4260 -0.0737
## I(week^2)-Sylvilagus_floridanus -0.1862 0.1633 -0.5125 -0.1817 0.1259
## I(week^2)-Meleagris_gallopavo -0.4237 0.2608 -1.0267 -0.3939 0.0095
## I(week^2)-Sciurus_carolinensis -0.2887 0.1459 -0.5805 -0.2855 -0.0124
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0077 2696
## (Intercept)-Procyon_lotor 1.0002 3268
## (Intercept)-Dasypus_novemcinctus 1.0011 2931
## (Intercept)-Lynx_rufus 1.0102 496
## (Intercept)-Didelphis_virginiana 1.0012 1554
## (Intercept)-Sylvilagus_floridanus 1.0104 1862
## (Intercept)-Meleagris_gallopavo 1.0047 456
## (Intercept)-Sciurus_carolinensis 0.9998 1439
## shrub_cover-Odocoileus_virginianus 1.0022 5250
## shrub_cover-Canis_latrans 1.0137 1600
## shrub_cover-Procyon_lotor 1.0017 3059
## shrub_cover-Dasypus_novemcinctus 1.0009 1722
## shrub_cover-Lynx_rufus 1.0044 687
## shrub_cover-Didelphis_virginiana 1.0001 1332
## shrub_cover-Sylvilagus_floridanus 1.0018 1373
## shrub_cover-Meleagris_gallopavo 1.0037 452
## shrub_cover-Sciurus_carolinensis 1.0025 1154
## veg_height-Odocoileus_virginianus 1.0016 5250
## veg_height-Canis_latrans 1.0031 2363
## veg_height-Procyon_lotor 1.0019 3447
## veg_height-Dasypus_novemcinctus 1.0008 3511
## veg_height-Lynx_rufus 1.0025 1641
## veg_height-Didelphis_virginiana 1.0005 2347
## veg_height-Sylvilagus_floridanus 1.0043 1988
## veg_height-Meleagris_gallopavo 1.0037 876
## veg_height-Sciurus_carolinensis 1.0011 2509
## week-Odocoileus_virginianus 1.0000 5250
## week-Canis_latrans 1.0001 3823
## week-Procyon_lotor 1.0007 4394
## week-Dasypus_novemcinctus 1.0006 4782
## week-Lynx_rufus 1.0027 2199
## week-Didelphis_virginiana 1.0019 2589
## week-Sylvilagus_floridanus 1.0005 2899
## week-Meleagris_gallopavo 1.0017 1364
## week-Sciurus_carolinensis 1.0028 3706
## I(week^2)-Odocoileus_virginianus 1.0002 5250
## I(week^2)-Canis_latrans 1.0000 3938
## I(week^2)-Procyon_lotor 1.0009 4259
## I(week^2)-Dasypus_novemcinctus 1.0002 4411
## I(week^2)-Lynx_rufus 1.0004 2054
## I(week^2)-Didelphis_virginiana 1.0138 1292
## I(week^2)-Sylvilagus_floridanus 1.0008 2327
## I(week^2)-Meleagris_gallopavo 1.0267 647
## I(week^2)-Sciurus_carolinensis 1.0023 3993
names(ms_fullQ_fullQ_T10)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T10$beta.samples)
## 'mcmc' num [1:5250, 1:81] 5.36 7.47 6.91 5.15 5.42 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:81] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.5264762
MCMCplot(ms_fullQ_fullQ_T10$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T10$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T10, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:5250, 1:9, 1:100] 0.998 0.999 0.999 0.988 0.998 ...
## $ z.0.samples : int [1:5250, 1:9, 1:100] 1 1 1 1 1 1 1 1 1 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T10, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:5250, 1:9, 1:100] 0.998 0.999 0.999 0.988 0.998 ...
psi.0.quants <- apply(out.pred$psi.0.samples, c(3), function(x) quantile(x, prob = c(0.025, 0.5, 0.975)))
str(psi.0.quants)
## num [1:3, 1:100] 0.000352 0.183428 0.999965 0.000406 0.181669 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:3] "2.5%" "50%" "97.5%"
## ..$ : NULL
psi.plot.dat <- data.frame(
psi.med = psi.0.quants[2, ],
psi.low = psi.0.quants[1, ],
psi.high = psi.0.quants[3, ],
Avg_Cogongrass_Cover = cogon.pred.vals
)
str(psi.plot.dat)
## 'data.frame': 100 obs. of 4 variables:
## $ psi.med : num 0.183 0.182 0.183 0.186 0.189 ...
## $ psi.low : num 0.000352 0.000406 0.000438 0.000422 0.000422 ...
## $ psi.high : num 1 1 1 1 1 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T10) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 2.0147
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4685 1.1492 -2.6444 -0.5057 1.9260 1.0056 2316
## Cogon_Patch_Size 0.2170 0.7677 -1.3115 0.2195 1.7107 1.0022 1179
## Veg_shannon_index 0.9341 0.5274 -0.0460 0.9076 2.0911 1.0172 571
## total_shrub_cover -0.8225 0.6568 -2.2526 -0.7720 0.3816 1.0011 729
## Avg_Cogongrass_Cover -0.2221 1.0220 -2.2627 -0.2099 1.7694 1.0234 496
## Tree_Density -1.8806 0.8858 -3.5837 -1.8757 -0.0590 1.0019 1012
## Avg_Canopy_Cover 1.8777 0.8160 0.2651 1.8509 3.5457 1.0133 1569
## I(Avg_Cogongrass_Cover^2) 1.5605 0.6469 0.3802 1.5231 2.9740 1.0337 495
## avg_veg_height 0.0369 0.5736 -1.0939 0.0397 1.1481 1.0064 683
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.9328 21.4222 3.0883 14.5553 79.6968 1.0770 368
## Cogon_Patch_Size 3.5410 5.3737 0.1106 1.7800 17.3044 1.0497 573
## Veg_shannon_index 0.8238 1.3533 0.0470 0.3930 4.2940 1.0433 1041
## total_shrub_cover 2.0399 3.1436 0.0713 1.0325 9.8879 1.0531 416
## Avg_Cogongrass_Cover 1.5275 3.2366 0.0513 0.5775 8.9153 1.0873 788
## Tree_Density 5.8015 12.9544 0.0789 1.8440 37.6491 1.0215 331
## Avg_Canopy_Cover 5.8910 10.0076 0.2600 3.0048 29.5992 1.1322 254
## I(Avg_Cogongrass_Cover^2) 1.4018 3.4337 0.0506 0.4822 8.4980 1.0277 514
## avg_veg_height 0.6908 1.0564 0.0464 0.3454 3.4751 1.0033 1392
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2539 4.5759 0.062 0.8239 13.6201 1.1308 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1866 0.5120 -3.1492 -2.2082 -1.1172 1.0003 5250
## shrub_cover 0.2819 0.3007 -0.3226 0.2813 0.8749 1.0003 2969
## veg_height 0.0300 0.1801 -0.3216 0.0277 0.3948 1.0042 2663
## week 0.3664 0.2482 -0.1390 0.3746 0.8434 1.0012 3661
## I(week^2) -0.2994 0.1189 -0.5507 -0.2972 -0.0718 1.0050 2656
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4224 1.8321 0.7876 1.9647 6.6811 1.0091 3977
## shrub_cover 0.6929 0.5631 0.1485 0.5466 2.0894 1.0000 2468
## veg_height 0.2463 0.1953 0.0660 0.1938 0.7316 1.0080 3224
## week 0.4332 0.3546 0.1081 0.3315 1.3003 1.0021 3581
## I(week^2) 0.0902 0.0783 0.0240 0.0677 0.2832 1.0252 1770
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.6227 3.8664 2.3169
## (Intercept)-Canis_latrans -0.6588 1.3992 -3.2514
## (Intercept)-Procyon_lotor -0.2902 1.2173 -2.9598
## (Intercept)-Dasypus_novemcinctus -2.5801 1.3717 -5.8454
## (Intercept)-Lynx_rufus 0.5404 2.8045 -4.0082
## (Intercept)-Didelphis_virginiana -4.0073 1.6130 -7.5568
## (Intercept)-Sylvilagus_floridanus -2.0590 1.6541 -5.4247
## (Intercept)-Meleagris_gallopavo -0.4203 2.6778 -4.6688
## (Intercept)-Sciurus_carolinensis -4.4807 1.8373 -8.7532
## Cogon_Patch_Size-Odocoileus_virginianus 0.2494 1.4852 -2.6412
## Cogon_Patch_Size-Canis_latrans 1.7573 1.5015 -0.3421
## Cogon_Patch_Size-Procyon_lotor -0.3562 0.9083 -2.1609
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1241 0.8823 -1.6156
## Cogon_Patch_Size-Lynx_rufus 0.0012 1.7061 -3.3176
## Cogon_Patch_Size-Didelphis_virginiana 1.7268 1.1546 -0.0800
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1283 1.7372 -5.4239
## Cogon_Patch_Size-Meleagris_gallopavo 0.6717 1.6034 -2.0234
## Cogon_Patch_Size-Sciurus_carolinensis -0.8700 1.4707 -4.5148
## Veg_shannon_index-Odocoileus_virginianus 0.7742 0.9098 -1.2088
## Veg_shannon_index-Canis_latrans 1.2983 0.7890 0.0192
## Veg_shannon_index-Procyon_lotor 1.1727 0.6665 0.0369
## Veg_shannon_index-Dasypus_novemcinctus 0.5959 0.6396 -0.7365
## Veg_shannon_index-Lynx_rufus 1.0155 0.9471 -0.8270
## Veg_shannon_index-Didelphis_virginiana 1.1774 0.8023 -0.1793
## Veg_shannon_index-Sylvilagus_floridanus 1.0384 0.7700 -0.3792
## Veg_shannon_index-Meleagris_gallopavo 1.2493 0.9243 -0.3146
## Veg_shannon_index-Sciurus_carolinensis 0.3477 0.8710 -1.6172
## total_shrub_cover-Odocoileus_virginianus -0.3919 1.1887 -2.7521
## total_shrub_cover-Canis_latrans 0.3109 0.9654 -1.2767
## total_shrub_cover-Procyon_lotor -1.3626 0.7565 -3.0452
## total_shrub_cover-Dasypus_novemcinctus -0.3657 0.8205 -2.1194
## total_shrub_cover-Lynx_rufus -1.3490 1.4959 -4.8860
## total_shrub_cover-Didelphis_virginiana -1.1822 1.0940 -3.8041
## total_shrub_cover-Sylvilagus_floridanus -1.0379 1.2667 -4.1776
## total_shrub_cover-Meleagris_gallopavo -1.9096 1.5642 -5.6124
## total_shrub_cover-Sciurus_carolinensis -0.7857 1.1760 -3.5491
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.3026 1.4744 -3.2424
## Avg_Cogongrass_Cover-Canis_latrans -0.0122 1.3439 -2.4952
## Avg_Cogongrass_Cover-Procyon_lotor -0.1991 1.2319 -2.6176
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4281 1.3888 -2.0121
## Avg_Cogongrass_Cover-Lynx_rufus -0.1572 1.4136 -2.9190
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1948 1.3124 -2.8237
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8885 1.4413 -4.0473
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5304 1.6299 -3.9856
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2393 1.3444 -2.9021
## Tree_Density-Odocoileus_virginianus -0.7294 1.6557 -3.1624
## Tree_Density-Canis_latrans -2.9190 1.6097 -6.9499
## Tree_Density-Procyon_lotor -1.9020 1.0697 -4.1154
## Tree_Density-Dasypus_novemcinctus -4.2246 2.6157 -11.2857
## Tree_Density-Lynx_rufus -0.5469 2.0239 -3.4383
## Tree_Density-Didelphis_virginiana -2.2968 1.4789 -5.7370
## Tree_Density-Sylvilagus_floridanus -2.6574 1.8401 -7.3130
## Tree_Density-Meleagris_gallopavo -2.2915 1.8256 -6.3880
## Tree_Density-Sciurus_carolinensis -2.6128 1.7995 -7.0294
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0041 1.6767 -2.4123
## Avg_Canopy_Cover-Canis_latrans -0.0137 0.7497 -1.5632
## Avg_Canopy_Cover-Procyon_lotor 1.6597 0.8788 0.1202
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3088 1.0104 0.7535
## Avg_Canopy_Cover-Lynx_rufus 1.4998 1.9961 -2.0886
## Avg_Canopy_Cover-Didelphis_virginiana 3.5809 1.8118 1.3105
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.3662 2.3438 1.3196
## Avg_Canopy_Cover-Meleagris_gallopavo 3.0171 2.1643 0.3456
## Avg_Canopy_Cover-Sciurus_carolinensis 3.3279 1.8440 1.0590
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9667 1.3266 0.0154
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0299 1.0740 0.5157
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9384 1.0077 0.4529
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5126 0.7731 0.1514
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1813 1.1624 0.4996
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2094 0.7910 -0.3288
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3076 0.8994 -0.3705
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.8736 1.4001 -2.4276
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7979 0.8643 0.3744
## avg_veg_height-Odocoileus_virginianus 0.0151 0.9078 -1.8399
## avg_veg_height-Canis_latrans -0.1053 0.7041 -1.5471
## avg_veg_height-Procyon_lotor 0.1460 0.7066 -1.2349
## avg_veg_height-Dasypus_novemcinctus 0.3570 0.7079 -0.9344
## avg_veg_height-Lynx_rufus -0.2309 0.9855 -2.5796
## avg_veg_height-Didelphis_virginiana -0.2186 0.8274 -2.0787
## avg_veg_height-Sylvilagus_floridanus -0.1288 0.8202 -1.8849
## avg_veg_height-Meleagris_gallopavo 0.0474 1.0536 -2.2106
## avg_veg_height-Sciurus_carolinensis 0.3956 0.8151 -1.0327
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8118 17.8014 1.0501 321
## (Intercept)-Canis_latrans -0.7198 2.3278 1.0184 1318
## (Intercept)-Procyon_lotor -0.2274 1.9758 1.0551 924
## (Intercept)-Dasypus_novemcinctus -2.4033 -0.3426 1.0233 486
## (Intercept)-Lynx_rufus 0.1578 7.2435 1.0074 322
## (Intercept)-Didelphis_virginiana -3.8885 -1.1380 1.0324 659
## (Intercept)-Sylvilagus_floridanus -2.0422 1.2569 1.0335 635
## (Intercept)-Meleagris_gallopavo -0.7929 5.9661 1.0236 273
## (Intercept)-Sciurus_carolinensis -4.3334 -1.3232 1.0509 515
## Cogon_Patch_Size-Odocoileus_virginianus 0.2062 3.4699 1.0107 1732
## Cogon_Patch_Size-Canis_latrans 1.4810 5.5647 1.0469 800
## Cogon_Patch_Size-Procyon_lotor -0.3301 1.2369 1.0420 523
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1012 1.9375 1.0172 1197
## Cogon_Patch_Size-Lynx_rufus 0.0058 3.6013 1.0244 648
## Cogon_Patch_Size-Didelphis_virginiana 1.5795 4.3607 1.0481 600
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8141 1.4486 1.0095 756
## Cogon_Patch_Size-Meleagris_gallopavo 0.4832 4.4692 1.0056 791
## Cogon_Patch_Size-Sciurus_carolinensis -0.6163 1.2742 1.0016 715
## Veg_shannon_index-Odocoileus_virginianus 0.7744 2.5623 1.0107 1127
## Veg_shannon_index-Canis_latrans 1.2188 3.0920 1.0260 719
## Veg_shannon_index-Procyon_lotor 1.1035 2.7100 1.0365 548
## Veg_shannon_index-Dasypus_novemcinctus 0.6112 1.8552 1.0041 1401
## Veg_shannon_index-Lynx_rufus 0.9808 3.0524 1.0092 1431
## Veg_shannon_index-Didelphis_virginiana 1.0909 3.0592 1.0176 1120
## Veg_shannon_index-Sylvilagus_floridanus 0.9994 2.7665 1.0174 1061
## Veg_shannon_index-Meleagris_gallopavo 1.1542 3.4356 1.0171 1238
## Veg_shannon_index-Sciurus_carolinensis 0.4369 1.8767 1.0149 1376
## total_shrub_cover-Odocoileus_virginianus -0.4331 2.1547 1.0049 1889
## total_shrub_cover-Canis_latrans 0.1760 2.6154 1.0281 675
## total_shrub_cover-Procyon_lotor -1.2972 -0.1094 1.0035 1207
## total_shrub_cover-Dasypus_novemcinctus -0.3241 1.1487 1.0030 1160
## total_shrub_cover-Lynx_rufus -1.1403 1.0864 1.0121 499
## total_shrub_cover-Didelphis_virginiana -1.0168 0.4969 1.0080 762
## total_shrub_cover-Sylvilagus_floridanus -0.8720 0.9988 1.0028 868
## total_shrub_cover-Meleagris_gallopavo -1.6191 0.4955 1.0086 373
## total_shrub_cover-Sciurus_carolinensis -0.6509 1.1818 1.0053 679
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2803 2.6526 1.0137 782
## Avg_Cogongrass_Cover-Canis_latrans -0.0537 2.8023 1.0166 722
## Avg_Cogongrass_Cover-Procyon_lotor -0.2179 2.2627 1.0098 529
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3099 3.5648 1.0141 499
## Avg_Cogongrass_Cover-Lynx_rufus -0.1607 2.7044 1.0100 840
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1991 2.4719 1.0153 724
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7765 1.6424 1.0098 763
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4160 2.3273 1.0381 568
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.2388 2.4608 1.0134 502
## Tree_Density-Odocoileus_virginianus -0.9822 3.3315 1.0110 855
## Tree_Density-Canis_latrans -2.6004 -0.6830 1.0067 610
## Tree_Density-Procyon_lotor -1.8685 0.0674 1.0126 998
## Tree_Density-Dasypus_novemcinctus -3.4941 -1.2537 1.0099 231
## Tree_Density-Lynx_rufus -0.9077 4.7375 1.0002 359
## Tree_Density-Didelphis_virginiana -2.1450 0.3379 1.0126 770
## Tree_Density-Sylvilagus_floridanus -2.3674 0.1853 1.0076 515
## Tree_Density-Meleagris_gallopavo -2.1516 1.0364 1.0194 672
## Tree_Density-Sciurus_carolinensis -2.3458 0.2664 1.0040 505
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0668 4.3848 1.0067 1445
## Avg_Canopy_Cover-Canis_latrans 0.0045 1.4495 1.0416 1131
## Avg_Canopy_Cover-Procyon_lotor 1.6118 3.6320 1.0204 1023
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1704 4.7503 1.0338 368
## Avg_Canopy_Cover-Lynx_rufus 1.3951 5.8272 1.0201 461
## Avg_Canopy_Cover-Didelphis_virginiana 3.1906 8.0834 1.0451 326
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9274 10.2811 1.1012 262
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5259 8.4893 1.0693 270
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8963 8.1245 1.1237 258
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7467 5.3038 1.0099 688
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8363 4.6500 1.0256 663
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8090 4.1853 1.0337 512
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4525 3.2285 1.0365 627
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9860 5.1125 1.0341 612
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1975 2.8483 1.0343 700
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2734 3.2320 1.0163 864
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0472 3.1947 1.0173 389
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7061 3.7417 1.0395 711
## avg_veg_height-Odocoileus_virginianus 0.0500 1.7318 1.0015 1186
## avg_veg_height-Canis_latrans -0.0980 1.2260 1.0032 1225
## avg_veg_height-Procyon_lotor 0.1415 1.5324 1.0095 906
## avg_veg_height-Dasypus_novemcinctus 0.3271 1.8627 1.0050 1003
## avg_veg_height-Lynx_rufus -0.1441 1.4868 1.0074 1005
## avg_veg_height-Didelphis_virginiana -0.1765 1.2715 1.0058 889
## avg_veg_height-Sylvilagus_floridanus -0.0928 1.4132 1.0031 1127
## avg_veg_height-Meleagris_gallopavo 0.0833 2.0650 1.0054 781
## avg_veg_height-Sciurus_carolinensis 0.3205 2.2413 1.0026 1244
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5377 0.0805 0.3813 0.5372 0.7026
## (Intercept)-Canis_latrans -2.5346 0.1988 -2.9398 -2.5267 -2.1655
## (Intercept)-Procyon_lotor -2.1863 0.1647 -2.5318 -2.1826 -1.8704
## (Intercept)-Dasypus_novemcinctus -1.6273 0.1849 -2.0057 -1.6234 -1.2719
## (Intercept)-Lynx_rufus -3.7105 0.3815 -4.4674 -3.7106 -2.9710
## (Intercept)-Didelphis_virginiana -2.3838 0.3208 -3.0394 -2.3726 -1.7908
## (Intercept)-Sylvilagus_floridanus -3.0882 0.2923 -3.6821 -3.0800 -2.5411
## (Intercept)-Meleagris_gallopavo -3.8862 0.5393 -4.9580 -3.8769 -2.8587
## (Intercept)-Sciurus_carolinensis -2.5396 0.3494 -3.2219 -2.5331 -1.8752
## shrub_cover-Odocoileus_virginianus -0.0600 0.0682 -0.1921 -0.0602 0.0727
## shrub_cover-Canis_latrans -0.3258 0.2331 -0.7777 -0.3275 0.1381
## shrub_cover-Procyon_lotor 0.2735 0.1628 -0.0613 0.2783 0.5858
## shrub_cover-Dasypus_novemcinctus 0.9642 0.3224 0.3396 0.9569 1.5972
## shrub_cover-Lynx_rufus -0.1800 0.3746 -0.8841 -0.1922 0.5700
## shrub_cover-Didelphis_virginiana 1.0448 0.3884 0.3430 1.0233 1.8684
## shrub_cover-Sylvilagus_floridanus 0.5432 0.3936 -0.2070 0.5352 1.3301
## shrub_cover-Meleagris_gallopavo -0.6871 0.4697 -1.6291 -0.6778 0.2224
## shrub_cover-Sciurus_carolinensis 1.0121 0.4374 0.1691 1.0140 1.8448
## veg_height-Odocoileus_virginianus -0.3332 0.0694 -0.4687 -0.3332 -0.1976
## veg_height-Canis_latrans -0.5741 0.1822 -0.9405 -0.5697 -0.2302
## veg_height-Procyon_lotor 0.3600 0.1236 0.1251 0.3583 0.6026
## veg_height-Dasypus_novemcinctus 0.2590 0.1377 -0.0014 0.2562 0.5333
## veg_height-Lynx_rufus 0.1273 0.2428 -0.3586 0.1275 0.5923
## veg_height-Didelphis_virginiana 0.4627 0.2445 0.0036 0.4535 0.9714
## veg_height-Sylvilagus_floridanus 0.1277 0.2475 -0.3503 0.1296 0.6128
## veg_height-Meleagris_gallopavo -0.2475 0.3736 -1.0238 -0.2397 0.4831
## veg_height-Sciurus_carolinensis 0.1186 0.2252 -0.3260 0.1155 0.5671
## week-Odocoileus_virginianus 1.3141 0.1243 1.0718 1.3135 1.5653
## week-Canis_latrans 0.5942 0.2613 0.0884 0.5932 1.1158
## week-Procyon_lotor 0.2076 0.2139 -0.2121 0.2114 0.6187
## week-Dasypus_novemcinctus 0.1122 0.2292 -0.3313 0.1136 0.5612
## week-Lynx_rufus 0.3854 0.3562 -0.3146 0.3864 1.0720
## week-Didelphis_virginiana 0.0663 0.3752 -0.7211 0.0793 0.7583
## week-Sylvilagus_floridanus 0.0770 0.3479 -0.6428 0.0850 0.7347
## week-Meleagris_gallopavo -0.1840 0.4330 -1.0609 -0.1673 0.6371
## week-Sciurus_carolinensis 0.8146 0.3716 0.1176 0.8060 1.5785
## I(week^2)-Odocoileus_virginianus -0.5418 0.0513 -0.6418 -0.5416 -0.4414
## I(week^2)-Canis_latrans -0.2468 0.1085 -0.4619 -0.2461 -0.0393
## I(week^2)-Procyon_lotor -0.1329 0.0917 -0.3157 -0.1315 0.0452
## I(week^2)-Dasypus_novemcinctus -0.1817 0.1048 -0.3924 -0.1812 0.0177
## I(week^2)-Lynx_rufus -0.2484 0.1593 -0.5789 -0.2435 0.0490
## I(week^2)-Didelphis_virginiana -0.4526 0.2342 -0.9894 -0.4260 -0.0737
## I(week^2)-Sylvilagus_floridanus -0.1862 0.1633 -0.5125 -0.1817 0.1259
## I(week^2)-Meleagris_gallopavo -0.4237 0.2608 -1.0267 -0.3939 0.0095
## I(week^2)-Sciurus_carolinensis -0.2887 0.1459 -0.5805 -0.2855 -0.0124
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0077 2696
## (Intercept)-Procyon_lotor 1.0002 3268
## (Intercept)-Dasypus_novemcinctus 1.0011 2931
## (Intercept)-Lynx_rufus 1.0102 496
## (Intercept)-Didelphis_virginiana 1.0012 1554
## (Intercept)-Sylvilagus_floridanus 1.0104 1862
## (Intercept)-Meleagris_gallopavo 1.0047 456
## (Intercept)-Sciurus_carolinensis 0.9998 1439
## shrub_cover-Odocoileus_virginianus 1.0022 5250
## shrub_cover-Canis_latrans 1.0137 1600
## shrub_cover-Procyon_lotor 1.0017 3059
## shrub_cover-Dasypus_novemcinctus 1.0009 1722
## shrub_cover-Lynx_rufus 1.0044 687
## shrub_cover-Didelphis_virginiana 1.0001 1332
## shrub_cover-Sylvilagus_floridanus 1.0018 1373
## shrub_cover-Meleagris_gallopavo 1.0037 452
## shrub_cover-Sciurus_carolinensis 1.0025 1154
## veg_height-Odocoileus_virginianus 1.0016 5250
## veg_height-Canis_latrans 1.0031 2363
## veg_height-Procyon_lotor 1.0019 3447
## veg_height-Dasypus_novemcinctus 1.0008 3511
## veg_height-Lynx_rufus 1.0025 1641
## veg_height-Didelphis_virginiana 1.0005 2347
## veg_height-Sylvilagus_floridanus 1.0043 1988
## veg_height-Meleagris_gallopavo 1.0037 876
## veg_height-Sciurus_carolinensis 1.0011 2509
## week-Odocoileus_virginianus 1.0000 5250
## week-Canis_latrans 1.0001 3823
## week-Procyon_lotor 1.0007 4394
## week-Dasypus_novemcinctus 1.0006 4782
## week-Lynx_rufus 1.0027 2199
## week-Didelphis_virginiana 1.0019 2589
## week-Sylvilagus_floridanus 1.0005 2899
## week-Meleagris_gallopavo 1.0017 1364
## week-Sciurus_carolinensis 1.0028 3706
## I(week^2)-Odocoileus_virginianus 1.0002 5250
## I(week^2)-Canis_latrans 1.0000 3938
## I(week^2)-Procyon_lotor 1.0009 4259
## I(week^2)-Dasypus_novemcinctus 1.0002 4411
## I(week^2)-Lynx_rufus 1.0004 2054
## I(week^2)-Didelphis_virginiana 1.0138 1292
## I(week^2)-Sylvilagus_floridanus 1.0008 2327
## I(week^2)-Meleagris_gallopavo 1.0267 647
## I(week^2)-Sciurus_carolinensis 1.0023 3993
names(ms_fullQ_fullQ_T10)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T10$beta.samples)
## 'mcmc' num [1:5250, 1:81] 5.36 7.47 6.91 5.15 5.42 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:81] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.5264762
MCMCplot(ms_fullQ_fullQ_T10$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T10$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T10$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T10$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 174 × 2
## Plot Veg_shannon_index
## <chr> <dbl>
## 1 BI200 2.75
## 2 BI201 2.70
## 3 BI202 2.59
## 4 BI97 1.61
## 5 BI99 2.97
## 6 BN210 2.97
## 7 BN211 2.43
## 8 BN212 2.22
## 9 BN96 3.05
## 10 BN98 2.79
## # ℹ 164 more rows
if (!is.numeric(fuel_data$Height)) {
fuel_data$Height <- as.numeric(as.character(fuel_data$Height))
}
## Warning: NAs introduced by coercion
# Calculate average vegetation height per plot
veg_height <- fuel_data %>%
group_by(Plot) %>%
summarize(avg_veg_height = mean(Height, na.rm = TRUE), .groups = "drop")
# Tree density from point-centered quarter data
if (!is.numeric(tree_data$Distance)) {
tree_data$Distance <- as.numeric(as.character(tree_data$Distance))
}
tree_density_data <- tree_data %>%
group_by(Plot) %>%
summarize(Average_Distance = mean(Distance) / 100, # Convert to meters
Tree_Density = 10000 / (Average_Distance^2)) # Convert to trees per hectare
# Average canopy cover from vegetation quadrats
tree_canopy_data <- Veg_Cover %>%
distinct(Plot, Quadrat, .keep_all = TRUE) %>% # Ensure each quadrat counts once per plot
group_by(Plot) %>%
summarize(Avg_Canopy_Cover = mean(Canopy_Cover, na.rm = TRUE), .groups = "drop") # Calculate the average canopy cover per plot
cor(tree_density_data$Tree_Density, tree_canopy_data$Avg_Canopy_Cover)
## [1] 0.2836106
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter(Class == "Mammalia" | Name == "Meleagris_gallopavo") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 25
species_subset <- species_counts %>%
filter(count > 25) %>%
pull(Name)
# Filter CameraData to only include species with count > 25
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 5
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$week)
week_matrix <- matrix(rep(week_vals, each = num_sites), nrow = num_sites, ncol = num_weeks, byrow = FALSE)
# Create detection covariate list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_matrix
)
# Remove dash and convert to numeric
week_numeric <- as.numeric(gsub("-", "", det.covs$week))
## Scale and center week_numeric
week_numeric <- scale(week_numeric)
# Reshape into the original 32x36 matrix
det.covs$week <- matrix(week_numeric, nrow = 32, ncol = 36)
str(det.covs)
## List of 3
## $ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
This requires combining the presence data and the site covariate data into a single list. This also means that the presence data is in a 3-d format.
# Combine into a named list
data_list <- list(
y = detection_array,
occ.covs = covariates_matrix,
det.covs = det.covs
)
str(data_list)
## List of 3
## $ y : num [1:32, 1:36, 1:5] 1 1 0 1 0 0 0 1 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## .. ..$ : chr [1:5] "Odocoileus_virginianus" "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" ...
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:5, 1:32, 1:36] 1 0 0 0 0 1 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:5] "Odocoileus_virginianus" "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
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## -------------------------------------------------
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## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8807
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6155 0.8704 -1.1289 0.6268 2.3474 1.0013 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.4814 15.5625 0.6449 3.6803 37.7146 1.0029 1583
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6396 0.6754 -2.827 -1.6979 -0.1368 1.0027 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.7481 3.8241 0.534 1.7639 11.2122 1.0044 4186
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Odocoileus_virginianus 4.2346 1.9086 1.9041 3.7994 9.2075 1.0035
## (Intercept)-Canis_latrans 0.3756 0.4231 -0.3985 0.3640 1.2655 1.0028
## (Intercept)-Procyon_lotor 0.7660 0.4059 -0.0006 0.7522 1.6109 1.0008
## (Intercept)-Dasypus_novemcinctus -0.5936 0.3672 -1.3320 -0.5907 0.1041 1.0026
## (Intercept)-Sylvilagus_floridanus -0.1955 0.7707 -1.1874 -0.2851 1.1482 1.0450
## ESS
## (Intercept)-Odocoileus_virginianus 634
## (Intercept)-Canis_latrans 4375
## (Intercept)-Procyon_lotor 5250
## (Intercept)-Dasypus_novemcinctus 5250
## (Intercept)-Sylvilagus_floridanus 599
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0053 0.0588 -0.1094 0.0051 0.1221
## (Intercept)-Canis_latrans -2.6013 0.1763 -2.9557 -2.5955 -2.2702
## (Intercept)-Procyon_lotor -2.2557 0.1279 -2.5149 -2.2527 -2.0130
## (Intercept)-Dasypus_novemcinctus -1.5617 0.1354 -1.8409 -1.5583 -1.3052
## (Intercept)-Sylvilagus_floridanus -3.1796 0.3262 -3.8742 -3.1627 -2.5953
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5578
## (Intercept)-Canis_latrans 1.0116 3110
## (Intercept)-Procyon_lotor 0.9999 3909
## (Intercept)-Dasypus_novemcinctus 0.9999 5250
## (Intercept)-Sylvilagus_floridanus 1.0083 1083
# Includes all covariates of detection and occupancy
ms_full_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
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## Sampled: 1500 of 10000, 15.00%
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Sampled: 9500 of 10000, 95.00%
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## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
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## Sampled: 5500 of 10000, 55.00%
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## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
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## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
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## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1907
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6475 1.3446 -2.1341 0.6881 3.2449 1.0009 5250
## Cogon_Patch_Size -0.6008 0.8622 -2.2793 -0.6389 1.2595 1.0030 1596
## Veg_shannon_index 1.0248 0.6180 -0.1595 1.0154 2.2552 1.0026 1362
## total_shrub_cover -0.0970 0.7334 -1.5570 -0.1088 1.4482 1.0045 2355
## Avg_Cogongrass_Cover 1.7766 0.9542 -0.1268 1.7833 3.6139 1.0061 1486
## Tree_Density -1.6393 1.1186 -3.7125 -1.7153 0.8998 1.0006 2241
## Avg_Canopy_Cover 1.2819 0.9586 -0.7710 1.3008 3.1048 1.0036 3445
## avg_veg_height -0.3385 0.6168 -1.5868 -0.3294 0.8841 1.0074 1541
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 51.2934 108.1474 2.2070 23.3056 249.5093 1.0489 526
## Cogon_Patch_Size 4.7446 22.2221 0.0597 0.9369 27.2313 1.0417 1000
## Veg_shannon_index 0.9138 2.5742 0.0473 0.3584 4.9371 1.1184 1905
## total_shrub_cover 2.4720 5.4088 0.0704 0.9641 14.3008 1.0179 1686
## Avg_Cogongrass_Cover 3.1735 7.5572 0.0616 0.9854 20.0439 1.0399 1717
## Tree_Density 12.7367 37.2844 0.0855 3.0578 87.0449 1.0478 1090
## Avg_Canopy_Cover 8.2441 22.5568 0.1774 3.0801 47.5806 1.1251 917
## avg_veg_height 0.7416 1.8086 0.0448 0.2989 4.1258 1.0802 3447
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9711 3.8277 0.0605 0.7636 11.824 1.168 285
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6903 0.6845 -2.8952 -1.7437 -0.1565 1.0012 5250
## shrub_cover 0.2009 0.3379 -0.4657 0.1907 0.8976 1.0040 4706
## veg_height -0.0384 0.2982 -0.6235 -0.0390 0.5553 1.0001 4777
## week -0.0103 0.2020 -0.4106 -0.0067 0.3684 1.0056 4899
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0385 5.6971 0.5467 1.8239 12.4987 1.0195 5250
## shrub_cover 0.5426 0.7628 0.0697 0.3422 2.3044 1.0065 4342
## veg_height 0.4344 0.5962 0.0712 0.2680 1.8682 1.0314 4235
## week 0.1787 0.4257 0.0285 0.1028 0.7289 1.1173 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 10.8432 6.0863 3.1513 9.3500
## (Intercept)-Canis_latrans 1.2720 1.3214 -0.9073 1.1174
## (Intercept)-Procyon_lotor 1.0733 1.0602 -0.9681 1.0317
## (Intercept)-Dasypus_novemcinctus -1.6724 1.2864 -4.5369 -1.5292
## (Intercept)-Sylvilagus_floridanus -1.1110 1.6250 -4.3628 -1.1046
## Cogon_Patch_Size-Odocoileus_virginianus -0.5227 1.6158 -3.3590 -0.6503
## Cogon_Patch_Size-Canis_latrans 0.6350 1.8783 -1.5557 0.2475
## Cogon_Patch_Size-Procyon_lotor -0.9948 0.7692 -2.6372 -0.9605
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7315 0.8192 -2.3642 -0.7311
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8105 2.0099 -6.7203 -1.3981
## Veg_shannon_index-Odocoileus_virginianus 0.9665 0.9443 -0.9747 0.9820
## Veg_shannon_index-Canis_latrans 1.3571 0.7786 -0.0048 1.2954
## Veg_shannon_index-Procyon_lotor 1.2557 0.6625 0.0647 1.2181
## Veg_shannon_index-Dasypus_novemcinctus 0.7229 0.6112 -0.4926 0.7210
## Veg_shannon_index-Sylvilagus_floridanus 1.1360 0.7816 -0.2412 1.0937
## total_shrub_cover-Odocoileus_virginianus 0.1116 1.2558 -2.2718 0.0321
## total_shrub_cover-Canis_latrans 0.8963 1.1425 -0.7128 0.6623
## total_shrub_cover-Procyon_lotor -0.9820 0.7601 -2.6772 -0.9044
## total_shrub_cover-Dasypus_novemcinctus -0.0294 0.7585 -1.6003 0.0180
## total_shrub_cover-Sylvilagus_floridanus -0.5092 1.2794 -3.5856 -0.3739
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9021 1.5561 -1.0567 1.8158
## Avg_Cogongrass_Cover-Canis_latrans 2.6068 1.3383 0.4577 2.4379
## Avg_Cogongrass_Cover-Procyon_lotor 2.0739 1.0338 0.2124 2.0123
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.8118 1.3390 0.6893 2.6311
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0996 1.2381 -1.4709 1.1439
## Tree_Density-Odocoileus_virginianus -0.4584 2.1289 -3.7906 -0.8073
## Tree_Density-Canis_latrans -3.2146 1.9744 -8.5473 -2.7872
## Tree_Density-Procyon_lotor -1.4061 0.8978 -3.2324 -1.3983
## Tree_Density-Dasypus_novemcinctus -4.5261 2.8992 -12.1180 -3.7279
## Tree_Density-Sylvilagus_floridanus -2.8462 2.1175 -8.1340 -2.4466
## Avg_Canopy_Cover-Odocoileus_virginianus 0.9294 1.9449 -2.9485 0.9144
## Avg_Canopy_Cover-Canis_latrans 0.0055 0.8138 -1.4740 0.0256
## Avg_Canopy_Cover-Procyon_lotor 1.6470 0.8536 0.1551 1.5714
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2624 0.9385 0.8492 2.1181
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0969 2.5575 1.0052 3.5610
## avg_veg_height-Odocoileus_virginianus -0.3666 0.9100 -2.2780 -0.3479
## avg_veg_height-Canis_latrans -0.3836 0.7190 -1.7878 -0.3993
## avg_veg_height-Procyon_lotor -0.3040 0.6624 -1.6286 -0.3106
## avg_veg_height-Dasypus_novemcinctus -0.1617 0.6856 -1.4680 -0.1781
## avg_veg_height-Sylvilagus_floridanus -0.5466 0.8206 -2.3593 -0.5175
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 27.2282 1.0071 193
## (Intercept)-Canis_latrans 4.3451 1.0031 794
## (Intercept)-Procyon_lotor 3.3479 1.0016 1305
## (Intercept)-Dasypus_novemcinctus 0.3890 1.0180 644
## (Intercept)-Sylvilagus_floridanus 2.0350 1.0137 702
## Cogon_Patch_Size-Odocoileus_virginianus 2.9879 1.0204 1259
## Cogon_Patch_Size-Canis_latrans 4.9889 1.0098 588
## Cogon_Patch_Size-Procyon_lotor 0.4500 1.0061 806
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9702 1.0000 1425
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4811 1.0746 542
## Veg_shannon_index-Odocoileus_virginianus 2.8118 1.0014 2039
## Veg_shannon_index-Canis_latrans 3.0387 1.0119 1347
## Veg_shannon_index-Procyon_lotor 2.6906 1.0081 840
## Veg_shannon_index-Dasypus_novemcinctus 1.9441 1.0026 1456
## Veg_shannon_index-Sylvilagus_floridanus 2.8460 1.0048 1233
## total_shrub_cover-Odocoileus_virginianus 2.8095 1.0061 1804
## total_shrub_cover-Canis_latrans 3.7402 1.0220 757
## total_shrub_cover-Procyon_lotor 0.2758 1.0016 1678
## total_shrub_cover-Dasypus_novemcinctus 1.2751 1.0184 1402
## total_shrub_cover-Sylvilagus_floridanus 1.6960 1.0155 683
## Avg_Cogongrass_Cover-Odocoileus_virginianus 5.3696 1.0013 1252
## Avg_Cogongrass_Cover-Canis_latrans 5.7573 1.0058 984
## Avg_Cogongrass_Cover-Procyon_lotor 4.2571 1.0051 1279
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 6.0744 1.0061 624
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4196 1.0129 1446
## Tree_Density-Odocoileus_virginianus 4.9927 1.0041 909
## Tree_Density-Canis_latrans -0.6728 1.0168 582
## Tree_Density-Procyon_lotor 0.3446 1.0060 1543
## Tree_Density-Dasypus_novemcinctus -1.2190 1.0327 497
## Tree_Density-Sylvilagus_floridanus 0.2808 1.0595 458
## Avg_Canopy_Cover-Odocoileus_virginianus 5.0951 1.0219 1202
## Avg_Canopy_Cover-Canis_latrans 1.4268 1.0423 783
## Avg_Canopy_Cover-Procyon_lotor 3.5568 1.0118 1097
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.4275 1.0281 577
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.1584 1.0694 391
## avg_veg_height-Odocoileus_virginianus 1.4368 1.0078 2224
## avg_veg_height-Canis_latrans 1.0899 1.0047 1708
## avg_veg_height-Procyon_lotor 1.0287 1.0023 1828
## avg_veg_height-Dasypus_novemcinctus 1.2566 1.0051 1717
## avg_veg_height-Sylvilagus_floridanus 0.9966 1.0026 1576
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0070 0.0614 -0.1112 0.0060 0.1281
## (Intercept)-Canis_latrans -2.7829 0.1889 -3.1635 -2.7790 -2.4290
## (Intercept)-Procyon_lotor -2.2983 0.1466 -2.5995 -2.2949 -2.0241
## (Intercept)-Dasypus_novemcinctus -1.7283 0.1594 -2.0589 -1.7231 -1.4259
## (Intercept)-Sylvilagus_floridanus -3.1822 0.2792 -3.7735 -3.1723 -2.6665
## shrub_cover-Odocoileus_virginianus -0.0539 0.0632 -0.1771 -0.0527 0.0677
## shrub_cover-Canis_latrans -0.3780 0.2253 -0.8100 -0.3803 0.0709
## shrub_cover-Procyon_lotor 0.2626 0.1647 -0.0715 0.2648 0.5726
## shrub_cover-Dasypus_novemcinctus 0.8078 0.3155 0.2210 0.7927 1.4387
## shrub_cover-Sylvilagus_floridanus 0.4044 0.4031 -0.3632 0.3945 1.2218
## veg_height-Odocoileus_virginianus -0.3008 0.0655 -0.4261 -0.3014 -0.1690
## veg_height-Canis_latrans -0.6336 0.1844 -1.0089 -0.6288 -0.2866
## veg_height-Procyon_lotor 0.3469 0.1267 0.0962 0.3460 0.5948
## veg_height-Dasypus_novemcinctus 0.2359 0.1335 -0.0232 0.2346 0.4985
## veg_height-Sylvilagus_floridanus 0.1549 0.2587 -0.3588 0.1564 0.6600
## week-Odocoileus_virginianus 0.2158 0.0611 0.0983 0.2146 0.3361
## week-Canis_latrans 0.0827 0.1306 -0.1782 0.0865 0.3325
## week-Procyon_lotor -0.0426 0.1202 -0.2871 -0.0395 0.1821
## week-Dasypus_novemcinctus -0.1596 0.1411 -0.4456 -0.1541 0.1004
## week-Sylvilagus_floridanus -0.1394 0.2139 -0.5916 -0.1269 0.2406
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0022 5250
## (Intercept)-Canis_latrans 1.0242 1892
## (Intercept)-Procyon_lotor 1.0007 3456
## (Intercept)-Dasypus_novemcinctus 1.0016 3444
## (Intercept)-Sylvilagus_floridanus 1.0076 1632
## shrub_cover-Odocoileus_virginianus 1.0023 5466
## shrub_cover-Canis_latrans 1.0020 1918
## shrub_cover-Procyon_lotor 1.0004 4053
## shrub_cover-Dasypus_novemcinctus 1.0016 1931
## shrub_cover-Sylvilagus_floridanus 1.0011 1249
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0022 2495
## veg_height-Procyon_lotor 1.0037 4085
## veg_height-Dasypus_novemcinctus 1.0011 4491
## veg_height-Sylvilagus_floridanus 1.0060 2452
## week-Odocoileus_virginianus 1.0049 5250
## week-Canis_latrans 1.0012 4280
## week-Procyon_lotor 1.0090 4533
## week-Dasypus_novemcinctus 1.0020 4733
## week-Sylvilagus_floridanus 1.0039 2976
#Includes all covariates of detection and only null for occupancy
ms_full_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1697
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6556 0.8598 -1.039 0.6632 2.3901 1.0011 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.6784 11.6445 0.6087 3.5778 32.4596 1.0467 1451
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6843 0.6784 -2.8676 -1.7389 -0.1762 1.0101 5250
## shrub_cover 0.1603 0.3021 -0.4087 0.1548 0.8057 1.0007 4423
## veg_height -0.0479 0.2809 -0.6190 -0.0489 0.5261 1.0020 4868
## week -0.0065 0.1872 -0.3916 -0.0027 0.3654 1.0000 4942
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9488 4.5817 0.5547 1.8370 11.4745 1.0385 5250
## shrub_cover 0.4280 0.6627 0.0525 0.2548 1.8014 1.0128 4750
## veg_height 0.4078 0.6418 0.0694 0.2535 1.5801 1.0467 5250
## week 0.1678 0.2960 0.0277 0.1040 0.7066 1.0563 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Odocoileus_virginianus 4.2001 1.8708 1.9289 3.7440 9.2791 1.0014
## (Intercept)-Canis_latrans 0.4743 0.4368 -0.3448 0.4577 1.3652 1.0028
## (Intercept)-Procyon_lotor 0.8016 0.4181 0.0413 0.7755 1.6915 0.9999
## (Intercept)-Dasypus_novemcinctus -0.5478 0.3802 -1.3033 -0.5438 0.1933 0.9998
## (Intercept)-Sylvilagus_floridanus -0.2486 0.5293 -1.1851 -0.2782 0.8978 1.0031
## ESS
## (Intercept)-Odocoileus_virginianus 637
## (Intercept)-Canis_latrans 4451
## (Intercept)-Procyon_lotor 4688
## (Intercept)-Dasypus_novemcinctus 4940
## (Intercept)-Sylvilagus_floridanus 2447
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0082 0.0598 -0.1130 0.0080 0.1257
## (Intercept)-Canis_latrans -2.7581 0.1933 -3.1645 -2.7516 -2.3963
## (Intercept)-Procyon_lotor -2.2940 0.1451 -2.5915 -2.2889 -2.0276
## (Intercept)-Dasypus_novemcinctus -1.7110 0.1541 -2.0238 -1.7089 -1.4195
## (Intercept)-Sylvilagus_floridanus -3.1897 0.3165 -3.8712 -3.1647 -2.6361
## shrub_cover-Odocoileus_virginianus -0.0542 0.0641 -0.1811 -0.0543 0.0698
## shrub_cover-Canis_latrans -0.2740 0.2152 -0.6961 -0.2738 0.1357
## shrub_cover-Procyon_lotor 0.2409 0.1642 -0.0944 0.2451 0.5524
## shrub_cover-Dasypus_novemcinctus 0.7263 0.2965 0.1791 0.7138 1.3412
## shrub_cover-Sylvilagus_floridanus 0.2011 0.3784 -0.5156 0.1837 0.9746
## veg_height-Odocoileus_virginianus -0.3022 0.0648 -0.4323 -0.3013 -0.1777
## veg_height-Canis_latrans -0.6175 0.1869 -0.9996 -0.6127 -0.2667
## veg_height-Procyon_lotor 0.3392 0.1267 0.0908 0.3384 0.5957
## veg_height-Dasypus_novemcinctus 0.2258 0.1336 -0.0328 0.2252 0.4909
## veg_height-Sylvilagus_floridanus 0.1186 0.2590 -0.3901 0.1188 0.6320
## week-Odocoileus_virginianus 0.2172 0.0620 0.0977 0.2159 0.3399
## week-Canis_latrans 0.0876 0.1327 -0.1762 0.0904 0.3373
## week-Procyon_lotor -0.0413 0.1170 -0.2750 -0.0396 0.1794
## week-Dasypus_novemcinctus -0.1584 0.1387 -0.4466 -0.1545 0.1038
## week-Sylvilagus_floridanus -0.1508 0.2185 -0.6235 -0.1366 0.2411
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5515
## (Intercept)-Canis_latrans 1.0119 2086
## (Intercept)-Procyon_lotor 1.0067 3564
## (Intercept)-Dasypus_novemcinctus 1.0000 4244
## (Intercept)-Sylvilagus_floridanus 1.0102 1378
## shrub_cover-Odocoileus_virginianus 1.0005 5250
## shrub_cover-Canis_latrans 1.0005 2407
## shrub_cover-Procyon_lotor 0.9999 3952
## shrub_cover-Dasypus_novemcinctus 1.0009 3655
## shrub_cover-Sylvilagus_floridanus 0.9999 1789
## veg_height-Odocoileus_virginianus 1.0014 5250
## veg_height-Canis_latrans 1.0018 2193
## veg_height-Procyon_lotor 1.0007 3880
## veg_height-Dasypus_novemcinctus 1.0002 4632
## veg_height-Sylvilagus_floridanus 1.0011 2533
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0033 4087
## week-Procyon_lotor 1.0000 4331
## week-Dasypus_novemcinctus 1.0025 4959
## week-Sylvilagus_floridanus 1.0027 2703
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2603
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0802 1.0316 -1.1251 1.0807 3.0885 1.0082 1896
## Avg_Cogongrass_Cover -0.0697 0.5656 -1.2340 -0.0622 1.0269 1.0032 2208
## total_shrub_cover -0.4787 0.7909 -2.1754 -0.4700 1.1655 1.0008 1559
## avg_veg_height 0.3888 0.5531 -0.6673 0.3634 1.5091 1.0104 833
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.1659 20.5644 0.4058 4.6896 52.0616 1.0318 772
## Avg_Cogongrass_Cover 0.9081 2.0775 0.0459 0.3410 5.0463 1.0185 3285
## total_shrub_cover 4.1703 9.8817 0.0926 1.6401 23.8980 1.0399 1517
## avg_veg_height 0.5387 1.0666 0.0411 0.2551 2.7452 1.0466 2614
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8848 1.3244 0.049 0.4361 4.6378 1.0208 790
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7167 0.7187 -2.9749 -1.7709 -0.1042 1.0020 5250
## shrub_cover 0.2912 0.3936 -0.4739 0.2788 1.1319 1.0039 2541
## veg_height -0.0710 0.2883 -0.6386 -0.0738 0.5043 1.0034 4973
## week -0.0117 0.1876 -0.3859 -0.0094 0.3413 1.0018 4498
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1576 4.7455 0.6022 1.9974 12.1886 1.0664 5250
## shrub_cover 0.7498 1.1690 0.0858 0.4405 3.3035 1.0073 3051
## veg_height 0.4350 0.6171 0.0699 0.2678 1.8272 1.0072 4998
## week 0.1673 0.2661 0.0286 0.1011 0.6922 1.0219 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.1382 2.4632 1.8701 4.6243
## (Intercept)-Canis_latrans 1.0746 0.9373 -0.4751 0.9793
## (Intercept)-Procyon_lotor 1.2768 0.8365 -0.2194 1.2268
## (Intercept)-Dasypus_novemcinctus -0.2234 0.9672 -1.7290 -0.3819
## (Intercept)-Sylvilagus_floridanus 1.0185 1.4999 -1.1564 0.7994
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0549 0.8220 -1.6253 -0.0602
## Avg_Cogongrass_Cover-Canis_latrans 0.3430 0.6629 -0.8630 0.2991
## Avg_Cogongrass_Cover-Procyon_lotor -0.2015 0.6023 -1.4600 -0.1869
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.0901 0.5326 -0.9272 0.0849
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5645 0.7898 -2.3787 -0.4855
## total_shrub_cover-Odocoileus_virginianus -0.1019 1.1311 -2.2680 -0.1638
## total_shrub_cover-Canis_latrans 0.7519 1.0379 -0.7909 0.5528
## total_shrub_cover-Procyon_lotor -1.4865 0.8272 -3.3921 -1.3740
## total_shrub_cover-Dasypus_novemcinctus -0.4598 0.9955 -3.2843 -0.2106
## total_shrub_cover-Sylvilagus_floridanus -1.9000 1.8640 -6.4917 -1.5547
## avg_veg_height-Odocoileus_virginianus 0.3399 0.7492 -1.1360 0.3212
## avg_veg_height-Canis_latrans 0.3887 0.6142 -0.7302 0.3614
## avg_veg_height-Procyon_lotor 0.3532 0.5916 -0.7555 0.3301
## avg_veg_height-Dasypus_novemcinctus 0.5563 0.6157 -0.4738 0.4989
## avg_veg_height-Sylvilagus_floridanus 0.3334 0.7493 -1.0878 0.2928
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.9551 1.0228 471
## (Intercept)-Canis_latrans 3.2448 1.0004 1114
## (Intercept)-Procyon_lotor 3.0885 1.0000 1923
## (Intercept)-Dasypus_novemcinctus 2.1872 1.0689 388
## (Intercept)-Sylvilagus_floridanus 4.5303 1.0307 385
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6419 1.0024 2517
## Avg_Cogongrass_Cover-Canis_latrans 1.7876 1.0056 2065
## Avg_Cogongrass_Cover-Procyon_lotor 0.9434 1.0029 2044
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1325 1.0043 2910
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7641 1.0106 1592
## total_shrub_cover-Odocoileus_virginianus 2.3376 1.0044 1875
## total_shrub_cover-Canis_latrans 3.2327 1.0031 717
## total_shrub_cover-Procyon_lotor -0.1845 1.0004 1049
## total_shrub_cover-Dasypus_novemcinctus 0.7268 1.0611 303
## total_shrub_cover-Sylvilagus_floridanus 0.7740 1.0081 306
## avg_veg_height-Odocoileus_virginianus 1.8905 1.0083 1506
## avg_veg_height-Canis_latrans 1.6673 1.0101 1402
## avg_veg_height-Procyon_lotor 1.5821 1.0181 1390
## avg_veg_height-Dasypus_novemcinctus 1.9730 1.0238 678
## avg_veg_height-Sylvilagus_floridanus 1.8934 1.0092 629
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0078 0.0599 -0.1087 0.0074 0.1244
## (Intercept)-Canis_latrans -2.8346 0.2025 -3.2489 -2.8270 -2.4538
## (Intercept)-Procyon_lotor -2.2991 0.1374 -2.5754 -2.2965 -2.0339
## (Intercept)-Dasypus_novemcinctus -1.7838 0.1919 -2.1874 -1.7743 -1.4356
## (Intercept)-Sylvilagus_floridanus -3.3966 0.3082 -4.0064 -3.3896 -2.7896
## shrub_cover-Odocoileus_virginianus -0.0544 0.0638 -0.1829 -0.0539 0.0724
## shrub_cover-Canis_latrans -0.3636 0.2538 -0.8435 -0.3691 0.1422
## shrub_cover-Procyon_lotor 0.3238 0.1600 0.0028 0.3296 0.6282
## shrub_cover-Dasypus_novemcinctus 0.9822 0.4154 0.2909 0.9292 1.8510
## shrub_cover-Sylvilagus_floridanus 0.6901 0.4930 -0.3383 0.7181 1.5892
## veg_height-Odocoileus_virginianus -0.3018 0.0654 -0.4292 -0.3018 -0.1736
## veg_height-Canis_latrans -0.6628 0.1979 -1.0670 -0.6549 -0.2919
## veg_height-Procyon_lotor 0.3374 0.1242 0.0862 0.3370 0.5821
## veg_height-Dasypus_novemcinctus 0.2400 0.1400 -0.0254 0.2368 0.5184
## veg_height-Sylvilagus_floridanus 0.0196 0.2765 -0.5005 0.0113 0.5851
## week-Odocoileus_virginianus 0.2144 0.0612 0.0976 0.2141 0.3350
## week-Canis_latrans 0.0858 0.1315 -0.1856 0.0890 0.3335
## week-Procyon_lotor -0.0440 0.1195 -0.2850 -0.0400 0.1876
## week-Dasypus_novemcinctus -0.1628 0.1395 -0.4455 -0.1603 0.1018
## week-Sylvilagus_floridanus -0.1423 0.2176 -0.6108 -0.1297 0.2531
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0052 5289
## (Intercept)-Canis_latrans 1.0055 1429
## (Intercept)-Procyon_lotor 1.0012 3533
## (Intercept)-Dasypus_novemcinctus 1.0187 720
## (Intercept)-Sylvilagus_floridanus 1.0120 860
## shrub_cover-Odocoileus_virginianus 1.0039 5250
## shrub_cover-Canis_latrans 1.0000 1176
## shrub_cover-Procyon_lotor 1.0002 3828
## shrub_cover-Dasypus_novemcinctus 1.0266 465
## shrub_cover-Sylvilagus_floridanus 1.0054 439
## veg_height-Odocoileus_virginianus 1.0014 5250
## veg_height-Canis_latrans 1.0016 2065
## veg_height-Procyon_lotor 1.0083 3996
## veg_height-Dasypus_novemcinctus 1.0002 3986
## veg_height-Sylvilagus_floridanus 1.0062 1169
## week-Odocoileus_virginianus 1.0008 4730
## week-Canis_latrans 1.0001 4053
## week-Procyon_lotor 0.9999 4150
## week-Dasypus_novemcinctus 1.0021 4559
## week-Sylvilagus_floridanus 1.0023 2442
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1637
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6339 1.0471 -1.5994 0.6638 2.6437 1.0021 4757
## Tree_Density -0.8706 0.5878 -2.1542 -0.8447 0.2517 1.0028 3121
## Avg_Canopy_Cover 0.8183 0.6815 -0.5368 0.8229 2.1965 1.0017 4288
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.3595 22.0636 1.1437 7.2552 63.4473 1.0061 1256
## Tree_Density 1.4745 4.0295 0.0493 0.4752 8.9898 1.0608 1811
## Avg_Canopy_Cover 2.8517 5.7314 0.1150 1.3266 14.8226 1.0354 2337
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4922 0.7028 0.041 0.264 2.3437 1.0334 1105
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6933 0.6778 -2.8961 -1.7499 -0.1407 1.0024 5238
## shrub_cover 0.2074 0.3138 -0.4070 0.2023 0.8551 1.0025 4253
## veg_height -0.0429 0.2882 -0.6000 -0.0437 0.5633 1.0010 4935
## week -0.0094 0.1877 -0.4045 -0.0052 0.3542 0.9999 4905
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.7926 3.5323 0.5445 1.7990 11.1214 1.0126 5250
## shrub_cover 0.4646 0.7244 0.0575 0.2756 2.0160 1.0209 4522
## veg_height 0.4275 0.6107 0.0704 0.2699 1.7607 1.0077 5250
## week 0.1761 0.3130 0.0290 0.1033 0.7758 1.0008 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 5.7094 2.4935 2.3463 5.2186 12.1556
## (Intercept)-Canis_latrans 0.4907 0.6827 -0.7212 0.4566 1.8692
## (Intercept)-Procyon_lotor 0.9020 0.6764 -0.3821 0.8846 2.3669
## (Intercept)-Dasypus_novemcinctus -0.9840 0.6758 -2.4365 -0.9520 0.2493
## (Intercept)-Sylvilagus_floridanus -0.5502 0.8179 -2.0955 -0.5716 1.1327
## Tree_Density-Odocoileus_virginianus -0.4174 0.8728 -1.8168 -0.5213 1.7332
## Tree_Density-Canis_latrans -1.0581 0.6366 -2.5549 -0.9933 -0.0050
## Tree_Density-Procyon_lotor -0.5229 0.4539 -1.4258 -0.5255 0.3477
## Tree_Density-Dasypus_novemcinctus -1.6010 1.0810 -4.3815 -1.3454 -0.2118
## Tree_Density-Sylvilagus_floridanus -1.2360 0.9477 -3.6063 -1.0819 0.1466
## Avg_Canopy_Cover-Odocoileus_virginianus 0.5897 1.0489 -1.5139 0.5705 2.8218
## Avg_Canopy_Cover-Canis_latrans -0.2345 0.4968 -1.2431 -0.2323 0.7118
## Avg_Canopy_Cover-Procyon_lotor 1.0458 0.5591 0.0774 1.0039 2.2672
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0476 0.5116 0.1414 1.0154 2.1371
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4758 1.3922 0.6320 2.2056 5.8684
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0031 613
## (Intercept)-Canis_latrans 1.0072 2472
## (Intercept)-Procyon_lotor 1.0063 2827
## (Intercept)-Dasypus_novemcinctus 1.0063 2375
## (Intercept)-Sylvilagus_floridanus 1.0030 1939
## Tree_Density-Odocoileus_virginianus 1.0019 2003
## Tree_Density-Canis_latrans 1.0009 3079
## Tree_Density-Procyon_lotor 1.0063 3525
## Tree_Density-Dasypus_novemcinctus 1.0040 1454
## Tree_Density-Sylvilagus_floridanus 1.0032 1387
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0019 2372
## Avg_Canopy_Cover-Canis_latrans 1.0019 3508
## Avg_Canopy_Cover-Procyon_lotor 1.0018 3943
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0050 4392
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0062 1202
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0062 0.0595 -0.1100 0.0065 0.1220
## (Intercept)-Canis_latrans -2.7724 0.1913 -3.1680 -2.7665 -2.4017
## (Intercept)-Procyon_lotor -2.2952 0.1482 -2.5971 -2.2894 -2.0235
## (Intercept)-Dasypus_novemcinctus -1.7188 0.1579 -2.0493 -1.7113 -1.4249
## (Intercept)-Sylvilagus_floridanus -3.1397 0.2740 -3.7225 -3.1260 -2.6374
## shrub_cover-Odocoileus_virginianus -0.0544 0.0643 -0.1797 -0.0531 0.0692
## shrub_cover-Canis_latrans -0.2804 0.2213 -0.7234 -0.2747 0.1326
## shrub_cover-Procyon_lotor 0.2488 0.1587 -0.0734 0.2546 0.5602
## shrub_cover-Dasypus_novemcinctus 0.7635 0.2926 0.2169 0.7579 1.3551
## shrub_cover-Sylvilagus_floridanus 0.3791 0.3647 -0.3071 0.3736 1.1217
## veg_height-Odocoileus_virginianus -0.3013 0.0647 -0.4254 -0.3014 -0.1741
## veg_height-Canis_latrans -0.6284 0.1875 -0.9969 -0.6256 -0.2693
## veg_height-Procyon_lotor 0.3420 0.1257 0.1013 0.3418 0.5853
## veg_height-Dasypus_novemcinctus 0.2320 0.1348 -0.0286 0.2301 0.4980
## veg_height-Sylvilagus_floridanus 0.1540 0.2511 -0.3441 0.1550 0.6480
## week-Odocoileus_virginianus 0.2154 0.0632 0.0934 0.2128 0.3406
## week-Canis_latrans 0.0805 0.1327 -0.1966 0.0835 0.3312
## week-Procyon_lotor -0.0435 0.1204 -0.2948 -0.0378 0.1827
## week-Dasypus_novemcinctus -0.1601 0.1394 -0.4591 -0.1527 0.0958
## week-Sylvilagus_floridanus -0.1509 0.2218 -0.6133 -0.1358 0.2452
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0048 2164
## (Intercept)-Procyon_lotor 1.0002 3673
## (Intercept)-Dasypus_novemcinctus 1.0002 4437
## (Intercept)-Sylvilagus_floridanus 1.0001 1827
## shrub_cover-Odocoileus_virginianus 1.0011 5250
## shrub_cover-Canis_latrans 1.0035 2823
## shrub_cover-Procyon_lotor 1.0025 4028
## shrub_cover-Dasypus_novemcinctus 1.0011 3501
## shrub_cover-Sylvilagus_floridanus 1.0044 2183
## veg_height-Odocoileus_virginianus 1.0042 5250
## veg_height-Canis_latrans 1.0063 2460
## veg_height-Procyon_lotor 1.0013 3682
## veg_height-Dasypus_novemcinctus 1.0002 4690
## veg_height-Sylvilagus_floridanus 1.0010 2992
## week-Odocoileus_virginianus 1.0035 5250
## week-Canis_latrans 1.0006 4349
## week-Procyon_lotor 0.9999 4364
## week-Dasypus_novemcinctus 1.0003 5250
## week-Sylvilagus_floridanus 1.0066 3032
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2773
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8888 1.0147 -1.2506 0.9238 2.8481 1.0056 3043
## Cogon_Patch_Size 0.0250 0.6249 -1.2152 0.0075 1.3639 1.0080 2600
## Avg_Cogongrass_Cover 0.1745 0.4810 -0.7652 0.1648 1.1774 1.0018 2654
## total_shrub_cover -0.4436 0.6771 -1.8606 -0.4264 0.9173 1.0044 2234
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.9608 20.0031 0.4574 5.0431 48.9106 1.0023 1501
## Cogon_Patch_Size 2.0403 4.9714 0.0568 0.6293 13.3193 1.0474 1550
## Avg_Cogongrass_Cover 0.6545 1.4175 0.0415 0.2885 3.3577 1.0166 3870
## total_shrub_cover 2.2567 4.9745 0.0710 0.8930 12.9972 1.0415 1109
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8559 1.3152 0.0527 0.4146 4.1986 1.0179 472
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6878 0.7156 -2.9302 -1.7545 -0.0493 1.0039 5250
## shrub_cover 0.2777 0.3653 -0.4155 0.2660 1.0386 1.0018 3365
## veg_height -0.0628 0.2918 -0.6602 -0.0609 0.5093 1.0002 5250
## week -0.0135 0.1933 -0.3978 -0.0093 0.3546 1.0005 4982
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1354 4.7095 0.5727 1.9613 13.5647 1.0516 5250
## shrub_cover 0.6719 2.0426 0.0682 0.3760 2.8401 1.1763 4520
## veg_height 0.4104 0.5591 0.0677 0.2556 1.6971 1.0021 5250
## week 0.1669 0.2563 0.0283 0.1047 0.6833 1.0399 4830
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0543 2.3236 1.8066 4.6506
## (Intercept)-Canis_latrans 0.9859 0.8590 -0.4849 0.9136
## (Intercept)-Procyon_lotor 1.1318 0.7846 -0.2894 1.0808
## (Intercept)-Dasypus_novemcinctus -0.4220 0.7498 -1.7603 -0.4759
## (Intercept)-Sylvilagus_floridanus 0.3453 1.2141 -1.6612 0.2304
## Cogon_Patch_Size-Odocoileus_virginianus 0.2401 1.0406 -1.3460 0.1014
## Cogon_Patch_Size-Canis_latrans 0.9721 1.0820 -0.4324 0.7375
## Cogon_Patch_Size-Procyon_lotor -0.1316 0.5035 -1.1379 -0.1229
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0448 0.4825 -0.9968 -0.0436
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9006 1.2556 -4.1406 -0.6309
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1825 0.7396 -1.1943 0.1598
## Avg_Cogongrass_Cover-Canis_latrans 0.3860 0.5512 -0.5545 0.3369
## Avg_Cogongrass_Cover-Procyon_lotor 0.0999 0.5188 -0.9423 0.0967
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3726 0.4584 -0.4497 0.3550
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1666 0.6747 -1.6206 -0.1457
## total_shrub_cover-Odocoileus_virginianus -0.1978 0.9452 -1.9956 -0.2314
## total_shrub_cover-Canis_latrans 0.4090 0.8441 -0.8795 0.2628
## total_shrub_cover-Procyon_lotor -1.2862 0.7652 -3.1078 -1.1805
## total_shrub_cover-Dasypus_novemcinctus -0.2901 0.7000 -1.9818 -0.1966
## total_shrub_cover-Sylvilagus_floridanus -1.2372 1.3504 -4.4990 -0.9962
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.0140 1.0111 628
## (Intercept)-Canis_latrans 2.8781 1.0197 1459
## (Intercept)-Procyon_lotor 2.7942 1.0034 1776
## (Intercept)-Dasypus_novemcinctus 1.2237 1.0112 700
## (Intercept)-Sylvilagus_floridanus 3.1583 1.0123 673
## Cogon_Patch_Size-Odocoileus_virginianus 2.7582 1.0233 1799
## Cogon_Patch_Size-Canis_latrans 3.8566 1.0066 1367
## Cogon_Patch_Size-Procyon_lotor 0.8635 1.0019 3045
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9129 1.0056 2489
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7176 1.0158 1096
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7647 1.0014 2689
## Avg_Cogongrass_Cover-Canis_latrans 1.6429 1.0020 2859
## Avg_Cogongrass_Cover-Procyon_lotor 1.1299 1.0013 2783
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3534 1.0005 3321
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0835 1.0008 2110
## total_shrub_cover-Odocoileus_virginianus 1.8489 1.0010 2535
## total_shrub_cover-Canis_latrans 2.4736 1.0120 1138
## total_shrub_cover-Procyon_lotor -0.1170 1.0074 1169
## total_shrub_cover-Dasypus_novemcinctus 0.6954 1.0608 564
## total_shrub_cover-Sylvilagus_floridanus 0.7570 1.0313 518
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0084 0.0598 -0.1093 0.0087 0.1254
## (Intercept)-Canis_latrans -2.7817 0.1973 -3.1811 -2.7755 -2.4183
## (Intercept)-Procyon_lotor -2.3042 0.1429 -2.5863 -2.3014 -2.0315
## (Intercept)-Dasypus_novemcinctus -1.7544 0.1761 -2.1371 -1.7423 -1.4378
## (Intercept)-Sylvilagus_floridanus -3.3389 0.3102 -3.9714 -3.3298 -2.7576
## shrub_cover-Odocoileus_virginianus -0.0547 0.0632 -0.1762 -0.0548 0.0655
## shrub_cover-Canis_latrans -0.3270 0.2509 -0.8246 -0.3206 0.1536
## shrub_cover-Procyon_lotor 0.3060 0.1623 -0.0199 0.3083 0.6128
## shrub_cover-Dasypus_novemcinctus 0.8891 0.3698 0.2497 0.8604 1.7039
## shrub_cover-Sylvilagus_floridanus 0.6151 0.4712 -0.3611 0.6310 1.4938
## veg_height-Odocoileus_virginianus -0.3008 0.0649 -0.4277 -0.3012 -0.1742
## veg_height-Canis_latrans -0.6318 0.1935 -1.0240 -0.6278 -0.2668
## veg_height-Procyon_lotor 0.3372 0.1249 0.0928 0.3367 0.5875
## veg_height-Dasypus_novemcinctus 0.2370 0.1399 -0.0323 0.2335 0.5095
## veg_height-Sylvilagus_floridanus 0.0387 0.2672 -0.4762 0.0338 0.5866
## week-Odocoileus_virginianus 0.2159 0.0611 0.0983 0.2160 0.3338
## week-Canis_latrans 0.0830 0.1341 -0.1865 0.0881 0.3314
## week-Procyon_lotor -0.0426 0.1207 -0.2883 -0.0382 0.1842
## week-Dasypus_novemcinctus -0.1612 0.1389 -0.4512 -0.1565 0.1021
## week-Sylvilagus_floridanus -0.1503 0.2238 -0.6202 -0.1312 0.2423
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0037 1823
## (Intercept)-Procyon_lotor 1.0014 3287
## (Intercept)-Dasypus_novemcinctus 1.0048 1785
## (Intercept)-Sylvilagus_floridanus 1.0198 949
## shrub_cover-Odocoileus_virginianus 1.0007 5250
## shrub_cover-Canis_latrans 1.0116 1514
## shrub_cover-Procyon_lotor 1.0003 3272
## shrub_cover-Dasypus_novemcinctus 1.0166 965
## shrub_cover-Sylvilagus_floridanus 1.0189 748
## veg_height-Odocoileus_virginianus 1.0009 5250
## veg_height-Canis_latrans 1.0059 1962
## veg_height-Procyon_lotor 1.0021 3936
## veg_height-Dasypus_novemcinctus 1.0019 3953
## veg_height-Sylvilagus_floridanus 1.0173 1650
## week-Odocoileus_virginianus 1.0044 5250
## week-Canis_latrans 1.0024 4663
## week-Procyon_lotor 1.0006 4363
## week-Dasypus_novemcinctus 1.0066 4241
## week-Sylvilagus_floridanus 0.9998 2327
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1473
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7024 0.9566 -1.2748 0.7029 2.5796 1.0016 4779
## Veg_shannon_index 0.4836 0.4076 -0.3117 0.4777 1.3294 1.0077 2807
## Avg_Cogongrass_Cover 0.3944 0.4552 -0.4778 0.3798 1.3375 1.0071 2711
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.2345 17.8155 0.5757 4.6905 47.3513 1.0032 1318
## Veg_shannon_index 0.4847 1.0504 0.0395 0.2312 2.4864 1.0185 4124
## Avg_Cogongrass_Cover 0.6924 1.1936 0.0451 0.3230 3.7179 1.0010 3407
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6652 0.9981 0.0466 0.339 3.2106 1.0641 943
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6751 0.6908 -2.9075 -1.7317 -0.0882 1.0011 5250
## shrub_cover 0.1603 0.3027 -0.4041 0.1457 0.7824 1.0008 4488
## veg_height -0.0520 0.2804 -0.6021 -0.0570 0.5267 1.0006 4979
## week -0.0069 0.1924 -0.4188 -0.0004 0.3569 1.0055 4673
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0118 5.2674 0.5553 1.8548 11.9854 1.0436 5009
## shrub_cover 0.4189 0.7000 0.0503 0.2447 1.8152 1.0246 4802
## veg_height 0.4063 0.5956 0.0649 0.2542 1.6492 1.0045 4921
## week 0.1655 0.2197 0.0284 0.1020 0.7103 1.0106 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.7131 2.2495 1.6386 4.2622
## (Intercept)-Canis_latrans 0.6026 0.6768 -0.6427 0.5707
## (Intercept)-Procyon_lotor 0.7457 0.6441 -0.5349 0.7482
## (Intercept)-Dasypus_novemcinctus -0.5748 0.5978 -1.7328 -0.5762
## (Intercept)-Sylvilagus_floridanus -0.1198 1.0482 -1.6556 -0.2493
## Veg_shannon_index-Odocoileus_virginianus 0.3986 0.6363 -0.9216 0.4121
## Veg_shannon_index-Canis_latrans 0.7627 0.4707 -0.0591 0.7227
## Veg_shannon_index-Procyon_lotor 0.5418 0.4360 -0.2318 0.5193
## Veg_shannon_index-Dasypus_novemcinctus 0.2412 0.3855 -0.5307 0.2456
## Veg_shannon_index-Sylvilagus_floridanus 0.5568 0.5212 -0.3505 0.5272
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3970 0.6988 -0.9299 0.3762
## Avg_Cogongrass_Cover-Canis_latrans 0.8063 0.5648 -0.0654 0.7369
## Avg_Cogongrass_Cover-Procyon_lotor 0.4577 0.4596 -0.3730 0.4207
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4838 0.3731 -0.2329 0.4778
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1262 0.5785 -1.3486 -0.1044
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.6723 1.0112 543
## (Intercept)-Canis_latrans 2.0486 1.0083 2585
## (Intercept)-Procyon_lotor 2.0395 1.0021 2465
## (Intercept)-Dasypus_novemcinctus 0.6367 1.0010 2917
## (Intercept)-Sylvilagus_floridanus 2.1256 1.0698 418
## Veg_shannon_index-Odocoileus_virginianus 1.6389 1.0012 2826
## Veg_shannon_index-Canis_latrans 1.7931 1.0074 2417
## Veg_shannon_index-Procyon_lotor 1.4662 1.0033 3128
## Veg_shannon_index-Dasypus_novemcinctus 0.9806 1.0013 4228
## Veg_shannon_index-Sylvilagus_floridanus 1.7005 1.0100 2290
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9058 1.0057 3454
## Avg_Cogongrass_Cover-Canis_latrans 2.1628 1.0036 2655
## Avg_Cogongrass_Cover-Procyon_lotor 1.4698 1.0005 3002
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2444 1.0005 4607
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9574 1.0017 2239
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0067 0.0601 -0.1100 0.0061 0.1251
## (Intercept)-Canis_latrans -2.7586 0.1882 -3.1425 -2.7555 -2.4038
## (Intercept)-Procyon_lotor -2.3010 0.1491 -2.6028 -2.2958 -2.0226
## (Intercept)-Dasypus_novemcinctus -1.7066 0.1580 -2.0277 -1.7018 -1.4106
## (Intercept)-Sylvilagus_floridanus -3.2489 0.3510 -4.0330 -3.2207 -2.6555
## shrub_cover-Odocoileus_virginianus -0.0538 0.0655 -0.1840 -0.0534 0.0741
## shrub_cover-Canis_latrans -0.2563 0.2116 -0.6760 -0.2530 0.1467
## shrub_cover-Procyon_lotor 0.2234 0.1742 -0.1393 0.2309 0.5391
## shrub_cover-Dasypus_novemcinctus 0.7174 0.3036 0.1617 0.7100 1.3288
## shrub_cover-Sylvilagus_floridanus 0.1687 0.3640 -0.4963 0.1469 0.9572
## veg_height-Odocoileus_virginianus -0.3008 0.0650 -0.4296 -0.2998 -0.1746
## veg_height-Canis_latrans -0.6264 0.1873 -1.0069 -0.6181 -0.2681
## veg_height-Procyon_lotor 0.3343 0.1238 0.0961 0.3352 0.5817
## veg_height-Dasypus_novemcinctus 0.2186 0.1334 -0.0405 0.2176 0.4799
## veg_height-Sylvilagus_floridanus 0.1246 0.2574 -0.3739 0.1259 0.6237
## week-Odocoileus_virginianus 0.2158 0.0620 0.0944 0.2150 0.3405
## week-Canis_latrans 0.0865 0.1339 -0.1941 0.0907 0.3373
## week-Procyon_lotor -0.0445 0.1200 -0.2922 -0.0404 0.1794
## week-Dasypus_novemcinctus -0.1609 0.1402 -0.4451 -0.1576 0.1025
## week-Sylvilagus_floridanus -0.1374 0.2176 -0.6085 -0.1252 0.2523
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0035 2048
## (Intercept)-Procyon_lotor 1.0026 3287
## (Intercept)-Dasypus_novemcinctus 1.0010 4286
## (Intercept)-Sylvilagus_floridanus 1.0105 761
## shrub_cover-Odocoileus_virginianus 1.0024 5250
## shrub_cover-Canis_latrans 1.0048 2955
## shrub_cover-Procyon_lotor 1.0006 3299
## shrub_cover-Dasypus_novemcinctus 1.0013 3567
## shrub_cover-Sylvilagus_floridanus 1.0025 2046
## veg_height-Odocoileus_virginianus 1.0034 4500
## veg_height-Canis_latrans 1.0032 2077
## veg_height-Procyon_lotor 1.0019 4290
## veg_height-Dasypus_novemcinctus 1.0017 4860
## veg_height-Sylvilagus_floridanus 1.0027 2379
## week-Odocoileus_virginianus 1.0021 5503
## week-Canis_latrans 1.0018 3841
## week-Procyon_lotor 1.0035 4701
## week-Dasypus_novemcinctus 1.0002 4754
## week-Sylvilagus_floridanus 1.0038 2864
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.15
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1156 0.9380 -1.6583 0.0844 2.0870 1.0030 3432
## Avg_Cogongrass_Cover -0.4987 0.6318 -1.7249 -0.5146 0.8123 1.0088 2165
## I(Avg_Cogongrass_Cover^2) 1.0869 0.6903 -0.0834 1.0170 2.7074 1.0156 1054
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.4675 14.3381 0.3732 3.8382 37.2876 1.0373 1535
## Avg_Cogongrass_Cover 1.4660 4.0836 0.0515 0.5728 8.0443 1.0717 2685
## I(Avg_Cogongrass_Cover^2) 6.0685 149.9226 0.0487 0.4577 17.5201 1.3738 1089
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5081 0.7708 0.0419 0.2716 2.4125 1.0127 845
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6971 0.6803 -2.9289 -1.7492 -0.2051 1.0056 5250
## shrub_cover 0.1571 0.2978 -0.4381 0.1514 0.7606 1.0023 4171
## veg_height -0.0424 0.2865 -0.6184 -0.0454 0.5247 1.0037 5003
## week -0.0119 0.1952 -0.4143 -0.0075 0.3668 1.0037 4955
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9460 4.2032 0.5556 1.8571 11.7709 1.0116 5250
## shrub_cover 0.4199 0.6652 0.0498 0.2424 1.9023 1.0090 4333
## veg_height 0.4309 0.7295 0.0690 0.2650 1.7810 1.0695 4564
## week 0.1683 0.2308 0.0281 0.1009 0.8002 1.0108 4649
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5106 2.1958 0.4009 3.1516
## (Intercept)-Canis_latrans -0.3510 0.8184 -2.0271 -0.3442
## (Intercept)-Procyon_lotor -0.0292 0.6829 -1.4055 -0.0251
## (Intercept)-Dasypus_novemcinctus -1.1589 0.6950 -2.5349 -1.1566
## (Intercept)-Sylvilagus_floridanus -0.9706 0.8630 -2.7124 -0.9564
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.5251 1.0263 -2.5155 -0.5272
## Avg_Cogongrass_Cover-Canis_latrans 0.0103 0.7697 -1.2542 -0.0715
## Avg_Cogongrass_Cover-Procyon_lotor -0.5073 0.6762 -1.8211 -0.5173
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3456 0.5975 -1.5087 -0.3544
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3450 1.0058 -3.6955 -1.2093
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9084 2.9650 -0.1048 1.2995
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6562 1.1783 0.1099 1.3849
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.4220 1.1433 0.1399 1.1301
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6567 0.4534 -0.1881 0.6382
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8932 0.7436 -0.2105 0.7943
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.7699 1.0394 555
## (Intercept)-Canis_latrans 1.2521 1.0062 1509
## (Intercept)-Procyon_lotor 1.3187 1.0100 1973
## (Intercept)-Dasypus_novemcinctus 0.2086 1.0081 2795
## (Intercept)-Sylvilagus_floridanus 0.6947 1.0024 2087
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6181 1.0056 2571
## Avg_Cogongrass_Cover-Canis_latrans 1.7358 1.0026 2038
## Avg_Cogongrass_Cover-Procyon_lotor 0.8739 1.0056 2896
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8742 1.0023 2917
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2308 1.0063 1275
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 7.3591 1.4588 147
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7299 1.0305 547
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.4746 1.0660 371
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5902 1.0010 2812
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.6722 1.0331 901
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0082 0.0600 -0.1104 0.0076 0.1261
## (Intercept)-Canis_latrans -2.7679 0.1875 -3.1514 -2.7594 -2.4163
## (Intercept)-Procyon_lotor -2.3344 0.1573 -2.6470 -2.3282 -2.0446
## (Intercept)-Dasypus_novemcinctus -1.7088 0.1597 -2.0359 -1.7043 -1.4054
## (Intercept)-Sylvilagus_floridanus -3.2351 0.3274 -3.9436 -3.2077 -2.6620
## shrub_cover-Odocoileus_virginianus -0.0545 0.0642 -0.1790 -0.0541 0.0711
## shrub_cover-Canis_latrans -0.2350 0.2169 -0.6660 -0.2336 0.1778
## shrub_cover-Procyon_lotor 0.2038 0.1717 -0.1369 0.2091 0.5225
## shrub_cover-Dasypus_novemcinctus 0.7224 0.3021 0.1691 0.7056 1.3522
## shrub_cover-Sylvilagus_floridanus 0.1834 0.3656 -0.4746 0.1622 0.9676
## veg_height-Odocoileus_virginianus -0.3015 0.0640 -0.4256 -0.3009 -0.1771
## veg_height-Canis_latrans -0.6180 0.1887 -1.0130 -0.6115 -0.2687
## veg_height-Procyon_lotor 0.3472 0.1291 0.0936 0.3466 0.6047
## veg_height-Dasypus_novemcinctus 0.2197 0.1344 -0.0410 0.2150 0.4921
## veg_height-Sylvilagus_floridanus 0.1544 0.2718 -0.3721 0.1501 0.7080
## week-Odocoileus_virginianus 0.2154 0.0613 0.0982 0.2146 0.3387
## week-Canis_latrans 0.0863 0.1325 -0.1785 0.0879 0.3399
## week-Procyon_lotor -0.0448 0.1191 -0.2923 -0.0416 0.1799
## week-Dasypus_novemcinctus -0.1597 0.1396 -0.4407 -0.1565 0.1009
## week-Sylvilagus_floridanus -0.1444 0.2199 -0.6236 -0.1246 0.2387
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0018 2296
## (Intercept)-Procyon_lotor 1.0110 2173
## (Intercept)-Dasypus_novemcinctus 1.0015 4086
## (Intercept)-Sylvilagus_floridanus 1.0162 882
## shrub_cover-Odocoileus_virginianus 0.9999 5250
## shrub_cover-Canis_latrans 1.0004 2651
## shrub_cover-Procyon_lotor 1.0039 2352
## shrub_cover-Dasypus_novemcinctus 1.0000 3676
## shrub_cover-Sylvilagus_floridanus 0.9999 1984
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 0.9999 2394
## veg_height-Procyon_lotor 1.0006 3948
## veg_height-Dasypus_novemcinctus 1.0000 4722
## veg_height-Sylvilagus_floridanus 1.0075 1579
## week-Odocoileus_virginianus 1.0028 5588
## week-Canis_latrans 1.0021 4543
## week-Procyon_lotor 1.0033 4237
## week-Dasypus_novemcinctus 1.0013 5250
## week-Sylvilagus_floridanus 1.0014 2733
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1707
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1586 1.3876 -2.5278 0.1207 2.9468 1.0004 3902
## Cogon_Patch_Size -0.1733 0.9539 -2.1245 -0.1904 1.7983 1.0018 2046
## Veg_shannon_index 1.0385 0.6926 -0.2400 1.0154 2.4602 1.0121 659
## total_shrub_cover -0.2196 0.7442 -1.6929 -0.2274 1.3063 1.0061 2145
## Avg_Cogongrass_Cover 0.1939 1.1692 -2.0697 0.1619 2.4590 1.0014 1175
## Tree_Density -1.8224 1.2991 -4.1501 -1.9300 1.1186 1.0055 1564
## Avg_Canopy_Cover 1.2629 1.0576 -0.9896 1.3212 3.3123 1.0102 2936
## I(Avg_Cogongrass_Cover^2) 1.7229 0.8848 0.0552 1.6617 3.5905 1.0083 1019
## avg_veg_height -0.0362 0.7058 -1.5149 -0.0215 1.3202 1.0009 1106
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 68.9396 187.1263 3.0649 27.7346 392.7605 1.0702 620
## Cogon_Patch_Size 6.8255 21.1701 0.0785 1.6539 44.2219 1.0291 794
## Veg_shannon_index 1.0854 3.3824 0.0477 0.4038 5.6652 1.1889 2530
## total_shrub_cover 2.5086 5.1294 0.0726 1.0069 14.9434 1.0448 1883
## Avg_Cogongrass_Cover 5.5701 15.8939 0.0653 1.5210 34.6404 1.0743 766
## Tree_Density 22.3052 58.4125 0.1069 5.4813 157.1953 1.0033 406
## Avg_Canopy_Cover 12.7186 28.2514 0.2154 4.4757 81.0041 1.0070 315
## I(Avg_Cogongrass_Cover^2) 2.9816 11.4220 0.0500 0.5485 20.5914 1.0107 565
## avg_veg_height 0.9545 2.1422 0.0440 0.3726 5.4230 1.0010 3695
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.0489 11.2722 0.0544 0.7092 18.1989 1.4207 136
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6998 0.6831 -2.9399 -1.7525 -0.1843 1.0004 5250
## shrub_cover 0.2166 0.3306 -0.4440 0.2154 0.8694 1.0015 4236
## veg_height -0.0311 0.2877 -0.6038 -0.0328 0.5326 1.0024 4836
## week -0.0066 0.1955 -0.3919 -0.0008 0.3530 1.0001 4832
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8452 3.9753 0.5485 1.8033 11.2281 1.0163 5250
## shrub_cover 0.5279 0.7275 0.0662 0.3290 2.2177 1.0193 4577
## veg_height 0.4303 0.7242 0.0718 0.2685 1.7729 1.0427 4930
## week 0.1745 0.4813 0.0276 0.1023 0.6941 1.1588 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 11.1243 7.5049 2.6462
## (Intercept)-Canis_latrans -0.6032 1.6972 -3.9382
## (Intercept)-Procyon_lotor -0.2818 1.3161 -3.0106
## (Intercept)-Dasypus_novemcinctus -3.1528 1.8453 -7.7025
## (Intercept)-Sylvilagus_floridanus -2.6983 2.0770 -7.5047
## Cogon_Patch_Size-Odocoileus_virginianus -0.0356 1.8644 -3.5241
## Cogon_Patch_Size-Canis_latrans 1.6643 2.2392 -1.0710
## Cogon_Patch_Size-Procyon_lotor -0.6370 1.0067 -2.6622
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3947 0.9867 -2.4639
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6295 2.4067 -7.7111
## Veg_shannon_index-Odocoileus_virginianus 0.9494 1.0937 -1.2531
## Veg_shannon_index-Canis_latrans 1.4481 0.8813 0.0283
## Veg_shannon_index-Procyon_lotor 1.2802 0.7712 0.0183
## Veg_shannon_index-Dasypus_novemcinctus 0.7247 0.7125 -0.6444
## Veg_shannon_index-Sylvilagus_floridanus 1.1762 0.8747 -0.3701
## total_shrub_cover-Odocoileus_virginianus -0.0470 1.3774 -2.7012
## total_shrub_cover-Canis_latrans 0.6506 1.1506 -1.0542
## total_shrub_cover-Procyon_lotor -1.1969 0.8347 -3.0401
## total_shrub_cover-Dasypus_novemcinctus -0.0523 0.7851 -1.7877
## total_shrub_cover-Sylvilagus_floridanus -0.5850 1.2110 -3.4335
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1015 2.1788 -4.2193
## Avg_Cogongrass_Cover-Canis_latrans 0.6914 1.8128 -2.4195
## Avg_Cogongrass_Cover-Procyon_lotor 0.2040 1.5356 -2.6998
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4471 1.9428 -1.4804
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0764 1.9622 -5.6388
## Tree_Density-Odocoileus_virginianus -0.4308 2.8832 -4.4155
## Tree_Density-Canis_latrans -3.9521 2.3470 -9.8395
## Tree_Density-Procyon_lotor -2.1030 1.2699 -4.7869
## Tree_Density-Dasypus_novemcinctus -6.1660 4.2197 -17.0089
## Tree_Density-Sylvilagus_floridanus -3.4483 2.4653 -9.9104
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7218 2.2246 -3.8539
## Avg_Canopy_Cover-Canis_latrans -0.0693 0.7960 -1.7356
## Avg_Canopy_Cover-Procyon_lotor 1.6707 1.0010 -0.0018
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.6212 1.4239 0.7941
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.0749 3.4541 1.0240
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.2762 1.9419 -0.2185
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3478 1.4826 0.4066
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1545 1.2108 0.4479
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7653 1.0306 0.0988
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4939 1.1011 -0.4665
## avg_veg_height-Odocoileus_virginianus -0.0370 1.1130 -2.3774
## avg_veg_height-Canis_latrans -0.2031 0.8145 -1.8876
## avg_veg_height-Procyon_lotor 0.0876 0.7914 -1.5219
## avg_veg_height-Dasypus_novemcinctus 0.1571 0.7850 -1.4069
## avg_veg_height-Sylvilagus_floridanus -0.2223 0.9210 -2.1996
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.1615 33.1445 1.0243 140
## (Intercept)-Canis_latrans -0.6284 3.0705 1.0136 913
## (Intercept)-Procyon_lotor -0.2416 2.0679 1.0077 826
## (Intercept)-Dasypus_novemcinctus -2.8953 -0.3893 1.0654 359
## (Intercept)-Sylvilagus_floridanus -2.5460 1.0325 1.0039 495
## Cogon_Patch_Size-Odocoileus_virginianus -0.1688 4.3321 1.0040 1754
## Cogon_Patch_Size-Canis_latrans 1.1190 7.6254 1.0015 785
## Cogon_Patch_Size-Procyon_lotor -0.6253 1.1320 1.0078 791
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3596 1.4945 1.0012 1168
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0684 1.0341 1.0090 575
## Veg_shannon_index-Odocoileus_virginianus 0.9501 3.2335 1.0046 973
## Veg_shannon_index-Canis_latrans 1.3385 3.4883 1.0015 1015
## Veg_shannon_index-Procyon_lotor 1.2052 3.0257 1.0176 426
## Veg_shannon_index-Dasypus_novemcinctus 0.7222 2.1581 1.0090 699
## Veg_shannon_index-Sylvilagus_floridanus 1.1169 3.0210 1.0166 841
## total_shrub_cover-Odocoileus_virginianus -0.1061 2.8460 1.0018 2086
## total_shrub_cover-Canis_latrans 0.4451 3.5117 1.0090 689
## total_shrub_cover-Procyon_lotor -1.1264 0.2126 1.0025 1587
## total_shrub_cover-Dasypus_novemcinctus -0.0164 1.4044 1.0019 1378
## total_shrub_cover-Sylvilagus_floridanus -0.4783 1.5658 1.0062 980
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0953 4.4555 1.0048 1387
## Avg_Cogongrass_Cover-Canis_latrans 0.5490 4.6217 1.0041 1121
## Avg_Cogongrass_Cover-Procyon_lotor 0.1689 3.3417 1.0001 1196
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1868 5.9631 1.0122 572
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8375 2.1764 1.0035 1068
## Tree_Density-Odocoileus_virginianus -0.9329 6.5645 1.0109 767
## Tree_Density-Canis_latrans -3.4545 -0.9098 1.0243 361
## Tree_Density-Procyon_lotor -2.0498 0.2185 1.0079 1046
## Tree_Density-Dasypus_novemcinctus -4.9140 -1.6501 1.0217 238
## Tree_Density-Sylvilagus_floridanus -3.0151 0.2065 1.0101 533
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7783 5.0561 1.0054 1049
## Avg_Canopy_Cover-Canis_latrans -0.0514 1.4280 1.0036 1644
## Avg_Canopy_Cover-Procyon_lotor 1.5695 3.9711 1.0045 445
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3334 6.0844 1.0135 274
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.3034 14.4980 1.0194 233
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9305 7.5087 1.0105 556
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0808 5.9355 1.0171 307
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0015 4.8674 1.0162 626
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6585 4.0659 1.0151 720
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4106 3.8338 1.0304 887
## avg_veg_height-Odocoileus_virginianus -0.0165 2.1322 1.0007 1719
## avg_veg_height-Canis_latrans -0.1811 1.3098 1.0051 1406
## avg_veg_height-Procyon_lotor 0.0969 1.6784 1.0014 1493
## avg_veg_height-Dasypus_novemcinctus 0.1520 1.7555 1.0027 1396
## avg_veg_height-Sylvilagus_floridanus -0.1770 1.4707 1.0015 1734
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0064 0.0596 -0.1088 0.0051 0.1246
## (Intercept)-Canis_latrans -2.7426 0.1888 -3.1355 -2.7366 -2.3850
## (Intercept)-Procyon_lotor -2.3093 0.1460 -2.6095 -2.3042 -2.0363
## (Intercept)-Dasypus_novemcinctus -1.7348 0.1619 -2.0655 -1.7303 -1.4321
## (Intercept)-Sylvilagus_floridanus -3.1903 0.2737 -3.7444 -3.1824 -2.6809
## shrub_cover-Odocoileus_virginianus -0.0559 0.0640 -0.1831 -0.0552 0.0647
## shrub_cover-Canis_latrans -0.3320 0.2375 -0.7925 -0.3321 0.1431
## shrub_cover-Procyon_lotor 0.2574 0.1674 -0.0921 0.2631 0.5722
## shrub_cover-Dasypus_novemcinctus 0.8262 0.3196 0.2314 0.8146 1.4718
## shrub_cover-Sylvilagus_floridanus 0.4245 0.3857 -0.3241 0.4182 1.1974
## veg_height-Odocoileus_virginianus -0.3015 0.0658 -0.4284 -0.3013 -0.1752
## veg_height-Canis_latrans -0.5968 0.1873 -0.9719 -0.5921 -0.2431
## veg_height-Procyon_lotor 0.3599 0.1249 0.1125 0.3602 0.6113
## veg_height-Dasypus_novemcinctus 0.2393 0.1352 -0.0174 0.2388 0.5088
## veg_height-Sylvilagus_floridanus 0.1629 0.2606 -0.3484 0.1624 0.6740
## week-Odocoileus_virginianus 0.2151 0.0618 0.0974 0.2139 0.3399
## week-Canis_latrans 0.0797 0.1333 -0.1974 0.0849 0.3308
## week-Procyon_lotor -0.0411 0.1196 -0.2912 -0.0372 0.1795
## week-Dasypus_novemcinctus -0.1589 0.1397 -0.4507 -0.1545 0.0982
## week-Sylvilagus_floridanus -0.1467 0.2203 -0.6134 -0.1363 0.2414
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0006 1875
## (Intercept)-Procyon_lotor 1.0032 2717
## (Intercept)-Dasypus_novemcinctus 1.0008 3239
## (Intercept)-Sylvilagus_floridanus 1.0003 1322
## shrub_cover-Odocoileus_virginianus 1.0011 5250
## shrub_cover-Canis_latrans 1.0036 1620
## shrub_cover-Procyon_lotor 1.0062 2267
## shrub_cover-Dasypus_novemcinctus 1.0037 2361
## shrub_cover-Sylvilagus_floridanus 1.0020 1412
## veg_height-Odocoileus_virginianus 1.0017 5250
## veg_height-Canis_latrans 1.0005 2320
## veg_height-Procyon_lotor 1.0019 3747
## veg_height-Dasypus_novemcinctus 1.0001 3963
## veg_height-Sylvilagus_floridanus 1.0011 2025
## week-Odocoileus_virginianus 1.0001 5250
## week-Canis_latrans 1.0019 4702
## week-Procyon_lotor 0.9998 4403
## week-Dasypus_novemcinctus 1.0006 4980
## week-Sylvilagus_floridanus 1.0019 2694
# Includes all covariates of occupancy and null for detection
ms_null_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.882
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5935 1.3300 -2.0655 0.6143 3.2038 1.0007 5250
## Cogon_Patch_Size -0.5745 0.7773 -2.0560 -0.6058 1.0906 1.0098 2102
## Veg_shannon_index 1.0001 0.5711 -0.0896 0.9892 2.1556 1.0101 1679
## total_shrub_cover -0.0139 0.5811 -1.1263 -0.0270 1.2017 1.0007 2653
## Avg_Cogongrass_Cover 1.8524 0.8492 0.1633 1.8549 3.5057 1.0019 1451
## Tree_Density -1.6538 0.9791 -3.5076 -1.7057 0.5394 1.0037 2373
## Avg_Canopy_Cover 1.2709 0.8816 -0.6507 1.3102 2.9230 1.0023 3337
## avg_veg_height -0.4091 0.5904 -1.6137 -0.4123 0.7659 1.0009 1851
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 57.0596 159.4254 2.7975 20.1146 355.0118 1.4077 86
## Cogon_Patch_Size 3.1765 8.9397 0.0621 0.9137 19.6904 1.0393 1502
## Veg_shannon_index 0.8065 2.5093 0.0446 0.3218 4.4959 1.0905 3993
## total_shrub_cover 1.1597 2.8544 0.0555 0.4572 6.2562 1.0148 3195
## Avg_Cogongrass_Cover 2.1742 5.8704 0.0533 0.6691 13.4539 1.0356 1445
## Tree_Density 6.9313 19.8052 0.0730 1.7220 46.1173 1.0188 1196
## Avg_Canopy_Cover 5.3402 11.7478 0.1340 2.1861 30.4004 1.0576 1423
## avg_veg_height 0.7428 1.7173 0.0447 0.3208 4.1399 1.0138 3964
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4379 2.2028 0.0617 0.701 7.1161 1.0553 528
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.647 0.67 -2.8689 -1.6789 -0.1465 1.0004 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8393 4.6842 0.5402 1.7558 11.8326 1.0453 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 10.6333 8.0943 3.6745 8.6247
## (Intercept)-Canis_latrans 0.8584 1.0966 -1.0588 0.7693
## (Intercept)-Procyon_lotor 0.9737 0.9414 -0.9067 0.9684
## (Intercept)-Dasypus_novemcinctus -1.5987 1.0304 -3.9692 -1.4862
## (Intercept)-Sylvilagus_floridanus -1.2176 1.3213 -3.8106 -1.2252
## Cogon_Patch_Size-Odocoileus_virginianus -0.5183 1.4443 -3.0351 -0.6177
## Cogon_Patch_Size-Canis_latrans 0.5332 1.3825 -1.3304 0.2625
## Cogon_Patch_Size-Procyon_lotor -0.9093 0.7215 -2.3230 -0.9076
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8233 0.6443 -2.1914 -0.7925
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6839 1.5795 -5.6521 -1.3561
## Veg_shannon_index-Odocoileus_virginianus 0.8950 0.9001 -0.9916 0.9133
## Veg_shannon_index-Canis_latrans 1.3392 0.7293 0.1532 1.2561
## Veg_shannon_index-Procyon_lotor 1.1715 0.6173 0.0584 1.1330
## Veg_shannon_index-Dasypus_novemcinctus 0.7467 0.5390 -0.2977 0.7395
## Veg_shannon_index-Sylvilagus_floridanus 1.1114 0.7106 -0.1311 1.0514
## total_shrub_cover-Odocoileus_virginianus 0.1434 0.9974 -1.6323 0.0741
## total_shrub_cover-Canis_latrans 0.3336 0.7190 -0.8256 0.2574
## total_shrub_cover-Procyon_lotor -0.6801 0.6261 -2.0359 -0.6287
## total_shrub_cover-Dasypus_novemcinctus 0.1905 0.5313 -0.8119 0.1747
## total_shrub_cover-Sylvilagus_floridanus 0.0132 0.7884 -1.5339 -0.0143
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9034 1.3482 -0.6384 1.8704
## Avg_Cogongrass_Cover-Canis_latrans 2.3797 1.1008 0.5451 2.2816
## Avg_Cogongrass_Cover-Procyon_lotor 2.1609 0.9786 0.4463 2.0898
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6249 1.1310 0.8027 2.5018
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3039 1.0798 -0.8729 1.3261
## Tree_Density-Odocoileus_virginianus -0.6286 1.8716 -3.2556 -0.9670
## Tree_Density-Canis_latrans -2.5585 1.4133 -6.2517 -2.3163
## Tree_Density-Procyon_lotor -1.3509 0.8717 -3.0171 -1.3723
## Tree_Density-Dasypus_novemcinctus -3.6587 2.0651 -8.9763 -3.1136
## Tree_Density-Sylvilagus_floridanus -2.5392 1.5034 -6.1809 -2.2998
## Avg_Canopy_Cover-Odocoileus_virginianus 0.9222 1.6775 -2.5103 0.9838
## Avg_Canopy_Cover-Canis_latrans 0.1396 0.7180 -1.3113 0.1539
## Avg_Canopy_Cover-Procyon_lotor 1.6476 0.7590 0.3275 1.5882
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9910 0.7688 0.7389 1.9089
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4934 1.9596 0.9239 3.1010
## avg_veg_height-Odocoileus_virginianus -0.4321 0.9071 -2.3240 -0.4047
## avg_veg_height-Canis_latrans -0.6327 0.6724 -1.9912 -0.6272
## avg_veg_height-Procyon_lotor -0.2745 0.6387 -1.4882 -0.2925
## avg_veg_height-Dasypus_novemcinctus -0.2044 0.6379 -1.4498 -0.2094
## avg_veg_height-Sylvilagus_floridanus -0.6312 0.7498 -2.2225 -0.5881
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 29.7460 1.3051 89
## (Intercept)-Canis_latrans 3.3523 1.0079 1503
## (Intercept)-Procyon_lotor 2.9418 1.0060 1842
## (Intercept)-Dasypus_novemcinctus 0.1543 1.0092 926
## (Intercept)-Sylvilagus_floridanus 1.4345 1.0052 1090
## Cogon_Patch_Size-Odocoileus_virginianus 2.7391 1.0090 1936
## Cogon_Patch_Size-Canis_latrans 4.1270 1.0103 1269
## Cogon_Patch_Size-Procyon_lotor 0.4861 1.0063 1503
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3458 1.0117 1384
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3125 1.0063 944
## Veg_shannon_index-Odocoileus_virginianus 2.5993 1.0089 2131
## Veg_shannon_index-Canis_latrans 3.0939 1.0177 1276
## Veg_shannon_index-Procyon_lotor 2.4893 1.0128 1257
## Veg_shannon_index-Dasypus_novemcinctus 1.8198 1.0046 1680
## Veg_shannon_index-Sylvilagus_floridanus 2.6700 1.0115 1510
## total_shrub_cover-Odocoileus_virginianus 2.2448 1.0041 2423
## total_shrub_cover-Canis_latrans 2.0310 1.0026 1994
## total_shrub_cover-Procyon_lotor 0.4326 0.9998 2993
## total_shrub_cover-Dasypus_novemcinctus 1.2691 1.0031 2782
## total_shrub_cover-Sylvilagus_floridanus 1.6505 1.0004 2238
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.5828 1.0030 1751
## Avg_Cogongrass_Cover-Canis_latrans 4.9428 1.0123 1016
## Avg_Cogongrass_Cover-Procyon_lotor 4.2544 1.0063 1360
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2906 1.0159 877
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3487 1.0109 1785
## Tree_Density-Odocoileus_virginianus 4.1991 1.0206 924
## Tree_Density-Canis_latrans -0.5568 1.0275 1161
## Tree_Density-Procyon_lotor 0.3231 1.0061 1984
## Tree_Density-Dasypus_novemcinctus -1.1291 1.0205 664
## Tree_Density-Sylvilagus_floridanus -0.1673 1.0212 1164
## Avg_Canopy_Cover-Odocoileus_virginianus 4.0994 1.0185 1543
## Avg_Canopy_Cover-Canis_latrans 1.5402 1.0069 2369
## Avg_Canopy_Cover-Procyon_lotor 3.3065 1.0073 2072
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7626 1.0133 1172
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.2089 1.0135 728
## avg_veg_height-Odocoileus_virginianus 1.2948 1.0040 2572
## avg_veg_height-Canis_latrans 0.6561 1.0019 1903
## avg_veg_height-Procyon_lotor 1.0244 1.0029 2374
## avg_veg_height-Dasypus_novemcinctus 1.0516 1.0016 1900
## avg_veg_height-Sylvilagus_floridanus 0.7732 1.0001 2305
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0060 0.0594 -0.1107 0.0054 0.1246
## (Intercept)-Canis_latrans -2.6413 0.1785 -3.0080 -2.6343 -2.3069
## (Intercept)-Procyon_lotor -2.2612 0.1321 -2.5373 -2.2570 -2.0130
## (Intercept)-Dasypus_novemcinctus -1.5666 0.1314 -1.8280 -1.5641 -1.3088
## (Intercept)-Sylvilagus_floridanus -3.1544 0.2818 -3.7358 -3.1476 -2.6346
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 4771
## (Intercept)-Canis_latrans 1.0066 2381
## (Intercept)-Procyon_lotor 1.0009 3974
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## (Intercept)-Sylvilagus_floridanus 1.0028 1496
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8865
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7401 1.0029 -1.3516 0.7557 2.7419 1.0037 4413
## Avg_Cogongrass_Cover 0.0761 0.4825 -0.8876 0.0727 1.0535 1.0030 2539
## total_shrub_cover -0.2396 0.5235 -1.3406 -0.2267 0.7965 1.0026 3428
## avg_veg_height 0.0959 0.4508 -0.7784 0.0955 1.0246 1.0024 2449
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.5023 19.8720 0.6491 5.1148 57.2626 1.0285 1077
## Avg_Cogongrass_Cover 0.6322 1.2075 0.0416 0.2967 3.3329 1.0027 3712
## total_shrub_cover 1.1128 2.4358 0.0549 0.4798 5.9223 1.0167 1225
## avg_veg_height 0.4772 0.9594 0.0373 0.2287 2.5054 1.0197 3951
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6657 1.1438 0.0511 0.3496 3.0479 1.0587 902
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6601 0.6911 -2.9329 -1.7047 -0.1371 1.0046 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0338 5.3769 0.5504 1.9138 12.2746 1.103 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0314 2.5695 1.8253 4.4609
## (Intercept)-Canis_latrans 0.5230 0.6577 -0.6389 0.4819
## (Intercept)-Procyon_lotor 0.9190 0.6660 -0.3850 0.9013
## (Intercept)-Dasypus_novemcinctus -0.6424 0.5862 -1.8063 -0.6400
## (Intercept)-Sylvilagus_floridanus 0.2331 1.5009 -1.5639 -0.0564
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0945 0.7392 -1.3051 0.0584
## Avg_Cogongrass_Cover-Canis_latrans 0.4007 0.5426 -0.5802 0.3639
## Avg_Cogongrass_Cover-Procyon_lotor 0.0319 0.5176 -0.9838 0.0281
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2088 0.4538 -0.6572 0.2009
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3394 0.6318 -1.7170 -0.3062
## total_shrub_cover-Odocoileus_virginianus -0.1503 0.8061 -1.7489 -0.1632
## total_shrub_cover-Canis_latrans 0.2359 0.5438 -0.6991 0.1936
## total_shrub_cover-Procyon_lotor -0.9423 0.5944 -2.3495 -0.8718
## total_shrub_cover-Dasypus_novemcinctus 0.0217 0.3904 -0.7438 0.0177
## total_shrub_cover-Sylvilagus_floridanus -0.4325 0.8851 -2.5692 -0.3387
## avg_veg_height-Odocoileus_virginianus 0.1005 0.6970 -1.2551 0.0849
## avg_veg_height-Canis_latrans -0.0316 0.4844 -0.9961 -0.0211
## avg_veg_height-Procyon_lotor 0.1949 0.4876 -0.7126 0.1860
## avg_veg_height-Dasypus_novemcinctus 0.2817 0.4564 -0.5693 0.2738
## avg_veg_height-Sylvilagus_floridanus -0.0501 0.5568 -1.1329 -0.0382
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.5838 1.0139 449
## (Intercept)-Canis_latrans 1.9172 1.0097 2730
## (Intercept)-Procyon_lotor 2.2707 1.0002 3020
## (Intercept)-Dasypus_novemcinctus 0.5416 1.0096 2872
## (Intercept)-Sylvilagus_floridanus 4.0253 1.0617 241
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6714 1.0003 2667
## Avg_Cogongrass_Cover-Canis_latrans 1.5862 1.0006 3079
## Avg_Cogongrass_Cover-Procyon_lotor 1.0682 1.0030 2988
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1147 1.0034 3041
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8004 1.0017 2462
## total_shrub_cover-Odocoileus_virginianus 1.5221 1.0031 2990
## total_shrub_cover-Canis_latrans 1.4453 1.0115 2838
## total_shrub_cover-Procyon_lotor 0.0109 1.0044 2028
## total_shrub_cover-Dasypus_novemcinctus 0.8255 1.0007 4281
## total_shrub_cover-Sylvilagus_floridanus 0.9432 1.0089 565
## avg_veg_height-Odocoileus_virginianus 1.5528 1.0068 2806
## avg_veg_height-Canis_latrans 0.9080 1.0020 2967
## avg_veg_height-Procyon_lotor 1.2189 1.0032 2913
## avg_veg_height-Dasypus_novemcinctus 1.2374 1.0014 3130
## avg_veg_height-Sylvilagus_floridanus 1.0712 1.0038 2164
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0596 -0.1147 0.0065 0.1184
## (Intercept)-Canis_latrans -2.6319 0.1814 -3.0103 -2.6257 -2.2886
## (Intercept)-Procyon_lotor -2.2723 0.1301 -2.5340 -2.2699 -2.0288
## (Intercept)-Dasypus_novemcinctus -1.5680 0.1311 -1.8291 -1.5639 -1.3153
## (Intercept)-Sylvilagus_floridanus -3.3006 0.3732 -4.0770 -3.2721 -2.6488
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0016 5250
## (Intercept)-Canis_latrans 1.0040 2340
## (Intercept)-Procyon_lotor 1.0001 3896
## (Intercept)-Dasypus_novemcinctus 1.0002 5250
## (Intercept)-Sylvilagus_floridanus 1.0097 564
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.86
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5542 1.0365 -1.5596 0.5480 2.6205 1.0048 4708
## Tree_Density -0.8534 0.5678 -2.0445 -0.8275 0.2358 1.0062 3022
## Avg_Canopy_Cover 0.7957 0.6072 -0.4330 0.7921 2.0266 1.0050 4250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.7793 57.5144 1.0174 6.9816 79.4946 1.5546 127
## Tree_Density 1.4385 4.0756 0.0473 0.4523 9.1347 1.0272 2450
## Avg_Canopy_Cover 1.8828 3.4121 0.0846 0.8737 10.2664 1.0021 2399
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4578 0.6669 0.0392 0.2529 2.0923 1.0696 1050
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6417 0.6553 -2.7982 -1.6918 -0.1747 1.0012 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.7 3.8276 0.52 1.7496 10.2793 1.0483 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 5.9262 4.0079 2.1816 5.0670 14.2937
## (Intercept)-Canis_latrans 0.3788 0.6728 -0.8232 0.3346 1.7688
## (Intercept)-Procyon_lotor 0.8114 0.6221 -0.4310 0.8000 2.0769
## (Intercept)-Dasypus_novemcinctus -1.0368 0.6521 -2.3847 -1.0044 0.1701
## (Intercept)-Sylvilagus_floridanus -0.6500 0.7946 -2.1372 -0.6762 1.0039
## Tree_Density-Odocoileus_virginianus -0.4073 0.8837 -1.7511 -0.5175 1.6878
## Tree_Density-Canis_latrans -0.9598 0.5887 -2.3054 -0.8953 0.0175
## Tree_Density-Procyon_lotor -0.5080 0.4504 -1.4005 -0.5040 0.3835
## Tree_Density-Dasypus_novemcinctus -1.5317 1.0401 -4.2997 -1.2967 -0.2094
## Tree_Density-Sylvilagus_floridanus -1.2170 0.8925 -3.4187 -1.0627 0.1236
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6059 0.9510 -1.2847 0.6033 2.5765
## Avg_Canopy_Cover-Canis_latrans -0.1029 0.4934 -1.0928 -0.1074 0.8247
## Avg_Canopy_Cover-Procyon_lotor 0.9999 0.5229 0.0924 0.9560 2.1216
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9760 0.4661 0.1388 0.9522 1.9803
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9920 1.1233 0.5046 1.7590 4.8277
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.4335 68
## (Intercept)-Canis_latrans 1.0026 2657
## (Intercept)-Procyon_lotor 1.0056 3185
## (Intercept)-Dasypus_novemcinctus 1.0064 2509
## (Intercept)-Sylvilagus_floridanus 1.0011 2404
## Tree_Density-Odocoileus_virginianus 1.0088 2199
## Tree_Density-Canis_latrans 1.0024 3248
## Tree_Density-Procyon_lotor 1.0040 3805
## Tree_Density-Dasypus_novemcinctus 1.0009 1746
## Tree_Density-Sylvilagus_floridanus 1.0077 1702
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0027 2298
## Avg_Canopy_Cover-Canis_latrans 1.0001 3184
## Avg_Canopy_Cover-Procyon_lotor 1.0012 3384
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0017 4451
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0007 1277
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0065 0.0592 -0.1102 0.0063 0.1206
## (Intercept)-Canis_latrans -2.6265 0.1841 -3.0115 -2.6151 -2.2918
## (Intercept)-Procyon_lotor -2.2574 0.1310 -2.5217 -2.2571 -2.0022
## (Intercept)-Dasypus_novemcinctus -1.5622 0.1345 -1.8288 -1.5574 -1.3077
## (Intercept)-Sylvilagus_floridanus -3.1002 0.2868 -3.6881 -3.0884 -2.5777
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0035 2527
## (Intercept)-Procyon_lotor 1.0000 4144
## (Intercept)-Dasypus_novemcinctus 1.0012 4966
## (Intercept)-Sylvilagus_floridanus 1.0012 1754
# Includes movement covariates of occupancy and null for detection
ms_null_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8822
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7547 1.0083 -1.3520 0.7666 2.7646 1.0025 4258
## Cogon_Patch_Size 0.0157 0.6368 -1.2618 0.0054 1.3519 1.0045 3786
## Avg_Cogongrass_Cover 0.1666 0.4411 -0.6788 0.1611 1.0629 1.0050 2713
## total_shrub_cover -0.2323 0.5065 -1.2919 -0.2239 0.7973 1.0058 2613
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.0889 23.0487 0.8216 5.5815 53.7432 1.0106 1362
## Cogon_Patch_Size 2.2572 6.8040 0.0610 0.7109 14.0468 1.1118 1464
## Avg_Cogongrass_Cover 0.5261 1.0899 0.0403 0.2470 2.9242 1.0463 4512
## total_shrub_cover 0.8879 1.7620 0.0508 0.4171 4.8078 1.0098 1929
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5973 0.8365 0.0476 0.3267 2.7132 1.0202 811
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6418 0.6934 -2.8856 -1.6893 -0.0843 1.0003 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9343 3.7689 0.558 1.8422 11.9939 1.0021 4553
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.2063 2.5592 2.0402 4.6325
## (Intercept)-Canis_latrans 0.6768 0.7252 -0.6227 0.6217
## (Intercept)-Procyon_lotor 0.9163 0.6827 -0.3568 0.8920
## (Intercept)-Dasypus_novemcinctus -0.6613 0.5761 -1.8212 -0.6541
## (Intercept)-Sylvilagus_floridanus -0.0999 1.2260 -1.8949 -0.2850
## Cogon_Patch_Size-Odocoileus_virginianus 0.2470 1.1059 -1.3804 0.1037
## Cogon_Patch_Size-Canis_latrans 1.0468 1.0845 -0.3112 0.8002
## Cogon_Patch_Size-Procyon_lotor -0.1137 0.5496 -1.0773 -0.1293
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0966 0.4422 -1.0023 -0.0815
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9587 1.2733 -4.0525 -0.6839
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1834 0.6802 -1.1232 0.1669
## Avg_Cogongrass_Cover-Canis_latrans 0.2468 0.4780 -0.6136 0.2234
## Avg_Cogongrass_Cover-Procyon_lotor 0.2016 0.4889 -0.7076 0.1810
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3742 0.4052 -0.4018 0.3598
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1384 0.5623 -1.3208 -0.1170
## total_shrub_cover-Odocoileus_virginianus -0.1585 0.7593 -1.6587 -0.1755
## total_shrub_cover-Canis_latrans 0.1468 0.5184 -0.7337 0.1016
## total_shrub_cover-Procyon_lotor -0.8972 0.5934 -2.2830 -0.8225
## total_shrub_cover-Dasypus_novemcinctus -0.0203 0.3941 -0.7825 -0.0256
## total_shrub_cover-Sylvilagus_floridanus -0.3018 0.7820 -1.9577 -0.2339
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.3972 1.0089 498
## (Intercept)-Canis_latrans 2.3116 1.0051 1750
## (Intercept)-Procyon_lotor 2.3598 1.0076 2732
## (Intercept)-Dasypus_novemcinctus 0.4817 1.0061 2988
## (Intercept)-Sylvilagus_floridanus 3.0488 1.0592 495
## Cogon_Patch_Size-Odocoileus_virginianus 2.8738 1.0127 1763
## Cogon_Patch_Size-Canis_latrans 3.9349 1.0009 1559
## Cogon_Patch_Size-Procyon_lotor 0.8941 1.0073 2662
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7453 1.0000 4196
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6654 1.0285 1009
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6481 1.0089 3179
## Avg_Cogongrass_Cover-Canis_latrans 1.2506 1.0007 2967
## Avg_Cogongrass_Cover-Procyon_lotor 1.2697 1.0026 3427
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1937 1.0006 3647
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9299 1.0024 2726
## total_shrub_cover-Odocoileus_virginianus 1.3696 1.0014 2745
## total_shrub_cover-Canis_latrans 1.3088 1.0021 2923
## total_shrub_cover-Procyon_lotor 0.0539 1.0099 2359
## total_shrub_cover-Dasypus_novemcinctus 0.7606 1.0025 4635
## total_shrub_cover-Sylvilagus_floridanus 0.9599 1.0206 934
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0067 0.0590 -0.1097 0.0071 0.1245
## (Intercept)-Canis_latrans -2.6095 0.1793 -2.9830 -2.6030 -2.2782
## (Intercept)-Procyon_lotor -2.2682 0.1323 -2.5407 -2.2647 -2.0174
## (Intercept)-Dasypus_novemcinctus -1.5696 0.1344 -1.8390 -1.5690 -1.3100
## (Intercept)-Sylvilagus_floridanus -3.2868 0.3683 -4.0727 -3.2608 -2.6394
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 4795
## (Intercept)-Canis_latrans 1.0036 2346
## (Intercept)-Procyon_lotor 1.0044 3864
## (Intercept)-Dasypus_novemcinctus 1.0025 5250
## (Intercept)-Sylvilagus_floridanus 1.0411 617
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.881
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6847 0.9603 -1.1994 0.7051 2.6278 1.0005 4675
## Veg_shannon_index 0.5226 0.4017 -0.2467 0.5204 1.3280 1.0034 2374
## Avg_Cogongrass_Cover 0.3854 0.4286 -0.4455 0.3781 1.2455 1.0126 2923
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.8673 16.7276 0.7151 4.7904 41.5946 1.0212 1409
## Veg_shannon_index 0.4933 1.0009 0.0383 0.2290 2.6167 1.0004 2687
## Avg_Cogongrass_Cover 0.5857 1.0754 0.0413 0.2802 3.0759 1.0025 3790
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6111 0.8327 0.0466 0.3431 2.6893 1.0567 791
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.632 0.6973 -2.8602 -1.6873 -0.0273 1.0016 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9134 4.2274 0.5525 1.85 12.0183 1.0137 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.7146 2.1014 1.9440 4.2970
## (Intercept)-Canis_latrans 0.4219 0.6294 -0.7540 0.4000
## (Intercept)-Procyon_lotor 0.7517 0.6144 -0.4276 0.7525
## (Intercept)-Dasypus_novemcinctus -0.6844 0.5750 -1.8599 -0.6764
## (Intercept)-Sylvilagus_floridanus -0.0325 1.1155 -1.5829 -0.2091
## Veg_shannon_index-Odocoileus_virginianus 0.4290 0.6356 -0.8901 0.4550
## Veg_shannon_index-Canis_latrans 0.7778 0.4303 0.0010 0.7475
## Veg_shannon_index-Procyon_lotor 0.5870 0.4522 -0.2282 0.5611
## Veg_shannon_index-Dasypus_novemcinctus 0.2687 0.3647 -0.4824 0.2680
## Veg_shannon_index-Sylvilagus_floridanus 0.6110 0.5638 -0.3189 0.5625
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4032 0.6747 -0.8431 0.3726
## Avg_Cogongrass_Cover-Canis_latrans 0.6621 0.4609 -0.1374 0.6214
## Avg_Cogongrass_Cover-Procyon_lotor 0.5425 0.4827 -0.2833 0.4970
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4799 0.3706 -0.2333 0.4697
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0526 0.5668 -1.2148 -0.0317
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.1128 1.0108 721
## (Intercept)-Canis_latrans 1.7086 1.0007 3196
## (Intercept)-Procyon_lotor 1.9738 1.0065 3427
## (Intercept)-Dasypus_novemcinctus 0.4432 1.0001 3139
## (Intercept)-Sylvilagus_floridanus 2.7621 1.0107 439
## Veg_shannon_index-Odocoileus_virginianus 1.6295 1.0018 2931
## Veg_shannon_index-Canis_latrans 1.7090 1.0052 3014
## Veg_shannon_index-Procyon_lotor 1.5748 1.0100 2433
## Veg_shannon_index-Dasypus_novemcinctus 0.9602 1.0004 4418
## Veg_shannon_index-Sylvilagus_floridanus 1.8181 1.0193 1813
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8626 1.0076 3331
## Avg_Cogongrass_Cover-Canis_latrans 1.7095 1.0115 3152
## Avg_Cogongrass_Cover-Procyon_lotor 1.6170 1.0091 2438
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2177 1.0035 4376
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0388 1.0036 2122
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0072 0.0587 -0.1075 0.0066 0.1244
## (Intercept)-Canis_latrans -2.6073 0.1770 -2.9750 -2.5996 -2.2793
## (Intercept)-Procyon_lotor -2.2736 0.1348 -2.5466 -2.2688 -2.0185
## (Intercept)-Dasypus_novemcinctus -1.5648 0.1319 -1.8288 -1.5614 -1.3090
## (Intercept)-Sylvilagus_floridanus -3.2644 0.3596 -4.0440 -3.2360 -2.6448
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0001 2955
## (Intercept)-Procyon_lotor 1.0043 3948
## (Intercept)-Dasypus_novemcinctus 1.0022 5250
## (Intercept)-Sylvilagus_floridanus 1.0153 555
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9005
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0634 0.9346 -1.7031 0.0256 1.9829 1.0009 4048
## Avg_Cogongrass_Cover -0.5125 0.5916 -1.6571 -0.5191 0.6577 1.0025 2914
## I(Avg_Cogongrass_Cover^2) 1.1697 0.7132 0.0111 1.0720 2.7905 0.9999 1012
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.2231 15.7371 0.4279 3.9798 43.8969 1.0532 702
## Avg_Cogongrass_Cover 1.2058 2.8832 0.0492 0.4646 7.0130 1.0431 2451
## I(Avg_Cogongrass_Cover^2) 2.5323 8.1339 0.0496 0.6011 17.7384 1.0608 775
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4757 0.6352 0.0432 0.2781 2.1975 1.0221 1182
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6524 0.6713 -2.8139 -1.7114 -0.1849 1.0013 5972
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8265 3.9907 0.5476 1.8121 10.8806 1.0021 4915
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6263 2.3265 0.4902 3.1750
## (Intercept)-Canis_latrans -0.5138 0.7818 -2.1668 -0.4925
## (Intercept)-Procyon_lotor -0.1544 0.6961 -1.5735 -0.1433
## (Intercept)-Dasypus_novemcinctus -1.2375 0.6729 -2.6257 -1.2175
## (Intercept)-Sylvilagus_floridanus -0.9376 0.9059 -2.7675 -0.9357
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.5512 0.9534 -2.4728 -0.5527
## Avg_Cogongrass_Cover-Canis_latrans -0.1853 0.6796 -1.3513 -0.2384
## Avg_Cogongrass_Cover-Procyon_lotor -0.3678 0.6699 -1.5905 -0.4068
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3947 0.5843 -1.5466 -0.3931
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2512 0.9211 -3.3977 -1.1205
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8711 1.7959 -0.0287 1.3916
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8564 1.3284 0.2017 1.5062
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.5826 1.1713 0.1594 1.2651
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6590 0.4307 -0.1300 0.6430
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9481 0.7578 -0.2313 0.8278
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.8331 1.0130 525
## (Intercept)-Canis_latrans 0.9524 1.0002 1643
## (Intercept)-Procyon_lotor 1.1907 1.0011 2349
## (Intercept)-Dasypus_novemcinctus 0.0375 1.0049 3231
## (Intercept)-Sylvilagus_floridanus 0.8666 1.0033 1859
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3266 1.0092 3085
## Avg_Cogongrass_Cover-Canis_latrans 1.2864 1.0002 2944
## Avg_Cogongrass_Cover-Procyon_lotor 1.0795 1.0022 2542
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7594 1.0018 2844
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2135 1.0226 1756
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 6.6479 1.0193 368
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.2886 1.0015 543
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6388 1.0040 535
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5497 1.0041 3136
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8020 1.0008 901
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0590 -0.1104 0.0057 0.1234
## (Intercept)-Canis_latrans -2.6392 0.1769 -2.9962 -2.6337 -2.3072
## (Intercept)-Procyon_lotor -2.2791 0.1322 -2.5464 -2.2758 -2.0283
## (Intercept)-Dasypus_novemcinctus -1.5670 0.1332 -1.8320 -1.5629 -1.3125
## (Intercept)-Sylvilagus_floridanus -3.2431 0.3298 -3.9518 -3.2227 -2.6469
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0021 5250
## (Intercept)-Canis_latrans 0.9998 2795
## (Intercept)-Procyon_lotor 1.0004 3967
## (Intercept)-Dasypus_novemcinctus 1.0009 5250
## (Intercept)-Sylvilagus_floridanus 1.0018 805
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9083
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0532 1.3720 -2.6194 0.0350 2.8566 1.0059 4582
## Cogon_Patch_Size -0.1630 0.9215 -1.9731 -0.1798 1.7906 1.0022 2084
## Veg_shannon_index 1.0418 0.6289 -0.1504 1.0162 2.4059 1.0388 1108
## total_shrub_cover -0.0862 0.6171 -1.2818 -0.1051 1.2171 1.0029 2250
## Avg_Cogongrass_Cover 0.2540 1.1382 -2.0557 0.2693 2.4892 1.0011 1174
## Tree_Density -1.8255 1.2352 -4.0129 -1.9391 1.0166 1.0033 1135
## Avg_Canopy_Cover 1.1970 1.0498 -1.2196 1.2252 3.2101 1.0025 3225
## I(Avg_Cogongrass_Cover^2) 1.7472 0.9196 -0.0530 1.7052 3.6211 1.0059 895
## avg_veg_height -0.0985 0.6758 -1.4817 -0.0892 1.2189 1.0031 1674
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 59.5946 148.6632 3.1747 25.2609 333.9002 1.1018 325
## Cogon_Patch_Size 6.3408 15.0240 0.0845 1.9218 40.4597 1.0788 1025
## Veg_shannon_index 1.0147 2.8539 0.0439 0.3506 6.1798 1.1356 2255
## total_shrub_cover 1.2715 3.6632 0.0542 0.4941 6.5909 1.0819 2902
## Avg_Cogongrass_Cover 4.5408 15.5268 0.0658 1.2470 26.9265 1.2075 332
## Tree_Density 19.8678 65.6789 0.0878 4.1061 138.6707 1.1172 215
## Avg_Canopy_Cover 12.2513 34.6083 0.1931 4.2239 71.2554 1.1563 288
## I(Avg_Cogongrass_Cover^2) 5.9307 33.8008 0.0497 0.5990 40.9906 1.3339 364
## avg_veg_height 1.1081 2.4696 0.0503 0.4175 6.6956 1.0049 1555
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2633 4.7782 0.0565 0.8528 13.2903 1.2704 142
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6494 0.6593 -2.853 -1.6908 -0.1542 1.0008 4476
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8221 4.0631 0.5417 1.7813 11.1866 1.0083 4834
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 10.3544 6.8349 2.6990
## (Intercept)-Canis_latrans -1.1119 1.8616 -4.7549
## (Intercept)-Procyon_lotor -0.4153 1.2967 -2.9971
## (Intercept)-Dasypus_novemcinctus -3.3728 1.8229 -7.7572
## (Intercept)-Sylvilagus_floridanus -2.8271 2.0542 -7.1112
## Cogon_Patch_Size-Odocoileus_virginianus 0.0279 2.0035 -3.3095
## Cogon_Patch_Size-Canis_latrans 1.7980 2.1898 -0.7414
## Cogon_Patch_Size-Procyon_lotor -0.4988 1.1674 -2.3677
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5724 0.9466 -2.6205
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6043 2.0176 -6.9588
## Veg_shannon_index-Odocoileus_virginianus 0.9326 1.0055 -1.1416
## Veg_shannon_index-Canis_latrans 1.4194 0.8331 0.1163
## Veg_shannon_index-Procyon_lotor 1.2513 0.7730 -0.0120
## Veg_shannon_index-Dasypus_novemcinctus 0.7522 0.6370 -0.4696
## Veg_shannon_index-Sylvilagus_floridanus 1.1610 0.8307 -0.3246
## total_shrub_cover-Odocoileus_virginianus 0.0681 1.0668 -1.8981
## total_shrub_cover-Canis_latrans 0.1361 0.7430 -1.1672
## total_shrub_cover-Procyon_lotor -0.7745 0.7140 -2.3347
## total_shrub_cover-Dasypus_novemcinctus 0.1968 0.5837 -0.8847
## total_shrub_cover-Sylvilagus_floridanus -0.0844 0.8719 -1.8531
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2069 2.0377 -3.9766
## Avg_Cogongrass_Cover-Canis_latrans 0.2335 1.5935 -3.0731
## Avg_Cogongrass_Cover-Procyon_lotor 0.5350 1.4709 -2.2517
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3181 1.7691 -1.5592
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7621 1.8576 -5.1517
## Tree_Density-Odocoileus_virginianus -0.3622 2.9474 -4.0553
## Tree_Density-Canis_latrans -3.6324 2.5400 -9.2092
## Tree_Density-Procyon_lotor -1.8924 1.2783 -4.4550
## Tree_Density-Dasypus_novemcinctus -5.5696 3.7713 -14.8361
## Tree_Density-Sylvilagus_floridanus -3.3152 2.3258 -9.3384
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7449 2.1658 -3.7498
## Avg_Canopy_Cover-Canis_latrans -0.1056 0.8706 -1.9899
## Avg_Canopy_Cover-Procyon_lotor 1.6963 0.9024 0.1883
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3632 1.1344 0.7585
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.8213 3.2196 0.9988
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.4453 2.6054 -0.0745
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.7719 2.3837 0.5754
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2733 1.3320 0.4259
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7797 1.1395 0.1041
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5435 1.1591 -0.4506
## avg_veg_height-Odocoileus_virginianus -0.0861 1.1046 -2.3062
## avg_veg_height-Canis_latrans -0.4893 0.8168 -2.2050
## avg_veg_height-Procyon_lotor 0.2004 0.7691 -1.2729
## avg_veg_height-Dasypus_novemcinctus 0.1201 0.7538 -1.3535
## avg_veg_height-Sylvilagus_floridanus -0.3052 0.9141 -2.2279
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.5163 28.8589 1.1271 150
## (Intercept)-Canis_latrans -1.0406 2.1746 1.0880 344
## (Intercept)-Procyon_lotor -0.4110 2.1248 1.0116 1098
## (Intercept)-Dasypus_novemcinctus -3.0521 -0.8088 1.0790 287
## (Intercept)-Sylvilagus_floridanus -2.6541 0.6309 1.0874 311
## Cogon_Patch_Size-Odocoileus_virginianus -0.1732 4.5856 1.0203 1201
## Cogon_Patch_Size-Canis_latrans 1.2836 7.7723 1.0360 775
## Cogon_Patch_Size-Procyon_lotor -0.5438 1.5077 1.0142 791
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4773 0.9921 1.0489 437
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1842 1.0239 1.0431 741
## Veg_shannon_index-Odocoileus_virginianus 0.9457 2.9014 1.0221 1731
## Veg_shannon_index-Canis_latrans 1.3100 3.3497 1.0761 560
## Veg_shannon_index-Procyon_lotor 1.1683 2.9132 1.0879 361
## Veg_shannon_index-Dasypus_novemcinctus 0.7548 2.0527 1.0164 1581
## Veg_shannon_index-Sylvilagus_floridanus 1.1114 3.0211 1.0304 742
## total_shrub_cover-Odocoileus_virginianus -0.0002 2.3879 1.0030 2535
## total_shrub_cover-Canis_latrans 0.0757 1.8230 1.0051 1836
## total_shrub_cover-Procyon_lotor -0.7195 0.4551 1.0034 1911
## total_shrub_cover-Dasypus_novemcinctus 0.1787 1.4247 1.0022 2958
## total_shrub_cover-Sylvilagus_floridanus -0.0936 1.7240 1.0036 1819
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2539 4.0818 1.0215 1194
## Avg_Cogongrass_Cover-Canis_latrans 0.2714 3.3299 1.0014 1261
## Avg_Cogongrass_Cover-Procyon_lotor 0.4720 3.6908 1.0140 1312
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1027 5.5070 1.0357 517
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5342 2.2067 1.0136 1175
## Tree_Density-Odocoileus_virginianus -0.9700 7.2951 1.0039 547
## Tree_Density-Canis_latrans -3.1279 -0.6911 1.1060 328
## Tree_Density-Procyon_lotor -1.8692 0.4302 1.0156 1498
## Tree_Density-Dasypus_novemcinctus -4.4801 -1.6228 1.0964 159
## Tree_Density-Sylvilagus_floridanus -2.8640 -0.1705 1.0603 264
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7688 5.1531 1.0066 1134
## Avg_Canopy_Cover-Canis_latrans -0.0428 1.4291 1.0284 1067
## Avg_Canopy_Cover-Procyon_lotor 1.6026 3.7060 1.0120 1504
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1585 5.0710 1.0643 231
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0806 13.6855 1.0810 216
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9785 8.6058 1.1503 221
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2650 8.9323 1.2283 225
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0584 5.5580 1.0129 615
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6451 4.2349 1.0543 547
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4638 4.0163 1.0156 631
## avg_veg_height-Odocoileus_virginianus -0.0726 2.1335 1.0036 2177
## avg_veg_height-Canis_latrans -0.4288 0.9622 1.0109 847
## avg_veg_height-Procyon_lotor 0.1684 1.8096 1.0079 1850
## avg_veg_height-Dasypus_novemcinctus 0.1148 1.6189 1.0025 1408
## avg_veg_height-Sylvilagus_floridanus -0.2638 1.3692 1.0031 1663
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0062 0.0589 -0.1094 0.0065 0.1230
## (Intercept)-Canis_latrans -2.6205 0.1761 -2.9683 -2.6167 -2.2837
## (Intercept)-Procyon_lotor -2.2721 0.1324 -2.5392 -2.2692 -2.0158
## (Intercept)-Dasypus_novemcinctus -1.5649 0.1350 -1.8326 -1.5626 -1.3030
## (Intercept)-Sylvilagus_floridanus -3.1797 0.2760 -3.7527 -3.1718 -2.6552
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0027 5250
## (Intercept)-Canis_latrans 1.0026 2608
## (Intercept)-Procyon_lotor 1.0018 2816
## (Intercept)-Dasypus_novemcinctus 1.0017 5250
## (Intercept)-Sylvilagus_floridanus 1.0005 1483
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2073
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6862 0.9053 -1.1966 0.7059 2.4665 1.0001 4947
## Avg_Cogongrass_Cover 0.1885 0.4126 -0.6126 0.1910 1.0229 1.0053 3884
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.7121 12.5195 0.5319 4.1499 37.3080 1.0014 1300
## Avg_Cogongrass_Cover 0.5794 1.0553 0.0455 0.2953 2.8813 1.0132 4206
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5631 0.982 0.044 0.2915 2.9337 1.0419 503
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6959 0.6606 -2.8895 -1.7517 -0.2265 1.0006 5250
## shrub_cover 0.1696 0.2983 -0.3998 0.1627 0.8059 1.0003 4635
## veg_height -0.0461 0.2850 -0.6019 -0.0457 0.5208 1.0023 5011
## week -0.0166 0.1956 -0.4220 -0.0107 0.3370 1.0025 4772
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8088 3.6282 0.5473 1.8583 10.4399 1.0135 4836
## shrub_cover 0.4183 0.6388 0.0539 0.2491 1.8131 1.0091 4747
## veg_height 0.4133 0.5630 0.0710 0.2638 1.6432 1.0090 5250
## week 0.1711 0.2760 0.0277 0.1023 0.7203 1.0119 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.4363 2.1257 1.6418 4.0301
## (Intercept)-Canis_latrans 0.6113 0.6215 -0.5086 0.5855
## (Intercept)-Procyon_lotor 0.7413 0.5897 -0.4281 0.7314
## (Intercept)-Dasypus_novemcinctus -0.5505 0.5741 -1.6582 -0.5623
## (Intercept)-Sylvilagus_floridanus -0.2240 0.7876 -1.5502 -0.2918
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2052 0.6641 -1.0377 0.1742
## Avg_Cogongrass_Cover-Canis_latrans 0.5168 0.4884 -0.2884 0.4568
## Avg_Cogongrass_Cover-Procyon_lotor 0.2338 0.3913 -0.4896 0.2189
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3636 0.3646 -0.3388 0.3530
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2840 0.5189 -1.3727 -0.2459
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.9538 1.0170 615
## (Intercept)-Canis_latrans 1.9308 1.0022 2569
## (Intercept)-Procyon_lotor 1.9224 1.0010 3064
## (Intercept)-Dasypus_novemcinctus 0.5786 1.0032 1689
## (Intercept)-Sylvilagus_floridanus 1.4676 1.0066 916
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6799 1.0025 3420
## Avg_Cogongrass_Cover-Canis_latrans 1.6595 1.0044 3160
## Avg_Cogongrass_Cover-Procyon_lotor 1.0760 1.0021 4419
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1129 1.0019 4171
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6298 1.0025 2691
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0067 0.0593 -0.1104 0.0055 0.1229
## (Intercept)-Canis_latrans -2.7792 0.1962 -3.1770 -2.7759 -2.4051
## (Intercept)-Procyon_lotor -2.2935 0.1452 -2.5902 -2.2883 -2.0222
## (Intercept)-Dasypus_novemcinctus -1.7073 0.1574 -2.0227 -1.7028 -1.4096
## (Intercept)-Sylvilagus_floridanus -3.1873 0.3222 -3.8988 -3.1626 -2.6292
## shrub_cover-Odocoileus_virginianus -0.0539 0.0650 -0.1817 -0.0523 0.0738
## shrub_cover-Canis_latrans -0.2515 0.2156 -0.6853 -0.2482 0.1492
## shrub_cover-Procyon_lotor 0.2421 0.1642 -0.0947 0.2453 0.5470
## shrub_cover-Dasypus_novemcinctus 0.7223 0.3008 0.1676 0.7096 1.3307
## shrub_cover-Sylvilagus_floridanus 0.1938 0.3719 -0.5103 0.1792 0.9843
## veg_height-Odocoileus_virginianus -0.3013 0.0643 -0.4248 -0.3020 -0.1732
## veg_height-Canis_latrans -0.6367 0.1904 -1.0235 -0.6311 -0.2849
## veg_height-Procyon_lotor 0.3365 0.1241 0.0950 0.3349 0.5836
## veg_height-Dasypus_novemcinctus 0.2194 0.1306 -0.0291 0.2188 0.4769
## veg_height-Sylvilagus_floridanus 0.1385 0.2627 -0.3701 0.1404 0.6563
## week-Odocoileus_virginianus 0.2162 0.0610 0.0963 0.2166 0.3341
## week-Canis_latrans 0.0836 0.1353 -0.1980 0.0882 0.3398
## week-Procyon_lotor -0.0419 0.1207 -0.2839 -0.0386 0.1875
## week-Dasypus_novemcinctus -0.1587 0.1402 -0.4467 -0.1547 0.1037
## week-Sylvilagus_floridanus -0.1507 0.2216 -0.6341 -0.1379 0.2520
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0013 2154
## (Intercept)-Procyon_lotor 1.0027 3772
## (Intercept)-Dasypus_novemcinctus 1.0033 4539
## (Intercept)-Sylvilagus_floridanus 1.0127 1202
## shrub_cover-Odocoileus_virginianus 1.0035 5250
## shrub_cover-Canis_latrans 1.0033 2699
## shrub_cover-Procyon_lotor 1.0022 3862
## shrub_cover-Dasypus_novemcinctus 1.0001 3419
## shrub_cover-Sylvilagus_floridanus 1.0004 1963
## veg_height-Odocoileus_virginianus 1.0002 5250
## veg_height-Canis_latrans 1.0022 2137
## veg_height-Procyon_lotor 1.0013 4043
## veg_height-Dasypus_novemcinctus 1.0005 4790
## veg_height-Sylvilagus_floridanus 1.0015 2304
## week-Odocoileus_virginianus 1.0026 5250
## week-Canis_latrans 1.0086 4213
## week-Procyon_lotor 1.0009 4112
## week-Dasypus_novemcinctus 1.0001 4866
## week-Sylvilagus_floridanus 1.0003 2899
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8708
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6317 0.9294 -1.3735 0.6414 2.4880 1.0012 4931
## Avg_Cogongrass_Cover 0.1974 0.3879 -0.5648 0.1928 0.9672 1.0006 4002
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.4436 53.8644 0.5837 4.3118 54.5120 1.1679 1498
## Avg_Cogongrass_Cover 0.4970 0.8692 0.0401 0.2490 2.5844 1.0078 4315
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5355 0.7717 0.0438 0.3008 2.5971 1.0191 1034
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6318 0.6908 -2.8453 -1.6836 -0.0722 1.0005 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9571 4.5755 0.5278 1.7983 12.2555 1.0011 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.5898 2.7261 1.6936 4.0110
## (Intercept)-Canis_latrans 0.4215 0.5869 -0.6663 0.4037
## (Intercept)-Procyon_lotor 0.7163 0.5656 -0.4100 0.7219
## (Intercept)-Dasypus_novemcinctus -0.6073 0.5502 -1.6982 -0.6071
## (Intercept)-Sylvilagus_floridanus -0.2228 0.7537 -1.5757 -0.2662
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2077 0.6301 -0.9744 0.1824
## Avg_Cogongrass_Cover-Canis_latrans 0.3944 0.3940 -0.3169 0.3760
## Avg_Cogongrass_Cover-Procyon_lotor 0.2685 0.4009 -0.4525 0.2427
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3501 0.3441 -0.2931 0.3407
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2291 0.4811 -1.2965 -0.1987
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.5373 1.0327 290
## (Intercept)-Canis_latrans 1.6493 1.0043 3011
## (Intercept)-Procyon_lotor 1.8226 1.0014 3612
## (Intercept)-Dasypus_novemcinctus 0.4862 1.0022 3194
## (Intercept)-Sylvilagus_floridanus 1.3915 1.0184 1456
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5561 1.0048 3659
## Avg_Cogongrass_Cover-Canis_latrans 1.2644 1.0006 4164
## Avg_Cogongrass_Cover-Procyon_lotor 1.1083 1.0016 4070
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0548 1.0010 4584
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6268 1.0000 3369
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0068 0.0594 -0.1114 0.0069 0.1221
## (Intercept)-Canis_latrans -2.6068 0.1731 -2.9615 -2.6007 -2.2809
## (Intercept)-Procyon_lotor -2.2628 0.1356 -2.5412 -2.2562 -2.0139
## (Intercept)-Dasypus_novemcinctus -1.5630 0.1325 -1.8293 -1.5613 -1.3109
## (Intercept)-Sylvilagus_floridanus -3.1897 0.3214 -3.8665 -3.1675 -2.6163
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0004 2978
## (Intercept)-Procyon_lotor 1.0012 4118
## (Intercept)-Dasypus_novemcinctus 1.0004 5250
## (Intercept)-Sylvilagus_floridanus 1.0168 1254
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.0845
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6442 0.9194 -1.2620 0.6510 2.4990 1.0014 4635
## Avg_Cogongrass_Cover 0.1835 0.3952 -0.6379 0.1855 0.9509 1.0012 4135
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.6719 17.9468 0.5793 4.2173 44.3078 1.0787 672
## Avg_Cogongrass_Cover 0.5249 1.0925 0.0429 0.2541 2.6793 1.0075 4215
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5228 0.7488 0.0456 0.2899 2.3439 1.0998 824
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6418 0.6735 -2.8392 -1.6988 -0.1021 1.0014 4961
## week -0.0131 0.1926 -0.4209 -0.0084 0.3699 1.0014 4375
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9103 5.0839 0.5256 1.7987 11.8162 1.1173 4547
## week 0.1644 0.2272 0.0282 0.1013 0.6855 1.0152 4786
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.5484 2.3752 1.7577 4.0310
## (Intercept)-Canis_latrans 0.4094 0.5936 -0.6787 0.3920
## (Intercept)-Procyon_lotor 0.6937 0.5655 -0.3870 0.6899
## (Intercept)-Dasypus_novemcinctus -0.6126 0.5562 -1.7159 -0.6140
## (Intercept)-Sylvilagus_floridanus -0.2120 0.8167 -1.6289 -0.2869
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1953 0.6413 -1.0182 0.1665
## Avg_Cogongrass_Cover-Canis_latrans 0.3931 0.4029 -0.3059 0.3638
## Avg_Cogongrass_Cover-Procyon_lotor 0.2779 0.4014 -0.4441 0.2617
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3497 0.3438 -0.3156 0.3452
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2351 0.4946 -1.3339 -0.2059
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.7501 1.0517 389
## (Intercept)-Canis_latrans 1.6257 1.0049 3066
## (Intercept)-Procyon_lotor 1.8399 1.0036 3696
## (Intercept)-Dasypus_novemcinctus 0.4805 1.0084 2849
## (Intercept)-Sylvilagus_floridanus 1.6722 1.0033 1408
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5399 1.0008 3362
## Avg_Cogongrass_Cover-Canis_latrans 1.2967 1.0002 4529
## Avg_Cogongrass_Cover-Procyon_lotor 1.1380 1.0010 4550
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0363 1.0031 4710
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6443 1.0001 3306
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0082 0.0597 -0.1086 0.0087 0.1254
## (Intercept)-Canis_latrans -2.6208 0.1778 -2.9953 -2.6128 -2.2943
## (Intercept)-Procyon_lotor -2.2667 0.1336 -2.5445 -2.2642 -2.0161
## (Intercept)-Dasypus_novemcinctus -1.5766 0.1352 -1.8499 -1.5730 -1.3148
## (Intercept)-Sylvilagus_floridanus -3.2079 0.3280 -3.9116 -3.1836 -2.6312
## week-Odocoileus_virginianus 0.2103 0.0589 0.0934 0.2100 0.3257
## week-Canis_latrans 0.0788 0.1317 -0.1906 0.0812 0.3226
## week-Procyon_lotor -0.0407 0.1184 -0.2842 -0.0372 0.1798
## week-Dasypus_novemcinctus -0.1564 0.1402 -0.4470 -0.1492 0.1121
## week-Sylvilagus_floridanus -0.1474 0.2172 -0.6075 -0.1310 0.2505
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0014 2837
## (Intercept)-Procyon_lotor 1.0026 3958
## (Intercept)-Dasypus_novemcinctus 1.0034 5250
## (Intercept)-Sylvilagus_floridanus 1.0021 1190
## week-Odocoileus_virginianus 1.0026 5496
## week-Canis_latrans 1.0020 4282
## week-Procyon_lotor 1.0004 5192
## week-Dasypus_novemcinctus 1.0033 5015
## week-Sylvilagus_floridanus 1.0012 2943
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1032
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6110 1.2939 -2.0304 0.6345 3.1397 1.0004 5250
## Cogon_Patch_Size -0.5987 0.7979 -2.1530 -0.6235 1.1184 1.0019 2057
## Veg_shannon_index 1.0502 0.5867 -0.0464 1.0265 2.2929 1.0019 1078
## total_shrub_cover -0.0010 0.5892 -1.1219 -0.0199 1.2989 1.0035 2091
## Avg_Cogongrass_Cover 1.8754 0.8979 0.0545 1.8699 3.6481 1.0014 1052
## Tree_Density -1.6494 1.0182 -3.5303 -1.6752 0.6421 1.0121 2250
## Avg_Canopy_Cover 1.2638 0.9283 -0.7515 1.2713 3.0691 1.0031 4068
## avg_veg_height -0.4036 0.5950 -1.5759 -0.4022 0.7817 1.0026 1508
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 39.9271 96.2211 2.7696 19.1387 189.2512 1.1875 348
## Cogon_Patch_Size 3.5646 10.0715 0.0696 1.0034 23.8450 1.0264 949
## Veg_shannon_index 0.8128 1.6931 0.0458 0.3343 4.6092 1.0050 2430
## total_shrub_cover 1.1817 2.6914 0.0490 0.4802 6.9996 1.0006 2411
## Avg_Cogongrass_Cover 2.2874 6.0342 0.0598 0.7224 14.6991 1.0007 1654
## Tree_Density 8.4456 28.4714 0.0809 2.0955 51.5657 1.1507 1312
## Avg_Canopy_Cover 6.9515 17.2773 0.1403 2.6936 37.7317 1.0342 1258
## avg_veg_height 0.7314 1.5928 0.0446 0.3083 3.9302 1.0182 3097
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8054 3.1973 0.0614 0.8141 9.1966 1.0904 345
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6538 0.668 -2.8523 -1.7082 -0.1533 1.0033 5250
## week -0.0104 0.196 -0.3932 -0.0052 0.3593 1.0057 4987
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8151 4.5189 0.5183 1.7729 10.9863 1.0011 5250
## week 0.1629 0.2417 0.0286 0.1015 0.6893 1.0045 5080
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 9.5780 5.1929 3.4478 8.4510
## (Intercept)-Canis_latrans 0.9237 1.2271 -1.0125 0.7836
## (Intercept)-Procyon_lotor 0.8953 1.0597 -1.3104 0.9102
## (Intercept)-Dasypus_novemcinctus -1.6935 1.1349 -4.3686 -1.5459
## (Intercept)-Sylvilagus_floridanus -1.2163 1.4181 -3.9818 -1.2225
## Cogon_Patch_Size-Odocoileus_virginianus -0.4948 1.5722 -3.2601 -0.6161
## Cogon_Patch_Size-Canis_latrans 0.6047 1.4751 -1.2997 0.2974
## Cogon_Patch_Size-Procyon_lotor -0.9723 0.7778 -2.5197 -0.9608
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8630 0.6764 -2.3038 -0.8220
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7952 1.7848 -6.4510 -1.4043
## Veg_shannon_index-Odocoileus_virginianus 0.9311 0.9276 -0.9872 0.9271
## Veg_shannon_index-Canis_latrans 1.3962 0.7887 0.1512 1.2955
## Veg_shannon_index-Procyon_lotor 1.2384 0.6664 0.0877 1.1823
## Veg_shannon_index-Dasypus_novemcinctus 0.7668 0.5637 -0.3496 0.7605
## Veg_shannon_index-Sylvilagus_floridanus 1.1818 0.7589 -0.1330 1.1241
## total_shrub_cover-Odocoileus_virginianus 0.1494 0.9522 -1.5840 0.0808
## total_shrub_cover-Canis_latrans 0.3349 0.8009 -0.8532 0.2121
## total_shrub_cover-Procyon_lotor -0.6658 0.6474 -2.0712 -0.6157
## total_shrub_cover-Dasypus_novemcinctus 0.1851 0.5405 -0.8330 0.1755
## total_shrub_cover-Sylvilagus_floridanus 0.0010 0.8518 -1.6316 -0.0046
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9422 1.3870 -0.6589 1.8912
## Avg_Cogongrass_Cover-Canis_latrans 2.4044 1.1236 0.5473 2.3043
## Avg_Cogongrass_Cover-Procyon_lotor 2.2013 1.0247 0.4231 2.1281
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6824 1.1650 0.8119 2.5331
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3160 1.1222 -1.0342 1.3419
## Tree_Density-Odocoileus_virginianus -0.4681 1.9847 -3.1862 -0.8301
## Tree_Density-Canis_latrans -2.6888 1.5070 -6.4541 -2.4066
## Tree_Density-Procyon_lotor -1.3607 0.8931 -3.1104 -1.3675
## Tree_Density-Dasypus_novemcinctus -3.9186 2.3206 -10.1798 -3.3159
## Tree_Density-Sylvilagus_floridanus -2.6478 1.7329 -7.0554 -2.3595
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7692 1.7606 -2.8599 0.8355
## Avg_Canopy_Cover-Canis_latrans 0.0744 0.7644 -1.5570 0.1012
## Avg_Canopy_Cover-Procyon_lotor 1.6964 0.8347 0.3080 1.6145
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0466 0.8119 0.7661 1.9452
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8788 2.3086 0.9539 3.3452
## avg_veg_height-Odocoileus_virginianus -0.4230 0.9296 -2.2859 -0.4195
## avg_veg_height-Canis_latrans -0.6328 0.7029 -2.0684 -0.6218
## avg_veg_height-Procyon_lotor -0.2462 0.6335 -1.4404 -0.2551
## avg_veg_height-Dasypus_novemcinctus -0.2159 0.6382 -1.4450 -0.2316
## avg_veg_height-Sylvilagus_floridanus -0.6164 0.7718 -2.1804 -0.5881
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 21.1528 1.0848 187
## (Intercept)-Canis_latrans 3.6960 1.0218 734
## (Intercept)-Procyon_lotor 2.9378 1.0074 1263
## (Intercept)-Dasypus_novemcinctus 0.1332 1.0207 863
## (Intercept)-Sylvilagus_floridanus 1.7178 1.0212 1064
## Cogon_Patch_Size-Odocoileus_virginianus 3.0287 1.0234 1664
## Cogon_Patch_Size-Canis_latrans 4.4323 1.0245 1012
## Cogon_Patch_Size-Procyon_lotor 0.4448 1.0046 1323
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3607 1.0023 1185
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3243 1.0086 870
## Veg_shannon_index-Odocoileus_virginianus 2.7751 1.0037 1562
## Veg_shannon_index-Canis_latrans 3.2606 1.0140 922
## Veg_shannon_index-Procyon_lotor 2.7519 1.0114 708
## Veg_shannon_index-Dasypus_novemcinctus 1.8764 1.0015 1569
## Veg_shannon_index-Sylvilagus_floridanus 2.8637 1.0013 1129
## total_shrub_cover-Odocoileus_virginianus 2.2369 1.0012 2136
## total_shrub_cover-Canis_latrans 2.3231 1.0020 935
## total_shrub_cover-Procyon_lotor 0.4592 1.0020 2858
## total_shrub_cover-Dasypus_novemcinctus 1.3085 1.0002 2621
## total_shrub_cover-Sylvilagus_floridanus 1.6948 1.0024 1926
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.8147 1.0036 1424
## Avg_Cogongrass_Cover-Canis_latrans 5.0167 1.0043 894
## Avg_Cogongrass_Cover-Procyon_lotor 4.4472 1.0059 991
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.3973 1.0036 778
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4562 1.0007 1598
## Tree_Density-Odocoileus_virginianus 4.6643 1.0224 853
## Tree_Density-Canis_latrans -0.5512 1.0236 691
## Tree_Density-Procyon_lotor 0.3678 1.0082 1692
## Tree_Density-Dasypus_novemcinctus -1.1331 1.0109 593
## Tree_Density-Sylvilagus_floridanus 0.0418 1.0180 630
## Avg_Canopy_Cover-Odocoileus_virginianus 4.1733 1.0037 1541
## Avg_Canopy_Cover-Canis_latrans 1.4523 1.0125 1516
## Avg_Canopy_Cover-Procyon_lotor 3.6311 1.0020 1596
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9175 1.0027 986
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.9683 1.0164 581
## avg_veg_height-Odocoileus_virginianus 1.3830 1.0003 2067
## avg_veg_height-Canis_latrans 0.7417 1.0037 1657
## avg_veg_height-Procyon_lotor 1.0065 1.0052 2138
## avg_veg_height-Dasypus_novemcinctus 1.1078 1.0008 1987
## avg_veg_height-Sylvilagus_floridanus 0.8503 1.0051 1825
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0098 0.0590 -0.1045 0.0089 0.1269
## (Intercept)-Canis_latrans -2.6568 0.1851 -3.0371 -2.6506 -2.3132
## (Intercept)-Procyon_lotor -2.2669 0.1313 -2.5394 -2.2629 -2.0167
## (Intercept)-Dasypus_novemcinctus -1.5785 0.1339 -1.8502 -1.5767 -1.3248
## (Intercept)-Sylvilagus_floridanus -3.1845 0.2842 -3.7685 -3.1751 -2.6498
## week-Odocoileus_virginianus 0.2127 0.0622 0.0955 0.2108 0.3353
## week-Canis_latrans 0.0820 0.1317 -0.1896 0.0875 0.3269
## week-Procyon_lotor -0.0409 0.1209 -0.2972 -0.0360 0.1803
## week-Dasypus_novemcinctus -0.1580 0.1415 -0.4554 -0.1525 0.1101
## week-Sylvilagus_floridanus -0.1413 0.2154 -0.5983 -0.1277 0.2487
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0050 4975
## (Intercept)-Canis_latrans 1.0016 2167
## (Intercept)-Procyon_lotor 1.0033 3931
## (Intercept)-Dasypus_novemcinctus 1.0007 5250
## (Intercept)-Sylvilagus_floridanus 1.0028 1667
## week-Odocoileus_virginianus 1.0017 5250
## week-Canis_latrans 1.0009 4154
## week-Procyon_lotor 1.0012 4611
## week-Dasypus_novemcinctus 1.0006 4988
## week-Sylvilagus_floridanus 1.0011 2962
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1167
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7539 0.9767 -1.3020 0.7768 2.6462 1.0025 4474
## Avg_Cogongrass_Cover 0.0808 0.4945 -0.8848 0.0799 1.0699 1.0010 2534
## total_shrub_cover -0.2126 0.4869 -1.2284 -0.2088 0.7512 1.0002 3188
## avg_veg_height 0.0949 0.4537 -0.7648 0.0890 1.0162 1.0001 2417
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.3440 27.4013 0.7001 5.0712 60.7937 1.0777 750
## Avg_Cogongrass_Cover 0.6839 1.3968 0.0437 0.3056 3.5456 1.0183 3822
## total_shrub_cover 1.0235 2.6935 0.0523 0.4301 5.5481 1.0329 2986
## avg_veg_height 0.4687 0.9862 0.0387 0.2192 2.4171 1.0344 4536
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6284 0.8897 0.0476 0.3397 3.0027 1.0065 927
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6514 0.6796 -2.8924 -1.7031 -0.1404 1.0010 5250
## week -0.0124 0.1927 -0.3797 -0.0118 0.3506 1.0003 4758
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0412 5.0395 0.5531 1.8901 11.9798 1.0605 5250
## week 0.1687 0.2728 0.0283 0.1021 0.7187 1.0272 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0545 2.6704 1.8850 4.5066
## (Intercept)-Canis_latrans 0.5230 0.6706 -0.7089 0.4758
## (Intercept)-Procyon_lotor 0.9161 0.6730 -0.3821 0.8989
## (Intercept)-Dasypus_novemcinctus -0.6544 0.5784 -1.7819 -0.6591
## (Intercept)-Sylvilagus_floridanus 0.1802 1.5587 -1.5569 -0.0944
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0930 0.7582 -1.3952 0.0766
## Avg_Cogongrass_Cover-Canis_latrans 0.4161 0.5549 -0.5633 0.3850
## Avg_Cogongrass_Cover-Procyon_lotor 0.0338 0.5265 -0.9877 0.0240
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2140 0.4396 -0.6380 0.2093
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3548 0.6410 -1.7526 -0.3131
## total_shrub_cover-Odocoileus_virginianus -0.1234 0.7750 -1.5942 -0.1458
## total_shrub_cover-Canis_latrans 0.2131 0.5291 -0.6784 0.1637
## total_shrub_cover-Procyon_lotor -0.9125 0.6051 -2.3328 -0.8261
## total_shrub_cover-Dasypus_novemcinctus 0.0108 0.3858 -0.7181 0.0028
## total_shrub_cover-Sylvilagus_floridanus -0.3769 0.7839 -2.2911 -0.3092
## avg_veg_height-Odocoileus_virginianus 0.0949 0.6721 -1.2453 0.1015
## avg_veg_height-Canis_latrans -0.0196 0.4946 -1.0134 -0.0147
## avg_veg_height-Procyon_lotor 0.1926 0.5110 -0.7863 0.1805
## avg_veg_height-Dasypus_novemcinctus 0.2716 0.4489 -0.6047 0.2656
## avg_veg_height-Sylvilagus_floridanus -0.0577 0.5549 -1.1411 -0.0522
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.3468 1.0608 352
## (Intercept)-Canis_latrans 1.9503 1.0060 2363
## (Intercept)-Procyon_lotor 2.3031 0.9998 2453
## (Intercept)-Dasypus_novemcinctus 0.4958 1.0000 3021
## (Intercept)-Sylvilagus_floridanus 3.4629 1.1580 229
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6475 1.0055 2761
## Avg_Cogongrass_Cover-Canis_latrans 1.6230 1.0033 2722
## Avg_Cogongrass_Cover-Procyon_lotor 1.1124 1.0007 3236
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1006 1.0010 3114
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7748 1.0018 2099
## total_shrub_cover-Odocoileus_virginianus 1.5233 1.0041 3042
## total_shrub_cover-Canis_latrans 1.4555 1.0074 2614
## total_shrub_cover-Procyon_lotor 0.0378 1.0105 2457
## total_shrub_cover-Dasypus_novemcinctus 0.7992 1.0040 4673
## total_shrub_cover-Sylvilagus_floridanus 0.9428 1.0026 1009
## avg_veg_height-Odocoileus_virginianus 1.4287 1.0006 2899
## avg_veg_height-Canis_latrans 0.9668 1.0021 2884
## avg_veg_height-Procyon_lotor 1.2752 1.0006 2960
## avg_veg_height-Dasypus_novemcinctus 1.1673 1.0012 2849
## avg_veg_height-Sylvilagus_floridanus 1.0475 1.0003 2234
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0085 0.0592 -0.1102 0.0093 0.1227
## (Intercept)-Canis_latrans -2.6438 0.1831 -3.0275 -2.6394 -2.3023
## (Intercept)-Procyon_lotor -2.2799 0.1313 -2.5335 -2.2772 -2.0278
## (Intercept)-Dasypus_novemcinctus -1.5783 0.1334 -1.8407 -1.5782 -1.3231
## (Intercept)-Sylvilagus_floridanus -3.3176 0.3722 -4.0985 -3.2907 -2.6558
## week-Odocoileus_virginianus 0.2118 0.0610 0.0946 0.2109 0.3338
## week-Canis_latrans 0.0821 0.1302 -0.1846 0.0864 0.3254
## week-Procyon_lotor -0.0406 0.1220 -0.2944 -0.0346 0.1815
## week-Dasypus_novemcinctus -0.1584 0.1413 -0.4495 -0.1504 0.1030
## week-Sylvilagus_floridanus -0.1447 0.2186 -0.6054 -0.1277 0.2455
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0039 2425
## (Intercept)-Procyon_lotor 1.0001 3856
## (Intercept)-Dasypus_novemcinctus 1.0057 5250
## (Intercept)-Sylvilagus_floridanus 1.0133 536
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0015 4497
## week-Procyon_lotor 1.0017 4868
## week-Dasypus_novemcinctus 1.0041 4927
## week-Sylvilagus_floridanus 1.0035 2932
# Includes week covariate for detection and none for occupancy
ms_week_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.085
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6223 0.8973 -1.2296 0.6209 2.3831 1.0009 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.4695 14.2016 0.6564 3.8525 37.2384 1.0596 701
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6562 0.6708 -2.8812 -1.7108 -0.1573 1.0008 5099
## week -0.0120 0.1919 -0.4023 -0.0104 0.3570 1.0006 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8976 4.1910 0.5309 1.8294 11.7833 1.0203 5250
## week 0.1631 0.2348 0.0282 0.0999 0.6727 1.0085 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Odocoileus_virginianus 4.2617 1.9612 1.9256 3.8315 9.2211 1.0338
## (Intercept)-Canis_latrans 0.3645 0.4152 -0.3866 0.3436 1.2119 1.0007
## (Intercept)-Procyon_lotor 0.7652 0.4083 0.0176 0.7483 1.5889 1.0020
## (Intercept)-Dasypus_novemcinctus -0.6041 0.3772 -1.3652 -0.5949 0.1383 1.0006
## (Intercept)-Sylvilagus_floridanus -0.1685 0.8295 -1.2036 -0.2740 1.4547 1.0682
## ESS
## (Intercept)-Odocoileus_virginianus 498
## (Intercept)-Canis_latrans 4734
## (Intercept)-Procyon_lotor 5250
## (Intercept)-Dasypus_novemcinctus 4697
## (Intercept)-Sylvilagus_floridanus 527
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0077 0.0593 -0.1118 0.0072 0.1229
## (Intercept)-Canis_latrans -2.6167 0.1745 -2.9680 -2.6113 -2.2892
## (Intercept)-Procyon_lotor -2.2654 0.1294 -2.5382 -2.2607 -2.0209
## (Intercept)-Dasypus_novemcinctus -1.5792 0.1324 -1.8410 -1.5799 -1.3233
## (Intercept)-Sylvilagus_floridanus -3.2235 0.3429 -3.9939 -3.1939 -2.6197
## week-Odocoileus_virginianus 0.2106 0.0612 0.0946 0.2098 0.3321
## week-Canis_latrans 0.0820 0.1360 -0.2001 0.0868 0.3361
## week-Procyon_lotor -0.0427 0.1214 -0.2881 -0.0379 0.1818
## week-Dasypus_novemcinctus -0.1576 0.1375 -0.4346 -0.1541 0.0992
## week-Sylvilagus_floridanus -0.1483 0.2227 -0.6491 -0.1325 0.2436
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0002 2963
## (Intercept)-Procyon_lotor 1.0029 4245
## (Intercept)-Dasypus_novemcinctus 1.0003 5250
## (Intercept)-Sylvilagus_floridanus 1.0024 1050
## week-Odocoileus_virginianus 1.0023 5250
## week-Canis_latrans 0.9999 4267
## week-Procyon_lotor 1.0027 4534
## week-Dasypus_novemcinctus 1.0010 5250
## week-Sylvilagus_floridanus 1.0026 3002
#Includes week for detection and only foraging for occupancy
ms_week_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
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## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6646 0.966 -1.3666 0.6774 2.5749 1.0016 4429
## Veg_shannon_index 0.5203 0.400 -0.2553 0.5132 1.3386 1.0024 2708
## Avg_Cogongrass_Cover 0.3972 0.434 -0.4629 0.3969 1.2648 1.0045 2612
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.3506 17.8367 0.7004 4.8256 44.6825 1.0225 1627
## Veg_shannon_index 0.4487 0.7200 0.0395 0.2256 2.2242 1.0030 3109
## Avg_Cogongrass_Cover 0.6838 2.7090 0.0431 0.2850 3.4307 1.0708 4493
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.689 1.1012 0.0478 0.3662 3.3024 1.0511 741
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6544 0.6779 -2.8632 -1.7117 -0.2003 1.0009 5250
## week -0.0120 0.1965 -0.4086 -0.0074 0.3528 1.0008 4803
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9070 4.6561 0.5504 1.8207 11.4105 1.0479 5250
## week 0.1756 0.3518 0.0283 0.1032 0.7144 1.0294 4388
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.7513 2.1569 1.8206 4.3108
## (Intercept)-Canis_latrans 0.4254 0.6628 -0.8332 0.3992
## (Intercept)-Procyon_lotor 0.7458 0.6429 -0.5412 0.7605
## (Intercept)-Dasypus_novemcinctus -0.6480 0.5878 -1.8381 -0.6467
## (Intercept)-Sylvilagus_floridanus -0.0678 1.1816 -1.6558 -0.2485
## Veg_shannon_index-Odocoileus_virginianus 0.4310 0.6201 -0.8818 0.4543
## Veg_shannon_index-Canis_latrans 0.7738 0.4362 0.0031 0.7404
## Veg_shannon_index-Procyon_lotor 0.6040 0.4613 -0.2205 0.5808
## Veg_shannon_index-Dasypus_novemcinctus 0.2777 0.3759 -0.4717 0.2906
## Veg_shannon_index-Sylvilagus_floridanus 0.6055 0.5332 -0.3256 0.5704
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4330 0.6960 -0.8630 0.4069
## Avg_Cogongrass_Cover-Canis_latrans 0.6765 0.4896 -0.1564 0.6328
## Avg_Cogongrass_Cover-Procyon_lotor 0.5532 0.4987 -0.3125 0.5048
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4875 0.3677 -0.2269 0.4841
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0676 0.5652 -1.2554 -0.0409
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.3165 1.0151 701
## (Intercept)-Canis_latrans 1.8211 1.0085 2912
## (Intercept)-Procyon_lotor 2.0064 1.0012 2872
## (Intercept)-Dasypus_novemcinctus 0.5048 1.0067 2947
## (Intercept)-Sylvilagus_floridanus 2.9112 1.0626 436
## Veg_shannon_index-Odocoileus_virginianus 1.6292 1.0009 2477
## Veg_shannon_index-Canis_latrans 1.7156 0.9999 2816
## Veg_shannon_index-Procyon_lotor 1.5968 1.0015 1876
## Veg_shannon_index-Dasypus_novemcinctus 0.9860 1.0028 4018
## Veg_shannon_index-Sylvilagus_floridanus 1.7237 1.0087 1935
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9019 1.0076 3055
## Avg_Cogongrass_Cover-Canis_latrans 1.7886 1.0003 3278
## Avg_Cogongrass_Cover-Procyon_lotor 1.6959 1.0021 2282
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2275 1.0004 3934
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0062 1.0020 1996
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0085 0.0590 -0.1059 0.0090 0.1244
## (Intercept)-Canis_latrans -2.6138 0.1735 -2.9659 -2.6077 -2.2964
## (Intercept)-Procyon_lotor -2.2817 0.1320 -2.5458 -2.2781 -2.0312
## (Intercept)-Dasypus_novemcinctus -1.5819 0.1355 -1.8510 -1.5796 -1.3210
## (Intercept)-Sylvilagus_floridanus -3.2719 0.3562 -4.0614 -3.2422 -2.6508
## week-Odocoileus_virginianus 0.2109 0.0601 0.0961 0.2097 0.3319
## week-Canis_latrans 0.0755 0.1320 -0.1953 0.0805 0.3247
## week-Procyon_lotor -0.0411 0.1201 -0.2835 -0.0385 0.1842
## week-Dasypus_novemcinctus -0.1585 0.1418 -0.4610 -0.1529 0.1011
## week-Sylvilagus_floridanus -0.1383 0.2170 -0.6000 -0.1218 0.2463
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0018 5250
## (Intercept)-Canis_latrans 1.0005 3168
## (Intercept)-Procyon_lotor 1.0007 4141
## (Intercept)-Dasypus_novemcinctus 1.0020 5045
## (Intercept)-Sylvilagus_floridanus 1.0097 722
## week-Odocoileus_virginianus 1.0016 5483
## week-Canis_latrans 1.0013 4202
## week-Procyon_lotor 1.0014 4339
## week-Dasypus_novemcinctus 1.0039 4653
## week-Sylvilagus_floridanus 1.0032 2687
# Includes movement covariates of occupancy and week for detection
ms_week_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
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## -------------------------------------------------
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## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1207
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7210 1.0132 -1.3921 0.7408 2.7395 1.0051 4734
## Cogon_Patch_Size 0.0071 0.6302 -1.2347 -0.0022 1.3032 1.0027 3126
## Avg_Cogongrass_Cover 0.1543 0.4344 -0.6668 0.1410 1.0475 1.0039 3075
## total_shrub_cover -0.2376 0.4766 -1.1877 -0.2305 0.7321 1.0023 2939
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.3875 38.8051 0.7686 5.5980 58.0523 1.2428 708
## Cogon_Patch_Size 2.1339 6.7604 0.0593 0.6917 12.7596 1.1085 1452
## Avg_Cogongrass_Cover 0.5226 1.1568 0.0399 0.2481 2.5707 1.0810 3689
## total_shrub_cover 0.9016 2.4852 0.0517 0.3861 4.7379 1.1157 2415
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6355 0.8684 0.045 0.3436 2.8589 1.0047 1058
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6602 0.6941 -2.8790 -1.7217 -0.1227 1.0022 5250
## week -0.0062 0.1882 -0.3918 -0.0038 0.3534 1.0004 4613
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0031 5.9981 0.5529 1.8679 12.3704 1.1402 5250
## week 0.1703 0.2752 0.0284 0.1020 0.7299 1.0115 4885
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.2923 2.9321 1.9814 4.6398
## (Intercept)-Canis_latrans 0.6615 0.7442 -0.5866 0.5905
## (Intercept)-Procyon_lotor 0.8889 0.6736 -0.3836 0.8795
## (Intercept)-Dasypus_novemcinctus -0.6598 0.5969 -1.8396 -0.6656
## (Intercept)-Sylvilagus_floridanus -0.1343 1.1556 -1.9444 -0.2909
## Cogon_Patch_Size-Odocoileus_virginianus 0.2018 1.0155 -1.3632 0.0691
## Cogon_Patch_Size-Canis_latrans 1.0166 1.0499 -0.2944 0.7834
## Cogon_Patch_Size-Procyon_lotor -0.1240 0.5156 -1.1183 -0.1280
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1012 0.4383 -1.0313 -0.0885
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9209 1.1706 -3.9114 -0.6858
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1834 0.7132 -1.1353 0.1482
## Avg_Cogongrass_Cover-Canis_latrans 0.2427 0.4546 -0.5663 0.2152
## Avg_Cogongrass_Cover-Procyon_lotor 0.1883 0.4792 -0.7099 0.1713
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3588 0.4106 -0.4193 0.3486
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1675 0.5562 -1.3139 -0.1433
## total_shrub_cover-Odocoileus_virginianus -0.1536 0.7540 -1.6056 -0.1675
## total_shrub_cover-Canis_latrans 0.1130 0.5312 -0.7661 0.0843
## total_shrub_cover-Procyon_lotor -0.8842 0.5888 -2.2570 -0.8123
## total_shrub_cover-Dasypus_novemcinctus -0.0350 0.3823 -0.7661 -0.0413
## total_shrub_cover-Sylvilagus_floridanus -0.2891 0.7256 -1.8271 -0.2570
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.3299 1.0532 324
## (Intercept)-Canis_latrans 2.2751 1.0105 2006
## (Intercept)-Procyon_lotor 2.2840 1.0005 2810
## (Intercept)-Dasypus_novemcinctus 0.4957 1.0041 2966
## (Intercept)-Sylvilagus_floridanus 2.7248 1.0051 579
## Cogon_Patch_Size-Odocoileus_virginianus 2.5855 1.0064 2179
## Cogon_Patch_Size-Canis_latrans 3.5958 1.0011 1554
## Cogon_Patch_Size-Procyon_lotor 0.8961 1.0031 3912
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7274 1.0009 4174
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6946 1.0020 1165
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7551 1.0014 2876
## Avg_Cogongrass_Cover-Canis_latrans 1.2461 1.0022 3450
## Avg_Cogongrass_Cover-Procyon_lotor 1.1776 1.0062 3284
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2252 1.0012 3759
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8756 1.0120 3041
## total_shrub_cover-Odocoileus_virginianus 1.4453 1.0008 3263
## total_shrub_cover-Canis_latrans 1.2045 1.0121 1936
## total_shrub_cover-Procyon_lotor 0.0477 1.0034 2623
## total_shrub_cover-Dasypus_novemcinctus 0.7455 1.0025 4187
## total_shrub_cover-Sylvilagus_floridanus 1.0144 1.0072 1369
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0080 0.0597 -0.1067 0.0057 0.1271
## (Intercept)-Canis_latrans -2.6174 0.1794 -2.9891 -2.6115 -2.2831
## (Intercept)-Procyon_lotor -2.2739 0.1321 -2.5432 -2.2703 -2.0250
## (Intercept)-Dasypus_novemcinctus -1.5761 0.1358 -1.8516 -1.5724 -1.3204
## (Intercept)-Sylvilagus_floridanus -3.2927 0.3628 -4.0557 -3.2608 -2.6662
## week-Odocoileus_virginianus 0.2124 0.0602 0.0952 0.2131 0.3304
## week-Canis_latrans 0.0839 0.1340 -0.1949 0.0876 0.3277
## week-Procyon_lotor -0.0393 0.1187 -0.2755 -0.0372 0.1864
## week-Dasypus_novemcinctus -0.1583 0.1402 -0.4494 -0.1530 0.1045
## week-Sylvilagus_floridanus -0.1465 0.2163 -0.6119 -0.1328 0.2389
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 4857
## (Intercept)-Canis_latrans 1.0010 2454
## (Intercept)-Procyon_lotor 1.0004 3840
## (Intercept)-Dasypus_novemcinctus 1.0010 5250
## (Intercept)-Sylvilagus_floridanus 1.0058 662
## week-Odocoileus_virginianus 1.0011 5250
## week-Canis_latrans 1.0000 4408
## week-Procyon_lotor 1.0016 4667
## week-Dasypus_novemcinctus 1.0006 4888
## week-Sylvilagus_floridanus 1.0008 2879
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.081
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6100 1.0806 -1.6139 0.6176 2.7327 1.0033 4961
## Tree_Density -0.8574 0.5943 -2.1319 -0.8328 0.2700 1.0012 2991
## Avg_Canopy_Cover 0.8064 0.6223 -0.4331 0.7871 2.1492 1.0032 3841
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.9407 36.0622 1.1134 7.7329 80.6892 1.0454 1326
## Tree_Density 1.4891 3.6976 0.0473 0.4617 9.8096 1.0068 2192
## Avg_Canopy_Cover 2.1813 4.8153 0.0867 0.9580 11.4608 1.0031 2228
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4732 0.7426 0.0413 0.2559 2.2592 1.0942 887
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6344 0.6646 -2.8017 -1.6778 -0.1651 1.0012 5250
## week -0.0173 0.1894 -0.4118 -0.0123 0.3434 1.0011 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8008 3.6416 0.5375 1.7850 11.7955 1.0033 5250
## week 0.1624 0.2697 0.0284 0.0988 0.6753 1.0535 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 5.9743 2.9728 2.3105 5.3239 13.5943
## (Intercept)-Canis_latrans 0.3994 0.7593 -0.8197 0.3421 1.8850
## (Intercept)-Procyon_lotor 0.8266 0.6407 -0.4330 0.8154 2.1778
## (Intercept)-Dasypus_novemcinctus -1.0629 0.6716 -2.5684 -1.0167 0.1291
## (Intercept)-Sylvilagus_floridanus -0.6253 0.8465 -2.1579 -0.6558 1.1242
## Tree_Density-Odocoileus_virginianus -0.4194 0.9302 -1.9049 -0.5170 1.7991
## Tree_Density-Canis_latrans -0.9828 0.6125 -2.4002 -0.9050 -0.0116
## Tree_Density-Procyon_lotor -0.5119 0.4449 -1.3756 -0.5145 0.3659
## Tree_Density-Dasypus_novemcinctus -1.5799 1.1270 -4.6239 -1.2944 -0.1816
## Tree_Density-Sylvilagus_floridanus -1.2197 0.9046 -3.5069 -1.0790 0.1098
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6164 1.0158 -1.3520 0.6284 2.7305
## Avg_Canopy_Cover-Canis_latrans -0.1221 0.5030 -1.1117 -0.1188 0.8536
## Avg_Canopy_Cover-Procyon_lotor 1.0078 0.5335 0.0921 0.9562 2.1970
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9980 0.4786 0.1581 0.9610 2.0382
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.0870 1.2473 0.5111 1.8023 5.1937
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0141 456
## (Intercept)-Canis_latrans 1.0344 1295
## (Intercept)-Procyon_lotor 1.0000 3031
## (Intercept)-Dasypus_novemcinctus 1.0012 1953
## (Intercept)-Sylvilagus_floridanus 1.0087 1856
## Tree_Density-Odocoileus_virginianus 1.0056 1619
## Tree_Density-Canis_latrans 1.0047 3011
## Tree_Density-Procyon_lotor 0.9999 3757
## Tree_Density-Dasypus_novemcinctus 1.0011 1603
## Tree_Density-Sylvilagus_floridanus 1.0021 1708
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0027 2637
## Avg_Canopy_Cover-Canis_latrans 1.0049 3152
## Avg_Canopy_Cover-Procyon_lotor 1.0010 4175
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0024 4029
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0162 1014
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0077 0.0595 -0.1098 0.0090 0.1245
## (Intercept)-Canis_latrans -2.6465 0.1855 -3.0315 -2.6418 -2.3033
## (Intercept)-Procyon_lotor -2.2634 0.1331 -2.5353 -2.2607 -2.0138
## (Intercept)-Dasypus_novemcinctus -1.5782 0.1338 -1.8526 -1.5742 -1.3241
## (Intercept)-Sylvilagus_floridanus -3.1344 0.2863 -3.7275 -3.1241 -2.6082
## week-Odocoileus_virginianus 0.2116 0.0605 0.0941 0.2106 0.3317
## week-Canis_latrans 0.0796 0.1343 -0.1945 0.0837 0.3241
## week-Procyon_lotor -0.0420 0.1202 -0.2917 -0.0389 0.1847
## week-Dasypus_novemcinctus -0.1606 0.1384 -0.4461 -0.1551 0.0959
## week-Sylvilagus_floridanus -0.1434 0.2177 -0.6172 -0.1266 0.2487
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0028 2437
## (Intercept)-Procyon_lotor 1.0002 4135
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## (Intercept)-Sylvilagus_floridanus 1.0001 1725
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0015 4299
## week-Procyon_lotor 1.0017 4120
## week-Dasypus_novemcinctus 1.0045 4937
## week-Sylvilagus_floridanus 1.0037 3043
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1128
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0549 0.9335 -1.7439 0.0175 2.0246 1.0040 3216
## Avg_Cogongrass_Cover -0.5194 0.6134 -1.7160 -0.5259 0.7236 1.0046 2750
## I(Avg_Cogongrass_Cover^2) 1.1687 0.7267 -0.0629 1.0730 2.8198 1.0137 801
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.4301 16.7115 0.4187 3.9523 43.4586 1.0671 747
## Avg_Cogongrass_Cover 1.3116 4.0674 0.0499 0.4782 7.1118 1.0258 2023
## I(Avg_Cogongrass_Cover^2) 3.2608 31.0739 0.0465 0.5521 17.2959 1.3495 1276
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4624 0.6079 0.0413 0.2658 2.1221 1.014 1194
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6537 0.6847 -2.8951 -1.7059 -0.0799 1.0024 5250
## week -0.0078 0.1848 -0.3845 -0.0041 0.3493 1.0025 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9955 4.6671 0.5526 1.8616 12.1790 1.0139 4693
## week 0.1620 0.2456 0.0272 0.0988 0.6595 1.0079 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6983 2.4785 0.4232 3.2340
## (Intercept)-Canis_latrans -0.5239 0.7682 -2.1155 -0.5073
## (Intercept)-Procyon_lotor -0.1389 0.6922 -1.5564 -0.1176
## (Intercept)-Dasypus_novemcinctus -1.2631 0.6707 -2.6085 -1.2530
## (Intercept)-Sylvilagus_floridanus -0.9364 0.9312 -2.7155 -0.9614
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.5592 1.0009 -2.6425 -0.5555
## Avg_Cogongrass_Cover-Canis_latrans -0.2068 0.6874 -1.4024 -0.2570
## Avg_Cogongrass_Cover-Procyon_lotor -0.3797 0.6941 -1.6276 -0.4231
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4116 0.5794 -1.5510 -0.4185
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2754 0.9871 -3.5515 -1.1325
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9021 1.9101 -0.0491 1.3952
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8302 1.3117 0.1935 1.4907
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.5277 1.1413 0.1453 1.2410
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6831 0.4256 -0.1320 0.6760
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9898 0.8359 -0.2020 0.8567
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.5914 1.0654 407
## (Intercept)-Canis_latrans 0.9542 1.0023 1599
## (Intercept)-Procyon_lotor 1.1945 1.0014 1768
## (Intercept)-Dasypus_novemcinctus 0.0015 1.0019 2998
## (Intercept)-Sylvilagus_floridanus 0.8526 1.0104 1589
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3804 1.0112 2630
## Avg_Cogongrass_Cover-Canis_latrans 1.2071 1.0073 2610
## Avg_Cogongrass_Cover-Procyon_lotor 1.0974 1.0112 2039
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7039 1.0032 3137
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2469 1.0049 1259
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 7.1407 1.0525 316
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.2225 1.0047 452
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.5390 1.0181 394
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5643 1.0012 2968
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.0727 1.0407 692
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0079 0.0590 -0.1080 0.0086 0.1213
## (Intercept)-Canis_latrans -2.6470 0.1762 -3.0084 -2.6391 -2.3251
## (Intercept)-Procyon_lotor -2.2871 0.1328 -2.5504 -2.2853 -2.0289
## (Intercept)-Dasypus_novemcinctus -1.5780 0.1338 -1.8447 -1.5760 -1.3120
## (Intercept)-Sylvilagus_floridanus -3.2726 0.3377 -3.9783 -3.2567 -2.6684
## week-Odocoileus_virginianus 0.2123 0.0608 0.0943 0.2123 0.3317
## week-Canis_latrans 0.0813 0.1304 -0.1774 0.0836 0.3263
## week-Procyon_lotor -0.0412 0.1165 -0.2818 -0.0396 0.1774
## week-Dasypus_novemcinctus -0.1563 0.1399 -0.4478 -0.1514 0.1005
## week-Sylvilagus_floridanus -0.1456 0.2172 -0.6222 -0.1308 0.2382
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0031 5250
## (Intercept)-Canis_latrans 1.0029 2380
## (Intercept)-Procyon_lotor 0.9998 3840
## (Intercept)-Dasypus_novemcinctus 1.0004 5250
## (Intercept)-Sylvilagus_floridanus 1.0078 1052
## week-Odocoileus_virginianus 1.0005 4435
## week-Canis_latrans 1.0001 4460
## week-Procyon_lotor 1.0001 4508
## week-Dasypus_novemcinctus 1.0018 4913
## week-Sylvilagus_floridanus 1.0003 2850
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1157
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0181 1.4002 -2.6836 -0.0138 2.8654 1.0029 3929
## Cogon_Patch_Size -0.1634 0.9296 -2.0569 -0.1788 1.7901 1.0024 2488
## Veg_shannon_index 1.0657 0.6955 -0.2047 1.0259 2.5378 1.0178 978
## total_shrub_cover -0.0887 0.6323 -1.3077 -0.1061 1.2272 1.0111 1359
## Avg_Cogongrass_Cover 0.2655 1.1533 -1.9940 0.2704 2.5525 1.0040 1031
## Tree_Density -1.7835 1.3367 -4.1408 -1.9249 1.2951 1.0116 1396
## Avg_Canopy_Cover 1.2600 1.0520 -1.0958 1.2891 3.2780 1.0042 3787
## I(Avg_Cogongrass_Cover^2) 1.8445 0.9710 -0.0555 1.8120 3.7926 1.0017 1123
## avg_veg_height -0.0894 0.6881 -1.5025 -0.0743 1.2295 1.0045 1629
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 67.8190 162.6129 3.3050 29.9903 360.4432 1.0514 320
## Cogon_Patch_Size 7.9880 34.3393 0.0883 1.9910 47.6125 1.1507 741
## Veg_shannon_index 1.3699 4.9357 0.0445 0.4035 8.5361 1.0463 761
## total_shrub_cover 1.3086 3.7130 0.0537 0.5153 7.7007 1.0623 3223
## Avg_Cogongrass_Cover 4.8385 13.8406 0.0626 1.2620 31.7448 1.0601 493
## Tree_Density 24.7015 73.1088 0.1008 5.8239 163.4644 1.0419 501
## Avg_Canopy_Cover 12.5353 39.0501 0.2158 4.3413 76.7905 1.1142 548
## I(Avg_Cogongrass_Cover^2) 6.9331 29.8787 0.0528 0.7073 63.5450 1.0979 103
## avg_veg_height 1.2332 3.2241 0.0495 0.4411 7.2195 1.0678 1095
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1675 7.294 0.0591 1.0467 18.7716 1.2464 81
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6388 0.6948 -2.9092 -1.7021 -0.0329 1.0023 5250
## week -0.0054 0.1923 -0.3906 -0.0007 0.3619 1.0030 4663
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8461 3.6134 0.5304 1.8356 11.3499 1.0183 5250
## week 0.1734 0.2976 0.0285 0.1029 0.7208 1.0050 4644
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 11.0049 7.0244 2.5657
## (Intercept)-Canis_latrans -1.1812 1.8440 -5.1825
## (Intercept)-Procyon_lotor -0.5643 1.4347 -3.5477
## (Intercept)-Dasypus_novemcinctus -3.6865 2.0714 -9.1626
## (Intercept)-Sylvilagus_floridanus -2.8237 2.2317 -7.6231
## Cogon_Patch_Size-Odocoileus_virginianus 0.0432 2.1403 -3.5326
## Cogon_Patch_Size-Canis_latrans 1.8364 2.2409 -0.6905
## Cogon_Patch_Size-Procyon_lotor -0.4838 1.2798 -2.5960
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6060 0.9668 -2.9454
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7699 2.6054 -7.9712
## Veg_shannon_index-Odocoileus_virginianus 0.9474 1.1613 -1.4946
## Veg_shannon_index-Canis_latrans 1.5158 0.9925 0.1216
## Veg_shannon_index-Procyon_lotor 1.3192 0.9437 0.0226
## Veg_shannon_index-Dasypus_novemcinctus 0.7634 0.6561 -0.4983
## Veg_shannon_index-Sylvilagus_floridanus 1.2194 0.9613 -0.2823
## total_shrub_cover-Odocoileus_virginianus 0.0343 1.0671 -1.9670
## total_shrub_cover-Canis_latrans 0.1665 0.8076 -1.1575
## total_shrub_cover-Procyon_lotor -0.7472 0.7283 -2.3021
## total_shrub_cover-Dasypus_novemcinctus 0.2277 0.6126 -0.8813
## total_shrub_cover-Sylvilagus_floridanus -0.0862 0.9266 -1.9692
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1994 2.0939 -3.9252
## Avg_Cogongrass_Cover-Canis_latrans 0.2310 1.6676 -3.0174
## Avg_Cogongrass_Cover-Procyon_lotor 0.6175 1.5731 -2.1891
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3361 1.8282 -1.4531
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7532 1.9852 -4.9358
## Tree_Density-Odocoileus_virginianus -0.4294 2.9296 -4.3443
## Tree_Density-Canis_latrans -3.9252 2.5223 -10.8893
## Tree_Density-Procyon_lotor -1.9854 1.3605 -4.8646
## Tree_Density-Dasypus_novemcinctus -6.2335 4.1902 -17.3016
## Tree_Density-Sylvilagus_floridanus -3.4594 2.6437 -9.8509
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7456 2.2984 -3.8517
## Avg_Canopy_Cover-Canis_latrans -0.1395 0.9394 -2.2035
## Avg_Canopy_Cover-Procyon_lotor 1.7662 0.9722 0.2266
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5112 1.2768 0.7372
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.9583 3.2914 1.0253
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.7302 2.7905 -0.1294
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9425 2.1577 0.6949
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6255 2.0067 0.4180
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.9526 1.3061 0.1263
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7278 1.4313 -0.4031
## avg_veg_height-Odocoileus_virginianus -0.0959 1.1537 -2.4546
## avg_veg_height-Canis_latrans -0.5198 0.8750 -2.5480
## avg_veg_height-Procyon_lotor 0.2336 0.7910 -1.2288
## avg_veg_height-Dasypus_novemcinctus 0.1212 0.7654 -1.3996
## avg_veg_height-Sylvilagus_floridanus -0.2974 0.9781 -2.4172
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.3608 30.0976 1.0394 161
## (Intercept)-Canis_latrans -1.1342 2.3451 1.0419 704
## (Intercept)-Procyon_lotor -0.4800 1.9905 1.0314 591
## (Intercept)-Dasypus_novemcinctus -3.2880 -0.8360 1.0407 182
## (Intercept)-Sylvilagus_floridanus -2.6851 1.1577 1.0203 556
## Cogon_Patch_Size-Odocoileus_virginianus -0.1507 4.7917 1.0074 1165
## Cogon_Patch_Size-Canis_latrans 1.2808 7.5535 1.0363 499
## Cogon_Patch_Size-Procyon_lotor -0.5510 2.1256 1.0163 736
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5075 0.9558 1.0092 733
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2100 1.0710 1.0612 480
## Veg_shannon_index-Odocoileus_virginianus 0.9738 3.2003 1.0165 1675
## Veg_shannon_index-Canis_latrans 1.3635 3.8888 1.0162 611
## Veg_shannon_index-Procyon_lotor 1.1983 3.3018 1.0849 299
## Veg_shannon_index-Dasypus_novemcinctus 0.7512 2.0714 1.0026 1601
## Veg_shannon_index-Sylvilagus_floridanus 1.1139 3.3618 1.0617 696
## total_shrub_cover-Odocoileus_virginianus -0.0244 2.3579 1.0148 1906
## total_shrub_cover-Canis_latrans 0.0932 2.0842 1.0187 1492
## total_shrub_cover-Procyon_lotor -0.7049 0.5961 1.0010 1237
## total_shrub_cover-Dasypus_novemcinctus 0.1977 1.5058 1.0085 1442
## total_shrub_cover-Sylvilagus_floridanus -0.0944 1.8636 1.0108 1774
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2220 4.4298 1.0147 1230
## Avg_Cogongrass_Cover-Canis_latrans 0.2566 3.5814 1.0024 1299
## Avg_Cogongrass_Cover-Procyon_lotor 0.5080 4.2081 1.0039 923
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0926 5.8621 1.0100 699
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5472 2.4296 1.0189 1108
## Tree_Density-Odocoileus_virginianus -0.9820 6.8493 1.0461 643
## Tree_Density-Canis_latrans -3.3425 -0.7762 1.0896 171
## Tree_Density-Procyon_lotor -1.9412 0.4703 1.0064 1382
## Tree_Density-Dasypus_novemcinctus -4.9733 -1.6742 1.0587 224
## Tree_Density-Sylvilagus_floridanus -3.0061 0.3607 1.1006 298
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8121 5.4235 1.0466 937
## Avg_Canopy_Cover-Canis_latrans -0.0731 1.4534 1.0478 484
## Avg_Canopy_Cover-Procyon_lotor 1.6644 3.9751 1.0129 1006
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2738 5.7150 1.0499 245
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2478 13.2557 1.1299 166
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.1462 9.8345 1.0183 123
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4132 9.3917 1.0480 91
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1432 8.6995 1.0998 198
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7646 5.0742 1.0700 394
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5559 4.7070 1.0069 404
## avg_veg_height-Odocoileus_virginianus -0.0975 2.2190 1.0074 2146
## avg_veg_height-Canis_latrans -0.4389 0.9601 1.0391 542
## avg_veg_height-Procyon_lotor 0.2038 1.8969 1.0059 1959
## avg_veg_height-Dasypus_novemcinctus 0.1264 1.6359 1.0047 1477
## avg_veg_height-Sylvilagus_floridanus -0.2379 1.4254 1.0109 1451
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0076 0.0588 -0.1093 0.0095 0.1206
## (Intercept)-Canis_latrans -2.6269 0.1762 -2.9894 -2.6183 -2.3081
## (Intercept)-Procyon_lotor -2.2832 0.1346 -2.5536 -2.2788 -2.0239
## (Intercept)-Dasypus_novemcinctus -1.5767 0.1369 -1.8535 -1.5756 -1.3124
## (Intercept)-Sylvilagus_floridanus -3.2411 0.2852 -3.8140 -3.2260 -2.7106
## week-Odocoileus_virginianus 0.2124 0.0611 0.0952 0.2117 0.3337
## week-Canis_latrans 0.0827 0.1327 -0.1865 0.0893 0.3253
## week-Procyon_lotor -0.0408 0.1179 -0.2802 -0.0390 0.1833
## week-Dasypus_novemcinctus -0.1580 0.1403 -0.4488 -0.1538 0.1057
## week-Sylvilagus_floridanus -0.1429 0.2163 -0.6029 -0.1307 0.2527
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0045 2259
## (Intercept)-Procyon_lotor 1.0076 2749
## (Intercept)-Dasypus_novemcinctus 1.0003 5002
## (Intercept)-Sylvilagus_floridanus 1.0019 1028
## week-Odocoileus_virginianus 1.0007 5250
## week-Canis_latrans 1.0004 4201
## week-Procyon_lotor 1.0010 4543
## week-Dasypus_novemcinctus 0.9998 4780
## week-Sylvilagus_floridanus 1.0038 2670
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.896
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6548 0.9141 -1.2401 0.6516 2.4700 1.0013 4517
## Avg_Cogongrass_Cover 0.1943 0.4101 -0.6013 0.1898 1.0237 1.0042 3997
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.9828 14.8625 0.5305 4.2394 38.4347 1.0198 1438
## Avg_Cogongrass_Cover 0.6363 1.3762 0.0420 0.2823 3.2412 1.0019 2902
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5445 0.7488 0.0463 0.3029 2.4564 1.0375 954
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6710 0.6737 -2.8741 -1.7169 -0.1625 1.0001 5250
## shrub_cover 0.1668 0.2987 -0.4087 0.1604 0.7886 1.0007 4834
## veg_height -0.0499 0.2968 -0.6257 -0.0507 0.5344 1.0003 4667
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9854 5.4435 0.5383 1.8266 11.4378 1.0358 5250
## shrub_cover 0.4324 0.7504 0.0531 0.2541 1.9153 1.0430 4818
## veg_height 0.4355 0.7641 0.0674 0.2612 1.8541 1.0107 4817
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.4509 2.0224 1.6481 4.0638
## (Intercept)-Canis_latrans 0.6153 0.6490 -0.5753 0.5810
## (Intercept)-Procyon_lotor 0.7148 0.5933 -0.4448 0.7167
## (Intercept)-Dasypus_novemcinctus -0.5453 0.5699 -1.6644 -0.5535
## (Intercept)-Sylvilagus_floridanus -0.2478 0.7650 -1.5799 -0.2927
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2009 0.6715 -1.0817 0.1744
## Avg_Cogongrass_Cover-Canis_latrans 0.5319 0.5026 -0.2445 0.4678
## Avg_Cogongrass_Cover-Procyon_lotor 0.2256 0.3914 -0.5067 0.2131
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3576 0.3565 -0.3235 0.3445
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2786 0.5100 -1.3483 -0.2491
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.7401 1.0048 625
## (Intercept)-Canis_latrans 2.0205 1.0027 2852
## (Intercept)-Procyon_lotor 1.9011 1.0014 3145
## (Intercept)-Dasypus_novemcinctus 0.6262 1.0019 3171
## (Intercept)-Sylvilagus_floridanus 1.3019 1.0046 1035
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5902 1.0004 3151
## Avg_Cogongrass_Cover-Canis_latrans 1.6424 1.0001 3010
## Avg_Cogongrass_Cover-Procyon_lotor 1.0336 1.0003 4356
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0836 1.0006 4805
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6251 1.0012 2809
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0074 0.0589 -0.1067 0.0068 0.1246
## (Intercept)-Canis_latrans -2.7692 0.1963 -3.1686 -2.7670 -2.4000
## (Intercept)-Procyon_lotor -2.2862 0.1454 -2.5955 -2.2783 -2.0208
## (Intercept)-Dasypus_novemcinctus -1.6989 0.1545 -2.0175 -1.6948 -1.4111
## (Intercept)-Sylvilagus_floridanus -3.1538 0.3054 -3.8231 -3.1360 -2.6091
## shrub_cover-Odocoileus_virginianus -0.0529 0.0644 -0.1758 -0.0540 0.0753
## shrub_cover-Canis_latrans -0.2560 0.2178 -0.6848 -0.2532 0.1604
## shrub_cover-Procyon_lotor 0.2423 0.1620 -0.0864 0.2432 0.5588
## shrub_cover-Dasypus_novemcinctus 0.7315 0.3046 0.1730 0.7143 1.3589
## shrub_cover-Sylvilagus_floridanus 0.2090 0.3755 -0.5012 0.1931 0.9922
## veg_height-Odocoileus_virginianus -0.2985 0.0639 -0.4282 -0.2974 -0.1760
## veg_height-Canis_latrans -0.6330 0.1899 -1.0137 -0.6292 -0.2757
## veg_height-Procyon_lotor 0.3343 0.1241 0.0889 0.3330 0.5741
## veg_height-Dasypus_novemcinctus 0.2231 0.1372 -0.0426 0.2226 0.5044
## veg_height-Sylvilagus_floridanus 0.1411 0.2618 -0.3736 0.1420 0.6453
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5016
## (Intercept)-Canis_latrans 1.0023 2080
## (Intercept)-Procyon_lotor 1.0029 3606
## (Intercept)-Dasypus_novemcinctus 1.0007 4489
## (Intercept)-Sylvilagus_floridanus 1.0019 1312
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0027 2716
## shrub_cover-Procyon_lotor 1.0026 3904
## shrub_cover-Dasypus_novemcinctus 1.0026 3499
## shrub_cover-Sylvilagus_floridanus 1.0005 1942
## veg_height-Odocoileus_virginianus 1.0019 5722
## veg_height-Canis_latrans 1.0048 2063
## veg_height-Procyon_lotor 1.0017 3830
## veg_height-Dasypus_novemcinctus 1.0002 4703
## veg_height-Sylvilagus_floridanus 1.0009 2381
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9232
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6366 1.3346 -2.1402 0.6853 3.1282 1.0028 4867
## Cogon_Patch_Size -0.5863 0.8585 -2.2103 -0.6091 1.2465 1.0077 2045
## Veg_shannon_index 1.0292 0.6630 -0.2942 1.0278 2.3282 1.0121 668
## total_shrub_cover -0.0769 0.7484 -1.5875 -0.0807 1.4598 1.0064 2083
## Avg_Cogongrass_Cover 1.7851 1.0145 -0.3038 1.7916 3.7386 1.0093 1521
## Tree_Density -1.6316 1.1654 -3.7473 -1.7116 0.9880 1.0032 1840
## Avg_Canopy_Cover 1.2950 0.9821 -0.8738 1.3248 3.1598 1.0090 2612
## avg_veg_height -0.3533 0.6455 -1.6862 -0.3357 0.9031 1.0083 821
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 62.3357 185.1241 2.7078 24.3743 357.3082 1.2059 380
## Cogon_Patch_Size 5.0469 19.3729 0.0615 1.0315 34.1594 1.1369 715
## Veg_shannon_index 1.0998 4.3855 0.0459 0.3834 5.9096 1.1463 3295
## total_shrub_cover 4.0097 14.3453 0.0754 0.9611 28.4640 1.6732 116
## Avg_Cogongrass_Cover 4.9153 15.8647 0.0616 1.0620 34.1684 1.3195 124
## Tree_Density 27.0915 160.3512 0.0899 3.5168 140.2600 1.7044 124
## Avg_Canopy_Cover 9.9337 24.6331 0.1554 3.4919 61.4253 1.2327 232
## avg_veg_height 0.7895 1.7909 0.0416 0.3142 4.3792 1.0375 2663
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8874 7.4016 0.0701 0.9173 18.4025 1.0527 270
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7020 0.6757 -2.9160 -1.7525 -0.2099 1.0020 5480
## shrub_cover 0.1961 0.3397 -0.4462 0.1816 0.9201 1.0004 3943
## veg_height -0.0379 0.2891 -0.6328 -0.0373 0.5401 1.0055 4742
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8947 5.5138 0.5692 1.8094 11.2554 1.1322 5250
## shrub_cover 0.5638 1.0121 0.0698 0.3245 2.4375 1.0066 4830
## veg_height 0.4339 0.6371 0.0698 0.2715 1.8267 1.0190 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 11.2347 6.9957 3.4873 9.5641
## (Intercept)-Canis_latrans 1.4007 1.6831 -0.9608 1.1491
## (Intercept)-Procyon_lotor 1.3411 2.3512 -1.2267 1.0536
## (Intercept)-Dasypus_novemcinctus -1.9192 1.7857 -6.0124 -1.6194
## (Intercept)-Sylvilagus_floridanus -1.2410 1.8018 -5.1803 -1.1878
## Cogon_Patch_Size-Odocoileus_virginianus -0.4417 1.6868 -3.2650 -0.5934
## Cogon_Patch_Size-Canis_latrans 0.6647 1.8094 -1.6196 0.2712
## Cogon_Patch_Size-Procyon_lotor -1.0615 0.9241 -2.8768 -0.9867
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7651 0.8571 -2.4875 -0.7387
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9065 2.1762 -7.6778 -1.3789
## Veg_shannon_index-Odocoileus_virginianus 0.9302 1.0528 -1.3009 0.9623
## Veg_shannon_index-Canis_latrans 1.3811 0.8956 -0.2505 1.3195
## Veg_shannon_index-Procyon_lotor 1.2662 0.8309 -0.1212 1.2156
## Veg_shannon_index-Dasypus_novemcinctus 0.7516 0.6478 -0.5430 0.7407
## Veg_shannon_index-Sylvilagus_floridanus 1.1707 0.8369 -0.2982 1.1071
## total_shrub_cover-Odocoileus_virginianus 0.1030 1.4009 -2.5797 0.0592
## total_shrub_cover-Canis_latrans 1.0923 1.7780 -0.7234 0.7025
## total_shrub_cover-Procyon_lotor -1.0795 1.2067 -3.5313 -0.8934
## total_shrub_cover-Dasypus_novemcinctus -0.0445 0.8898 -1.8608 0.0251
## total_shrub_cover-Sylvilagus_floridanus -0.6272 1.7583 -4.5371 -0.3485
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8621 1.7024 -1.3221 1.8417
## Avg_Cogongrass_Cover-Canis_latrans 2.8021 1.8109 0.5212 2.4649
## Avg_Cogongrass_Cover-Procyon_lotor 2.1251 1.2156 0.1148 2.0415
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.0411 1.8560 0.7337 2.7213
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0452 1.3665 -1.7003 1.1224
## Tree_Density-Odocoileus_virginianus -0.0898 3.1941 -3.4688 -0.7677
## Tree_Density-Canis_latrans -3.6644 3.2982 -11.0406 -2.9130
## Tree_Density-Procyon_lotor -1.5135 1.1290 -3.7554 -1.4504
## Tree_Density-Dasypus_novemcinctus -5.2655 5.2414 -16.3969 -3.9437
## Tree_Density-Sylvilagus_floridanus -3.1281 2.7564 -9.8327 -2.5625
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8853 2.0308 -3.3092 0.9111
## Avg_Canopy_Cover-Canis_latrans -0.0313 0.8438 -1.7568 0.0158
## Avg_Canopy_Cover-Procyon_lotor 1.7587 1.0348 0.1640 1.6605
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.4510 1.3383 0.8427 2.2127
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.3819 2.9283 0.9921 3.7007
## avg_veg_height-Odocoileus_virginianus -0.3930 1.0121 -2.5022 -0.3632
## avg_veg_height-Canis_latrans -0.4025 0.7528 -1.9260 -0.3860
## avg_veg_height-Procyon_lotor -0.3365 0.7354 -1.8232 -0.3099
## avg_veg_height-Dasypus_novemcinctus -0.2009 0.7138 -1.6248 -0.1991
## avg_veg_height-Sylvilagus_floridanus -0.5604 0.8405 -2.3990 -0.5068
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 29.8508 1.1627 126
## (Intercept)-Canis_latrans 5.6382 1.0972 184
## (Intercept)-Procyon_lotor 5.4703 1.1800 105
## (Intercept)-Dasypus_novemcinctus 0.4050 1.2083 112
## (Intercept)-Sylvilagus_floridanus 2.3120 1.0425 442
## Cogon_Patch_Size-Odocoileus_virginianus 3.7193 1.0043 1255
## Cogon_Patch_Size-Canis_latrans 5.4548 1.0117 551
## Cogon_Patch_Size-Procyon_lotor 0.3986 1.0520 390
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9376 1.0205 1124
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3861 1.0356 433
## Veg_shannon_index-Odocoileus_virginianus 2.9670 1.0058 1554
## Veg_shannon_index-Canis_latrans 3.3613 1.0060 840
## Veg_shannon_index-Procyon_lotor 2.9748 1.0204 368
## Veg_shannon_index-Dasypus_novemcinctus 2.0615 1.0101 1007
## Veg_shannon_index-Sylvilagus_floridanus 3.0399 1.0236 1020
## total_shrub_cover-Odocoileus_virginianus 3.0082 1.0108 1569
## total_shrub_cover-Canis_latrans 5.5948 1.2832 96
## total_shrub_cover-Procyon_lotor 0.3456 1.0932 153
## total_shrub_cover-Dasypus_novemcinctus 1.3887 1.0954 215
## total_shrub_cover-Sylvilagus_floridanus 1.7114 1.2533 246
## Avg_Cogongrass_Cover-Odocoileus_virginianus 5.2582 1.0280 1457
## Avg_Cogongrass_Cover-Canis_latrans 7.5725 1.1263 186
## Avg_Cogongrass_Cover-Procyon_lotor 4.6425 1.0283 1047
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 7.0679 1.2041 123
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4916 1.0385 1203
## Tree_Density-Odocoileus_virginianus 7.8655 1.1107 254
## Tree_Density-Canis_latrans -0.6657 1.3143 88
## Tree_Density-Procyon_lotor 0.3568 1.0694 339
## Tree_Density-Dasypus_novemcinctus -1.2342 1.4102 87
## Tree_Density-Sylvilagus_floridanus 0.2201 1.1720 190
## Avg_Canopy_Cover-Odocoileus_virginianus 5.1546 1.0231 1177
## Avg_Canopy_Cover-Canis_latrans 1.4330 1.0207 1526
## Avg_Canopy_Cover-Procyon_lotor 4.0225 1.0287 612
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.3783 1.2184 122
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.9538 1.1960 191
## avg_veg_height-Odocoileus_virginianus 1.5020 1.0063 1393
## avg_veg_height-Canis_latrans 1.0291 1.0062 938
## avg_veg_height-Procyon_lotor 0.9987 1.0077 766
## avg_veg_height-Dasypus_novemcinctus 1.1813 1.0157 792
## avg_veg_height-Sylvilagus_floridanus 0.9731 1.0059 1195
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0056 0.0600 -0.1138 0.0050 0.1201
## (Intercept)-Canis_latrans -2.7763 0.1897 -3.1726 -2.7696 -2.4219
## (Intercept)-Procyon_lotor -2.3029 0.1550 -2.6430 -2.2896 -2.0201
## (Intercept)-Dasypus_novemcinctus -1.7134 0.1574 -2.0293 -1.7091 -1.4095
## (Intercept)-Sylvilagus_floridanus -3.1602 0.2704 -3.7037 -3.1527 -2.6564
## shrub_cover-Odocoileus_virginianus -0.0530 0.0642 -0.1803 -0.0536 0.0724
## shrub_cover-Canis_latrans -0.3786 0.2264 -0.8185 -0.3793 0.0653
## shrub_cover-Procyon_lotor 0.2550 0.1653 -0.0807 0.2556 0.5725
## shrub_cover-Dasypus_novemcinctus 0.8048 0.3126 0.2257 0.8007 1.4357
## shrub_cover-Sylvilagus_floridanus 0.3938 0.4005 -0.3547 0.3765 1.2206
## veg_height-Odocoileus_virginianus -0.2985 0.0648 -0.4269 -0.2982 -0.1710
## veg_height-Canis_latrans -0.6367 0.1847 -1.0161 -0.6318 -0.2873
## veg_height-Procyon_lotor 0.3504 0.1249 0.1099 0.3477 0.6042
## veg_height-Dasypus_novemcinctus 0.2334 0.1348 -0.0258 0.2296 0.4998
## veg_height-Sylvilagus_floridanus 0.1673 0.2587 -0.3359 0.1657 0.6679
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0098 1931
## (Intercept)-Procyon_lotor 1.0234 785
## (Intercept)-Dasypus_novemcinctus 1.0018 3371
## (Intercept)-Sylvilagus_floridanus 1.0080 1610
## shrub_cover-Odocoileus_virginianus 1.0012 5250
## shrub_cover-Canis_latrans 1.0175 1717
## shrub_cover-Procyon_lotor 1.0023 3223
## shrub_cover-Dasypus_novemcinctus 1.0011 2506
## shrub_cover-Sylvilagus_floridanus 1.0042 1268
## veg_height-Odocoileus_virginianus 1.0029 5798
## veg_height-Canis_latrans 1.0054 2264
## veg_height-Procyon_lotor 1.0020 3788
## veg_height-Dasypus_novemcinctus 1.0008 4444
## veg_height-Sylvilagus_floridanus 1.0020 2357
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9752
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0887 1.0460 -1.0826 1.0926 3.1028 1.0045 2012
## Avg_Cogongrass_Cover -0.0968 0.5535 -1.2363 -0.0836 0.9613 1.0049 2461
## total_shrub_cover -0.5297 0.7765 -2.1297 -0.4996 1.0280 1.0030 1707
## avg_veg_height 0.3850 0.5293 -0.5954 0.3665 1.4535 1.0099 869
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.9461 24.6988 0.4104 4.7512 59.0583 1.1336 640
## Avg_Cogongrass_Cover 0.8884 1.9345 0.0457 0.3576 4.9649 1.0230 2740
## total_shrub_cover 3.9566 8.9735 0.0938 1.5917 22.4665 1.0084 736
## avg_veg_height 0.5692 1.4457 0.0426 0.2501 2.8022 1.0123 2306
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6951 1.0066 0.0439 0.3621 3.3193 1.0085 898
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7326 0.6887 -2.9791 -1.7679 -0.1776 1.0002 5250
## shrub_cover 0.3033 0.3781 -0.4160 0.2874 1.0849 1.0028 2428
## veg_height -0.0760 0.2958 -0.6421 -0.0757 0.5024 1.0027 4555
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9558 3.7452 0.5897 1.9801 11.5325 1.0050 5250
## shrub_cover 0.7145 0.9877 0.0790 0.4271 3.1942 1.0061 2033
## veg_height 0.4213 0.6452 0.0682 0.2678 1.6525 1.0089 4656
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.3538 2.8694 1.9156 4.7333
## (Intercept)-Canis_latrans 1.0750 0.9053 -0.3981 0.9610
## (Intercept)-Procyon_lotor 1.2782 0.8026 -0.1561 1.2249
## (Intercept)-Dasypus_novemcinctus -0.2278 0.9488 -1.6973 -0.3829
## (Intercept)-Sylvilagus_floridanus 0.9511 1.3457 -1.1027 0.7919
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0789 0.8317 -1.7283 -0.0871
## Avg_Cogongrass_Cover-Canis_latrans 0.3440 0.6926 -0.8585 0.2852
## Avg_Cogongrass_Cover-Procyon_lotor -0.2101 0.5832 -1.4556 -0.1817
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.0796 0.5181 -0.9521 0.0781
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5962 0.7941 -2.4219 -0.5084
## total_shrub_cover-Odocoileus_virginianus -0.1651 1.1178 -2.3132 -0.2126
## total_shrub_cover-Canis_latrans 0.7197 1.0448 -0.8193 0.5064
## total_shrub_cover-Procyon_lotor -1.4768 0.7940 -3.3132 -1.3702
## total_shrub_cover-Dasypus_novemcinctus -0.4613 1.0705 -3.6057 -0.1974
## total_shrub_cover-Sylvilagus_floridanus -1.9028 1.6313 -5.7814 -1.6050
## avg_veg_height-Odocoileus_virginianus 0.3393 0.7427 -1.1469 0.3304
## avg_veg_height-Canis_latrans 0.3924 0.6175 -0.7831 0.3695
## avg_veg_height-Procyon_lotor 0.3613 0.5667 -0.7269 0.3509
## avg_veg_height-Dasypus_novemcinctus 0.5753 0.6084 -0.4203 0.5150
## avg_veg_height-Sylvilagus_floridanus 0.3628 0.7001 -0.9527 0.3404
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.5418 1.0605 329
## (Intercept)-Canis_latrans 3.1801 1.0007 1201
## (Intercept)-Procyon_lotor 2.9983 1.0001 1618
## (Intercept)-Dasypus_novemcinctus 2.2658 1.0067 375
## (Intercept)-Sylvilagus_floridanus 3.9441 1.0174 500
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6379 1.0037 2928
## Avg_Cogongrass_Cover-Canis_latrans 1.9016 1.0076 2071
## Avg_Cogongrass_Cover-Procyon_lotor 0.8514 1.0026 2264
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1046 1.0000 2587
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7253 1.0023 1536
## total_shrub_cover-Odocoileus_virginianus 2.2432 1.0028 1601
## total_shrub_cover-Canis_latrans 3.3091 1.0102 709
## total_shrub_cover-Procyon_lotor -0.2139 1.0009 1137
## total_shrub_cover-Dasypus_novemcinctus 0.7182 1.0166 291
## total_shrub_cover-Sylvilagus_floridanus 0.4188 1.0071 347
## avg_veg_height-Odocoileus_virginianus 1.8425 1.0099 2170
## avg_veg_height-Canis_latrans 1.6880 1.0055 1457
## avg_veg_height-Procyon_lotor 1.5080 1.0041 1796
## avg_veg_height-Dasypus_novemcinctus 1.9550 1.0096 620
## avg_veg_height-Sylvilagus_floridanus 1.8437 1.0004 825
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0051 0.0600 -0.1168 0.0054 0.1203
## (Intercept)-Canis_latrans -2.8263 0.2048 -3.2351 -2.8217 -2.4432
## (Intercept)-Procyon_lotor -2.2936 0.1387 -2.5707 -2.2912 -2.0335
## (Intercept)-Dasypus_novemcinctus -1.7668 0.1918 -2.1861 -1.7510 -1.4243
## (Intercept)-Sylvilagus_floridanus -3.3549 0.2972 -3.9496 -3.3485 -2.7906
## shrub_cover-Odocoileus_virginianus -0.0528 0.0640 -0.1776 -0.0523 0.0714
## shrub_cover-Canis_latrans -0.3500 0.2587 -0.8566 -0.3567 0.1544
## shrub_cover-Procyon_lotor 0.3267 0.1574 0.0103 0.3287 0.6245
## shrub_cover-Dasypus_novemcinctus 0.9717 0.4137 0.2651 0.9261 1.8580
## shrub_cover-Sylvilagus_floridanus 0.7059 0.4594 -0.2723 0.7271 1.5469
## veg_height-Odocoileus_virginianus -0.2988 0.0647 -0.4249 -0.2974 -0.1721
## veg_height-Canis_latrans -0.6639 0.1960 -1.0530 -0.6631 -0.2965
## veg_height-Procyon_lotor 0.3343 0.1260 0.0877 0.3331 0.5811
## veg_height-Dasypus_novemcinctus 0.2362 0.1387 -0.0317 0.2350 0.5160
## veg_height-Sylvilagus_floridanus 0.0112 0.2659 -0.5006 0.0071 0.5424
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 0.9999 1693
## (Intercept)-Procyon_lotor 1.0004 3874
## (Intercept)-Dasypus_novemcinctus 1.0070 620
## (Intercept)-Sylvilagus_floridanus 1.0013 980
## shrub_cover-Odocoileus_virginianus 1.0022 5250
## shrub_cover-Canis_latrans 1.0188 1140
## shrub_cover-Procyon_lotor 1.0048 3892
## shrub_cover-Dasypus_novemcinctus 1.0039 415
## shrub_cover-Sylvilagus_floridanus 1.0068 543
## veg_height-Odocoileus_virginianus 1.0025 5250
## veg_height-Canis_latrans 1.0004 1932
## veg_height-Procyon_lotor 1.0024 4119
## veg_height-Dasypus_novemcinctus 1.0052 3056
## veg_height-Sylvilagus_floridanus 1.0017 1015
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8863
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6566 0.8631 -1.1185 0.6631 2.3574 1.0038 5026
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.8832 12.6669 0.6449 3.6804 32.2738 1.0762 999
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6896 0.6658 -2.8833 -1.7332 -0.2349 1.0002 5250
## shrub_cover 0.1659 0.2999 -0.4014 0.1616 0.7875 1.0009 4148
## veg_height -0.0447 0.2842 -0.6131 -0.0441 0.5283 0.9998 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8045 4.2171 0.5408 1.7896 10.9854 1.0043 5250
## shrub_cover 0.4081 0.5684 0.0557 0.2506 1.8161 1.0043 4879
## veg_height 0.4112 0.6817 0.0663 0.2582 1.6405 1.0580 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Odocoileus_virginianus 4.1910 1.8186 1.9324 3.7961 8.7019 1.0145
## (Intercept)-Canis_latrans 0.4759 0.4396 -0.3489 0.4564 1.3978 1.0016
## (Intercept)-Procyon_lotor 0.8067 0.4095 0.0635 0.7897 1.6691 0.9998
## (Intercept)-Dasypus_novemcinctus -0.5462 0.3815 -1.3057 -0.5436 0.1921 1.0003
## (Intercept)-Sylvilagus_floridanus -0.2559 0.5565 -1.2317 -0.2888 0.8866 1.0068
## ESS
## (Intercept)-Odocoileus_virginianus 768
## (Intercept)-Canis_latrans 4604
## (Intercept)-Procyon_lotor 4900
## (Intercept)-Dasypus_novemcinctus 5250
## (Intercept)-Sylvilagus_floridanus 1653
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0052 0.0591 -0.1118 0.0058 0.1204
## (Intercept)-Canis_latrans -2.7425 0.1948 -3.1438 -2.7338 -2.3845
## (Intercept)-Procyon_lotor -2.2885 0.1453 -2.5821 -2.2824 -2.0188
## (Intercept)-Dasypus_novemcinctus -1.6901 0.1540 -1.9970 -1.6861 -1.3989
## (Intercept)-Sylvilagus_floridanus -3.1527 0.3096 -3.8088 -3.1341 -2.5873
## shrub_cover-Odocoileus_virginianus -0.0549 0.0638 -0.1797 -0.0553 0.0682
## shrub_cover-Canis_latrans -0.2702 0.2196 -0.7056 -0.2713 0.1611
## shrub_cover-Procyon_lotor 0.2433 0.1610 -0.0777 0.2446 0.5536
## shrub_cover-Dasypus_novemcinctus 0.7145 0.2904 0.1782 0.7020 1.3160
## shrub_cover-Sylvilagus_floridanus 0.2124 0.3795 -0.4888 0.1942 0.9969
## veg_height-Odocoileus_virginianus -0.2986 0.0641 -0.4217 -0.2998 -0.1730
## veg_height-Canis_latrans -0.6063 0.1878 -0.9904 -0.5988 -0.2635
## veg_height-Procyon_lotor 0.3381 0.1252 0.0969 0.3366 0.5860
## veg_height-Dasypus_novemcinctus 0.2178 0.1323 -0.0383 0.2166 0.4804
## veg_height-Sylvilagus_floridanus 0.1137 0.2600 -0.3960 0.1121 0.6340
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0016 2089
## (Intercept)-Procyon_lotor 1.0017 3134
## (Intercept)-Dasypus_novemcinctus 1.0009 4474
## (Intercept)-Sylvilagus_floridanus 1.0031 1523
## shrub_cover-Odocoileus_virginianus 1.0016 4951
## shrub_cover-Canis_latrans 1.0074 2614
## shrub_cover-Procyon_lotor 1.0005 3996
## shrub_cover-Dasypus_novemcinctus 1.0009 3797
## shrub_cover-Sylvilagus_floridanus 1.0087 1900
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0087 2124
## veg_height-Procyon_lotor 1.0000 4281
## veg_height-Dasypus_novemcinctus 1.0001 4815
## veg_height-Sylvilagus_floridanus 1.0027 2499
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.907
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6784 0.9443 -1.3518 0.6761 2.5214 1.0001 4621
## Veg_shannon_index 0.4713 0.4099 -0.3442 0.4724 1.2855 1.0047 3029
## Avg_Cogongrass_Cover 0.3844 0.4600 -0.5048 0.3723 1.3244 1.0116 3023
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.4148 18.7918 0.6525 4.6627 47.7232 1.0671 951
## Veg_shannon_index 0.4931 1.3794 0.0377 0.2228 2.4157 1.1021 4748
## Avg_Cogongrass_Cover 0.7752 1.7484 0.0460 0.3371 4.2585 1.0165 3624
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5923 0.7923 0.0457 0.3259 2.7024 1.0067 883
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6770 0.6929 -2.9303 -1.7317 -0.1089 1.0008 5250
## shrub_cover 0.1538 0.3111 -0.4680 0.1457 0.7666 1.0008 4823
## veg_height -0.0490 0.2867 -0.6252 -0.0502 0.5191 1.0012 4935
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9035 3.7585 0.5279 1.8850 11.4340 1.0039 4996
## shrub_cover 0.4539 0.9776 0.0533 0.2524 1.9047 1.0544 4901
## veg_height 0.4238 0.7472 0.0651 0.2535 1.7718 1.1020 5036
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.7783 2.4260 1.8157 4.2867
## (Intercept)-Canis_latrans 0.5975 0.6577 -0.6280 0.5753
## (Intercept)-Procyon_lotor 0.7577 0.6303 -0.4416 0.7558
## (Intercept)-Dasypus_novemcinctus -0.5765 0.5746 -1.7208 -0.5734
## (Intercept)-Sylvilagus_floridanus -0.1593 1.0313 -1.6183 -0.2601
## Veg_shannon_index-Odocoileus_virginianus 0.3942 0.6131 -0.8842 0.4082
## Veg_shannon_index-Canis_latrans 0.7575 0.4612 -0.0778 0.7210
## Veg_shannon_index-Procyon_lotor 0.5345 0.4244 -0.2491 0.5196
## Veg_shannon_index-Dasypus_novemcinctus 0.2448 0.3832 -0.5376 0.2559
## Veg_shannon_index-Sylvilagus_floridanus 0.5366 0.5174 -0.3667 0.5062
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3999 0.7392 -0.9696 0.3596
## Avg_Cogongrass_Cover-Canis_latrans 0.8101 0.5742 -0.0830 0.7316
## Avg_Cogongrass_Cover-Procyon_lotor 0.4621 0.4606 -0.3460 0.4334
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4899 0.3888 -0.2381 0.4719
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1517 0.5734 -1.3772 -0.1153
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.4607 1.0235 450
## (Intercept)-Canis_latrans 1.9344 1.0027 2701
## (Intercept)-Procyon_lotor 2.0296 1.0022 3003
## (Intercept)-Dasypus_novemcinctus 0.5655 1.0000 3190
## (Intercept)-Sylvilagus_floridanus 1.8147 1.0920 393
## Veg_shannon_index-Odocoileus_virginianus 1.5876 1.0030 3356
## Veg_shannon_index-Canis_latrans 1.7511 1.0070 3256
## Veg_shannon_index-Procyon_lotor 1.4182 1.0080 2806
## Veg_shannon_index-Dasypus_novemcinctus 0.9855 1.0029 4433
## Veg_shannon_index-Sylvilagus_floridanus 1.6804 1.0069 2347
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0493 1.0183 3130
## Avg_Cogongrass_Cover-Canis_latrans 2.2207 1.0056 2467
## Avg_Cogongrass_Cover-Procyon_lotor 1.4291 1.0018 2616
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3087 1.0018 3862
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8954 1.0049 2641
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0600 -0.1135 0.0064 0.1195
## (Intercept)-Canis_latrans -2.7417 0.1884 -3.1335 -2.7351 -2.3878
## (Intercept)-Procyon_lotor -2.2945 0.1505 -2.6116 -2.2877 -2.0220
## (Intercept)-Dasypus_novemcinctus -1.6955 0.1527 -2.0115 -1.6922 -1.4056
## (Intercept)-Sylvilagus_floridanus -3.2044 0.3317 -3.9218 -3.1781 -2.6321
## shrub_cover-Odocoileus_virginianus -0.0531 0.0637 -0.1749 -0.0536 0.0704
## shrub_cover-Canis_latrans -0.2609 0.2135 -0.6892 -0.2552 0.1495
## shrub_cover-Procyon_lotor 0.2241 0.1714 -0.1281 0.2293 0.5435
## shrub_cover-Dasypus_novemcinctus 0.7252 0.3019 0.1722 0.7115 1.3485
## shrub_cover-Sylvilagus_floridanus 0.1667 0.3730 -0.5228 0.1463 0.9415
## veg_height-Odocoileus_virginianus -0.2984 0.0642 -0.4259 -0.2980 -0.1732
## veg_height-Canis_latrans -0.6183 0.1939 -1.0262 -0.6115 -0.2543
## veg_height-Procyon_lotor 0.3298 0.1231 0.0865 0.3297 0.5683
## veg_height-Dasypus_novemcinctus 0.2195 0.1342 -0.0349 0.2172 0.4948
## veg_height-Sylvilagus_floridanus 0.1419 0.2585 -0.3547 0.1389 0.6431
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0028 5544
## (Intercept)-Canis_latrans 1.0004 2223
## (Intercept)-Procyon_lotor 1.0000 3434
## (Intercept)-Dasypus_novemcinctus 1.0030 4347
## (Intercept)-Sylvilagus_floridanus 1.0063 935
## shrub_cover-Odocoileus_virginianus 1.0029 5250
## shrub_cover-Canis_latrans 1.0001 2852
## shrub_cover-Procyon_lotor 1.0007 3170
## shrub_cover-Dasypus_novemcinctus 1.0011 3642
## shrub_cover-Sylvilagus_floridanus 1.0006 1845
## veg_height-Odocoileus_virginianus 1.0009 6022
## veg_height-Canis_latrans 1.0092 2013
## veg_height-Procyon_lotor 1.0001 4104
## veg_height-Dasypus_novemcinctus 1.0004 4788
## veg_height-Sylvilagus_floridanus 1.0016 2454
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8953 1.0129 -1.2487 0.9242 2.8330 1.0030 4047
## Cogon_Patch_Size -0.0066 0.6406 -1.2741 -0.0073 1.3085 0.9999 3063
## Avg_Cogongrass_Cover 0.1694 0.4901 -0.7915 0.1673 1.1573 1.0042 2453
## total_shrub_cover -0.4128 0.6757 -1.8302 -0.4054 0.9472 1.0000 1839
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.7059 28.1364 0.5531 5.2730 59.8057 1.0369 877
## Cogon_Patch_Size 2.1931 6.3523 0.0543 0.6541 14.4419 1.1003 1733
## Avg_Cogongrass_Cover 0.6413 1.5284 0.0405 0.2917 3.6689 1.1722 1777
## total_shrub_cover 2.5064 6.7165 0.0651 0.9090 13.7064 1.0208 1173
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9027 2.0642 0.0487 0.3957 4.6673 1.0394 613
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7033 0.6869 -2.9365 -1.7589 -0.1317 1.0011 5250
## shrub_cover 0.2647 0.3608 -0.4646 0.2579 1.0246 1.0004 3039
## veg_height -0.0574 0.2829 -0.6249 -0.0590 0.5049 1.0031 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0534 4.9935 0.5685 1.9412 12.1625 1.0304 5250
## shrub_cover 0.5916 0.7475 0.0738 0.3651 2.5127 1.0019 3656
## veg_height 0.4177 0.6168 0.0684 0.2603 1.7656 1.0084 4955
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.2501 2.6869 1.9410 4.6859
## (Intercept)-Canis_latrans 1.0368 0.9159 -0.3987 0.9211
## (Intercept)-Procyon_lotor 1.1110 0.8059 -0.3290 1.0715
## (Intercept)-Dasypus_novemcinctus -0.4616 0.7130 -1.7471 -0.4870
## (Intercept)-Sylvilagus_floridanus 0.2990 1.3262 -1.8631 0.1581
## Cogon_Patch_Size-Odocoileus_virginianus 0.2142 1.1044 -1.4901 0.0735
## Cogon_Patch_Size-Canis_latrans 0.9662 1.1228 -0.4711 0.7289
## Cogon_Patch_Size-Procyon_lotor -0.1342 0.5009 -1.1348 -0.1324
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0587 0.4788 -1.0070 -0.0497
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9627 1.3086 -4.4030 -0.6657
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1937 0.7414 -1.2005 0.1666
## Avg_Cogongrass_Cover-Canis_latrans 0.4042 0.5745 -0.5309 0.3443
## Avg_Cogongrass_Cover-Procyon_lotor 0.1233 0.5305 -0.9010 0.1159
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3712 0.4418 -0.4735 0.3551
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1776 0.6887 -1.6431 -0.1491
## total_shrub_cover-Odocoileus_virginianus -0.1848 0.9734 -2.0511 -0.2297
## total_shrub_cover-Canis_latrans 0.4414 0.8686 -0.8545 0.2890
## total_shrub_cover-Procyon_lotor -1.2664 0.7556 -3.0511 -1.1620
## total_shrub_cover-Dasypus_novemcinctus -0.2262 0.5775 -1.5995 -0.1694
## total_shrub_cover-Sylvilagus_floridanus -1.2420 1.4325 -4.9231 -0.9495
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.8615 1.0325 437
## (Intercept)-Canis_latrans 3.1549 1.0021 899
## (Intercept)-Procyon_lotor 2.7636 1.0034 1354
## (Intercept)-Dasypus_novemcinctus 1.1560 1.0043 1419
## (Intercept)-Sylvilagus_floridanus 3.2043 1.0169 555
## Cogon_Patch_Size-Odocoileus_virginianus 2.9421 1.0055 1730
## Cogon_Patch_Size-Canis_latrans 3.8754 1.0106 1327
## Cogon_Patch_Size-Procyon_lotor 0.8664 1.0036 2826
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8718 1.0003 3676
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6547 1.0080 997
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7601 1.0015 2249
## Avg_Cogongrass_Cover-Canis_latrans 1.7108 1.0048 2180
## Avg_Cogongrass_Cover-Procyon_lotor 1.1937 1.0004 2928
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2801 1.0046 3060
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1054 1.0090 1991
## total_shrub_cover-Odocoileus_virginianus 1.9205 1.0070 2372
## total_shrub_cover-Canis_latrans 2.6131 1.0042 1003
## total_shrub_cover-Procyon_lotor -0.0871 1.0010 975
## total_shrub_cover-Dasypus_novemcinctus 0.7158 1.0040 1519
## total_shrub_cover-Sylvilagus_floridanus 0.7939 1.0104 398
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0058 0.0604 -0.1157 0.0055 0.1209
## (Intercept)-Canis_latrans -2.7782 0.2023 -3.1984 -2.7720 -2.3934
## (Intercept)-Procyon_lotor -2.2913 0.1396 -2.5792 -2.2894 -2.0318
## (Intercept)-Dasypus_novemcinctus -1.7378 0.1716 -2.0961 -1.7303 -1.4217
## (Intercept)-Sylvilagus_floridanus -3.3003 0.3126 -3.9246 -3.2962 -2.7174
## shrub_cover-Odocoileus_virginianus -0.0546 0.0641 -0.1800 -0.0538 0.0695
## shrub_cover-Canis_latrans -0.3277 0.2450 -0.7903 -0.3298 0.1498
## shrub_cover-Procyon_lotor 0.3085 0.1613 -0.0074 0.3100 0.6185
## shrub_cover-Dasypus_novemcinctus 0.8802 0.3500 0.2591 0.8550 1.6206
## shrub_cover-Sylvilagus_floridanus 0.5874 0.4617 -0.3801 0.6042 1.4647
## veg_height-Odocoileus_virginianus -0.2996 0.0645 -0.4245 -0.2995 -0.1723
## veg_height-Canis_latrans -0.6307 0.1958 -1.0291 -0.6228 -0.2688
## veg_height-Procyon_lotor 0.3390 0.1232 0.0949 0.3399 0.5799
## veg_height-Dasypus_novemcinctus 0.2356 0.1385 -0.0302 0.2348 0.5119
## veg_height-Sylvilagus_floridanus 0.0518 0.2634 -0.4615 0.0505 0.5711
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0012 1642
## (Intercept)-Procyon_lotor 1.0022 3489
## (Intercept)-Dasypus_novemcinctus 1.0008 2172
## (Intercept)-Sylvilagus_floridanus 1.0021 916
## shrub_cover-Odocoileus_virginianus 1.0012 4906
## shrub_cover-Canis_latrans 1.0007 1358
## shrub_cover-Procyon_lotor 1.0057 3983
## shrub_cover-Dasypus_novemcinctus 1.0030 1437
## shrub_cover-Sylvilagus_floridanus 1.0028 669
## veg_height-Odocoileus_virginianus 1.0004 5030
## veg_height-Canis_latrans 0.9997 1893
## veg_height-Procyon_lotor 1.0018 4148
## veg_height-Dasypus_novemcinctus 1.0008 4319
## veg_height-Sylvilagus_floridanus 1.0024 1458
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8983
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6342 1.0564 -1.6034 0.6452 2.7435 1.0044 4781
## Tree_Density -0.8771 0.5970 -2.1704 -0.8586 0.3186 1.0004 2895
## Avg_Canopy_Cover 0.8388 0.6956 -0.5313 0.8244 2.2875 1.0033 3651
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.5015 26.0514 0.9752 7.3209 73.8889 1.0358 979
## Tree_Density 1.5706 4.1464 0.0483 0.4713 10.2726 1.0075 1715
## Avg_Canopy_Cover 2.8727 5.9776 0.1225 1.3272 14.8362 1.0146 1335
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5663 0.9091 0.0396 0.2725 3.0541 1.0116 853
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6844 0.6729 -2.8711 -1.7332 -0.1352 1.0022 5250
## shrub_cover 0.1975 0.3102 -0.4021 0.1869 0.8234 0.9999 4573
## veg_height -0.0414 0.2867 -0.6267 -0.0416 0.5325 1.0038 4810
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8980 5.4207 0.5422 1.7750 11.4952 1.0406 4824
## shrub_cover 0.4665 0.7705 0.0575 0.2775 1.9675 1.0245 4475
## veg_height 0.4358 1.2581 0.0692 0.2672 1.6810 1.2063 3928
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 5.9525 3.0495 2.1811 5.2255 14.0205
## (Intercept)-Canis_latrans 0.5137 0.6832 -0.7549 0.4794 1.9399
## (Intercept)-Procyon_lotor 0.9440 0.6949 -0.3340 0.9054 2.4205
## (Intercept)-Dasypus_novemcinctus -0.9702 0.7194 -2.4993 -0.9327 0.3818
## (Intercept)-Sylvilagus_floridanus -0.5414 0.8498 -2.1564 -0.5631 1.1976
## Tree_Density-Odocoileus_virginianus -0.4052 0.9728 -1.8989 -0.5321 1.8349
## Tree_Density-Canis_latrans -1.0661 0.6555 -2.5790 -0.9921 0.0178
## Tree_Density-Procyon_lotor -0.5272 0.4608 -1.4414 -0.5269 0.3628
## Tree_Density-Dasypus_novemcinctus -1.5829 1.0893 -4.4012 -1.3263 -0.2101
## Tree_Density-Sylvilagus_floridanus -1.2416 0.9204 -3.5625 -1.1128 0.1551
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6261 1.1521 -1.4790 0.5926 3.1245
## Avg_Canopy_Cover-Canis_latrans -0.2468 0.5078 -1.2952 -0.2358 0.7290
## Avg_Canopy_Cover-Procyon_lotor 1.0709 0.5809 0.0893 1.0191 2.3547
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0612 0.5081 0.1451 1.0301 2.1479
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.4260 1.3778 0.6110 2.1352 5.8990
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0474 368
## (Intercept)-Canis_latrans 1.0041 2856
## (Intercept)-Procyon_lotor 1.0104 2688
## (Intercept)-Dasypus_novemcinctus 1.0038 2309
## (Intercept)-Sylvilagus_floridanus 1.0028 1915
## Tree_Density-Odocoileus_virginianus 1.0111 1405
## Tree_Density-Canis_latrans 1.0008 3208
## Tree_Density-Procyon_lotor 1.0008 3761
## Tree_Density-Dasypus_novemcinctus 1.0000 1406
## Tree_Density-Sylvilagus_floridanus 1.0049 1878
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0022 1493
## Avg_Canopy_Cover-Canis_latrans 1.0003 3234
## Avg_Canopy_Cover-Procyon_lotor 1.0073 3605
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0008 3735
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0101 979
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0051 0.0593 -0.1115 0.0057 0.1224
## (Intercept)-Canis_latrans -2.7694 0.1965 -3.1786 -2.7634 -2.4032
## (Intercept)-Procyon_lotor -2.2979 0.1493 -2.5970 -2.2925 -2.0169
## (Intercept)-Dasypus_novemcinctus -1.7077 0.1579 -2.0352 -1.7033 -1.4115
## (Intercept)-Sylvilagus_floridanus -3.1067 0.2744 -3.6792 -3.0940 -2.5987
## shrub_cover-Odocoileus_virginianus -0.0550 0.0637 -0.1817 -0.0551 0.0688
## shrub_cover-Canis_latrans -0.2880 0.2224 -0.7317 -0.2784 0.1357
## shrub_cover-Procyon_lotor 0.2426 0.1629 -0.0799 0.2457 0.5526
## shrub_cover-Dasypus_novemcinctus 0.7631 0.3025 0.2134 0.7522 1.3793
## shrub_cover-Sylvilagus_floridanus 0.3655 0.3626 -0.3319 0.3563 1.0886
## veg_height-Odocoileus_virginianus -0.2970 0.0650 -0.4277 -0.2959 -0.1718
## veg_height-Canis_latrans -0.6275 0.1868 -1.0085 -0.6229 -0.2670
## veg_height-Procyon_lotor 0.3467 0.1276 0.0920 0.3454 0.5971
## veg_height-Dasypus_novemcinctus 0.2302 0.1351 -0.0309 0.2284 0.5003
## veg_height-Sylvilagus_floridanus 0.1604 0.2481 -0.3334 0.1617 0.6414
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0002 1934
## (Intercept)-Procyon_lotor 1.0052 3388
## (Intercept)-Dasypus_novemcinctus 1.0018 4029
## (Intercept)-Sylvilagus_floridanus 1.0035 2219
## shrub_cover-Odocoileus_virginianus 1.0032 5250
## shrub_cover-Canis_latrans 1.0028 2581
## shrub_cover-Procyon_lotor 1.0022 3909
## shrub_cover-Dasypus_novemcinctus 1.0041 3311
## shrub_cover-Sylvilagus_floridanus 1.0029 2172
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0004 2259
## veg_height-Procyon_lotor 1.0007 4176
## veg_height-Dasypus_novemcinctus 1.0006 4664
## veg_height-Sylvilagus_floridanus 1.0018 3170
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0856 0.9160 -1.6362 0.0570 2.0284 1.0012 3606
## Avg_Cogongrass_Cover -0.5086 0.6115 -1.7046 -0.5146 0.7447 1.0138 3123
## I(Avg_Cogongrass_Cover^2) 1.0959 0.6808 -0.0319 1.0353 2.6211 1.0034 967
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.7149 13.5636 0.4073 3.9112 37.0894 1.0576 1423
## Avg_Cogongrass_Cover 1.4561 3.8872 0.0554 0.5485 8.2917 1.0229 3608
## I(Avg_Cogongrass_Cover^2) 4.6957 37.1738 0.0440 0.4282 20.1148 1.2757 251
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4737 0.7303 0.04 0.2615 2.215 1.0501 536
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7065 0.6846 -2.9468 -1.7517 -0.1627 1.0010 5745
## shrub_cover 0.1676 0.2979 -0.4021 0.1585 0.8048 1.0018 4554
## veg_height -0.0455 0.2893 -0.6359 -0.0436 0.5427 1.0016 4912
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9552 4.9205 0.5379 1.8483 11.8452 1.0474 5250
## shrub_cover 0.4162 0.7237 0.0516 0.2411 1.8796 1.0975 3734
## veg_height 0.4192 0.5702 0.0677 0.2661 1.7763 1.0075 4747
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5455 2.1535 0.4255 3.2038
## (Intercept)-Canis_latrans -0.3489 0.8030 -1.9677 -0.3452
## (Intercept)-Procyon_lotor -0.0540 0.7065 -1.4512 -0.0491
## (Intercept)-Dasypus_novemcinctus -1.1978 0.6761 -2.5631 -1.1936
## (Intercept)-Sylvilagus_floridanus -1.0021 0.8588 -2.6829 -1.0082
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.5552 1.0525 -2.5748 -0.5742
## Avg_Cogongrass_Cover-Canis_latrans -0.0157 0.7555 -1.2699 -0.0909
## Avg_Cogongrass_Cover-Procyon_lotor -0.5191 0.6838 -1.8400 -0.5249
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3742 0.5953 -1.5356 -0.3869
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3585 0.9379 -3.5035 -1.2488
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9250 3.3229 -0.0401 1.2940
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6528 1.1743 0.1564 1.3873
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.4401 1.1970 0.1346 1.1195
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6987 0.4552 -0.1528 0.6850
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9105 0.7723 -0.1898 0.7962
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.4969 1.0244 665
## (Intercept)-Canis_latrans 1.2373 1.0009 1643
## (Intercept)-Procyon_lotor 1.3290 1.0042 1782
## (Intercept)-Dasypus_novemcinctus 0.1023 1.0000 3238
## (Intercept)-Sylvilagus_floridanus 0.7155 1.0039 2074
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5808 1.0025 2423
## Avg_Cogongrass_Cover-Canis_latrans 1.6560 1.0132 2472
## Avg_Cogongrass_Cover-Procyon_lotor 0.8660 1.0009 3066
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8220 1.0024 3569
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1843 1.0024 1712
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 6.7334 1.2373 173
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6901 1.0018 516
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.7606 1.0039 397
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6821 1.0020 3173
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7305 1.0184 447
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0601 -0.1109 0.0054 0.1190
## (Intercept)-Canis_latrans -2.7620 0.1906 -3.1629 -2.7562 -2.4130
## (Intercept)-Procyon_lotor -2.3205 0.1567 -2.6466 -2.3143 -2.0346
## (Intercept)-Dasypus_novemcinctus -1.7004 0.1583 -2.0196 -1.6952 -1.3992
## (Intercept)-Sylvilagus_floridanus -3.1905 0.3226 -3.8872 -3.1721 -2.6244
## shrub_cover-Odocoileus_virginianus -0.0531 0.0643 -0.1780 -0.0546 0.0742
## shrub_cover-Canis_latrans -0.2271 0.2149 -0.6499 -0.2205 0.1820
## shrub_cover-Procyon_lotor 0.2074 0.1726 -0.1380 0.2100 0.5406
## shrub_cover-Dasypus_novemcinctus 0.7279 0.3005 0.1780 0.7112 1.3581
## shrub_cover-Sylvilagus_floridanus 0.1905 0.3682 -0.4783 0.1676 0.9817
## veg_height-Odocoileus_virginianus -0.2980 0.0663 -0.4263 -0.2980 -0.1703
## veg_height-Canis_latrans -0.6268 0.1889 -1.0080 -0.6168 -0.2675
## veg_height-Procyon_lotor 0.3461 0.1285 0.0929 0.3470 0.5991
## veg_height-Dasypus_novemcinctus 0.2226 0.1339 -0.0277 0.2212 0.4928
## veg_height-Sylvilagus_floridanus 0.1580 0.2733 -0.3776 0.1459 0.7034
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 6021
## (Intercept)-Canis_latrans 1.0010 2389
## (Intercept)-Procyon_lotor 1.0125 2288
## (Intercept)-Dasypus_novemcinctus 1.0000 4345
## (Intercept)-Sylvilagus_floridanus 1.0027 893
## shrub_cover-Odocoileus_virginianus 1.0005 5250
## shrub_cover-Canis_latrans 1.0039 2873
## shrub_cover-Procyon_lotor 1.0044 3110
## shrub_cover-Dasypus_novemcinctus 1.0032 3579
## shrub_cover-Sylvilagus_floridanus 1.0023 1816
## veg_height-Odocoileus_virginianus 1.0009 6410
## veg_height-Canis_latrans 1.0035 2447
## veg_height-Procyon_lotor 1.0008 3699
## veg_height-Dasypus_novemcinctus 1.0012 4802
## veg_height-Sylvilagus_floridanus 1.0012 1662
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9338
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1686 1.3440 -2.4406 0.1498 2.8452 1.0023 4824
## Cogon_Patch_Size -0.1873 0.9436 -2.0501 -0.2042 1.7546 1.0038 2232
## Veg_shannon_index 1.0261 0.6788 -0.3037 1.0155 2.3930 1.0034 1110
## total_shrub_cover -0.2134 0.7715 -1.8091 -0.2231 1.4267 1.0163 1290
## Avg_Cogongrass_Cover 0.3672 1.1505 -1.8894 0.3579 2.6535 1.0037 1094
## Tree_Density -1.7311 1.3341 -4.0954 -1.8617 1.2660 1.0058 1490
## Avg_Canopy_Cover 1.2825 1.0798 -1.0535 1.3118 3.3093 1.0004 3223
## I(Avg_Cogongrass_Cover^2) 1.7132 0.9056 -0.0078 1.6761 3.6673 1.0180 945
## avg_veg_height -0.0356 0.6868 -1.4258 -0.0380 1.3520 1.0044 1189
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 52.3509 113.4084 3.0090 26.0055 265.5752 1.0796 694
## Cogon_Patch_Size 7.8575 44.9546 0.0768 1.8299 47.6131 1.2303 1604
## Veg_shannon_index 1.2167 4.4089 0.0463 0.4409 6.7675 1.2135 3629
## total_shrub_cover 2.6132 6.1732 0.0720 0.9176 15.8590 1.0975 677
## Avg_Cogongrass_Cover 4.7318 11.5563 0.0674 1.4137 28.4818 1.0080 1429
## Tree_Density 27.7939 74.3883 0.1267 7.4378 183.6705 1.0623 383
## Avg_Canopy_Cover 14.1880 42.1737 0.2224 4.8885 75.9419 1.0913 650
## I(Avg_Cogongrass_Cover^2) 2.9936 12.2572 0.0516 0.5745 20.3462 1.2222 503
## avg_veg_height 0.8530 1.6735 0.0452 0.3489 4.9235 1.0114 3464
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8051 7.5793 0.0565 0.8811 15.3202 1.3067 151
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6973 0.6612 -2.8841 -1.7404 -0.1874 1.0021 5250
## shrub_cover 0.2177 0.3356 -0.4100 0.2085 0.9074 1.0081 3821
## veg_height -0.0350 0.2820 -0.6237 -0.0376 0.5229 1.0007 5000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8585 4.1292 0.5384 1.7942 11.4386 1.0062 5250
## shrub_cover 0.5445 0.7894 0.0692 0.3269 2.3221 1.0053 4569
## veg_height 0.4138 0.6182 0.0687 0.2527 1.7531 1.0055 4791
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 9.9518 5.6483 2.5089
## (Intercept)-Canis_latrans -0.5762 1.8512 -3.9940
## (Intercept)-Procyon_lotor -0.2456 1.3529 -2.9146
## (Intercept)-Dasypus_novemcinctus -3.2739 1.9087 -7.9282
## (Intercept)-Sylvilagus_floridanus -2.4965 2.1612 -7.0616
## Cogon_Patch_Size-Odocoileus_virginianus 0.0148 1.9579 -3.3068
## Cogon_Patch_Size-Canis_latrans 1.6958 2.2724 -0.9868
## Cogon_Patch_Size-Procyon_lotor -0.7158 0.9736 -2.6705
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4007 0.9916 -2.5392
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7532 2.6148 -8.4385
## Veg_shannon_index-Odocoileus_virginianus 0.8905 1.0743 -1.4236
## Veg_shannon_index-Canis_latrans 1.4985 0.9323 -0.0115
## Veg_shannon_index-Procyon_lotor 1.3034 0.7617 0.0171
## Veg_shannon_index-Dasypus_novemcinctus 0.7026 0.7005 -0.7245
## Veg_shannon_index-Sylvilagus_floridanus 1.1736 0.8823 -0.3702
## total_shrub_cover-Odocoileus_virginianus -0.0447 1.3639 -2.6325
## total_shrub_cover-Canis_latrans 0.6639 1.2544 -1.1410
## total_shrub_cover-Procyon_lotor -1.1570 0.8297 -3.0106
## total_shrub_cover-Dasypus_novemcinctus -0.0844 0.8374 -1.8601
## total_shrub_cover-Sylvilagus_floridanus -0.5789 1.3815 -3.7709
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3229 1.9377 -3.7426
## Avg_Cogongrass_Cover-Canis_latrans 0.7980 1.7213 -2.2534
## Avg_Cogongrass_Cover-Procyon_lotor 0.3673 1.4722 -2.4937
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.5188 1.8434 -1.4192
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7902 1.9306 -5.2237
## Tree_Density-Odocoileus_virginianus -0.0446 2.9411 -3.9480
## Tree_Density-Canis_latrans -4.2661 2.6435 -10.8821
## Tree_Density-Procyon_lotor -2.1733 1.3381 -4.9371
## Tree_Density-Dasypus_novemcinctus -6.6170 4.4880 -18.5879
## Tree_Density-Sylvilagus_floridanus -3.4339 2.7897 -10.8365
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6891 2.1154 -3.7673
## Avg_Canopy_Cover-Canis_latrans -0.0683 0.8382 -1.8354
## Avg_Canopy_Cover-Procyon_lotor 1.7042 1.0023 0.0123
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.7209 1.3623 0.8631
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.1929 3.5009 1.0329
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.1664 1.6440 -0.3685
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3860 1.6393 0.3550
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1771 1.3903 0.2831
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7612 1.0519 0.0439
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5022 1.1532 -0.5698
## avg_veg_height-Odocoileus_virginianus -0.0311 1.0335 -2.1319
## avg_veg_height-Canis_latrans -0.2062 0.8262 -1.8933
## avg_veg_height-Procyon_lotor 0.0843 0.7714 -1.3900
## avg_veg_height-Dasypus_novemcinctus 0.1426 0.7863 -1.3500
## avg_veg_height-Sylvilagus_floridanus -0.2297 0.9094 -2.2174
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.8105 24.1819 1.0123 258
## (Intercept)-Canis_latrans -0.6183 3.2196 1.0237 574
## (Intercept)-Procyon_lotor -0.2573 2.3967 1.0148 1398
## (Intercept)-Dasypus_novemcinctus -2.9821 -0.3735 1.0049 348
## (Intercept)-Sylvilagus_floridanus -2.4026 1.5800 1.0054 601
## Cogon_Patch_Size-Odocoileus_virginianus -0.1839 4.4993 1.0067 1222
## Cogon_Patch_Size-Canis_latrans 1.1759 7.1565 1.0109 557
## Cogon_Patch_Size-Procyon_lotor -0.6867 0.9748 1.0165 760
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3858 1.4679 1.0028 1646
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1423 1.2293 1.0137 465
## Veg_shannon_index-Odocoileus_virginianus 0.9296 2.9432 1.0065 1803
## Veg_shannon_index-Canis_latrans 1.3879 3.7093 1.0039 884
## Veg_shannon_index-Procyon_lotor 1.2364 3.0198 1.0044 797
## Veg_shannon_index-Dasypus_novemcinctus 0.6937 2.0901 1.0045 1409
## Veg_shannon_index-Sylvilagus_floridanus 1.1202 3.0756 1.0013 1064
## total_shrub_cover-Odocoileus_virginianus -0.1004 2.8924 1.0139 1692
## total_shrub_cover-Canis_latrans 0.4214 3.8684 1.0013 566
## total_shrub_cover-Procyon_lotor -1.0838 0.2774 1.0239 1588
## total_shrub_cover-Dasypus_novemcinctus -0.0249 1.4082 1.0274 1285
## total_shrub_cover-Sylvilagus_floridanus -0.4551 1.8198 1.0575 746
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3264 4.2880 1.0022 1283
## Avg_Cogongrass_Cover-Canis_latrans 0.6291 4.7254 1.0022 1055
## Avg_Cogongrass_Cover-Procyon_lotor 0.3547 3.5004 1.0039 1314
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2760 5.8663 1.0036 891
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5553 2.4714 1.0024 1063
## Tree_Density-Odocoileus_virginianus -0.6606 7.3512 1.0191 684
## Tree_Density-Canis_latrans -3.6653 -0.8217 1.0099 324
## Tree_Density-Procyon_lotor -2.0929 0.1596 1.0069 1384
## Tree_Density-Dasypus_novemcinctus -5.4210 -1.7466 1.0291 206
## Tree_Density-Sylvilagus_floridanus -2.9094 0.5838 1.0097 554
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7454 4.9951 1.0119 1031
## Avg_Canopy_Cover-Canis_latrans -0.0336 1.4466 1.0085 1083
## Avg_Canopy_Cover-Procyon_lotor 1.6063 3.9322 1.0024 857
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.4685 6.0705 1.0113 299
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.4430 13.7739 1.0161 303
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9259 6.3420 1.0119 792
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0654 6.1569 1.0249 372
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9853 5.1036 1.0457 350
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6328 4.2397 1.0032 693
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4223 4.0712 1.0205 663
## avg_veg_height-Odocoileus_virginianus -0.0351 2.0011 1.0015 1869
## avg_veg_height-Canis_latrans -0.1922 1.3779 1.0062 884
## avg_veg_height-Procyon_lotor 0.0760 1.6701 1.0069 1411
## avg_veg_height-Dasypus_novemcinctus 0.1235 1.7137 1.0039 1467
## avg_veg_height-Sylvilagus_floridanus -0.2044 1.4630 1.0038 1556
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0597 -0.1138 0.0062 0.1270
## (Intercept)-Canis_latrans -2.7368 0.1859 -3.1212 -2.7270 -2.3930
## (Intercept)-Procyon_lotor -2.3059 0.1477 -2.6016 -2.2992 -2.0288
## (Intercept)-Dasypus_novemcinctus -1.7270 0.1627 -2.0556 -1.7223 -1.4249
## (Intercept)-Sylvilagus_floridanus -3.1706 0.2731 -3.7471 -3.1583 -2.6652
## shrub_cover-Odocoileus_virginianus -0.0521 0.0643 -0.1754 -0.0512 0.0732
## shrub_cover-Canis_latrans -0.3267 0.2326 -0.7694 -0.3320 0.1261
## shrub_cover-Procyon_lotor 0.2613 0.1639 -0.0747 0.2656 0.5755
## shrub_cover-Dasypus_novemcinctus 0.8337 0.3181 0.2311 0.8318 1.4570
## shrub_cover-Sylvilagus_floridanus 0.4333 0.3935 -0.3245 0.4272 1.2304
## veg_height-Odocoileus_virginianus -0.2987 0.0651 -0.4265 -0.2980 -0.1737
## veg_height-Canis_latrans -0.5942 0.1851 -0.9726 -0.5908 -0.2421
## veg_height-Procyon_lotor 0.3581 0.1257 0.1071 0.3570 0.6087
## veg_height-Dasypus_novemcinctus 0.2398 0.1363 -0.0247 0.2385 0.5126
## veg_height-Sylvilagus_floridanus 0.1381 0.2551 -0.3586 0.1396 0.6586
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5750
## (Intercept)-Canis_latrans 1.0015 1929
## (Intercept)-Procyon_lotor 1.0022 2583
## (Intercept)-Dasypus_novemcinctus 1.0022 3344
## (Intercept)-Sylvilagus_floridanus 0.9999 1501
## shrub_cover-Odocoileus_virginianus 1.0014 5250
## shrub_cover-Canis_latrans 0.9998 1677
## shrub_cover-Procyon_lotor 1.0068 2370
## shrub_cover-Dasypus_novemcinctus 1.0029 1947
## shrub_cover-Sylvilagus_floridanus 1.0036 1453
## veg_height-Odocoileus_virginianus 1.0015 5293
## veg_height-Canis_latrans 1.0004 2365
## veg_height-Procyon_lotor 1.0062 3488
## veg_height-Dasypus_novemcinctus 1.0027 4579
## veg_height-Sylvilagus_floridanus 1.0104 2022
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T25<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1138
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6331 0.8958 -1.2381 0.6322 2.452 1.0016 5514
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.6501 29.6599 0.687 3.8015 40.054 1.261 610
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4255 0.7266 -2.7583 -1.4709 0.1613 1.0003 5250
## week 0.4490 0.3638 -0.3001 0.4563 1.1307 1.0033 4552
## I(week^2) -0.2458 0.1817 -0.5989 -0.2489 0.1268 1.0028 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5675 7.0993 0.6803 2.2116 12.9771 1.0895 3640
## week 0.6739 1.0532 0.1077 0.4129 2.9149 1.0149 4890
## I(week^2) 0.1580 0.2352 0.0280 0.0993 0.6378 1.0079 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.4004 2.2852 1.9892 3.8689 10.1545
## (Intercept)-Canis_latrans 0.3626 0.4196 -0.4112 0.3486 1.2400
## (Intercept)-Procyon_lotor 0.7670 0.4006 0.0146 0.7522 1.5965
## (Intercept)-Dasypus_novemcinctus -0.5981 0.3671 -1.3401 -0.5891 0.0983
## (Intercept)-Sylvilagus_floridanus -0.1375 1.0421 -1.2247 -0.2654 1.5198
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0541 379
## (Intercept)-Canis_latrans 1.0009 4825
## (Intercept)-Procyon_lotor 1.0010 5250
## (Intercept)-Dasypus_novemcinctus 1.0023 5321
## (Intercept)-Sylvilagus_floridanus 1.2299 301
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5348 0.0804 0.3782 0.5341 0.6977
## (Intercept)-Canis_latrans -2.4227 0.1936 -2.8207 -2.4165 -2.0633
## (Intercept)-Procyon_lotor -2.1526 0.1525 -2.4629 -2.1490 -1.8657
## (Intercept)-Dasypus_novemcinctus -1.4359 0.1573 -1.7532 -1.4330 -1.1329
## (Intercept)-Sylvilagus_floridanus -3.1174 0.3630 -3.9084 -3.0931 -2.4754
## week-Odocoileus_virginianus 1.3001 0.1231 1.0640 1.3003 1.5439
## week-Canis_latrans 0.6085 0.2747 0.0887 0.6013 1.1533
## week-Procyon_lotor 0.2020 0.2180 -0.2147 0.1993 0.6385
## week-Dasypus_novemcinctus 0.1032 0.2310 -0.3473 0.1027 0.5688
## week-Sylvilagus_floridanus 0.0516 0.3595 -0.6694 0.0552 0.7481
## I(week^2)-Odocoileus_virginianus -0.5362 0.0513 -0.6381 -0.5353 -0.4376
## I(week^2)-Canis_latrans -0.2504 0.1133 -0.4810 -0.2475 -0.0351
## I(week^2)-Procyon_lotor -0.1267 0.0939 -0.3146 -0.1257 0.0538
## I(week^2)-Dasypus_novemcinctus -0.1737 0.1079 -0.3922 -0.1720 0.0349
## I(week^2)-Sylvilagus_floridanus -0.1657 0.1741 -0.5177 -0.1622 0.1596
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0002 3822
## (Intercept)-Procyon_lotor 1.0004 4695
## (Intercept)-Dasypus_novemcinctus 1.0036 5250
## (Intercept)-Sylvilagus_floridanus 1.0096 1097
## week-Odocoileus_virginianus 1.0003 5041
## week-Canis_latrans 0.9999 3414
## week-Procyon_lotor 1.0006 4770
## week-Dasypus_novemcinctus 1.0021 5250
## week-Sylvilagus_floridanus 1.0002 3017
## I(week^2)-Odocoileus_virginianus 1.0002 5028
## I(week^2)-Canis_latrans 1.0006 3657
## I(week^2)-Procyon_lotor 1.0008 4454
## I(week^2)-Dasypus_novemcinctus 1.0004 4407
## I(week^2)-Sylvilagus_floridanus 1.0045 2396
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1445
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5650 1.2982 -2.0877 0.5804 3.1320 1.0004 4410
## Cogon_Patch_Size -0.6087 0.7681 -2.0959 -0.6289 1.0299 1.0015 2531
## Veg_shannon_index 1.0248 0.5854 -0.0918 1.0129 2.2079 1.0069 984
## total_shrub_cover -0.0258 0.5929 -1.1886 -0.0381 1.2430 1.0119 2070
## Avg_Cogongrass_Cover 1.8694 0.8850 0.0593 1.9010 3.5400 1.0161 1597
## Tree_Density -1.6602 0.9990 -3.5230 -1.7225 0.6614 1.0108 1596
## Avg_Canopy_Cover 1.2783 0.9076 -0.6667 1.2986 2.9901 1.0023 3625
## avg_veg_height -0.4068 0.5984 -1.5899 -0.3998 0.7742 1.0095 1908
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 43.4106 90.9941 2.6557 20.5571 213.1942 1.0027 759
## Cogon_Patch_Size 3.6253 9.9607 0.0599 0.9586 23.4567 1.0053 1335
## Veg_shannon_index 0.9139 3.0277 0.0427 0.3228 5.3374 1.1352 2086
## total_shrub_cover 1.1297 2.6532 0.0533 0.4747 5.8588 1.0413 2468
## Avg_Cogongrass_Cover 2.4558 9.0439 0.0551 0.7479 14.9325 1.1546 2867
## Tree_Density 8.0530 19.2650 0.0801 2.1056 53.0383 1.0217 1057
## Avg_Canopy_Cover 6.2299 13.4738 0.1430 2.4091 37.7786 1.0298 1658
## avg_veg_height 0.7946 2.0380 0.0479 0.3379 4.1063 1.0616 4467
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7628 3.0467 0.0589 0.7031 10.1644 1.0033 340
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4304 0.7142 -2.7254 -1.4791 0.1235 1.0020 5014
## week 0.4310 0.3696 -0.3403 0.4504 1.1218 1.0009 4743
## I(week^2) -0.2479 0.1859 -0.5958 -0.2544 0.1243 1.0003 5010
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4541 5.7268 0.6771 2.2161 13.0959 1.0022 5250
## week 0.6680 1.1521 0.1066 0.4073 2.6989 1.0468 5250
## I(week^2) 0.1615 0.2482 0.0289 0.1004 0.6619 1.0147 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 9.9709 5.2185 3.3413 8.7469
## (Intercept)-Canis_latrans 0.8428 1.2071 -1.1641 0.7168
## (Intercept)-Procyon_lotor 0.9066 1.0297 -1.1269 0.9063
## (Intercept)-Dasypus_novemcinctus -1.6628 1.0885 -4.2058 -1.5447
## (Intercept)-Sylvilagus_floridanus -1.3178 1.4183 -4.2434 -1.2786
## Cogon_Patch_Size-Odocoileus_virginianus -0.5326 1.4611 -3.1557 -0.6405
## Cogon_Patch_Size-Canis_latrans 0.5641 1.3851 -1.3268 0.2583
## Cogon_Patch_Size-Procyon_lotor -0.9164 0.8257 -2.4003 -0.9242
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8695 0.6514 -2.2844 -0.8431
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8303 1.8056 -6.3055 -1.4301
## Veg_shannon_index-Odocoileus_virginianus 0.9064 0.9421 -1.1560 0.9324
## Veg_shannon_index-Canis_latrans 1.3708 0.7677 0.1717 1.2794
## Veg_shannon_index-Procyon_lotor 1.2062 0.6382 0.0859 1.1518
## Veg_shannon_index-Dasypus_novemcinctus 0.7602 0.5555 -0.2996 0.7439
## Veg_shannon_index-Sylvilagus_floridanus 1.1538 0.7360 -0.1778 1.0885
## total_shrub_cover-Odocoileus_virginianus 0.1040 0.9747 -1.7339 0.0768
## total_shrub_cover-Canis_latrans 0.3217 0.7450 -0.8439 0.2309
## total_shrub_cover-Procyon_lotor -0.6742 0.6339 -2.0352 -0.6273
## total_shrub_cover-Dasypus_novemcinctus 0.1800 0.5439 -0.8366 0.1748
## total_shrub_cover-Sylvilagus_floridanus -0.0086 0.8250 -1.5907 -0.0293
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9045 1.3669 -0.7373 1.8770
## Avg_Cogongrass_Cover-Canis_latrans 2.4389 1.1316 0.5991 2.3235
## Avg_Cogongrass_Cover-Procyon_lotor 2.1848 0.9965 0.4099 2.1301
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7061 1.1444 0.8530 2.5475
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2814 1.1263 -1.0954 1.3194
## Tree_Density-Odocoileus_virginianus -0.5433 1.9118 -3.2888 -0.8879
## Tree_Density-Canis_latrans -2.6741 1.5154 -6.2737 -2.4480
## Tree_Density-Procyon_lotor -1.3571 0.8712 -3.0217 -1.3769
## Tree_Density-Dasypus_novemcinctus -3.8725 2.2195 -9.9004 -3.2686
## Tree_Density-Sylvilagus_floridanus -2.7632 1.8005 -7.3816 -2.4293
## Avg_Canopy_Cover-Odocoileus_virginianus 0.9039 1.6689 -2.5329 0.9456
## Avg_Canopy_Cover-Canis_latrans 0.0979 0.7525 -1.4270 0.1163
## Avg_Canopy_Cover-Procyon_lotor 1.6503 0.7731 0.3077 1.5726
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0357 0.8017 0.7677 1.9328
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7451 2.2306 0.9455 3.2772
## avg_veg_height-Odocoileus_virginianus -0.4198 0.9306 -2.2495 -0.4146
## avg_veg_height-Canis_latrans -0.6630 0.6844 -2.0697 -0.6433
## avg_veg_height-Procyon_lotor -0.2647 0.6390 -1.5026 -0.2877
## avg_veg_height-Dasypus_novemcinctus -0.2152 0.6357 -1.4399 -0.2250
## avg_veg_height-Sylvilagus_floridanus -0.6377 0.7657 -2.3123 -0.6043
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 23.6544 1.0035 238
## (Intercept)-Canis_latrans 3.7201 1.0202 1074
## (Intercept)-Procyon_lotor 2.9687 1.0027 1565
## (Intercept)-Dasypus_novemcinctus 0.1339 1.0012 967
## (Intercept)-Sylvilagus_floridanus 1.4855 1.0013 1007
## Cogon_Patch_Size-Odocoileus_virginianus 2.5784 1.0132 1450
## Cogon_Patch_Size-Canis_latrans 4.1316 1.0052 1253
## Cogon_Patch_Size-Procyon_lotor 0.5331 1.0247 1134
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3147 1.0030 1552
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2743 1.0180 826
## Veg_shannon_index-Odocoileus_virginianus 2.7083 1.0035 2208
## Veg_shannon_index-Canis_latrans 3.1278 1.0065 1059
## Veg_shannon_index-Procyon_lotor 2.5646 1.0050 759
## Veg_shannon_index-Dasypus_novemcinctus 1.9133 1.0048 1862
## Veg_shannon_index-Sylvilagus_floridanus 2.7626 1.0033 982
## total_shrub_cover-Odocoileus_virginianus 2.2723 1.0117 2561
## total_shrub_cover-Canis_latrans 2.0530 1.0219 1691
## total_shrub_cover-Procyon_lotor 0.4576 1.0014 2197
## total_shrub_cover-Dasypus_novemcinctus 1.2965 1.0132 2775
## total_shrub_cover-Sylvilagus_floridanus 1.6728 1.0069 1979
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.7767 1.0097 1833
## Avg_Cogongrass_Cover-Canis_latrans 4.9425 1.0065 1114
## Avg_Cogongrass_Cover-Procyon_lotor 4.3664 1.0027 1127
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.3313 1.0021 830
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4003 1.0265 1480
## Tree_Density-Odocoileus_virginianus 4.3877 1.0182 790
## Tree_Density-Canis_latrans -0.6043 1.0129 927
## Tree_Density-Procyon_lotor 0.3829 1.0009 2027
## Tree_Density-Dasypus_novemcinctus -1.2199 1.0009 511
## Tree_Density-Sylvilagus_floridanus -0.1575 1.0041 822
## Avg_Canopy_Cover-Odocoileus_virginianus 4.2952 1.0081 1799
## Avg_Canopy_Cover-Canis_latrans 1.4931 0.9999 1498
## Avg_Canopy_Cover-Procyon_lotor 3.4158 1.0007 1605
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9163 1.0026 926
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.4707 1.0118 621
## avg_veg_height-Odocoileus_virginianus 1.4490 1.0043 2417
## avg_veg_height-Canis_latrans 0.6244 1.0053 1945
## avg_veg_height-Procyon_lotor 1.0404 1.0040 2184
## avg_veg_height-Dasypus_novemcinctus 1.0691 1.0022 2337
## avg_veg_height-Sylvilagus_floridanus 0.7676 1.0091 2162
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5367 0.0809 0.3793 0.5357 0.6970
## (Intercept)-Canis_latrans -2.4618 0.1983 -2.8734 -2.4542 -2.0983
## (Intercept)-Procyon_lotor -2.1571 0.1542 -2.4740 -2.1511 -1.8691
## (Intercept)-Dasypus_novemcinctus -1.4355 0.1583 -1.7503 -1.4337 -1.1237
## (Intercept)-Sylvilagus_floridanus -3.0809 0.3202 -3.7388 -3.0678 -2.5005
## week-Odocoileus_virginianus 1.3008 0.1248 1.0600 1.2972 1.5460
## week-Canis_latrans 0.5998 0.2700 0.0690 0.6015 1.1395
## week-Procyon_lotor 0.1993 0.2158 -0.2202 0.2051 0.6183
## week-Dasypus_novemcinctus 0.1064 0.2323 -0.3425 0.1055 0.5633
## week-Sylvilagus_floridanus 0.0560 0.3666 -0.6913 0.0677 0.7615
## I(week^2)-Odocoileus_virginianus -0.5362 0.0512 -0.6392 -0.5364 -0.4355
## I(week^2)-Canis_latrans -0.2477 0.1114 -0.4704 -0.2469 -0.0236
## I(week^2)-Procyon_lotor -0.1273 0.0941 -0.3086 -0.1267 0.0558
## I(week^2)-Dasypus_novemcinctus -0.1760 0.1061 -0.3892 -0.1731 0.0328
## I(week^2)-Sylvilagus_floridanus -0.1686 0.1717 -0.5124 -0.1694 0.1604
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0021 2672
## (Intercept)-Procyon_lotor 1.0014 4345
## (Intercept)-Dasypus_novemcinctus 1.0022 5031
## (Intercept)-Sylvilagus_floridanus 1.0142 1434
## week-Odocoileus_virginianus 1.0014 4852
## week-Canis_latrans 0.9999 3921
## week-Procyon_lotor 1.0030 4716
## week-Dasypus_novemcinctus 1.0004 5250
## week-Sylvilagus_floridanus 1.0001 2552
## I(week^2)-Odocoileus_virginianus 1.0010 4996
## I(week^2)-Canis_latrans 1.0001 3913
## I(week^2)-Procyon_lotor 1.0009 4455
## I(week^2)-Dasypus_novemcinctus 1.0020 4945
## I(week^2)-Sylvilagus_floridanus 1.0045 2278
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.153
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7655 0.9860 -1.2844 0.7799 2.7166 1.0003 4140
## Avg_Cogongrass_Cover 0.0763 0.4879 -0.8824 0.0791 1.0504 1.0048 2389
## total_shrub_cover -0.2235 0.5125 -1.2514 -0.2210 0.8126 1.0012 3021
## avg_veg_height 0.0914 0.4532 -0.7968 0.0898 0.9854 1.0000 1926
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.2548 27.0035 0.6844 5.0643 60.7854 1.1119 288
## Avg_Cogongrass_Cover 0.6476 1.5060 0.0443 0.2990 3.1711 1.0087 3061
## total_shrub_cover 1.0816 2.7161 0.0561 0.4610 5.6810 1.0343 1688
## avg_veg_height 0.4783 0.8800 0.0395 0.2331 2.5299 1.0066 3952
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6691 1.9584 0.0462 0.3097 3.0017 1.2926 398
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4381 0.7210 -2.8045 -1.4797 0.1254 1.0018 5250
## week 0.4289 0.3573 -0.3257 0.4431 1.1374 1.0008 4979
## I(week^2) -0.2420 0.1874 -0.5933 -0.2449 0.1534 1.0022 4542
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6531 4.9476 0.7116 2.3935 14.5845 1.0094 5250
## week 0.6473 0.8875 0.1052 0.4094 2.6753 1.0244 5250
## I(week^2) 0.1658 0.2737 0.0290 0.1026 0.6887 1.0009 4818
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0784 3.1013 1.8374 4.4637
## (Intercept)-Canis_latrans 0.5110 0.6610 -0.6626 0.4674
## (Intercept)-Procyon_lotor 0.9262 0.6753 -0.3279 0.9022
## (Intercept)-Dasypus_novemcinctus -0.6464 0.5761 -1.8079 -0.6542
## (Intercept)-Sylvilagus_floridanus 0.2466 1.4214 -1.5651 -0.0350
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0788 0.7369 -1.3547 0.0643
## Avg_Cogongrass_Cover-Canis_latrans 0.4173 0.5627 -0.5593 0.3697
## Avg_Cogongrass_Cover-Procyon_lotor 0.0253 0.5239 -1.0320 0.0210
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1928 0.4416 -0.6674 0.1875
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3270 0.6597 -1.6884 -0.3009
## total_shrub_cover-Odocoileus_virginianus -0.1042 0.7959 -1.6439 -0.1336
## total_shrub_cover-Canis_latrans 0.2355 0.5288 -0.6710 0.1929
## total_shrub_cover-Procyon_lotor -0.9244 0.6010 -2.3377 -0.8476
## total_shrub_cover-Dasypus_novemcinctus 0.0097 0.3859 -0.7128 0.0001
## total_shrub_cover-Sylvilagus_floridanus -0.4101 0.8688 -2.3959 -0.3234
## avg_veg_height-Odocoileus_virginianus 0.0822 0.6866 -1.2507 0.0776
## avg_veg_height-Canis_latrans -0.0303 0.4993 -1.0066 -0.0273
## avg_veg_height-Procyon_lotor 0.2065 0.5018 -0.7451 0.1892
## avg_veg_height-Dasypus_novemcinctus 0.2850 0.4565 -0.5920 0.2826
## avg_veg_height-Sylvilagus_floridanus -0.0550 0.5792 -1.2231 -0.0387
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.5352 1.0784 144
## (Intercept)-Canis_latrans 1.9854 1.0008 2663
## (Intercept)-Procyon_lotor 2.3199 1.0045 1944
## (Intercept)-Dasypus_novemcinctus 0.5193 1.0019 2964
## (Intercept)-Sylvilagus_floridanus 3.8902 1.0807 370
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5979 1.0056 2700
## Avg_Cogongrass_Cover-Canis_latrans 1.6631 1.0003 2387
## Avg_Cogongrass_Cover-Procyon_lotor 1.0819 1.0016 3211
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0846 1.0012 2889
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9050 1.0041 1955
## total_shrub_cover-Odocoileus_virginianus 1.6279 1.0010 3058
## total_shrub_cover-Canis_latrans 1.4289 1.0078 2514
## total_shrub_cover-Procyon_lotor 0.0196 1.0010 2111
## total_shrub_cover-Dasypus_novemcinctus 0.7944 1.0022 4901
## total_shrub_cover-Sylvilagus_floridanus 1.0130 1.0088 803
## avg_veg_height-Odocoileus_virginianus 1.4906 1.0017 2360
## avg_veg_height-Canis_latrans 0.9494 1.0013 3016
## avg_veg_height-Procyon_lotor 1.2365 0.9999 2593
## avg_veg_height-Dasypus_novemcinctus 1.1757 1.0001 3096
## avg_veg_height-Sylvilagus_floridanus 1.0916 1.0061 1839
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5343 0.0820 0.3758 0.5337 0.6982
## (Intercept)-Canis_latrans -2.4519 0.2012 -2.8680 -2.4426 -2.0789
## (Intercept)-Procyon_lotor -2.1714 0.1547 -2.4844 -2.1647 -1.8781
## (Intercept)-Dasypus_novemcinctus -1.4359 0.1604 -1.7603 -1.4323 -1.1282
## (Intercept)-Sylvilagus_floridanus -3.2443 0.3996 -4.0732 -3.2168 -2.5312
## week-Odocoileus_virginianus 1.2989 0.1238 1.0584 1.2971 1.5411
## week-Canis_latrans 0.5913 0.2683 0.0736 0.5910 1.1273
## week-Procyon_lotor 0.1947 0.2179 -0.2364 0.1945 0.6256
## week-Dasypus_novemcinctus 0.1047 0.2359 -0.3571 0.1106 0.5613
## week-Sylvilagus_floridanus 0.0508 0.3598 -0.6773 0.0562 0.7453
## I(week^2)-Odocoileus_virginianus -0.5358 0.0513 -0.6382 -0.5343 -0.4374
## I(week^2)-Canis_latrans -0.2441 0.1120 -0.4683 -0.2420 -0.0297
## I(week^2)-Procyon_lotor -0.1262 0.0956 -0.3165 -0.1262 0.0573
## I(week^2)-Dasypus_novemcinctus -0.1761 0.1094 -0.4018 -0.1752 0.0304
## I(week^2)-Sylvilagus_floridanus -0.1642 0.1702 -0.5138 -0.1618 0.1525
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 6190
## (Intercept)-Canis_latrans 1.0009 2788
## (Intercept)-Procyon_lotor 1.0031 4016
## (Intercept)-Dasypus_novemcinctus 1.0005 5250
## (Intercept)-Sylvilagus_floridanus 1.0482 615
## week-Odocoileus_virginianus 1.0015 5392
## week-Canis_latrans 1.0012 3649
## week-Procyon_lotor 1.0025 4320
## week-Dasypus_novemcinctus 0.9998 4903
## week-Sylvilagus_floridanus 1.0033 2992
## I(week^2)-Odocoileus_virginianus 1.0025 5038
## I(week^2)-Canis_latrans 1.0007 3708
## I(week^2)-Procyon_lotor 1.0026 4115
## I(week^2)-Dasypus_novemcinctus 1.0014 4201
## I(week^2)-Sylvilagus_floridanus 1.0084 2155
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1195
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5830 1.0413 -1.5509 0.5934 2.6461 1.0024 5250
## Tree_Density -0.8450 0.5869 -2.1255 -0.8162 0.3307 1.0051 3350
## Avg_Canopy_Cover 0.7955 0.6045 -0.4494 0.7916 2.0534 1.0011 4317
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.5366 55.2518 1.0607 6.8677 61.3269 1.3226 2698
## Tree_Density 1.5000 3.9084 0.0474 0.4677 9.9453 1.0666 2408
## Avg_Canopy_Cover 1.9653 4.1444 0.0868 0.9131 10.3587 1.0113 2966
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4974 0.7993 0.0401 0.2568 2.3687 1.034 868
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4041 0.7120 -2.7411 -1.4453 0.1712 0.9999 5250
## week 0.4278 0.3657 -0.3283 0.4384 1.1246 1.0025 4744
## I(week^2) -0.2505 0.1843 -0.6129 -0.2479 0.1159 1.0039 4744
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3751 4.3076 0.6650 2.2260 13.2327 1.0242 5250
## week 0.6690 0.9634 0.1081 0.4221 2.5896 1.0023 4257
## I(week^2) 0.1574 0.2142 0.0294 0.0994 0.6294 1.0100 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 5.4795 2.5530 2.1930 5.0070 11.4927
## (Intercept)-Canis_latrans 0.3610 0.6449 -0.8233 0.3242 1.7558
## (Intercept)-Procyon_lotor 0.8055 0.6222 -0.4378 0.8030 2.0426
## (Intercept)-Dasypus_novemcinctus -1.0550 0.6802 -2.5043 -1.0175 0.1728
## (Intercept)-Sylvilagus_floridanus -0.6047 0.8244 -2.2005 -0.6366 1.0986
## Tree_Density-Odocoileus_virginianus -0.3895 0.9071 -1.7890 -0.5215 1.7852
## Tree_Density-Canis_latrans -0.9537 0.5950 -2.3263 -0.8850 0.0195
## Tree_Density-Procyon_lotor -0.4948 0.4507 -1.4020 -0.4931 0.4083
## Tree_Density-Dasypus_novemcinctus -1.5739 1.1045 -4.4664 -1.3109 -0.1972
## Tree_Density-Sylvilagus_floridanus -1.2352 0.9211 -3.4635 -1.0736 0.1532
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6009 0.9621 -1.3850 0.6058 2.5474
## Avg_Canopy_Cover-Canis_latrans -0.1111 0.4989 -1.0945 -0.1046 0.8296
## Avg_Canopy_Cover-Procyon_lotor 1.0003 0.5191 0.0846 0.9633 2.1267
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9982 0.4755 0.1595 0.9669 2.0204
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.0259 1.1245 0.4971 1.8041 4.9366
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0566 478
## (Intercept)-Canis_latrans 1.0049 2621
## (Intercept)-Procyon_lotor 1.0050 3380
## (Intercept)-Dasypus_novemcinctus 1.0048 1960
## (Intercept)-Sylvilagus_floridanus 1.0016 1984
## Tree_Density-Odocoileus_virginianus 1.0050 2129
## Tree_Density-Canis_latrans 1.0019 3143
## Tree_Density-Procyon_lotor 1.0010 4037
## Tree_Density-Dasypus_novemcinctus 1.0139 1619
## Tree_Density-Sylvilagus_floridanus 1.0110 1777
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0005 2774
## Avg_Canopy_Cover-Canis_latrans 1.0055 3537
## Avg_Canopy_Cover-Procyon_lotor 1.0003 3872
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0019 3380
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0036 1331
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5352 0.0794 0.3783 0.5354 0.6920
## (Intercept)-Canis_latrans -2.4475 0.1968 -2.8549 -2.4423 -2.0790
## (Intercept)-Procyon_lotor -2.1586 0.1536 -2.4678 -2.1557 -1.8641
## (Intercept)-Dasypus_novemcinctus -1.4318 0.1592 -1.7508 -1.4292 -1.1289
## (Intercept)-Sylvilagus_floridanus -3.0506 0.3225 -3.7118 -3.0398 -2.4566
## week-Odocoileus_virginianus 1.2980 0.1212 1.0580 1.2974 1.5419
## week-Canis_latrans 0.5983 0.2702 0.0798 0.5960 1.1412
## week-Procyon_lotor 0.1904 0.2166 -0.2222 0.1907 0.6221
## week-Dasypus_novemcinctus 0.1064 0.2319 -0.3383 0.1034 0.5709
## week-Sylvilagus_floridanus 0.0480 0.3661 -0.6851 0.0554 0.7555
## I(week^2)-Odocoileus_virginianus -0.5351 0.0501 -0.6362 -0.5348 -0.4398
## I(week^2)-Canis_latrans -0.2447 0.1105 -0.4718 -0.2427 -0.0341
## I(week^2)-Procyon_lotor -0.1253 0.0937 -0.3062 -0.1242 0.0547
## I(week^2)-Dasypus_novemcinctus -0.1745 0.1080 -0.3967 -0.1714 0.0297
## I(week^2)-Sylvilagus_floridanus -0.1641 0.1727 -0.5209 -0.1597 0.1640
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0040 5250
## (Intercept)-Canis_latrans 1.0070 2883
## (Intercept)-Procyon_lotor 1.0023 4273
## (Intercept)-Dasypus_novemcinctus 1.0019 4931
## (Intercept)-Sylvilagus_floridanus 0.9998 1944
## week-Odocoileus_virginianus 1.0014 5467
## week-Canis_latrans 1.0027 3791
## week-Procyon_lotor 1.0019 4416
## week-Dasypus_novemcinctus 1.0020 5047
## week-Sylvilagus_floridanus 1.0004 3236
## I(week^2)-Odocoileus_virginianus 1.0013 5542
## I(week^2)-Canis_latrans 1.0005 3845
## I(week^2)-Procyon_lotor 1.0004 4300
## I(week^2)-Dasypus_novemcinctus 1.0018 4304
## I(week^2)-Sylvilagus_floridanus 1.0028 2461
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7434 0.9812 -1.2689 0.7498 2.7089 1.0024 4082
## Cogon_Patch_Size 0.0068 0.6437 -1.3066 0.0034 1.3297 1.0000 3124
## Avg_Cogongrass_Cover 0.1535 0.4313 -0.7059 0.1485 1.0153 1.0031 3035
## total_shrub_cover -0.2406 0.4788 -1.2025 -0.2400 0.7349 1.0056 3253
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.0246 18.4064 0.7247 5.3280 50.0127 1.0592 1231
## Cogon_Patch_Size 2.3751 7.7429 0.0602 0.7189 14.0261 1.0958 2279
## Avg_Cogongrass_Cover 0.5460 1.3734 0.0399 0.2438 2.7803 1.0436 4166
## total_shrub_cover 0.8755 1.8788 0.0533 0.4054 4.2449 1.0128 3922
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5936 0.8253 0.0455 0.3223 2.8935 1.021 1009
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4479 0.7438 -2.8121 -1.4944 0.1888 1.0012 5486
## week 0.4287 0.3632 -0.3500 0.4430 1.1222 1.0005 4886
## I(week^2) -0.2460 0.1804 -0.6013 -0.2498 0.1306 1.0021 4818
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7407 6.9547 0.7037 2.3563 13.6965 1.1066 5250
## week 0.6674 0.9969 0.1082 0.4073 2.7511 1.0257 5250
## I(week^2) 0.1609 0.2767 0.0295 0.1004 0.6956 1.1036 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0447 2.2737 1.8873 4.5941
## (Intercept)-Canis_latrans 0.6736 0.7179 -0.5580 0.6146
## (Intercept)-Procyon_lotor 0.9087 0.6667 -0.3670 0.8829
## (Intercept)-Dasypus_novemcinctus -0.6336 0.5798 -1.7838 -0.6301
## (Intercept)-Sylvilagus_floridanus -0.0743 1.1402 -1.8494 -0.2347
## Cogon_Patch_Size-Odocoileus_virginianus 0.2296 1.0497 -1.4027 0.0962
## Cogon_Patch_Size-Canis_latrans 1.0335 1.0825 -0.3113 0.7774
## Cogon_Patch_Size-Procyon_lotor -0.1133 0.5084 -1.0595 -0.1243
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1056 0.4302 -1.0118 -0.0862
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9558 1.2579 -4.3133 -0.6736
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1819 0.6891 -1.0208 0.1495
## Avg_Cogongrass_Cover-Canis_latrans 0.2394 0.4449 -0.5792 0.2252
## Avg_Cogongrass_Cover-Procyon_lotor 0.1892 0.4734 -0.6907 0.1698
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3652 0.4019 -0.3941 0.3472
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1532 0.5626 -1.3510 -0.1361
## total_shrub_cover-Odocoileus_virginianus -0.1395 0.7481 -1.5202 -0.1584
## total_shrub_cover-Canis_latrans 0.1241 0.5096 -0.7651 0.0885
## total_shrub_cover-Procyon_lotor -0.9013 0.5876 -2.2846 -0.8271
## total_shrub_cover-Dasypus_novemcinctus -0.0382 0.3899 -0.7774 -0.0451
## total_shrub_cover-Sylvilagus_floridanus -0.2927 0.7028 -1.7552 -0.2677
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.5004 1.0190 655
## (Intercept)-Canis_latrans 2.2159 1.0096 2359
## (Intercept)-Procyon_lotor 2.3041 1.0003 3034
## (Intercept)-Dasypus_novemcinctus 0.5050 1.0009 3137
## (Intercept)-Sylvilagus_floridanus 2.6565 1.0245 662
## Cogon_Patch_Size-Odocoileus_virginianus 2.7372 1.0076 2120
## Cogon_Patch_Size-Canis_latrans 3.8633 1.0141 1299
## Cogon_Patch_Size-Procyon_lotor 0.9026 1.0010 3660
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7049 1.0006 4248
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6823 1.0001 1064
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6358 1.0031 3193
## Avg_Cogongrass_Cover-Canis_latrans 1.1580 1.0050 3932
## Avg_Cogongrass_Cover-Procyon_lotor 1.1710 1.0018 3519
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2030 1.0014 3818
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8981 1.0048 2643
## total_shrub_cover-Odocoileus_virginianus 1.4356 1.0031 3051
## total_shrub_cover-Canis_latrans 1.2663 1.0005 3173
## total_shrub_cover-Procyon_lotor 0.0321 1.0005 3018
## total_shrub_cover-Dasypus_novemcinctus 0.7614 1.0087 4244
## total_shrub_cover-Sylvilagus_floridanus 1.0725 1.0073 1850
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5355 0.0812 0.3768 0.5342 0.6954
## (Intercept)-Canis_latrans -2.4281 0.1955 -2.8363 -2.4255 -2.0653
## (Intercept)-Procyon_lotor -2.1707 0.1532 -2.4780 -2.1656 -1.8873
## (Intercept)-Dasypus_novemcinctus -1.4326 0.1591 -1.7559 -1.4288 -1.1316
## (Intercept)-Sylvilagus_floridanus -3.2169 0.3858 -4.0130 -3.1930 -2.5265
## week-Odocoileus_virginianus 1.3000 0.1234 1.0614 1.3018 1.5449
## week-Canis_latrans 0.6045 0.2717 0.0748 0.5993 1.1370
## week-Procyon_lotor 0.1982 0.2156 -0.2252 0.1983 0.6101
## week-Dasypus_novemcinctus 0.1046 0.2349 -0.3691 0.1073 0.5767
## week-Sylvilagus_floridanus 0.0402 0.3675 -0.6779 0.0440 0.7429
## I(week^2)-Odocoileus_virginianus -0.5356 0.0513 -0.6351 -0.5361 -0.4373
## I(week^2)-Canis_latrans -0.2485 0.1108 -0.4681 -0.2486 -0.0308
## I(week^2)-Procyon_lotor -0.1258 0.0938 -0.3049 -0.1262 0.0548
## I(week^2)-Dasypus_novemcinctus -0.1765 0.1085 -0.3988 -0.1735 0.0304
## I(week^2)-Sylvilagus_floridanus -0.1652 0.1755 -0.5279 -0.1609 0.1699
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0033 2957
## (Intercept)-Procyon_lotor 1.0002 3908
## (Intercept)-Dasypus_novemcinctus 1.0009 5250
## (Intercept)-Sylvilagus_floridanus 1.0107 821
## week-Odocoileus_virginianus 1.0004 4990
## week-Canis_latrans 1.0007 3983
## week-Procyon_lotor 1.0008 4489
## week-Dasypus_novemcinctus 1.0001 4991
## week-Sylvilagus_floridanus 1.0013 2901
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0007 4169
## I(week^2)-Procyon_lotor 1.0005 4304
## I(week^2)-Dasypus_novemcinctus 1.0006 4555
## I(week^2)-Sylvilagus_floridanus 1.0021 2318
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1315
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6710 0.9672 -1.2850 0.6672 2.6369 1.0009 4512
## Veg_shannon_index 0.5305 0.4078 -0.3035 0.5361 1.3724 1.0099 3142
## Avg_Cogongrass_Cover 0.4095 0.4332 -0.4242 0.3986 1.2877 1.0072 2942
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.3782 29.7760 0.5372 4.6971 43.1094 1.2212 2003
## Veg_shannon_index 0.4788 0.9050 0.0402 0.2370 2.3903 1.0043 3272
## Avg_Cogongrass_Cover 0.6103 1.2178 0.0437 0.2799 3.2324 1.0097 3987
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7145 1.1074 0.0496 0.3696 3.3098 1.0502 874
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4237 0.7196 -2.7300 -1.4735 0.1527 1.0024 5250
## week 0.4418 0.3595 -0.3090 0.4504 1.1210 1.0010 5065
## I(week^2) -0.2487 0.1832 -0.6096 -0.2505 0.1201 1.0063 4734
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4737 5.0771 0.7069 2.2234 14.1413 1.0233 5031
## week 0.6585 0.9846 0.1047 0.4043 2.7523 1.0021 4948
## I(week^2) 0.1609 0.2362 0.0294 0.0999 0.6816 1.0044 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.6387 2.1934 1.4820 4.2434
## (Intercept)-Canis_latrans 0.4100 0.6372 -0.8060 0.3949
## (Intercept)-Procyon_lotor 0.7465 0.6287 -0.4950 0.7497
## (Intercept)-Dasypus_novemcinctus -0.6430 0.5957 -1.8541 -0.6360
## (Intercept)-Sylvilagus_floridanus -0.0164 1.2106 -1.6737 -0.1940
## Veg_shannon_index-Odocoileus_virginianus 0.4332 0.6258 -0.8805 0.4501
## Veg_shannon_index-Canis_latrans 0.7923 0.4457 -0.0013 0.7555
## Veg_shannon_index-Procyon_lotor 0.6061 0.4521 -0.2018 0.5836
## Veg_shannon_index-Dasypus_novemcinctus 0.2827 0.3782 -0.4639 0.2858
## Veg_shannon_index-Sylvilagus_floridanus 0.6340 0.5596 -0.2974 0.5905
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4480 0.6988 -0.8600 0.4192
## Avg_Cogongrass_Cover-Canis_latrans 0.6828 0.4747 -0.1382 0.6288
## Avg_Cogongrass_Cover-Procyon_lotor 0.5622 0.4951 -0.2891 0.5164
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4886 0.3669 -0.2206 0.4787
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0425 0.5696 -1.2330 -0.0221
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.9285 1.0507 648
## (Intercept)-Canis_latrans 1.6889 1.0000 3134
## (Intercept)-Procyon_lotor 2.0047 1.0070 3361
## (Intercept)-Dasypus_novemcinctus 0.5797 1.0084 2290
## (Intercept)-Sylvilagus_floridanus 2.7589 1.0043 390
## Veg_shannon_index-Odocoileus_virginianus 1.6487 1.0095 3296
## Veg_shannon_index-Canis_latrans 1.7766 1.0120 3327
## Veg_shannon_index-Procyon_lotor 1.5796 1.0128 2690
## Veg_shannon_index-Dasypus_novemcinctus 1.0165 1.0053 4494
## Veg_shannon_index-Sylvilagus_floridanus 1.8900 1.0024 2167
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9549 1.0076 2952
## Avg_Cogongrass_Cover-Canis_latrans 1.7662 1.0083 3396
## Avg_Cogongrass_Cover-Procyon_lotor 1.6731 1.0052 3396
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2297 1.0001 3864
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0217 1.0014 2046
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5351 0.0806 0.3789 0.5345 0.6939
## (Intercept)-Canis_latrans -2.4190 0.1886 -2.8059 -2.4119 -2.0667
## (Intercept)-Procyon_lotor -2.1732 0.1560 -2.4848 -2.1697 -1.8805
## (Intercept)-Dasypus_novemcinctus -1.4351 0.1595 -1.7530 -1.4294 -1.1291
## (Intercept)-Sylvilagus_floridanus -3.1696 0.3812 -3.9560 -3.1450 -2.4899
## week-Odocoileus_virginianus 1.3000 0.1236 1.0630 1.2964 1.5473
## week-Canis_latrans 0.5971 0.2688 0.0949 0.5902 1.1470
## week-Procyon_lotor 0.2015 0.2185 -0.2285 0.2020 0.6420
## week-Dasypus_novemcinctus 0.1058 0.2319 -0.3572 0.1045 0.5638
## week-Sylvilagus_floridanus 0.0629 0.3652 -0.6700 0.0669 0.7657
## I(week^2)-Odocoileus_virginianus -0.5357 0.0504 -0.6362 -0.5352 -0.4372
## I(week^2)-Canis_latrans -0.2456 0.1114 -0.4742 -0.2450 -0.0355
## I(week^2)-Procyon_lotor -0.1283 0.0937 -0.3141 -0.1274 0.0545
## I(week^2)-Dasypus_novemcinctus -0.1737 0.1074 -0.3944 -0.1717 0.0285
## I(week^2)-Sylvilagus_floridanus -0.1721 0.1708 -0.5370 -0.1636 0.1459
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0045 5250
## (Intercept)-Canis_latrans 1.0006 3529
## (Intercept)-Procyon_lotor 1.0055 4190
## (Intercept)-Dasypus_novemcinctus 1.0038 5250
## (Intercept)-Sylvilagus_floridanus 1.0040 735
## week-Odocoileus_virginianus 1.0010 5250
## week-Canis_latrans 1.0008 3753
## week-Procyon_lotor 1.0005 4446
## week-Dasypus_novemcinctus 1.0001 4916
## week-Sylvilagus_floridanus 1.0008 2645
## I(week^2)-Odocoileus_virginianus 1.0004 5250
## I(week^2)-Canis_latrans 1.0005 4002
## I(week^2)-Procyon_lotor 1.0024 4478
## I(week^2)-Dasypus_novemcinctus 1.0010 4507
## I(week^2)-Sylvilagus_floridanus 1.0037 2240
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6454 0.9314 -1.2847 0.6471 2.5249 1.0039 4632
## Avg_Cogongrass_Cover 0.1954 0.3976 -0.6004 0.1920 0.9824 1.0020 3384
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.6116 15.1044 0.6356 4.3381 43.3353 1.0634 989
## Avg_Cogongrass_Cover 0.5443 1.2154 0.0424 0.2578 2.7397 1.0056 4078
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4981 0.6658 0.0422 0.2812 2.3105 1.0023 1098
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4314 0.7271 -2.7525 -1.4895 0.1605 1.0012 5250
## week 0.4352 0.3673 -0.3032 0.4403 1.1491 1.0016 4659
## I(week^2) -0.2454 0.1821 -0.5988 -0.2490 0.1155 1.0006 4752
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5436 4.5191 0.6865 2.3117 15.0601 1.0120 4603
## week 0.6713 1.1451 0.1017 0.4095 2.7197 1.0247 4803
## I(week^2) 0.1584 0.2409 0.0287 0.0996 0.6523 1.0257 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.5663 2.2064 1.7858 4.0982
## (Intercept)-Canis_latrans 0.4614 0.8490 -0.6829 0.3993
## (Intercept)-Procyon_lotor 0.7142 0.5737 -0.4228 0.7105
## (Intercept)-Dasypus_novemcinctus -0.6328 0.5391 -1.7061 -0.6244
## (Intercept)-Sylvilagus_floridanus -0.1173 1.2946 -1.5318 -0.2644
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1973 0.6462 -1.0875 0.1796
## Avg_Cogongrass_Cover-Canis_latrans 0.4077 0.4041 -0.2978 0.3801
## Avg_Cogongrass_Cover-Procyon_lotor 0.2795 0.4013 -0.4450 0.2534
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3581 0.3427 -0.2844 0.3494
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2379 0.5099 -1.3566 -0.2008
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.3201 1.0253 531
## (Intercept)-Canis_latrans 1.7695 1.1359 379
## (Intercept)-Procyon_lotor 1.8348 1.0015 4007
## (Intercept)-Dasypus_novemcinctus 0.4242 1.0053 3685
## (Intercept)-Sylvilagus_floridanus 1.8804 1.3372 123
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5379 1.0003 3402
## Avg_Cogongrass_Cover-Canis_latrans 1.2982 1.0015 4302
## Avg_Cogongrass_Cover-Procyon_lotor 1.1297 1.0033 3753
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0746 1.0003 3560
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6486 1.0085 2195
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5345 0.0811 0.3745 0.5346 0.6958
## (Intercept)-Canis_latrans -2.4354 0.2031 -2.8590 -2.4298 -2.0639
## (Intercept)-Procyon_lotor -2.1649 0.1528 -2.4728 -2.1646 -1.8675
## (Intercept)-Dasypus_novemcinctus -1.4366 0.1580 -1.7512 -1.4365 -1.1321
## (Intercept)-Sylvilagus_floridanus -3.1257 0.3676 -3.9481 -3.1039 -2.4615
## week-Odocoileus_virginianus 1.2984 0.1238 1.0596 1.2953 1.5440
## week-Canis_latrans 0.6026 0.2720 0.0759 0.5969 1.1495
## week-Procyon_lotor 0.1930 0.2180 -0.2374 0.1909 0.6138
## week-Dasypus_novemcinctus 0.1018 0.2330 -0.3602 0.1015 0.5574
## week-Sylvilagus_floridanus 0.0390 0.3728 -0.7222 0.0453 0.7450
## I(week^2)-Odocoileus_virginianus -0.5358 0.0509 -0.6378 -0.5358 -0.4380
## I(week^2)-Canis_latrans -0.2468 0.1117 -0.4686 -0.2456 -0.0334
## I(week^2)-Procyon_lotor -0.1264 0.0942 -0.3180 -0.1262 0.0556
## I(week^2)-Dasypus_novemcinctus -0.1709 0.1064 -0.3836 -0.1708 0.0263
## I(week^2)-Sylvilagus_floridanus -0.1647 0.1787 -0.5170 -0.1630 0.1773
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0033 5250
## (Intercept)-Canis_latrans 1.0016 1418
## (Intercept)-Procyon_lotor 1.0039 3770
## (Intercept)-Dasypus_novemcinctus 1.0018 5250
## (Intercept)-Sylvilagus_floridanus 1.0297 953
## week-Odocoileus_virginianus 1.0026 5250
## week-Canis_latrans 1.0055 3684
## week-Procyon_lotor 1.0096 4463
## week-Dasypus_novemcinctus 1.0024 4939
## week-Sylvilagus_floridanus 1.0004 2518
## I(week^2)-Odocoileus_virginianus 1.0012 5250
## I(week^2)-Canis_latrans 1.0027 3760
## I(week^2)-Procyon_lotor 1.0063 4458
## I(week^2)-Dasypus_novemcinctus 1.0004 4791
## I(week^2)-Sylvilagus_floridanus 1.0033 2055
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1502
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0799 0.9468 -1.7423 0.0432 2.0789 1.0000 2606
## Avg_Cogongrass_Cover -0.4945 0.6025 -1.6858 -0.4963 0.6737 1.0052 2440
## I(Avg_Cogongrass_Cover^2) 1.1925 0.7519 -0.0690 1.0922 2.9542 1.0032 968
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.1047 15.5812 0.4372 4.0419 41.1734 1.0047 1118
## Avg_Cogongrass_Cover 1.2951 3.2328 0.0498 0.4902 7.1983 1.0134 1932
## I(Avg_Cogongrass_Cover^2) 3.1035 11.9909 0.0504 0.6404 20.6266 1.0306 833
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4781 0.6615 0.0436 0.2723 2.1424 1.0118 1098
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4170 0.7419 -2.7175 -1.4666 0.1987 1.0009 5250
## week 0.4378 0.3544 -0.3142 0.4472 1.1122 1.0000 5250
## I(week^2) -0.2486 0.1857 -0.6187 -0.2501 0.1131 1.0012 4946
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6991 5.3461 0.6981 2.3525 14.6391 1.0133 5250
## week 0.6560 0.9466 0.1067 0.4102 2.7029 1.0604 4792
## I(week^2) 0.1602 0.2777 0.0294 0.1007 0.6811 1.0936 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6453 2.3573 0.4182 3.1758
## (Intercept)-Canis_latrans -0.5422 0.7903 -2.2038 -0.5035
## (Intercept)-Procyon_lotor -0.1524 0.7085 -1.6282 -0.1282
## (Intercept)-Dasypus_novemcinctus -1.2466 0.6664 -2.5988 -1.2364
## (Intercept)-Sylvilagus_floridanus -0.8962 1.0140 -2.8148 -0.9338
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.5371 1.0147 -2.6067 -0.5430
## Avg_Cogongrass_Cover-Canis_latrans -0.1640 0.6840 -1.3957 -0.1951
## Avg_Cogongrass_Cover-Procyon_lotor -0.3308 0.7153 -1.6213 -0.3813
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3655 0.5907 -1.5019 -0.3711
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2648 0.9947 -3.5671 -1.1245
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9573 1.9567 -0.0884 1.4012
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9345 1.3943 0.2278 1.5550
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.6460 1.2892 0.1662 1.2753
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6456 0.4333 -0.1839 0.6374
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0031 0.8664 -0.2165 0.8608
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.4779 1.0062 641
## (Intercept)-Canis_latrans 0.9678 1.0033 1665
## (Intercept)-Procyon_lotor 1.1928 1.0044 1979
## (Intercept)-Dasypus_novemcinctus 0.0074 1.0012 3003
## (Intercept)-Sylvilagus_floridanus 1.1592 1.0058 1172
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4917 1.0003 2631
## Avg_Cogongrass_Cover-Canis_latrans 1.2938 1.0054 2533
## Avg_Cogongrass_Cover-Procyon_lotor 1.2254 1.0040 1873
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8293 1.0064 3016
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2955 1.0033 1340
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 7.6092 1.0056 352
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.5116 1.0040 519
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.1261 1.0026 433
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5418 1.0028 3275
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2087 1.0042 720
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5350 0.0814 0.3762 0.5347 0.6939
## (Intercept)-Canis_latrans -2.4516 0.1883 -2.8260 -2.4495 -2.0888
## (Intercept)-Procyon_lotor -2.1795 0.1559 -2.4977 -2.1740 -1.8819
## (Intercept)-Dasypus_novemcinctus -1.4376 0.1594 -1.7578 -1.4359 -1.1354
## (Intercept)-Sylvilagus_floridanus -3.1796 0.3805 -3.9856 -3.1556 -2.4978
## week-Odocoileus_virginianus 1.3009 0.1235 1.0674 1.2992 1.5479
## week-Canis_latrans 0.6023 0.2770 0.0731 0.5932 1.1610
## week-Procyon_lotor 0.1972 0.2134 -0.2149 0.1976 0.6100
## week-Dasypus_novemcinctus 0.1086 0.2327 -0.3355 0.1044 0.5592
## week-Sylvilagus_floridanus 0.0624 0.3722 -0.6824 0.0737 0.7574
## I(week^2)-Odocoileus_virginianus -0.5362 0.0511 -0.6387 -0.5356 -0.4387
## I(week^2)-Canis_latrans -0.2492 0.1119 -0.4740 -0.2461 -0.0327
## I(week^2)-Procyon_lotor -0.1262 0.0920 -0.3043 -0.1264 0.0543
## I(week^2)-Dasypus_novemcinctus -0.1748 0.1070 -0.3899 -0.1750 0.0273
## I(week^2)-Sylvilagus_floridanus -0.1661 0.1691 -0.4989 -0.1674 0.1658
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0010 3393
## (Intercept)-Procyon_lotor 1.0009 3670
## (Intercept)-Dasypus_novemcinctus 1.0031 4566
## (Intercept)-Sylvilagus_floridanus 1.0042 892
## week-Odocoileus_virginianus 1.0000 5250
## week-Canis_latrans 1.0003 3632
## week-Procyon_lotor 1.0010 4103
## week-Dasypus_novemcinctus 0.9999 4784
## week-Sylvilagus_floridanus 1.0001 2909
## I(week^2)-Odocoileus_virginianus 0.9999 5250
## I(week^2)-Canis_latrans 1.0008 3783
## I(week^2)-Procyon_lotor 1.0011 4248
## I(week^2)-Dasypus_novemcinctus 0.9999 4568
## I(week^2)-Sylvilagus_floridanus 1.0011 2607
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1402
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0493 1.3522 -2.5650 0.0197 2.7039 1.0009 3722
## Cogon_Patch_Size -0.1359 0.9084 -1.9092 -0.1432 1.7519 1.0002 1991
## Veg_shannon_index 0.9646 0.6229 -0.2276 0.9390 2.2448 1.0104 1399
## total_shrub_cover -0.0966 0.6014 -1.2516 -0.1092 1.1666 1.0015 2317
## Avg_Cogongrass_Cover 0.1768 1.1389 -1.9849 0.1896 2.4374 1.0002 1045
## Tree_Density -1.8044 1.2193 -3.9560 -1.8862 0.9575 1.0070 1836
## Avg_Canopy_Cover 1.2094 1.0110 -1.0294 1.2422 3.1054 1.0013 3428
## I(Avg_Cogongrass_Cover^2) 1.7843 0.9388 0.0255 1.7578 3.7067 1.0090 1071
## avg_veg_height -0.0534 0.6851 -1.3926 -0.0496 1.2883 1.0035 1376
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 51.0854 144.0660 2.6796 24.6134 244.1395 1.1252 1216
## Cogon_Patch_Size 6.2368 18.3501 0.0719 1.6339 41.2993 1.0461 999
## Veg_shannon_index 0.9841 2.3242 0.0450 0.3780 5.4924 1.0311 1015
## total_shrub_cover 1.1845 2.6226 0.0554 0.5015 6.2661 1.0287 2876
## Avg_Cogongrass_Cover 4.3763 13.0022 0.0588 1.2072 26.9957 1.1053 877
## Tree_Density 17.5379 58.4925 0.0961 3.9291 116.3297 1.0800 610
## Avg_Canopy_Cover 11.1580 41.3531 0.1478 3.6558 62.3868 1.2044 819
## I(Avg_Cogongrass_Cover^2) 6.9007 99.3368 0.0528 0.7090 43.7139 1.3101 1398
## avg_veg_height 1.0711 2.2603 0.0497 0.4335 6.0131 1.0156 2903
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9893 6.1217 0.0552 0.6818 9.8476 1.4556 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4140 0.7266 -2.7072 -1.4664 0.1921 1.0031 5534
## week 0.4302 0.3628 -0.3260 0.4470 1.1204 1.0005 4654
## I(week^2) -0.2444 0.1815 -0.5970 -0.2491 0.1289 1.0015 4799
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5363 4.8612 0.6838 2.2720 14.1431 1.0034 5250
## week 0.6757 1.0706 0.1041 0.4185 2.7153 1.0290 5250
## I(week^2) 0.1597 0.2329 0.0294 0.0981 0.6748 1.0039 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 9.6576 5.6702 2.3184
## (Intercept)-Canis_latrans -1.1432 1.5808 -4.3880
## (Intercept)-Procyon_lotor -0.4511 1.2566 -2.9241
## (Intercept)-Dasypus_novemcinctus -3.2553 1.6741 -7.3423
## (Intercept)-Sylvilagus_floridanus -2.5814 2.0328 -6.6963
## Cogon_Patch_Size-Odocoileus_virginianus 0.0210 1.8075 -3.1287
## Cogon_Patch_Size-Canis_latrans 1.6623 2.1102 -0.8055
## Cogon_Patch_Size-Procyon_lotor -0.4665 1.0772 -2.3009
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5211 0.9038 -2.5315
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4234 2.2157 -6.9039
## Veg_shannon_index-Odocoileus_virginianus 0.8405 1.0448 -1.3372
## Veg_shannon_index-Canis_latrans 1.3851 0.8196 0.1158
## Veg_shannon_index-Procyon_lotor 1.1553 0.6879 -0.0040
## Veg_shannon_index-Dasypus_novemcinctus 0.7064 0.6162 -0.5139
## Veg_shannon_index-Sylvilagus_floridanus 1.0921 0.8526 -0.4114
## total_shrub_cover-Odocoileus_virginianus 0.0343 1.0255 -1.8916
## total_shrub_cover-Canis_latrans 0.1333 0.7271 -1.1327
## total_shrub_cover-Procyon_lotor -0.7937 0.7043 -2.3983
## total_shrub_cover-Dasypus_novemcinctus 0.1929 0.5733 -0.8917
## total_shrub_cover-Sylvilagus_floridanus -0.0906 0.8653 -1.8791
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1491 1.9785 -3.6974
## Avg_Cogongrass_Cover-Canis_latrans 0.2045 1.5707 -2.8100
## Avg_Cogongrass_Cover-Procyon_lotor 0.4271 1.4891 -2.3284
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2072 1.7287 -1.6603
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9185 1.8576 -5.3205
## Tree_Density-Odocoileus_virginianus -0.4107 2.5739 -3.9003
## Tree_Density-Canis_latrans -3.6116 2.1422 -9.0103
## Tree_Density-Procyon_lotor -1.9041 1.2425 -4.4722
## Tree_Density-Dasypus_novemcinctus -5.3446 3.4357 -14.4408
## Tree_Density-Sylvilagus_floridanus -3.2246 2.4332 -9.0857
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7132 1.9691 -3.3575
## Avg_Canopy_Cover-Canis_latrans -0.0310 0.8218 -1.7470
## Avg_Canopy_Cover-Procyon_lotor 1.6563 0.8895 0.1057
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2632 1.0877 0.6918
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.6577 3.3169 0.9121
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.6154 2.7371 -0.0859
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.7082 1.7774 0.6767
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3784 1.5094 0.4718
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7836 1.0492 0.1519
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.5886 1.2863 -0.4519
## avg_veg_height-Odocoileus_virginianus -0.0403 1.0995 -2.2076
## avg_veg_height-Canis_latrans -0.4444 0.8041 -2.1605
## avg_veg_height-Procyon_lotor 0.2416 0.7704 -1.1878
## avg_veg_height-Dasypus_novemcinctus 0.1611 0.7399 -1.2785
## avg_veg_height-Sylvilagus_floridanus -0.2592 0.9038 -2.1753
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.3717 24.6365 1.0158 232
## (Intercept)-Canis_latrans -1.1078 1.9284 1.0261 678
## (Intercept)-Procyon_lotor -0.3926 1.8755 1.0185 951
## (Intercept)-Dasypus_novemcinctus -3.0051 -0.8071 1.0255 415
## (Intercept)-Sylvilagus_floridanus -2.4919 1.0620 1.0157 650
## Cogon_Patch_Size-Odocoileus_virginianus -0.1445 4.3792 1.0071 1313
## Cogon_Patch_Size-Canis_latrans 1.1905 6.9459 1.0265 612
## Cogon_Patch_Size-Procyon_lotor -0.4932 1.4240 1.0053 946
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4465 0.9649 1.0145 945
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0282 1.3722 1.0248 563
## Veg_shannon_index-Odocoileus_virginianus 0.8466 2.8156 1.0095 1488
## Veg_shannon_index-Canis_latrans 1.2734 3.2840 1.0096 1100
## Veg_shannon_index-Procyon_lotor 1.1037 2.6895 1.0175 697
## Veg_shannon_index-Dasypus_novemcinctus 0.7032 1.9362 1.0064 1749
## Veg_shannon_index-Sylvilagus_floridanus 1.0249 2.8762 1.0055 1203
## total_shrub_cover-Odocoileus_virginianus -0.0304 2.2155 1.0036 2691
## total_shrub_cover-Canis_latrans 0.0735 1.6712 1.0096 1840
## total_shrub_cover-Procyon_lotor -0.7377 0.4498 1.0006 2279
## total_shrub_cover-Dasypus_novemcinctus 0.1746 1.3758 1.0039 3033
## total_shrub_cover-Sylvilagus_floridanus -0.0966 1.6589 1.0031 2115
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1673 4.1349 1.0068 1371
## Avg_Cogongrass_Cover-Canis_latrans 0.1821 3.3802 1.0018 1215
## Avg_Cogongrass_Cover-Procyon_lotor 0.3848 3.4922 1.0024 1123
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0195 5.1027 1.0208 740
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7305 2.1509 1.0042 1155
## Tree_Density-Odocoileus_virginianus -0.9462 6.3183 1.0414 630
## Tree_Density-Canis_latrans -3.1427 -0.7829 1.0307 532
## Tree_Density-Procyon_lotor -1.8781 0.4103 1.0100 1572
## Tree_Density-Dasypus_novemcinctus -4.3979 -1.5189 1.0433 237
## Tree_Density-Sylvilagus_floridanus -2.8058 0.1286 1.0554 461
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7797 4.6106 1.0046 1074
## Avg_Canopy_Cover-Canis_latrans -0.0054 1.5430 1.0062 1398
## Avg_Canopy_Cover-Procyon_lotor 1.5869 3.6144 1.0137 1014
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0694 4.8917 1.0359 370
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9350 12.1987 1.0484 293
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.0516 9.5540 1.0481 179
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2952 7.3504 1.0235 342
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0754 6.2665 1.0160 345
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6509 4.2663 1.0097 712
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4608 4.2338 1.0649 449
## avg_veg_height-Odocoileus_virginianus -0.0508 2.1964 1.0033 2060
## avg_veg_height-Canis_latrans -0.3979 1.0272 1.0058 1071
## avg_veg_height-Procyon_lotor 0.2032 1.8385 1.0099 1805
## avg_veg_height-Dasypus_novemcinctus 0.1455 1.6534 1.0051 1393
## avg_veg_height-Sylvilagus_floridanus -0.2178 1.4320 1.0052 1969
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5339 0.0813 0.3766 0.5324 0.6945
## (Intercept)-Canis_latrans -2.4345 0.1884 -2.8184 -2.4301 -2.0874
## (Intercept)-Procyon_lotor -2.1673 0.1542 -2.4876 -2.1639 -1.8724
## (Intercept)-Dasypus_novemcinctus -1.4385 0.1572 -1.7541 -1.4367 -1.1357
## (Intercept)-Sylvilagus_floridanus -3.1115 0.3129 -3.7474 -3.0971 -2.5361
## week-Odocoileus_virginianus 1.2987 0.1241 1.0578 1.2982 1.5455
## week-Canis_latrans 0.5950 0.2656 0.0852 0.5910 1.1186
## week-Procyon_lotor 0.1956 0.2218 -0.2436 0.1976 0.6377
## week-Dasypus_novemcinctus 0.0984 0.2338 -0.3720 0.0998 0.5460
## week-Sylvilagus_floridanus 0.0578 0.3634 -0.6739 0.0657 0.7546
## I(week^2)-Odocoileus_virginianus -0.5359 0.0514 -0.6363 -0.5364 -0.4361
## I(week^2)-Canis_latrans -0.2439 0.1093 -0.4631 -0.2427 -0.0317
## I(week^2)-Procyon_lotor -0.1261 0.0956 -0.3165 -0.1258 0.0591
## I(week^2)-Dasypus_novemcinctus -0.1697 0.1070 -0.3774 -0.1672 0.0451
## I(week^2)-Sylvilagus_floridanus -0.1669 0.1701 -0.5121 -0.1630 0.1595
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0147 2623
## (Intercept)-Procyon_lotor 1.0037 3656
## (Intercept)-Dasypus_novemcinctus 1.0015 4985
## (Intercept)-Sylvilagus_floridanus 1.0033 1708
## week-Odocoileus_virginianus 1.0025 5250
## week-Canis_latrans 1.0009 3792
## week-Procyon_lotor 0.9999 4270
## week-Dasypus_novemcinctus 1.0014 5038
## week-Sylvilagus_floridanus 1.0019 2920
## I(week^2)-Odocoileus_virginianus 1.0015 5022
## I(week^2)-Canis_latrans 0.9998 3755
## I(week^2)-Procyon_lotor 1.0003 4228
## I(week^2)-Dasypus_novemcinctus 1.0005 4516
## I(week^2)-Sylvilagus_floridanus 1.0048 2471
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.1932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.644 0.8413 -1.0657 0.6455 2.3566 1.001 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5841 15.6267 0.6898 3.6542 29.2286 1.1202 3503
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4675 0.7343 -2.7851 -1.5135 0.1535 1.0004 5642
## shrub_cover 0.1670 0.3113 -0.4458 0.1577 0.8107 1.0004 4413
## veg_height -0.0566 0.2913 -0.6524 -0.0562 0.5457 1.0015 4930
## week 0.4419 0.3734 -0.3594 0.4563 1.1534 1.0052 5302
## I(week^2) -0.2505 0.1843 -0.6156 -0.2524 0.1186 1.0010 4928
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5011 3.9719 0.6942 2.3261 14.1354 1.0032 4964
## shrub_cover 0.4493 0.9449 0.0552 0.2627 1.9954 1.0837 4984
## veg_height 0.4220 0.5193 0.0737 0.2733 1.7373 1.0316 4731
## week 0.6878 1.1140 0.1093 0.4282 2.7529 1.0101 4965
## I(week^2) 0.1598 0.2132 0.0297 0.1041 0.6275 1.0256 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Odocoileus_virginianus 4.0719 1.5957 1.9649 3.7552 8.0634 1.0254
## (Intercept)-Canis_latrans 0.4732 0.4340 -0.3302 0.4528 1.3791 1.0006
## (Intercept)-Procyon_lotor 0.8033 0.4180 0.0340 0.7898 1.6992 1.0028
## (Intercept)-Dasypus_novemcinctus -0.5355 0.3791 -1.2862 -0.5337 0.2139 1.0027
## (Intercept)-Sylvilagus_floridanus -0.2834 0.5074 -1.2024 -0.3075 0.8155 1.0001
## ESS
## (Intercept)-Odocoileus_virginianus 964
## (Intercept)-Canis_latrans 4803
## (Intercept)-Procyon_lotor 4712
## (Intercept)-Dasypus_novemcinctus 5250
## (Intercept)-Sylvilagus_floridanus 2536
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5499 0.0819 0.3914 0.5491 0.7150
## (Intercept)-Canis_latrans -2.5679 0.2092 -2.9919 -2.5597 -2.1778
## (Intercept)-Procyon_lotor -2.1792 0.1645 -2.5205 -2.1711 -1.8728
## (Intercept)-Dasypus_novemcinctus -1.5674 0.1778 -1.9249 -1.5659 -1.2310
## (Intercept)-Sylvilagus_floridanus -3.0585 0.3346 -3.7496 -3.0409 -2.4461
## shrub_cover-Odocoileus_virginianus -0.0592 0.0685 -0.1897 -0.0600 0.0783
## shrub_cover-Canis_latrans -0.2831 0.2191 -0.7167 -0.2820 0.1365
## shrub_cover-Procyon_lotor 0.2442 0.1635 -0.0959 0.2456 0.5567
## shrub_cover-Dasypus_novemcinctus 0.7376 0.3027 0.1766 0.7292 1.3451
## shrub_cover-Sylvilagus_floridanus 0.2086 0.3776 -0.4889 0.1980 0.9955
## veg_height-Odocoileus_virginianus -0.3377 0.0680 -0.4726 -0.3373 -0.2075
## veg_height-Canis_latrans -0.6313 0.1937 -1.0315 -0.6244 -0.2700
## veg_height-Procyon_lotor 0.3417 0.1231 0.1025 0.3423 0.5832
## veg_height-Dasypus_novemcinctus 0.2278 0.1326 -0.0282 0.2253 0.4928
## veg_height-Sylvilagus_floridanus 0.1198 0.2542 -0.3747 0.1194 0.6167
## week-Odocoileus_virginianus 1.3346 0.1278 1.0866 1.3308 1.5891
## week-Canis_latrans 0.6146 0.2750 0.0919 0.6085 1.1660
## week-Procyon_lotor 0.2051 0.2173 -0.2251 0.2057 0.6287
## week-Dasypus_novemcinctus 0.1020 0.2377 -0.3621 0.0996 0.5605
## week-Sylvilagus_floridanus 0.0544 0.3759 -0.6994 0.0581 0.7669
## I(week^2)-Odocoileus_virginianus -0.5507 0.0519 -0.6548 -0.5497 -0.4504
## I(week^2)-Canis_latrans -0.2529 0.1125 -0.4773 -0.2531 -0.0311
## I(week^2)-Procyon_lotor -0.1281 0.0924 -0.3096 -0.1295 0.0522
## I(week^2)-Dasypus_novemcinctus -0.1763 0.1102 -0.3973 -0.1736 0.0307
## I(week^2)-Sylvilagus_floridanus -0.1616 0.1739 -0.5109 -0.1579 0.1880
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0088 2449
## (Intercept)-Procyon_lotor 1.0003 3926
## (Intercept)-Dasypus_novemcinctus 1.0026 4395
## (Intercept)-Sylvilagus_floridanus 1.0070 1926
## shrub_cover-Odocoileus_virginianus 1.0012 5044
## shrub_cover-Canis_latrans 1.0046 2711
## shrub_cover-Procyon_lotor 1.0039 3973
## shrub_cover-Dasypus_novemcinctus 1.0035 3606
## shrub_cover-Sylvilagus_floridanus 1.0019 1998
## veg_height-Odocoileus_virginianus 1.0038 5250
## veg_height-Canis_latrans 1.0149 2493
## veg_height-Procyon_lotor 1.0018 4124
## veg_height-Dasypus_novemcinctus 1.0023 4620
## veg_height-Sylvilagus_floridanus 1.0100 2544
## week-Odocoileus_virginianus 1.0003 5515
## week-Canis_latrans 1.0027 3841
## week-Procyon_lotor 1.0010 4586
## week-Dasypus_novemcinctus 1.0002 4850
## week-Sylvilagus_floridanus 1.0026 2979
## I(week^2)-Odocoileus_virginianus 1.0003 5250
## I(week^2)-Canis_latrans 1.0022 3882
## I(week^2)-Procyon_lotor 1.0017 4456
## I(week^2)-Dasypus_novemcinctus 1.0017 4708
## I(week^2)-Sylvilagus_floridanus 1.0005 3106
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.227
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6596 1.3197 -2.0574 0.6946 3.1826 1.0044 4409
## Cogon_Patch_Size -0.5468 0.8306 -2.1157 -0.5712 1.2531 1.0090 1497
## Veg_shannon_index 1.0188 0.6224 -0.1779 1.0035 2.3086 1.0203 1018
## total_shrub_cover -0.0802 0.7417 -1.6319 -0.0682 1.4359 1.0226 1768
## Avg_Cogongrass_Cover 1.7452 0.9607 -0.2183 1.7440 3.6013 1.0021 1413
## Tree_Density -1.6664 1.0973 -3.7015 -1.7209 0.8653 1.0064 1879
## Avg_Canopy_Cover 1.2865 0.9581 -0.7672 1.3195 3.0958 1.0032 3698
## avg_veg_height -0.2836 0.6405 -1.5463 -0.2874 1.0079 1.0220 1454
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 49.3330 134.1732 2.3941 21.2452 256.5338 1.3463 402
## Cogon_Patch_Size 3.9635 15.3860 0.0668 0.9459 25.4414 1.1425 994
## Veg_shannon_index 0.9590 3.0092 0.0451 0.3492 5.2623 1.0729 2510
## total_shrub_cover 2.4876 5.6920 0.0727 0.9400 15.6665 1.1128 1034
## Avg_Cogongrass_Cover 3.1967 8.8574 0.0628 0.9371 20.2731 1.0340 868
## Tree_Density 12.5663 41.4187 0.0947 3.0079 87.0830 1.0596 756
## Avg_Canopy_Cover 8.3654 32.1289 0.1918 3.1755 46.6821 1.1635 1865
## avg_veg_height 0.7424 1.4963 0.0453 0.3181 4.2214 1.0363 3984
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2225 4.4348 0.0595 0.8394 12.3306 1.1601 371
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4459 0.7382 -2.7397 -1.4976 0.1987 1.0017 5250
## shrub_cover 0.2027 0.3468 -0.4798 0.1941 0.9295 1.0023 3962
## veg_height -0.0431 0.3036 -0.6465 -0.0463 0.5642 0.9997 5004
## week 0.4387 0.3815 -0.3709 0.4525 1.1773 1.0003 4779
## I(week^2) -0.2513 0.1827 -0.6033 -0.2563 0.1136 1.0000 4787
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6408 5.4181 0.6939 2.3626 13.7929 1.0507 4946
## shrub_cover 0.5544 0.8842 0.0654 0.3321 2.4238 1.0098 4514
## veg_height 0.4465 0.6398 0.0755 0.2794 1.8198 1.0120 5250
## week 0.7034 1.1176 0.1092 0.4302 2.8329 1.0085 5250
## I(week^2) 0.1645 0.2787 0.0296 0.1013 0.6366 1.0317 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 10.4513 6.1623 3.2551 8.8948
## (Intercept)-Canis_latrans 1.3151 1.3256 -0.7798 1.1414
## (Intercept)-Procyon_lotor 1.0411 1.0647 -1.1234 1.0286
## (Intercept)-Dasypus_novemcinctus -1.6779 1.2746 -4.6770 -1.5171
## (Intercept)-Sylvilagus_floridanus -1.1511 1.6283 -4.5446 -1.1089
## Cogon_Patch_Size-Odocoileus_virginianus -0.5010 1.5315 -3.2285 -0.6013
## Cogon_Patch_Size-Canis_latrans 0.6018 1.5706 -1.4817 0.2772
## Cogon_Patch_Size-Procyon_lotor -0.9641 0.7757 -2.5508 -0.9536
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7065 0.8136 -2.3349 -0.6949
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7017 1.8567 -6.5108 -1.3091
## Veg_shannon_index-Odocoileus_virginianus 0.9192 1.0373 -1.0838 0.9345
## Veg_shannon_index-Canis_latrans 1.3745 0.8485 -0.0476 1.2866
## Veg_shannon_index-Procyon_lotor 1.2522 0.6701 0.0638 1.2133
## Veg_shannon_index-Dasypus_novemcinctus 0.7329 0.6192 -0.4806 0.7242
## Veg_shannon_index-Sylvilagus_floridanus 1.1358 0.7610 -0.2450 1.0842
## total_shrub_cover-Odocoileus_virginianus 0.1264 1.2335 -2.2594 0.0723
## total_shrub_cover-Canis_latrans 0.8580 1.0942 -0.7663 0.6581
## total_shrub_cover-Procyon_lotor -0.9431 0.7570 -2.6303 -0.8806
## total_shrub_cover-Dasypus_novemcinctus -0.0168 0.7839 -1.7340 0.0464
## total_shrub_cover-Sylvilagus_floridanus -0.4963 1.3758 -3.8491 -0.3360
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7511 1.4767 -1.1920 1.7512
## Avg_Cogongrass_Cover-Canis_latrans 2.5787 1.4791 0.4936 2.3582
## Avg_Cogongrass_Cover-Procyon_lotor 2.0413 1.0539 0.1860 1.9725
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7652 1.3611 0.7021 2.5692
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0932 1.2623 -1.5288 1.1112
## Tree_Density-Odocoileus_virginianus -0.3783 2.3472 -3.5723 -0.8189
## Tree_Density-Canis_latrans -3.1697 1.9545 -7.9324 -2.7554
## Tree_Density-Procyon_lotor -1.4105 0.8770 -3.1818 -1.4001
## Tree_Density-Dasypus_novemcinctus -4.4904 3.0017 -12.5327 -3.7014
## Tree_Density-Sylvilagus_floridanus -2.8530 1.9935 -8.0814 -2.4697
## Avg_Canopy_Cover-Odocoileus_virginianus 0.9890 1.7831 -2.5813 0.9975
## Avg_Canopy_Cover-Canis_latrans 0.0099 0.7415 -1.5535 0.0288
## Avg_Canopy_Cover-Procyon_lotor 1.6595 0.8435 0.2077 1.5869
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2817 0.9876 0.7970 2.1330
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1389 2.4141 1.0349 3.6641
## avg_veg_height-Odocoileus_virginianus -0.2867 0.9480 -2.1461 -0.2858
## avg_veg_height-Canis_latrans -0.3350 0.7343 -1.7478 -0.3502
## avg_veg_height-Procyon_lotor -0.2570 0.6804 -1.5995 -0.2564
## avg_veg_height-Dasypus_novemcinctus -0.1127 0.6999 -1.4461 -0.1320
## avg_veg_height-Sylvilagus_floridanus -0.5055 0.8424 -2.2704 -0.4770
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 26.5485 1.1777 159
## (Intercept)-Canis_latrans 4.3495 1.0049 594
## (Intercept)-Procyon_lotor 3.2046 1.0016 1261
## (Intercept)-Dasypus_novemcinctus 0.3853 1.0151 690
## (Intercept)-Sylvilagus_floridanus 1.9828 1.0232 538
## Cogon_Patch_Size-Odocoileus_virginianus 2.9620 1.0253 1594
## Cogon_Patch_Size-Canis_latrans 4.6497 1.0157 1006
## Cogon_Patch_Size-Procyon_lotor 0.4730 1.0071 919
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9548 1.0081 1375
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5668 1.0305 588
## Veg_shannon_index-Odocoileus_virginianus 2.8527 1.0178 1600
## Veg_shannon_index-Canis_latrans 3.3267 1.0368 906
## Veg_shannon_index-Procyon_lotor 2.6857 1.0273 736
## Veg_shannon_index-Dasypus_novemcinctus 2.0070 1.0216 1270
## Veg_shannon_index-Sylvilagus_floridanus 2.8147 1.0236 1061
## total_shrub_cover-Odocoileus_virginianus 2.8347 1.0206 1977
## total_shrub_cover-Canis_latrans 3.5814 1.0150 733
## total_shrub_cover-Procyon_lotor 0.3455 1.0056 1808
## total_shrub_cover-Dasypus_novemcinctus 1.3217 1.0102 1007
## total_shrub_cover-Sylvilagus_floridanus 1.7452 1.0698 528
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.7712 1.0011 1870
## Avg_Cogongrass_Cover-Canis_latrans 5.8177 1.0461 619
## Avg_Cogongrass_Cover-Procyon_lotor 4.3230 1.0069 1246
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 6.0778 1.0179 540
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4734 1.0054 1381
## Tree_Density-Odocoileus_virginianus 5.7647 1.0325 582
## Tree_Density-Canis_latrans -0.6969 1.0040 447
## Tree_Density-Procyon_lotor 0.2990 1.0022 2008
## Tree_Density-Dasypus_novemcinctus -1.2420 1.0082 357
## Tree_Density-Sylvilagus_floridanus 0.0516 1.0125 717
## Avg_Canopy_Cover-Odocoileus_virginianus 4.7051 1.0123 1546
## Avg_Canopy_Cover-Canis_latrans 1.4387 1.0050 2119
## Avg_Canopy_Cover-Procyon_lotor 3.5455 1.0073 1393
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.5610 1.0143 600
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.4831 1.0351 479
## avg_veg_height-Odocoileus_virginianus 1.5735 1.0195 2191
## avg_veg_height-Canis_latrans 1.1586 1.0102 1712
## avg_veg_height-Procyon_lotor 1.0751 1.0188 1844
## avg_veg_height-Dasypus_novemcinctus 1.2916 1.0145 1567
## avg_veg_height-Sylvilagus_floridanus 1.0930 1.0126 1730
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5482 0.0817 0.3895 0.5462 0.7118
## (Intercept)-Canis_latrans -2.5912 0.2075 -3.0173 -2.5864 -2.2023
## (Intercept)-Procyon_lotor -2.1851 0.1632 -2.5119 -2.1811 -1.8724
## (Intercept)-Dasypus_novemcinctus -1.5880 0.1805 -1.9483 -1.5870 -1.2391
## (Intercept)-Sylvilagus_floridanus -3.0863 0.3152 -3.7370 -3.0774 -2.5027
## shrub_cover-Odocoileus_virginianus -0.0596 0.0676 -0.1906 -0.0607 0.0733
## shrub_cover-Canis_latrans -0.3713 0.2259 -0.8039 -0.3746 0.0773
## shrub_cover-Procyon_lotor 0.2682 0.1641 -0.0640 0.2710 0.5805
## shrub_cover-Dasypus_novemcinctus 0.8190 0.3176 0.2191 0.8118 1.4654
## shrub_cover-Sylvilagus_floridanus 0.3838 0.4066 -0.3832 0.3696 1.2220
## veg_height-Odocoileus_virginianus -0.3367 0.0677 -0.4699 -0.3370 -0.2020
## veg_height-Canis_latrans -0.6455 0.1836 -1.0134 -0.6451 -0.2875
## veg_height-Procyon_lotor 0.3518 0.1247 0.1126 0.3502 0.5926
## veg_height-Dasypus_novemcinctus 0.2371 0.1363 -0.0243 0.2329 0.5066
## veg_height-Sylvilagus_floridanus 0.1665 0.2602 -0.3445 0.1652 0.6538
## week-Odocoileus_virginianus 1.3319 0.1266 1.0862 1.3319 1.5846
## week-Canis_latrans 0.6185 0.2723 0.0989 0.6125 1.1610
## week-Procyon_lotor 0.1989 0.2213 -0.2336 0.1989 0.6441
## week-Dasypus_novemcinctus 0.1107 0.2358 -0.3590 0.1149 0.5578
## week-Sylvilagus_floridanus 0.0506 0.3678 -0.6807 0.0567 0.7700
## I(week^2)-Odocoileus_virginianus -0.5492 0.0522 -0.6521 -0.5485 -0.4480
## I(week^2)-Canis_latrans -0.2540 0.1120 -0.4860 -0.2509 -0.0471
## I(week^2)-Procyon_lotor -0.1284 0.0965 -0.3178 -0.1271 0.0546
## I(week^2)-Dasypus_novemcinctus -0.1770 0.1081 -0.3933 -0.1773 0.0329
## I(week^2)-Sylvilagus_floridanus -0.1616 0.1749 -0.5149 -0.1580 0.1731
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0023 5250
## (Intercept)-Canis_latrans 1.0042 2033
## (Intercept)-Procyon_lotor 1.0012 3616
## (Intercept)-Dasypus_novemcinctus 1.0008 3313
## (Intercept)-Sylvilagus_floridanus 1.0010 1742
## shrub_cover-Odocoileus_virginianus 1.0025 5250
## shrub_cover-Canis_latrans 1.0027 1811
## shrub_cover-Procyon_lotor 1.0008 3745
## shrub_cover-Dasypus_novemcinctus 1.0013 2126
## shrub_cover-Sylvilagus_floridanus 1.0149 1203
## veg_height-Odocoileus_virginianus 1.0030 5250
## veg_height-Canis_latrans 1.0043 2287
## veg_height-Procyon_lotor 1.0044 4021
## veg_height-Dasypus_novemcinctus 1.0008 4376
## veg_height-Sylvilagus_floridanus 1.0153 1862
## week-Odocoileus_virginianus 1.0028 5250
## week-Canis_latrans 1.0009 3769
## week-Procyon_lotor 0.9998 4309
## week-Dasypus_novemcinctus 1.0009 4872
## week-Sylvilagus_floridanus 1.0018 3121
## I(week^2)-Odocoileus_virginianus 1.0032 5250
## I(week^2)-Canis_latrans 1.0014 3766
## I(week^2)-Procyon_lotor 0.9999 4496
## I(week^2)-Dasypus_novemcinctus 1.0003 4353
## I(week^2)-Sylvilagus_floridanus 1.0039 2371
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2925
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1204 1.0402 -1.0492 1.1391 3.1685 1.0032 2250
## Avg_Cogongrass_Cover -0.0827 0.5567 -1.2369 -0.0718 1.0052 1.0137 2341
## total_shrub_cover -0.5050 0.7910 -2.2172 -0.4743 1.0596 1.0049 1816
## avg_veg_height 0.4042 0.5465 -0.6016 0.3804 1.5869 1.0275 782
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 10.2541 21.6128 0.3680 4.9292 52.5292 1.0531 1540
## Avg_Cogongrass_Cover 0.8671 1.6160 0.0490 0.3729 4.6923 1.0003 3476
## total_shrub_cover 4.1540 7.5778 0.1005 1.6888 23.4017 1.0663 615
## avg_veg_height 0.5586 1.1296 0.0389 0.2448 3.0687 1.0040 3131
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8723 1.3954 0.0485 0.4092 4.3639 1.0409 723
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4850 0.7505 -2.8767 -1.5320 0.1563 1.0004 5250
## shrub_cover 0.3039 0.3905 -0.4822 0.2922 1.1012 1.0020 3363
## veg_height -0.0820 0.2982 -0.6743 -0.0842 0.5043 1.0006 4058
## week 0.4401 0.3705 -0.3483 0.4526 1.1853 1.0022 4882
## I(week^2) -0.2487 0.1867 -0.6105 -0.2521 0.1374 1.0009 4489
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9602 5.4157 0.7552 2.5625 16.4046 1.0256 5250
## shrub_cover 0.7644 1.0336 0.0899 0.4460 3.4820 1.0280 2430
## veg_height 0.4454 0.6916 0.0731 0.2784 1.8356 1.0732 4273
## week 0.6950 0.9171 0.1083 0.4313 3.0791 1.0021 4838
## I(week^2) 0.1610 0.2422 0.0305 0.1026 0.6313 1.0056 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.2029 2.4693 1.9081 4.6612
## (Intercept)-Canis_latrans 1.0916 0.9044 -0.4418 0.9884
## (Intercept)-Procyon_lotor 1.2668 0.7992 -0.1849 1.2227
## (Intercept)-Dasypus_novemcinctus -0.1745 1.0130 -1.7347 -0.3421
## (Intercept)-Sylvilagus_floridanus 1.0164 1.4427 -1.1125 0.7932
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0737 0.8356 -1.7191 -0.0689
## Avg_Cogongrass_Cover-Canis_latrans 0.3418 0.6774 -0.8615 0.2860
## Avg_Cogongrass_Cover-Procyon_lotor -0.2149 0.5985 -1.4852 -0.1938
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.0810 0.5326 -0.9647 0.0815
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5818 0.7857 -2.2942 -0.5015
## total_shrub_cover-Odocoileus_virginianus -0.0949 1.1563 -2.1872 -0.1712
## total_shrub_cover-Canis_latrans 0.7806 1.0432 -0.7543 0.5987
## total_shrub_cover-Procyon_lotor -1.4955 0.8023 -3.3373 -1.3898
## total_shrub_cover-Dasypus_novemcinctus -0.5238 1.1219 -3.8747 -0.2304
## total_shrub_cover-Sylvilagus_floridanus -1.9743 1.8311 -6.4713 -1.6319
## avg_veg_height-Odocoileus_virginianus 0.3481 0.7599 -1.1038 0.3235
## avg_veg_height-Canis_latrans 0.3962 0.6264 -0.7510 0.3668
## avg_veg_height-Procyon_lotor 0.3688 0.5751 -0.7266 0.3459
## avg_veg_height-Dasypus_novemcinctus 0.5929 0.6426 -0.4511 0.5174
## avg_veg_height-Sylvilagus_floridanus 0.3995 0.7266 -0.9301 0.3658
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.3246 1.0406 503
## (Intercept)-Canis_latrans 3.1878 1.0065 913
## (Intercept)-Procyon_lotor 2.9917 1.0163 1777
## (Intercept)-Dasypus_novemcinctus 2.4188 1.0657 430
## (Intercept)-Sylvilagus_floridanus 4.5891 1.0602 477
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.6399 1.0054 2400
## Avg_Cogongrass_Cover-Canis_latrans 1.9195 1.0073 2538
## Avg_Cogongrass_Cover-Procyon_lotor 0.8881 1.0071 1897
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1535 1.0041 2868
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7542 1.0135 1898
## total_shrub_cover-Odocoileus_virginianus 2.4130 1.0072 1571
## total_shrub_cover-Canis_latrans 3.2839 1.0561 598
## total_shrub_cover-Procyon_lotor -0.2161 1.0224 1129
## total_shrub_cover-Dasypus_novemcinctus 0.7426 1.1018 310
## total_shrub_cover-Sylvilagus_floridanus 0.4947 1.0766 347
## avg_veg_height-Odocoileus_virginianus 1.9089 1.0167 1431
## avg_veg_height-Canis_latrans 1.7348 1.0167 1179
## avg_veg_height-Procyon_lotor 1.5836 1.0111 1888
## avg_veg_height-Dasypus_novemcinctus 2.1775 1.0488 529
## avg_veg_height-Sylvilagus_floridanus 1.9793 1.0245 634
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5488 0.0802 0.3945 0.5475 0.7105
## (Intercept)-Canis_latrans -2.6504 0.2226 -3.1048 -2.6452 -2.2425
## (Intercept)-Procyon_lotor -2.1886 0.1608 -2.5167 -2.1828 -1.8841
## (Intercept)-Dasypus_novemcinctus -1.6446 0.2078 -2.0919 -1.6369 -1.2617
## (Intercept)-Sylvilagus_floridanus -3.2817 0.3339 -3.9652 -3.2690 -2.6565
## shrub_cover-Odocoileus_virginianus -0.0610 0.0674 -0.1920 -0.0611 0.0740
## shrub_cover-Canis_latrans -0.3742 0.2601 -0.8695 -0.3783 0.1499
## shrub_cover-Procyon_lotor 0.3286 0.1594 0.0142 0.3308 0.6392
## shrub_cover-Dasypus_novemcinctus 1.0053 0.4123 0.2939 0.9588 1.8552
## shrub_cover-Sylvilagus_floridanus 0.7032 0.4621 -0.2788 0.7234 1.5517
## veg_height-Odocoileus_virginianus -0.3367 0.0685 -0.4724 -0.3368 -0.2027
## veg_height-Canis_latrans -0.6778 0.1979 -1.0791 -0.6762 -0.3062
## veg_height-Procyon_lotor 0.3352 0.1276 0.0840 0.3330 0.5857
## veg_height-Dasypus_novemcinctus 0.2442 0.1413 -0.0315 0.2474 0.5247
## veg_height-Sylvilagus_floridanus -0.0008 0.2709 -0.5159 -0.0060 0.5442
## week-Odocoileus_virginianus 1.3319 0.1258 1.0930 1.3328 1.5762
## week-Canis_latrans 0.6067 0.2783 0.0849 0.5983 1.1593
## week-Procyon_lotor 0.2038 0.2185 -0.2271 0.2043 0.6277
## week-Dasypus_novemcinctus 0.1034 0.2352 -0.3619 0.0997 0.5664
## week-Sylvilagus_floridanus 0.0467 0.3698 -0.6824 0.0441 0.7590
## I(week^2)-Odocoileus_virginianus -0.5496 0.0516 -0.6527 -0.5500 -0.4492
## I(week^2)-Canis_latrans -0.2495 0.1156 -0.4752 -0.2482 -0.0276
## I(week^2)-Procyon_lotor -0.1288 0.0950 -0.3238 -0.1278 0.0545
## I(week^2)-Dasypus_novemcinctus -0.1747 0.1086 -0.3967 -0.1714 0.0322
## I(week^2)-Sylvilagus_floridanus -0.1715 0.1759 -0.5283 -0.1678 0.1608
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 4960
## (Intercept)-Canis_latrans 1.0043 1731
## (Intercept)-Procyon_lotor 1.0019 4117
## (Intercept)-Dasypus_novemcinctus 1.0257 880
## (Intercept)-Sylvilagus_floridanus 1.0046 1292
## shrub_cover-Odocoileus_virginianus 1.0014 5543
## shrub_cover-Canis_latrans 1.0191 939
## shrub_cover-Procyon_lotor 1.0021 4079
## shrub_cover-Dasypus_novemcinctus 1.0297 501
## shrub_cover-Sylvilagus_floridanus 1.0033 553
## veg_height-Odocoileus_virginianus 1.0027 5250
## veg_height-Canis_latrans 1.0081 1960
## veg_height-Procyon_lotor 1.0021 4117
## veg_height-Dasypus_novemcinctus 1.0088 3334
## veg_height-Sylvilagus_floridanus 1.0061 1253
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0030 3389
## week-Procyon_lotor 1.0018 4439
## week-Dasypus_novemcinctus 1.0006 4757
## week-Sylvilagus_floridanus 1.0010 2412
## I(week^2)-Odocoileus_virginianus 1.0010 5250
## I(week^2)-Canis_latrans 1.0019 3727
## I(week^2)-Procyon_lotor 1.0019 4090
## I(week^2)-Dasypus_novemcinctus 1.0008 4281
## I(week^2)-Sylvilagus_floridanus 1.0018 1573
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2063
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6181 1.0638 -1.5690 0.6169 2.7160 1.0013 5250
## Tree_Density -0.8833 0.5938 -2.1290 -0.8724 0.2982 1.0028 3035
## Avg_Canopy_Cover 0.8215 0.6874 -0.5455 0.8154 2.2634 1.0010 4329
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.8327 22.6201 1.0264 6.9668 68.4609 1.0940 636
## Tree_Density 1.5261 3.9228 0.0517 0.4697 9.9900 1.0086 2178
## Avg_Canopy_Cover 2.6752 5.8459 0.1266 1.2513 13.5701 1.0046 3082
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.499 0.7341 0.0423 0.263 2.4174 1.0201 1080
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4681 0.7337 -2.8021 -1.5130 0.1327 1.0013 5250
## shrub_cover 0.1994 0.3241 -0.4230 0.1852 0.8706 1.0001 4288
## veg_height -0.0488 0.2870 -0.6467 -0.0452 0.5231 1.0074 5174
## week 0.4388 0.3733 -0.3426 0.4514 1.1210 1.0044 4423
## I(week^2) -0.2541 0.1823 -0.6253 -0.2527 0.1159 1.0004 4939
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4562 5.8895 0.6724 2.2098 13.9971 1.0705 4385
## shrub_cover 0.4678 0.7274 0.0603 0.2836 1.9748 1.0343 4557
## veg_height 0.4492 1.0992 0.0740 0.2816 1.8525 1.2205 5250
## week 0.6915 1.0751 0.1115 0.4269 2.7974 1.0586 5250
## I(week^2) 0.1573 0.2175 0.0305 0.1007 0.6597 1.0091 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 5.7713 2.8461 2.2288 5.1404 13.0816
## (Intercept)-Canis_latrans 0.4821 0.6552 -0.7357 0.4552 1.8625
## (Intercept)-Procyon_lotor 0.9212 0.7085 -0.3343 0.8751 2.3769
## (Intercept)-Dasypus_novemcinctus -0.9788 0.7096 -2.4881 -0.9448 0.2825
## (Intercept)-Sylvilagus_floridanus -0.5572 0.7983 -2.0802 -0.5709 1.0702
## Tree_Density-Odocoileus_virginianus -0.4183 0.9358 -1.8523 -0.5435 1.8301
## Tree_Density-Canis_latrans -1.0396 0.6262 -2.4696 -0.9729 0.0002
## Tree_Density-Procyon_lotor -0.5342 0.4609 -1.4293 -0.5357 0.3569
## Tree_Density-Dasypus_novemcinctus -1.6299 1.1352 -4.7203 -1.3642 -0.2317
## Tree_Density-Sylvilagus_floridanus -1.2207 0.8882 -3.4618 -1.0738 0.1610
## Avg_Canopy_Cover-Odocoileus_virginianus 0.5968 1.0532 -1.4910 0.5852 2.8220
## Avg_Canopy_Cover-Canis_latrans -0.2477 0.5039 -1.2533 -0.2391 0.7187
## Avg_Canopy_Cover-Procyon_lotor 1.0491 0.5600 0.0834 0.9922 2.3024
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0747 0.5140 0.1778 1.0325 2.1882
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.3673 1.2837 0.6045 2.1183 5.5563
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0397 462
## (Intercept)-Canis_latrans 1.0003 2939
## (Intercept)-Procyon_lotor 1.0146 2079
## (Intercept)-Dasypus_novemcinctus 1.0092 2253
## (Intercept)-Sylvilagus_floridanus 1.0025 2331
## Tree_Density-Odocoileus_virginianus 1.0014 1741
## Tree_Density-Canis_latrans 1.0025 2987
## Tree_Density-Procyon_lotor 1.0028 3198
## Tree_Density-Dasypus_novemcinctus 1.0158 1362
## Tree_Density-Sylvilagus_floridanus 1.0075 2006
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0016 2347
## Avg_Canopy_Cover-Canis_latrans 1.0028 3182
## Avg_Canopy_Cover-Procyon_lotor 1.0001 3709
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0046 4163
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0112 1185
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5465 0.0800 0.3900 0.5464 0.7029
## (Intercept)-Canis_latrans -2.5853 0.2133 -3.0181 -2.5794 -2.1822
## (Intercept)-Procyon_lotor -2.1901 0.1660 -2.5247 -2.1820 -1.8870
## (Intercept)-Dasypus_novemcinctus -1.5746 0.1819 -1.9448 -1.5678 -1.2380
## (Intercept)-Sylvilagus_floridanus -3.0156 0.3010 -3.6463 -3.0038 -2.4655
## shrub_cover-Odocoileus_virginianus -0.0596 0.0676 -0.1908 -0.0600 0.0754
## shrub_cover-Canis_latrans -0.2891 0.2237 -0.7311 -0.2869 0.1460
## shrub_cover-Procyon_lotor 0.2436 0.1611 -0.0739 0.2456 0.5524
## shrub_cover-Dasypus_novemcinctus 0.7732 0.3022 0.1974 0.7642 1.3937
## shrub_cover-Sylvilagus_floridanus 0.3638 0.3609 -0.3373 0.3560 1.0813
## veg_height-Odocoileus_virginianus -0.3359 0.0698 -0.4717 -0.3358 -0.2010
## veg_height-Canis_latrans -0.6376 0.1897 -1.0200 -0.6338 -0.2790
## veg_height-Procyon_lotor 0.3456 0.1247 0.1094 0.3435 0.5964
## veg_height-Dasypus_novemcinctus 0.2336 0.1358 -0.0299 0.2343 0.4994
## veg_height-Sylvilagus_floridanus 0.1646 0.2471 -0.3267 0.1665 0.6456
## week-Odocoileus_virginianus 1.3313 0.1258 1.0890 1.3287 1.5856
## week-Canis_latrans 0.6005 0.2756 0.0642 0.5952 1.1478
## week-Procyon_lotor 0.1972 0.2179 -0.2488 0.1997 0.6241
## week-Dasypus_novemcinctus 0.1073 0.2324 -0.3486 0.1062 0.5660
## week-Sylvilagus_floridanus 0.0603 0.3669 -0.6803 0.0687 0.7601
## I(week^2)-Odocoileus_virginianus -0.5491 0.0518 -0.6546 -0.5481 -0.4486
## I(week^2)-Canis_latrans -0.2482 0.1152 -0.4759 -0.2469 -0.0261
## I(week^2)-Procyon_lotor -0.1268 0.0943 -0.3108 -0.1241 0.0548
## I(week^2)-Dasypus_novemcinctus -0.1775 0.1086 -0.3980 -0.1752 0.0305
## I(week^2)-Sylvilagus_floridanus -0.1696 0.1726 -0.5256 -0.1629 0.1567
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5401
## (Intercept)-Canis_latrans 1.0004 2351
## (Intercept)-Procyon_lotor 1.0029 3674
## (Intercept)-Dasypus_novemcinctus 1.0064 4034
## (Intercept)-Sylvilagus_floridanus 1.0002 2347
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0016 2700
## shrub_cover-Procyon_lotor 1.0020 4093
## shrub_cover-Dasypus_novemcinctus 1.0013 3349
## shrub_cover-Sylvilagus_floridanus 1.0016 1936
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0025 2289
## veg_height-Procyon_lotor 1.0008 4289
## veg_height-Dasypus_novemcinctus 1.0016 4102
## veg_height-Sylvilagus_floridanus 1.0017 3017
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0040 3949
## week-Procyon_lotor 1.0002 4341
## week-Dasypus_novemcinctus 1.0038 5044
## week-Sylvilagus_floridanus 0.9999 3160
## I(week^2)-Odocoileus_virginianus 1.0009 5250
## I(week^2)-Canis_latrans 1.0012 3726
## I(week^2)-Procyon_lotor 1.0000 4877
## I(week^2)-Dasypus_novemcinctus 1.0018 4574
## I(week^2)-Sylvilagus_floridanus 1.0040 2036
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2613
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8801 1.0403 -1.2989 0.9148 2.9062 1.0041 3534
## Cogon_Patch_Size -0.0107 0.6282 -1.2663 -0.0210 1.2964 1.0008 3666
## Avg_Cogongrass_Cover 0.1929 0.4858 -0.7359 0.1820 1.1736 1.0067 2384
## total_shrub_cover -0.4314 0.6976 -1.9317 -0.4132 0.9803 1.0048 1868
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.0544 24.3883 0.5825 5.9055 61.7102 1.0551 967
## Cogon_Patch_Size 2.1318 7.0908 0.0535 0.6042 12.9403 1.0207 1001
## Avg_Cogongrass_Cover 0.6553 1.3805 0.0435 0.2864 3.3993 1.0830 2395
## total_shrub_cover 2.3617 5.3164 0.0707 0.9200 13.2134 1.0407 757
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9008 1.5188 0.0444 0.4261 4.5998 1.0133 695
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4606 0.7431 -2.7697 -1.5141 0.1899 1.0017 5539
## shrub_cover 0.2705 0.3501 -0.3941 0.2632 1.0036 1.0055 2682
## veg_height -0.0729 0.2864 -0.6555 -0.0698 0.4836 1.0004 4322
## week 0.4335 0.3728 -0.3385 0.4450 1.1378 1.0027 4167
## I(week^2) -0.2544 0.1928 -0.6255 -0.2563 0.1308 1.0023 4843
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7082 4.5179 0.7453 2.4089 14.7188 1.0073 5250
## shrub_cover 0.6189 0.9424 0.0742 0.3693 2.7979 1.0679 4726
## veg_height 0.4337 0.8950 0.0702 0.2640 1.7146 1.1401 5250
## week 0.6852 0.9594 0.1107 0.4362 2.9014 1.0045 5250
## I(week^2) 0.1706 0.3694 0.0297 0.1032 0.7044 1.1306 3942
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.4408 2.6973 1.8801 4.9082
## (Intercept)-Canis_latrans 1.0173 0.9356 -0.4281 0.8995
## (Intercept)-Procyon_lotor 1.1148 0.8127 -0.3491 1.0657
## (Intercept)-Dasypus_novemcinctus -0.5052 0.7233 -1.8709 -0.5270
## (Intercept)-Sylvilagus_floridanus 0.3147 1.2318 -1.7728 0.1734
## Cogon_Patch_Size-Odocoileus_virginianus 0.1570 1.0267 -1.5520 0.0457
## Cogon_Patch_Size-Canis_latrans 0.9392 1.1948 -0.4679 0.6734
## Cogon_Patch_Size-Procyon_lotor -0.1481 0.5129 -1.1615 -0.1553
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0749 0.4759 -1.0439 -0.0707
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9042 1.2074 -3.9165 -0.6646
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1999 0.7627 -1.1850 0.1696
## Avg_Cogongrass_Cover-Canis_latrans 0.4241 0.5913 -0.5038 0.3639
## Avg_Cogongrass_Cover-Procyon_lotor 0.1292 0.5302 -0.8953 0.1225
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3900 0.4488 -0.4414 0.3789
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1720 0.6950 -1.6615 -0.1391
## total_shrub_cover-Odocoileus_virginianus -0.2105 0.9814 -2.1409 -0.2632
## total_shrub_cover-Canis_latrans 0.4307 0.8848 -0.8772 0.2985
## total_shrub_cover-Procyon_lotor -1.2808 0.7926 -3.1563 -1.1535
## total_shrub_cover-Dasypus_novemcinctus -0.2170 0.5733 -1.4731 -0.1738
## total_shrub_cover-Sylvilagus_floridanus -1.2337 1.3679 -4.6727 -0.9505
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.8149 1.0303 476
## (Intercept)-Canis_latrans 3.2249 1.0027 1111
## (Intercept)-Procyon_lotor 2.8263 1.0009 1670
## (Intercept)-Dasypus_novemcinctus 1.0436 1.0066 1699
## (Intercept)-Sylvilagus_floridanus 3.1630 1.0295 578
## Cogon_Patch_Size-Odocoileus_virginianus 2.5344 1.0069 2010
## Cogon_Patch_Size-Canis_latrans 3.9543 1.0202 904
## Cogon_Patch_Size-Procyon_lotor 0.8795 1.0027 2923
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8345 1.0006 4108
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6263 1.0158 1174
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8041 1.0124 2513
## Avg_Cogongrass_Cover-Canis_latrans 1.7107 1.0099 1854
## Avg_Cogongrass_Cover-Procyon_lotor 1.2021 1.0012 3015
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3403 1.0035 3364
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0797 1.0050 1979
## total_shrub_cover-Odocoileus_virginianus 1.9188 1.0054 2429
## total_shrub_cover-Canis_latrans 2.5868 1.0098 855
## total_shrub_cover-Procyon_lotor -0.0837 1.0023 1334
## total_shrub_cover-Dasypus_novemcinctus 0.7110 1.0091 2159
## total_shrub_cover-Sylvilagus_floridanus 0.7470 1.0233 421
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5472 0.0801 0.3897 0.5477 0.7084
## (Intercept)-Canis_latrans -2.5958 0.2156 -3.0351 -2.5914 -2.1877
## (Intercept)-Procyon_lotor -2.1928 0.1612 -2.5083 -2.1881 -1.8873
## (Intercept)-Dasypus_novemcinctus -1.5959 0.1908 -1.9868 -1.5915 -1.2362
## (Intercept)-Sylvilagus_floridanus -3.2269 0.3358 -3.9060 -3.2209 -2.6017
## shrub_cover-Odocoileus_virginianus -0.0604 0.0676 -0.1918 -0.0604 0.0700
## shrub_cover-Canis_latrans -0.3346 0.2512 -0.8253 -0.3352 0.1538
## shrub_cover-Procyon_lotor 0.3108 0.1624 -0.0089 0.3145 0.6258
## shrub_cover-Dasypus_novemcinctus 0.8635 0.3421 0.2586 0.8434 1.5627
## shrub_cover-Sylvilagus_floridanus 0.5994 0.4644 -0.3343 0.6191 1.4811
## veg_height-Odocoileus_virginianus -0.3354 0.0689 -0.4732 -0.3343 -0.2014
## veg_height-Canis_latrans -0.6371 0.1906 -1.0231 -0.6307 -0.2782
## veg_height-Procyon_lotor 0.3392 0.1268 0.0889 0.3413 0.5926
## veg_height-Dasypus_novemcinctus 0.2351 0.1372 -0.0279 0.2339 0.5121
## veg_height-Sylvilagus_floridanus 0.0480 0.2722 -0.4728 0.0386 0.6060
## week-Odocoileus_virginianus 1.3302 0.1263 1.0858 1.3265 1.5896
## week-Canis_latrans 0.6062 0.2745 0.0842 0.6012 1.1583
## week-Procyon_lotor 0.1945 0.2157 -0.2357 0.1918 0.6166
## week-Dasypus_novemcinctus 0.1101 0.2317 -0.3521 0.1153 0.5640
## week-Sylvilagus_floridanus 0.0397 0.3689 -0.6984 0.0447 0.7518
## I(week^2)-Odocoileus_virginianus -0.5483 0.0515 -0.6518 -0.5476 -0.4490
## I(week^2)-Canis_latrans -0.2483 0.1135 -0.4796 -0.2472 -0.0292
## I(week^2)-Procyon_lotor -0.1260 0.0934 -0.3120 -0.1258 0.0590
## I(week^2)-Dasypus_novemcinctus -0.1772 0.1072 -0.3931 -0.1756 0.0296
## I(week^2)-Sylvilagus_floridanus -0.1601 0.1736 -0.5102 -0.1565 0.1692
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0023 5250
## (Intercept)-Canis_latrans 1.0011 1885
## (Intercept)-Procyon_lotor 1.0010 4298
## (Intercept)-Dasypus_novemcinctus 1.0025 3031
## (Intercept)-Sylvilagus_floridanus 1.0087 1114
## shrub_cover-Odocoileus_virginianus 1.0018 5250
## shrub_cover-Canis_latrans 1.0005 1302
## shrub_cover-Procyon_lotor 1.0026 3343
## shrub_cover-Dasypus_novemcinctus 1.0064 1843
## shrub_cover-Sylvilagus_floridanus 1.0146 607
## veg_height-Odocoileus_virginianus 1.0013 4959
## veg_height-Canis_latrans 1.0029 2222
## veg_height-Procyon_lotor 1.0020 4104
## veg_height-Dasypus_novemcinctus 0.9999 4315
## veg_height-Sylvilagus_floridanus 1.0049 1312
## week-Odocoileus_virginianus 1.0011 4856
## week-Canis_latrans 1.0021 3770
## week-Procyon_lotor 1.0038 4502
## week-Dasypus_novemcinctus 1.0006 4442
## week-Sylvilagus_floridanus 1.0019 2528
## I(week^2)-Odocoileus_virginianus 1.0006 5250
## I(week^2)-Canis_latrans 1.0013 3792
## I(week^2)-Procyon_lotor 1.0043 4521
## I(week^2)-Dasypus_novemcinctus 0.9998 4270
## I(week^2)-Sylvilagus_floridanus 1.0028 2185
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2737
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6847 0.9411 -1.2881 0.7013 2.5245 1.0017 4826
## Veg_shannon_index 0.4891 0.4042 -0.3183 0.4865 1.2779 1.0008 2630
## Avg_Cogongrass_Cover 0.3733 0.4484 -0.5308 0.3728 1.2646 1.0002 3385
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.6269 16.8191 0.6236 4.4457 42.4879 1.0303 1742
## Veg_shannon_index 0.4783 1.0633 0.0393 0.2295 2.3251 1.0046 4231
## Avg_Cogongrass_Cover 0.7390 1.4806 0.0463 0.3321 3.7187 1.0074 3811
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6146 0.9433 0.0493 0.3238 2.9576 1.0072 705
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4640 0.7208 -2.7920 -1.5070 0.0986 1.0013 5250
## shrub_cover 0.1541 0.3031 -0.4649 0.1516 0.7898 1.0000 4050
## veg_height -0.0554 0.2907 -0.6362 -0.0583 0.5280 1.0015 4628
## week 0.4475 0.3738 -0.3534 0.4599 1.1677 1.0006 4827
## I(week^2) -0.2526 0.1850 -0.6245 -0.2543 0.1203 1.0023 4974
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6522 5.1383 0.7226 2.3231 14.8332 1.0091 4521
## shrub_cover 0.4339 0.7970 0.0531 0.2483 1.8933 1.0429 4986
## veg_height 0.4336 0.8514 0.0720 0.2606 1.7452 1.0482 4530
## week 0.6925 1.0618 0.1108 0.4257 2.9789 1.0293 5007
## I(week^2) 0.1642 0.2740 0.0303 0.1019 0.6469 1.0113 4837
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.5917 2.0517 1.7238 4.2026
## (Intercept)-Canis_latrans 0.5892 0.6495 -0.6460 0.5677
## (Intercept)-Procyon_lotor 0.7474 0.6198 -0.5254 0.7498
## (Intercept)-Dasypus_novemcinctus -0.5839 0.5887 -1.7452 -0.5881
## (Intercept)-Sylvilagus_floridanus -0.1361 0.8434 -1.5463 -0.2131
## Veg_shannon_index-Odocoileus_virginianus 0.4076 0.6186 -0.8910 0.4169
## Veg_shannon_index-Canis_latrans 0.7652 0.4566 -0.0456 0.7367
## Veg_shannon_index-Procyon_lotor 0.5208 0.4230 -0.2926 0.5074
## Veg_shannon_index-Dasypus_novemcinctus 0.2591 0.3789 -0.5118 0.2659
## Veg_shannon_index-Sylvilagus_floridanus 0.5484 0.4976 -0.3770 0.5263
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3876 0.7031 -0.9706 0.3604
## Avg_Cogongrass_Cover-Canis_latrans 0.7978 0.5511 -0.0746 0.7251
## Avg_Cogongrass_Cover-Procyon_lotor 0.4575 0.4554 -0.3457 0.4251
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4835 0.3863 -0.2849 0.4773
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1387 0.5771 -1.3828 -0.1184
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.7386 1.0171 735
## (Intercept)-Canis_latrans 1.9718 1.0052 3072
## (Intercept)-Procyon_lotor 1.9725 1.0080 2915
## (Intercept)-Dasypus_novemcinctus 0.6169 1.0023 2420
## (Intercept)-Sylvilagus_floridanus 1.8320 1.0018 1172
## Veg_shannon_index-Odocoileus_virginianus 1.6205 1.0004 3058
## Veg_shannon_index-Canis_latrans 1.7628 1.0011 3217
## Veg_shannon_index-Procyon_lotor 1.4075 1.0078 3096
## Veg_shannon_index-Dasypus_novemcinctus 0.9922 1.0004 4156
## Veg_shannon_index-Sylvilagus_floridanus 1.5536 1.0002 3127
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8983 1.0016 3337
## Avg_Cogongrass_Cover-Canis_latrans 2.0574 1.0006 2916
## Avg_Cogongrass_Cover-Procyon_lotor 1.4357 1.0010 3265
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2674 1.0018 3954
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9643 1.0049 2475
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5483 0.0809 0.3927 0.5490 0.7098
## (Intercept)-Canis_latrans -2.5718 0.2069 -2.9925 -2.5660 -2.1823
## (Intercept)-Procyon_lotor -2.1906 0.1693 -2.5320 -2.1847 -1.8644
## (Intercept)-Dasypus_novemcinctus -1.5589 0.1761 -1.9077 -1.5542 -1.2294
## (Intercept)-Sylvilagus_floridanus -3.1443 0.3597 -3.9133 -3.1244 -2.5000
## shrub_cover-Odocoileus_virginianus -0.0626 0.0668 -0.1951 -0.0632 0.0689
## shrub_cover-Canis_latrans -0.2634 0.2172 -0.7026 -0.2577 0.1556
## shrub_cover-Procyon_lotor 0.2233 0.1728 -0.1277 0.2304 0.5428
## shrub_cover-Dasypus_novemcinctus 0.7262 0.3063 0.1662 0.7081 1.3682
## shrub_cover-Sylvilagus_floridanus 0.1718 0.3687 -0.5067 0.1535 0.9538
## veg_height-Odocoileus_virginianus -0.3382 0.0697 -0.4722 -0.3385 -0.1997
## veg_height-Canis_latrans -0.6382 0.1891 -1.0237 -0.6297 -0.2823
## veg_height-Procyon_lotor 0.3339 0.1258 0.0896 0.3328 0.5863
## veg_height-Dasypus_novemcinctus 0.2195 0.1340 -0.0398 0.2183 0.4887
## veg_height-Sylvilagus_floridanus 0.1257 0.2667 -0.3960 0.1266 0.6438
## week-Odocoileus_virginianus 1.3323 0.1270 1.0889 1.3313 1.5872
## week-Canis_latrans 0.6121 0.2715 0.0866 0.6104 1.1546
## week-Procyon_lotor 0.2016 0.2201 -0.2264 0.2007 0.6350
## week-Dasypus_novemcinctus 0.1053 0.2382 -0.3709 0.1067 0.5624
## week-Sylvilagus_floridanus 0.0626 0.3777 -0.7034 0.0736 0.7871
## I(week^2)-Odocoileus_virginianus -0.5495 0.0524 -0.6550 -0.5490 -0.4483
## I(week^2)-Canis_latrans -0.2521 0.1129 -0.4747 -0.2507 -0.0340
## I(week^2)-Procyon_lotor -0.1290 0.0946 -0.3169 -0.1286 0.0544
## I(week^2)-Dasypus_novemcinctus -0.1778 0.1104 -0.3983 -0.1776 0.0346
## I(week^2)-Sylvilagus_floridanus -0.1679 0.1773 -0.5283 -0.1630 0.1815
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5554
## (Intercept)-Canis_latrans 1.0027 2902
## (Intercept)-Procyon_lotor 1.0025 3725
## (Intercept)-Dasypus_novemcinctus 1.0003 5124
## (Intercept)-Sylvilagus_floridanus 1.0052 1272
## shrub_cover-Odocoileus_virginianus 1.0002 5250
## shrub_cover-Canis_latrans 1.0038 2821
## shrub_cover-Procyon_lotor 1.0006 3579
## shrub_cover-Dasypus_novemcinctus 1.0018 3586
## shrub_cover-Sylvilagus_floridanus 1.0000 1851
## veg_height-Odocoileus_virginianus 1.0014 5608
## veg_height-Canis_latrans 1.0052 2298
## veg_height-Procyon_lotor 1.0023 4048
## veg_height-Dasypus_novemcinctus 1.0065 4780
## veg_height-Sylvilagus_floridanus 1.0018 2261
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0023 4008
## week-Procyon_lotor 0.9999 4585
## week-Dasypus_novemcinctus 1.0019 4994
## week-Sylvilagus_floridanus 1.0000 2944
## I(week^2)-Odocoileus_virginianus 1.0002 5250
## I(week^2)-Canis_latrans 1.0010 3736
## I(week^2)-Procyon_lotor 1.0000 4276
## I(week^2)-Dasypus_novemcinctus 1.0010 4550
## I(week^2)-Sylvilagus_floridanus 1.0007 2115
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2178
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6890 0.9261 -1.2155 0.6925 2.5028 1.0036 4810
## Avg_Cogongrass_Cover 0.1961 0.4228 -0.6499 0.1940 1.0204 1.0008 3686
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 8.9720 19.0953 0.5698 4.3623 46.8469 1.0411 1436
## Avg_Cogongrass_Cover 0.6314 1.4883 0.0464 0.2905 3.0721 1.1250 4551
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5151 0.6742 0.0447 0.2973 2.3159 1.045 1045
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4746 0.7351 -2.7773 -1.5400 0.1744 1.0014 5250
## shrub_cover 0.1734 0.3074 -0.4272 0.1653 0.8004 1.0000 4313
## veg_height -0.0572 0.3053 -0.6611 -0.0574 0.5457 1.0010 4563
## week 0.4477 0.3676 -0.3097 0.4674 1.1359 1.0030 4827
## I(week^2) -0.2517 0.1898 -0.6247 -0.2538 0.1360 1.0010 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5621 4.4087 0.6924 2.3108 14.2357 1.0075 5250
## shrub_cover 0.4438 0.7972 0.0526 0.2607 1.8981 1.0848 4994
## veg_height 0.4786 0.9414 0.0720 0.2783 1.9638 1.0881 5250
## week 0.6927 1.0631 0.1101 0.4206 2.9369 1.0210 5336
## I(week^2) 0.1679 0.2839 0.0292 0.0997 0.6940 1.0819 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.6408 2.3603 1.6839 4.1359
## (Intercept)-Canis_latrans 0.6215 0.6529 -0.5617 0.5763
## (Intercept)-Procyon_lotor 0.7233 0.5694 -0.3809 0.7239
## (Intercept)-Dasypus_novemcinctus -0.5462 0.5612 -1.6693 -0.5371
## (Intercept)-Sylvilagus_floridanus -0.2262 0.7819 -1.5705 -0.2879
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1985 0.6644 -1.0693 0.1758
## Avg_Cogongrass_Cover-Canis_latrans 0.5409 0.5120 -0.2794 0.4820
## Avg_Cogongrass_Cover-Procyon_lotor 0.2347 0.3927 -0.4990 0.2149
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3646 0.3677 -0.3441 0.3493
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2941 0.5243 -1.4089 -0.2636
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.8200 1.0751 519
## (Intercept)-Canis_latrans 1.9790 1.0067 2572
## (Intercept)-Procyon_lotor 1.8586 1.0024 3820
## (Intercept)-Dasypus_novemcinctus 0.5988 1.0032 3114
## (Intercept)-Sylvilagus_floridanus 1.4377 1.0090 1104
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.5986 1.0022 3412
## Avg_Cogongrass_Cover-Canis_latrans 1.7155 1.0015 2926
## Avg_Cogongrass_Cover-Procyon_lotor 1.0753 1.0013 4457
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1379 1.0001 4532
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6376 1.0113 2692
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5489 0.0816 0.3895 0.5472 0.7093
## (Intercept)-Canis_latrans -2.5934 0.2135 -3.0475 -2.5873 -2.1921
## (Intercept)-Procyon_lotor -2.1814 0.1646 -2.5262 -2.1772 -1.8750
## (Intercept)-Dasypus_novemcinctus -1.5639 0.1773 -1.9236 -1.5608 -1.2309
## (Intercept)-Sylvilagus_floridanus -3.0922 0.3592 -3.8969 -3.0621 -2.4662
## shrub_cover-Odocoileus_virginianus -0.0608 0.0676 -0.1923 -0.0613 0.0721
## shrub_cover-Canis_latrans -0.2613 0.2217 -0.6959 -0.2564 0.1746
## shrub_cover-Procyon_lotor 0.2460 0.1653 -0.0928 0.2497 0.5578
## shrub_cover-Dasypus_novemcinctus 0.7376 0.3006 0.1816 0.7295 1.3561
## shrub_cover-Sylvilagus_floridanus 0.2150 0.3726 -0.4870 0.2005 1.0007
## veg_height-Odocoileus_virginianus -0.3350 0.0680 -0.4679 -0.3342 -0.2019
## veg_height-Canis_latrans -0.6522 0.1927 -1.0444 -0.6458 -0.3003
## veg_height-Procyon_lotor 0.3384 0.1246 0.0999 0.3384 0.5866
## veg_height-Dasypus_novemcinctus 0.2231 0.1341 -0.0375 0.2244 0.4948
## veg_height-Sylvilagus_floridanus 0.1517 0.2623 -0.3633 0.1596 0.6572
## week-Odocoileus_virginianus 1.3328 0.1250 1.0924 1.3317 1.5812
## week-Canis_latrans 0.6189 0.2735 0.0820 0.6178 1.1642
## week-Procyon_lotor 0.1995 0.2149 -0.2237 0.2022 0.6244
## week-Dasypus_novemcinctus 0.1132 0.2338 -0.3377 0.1100 0.5750
## week-Sylvilagus_floridanus 0.0539 0.3686 -0.6744 0.0604 0.7728
## I(week^2)-Odocoileus_virginianus -0.5500 0.0515 -0.6509 -0.5498 -0.4502
## I(week^2)-Canis_latrans -0.2538 0.1127 -0.4824 -0.2518 -0.0347
## I(week^2)-Procyon_lotor -0.1287 0.0939 -0.3146 -0.1285 0.0531
## I(week^2)-Dasypus_novemcinctus -0.1759 0.1082 -0.3886 -0.1739 0.0340
## I(week^2)-Sylvilagus_floridanus -0.1637 0.1704 -0.5084 -0.1582 0.1628
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0002 2053
## (Intercept)-Procyon_lotor 1.0002 3630
## (Intercept)-Dasypus_novemcinctus 1.0002 4631
## (Intercept)-Sylvilagus_floridanus 1.0005 1201
## shrub_cover-Odocoileus_virginianus 1.0033 4876
## shrub_cover-Canis_latrans 1.0052 2604
## shrub_cover-Procyon_lotor 1.0022 3786
## shrub_cover-Dasypus_novemcinctus 1.0032 3507
## shrub_cover-Sylvilagus_floridanus 1.0016 1821
## veg_height-Odocoileus_virginianus 1.0025 5250
## veg_height-Canis_latrans 1.0036 2208
## veg_height-Procyon_lotor 1.0037 4126
## veg_height-Dasypus_novemcinctus 1.0012 4894
## veg_height-Sylvilagus_floridanus 1.0051 2013
## week-Odocoileus_virginianus 1.0004 5042
## week-Canis_latrans 1.0013 3562
## week-Procyon_lotor 1.0045 4448
## week-Dasypus_novemcinctus 1.0007 4948
## week-Sylvilagus_floridanus 1.0024 2689
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 0.9999 3855
## I(week^2)-Procyon_lotor 1.0009 4034
## I(week^2)-Dasypus_novemcinctus 1.0002 4622
## I(week^2)-Sylvilagus_floridanus 0.9999 2307
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2052
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1415 0.9491 -1.7034 0.1250 2.1663 1.0008 3663
## Avg_Cogongrass_Cover -0.5178 0.6176 -1.7703 -0.5169 0.6920 1.0006 2707
## I(Avg_Cogongrass_Cover^2) 1.0888 0.6938 -0.0617 1.0145 2.7019 1.0228 1157
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 9.1485 28.6416 0.4388 4.1001 43.0287 1.0739 453
## Avg_Cogongrass_Cover 1.3730 3.0933 0.0545 0.5474 7.5365 1.0198 3266
## I(Avg_Cogongrass_Cover^2) 2.8369 16.7418 0.0462 0.4656 18.6817 1.3719 517
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4641 0.6496 0.0403 0.2482 2.1895 1.0103 1228
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4506 0.7410 -2.8000 -1.4919 0.2093 1.0005 5250
## shrub_cover 0.1612 0.2960 -0.3971 0.1509 0.7819 1.0021 4667
## veg_height -0.0432 0.2883 -0.6368 -0.0385 0.5319 0.9999 4729
## week 0.4479 0.3694 -0.3165 0.4608 1.1307 1.0008 4431
## I(week^2) -0.2527 0.1819 -0.6136 -0.2523 0.1111 1.0006 4636
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4927 4.2616 0.7236 2.3238 13.0438 1.0106 5250
## shrub_cover 0.4311 0.7091 0.0488 0.2519 1.8886 1.0168 4862
## veg_height 0.4483 0.6476 0.0704 0.2760 1.7565 1.0003 4978
## week 0.7037 1.2953 0.1113 0.4257 2.9051 1.0567 5250
## I(week^2) 0.1598 0.2761 0.0291 0.1012 0.5927 1.0850 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8101 2.9431 0.5751 3.3010
## (Intercept)-Canis_latrans -0.3631 0.7994 -1.9894 -0.3687
## (Intercept)-Procyon_lotor -0.0569 0.7050 -1.4791 -0.0385
## (Intercept)-Dasypus_novemcinctus -1.1801 0.6672 -2.5515 -1.1708
## (Intercept)-Sylvilagus_floridanus -0.9622 0.8752 -2.6657 -0.9609
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.5657 1.0278 -2.6600 -0.5576
## Avg_Cogongrass_Cover-Canis_latrans -0.0017 0.7446 -1.2741 -0.0711
## Avg_Cogongrass_Cover-Procyon_lotor -0.5390 0.6663 -1.7990 -0.5379
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3739 0.5946 -1.5443 -0.3791
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3326 0.9422 -3.5473 -1.2153
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7873 1.8633 -0.0535 1.2928
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6707 1.2127 0.1583 1.3789
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.4356 1.2816 0.1357 1.1131
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6759 0.4512 -0.1741 0.6615
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8674 0.7420 -0.2372 0.7718
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.0090 1.0735 257
## (Intercept)-Canis_latrans 1.2250 1.0147 1739
## (Intercept)-Procyon_lotor 1.3377 1.0038 2334
## (Intercept)-Dasypus_novemcinctus 0.0825 1.0002 2896
## (Intercept)-Sylvilagus_floridanus 0.7430 1.0076 1561
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4974 1.0005 2753
## Avg_Cogongrass_Cover-Canis_latrans 1.6166 1.0047 2522
## Avg_Cogongrass_Cover-Procyon_lotor 0.8108 1.0020 3086
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8033 1.0000 3050
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2254 1.0022 1744
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 6.7673 1.0949 275
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6992 1.0619 550
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.8911 1.1199 418
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6136 1.0002 2952
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.5739 1.0353 826
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5483 0.0821 0.3892 0.5479 0.7115
## (Intercept)-Canis_latrans -2.5777 0.2064 -2.9957 -2.5735 -2.1854
## (Intercept)-Procyon_lotor -2.2187 0.1738 -2.5672 -2.2135 -1.8946
## (Intercept)-Dasypus_novemcinctus -1.5629 0.1781 -1.9285 -1.5602 -1.2177
## (Intercept)-Sylvilagus_floridanus -3.1067 0.3494 -3.8442 -3.0834 -2.4833
## shrub_cover-Odocoileus_virginianus -0.0610 0.0689 -0.1987 -0.0602 0.0725
## shrub_cover-Canis_latrans -0.2415 0.2209 -0.6775 -0.2355 0.1749
## shrub_cover-Procyon_lotor 0.2084 0.1708 -0.1330 0.2137 0.5296
## shrub_cover-Dasypus_novemcinctus 0.7319 0.3018 0.1915 0.7182 1.3647
## shrub_cover-Sylvilagus_floridanus 0.1769 0.3701 -0.5018 0.1555 0.9578
## veg_height-Odocoileus_virginianus -0.3372 0.0692 -0.4714 -0.3365 -0.2039
## veg_height-Canis_latrans -0.6385 0.1893 -1.0252 -0.6324 -0.2766
## veg_height-Procyon_lotor 0.3476 0.1274 0.0970 0.3477 0.6034
## veg_height-Dasypus_novemcinctus 0.2239 0.1316 -0.0314 0.2223 0.4965
## veg_height-Sylvilagus_floridanus 0.1576 0.2726 -0.3652 0.1579 0.6962
## week-Odocoileus_virginianus 1.3314 0.1268 1.0870 1.3311 1.5810
## week-Canis_latrans 0.6244 0.2747 0.0858 0.6176 1.1650
## week-Procyon_lotor 0.1980 0.2190 -0.2267 0.1943 0.6379
## week-Dasypus_novemcinctus 0.1089 0.2343 -0.3474 0.1089 0.5792
## week-Sylvilagus_floridanus 0.0572 0.3626 -0.6552 0.0640 0.7606
## I(week^2)-Odocoileus_virginianus -0.5490 0.0519 -0.6493 -0.5488 -0.4484
## I(week^2)-Canis_latrans -0.2592 0.1133 -0.4819 -0.2595 -0.0404
## I(week^2)-Procyon_lotor -0.1262 0.0938 -0.3095 -0.1247 0.0574
## I(week^2)-Dasypus_novemcinctus -0.1786 0.1093 -0.4037 -0.1759 0.0262
## I(week^2)-Sylvilagus_floridanus -0.1670 0.1745 -0.5159 -0.1634 0.1630
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0005 5572
## (Intercept)-Canis_latrans 1.0018 2698
## (Intercept)-Procyon_lotor 1.0096 2500
## (Intercept)-Dasypus_novemcinctus 1.0019 4746
## (Intercept)-Sylvilagus_floridanus 1.0026 1050
## shrub_cover-Odocoileus_virginianus 1.0028 5250
## shrub_cover-Canis_latrans 1.0006 2657
## shrub_cover-Procyon_lotor 1.0045 2433
## shrub_cover-Dasypus_novemcinctus 1.0032 3277
## shrub_cover-Sylvilagus_floridanus 1.0039 1733
## veg_height-Odocoileus_virginianus 1.0032 5250
## veg_height-Canis_latrans 1.0014 2389
## veg_height-Procyon_lotor 1.0007 3682
## veg_height-Dasypus_novemcinctus 1.0021 4723
## veg_height-Sylvilagus_floridanus 1.0010 1687
## week-Odocoileus_virginianus 1.0004 4868
## week-Canis_latrans 1.0014 3523
## week-Procyon_lotor 1.0008 4371
## week-Dasypus_novemcinctus 1.0074 4975
## week-Sylvilagus_floridanus 1.0031 3217
## I(week^2)-Odocoileus_virginianus 0.9999 5330
## I(week^2)-Canis_latrans 1.0006 3279
## I(week^2)-Procyon_lotor 1.0007 3632
## I(week^2)-Dasypus_novemcinctus 1.0036 4944
## I(week^2)-Sylvilagus_floridanus 1.0005 2093
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 5 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1762 1.3599 -2.4652 0.1709 2.8719 1.0025 4207
## Cogon_Patch_Size -0.1715 0.9360 -2.0429 -0.1795 1.7365 1.0212 1816
## Veg_shannon_index 1.0132 0.6641 -0.3057 0.9916 2.4123 1.0171 671
## total_shrub_cover -0.2336 0.7411 -1.7114 -0.2424 1.2577 1.0047 1931
## Avg_Cogongrass_Cover 0.2408 1.1728 -2.1125 0.2456 2.5317 1.0224 1000
## Tree_Density -1.8019 1.2824 -4.0992 -1.8922 1.1459 1.0104 1384
## Avg_Canopy_Cover 1.2469 1.0277 -1.0097 1.2837 3.2307 1.0016 3501
## I(Avg_Cogongrass_Cover^2) 1.6887 0.8563 0.1567 1.6436 3.4718 1.0004 777
## avg_veg_height 0.0019 0.6940 -1.4000 0.0151 1.3449 1.0047 1283
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 56.2218 217.7103 3.1523 24.3605 293.1731 1.1629 1461
## Cogon_Patch_Size 6.6693 23.4132 0.0709 1.5763 43.7025 1.1039 963
## Veg_shannon_index 1.1193 4.0587 0.0468 0.3919 6.6164 1.0913 4749
## total_shrub_cover 2.6332 6.7997 0.0699 0.9730 15.3857 1.1202 361
## Avg_Cogongrass_Cover 5.0161 14.8722 0.0657 1.3833 31.6416 1.1657 754
## Tree_Density 22.6130 62.4561 0.0893 5.0812 158.0246 1.1122 296
## Avg_Canopy_Cover 10.6547 28.1409 0.2029 4.3220 60.2308 1.0946 1560
## I(Avg_Cogongrass_Cover^2) 3.5885 24.6541 0.0501 0.5451 20.3442 1.3006 497
## avg_veg_height 0.9267 2.6608 0.0451 0.3499 5.2041 1.0078 3122
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4493 5.759 0.0549 0.783 15.6338 1.3283 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4455 0.7419 -2.7308 -1.5091 0.1630 1.0012 5271
## shrub_cover 0.2134 0.3387 -0.4348 0.2097 0.8884 1.0057 4068
## veg_height -0.0388 0.2883 -0.5904 -0.0384 0.5357 1.0011 4140
## week 0.4418 0.3708 -0.3356 0.4573 1.1299 1.0012 5039
## I(week^2) -0.2514 0.1817 -0.6235 -0.2524 0.1224 1.0024 4979
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5309 6.2474 0.6809 2.2987 13.7029 1.1348 5250
## shrub_cover 0.5577 0.9279 0.0692 0.3337 2.5155 1.0226 4737
## veg_height 0.4411 0.6973 0.0720 0.2674 1.8742 1.0145 5250
## week 0.6543 0.9007 0.1077 0.4200 2.6307 1.0096 4109
## I(week^2) 0.1540 0.1810 0.0286 0.1012 0.6141 1.0000 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 9.8645 5.8523 2.7194
## (Intercept)-Canis_latrans -0.5086 1.7683 -3.8463
## (Intercept)-Procyon_lotor -0.3115 1.3312 -3.1247
## (Intercept)-Dasypus_novemcinctus -3.1287 1.7131 -7.3374
## (Intercept)-Sylvilagus_floridanus -2.6208 2.0244 -7.2321
## Cogon_Patch_Size-Odocoileus_virginianus -0.0390 1.7816 -3.3033
## Cogon_Patch_Size-Canis_latrans 1.5878 2.2587 -1.0337
## Cogon_Patch_Size-Procyon_lotor -0.6443 0.9407 -2.5330
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3542 0.9677 -2.3702
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6069 2.3907 -7.8013
## Veg_shannon_index-Odocoileus_virginianus 0.8909 1.0714 -1.4445
## Veg_shannon_index-Canis_latrans 1.4180 0.8620 0.0050
## Veg_shannon_index-Procyon_lotor 1.2654 0.7165 0.0477
## Veg_shannon_index-Dasypus_novemcinctus 0.7110 0.6869 -0.6843
## Veg_shannon_index-Sylvilagus_floridanus 1.1442 0.8554 -0.3376
## total_shrub_cover-Odocoileus_virginianus -0.0061 1.3428 -2.4595
## total_shrub_cover-Canis_latrans 0.6752 1.2511 -1.0456
## total_shrub_cover-Procyon_lotor -1.1683 0.8152 -2.9926
## total_shrub_cover-Dasypus_novemcinctus -0.0481 0.8053 -1.7640
## total_shrub_cover-Sylvilagus_floridanus -0.6343 1.3473 -3.8451
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2032 2.0427 -3.5580
## Avg_Cogongrass_Cover-Canis_latrans 0.7282 1.7396 -2.3004
## Avg_Cogongrass_Cover-Procyon_lotor 0.2194 1.4366 -2.5534
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3375 1.7881 -1.6707
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9259 1.9156 -5.2185
## Tree_Density-Odocoileus_virginianus -0.1574 3.4705 -4.1109
## Tree_Density-Canis_latrans -3.8941 2.3625 -9.9945
## Tree_Density-Procyon_lotor -2.1256 1.3350 -4.9243
## Tree_Density-Dasypus_novemcinctus -5.9325 3.8844 -16.3144
## Tree_Density-Sylvilagus_floridanus -3.4150 2.5565 -9.7680
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7219 2.0705 -3.3779
## Avg_Canopy_Cover-Canis_latrans -0.0504 0.7723 -1.6114
## Avg_Canopy_Cover-Procyon_lotor 1.6472 0.9654 0.0004
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5604 1.2520 0.7459
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7878 2.9165 1.0418
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.2382 2.2820 -0.1683
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2697 1.4236 0.4143
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1113 1.1709 0.3435
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6978 1.0001 0.0726
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4705 1.0689 -0.4178
## avg_veg_height-Odocoileus_virginianus 0.0138 1.0365 -2.0567
## avg_veg_height-Canis_latrans -0.1546 0.8223 -1.7724
## avg_veg_height-Procyon_lotor 0.1126 0.7623 -1.4185
## avg_veg_height-Dasypus_novemcinctus 0.2080 0.7810 -1.2911
## avg_veg_height-Sylvilagus_floridanus -0.1684 0.9270 -2.0830
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.5119 25.1069 1.1652 213
## (Intercept)-Canis_latrans -0.5767 3.2551 1.0213 778
## (Intercept)-Procyon_lotor -0.2731 2.1539 1.0494 734
## (Intercept)-Dasypus_novemcinctus -2.8570 -0.4405 1.0173 377
## (Intercept)-Sylvilagus_floridanus -2.4824 1.0348 1.0235 601
## Cogon_Patch_Size-Odocoileus_virginianus -0.1725 4.0847 1.0164 1603
## Cogon_Patch_Size-Canis_latrans 1.0458 7.4849 1.0080 774
## Cogon_Patch_Size-Procyon_lotor -0.6172 1.0205 1.0615 795
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3392 1.5327 1.0257 1340
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0556 1.1062 1.0079 519
## Veg_shannon_index-Odocoileus_virginianus 0.9134 2.9493 1.0212 1105
## Veg_shannon_index-Canis_latrans 1.3161 3.4116 1.0056 961
## Veg_shannon_index-Procyon_lotor 1.2001 2.8483 1.0080 508
## Veg_shannon_index-Dasypus_novemcinctus 0.7205 2.0667 1.0187 921
## Veg_shannon_index-Sylvilagus_floridanus 1.0807 3.0976 1.0092 807
## total_shrub_cover-Odocoileus_virginianus -0.0737 3.0024 1.0168 1577
## total_shrub_cover-Canis_latrans 0.4331 3.8598 1.0168 638
## total_shrub_cover-Procyon_lotor -1.0931 0.2006 1.0059 1477
## total_shrub_cover-Dasypus_novemcinctus -0.0237 1.5014 1.0069 1317
## total_shrub_cover-Sylvilagus_floridanus -0.4987 1.6096 1.0135 923
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1461 4.3935 1.0396 1330
## Avg_Cogongrass_Cover-Canis_latrans 0.5938 4.6273 1.0144 1173
## Avg_Cogongrass_Cover-Procyon_lotor 0.2130 3.0755 1.0162 1042
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1238 5.4825 1.0244 722
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6976 2.2334 1.0078 1049
## Tree_Density-Odocoileus_virginianus -0.8923 8.0269 1.1198 363
## Tree_Density-Canis_latrans -3.3616 -0.7829 1.0220 447
## Tree_Density-Procyon_lotor -2.0425 0.1089 1.0442 601
## Tree_Density-Dasypus_novemcinctus -4.8795 -1.5146 1.0849 208
## Tree_Density-Sylvilagus_floridanus -2.9098 0.1917 1.0276 390
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7711 4.7803 1.0066 1147
## Avg_Canopy_Cover-Canis_latrans -0.0333 1.4154 1.0010 2120
## Avg_Canopy_Cover-Procyon_lotor 1.5568 3.8208 1.0312 670
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3392 5.5377 1.0413 341
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1704 11.9973 1.0303 383
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8808 6.7025 1.0745 384
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9959 6.1186 1.0050 535
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9455 4.8869 1.0103 626
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5709 3.9992 1.0123 577
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3952 3.6965 1.0017 1010
## avg_veg_height-Odocoileus_virginianus 0.0231 2.1019 1.0101 1957
## avg_veg_height-Canis_latrans -0.1538 1.4545 1.0078 1222
## avg_veg_height-Procyon_lotor 0.1199 1.5830 1.0031 1478
## avg_veg_height-Dasypus_novemcinctus 0.1956 1.7872 1.0113 1564
## avg_veg_height-Sylvilagus_floridanus -0.1305 1.5394 1.0026 1363
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5463 0.0807 0.3925 0.5440 0.7057
## (Intercept)-Canis_latrans -2.5545 0.2047 -2.9777 -2.5467 -2.1776
## (Intercept)-Procyon_lotor -2.1985 0.1676 -2.5412 -2.1938 -1.8803
## (Intercept)-Dasypus_novemcinctus -1.5894 0.1811 -1.9525 -1.5851 -1.2547
## (Intercept)-Sylvilagus_floridanus -3.0988 0.3058 -3.7234 -3.0927 -2.5346
## shrub_cover-Odocoileus_virginianus -0.0600 0.0690 -0.1929 -0.0604 0.0764
## shrub_cover-Canis_latrans -0.3413 0.2357 -0.7927 -0.3451 0.1139
## shrub_cover-Procyon_lotor 0.2657 0.1638 -0.0736 0.2682 0.5705
## shrub_cover-Dasypus_novemcinctus 0.8328 0.3247 0.2257 0.8219 1.4777
## shrub_cover-Sylvilagus_floridanus 0.4463 0.3952 -0.2999 0.4342 1.2643
## veg_height-Odocoileus_virginianus -0.3361 0.0693 -0.4701 -0.3356 -0.1990
## veg_height-Canis_latrans -0.6106 0.1882 -0.9921 -0.6060 -0.2540
## veg_height-Procyon_lotor 0.3605 0.1271 0.1132 0.3605 0.6098
## veg_height-Dasypus_novemcinctus 0.2371 0.1385 -0.0258 0.2364 0.5168
## veg_height-Sylvilagus_floridanus 0.1354 0.2644 -0.3747 0.1312 0.6535
## week-Odocoileus_virginianus 1.3301 0.1270 1.0865 1.3294 1.5792
## week-Canis_latrans 0.6087 0.2730 0.0712 0.5977 1.1598
## week-Procyon_lotor 0.2024 0.2188 -0.2288 0.2055 0.6226
## week-Dasypus_novemcinctus 0.1075 0.2381 -0.3650 0.1067 0.5653
## week-Sylvilagus_floridanus 0.0624 0.3735 -0.6806 0.0727 0.7792
## I(week^2)-Odocoileus_virginianus -0.5477 0.0521 -0.6498 -0.5474 -0.4467
## I(week^2)-Canis_latrans -0.2496 0.1122 -0.4751 -0.2466 -0.0314
## I(week^2)-Procyon_lotor -0.1286 0.0949 -0.3159 -0.1281 0.0549
## I(week^2)-Dasypus_novemcinctus -0.1764 0.1079 -0.3936 -0.1766 0.0299
## I(week^2)-Sylvilagus_floridanus -0.1674 0.1734 -0.5111 -0.1630 0.1613
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0040 2049
## (Intercept)-Procyon_lotor 1.0007 3402
## (Intercept)-Dasypus_novemcinctus 1.0049 2850
## (Intercept)-Sylvilagus_floridanus 1.0039 1774
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0020 1525
## shrub_cover-Procyon_lotor 1.0005 2450
## shrub_cover-Dasypus_novemcinctus 1.0053 2038
## shrub_cover-Sylvilagus_floridanus 1.0114 1146
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0015 2184
## veg_height-Procyon_lotor 1.0005 3388
## veg_height-Dasypus_novemcinctus 1.0018 4165
## veg_height-Sylvilagus_floridanus 1.0030 1580
## week-Odocoileus_virginianus 1.0005 5250
## week-Canis_latrans 1.0017 3645
## week-Procyon_lotor 1.0005 4705
## week-Dasypus_novemcinctus 1.0004 4799
## week-Sylvilagus_floridanus 1.0002 2922
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0011 3519
## I(week^2)-Procyon_lotor 1.0015 4332
## I(week^2)-Dasypus_novemcinctus 1.0021 4937
## I(week^2)-Sylvilagus_floridanus 1.0013 2203
waicOcc(ms_full_full_T25, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1451.44584 95.86875 3094.62920
waicOcc(ms_full_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -1472.43618 99.88116 3144.63470
waicOcc(ms_full_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -1469.76462 80.71149 3100.95222
waicOcc(ms_full_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -1473.23409 94.00655 3134.48128
waicOcc(ms_full_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -1478.61420 86.87196 3130.97232
waicOcc(ms_full_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -1481.69799 80.89166 3125.17929
waicOcc(ms_full_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -1486.34616 73.70057 3120.09345
waicOcc(ms_full_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1477.80610 85.92775 3127.46769
waicOcc(ms_full_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1447.56743 97.96662 3091.06811
waicOcc(ms_null_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -1534.5089 26.4767 3121.9711
waicOcc(ms_null_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -1502.15762 47.90001 3100.11525
waicOcc(ms_null_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -1526.5592 40.5199 3134.1581
waicOcc(ms_null_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -1518.89653 35.56219 3108.91743
waicOcc(ms_null_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -1525.0923 40.2673 3130.7193
waicOcc(ms_null_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -1526.93881 36.98412 3127.84586
waicOcc(ms_null_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -1529.99525 33.53729 3127.06507
waicOcc(ms_null_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1526.47674 36.30736 3125.56819
waicOcc(ms_null_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1496.56646 50.70269 3094.53831
waicOcc(ms_week_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -1492.8487 55.2445 3096.1864
waicOcc(ms_week_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -1517.44872 47.11259 3129.12261
waicOcc(ms_week_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -1525.8593 32.5976 3116.9139
waicOcc(ms_week_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -1518.04343 43.65017 3123.38718
waicOcc(ms_week_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -1516.19262 46.60044 3125.58612
waicOcc(ms_week_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -1510.15986 41.90353 3104.12677
waicOcc(ms_week_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -1521.27721 40.11139 3122.77721
waicOcc(ms_week_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1517.48349 42.49441 3119.95580
waicOcc(ms_week_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1487.02496 57.34125 3088.73241
waicOcc(ms_cover_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -1459.64320 90.87496 3101.03632
waicOcc(ms_cover_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -1481.79151 92.37603 3148.33507
waicOcc(ms_cover_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -1495.95334 66.18471 3124.27610
waicOcc(ms_cover_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -1487.43943 79.06228 3133.00342
waicOcc(ms_cover_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -1482.7271 86.2703 3137.9949
waicOcc(ms_cover_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -1478.65574 75.72735 3108.76619
waicOcc(ms_cover_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -1490.37446 75.46106 3131.67104
waicOcc(ms_cover_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1486.68285 79.99203 3133.34976
waicOcc(ms_cover_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1456.66843 91.88817 3097.11318
waicOcc(ms_weekQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -1423.47908 64.34259 2975.64334
waicOcc(ms_weekQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -1447.88955 56.28184 3008.34279
waicOcc(ms_weekQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -1456.13560 41.35397 2994.97915
waicOcc(ms_weekQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -1448.27876 52.81872 3002.19498
waicOcc(ms_weekQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -1446.90701 54.96101 3003.73606
waicOcc(ms_weekQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -1440.83001 50.18829 2982.03659
waicOcc(ms_weekQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -1451.25079 49.27699 3001.05556
waicOcc(ms_weekQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1447.76849 52.36692 3000.27082
waicOcc(ms_weekQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1418.94837 65.02088 2967.93849
waicOcc(ms_fullQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -1379.5738 105.9047 2970.9570
waicOcc(ms_fullQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -1400.8861 109.8609 3021.4940
waicOcc(ms_fullQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -1414.56480 84.07479 2997.27919
waicOcc(ms_fullQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -1406.67289 97.04769 3007.44115
waicOcc(ms_fullQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -1401.9820 102.1668 3008.2977
waicOcc(ms_fullQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -1397.83573 92.48084 2980.63313
waicOcc(ms_fullQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -1409.73986 91.03498 3001.54969
waicOcc(ms_fullQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1405.55497 95.84637 3002.80269
waicOcc(ms_fullQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -1376.0300 108.9991 2970.0583
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T25 <- ppcOcc(ms_fullQ_fullQ_T25, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 5
## Currently on species 2 out of 5
## Currently on species 3 out of 5
## Currently on species 4 out of 5
## Currently on species 5 out of 5
summary(ppc.ms_fullQ_fullQ_T25)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T25, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.2026
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Odocoileus_virginianus Bayesian p-value: 0
## Canis_latrans Bayesian p-value: 0.5775
## Procyon_lotor Bayesian p-value: 0.065
## Dasypus_novemcinctus Bayesian p-value: 0
## Sylvilagus_floridanus Bayesian p-value: 0.3705
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T25) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1762 1.3599 -2.4652 0.1709 2.8719 1.0025 4207
## Cogon_Patch_Size -0.1715 0.9360 -2.0429 -0.1795 1.7365 1.0212 1816
## Veg_shannon_index 1.0132 0.6641 -0.3057 0.9916 2.4123 1.0171 671
## total_shrub_cover -0.2336 0.7411 -1.7114 -0.2424 1.2577 1.0047 1931
## Avg_Cogongrass_Cover 0.2408 1.1728 -2.1125 0.2456 2.5317 1.0224 1000
## Tree_Density -1.8019 1.2824 -4.0992 -1.8922 1.1459 1.0104 1384
## Avg_Canopy_Cover 1.2469 1.0277 -1.0097 1.2837 3.2307 1.0016 3501
## I(Avg_Cogongrass_Cover^2) 1.6887 0.8563 0.1567 1.6436 3.4718 1.0004 777
## avg_veg_height 0.0019 0.6940 -1.4000 0.0151 1.3449 1.0047 1283
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 56.2218 217.7103 3.1523 24.3605 293.1731 1.1629 1461
## Cogon_Patch_Size 6.6693 23.4132 0.0709 1.5763 43.7025 1.1039 963
## Veg_shannon_index 1.1193 4.0587 0.0468 0.3919 6.6164 1.0913 4749
## total_shrub_cover 2.6332 6.7997 0.0699 0.9730 15.3857 1.1202 361
## Avg_Cogongrass_Cover 5.0161 14.8722 0.0657 1.3833 31.6416 1.1657 754
## Tree_Density 22.6130 62.4561 0.0893 5.0812 158.0246 1.1122 296
## Avg_Canopy_Cover 10.6547 28.1409 0.2029 4.3220 60.2308 1.0946 1560
## I(Avg_Cogongrass_Cover^2) 3.5885 24.6541 0.0501 0.5451 20.3442 1.3006 497
## avg_veg_height 0.9267 2.6608 0.0451 0.3499 5.2041 1.0078 3122
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4493 5.759 0.0549 0.783 15.6338 1.3283 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4455 0.7419 -2.7308 -1.5091 0.1630 1.0012 5271
## shrub_cover 0.2134 0.3387 -0.4348 0.2097 0.8884 1.0057 4068
## veg_height -0.0388 0.2883 -0.5904 -0.0384 0.5357 1.0011 4140
## week 0.4418 0.3708 -0.3356 0.4573 1.1299 1.0012 5039
## I(week^2) -0.2514 0.1817 -0.6235 -0.2524 0.1224 1.0024 4979
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5309 6.2474 0.6809 2.2987 13.7029 1.1348 5250
## shrub_cover 0.5577 0.9279 0.0692 0.3337 2.5155 1.0226 4737
## veg_height 0.4411 0.6973 0.0720 0.2674 1.8742 1.0145 5250
## week 0.6543 0.9007 0.1077 0.4200 2.6307 1.0096 4109
## I(week^2) 0.1540 0.1810 0.0286 0.1012 0.6141 1.0000 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 9.8645 5.8523 2.7194
## (Intercept)-Canis_latrans -0.5086 1.7683 -3.8463
## (Intercept)-Procyon_lotor -0.3115 1.3312 -3.1247
## (Intercept)-Dasypus_novemcinctus -3.1287 1.7131 -7.3374
## (Intercept)-Sylvilagus_floridanus -2.6208 2.0244 -7.2321
## Cogon_Patch_Size-Odocoileus_virginianus -0.0390 1.7816 -3.3033
## Cogon_Patch_Size-Canis_latrans 1.5878 2.2587 -1.0337
## Cogon_Patch_Size-Procyon_lotor -0.6443 0.9407 -2.5330
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3542 0.9677 -2.3702
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6069 2.3907 -7.8013
## Veg_shannon_index-Odocoileus_virginianus 0.8909 1.0714 -1.4445
## Veg_shannon_index-Canis_latrans 1.4180 0.8620 0.0050
## Veg_shannon_index-Procyon_lotor 1.2654 0.7165 0.0477
## Veg_shannon_index-Dasypus_novemcinctus 0.7110 0.6869 -0.6843
## Veg_shannon_index-Sylvilagus_floridanus 1.1442 0.8554 -0.3376
## total_shrub_cover-Odocoileus_virginianus -0.0061 1.3428 -2.4595
## total_shrub_cover-Canis_latrans 0.6752 1.2511 -1.0456
## total_shrub_cover-Procyon_lotor -1.1683 0.8152 -2.9926
## total_shrub_cover-Dasypus_novemcinctus -0.0481 0.8053 -1.7640
## total_shrub_cover-Sylvilagus_floridanus -0.6343 1.3473 -3.8451
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2032 2.0427 -3.5580
## Avg_Cogongrass_Cover-Canis_latrans 0.7282 1.7396 -2.3004
## Avg_Cogongrass_Cover-Procyon_lotor 0.2194 1.4366 -2.5534
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3375 1.7881 -1.6707
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9259 1.9156 -5.2185
## Tree_Density-Odocoileus_virginianus -0.1574 3.4705 -4.1109
## Tree_Density-Canis_latrans -3.8941 2.3625 -9.9945
## Tree_Density-Procyon_lotor -2.1256 1.3350 -4.9243
## Tree_Density-Dasypus_novemcinctus -5.9325 3.8844 -16.3144
## Tree_Density-Sylvilagus_floridanus -3.4150 2.5565 -9.7680
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7219 2.0705 -3.3779
## Avg_Canopy_Cover-Canis_latrans -0.0504 0.7723 -1.6114
## Avg_Canopy_Cover-Procyon_lotor 1.6472 0.9654 0.0004
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5604 1.2520 0.7459
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7878 2.9165 1.0418
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.2382 2.2820 -0.1683
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2697 1.4236 0.4143
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1113 1.1709 0.3435
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6978 1.0001 0.0726
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4705 1.0689 -0.4178
## avg_veg_height-Odocoileus_virginianus 0.0138 1.0365 -2.0567
## avg_veg_height-Canis_latrans -0.1546 0.8223 -1.7724
## avg_veg_height-Procyon_lotor 0.1126 0.7623 -1.4185
## avg_veg_height-Dasypus_novemcinctus 0.2080 0.7810 -1.2911
## avg_veg_height-Sylvilagus_floridanus -0.1684 0.9270 -2.0830
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.5119 25.1069 1.1652 213
## (Intercept)-Canis_latrans -0.5767 3.2551 1.0213 778
## (Intercept)-Procyon_lotor -0.2731 2.1539 1.0494 734
## (Intercept)-Dasypus_novemcinctus -2.8570 -0.4405 1.0173 377
## (Intercept)-Sylvilagus_floridanus -2.4824 1.0348 1.0235 601
## Cogon_Patch_Size-Odocoileus_virginianus -0.1725 4.0847 1.0164 1603
## Cogon_Patch_Size-Canis_latrans 1.0458 7.4849 1.0080 774
## Cogon_Patch_Size-Procyon_lotor -0.6172 1.0205 1.0615 795
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3392 1.5327 1.0257 1340
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0556 1.1062 1.0079 519
## Veg_shannon_index-Odocoileus_virginianus 0.9134 2.9493 1.0212 1105
## Veg_shannon_index-Canis_latrans 1.3161 3.4116 1.0056 961
## Veg_shannon_index-Procyon_lotor 1.2001 2.8483 1.0080 508
## Veg_shannon_index-Dasypus_novemcinctus 0.7205 2.0667 1.0187 921
## Veg_shannon_index-Sylvilagus_floridanus 1.0807 3.0976 1.0092 807
## total_shrub_cover-Odocoileus_virginianus -0.0737 3.0024 1.0168 1577
## total_shrub_cover-Canis_latrans 0.4331 3.8598 1.0168 638
## total_shrub_cover-Procyon_lotor -1.0931 0.2006 1.0059 1477
## total_shrub_cover-Dasypus_novemcinctus -0.0237 1.5014 1.0069 1317
## total_shrub_cover-Sylvilagus_floridanus -0.4987 1.6096 1.0135 923
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1461 4.3935 1.0396 1330
## Avg_Cogongrass_Cover-Canis_latrans 0.5938 4.6273 1.0144 1173
## Avg_Cogongrass_Cover-Procyon_lotor 0.2130 3.0755 1.0162 1042
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1238 5.4825 1.0244 722
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6976 2.2334 1.0078 1049
## Tree_Density-Odocoileus_virginianus -0.8923 8.0269 1.1198 363
## Tree_Density-Canis_latrans -3.3616 -0.7829 1.0220 447
## Tree_Density-Procyon_lotor -2.0425 0.1089 1.0442 601
## Tree_Density-Dasypus_novemcinctus -4.8795 -1.5146 1.0849 208
## Tree_Density-Sylvilagus_floridanus -2.9098 0.1917 1.0276 390
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7711 4.7803 1.0066 1147
## Avg_Canopy_Cover-Canis_latrans -0.0333 1.4154 1.0010 2120
## Avg_Canopy_Cover-Procyon_lotor 1.5568 3.8208 1.0312 670
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3392 5.5377 1.0413 341
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1704 11.9973 1.0303 383
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8808 6.7025 1.0745 384
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9959 6.1186 1.0050 535
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9455 4.8869 1.0103 626
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5709 3.9992 1.0123 577
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3952 3.6965 1.0017 1010
## avg_veg_height-Odocoileus_virginianus 0.0231 2.1019 1.0101 1957
## avg_veg_height-Canis_latrans -0.1538 1.4545 1.0078 1222
## avg_veg_height-Procyon_lotor 0.1199 1.5830 1.0031 1478
## avg_veg_height-Dasypus_novemcinctus 0.1956 1.7872 1.0113 1564
## avg_veg_height-Sylvilagus_floridanus -0.1305 1.5394 1.0026 1363
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5463 0.0807 0.3925 0.5440 0.7057
## (Intercept)-Canis_latrans -2.5545 0.2047 -2.9777 -2.5467 -2.1776
## (Intercept)-Procyon_lotor -2.1985 0.1676 -2.5412 -2.1938 -1.8803
## (Intercept)-Dasypus_novemcinctus -1.5894 0.1811 -1.9525 -1.5851 -1.2547
## (Intercept)-Sylvilagus_floridanus -3.0988 0.3058 -3.7234 -3.0927 -2.5346
## shrub_cover-Odocoileus_virginianus -0.0600 0.0690 -0.1929 -0.0604 0.0764
## shrub_cover-Canis_latrans -0.3413 0.2357 -0.7927 -0.3451 0.1139
## shrub_cover-Procyon_lotor 0.2657 0.1638 -0.0736 0.2682 0.5705
## shrub_cover-Dasypus_novemcinctus 0.8328 0.3247 0.2257 0.8219 1.4777
## shrub_cover-Sylvilagus_floridanus 0.4463 0.3952 -0.2999 0.4342 1.2643
## veg_height-Odocoileus_virginianus -0.3361 0.0693 -0.4701 -0.3356 -0.1990
## veg_height-Canis_latrans -0.6106 0.1882 -0.9921 -0.6060 -0.2540
## veg_height-Procyon_lotor 0.3605 0.1271 0.1132 0.3605 0.6098
## veg_height-Dasypus_novemcinctus 0.2371 0.1385 -0.0258 0.2364 0.5168
## veg_height-Sylvilagus_floridanus 0.1354 0.2644 -0.3747 0.1312 0.6535
## week-Odocoileus_virginianus 1.3301 0.1270 1.0865 1.3294 1.5792
## week-Canis_latrans 0.6087 0.2730 0.0712 0.5977 1.1598
## week-Procyon_lotor 0.2024 0.2188 -0.2288 0.2055 0.6226
## week-Dasypus_novemcinctus 0.1075 0.2381 -0.3650 0.1067 0.5653
## week-Sylvilagus_floridanus 0.0624 0.3735 -0.6806 0.0727 0.7792
## I(week^2)-Odocoileus_virginianus -0.5477 0.0521 -0.6498 -0.5474 -0.4467
## I(week^2)-Canis_latrans -0.2496 0.1122 -0.4751 -0.2466 -0.0314
## I(week^2)-Procyon_lotor -0.1286 0.0949 -0.3159 -0.1281 0.0549
## I(week^2)-Dasypus_novemcinctus -0.1764 0.1079 -0.3936 -0.1766 0.0299
## I(week^2)-Sylvilagus_floridanus -0.1674 0.1734 -0.5111 -0.1630 0.1613
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0040 2049
## (Intercept)-Procyon_lotor 1.0007 3402
## (Intercept)-Dasypus_novemcinctus 1.0049 2850
## (Intercept)-Sylvilagus_floridanus 1.0039 1774
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0020 1525
## shrub_cover-Procyon_lotor 1.0005 2450
## shrub_cover-Dasypus_novemcinctus 1.0053 2038
## shrub_cover-Sylvilagus_floridanus 1.0114 1146
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0015 2184
## veg_height-Procyon_lotor 1.0005 3388
## veg_height-Dasypus_novemcinctus 1.0018 4165
## veg_height-Sylvilagus_floridanus 1.0030 1580
## week-Odocoileus_virginianus 1.0005 5250
## week-Canis_latrans 1.0017 3645
## week-Procyon_lotor 1.0005 4705
## week-Dasypus_novemcinctus 1.0004 4799
## week-Sylvilagus_floridanus 1.0002 2922
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0011 3519
## I(week^2)-Procyon_lotor 1.0015 4332
## I(week^2)-Dasypus_novemcinctus 1.0021 4937
## I(week^2)-Sylvilagus_floridanus 1.0013 2203
names(ms_fullQ_fullQ_T25)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T25$beta.samples)
## 'mcmc' num [1:5250, 1:45] 3.67 3.02 3.73 3.19 2.38 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:45] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T25$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.07161905
MCMCplot(ms_fullQ_fullQ_T25$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T25$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T25, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:5250, 1:5, 1:100] 0.984 0.942 0.914 0.904 0.688 ...
## $ z.0.samples : int [1:5250, 1:5, 1:100] 1 1 0 1 0 1 1 1 1 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T25, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:5250, 1:5, 1:100] 0.984 0.942 0.914 0.904 0.688 ...
psi.0.quants <- apply(out.pred$psi.0.samples, c(3), function(x) quantile(x, prob = c(0.025, 0.5, 0.975)))
str(psi.0.quants)
## num [1:3, 1:100] 0.00133 0.32995 1 0.00136 0.33148 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:3] "2.5%" "50%" "97.5%"
## ..$ : NULL
psi.plot.dat <- data.frame(
psi.med = psi.0.quants[2, ],
psi.low = psi.0.quants[1, ],
psi.high = psi.0.quants[3, ],
Avg_Cogongrass_Cover = cogon.pred.vals
)
str(psi.plot.dat)
## 'data.frame': 100 obs. of 4 variables:
## $ psi.med : num 0.33 0.331 0.324 0.328 0.328 ...
## $ psi.low : num 0.00133 0.00136 0.00141 0.00151 0.00154 ...
## $ psi.high : num 1 1 1 1 1 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T25) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.2273
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1762 1.3599 -2.4652 0.1709 2.8719 1.0025 4207
## Cogon_Patch_Size -0.1715 0.9360 -2.0429 -0.1795 1.7365 1.0212 1816
## Veg_shannon_index 1.0132 0.6641 -0.3057 0.9916 2.4123 1.0171 671
## total_shrub_cover -0.2336 0.7411 -1.7114 -0.2424 1.2577 1.0047 1931
## Avg_Cogongrass_Cover 0.2408 1.1728 -2.1125 0.2456 2.5317 1.0224 1000
## Tree_Density -1.8019 1.2824 -4.0992 -1.8922 1.1459 1.0104 1384
## Avg_Canopy_Cover 1.2469 1.0277 -1.0097 1.2837 3.2307 1.0016 3501
## I(Avg_Cogongrass_Cover^2) 1.6887 0.8563 0.1567 1.6436 3.4718 1.0004 777
## avg_veg_height 0.0019 0.6940 -1.4000 0.0151 1.3449 1.0047 1283
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 56.2218 217.7103 3.1523 24.3605 293.1731 1.1629 1461
## Cogon_Patch_Size 6.6693 23.4132 0.0709 1.5763 43.7025 1.1039 963
## Veg_shannon_index 1.1193 4.0587 0.0468 0.3919 6.6164 1.0913 4749
## total_shrub_cover 2.6332 6.7997 0.0699 0.9730 15.3857 1.1202 361
## Avg_Cogongrass_Cover 5.0161 14.8722 0.0657 1.3833 31.6416 1.1657 754
## Tree_Density 22.6130 62.4561 0.0893 5.0812 158.0246 1.1122 296
## Avg_Canopy_Cover 10.6547 28.1409 0.2029 4.3220 60.2308 1.0946 1560
## I(Avg_Cogongrass_Cover^2) 3.5885 24.6541 0.0501 0.5451 20.3442 1.3006 497
## avg_veg_height 0.9267 2.6608 0.0451 0.3499 5.2041 1.0078 3122
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.4493 5.759 0.0549 0.783 15.6338 1.3283 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4455 0.7419 -2.7308 -1.5091 0.1630 1.0012 5271
## shrub_cover 0.2134 0.3387 -0.4348 0.2097 0.8884 1.0057 4068
## veg_height -0.0388 0.2883 -0.5904 -0.0384 0.5357 1.0011 4140
## week 0.4418 0.3708 -0.3356 0.4573 1.1299 1.0012 5039
## I(week^2) -0.2514 0.1817 -0.6235 -0.2524 0.1224 1.0024 4979
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5309 6.2474 0.6809 2.2987 13.7029 1.1348 5250
## shrub_cover 0.5577 0.9279 0.0692 0.3337 2.5155 1.0226 4737
## veg_height 0.4411 0.6973 0.0720 0.2674 1.8742 1.0145 5250
## week 0.6543 0.9007 0.1077 0.4200 2.6307 1.0096 4109
## I(week^2) 0.1540 0.1810 0.0286 0.1012 0.6141 1.0000 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 9.8645 5.8523 2.7194
## (Intercept)-Canis_latrans -0.5086 1.7683 -3.8463
## (Intercept)-Procyon_lotor -0.3115 1.3312 -3.1247
## (Intercept)-Dasypus_novemcinctus -3.1287 1.7131 -7.3374
## (Intercept)-Sylvilagus_floridanus -2.6208 2.0244 -7.2321
## Cogon_Patch_Size-Odocoileus_virginianus -0.0390 1.7816 -3.3033
## Cogon_Patch_Size-Canis_latrans 1.5878 2.2587 -1.0337
## Cogon_Patch_Size-Procyon_lotor -0.6443 0.9407 -2.5330
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3542 0.9677 -2.3702
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6069 2.3907 -7.8013
## Veg_shannon_index-Odocoileus_virginianus 0.8909 1.0714 -1.4445
## Veg_shannon_index-Canis_latrans 1.4180 0.8620 0.0050
## Veg_shannon_index-Procyon_lotor 1.2654 0.7165 0.0477
## Veg_shannon_index-Dasypus_novemcinctus 0.7110 0.6869 -0.6843
## Veg_shannon_index-Sylvilagus_floridanus 1.1442 0.8554 -0.3376
## total_shrub_cover-Odocoileus_virginianus -0.0061 1.3428 -2.4595
## total_shrub_cover-Canis_latrans 0.6752 1.2511 -1.0456
## total_shrub_cover-Procyon_lotor -1.1683 0.8152 -2.9926
## total_shrub_cover-Dasypus_novemcinctus -0.0481 0.8053 -1.7640
## total_shrub_cover-Sylvilagus_floridanus -0.6343 1.3473 -3.8451
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2032 2.0427 -3.5580
## Avg_Cogongrass_Cover-Canis_latrans 0.7282 1.7396 -2.3004
## Avg_Cogongrass_Cover-Procyon_lotor 0.2194 1.4366 -2.5534
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3375 1.7881 -1.6707
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9259 1.9156 -5.2185
## Tree_Density-Odocoileus_virginianus -0.1574 3.4705 -4.1109
## Tree_Density-Canis_latrans -3.8941 2.3625 -9.9945
## Tree_Density-Procyon_lotor -2.1256 1.3350 -4.9243
## Tree_Density-Dasypus_novemcinctus -5.9325 3.8844 -16.3144
## Tree_Density-Sylvilagus_floridanus -3.4150 2.5565 -9.7680
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7219 2.0705 -3.3779
## Avg_Canopy_Cover-Canis_latrans -0.0504 0.7723 -1.6114
## Avg_Canopy_Cover-Procyon_lotor 1.6472 0.9654 0.0004
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5604 1.2520 0.7459
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7878 2.9165 1.0418
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.2382 2.2820 -0.1683
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2697 1.4236 0.4143
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1113 1.1709 0.3435
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.6978 1.0001 0.0726
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.4705 1.0689 -0.4178
## avg_veg_height-Odocoileus_virginianus 0.0138 1.0365 -2.0567
## avg_veg_height-Canis_latrans -0.1546 0.8223 -1.7724
## avg_veg_height-Procyon_lotor 0.1126 0.7623 -1.4185
## avg_veg_height-Dasypus_novemcinctus 0.2080 0.7810 -1.2911
## avg_veg_height-Sylvilagus_floridanus -0.1684 0.9270 -2.0830
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.5119 25.1069 1.1652 213
## (Intercept)-Canis_latrans -0.5767 3.2551 1.0213 778
## (Intercept)-Procyon_lotor -0.2731 2.1539 1.0494 734
## (Intercept)-Dasypus_novemcinctus -2.8570 -0.4405 1.0173 377
## (Intercept)-Sylvilagus_floridanus -2.4824 1.0348 1.0235 601
## Cogon_Patch_Size-Odocoileus_virginianus -0.1725 4.0847 1.0164 1603
## Cogon_Patch_Size-Canis_latrans 1.0458 7.4849 1.0080 774
## Cogon_Patch_Size-Procyon_lotor -0.6172 1.0205 1.0615 795
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3392 1.5327 1.0257 1340
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0556 1.1062 1.0079 519
## Veg_shannon_index-Odocoileus_virginianus 0.9134 2.9493 1.0212 1105
## Veg_shannon_index-Canis_latrans 1.3161 3.4116 1.0056 961
## Veg_shannon_index-Procyon_lotor 1.2001 2.8483 1.0080 508
## Veg_shannon_index-Dasypus_novemcinctus 0.7205 2.0667 1.0187 921
## Veg_shannon_index-Sylvilagus_floridanus 1.0807 3.0976 1.0092 807
## total_shrub_cover-Odocoileus_virginianus -0.0737 3.0024 1.0168 1577
## total_shrub_cover-Canis_latrans 0.4331 3.8598 1.0168 638
## total_shrub_cover-Procyon_lotor -1.0931 0.2006 1.0059 1477
## total_shrub_cover-Dasypus_novemcinctus -0.0237 1.5014 1.0069 1317
## total_shrub_cover-Sylvilagus_floridanus -0.4987 1.6096 1.0135 923
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1461 4.3935 1.0396 1330
## Avg_Cogongrass_Cover-Canis_latrans 0.5938 4.6273 1.0144 1173
## Avg_Cogongrass_Cover-Procyon_lotor 0.2130 3.0755 1.0162 1042
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1238 5.4825 1.0244 722
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6976 2.2334 1.0078 1049
## Tree_Density-Odocoileus_virginianus -0.8923 8.0269 1.1198 363
## Tree_Density-Canis_latrans -3.3616 -0.7829 1.0220 447
## Tree_Density-Procyon_lotor -2.0425 0.1089 1.0442 601
## Tree_Density-Dasypus_novemcinctus -4.8795 -1.5146 1.0849 208
## Tree_Density-Sylvilagus_floridanus -2.9098 0.1917 1.0276 390
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7711 4.7803 1.0066 1147
## Avg_Canopy_Cover-Canis_latrans -0.0333 1.4154 1.0010 2120
## Avg_Canopy_Cover-Procyon_lotor 1.5568 3.8208 1.0312 670
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3392 5.5377 1.0413 341
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1704 11.9973 1.0303 383
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8808 6.7025 1.0745 384
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9959 6.1186 1.0050 535
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9455 4.8869 1.0103 626
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5709 3.9992 1.0123 577
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3952 3.6965 1.0017 1010
## avg_veg_height-Odocoileus_virginianus 0.0231 2.1019 1.0101 1957
## avg_veg_height-Canis_latrans -0.1538 1.4545 1.0078 1222
## avg_veg_height-Procyon_lotor 0.1199 1.5830 1.0031 1478
## avg_veg_height-Dasypus_novemcinctus 0.1956 1.7872 1.0113 1564
## avg_veg_height-Sylvilagus_floridanus -0.1305 1.5394 1.0026 1363
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5463 0.0807 0.3925 0.5440 0.7057
## (Intercept)-Canis_latrans -2.5545 0.2047 -2.9777 -2.5467 -2.1776
## (Intercept)-Procyon_lotor -2.1985 0.1676 -2.5412 -2.1938 -1.8803
## (Intercept)-Dasypus_novemcinctus -1.5894 0.1811 -1.9525 -1.5851 -1.2547
## (Intercept)-Sylvilagus_floridanus -3.0988 0.3058 -3.7234 -3.0927 -2.5346
## shrub_cover-Odocoileus_virginianus -0.0600 0.0690 -0.1929 -0.0604 0.0764
## shrub_cover-Canis_latrans -0.3413 0.2357 -0.7927 -0.3451 0.1139
## shrub_cover-Procyon_lotor 0.2657 0.1638 -0.0736 0.2682 0.5705
## shrub_cover-Dasypus_novemcinctus 0.8328 0.3247 0.2257 0.8219 1.4777
## shrub_cover-Sylvilagus_floridanus 0.4463 0.3952 -0.2999 0.4342 1.2643
## veg_height-Odocoileus_virginianus -0.3361 0.0693 -0.4701 -0.3356 -0.1990
## veg_height-Canis_latrans -0.6106 0.1882 -0.9921 -0.6060 -0.2540
## veg_height-Procyon_lotor 0.3605 0.1271 0.1132 0.3605 0.6098
## veg_height-Dasypus_novemcinctus 0.2371 0.1385 -0.0258 0.2364 0.5168
## veg_height-Sylvilagus_floridanus 0.1354 0.2644 -0.3747 0.1312 0.6535
## week-Odocoileus_virginianus 1.3301 0.1270 1.0865 1.3294 1.5792
## week-Canis_latrans 0.6087 0.2730 0.0712 0.5977 1.1598
## week-Procyon_lotor 0.2024 0.2188 -0.2288 0.2055 0.6226
## week-Dasypus_novemcinctus 0.1075 0.2381 -0.3650 0.1067 0.5653
## week-Sylvilagus_floridanus 0.0624 0.3735 -0.6806 0.0727 0.7792
## I(week^2)-Odocoileus_virginianus -0.5477 0.0521 -0.6498 -0.5474 -0.4467
## I(week^2)-Canis_latrans -0.2496 0.1122 -0.4751 -0.2466 -0.0314
## I(week^2)-Procyon_lotor -0.1286 0.0949 -0.3159 -0.1281 0.0549
## I(week^2)-Dasypus_novemcinctus -0.1764 0.1079 -0.3936 -0.1766 0.0299
## I(week^2)-Sylvilagus_floridanus -0.1674 0.1734 -0.5111 -0.1630 0.1613
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0040 2049
## (Intercept)-Procyon_lotor 1.0007 3402
## (Intercept)-Dasypus_novemcinctus 1.0049 2850
## (Intercept)-Sylvilagus_floridanus 1.0039 1774
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0020 1525
## shrub_cover-Procyon_lotor 1.0005 2450
## shrub_cover-Dasypus_novemcinctus 1.0053 2038
## shrub_cover-Sylvilagus_floridanus 1.0114 1146
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0015 2184
## veg_height-Procyon_lotor 1.0005 3388
## veg_height-Dasypus_novemcinctus 1.0018 4165
## veg_height-Sylvilagus_floridanus 1.0030 1580
## week-Odocoileus_virginianus 1.0005 5250
## week-Canis_latrans 1.0017 3645
## week-Procyon_lotor 1.0005 4705
## week-Dasypus_novemcinctus 1.0004 4799
## week-Sylvilagus_floridanus 1.0002 2922
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0011 3519
## I(week^2)-Procyon_lotor 1.0015 4332
## I(week^2)-Dasypus_novemcinctus 1.0021 4937
## I(week^2)-Sylvilagus_floridanus 1.0013 2203
names(ms_fullQ_fullQ_T25)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T25$beta.samples)
## 'mcmc' num [1:5250, 1:45] 3.67 3.02 3.73 3.19 2.38 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:45] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T25$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.07161905
MCMCplot(ms_fullQ_fullQ_T25$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T25$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T25$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T25$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 174 × 2
## Plot Veg_shannon_index
## <chr> <dbl>
## 1 BI200 2.75
## 2 BI201 2.70
## 3 BI202 2.59
## 4 BI97 1.61
## 5 BI99 2.97
## 6 BN210 2.97
## 7 BN211 2.43
## 8 BN212 2.22
## 9 BN96 3.05
## 10 BN98 2.79
## # ℹ 164 more rows
if (!is.numeric(fuel_data$Height)) {
fuel_data$Height <- as.numeric(as.character(fuel_data$Height))
}
## Warning: NAs introduced by coercion
# Calculate average vegetation height per plot
veg_height <- fuel_data %>%
group_by(Plot) %>%
summarize(avg_veg_height = mean(Height, na.rm = TRUE), .groups = "drop")
# Tree density from point-centered quarter data
if (!is.numeric(tree_data$Distance)) {
tree_data$Distance <- as.numeric(as.character(tree_data$Distance))
}
tree_density_data <- tree_data %>%
group_by(Plot) %>%
summarize(Average_Distance = mean(Distance) / 100, # Convert to meters
Tree_Density = 10000 / (Average_Distance^2)) # Convert to trees per hectare
# Average canopy cover from vegetation quadrats
tree_canopy_data <- Veg_Cover %>%
distinct(Plot, Quadrat, .keep_all = TRUE) %>% # Ensure each quadrat counts once per plot
group_by(Plot) %>%
summarize(Avg_Canopy_Cover = mean(Canopy_Cover, na.rm = TRUE), .groups = "drop") # Calculate the average canopy cover per plot
cor(tree_density_data$Tree_Density, tree_canopy_data$Avg_Canopy_Cover)
## [1] 0.2836106
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter(Class == "Mammalia" | Name == "Meleagris_gallopavo") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 25
species_subset <- species_counts %>%
filter(count > 50) %>%
pull(Name)
# Filter CameraData to only include species with count > 25
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 4
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$week)
week_matrix <- matrix(rep(week_vals, each = num_sites), nrow = num_sites, ncol = num_weeks, byrow = FALSE)
# Create detection covariate list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_matrix
)
# Remove dash and convert to numeric
week_numeric <- as.numeric(gsub("-", "", det.covs$week))
## Scale and center week_numeric
week_numeric <- scale(week_numeric)
# Reshape into the original 32x36 matrix
det.covs$week <- matrix(week_numeric, nrow = 32, ncol = 36)
str(det.covs)
## List of 3
## $ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
This requires combining the presence data and the site covariate data into a single list. This also means that the presence data is in a 3-d format.
# Combine into a named list
data_list <- list(
y = detection_array,
occ.covs = covariates_matrix,
det.covs = det.covs
)
str(data_list)
## List of 3
## $ y : num [1:32, 1:36, 1:4] 1 1 0 1 0 0 0 1 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## .. ..$ : chr [1:4] "Odocoileus_virginianus" "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus"
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:4, 1:32, 1:36] 1 0 0 0 1 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:4] "Odocoileus_virginianus" "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus"
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7000 of 10000, 70.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
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## Sampled: 8500 of 10000, 85.00%
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## Sampled: 9000 of 10000, 90.00%
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## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7513
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7482 1.041 -1.4948 0.796 2.688 1.0021 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.9237 127.5962 0.7775 5.3059 81.474 1.2537 1463
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3341 0.71 -2.5868 -1.3934 0.2776 1.0048 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8885 6.7419 0.421 1.5699 12.5261 1.0637 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8044 2.6780 2.0785 4.1383 11.6265
## (Intercept)-Canis_latrans 0.3785 0.4252 -0.4124 0.3607 1.2617
## (Intercept)-Procyon_lotor 0.7781 0.4061 0.0300 0.7681 1.6074
## (Intercept)-Dasypus_novemcinctus -0.6106 0.3780 -1.3732 -0.5970 0.0903
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0660 282
## (Intercept)-Canis_latrans 1.0001 4254
## (Intercept)-Procyon_lotor 1.0001 5250
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0077 0.0591 -0.1087 0.0083 0.1247
## (Intercept)-Canis_latrans -2.5986 0.1730 -2.9484 -2.5929 -2.2741
## (Intercept)-Procyon_lotor -2.2531 0.1306 -2.5128 -2.2499 -2.0035
## (Intercept)-Dasypus_novemcinctus -1.5597 0.1340 -1.8234 -1.5585 -1.2990
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0037 5250
## (Intercept)-Canis_latrans 1.0043 3037
## (Intercept)-Procyon_lotor 1.0017 4368
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
# Includes all covariates of detection and occupancy
ms_full_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.001
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7006 1.4357 -2.2427 0.7614 3.4022 1.0017 4914
## Cogon_Patch_Size -0.2595 0.8809 -1.8983 -0.3070 1.6949 1.0017 1833
## Veg_shannon_index 0.9011 0.7393 -0.6318 0.8993 2.3534 1.0022 1876
## total_shrub_cover 0.0342 0.8344 -1.6234 0.0130 1.8114 1.0015 2584
## Avg_Cogongrass_Cover 2.0302 1.0079 0.0063 2.0498 3.9637 1.0017 1463
## Tree_Density -1.3625 1.2312 -3.5466 -1.4808 1.3902 1.0011 1683
## Avg_Canopy_Cover 0.8862 0.8031 -0.8227 0.9122 2.4255 1.0015 3413
## avg_veg_height -0.2304 0.6789 -1.5280 -0.2431 1.1374 1.0096 1727
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 80.0855 231.4180 2.6498 27.6846 471.1933 1.0057 510
## Cogon_Patch_Size 3.4188 17.5915 0.0656 0.8848 20.4061 1.1469 3385
## Veg_shannon_index 1.7513 6.8372 0.0479 0.5158 9.9688 1.0642 2821
## total_shrub_cover 3.7432 10.5292 0.0810 1.3040 21.6333 1.0352 1501
## Avg_Cogongrass_Cover 3.3365 14.1941 0.0510 0.6294 22.6723 1.0490 1200
## Tree_Density 20.8914 58.6010 0.1164 4.8919 149.8133 1.0207 818
## Avg_Canopy_Cover 3.7720 31.9643 0.0912 1.3171 20.2568 1.2840 5250
## avg_veg_height 0.9498 3.0772 0.0440 0.3342 5.6358 1.0554 4822
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.5514 6.5419 0.0652 0.8708 14.9329 1.1606 317
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3926 0.7253 -2.6942 -1.4344 0.2600 1.0010 5250
## shrub_cover 0.1319 0.4242 -0.7394 0.1303 1.0141 1.0030 4912
## veg_height -0.0818 0.3720 -0.8460 -0.0850 0.6828 1.0016 5250
## week 0.0209 0.2417 -0.4753 0.0253 0.4874 1.0012 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1381 5.5016 0.4566 1.7762 13.6893 1.0132 5250
## shrub_cover 0.8805 2.0888 0.0752 0.4162 4.2238 1.0180 4497
## veg_height 0.6635 1.7569 0.0771 0.3318 3.0388 1.0406 5250
## week 0.2382 0.5424 0.0302 0.1207 1.1620 1.0227 4933
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 11.1433 6.7263 3.5516 9.2819
## (Intercept)-Canis_latrans 1.4706 1.4486 -0.7791 1.2568
## (Intercept)-Procyon_lotor 1.0597 1.2255 -1.3165 1.0034
## (Intercept)-Dasypus_novemcinctus -1.7378 1.3791 -5.0043 -1.5664
## Cogon_Patch_Size-Odocoileus_virginianus -0.2293 1.6067 -2.8998 -0.3778
## Cogon_Patch_Size-Canis_latrans 0.7160 1.5666 -1.3681 0.3680
## Cogon_Patch_Size-Procyon_lotor -0.9234 0.8026 -2.6196 -0.8975
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6395 0.8279 -2.2982 -0.6340
## Veg_shannon_index-Odocoileus_virginianus 0.7916 1.2457 -1.8329 0.8411
## Veg_shannon_index-Canis_latrans 1.3703 0.9571 -0.2830 1.2894
## Veg_shannon_index-Procyon_lotor 1.2382 0.7729 -0.0628 1.1789
## Veg_shannon_index-Dasypus_novemcinctus 0.6166 0.6365 -0.6628 0.6245
## total_shrub_cover-Odocoileus_virginianus 0.2017 1.4648 -2.5798 0.1473
## total_shrub_cover-Canis_latrans 1.1152 1.3437 -0.7146 0.8362
## total_shrub_cover-Procyon_lotor -1.0223 0.8245 -2.8378 -0.9325
## total_shrub_cover-Dasypus_novemcinctus -0.0431 0.7749 -1.7562 0.0087
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1298 1.5443 -0.7096 2.0905
## Avg_Cogongrass_Cover-Canis_latrans 2.6825 1.3856 0.5988 2.5142
## Avg_Cogongrass_Cover-Procyon_lotor 2.0862 1.0490 0.1628 2.0442
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6962 1.2836 0.6858 2.5495
## Tree_Density-Odocoileus_virginianus 0.0620 2.9593 -3.5700 -0.5564
## Tree_Density-Canis_latrans -3.5134 2.2352 -9.0824 -3.0051
## Tree_Density-Procyon_lotor -1.3404 0.9237 -3.1450 -1.3418
## Tree_Density-Dasypus_novemcinctus -4.8150 3.1358 -12.9448 -3.9959
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7722 1.4619 -2.2851 0.7983
## Avg_Canopy_Cover-Canis_latrans 0.0766 0.7943 -1.5057 0.1204
## Avg_Canopy_Cover-Procyon_lotor 1.4925 0.8010 0.1418 1.4138
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0384 0.9370 0.6445 1.8874
## avg_veg_height-Odocoileus_virginianus -0.2611 1.0124 -2.1908 -0.2705
## avg_veg_height-Canis_latrans -0.3157 0.7637 -1.8427 -0.3127
## avg_veg_height-Procyon_lotor -0.2840 0.6898 -1.6698 -0.2778
## avg_veg_height-Dasypus_novemcinctus -0.1279 0.6907 -1.4650 -0.1322
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 30.5698 1.0050 142
## (Intercept)-Canis_latrans 5.0770 1.0321 729
## (Intercept)-Procyon_lotor 3.6745 1.0246 590
## (Intercept)-Dasypus_novemcinctus 0.4572 1.0049 621
## Cogon_Patch_Size-Odocoileus_virginianus 3.4750 1.0053 1521
## Cogon_Patch_Size-Canis_latrans 4.9269 1.0007 1149
## Cogon_Patch_Size-Procyon_lotor 0.5449 1.0038 775
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0067 1.0023 1468
## Veg_shannon_index-Odocoileus_virginianus 3.0654 1.0067 1935
## Veg_shannon_index-Canis_latrans 3.4150 1.0080 978
## Veg_shannon_index-Procyon_lotor 2.8470 1.0068 616
## Veg_shannon_index-Dasypus_novemcinctus 1.8437 0.9998 1953
## total_shrub_cover-Odocoileus_virginianus 3.2034 1.0066 1606
## total_shrub_cover-Canis_latrans 4.5484 1.0079 675
## total_shrub_cover-Procyon_lotor 0.3098 1.0062 880
## total_shrub_cover-Dasypus_novemcinctus 1.3100 1.0115 1118
## Avg_Cogongrass_Cover-Odocoileus_virginianus 5.3203 1.0045 1137
## Avg_Cogongrass_Cover-Canis_latrans 6.1190 1.0102 809
## Avg_Cogongrass_Cover-Procyon_lotor 4.3102 1.0071 1150
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.8342 1.0090 788
## Tree_Density-Odocoileus_virginianus 7.9204 1.0102 485
## Tree_Density-Canis_latrans -0.6607 1.0352 416
## Tree_Density-Procyon_lotor 0.4129 1.0015 2019
## Tree_Density-Dasypus_novemcinctus -1.2104 1.0163 357
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6928 1.0014 2060
## Avg_Canopy_Cover-Canis_latrans 1.4381 1.0317 673
## Avg_Canopy_Cover-Procyon_lotor 3.2741 1.0011 1536
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2549 1.0014 508
## avg_veg_height-Odocoileus_virginianus 1.8415 1.0041 2227
## avg_veg_height-Canis_latrans 1.1831 1.0035 1699
## avg_veg_height-Procyon_lotor 1.0275 1.0012 1782
## avg_veg_height-Dasypus_novemcinctus 1.2671 1.0104 1856
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0093 0.0597 -0.1079 0.0088 0.1259
## (Intercept)-Canis_latrans -2.7894 0.1885 -3.1699 -2.7835 -2.4341
## (Intercept)-Procyon_lotor -2.2908 0.1472 -2.5990 -2.2872 -2.0180
## (Intercept)-Dasypus_novemcinctus -1.7265 0.1626 -2.0607 -1.7223 -1.4226
## shrub_cover-Odocoileus_virginianus -0.0562 0.0633 -0.1820 -0.0561 0.0675
## shrub_cover-Canis_latrans -0.4161 0.2249 -0.8474 -0.4155 0.0348
## shrub_cover-Procyon_lotor 0.2653 0.1647 -0.0833 0.2688 0.5837
## shrub_cover-Dasypus_novemcinctus 0.8339 0.3237 0.2315 0.8242 1.4816
## veg_height-Odocoileus_virginianus -0.3029 0.0653 -0.4330 -0.3019 -0.1757
## veg_height-Canis_latrans -0.6625 0.1846 -1.0295 -0.6583 -0.3146
## veg_height-Procyon_lotor 0.3456 0.1260 0.1011 0.3451 0.5967
## veg_height-Dasypus_novemcinctus 0.2342 0.1350 -0.0250 0.2323 0.5060
## week-Odocoileus_virginianus 0.2170 0.0623 0.0937 0.2173 0.3403
## week-Canis_latrans 0.0880 0.1354 -0.1919 0.0924 0.3488
## week-Procyon_lotor -0.0388 0.1213 -0.2813 -0.0341 0.1878
## week-Dasypus_novemcinctus -0.1594 0.1432 -0.4542 -0.1549 0.1137
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5576
## (Intercept)-Canis_latrans 1.0006 1793
## (Intercept)-Procyon_lotor 1.0022 1599
## (Intercept)-Dasypus_novemcinctus 1.0031 2770
## shrub_cover-Odocoileus_virginianus 1.0004 6057
## shrub_cover-Canis_latrans 1.0032 1970
## shrub_cover-Procyon_lotor 1.0027 3243
## shrub_cover-Dasypus_novemcinctus 1.0019 2206
## veg_height-Odocoileus_virginianus 1.0023 5536
## veg_height-Canis_latrans 1.0027 2050
## veg_height-Procyon_lotor 1.0002 3812
## veg_height-Dasypus_novemcinctus 1.0021 4877
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0004 4231
## week-Procyon_lotor 1.0010 4615
## week-Dasypus_novemcinctus 1.0003 4802
#Includes all covariates of detection and only null for occupancy
ms_full_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9805
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7775 1.0189 -1.4055 0.8157 2.7124 1.002 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.8047 29.1399 0.7257 4.8431 65.4187 1.0811 902
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3702 0.7571 -2.6841 -1.4369 0.4012 1.0002 5250
## shrub_cover 0.1477 0.3749 -0.6328 0.1403 0.9380 1.0014 5014
## veg_height -0.0936 0.3704 -0.8226 -0.0915 0.6545 1.0016 5250
## week 0.0269 0.2406 -0.4648 0.0293 0.4915 1.0002 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2553 6.8606 0.4664 1.7680 13.5942 1.0339 5250
## shrub_cover 0.6484 1.3633 0.0609 0.3315 3.0431 1.0734 5037
## veg_height 0.6310 1.2093 0.0771 0.3381 2.8870 1.0190 5250
## week 0.2425 0.5518 0.0299 0.1214 1.1861 1.0758 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6144 2.3540 2.0738 4.0417 10.3209
## (Intercept)-Canis_latrans 0.4843 0.4421 -0.3379 0.4670 1.4113
## (Intercept)-Procyon_lotor 0.8232 0.4214 0.0331 0.8098 1.6903
## (Intercept)-Dasypus_novemcinctus -0.5323 0.3882 -1.3238 -0.5197 0.2084
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0115 457
## (Intercept)-Canis_latrans 1.0039 4565
## (Intercept)-Procyon_lotor 0.9999 4822
## (Intercept)-Dasypus_novemcinctus 1.0010 4591
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0085 0.0595 -0.1058 0.0081 0.1278
## (Intercept)-Canis_latrans -2.7643 0.1955 -3.1718 -2.7586 -2.3967
## (Intercept)-Procyon_lotor -2.2851 0.1463 -2.5911 -2.2803 -2.0170
## (Intercept)-Dasypus_novemcinctus -1.7079 0.1567 -2.0177 -1.7040 -1.4157
## shrub_cover-Odocoileus_virginianus -0.0563 0.0634 -0.1808 -0.0565 0.0672
## shrub_cover-Canis_latrans -0.2957 0.2193 -0.7293 -0.2947 0.1269
## shrub_cover-Procyon_lotor 0.2475 0.1650 -0.0859 0.2526 0.5594
## shrub_cover-Dasypus_novemcinctus 0.7502 0.3062 0.1908 0.7411 1.3762
## veg_height-Odocoileus_virginianus -0.3031 0.0654 -0.4355 -0.3020 -0.1799
## veg_height-Canis_latrans -0.6392 0.1931 -1.0459 -0.6370 -0.2806
## veg_height-Procyon_lotor 0.3446 0.1263 0.1066 0.3425 0.6016
## veg_height-Dasypus_novemcinctus 0.2274 0.1369 -0.0343 0.2233 0.4994
## week-Odocoileus_virginianus 0.2196 0.0611 0.1012 0.2188 0.3408
## week-Canis_latrans 0.0915 0.1348 -0.1845 0.0952 0.3464
## week-Procyon_lotor -0.0394 0.1216 -0.2906 -0.0351 0.1905
## week-Dasypus_novemcinctus -0.1588 0.1417 -0.4513 -0.1516 0.1067
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0002 2426
## (Intercept)-Procyon_lotor 1.0015 3665
## (Intercept)-Dasypus_novemcinctus 1.0009 4245
## shrub_cover-Odocoileus_virginianus 1.0024 5508
## shrub_cover-Canis_latrans 1.0021 2653
## shrub_cover-Procyon_lotor 1.0029 4166
## shrub_cover-Dasypus_novemcinctus 1.0024 3848
## veg_height-Odocoileus_virginianus 0.9999 4808
## veg_height-Canis_latrans 1.0009 2220
## veg_height-Procyon_lotor 1.0023 3972
## veg_height-Dasypus_novemcinctus 1.0027 4520
## week-Odocoileus_virginianus 1.0005 5250
## week-Canis_latrans 1.0030 4140
## week-Procyon_lotor 1.0041 4277
## week-Dasypus_novemcinctus 1.0001 4733
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.0092
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9608 1.1901 -1.6160 1.0137 3.2032 1.0014 4795
## Avg_Cogongrass_Cover 0.2294 0.5894 -0.9424 0.2244 1.3913 1.0050 2302
## total_shrub_cover -0.1776 0.7425 -1.6398 -0.1847 1.4075 1.0048 3372
## avg_veg_height 0.2794 0.5693 -0.8548 0.2826 1.4167 1.0106 1871
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.0641 63.6085 0.7170 7.9081 138.7274 1.1134 514
## Avg_Cogongrass_Cover 0.8512 1.9187 0.0439 0.3284 5.2467 1.0036 3428
## total_shrub_cover 2.9335 7.1909 0.0828 1.0652 17.6290 1.0243 1894
## avg_veg_height 0.7240 2.1686 0.0411 0.2700 4.1623 1.0166 4945
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6459 0.9542 0.0442 0.3272 3.2121 1.0428 1065
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3877 0.7654 -2.7399 -1.4622 0.2866 1.0018 5250
## shrub_cover 0.1886 0.4457 -0.6958 0.1717 1.1782 1.0007 4823
## veg_height -0.0976 0.3751 -0.8425 -0.0919 0.6634 1.0010 5250
## week 0.0282 0.2517 -0.4640 0.0295 0.5190 1.0021 5349
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4740 9.2615 0.4799 1.8161 15.5585 1.1203 5250
## shrub_cover 0.8446 1.4326 0.0748 0.4321 4.3839 1.0239 4197
## veg_height 0.6486 1.2003 0.0816 0.3523 3.0921 1.0341 4761
## week 0.2516 0.7155 0.0293 0.1204 1.2468 1.0784 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 6.1377 3.5710 2.1762 5.3173
## (Intercept)-Canis_latrans 1.0240 0.9001 -0.4203 0.9310
## (Intercept)-Procyon_lotor 1.1852 0.7785 -0.1843 1.1322
## (Intercept)-Dasypus_novemcinctus -0.4079 0.8355 -1.7781 -0.5081
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2120 0.8807 -1.4829 0.1950
## Avg_Cogongrass_Cover-Canis_latrans 0.5359 0.6738 -0.6485 0.4760
## Avg_Cogongrass_Cover-Procyon_lotor -0.0016 0.5717 -1.1774 0.0113
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2347 0.5150 -0.7485 0.2331
## total_shrub_cover-Odocoileus_virginianus -0.0650 1.0807 -2.1859 -0.1026
## total_shrub_cover-Canis_latrans 0.7280 0.9557 -0.6144 0.5379
## total_shrub_cover-Procyon_lotor -1.2564 0.7649 -3.0395 -1.1434
## total_shrub_cover-Dasypus_novemcinctus -0.2434 0.8598 -2.6365 -0.0970
## avg_veg_height-Odocoileus_virginianus 0.2433 0.8055 -1.4003 0.2387
## avg_veg_height-Canis_latrans 0.2672 0.6010 -0.8495 0.2468
## avg_veg_height-Procyon_lotor 0.2399 0.5615 -0.8603 0.2353
## avg_veg_height-Dasypus_novemcinctus 0.4382 0.5677 -0.5598 0.3980
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.3204 1.0716 232
## (Intercept)-Canis_latrans 3.0914 1.0126 1128
## (Intercept)-Procyon_lotor 2.8697 1.0099 2228
## (Intercept)-Dasypus_novemcinctus 1.7036 1.0004 728
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0225 1.0032 2224
## Avg_Cogongrass_Cover-Canis_latrans 2.0007 1.0065 2374
## Avg_Cogongrass_Cover-Procyon_lotor 1.0992 1.0021 2508
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3003 1.0131 2712
## total_shrub_cover-Odocoileus_virginianus 2.2969 1.0069 2367
## total_shrub_cover-Canis_latrans 3.1422 1.0134 926
## total_shrub_cover-Procyon_lotor -0.0661 1.0128 1628
## total_shrub_cover-Dasypus_novemcinctus 0.7965 1.0009 608
## avg_veg_height-Odocoileus_virginianus 1.8815 1.0079 2525
## avg_veg_height-Canis_latrans 1.5262 1.0117 1916
## avg_veg_height-Procyon_lotor 1.3553 1.0156 2344
## avg_veg_height-Dasypus_novemcinctus 1.7027 1.0048 1459
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0087 0.0599 -0.1115 0.0087 0.1266
## (Intercept)-Canis_latrans -2.8296 0.1988 -3.2400 -2.8243 -2.4583
## (Intercept)-Procyon_lotor -2.2923 0.1427 -2.5868 -2.2877 -2.0227
## (Intercept)-Dasypus_novemcinctus -1.7526 0.1826 -2.1390 -1.7400 -1.4248
## shrub_cover-Odocoileus_virginianus -0.0569 0.0651 -0.1843 -0.0571 0.0724
## shrub_cover-Canis_latrans -0.3782 0.2496 -0.8618 -0.3795 0.1164
## shrub_cover-Procyon_lotor 0.3092 0.1604 -0.0137 0.3146 0.6211
## shrub_cover-Dasypus_novemcinctus 0.8936 0.3884 0.2439 0.8548 1.7216
## veg_height-Odocoileus_virginianus -0.3047 0.0649 -0.4344 -0.3053 -0.1796
## veg_height-Canis_latrans -0.6769 0.1897 -1.0563 -0.6718 -0.3279
## veg_height-Procyon_lotor 0.3357 0.1267 0.0829 0.3370 0.5831
## veg_height-Dasypus_novemcinctus 0.2345 0.1373 -0.0298 0.2335 0.5053
## week-Odocoileus_virginianus 0.2185 0.0604 0.1039 0.2182 0.3396
## week-Canis_latrans 0.0885 0.1361 -0.1868 0.0916 0.3471
## week-Procyon_lotor -0.0377 0.1223 -0.2875 -0.0319 0.1906
## week-Dasypus_novemcinctus -0.1609 0.1446 -0.4603 -0.1547 0.1025
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5465
## (Intercept)-Canis_latrans 1.0047 1543
## (Intercept)-Procyon_lotor 1.0037 3760
## (Intercept)-Dasypus_novemcinctus 1.0026 1239
## shrub_cover-Odocoileus_virginianus 1.0029 5250
## shrub_cover-Canis_latrans 1.0042 1292
## shrub_cover-Procyon_lotor 1.0018 3791
## shrub_cover-Dasypus_novemcinctus 1.0036 948
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0020 1627
## veg_height-Procyon_lotor 1.0018 4171
## veg_height-Dasypus_novemcinctus 1.0005 4018
## week-Odocoileus_virginianus 1.0010 5250
## week-Canis_latrans 1.0064 4081
## week-Procyon_lotor 1.0001 4411
## week-Dasypus_novemcinctus 1.0006 3765
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9852
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7211 1.1949 -1.7424 0.7556 3.0845 1.0031 5250
## Tree_Density -0.7478 0.6531 -2.0528 -0.7383 0.5653 1.0039 3514
## Avg_Canopy_Cover 0.4318 0.5787 -0.7637 0.4347 1.5982 1.0000 3938
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.8066 87.2617 1.1201 8.8729 136.5313 1.1415 907
## Tree_Density 2.2081 9.1178 0.0511 0.5431 13.8064 1.0303 2537
## Avg_Canopy_Cover 1.2728 2.5560 0.0669 0.5892 6.9035 1.0457 3944
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6051 1.0187 0.0436 0.2921 3.3363 1.0485 878
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3969 0.7323 -2.6597 -1.4686 0.2848 1.0040 5250
## shrub_cover 0.1483 0.3839 -0.6616 0.1437 0.9307 0.9998 5030
## veg_height -0.0958 0.3700 -0.8624 -0.0867 0.6402 1.0000 5250
## week 0.0255 0.2343 -0.4292 0.0266 0.4925 1.0023 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1947 5.9083 0.4782 1.7871 14.3727 1.0021 5250
## shrub_cover 0.7002 1.8740 0.0633 0.3390 3.1993 1.0466 5250
## veg_height 0.6379 1.5956 0.0774 0.3259 2.9223 1.0711 5250
## week 0.2324 0.4752 0.0301 0.1211 1.0913 1.0032 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 6.2965 3.8272 2.2551 5.2876 17.1056
## (Intercept)-Canis_latrans 0.5142 0.6823 -0.7523 0.4882 1.9596
## (Intercept)-Procyon_lotor 0.8355 0.6583 -0.4503 0.8289 2.1731
## (Intercept)-Dasypus_novemcinctus -0.9744 0.7312 -2.5050 -0.9385 0.3364
## Tree_Density-Odocoileus_virginianus -0.2989 1.0715 -1.9147 -0.4636 2.2479
## Tree_Density-Canis_latrans -1.0209 0.6656 -2.5753 -0.9361 0.0300
## Tree_Density-Procyon_lotor -0.4768 0.4763 -1.3791 -0.4873 0.4241
## Tree_Density-Dasypus_novemcinctus -1.5823 1.1567 -4.6632 -1.2929 -0.1505
## Avg_Canopy_Cover-Odocoileus_virginianus 0.3672 0.9134 -1.4929 0.3685 2.2134
## Avg_Canopy_Cover-Canis_latrans -0.2141 0.4872 -1.2113 -0.1894 0.6897
## Avg_Canopy_Cover-Procyon_lotor 0.8685 0.5321 -0.0446 0.8171 2.0774
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8952 0.4927 0.0336 0.8630 1.9754
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.2202 213
## (Intercept)-Canis_latrans 1.0036 2717
## (Intercept)-Procyon_lotor 1.0007 2682
## (Intercept)-Dasypus_novemcinctus 1.0031 2100
## Tree_Density-Odocoileus_virginianus 1.0036 1216
## Tree_Density-Canis_latrans 1.0071 2635
## Tree_Density-Procyon_lotor 1.0024 3444
## Tree_Density-Dasypus_novemcinctus 1.0032 1348
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0039 2955
## Avg_Canopy_Cover-Canis_latrans 1.0052 3920
## Avg_Canopy_Cover-Procyon_lotor 1.0018 3742
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0012 3736
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0071 0.0594 -0.1097 0.0070 0.1218
## (Intercept)-Canis_latrans -2.7790 0.1933 -3.1801 -2.7723 -2.4210
## (Intercept)-Procyon_lotor -2.2927 0.1442 -2.5851 -2.2895 -2.0185
## (Intercept)-Dasypus_novemcinctus -1.7144 0.1591 -2.0383 -1.7082 -1.4229
## shrub_cover-Odocoileus_virginianus -0.0567 0.0632 -0.1815 -0.0580 0.0657
## shrub_cover-Canis_latrans -0.3089 0.2222 -0.7462 -0.3044 0.1202
## shrub_cover-Procyon_lotor 0.2458 0.1632 -0.0844 0.2480 0.5574
## shrub_cover-Dasypus_novemcinctus 0.7731 0.3056 0.2138 0.7662 1.3901
## veg_height-Odocoileus_virginianus -0.3048 0.0650 -0.4313 -0.3043 -0.1781
## veg_height-Canis_latrans -0.6464 0.1889 -1.0274 -0.6416 -0.2952
## veg_height-Procyon_lotor 0.3391 0.1273 0.0887 0.3377 0.5852
## veg_height-Dasypus_novemcinctus 0.2328 0.1350 -0.0273 0.2320 0.5016
## week-Odocoileus_virginianus 0.2188 0.0607 0.1015 0.2187 0.3385
## week-Canis_latrans 0.0875 0.1356 -0.1899 0.0923 0.3449
## week-Procyon_lotor -0.0398 0.1224 -0.2881 -0.0376 0.1925
## week-Dasypus_novemcinctus -0.1574 0.1428 -0.4444 -0.1525 0.1038
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0056 2022
## (Intercept)-Procyon_lotor 0.9999 3302
## (Intercept)-Dasypus_novemcinctus 1.0004 4082
## shrub_cover-Odocoileus_virginianus 1.0003 5404
## shrub_cover-Canis_latrans 1.0024 2636
## shrub_cover-Procyon_lotor 1.0045 3950
## shrub_cover-Dasypus_novemcinctus 1.0009 3303
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0069 2245
## veg_height-Procyon_lotor 1.0022 4066
## veg_height-Dasypus_novemcinctus 1.0041 4544
## week-Odocoileus_virginianus 1.0019 5250
## week-Canis_latrans 1.0083 4165
## week-Procyon_lotor 0.9998 4492
## week-Dasypus_novemcinctus 1.0037 5215
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.957
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9406 1.1524 -1.5450 0.9913 3.1720 1.0021 5006
## Cogon_Patch_Size 0.2002 0.6187 -0.9440 0.1701 1.5555 1.0015 3080
## Avg_Cogongrass_Cover 0.3345 0.5338 -0.6978 0.3146 1.4306 1.0003 2847
## total_shrub_cover -0.1908 0.7027 -1.5426 -0.2117 1.3497 1.0014 2958
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.5033 46.0854 0.7192 7.5557 105.9400 1.0555 1099
## Cogon_Patch_Size 1.6718 7.3090 0.0497 0.4544 10.1554 1.1105 2464
## Avg_Cogongrass_Cover 0.7535 2.4328 0.0388 0.2745 4.2533 1.0759 4316
## total_shrub_cover 2.3747 6.0364 0.0713 0.8430 14.0201 1.0159 3067
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.721 1.3126 0.0445 0.3382 3.7303 1.0391 545
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3851 0.7311 -2.6590 -1.4453 0.2490 1.0000 5250
## shrub_cover 0.1716 0.4282 -0.7068 0.1710 1.0499 1.0033 5039
## veg_height -0.0863 0.3844 -0.8582 -0.0849 0.7288 1.0015 4974
## week 0.0292 0.2501 -0.4646 0.0241 0.5217 1.0009 5496
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2781 5.8625 0.4810 1.7978 16.2109 1.0037 5250
## shrub_cover 0.8604 2.5336 0.0727 0.4162 3.9469 1.0934 5250
## veg_height 0.6656 1.2971 0.0801 0.3477 3.2794 1.0020 4684
## week 0.2478 0.7010 0.0287 0.1242 1.1987 1.0447 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8530 3.0650 2.0716 5.1960
## (Intercept)-Canis_latrans 1.0256 0.8783 -0.3816 0.9290
## (Intercept)-Procyon_lotor 1.1375 0.7650 -0.2325 1.0836
## (Intercept)-Dasypus_novemcinctus -0.4976 0.6814 -1.7774 -0.5276
## Cogon_Patch_Size-Odocoileus_virginianus 0.2539 0.9921 -1.3943 0.1564
## Cogon_Patch_Size-Canis_latrans 0.9067 1.0872 -0.4374 0.6731
## Cogon_Patch_Size-Procyon_lotor -0.0948 0.5141 -1.1217 -0.0970
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0395 0.4640 -0.9575 -0.0381
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3223 0.8225 -1.2357 0.2852
## Avg_Cogongrass_Cover-Canis_latrans 0.4834 0.5692 -0.4763 0.4294
## Avg_Cogongrass_Cover-Procyon_lotor 0.1959 0.5424 -0.8570 0.1970
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4378 0.4547 -0.4018 0.4149
## total_shrub_cover-Odocoileus_virginianus -0.0568 1.0162 -2.0240 -0.0971
## total_shrub_cover-Canis_latrans 0.5187 0.8757 -0.7292 0.3603
## total_shrub_cover-Procyon_lotor -1.1910 0.7598 -3.0125 -1.0893
## total_shrub_cover-Dasypus_novemcinctus -0.1589 0.5719 -1.3769 -0.1126
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.8338 1.0274 394
## (Intercept)-Canis_latrans 3.0738 1.0136 1722
## (Intercept)-Procyon_lotor 2.7855 1.0100 2418
## (Intercept)-Dasypus_novemcinctus 0.9649 1.0048 1604
## Cogon_Patch_Size-Odocoileus_virginianus 2.5262 1.0067 2248
## Cogon_Patch_Size-Canis_latrans 3.6491 1.0110 1544
## Cogon_Patch_Size-Procyon_lotor 0.9322 1.0043 3443
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8816 1.0013 3493
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0531 1.0027 2580
## Avg_Cogongrass_Cover-Canis_latrans 1.7588 1.0015 2186
## Avg_Cogongrass_Cover-Procyon_lotor 1.2895 1.0025 2892
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3892 1.0006 3277
## total_shrub_cover-Odocoileus_virginianus 2.1360 1.0035 2552
## total_shrub_cover-Canis_latrans 2.7201 1.0147 1119
## total_shrub_cover-Procyon_lotor -0.0360 1.0105 2012
## total_shrub_cover-Dasypus_novemcinctus 0.7531 1.0037 1447
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0079 0.0606 -0.1141 0.0075 0.1268
## (Intercept)-Canis_latrans -2.7949 0.1975 -3.2011 -2.7914 -2.4240
## (Intercept)-Procyon_lotor -2.2963 0.1432 -2.5857 -2.2919 -2.0209
## (Intercept)-Dasypus_novemcinctus -1.7408 0.1696 -2.0892 -1.7326 -1.4322
## shrub_cover-Odocoileus_virginianus -0.0556 0.0641 -0.1811 -0.0543 0.0693
## shrub_cover-Canis_latrans -0.3590 0.2438 -0.8425 -0.3530 0.0981
## shrub_cover-Procyon_lotor 0.3057 0.1619 -0.0227 0.3081 0.6161
## shrub_cover-Dasypus_novemcinctus 0.8449 0.3443 0.2284 0.8220 1.5673
## veg_height-Odocoileus_virginianus -0.3031 0.0650 -0.4322 -0.3038 -0.1775
## veg_height-Canis_latrans -0.6618 0.1910 -1.0495 -0.6555 -0.2992
## veg_height-Procyon_lotor 0.3422 0.1261 0.0998 0.3418 0.5877
## veg_height-Dasypus_novemcinctus 0.2361 0.1379 -0.0284 0.2359 0.5087
## week-Odocoileus_virginianus 0.2178 0.0614 0.0994 0.2177 0.3375
## week-Canis_latrans 0.0924 0.1333 -0.1826 0.0946 0.3428
## week-Procyon_lotor -0.0388 0.1210 -0.2908 -0.0332 0.1838
## week-Dasypus_novemcinctus -0.1562 0.1423 -0.4512 -0.1498 0.1083
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0077 1878
## (Intercept)-Procyon_lotor 1.0063 3699
## (Intercept)-Dasypus_novemcinctus 1.0003 2312
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0104 1554
## shrub_cover-Procyon_lotor 1.0049 4073
## shrub_cover-Dasypus_novemcinctus 1.0003 1795
## veg_height-Odocoileus_virginianus 1.0011 5040
## veg_height-Canis_latrans 1.0044 2163
## veg_height-Procyon_lotor 1.0023 3917
## veg_height-Dasypus_novemcinctus 1.0017 4110
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0001 4295
## week-Procyon_lotor 1.0032 4196
## week-Dasypus_novemcinctus 1.0004 5053
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8918
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8209 1.1518 -1.6345 0.8630 3.0068 1.0012 6964
## Veg_shannon_index 0.5135 0.5248 -0.5149 0.5180 1.5212 1.0018 3666
## Avg_Cogongrass_Cover 0.6613 0.5255 -0.3497 0.6542 1.7441 1.0016 3072
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.1339 49.1404 0.7118 7.3658 123.6510 1.1060 443
## Veg_shannon_index 0.8879 5.2096 0.0439 0.3191 4.9183 1.2632 5250
## Avg_Cogongrass_Cover 0.7979 3.2729 0.0410 0.2865 4.5394 1.1444 4042
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6956 1.0686 0.0503 0.3509 3.5917 1.0292 902
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3827 0.7524 -2.7248 -1.4599 0.3260 1.0006 5250
## shrub_cover 0.1520 0.3873 -0.6291 0.1512 0.9389 1.0024 4940
## veg_height -0.1015 0.3789 -0.8972 -0.0991 0.6892 1.0006 5250
## week 0.0271 0.2414 -0.4711 0.0292 0.5106 1.0009 4995
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3203 6.3745 0.4850 1.7925 15.7672 1.0782 5250
## shrub_cover 0.6893 2.5940 0.0628 0.3322 3.2369 1.0821 5250
## veg_height 0.6668 1.6751 0.0764 0.3338 3.2504 1.0344 5250
## week 0.2316 0.4798 0.0303 0.1178 1.1097 1.0422 4934
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8527 3.6034 1.9440 4.9952
## (Intercept)-Canis_latrans 0.6684 0.7081 -0.6224 0.6290
## (Intercept)-Procyon_lotor 0.8129 0.6585 -0.4420 0.7948
## (Intercept)-Dasypus_novemcinctus -0.5861 0.6237 -1.8658 -0.5877
## Veg_shannon_index-Odocoileus_virginianus 0.4236 0.8061 -1.2313 0.4573
## Veg_shannon_index-Canis_latrans 0.8614 0.5200 -0.0561 0.8217
## Veg_shannon_index-Procyon_lotor 0.6183 0.4755 -0.2363 0.5896
## Veg_shannon_index-Dasypus_novemcinctus 0.2590 0.4147 -0.5569 0.2665
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6720 0.7895 -0.8340 0.6404
## Avg_Cogongrass_Cover-Canis_latrans 0.9709 0.5946 0.0416 0.8824
## Avg_Cogongrass_Cover-Procyon_lotor 0.6092 0.4946 -0.2589 0.5749
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6030 0.3964 -0.1608 0.5917
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.4273 1.0746 264
## (Intercept)-Canis_latrans 2.1865 1.0079 2421
## (Intercept)-Procyon_lotor 2.1834 1.0033 2865
## (Intercept)-Dasypus_novemcinctus 0.6669 1.0116 2034
## Veg_shannon_index-Odocoileus_virginianus 1.9093 1.0027 2733
## Veg_shannon_index-Canis_latrans 1.9818 1.0004 2980
## Veg_shannon_index-Procyon_lotor 1.6509 1.0038 2166
## Veg_shannon_index-Dasypus_novemcinctus 1.0573 1.0024 3688
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.3519 1.0066 2871
## Avg_Cogongrass_Cover-Canis_latrans 2.3653 1.0087 2116
## Avg_Cogongrass_Cover-Procyon_lotor 1.6958 1.0003 2818
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3951 1.0009 3605
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0077 0.0595 -0.1074 0.0084 0.1229
## (Intercept)-Canis_latrans -2.7654 0.1911 -3.1612 -2.7567 -2.4045
## (Intercept)-Procyon_lotor -2.3068 0.1518 -2.6177 -2.2994 -2.0293
## (Intercept)-Dasypus_novemcinctus -1.7118 0.1582 -2.0322 -1.7086 -1.4136
## shrub_cover-Odocoileus_virginianus -0.0565 0.0646 -0.1824 -0.0563 0.0691
## shrub_cover-Canis_latrans -0.2810 0.2155 -0.7009 -0.2778 0.1368
## shrub_cover-Procyon_lotor 0.2160 0.1778 -0.1455 0.2260 0.5460
## shrub_cover-Dasypus_novemcinctus 0.7603 0.3072 0.1831 0.7541 1.3789
## veg_height-Odocoileus_virginianus -0.3053 0.0648 -0.4333 -0.3054 -0.1772
## veg_height-Canis_latrans -0.6547 0.1910 -1.0423 -0.6510 -0.2992
## veg_height-Procyon_lotor 0.3358 0.1281 0.0910 0.3358 0.5817
## veg_height-Dasypus_novemcinctus 0.2232 0.1354 -0.0423 0.2244 0.4927
## week-Odocoileus_virginianus 0.2183 0.0617 0.0978 0.2181 0.3423
## week-Canis_latrans 0.0906 0.1342 -0.1818 0.0952 0.3430
## week-Procyon_lotor -0.0404 0.1215 -0.2869 -0.0351 0.1895
## week-Dasypus_novemcinctus -0.1575 0.1422 -0.4510 -0.1528 0.1034
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0017 4485
## (Intercept)-Canis_latrans 1.0152 2261
## (Intercept)-Procyon_lotor 1.0029 2763
## (Intercept)-Dasypus_novemcinctus 1.0063 4321
## shrub_cover-Odocoileus_virginianus 1.0041 5250
## shrub_cover-Canis_latrans 1.0016 2743
## shrub_cover-Procyon_lotor 1.0002 2786
## shrub_cover-Dasypus_novemcinctus 1.0058 3619
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0054 2269
## veg_height-Procyon_lotor 0.9999 4021
## veg_height-Dasypus_novemcinctus 1.0033 4751
## week-Odocoileus_virginianus 1.0024 5250
## week-Canis_latrans 1.0011 4208
## week-Procyon_lotor 1.0022 4530
## week-Dasypus_novemcinctus 1.0017 4691
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9083
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3252 1.0541 -1.7398 0.3022 2.5245 1.0002 4021
## Avg_Cogongrass_Cover -0.0529 0.6449 -1.2681 -0.0781 1.3237 1.0015 2001
## I(Avg_Cogongrass_Cover^2) 1.1392 0.8982 -0.5634 1.0600 3.1350 1.0117 1452
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.6241 29.2246 0.3157 5.0632 73.5669 1.0227 1568
## Avg_Cogongrass_Cover 1.0331 3.3622 0.0464 0.3596 5.8723 1.0871 2691
## I(Avg_Cogongrass_Cover^2) 7.3900 27.2487 0.0560 0.9629 61.2200 1.1233 186
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5111 0.8157 0.0436 0.2656 2.4792 1.0146 1196
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3905 0.7303 -2.6818 -1.4576 0.2787 1.0071 5250
## shrub_cover 0.1505 0.3813 -0.5938 0.1438 0.8899 1.0011 5250
## veg_height -0.0908 0.3762 -0.8423 -0.0962 0.7422 1.0003 5250
## week 0.0292 0.2450 -0.4440 0.0286 0.4957 1.0017 5463
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3222 6.8778 0.4750 1.7880 14.8280 1.0233 4301
## shrub_cover 0.6541 1.6708 0.0574 0.3111 3.1022 1.0514 5250
## veg_height 0.6420 1.2874 0.0753 0.3342 3.0937 1.0044 5250
## week 0.2386 0.4861 0.0304 0.1245 1.1380 1.0215 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.1024 2.7777 0.3570 3.5426
## (Intercept)-Canis_latrans -0.3709 0.8735 -2.1673 -0.3413
## (Intercept)-Procyon_lotor -0.0338 0.7428 -1.6118 -0.0072
## (Intercept)-Dasypus_novemcinctus -1.0477 0.6964 -2.4685 -1.0285
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1296 0.9703 -2.0750 -0.1495
## Avg_Cogongrass_Cover-Canis_latrans 0.2155 0.7298 -1.0136 0.1550
## Avg_Cogongrass_Cover-Procyon_lotor -0.2046 0.7105 -1.5617 -0.2228
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.1193 0.6049 -1.3035 -0.1205
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.3606 2.9499 -0.2570 1.4890
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9629 1.4749 0.0362 1.6231
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7612 1.7272 0.0145 1.2020
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5629 0.4880 -0.3183 0.5436
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.8140 1.0027 498
## (Intercept)-Canis_latrans 1.3463 1.0095 1467
## (Intercept)-Procyon_lotor 1.3310 1.0022 1534
## (Intercept)-Dasypus_novemcinctus 0.2998 1.0002 3147
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8757 1.0037 2464
## Avg_Cogongrass_Cover-Canis_latrans 1.8591 1.0023 1863
## Avg_Cogongrass_Cover-Procyon_lotor 1.2599 1.0005 1557
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0804 1.0014 2714
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 10.9570 1.0801 103
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6754 1.0143 535
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.6725 1.0334 230
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5956 1.0049 2735
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0066 0.0602 -0.1130 0.0070 0.1260
## (Intercept)-Canis_latrans -2.7772 0.1886 -3.1678 -2.7708 -2.4295
## (Intercept)-Procyon_lotor -2.3297 0.1564 -2.6515 -2.3240 -2.0367
## (Intercept)-Dasypus_novemcinctus -1.7135 0.1590 -2.0393 -1.7119 -1.4157
## shrub_cover-Odocoileus_virginianus -0.0564 0.0657 -0.1882 -0.0561 0.0734
## shrub_cover-Canis_latrans -0.2477 0.2173 -0.6864 -0.2421 0.1652
## shrub_cover-Procyon_lotor 0.1982 0.1721 -0.1438 0.2018 0.5299
## shrub_cover-Dasypus_novemcinctus 0.7560 0.3117 0.1859 0.7408 1.4003
## veg_height-Odocoileus_virginianus -0.3046 0.0646 -0.4358 -0.3038 -0.1808
## veg_height-Canis_latrans -0.6503 0.1901 -1.0440 -0.6425 -0.2982
## veg_height-Procyon_lotor 0.3464 0.1274 0.1000 0.3445 0.5922
## veg_height-Dasypus_novemcinctus 0.2258 0.1345 -0.0376 0.2237 0.4951
## week-Odocoileus_virginianus 0.2191 0.0619 0.1004 0.2177 0.3440
## week-Canis_latrans 0.0921 0.1351 -0.1875 0.0966 0.3449
## week-Procyon_lotor -0.0399 0.1200 -0.2851 -0.0364 0.1840
## week-Dasypus_novemcinctus -0.1562 0.1415 -0.4556 -0.1495 0.1047
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0025 2310
## (Intercept)-Procyon_lotor 1.0042 2151
## (Intercept)-Dasypus_novemcinctus 1.0006 4102
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 0.9999 2812
## shrub_cover-Procyon_lotor 1.0045 1808
## shrub_cover-Dasypus_novemcinctus 1.0006 3190
## veg_height-Odocoileus_virginianus 1.0031 4828
## veg_height-Canis_latrans 1.0026 2346
## veg_height-Procyon_lotor 0.9999 3689
## veg_height-Dasypus_novemcinctus 1.0003 4668
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 0.9999 4215
## week-Procyon_lotor 1.0009 4281
## week-Dasypus_novemcinctus 1.0007 4654
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2298 1.4704 -2.6629 0.2492 3.0933 1.0217 3438
## Cogon_Patch_Size 0.1003 1.0096 -1.8260 0.0662 2.3024 1.0237 1878
## Veg_shannon_index 0.9524 0.8588 -0.8340 0.9637 2.6659 1.0213 1812
## total_shrub_cover -0.0902 0.9271 -1.9970 -0.1044 1.8563 1.0065 1247
## Avg_Cogongrass_Cover 0.7915 1.2985 -1.7200 0.7618 3.4381 1.0034 1055
## Tree_Density -1.2648 1.5585 -4.0496 -1.3891 2.0980 1.0543 1140
## Avg_Canopy_Cover 0.8752 0.9519 -1.2154 0.9179 2.6662 1.0220 1941
## I(Avg_Cogongrass_Cover^2) 1.4902 1.3055 -1.6381 1.6212 3.7422 1.4041 160
## avg_veg_height 0.0064 0.8345 -1.6715 0.0164 1.6974 1.0205 972
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept) 191.6422 684.7769 2.5724 41.6630 1420.9888 1.0632
## Cogon_Patch_Size 8.2324 49.2797 0.0670 1.4222 53.0279 1.1090
## Veg_shannon_index 7.4936 54.8409 0.0583 0.6949 45.0749 1.8850
## total_shrub_cover 4.4802 13.9985 0.0712 1.2655 29.6853 1.1328
## Avg_Cogongrass_Cover 6.5604 37.7592 0.0549 0.9557 46.1410 1.4311
## Tree_Density 185.0187 1356.2293 0.1370 15.7270 1403.9695 2.0575
## Avg_Canopy_Cover 8.7890 31.2704 0.1075 1.9259 67.5967 1.8069
## I(Avg_Cogongrass_Cover^2) 624.5205 3802.8821 0.0590 1.3311 5419.3838 2.8703
## avg_veg_height 1.6413 5.4444 0.0468 0.4622 10.2832 1.0927
## ESS
## (Intercept) 144
## Cogon_Patch_Size 1626
## Veg_shannon_index 322
## total_shrub_cover 1022
## Avg_Cogongrass_Cover 1947
## Tree_Density 128
## Avg_Canopy_Cover 108
## I(Avg_Cogongrass_Cover^2) 155
## avg_veg_height 1375
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.7679 20.8087 0.0548 0.8243 30.3901 2.1929 91
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3864 0.7481 -2.7243 -1.4459 0.3392 1.0025 5250
## shrub_cover 0.1461 0.4113 -0.6634 0.1422 0.9986 1.0022 5250
## veg_height -0.0767 0.3754 -0.8697 -0.0740 0.6707 1.0062 5250
## week 0.0249 0.2415 -0.4544 0.0274 0.4823 1.0021 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3347 6.6845 0.4766 1.7850 15.2340 1.0100 5250
## shrub_cover 0.7726 1.6422 0.0692 0.3984 3.7212 1.0064 5250
## veg_height 0.6305 1.3116 0.0803 0.3218 3.0558 1.0180 5250
## week 0.2433 0.5541 0.0305 0.1195 1.1778 1.0371 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 13.7682 12.1243 1.1939
## (Intercept)-Canis_latrans -2.1044 5.2911 -20.1030
## (Intercept)-Procyon_lotor -0.3027 2.6859 -4.4701
## (Intercept)-Dasypus_novemcinctus -4.2422 4.1992 -15.5580
## Cogon_Patch_Size-Odocoileus_virginianus 0.2158 2.0024 -3.3719
## Cogon_Patch_Size-Canis_latrans 1.7326 2.4859 -1.0702
## Cogon_Patch_Size-Procyon_lotor -0.5331 1.4008 -3.0586
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4064 1.2321 -2.9618
## Veg_shannon_index-Odocoileus_virginianus 0.8008 2.3743 -3.0191
## Veg_shannon_index-Canis_latrans 1.7980 1.9637 -0.1616
## Veg_shannon_index-Procyon_lotor 1.7745 2.2671 -0.0256
## Veg_shannon_index-Dasypus_novemcinctus 0.5949 0.8274 -1.0936
## total_shrub_cover-Odocoileus_virginianus 0.0962 1.7506 -3.2586
## total_shrub_cover-Canis_latrans 0.8405 1.4986 -1.3852
## total_shrub_cover-Procyon_lotor -1.1718 1.1924 -3.8257
## total_shrub_cover-Dasypus_novemcinctus -0.1266 1.0000 -2.5955
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.7244 2.3193 -3.9263
## Avg_Cogongrass_Cover-Canis_latrans 1.1703 2.1311 -2.2111
## Avg_Cogongrass_Cover-Procyon_lotor 0.6010 1.8633 -2.8626
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.6559 2.0493 -1.3748
## Tree_Density-Odocoileus_virginianus 0.9199 7.2367 -7.0618
## Tree_Density-Canis_latrans -7.1578 11.3849 -43.9242
## Tree_Density-Procyon_lotor -0.7766 5.6788 -5.3087
## Tree_Density-Dasypus_novemcinctus -8.5379 7.6837 -31.6586
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6277 2.2115 -4.3950
## Avg_Canopy_Cover-Canis_latrans -0.0886 1.2838 -3.0029
## Avg_Canopy_Cover-Procyon_lotor 1.3689 1.5518 -2.2623
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.7714 2.0864 0.6419
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 12.3584 24.7290 -0.4241
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.7898 10.5960 0.4467
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 9.4984 19.5443 0.4113
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.1847 2.1775 -0.1161
## avg_veg_height-Odocoileus_virginianus 0.0149 1.3542 -2.7163
## avg_veg_height-Canis_latrans -0.2550 1.0758 -2.3824
## avg_veg_height-Procyon_lotor 0.0509 1.0967 -1.9528
## avg_veg_height-Dasypus_novemcinctus 0.1417 0.8800 -1.6319
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.1572 51.2807 1.1548 75
## (Intercept)-Canis_latrans -1.0073 2.9044 3.3830 31
## (Intercept)-Procyon_lotor -0.3836 5.0066 1.1935 388
## (Intercept)-Dasypus_novemcinctus -3.3323 -0.4470 2.1218 55
## Cogon_Patch_Size-Odocoileus_virginianus 0.0791 4.7269 1.0452 1293
## Cogon_Patch_Size-Canis_latrans 1.1156 8.9410 1.1005 376
## Cogon_Patch_Size-Procyon_lotor -0.5495 2.2448 1.0952 950
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3525 1.8154 1.0759 807
## Veg_shannon_index-Odocoileus_virginianus 0.9135 4.3110 1.1702 347
## Veg_shannon_index-Canis_latrans 1.4329 6.1833 1.6060 122
## Veg_shannon_index-Procyon_lotor 1.3262 7.0717 2.5741 70
## Veg_shannon_index-Dasypus_novemcinctus 0.5972 2.1993 1.0370 925
## total_shrub_cover-Odocoileus_virginianus -0.0010 4.0148 1.0151 1563
## total_shrub_cover-Canis_latrans 0.5848 4.2957 1.0209 386
## total_shrub_cover-Procyon_lotor -1.0610 0.8691 1.0969 564
## total_shrub_cover-Dasypus_novemcinctus -0.0138 1.4494 1.0288 757
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6984 5.1485 1.0265 1465
## Avg_Cogongrass_Cover-Canis_latrans 1.0011 5.6261 1.0358 801
## Avg_Cogongrass_Cover-Procyon_lotor 0.5829 4.0575 1.0511 853
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3361 6.9102 1.0829 352
## Tree_Density-Odocoileus_virginianus -0.4666 15.6680 1.3320 217
## Tree_Density-Canis_latrans -4.2432 -0.7955 2.9380 21
## Tree_Density-Procyon_lotor -1.9490 16.1728 3.1866 23
## Tree_Density-Dasypus_novemcinctus -6.2615 -1.6268 1.8512 79
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8012 4.6205 1.1035 1036
## Avg_Canopy_Cover-Canis_latrans 0.0800 1.6336 1.3822 83
## Avg_Canopy_Cover-Procyon_lotor 1.3855 3.9796 1.3028 171
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2313 8.9047 1.9124 69
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.4520 93.4300 6.1901 12
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5958 42.5090 4.7718 18
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4395 78.6211 6.2901 11
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7863 8.1137 1.7777 49
## avg_veg_height-Odocoileus_virginianus 0.0258 2.6962 1.0364 1935
## avg_veg_height-Canis_latrans -0.2056 1.6463 1.0595 723
## avg_veg_height-Procyon_lotor 0.0783 1.9710 1.0842 754
## avg_veg_height-Dasypus_novemcinctus 0.1452 1.8977 1.0143 1134
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0065 0.0589 -0.1067 0.0069 0.1212
## (Intercept)-Canis_latrans -2.7667 0.1930 -3.1635 -2.7598 -2.4067
## (Intercept)-Procyon_lotor -2.3545 0.1759 -2.7376 -2.3420 -2.0383
## (Intercept)-Dasypus_novemcinctus -1.7335 0.1663 -2.0690 -1.7293 -1.4252
## shrub_cover-Odocoileus_virginianus -0.0568 0.0648 -0.1833 -0.0570 0.0707
## shrub_cover-Canis_latrans -0.3539 0.2363 -0.8032 -0.3591 0.1192
## shrub_cover-Procyon_lotor 0.2089 0.1874 -0.1827 0.2158 0.5480
## shrub_cover-Dasypus_novemcinctus 0.8464 0.3373 0.2143 0.8430 1.5206
## veg_height-Odocoileus_virginianus -0.3056 0.0658 -0.4376 -0.3055 -0.1720
## veg_height-Canis_latrans -0.6166 0.1836 -0.9884 -0.6116 -0.2686
## veg_height-Procyon_lotor 0.3679 0.1281 0.1204 0.3677 0.6227
## veg_height-Dasypus_novemcinctus 0.2382 0.1383 -0.0273 0.2384 0.5200
## week-Odocoileus_virginianus 0.2167 0.0610 0.1015 0.2159 0.3393
## week-Canis_latrans 0.0889 0.1350 -0.1916 0.0943 0.3402
## week-Procyon_lotor -0.0377 0.1202 -0.2836 -0.0334 0.1871
## week-Dasypus_novemcinctus -0.1609 0.1414 -0.4597 -0.1551 0.1083
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0227 753
## (Intercept)-Procyon_lotor 1.4022 92
## (Intercept)-Dasypus_novemcinctus 1.0029 2719
## shrub_cover-Odocoileus_virginianus 1.0027 5490
## shrub_cover-Canis_latrans 1.0302 602
## shrub_cover-Procyon_lotor 1.2514 217
## shrub_cover-Dasypus_novemcinctus 1.0053 1669
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0172 2000
## veg_height-Procyon_lotor 1.0370 645
## veg_height-Dasypus_novemcinctus 1.0008 4096
## week-Odocoileus_virginianus 0.9999 5587
## week-Canis_latrans 1.0002 4341
## week-Procyon_lotor 1.0022 4401
## week-Dasypus_novemcinctus 1.0040 4909
# Includes all covariates of occupancy and null for detection
ms_null_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7053
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6831 1.4367 -2.2492 0.7180 3.4413 1.0013 4796
## Cogon_Patch_Size -0.3268 0.8288 -1.9048 -0.3731 1.5407 1.0039 2226
## Veg_shannon_index 0.9350 0.6649 -0.4266 0.9379 2.2207 1.0000 1439
## total_shrub_cover 0.0088 0.6693 -1.2940 0.0011 1.4403 1.0041 2888
## Avg_Cogongrass_Cover 2.0795 0.9821 0.0844 2.0907 4.0182 1.0024 1334
## Tree_Density -1.4232 1.1014 -3.4594 -1.5185 1.1013 1.0026 2108
## Avg_Canopy_Cover 0.8933 0.7856 -0.7879 0.9143 2.3620 1.0042 3321
## avg_veg_height -0.3280 0.6785 -1.6418 -0.3286 1.0203 1.0015 2025
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 78.5483 233.9399 2.9974 30.5125 391.0696 1.0336 855
## Cogon_Patch_Size 3.1112 13.1239 0.0581 0.8213 17.0283 1.0755 2272
## Veg_shannon_index 1.5364 5.6548 0.0495 0.4504 9.3827 1.1414 1916
## total_shrub_cover 1.7552 4.8195 0.0586 0.6100 10.4206 1.0723 3863
## Avg_Cogongrass_Cover 2.4149 7.9962 0.0490 0.5283 17.9371 1.0431 1876
## Tree_Density 12.0339 42.6420 0.0921 2.7564 74.8164 1.0853 1617
## Avg_Canopy_Cover 2.9407 11.6620 0.0806 1.0568 16.0974 1.1060 4455
## avg_veg_height 1.1112 3.8080 0.0441 0.3684 5.9107 1.0645 4180
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8748 3.4388 0.0635 0.8096 10.25 1.069 592
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3305 0.7255 -2.6344 -1.3887 0.3424 0.9999 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9378 4.9186 0.4314 1.6674 13.3577 1.0183 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 11.2161 6.1720 3.7985 9.7772
## (Intercept)-Canis_latrans 0.9298 1.1852 -0.9992 0.7866
## (Intercept)-Procyon_lotor 0.9062 1.0630 -1.2492 0.9191
## (Intercept)-Dasypus_novemcinctus -1.7183 1.1290 -4.3383 -1.5992
## Cogon_Patch_Size-Odocoileus_virginianus -0.2851 1.5247 -2.8103 -0.4213
## Cogon_Patch_Size-Canis_latrans 0.6160 1.3680 -1.2492 0.3468
## Cogon_Patch_Size-Procyon_lotor -0.8098 0.8721 -2.3368 -0.8279
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7961 0.6766 -2.2580 -0.7663
## Veg_shannon_index-Odocoileus_virginianus 0.8068 1.1787 -1.7173 0.8719
## Veg_shannon_index-Canis_latrans 1.4186 0.8370 0.1425 1.3059
## Veg_shannon_index-Procyon_lotor 1.2123 0.6755 0.0194 1.1673
## Veg_shannon_index-Dasypus_novemcinctus 0.6953 0.5687 -0.4225 0.6949
## total_shrub_cover-Odocoileus_virginianus 0.1822 1.1454 -1.9375 0.1161
## total_shrub_cover-Canis_latrans 0.4100 0.8399 -0.8905 0.2919
## total_shrub_cover-Procyon_lotor -0.7315 0.6743 -2.1798 -0.6905
## total_shrub_cover-Dasypus_novemcinctus 0.1967 0.5469 -0.8790 0.1924
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1103 1.4456 -0.6798 2.0940
## Avg_Cogongrass_Cover-Canis_latrans 2.5086 1.1551 0.6280 2.3776
## Avg_Cogongrass_Cover-Procyon_lotor 2.1995 1.0334 0.3442 2.1454
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6384 1.1291 0.7901 2.5191
## Tree_Density-Odocoileus_virginianus -0.4078 2.3116 -3.6025 -0.8451
## Tree_Density-Canis_latrans -2.7469 1.6519 -7.0960 -2.4071
## Tree_Density-Procyon_lotor -1.2651 0.9082 -3.0068 -1.2763
## Tree_Density-Dasypus_novemcinctus -4.0156 2.4049 -10.1575 -3.3985
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7148 1.4555 -2.3000 0.8031
## Avg_Canopy_Cover-Canis_latrans 0.1781 0.7180 -1.3031 0.2017
## Avg_Canopy_Cover-Procyon_lotor 1.4652 0.7298 0.1892 1.3910
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8307 0.7674 0.6024 1.7332
## avg_veg_height-Odocoileus_virginianus -0.3451 1.0526 -2.4625 -0.3379
## avg_veg_height-Canis_latrans -0.6298 0.7307 -2.1188 -0.6208
## avg_veg_height-Procyon_lotor -0.2452 0.6680 -1.5319 -0.2580
## avg_veg_height-Dasypus_novemcinctus -0.1951 0.6666 -1.4381 -0.2118
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 28.0527 1.0173 188
## (Intercept)-Canis_latrans 3.6374 1.0201 1142
## (Intercept)-Procyon_lotor 2.9969 1.0058 958
## (Intercept)-Dasypus_novemcinctus 0.1672 1.0123 1001
## Cogon_Patch_Size-Odocoileus_virginianus 3.1450 1.0074 1633
## Cogon_Patch_Size-Canis_latrans 4.2774 1.0227 1319
## Cogon_Patch_Size-Procyon_lotor 0.7287 1.0126 1119
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4471 1.0067 1503
## Veg_shannon_index-Odocoileus_virginianus 2.9705 1.0069 1943
## Veg_shannon_index-Canis_latrans 3.4529 1.0086 1053
## Veg_shannon_index-Procyon_lotor 2.7014 1.0012 1134
## Veg_shannon_index-Dasypus_novemcinctus 1.8110 1.0016 2063
## total_shrub_cover-Odocoileus_virginianus 2.6940 1.0050 2202
## total_shrub_cover-Canis_latrans 2.3973 1.0012 1340
## total_shrub_cover-Procyon_lotor 0.4957 1.0025 2634
## total_shrub_cover-Dasypus_novemcinctus 1.3175 1.0016 2715
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.9328 1.0069 1433
## Avg_Cogongrass_Cover-Canis_latrans 5.1828 1.0083 874
## Avg_Cogongrass_Cover-Procyon_lotor 4.4477 1.0031 1277
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.1988 1.0112 828
## Tree_Density-Odocoileus_virginianus 5.2607 1.0293 700
## Tree_Density-Canis_latrans -0.5202 1.0171 785
## Tree_Density-Procyon_lotor 0.5108 1.0040 1859
## Tree_Density-Dasypus_novemcinctus -1.1013 1.0159 595
## Avg_Canopy_Cover-Odocoileus_virginianus 3.4995 1.0041 2081
## Avg_Canopy_Cover-Canis_latrans 1.5307 1.0002 2324
## Avg_Canopy_Cover-Procyon_lotor 3.0955 1.0019 2062
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.5819 1.0039 1055
## avg_veg_height-Odocoileus_virginianus 1.7743 1.0023 2179
## avg_veg_height-Canis_latrans 0.7964 1.0006 1629
## avg_veg_height-Procyon_lotor 1.1346 1.0030 2142
## avg_veg_height-Dasypus_novemcinctus 1.1717 1.0011 1874
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0060 0.0583 -0.1110 0.0064 0.1184
## (Intercept)-Canis_latrans -2.6359 0.1800 -2.9994 -2.6302 -2.2948
## (Intercept)-Procyon_lotor -2.2570 0.1304 -2.5202 -2.2520 -2.0100
## (Intercept)-Dasypus_novemcinctus -1.5615 0.1313 -1.8243 -1.5627 -1.3101
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0069 2503
## (Intercept)-Procyon_lotor 1.0017 3582
## (Intercept)-Dasypus_novemcinctus 1.0003 5250
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.6918
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8346 1.1534 -1.6263 0.8821 3.0510 1.0020 5250
## Avg_Cogongrass_Cover 0.3027 0.5468 -0.7725 0.2927 1.3990 1.0029 2659
## total_shrub_cover -0.1553 0.6096 -1.3643 -0.1516 1.1013 1.0018 4359
## avg_veg_height 0.1142 0.5378 -0.9241 0.1139 1.2020 1.0054 2804
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.8598 47.2339 0.9454 7.8093 104.4259 1.0138 943
## Avg_Cogongrass_Cover 0.7341 1.7635 0.0407 0.2766 4.2758 1.0219 3265
## total_shrub_cover 1.4921 4.4542 0.0628 0.6318 7.5575 1.1674 4897
## avg_veg_height 0.7417 2.1523 0.0420 0.2721 4.1265 1.0028 3933
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.59 0.9482 0.0448 0.3125 2.8183 1.0401 1003
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3361 0.7259 -2.6766 -1.3887 0.2684 1.0016 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0136 7.1931 0.4362 1.6461 12.817 1.0894 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8026 3.0324 2.2422 5.0916
## (Intercept)-Canis_latrans 0.5604 0.7093 -0.6762 0.5050
## (Intercept)-Procyon_lotor 0.9676 0.6893 -0.3253 0.9402
## (Intercept)-Dasypus_novemcinctus -0.6819 0.5858 -1.8259 -0.6835
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3084 0.8608 -1.2616 0.2762
## Avg_Cogongrass_Cover-Canis_latrans 0.5405 0.5731 -0.4745 0.5020
## Avg_Cogongrass_Cover-Procyon_lotor 0.1415 0.5501 -0.9474 0.1279
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2994 0.4601 -0.5920 0.2925
## total_shrub_cover-Odocoileus_virginianus -0.0712 0.9074 -1.8969 -0.0956
## total_shrub_cover-Canis_latrans 0.2951 0.5623 -0.6507 0.2445
## total_shrub_cover-Procyon_lotor -0.9654 0.6311 -2.4534 -0.8826
## total_shrub_cover-Dasypus_novemcinctus 0.0538 0.4067 -0.7358 0.0534
## avg_veg_height-Odocoileus_virginianus 0.0875 0.7837 -1.4184 0.0954
## avg_veg_height-Canis_latrans -0.0671 0.5298 -1.1813 -0.0582
## avg_veg_height-Procyon_lotor 0.1892 0.5289 -0.8185 0.1704
## avg_veg_height-Dasypus_novemcinctus 0.2741 0.4770 -0.6451 0.2686
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.3829 1.0215 362
## (Intercept)-Canis_latrans 2.1377 1.0038 2164
## (Intercept)-Procyon_lotor 2.4169 1.0019 2992
## (Intercept)-Dasypus_novemcinctus 0.4814 1.0062 2879
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1077 1.0061 2655
## Avg_Cogongrass_Cover-Canis_latrans 1.8309 1.0024 3047
## Avg_Cogongrass_Cover-Procyon_lotor 1.2765 1.0037 3112
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2320 1.0016 2851
## total_shrub_cover-Odocoileus_virginianus 1.8684 1.0009 2898
## total_shrub_cover-Canis_latrans 1.5825 1.0053 2510
## total_shrub_cover-Procyon_lotor 0.0533 1.0022 2600
## total_shrub_cover-Dasypus_novemcinctus 0.8748 1.0034 4793
## avg_veg_height-Odocoileus_virginianus 1.6228 1.0013 2760
## avg_veg_height-Canis_latrans 0.9432 1.0024 3013
## avg_veg_height-Procyon_lotor 1.2704 1.0023 2998
## avg_veg_height-Dasypus_novemcinctus 1.2126 1.0021 3257
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0598 -0.1097 0.0058 0.1230
## (Intercept)-Canis_latrans -2.6285 0.1859 -3.0057 -2.6212 -2.2770
## (Intercept)-Procyon_lotor -2.2698 0.1286 -2.5249 -2.2681 -2.0218
## (Intercept)-Dasypus_novemcinctus -1.5642 0.1324 -1.8277 -1.5627 -1.3136
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0096 2433
## (Intercept)-Procyon_lotor 1.0002 3788
## (Intercept)-Dasypus_novemcinctus 1.0000 5250
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.6893
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6731 1.2193 -1.8363 0.7252 3.0516 1.0026 6413
## Tree_Density -0.7177 0.6754 -2.0631 -0.7252 0.7223 1.0023 3313
## Avg_Canopy_Cover 0.4542 0.5701 -0.7066 0.4554 1.5513 1.0013 4112
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 28.4032 75.1564 1.1892 9.9772 178.5916 1.0191 432
## Tree_Density 2.1963 7.0562 0.0519 0.5701 14.6670 1.0195 2973
## Avg_Canopy_Cover 1.2761 2.9870 0.0604 0.5333 7.3074 1.0396 3094
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6186 1.2086 0.0427 0.2939 2.9588 1.2237 732
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.335 0.709 -2.5751 -1.3938 0.2956 1.0011 5008
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9813 5.4464 0.4297 1.6035 13.9634 1.0101 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 6.6351 4.1923 2.3262 5.5915 18.9999
## (Intercept)-Canis_latrans 0.3967 0.7258 -0.8521 0.3471 1.9180
## (Intercept)-Procyon_lotor 0.7673 0.6455 -0.4589 0.7612 2.0935
## (Intercept)-Dasypus_novemcinctus -1.0578 0.7245 -2.5730 -1.0214 0.2585
## Tree_Density-Odocoileus_virginianus -0.3020 1.1114 -2.0378 -0.4479 2.4728
## Tree_Density-Canis_latrans -0.9428 0.6175 -2.3467 -0.8745 0.0571
## Tree_Density-Procyon_lotor -0.4488 0.4693 -1.3515 -0.4532 0.4530
## Tree_Density-Dasypus_novemcinctus -1.6107 1.2231 -4.8159 -1.2966 -0.1335
## Avg_Canopy_Cover-Odocoileus_virginianus 0.3917 0.9057 -1.4033 0.3988 2.2323
## Avg_Canopy_Cover-Canis_latrans -0.1269 0.4621 -1.0897 -0.1032 0.7400
## Avg_Canopy_Cover-Procyon_lotor 0.8504 0.5091 -0.0354 0.8101 1.9721
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8631 0.4651 0.0388 0.8336 1.8684
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0038 166
## (Intercept)-Canis_latrans 1.0130 1852
## (Intercept)-Procyon_lotor 1.0010 3098
## (Intercept)-Dasypus_novemcinctus 1.0101 1758
## Tree_Density-Odocoileus_virginianus 1.0017 1680
## Tree_Density-Canis_latrans 1.0015 3484
## Tree_Density-Procyon_lotor 1.0027 3642
## Tree_Density-Dasypus_novemcinctus 1.0022 1441
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0094 2621
## Avg_Canopy_Cover-Canis_latrans 1.0017 4039
## Avg_Canopy_Cover-Procyon_lotor 1.0024 3874
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0013 3691
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0064 0.0586 -0.1036 0.0060 0.1240
## (Intercept)-Canis_latrans -2.6252 0.1830 -3.0048 -2.6146 -2.2928
## (Intercept)-Procyon_lotor -2.2519 0.1296 -2.5165 -2.2502 -2.0100
## (Intercept)-Dasypus_novemcinctus -1.5648 0.1326 -1.8303 -1.5630 -1.3112
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0001 2288
## (Intercept)-Procyon_lotor 1.0010 4164
## (Intercept)-Dasypus_novemcinctus 1.0010 5759
# Includes movement covariates of occupancy and null for detection
ms_null_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.6915
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8126 1.1295 -1.6488 0.8549 2.9675 1.0016 5250
## Cogon_Patch_Size 0.2342 0.6251 -0.8717 0.1997 1.6050 1.0033 2928
## Avg_Cogongrass_Cover 0.3180 0.4916 -0.6172 0.3107 1.3205 1.0007 2509
## total_shrub_cover -0.1899 0.5796 -1.3353 -0.1922 1.0381 1.0021 3589
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.6332 43.9747 0.9064 7.5951 116.6431 1.0022 942
## Cogon_Patch_Size 1.9287 7.9478 0.0505 0.4872 11.5099 1.0303 2007
## Avg_Cogongrass_Cover 0.6018 1.5424 0.0401 0.2464 3.3576 1.0238 4698
## total_shrub_cover 1.3499 3.4017 0.0621 0.5384 7.3517 1.0445 4525
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6035 0.9604 0.0429 0.3161 2.9334 1.019 1192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3301 0.7252 -2.6425 -1.3891 0.3199 0.9998 4785
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0636 7.8803 0.4204 1.6155 14.2551 1.1759 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8221 3.0562 2.2094 5.1411
## (Intercept)-Canis_latrans 0.6913 0.7504 -0.5718 0.6260
## (Intercept)-Procyon_lotor 0.9496 0.6833 -0.3577 0.9362
## (Intercept)-Dasypus_novemcinctus -0.6850 0.5903 -1.8634 -0.6724
## Cogon_Patch_Size-Odocoileus_virginianus 0.3051 1.0671 -1.3854 0.1865
## Cogon_Patch_Size-Canis_latrans 1.0013 1.1089 -0.3001 0.7784
## Cogon_Patch_Size-Procyon_lotor -0.0563 0.5379 -1.0620 -0.0751
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0537 0.4335 -0.9388 -0.0465
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3154 0.7358 -1.1347 0.2934
## Avg_Cogongrass_Cover-Canis_latrans 0.3329 0.4763 -0.5465 0.3070
## Avg_Cogongrass_Cover-Procyon_lotor 0.2578 0.5026 -0.7209 0.2403
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4181 0.4110 -0.3643 0.4062
## total_shrub_cover-Odocoileus_virginianus -0.0979 0.8925 -1.8350 -0.1327
## total_shrub_cover-Canis_latrans 0.1972 0.5450 -0.7246 0.1566
## total_shrub_cover-Procyon_lotor -0.9410 0.6100 -2.3349 -0.8698
## total_shrub_cover-Dasypus_novemcinctus -0.0109 0.4002 -0.7892 -0.0098
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.3951 1.0043 370
## (Intercept)-Canis_latrans 2.3684 1.0080 2010
## (Intercept)-Procyon_lotor 2.3529 1.0028 2519
## (Intercept)-Dasypus_novemcinctus 0.4703 1.0075 2950
## Cogon_Patch_Size-Odocoileus_virginianus 2.6745 1.0040 1504
## Cogon_Patch_Size-Canis_latrans 3.8400 1.0074 1318
## Cogon_Patch_Size-Procyon_lotor 1.0224 1.0041 2409
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7558 1.0001 4077
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7867 1.0033 2997
## Avg_Cogongrass_Cover-Canis_latrans 1.3389 1.0007 2797
## Avg_Cogongrass_Cover-Procyon_lotor 1.3186 1.0015 3310
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2747 1.0000 3683
## total_shrub_cover-Odocoileus_virginianus 1.8834 1.0035 2676
## total_shrub_cover-Canis_latrans 1.3901 1.0038 2840
## total_shrub_cover-Procyon_lotor 0.0656 1.0117 3028
## total_shrub_cover-Dasypus_novemcinctus 0.7958 1.0009 4396
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0078 0.0590 -0.1062 0.0076 0.1255
## (Intercept)-Canis_latrans -2.5993 0.1776 -2.9747 -2.5883 -2.2770
## (Intercept)-Procyon_lotor -2.2677 0.1326 -2.5406 -2.2645 -2.0159
## (Intercept)-Dasypus_novemcinctus -1.5651 0.1342 -1.8233 -1.5654 -1.3061
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5250
## (Intercept)-Canis_latrans 1.0035 2330
## (Intercept)-Procyon_lotor 1.0021 4068
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.6993
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7712 1.1335 -1.6386 0.8110 2.9876 1.0003 4978
## Veg_shannon_index 0.5527 0.5160 -0.4650 0.5574 1.5992 1.0135 3286
## Avg_Cogongrass_Cover 0.6562 0.4966 -0.2838 0.6482 1.6981 1.0148 3131
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.7617 40.2526 0.7965 6.8115 79.9406 1.0464 2202
## Veg_shannon_index 0.8910 5.7746 0.0455 0.3193 4.7839 1.2811 2743
## Avg_Cogongrass_Cover 0.7098 2.5636 0.0408 0.2472 3.9392 1.0026 2454
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.874 1.5133 0.0496 0.4224 4.725 1.0653 737
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.329 0.7049 -2.5914 -1.3798 0.2596 1.0019 5605
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0538 7.7797 0.4152 1.6339 13.3681 1.054 4372
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.2547 2.3398 1.9735 4.7750
## (Intercept)-Canis_latrans 0.4682 0.7278 -0.8737 0.4360
## (Intercept)-Procyon_lotor 0.7848 0.6955 -0.5814 0.7790
## (Intercept)-Dasypus_novemcinctus -0.6811 0.6624 -2.0231 -0.6634
## Veg_shannon_index-Odocoileus_virginianus 0.4765 0.7593 -1.0843 0.5076
## Veg_shannon_index-Canis_latrans 0.8923 0.5513 0.0270 0.8397
## Veg_shannon_index-Procyon_lotor 0.7035 0.5548 -0.2134 0.6446
## Veg_shannon_index-Dasypus_novemcinctus 0.2849 0.4028 -0.5090 0.2939
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6321 0.7119 -0.7490 0.6185
## Avg_Cogongrass_Cover-Canis_latrans 0.8282 0.5693 -0.0412 0.7695
## Avg_Cogongrass_Cover-Procyon_lotor 0.7171 0.5430 -0.1977 0.6700
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5941 0.3781 -0.1486 0.5835
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.2319 1.0326 469
## (Intercept)-Canis_latrans 2.0482 1.0026 2191
## (Intercept)-Procyon_lotor 2.1928 1.0113 2645
## (Intercept)-Dasypus_novemcinctus 0.5875 1.0042 2346
## Veg_shannon_index-Odocoileus_virginianus 1.9430 1.0031 2980
## Veg_shannon_index-Canis_latrans 1.9649 1.0430 1396
## Veg_shannon_index-Procyon_lotor 1.9118 1.0384 1367
## Veg_shannon_index-Dasypus_novemcinctus 1.0804 1.0006 4199
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1559 1.0021 2687
## Avg_Cogongrass_Cover-Canis_latrans 1.9880 1.0454 1572
## Avg_Cogongrass_Cover-Procyon_lotor 1.8887 1.0239 2035
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3562 1.0078 3232
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0073 0.0587 -0.1062 0.0064 0.1255
## (Intercept)-Canis_latrans -2.5965 0.1764 -2.9582 -2.5913 -2.2718
## (Intercept)-Procyon_lotor -2.2777 0.1356 -2.5498 -2.2762 -2.0186
## (Intercept)-Dasypus_novemcinctus -1.5633 0.1325 -1.8270 -1.5619 -1.3091
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0027 5250
## (Intercept)-Canis_latrans 1.0013 2835
## (Intercept)-Procyon_lotor 1.0031 3885
## (Intercept)-Dasypus_novemcinctus 1.0000 5050
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.6905
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2246 1.0602 -1.9155 0.1929 2.4265 1.0016 3351
## Avg_Cogongrass_Cover -0.0371 0.6492 -1.2682 -0.0461 1.2300 1.0134 1506
## I(Avg_Cogongrass_Cover^2) 1.2230 1.0121 -0.8059 1.1456 3.2909 1.0050 1612
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 12.9981 34.9216 0.3246 5.0802 70.2801 1.0105 1926
## Avg_Cogongrass_Cover 0.9076 1.9903 0.0451 0.3337 5.5853 1.0204 3537
## I(Avg_Cogongrass_Cover^2) 19.1173 137.7975 0.0629 1.5779 120.8553 1.2373 239
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5296 0.7709 0.0437 0.2874 2.4606 1.0253 1139
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3372 0.7221 -2.5635 -1.4127 0.356 1.0001 5009
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8686 4.4886 0.4455 1.6506 12.2096 1.0106 5159
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9329 2.6712 0.2411 3.3913
## (Intercept)-Canis_latrans -0.6401 0.8798 -2.5966 -0.5942
## (Intercept)-Procyon_lotor -0.1831 0.7918 -1.8591 -0.1389
## (Intercept)-Dasypus_novemcinctus -1.0854 0.6898 -2.4744 -1.0686
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0942 0.9753 -2.0452 -0.0924
## Avg_Cogongrass_Cover-Canis_latrans 0.0918 0.7019 -1.2000 0.0546
## Avg_Cogongrass_Cover-Procyon_lotor -0.0284 0.7268 -1.3465 -0.0658
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.1068 0.6028 -1.3058 -0.1113
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.3503 4.8731 -0.2667 1.8255
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4415 1.8124 0.1415 2.0145
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0803 1.7493 0.0566 1.5819
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4932 0.4519 -0.3613 0.4848
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.8414 1.0131 494
## (Intercept)-Canis_latrans 1.0026 1.0036 894
## (Intercept)-Procyon_lotor 1.2531 1.0029 1195
## (Intercept)-Dasypus_novemcinctus 0.2299 1.0010 2693
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8848 1.0179 1935
## Avg_Cogongrass_Cover-Canis_latrans 1.5920 1.0037 1633
## Avg_Cogongrass_Cover-Procyon_lotor 1.5350 1.0130 1350
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0762 1.0025 2636
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 17.0067 1.0316 125
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.9327 1.0120 296
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.5791 1.0196 427
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4138 1.0012 2448
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0066 0.0587 -0.1093 0.0068 0.1226
## (Intercept)-Canis_latrans -2.6290 0.1718 -2.9778 -2.6238 -2.3104
## (Intercept)-Procyon_lotor -2.2851 0.1325 -2.5500 -2.2796 -2.0335
## (Intercept)-Dasypus_novemcinctus -1.5651 0.1363 -1.8431 -1.5601 -1.3101
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0076 3053
## (Intercept)-Procyon_lotor 1.0041 3236
## (Intercept)-Dasypus_novemcinctus 1.0007 5250
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
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## Sampled: 6500 of 10000, 65.00%
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7185
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1958 1.4577 -2.6988 0.2115 3.0971 1.0001 4741
## Cogon_Patch_Size 0.0950 0.9444 -1.7417 0.0703 2.1361 1.0022 2253
## Veg_shannon_index 0.9710 0.7633 -0.6290 0.9565 2.5309 0.9999 1379
## total_shrub_cover -0.0915 0.6905 -1.4432 -0.1018 1.3464 1.0009 2255
## Avg_Cogongrass_Cover 0.6301 1.1746 -1.6415 0.6262 2.9739 1.0127 1181
## Tree_Density -1.4096 1.3991 -3.7884 -1.5545 1.7167 1.0056 1763
## Avg_Canopy_Cover 0.8989 0.8802 -1.0329 0.9469 2.6082 1.0043 3066
## I(Avg_Cogongrass_Cover^2) 1.8357 1.1218 -0.6437 1.8575 3.9958 1.0033 899
## avg_veg_height -0.0575 0.7773 -1.6027 -0.0545 1.4389 1.0075 1905
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 96.7726 304.6650 4.2240 35.9087 589.3658 1.0250 647
## Cogon_Patch_Size 6.1687 20.9754 0.0803 1.5295 41.9620 1.0143 1664
## Veg_shannon_index 2.2882 10.7971 0.0494 0.5533 13.8681 1.1003 1617
## total_shrub_cover 1.7986 9.5919 0.0547 0.5826 9.4377 1.2490 2709
## Avg_Cogongrass_Cover 4.3335 18.6241 0.0530 0.7149 30.6963 1.0499 1613
## Tree_Density 37.7040 118.6879 0.1349 9.7461 256.8851 1.0304 700
## Avg_Canopy_Cover 4.1994 10.4183 0.0945 1.4870 25.2473 1.0349 1163
## I(Avg_Cogongrass_Cover^2) 13.5256 61.3399 0.0538 0.9081 125.5478 1.0472 292
## avg_veg_height 1.6581 5.2080 0.0538 0.5312 10.2314 1.0327 3648
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2391 7.5411 0.0639 1.0742 20.7625 1.1247 331
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3354 0.7166 -2.6072 -1.3842 0.2316 1.0025 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9746 5.3024 0.4503 1.6628 13.3113 1.0515 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 11.3264 7.2785 2.8192
## (Intercept)-Canis_latrans -1.4876 2.0343 -6.4866
## (Intercept)-Procyon_lotor -0.6317 1.4228 -3.8496
## (Intercept)-Dasypus_novemcinctus -3.7646 2.1537 -9.3647
## Cogon_Patch_Size-Odocoileus_virginianus 0.2031 2.0065 -3.1710
## Cogon_Patch_Size-Canis_latrans 1.6850 1.9837 -0.7906
## Cogon_Patch_Size-Procyon_lotor -0.4313 1.1481 -2.5791
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5514 0.9286 -2.7142
## Veg_shannon_index-Odocoileus_virginianus 0.8327 1.4659 -1.9980
## Veg_shannon_index-Canis_latrans 1.6127 1.1076 0.1132
## Veg_shannon_index-Procyon_lotor 1.2916 0.8282 -0.0116
## Veg_shannon_index-Dasypus_novemcinctus 0.6614 0.6706 -0.6421
## total_shrub_cover-Odocoileus_virginianus 0.0341 1.2610 -2.2119
## total_shrub_cover-Canis_latrans 0.1544 0.8112 -1.2629
## total_shrub_cover-Procyon_lotor -0.7812 0.7560 -2.4235
## total_shrub_cover-Dasypus_novemcinctus 0.2012 0.6099 -0.9102
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5535 1.9461 -3.4047
## Avg_Cogongrass_Cover-Canis_latrans 0.5434 1.6869 -2.6892
## Avg_Cogongrass_Cover-Procyon_lotor 0.7297 1.5460 -2.0328
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2796 1.6837 -1.4077
## Tree_Density-Odocoileus_virginianus 0.1256 3.3264 -4.2833
## Tree_Density-Canis_latrans -4.4856 3.0303 -12.7014
## Tree_Density-Procyon_lotor -1.8498 1.4551 -4.9168
## Tree_Density-Dasypus_novemcinctus -6.2574 3.9295 -16.7607
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7287 1.6955 -2.7992
## Avg_Canopy_Cover-Canis_latrans 0.0246 0.8946 -2.0154
## Avg_Canopy_Cover-Procyon_lotor 1.5491 0.8987 0.0756
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1920 1.1210 0.6579
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.8821 3.2754 -0.6520
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2983 2.7803 0.6829
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.8614 2.1776 0.4266
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.0054 1.2366 0.1115
## avg_veg_height-Odocoileus_virginianus -0.0595 1.2578 -2.5841
## avg_veg_height-Canis_latrans -0.5905 0.9220 -2.5526
## avg_veg_height-Procyon_lotor 0.2277 0.8140 -1.2911
## avg_veg_height-Dasypus_novemcinctus 0.1258 0.7855 -1.4070
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.5472 31.0893 1.0114 149
## (Intercept)-Canis_latrans -1.3086 2.1267 1.0180 504
## (Intercept)-Procyon_lotor -0.5384 1.9197 1.0044 980
## (Intercept)-Dasypus_novemcinctus -3.3523 -0.8315 1.0127 257
## Cogon_Patch_Size-Odocoileus_virginianus 0.0493 4.7105 1.0133 1351
## Cogon_Patch_Size-Canis_latrans 1.2534 6.9846 1.0018 892
## Cogon_Patch_Size-Procyon_lotor -0.4526 1.8441 1.0193 990
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4484 0.9801 1.0079 973
## Veg_shannon_index-Odocoileus_virginianus 0.8892 3.3824 1.0181 1442
## Veg_shannon_index-Canis_latrans 1.4276 4.2946 1.0143 595
## Veg_shannon_index-Procyon_lotor 1.1991 3.1617 1.0078 564
## Veg_shannon_index-Dasypus_novemcinctus 0.6507 1.9915 1.0006 1425
## total_shrub_cover-Odocoileus_virginianus -0.0035 2.6866 1.0173 1963
## total_shrub_cover-Canis_latrans 0.1010 2.0259 1.0058 1688
## total_shrub_cover-Procyon_lotor -0.7170 0.5529 1.0085 1672
## total_shrub_cover-Dasypus_novemcinctus 0.1739 1.5102 1.0003 2768
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5606 4.2969 1.0105 1501
## Avg_Cogongrass_Cover-Canis_latrans 0.5437 3.6978 1.0174 1244
## Avg_Cogongrass_Cover-Procyon_lotor 0.6451 4.0540 1.0040 1020
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1132 5.1334 1.0098 944
## Tree_Density-Odocoileus_virginianus -0.6443 8.9304 1.0090 647
## Tree_Density-Canis_latrans -3.7486 -0.8298 1.0126 286
## Tree_Density-Procyon_lotor -1.7659 0.6569 1.0055 1457
## Tree_Density-Dasypus_novemcinctus -5.3399 -1.6492 1.0161 267
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8118 4.0441 1.0100 1623
## Avg_Canopy_Cover-Canis_latrans 0.0801 1.5935 1.0064 1269
## Avg_Canopy_Cover-Procyon_lotor 1.4558 3.6448 1.0136 819
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9697 5.0279 1.0124 292
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.2422 12.4836 1.0154 285
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5484 12.0698 1.0358 136
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3208 9.2347 1.0081 286
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.8252 4.9142 1.0006 568
## avg_veg_height-Odocoileus_virginianus -0.0580 2.4146 1.0040 2205
## avg_veg_height-Canis_latrans -0.5444 1.0217 1.0046 1535
## avg_veg_height-Procyon_lotor 0.2009 1.9251 1.0025 2040
## avg_veg_height-Dasypus_novemcinctus 0.1314 1.7105 1.0045 1979
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0586 -0.1115 0.0061 0.1176
## (Intercept)-Canis_latrans -2.6204 0.1776 -2.9723 -2.6197 -2.2920
## (Intercept)-Procyon_lotor -2.2731 0.1332 -2.5388 -2.2699 -2.0200
## (Intercept)-Dasypus_novemcinctus -1.5630 0.1329 -1.8265 -1.5597 -1.3068
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0017 5250
## (Intercept)-Canis_latrans 1.0004 1833
## (Intercept)-Procyon_lotor 1.0012 2763
## (Intercept)-Dasypus_novemcinctus 1.0000 5429
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_full_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9443
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8370 1.0767 -1.3981 0.8803 2.9257 1.0016 5250
## Avg_Cogongrass_Cover 0.4449 0.4744 -0.4572 0.4369 1.4082 1.0003 3110
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.8821 42.5022 0.6456 5.9195 92.3912 1.0372 1195
## Avg_Cogongrass_Cover 0.6696 2.2615 0.0385 0.2526 3.6335 1.0936 4903
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5886 0.9822 0.0424 0.2969 3.0615 1.032 867
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4002 0.7422 -2.7130 -1.4552 0.2917 1.0003 5250
## shrub_cover 0.1530 0.3748 -0.6021 0.1544 0.9173 1.0006 5250
## veg_height -0.0885 0.3773 -0.8298 -0.0920 0.6809 1.0016 5250
## week 0.0263 0.2364 -0.4608 0.0293 0.4856 1.0035 5014
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4736 12.4704 0.4587 1.7491 15.2847 1.0327 5250
## shrub_cover 0.6848 1.9359 0.0614 0.3261 3.3410 1.0536 5250
## veg_height 0.6578 1.3733 0.0762 0.3432 3.2723 1.0500 4855
## week 0.2486 0.7468 0.0302 0.1213 1.1268 1.0620 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.2514 2.9146 1.8966 4.4868
## (Intercept)-Canis_latrans 0.6940 0.6756 -0.5402 0.6510
## (Intercept)-Procyon_lotor 0.7382 0.5985 -0.4305 0.7386
## (Intercept)-Dasypus_novemcinctus -0.5491 0.5962 -1.6894 -0.5483
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4176 0.7268 -0.9539 0.3950
## Avg_Cogongrass_Cover-Canis_latrans 0.6570 0.5315 -0.1864 0.5918
## Avg_Cogongrass_Cover-Procyon_lotor 0.3424 0.4088 -0.4238 0.3205
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4601 0.3687 -0.2353 0.4499
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.3139 1.0211 363
## (Intercept)-Canis_latrans 2.1763 1.0020 2181
## (Intercept)-Procyon_lotor 1.9115 1.0037 2460
## (Intercept)-Dasypus_novemcinctus 0.6710 1.0089 1664
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9286 1.0011 2707
## Avg_Cogongrass_Cover-Canis_latrans 1.9052 1.0066 2623
## Avg_Cogongrass_Cover-Procyon_lotor 1.1724 1.0020 4201
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2146 1.0031 3497
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0075 0.0602 -0.1102 0.0088 0.1266
## (Intercept)-Canis_latrans -2.7869 0.2003 -3.2024 -2.7792 -2.4159
## (Intercept)-Procyon_lotor -2.2971 0.1463 -2.5968 -2.2918 -2.0270
## (Intercept)-Dasypus_novemcinctus -1.7094 0.1582 -2.0259 -1.7042 -1.4157
## shrub_cover-Odocoileus_virginianus -0.0540 0.0645 -0.1771 -0.0534 0.0753
## shrub_cover-Canis_latrans -0.2740 0.2159 -0.6988 -0.2725 0.1342
## shrub_cover-Procyon_lotor 0.2373 0.1695 -0.1073 0.2417 0.5608
## shrub_cover-Dasypus_novemcinctus 0.7607 0.3048 0.1982 0.7497 1.3932
## veg_height-Odocoileus_virginianus -0.3015 0.0655 -0.4322 -0.3012 -0.1726
## veg_height-Canis_latrans -0.6585 0.1975 -1.0651 -0.6529 -0.2869
## veg_height-Procyon_lotor 0.3389 0.1261 0.0934 0.3394 0.5858
## veg_height-Dasypus_novemcinctus 0.2252 0.1356 -0.0416 0.2271 0.4955
## week-Odocoileus_virginianus 0.2199 0.0617 0.0988 0.2198 0.3399
## week-Canis_latrans 0.0897 0.1330 -0.1832 0.0938 0.3413
## week-Procyon_lotor -0.0384 0.1210 -0.2866 -0.0348 0.1898
## week-Dasypus_novemcinctus -0.1557 0.1415 -0.4501 -0.1484 0.1114
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5025
## (Intercept)-Canis_latrans 1.0021 2109
## (Intercept)-Procyon_lotor 1.0015 3484
## (Intercept)-Dasypus_novemcinctus 1.0039 4172
## shrub_cover-Odocoileus_virginianus 1.0020 5250
## shrub_cover-Canis_latrans 1.0014 2617
## shrub_cover-Procyon_lotor 1.0005 3606
## shrub_cover-Dasypus_novemcinctus 1.0008 3588
## veg_height-Odocoileus_virginianus 1.0005 5250
## veg_height-Canis_latrans 0.9999 2088
## veg_height-Procyon_lotor 1.0001 3896
## veg_height-Dasypus_novemcinctus 1.0017 4684
## week-Odocoileus_virginianus 1.0007 4953
## week-Canis_latrans 1.0004 4209
## week-Procyon_lotor 1.0004 4449
## week-Dasypus_novemcinctus 1.0010 4751
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_null_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7288
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7618 1.0756 -1.4627 0.7829 2.8861 1.001 4629
## Avg_Cogongrass_Cover 0.4112 0.4379 -0.4565 0.4047 1.2853 1.002 3502
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.7621 44.4052 0.6814 6.0214 80.7662 1.0638 1881
## Avg_Cogongrass_Cover 0.5519 1.4400 0.0372 0.2245 3.0316 1.0116 4178
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5579 0.8769 0.0434 0.2884 2.8256 1.028 1298
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.312 0.7215 -2.5569 -1.3753 0.3306 1.0011 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8742 4.4824 0.4462 1.6181 13.2355 1.042 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0837 2.6832 1.9227 4.4283
## (Intercept)-Canis_latrans 0.4411 0.6079 -0.7145 0.4274
## (Intercept)-Procyon_lotor 0.7203 0.5852 -0.4857 0.7262
## (Intercept)-Dasypus_novemcinctus -0.6191 0.5798 -1.8274 -0.5977
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3814 0.6724 -0.8961 0.3621
## Avg_Cogongrass_Cover-Canis_latrans 0.5018 0.4244 -0.2468 0.4746
## Avg_Cogongrass_Cover-Procyon_lotor 0.3862 0.4178 -0.3690 0.3705
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4329 0.3523 -0.2486 0.4324
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.6078 1.0115 428
## (Intercept)-Canis_latrans 1.7136 1.0025 2812
## (Intercept)-Procyon_lotor 1.8689 1.0024 3732
## (Intercept)-Dasypus_novemcinctus 0.5061 1.0130 2891
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7832 1.0087 3112
## Avg_Cogongrass_Cover-Canis_latrans 1.4281 1.0010 3807
## Avg_Cogongrass_Cover-Procyon_lotor 1.2615 1.0033 3883
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1607 1.0009 4348
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0063 0.0596 -0.1096 0.0067 0.1233
## (Intercept)-Canis_latrans -2.6064 0.1744 -2.9652 -2.6049 -2.2827
## (Intercept)-Procyon_lotor -2.2589 0.1316 -2.5217 -2.2569 -2.0083
## (Intercept)-Dasypus_novemcinctus -1.5625 0.1325 -1.8278 -1.5599 -1.3127
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0032 5250
## (Intercept)-Canis_latrans 1.0028 2811
## (Intercept)-Procyon_lotor 0.9999 3927
## (Intercept)-Dasypus_novemcinctus 1.0008 5181
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.878
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7727 1.0819 -1.5498 0.7939 2.8841 1.0046 5250
## Avg_Cogongrass_Cover 0.4105 0.4364 -0.4666 0.4092 1.2776 1.0011 3578
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.2627 87.7280 0.6993 6.344 88.5624 1.2529 646
## Avg_Cogongrass_Cover 0.5156 1.1081 0.0387 0.218 2.9528 1.0221 4425
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5399 0.8457 0.0422 0.28 2.6159 1.0073 1141
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3044 0.7449 -2.5833 -1.3668 0.4362 1.0038 5553
## week 0.0266 0.2439 -0.4679 0.0300 0.4924 1.0016 5044
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0497 5.7195 0.4288 1.6346 13.7558 1.0078 5250
## week 0.2681 1.1814 0.0304 0.1212 1.2420 1.0159 4727
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.3086 3.2401 1.8708 4.5570
## (Intercept)-Canis_latrans 0.4314 0.6066 -0.7402 0.4209
## (Intercept)-Procyon_lotor 0.7091 0.5883 -0.4752 0.7101
## (Intercept)-Dasypus_novemcinctus -0.6212 0.5686 -1.7599 -0.6073
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3831 0.6639 -0.8807 0.3599
## Avg_Cogongrass_Cover-Canis_latrans 0.4952 0.4185 -0.2491 0.4673
## Avg_Cogongrass_Cover-Procyon_lotor 0.3812 0.4206 -0.3920 0.3545
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4374 0.3535 -0.2188 0.4295
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.1555 1.1467 280
## (Intercept)-Canis_latrans 1.6692 1.0004 2791
## (Intercept)-Procyon_lotor 1.8424 1.0005 3245
## (Intercept)-Dasypus_novemcinctus 0.5135 1.0071 3156
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7544 1.0009 3119
## Avg_Cogongrass_Cover-Canis_latrans 1.3979 0.9999 3667
## Avg_Cogongrass_Cover-Procyon_lotor 1.2625 1.0001 4013
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1641 1.0019 4345
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0075 0.0586 -0.1071 0.0075 0.1261
## (Intercept)-Canis_latrans -2.6076 0.1763 -2.9614 -2.6037 -2.2741
## (Intercept)-Procyon_lotor -2.2626 0.1303 -2.5205 -2.2612 -2.0155
## (Intercept)-Dasypus_novemcinctus -1.5743 0.1338 -1.8445 -1.5728 -1.3224
## week-Odocoileus_virginianus 0.2139 0.0612 0.0950 0.2141 0.3343
## week-Canis_latrans 0.0890 0.1296 -0.1711 0.0921 0.3291
## week-Procyon_lotor -0.0370 0.1197 -0.2803 -0.0332 0.1832
## week-Dasypus_novemcinctus -0.1581 0.1427 -0.4507 -0.1530 0.1020
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0084 3129
## (Intercept)-Procyon_lotor 1.0027 4003
## (Intercept)-Dasypus_novemcinctus 1.0009 5031
## week-Odocoileus_virginianus 1.0015 5250
## week-Canis_latrans 1.0040 4427
## week-Procyon_lotor 1.0028 4219
## week-Dasypus_novemcinctus 1.0010 4859
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9293
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6804 1.4200 -2.2248 0.7345 3.3603 1.0012 5250
## Cogon_Patch_Size -0.3265 0.8240 -1.8638 -0.3639 1.5079 1.0058 2304
## Veg_shannon_index 0.9449 0.7159 -0.5537 0.9413 2.3816 1.0021 1520
## total_shrub_cover -0.0007 0.6954 -1.3420 -0.0394 1.4684 1.0016 2352
## Avg_Cogongrass_Cover 2.0810 0.9980 -0.0101 2.0692 4.1058 1.0079 1125
## Tree_Density -1.3737 1.1579 -3.3978 -1.4766 1.2949 1.0007 2335
## Avg_Canopy_Cover 0.8916 0.7948 -0.8963 0.9165 2.4477 1.0008 2700
## avg_veg_height -0.3534 0.6629 -1.7076 -0.3436 0.9628 1.0026 1931
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 82.6871 215.3828 3.0632 30.0613 506.7357 1.1315 520
## Cogon_Patch_Size 3.1898 10.8227 0.0594 0.8419 19.5496 1.0575 2761
## Veg_shannon_index 1.8772 19.3859 0.0503 0.4489 10.2501 1.2909 3908
## total_shrub_cover 1.9634 5.3628 0.0603 0.6492 12.4784 1.0458 1751
## Avg_Cogongrass_Cover 3.0604 17.7790 0.0518 0.5279 21.0486 1.1638 2880
## Tree_Density 14.8652 57.6507 0.0903 2.9141 103.1122 1.0887 1152
## Avg_Canopy_Cover 2.8210 5.8249 0.0896 1.1120 16.7360 1.0046 2051
## avg_veg_height 1.1455 4.9249 0.0449 0.3450 6.3234 1.0253 3397
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3003 5.1707 0.0582 0.7845 14.824 1.0367 356
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3332 0.7302 -2.6069 -1.3895 0.3192 1.0016 5250
## week 0.0200 0.2469 -0.4666 0.0226 0.4979 1.0039 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9366 4.7996 0.4349 1.6567 13.8900 1.0195 5250
## week 0.2547 0.6811 0.0300 0.1192 1.1358 1.0351 4082
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 11.6072 6.8636 3.8709 9.8316
## (Intercept)-Canis_latrans 1.0480 1.3506 -0.9984 0.8610
## (Intercept)-Procyon_lotor 0.9457 1.0618 -1.2569 0.9426
## (Intercept)-Dasypus_novemcinctus -1.7718 1.3372 -4.8452 -1.5931
## Cogon_Patch_Size-Odocoileus_virginianus -0.2795 1.5403 -2.9412 -0.4215
## Cogon_Patch_Size-Canis_latrans 0.6541 1.4605 -1.2338 0.3413
## Cogon_Patch_Size-Procyon_lotor -0.8631 0.8128 -2.4885 -0.8463
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8242 0.7177 -2.4335 -0.7486
## Veg_shannon_index-Odocoileus_virginianus 0.8500 1.1996 -1.7574 0.8982
## Veg_shannon_index-Canis_latrans 1.4545 0.9402 0.1089 1.3233
## Veg_shannon_index-Procyon_lotor 1.2221 0.7082 0.0019 1.1679
## Veg_shannon_index-Dasypus_novemcinctus 0.7022 0.5998 -0.4526 0.6859
## total_shrub_cover-Odocoileus_virginianus 0.1673 1.1938 -2.0837 0.0801
## total_shrub_cover-Canis_latrans 0.4427 0.9363 -0.8838 0.3031
## total_shrub_cover-Procyon_lotor -0.7482 0.6792 -2.2195 -0.6982
## total_shrub_cover-Dasypus_novemcinctus 0.1981 0.5624 -0.8506 0.1747
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1197 1.4615 -0.7282 2.0709
## Avg_Cogongrass_Cover-Canis_latrans 2.5742 1.2793 0.6367 2.4316
## Avg_Cogongrass_Cover-Procyon_lotor 2.2023 1.0556 0.3594 2.1267
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6841 1.2229 0.7855 2.5550
## Tree_Density-Odocoileus_virginianus -0.1976 2.6846 -3.6499 -0.7895
## Tree_Density-Canis_latrans -2.8538 1.8195 -7.2544 -2.4679
## Tree_Density-Procyon_lotor -1.2900 0.8762 -3.0153 -1.3032
## Tree_Density-Dasypus_novemcinctus -4.1665 2.8493 -11.5294 -3.3996
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7606 1.4182 -2.3347 0.8164
## Avg_Canopy_Cover-Canis_latrans 0.1467 0.7727 -1.4869 0.1890
## Avg_Canopy_Cover-Procyon_lotor 1.4782 0.7694 0.2187 1.3984
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8808 0.8434 0.6121 1.7569
## avg_veg_height-Odocoileus_virginianus -0.3762 1.0230 -2.4189 -0.3600
## avg_veg_height-Canis_latrans -0.6466 0.7734 -2.2324 -0.5961
## avg_veg_height-Procyon_lotor -0.2586 0.6578 -1.5235 -0.2786
## avg_veg_height-Dasypus_novemcinctus -0.2155 0.6669 -1.5261 -0.2228
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 29.6160 1.0555 157
## (Intercept)-Canis_latrans 4.2720 1.0063 814
## (Intercept)-Procyon_lotor 3.1812 1.0009 1271
## (Intercept)-Dasypus_novemcinctus 0.2520 1.0033 606
## Cogon_Patch_Size-Odocoileus_virginianus 3.4757 1.0061 1662
## Cogon_Patch_Size-Canis_latrans 4.4840 1.0079 1383
## Cogon_Patch_Size-Procyon_lotor 0.6544 1.0052 761
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4115 1.0023 964
## Veg_shannon_index-Odocoileus_virginianus 3.0749 1.0058 1833
## Veg_shannon_index-Canis_latrans 3.5732 1.0128 849
## Veg_shannon_index-Procyon_lotor 2.7871 1.0020 755
## Veg_shannon_index-Dasypus_novemcinctus 1.9276 1.0014 1582
## total_shrub_cover-Odocoileus_virginianus 2.8473 1.0034 2166
## total_shrub_cover-Canis_latrans 2.5725 1.0045 864
## total_shrub_cover-Procyon_lotor 0.4539 1.0041 2255
## total_shrub_cover-Dasypus_novemcinctus 1.3809 1.0013 3169
## Avg_Cogongrass_Cover-Odocoileus_virginianus 5.1758 1.0027 1027
## Avg_Cogongrass_Cover-Canis_latrans 5.3710 1.0065 674
## Avg_Cogongrass_Cover-Procyon_lotor 4.4876 1.0094 934
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.4751 1.0019 612
## Tree_Density-Odocoileus_virginianus 7.0404 1.0039 531
## Tree_Density-Canis_latrans -0.5665 1.0117 500
## Tree_Density-Procyon_lotor 0.4034 1.0027 2196
## Tree_Density-Dasypus_novemcinctus -1.1060 1.0022 471
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5849 1.0038 1951
## Avg_Canopy_Cover-Canis_latrans 1.5541 1.0029 1424
## Avg_Canopy_Cover-Procyon_lotor 3.2725 1.0006 1114
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.8396 1.0004 792
## avg_veg_height-Odocoileus_virginianus 1.6763 1.0006 2378
## avg_veg_height-Canis_latrans 0.7084 1.0023 1529
## avg_veg_height-Procyon_lotor 1.0811 1.0053 2054
## avg_veg_height-Dasypus_novemcinctus 1.0677 1.0013 1875
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0072 0.0590 -0.1078 0.0071 0.1237
## (Intercept)-Canis_latrans -2.6541 0.1830 -3.0231 -2.6486 -2.3140
## (Intercept)-Procyon_lotor -2.2674 0.1322 -2.5439 -2.2636 -2.0182
## (Intercept)-Dasypus_novemcinctus -1.5746 0.1336 -1.8372 -1.5708 -1.3210
## week-Odocoileus_virginianus 0.2141 0.0615 0.0922 0.2137 0.3379
## week-Canis_latrans 0.0886 0.1314 -0.1792 0.0918 0.3337
## week-Procyon_lotor -0.0395 0.1202 -0.2819 -0.0339 0.1794
## week-Dasypus_novemcinctus -0.1563 0.1408 -0.4388 -0.1518 0.0994
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 4959
## (Intercept)-Canis_latrans 1.0007 1863
## (Intercept)-Procyon_lotor 1.0023 3382
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## week-Odocoileus_virginianus 1.0024 5250
## week-Canis_latrans 1.0022 4311
## week-Procyon_lotor 1.0014 4637
## week-Dasypus_novemcinctus 1.0000 4274
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8978
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8176 1.1641 -1.7032 0.8494 3.0469 1.0002 5250
## Avg_Cogongrass_Cover 0.2972 0.5403 -0.7413 0.2992 1.4061 1.0061 2947
## total_shrub_cover -0.1600 0.5936 -1.3509 -0.1603 1.0800 1.0004 4174
## avg_veg_height 0.1270 0.5276 -0.8787 0.1259 1.1639 1.0036 2695
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 27.6105 110.7987 0.9531 8.0257 161.4074 1.2914 249
## Avg_Cogongrass_Cover 0.8361 3.2477 0.0416 0.2970 4.6093 1.0916 5250
## total_shrub_cover 1.4758 3.2014 0.0611 0.6145 7.9499 1.0015 3671
## avg_veg_height 0.7368 1.9715 0.0433 0.2667 3.8953 1.0325 5250
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5933 0.9087 0.0443 0.3086 2.8104 1.0011 1070
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3329 0.7228 -2.6044 -1.3878 0.3282 1.0014 5250
## week 0.0224 0.2373 -0.4671 0.0301 0.4716 1.0017 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1045 6.0515 0.4396 1.7237 13.9170 1.0222 5250
## week 0.2416 0.5201 0.0298 0.1171 1.2733 1.0405 4295
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 6.2875 4.6530 2.2735 5.1854
## (Intercept)-Canis_latrans 0.5410 0.7173 -0.7107 0.4720
## (Intercept)-Procyon_lotor 0.9856 0.6871 -0.2732 0.9419
## (Intercept)-Dasypus_novemcinctus -0.6857 0.5880 -1.8497 -0.6823
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3059 0.8186 -1.2976 0.2898
## Avg_Cogongrass_Cover-Canis_latrans 0.5278 0.5700 -0.4956 0.4874
## Avg_Cogongrass_Cover-Procyon_lotor 0.1291 0.5491 -0.9749 0.1312
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2900 0.4558 -0.5854 0.2887
## total_shrub_cover-Odocoileus_virginianus -0.0587 0.9112 -1.8276 -0.0999
## total_shrub_cover-Canis_latrans 0.2677 0.5691 -0.6799 0.2243
## total_shrub_cover-Procyon_lotor -0.9689 0.6381 -2.4958 -0.8949
## total_shrub_cover-Dasypus_novemcinctus 0.0425 0.4026 -0.7208 0.0389
## avg_veg_height-Odocoileus_virginianus 0.0982 0.7677 -1.4266 0.0977
## avg_veg_height-Canis_latrans -0.0632 0.5328 -1.1070 -0.0590
## avg_veg_height-Procyon_lotor 0.1901 0.5386 -0.8397 0.1787
## avg_veg_height-Dasypus_novemcinctus 0.2737 0.4731 -0.6375 0.2740
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.5876 1.2276 102
## (Intercept)-Canis_latrans 2.1772 1.0020 2134
## (Intercept)-Procyon_lotor 2.4451 1.0065 2488
## (Intercept)-Dasypus_novemcinctus 0.4651 1.0020 3049
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0826 1.0020 3027
## Avg_Cogongrass_Cover-Canis_latrans 1.8075 1.0003 2930
## Avg_Cogongrass_Cover-Procyon_lotor 1.2174 1.0026 3406
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1893 1.0050 3300
## total_shrub_cover-Odocoileus_virginianus 1.9168 1.0011 2805
## total_shrub_cover-Canis_latrans 1.5380 1.0043 2210
## total_shrub_cover-Procyon_lotor 0.0536 1.0032 2941
## total_shrub_cover-Dasypus_novemcinctus 0.8626 1.0008 4872
## avg_veg_height-Odocoileus_virginianus 1.6507 1.0024 3165
## avg_veg_height-Canis_latrans 0.9664 1.0017 2917
## avg_veg_height-Procyon_lotor 1.2707 1.0012 3234
## avg_veg_height-Dasypus_novemcinctus 1.2333 1.0017 3434
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0096 0.0599 -0.1069 0.0088 0.1269
## (Intercept)-Canis_latrans -2.6399 0.1838 -3.0194 -2.6330 -2.2961
## (Intercept)-Procyon_lotor -2.2750 0.1308 -2.5360 -2.2721 -2.0269
## (Intercept)-Dasypus_novemcinctus -1.5765 0.1352 -1.8413 -1.5764 -1.3152
## week-Odocoileus_virginianus 0.2128 0.0610 0.0953 0.2124 0.3362
## week-Canis_latrans 0.0843 0.1311 -0.1840 0.0877 0.3335
## week-Procyon_lotor -0.0376 0.1214 -0.2887 -0.0346 0.1918
## week-Dasypus_novemcinctus -0.1538 0.1426 -0.4429 -0.1485 0.1133
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5000
## (Intercept)-Canis_latrans 1.0009 2468
## (Intercept)-Procyon_lotor 1.0005 3736
## (Intercept)-Dasypus_novemcinctus 1.0017 5047
## week-Odocoileus_virginianus 1.0007 5250
## week-Canis_latrans 1.0015 4245
## week-Procyon_lotor 1.0029 4585
## week-Dasypus_novemcinctus 0.9999 4970
# Includes week covariate for detection and none for occupancy
ms_week_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8857
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7426 1.0258 -1.4283 0.7818 2.7334 1.0008 5250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.9799 75.5167 0.708 5.456 74.9658 1.1239 1525
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3142 0.7323 -2.5977 -1.3795 0.3606 1.0014 5250
## week 0.0251 0.2364 -0.4555 0.0260 0.5056 1.0016 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2236 8.1059 0.4245 1.6825 15.4826 1.1334 5250
## week 0.2362 0.5783 0.0304 0.1207 1.0848 1.1689 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8299 2.6085 2.0281 4.1848 11.7273
## (Intercept)-Canis_latrans 0.3785 0.4225 -0.3976 0.3616 1.2601
## (Intercept)-Procyon_lotor 0.7788 0.4031 0.0029 0.7680 1.6230
## (Intercept)-Dasypus_novemcinctus -0.6070 0.3782 -1.3740 -0.5898 0.1127
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0563 350
## (Intercept)-Canis_latrans 1.0009 4438
## (Intercept)-Procyon_lotor 1.0010 5250
## (Intercept)-Dasypus_novemcinctus 1.0002 4465
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0083 0.0599 -0.1079 0.0084 0.1273
## (Intercept)-Canis_latrans -2.6116 0.1785 -2.9746 -2.6062 -2.2830
## (Intercept)-Procyon_lotor -2.2603 0.1319 -2.5208 -2.2587 -2.0035
## (Intercept)-Dasypus_novemcinctus -1.5713 0.1344 -1.8414 -1.5695 -1.3156
## week-Odocoileus_virginianus 0.2153 0.0603 0.0994 0.2154 0.3348
## week-Canis_latrans 0.0881 0.1355 -0.1901 0.0919 0.3416
## week-Procyon_lotor -0.0351 0.1179 -0.2763 -0.0346 0.1809
## week-Dasypus_novemcinctus -0.1558 0.1421 -0.4484 -0.1518 0.1089
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0013 2863
## (Intercept)-Procyon_lotor 0.9999 4307
## (Intercept)-Dasypus_novemcinctus 1.0000 5250
## week-Odocoileus_virginianus 1.0005 4918
## week-Canis_latrans 1.0000 4344
## week-Procyon_lotor 1.0012 4146
## week-Dasypus_novemcinctus 1.0018 4927
#Includes week for detection and only foraging for occupancy
ms_week_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9218
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7545 1.1329 -1.6414 0.7970 2.9726 1.0013 4405
## Veg_shannon_index 0.5358 0.5039 -0.4636 0.5396 1.5379 1.0083 3058
## Avg_Cogongrass_Cover 0.6484 0.4832 -0.3107 0.6488 1.6370 1.0054 2715
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.1966 52.7715 0.8671 7.0101 92.6502 1.1221 3163
## Veg_shannon_index 0.8024 2.2346 0.0422 0.3131 4.2283 1.0251 4944
## Avg_Cogongrass_Cover 0.6629 2.2221 0.0397 0.2518 3.4174 1.0314 4885
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7322 1.1708 0.0485 0.3693 3.5445 1.0323 1087
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3201 0.7352 -2.6385 -1.3748 0.3608 1.0001 5250
## week 0.0288 0.2497 -0.4547 0.0278 0.5181 1.0034 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0619 5.9567 0.4404 1.6399 14.3903 1.0144 5250
## week 0.2533 0.7774 0.0298 0.1236 1.2012 1.0592 4776
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.4041 2.5794 2.0655 4.8275
## (Intercept)-Canis_latrans 0.4345 0.6909 -0.8289 0.4035
## (Intercept)-Procyon_lotor 0.8096 0.6685 -0.5513 0.8085
## (Intercept)-Dasypus_novemcinctus -0.6722 0.6114 -1.9062 -0.6662
## Veg_shannon_index-Odocoileus_virginianus 0.4495 0.7664 -1.1907 0.4698
## Veg_shannon_index-Canis_latrans 0.8611 0.4737 0.0208 0.8223
## Veg_shannon_index-Procyon_lotor 0.6646 0.5116 -0.2076 0.6161
## Veg_shannon_index-Dasypus_novemcinctus 0.2836 0.3975 -0.5335 0.2997
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6481 0.7413 -0.7899 0.6263
## Avg_Cogongrass_Cover-Canis_latrans 0.8024 0.4890 -0.0428 0.7618
## Avg_Cogongrass_Cover-Procyon_lotor 0.6905 0.5179 -0.1926 0.6469
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5889 0.3823 -0.1696 0.5845
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.0894 1.0036 534
## (Intercept)-Canis_latrans 1.8499 1.0066 2625
## (Intercept)-Procyon_lotor 2.1531 1.0004 2816
## (Intercept)-Dasypus_novemcinctus 0.4945 1.0024 2813
## Veg_shannon_index-Odocoileus_virginianus 1.8819 1.0029 3083
## Veg_shannon_index-Canis_latrans 1.8991 1.0083 2919
## Veg_shannon_index-Procyon_lotor 1.7838 1.0032 2211
## Veg_shannon_index-Dasypus_novemcinctus 1.0313 1.0036 4443
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1828 1.0029 2881
## Avg_Cogongrass_Cover-Canis_latrans 1.8807 1.0104 2890
## Avg_Cogongrass_Cover-Procyon_lotor 1.8435 1.0029 2691
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3618 1.0014 3692
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0084 0.0588 -0.1080 0.0084 0.1225
## (Intercept)-Canis_latrans -2.6070 0.1745 -2.9679 -2.5977 -2.2864
## (Intercept)-Procyon_lotor -2.2763 0.1339 -2.5452 -2.2739 -2.0231
## (Intercept)-Dasypus_novemcinctus -1.5756 0.1331 -1.8396 -1.5752 -1.3225
## week-Odocoileus_virginianus 0.2140 0.0616 0.0949 0.2118 0.3366
## week-Canis_latrans 0.0851 0.1306 -0.1764 0.0883 0.3275
## week-Procyon_lotor -0.0383 0.1212 -0.2886 -0.0327 0.1902
## week-Dasypus_novemcinctus -0.1561 0.1423 -0.4489 -0.1494 0.1057
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 0.9998 2990
## (Intercept)-Procyon_lotor 1.0011 3787
## (Intercept)-Dasypus_novemcinctus 0.9998 5250
## week-Odocoileus_virginianus 1.0020 5250
## week-Canis_latrans 1.0014 4941
## week-Procyon_lotor 1.0010 4244
## week-Dasypus_novemcinctus 1.0009 4866
# Includes movement covariates of occupancy and week for detection
ms_week_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
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## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
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## Sampled: 1000 of 10000, 10.00%
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## Sampled: 4500 of 10000, 45.00%
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## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
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## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8878
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8522 1.1555 -1.5966 0.8920 3.0603 1.0014 5250
## Cogon_Patch_Size 0.2651 0.6197 -0.8608 0.2353 1.6357 1.0096 3327
## Avg_Cogongrass_Cover 0.3064 0.4941 -0.5995 0.2988 1.2887 1.0015 3435
## total_shrub_cover -0.2035 0.5789 -1.3756 -0.2059 0.9864 1.0000 3562
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.0140 45.5400 0.8671 7.7415 107.5546 1.0159 1370
## Cogon_Patch_Size 1.7043 6.1767 0.0521 0.5233 9.9976 1.1073 3689
## Avg_Cogongrass_Cover 0.6483 1.9611 0.0376 0.2370 3.7701 1.1413 4723
## total_shrub_cover 1.5292 7.4346 0.0608 0.5446 8.0507 1.2305 2532
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5836 0.8783 0.0455 0.3053 2.9139 1.001 1224
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3231 0.7223 -2.5984 -1.3880 0.3459 1.0027 5250
## week 0.0200 0.2447 -0.4782 0.0233 0.4985 1.0030 5024
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1854 9.592 0.4314 1.6804 14.2404 1.1671 4816
## week 0.2617 1.321 0.0300 0.1216 1.1913 1.2504 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8513 2.9630 2.2301 5.1678
## (Intercept)-Canis_latrans 0.6681 0.6908 -0.5861 0.6111
## (Intercept)-Procyon_lotor 0.9888 0.6892 -0.2642 0.9419
## (Intercept)-Dasypus_novemcinctus -0.6682 0.5964 -1.8936 -0.6529
## Cogon_Patch_Size-Odocoileus_virginianus 0.3210 0.9855 -1.3900 0.2322
## Cogon_Patch_Size-Canis_latrans 1.0048 0.9505 -0.2313 0.8147
## Cogon_Patch_Size-Procyon_lotor -0.0309 0.5430 -1.0335 -0.0407
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0332 0.4359 -0.9305 -0.0252
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3006 0.7328 -1.0877 0.2818
## Avg_Cogongrass_Cover-Canis_latrans 0.3247 0.4580 -0.5357 0.3057
## Avg_Cogongrass_Cover-Procyon_lotor 0.2617 0.5079 -0.6754 0.2476
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3917 0.3993 -0.3798 0.3870
## total_shrub_cover-Odocoileus_virginianus -0.0977 0.9162 -1.7837 -0.1429
## total_shrub_cover-Canis_latrans 0.1835 0.5347 -0.7325 0.1464
## total_shrub_cover-Procyon_lotor -0.9577 0.6562 -2.5120 -0.8714
## total_shrub_cover-Dasypus_novemcinctus -0.0125 0.3983 -0.8015 -0.0201
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.8941 1.0097 419
## (Intercept)-Canis_latrans 2.1768 1.0002 2222
## (Intercept)-Procyon_lotor 2.5020 1.0053 2860
## (Intercept)-Dasypus_novemcinctus 0.4818 1.0009 2831
## Cogon_Patch_Size-Odocoileus_virginianus 2.4535 1.0063 2486
## Cogon_Patch_Size-Canis_latrans 3.5780 1.0009 1907
## Cogon_Patch_Size-Procyon_lotor 1.0389 1.0077 3637
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7990 1.0024 4110
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8220 1.0010 3075
## Avg_Cogongrass_Cover-Canis_latrans 1.3006 1.0005 3463
## Avg_Cogongrass_Cover-Procyon_lotor 1.3311 1.0012 3199
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2139 1.0021 3569
## total_shrub_cover-Odocoileus_virginianus 1.7622 1.0041 2012
## total_shrub_cover-Canis_latrans 1.3848 1.0000 3177
## total_shrub_cover-Procyon_lotor 0.0821 1.0037 2465
## total_shrub_cover-Dasypus_novemcinctus 0.7633 1.0012 4287
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0072 0.0597 -0.1108 0.0080 0.1198
## (Intercept)-Canis_latrans -2.6094 0.1772 -2.9804 -2.5998 -2.2855
## (Intercept)-Procyon_lotor -2.2744 0.1315 -2.5327 -2.2714 -2.0223
## (Intercept)-Dasypus_novemcinctus -1.5761 0.1352 -1.8475 -1.5749 -1.3184
## week-Odocoileus_virginianus 0.2134 0.0612 0.0937 0.2131 0.3333
## week-Canis_latrans 0.0917 0.1331 -0.1853 0.0939 0.3382
## week-Procyon_lotor -0.0397 0.1206 -0.2858 -0.0364 0.1879
## week-Dasypus_novemcinctus -0.1583 0.1430 -0.4561 -0.1523 0.1069
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0017 5250
## (Intercept)-Canis_latrans 1.0016 2727
## (Intercept)-Procyon_lotor 1.0084 3980
## (Intercept)-Dasypus_novemcinctus 1.0007 4659
## week-Odocoileus_virginianus 1.0045 5250
## week-Canis_latrans 1.0111 4327
## week-Procyon_lotor 1.0020 4525
## week-Dasypus_novemcinctus 1.0007 4805
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2500 of 10000, 25.00%
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## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.918
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7239 1.1999 -1.7968 0.7598 3.0104 1.0033 5057
## Tree_Density -0.7115 0.6421 -2.0197 -0.7054 0.6088 1.0000 3686
## Avg_Canopy_Cover 0.4597 0.5601 -0.7010 0.4688 1.5517 1.0008 4425
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.2777 49.3824 1.1334 9.2269 110.7323 1.0169 1576
## Tree_Density 1.9469 6.8806 0.0489 0.5081 11.3822 1.0289 3353
## Avg_Canopy_Cover 1.2629 3.8385 0.0628 0.5257 6.8887 1.0477 4553
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6571 1.3564 0.0437 0.2904 3.6937 1.0244 888
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3302 0.7383 -2.6468 -1.3927 0.3382 1.0027 5554
## week 0.0276 0.2380 -0.4422 0.0351 0.4624 1.0028 5272
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1471 8.1915 0.4306 1.6531 14.4005 1.1294 5250
## week 0.2357 0.6221 0.0296 0.1189 1.0489 1.0070 5044
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 6.0402 2.9694 2.2166 5.3985 13.5576
## (Intercept)-Canis_latrans 0.3972 0.7274 -0.8625 0.3535 1.9502
## (Intercept)-Procyon_lotor 0.7804 0.6601 -0.5273 0.7843 2.0793
## (Intercept)-Dasypus_novemcinctus -1.0311 0.7155 -2.5332 -0.9950 0.2704
## Tree_Density-Odocoileus_virginianus -0.3052 0.9636 -1.7894 -0.4503 2.1244
## Tree_Density-Canis_latrans -0.9290 0.6109 -2.3378 -0.8613 0.0808
## Tree_Density-Procyon_lotor -0.4534 0.4528 -1.3559 -0.4533 0.4780
## Tree_Density-Dasypus_novemcinctus -1.5428 1.1297 -4.5737 -1.2803 -0.1047
## Avg_Canopy_Cover-Odocoileus_virginianus 0.3731 0.8699 -1.4215 0.3900 2.1310
## Avg_Canopy_Cover-Canis_latrans -0.1238 0.4860 -1.1136 -0.1160 0.8011
## Avg_Canopy_Cover-Procyon_lotor 0.8620 0.5041 -0.0146 0.8245 1.9832
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8478 0.4572 0.0270 0.8213 1.8407
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0191 423
## (Intercept)-Canis_latrans 1.0016 1709
## (Intercept)-Procyon_lotor 1.0008 2637
## (Intercept)-Dasypus_novemcinctus 1.0016 1836
## Tree_Density-Odocoileus_virginianus 1.0020 2289
## Tree_Density-Canis_latrans 1.0011 3708
## Tree_Density-Procyon_lotor 1.0011 4114
## Tree_Density-Dasypus_novemcinctus 1.0016 1555
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0031 2880
## Avg_Canopy_Cover-Canis_latrans 1.0025 3346
## Avg_Canopy_Cover-Procyon_lotor 1.0028 4010
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0023 3102
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0087 0.0585 -0.1038 0.0087 0.1224
## (Intercept)-Canis_latrans -2.6356 0.1898 -3.0393 -2.6290 -2.2895
## (Intercept)-Procyon_lotor -2.2609 0.1306 -2.5258 -2.2577 -2.0094
## (Intercept)-Dasypus_novemcinctus -1.5743 0.1345 -1.8338 -1.5732 -1.3156
## week-Odocoileus_virginianus 0.2128 0.0609 0.0930 0.2117 0.3333
## week-Canis_latrans 0.0895 0.1297 -0.1786 0.0926 0.3313
## week-Procyon_lotor -0.0358 0.1195 -0.2801 -0.0306 0.1867
## week-Dasypus_novemcinctus -0.1557 0.1394 -0.4482 -0.1491 0.0987
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 4960
## (Intercept)-Canis_latrans 1.0011 2539
## (Intercept)-Procyon_lotor 1.0013 4306
## (Intercept)-Dasypus_novemcinctus 1.0007 5250
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0052 4364
## week-Procyon_lotor 0.9999 4621
## week-Dasypus_novemcinctus 1.0001 5250
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
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## Sampled: 8500 of 10000, 85.00%
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## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## -------------------------------------------------
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## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9282
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2480 1.0511 -1.8124 0.2279 2.3889 1.0014 4070
## Avg_Cogongrass_Cover -0.0465 0.6224 -1.2403 -0.0526 1.2237 1.0130 2101
## I(Avg_Cogongrass_Cover^2) 1.2180 0.9569 -0.6721 1.1663 3.2437 1.0088 1802
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.6362 38.3208 0.3744 5.1619 78.5305 1.0241 1263
## Avg_Cogongrass_Cover 0.8548 2.2909 0.0419 0.3211 4.8329 1.0323 4573
## I(Avg_Cogongrass_Cover^2) 8.2633 28.9704 0.0643 1.4584 62.5947 1.3288 405
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4884 0.7144 0.0427 0.2666 2.3482 1.0126 1322
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3259 0.7327 -2.6275 -1.3801 0.3785 1.0020 5250
## week 0.0269 0.2377 -0.4631 0.0278 0.4967 1.0006 5047
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9601 4.7240 0.4345 1.6612 13.4894 1.0062 5250
## week 0.2329 0.4829 0.0298 0.1222 1.1323 1.0562 4569
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.0659 2.8259 0.3202 3.5167
## (Intercept)-Canis_latrans -0.6146 0.8687 -2.4620 -0.5681
## (Intercept)-Procyon_lotor -0.1560 0.7896 -1.8070 -0.1349
## (Intercept)-Dasypus_novemcinctus -1.0905 0.6745 -2.4383 -1.0871
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1006 0.9169 -1.9243 -0.1086
## Avg_Cogongrass_Cover-Canis_latrans 0.0680 0.6819 -1.1751 0.0422
## Avg_Cogongrass_Cover-Procyon_lotor -0.0501 0.7031 -1.3594 -0.0796
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.1179 0.5819 -1.2926 -0.1103
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.5382 2.9669 -0.2925 1.7164
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3437 1.7141 0.1311 1.9521
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9472 1.6155 0.0565 1.4717
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5064 0.4481 -0.3088 0.4856
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.5037 1.0188 493
## (Intercept)-Canis_latrans 1.0287 1.0177 1100
## (Intercept)-Procyon_lotor 1.2996 1.0062 1881
## (Intercept)-Dasypus_novemcinctus 0.1925 1.0028 3190
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7301 1.0042 2416
## Avg_Cogongrass_Cover-Canis_latrans 1.4874 1.0104 1608
## Avg_Cogongrass_Cover-Procyon_lotor 1.3872 1.0154 1611
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9821 1.0093 2613
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 9.7155 1.3050 246
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.4843 1.0511 427
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2068 1.0387 516
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4190 1.0042 3128
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0093 0.0591 -0.1085 0.0098 0.1238
## (Intercept)-Canis_latrans -2.6486 0.1735 -3.0030 -2.6453 -2.3209
## (Intercept)-Procyon_lotor -2.2881 0.1330 -2.5630 -2.2840 -2.0438
## (Intercept)-Dasypus_novemcinctus -1.5728 0.1348 -1.8423 -1.5716 -1.3133
## week-Odocoileus_virginianus 0.2140 0.0610 0.0970 0.2135 0.3360
## week-Canis_latrans 0.0882 0.1328 -0.1864 0.0925 0.3339
## week-Procyon_lotor -0.0385 0.1202 -0.2807 -0.0345 0.1897
## week-Dasypus_novemcinctus -0.1575 0.1419 -0.4499 -0.1513 0.0984
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5250
## (Intercept)-Canis_latrans 1.0083 2859
## (Intercept)-Procyon_lotor 1.0002 4011
## (Intercept)-Dasypus_novemcinctus 1.0026 5250
## week-Odocoileus_virginianus 1.0014 5250
## week-Canis_latrans 1.0018 4377
## week-Procyon_lotor 1.0004 4075
## week-Dasypus_novemcinctus 1.0004 4897
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
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## Sampled: 1500 of 10000, 15.00%
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## Sampled: 4500 of 10000, 45.00%
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## Sampled: 5000 of 10000, 50.00%
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## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
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## Sampled: 7500 of 10000, 75.00%
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## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 4500 of 10000, 45.00%
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## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_week_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9052
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2238 1.4308 -2.5259 0.2177 3.0878 1.0010 3509
## Cogon_Patch_Size 0.0908 0.9027 -1.6181 0.0562 1.9879 1.0047 2286
## Veg_shannon_index 0.9027 0.7184 -0.5642 0.8981 2.3255 1.0084 1836
## total_shrub_cover -0.0914 0.6898 -1.4883 -0.0926 1.3201 1.0023 2345
## Avg_Cogongrass_Cover 0.5898 1.1523 -1.7224 0.5898 2.8229 1.0352 1005
## Tree_Density -1.4785 1.3590 -3.8942 -1.5906 1.5542 1.0013 1939
## Avg_Canopy_Cover 0.8756 0.8700 -1.0023 0.9025 2.5608 1.0019 2938
## I(Avg_Cogongrass_Cover^2) 1.8745 1.0906 -0.3980 1.8695 3.9789 1.0039 1029
## avg_veg_height -0.0425 0.7310 -1.4554 -0.0407 1.3957 1.0175 1388
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 64.7552 125.5394 3.3172 30.0768 333.5158 1.0450 949
## Cogon_Patch_Size 5.1811 23.1487 0.0729 1.3480 33.8139 1.2127 1939
## Veg_shannon_index 1.7157 5.9835 0.0510 0.5043 11.1517 1.0386 3381
## total_shrub_cover 1.6126 3.8676 0.0625 0.5975 9.2725 1.0365 2854
## Avg_Cogongrass_Cover 2.8046 8.2247 0.0567 0.7016 19.5548 1.0046 2416
## Tree_Density 30.3181 120.7972 0.1107 7.3802 185.8068 1.0770 1605
## Avg_Canopy_Cover 3.6573 10.7020 0.0864 1.3359 21.0406 1.0958 1271
## I(Avg_Cogongrass_Cover^2) 10.2906 58.8776 0.0565 0.8013 82.5849 1.2823 291
## avg_veg_height 1.4600 4.8330 0.0521 0.4784 8.4264 1.0619 3837
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1721 4.0757 0.0579 0.7912 12.3616 1.0794 433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3361 0.7321 -2.6390 -1.4007 0.3240 1.0019 5250
## week 0.0224 0.2405 -0.4573 0.0268 0.4795 1.0010 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1990 7.2908 0.4355 1.737 13.775 1.0245 5250
## week 0.2514 0.7627 0.0291 0.120 1.156 1.1780 5029
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 9.7996 5.5671 2.3554
## (Intercept)-Canis_latrans -1.2779 1.7238 -4.9581
## (Intercept)-Procyon_lotor -0.4700 1.3045 -3.1856
## (Intercept)-Dasypus_novemcinctus -3.5005 1.8323 -8.0623
## Cogon_Patch_Size-Odocoileus_virginianus 0.1527 1.7843 -2.8780
## Cogon_Patch_Size-Canis_latrans 1.5706 1.8670 -0.7205
## Cogon_Patch_Size-Procyon_lotor -0.4211 1.0234 -2.2176
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4834 0.8235 -2.2426
## Veg_shannon_index-Odocoileus_virginianus 0.7843 1.1744 -1.8586
## Veg_shannon_index-Canis_latrans 1.4471 0.9199 0.0344
## Veg_shannon_index-Procyon_lotor 1.1941 0.7650 -0.0971
## Veg_shannon_index-Dasypus_novemcinctus 0.6200 0.6409 -0.6190
## total_shrub_cover-Odocoileus_virginianus 0.0385 1.1344 -2.1580
## total_shrub_cover-Canis_latrans 0.1900 0.7908 -1.1521
## total_shrub_cover-Procyon_lotor -0.7971 0.7535 -2.4565
## total_shrub_cover-Dasypus_novemcinctus 0.1982 0.5906 -0.9340
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4593 1.7581 -3.1487
## Avg_Cogongrass_Cover-Canis_latrans 0.5327 1.5108 -2.3704
## Avg_Cogongrass_Cover-Procyon_lotor 0.5996 1.4100 -2.1238
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1288 1.4499 -1.4563
## Tree_Density-Odocoileus_virginianus 0.1498 3.3157 -3.9951
## Tree_Density-Canis_latrans -3.9757 2.3923 -9.9995
## Tree_Density-Procyon_lotor -1.7608 1.3432 -4.4530
## Tree_Density-Dasypus_novemcinctus -5.7925 3.5696 -14.5851
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6545 1.5888 -2.7983
## Avg_Canopy_Cover-Canis_latrans 0.0658 0.8191 -1.6596
## Avg_Canopy_Cover-Procyon_lotor 1.4743 0.8527 0.0332
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0721 1.0259 0.5631
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.7599 2.5654 -0.2898
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0142 2.2808 0.6229
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.7183 2.0160 0.4021
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.8803 1.1206 0.0745
## avg_veg_height-Odocoileus_virginianus -0.0211 1.2018 -2.4590
## avg_veg_height-Canis_latrans -0.5205 0.8495 -2.3562
## avg_veg_height-Procyon_lotor 0.2049 0.7744 -1.2209
## avg_veg_height-Dasypus_novemcinctus 0.1475 0.7311 -1.2775
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.5805 24.4174 1.0448 274
## (Intercept)-Canis_latrans -1.2242 2.1355 1.0075 797
## (Intercept)-Procyon_lotor -0.4309 1.9932 1.0192 896
## (Intercept)-Dasypus_novemcinctus -3.2030 -0.8199 1.0338 539
## Cogon_Patch_Size-Odocoileus_virginianus 0.0103 3.9206 1.0113 1585
## Cogon_Patch_Size-Canis_latrans 1.1341 6.4868 1.0310 904
## Cogon_Patch_Size-Procyon_lotor -0.4589 1.5274 1.0180 925
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4208 0.9650 1.0252 1443
## Veg_shannon_index-Odocoileus_virginianus 0.8399 3.0284 1.0090 1951
## Veg_shannon_index-Canis_latrans 1.3214 3.6044 1.0161 1051
## Veg_shannon_index-Procyon_lotor 1.1226 2.9442 1.0248 871
## Veg_shannon_index-Dasypus_novemcinctus 0.6054 1.8757 1.0095 2508
## total_shrub_cover-Odocoileus_virginianus -0.0307 2.4841 1.0014 2633
## total_shrub_cover-Canis_latrans 0.1205 1.9080 1.0009 1954
## total_shrub_cover-Procyon_lotor -0.7446 0.5390 1.0090 1694
## total_shrub_cover-Dasypus_novemcinctus 0.1848 1.4302 1.0019 2821
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5070 3.7074 1.0066 1571
## Avg_Cogongrass_Cover-Canis_latrans 0.4992 3.5176 1.0121 1092
## Avg_Cogongrass_Cover-Procyon_lotor 0.5519 3.5117 1.0462 902
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0502 4.2941 1.0330 1081
## Tree_Density-Odocoileus_virginianus -0.5495 8.5841 1.0120 549
## Tree_Density-Canis_latrans -3.4823 -0.6719 1.0371 595
## Tree_Density-Procyon_lotor -1.7499 0.6421 1.0041 1651
## Tree_Density-Dasypus_novemcinctus -4.9569 -1.5475 1.0499 399
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7178 3.6818 1.0028 1559
## Avg_Canopy_Cover-Canis_latrans 0.1175 1.5717 1.0107 1398
## Avg_Canopy_Cover-Procyon_lotor 1.3873 3.4122 1.0211 1041
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8970 4.6057 1.0351 548
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.2422 9.7363 1.0281 313
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4696 9.5878 1.1010 197
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2551 8.5025 1.1373 252
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7450 4.4889 1.0193 810
## avg_veg_height-Odocoileus_virginianus -0.0328 2.3793 1.0132 2118
## avg_veg_height-Canis_latrans -0.4739 0.9926 1.0237 1463
## avg_veg_height-Procyon_lotor 0.1807 1.8578 1.0144 1808
## avg_veg_height-Dasypus_novemcinctus 0.1355 1.6476 1.0181 1790
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0083 0.0598 -0.1083 0.0088 0.1251
## (Intercept)-Canis_latrans -2.6243 0.1804 -2.9972 -2.6155 -2.2953
## (Intercept)-Procyon_lotor -2.2793 0.1356 -2.5600 -2.2768 -2.0259
## (Intercept)-Dasypus_novemcinctus -1.5736 0.1350 -1.8442 -1.5723 -1.3159
## week-Odocoileus_virginianus 0.2122 0.0605 0.0949 0.2128 0.3290
## week-Canis_latrans 0.0891 0.1340 -0.1846 0.0921 0.3329
## week-Procyon_lotor -0.0376 0.1167 -0.2737 -0.0334 0.1794
## week-Dasypus_novemcinctus -0.1583 0.1412 -0.4434 -0.1543 0.1102
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0016 5250
## (Intercept)-Canis_latrans 1.0008 1978
## (Intercept)-Procyon_lotor 1.0007 2912
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## week-Odocoileus_virginianus 0.9999 5250
## week-Canis_latrans 1.0002 4244
## week-Procyon_lotor 1.0002 4415
## week-Dasypus_novemcinctus 0.9998 5015
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7505
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8070 1.0751 -1.4898 0.8316 2.9228 1.0004 5250
## Avg_Cogongrass_Cover 0.4483 0.4685 -0.4723 0.4425 1.4312 1.0019 3737
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.1128 39.6027 0.6667 5.8164 67.6619 1.1295 2311
## Avg_Cogongrass_Cover 0.6433 1.7637 0.0370 0.2459 3.7367 1.0129 3665
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5254 0.7816 0.0411 0.2842 2.4565 1.0069 1316
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3693 0.7567 -2.7137 -1.4407 0.3107 1.0113 5250
## shrub_cover 0.1471 0.3874 -0.6205 0.1463 0.9267 1.0004 5011
## veg_height -0.0980 0.3722 -0.8591 -0.0955 0.6282 1.0014 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3386 7.1497 0.4633 1.7778 14.6315 1.0312 4911
## shrub_cover 0.6574 1.2787 0.0585 0.3298 3.2132 1.0127 5026
## veg_height 0.6410 1.5169 0.0786 0.3377 3.0374 1.0491 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.0030 2.5599 1.8650 4.4473
## (Intercept)-Canis_latrans 0.6747 0.6614 -0.5192 0.6435
## (Intercept)-Procyon_lotor 0.7484 0.5914 -0.4160 0.7408
## (Intercept)-Dasypus_novemcinctus -0.5506 0.5604 -1.6595 -0.5501
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4089 0.7227 -0.9479 0.3902
## Avg_Cogongrass_Cover-Canis_latrans 0.6612 0.5322 -0.1654 0.5946
## Avg_Cogongrass_Cover-Procyon_lotor 0.3511 0.4156 -0.4408 0.3364
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4586 0.3672 -0.2214 0.4459
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.1868 1.0199 498
## (Intercept)-Canis_latrans 2.0811 1.0030 2690
## (Intercept)-Procyon_lotor 1.9500 1.0005 3184
## (Intercept)-Dasypus_novemcinctus 0.5820 1.0040 2957
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8636 1.0027 3140
## Avg_Cogongrass_Cover-Canis_latrans 1.9057 1.0012 2416
## Avg_Cogongrass_Cover-Procyon_lotor 1.2252 1.0005 3970
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2070 1.0001 4271
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0059 0.0601 -0.1109 0.0066 0.1253
## (Intercept)-Canis_latrans -2.7734 0.2012 -3.1901 -2.7677 -2.3992
## (Intercept)-Procyon_lotor -2.2823 0.1441 -2.5834 -2.2784 -2.0151
## (Intercept)-Dasypus_novemcinctus -1.6990 0.1553 -2.0190 -1.6962 -1.4050
## shrub_cover-Odocoileus_virginianus -0.0552 0.0639 -0.1852 -0.0539 0.0689
## shrub_cover-Canis_latrans -0.2712 0.2199 -0.7145 -0.2660 0.1381
## shrub_cover-Procyon_lotor 0.2444 0.1645 -0.0852 0.2487 0.5521
## shrub_cover-Dasypus_novemcinctus 0.7505 0.3061 0.2022 0.7373 1.3864
## veg_height-Odocoileus_virginianus -0.3011 0.0656 -0.4293 -0.3017 -0.1732
## veg_height-Canis_latrans -0.6627 0.1952 -1.0695 -0.6579 -0.3026
## veg_height-Procyon_lotor 0.3342 0.1253 0.0893 0.3341 0.5836
## veg_height-Dasypus_novemcinctus 0.2217 0.1349 -0.0368 0.2201 0.4934
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0026 5813
## (Intercept)-Canis_latrans 1.0019 1937
## (Intercept)-Procyon_lotor 1.0005 3735
## (Intercept)-Dasypus_novemcinctus 1.0018 4337
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0034 2655
## shrub_cover-Procyon_lotor 1.0006 3809
## shrub_cover-Dasypus_novemcinctus 1.0006 3373
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0012 2345
## veg_height-Procyon_lotor 1.0002 4475
## veg_height-Dasypus_novemcinctus 1.0011 4862
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7617
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7295 1.4139 -2.1500 0.7539 3.3686 1.0002 4117
## Cogon_Patch_Size -0.2947 0.8784 -1.8927 -0.3399 1.6422 1.0011 1780
## Veg_shannon_index 0.9203 0.7509 -0.5483 0.9201 2.4212 1.0046 1465
## total_shrub_cover 0.0500 0.8174 -1.5258 0.0067 1.8690 1.0027 2197
## Avg_Cogongrass_Cover 2.0686 1.0480 -0.0121 2.0597 4.1693 1.0034 1312
## Tree_Density -1.4260 1.1965 -3.5036 -1.5321 1.3486 1.0016 2112
## Avg_Canopy_Cover 0.9142 0.8113 -0.8027 0.9399 2.4116 0.9999 3390
## avg_veg_height -0.2381 0.7051 -1.6019 -0.2482 1.1692 1.0020 1719
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 72.3451 241.3539 2.7343 29.1636 374.9344 1.1267 2195
## Cogon_Patch_Size 3.3539 13.9241 0.0609 0.8910 20.7169 1.1814 1238
## Veg_shannon_index 1.8808 6.3132 0.0511 0.5259 12.0158 1.0143 2108
## total_shrub_cover 3.3069 8.1949 0.0752 1.1821 19.2681 1.0014 2078
## Avg_Cogongrass_Cover 3.5906 23.0501 0.0550 0.6935 22.3976 1.2654 2323
## Tree_Density 17.0340 48.6774 0.1019 4.4669 111.6285 1.0649 800
## Avg_Canopy_Cover 3.4732 8.6387 0.0963 1.2986 20.4714 1.0114 3060
## avg_veg_height 1.0715 5.8845 0.0447 0.3509 6.1383 1.1739 4934
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 4.2663 26.9775 0.0633 1.0141 17.5493 1.5553 247
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3824 0.7430 -2.7109 -1.4397 0.2835 1.0030 5250
## shrub_cover 0.1514 0.4105 -0.6640 0.1441 0.9962 1.0024 5250
## veg_height -0.0965 0.3812 -0.8591 -0.0983 0.6595 1.0010 4916
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4711 8.5233 0.4542 1.7866 16.5556 1.0126 5250
## shrub_cover 0.8537 3.3394 0.0763 0.4118 3.6633 1.1220 5250
## veg_height 0.6758 1.5036 0.0779 0.3399 3.2763 1.0230 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 10.9452 5.6038 3.5086 9.6692
## (Intercept)-Canis_latrans 1.3915 1.3513 -0.8287 1.2297
## (Intercept)-Procyon_lotor 1.0752 1.2547 -1.2770 1.0368
## (Intercept)-Dasypus_novemcinctus -1.6643 1.2887 -4.5291 -1.5247
## Cogon_Patch_Size-Odocoileus_virginianus -0.2917 1.5663 -2.9755 -0.4105
## Cogon_Patch_Size-Canis_latrans 0.6755 1.7148 -1.3239 0.3351
## Cogon_Patch_Size-Procyon_lotor -0.8984 0.9034 -2.5875 -0.8919
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6156 0.8129 -2.2242 -0.6269
## Veg_shannon_index-Odocoileus_virginianus 0.7602 1.3115 -2.1927 0.8263
## Veg_shannon_index-Canis_latrans 1.4024 0.9457 -0.2484 1.3229
## Veg_shannon_index-Procyon_lotor 1.2792 0.8260 -0.1165 1.2036
## Veg_shannon_index-Dasypus_novemcinctus 0.6180 0.6553 -0.6886 0.6240
## total_shrub_cover-Odocoileus_virginianus 0.2244 1.4001 -2.4197 0.1320
## total_shrub_cover-Canis_latrans 1.1075 1.3073 -0.6223 0.8042
## total_shrub_cover-Procyon_lotor -0.9613 0.7948 -2.7020 -0.9017
## total_shrub_cover-Dasypus_novemcinctus -0.0168 0.7408 -1.6316 0.0312
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0874 1.5204 -0.9899 2.0532
## Avg_Cogongrass_Cover-Canis_latrans 2.7538 1.3996 0.5529 2.5498
## Avg_Cogongrass_Cover-Procyon_lotor 2.1629 1.1205 0.1726 2.1107
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7237 1.2954 0.6364 2.5480
## Tree_Density-Odocoileus_virginianus -0.0469 2.6295 -3.4938 -0.6015
## Tree_Density-Canis_latrans -3.3968 2.0489 -8.7529 -2.9508
## Tree_Density-Procyon_lotor -1.3711 1.0789 -3.3096 -1.3643
## Tree_Density-Dasypus_novemcinctus -4.6473 2.8919 -12.4268 -3.8881
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7644 1.4931 -2.2924 0.8243
## Avg_Canopy_Cover-Canis_latrans 0.1093 0.7211 -1.2894 0.1244
## Avg_Canopy_Cover-Procyon_lotor 1.5296 0.8321 0.1439 1.4397
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0463 0.8919 0.7105 1.9248
## avg_veg_height-Odocoileus_virginianus -0.2717 1.0551 -2.4152 -0.2749
## avg_veg_height-Canis_latrans -0.3173 0.7805 -1.8383 -0.3136
## avg_veg_height-Procyon_lotor -0.2885 0.7165 -1.7145 -0.2706
## avg_veg_height-Dasypus_novemcinctus -0.1434 0.7091 -1.5202 -0.1608
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 24.6368 1.0325 237
## (Intercept)-Canis_latrans 4.5737 1.0120 802
## (Intercept)-Procyon_lotor 3.6732 1.0205 1015
## (Intercept)-Dasypus_novemcinctus 0.5666 1.0193 907
## Cogon_Patch_Size-Odocoileus_virginianus 3.3110 1.0012 1528
## Cogon_Patch_Size-Canis_latrans 4.7250 1.0264 850
## Cogon_Patch_Size-Procyon_lotor 0.7868 1.0138 787
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0560 1.0018 1668
## Veg_shannon_index-Odocoileus_virginianus 3.1480 1.0048 1597
## Veg_shannon_index-Canis_latrans 3.4415 1.0132 909
## Veg_shannon_index-Procyon_lotor 2.9789 1.0306 590
## Veg_shannon_index-Dasypus_novemcinctus 1.9245 1.0069 1595
## total_shrub_cover-Odocoileus_virginianus 3.2718 1.0042 1927
## total_shrub_cover-Canis_latrans 4.4305 1.0189 673
## total_shrub_cover-Procyon_lotor 0.4155 1.0020 1469
## total_shrub_cover-Dasypus_novemcinctus 1.2904 1.0031 1869
## Avg_Cogongrass_Cover-Odocoileus_virginianus 5.1670 1.0019 1299
## Avg_Cogongrass_Cover-Canis_latrans 6.1418 1.0079 634
## Avg_Cogongrass_Cover-Procyon_lotor 4.5017 1.0055 533
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.6991 1.0040 788
## Tree_Density-Odocoileus_virginianus 6.9821 1.0008 574
## Tree_Density-Canis_latrans -0.6407 1.0143 644
## Tree_Density-Procyon_lotor 0.4904 1.0119 670
## Tree_Density-Dasypus_novemcinctus -1.1607 1.0069 575
## Avg_Canopy_Cover-Odocoileus_virginianus 3.6914 1.0042 1912
## Avg_Canopy_Cover-Canis_latrans 1.5061 1.0039 2023
## Avg_Canopy_Cover-Procyon_lotor 3.4448 1.0020 1388
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.1689 1.0070 739
## avg_veg_height-Odocoileus_virginianus 1.8772 1.0023 1790
## avg_veg_height-Canis_latrans 1.2495 1.0009 1751
## avg_veg_height-Procyon_lotor 1.1172 1.0013 1764
## avg_veg_height-Dasypus_novemcinctus 1.2650 1.0032 1815
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0060 0.0596 -0.1091 0.0060 0.1228
## (Intercept)-Canis_latrans -2.7743 0.1901 -3.1609 -2.7712 -2.4131
## (Intercept)-Procyon_lotor -2.2951 0.1515 -2.6091 -2.2871 -2.0107
## (Intercept)-Dasypus_novemcinctus -1.7122 0.1601 -2.0328 -1.7097 -1.4088
## shrub_cover-Odocoileus_virginianus -0.0568 0.0642 -0.1832 -0.0576 0.0681
## shrub_cover-Canis_latrans -0.4026 0.2198 -0.8344 -0.4008 0.0218
## shrub_cover-Procyon_lotor 0.2541 0.1712 -0.0913 0.2606 0.5729
## shrub_cover-Dasypus_novemcinctus 0.8215 0.3244 0.2297 0.8118 1.4720
## veg_height-Odocoileus_virginianus -0.2997 0.0652 -0.4291 -0.2998 -0.1727
## veg_height-Canis_latrans -0.6586 0.1822 -1.0159 -0.6572 -0.3144
## veg_height-Procyon_lotor 0.3447 0.1272 0.0956 0.3456 0.5962
## veg_height-Dasypus_novemcinctus 0.2344 0.1363 -0.0340 0.2332 0.4948
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5490
## (Intercept)-Canis_latrans 1.0022 2200
## (Intercept)-Procyon_lotor 1.0025 2327
## (Intercept)-Dasypus_novemcinctus 1.0012 3138
## shrub_cover-Odocoileus_virginianus 1.0017 5250
## shrub_cover-Canis_latrans 1.0015 2047
## shrub_cover-Procyon_lotor 1.0058 1252
## shrub_cover-Dasypus_novemcinctus 1.0024 2421
## veg_height-Odocoileus_virginianus 1.0017 5250
## veg_height-Canis_latrans 1.0014 2446
## veg_height-Procyon_lotor 1.0011 3757
## veg_height-Dasypus_novemcinctus 1.0010 4646
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9437 1.1583 -1.5857 0.9992 3.1092 1.0000 4706
## Avg_Cogongrass_Cover 0.2117 0.5825 -0.9502 0.2061 1.3388 1.0001 2451
## total_shrub_cover -0.1550 0.7223 -1.5922 -0.1800 1.4488 1.0012 3189
## avg_veg_height 0.2858 0.5643 -0.7830 0.2676 1.4576 1.0040 2064
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.9023 70.6445 0.7965 7.5548 130.4181 1.0380 423
## Avg_Cogongrass_Cover 0.9141 2.8394 0.0420 0.3236 5.5226 1.0776 4291
## total_shrub_cover 2.4805 6.4297 0.0872 1.0114 13.5169 1.0871 3731
## avg_veg_height 0.7438 2.5013 0.0407 0.2687 3.9316 1.0878 3680
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5736 0.8477 0.043 0.3001 2.8224 1.0201 1287
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3947 0.7354 -2.7457 -1.4465 0.2665 1.0008 5250
## shrub_cover 0.1768 0.4287 -0.6525 0.1750 1.0743 1.0000 4914
## veg_height -0.1007 0.3698 -0.8867 -0.0987 0.6152 1.0016 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2528 5.3543 0.4759 1.8009 15.4953 1.0278 3770
## shrub_cover 0.8354 1.5843 0.0708 0.4148 4.2523 1.0013 4340
## veg_height 0.6278 1.0195 0.0795 0.3438 2.9094 1.0066 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 6.1331 3.8740 2.3355 5.1676
## (Intercept)-Canis_latrans 1.0170 0.8615 -0.3758 0.9227
## (Intercept)-Procyon_lotor 1.1624 0.7501 -0.2002 1.1006
## (Intercept)-Dasypus_novemcinctus -0.4853 0.7556 -1.8199 -0.5377
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1995 0.8457 -1.4623 0.1916
## Avg_Cogongrass_Cover-Canis_latrans 0.5246 0.7023 -0.6475 0.4582
## Avg_Cogongrass_Cover-Procyon_lotor 0.0169 0.5735 -1.1716 0.0285
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2247 0.4976 -0.7532 0.2280
## total_shrub_cover-Odocoileus_virginianus -0.0334 1.0882 -2.0707 -0.0935
## total_shrub_cover-Canis_latrans 0.6766 0.9017 -0.6512 0.5072
## total_shrub_cover-Procyon_lotor -1.2189 0.7229 -2.9159 -1.1122
## total_shrub_cover-Dasypus_novemcinctus -0.1655 0.6923 -1.9044 -0.0757
## avg_veg_height-Odocoileus_virginianus 0.2556 0.8024 -1.3099 0.2357
## avg_veg_height-Canis_latrans 0.2705 0.6140 -0.8447 0.2403
## avg_veg_height-Procyon_lotor 0.2311 0.5602 -0.8369 0.2197
## avg_veg_height-Dasypus_novemcinctus 0.4204 0.5497 -0.5795 0.3934
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.9401 1.0265 210
## (Intercept)-Canis_latrans 3.0860 1.0071 1486
## (Intercept)-Procyon_lotor 2.8187 1.0007 2189
## (Intercept)-Dasypus_novemcinctus 1.3218 1.0095 757
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9251 1.0009 2601
## Avg_Cogongrass_Cover-Canis_latrans 2.1165 1.0006 2093
## Avg_Cogongrass_Cover-Procyon_lotor 1.1187 1.0008 2820
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2148 1.0002 2608
## total_shrub_cover-Odocoileus_virginianus 2.3214 1.0048 2303
## total_shrub_cover-Canis_latrans 2.8950 1.0094 1144
## total_shrub_cover-Procyon_lotor -0.0717 1.0051 1929
## total_shrub_cover-Dasypus_novemcinctus 0.8328 1.0051 641
## avg_veg_height-Odocoileus_virginianus 1.8336 1.0035 2380
## avg_veg_height-Canis_latrans 1.6158 1.0014 1975
## avg_veg_height-Procyon_lotor 1.3755 1.0019 2552
## avg_veg_height-Dasypus_novemcinctus 1.5524 1.0016 1582
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0048 0.0582 -0.1098 0.0050 0.1188
## (Intercept)-Canis_latrans -2.8154 0.2008 -3.2330 -2.8072 -2.4524
## (Intercept)-Procyon_lotor -2.2881 0.1393 -2.5678 -2.2834 -2.0265
## (Intercept)-Dasypus_novemcinctus -1.7320 0.1760 -2.1014 -1.7213 -1.4138
## shrub_cover-Odocoileus_virginianus -0.0559 0.0632 -0.1802 -0.0558 0.0689
## shrub_cover-Canis_latrans -0.3721 0.2504 -0.8581 -0.3746 0.1249
## shrub_cover-Procyon_lotor 0.3073 0.1605 -0.0091 0.3061 0.6191
## shrub_cover-Dasypus_novemcinctus 0.8730 0.3719 0.2271 0.8444 1.6880
## veg_height-Odocoileus_virginianus -0.3004 0.0650 -0.4287 -0.2991 -0.1766
## veg_height-Canis_latrans -0.6771 0.1954 -1.0790 -0.6687 -0.3186
## veg_height-Procyon_lotor 0.3372 0.1243 0.0910 0.3375 0.5785
## veg_height-Dasypus_novemcinctus 0.2304 0.1391 -0.0445 0.2294 0.5019
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5539
## (Intercept)-Canis_latrans 1.0011 1852
## (Intercept)-Procyon_lotor 1.0000 3785
## (Intercept)-Dasypus_novemcinctus 1.0094 1530
## shrub_cover-Odocoileus_virginianus 1.0038 5250
## shrub_cover-Canis_latrans 0.9999 1373
## shrub_cover-Procyon_lotor 1.0029 3833
## shrub_cover-Dasypus_novemcinctus 1.0060 1038
## veg_height-Odocoileus_virginianus 1.0018 5250
## veg_height-Canis_latrans 1.0003 1884
## veg_height-Procyon_lotor 1.0007 4212
## veg_height-Dasypus_novemcinctus 1.0005 4606
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 10000, # number of MCMC samples
n.thin = 4, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.722
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7943 1.0702 -1.4536 0.8177 2.972 1.0016 4694
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.4398 31.4715 0.7291 5.2933 83.3564 1.0852 756
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3688 0.7295 -2.6581 -1.4334 0.3168 1.0048 4857
## shrub_cover 0.1529 0.3877 -0.6161 0.1431 0.9558 1.0016 5250
## veg_height -0.0976 0.3623 -0.8133 -0.0942 0.6419 1.0008 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2805 6.0111 0.4528 1.7417 16.5994 1.0114 5815
## shrub_cover 0.6725 1.4435 0.0678 0.3211 3.2113 1.0049 5250
## veg_height 0.6169 1.1115 0.0761 0.3252 2.9434 1.0304 3964
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.9321 2.7529 2.0502 4.2150 12.1964
## (Intercept)-Canis_latrans 0.4891 0.4453 -0.3299 0.4712 1.4122
## (Intercept)-Procyon_lotor 0.8090 0.4196 0.0289 0.7895 1.6991
## (Intercept)-Dasypus_novemcinctus -0.5437 0.3885 -1.3042 -0.5398 0.1846
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.1403 350
## (Intercept)-Canis_latrans 1.0003 3626
## (Intercept)-Procyon_lotor 0.9998 4812
## (Intercept)-Dasypus_novemcinctus 1.0037 5250
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0599 -0.1159 0.0065 0.1227
## (Intercept)-Canis_latrans -2.7527 0.1974 -3.1715 -2.7485 -2.3817
## (Intercept)-Procyon_lotor -2.2866 0.1446 -2.5855 -2.2818 -2.0163
## (Intercept)-Dasypus_novemcinctus -1.6907 0.1569 -2.0074 -1.6850 -1.3896
## shrub_cover-Odocoileus_virginianus -0.0559 0.0646 -0.1861 -0.0550 0.0689
## shrub_cover-Canis_latrans -0.2967 0.2218 -0.7361 -0.2945 0.1296
## shrub_cover-Procyon_lotor 0.2426 0.1641 -0.0939 0.2456 0.5573
## shrub_cover-Dasypus_novemcinctus 0.7431 0.2997 0.1831 0.7368 1.3535
## veg_height-Odocoileus_virginianus -0.2993 0.0655 -0.4286 -0.2986 -0.1724
## veg_height-Canis_latrans -0.6400 0.1922 -1.0338 -0.6360 -0.2749
## veg_height-Procyon_lotor 0.3415 0.1247 0.0968 0.3402 0.5854
## veg_height-Dasypus_novemcinctus 0.2242 0.1324 -0.0329 0.2227 0.4882
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0011 2131
## (Intercept)-Procyon_lotor 1.0022 3337
## (Intercept)-Dasypus_novemcinctus 1.0029 3484
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0083 2720
## shrub_cover-Procyon_lotor 1.0003 3661
## shrub_cover-Dasypus_novemcinctus 1.0010 3488
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0025 2178
## veg_height-Procyon_lotor 0.9999 3952
## veg_height-Dasypus_novemcinctus 1.0028 4809
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7397
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7993 1.1397 -1.6657 0.8317 2.9715 1.0008 4905
## Veg_shannon_index 0.5122 0.5211 -0.5164 0.5011 1.5628 1.0047 2918
## Avg_Cogongrass_Cover 0.6778 0.5176 -0.3697 0.6641 1.7526 1.0017 2403
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.5962 91.2354 0.7953 6.9065 110.6724 1.0699 863
## Veg_shannon_index 0.8902 2.8774 0.0430 0.3124 4.8844 1.0513 3482
## Avg_Cogongrass_Cover 0.8108 2.1104 0.0422 0.2856 4.5496 1.0027 2105
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7559 1.4792 0.0435 0.3498 4.0354 1.0625 847
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3875 0.7383 -2.7264 -1.4410 0.3176 1.0002 5250
## shrub_cover 0.1532 0.3796 -0.6203 0.1524 0.9338 1.0010 4824
## veg_height -0.0974 0.3792 -0.8738 -0.0903 0.6521 1.0010 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4904 19.9486 0.4614 1.7936 14.8156 1.2590 5250
## shrub_cover 0.6494 1.3557 0.0560 0.3222 3.1429 1.0004 4697
## veg_height 0.6717 2.1575 0.0751 0.3361 2.9992 1.1355 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.7211 3.4127 2.1236 4.8732
## (Intercept)-Canis_latrans 0.7002 0.7489 -0.6303 0.6571
## (Intercept)-Procyon_lotor 0.8345 0.6785 -0.3996 0.8162
## (Intercept)-Dasypus_novemcinctus -0.6339 0.6308 -1.9038 -0.6248
## Veg_shannon_index-Odocoileus_virginianus 0.4381 0.8232 -1.3006 0.4673
## Veg_shannon_index-Canis_latrans 0.8719 0.5287 -0.0312 0.8278
## Veg_shannon_index-Procyon_lotor 0.6336 0.5355 -0.2370 0.5826
## Veg_shannon_index-Dasypus_novemcinctus 0.2564 0.4081 -0.5575 0.2628
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6717 0.8183 -0.8619 0.6410
## Avg_Cogongrass_Cover-Canis_latrans 1.0036 0.6531 0.0424 0.9058
## Avg_Cogongrass_Cover-Procyon_lotor 0.6465 0.5576 -0.2340 0.5922
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6003 0.3939 -0.1388 0.5852
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.5951 1.0521 273
## (Intercept)-Canis_latrans 2.2907 1.0015 1831
## (Intercept)-Procyon_lotor 2.2152 1.0091 1457
## (Intercept)-Dasypus_novemcinctus 0.5844 1.0011 2109
## Veg_shannon_index-Odocoileus_virginianus 1.9784 1.0085 2899
## Veg_shannon_index-Canis_latrans 2.0076 1.0018 1944
## Veg_shannon_index-Procyon_lotor 1.7461 1.0054 1208
## Veg_shannon_index-Dasypus_novemcinctus 1.0634 1.0013 4443
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.3811 1.0001 2366
## Avg_Cogongrass_Cover-Canis_latrans 2.5497 1.0029 1462
## Avg_Cogongrass_Cover-Procyon_lotor 1.8165 1.0055 1333
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4011 1.0028 3652
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0044 0.0600 -0.1128 0.0049 0.1240
## (Intercept)-Canis_latrans -2.7566 0.1934 -3.1657 -2.7487 -2.4027
## (Intercept)-Procyon_lotor -2.2963 0.1500 -2.6072 -2.2916 -2.0223
## (Intercept)-Dasypus_novemcinctus -1.6974 0.1567 -2.0162 -1.6920 -1.3954
## shrub_cover-Odocoileus_virginianus -0.0571 0.0637 -0.1809 -0.0579 0.0694
## shrub_cover-Canis_latrans -0.2718 0.2137 -0.6864 -0.2672 0.1357
## shrub_cover-Procyon_lotor 0.2146 0.1781 -0.1569 0.2250 0.5446
## shrub_cover-Dasypus_novemcinctus 0.7577 0.3090 0.1882 0.7472 1.3984
## veg_height-Odocoileus_virginianus -0.3012 0.0649 -0.4303 -0.3004 -0.1774
## veg_height-Canis_latrans -0.6560 0.1911 -1.0425 -0.6470 -0.2958
## veg_height-Procyon_lotor 0.3295 0.1266 0.0801 0.3301 0.5800
## veg_height-Dasypus_novemcinctus 0.2251 0.1344 -0.0379 0.2249 0.4896
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 5250
## (Intercept)-Canis_latrans 1.0022 2091
## (Intercept)-Procyon_lotor 1.0010 2963
## (Intercept)-Dasypus_novemcinctus 1.0001 4023
## shrub_cover-Odocoileus_virginianus 1.0056 5250
## shrub_cover-Canis_latrans 1.0100 2849
## shrub_cover-Procyon_lotor 0.9998 2613
## shrub_cover-Dasypus_novemcinctus 1.0025 3459
## veg_height-Odocoileus_virginianus 1.0009 5250
## veg_height-Canis_latrans 1.0015 2108
## veg_height-Procyon_lotor 1.0055 4054
## veg_height-Dasypus_novemcinctus 1.0002 4573
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7505
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9302 1.1470 -1.5325 0.9891 3.0740 1.0006 5250
## Cogon_Patch_Size 0.2637 0.6389 -0.9363 0.2286 1.6917 1.0035 3055
## Avg_Cogongrass_Cover 0.3126 0.5310 -0.7135 0.2950 1.3991 1.0034 2951
## total_shrub_cover -0.2203 0.6783 -1.5896 -0.2367 1.2149 1.0035 2674
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.0250 52.0667 0.7869 7.4248 108.2187 1.0112 1210
## Cogon_Patch_Size 1.6286 4.5447 0.0510 0.4942 10.4282 1.0311 2297
## Avg_Cogongrass_Cover 0.7251 2.0660 0.0391 0.2695 4.3393 1.0314 3581
## total_shrub_cover 2.2720 7.8574 0.0753 0.8065 12.6467 1.1232 3786
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6206 1.0371 0.0458 0.3 3.0713 1.0269 780
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3891 0.7510 -2.7121 -1.4677 0.3152 1.0028 4814
## shrub_cover 0.1744 0.4118 -0.6305 0.1625 1.0293 1.0001 4735
## veg_height -0.0945 0.3704 -0.8221 -0.0881 0.6403 1.0003 5168
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2074 6.7490 0.4601 1.7789 14.0705 1.0297 5250
## shrub_cover 0.8095 1.9194 0.0712 0.4015 3.9811 1.0757 4701
## veg_height 0.6405 1.3745 0.0753 0.3241 2.9767 1.0324 4849
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8572 3.0905 2.1546 5.1173
## (Intercept)-Canis_latrans 1.0114 0.8550 -0.3810 0.9077
## (Intercept)-Procyon_lotor 1.1002 0.7493 -0.2738 1.0529
## (Intercept)-Dasypus_novemcinctus -0.4877 0.6511 -1.7042 -0.5199
## Cogon_Patch_Size-Odocoileus_virginianus 0.3159 1.0271 -1.3502 0.2152
## Cogon_Patch_Size-Canis_latrans 0.9831 1.1092 -0.4130 0.7312
## Cogon_Patch_Size-Procyon_lotor -0.0732 0.5142 -1.0925 -0.0755
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0170 0.4769 -1.0088 -0.0104
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3017 0.7933 -1.1652 0.2697
## Avg_Cogongrass_Cover-Canis_latrans 0.4552 0.5864 -0.4683 0.3949
## Avg_Cogongrass_Cover-Procyon_lotor 0.1804 0.5344 -0.8613 0.1816
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4127 0.4460 -0.4490 0.4033
## total_shrub_cover-Odocoileus_virginianus -0.1113 1.0116 -2.0219 -0.1638
## total_shrub_cover-Canis_latrans 0.4922 0.8488 -0.7435 0.3375
## total_shrub_cover-Procyon_lotor -1.1710 0.7295 -2.8838 -1.0634
## total_shrub_cover-Dasypus_novemcinctus -0.1792 0.5477 -1.3670 -0.1339
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.7594 1.0264 355
## (Intercept)-Canis_latrans 3.0389 1.0149 1508
## (Intercept)-Procyon_lotor 2.7467 1.0058 2549
## (Intercept)-Dasypus_novemcinctus 0.9330 1.0055 2089
## Cogon_Patch_Size-Odocoileus_virginianus 2.7151 1.0055 2075
## Cogon_Patch_Size-Canis_latrans 3.9069 1.0208 1559
## Cogon_Patch_Size-Procyon_lotor 0.9402 1.0065 3500
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8850 1.0052 3359
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9560 1.0059 2768
## Avg_Cogongrass_Cover-Canis_latrans 1.7697 1.0214 1837
## Avg_Cogongrass_Cover-Procyon_lotor 1.2713 1.0025 2859
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3131 1.0040 2834
## total_shrub_cover-Odocoileus_virginianus 2.0323 1.0054 1756
## total_shrub_cover-Canis_latrans 2.5856 1.0084 1145
## total_shrub_cover-Procyon_lotor -0.0671 1.0040 2327
## total_shrub_cover-Dasypus_novemcinctus 0.7264 1.0139 1503
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0052 0.0591 -0.1114 0.0057 0.1204
## (Intercept)-Canis_latrans -2.7719 0.1972 -3.1797 -2.7633 -2.4115
## (Intercept)-Procyon_lotor -2.2852 0.1387 -2.5688 -2.2824 -2.0302
## (Intercept)-Dasypus_novemcinctus -1.7203 0.1665 -2.0655 -1.7122 -1.4129
## shrub_cover-Odocoileus_virginianus -0.0565 0.0643 -0.1787 -0.0564 0.0712
## shrub_cover-Canis_latrans -0.3513 0.2444 -0.8231 -0.3522 0.1162
## shrub_cover-Procyon_lotor 0.2978 0.1634 -0.0312 0.2991 0.6131
## shrub_cover-Dasypus_novemcinctus 0.8373 0.3451 0.2124 0.8153 1.5777
## veg_height-Odocoileus_virginianus -0.3020 0.0654 -0.4340 -0.3009 -0.1756
## veg_height-Canis_latrans -0.6455 0.1929 -1.0348 -0.6387 -0.2925
## veg_height-Procyon_lotor 0.3380 0.1236 0.0966 0.3376 0.5776
## veg_height-Dasypus_novemcinctus 0.2313 0.1378 -0.0379 0.2305 0.5035
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0020 1874
## (Intercept)-Procyon_lotor 1.0008 3873
## (Intercept)-Dasypus_novemcinctus 1.0015 2617
## shrub_cover-Odocoileus_virginianus 1.0022 5250
## shrub_cover-Canis_latrans 1.0032 1689
## shrub_cover-Procyon_lotor 1.0003 3750
## shrub_cover-Dasypus_novemcinctus 1.0086 1601
## veg_height-Odocoileus_virginianus 1.0019 5250
## veg_height-Canis_latrans 1.0055 1964
## veg_height-Procyon_lotor 1.0003 4212
## veg_height-Dasypus_novemcinctus 1.0033 4199
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 10000, n.report = 500, n.burn = 3000, n.thin = 4,
## n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.726
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7225 1.2160 -1.8415 0.7606 3.0694 1.0000 4750
## Tree_Density -0.7477 0.6934 -2.0830 -0.7552 0.7717 1.0015 3454
## Avg_Canopy_Cover 0.4330 0.5873 -0.7670 0.4394 1.5676 1.0045 3985
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.9838 68.7164 1.0250 9.0975 145.5273 1.0047 819
## Tree_Density 2.5046 9.3308 0.0477 0.5724 16.7430 1.0439 1532
## Avg_Canopy_Cover 1.5352 5.1405 0.0676 0.6130 7.6388 1.1045 4261
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6897 2.0431 0.0416 0.2845 3.2063 1.1548 479
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3813 0.7609 -2.7117 -1.4447 0.3775 0.9999 4998
## shrub_cover 0.1533 0.3845 -0.6166 0.1472 0.9684 1.0028 5584
## veg_height -0.0958 0.3727 -0.8610 -0.0977 0.6709 1.0030 4623
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3775 7.1893 0.4642 1.7976 16.2534 1.0836 5250
## shrub_cover 0.6969 1.6114 0.0637 0.3410 3.4213 1.0187 5250
## veg_height 0.6223 1.1715 0.0794 0.3364 2.8086 1.0267 4957
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 6.2664 3.5701 2.3088 5.3623 15.1157
## (Intercept)-Canis_latrans 0.5325 0.6847 -0.7466 0.4966 2.0250
## (Intercept)-Procyon_lotor 0.8478 0.6577 -0.4422 0.8324 2.2160
## (Intercept)-Dasypus_novemcinctus -0.9744 0.7362 -2.5752 -0.9286 0.3673
## Tree_Density-Odocoileus_virginianus -0.2724 1.1952 -1.9073 -0.4636 2.7245
## Tree_Density-Canis_latrans -1.0101 0.6597 -2.5332 -0.9283 0.0656
## Tree_Density-Procyon_lotor -0.4753 0.4818 -1.4276 -0.4745 0.4270
## Tree_Density-Dasypus_novemcinctus -1.6304 1.2238 -4.9683 -1.3354 -0.1485
## Avg_Canopy_Cover-Odocoileus_virginianus 0.3828 0.9098 -1.4871 0.3740 2.2561
## Avg_Canopy_Cover-Canis_latrans -0.2197 0.4854 -1.2175 -0.2015 0.6972
## Avg_Canopy_Cover-Procyon_lotor 0.8662 0.5348 -0.0382 0.8181 2.0754
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8938 0.4838 0.0559 0.8548 1.9683
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0073 285
## (Intercept)-Canis_latrans 1.0019 2590
## (Intercept)-Procyon_lotor 1.0002 2831
## (Intercept)-Dasypus_novemcinctus 1.0040 1980
## Tree_Density-Odocoileus_virginianus 1.0042 1227
## Tree_Density-Canis_latrans 1.0051 3108
## Tree_Density-Procyon_lotor 1.0025 3157
## Tree_Density-Dasypus_novemcinctus 1.0019 1284
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0047 2789
## Avg_Canopy_Cover-Canis_latrans 1.0031 2862
## Avg_Canopy_Cover-Procyon_lotor 1.0053 3552
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0038 3680
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0039 0.0599 -0.1130 0.0044 0.1204
## (Intercept)-Canis_latrans -2.7682 0.1974 -3.1838 -2.7585 -2.4118
## (Intercept)-Procyon_lotor -2.2933 0.1481 -2.6019 -2.2901 -2.0169
## (Intercept)-Dasypus_novemcinctus -1.7033 0.1569 -2.0175 -1.6980 -1.4088
## shrub_cover-Odocoileus_virginianus -0.0570 0.0639 -0.1874 -0.0562 0.0655
## shrub_cover-Canis_latrans -0.3065 0.2247 -0.7505 -0.3087 0.1353
## shrub_cover-Procyon_lotor 0.2396 0.1657 -0.0916 0.2452 0.5560
## shrub_cover-Dasypus_novemcinctus 0.7730 0.3075 0.1982 0.7672 1.3908
## veg_height-Odocoileus_virginianus -0.3012 0.0654 -0.4301 -0.3022 -0.1735
## veg_height-Canis_latrans -0.6489 0.1937 -1.0421 -0.6385 -0.2932
## veg_height-Procyon_lotor 0.3445 0.1262 0.1052 0.3432 0.5945
## veg_height-Dasypus_novemcinctus 0.2318 0.1375 -0.0336 0.2294 0.5012
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0026 5020
## (Intercept)-Canis_latrans 1.0048 1846
## (Intercept)-Procyon_lotor 0.9998 3406
## (Intercept)-Dasypus_novemcinctus 1.0002 4555
## shrub_cover-Odocoileus_virginianus 1.0020 5502
## shrub_cover-Canis_latrans 1.0062 2587
## shrub_cover-Procyon_lotor 1.0000 3781
## shrub_cover-Dasypus_novemcinctus 1.0012 3122
## veg_height-Odocoileus_virginianus 1.0005 6275
## veg_height-Canis_latrans 1.0018 2288
## veg_height-Procyon_lotor 1.0015 4969
## veg_height-Dasypus_novemcinctus 1.0012 5103
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.736
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3390 1.0662 -1.8029 0.3296 2.4577 1.0057 2990
## Avg_Cogongrass_Cover -0.0648 0.6400 -1.3076 -0.0741 1.2517 1.0030 2430
## I(Avg_Cogongrass_Cover^2) 1.1717 0.9316 -0.6942 1.1048 3.1764 1.0048 1992
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 15.1013 79.8034 0.4344 5.4457 79.3245 1.1985 2913
## Avg_Cogongrass_Cover 1.0824 3.7176 0.0435 0.3509 6.2375 1.0294 4500
## I(Avg_Cogongrass_Cover^2) 7.4718 28.5729 0.0567 1.0625 57.7777 1.0019 773
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.511 0.7315 0.0414 0.2661 2.4795 1.0218 1273
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3717 0.7342 -2.6677 -1.4379 0.3252 1.0005 5045
## shrub_cover 0.1530 0.3757 -0.6305 0.1452 0.9219 1.0043 4954
## veg_height -0.0821 0.3768 -0.8473 -0.0895 0.7260 1.0023 4628
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2860 7.3814 0.4707 1.7968 14.7357 1.0196 5250
## shrub_cover 0.6325 1.2434 0.0565 0.3180 3.2442 1.0182 4824
## veg_height 0.6437 1.2865 0.0767 0.3288 3.0577 1.0066 5338
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.2453 2.8562 0.5775 3.6699
## (Intercept)-Canis_latrans -0.4251 0.9012 -2.3838 -0.3863
## (Intercept)-Procyon_lotor -0.0485 0.7923 -1.7237 -0.0096
## (Intercept)-Dasypus_novemcinctus -1.0478 0.7091 -2.5038 -1.0329
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1380 0.9770 -2.1494 -0.1156
## Avg_Cogongrass_Cover-Canis_latrans 0.2042 0.7224 -1.0456 0.1396
## Avg_Cogongrass_Cover-Procyon_lotor -0.1771 0.7302 -1.5898 -0.1863
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.1140 0.6181 -1.3815 -0.1115
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.3411 2.7229 -0.3213 1.5591
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0743 1.5311 0.0505 1.7028
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9620 1.9186 0.0207 1.3314
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5562 0.4840 -0.3417 0.5338
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.6271 1.0065 512
## (Intercept)-Canis_latrans 1.3397 1.0020 1166
## (Intercept)-Procyon_lotor 1.4501 1.0016 1227
## (Intercept)-Dasypus_novemcinctus 0.3027 1.0010 3446
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7383 1.0104 2212
## Avg_Cogongrass_Cover-Canis_latrans 1.7808 1.0054 2283
## Avg_Cogongrass_Cover-Procyon_lotor 1.3054 1.0017 1492
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0913 1.0001 2744
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 9.9032 1.0125 297
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.9234 1.0093 539
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 7.3857 1.0161 310
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5990 1.0013 2719
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0598 -0.1124 0.0047 0.1250
## (Intercept)-Canis_latrans -2.7628 0.1887 -3.1515 -2.7581 -2.4166
## (Intercept)-Procyon_lotor -2.3370 0.1561 -2.6517 -2.3344 -2.0464
## (Intercept)-Dasypus_novemcinctus -1.7061 0.1564 -2.0200 -1.7020 -1.4095
## shrub_cover-Odocoileus_virginianus -0.0551 0.0640 -0.1835 -0.0542 0.0697
## shrub_cover-Canis_latrans -0.2523 0.2162 -0.6969 -0.2473 0.1635
## shrub_cover-Procyon_lotor 0.1838 0.1768 -0.1594 0.1847 0.5244
## shrub_cover-Dasypus_novemcinctus 0.7593 0.3117 0.1839 0.7477 1.4124
## veg_height-Odocoileus_virginianus -0.2989 0.0648 -0.4267 -0.2978 -0.1722
## veg_height-Canis_latrans -0.6407 0.1890 -1.0286 -0.6340 -0.2843
## veg_height-Procyon_lotor 0.3446 0.1279 0.0940 0.3451 0.5994
## veg_height-Dasypus_novemcinctus 0.2262 0.1363 -0.0402 0.2254 0.4979
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0016 2644
## (Intercept)-Procyon_lotor 1.0078 1639
## (Intercept)-Dasypus_novemcinctus 1.0062 3848
## shrub_cover-Odocoileus_virginianus 1.0009 5698
## shrub_cover-Canis_latrans 1.0010 3256
## shrub_cover-Procyon_lotor 1.0008 1938
## shrub_cover-Dasypus_novemcinctus 1.0028 3190
## veg_height-Odocoileus_virginianus 1.0027 5041
## veg_height-Canis_latrans 1.0062 2369
## veg_height-Procyon_lotor 1.0017 3932
## veg_height-Dasypus_novemcinctus 1.0047 4742
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_cover_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.7357
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2739 1.3938 -2.4773 0.2722 3.0276 1.0007 4740
## Cogon_Patch_Size 0.0610 0.9259 -1.7190 0.0409 2.0307 1.0023 2166
## Veg_shannon_index 0.8901 0.7757 -0.6432 0.8805 2.4316 1.0007 1640
## total_shrub_cover -0.0538 0.8445 -1.6901 -0.0747 1.7506 1.0036 1384
## Avg_Cogongrass_Cover 0.6423 1.2109 -1.7124 0.6307 3.0600 1.0128 1002
## Tree_Density -1.4773 1.4300 -3.9111 -1.6218 1.6897 1.0059 1952
## Avg_Canopy_Cover 0.9063 0.8939 -0.9887 0.9440 2.5856 1.0012 3020
## I(Avg_Cogongrass_Cover^2) 1.7914 1.0175 -0.2382 1.7811 3.8399 1.0019 1456
## avg_veg_height 0.0586 0.7587 -1.4752 0.0641 1.5369 1.0016 1128
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 82.7594 325.2504 3.2294 31.3851 436.2775 1.1468 1374
## Cogon_Patch_Size 5.1254 22.5104 0.0668 1.2780 33.2102 1.1613 1599
## Veg_shannon_index 1.9620 6.8961 0.0513 0.5791 12.7327 1.0301 3739
## total_shrub_cover 3.6544 16.6464 0.0741 1.1720 19.0189 1.1504 2876
## Avg_Cogongrass_Cover 3.8533 20.4653 0.0584 0.7757 25.9670 1.1890 2162
## Tree_Density 35.2848 91.9386 0.1090 9.9097 220.5783 1.1021 569
## Avg_Canopy_Cover 4.6788 13.3496 0.1060 1.5981 28.7783 1.0167 1951
## I(Avg_Cogongrass_Cover^2) 6.6476 44.5227 0.0536 0.7430 48.8660 1.0989 1462
## avg_veg_height 1.1247 3.4945 0.0417 0.3851 6.4196 1.0107 2890
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.2579 9.1336 0.0609 0.8768 21.4969 1.1969 119
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3790 0.7380 -2.6851 -1.4372 0.2817 1.0007 5250
## shrub_cover 0.1492 0.4128 -0.7094 0.1504 0.9832 1.0045 4624
## veg_height -0.0742 0.3627 -0.8027 -0.0736 0.6876 1.0052 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6195 12.6116 0.4614 1.7796 15.2948 1.1163 5250
## shrub_cover 0.7995 2.3406 0.0700 0.3841 3.7254 1.0621 5250
## veg_height 0.6229 1.2520 0.0782 0.3294 2.9105 1.0030 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 10.5587 6.4971 2.7709
## (Intercept)-Canis_latrans -0.7245 1.8017 -4.3589
## (Intercept)-Procyon_lotor -0.3847 1.5470 -3.5429
## (Intercept)-Dasypus_novemcinctus -3.3225 1.9012 -8.0429
## Cogon_Patch_Size-Odocoileus_virginianus 0.1197 1.9380 -3.1820
## Cogon_Patch_Size-Canis_latrans 1.4598 1.9238 -0.9324
## Cogon_Patch_Size-Procyon_lotor -0.5820 1.0006 -2.5780
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3403 0.9391 -2.2507
## Veg_shannon_index-Odocoileus_virginianus 0.7390 1.2989 -2.1469
## Veg_shannon_index-Canis_latrans 1.4646 0.9925 -0.1537
## Veg_shannon_index-Procyon_lotor 1.2489 0.8157 -0.1158
## Veg_shannon_index-Dasypus_novemcinctus 0.5798 0.7254 -0.8490
## total_shrub_cover-Odocoileus_virginianus 0.1057 1.4150 -2.5890
## total_shrub_cover-Canis_latrans 0.8520 1.2667 -0.9393
## total_shrub_cover-Procyon_lotor -1.1468 0.9657 -3.0629
## total_shrub_cover-Dasypus_novemcinctus -0.0122 0.8217 -1.8582
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5245 1.7995 -3.0953
## Avg_Cogongrass_Cover-Canis_latrans 0.9629 1.6692 -2.0034
## Avg_Cogongrass_Cover-Procyon_lotor 0.3946 1.5485 -2.6860
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3144 1.6460 -1.4598
## Tree_Density-Odocoileus_virginianus 0.2596 3.4123 -4.1485
## Tree_Density-Canis_latrans -4.3036 2.6388 -10.6927
## Tree_Density-Procyon_lotor -2.1081 1.3659 -4.9139
## Tree_Density-Dasypus_novemcinctus -6.4250 3.9330 -16.5457
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7116 1.6281 -2.6373
## Avg_Canopy_Cover-Canis_latrans 0.0482 0.7783 -1.5366
## Avg_Canopy_Cover-Procyon_lotor 1.5171 0.8944 0.0222
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2986 1.1888 0.6433
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.4593 2.3172 -0.5942
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6334 1.7931 0.3412
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4250 1.6868 0.3652
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.8232 1.1008 -0.0316
## avg_veg_height-Odocoileus_virginianus 0.0496 1.1209 -2.2718
## avg_veg_height-Canis_latrans -0.1358 0.8488 -1.8965
## avg_veg_height-Procyon_lotor 0.1147 0.8158 -1.5396
## avg_veg_height-Dasypus_novemcinctus 0.2035 0.7715 -1.3522
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.0522 28.3466 1.0187 200
## (Intercept)-Canis_latrans -0.7629 3.0321 1.0104 918
## (Intercept)-Procyon_lotor -0.3376 2.2331 1.0311 427
## (Intercept)-Dasypus_novemcinctus -3.0808 -0.3450 1.0069 467
## Cogon_Patch_Size-Odocoileus_virginianus -0.0131 4.1590 1.0152 1047
## Cogon_Patch_Size-Canis_latrans 1.0247 6.5573 1.0405 913
## Cogon_Patch_Size-Procyon_lotor -0.5701 1.2216 1.0096 1200
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3144 1.4931 1.0117 1521
## Veg_shannon_index-Odocoileus_virginianus 0.8040 3.1494 1.0046 1584
## Veg_shannon_index-Canis_latrans 1.3386 3.8147 1.0021 923
## Veg_shannon_index-Procyon_lotor 1.1750 3.0031 1.0026 675
## Veg_shannon_index-Dasypus_novemcinctus 0.5780 2.0467 1.0025 1502
## total_shrub_cover-Odocoileus_virginianus 0.0272 3.1498 1.0034 1906
## total_shrub_cover-Canis_latrans 0.5929 4.0642 1.0050 704
## total_shrub_cover-Procyon_lotor -1.0652 0.3984 1.0207 618
## total_shrub_cover-Dasypus_novemcinctus 0.0401 1.4663 1.0005 1478
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5377 4.0270 1.0036 1599
## Avg_Cogongrass_Cover-Canis_latrans 0.8206 4.7415 1.0227 694
## Avg_Cogongrass_Cover-Procyon_lotor 0.4176 3.3175 1.0053 849
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1534 5.0654 1.0204 871
## Tree_Density-Odocoileus_virginianus -0.5378 9.4797 1.0066 625
## Tree_Density-Canis_latrans -3.7487 -0.7917 1.0179 338
## Tree_Density-Procyon_lotor -2.0664 0.2764 1.0080 1176
## Tree_Density-Dasypus_novemcinctus -5.4742 -1.5869 1.0055 274
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7662 3.9206 1.0151 1725
## Avg_Canopy_Cover-Canis_latrans 0.0921 1.4460 1.0069 1665
## Avg_Canopy_Cover-Procyon_lotor 1.4294 3.5410 1.0006 1080
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0646 5.1982 1.0078 370
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.0388 8.1676 1.0742 406
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2747 7.4664 1.0332 516
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1274 6.6486 1.0255 359
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7328 4.3184 1.0063 1112
## avg_veg_height-Odocoileus_virginianus 0.0650 2.2311 1.0056 1479
## avg_veg_height-Canis_latrans -0.1161 1.5062 1.0022 1244
## avg_veg_height-Procyon_lotor 0.1143 1.7043 1.0042 1584
## avg_veg_height-Dasypus_novemcinctus 0.2080 1.6832 1.0017 1393
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0056 0.0596 -0.1125 0.0055 0.1239
## (Intercept)-Canis_latrans -2.7454 0.1929 -3.1383 -2.7428 -2.3779
## (Intercept)-Procyon_lotor -2.3089 0.1510 -2.6333 -2.3013 -2.0307
## (Intercept)-Dasypus_novemcinctus -1.7158 0.1598 -2.0317 -1.7147 -1.4052
## shrub_cover-Odocoileus_virginianus -0.0562 0.0638 -0.1810 -0.0564 0.0702
## shrub_cover-Canis_latrans -0.3564 0.2321 -0.7911 -0.3612 0.1009
## shrub_cover-Procyon_lotor 0.2479 0.1706 -0.1051 0.2551 0.5671
## shrub_cover-Dasypus_novemcinctus 0.8200 0.3256 0.2102 0.8157 1.4790
## veg_height-Odocoileus_virginianus -0.3005 0.0651 -0.4278 -0.3008 -0.1736
## veg_height-Canis_latrans -0.6222 0.1895 -1.0004 -0.6186 -0.2592
## veg_height-Procyon_lotor 0.3531 0.1261 0.1140 0.3536 0.6006
## veg_height-Dasypus_novemcinctus 0.2344 0.1348 -0.0267 0.2331 0.5014
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0002 1935
## (Intercept)-Procyon_lotor 1.0054 1326
## (Intercept)-Dasypus_novemcinctus 1.0006 2645
## shrub_cover-Odocoileus_virginianus 1.0018 5250
## shrub_cover-Canis_latrans 1.0003 1738
## shrub_cover-Procyon_lotor 1.0010 1737
## shrub_cover-Dasypus_novemcinctus 0.9999 2087
## veg_height-Odocoileus_virginianus 1.0027 5461
## veg_height-Canis_latrans 0.9999 2143
## veg_height-Procyon_lotor 1.0091 2926
## veg_height-Dasypus_novemcinctus 1.0004 4549
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T50<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8648
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7493 1.0412 -1.4678 0.7552 2.7941 1.0026 4966
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.0427 61.6 0.7695 5.2611 76.9331 1.1869 1391
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0854 0.7735 -2.4749 -1.1501 0.6702 1.0015 5250
## week 0.5339 0.4328 -0.4047 0.5523 1.3610 1.0025 4975
## I(week^2) -0.2743 0.2376 -0.7317 -0.2762 0.1992 1.0022 4938
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8392 7.0085 0.5624 2.1037 18.4522 1.0428 5250
## week 0.8498 1.5406 0.1037 0.4460 4.0868 1.0209 5250
## I(week^2) 0.2361 0.4357 0.0317 0.1267 1.1174 1.0093 4612
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8233 2.6810 2.0998 4.1639 11.3262
## (Intercept)-Canis_latrans 0.3732 0.4247 -0.4164 0.3590 1.2473
## (Intercept)-Procyon_lotor 0.7779 0.4079 0.0176 0.7676 1.6244
## (Intercept)-Dasypus_novemcinctus -0.6066 0.3791 -1.3734 -0.5959 0.0966
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0452 365
## (Intercept)-Canis_latrans 1.0000 4367
## (Intercept)-Procyon_lotor 1.0006 5250
## (Intercept)-Dasypus_novemcinctus 1.0003 4744
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5390 0.0811 0.3799 0.5378 0.6983
## (Intercept)-Canis_latrans -2.4119 0.1903 -2.7992 -2.4056 -2.0596
## (Intercept)-Procyon_lotor -2.1496 0.1516 -2.4545 -2.1457 -1.8557
## (Intercept)-Dasypus_novemcinctus -1.4271 0.1598 -1.7511 -1.4250 -1.1196
## week-Odocoileus_virginianus 1.3088 0.1246 1.0666 1.3085 1.5482
## week-Canis_latrans 0.6348 0.2769 0.1037 0.6298 1.1844
## week-Procyon_lotor 0.2101 0.2173 -0.2061 0.2092 0.6402
## week-Dasypus_novemcinctus 0.1176 0.2384 -0.3499 0.1166 0.5896
## I(week^2)-Odocoileus_virginianus -0.5398 0.0507 -0.6396 -0.5388 -0.4410
## I(week^2)-Canis_latrans -0.2606 0.1142 -0.4929 -0.2575 -0.0414
## I(week^2)-Procyon_lotor -0.1312 0.0944 -0.3194 -0.1312 0.0443
## I(week^2)-Dasypus_novemcinctus -0.1804 0.1095 -0.4011 -0.1785 0.0265
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0062 4997
## (Intercept)-Canis_latrans 1.0031 3058
## (Intercept)-Procyon_lotor 1.0040 4456
## (Intercept)-Dasypus_novemcinctus 1.0001 5250
## week-Odocoileus_virginianus 1.0042 5250
## week-Canis_latrans 1.0002 3744
## week-Procyon_lotor 1.0029 5120
## week-Dasypus_novemcinctus 1.0006 4996
## I(week^2)-Odocoileus_virginianus 1.0029 5042
## I(week^2)-Canis_latrans 1.0010 3877
## I(week^2)-Procyon_lotor 1.0008 4290
## I(week^2)-Dasypus_novemcinctus 1.0016 4763
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9578
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6511 1.3984 -2.1236 0.6646 3.3393 1.0006 5250
## Cogon_Patch_Size -0.3135 0.8219 -1.8176 -0.3456 1.4748 1.0048 2289
## Veg_shannon_index 0.9777 0.6948 -0.4265 0.9833 2.3869 1.0027 1459
## total_shrub_cover 0.0116 0.6695 -1.2130 -0.0252 1.4610 1.0034 2598
## Avg_Cogongrass_Cover 2.1014 0.9651 0.0976 2.1016 3.9617 1.0143 1201
## Tree_Density -1.3976 1.1326 -3.4109 -1.5081 1.1857 1.0133 1537
## Avg_Canopy_Cover 0.9065 0.7637 -0.8152 0.9417 2.3627 1.0030 3203
## avg_veg_height -0.3436 0.6683 -1.6335 -0.3465 1.0080 1.0160 2039
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 74.0973 270.1636 2.3729 26.4961 435.7111 1.0833 1343
## Cogon_Patch_Size 3.0840 12.2130 0.0568 0.8749 19.3152 1.0767 3666
## Veg_shannon_index 1.5599 5.5174 0.0513 0.4670 10.0978 1.0423 2982
## total_shrub_cover 1.6840 4.3938 0.0561 0.6359 9.2837 1.0150 3083
## Avg_Cogongrass_Cover 2.2813 7.6423 0.0491 0.5134 16.4335 1.0101 2630
## Tree_Density 10.7949 30.0183 0.0886 2.7539 70.3426 1.0395 1603
## Avg_Canopy_Cover 2.8904 6.7328 0.0787 1.1069 16.2491 1.0416 2684
## avg_veg_height 1.2698 11.5499 0.0471 0.3668 6.6204 1.2505 4819
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9615 9.6302 0.0571 0.9404 16.2123 1.4552 215
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0879 0.7804 -2.4574 -1.1563 0.6766 1.0028 5250
## week 0.5276 0.4444 -0.3817 0.5459 1.3439 0.9999 4952
## I(week^2) -0.2772 0.2381 -0.7360 -0.2780 0.1826 1.0022 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8571 7.2532 0.5705 2.1363 16.9095 1.0346 5458
## week 0.9105 2.7498 0.1030 0.4543 3.9295 1.0723 5250
## I(week^2) 0.2564 0.9188 0.0319 0.1238 1.2011 1.1446 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 10.7157 6.1156 3.2603 9.2725
## (Intercept)-Canis_latrans 0.9137 1.2567 -1.1400 0.7788
## (Intercept)-Procyon_lotor 0.8848 1.0662 -1.3260 0.8840
## (Intercept)-Dasypus_novemcinctus -1.6876 1.1870 -4.3522 -1.5624
## Cogon_Patch_Size-Odocoileus_virginianus -0.2805 1.4635 -2.8435 -0.3896
## Cogon_Patch_Size-Canis_latrans 0.6020 1.3286 -1.2542 0.3494
## Cogon_Patch_Size-Procyon_lotor -0.8356 0.8825 -2.3647 -0.8483
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7976 0.6807 -2.2335 -0.7592
## Veg_shannon_index-Odocoileus_virginianus 0.8720 1.1329 -1.4897 0.8873
## Veg_shannon_index-Canis_latrans 1.4261 0.8402 0.1381 1.3031
## Veg_shannon_index-Procyon_lotor 1.2592 0.7871 -0.0373 1.1907
## Veg_shannon_index-Dasypus_novemcinctus 0.6966 0.5828 -0.4507 0.6879
## total_shrub_cover-Odocoileus_virginianus 0.1567 1.1129 -1.9187 0.0790
## total_shrub_cover-Canis_latrans 0.4195 0.8715 -0.9112 0.2940
## total_shrub_cover-Procyon_lotor -0.7431 0.7035 -2.2091 -0.7016
## total_shrub_cover-Dasypus_novemcinctus 0.2131 0.5484 -0.8126 0.2010
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1218 1.3818 -0.6505 2.1237
## Avg_Cogongrass_Cover-Canis_latrans 2.5282 1.1265 0.6702 2.4107
## Avg_Cogongrass_Cover-Procyon_lotor 2.2242 1.0410 0.3175 2.1884
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6489 1.1017 0.7843 2.5413
## Tree_Density-Odocoileus_virginianus -0.3178 2.1024 -3.3042 -0.7173
## Tree_Density-Canis_latrans -2.7325 1.6003 -6.9501 -2.4170
## Tree_Density-Procyon_lotor -1.2637 0.9229 -3.0349 -1.2880
## Tree_Density-Dasypus_novemcinctus -3.9358 2.3203 -10.0363 -3.3601
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7301 1.4093 -2.3276 0.8000
## Avg_Canopy_Cover-Canis_latrans 0.1504 0.7248 -1.4178 0.1942
## Avg_Canopy_Cover-Procyon_lotor 1.4874 0.7417 0.2194 1.4222
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8591 0.7748 0.6432 1.7551
## avg_veg_height-Odocoileus_virginianus -0.4055 1.0585 -2.5589 -0.3852
## avg_veg_height-Canis_latrans -0.6359 0.7315 -2.1509 -0.6191
## avg_veg_height-Procyon_lotor -0.2615 0.6640 -1.5306 -0.2644
## avg_veg_height-Dasypus_novemcinctus -0.2026 0.6495 -1.4520 -0.2157
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 27.7893 1.1344 173
## (Intercept)-Canis_latrans 3.8244 1.0074 798
## (Intercept)-Procyon_lotor 3.0249 1.0032 1289
## (Intercept)-Dasypus_novemcinctus 0.2646 1.0034 968
## Cogon_Patch_Size-Odocoileus_virginianus 2.9450 1.0011 1921
## Cogon_Patch_Size-Canis_latrans 3.9211 1.0117 1507
## Cogon_Patch_Size-Procyon_lotor 0.7182 1.0062 1090
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4315 1.0058 1296
## Veg_shannon_index-Odocoileus_virginianus 3.0992 1.0029 2104
## Veg_shannon_index-Canis_latrans 3.4398 1.0042 1052
## Veg_shannon_index-Procyon_lotor 2.8716 1.0548 432
## Veg_shannon_index-Dasypus_novemcinctus 1.8653 1.0032 1851
## total_shrub_cover-Odocoileus_virginianus 2.6560 1.0014 2034
## total_shrub_cover-Canis_latrans 2.4312 1.0230 1358
## total_shrub_cover-Procyon_lotor 0.4942 1.0025 1674
## total_shrub_cover-Dasypus_novemcinctus 1.3275 1.0025 2291
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.8293 1.0091 1562
## Avg_Cogongrass_Cover-Canis_latrans 5.0276 1.0027 958
## Avg_Cogongrass_Cover-Procyon_lotor 4.3615 1.0046 1011
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0744 1.0075 817
## Tree_Density-Odocoileus_virginianus 5.2027 1.0189 893
## Tree_Density-Canis_latrans -0.5287 1.0147 930
## Tree_Density-Procyon_lotor 0.5976 1.0110 1716
## Tree_Density-Dasypus_novemcinctus -1.1102 1.0104 750
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5342 1.0041 2070
## Avg_Canopy_Cover-Canis_latrans 1.4647 1.0061 1838
## Avg_Canopy_Cover-Procyon_lotor 3.1556 1.0018 1675
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.6333 1.0089 906
## avg_veg_height-Odocoileus_virginianus 1.6044 1.0089 1989
## avg_veg_height-Canis_latrans 0.7405 1.0047 1684
## avg_veg_height-Procyon_lotor 1.1118 1.0048 2050
## avg_veg_height-Dasypus_novemcinctus 1.1097 1.0102 1833
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5397 0.0797 0.3887 0.5377 0.6981
## (Intercept)-Canis_latrans -2.4516 0.1974 -2.8514 -2.4452 -2.0775
## (Intercept)-Procyon_lotor -2.1563 0.1539 -2.4764 -2.1507 -1.8755
## (Intercept)-Dasypus_novemcinctus -1.4212 0.1578 -1.7363 -1.4189 -1.1269
## week-Odocoileus_virginianus 1.3091 0.1234 1.0676 1.3077 1.5524
## week-Canis_latrans 0.6215 0.2781 0.0953 0.6189 1.1799
## week-Procyon_lotor 0.2127 0.2243 -0.2219 0.2121 0.6673
## week-Dasypus_novemcinctus 0.1273 0.2344 -0.3364 0.1285 0.5777
## I(week^2)-Odocoileus_virginianus -0.5404 0.0511 -0.6421 -0.5392 -0.4427
## I(week^2)-Canis_latrans -0.2565 0.1150 -0.4900 -0.2551 -0.0329
## I(week^2)-Procyon_lotor -0.1320 0.0957 -0.3227 -0.1313 0.0539
## I(week^2)-Dasypus_novemcinctus -0.1836 0.1073 -0.4007 -0.1805 0.0196
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0015 2489
## (Intercept)-Procyon_lotor 0.9999 3974
## (Intercept)-Dasypus_novemcinctus 1.0002 5250
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0053 3374
## week-Procyon_lotor 1.0002 4535
## week-Dasypus_novemcinctus 1.0020 5250
## I(week^2)-Odocoileus_virginianus 1.0000 5250
## I(week^2)-Canis_latrans 1.0015 3520
## I(week^2)-Procyon_lotor 1.0015 4437
## I(week^2)-Dasypus_novemcinctus 1.0016 4711
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8912
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8155 1.1544 -1.6474 0.8546 3.0302 1.0029 5250
## Avg_Cogongrass_Cover 0.2757 0.5366 -0.7670 0.2668 1.3650 1.0027 2942
## total_shrub_cover -0.1500 0.5790 -1.3224 -0.1577 1.0384 1.0008 4616
## avg_veg_height 0.1391 0.5333 -0.8753 0.1289 1.2175 1.0010 3040
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.1193 38.3251 0.8746 7.6764 87.2633 1.0240 1961
## Avg_Cogongrass_Cover 0.7609 2.2508 0.0423 0.2883 4.4212 1.0537 4826
## total_shrub_cover 1.7245 11.1924 0.0629 0.6279 8.3979 1.2356 5250
## avg_veg_height 0.7330 2.1511 0.0413 0.2751 4.0425 1.0338 4708
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6153 1.1868 0.0468 0.3125 2.8706 1.1281 1011
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0808 0.7821 -2.5042 -1.1425 0.6873 1.0008 5471
## week 0.5244 0.4556 -0.4447 0.5417 1.3899 1.0004 5250
## I(week^2) -0.2741 0.2357 -0.7526 -0.2748 0.2167 1.0011 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8205 8.2590 0.5646 2.1362 17.8526 1.0873 4736
## week 0.9300 2.6464 0.1022 0.4724 4.2837 1.0565 4001
## I(week^2) 0.2452 0.4410 0.0323 0.1257 1.2598 1.0003 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.6029 2.6544 2.2385 5.0499
## (Intercept)-Canis_latrans 0.5504 0.6961 -0.6892 0.5019
## (Intercept)-Procyon_lotor 0.9823 0.6821 -0.2587 0.9569
## (Intercept)-Dasypus_novemcinctus -0.6710 0.5976 -1.9023 -0.6652
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2506 0.7947 -1.2780 0.2439
## Avg_Cogongrass_Cover-Canis_latrans 0.5219 0.5699 -0.5030 0.4821
## Avg_Cogongrass_Cover-Procyon_lotor 0.1186 0.5471 -0.9517 0.1171
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2854 0.4600 -0.6041 0.2771
## total_shrub_cover-Odocoileus_virginianus -0.0756 0.9153 -1.8150 -0.1161
## total_shrub_cover-Canis_latrans 0.2907 0.5621 -0.6583 0.2355
## total_shrub_cover-Procyon_lotor -0.9842 0.6292 -2.4011 -0.9118
## total_shrub_cover-Dasypus_novemcinctus 0.0505 0.4073 -0.7389 0.0411
## avg_veg_height-Odocoileus_virginianus 0.1236 0.7830 -1.4247 0.1092
## avg_veg_height-Canis_latrans -0.0526 0.5217 -1.1026 -0.0435
## avg_veg_height-Procyon_lotor 0.1962 0.5255 -0.7867 0.1877
## avg_veg_height-Dasypus_novemcinctus 0.2849 0.4718 -0.6293 0.2800
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.3178 1.0032 531
## (Intercept)-Canis_latrans 2.0435 1.0003 2277
## (Intercept)-Procyon_lotor 2.4260 1.0004 2548
## (Intercept)-Dasypus_novemcinctus 0.5254 1.0039 2519
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9370 1.0065 2570
## Avg_Cogongrass_Cover-Canis_latrans 1.7513 1.0007 3949
## Avg_Cogongrass_Cover-Procyon_lotor 1.2014 1.0016 3249
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2137 1.0004 3380
## total_shrub_cover-Odocoileus_virginianus 1.8672 1.0006 2619
## total_shrub_cover-Canis_latrans 1.5595 1.0008 2690
## total_shrub_cover-Procyon_lotor 0.0287 1.0003 2885
## total_shrub_cover-Dasypus_novemcinctus 0.8532 1.0044 4849
## avg_veg_height-Odocoileus_virginianus 1.7267 1.0076 2704
## avg_veg_height-Canis_latrans 0.9822 1.0030 3364
## avg_veg_height-Procyon_lotor 1.2552 1.0008 3211
## avg_veg_height-Dasypus_novemcinctus 1.2196 1.0020 3339
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5380 0.0787 0.3840 0.5378 0.6925
## (Intercept)-Canis_latrans -2.4345 0.2022 -2.8547 -2.4280 -2.0595
## (Intercept)-Procyon_lotor -2.1632 0.1517 -2.4731 -2.1577 -1.8816
## (Intercept)-Dasypus_novemcinctus -1.4278 0.1602 -1.7569 -1.4234 -1.1246
## week-Odocoileus_virginianus 1.3091 0.1226 1.0748 1.3081 1.5559
## week-Canis_latrans 0.6265 0.2729 0.0964 0.6233 1.1742
## week-Procyon_lotor 0.2062 0.2194 -0.2244 0.2052 0.6347
## week-Dasypus_novemcinctus 0.1164 0.2326 -0.3341 0.1138 0.5649
## I(week^2)-Odocoileus_virginianus -0.5399 0.0500 -0.6374 -0.5389 -0.4441
## I(week^2)-Canis_latrans -0.2583 0.1127 -0.4825 -0.2558 -0.0427
## I(week^2)-Procyon_lotor -0.1312 0.0943 -0.3169 -0.1299 0.0526
## I(week^2)-Dasypus_novemcinctus -0.1799 0.1101 -0.4029 -0.1763 0.0283
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0020 2549
## (Intercept)-Procyon_lotor 0.9999 4132
## (Intercept)-Dasypus_novemcinctus 1.0000 5250
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0010 3992
## week-Procyon_lotor 1.0031 4561
## week-Dasypus_novemcinctus 1.0003 4987
## I(week^2)-Odocoileus_virginianus 0.9998 5250
## I(week^2)-Canis_latrans 1.0026 4073
## I(week^2)-Procyon_lotor 1.0023 4537
## I(week^2)-Dasypus_novemcinctus 1.0003 4727
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6812 1.2156 -1.8726 0.7136 3.0110 1.0008 5011
## Tree_Density -0.6961 0.6603 -1.9703 -0.6980 0.6979 1.0035 3520
## Avg_Canopy_Cover 0.4591 0.5546 -0.7179 0.4630 1.5587 1.0021 4426
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.0458 71.2704 1.1489 9.5048 126.9011 1.0329 1645
## Tree_Density 2.3226 9.6158 0.0495 0.5611 14.5761 1.1216 1854
## Avg_Canopy_Cover 1.2454 3.2188 0.0625 0.5449 6.5637 1.0073 4648
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5802 1.0187 0.0413 0.2896 2.9501 1.0129 1058
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0783 0.7738 -2.5328 -1.1358 0.6807 1.0009 6024
## week 0.5299 0.4304 -0.4528 0.5480 1.3341 1.0000 5250
## I(week^2) -0.2731 0.2415 -0.7599 -0.2756 0.2183 1.0007 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8958 8.3816 0.5578 2.1088 16.9698 1.0526 4587
## week 0.8667 2.0825 0.1050 0.4446 4.2941 1.0443 5250
## I(week^2) 0.2612 0.9726 0.0320 0.1249 1.2291 1.2127 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 6.2133 3.2662 2.3769 5.4134 14.8036
## (Intercept)-Canis_latrans 0.3767 0.7003 -0.8575 0.3247 1.8650
## (Intercept)-Procyon_lotor 0.7613 0.6393 -0.5194 0.7669 2.0350
## (Intercept)-Dasypus_novemcinctus -1.0622 0.7058 -2.6629 -1.0059 0.1579
## Tree_Density-Odocoileus_virginianus -0.2421 1.2096 -1.8892 -0.4198 2.5748
## Tree_Density-Canis_latrans -0.9359 0.6225 -2.4055 -0.8634 0.1061
## Tree_Density-Procyon_lotor -0.4498 0.4692 -1.3784 -0.4536 0.4477
## Tree_Density-Dasypus_novemcinctus -1.5693 1.1625 -4.6042 -1.2890 -0.1605
## Avg_Canopy_Cover-Odocoileus_virginianus 0.4141 0.8862 -1.3656 0.4158 2.2710
## Avg_Canopy_Cover-Canis_latrans -0.1235 0.4783 -1.1106 -0.1039 0.7843
## Avg_Canopy_Cover-Procyon_lotor 0.8570 0.5063 -0.0302 0.8168 1.9635
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8564 0.4555 0.0676 0.8137 1.8611
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0227 375
## (Intercept)-Canis_latrans 1.0040 2212
## (Intercept)-Procyon_lotor 1.0021 2764
## (Intercept)-Dasypus_novemcinctus 1.0076 2035
## Tree_Density-Odocoileus_virginianus 1.0102 1394
## Tree_Density-Canis_latrans 1.0002 3555
## Tree_Density-Procyon_lotor 1.0034 3468
## Tree_Density-Dasypus_novemcinctus 1.0068 1363
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0060 2946
## Avg_Canopy_Cover-Canis_latrans 1.0038 3609
## Avg_Canopy_Cover-Procyon_lotor 1.0017 4913
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0061 4474
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5407 0.0804 0.3811 0.5424 0.6968
## (Intercept)-Canis_latrans -2.4281 0.2001 -2.8470 -2.4195 -2.0552
## (Intercept)-Procyon_lotor -2.1445 0.1508 -2.4470 -2.1402 -1.8586
## (Intercept)-Dasypus_novemcinctus -1.4267 0.1576 -1.7435 -1.4233 -1.1308
## week-Odocoileus_virginianus 1.3090 0.1247 1.0590 1.3101 1.5543
## week-Canis_latrans 0.6293 0.2749 0.0980 0.6306 1.1952
## week-Procyon_lotor 0.2096 0.2195 -0.2239 0.2076 0.6510
## week-Dasypus_novemcinctus 0.1170 0.2361 -0.3498 0.1154 0.5834
## I(week^2)-Odocoileus_virginianus -0.5403 0.0512 -0.6402 -0.5401 -0.4394
## I(week^2)-Canis_latrans -0.2584 0.1150 -0.4913 -0.2580 -0.0361
## I(week^2)-Procyon_lotor -0.1328 0.0959 -0.3226 -0.1314 0.0534
## I(week^2)-Dasypus_novemcinctus -0.1792 0.1099 -0.3997 -0.1795 0.0326
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0003 2999
## (Intercept)-Procyon_lotor 1.0009 4270
## (Intercept)-Dasypus_novemcinctus 0.9998 5273
## week-Odocoileus_virginianus 1.0011 5250
## week-Canis_latrans 1.0016 3675
## week-Procyon_lotor 1.0002 4064
## week-Dasypus_novemcinctus 1.0000 4988
## I(week^2)-Odocoileus_virginianus 1.0018 5250
## I(week^2)-Canis_latrans 1.0005 3723
## I(week^2)-Procyon_lotor 1.0007 4602
## I(week^2)-Dasypus_novemcinctus 1.0006 4672
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.8483
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8495 1.1619 -1.5880 0.8906 3.0759 1.0022 5197
## Cogon_Patch_Size 0.2758 0.6423 -0.9427 0.2369 1.6460 1.0003 3281
## Avg_Cogongrass_Cover 0.2932 0.4979 -0.6827 0.2890 1.2678 1.0016 3341
## total_shrub_cover -0.2146 0.5923 -1.4240 -0.2125 1.0459 1.0080 3518
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.2904 53.4438 1.0401 7.9705 110.4303 1.0144 806
## Cogon_Patch_Size 2.2356 22.2065 0.0497 0.5485 10.7234 1.2875 4962
## Avg_Cogongrass_Cover 0.6451 3.0268 0.0390 0.2471 3.3931 1.2066 5250
## total_shrub_cover 1.4052 2.8267 0.0545 0.5545 8.5227 1.0030 3624
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6254 1.0518 0.0447 0.3078 3.1404 1.0234 684
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0918 0.7695 -2.4895 -1.1428 0.5916 1.0031 5250
## week 0.5367 0.4283 -0.3637 0.5476 1.3765 1.0009 5250
## I(week^2) -0.2755 0.2379 -0.7489 -0.2810 0.2153 1.0016 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8331 7.8793 0.5710 2.0792 17.5349 1.0689 4408
## week 0.8354 1.5357 0.1049 0.4509 3.9790 1.0218 5250
## I(week^2) 0.2448 0.5672 0.0318 0.1256 1.1642 1.0158 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 6.0360 3.2989 2.4059 5.2137
## (Intercept)-Canis_latrans 0.6902 0.7490 -0.6013 0.6288
## (Intercept)-Procyon_lotor 0.9638 0.7058 -0.3475 0.9338
## (Intercept)-Dasypus_novemcinctus -0.6829 0.5940 -1.9172 -0.6621
## Cogon_Patch_Size-Odocoileus_virginianus 0.3479 1.0155 -1.3574 0.2308
## Cogon_Patch_Size-Canis_latrans 1.0425 1.0285 -0.2553 0.8088
## Cogon_Patch_Size-Procyon_lotor -0.0272 0.5421 -0.9787 -0.0446
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0547 0.4450 -0.9670 -0.0410
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2922 0.7552 -1.1539 0.2627
## Avg_Cogongrass_Cover-Canis_latrans 0.3112 0.4669 -0.5389 0.2848
## Avg_Cogongrass_Cover-Procyon_lotor 0.2431 0.5113 -0.7387 0.2283
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4067 0.4110 -0.3616 0.3940
## total_shrub_cover-Odocoileus_virginianus -0.1210 0.9359 -1.9983 -0.1557
## total_shrub_cover-Canis_latrans 0.1985 0.5725 -0.7283 0.1430
## total_shrub_cover-Procyon_lotor -0.9715 0.6467 -2.4945 -0.8823
## total_shrub_cover-Dasypus_novemcinctus -0.0111 0.4075 -0.8091 -0.0125
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.3081 1.0961 319
## (Intercept)-Canis_latrans 2.3627 1.0038 3166
## (Intercept)-Procyon_lotor 2.4419 1.0047 2827
## (Intercept)-Dasypus_novemcinctus 0.4455 1.0008 2785
## Cogon_Patch_Size-Odocoileus_virginianus 2.7880 1.0037 2510
## Cogon_Patch_Size-Canis_latrans 3.7210 1.0011 1845
## Cogon_Patch_Size-Procyon_lotor 0.9872 1.0048 2705
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7886 1.0035 4408
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8640 1.0030 2761
## Avg_Cogongrass_Cover-Canis_latrans 1.3168 1.0018 3208
## Avg_Cogongrass_Cover-Procyon_lotor 1.2779 1.0033 3491
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2685 1.0036 4022
## total_shrub_cover-Odocoileus_virginianus 1.8688 1.0016 2467
## total_shrub_cover-Canis_latrans 1.5473 1.0044 2585
## total_shrub_cover-Procyon_lotor 0.0662 1.0016 2740
## total_shrub_cover-Dasypus_novemcinctus 0.8086 1.0038 4828
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5394 0.0802 0.3813 0.5402 0.6986
## (Intercept)-Canis_latrans -2.4178 0.1933 -2.8140 -2.4116 -2.0581
## (Intercept)-Procyon_lotor -2.1620 0.1544 -2.4801 -2.1578 -1.8644
## (Intercept)-Dasypus_novemcinctus -1.4282 0.1606 -1.7485 -1.4249 -1.1258
## week-Odocoileus_virginianus 1.3085 0.1235 1.0665 1.3069 1.5520
## week-Canis_latrans 0.6204 0.2765 0.0951 0.6146 1.1927
## week-Procyon_lotor 0.2091 0.2162 -0.2163 0.2070 0.6354
## week-Dasypus_novemcinctus 0.1157 0.2411 -0.3659 0.1201 0.5823
## I(week^2)-Odocoileus_virginianus -0.5398 0.0504 -0.6383 -0.5397 -0.4413
## I(week^2)-Canis_latrans -0.2565 0.1134 -0.4853 -0.2536 -0.0353
## I(week^2)-Procyon_lotor -0.1301 0.0948 -0.3153 -0.1286 0.0542
## I(week^2)-Dasypus_novemcinctus -0.1780 0.1103 -0.3960 -0.1777 0.0347
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0016 2510
## (Intercept)-Procyon_lotor 1.0000 4069
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## week-Odocoileus_virginianus 1.0001 5250
## week-Canis_latrans 1.0064 3973
## week-Procyon_lotor 1.0032 4376
## week-Dasypus_novemcinctus 1.0007 4903
## I(week^2)-Odocoileus_virginianus 1.0004 4739
## I(week^2)-Canis_latrans 1.0084 3852
## I(week^2)-Procyon_lotor 1.0000 4143
## I(week^2)-Dasypus_novemcinctus 1.0000 4471
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.868
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7813 1.1398 -1.6360 0.8127 2.9924 1.0025 4849
## Veg_shannon_index 0.5400 0.5079 -0.5115 0.5485 1.5453 1.0009 3343
## Avg_Cogongrass_Cover 0.6736 0.5129 -0.3253 0.6525 1.7582 1.0017 2600
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 16.7747 45.0686 0.7784 6.9322 93.4146 1.0780 1645
## Veg_shannon_index 0.8261 3.3816 0.0437 0.3194 4.3342 1.1289 4507
## Avg_Cogongrass_Cover 0.7080 2.5225 0.0403 0.2631 4.0235 1.0530 4888
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7745 1.2664 0.0499 0.3947 3.7906 1.0233 1166
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1019 0.7797 -2.5146 -1.1425 0.6370 0.9999 5250
## week 0.5229 0.4476 -0.3948 0.5408 1.3647 1.0056 5250
## I(week^2) -0.2730 0.2327 -0.7212 -0.2754 0.1888 1.0015 4886
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7944 6.4243 0.5671 2.0924 17.5739 1.0010 5250
## week 0.8620 1.5408 0.1011 0.4572 4.2253 1.0187 5250
## I(week^2) 0.2430 0.5502 0.0318 0.1196 1.1395 1.0234 4332
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.3614 2.6365 2.0180 4.7494
## (Intercept)-Canis_latrans 0.4484 0.6837 -0.8386 0.4282
## (Intercept)-Procyon_lotor 0.8087 0.6656 -0.5163 0.8002
## (Intercept)-Dasypus_novemcinctus -0.6559 0.6240 -1.9184 -0.6480
## Veg_shannon_index-Odocoileus_virginianus 0.4549 0.7815 -1.2712 0.4880
## Veg_shannon_index-Canis_latrans 0.8672 0.4793 0.0378 0.8325
## Veg_shannon_index-Procyon_lotor 0.6942 0.5332 -0.2007 0.6450
## Veg_shannon_index-Dasypus_novemcinctus 0.2815 0.3946 -0.4915 0.2871
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6668 0.7732 -0.7678 0.6354
## Avg_Cogongrass_Cover-Canis_latrans 0.8234 0.5155 -0.0276 0.7748
## Avg_Cogongrass_Cover-Procyon_lotor 0.7286 0.5460 -0.2015 0.6810
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5964 0.3831 -0.1611 0.5937
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 12.4836 1.0297 527
## (Intercept)-Canis_latrans 1.8619 1.0001 2551
## (Intercept)-Procyon_lotor 2.1553 1.0008 2768
## (Intercept)-Dasypus_novemcinctus 0.5889 1.0025 2667
## Veg_shannon_index-Odocoileus_virginianus 1.9891 1.0022 2573
## Veg_shannon_index-Canis_latrans 1.9492 1.0013 2858
## Veg_shannon_index-Procyon_lotor 1.8638 1.0056 2177
## Veg_shannon_index-Dasypus_novemcinctus 1.0525 1.0014 4222
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.2958 0.9998 2490
## Avg_Cogongrass_Cover-Canis_latrans 2.0252 1.0031 2666
## Avg_Cogongrass_Cover-Procyon_lotor 1.9560 1.0021 2290
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3460 1.0028 3963
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5387 0.0794 0.3844 0.5376 0.6948
## (Intercept)-Canis_latrans -2.4115 0.1948 -2.8156 -2.4064 -2.0501
## (Intercept)-Procyon_lotor -2.1725 0.1526 -2.4784 -2.1674 -1.8845
## (Intercept)-Dasypus_novemcinctus -1.4288 0.1562 -1.7407 -1.4273 -1.1267
## week-Odocoileus_virginianus 1.3095 0.1223 1.0699 1.3107 1.5498
## week-Canis_latrans 0.6308 0.2761 0.1095 0.6235 1.1830
## week-Procyon_lotor 0.2079 0.2210 -0.2247 0.2057 0.6463
## week-Dasypus_novemcinctus 0.1183 0.2354 -0.3392 0.1171 0.5699
## I(week^2)-Odocoileus_virginianus -0.5397 0.0509 -0.6396 -0.5394 -0.4390
## I(week^2)-Canis_latrans -0.2586 0.1146 -0.4935 -0.2564 -0.0359
## I(week^2)-Procyon_lotor -0.1313 0.0952 -0.3186 -0.1306 0.0486
## I(week^2)-Dasypus_novemcinctus -0.1816 0.1094 -0.3976 -0.1793 0.0261
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9999 5015
## (Intercept)-Canis_latrans 1.0004 3365
## (Intercept)-Procyon_lotor 1.0007 4053
## (Intercept)-Dasypus_novemcinctus 1.0008 5250
## week-Odocoileus_virginianus 1.0007 5035
## week-Canis_latrans 1.0050 3942
## week-Procyon_lotor 1.0022 4568
## week-Dasypus_novemcinctus 1.0001 4715
## I(week^2)-Odocoileus_virginianus 0.9999 5250
## I(week^2)-Canis_latrans 1.0016 3976
## I(week^2)-Procyon_lotor 1.0019 4229
## I(week^2)-Dasypus_novemcinctus 1.0006 4287
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.908
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7728 1.0593 -1.4377 0.7928 2.8677 1.0010 5105
## Avg_Cogongrass_Cover 0.4172 0.4288 -0.4075 0.4131 1.2763 1.0038 3507
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.5343 49.7905 0.7180 5.5270 66.5048 1.0754 2086
## Avg_Cogongrass_Cover 0.4906 1.0895 0.0376 0.2146 2.5795 1.0184 3459
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5314 0.7682 0.0424 0.2892 2.568 1.0616 1110
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0921 0.7855 -2.5011 -1.1575 0.6901 1.0014 5250
## week 0.5336 0.4406 -0.4020 0.5514 1.3723 1.0024 5003
## I(week^2) -0.2723 0.2402 -0.7401 -0.2736 0.2172 1.0022 4943
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8212 8.7207 0.5621 2.0855 16.4069 1.0438 5250
## week 0.9617 3.3463 0.1050 0.4494 4.3829 1.0488 4738
## I(week^2) 0.2385 0.5439 0.0318 0.1246 1.1128 1.0657 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.8257 2.3319 1.8587 4.3416
## (Intercept)-Canis_latrans 0.4368 0.5963 -0.6950 0.4181
## (Intercept)-Procyon_lotor 0.7253 0.5878 -0.4174 0.7131
## (Intercept)-Dasypus_novemcinctus -0.6130 0.5525 -1.6874 -0.6058
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4079 0.6477 -0.8334 0.3921
## Avg_Cogongrass_Cover-Canis_latrans 0.5060 0.4045 -0.2267 0.4836
## Avg_Cogongrass_Cover-Procyon_lotor 0.3797 0.4074 -0.3749 0.3599
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4307 0.3474 -0.2397 0.4281
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.5457 1.0084 440
## (Intercept)-Canis_latrans 1.6554 1.0001 3409
## (Intercept)-Procyon_lotor 1.8672 1.0020 3678
## (Intercept)-Dasypus_novemcinctus 0.4945 1.0056 3417
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7965 1.0026 3142
## Avg_Cogongrass_Cover-Canis_latrans 1.3916 1.0006 4130
## Avg_Cogongrass_Cover-Procyon_lotor 1.2338 1.0000 4242
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1447 1.0018 4546
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5381 0.0801 0.3859 0.5369 0.6952
## (Intercept)-Canis_latrans -2.4134 0.1914 -2.8060 -2.4072 -2.0602
## (Intercept)-Procyon_lotor -2.1520 0.1538 -2.4633 -2.1444 -1.8610
## (Intercept)-Dasypus_novemcinctus -1.4244 0.1620 -1.7491 -1.4229 -1.1181
## week-Odocoileus_virginianus 1.3089 0.1251 1.0657 1.3079 1.5563
## week-Canis_latrans 0.6352 0.2769 0.1060 0.6325 1.1889
## week-Procyon_lotor 0.2067 0.2182 -0.2242 0.2112 0.6221
## week-Dasypus_novemcinctus 0.1206 0.2411 -0.3504 0.1174 0.5851
## I(week^2)-Odocoileus_virginianus -0.5401 0.0507 -0.6423 -0.5390 -0.4409
## I(week^2)-Canis_latrans -0.2609 0.1156 -0.4889 -0.2587 -0.0393
## I(week^2)-Procyon_lotor -0.1310 0.0939 -0.3110 -0.1311 0.0517
## I(week^2)-Dasypus_novemcinctus -0.1821 0.1091 -0.3983 -0.1810 0.0246
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0002 3451
## (Intercept)-Procyon_lotor 0.9999 4261
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0002 3794
## week-Procyon_lotor 1.0057 4485
## week-Dasypus_novemcinctus 0.9998 4816
## I(week^2)-Odocoileus_virginianus 0.9999 5250
## I(week^2)-Canis_latrans 0.9998 3869
## I(week^2)-Procyon_lotor 1.0066 3734
## I(week^2)-Dasypus_novemcinctus 1.0003 4408
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.933
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2501 1.0808 -1.8466 0.2081 2.5391 1.0011 4147
## Avg_Cogongrass_Cover -0.0595 0.6293 -1.2879 -0.0667 1.2221 1.0059 1473
## I(Avg_Cogongrass_Cover^2) 1.2391 0.9521 -0.5725 1.1525 3.3303 1.0099 1923
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.0661 90.3137 0.4107 5.3683 95.4770 1.1899 923
## Avg_Cogongrass_Cover 0.8773 2.2576 0.0428 0.3168 5.0139 1.0687 3391
## I(Avg_Cogongrass_Cover^2) 8.2925 39.1279 0.0633 1.4404 52.8198 1.0842 1192
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4929 0.7404 0.0385 0.2659 2.3536 1.0439 869
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0938 0.7838 -2.4990 -1.1522 0.6401 1.0012 5250
## week 0.5290 0.4330 -0.3912 0.5367 1.3822 0.9998 5250
## I(week^2) -0.2739 0.2397 -0.7376 -0.2779 0.2178 1.0036 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8336 6.3418 0.5644 2.1740 18.0248 1.0522 5482
## week 0.8678 1.6237 0.1035 0.4651 4.2539 1.0003 4018
## I(week^2) 0.2323 0.4503 0.0318 0.1217 1.0627 1.0182 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.4133 3.8522 0.4724 3.5414
## (Intercept)-Canis_latrans -0.6144 0.8748 -2.5033 -0.5546
## (Intercept)-Procyon_lotor -0.1667 0.7728 -1.7752 -0.1340
## (Intercept)-Dasypus_novemcinctus -1.1167 0.6795 -2.5008 -1.1169
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1220 0.9609 -2.1115 -0.1165
## Avg_Cogongrass_Cover-Canis_latrans 0.0550 0.6728 -1.1882 0.0235
## Avg_Cogongrass_Cover-Procyon_lotor -0.0444 0.7019 -1.3614 -0.0745
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.1334 0.5833 -1.2850 -0.1340
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.4599 2.6098 -0.2515 1.6727
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3062 1.6942 0.1487 1.9191
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9798 1.5751 0.0831 1.5341
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5174 0.4400 -0.2906 0.4997
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.5925 1.0051 218
## (Intercept)-Canis_latrans 1.0035 1.0066 1344
## (Intercept)-Procyon_lotor 1.3281 1.0050 1405
## (Intercept)-Dasypus_novemcinctus 0.1914 1.0021 3365
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.7549 1.0024 1831
## Avg_Cogongrass_Cover-Canis_latrans 1.5086 1.0029 2488
## Avg_Cogongrass_Cover-Procyon_lotor 1.4474 1.0073 1159
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0122 1.0034 1889
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 9.4987 1.0465 368
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.4815 1.0174 556
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.0017 1.0139 369
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4432 1.0054 3108
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5388 0.0792 0.3877 0.5389 0.6934
## (Intercept)-Canis_latrans -2.4456 0.1931 -2.8444 -2.4385 -2.0852
## (Intercept)-Procyon_lotor -2.1696 0.1517 -2.4738 -2.1705 -1.8833
## (Intercept)-Dasypus_novemcinctus -1.4290 0.1585 -1.7425 -1.4236 -1.1291
## week-Odocoileus_virginianus 1.3095 0.1252 1.0682 1.3113 1.5531
## week-Canis_latrans 0.6294 0.2772 0.0904 0.6213 1.1808
## week-Procyon_lotor 0.2162 0.2219 -0.2257 0.2165 0.6536
## week-Dasypus_novemcinctus 0.1192 0.2362 -0.3390 0.1161 0.5814
## I(week^2)-Odocoileus_virginianus -0.5396 0.0513 -0.6403 -0.5395 -0.4402
## I(week^2)-Canis_latrans -0.2589 0.1145 -0.4821 -0.2581 -0.0359
## I(week^2)-Procyon_lotor -0.1333 0.0968 -0.3254 -0.1325 0.0565
## I(week^2)-Dasypus_novemcinctus -0.1781 0.1073 -0.3951 -0.1775 0.0281
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0008 5250
## (Intercept)-Canis_latrans 1.0002 3127
## (Intercept)-Procyon_lotor 0.9999 4113
## (Intercept)-Dasypus_novemcinctus 1.0034 5250
## week-Odocoileus_virginianus 1.0009 5195
## week-Canis_latrans 1.0025 3515
## week-Procyon_lotor 1.0006 4331
## week-Dasypus_novemcinctus 1.0014 5250
## I(week^2)-Odocoileus_virginianus 0.9998 5250
## I(week^2)-Canis_latrans 1.0006 3720
## I(week^2)-Procyon_lotor 1.0001 4141
## I(week^2)-Dasypus_novemcinctus 1.0005 4730
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_weekQ_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9357
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2026 1.4245 -2.6222 0.1872 3.0669 1.0005 4836
## Cogon_Patch_Size 0.1516 0.9264 -1.6276 0.1077 2.1502 1.0007 2403
## Veg_shannon_index 0.9020 0.7412 -0.5639 0.8954 2.3584 1.0008 1821
## total_shrub_cover -0.0821 0.6861 -1.4626 -0.1019 1.3647 1.0029 2273
## Avg_Cogongrass_Cover 0.6363 1.1575 -1.6157 0.6147 2.9086 1.0026 1068
## Tree_Density -1.4717 1.3833 -3.9107 -1.5953 1.5649 1.0031 1471
## Avg_Canopy_Cover 0.8858 0.8294 -0.9149 0.9206 2.4773 1.0036 2559
## I(Avg_Cogongrass_Cover^2) 1.8828 1.1128 -0.5199 1.9002 3.9744 1.0025 1416
## avg_veg_height -0.0775 0.7392 -1.5364 -0.0778 1.4356 1.0035 1858
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 87.5397 223.4569 3.3646 31.5661 530.8559 1.0209 383
## Cogon_Patch_Size 5.3441 16.6567 0.0731 1.4522 34.2719 1.0082 1997
## Veg_shannon_index 2.2172 12.4667 0.0503 0.5257 12.2586 1.0133 1641
## total_shrub_cover 1.6673 4.2915 0.0566 0.6080 9.6323 1.0381 3750
## Avg_Cogongrass_Cover 2.9494 12.1404 0.0533 0.6874 19.4864 1.1286 2259
## Tree_Density 31.4699 124.9574 0.1136 7.1991 199.3155 1.1725 1438
## Avg_Canopy_Cover 3.8226 10.8356 0.0845 1.2617 23.2841 1.0169 1780
## I(Avg_Cogongrass_Cover^2) 10.7656 54.3754 0.0516 0.9135 90.0312 1.1346 491
## avg_veg_height 1.5930 5.3699 0.0512 0.5026 9.2440 1.0484 1683
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.558 6.3863 0.0561 0.8509 16.5298 1.3238 127
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0761 0.7831 -2.4438 -1.1415 0.7430 1.0032 5591
## week 0.5223 0.4309 -0.4222 0.5372 1.3330 1.0012 4972
## I(week^2) -0.2675 0.2438 -0.7350 -0.2751 0.2357 1.0008 4875
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7332 8.2810 0.5713 2.1043 15.3810 1.1287 5250
## week 1.0857 13.3993 0.1055 0.4494 4.5093 1.2837 5250
## I(week^2) 0.2477 0.5256 0.0324 0.1245 1.1780 1.0154 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 10.7634 7.4033 2.3575
## (Intercept)-Canis_latrans -1.3870 1.8517 -5.2644
## (Intercept)-Procyon_lotor -0.5315 1.3831 -3.4131
## (Intercept)-Dasypus_novemcinctus -3.4663 1.8952 -8.1016
## Cogon_Patch_Size-Odocoileus_virginianus 0.2314 1.7529 -2.8352
## Cogon_Patch_Size-Canis_latrans 1.6433 1.8730 -0.6746
## Cogon_Patch_Size-Procyon_lotor -0.3248 1.2910 -2.3203
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4902 0.8400 -2.4043
## Veg_shannon_index-Odocoileus_virginianus 0.7699 1.2764 -2.0288
## Veg_shannon_index-Canis_latrans 1.4913 1.0729 0.0009
## Veg_shannon_index-Procyon_lotor 1.2411 0.9305 -0.0682
## Veg_shannon_index-Dasypus_novemcinctus 0.6166 0.6457 -0.6347
## total_shrub_cover-Odocoileus_virginianus 0.0766 1.1490 -2.0819
## total_shrub_cover-Canis_latrans 0.1725 0.7790 -1.1983
## total_shrub_cover-Procyon_lotor -0.7603 0.7483 -2.3553
## total_shrub_cover-Dasypus_novemcinctus 0.2002 0.5926 -0.9017
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5970 1.7463 -2.8324
## Avg_Cogongrass_Cover-Canis_latrans 0.5671 1.4759 -2.3327
## Avg_Cogongrass_Cover-Procyon_lotor 0.7086 1.4414 -1.9133
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1703 1.4838 -1.3349
## Tree_Density-Odocoileus_virginianus 0.1448 3.4603 -4.1051
## Tree_Density-Canis_latrans -4.1957 2.7443 -11.3061
## Tree_Density-Procyon_lotor -1.8011 1.3268 -4.5759
## Tree_Density-Dasypus_novemcinctus -5.7122 3.5613 -15.1003
## Avg_Canopy_Cover-Odocoileus_virginianus 0.6842 1.5684 -2.7670
## Avg_Canopy_Cover-Canis_latrans 0.0959 0.8343 -1.7648
## Avg_Canopy_Cover-Procyon_lotor 1.4649 0.8511 0.0829
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0621 1.0415 0.5982
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.8489 2.8572 -0.4600
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1231 2.3068 0.7068
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.7076 1.8929 0.4718
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.9052 1.2237 0.0675
## avg_veg_height-Odocoileus_virginianus -0.0647 1.2003 -2.5152
## avg_veg_height-Canis_latrans -0.5561 0.8636 -2.4603
## avg_veg_height-Procyon_lotor 0.1706 0.7803 -1.2929
## avg_veg_height-Dasypus_novemcinctus 0.1099 0.7350 -1.3037
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 8.8634 31.6249 1.0128 134
## (Intercept)-Canis_latrans -1.2984 1.9164 1.0159 657
## (Intercept)-Procyon_lotor -0.4645 1.9127 1.0411 953
## (Intercept)-Dasypus_novemcinctus -3.1752 -0.7293 1.0465 349
## Cogon_Patch_Size-Odocoileus_virginianus 0.0890 4.4722 1.0083 1938
## Cogon_Patch_Size-Canis_latrans 1.1934 6.9853 1.0086 915
## Cogon_Patch_Size-Procyon_lotor -0.4135 2.3938 1.0020 865
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4115 0.9285 1.0103 955
## Veg_shannon_index-Odocoileus_virginianus 0.8147 3.1799 1.0038 1925
## Veg_shannon_index-Canis_latrans 1.3348 3.8869 1.0147 610
## Veg_shannon_index-Procyon_lotor 1.1250 3.0433 1.0546 400
## Veg_shannon_index-Dasypus_novemcinctus 0.6067 1.9216 1.0027 1669
## total_shrub_cover-Odocoileus_virginianus 0.0265 2.6509 1.0012 1955
## total_shrub_cover-Canis_latrans 0.1208 1.9751 1.0047 2009
## total_shrub_cover-Procyon_lotor -0.7221 0.5812 1.0017 1581
## total_shrub_cover-Dasypus_novemcinctus 0.1789 1.4444 1.0015 2424
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5629 4.0826 1.0022 1343
## Avg_Cogongrass_Cover-Canis_latrans 0.5600 3.4524 1.0037 1239
## Avg_Cogongrass_Cover-Procyon_lotor 0.6525 3.7059 1.0072 713
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0330 4.4876 1.0040 733
## Tree_Density-Odocoileus_virginianus -0.6674 9.0355 1.0473 594
## Tree_Density-Canis_latrans -3.5660 -0.7836 1.0171 341
## Tree_Density-Procyon_lotor -1.7569 0.5731 1.0041 1823
## Tree_Density-Dasypus_novemcinctus -4.7943 -1.5232 1.0621 285
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7837 3.7830 1.0039 1889
## Avg_Canopy_Cover-Canis_latrans 0.1514 1.5207 1.0349 1391
## Avg_Canopy_Cover-Procyon_lotor 1.3633 3.3922 1.0128 1209
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8740 4.6376 1.0461 265
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.3110 10.2561 1.0289 327
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5777 9.5854 1.0226 230
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3105 8.0359 1.0217 365
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7500 4.5453 1.0312 599
## avg_veg_height-Odocoileus_virginianus -0.0425 2.2127 1.0020 2131
## avg_veg_height-Canis_latrans -0.4878 0.9800 1.0045 1040
## avg_veg_height-Procyon_lotor 0.1381 1.8205 1.0052 2035
## avg_veg_height-Dasypus_novemcinctus 0.1016 1.6080 1.0027 2142
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5423 0.0791 0.3913 0.5423 0.7010
## (Intercept)-Canis_latrans -2.4222 0.1920 -2.8155 -2.4149 -2.0600
## (Intercept)-Procyon_lotor -2.1695 0.1533 -2.4780 -2.1663 -1.8845
## (Intercept)-Dasypus_novemcinctus -1.4254 0.1584 -1.7425 -1.4233 -1.1252
## week-Odocoileus_virginianus 1.3082 0.1207 1.0686 1.3086 1.5468
## week-Canis_latrans 0.6258 0.2796 0.0839 0.6212 1.1754
## week-Procyon_lotor 0.2095 0.2232 -0.2299 0.2083 0.6562
## week-Dasypus_novemcinctus 0.1149 0.2368 -0.3541 0.1155 0.5658
## I(week^2)-Odocoileus_virginianus -0.5401 0.0506 -0.6388 -0.5406 -0.4419
## I(week^2)-Canis_latrans -0.2569 0.1137 -0.4864 -0.2556 -0.0369
## I(week^2)-Procyon_lotor -0.1313 0.0954 -0.3248 -0.1307 0.0527
## I(week^2)-Dasypus_novemcinctus -0.1786 0.1090 -0.3981 -0.1782 0.0358
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0063 2312
## (Intercept)-Procyon_lotor 1.0029 3446
## (Intercept)-Dasypus_novemcinctus 1.0024 4860
## week-Odocoileus_virginianus 1.0027 5250
## week-Canis_latrans 1.0021 3797
## week-Procyon_lotor 1.0016 4388
## week-Dasypus_novemcinctus 1.0003 4898
## I(week^2)-Odocoileus_virginianus 1.0016 5250
## I(week^2)-Canis_latrans 1.0030 3908
## I(week^2)-Procyon_lotor 1.0003 4426
## I(week^2)-Dasypus_novemcinctus 0.9999 4670
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9327
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7907 1.0458 -1.4288 0.8253 2.842 1.0004 4754
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 13.2033 34.6425 0.7517 5.1824 79.3084 1.0036 978
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1091 0.8112 -2.5869 -1.1611 0.7225 1.0006 5250
## shrub_cover 0.1471 0.3905 -0.6732 0.1473 0.9172 1.0004 4988
## veg_height -0.1049 0.3724 -0.8780 -0.1103 0.6605 1.0009 4299
## week 0.5381 0.4443 -0.4029 0.5542 1.3860 1.0046 5250
## I(week^2) -0.2819 0.2421 -0.7656 -0.2828 0.1864 1.0037 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0515 7.1518 0.5912 2.2690 17.9815 1.0018 5046
## shrub_cover 0.6847 2.0389 0.0606 0.3311 3.2050 1.0411 5250
## veg_height 0.6161 1.0409 0.0791 0.3369 2.8999 1.0058 5250
## week 0.8922 1.8481 0.1141 0.4808 4.1763 1.0303 5250
## I(week^2) 0.2529 0.9210 0.0326 0.1291 1.1471 1.1662 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.8199 2.6796 2.0727 4.1594 11.6764
## (Intercept)-Canis_latrans 0.4740 0.4356 -0.3187 0.4524 1.3792
## (Intercept)-Procyon_lotor 0.8064 0.4239 0.0328 0.7913 1.6862
## (Intercept)-Dasypus_novemcinctus -0.5500 0.3884 -1.3259 -0.5452 0.1735
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0110 353
## (Intercept)-Canis_latrans 1.0035 4256
## (Intercept)-Procyon_lotor 1.0023 4885
## (Intercept)-Dasypus_novemcinctus 1.0031 4835
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5526 0.0814 0.3947 0.5523 0.7119
## (Intercept)-Canis_latrans -2.5573 0.2071 -2.9786 -2.5504 -2.1717
## (Intercept)-Procyon_lotor -2.1778 0.1698 -2.5276 -2.1740 -1.8582
## (Intercept)-Dasypus_novemcinctus -1.5591 0.1806 -1.9226 -1.5569 -1.2195
## shrub_cover-Odocoileus_virginianus -0.0608 0.0682 -0.1951 -0.0615 0.0750
## shrub_cover-Canis_latrans -0.3047 0.2260 -0.7516 -0.2979 0.1355
## shrub_cover-Procyon_lotor 0.2450 0.1648 -0.0826 0.2499 0.5617
## shrub_cover-Dasypus_novemcinctus 0.7571 0.3047 0.1960 0.7453 1.3833
## veg_height-Odocoileus_virginianus -0.3394 0.0694 -0.4771 -0.3383 -0.2052
## veg_height-Canis_latrans -0.6450 0.1881 -1.0220 -0.6390 -0.2989
## veg_height-Procyon_lotor 0.3460 0.1271 0.0927 0.3472 0.5888
## veg_height-Dasypus_novemcinctus 0.2260 0.1345 -0.0277 0.2254 0.4970
## week-Odocoileus_virginianus 1.3417 0.1275 1.0931 1.3405 1.5970
## week-Canis_latrans 0.6452 0.2783 0.1116 0.6407 1.2047
## week-Procyon_lotor 0.2108 0.2219 -0.2229 0.2102 0.6492
## week-Dasypus_novemcinctus 0.1219 0.2421 -0.3463 0.1220 0.6063
## I(week^2)-Odocoileus_virginianus -0.5533 0.0521 -0.6562 -0.5530 -0.4505
## I(week^2)-Canis_latrans -0.2635 0.1146 -0.4974 -0.2620 -0.0431
## I(week^2)-Procyon_lotor -0.1320 0.0958 -0.3183 -0.1301 0.0522
## I(week^2)-Dasypus_novemcinctus -0.1807 0.1084 -0.4007 -0.1781 0.0285
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0012 2654
## (Intercept)-Procyon_lotor 1.0020 3877
## (Intercept)-Dasypus_novemcinctus 1.0043 4525
## shrub_cover-Odocoileus_virginianus 1.0011 5250
## shrub_cover-Canis_latrans 1.0037 2678
## shrub_cover-Procyon_lotor 1.0041 4130
## shrub_cover-Dasypus_novemcinctus 1.0006 3828
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0111 2657
## veg_height-Procyon_lotor 1.0074 4214
## veg_height-Dasypus_novemcinctus 1.0015 4840
## week-Odocoileus_virginianus 1.0023 5250
## week-Canis_latrans 1.0052 3615
## week-Procyon_lotor 1.0013 4588
## week-Dasypus_novemcinctus 1.0005 5115
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0035 3832
## I(week^2)-Procyon_lotor 1.0002 4285
## I(week^2)-Dasypus_novemcinctus 1.0034 4624
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9957
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7265 1.4508 -2.2836 0.7790 3.5087 1.0024 4840
## Cogon_Patch_Size -0.2644 0.8903 -1.8930 -0.3029 1.7219 1.0023 2047
## Veg_shannon_index 0.8643 0.7343 -0.6507 0.8835 2.2488 1.0000 1790
## total_shrub_cover 0.0658 0.8383 -1.5550 0.0330 1.8921 1.0048 2783
## Avg_Cogongrass_Cover 2.0458 1.0556 -0.0715 2.0502 4.0897 1.0008 1394
## Tree_Density -1.3650 1.2314 -3.5428 -1.4806 1.4363 1.0024 2172
## Avg_Canopy_Cover 0.9151 0.8243 -0.8623 0.9492 2.4541 0.9999 3107
## avg_veg_height -0.2752 0.6931 -1.6461 -0.2567 1.0865 1.0020 1589
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 84.9712 237.9428 2.9713 29.6900 492.8471 1.0789 471
## Cogon_Patch_Size 3.2745 9.5187 0.0623 0.8490 21.5014 1.0353 1952
## Veg_shannon_index 1.7714 6.7399 0.0496 0.4877 11.1370 1.0371 3004
## total_shrub_cover 3.6835 9.2074 0.0844 1.3597 21.0618 1.0146 1475
## Avg_Cogongrass_Cover 3.5396 15.1837 0.0530 0.6658 24.7315 1.0298 766
## Tree_Density 21.8360 88.5802 0.1150 4.8721 126.1069 1.0291 1126
## Avg_Canopy_Cover 3.3669 8.8701 0.0918 1.3546 17.6995 1.0195 3495
## avg_veg_height 0.9502 3.4492 0.0438 0.3281 5.3302 1.1788 4709
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0312 3.6158 0.0611 0.8434 11.028 1.0085 399
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1305 0.8225 -2.6466 -1.1821 0.6286 1.0013 5250
## shrub_cover 0.1322 0.4295 -0.7507 0.1340 0.9829 1.0022 4733
## veg_height -0.1051 0.3782 -0.8613 -0.1094 0.6939 1.0042 4626
## week 0.5433 0.4416 -0.4065 0.5579 1.3744 1.0017 5250
## I(week^2) -0.2782 0.2398 -0.7467 -0.2791 0.2035 1.0047 5027
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.5013 19.9299 0.6324 2.3202 19.6901 1.2162 5250
## shrub_cover 0.9055 4.6035 0.0775 0.4230 4.2338 1.2738 5250
## veg_height 0.7182 1.8291 0.0823 0.3593 3.4735 1.0370 5250
## week 0.9013 1.9293 0.1112 0.4704 4.2505 1.0051 5250
## I(week^2) 0.2473 0.5100 0.0340 0.1266 1.1426 1.0218 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 11.5491 7.1663 3.8853 9.6465
## (Intercept)-Canis_latrans 1.4528 1.3470 -0.7329 1.2742
## (Intercept)-Procyon_lotor 1.0579 1.1144 -1.1013 1.0274
## (Intercept)-Dasypus_novemcinctus -1.7523 1.3154 -4.7981 -1.5732
## Cogon_Patch_Size-Odocoileus_virginianus -0.2489 1.5074 -2.9032 -0.3500
## Cogon_Patch_Size-Canis_latrans 0.7070 1.6446 -1.3799 0.3664
## Cogon_Patch_Size-Procyon_lotor -0.8875 0.8168 -2.5607 -0.8694
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6111 0.8311 -2.2425 -0.6111
## Veg_shannon_index-Odocoileus_virginianus 0.7424 1.1950 -1.9272 0.7935
## Veg_shannon_index-Canis_latrans 1.3360 0.9128 -0.2657 1.2568
## Veg_shannon_index-Procyon_lotor 1.1970 0.7054 -0.0619 1.1578
## Veg_shannon_index-Dasypus_novemcinctus 0.6029 0.6470 -0.6966 0.6170
## total_shrub_cover-Odocoileus_virginianus 0.2620 1.4518 -2.3953 0.1761
## total_shrub_cover-Canis_latrans 1.2192 1.3166 -0.5882 0.9543
## total_shrub_cover-Procyon_lotor -1.0033 0.7834 -2.7419 -0.9269
## total_shrub_cover-Dasypus_novemcinctus -0.0472 0.8003 -1.8025 0.0188
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1042 1.6098 -0.9997 2.0585
## Avg_Cogongrass_Cover-Canis_latrans 2.7692 1.4931 0.5342 2.5634
## Avg_Cogongrass_Cover-Procyon_lotor 2.1175 1.1303 0.1068 2.0473
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7309 1.3029 0.7032 2.5635
## Tree_Density-Odocoileus_virginianus -0.0290 2.7655 -3.6743 -0.5941
## Tree_Density-Canis_latrans -3.5556 2.3339 -9.2369 -2.9929
## Tree_Density-Procyon_lotor -1.3415 0.9656 -3.1595 -1.3448
## Tree_Density-Dasypus_novemcinctus -4.7775 3.1567 -13.0040 -3.9786
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7450 1.5164 -2.5225 0.7805
## Avg_Canopy_Cover-Canis_latrans 0.1090 0.7317 -1.3874 0.1285
## Avg_Canopy_Cover-Procyon_lotor 1.5019 0.8202 0.1197 1.4273
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0613 0.9504 0.6539 1.9257
## avg_veg_height-Odocoileus_virginianus -0.3153 1.0472 -2.4964 -0.3195
## avg_veg_height-Canis_latrans -0.3550 0.7850 -1.8885 -0.3591
## avg_veg_height-Procyon_lotor -0.3134 0.6914 -1.7033 -0.3019
## avg_veg_height-Dasypus_novemcinctus -0.1780 0.7045 -1.6084 -0.1871
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 31.9949 1.2360 145
## (Intercept)-Canis_latrans 4.6306 1.0100 1097
## (Intercept)-Procyon_lotor 3.2739 1.0098 1128
## (Intercept)-Dasypus_novemcinctus 0.3137 1.0141 595
## Cogon_Patch_Size-Odocoileus_virginianus 3.1157 1.0063 1751
## Cogon_Patch_Size-Canis_latrans 4.8805 1.0123 1023
## Cogon_Patch_Size-Procyon_lotor 0.5628 1.0063 1249
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0529 1.0033 1422
## Veg_shannon_index-Odocoileus_virginianus 2.9785 1.0043 1819
## Veg_shannon_index-Canis_latrans 3.3647 1.0007 1114
## Veg_shannon_index-Procyon_lotor 2.6754 1.0105 938
## Veg_shannon_index-Dasypus_novemcinctus 1.8349 1.0018 1786
## total_shrub_cover-Odocoileus_virginianus 3.3649 1.0220 1844
## total_shrub_cover-Canis_latrans 4.4378 1.0045 792
## total_shrub_cover-Procyon_lotor 0.3067 1.0050 1543
## total_shrub_cover-Dasypus_novemcinctus 1.3140 1.0074 1074
## Avg_Cogongrass_Cover-Odocoileus_virginianus 5.2873 1.0001 1681
## Avg_Cogongrass_Cover-Canis_latrans 6.3060 1.0017 581
## Avg_Cogongrass_Cover-Procyon_lotor 4.3763 1.0078 983
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.8341 1.0019 694
## Tree_Density-Odocoileus_virginianus 7.5672 1.0171 570
## Tree_Density-Canis_latrans -0.7204 1.0087 527
## Tree_Density-Procyon_lotor 0.5008 1.0044 1450
## Tree_Density-Dasypus_novemcinctus -1.2413 1.0023 304
## Avg_Canopy_Cover-Odocoileus_virginianus 3.7780 1.0036 1985
## Avg_Canopy_Cover-Canis_latrans 1.4962 1.0031 2313
## Avg_Canopy_Cover-Procyon_lotor 3.3338 1.0037 1197
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2883 1.0073 595
## avg_veg_height-Odocoileus_virginianus 1.7317 1.0018 1817
## avg_veg_height-Canis_latrans 1.2748 1.0044 1593
## avg_veg_height-Procyon_lotor 1.0184 1.0027 1862
## avg_veg_height-Dasypus_novemcinctus 1.2757 1.0018 1559
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5530 0.0804 0.3962 0.5530 0.7094
## (Intercept)-Canis_latrans -2.6018 0.2070 -3.0222 -2.5949 -2.2181
## (Intercept)-Procyon_lotor -2.1813 0.1671 -2.5222 -2.1788 -1.8675
## (Intercept)-Dasypus_novemcinctus -1.5750 0.1811 -1.9401 -1.5728 -1.2392
## shrub_cover-Odocoileus_virginianus -0.0651 0.0687 -0.2003 -0.0654 0.0709
## shrub_cover-Canis_latrans -0.4305 0.2279 -0.8705 -0.4390 0.0313
## shrub_cover-Procyon_lotor 0.2604 0.1648 -0.0734 0.2649 0.5745
## shrub_cover-Dasypus_novemcinctus 0.8324 0.3245 0.2333 0.8182 1.4904
## veg_height-Odocoileus_virginianus -0.3406 0.0698 -0.4772 -0.3418 -0.2019
## veg_height-Canis_latrans -0.6782 0.1891 -1.0580 -0.6703 -0.3136
## veg_height-Procyon_lotor 0.3472 0.1278 0.0953 0.3468 0.5921
## veg_height-Dasypus_novemcinctus 0.2334 0.1361 -0.0303 0.2321 0.5078
## week-Odocoileus_virginianus 1.3407 0.1254 1.0993 1.3418 1.5873
## week-Canis_latrans 0.6493 0.2789 0.1175 0.6410 1.2103
## week-Procyon_lotor 0.2125 0.2233 -0.2352 0.2137 0.6501
## week-Dasypus_novemcinctus 0.1267 0.2387 -0.3432 0.1303 0.5911
## I(week^2)-Odocoileus_virginianus -0.5531 0.0519 -0.6573 -0.5530 -0.4530
## I(week^2)-Canis_latrans -0.2656 0.1161 -0.5036 -0.2646 -0.0402
## I(week^2)-Procyon_lotor -0.1332 0.0962 -0.3250 -0.1335 0.0577
## I(week^2)-Dasypus_novemcinctus -0.1854 0.1117 -0.4073 -0.1825 0.0267
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0044 5250
## (Intercept)-Canis_latrans 1.0004 2224
## (Intercept)-Procyon_lotor 1.0001 3331
## (Intercept)-Dasypus_novemcinctus 1.0072 3607
## shrub_cover-Odocoileus_virginianus 1.0007 5474
## shrub_cover-Canis_latrans 1.0065 1667
## shrub_cover-Procyon_lotor 1.0017 2762
## shrub_cover-Dasypus_novemcinctus 1.0061 2299
## veg_height-Odocoileus_virginianus 1.0017 5089
## veg_height-Canis_latrans 1.0021 2122
## veg_height-Procyon_lotor 1.0023 4004
## veg_height-Dasypus_novemcinctus 1.0007 4402
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0019 3620
## week-Procyon_lotor 1.0015 4960
## week-Dasypus_novemcinctus 1.0024 4841
## I(week^2)-Odocoileus_virginianus 1.0002 5250
## I(week^2)-Canis_latrans 1.0015 3751
## I(week^2)-Procyon_lotor 1.0004 4344
## I(week^2)-Dasypus_novemcinctus 1.0004 4971
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.0045
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9773 1.1806 -1.5431 1.0285 3.2289 1.0036 4248
## Avg_Cogongrass_Cover 0.2114 0.5893 -0.9710 0.2090 1.3981 1.0100 2516
## total_shrub_cover -0.1634 0.7487 -1.6815 -0.1681 1.4779 1.0030 2856
## avg_veg_height 0.2974 0.5881 -0.8429 0.2786 1.4896 1.0163 2242
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.7029 37.1084 0.6950 7.4684 99.1674 1.0215 1397
## Avg_Cogongrass_Cover 0.9198 2.5088 0.0449 0.3322 5.2133 1.0349 4303
## total_shrub_cover 2.8859 6.8579 0.0803 1.1202 16.7959 1.0691 1586
## avg_veg_height 0.7975 2.8809 0.0403 0.2825 4.3023 1.0251 4483
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7028 1.3937 0.0464 0.3306 3.5215 1.1145 859
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1417 0.8084 -2.6181 -1.2043 0.6544 1.0013 5250
## shrub_cover 0.1723 0.4348 -0.6964 0.1563 1.1014 1.0026 4724
## veg_height -0.1152 0.3877 -0.9305 -0.1095 0.6627 1.0020 5250
## week 0.5351 0.4375 -0.3531 0.5518 1.3477 1.0007 5250
## I(week^2) -0.2778 0.2554 -0.7566 -0.2814 0.2365 1.0044 5575
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0048 6.4489 0.6066 2.2982 17.7467 1.0136 5250
## shrub_cover 0.8902 1.9799 0.0716 0.4452 4.4657 1.0190 4321
## veg_height 0.6972 1.4343 0.0830 0.3601 3.2388 1.0370 4677
## week 0.9054 2.0659 0.1108 0.4878 3.9754 1.0488 5250
## I(week^2) 0.2636 0.7874 0.0325 0.1266 1.1955 1.0337 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.8278 2.8991 2.1981 5.1506
## (Intercept)-Canis_latrans 1.0614 0.9078 -0.4397 0.9533
## (Intercept)-Procyon_lotor 1.1846 0.7777 -0.2452 1.1227
## (Intercept)-Dasypus_novemcinctus -0.4032 0.8069 -1.7805 -0.4906
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1877 0.8638 -1.4816 0.1599
## Avg_Cogongrass_Cover-Canis_latrans 0.5131 0.6840 -0.6825 0.4531
## Avg_Cogongrass_Cover-Procyon_lotor -0.0097 0.5711 -1.1818 0.0097
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2153 0.5065 -0.7647 0.2136
## total_shrub_cover-Odocoileus_virginianus 0.0087 1.1618 -2.1193 -0.0579
## total_shrub_cover-Canis_latrans 0.7238 0.9395 -0.6535 0.5280
## total_shrub_cover-Procyon_lotor -1.2605 0.7427 -2.9892 -1.1618
## total_shrub_cover-Dasypus_novemcinctus -0.2341 0.8150 -2.6303 -0.0967
## avg_veg_height-Odocoileus_virginianus 0.2587 0.8278 -1.3928 0.2540
## avg_veg_height-Canis_latrans 0.2906 0.6306 -0.8743 0.2571
## avg_veg_height-Procyon_lotor 0.2574 0.5765 -0.8664 0.2413
## avg_veg_height-Dasypus_novemcinctus 0.4633 0.5720 -0.5215 0.4283
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.7261 1.0309 468
## (Intercept)-Canis_latrans 3.1887 1.0074 1445
## (Intercept)-Procyon_lotor 2.8927 1.0032 1970
## (Intercept)-Dasypus_novemcinctus 1.6468 1.0646 662
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0690 1.0168 2579
## Avg_Cogongrass_Cover-Canis_latrans 2.0609 1.0143 2397
## Avg_Cogongrass_Cover-Procyon_lotor 1.1097 1.0156 2865
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2351 1.0043 2922
## total_shrub_cover-Odocoileus_virginianus 2.5352 1.0174 1628
## total_shrub_cover-Canis_latrans 2.9693 1.0149 1098
## total_shrub_cover-Procyon_lotor -0.0927 1.0044 1531
## total_shrub_cover-Dasypus_novemcinctus 0.8184 1.1342 387
## avg_veg_height-Odocoileus_virginianus 1.9436 1.0149 2426
## avg_veg_height-Canis_latrans 1.6434 1.0103 2083
## avg_veg_height-Procyon_lotor 1.4269 1.0096 2710
## avg_veg_height-Dasypus_novemcinctus 1.7384 1.0302 936
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5523 0.0816 0.3953 0.5510 0.7153
## (Intercept)-Canis_latrans -2.6326 0.2184 -3.0734 -2.6263 -2.2174
## (Intercept)-Procyon_lotor -2.1821 0.1589 -2.4963 -2.1773 -1.8835
## (Intercept)-Dasypus_novemcinctus -1.6015 0.2042 -2.0339 -1.5897 -1.2336
## shrub_cover-Odocoileus_virginianus -0.0620 0.0682 -0.1965 -0.0617 0.0708
## shrub_cover-Canis_latrans -0.3891 0.2533 -0.8728 -0.3941 0.1101
## shrub_cover-Procyon_lotor 0.3065 0.1635 -0.0193 0.3058 0.6145
## shrub_cover-Dasypus_novemcinctus 0.9061 0.3936 0.2329 0.8658 1.7576
## veg_height-Odocoileus_virginianus -0.3378 0.0687 -0.4705 -0.3384 -0.2034
## veg_height-Canis_latrans -0.6879 0.1986 -1.0914 -0.6811 -0.3114
## veg_height-Procyon_lotor 0.3406 0.1234 0.0978 0.3402 0.5826
## veg_height-Dasypus_novemcinctus 0.2324 0.1386 -0.0280 0.2292 0.5073
## week-Odocoileus_virginianus 1.3450 0.1254 1.1010 1.3422 1.5933
## week-Canis_latrans 0.6332 0.2777 0.1011 0.6283 1.1849
## week-Procyon_lotor 0.2095 0.2190 -0.2098 0.2065 0.6373
## week-Dasypus_novemcinctus 0.1242 0.2407 -0.3450 0.1223 0.5982
## I(week^2)-Odocoileus_virginianus -0.5540 0.0516 -0.6585 -0.5536 -0.4552
## I(week^2)-Canis_latrans -0.2612 0.1144 -0.4905 -0.2581 -0.0418
## I(week^2)-Procyon_lotor -0.1331 0.0947 -0.3216 -0.1336 0.0527
## I(week^2)-Dasypus_novemcinctus -0.1858 0.1115 -0.4108 -0.1823 0.0263
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0003 5250
## (Intercept)-Canis_latrans 1.0122 1948
## (Intercept)-Procyon_lotor 1.0037 4467
## (Intercept)-Dasypus_novemcinctus 1.0141 1378
## shrub_cover-Odocoileus_virginianus 1.0018 5017
## shrub_cover-Canis_latrans 1.0073 1378
## shrub_cover-Procyon_lotor 1.0021 3414
## shrub_cover-Dasypus_novemcinctus 1.0315 700
## veg_height-Odocoileus_virginianus 1.0012 5504
## veg_height-Canis_latrans 1.0068 2147
## veg_height-Procyon_lotor 1.0052 3917
## veg_height-Dasypus_novemcinctus 1.0008 4648
## week-Odocoileus_virginianus 1.0007 5034
## week-Canis_latrans 1.0023 3835
## week-Procyon_lotor 1.0009 4265
## week-Dasypus_novemcinctus 1.0007 4878
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0011 3948
## I(week^2)-Procyon_lotor 1.0004 4620
## I(week^2)-Dasypus_novemcinctus 0.9999 4434
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9848
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7620 1.1781 -1.7076 0.8208 2.9676 1.0001 4818
## Tree_Density -0.7256 0.6907 -2.1105 -0.7405 0.7578 1.0019 3866
## Avg_Canopy_Cover 0.4275 0.5704 -0.7867 0.4302 1.5735 1.0037 4464
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 23.3797 59.1254 1.0401 8.7501 134.1065 1.0053 840
## Tree_Density 2.4489 9.3092 0.0529 0.6129 15.3261 1.0925 2829
## Avg_Canopy_Cover 1.3080 3.6721 0.0694 0.5723 6.6139 1.0835 4257
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5511 1.0652 0.0427 0.2685 2.7155 1.1291 685
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1169 0.7966 -2.5323 -1.1890 0.6948 1.0017 5250
## shrub_cover 0.1524 0.3935 -0.6462 0.1491 1.0050 1.0020 5250
## veg_height -0.1019 0.3847 -0.8944 -0.1046 0.6795 1.0001 5250
## week 0.5413 0.4426 -0.4071 0.5603 1.3820 1.0017 5250
## I(week^2) -0.2746 0.2465 -0.7436 -0.2792 0.2376 1.0015 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0643 9.9796 0.6107 2.2397 17.4844 1.0878 5250
## shrub_cover 0.7360 1.8208 0.0665 0.3488 3.9459 1.0549 4867
## veg_height 0.7177 2.5980 0.0801 0.3477 3.2382 1.1693 5250
## week 0.9214 2.6187 0.1087 0.4650 4.2209 1.0464 4629
## I(week^2) 0.2493 0.4451 0.0328 0.1290 1.2266 1.0342 3930
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 6.1909 3.4219 2.3042 5.3235 15.7116
## (Intercept)-Canis_latrans 0.4881 0.6515 -0.7493 0.4619 1.8361
## (Intercept)-Procyon_lotor 0.8515 0.6382 -0.4038 0.8411 2.1481
## (Intercept)-Dasypus_novemcinctus -0.9669 0.7219 -2.4771 -0.9316 0.3428
## Tree_Density-Odocoileus_virginianus -0.2457 1.1331 -1.8670 -0.4196 2.5629
## Tree_Density-Canis_latrans -1.0221 0.6581 -2.5445 -0.9471 0.0325
## Tree_Density-Procyon_lotor -0.4596 0.4578 -1.3428 -0.4671 0.4725
## Tree_Density-Dasypus_novemcinctus -1.6397 1.2535 -4.9742 -1.3305 -0.1498
## Avg_Canopy_Cover-Odocoileus_virginianus 0.3566 0.8615 -1.4166 0.3510 2.1156
## Avg_Canopy_Cover-Canis_latrans -0.2049 0.4649 -1.1695 -0.1989 0.6618
## Avg_Canopy_Cover-Procyon_lotor 0.8557 0.5151 -0.0526 0.8140 1.9976
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8768 0.4798 0.0416 0.8368 1.9176
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0393 249
## (Intercept)-Canis_latrans 1.0040 2808
## (Intercept)-Procyon_lotor 1.0003 3107
## (Intercept)-Dasypus_novemcinctus 1.0052 1568
## Tree_Density-Odocoileus_virginianus 1.0165 1888
## Tree_Density-Canis_latrans 1.0030 3033
## Tree_Density-Procyon_lotor 1.0004 3893
## Tree_Density-Dasypus_novemcinctus 1.0168 1281
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0024 2764
## Avg_Canopy_Cover-Canis_latrans 1.0003 4241
## Avg_Canopy_Cover-Procyon_lotor 1.0007 4077
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0005 3872
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5541 0.0812 0.4006 0.5531 0.7152
## (Intercept)-Canis_latrans -2.5740 0.2127 -3.0052 -2.5708 -2.1711
## (Intercept)-Procyon_lotor -2.1769 0.1647 -2.5140 -2.1701 -1.8691
## (Intercept)-Dasypus_novemcinctus -1.5631 0.1793 -1.9369 -1.5582 -1.2287
## shrub_cover-Odocoileus_virginianus -0.0615 0.0682 -0.1970 -0.0617 0.0706
## shrub_cover-Canis_latrans -0.3221 0.2297 -0.7601 -0.3246 0.1269
## shrub_cover-Procyon_lotor 0.2478 0.1642 -0.0873 0.2503 0.5673
## shrub_cover-Dasypus_novemcinctus 0.7806 0.3083 0.2008 0.7761 1.4045
## veg_height-Odocoileus_virginianus -0.3394 0.0691 -0.4767 -0.3383 -0.2061
## veg_height-Canis_latrans -0.6595 0.1912 -1.0495 -0.6548 -0.2993
## veg_height-Procyon_lotor 0.3481 0.1257 0.1050 0.3470 0.5988
## veg_height-Dasypus_novemcinctus 0.2334 0.1369 -0.0290 0.2302 0.5108
## week-Odocoileus_virginianus 1.3453 0.1281 1.0966 1.3455 1.5941
## week-Canis_latrans 0.6438 0.2755 0.1080 0.6458 1.1883
## week-Procyon_lotor 0.2114 0.2205 -0.2111 0.2091 0.6474
## week-Dasypus_novemcinctus 0.1232 0.2413 -0.3521 0.1262 0.6014
## I(week^2)-Odocoileus_virginianus -0.5547 0.0536 -0.6583 -0.5546 -0.4519
## I(week^2)-Canis_latrans -0.2645 0.1166 -0.4939 -0.2638 -0.0348
## I(week^2)-Procyon_lotor -0.1334 0.0956 -0.3170 -0.1311 0.0503
## I(week^2)-Dasypus_novemcinctus -0.1810 0.1106 -0.4060 -0.1790 0.0358
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0035 5250
## (Intercept)-Canis_latrans 1.0048 2423
## (Intercept)-Procyon_lotor 1.0019 3933
## (Intercept)-Dasypus_novemcinctus 0.9999 4313
## shrub_cover-Odocoileus_virginianus 1.0009 5250
## shrub_cover-Canis_latrans 1.0023 2445
## shrub_cover-Procyon_lotor 1.0004 4444
## shrub_cover-Dasypus_novemcinctus 1.0002 3582
## veg_height-Odocoileus_virginianus 0.9998 5250
## veg_height-Canis_latrans 1.0087 2109
## veg_height-Procyon_lotor 1.0002 4314
## veg_height-Dasypus_novemcinctus 1.0016 4596
## week-Odocoileus_virginianus 1.0001 5250
## week-Canis_latrans 1.0024 3413
## week-Procyon_lotor 1.0006 4367
## week-Dasypus_novemcinctus 1.0008 5250
## I(week^2)-Odocoileus_virginianus 1.0002 5250
## I(week^2)-Canis_latrans 1.0039 3428
## I(week^2)-Procyon_lotor 1.0004 4303
## I(week^2)-Dasypus_novemcinctus 1.0016 4862
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
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## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
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## Sampled: 3000 of 10000, 30.00%
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## Sampled: 3500 of 10000, 35.00%
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## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
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## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9577
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9441 1.1750 -1.5332 0.9951 3.1312 1.0051 4861
## Cogon_Patch_Size 0.2291 0.6408 -0.9548 0.1867 1.6532 1.0007 2830
## Avg_Cogongrass_Cover 0.3477 0.5371 -0.6604 0.3401 1.4491 1.0022 2648
## total_shrub_cover -0.2014 0.6914 -1.5783 -0.2143 1.2669 1.0085 3014
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.2891 63.1581 0.7186 7.2982 130.6099 1.0201 919
## Cogon_Patch_Size 1.7584 6.3811 0.0525 0.4822 10.7961 1.0274 2545
## Avg_Cogongrass_Cover 0.7886 2.4001 0.0416 0.2801 4.5629 1.0485 3740
## total_shrub_cover 2.3512 7.6574 0.0682 0.8468 13.6738 1.0862 2257
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6795 1.5544 0.0423 0.3042 3.4805 1.0935 760
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1423 0.8147 -2.6020 -1.2070 0.6941 1.0003 4765
## shrub_cover 0.1692 0.4190 -0.6767 0.1661 1.0054 1.0007 4805
## veg_height -0.0966 0.3883 -0.8682 -0.1033 0.7106 1.0014 5250
## week 0.5304 0.4446 -0.3898 0.5478 1.3926 1.0036 4997
## I(week^2) -0.2781 0.2541 -0.7692 -0.2835 0.2544 1.0012 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0305 6.3495 0.6127 2.3038 18.1064 1.0088 5250
## shrub_cover 0.8082 1.4291 0.0707 0.4128 4.0551 1.0053 4540
## veg_height 0.6826 1.2959 0.0813 0.3554 3.2830 1.0336 4552
## week 0.9171 1.9043 0.1119 0.4782 4.1047 1.0382 4979
## I(week^2) 0.2518 0.5406 0.0328 0.1299 1.2025 1.0342 4452
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 6.0651 3.4570 2.1242 5.1870
## (Intercept)-Canis_latrans 1.0413 0.8748 -0.3728 0.9458
## (Intercept)-Procyon_lotor 1.0991 0.7419 -0.2885 1.0677
## (Intercept)-Dasypus_novemcinctus -0.4890 0.6962 -1.7307 -0.5243
## Cogon_Patch_Size-Odocoileus_virginianus 0.2916 1.0390 -1.4371 0.1864
## Cogon_Patch_Size-Canis_latrans 0.9346 1.0425 -0.4055 0.7101
## Cogon_Patch_Size-Procyon_lotor -0.0815 0.5120 -1.0977 -0.0863
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0301 0.4709 -0.9552 -0.0253
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3628 0.8236 -1.1595 0.3223
## Avg_Cogongrass_Cover-Canis_latrans 0.4825 0.5959 -0.4652 0.4274
## Avg_Cogongrass_Cover-Procyon_lotor 0.2018 0.5369 -0.8521 0.1924
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4433 0.4489 -0.3953 0.4255
## total_shrub_cover-Odocoileus_virginianus -0.0840 1.0498 -2.1295 -0.1269
## total_shrub_cover-Canis_latrans 0.5344 0.9001 -0.7719 0.3557
## total_shrub_cover-Procyon_lotor -1.1648 0.7368 -2.8931 -1.0676
## total_shrub_cover-Dasypus_novemcinctus -0.1836 0.6032 -1.4907 -0.1300
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.2597 1.0644 248
## (Intercept)-Canis_latrans 2.9984 1.0330 1195
## (Intercept)-Procyon_lotor 2.6922 1.0089 2174
## (Intercept)-Dasypus_novemcinctus 1.0833 1.0083 1682
## Cogon_Patch_Size-Odocoileus_virginianus 2.7122 1.0108 2152
## Cogon_Patch_Size-Canis_latrans 3.6903 1.0158 1272
## Cogon_Patch_Size-Procyon_lotor 0.9425 1.0012 3388
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9194 0.9999 3578
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.1149 1.0030 2303
## Avg_Cogongrass_Cover-Canis_latrans 1.7705 1.0084 1646
## Avg_Cogongrass_Cover-Procyon_lotor 1.2944 1.0005 2824
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3877 1.0028 3134
## total_shrub_cover-Odocoileus_virginianus 2.1798 1.0304 2063
## total_shrub_cover-Canis_latrans 2.8466 1.0392 953
## total_shrub_cover-Procyon_lotor -0.0208 1.0015 2070
## total_shrub_cover-Dasypus_novemcinctus 0.7636 1.0129 877
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5535 0.0803 0.3996 0.5529 0.7142
## (Intercept)-Canis_latrans -2.5995 0.2194 -3.0618 -2.5936 -2.1997
## (Intercept)-Procyon_lotor -2.1820 0.1640 -2.5134 -2.1756 -1.8759
## (Intercept)-Dasypus_novemcinctus -1.5893 0.1929 -1.9854 -1.5825 -1.2290
## shrub_cover-Odocoileus_virginianus -0.0611 0.0679 -0.1923 -0.0618 0.0715
## shrub_cover-Canis_latrans -0.3618 0.2449 -0.8304 -0.3644 0.1083
## shrub_cover-Procyon_lotor 0.2959 0.1620 -0.0285 0.2971 0.6045
## shrub_cover-Dasypus_novemcinctus 0.8608 0.3607 0.2136 0.8337 1.6459
## veg_height-Odocoileus_virginianus -0.3390 0.0692 -0.4768 -0.3390 -0.2062
## veg_height-Canis_latrans -0.6690 0.1974 -1.0755 -0.6633 -0.2982
## veg_height-Procyon_lotor 0.3409 0.1261 0.0880 0.3420 0.5807
## veg_height-Dasypus_novemcinctus 0.2387 0.1395 -0.0244 0.2372 0.5166
## week-Odocoileus_virginianus 1.3443 0.1261 1.0986 1.3451 1.5941
## week-Canis_latrans 0.6333 0.2785 0.0985 0.6272 1.1975
## week-Procyon_lotor 0.2123 0.2226 -0.2195 0.2122 0.6458
## week-Dasypus_novemcinctus 0.1189 0.2386 -0.3408 0.1165 0.5871
## I(week^2)-Odocoileus_virginianus -0.5544 0.0517 -0.6594 -0.5545 -0.4565
## I(week^2)-Canis_latrans -0.2617 0.1149 -0.4922 -0.2606 -0.0423
## I(week^2)-Procyon_lotor -0.1332 0.0968 -0.3228 -0.1332 0.0540
## I(week^2)-Dasypus_novemcinctus -0.1843 0.1110 -0.4062 -0.1818 0.0300
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0023 5250
## (Intercept)-Canis_latrans 1.0076 1989
## (Intercept)-Procyon_lotor 1.0001 4151
## (Intercept)-Dasypus_novemcinctus 1.0004 2505
## shrub_cover-Odocoileus_virginianus 1.0013 5250
## shrub_cover-Canis_latrans 1.0149 1652
## shrub_cover-Procyon_lotor 1.0005 3933
## shrub_cover-Dasypus_novemcinctus 1.0046 1629
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0008 1987
## veg_height-Procyon_lotor 1.0011 4262
## veg_height-Dasypus_novemcinctus 1.0006 4324
## week-Odocoileus_virginianus 1.0001 5250
## week-Canis_latrans 1.0051 3684
## week-Procyon_lotor 1.0002 4316
## week-Dasypus_novemcinctus 0.9999 4690
## I(week^2)-Odocoileus_virginianus 1.0003 5250
## I(week^2)-Canis_latrans 1.0040 3703
## I(week^2)-Procyon_lotor 1.0002 4471
## I(week^2)-Dasypus_novemcinctus 1.0012 4501
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9675
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8339 1.1197 -1.5102 0.8735 2.9750 1.0002 4746
## Veg_shannon_index 0.5007 0.4999 -0.5024 0.5085 1.4572 1.0033 3825
## Avg_Cogongrass_Cover 0.6632 0.5151 -0.3248 0.6539 1.6994 1.0019 2747
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.5530 51.6771 0.7531 6.8704 98.0887 1.0360 856
## Veg_shannon_index 0.8252 2.1799 0.0446 0.3161 4.9939 1.0371 4428
## Avg_Cogongrass_Cover 0.7537 2.2577 0.0414 0.2756 4.2995 1.0670 4642
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7324 1.4303 0.0448 0.3427 3.6066 1.0826 835
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1148 0.8245 -2.6232 -1.1724 0.7277 1.0010 5250
## shrub_cover 0.1534 0.3912 -0.5975 0.1464 0.9613 1.0045 5250
## veg_height -0.0980 0.3704 -0.8238 -0.1035 0.6702 1.0000 5250
## week 0.5529 0.4431 -0.4275 0.5641 1.4161 1.0012 4585
## I(week^2) -0.2721 0.2383 -0.7430 -0.2756 0.2367 1.0007 4939
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.1613 12.0324 0.6115 2.2473 18.0701 1.0996 4746
## shrub_cover 0.6869 1.4478 0.0611 0.3352 3.3685 1.0067 5250
## veg_height 0.6826 1.8570 0.0797 0.3414 3.0684 1.0256 5250
## week 0.9619 2.5426 0.1149 0.4716 4.4226 1.0255 5250
## I(week^2) 0.2464 0.4399 0.0336 0.1281 1.2320 1.0092 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 5.4775 2.9779 1.9267 4.8040
## (Intercept)-Canis_latrans 0.6689 0.7129 -0.6325 0.6452
## (Intercept)-Procyon_lotor 0.8024 0.6780 -0.4973 0.7919
## (Intercept)-Dasypus_novemcinctus -0.5931 0.6043 -1.7694 -0.6034
## Veg_shannon_index-Odocoileus_virginianus 0.4145 0.7455 -1.1961 0.4396
## Veg_shannon_index-Canis_latrans 0.8520 0.5028 -0.0195 0.8077
## Veg_shannon_index-Procyon_lotor 0.6114 0.4989 -0.2589 0.5861
## Veg_shannon_index-Dasypus_novemcinctus 0.2588 0.4039 -0.5595 0.2665
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6581 0.7770 -0.8088 0.6174
## Avg_Cogongrass_Cover-Canis_latrans 0.9620 0.5888 0.0155 0.8895
## Avg_Cogongrass_Cover-Procyon_lotor 0.6076 0.5044 -0.2812 0.5727
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6018 0.3940 -0.1442 0.5891
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 13.5039 1.0090 354
## (Intercept)-Canis_latrans 2.1909 1.0012 2260
## (Intercept)-Procyon_lotor 2.1078 1.0031 2289
## (Intercept)-Dasypus_novemcinctus 0.6628 1.0054 2378
## Veg_shannon_index-Odocoileus_virginianus 1.8027 1.0058 3164
## Veg_shannon_index-Canis_latrans 1.9643 1.0054 3159
## Veg_shannon_index-Procyon_lotor 1.6861 1.0028 1998
## Veg_shannon_index-Dasypus_novemcinctus 1.0561 1.0002 4323
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.3243 1.0018 2523
## Avg_Cogongrass_Cover-Canis_latrans 2.3186 1.0015 2403
## Avg_Cogongrass_Cover-Procyon_lotor 1.7564 1.0010 2728
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4109 1.0023 3997
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5539 0.0806 0.3971 0.5534 0.7132
## (Intercept)-Canis_latrans -2.5697 0.2111 -2.9977 -2.5634 -2.1811
## (Intercept)-Procyon_lotor -2.1886 0.1707 -2.5431 -2.1828 -1.8681
## (Intercept)-Dasypus_novemcinctus -1.5583 0.1813 -1.9323 -1.5527 -1.2267
## shrub_cover-Odocoileus_virginianus -0.0634 0.0683 -0.1947 -0.0622 0.0720
## shrub_cover-Canis_latrans -0.2881 0.2148 -0.7215 -0.2860 0.1253
## shrub_cover-Procyon_lotor 0.2192 0.1781 -0.1485 0.2241 0.5548
## shrub_cover-Dasypus_novemcinctus 0.7631 0.3053 0.2267 0.7489 1.3891
## veg_height-Odocoileus_virginianus -0.3393 0.0685 -0.4730 -0.3386 -0.2063
## veg_height-Canis_latrans -0.6623 0.1906 -1.0502 -0.6539 -0.3086
## veg_height-Procyon_lotor 0.3363 0.1286 0.0880 0.3350 0.5939
## veg_height-Dasypus_novemcinctus 0.2233 0.1363 -0.0416 0.2199 0.4976
## week-Odocoileus_virginianus 1.3469 0.1260 1.1032 1.3466 1.6008
## week-Canis_latrans 0.6417 0.2806 0.0961 0.6391 1.1861
## week-Procyon_lotor 0.2131 0.2239 -0.2163 0.2074 0.6687
## week-Dasypus_novemcinctus 0.1214 0.2417 -0.3433 0.1152 0.5976
## I(week^2)-Odocoileus_virginianus -0.5551 0.0517 -0.6577 -0.5556 -0.4555
## I(week^2)-Canis_latrans -0.2610 0.1150 -0.4831 -0.2638 -0.0313
## I(week^2)-Procyon_lotor -0.1330 0.0969 -0.3325 -0.1305 0.0528
## I(week^2)-Dasypus_novemcinctus -0.1805 0.1106 -0.3968 -0.1772 0.0345
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 4843
## (Intercept)-Canis_latrans 1.0050 2702
## (Intercept)-Procyon_lotor 1.0010 3879
## (Intercept)-Dasypus_novemcinctus 1.0024 4424
## shrub_cover-Odocoileus_virginianus 1.0035 4991
## shrub_cover-Canis_latrans 1.0038 2941
## shrub_cover-Procyon_lotor 1.0043 2977
## shrub_cover-Dasypus_novemcinctus 1.0074 3601
## veg_height-Odocoileus_virginianus 1.0006 5250
## veg_height-Canis_latrans 1.0002 2375
## veg_height-Procyon_lotor 1.0026 4261
## veg_height-Dasypus_novemcinctus 0.9999 5300
## week-Odocoileus_virginianus 1.0021 4983
## week-Canis_latrans 1.0018 3705
## week-Procyon_lotor 1.0019 4428
## week-Dasypus_novemcinctus 1.0006 5250
## I(week^2)-Odocoileus_virginianus 1.0047 5112
## I(week^2)-Canis_latrans 1.0006 3836
## I(week^2)-Procyon_lotor 1.0014 4267
## I(week^2)-Dasypus_novemcinctus 1.0024 4772
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.9397
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8070 1.0792 -1.5171 0.8282 2.9359 1.0040 4710
## Avg_Cogongrass_Cover 0.4448 0.4433 -0.4310 0.4467 1.3205 1.0023 3660
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 14.1285 33.5028 0.6104 5.7354 76.0732 1.0153 1333
## Avg_Cogongrass_Cover 0.5864 1.3987 0.0392 0.2429 3.4457 1.0361 3659
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5728 1.0149 0.0437 0.2929 2.892 1.0357 1081
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1369 0.8024 -2.6087 -1.2053 0.7034 1.0007 5250
## shrub_cover 0.1700 0.3719 -0.5954 0.1597 0.9467 1.0020 4908
## veg_height -0.1012 0.3754 -0.8702 -0.1032 0.6672 1.0014 5250
## week 0.5487 0.4361 -0.3865 0.5629 1.3996 1.0018 5250
## I(week^2) -0.2722 0.2364 -0.7466 -0.2742 0.2123 1.0005 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9886 6.8275 0.6114 2.2603 19.1078 1.0134 5250
## shrub_cover 0.6494 1.1986 0.0615 0.3345 3.1772 1.0102 4349
## veg_height 0.7042 1.5380 0.0841 0.3499 3.3479 1.0261 5250
## week 0.8922 1.7874 0.1089 0.4590 4.4138 1.0461 4966
## I(week^2) 0.2413 0.4383 0.0333 0.1276 1.1750 1.0095 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 4.9694 2.5551 1.8104 4.3999
## (Intercept)-Canis_latrans 0.6809 0.6540 -0.5109 0.6511
## (Intercept)-Procyon_lotor 0.7589 0.5842 -0.3782 0.7564
## (Intercept)-Dasypus_novemcinctus -0.5444 0.5945 -1.7220 -0.5453
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4135 0.6925 -0.9553 0.4000
## Avg_Cogongrass_Cover-Canis_latrans 0.6500 0.4988 -0.1496 0.5873
## Avg_Cogongrass_Cover-Procyon_lotor 0.3551 0.4118 -0.4088 0.3388
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4610 0.3561 -0.2081 0.4563
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 11.7552 1.0188 414
## (Intercept)-Canis_latrans 2.0580 1.0015 2619
## (Intercept)-Procyon_lotor 1.9249 1.0017 3847
## (Intercept)-Dasypus_novemcinctus 0.6387 1.0012 2202
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8985 1.0002 3134
## Avg_Cogongrass_Cover-Canis_latrans 1.7905 1.0008 2933
## Avg_Cogongrass_Cover-Procyon_lotor 1.2162 1.0060 3815
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2011 1.0021 4518
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5536 0.0812 0.4010 0.5531 0.7145
## (Intercept)-Canis_latrans -2.5927 0.2123 -3.0167 -2.5843 -2.1880
## (Intercept)-Procyon_lotor -2.1722 0.1669 -2.5182 -2.1650 -1.8611
## (Intercept)-Dasypus_novemcinctus -1.5615 0.1788 -1.9229 -1.5600 -1.2248
## shrub_cover-Odocoileus_virginianus -0.0618 0.0682 -0.1944 -0.0613 0.0695
## shrub_cover-Canis_latrans -0.2785 0.2220 -0.7306 -0.2726 0.1382
## shrub_cover-Procyon_lotor 0.2468 0.1636 -0.0777 0.2510 0.5642
## shrub_cover-Dasypus_novemcinctus 0.7686 0.3106 0.2014 0.7582 1.4095
## veg_height-Odocoileus_virginianus -0.3395 0.0680 -0.4719 -0.3386 -0.2089
## veg_height-Canis_latrans -0.6710 0.1955 -1.0662 -0.6681 -0.3042
## veg_height-Procyon_lotor 0.3379 0.1234 0.0922 0.3374 0.5809
## veg_height-Dasypus_novemcinctus 0.2262 0.1371 -0.0354 0.2233 0.5010
## week-Odocoileus_virginianus 1.3425 0.1256 1.0958 1.3417 1.5900
## week-Canis_latrans 0.6429 0.2761 0.1273 0.6433 1.1849
## week-Procyon_lotor 0.2157 0.2254 -0.2261 0.2135 0.6605
## week-Dasypus_novemcinctus 0.1226 0.2392 -0.3544 0.1228 0.5924
## I(week^2)-Odocoileus_virginianus -0.5534 0.0520 -0.6542 -0.5536 -0.4513
## I(week^2)-Canis_latrans -0.2629 0.1142 -0.4913 -0.2619 -0.0422
## I(week^2)-Procyon_lotor -0.1347 0.0963 -0.3264 -0.1333 0.0514
## I(week^2)-Dasypus_novemcinctus -0.1827 0.1106 -0.4045 -0.1837 0.0297
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5250
## (Intercept)-Canis_latrans 1.0007 2232
## (Intercept)-Procyon_lotor 1.0020 4223
## (Intercept)-Dasypus_novemcinctus 1.0059 4588
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0010 2276
## shrub_cover-Procyon_lotor 1.0017 3903
## shrub_cover-Dasypus_novemcinctus 1.0032 3664
## veg_height-Odocoileus_virginianus 1.0007 5250
## veg_height-Canis_latrans 1.0019 1897
## veg_height-Procyon_lotor 1.0016 4037
## veg_height-Dasypus_novemcinctus 1.0039 4794
## week-Odocoileus_virginianus 1.0009 5250
## week-Canis_latrans 1.0012 3007
## week-Procyon_lotor 1.0013 4390
## week-Dasypus_novemcinctus 1.0019 5015
## I(week^2)-Odocoileus_virginianus 1.0000 5250
## I(week^2)-Canis_latrans 1.0006 3772
## I(week^2)-Procyon_lotor 1.0033 4392
## I(week^2)-Dasypus_novemcinctus 1.0010 4653
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 0.987
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3362 1.0489 -1.7319 0.3103 2.4620 1.0017 3587
## Avg_Cogongrass_Cover -0.0585 0.6379 -1.3139 -0.0626 1.2226 1.0039 2298
## I(Avg_Cogongrass_Cover^2) 1.1506 0.9012 -0.5729 1.0814 3.1362 1.0037 1720
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 11.5841 23.7022 0.3731 5.0470 64.0967 1.0154 1209
## Avg_Cogongrass_Cover 1.0494 6.7211 0.0447 0.3628 5.6791 1.2510 5250
## I(Avg_Cogongrass_Cover^2) 7.3847 45.4752 0.0564 0.9767 47.4553 1.0986 987
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5031 0.7948 0.0428 0.2609 2.523 1.0112 918
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1371 0.8063 -2.5570 -1.2017 0.7056 1.0007 5250
## shrub_cover 0.1464 0.3696 -0.5942 0.1441 0.8806 1.0039 4772
## veg_height -0.1039 0.3712 -0.8576 -0.1043 0.6383 1.0032 5250
## week 0.5382 0.4422 -0.3818 0.5595 1.3710 1.0019 5250
## I(week^2) -0.2741 0.2368 -0.7412 -0.2793 0.2107 1.0019 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2789 9.8176 0.6286 2.2904 19.2492 1.0355 5250
## shrub_cover 0.6316 1.3581 0.0558 0.3180 3.1502 1.0521 4807
## veg_height 0.6444 1.0518 0.0808 0.3487 3.0730 1.0026 4557
## week 0.9055 1.7505 0.1092 0.4776 4.3467 1.0194 3823
## I(week^2) 0.2419 0.5010 0.0318 0.1278 1.1496 1.0350 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.9788 2.5483 0.4840 3.4909
## (Intercept)-Canis_latrans -0.3879 0.8863 -2.2762 -0.3679
## (Intercept)-Procyon_lotor -0.0104 0.7538 -1.5918 0.0189
## (Intercept)-Dasypus_novemcinctus -1.0312 0.6883 -2.4288 -1.0278
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1158 0.9659 -2.0902 -0.1231
## Avg_Cogongrass_Cover-Canis_latrans 0.1989 0.7163 -1.0665 0.1440
## Avg_Cogongrass_Cover-Procyon_lotor -0.1715 0.7129 -1.4942 -0.1906
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.1155 0.6049 -1.3074 -0.1170
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.1634 2.3239 -0.3204 1.4835
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9874 1.4798 0.1149 1.6336
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8276 1.8868 0.0227 1.2204
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5561 0.4757 -0.2985 0.5347
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 10.4993 1.0054 564
## (Intercept)-Canis_latrans 1.2774 1.0086 1709
## (Intercept)-Procyon_lotor 1.3816 1.0001 1452
## (Intercept)-Dasypus_novemcinctus 0.3283 1.0001 3123
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.8066 1.0042 2430
## Avg_Cogongrass_Cover-Canis_latrans 1.7674 1.0060 2174
## Avg_Cogongrass_Cover-Procyon_lotor 1.3263 1.0021 1654
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0809 1.0006 2556
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 8.3358 1.0156 342
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.8029 1.0170 515
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.6215 1.0141 317
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5411 1.0012 2959
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5508 0.0798 0.3908 0.5515 0.7052
## (Intercept)-Canis_latrans -2.5744 0.2054 -2.9930 -2.5693 -2.1953
## (Intercept)-Procyon_lotor -2.2245 0.1768 -2.5824 -2.2187 -1.8925
## (Intercept)-Dasypus_novemcinctus -1.5610 0.1805 -1.9292 -1.5588 -1.2173
## shrub_cover-Odocoileus_virginianus -0.0614 0.0675 -0.1963 -0.0617 0.0720
## shrub_cover-Canis_latrans -0.2535 0.2170 -0.6905 -0.2467 0.1555
## shrub_cover-Procyon_lotor 0.1901 0.1751 -0.1600 0.1918 0.5268
## shrub_cover-Dasypus_novemcinctus 0.7580 0.3145 0.1881 0.7400 1.4122
## veg_height-Odocoileus_virginianus -0.3384 0.0693 -0.4786 -0.3392 -0.2042
## veg_height-Canis_latrans -0.6563 0.1954 -1.0511 -0.6516 -0.2868
## veg_height-Procyon_lotor 0.3473 0.1265 0.0992 0.3481 0.5922
## veg_height-Dasypus_novemcinctus 0.2241 0.1352 -0.0480 0.2233 0.4918
## week-Odocoileus_virginianus 1.3400 0.1232 1.1028 1.3392 1.5851
## week-Canis_latrans 0.6439 0.2785 0.1141 0.6345 1.2072
## week-Procyon_lotor 0.2097 0.2216 -0.2189 0.2111 0.6517
## week-Dasypus_novemcinctus 0.1151 0.2419 -0.3564 0.1115 0.5905
## I(week^2)-Odocoileus_virginianus -0.5530 0.0515 -0.6543 -0.5531 -0.4522
## I(week^2)-Canis_latrans -0.2666 0.1163 -0.5013 -0.2652 -0.0431
## I(week^2)-Procyon_lotor -0.1306 0.0945 -0.3174 -0.1297 0.0508
## I(week^2)-Dasypus_novemcinctus -0.1829 0.1104 -0.4086 -0.1813 0.0284
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0004 2400
## (Intercept)-Procyon_lotor 1.0060 2417
## (Intercept)-Dasypus_novemcinctus 1.0007 4494
## shrub_cover-Odocoileus_virginianus 1.0023 5250
## shrub_cover-Canis_latrans 1.0018 2812
## shrub_cover-Procyon_lotor 1.0006 2042
## shrub_cover-Dasypus_novemcinctus 1.0002 3647
## veg_height-Odocoileus_virginianus 1.0024 5250
## veg_height-Canis_latrans 1.0008 2243
## veg_height-Procyon_lotor 1.0098 3632
## veg_height-Dasypus_novemcinctus 1.0028 4686
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0011 3831
## week-Procyon_lotor 1.0019 4448
## week-Dasypus_novemcinctus 1.0008 5742
## I(week^2)-Odocoileus_virginianus 1.0005 5250
## I(week^2)-Canis_latrans 1.0004 3913
## I(week^2)-Procyon_lotor 1.0001 4361
## I(week^2)-Dasypus_novemcinctus 1.0000 4119
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 10000,
n.thin = 4,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 10000, 5.00%
## -------------------------------------------------
## Sampled: 1000 of 10000, 10.00%
## -------------------------------------------------
## Sampled: 1500 of 10000, 15.00%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 8500 of 10000, 85.00%
## -------------------------------------------------
## Sampled: 9000 of 10000, 90.00%
## -------------------------------------------------
## Sampled: 9500 of 10000, 95.00%
## -------------------------------------------------
## Sampled: 10000 of 10000, 100.00%
summary(ms_fullQ_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.0175
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2963 1.4400 -2.5656 0.2898 3.2058 1.0025 4833
## Cogon_Patch_Size 0.0918 0.9578 -1.7176 0.0597 2.1918 1.0020 1777
## Veg_shannon_index 0.8938 0.7715 -0.6406 0.8834 2.4256 1.0016 1758
## total_shrub_cover -0.0446 0.8584 -1.7448 -0.0735 1.7952 1.0060 2203
## Avg_Cogongrass_Cover 0.7252 1.2303 -1.6148 0.7175 3.2714 1.0082 868
## Tree_Density -1.4553 1.4779 -4.0240 -1.6008 1.7792 1.0049 1668
## Avg_Canopy_Cover 0.8975 0.8904 -0.9839 0.9208 2.6393 1.0019 2759
## I(Avg_Cogongrass_Cover^2) 1.8323 1.0400 -0.3242 1.7978 4.0071 1.0008 1409
## avg_veg_height 0.0015 0.7445 -1.4571 -0.0060 1.4525 1.0010 1548
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 71.7982 224.7332 2.6898 30.7669 352.2972 1.0779 2635
## Cogon_Patch_Size 5.9688 25.4527 0.0692 1.3331 37.0210 1.1756 1065
## Veg_shannon_index 2.3175 11.7528 0.0563 0.5629 14.1663 1.1668 2643
## total_shrub_cover 3.8959 11.7146 0.0791 1.3177 22.3317 1.0066 1681
## Avg_Cogongrass_Cover 3.8937 19.6395 0.0539 0.7700 24.8111 1.1259 3161
## Tree_Density 40.0216 110.2022 0.1274 10.1898 255.4171 1.0295 703
## Avg_Canopy_Cover 4.6776 14.4276 0.0942 1.6120 27.0347 1.0490 1872
## I(Avg_Cogongrass_Cover^2) 8.3446 38.9732 0.0566 0.7363 66.5801 1.0811 576
## avg_veg_height 1.1870 3.1470 0.0443 0.3984 7.7557 1.0198 3392
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9008 7.0763 0.0552 0.8734 18.7874 1.0667 259
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1294 0.7920 -2.5672 -1.1827 0.6156 1.0008 5250
## shrub_cover 0.1500 0.4288 -0.7184 0.1418 1.0115 0.9998 4328
## veg_height -0.0851 0.3754 -0.8403 -0.0876 0.6716 1.0007 5250
## week 0.5487 0.4576 -0.4211 0.5636 1.4628 1.0008 5250
## I(week^2) -0.2756 0.2446 -0.7685 -0.2796 0.2136 1.0010 5809
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0055 7.3164 0.6045 2.2904 17.5966 1.1157 5250
## shrub_cover 0.8478 2.2359 0.0726 0.4097 4.1737 1.1322 5250
## veg_height 0.7093 1.8232 0.0790 0.3556 3.4518 1.0748 5046
## week 0.9514 2.2939 0.1061 0.4704 4.6635 1.0312 5250
## I(week^2) 0.2379 0.4265 0.0317 0.1288 1.1292 1.0062 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 10.1336 5.5042 2.3046
## (Intercept)-Canis_latrans -0.6943 1.8420 -4.4364
## (Intercept)-Procyon_lotor -0.2847 1.3599 -2.9523
## (Intercept)-Dasypus_novemcinctus -3.3875 1.9227 -8.0517
## Cogon_Patch_Size-Odocoileus_virginianus 0.2486 1.9540 -2.9263
## Cogon_Patch_Size-Canis_latrans 1.5966 2.3206 -1.0071
## Cogon_Patch_Size-Procyon_lotor -0.5997 1.0149 -2.6404
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3093 0.9971 -2.3716
## Veg_shannon_index-Odocoileus_virginianus 0.7359 1.2732 -2.0673
## Veg_shannon_index-Canis_latrans 1.4709 1.0411 -0.1617
## Veg_shannon_index-Procyon_lotor 1.2589 0.8102 -0.1145
## Veg_shannon_index-Dasypus_novemcinctus 0.5812 0.7176 -0.8440
## total_shrub_cover-Odocoileus_virginianus 0.1454 1.4992 -2.6718
## total_shrub_cover-Canis_latrans 0.9160 1.3374 -0.9208
## total_shrub_cover-Procyon_lotor -1.1808 0.8937 -3.1535
## total_shrub_cover-Dasypus_novemcinctus -0.0667 0.9541 -2.1144
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6895 1.9584 -3.0295
## Avg_Cogongrass_Cover-Canis_latrans 1.0079 1.6778 -1.9871
## Avg_Cogongrass_Cover-Procyon_lotor 0.4555 1.5556 -2.6376
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3641 1.6840 -1.4727
## Tree_Density-Odocoileus_virginianus 0.2031 3.1972 -3.9899
## Tree_Density-Canis_latrans -4.5913 3.0279 -12.7108
## Tree_Density-Procyon_lotor -2.1712 1.4469 -5.2446
## Tree_Density-Dasypus_novemcinctus -6.6684 4.3889 -17.8870
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7124 1.6799 -2.8341
## Avg_Canopy_Cover-Canis_latrans 0.0646 0.7711 -1.5902
## Avg_Canopy_Cover-Procyon_lotor 1.5429 0.9497 0.0007
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3568 1.2848 0.6171
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.7799 2.8456 -0.2968
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6193 1.7556 0.4108
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5499 1.8009 0.4183
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.8800 1.1435 0.0448
## avg_veg_height-Odocoileus_virginianus -0.0119 1.1560 -2.3512
## avg_veg_height-Canis_latrans -0.1991 0.8221 -1.8144
## avg_veg_height-Procyon_lotor 0.0451 0.8202 -1.5761
## avg_veg_height-Dasypus_novemcinctus 0.1668 0.7828 -1.3124
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.1536 23.7471 1.0096 279
## (Intercept)-Canis_latrans -0.7093 3.1682 1.0151 929
## (Intercept)-Procyon_lotor -0.2845 2.5178 1.0065 1156
## (Intercept)-Dasypus_novemcinctus -3.0583 -0.4263 1.0091 374
## Cogon_Patch_Size-Odocoileus_virginianus 0.0368 4.8582 1.0247 1021
## Cogon_Patch_Size-Canis_latrans 1.0991 7.1218 1.0555 622
## Cogon_Patch_Size-Procyon_lotor -0.5876 1.2662 1.0092 882
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3081 1.6739 1.0030 1439
## Veg_shannon_index-Odocoileus_virginianus 0.7854 3.1785 1.0026 1703
## Veg_shannon_index-Canis_latrans 1.3450 3.8978 1.0088 924
## Veg_shannon_index-Procyon_lotor 1.1871 3.0610 1.0028 571
## Veg_shannon_index-Dasypus_novemcinctus 0.5740 1.9901 1.0020 1894
## total_shrub_cover-Odocoileus_virginianus 0.0351 3.4737 1.0081 1490
## total_shrub_cover-Canis_latrans 0.6244 4.3802 1.0052 417
## total_shrub_cover-Procyon_lotor -1.1029 0.3424 1.0009 1354
## total_shrub_cover-Dasypus_novemcinctus 0.0117 1.4866 1.0140 536
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6951 4.4967 1.0107 911
## Avg_Cogongrass_Cover-Canis_latrans 0.9087 4.6261 1.0054 847
## Avg_Cogongrass_Cover-Procyon_lotor 0.4593 3.5532 1.0053 911
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1969 5.1155 1.0123 709
## Tree_Density-Odocoileus_virginianus -0.5044 8.4589 1.0121 717
## Tree_Density-Canis_latrans -3.8923 -0.9024 1.0287 355
## Tree_Density-Procyon_lotor -2.0959 0.3646 1.0019 1235
## Tree_Density-Dasypus_novemcinctus -5.4504 -1.5677 1.0169 264
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7624 4.1963 1.0007 1480
## Avg_Canopy_Cover-Canis_latrans 0.0969 1.4575 1.0148 2162
## Avg_Canopy_Cover-Procyon_lotor 1.4218 3.7411 1.0008 857
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0997 5.4779 1.0200 389
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.1617 10.8491 1.0683 288
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2693 7.3130 1.0209 507
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1741 7.9248 1.0048 352
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7470 4.4723 1.0098 786
## avg_veg_height-Odocoileus_virginianus -0.0040 2.2384 1.0023 1943
## avg_veg_height-Canis_latrans -0.2011 1.4378 1.0015 1545
## avg_veg_height-Procyon_lotor 0.0446 1.6525 1.0014 1728
## avg_veg_height-Dasypus_novemcinctus 0.1474 1.7636 1.0018 1557
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5527 0.0818 0.3944 0.5505 0.7153
## (Intercept)-Canis_latrans -2.5538 0.2071 -2.9718 -2.5497 -2.1738
## (Intercept)-Procyon_lotor -2.2050 0.1714 -2.5521 -2.2005 -1.8754
## (Intercept)-Dasypus_novemcinctus -1.5799 0.1857 -1.9577 -1.5737 -1.2319
## shrub_cover-Odocoileus_virginianus -0.0623 0.0685 -0.1969 -0.0629 0.0704
## shrub_cover-Canis_latrans -0.3871 0.2329 -0.8455 -0.3892 0.0691
## shrub_cover-Procyon_lotor 0.2454 0.1713 -0.0949 0.2503 0.5690
## shrub_cover-Dasypus_novemcinctus 0.8399 0.3332 0.2188 0.8311 1.5241
## veg_height-Odocoileus_virginianus -0.3395 0.0686 -0.4779 -0.3389 -0.2098
## veg_height-Canis_latrans -0.6352 0.1853 -1.0138 -0.6283 -0.2826
## veg_height-Procyon_lotor 0.3590 0.1270 0.1110 0.3591 0.6074
## veg_height-Dasypus_novemcinctus 0.2383 0.1370 -0.0213 0.2338 0.5081
## week-Odocoileus_virginianus 1.3446 0.1280 1.0961 1.3439 1.5940
## week-Canis_latrans 0.6429 0.2855 0.0947 0.6356 1.2313
## week-Procyon_lotor 0.2118 0.2249 -0.2322 0.2091 0.6527
## week-Dasypus_novemcinctus 0.1244 0.2418 -0.3512 0.1254 0.5994
## I(week^2)-Odocoileus_virginianus -0.5542 0.0524 -0.6579 -0.5539 -0.4532
## I(week^2)-Canis_latrans -0.2630 0.1164 -0.4992 -0.2613 -0.0345
## I(week^2)-Procyon_lotor -0.1332 0.0958 -0.3224 -0.1323 0.0549
## I(week^2)-Dasypus_novemcinctus -0.1833 0.1123 -0.4098 -0.1833 0.0281
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0034 2142
## (Intercept)-Procyon_lotor 1.0012 1883
## (Intercept)-Dasypus_novemcinctus 1.0023 2909
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0016 1609
## shrub_cover-Procyon_lotor 1.0047 1718
## shrub_cover-Dasypus_novemcinctus 1.0008 1190
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0060 2214
## veg_height-Procyon_lotor 1.0051 3691
## veg_height-Dasypus_novemcinctus 1.0008 4228
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0020 3377
## week-Procyon_lotor 1.0097 4311
## week-Dasypus_novemcinctus 1.0037 5250
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0009 3655
## I(week^2)-Procyon_lotor 1.0037 4308
## I(week^2)-Dasypus_novemcinctus 1.0044 4544
waicOcc(ms_full_full_T50, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1368.75867 88.97229 2915.46192
waicOcc(ms_full_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -1385.33617 87.17551 2945.02336
waicOcc(ms_full_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -1385.21668 75.96865 2922.37066
waicOcc(ms_full_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -1385.93339 82.31073 2936.48825
waicOcc(ms_full_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -1388.51773 78.15838 2933.35222
waicOcc(ms_full_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -1391.11811 74.30184 2930.83989
waicOcc(ms_full_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -1394.47699 68.31991 2925.59380
waicOcc(ms_full_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1388.41436 78.50201 2933.83275
waicOcc(ms_full_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1364.5666 98.2155 2925.5643
waicOcc(ms_null_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -1442.07458 23.82988 2931.80893
waicOcc(ms_null_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -1417.99252 43.61388 2923.21280
waicOcc(ms_null_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -1435.50898 35.15475 2941.32746
waicOcc(ms_null_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -1433.00318 31.63729 2929.28093
waicOcc(ms_null_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -1434.85872 34.72833 2939.17410
waicOcc(ms_null_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -1435.58304 32.76805 2936.70216
waicOcc(ms_null_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -1439.07115 29.08015 2936.30258
waicOcc(ms_null_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1436.05276 31.99753 2936.10058
waicOcc(ms_null_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1413.01955 45.77809 2917.59529
waicOcc(ms_week_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -1409.39859 49.51759 2917.83234
waicOcc(ms_week_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -1426.85758 40.88658 2935.48832
waicOcc(ms_week_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -1433.62025 29.44922 2926.13894
waicOcc(ms_week_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -1427.31740 37.67633 2929.98746
waicOcc(ms_week_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -1426.57165 40.42735 2933.99800
waicOcc(ms_week_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -1424.55689 37.33148 2923.77674
waicOcc(ms_week_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -1430.57234 34.30387 2929.75242
waicOcc(ms_week_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1427.82732 37.09642 2929.84747
waicOcc(ms_week_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1405.26556 51.24162 2913.01435
waicOcc(ms_cover_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -1377.25232 84.71575 2923.93615
waicOcc(ms_cover_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -1394.74223 78.94983 2947.38414
waicOcc(ms_cover_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -1403.80925 61.66565 2930.94978
waicOcc(ms_cover_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -1396.84209 72.78975 2939.26368
waicOcc(ms_cover_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -1394.91009 76.50994 2942.84008
waicOcc(ms_cover_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -1393.68121 71.38631 2930.13505
waicOcc(ms_cover_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -1400.05742 68.19048 2936.49580
waicOcc(ms_cover_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1397.16818 73.06782 2940.47200
waicOcc(ms_cover_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1374.46037 86.45861 2921.83795
waicOcc(ms_weekQ_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -1339.39252 57.34514 2793.47532
waicOcc(ms_weekQ_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -1357.42135 47.87873 2810.60016
waicOcc(ms_weekQ_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -1363.93898 36.71541 2801.30877
waicOcc(ms_weekQ_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -1357.35211 45.11472 2804.93366
waicOcc(ms_weekQ_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -1356.85074 47.58237 2808.86622
waicOcc(ms_weekQ_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -1354.73984 44.80951 2799.09870
waicOcc(ms_weekQ_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -1360.40920 42.49149 2805.80138
waicOcc(ms_weekQ_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1357.97603 44.17949 2804.31102
waicOcc(ms_weekQ_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1335.3672 58.2071 2787.1486
waicOcc(ms_fullQ_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -1297.02228 97.53895 2789.12246
waicOcc(ms_fullQ_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -1313.17849 96.45689 2819.27077
waicOcc(ms_fullQ_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -1322.44576 77.10072 2799.09296
waicOcc(ms_fullQ_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -1316.22850 87.12002 2806.69703
waicOcc(ms_fullQ_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -1313.64001 93.23588 2813.75179
waicOcc(ms_fullQ_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -1313.57670 84.94913 2797.05166
waicOcc(ms_fullQ_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -1319.53472 82.27077 2803.61099
waicOcc(ms_fullQ_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1316.74761 86.51569 2806.52660
waicOcc(ms_fullQ_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -1293.5577 101.3866 2789.8886
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T50 <- ppcOcc(ms_fullQ_fullQ_T50, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 4
## Currently on species 2 out of 4
## Currently on species 3 out of 4
## Currently on species 4 out of 4
summary(ppc.ms_fullQ_fullQ_T50)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T50, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.1595
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Odocoileus_virginianus Bayesian p-value: 0
## Canis_latrans Bayesian p-value: 0.5859
## Procyon_lotor Bayesian p-value: 0.052
## Dasypus_novemcinctus Bayesian p-value: 0
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T50) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.0175
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2963 1.4400 -2.5656 0.2898 3.2058 1.0025 4833
## Cogon_Patch_Size 0.0918 0.9578 -1.7176 0.0597 2.1918 1.0020 1777
## Veg_shannon_index 0.8938 0.7715 -0.6406 0.8834 2.4256 1.0016 1758
## total_shrub_cover -0.0446 0.8584 -1.7448 -0.0735 1.7952 1.0060 2203
## Avg_Cogongrass_Cover 0.7252 1.2303 -1.6148 0.7175 3.2714 1.0082 868
## Tree_Density -1.4553 1.4779 -4.0240 -1.6008 1.7792 1.0049 1668
## Avg_Canopy_Cover 0.8975 0.8904 -0.9839 0.9208 2.6393 1.0019 2759
## I(Avg_Cogongrass_Cover^2) 1.8323 1.0400 -0.3242 1.7978 4.0071 1.0008 1409
## avg_veg_height 0.0015 0.7445 -1.4571 -0.0060 1.4525 1.0010 1548
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 71.7982 224.7332 2.6898 30.7669 352.2972 1.0779 2635
## Cogon_Patch_Size 5.9688 25.4527 0.0692 1.3331 37.0210 1.1756 1065
## Veg_shannon_index 2.3175 11.7528 0.0563 0.5629 14.1663 1.1668 2643
## total_shrub_cover 3.8959 11.7146 0.0791 1.3177 22.3317 1.0066 1681
## Avg_Cogongrass_Cover 3.8937 19.6395 0.0539 0.7700 24.8111 1.1259 3161
## Tree_Density 40.0216 110.2022 0.1274 10.1898 255.4171 1.0295 703
## Avg_Canopy_Cover 4.6776 14.4276 0.0942 1.6120 27.0347 1.0490 1872
## I(Avg_Cogongrass_Cover^2) 8.3446 38.9732 0.0566 0.7363 66.5801 1.0811 576
## avg_veg_height 1.1870 3.1470 0.0443 0.3984 7.7557 1.0198 3392
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9008 7.0763 0.0552 0.8734 18.7874 1.0667 259
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1294 0.7920 -2.5672 -1.1827 0.6156 1.0008 5250
## shrub_cover 0.1500 0.4288 -0.7184 0.1418 1.0115 0.9998 4328
## veg_height -0.0851 0.3754 -0.8403 -0.0876 0.6716 1.0007 5250
## week 0.5487 0.4576 -0.4211 0.5636 1.4628 1.0008 5250
## I(week^2) -0.2756 0.2446 -0.7685 -0.2796 0.2136 1.0010 5809
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0055 7.3164 0.6045 2.2904 17.5966 1.1157 5250
## shrub_cover 0.8478 2.2359 0.0726 0.4097 4.1737 1.1322 5250
## veg_height 0.7093 1.8232 0.0790 0.3556 3.4518 1.0748 5046
## week 0.9514 2.2939 0.1061 0.4704 4.6635 1.0312 5250
## I(week^2) 0.2379 0.4265 0.0317 0.1288 1.1292 1.0062 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 10.1336 5.5042 2.3046
## (Intercept)-Canis_latrans -0.6943 1.8420 -4.4364
## (Intercept)-Procyon_lotor -0.2847 1.3599 -2.9523
## (Intercept)-Dasypus_novemcinctus -3.3875 1.9227 -8.0517
## Cogon_Patch_Size-Odocoileus_virginianus 0.2486 1.9540 -2.9263
## Cogon_Patch_Size-Canis_latrans 1.5966 2.3206 -1.0071
## Cogon_Patch_Size-Procyon_lotor -0.5997 1.0149 -2.6404
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3093 0.9971 -2.3716
## Veg_shannon_index-Odocoileus_virginianus 0.7359 1.2732 -2.0673
## Veg_shannon_index-Canis_latrans 1.4709 1.0411 -0.1617
## Veg_shannon_index-Procyon_lotor 1.2589 0.8102 -0.1145
## Veg_shannon_index-Dasypus_novemcinctus 0.5812 0.7176 -0.8440
## total_shrub_cover-Odocoileus_virginianus 0.1454 1.4992 -2.6718
## total_shrub_cover-Canis_latrans 0.9160 1.3374 -0.9208
## total_shrub_cover-Procyon_lotor -1.1808 0.8937 -3.1535
## total_shrub_cover-Dasypus_novemcinctus -0.0667 0.9541 -2.1144
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6895 1.9584 -3.0295
## Avg_Cogongrass_Cover-Canis_latrans 1.0079 1.6778 -1.9871
## Avg_Cogongrass_Cover-Procyon_lotor 0.4555 1.5556 -2.6376
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3641 1.6840 -1.4727
## Tree_Density-Odocoileus_virginianus 0.2031 3.1972 -3.9899
## Tree_Density-Canis_latrans -4.5913 3.0279 -12.7108
## Tree_Density-Procyon_lotor -2.1712 1.4469 -5.2446
## Tree_Density-Dasypus_novemcinctus -6.6684 4.3889 -17.8870
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7124 1.6799 -2.8341
## Avg_Canopy_Cover-Canis_latrans 0.0646 0.7711 -1.5902
## Avg_Canopy_Cover-Procyon_lotor 1.5429 0.9497 0.0007
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3568 1.2848 0.6171
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.7799 2.8456 -0.2968
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6193 1.7556 0.4108
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5499 1.8009 0.4183
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.8800 1.1435 0.0448
## avg_veg_height-Odocoileus_virginianus -0.0119 1.1560 -2.3512
## avg_veg_height-Canis_latrans -0.1991 0.8221 -1.8144
## avg_veg_height-Procyon_lotor 0.0451 0.8202 -1.5761
## avg_veg_height-Dasypus_novemcinctus 0.1668 0.7828 -1.3124
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.1536 23.7471 1.0096 279
## (Intercept)-Canis_latrans -0.7093 3.1682 1.0151 929
## (Intercept)-Procyon_lotor -0.2845 2.5178 1.0065 1156
## (Intercept)-Dasypus_novemcinctus -3.0583 -0.4263 1.0091 374
## Cogon_Patch_Size-Odocoileus_virginianus 0.0368 4.8582 1.0247 1021
## Cogon_Patch_Size-Canis_latrans 1.0991 7.1218 1.0555 622
## Cogon_Patch_Size-Procyon_lotor -0.5876 1.2662 1.0092 882
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3081 1.6739 1.0030 1439
## Veg_shannon_index-Odocoileus_virginianus 0.7854 3.1785 1.0026 1703
## Veg_shannon_index-Canis_latrans 1.3450 3.8978 1.0088 924
## Veg_shannon_index-Procyon_lotor 1.1871 3.0610 1.0028 571
## Veg_shannon_index-Dasypus_novemcinctus 0.5740 1.9901 1.0020 1894
## total_shrub_cover-Odocoileus_virginianus 0.0351 3.4737 1.0081 1490
## total_shrub_cover-Canis_latrans 0.6244 4.3802 1.0052 417
## total_shrub_cover-Procyon_lotor -1.1029 0.3424 1.0009 1354
## total_shrub_cover-Dasypus_novemcinctus 0.0117 1.4866 1.0140 536
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6951 4.4967 1.0107 911
## Avg_Cogongrass_Cover-Canis_latrans 0.9087 4.6261 1.0054 847
## Avg_Cogongrass_Cover-Procyon_lotor 0.4593 3.5532 1.0053 911
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1969 5.1155 1.0123 709
## Tree_Density-Odocoileus_virginianus -0.5044 8.4589 1.0121 717
## Tree_Density-Canis_latrans -3.8923 -0.9024 1.0287 355
## Tree_Density-Procyon_lotor -2.0959 0.3646 1.0019 1235
## Tree_Density-Dasypus_novemcinctus -5.4504 -1.5677 1.0169 264
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7624 4.1963 1.0007 1480
## Avg_Canopy_Cover-Canis_latrans 0.0969 1.4575 1.0148 2162
## Avg_Canopy_Cover-Procyon_lotor 1.4218 3.7411 1.0008 857
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0997 5.4779 1.0200 389
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.1617 10.8491 1.0683 288
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2693 7.3130 1.0209 507
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1741 7.9248 1.0048 352
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7470 4.4723 1.0098 786
## avg_veg_height-Odocoileus_virginianus -0.0040 2.2384 1.0023 1943
## avg_veg_height-Canis_latrans -0.2011 1.4378 1.0015 1545
## avg_veg_height-Procyon_lotor 0.0446 1.6525 1.0014 1728
## avg_veg_height-Dasypus_novemcinctus 0.1474 1.7636 1.0018 1557
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5527 0.0818 0.3944 0.5505 0.7153
## (Intercept)-Canis_latrans -2.5538 0.2071 -2.9718 -2.5497 -2.1738
## (Intercept)-Procyon_lotor -2.2050 0.1714 -2.5521 -2.2005 -1.8754
## (Intercept)-Dasypus_novemcinctus -1.5799 0.1857 -1.9577 -1.5737 -1.2319
## shrub_cover-Odocoileus_virginianus -0.0623 0.0685 -0.1969 -0.0629 0.0704
## shrub_cover-Canis_latrans -0.3871 0.2329 -0.8455 -0.3892 0.0691
## shrub_cover-Procyon_lotor 0.2454 0.1713 -0.0949 0.2503 0.5690
## shrub_cover-Dasypus_novemcinctus 0.8399 0.3332 0.2188 0.8311 1.5241
## veg_height-Odocoileus_virginianus -0.3395 0.0686 -0.4779 -0.3389 -0.2098
## veg_height-Canis_latrans -0.6352 0.1853 -1.0138 -0.6283 -0.2826
## veg_height-Procyon_lotor 0.3590 0.1270 0.1110 0.3591 0.6074
## veg_height-Dasypus_novemcinctus 0.2383 0.1370 -0.0213 0.2338 0.5081
## week-Odocoileus_virginianus 1.3446 0.1280 1.0961 1.3439 1.5940
## week-Canis_latrans 0.6429 0.2855 0.0947 0.6356 1.2313
## week-Procyon_lotor 0.2118 0.2249 -0.2322 0.2091 0.6527
## week-Dasypus_novemcinctus 0.1244 0.2418 -0.3512 0.1254 0.5994
## I(week^2)-Odocoileus_virginianus -0.5542 0.0524 -0.6579 -0.5539 -0.4532
## I(week^2)-Canis_latrans -0.2630 0.1164 -0.4992 -0.2613 -0.0345
## I(week^2)-Procyon_lotor -0.1332 0.0958 -0.3224 -0.1323 0.0549
## I(week^2)-Dasypus_novemcinctus -0.1833 0.1123 -0.4098 -0.1833 0.0281
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0034 2142
## (Intercept)-Procyon_lotor 1.0012 1883
## (Intercept)-Dasypus_novemcinctus 1.0023 2909
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0016 1609
## shrub_cover-Procyon_lotor 1.0047 1718
## shrub_cover-Dasypus_novemcinctus 1.0008 1190
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0060 2214
## veg_height-Procyon_lotor 1.0051 3691
## veg_height-Dasypus_novemcinctus 1.0008 4228
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0020 3377
## week-Procyon_lotor 1.0097 4311
## week-Dasypus_novemcinctus 1.0037 5250
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0009 3655
## I(week^2)-Procyon_lotor 1.0037 4308
## I(week^2)-Dasypus_novemcinctus 1.0044 4544
names(ms_fullQ_fullQ_T50)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T50$beta.samples)
## 'mcmc' num [1:5250, 1:36] 8.51 12.16 9.3 12.13 7.89 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:36] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T50$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.5104762
MCMCplot(ms_fullQ_fullQ_T50$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T50$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T50, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:5250, 1:4, 1:100] 0.993 1 1 1 1 ...
## $ z.0.samples : int [1:5250, 1:4, 1:100] 1 1 1 1 1 1 1 1 1 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T50, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:5250, 1:4, 1:100] 0.993 1 1 1 1 ...
psi.0.quants <- apply(out.pred$psi.0.samples, c(3), function(x) quantile(x, prob = c(0.025, 0.5, 0.975)))
str(psi.0.quants)
## num [1:3, 1:100] 0.000826 0.377067 1 0.000803 0.374438 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:3] "2.5%" "50%" "97.5%"
## ..$ : NULL
psi.plot.dat <- data.frame(
psi.med = psi.0.quants[2, ],
psi.low = psi.0.quants[1, ],
psi.high = psi.0.quants[3, ],
Avg_Cogongrass_Cover = cogon.pred.vals
)
str(psi.plot.dat)
## 'data.frame': 100 obs. of 4 variables:
## $ psi.med : num 0.377 0.374 0.383 0.38 0.375 ...
## $ psi.low : num 0.000826 0.000803 0.000847 0.000983 0.000849 ...
## $ psi.high : num 1 1 1 1 1 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T50) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 10000, n.report = 500, n.burn = 3000,
## n.thin = 4, n.chains = 3)
##
## Samples per Chain: 10000
## Burn-in: 3000
## Thinning Rate: 4
## Number of Chains: 3
## Total Posterior Samples: 5250
## Run Time (min): 1.0175
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2963 1.4400 -2.5656 0.2898 3.2058 1.0025 4833
## Cogon_Patch_Size 0.0918 0.9578 -1.7176 0.0597 2.1918 1.0020 1777
## Veg_shannon_index 0.8938 0.7715 -0.6406 0.8834 2.4256 1.0016 1758
## total_shrub_cover -0.0446 0.8584 -1.7448 -0.0735 1.7952 1.0060 2203
## Avg_Cogongrass_Cover 0.7252 1.2303 -1.6148 0.7175 3.2714 1.0082 868
## Tree_Density -1.4553 1.4779 -4.0240 -1.6008 1.7792 1.0049 1668
## Avg_Canopy_Cover 0.8975 0.8904 -0.9839 0.9208 2.6393 1.0019 2759
## I(Avg_Cogongrass_Cover^2) 1.8323 1.0400 -0.3242 1.7978 4.0071 1.0008 1409
## avg_veg_height 0.0015 0.7445 -1.4571 -0.0060 1.4525 1.0010 1548
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 71.7982 224.7332 2.6898 30.7669 352.2972 1.0779 2635
## Cogon_Patch_Size 5.9688 25.4527 0.0692 1.3331 37.0210 1.1756 1065
## Veg_shannon_index 2.3175 11.7528 0.0563 0.5629 14.1663 1.1668 2643
## total_shrub_cover 3.8959 11.7146 0.0791 1.3177 22.3317 1.0066 1681
## Avg_Cogongrass_Cover 3.8937 19.6395 0.0539 0.7700 24.8111 1.1259 3161
## Tree_Density 40.0216 110.2022 0.1274 10.1898 255.4171 1.0295 703
## Avg_Canopy_Cover 4.6776 14.4276 0.0942 1.6120 27.0347 1.0490 1872
## I(Avg_Cogongrass_Cover^2) 8.3446 38.9732 0.0566 0.7363 66.5801 1.0811 576
## avg_veg_height 1.1870 3.1470 0.0443 0.3984 7.7557 1.0198 3392
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.9008 7.0763 0.0552 0.8734 18.7874 1.0667 259
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1294 0.7920 -2.5672 -1.1827 0.6156 1.0008 5250
## shrub_cover 0.1500 0.4288 -0.7184 0.1418 1.0115 0.9998 4328
## veg_height -0.0851 0.3754 -0.8403 -0.0876 0.6716 1.0007 5250
## week 0.5487 0.4576 -0.4211 0.5636 1.4628 1.0008 5250
## I(week^2) -0.2756 0.2446 -0.7685 -0.2796 0.2136 1.0010 5809
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0055 7.3164 0.6045 2.2904 17.5966 1.1157 5250
## shrub_cover 0.8478 2.2359 0.0726 0.4097 4.1737 1.1322 5250
## veg_height 0.7093 1.8232 0.0790 0.3556 3.4518 1.0748 5046
## week 0.9514 2.2939 0.1061 0.4704 4.6635 1.0312 5250
## I(week^2) 0.2379 0.4265 0.0317 0.1288 1.1292 1.0062 5250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 10.1336 5.5042 2.3046
## (Intercept)-Canis_latrans -0.6943 1.8420 -4.4364
## (Intercept)-Procyon_lotor -0.2847 1.3599 -2.9523
## (Intercept)-Dasypus_novemcinctus -3.3875 1.9227 -8.0517
## Cogon_Patch_Size-Odocoileus_virginianus 0.2486 1.9540 -2.9263
## Cogon_Patch_Size-Canis_latrans 1.5966 2.3206 -1.0071
## Cogon_Patch_Size-Procyon_lotor -0.5997 1.0149 -2.6404
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3093 0.9971 -2.3716
## Veg_shannon_index-Odocoileus_virginianus 0.7359 1.2732 -2.0673
## Veg_shannon_index-Canis_latrans 1.4709 1.0411 -0.1617
## Veg_shannon_index-Procyon_lotor 1.2589 0.8102 -0.1145
## Veg_shannon_index-Dasypus_novemcinctus 0.5812 0.7176 -0.8440
## total_shrub_cover-Odocoileus_virginianus 0.1454 1.4992 -2.6718
## total_shrub_cover-Canis_latrans 0.9160 1.3374 -0.9208
## total_shrub_cover-Procyon_lotor -1.1808 0.8937 -3.1535
## total_shrub_cover-Dasypus_novemcinctus -0.0667 0.9541 -2.1144
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6895 1.9584 -3.0295
## Avg_Cogongrass_Cover-Canis_latrans 1.0079 1.6778 -1.9871
## Avg_Cogongrass_Cover-Procyon_lotor 0.4555 1.5556 -2.6376
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3641 1.6840 -1.4727
## Tree_Density-Odocoileus_virginianus 0.2031 3.1972 -3.9899
## Tree_Density-Canis_latrans -4.5913 3.0279 -12.7108
## Tree_Density-Procyon_lotor -2.1712 1.4469 -5.2446
## Tree_Density-Dasypus_novemcinctus -6.6684 4.3889 -17.8870
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7124 1.6799 -2.8341
## Avg_Canopy_Cover-Canis_latrans 0.0646 0.7711 -1.5902
## Avg_Canopy_Cover-Procyon_lotor 1.5429 0.9497 0.0007
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3568 1.2848 0.6171
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.7799 2.8456 -0.2968
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6193 1.7556 0.4108
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5499 1.8009 0.4183
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.8800 1.1435 0.0448
## avg_veg_height-Odocoileus_virginianus -0.0119 1.1560 -2.3512
## avg_veg_height-Canis_latrans -0.1991 0.8221 -1.8144
## avg_veg_height-Procyon_lotor 0.0451 0.8202 -1.5761
## avg_veg_height-Dasypus_novemcinctus 0.1668 0.7828 -1.3124
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 9.1536 23.7471 1.0096 279
## (Intercept)-Canis_latrans -0.7093 3.1682 1.0151 929
## (Intercept)-Procyon_lotor -0.2845 2.5178 1.0065 1156
## (Intercept)-Dasypus_novemcinctus -3.0583 -0.4263 1.0091 374
## Cogon_Patch_Size-Odocoileus_virginianus 0.0368 4.8582 1.0247 1021
## Cogon_Patch_Size-Canis_latrans 1.0991 7.1218 1.0555 622
## Cogon_Patch_Size-Procyon_lotor -0.5876 1.2662 1.0092 882
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3081 1.6739 1.0030 1439
## Veg_shannon_index-Odocoileus_virginianus 0.7854 3.1785 1.0026 1703
## Veg_shannon_index-Canis_latrans 1.3450 3.8978 1.0088 924
## Veg_shannon_index-Procyon_lotor 1.1871 3.0610 1.0028 571
## Veg_shannon_index-Dasypus_novemcinctus 0.5740 1.9901 1.0020 1894
## total_shrub_cover-Odocoileus_virginianus 0.0351 3.4737 1.0081 1490
## total_shrub_cover-Canis_latrans 0.6244 4.3802 1.0052 417
## total_shrub_cover-Procyon_lotor -1.1029 0.3424 1.0009 1354
## total_shrub_cover-Dasypus_novemcinctus 0.0117 1.4866 1.0140 536
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.6951 4.4967 1.0107 911
## Avg_Cogongrass_Cover-Canis_latrans 0.9087 4.6261 1.0054 847
## Avg_Cogongrass_Cover-Procyon_lotor 0.4593 3.5532 1.0053 911
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1969 5.1155 1.0123 709
## Tree_Density-Odocoileus_virginianus -0.5044 8.4589 1.0121 717
## Tree_Density-Canis_latrans -3.8923 -0.9024 1.0287 355
## Tree_Density-Procyon_lotor -2.0959 0.3646 1.0019 1235
## Tree_Density-Dasypus_novemcinctus -5.4504 -1.5677 1.0169 264
## Avg_Canopy_Cover-Odocoileus_virginianus 0.7624 4.1963 1.0007 1480
## Avg_Canopy_Cover-Canis_latrans 0.0969 1.4575 1.0148 2162
## Avg_Canopy_Cover-Procyon_lotor 1.4218 3.7411 1.0008 857
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0997 5.4779 1.0200 389
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 2.1617 10.8491 1.0683 288
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2693 7.3130 1.0209 507
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1741 7.9248 1.0048 352
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.7470 4.4723 1.0098 786
## avg_veg_height-Odocoileus_virginianus -0.0040 2.2384 1.0023 1943
## avg_veg_height-Canis_latrans -0.2011 1.4378 1.0015 1545
## avg_veg_height-Procyon_lotor 0.0446 1.6525 1.0014 1728
## avg_veg_height-Dasypus_novemcinctus 0.1474 1.7636 1.0018 1557
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5527 0.0818 0.3944 0.5505 0.7153
## (Intercept)-Canis_latrans -2.5538 0.2071 -2.9718 -2.5497 -2.1738
## (Intercept)-Procyon_lotor -2.2050 0.1714 -2.5521 -2.2005 -1.8754
## (Intercept)-Dasypus_novemcinctus -1.5799 0.1857 -1.9577 -1.5737 -1.2319
## shrub_cover-Odocoileus_virginianus -0.0623 0.0685 -0.1969 -0.0629 0.0704
## shrub_cover-Canis_latrans -0.3871 0.2329 -0.8455 -0.3892 0.0691
## shrub_cover-Procyon_lotor 0.2454 0.1713 -0.0949 0.2503 0.5690
## shrub_cover-Dasypus_novemcinctus 0.8399 0.3332 0.2188 0.8311 1.5241
## veg_height-Odocoileus_virginianus -0.3395 0.0686 -0.4779 -0.3389 -0.2098
## veg_height-Canis_latrans -0.6352 0.1853 -1.0138 -0.6283 -0.2826
## veg_height-Procyon_lotor 0.3590 0.1270 0.1110 0.3591 0.6074
## veg_height-Dasypus_novemcinctus 0.2383 0.1370 -0.0213 0.2338 0.5081
## week-Odocoileus_virginianus 1.3446 0.1280 1.0961 1.3439 1.5940
## week-Canis_latrans 0.6429 0.2855 0.0947 0.6356 1.2313
## week-Procyon_lotor 0.2118 0.2249 -0.2322 0.2091 0.6527
## week-Dasypus_novemcinctus 0.1244 0.2418 -0.3512 0.1254 0.5994
## I(week^2)-Odocoileus_virginianus -0.5542 0.0524 -0.6579 -0.5539 -0.4532
## I(week^2)-Canis_latrans -0.2630 0.1164 -0.4992 -0.2613 -0.0345
## I(week^2)-Procyon_lotor -0.1332 0.0958 -0.3224 -0.1323 0.0549
## I(week^2)-Dasypus_novemcinctus -0.1833 0.1123 -0.4098 -0.1833 0.0281
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0034 2142
## (Intercept)-Procyon_lotor 1.0012 1883
## (Intercept)-Dasypus_novemcinctus 1.0023 2909
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0016 1609
## shrub_cover-Procyon_lotor 1.0047 1718
## shrub_cover-Dasypus_novemcinctus 1.0008 1190
## veg_height-Odocoileus_virginianus 1.0001 5250
## veg_height-Canis_latrans 1.0060 2214
## veg_height-Procyon_lotor 1.0051 3691
## veg_height-Dasypus_novemcinctus 1.0008 4228
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0020 3377
## week-Procyon_lotor 1.0097 4311
## week-Dasypus_novemcinctus 1.0037 5250
## I(week^2)-Odocoileus_virginianus 1.0013 5250
## I(week^2)-Canis_latrans 1.0009 3655
## I(week^2)-Procyon_lotor 1.0037 4308
## I(week^2)-Dasypus_novemcinctus 1.0044 4544
names(ms_fullQ_fullQ_T50)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T50$beta.samples)
## 'mcmc' num [1:5250, 1:36] 8.51 12.16 9.3 12.13 7.89 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:36] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T50$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.5104762
MCMCplot(ms_fullQ_fullQ_T50$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T50$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T50$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T50$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}