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): 1.3807
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2129 0.5435 -1.2206 -0.2365 0.9341 1.0003 3229
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2431 2.501 0.8261 2.5638 9.6033 1.0053 1756
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4633 0.4057 -3.2556 -2.4697 -1.6209 1.0038 3942
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.766 1.1217 0.6061 1.4924 4.511 1.0032 2678
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4579 1.1436 1.7568 3.2688 6.2707
## (Intercept)-Canis_latrans 0.3173 0.4154 -0.4507 0.3037 1.1631
## (Intercept)-Sciurus_niger -0.6008 1.1542 -2.1353 -0.7899 2.2796
## (Intercept)-Procyon_lotor 0.7101 0.4085 -0.0638 0.6965 1.5539
## (Intercept)-Dasypus_novemcinctus -0.6429 0.3657 -1.3781 -0.6426 0.0677
## (Intercept)-Lynx_rufus 0.3712 0.9210 -0.8871 0.1893 2.7081
## (Intercept)-Didelphis_virginiana -1.3612 0.4475 -2.3001 -1.3356 -0.5303
## (Intercept)-Sylvilagus_floridanus -0.2888 0.5829 -1.2518 -0.3416 0.9927
## (Intercept)-Sciurus_carolinensis -1.3478 0.4495 -2.3078 -1.3341 -0.5163
## (Intercept)-Vulpes_vulpes -1.1884 1.0187 -2.8448 -1.3183 1.3786
## (Intercept)-Sus_scrofa -1.8703 0.6270 -3.1587 -1.8541 -0.6560
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0172 1660
## (Intercept)-Canis_latrans 1.0011 4712
## (Intercept)-Sciurus_niger 1.0287 371
## (Intercept)-Procyon_lotor 0.9999 5250
## (Intercept)-Dasypus_novemcinctus 0.9998 5250
## (Intercept)-Lynx_rufus 1.0021 797
## (Intercept)-Didelphis_virginiana 1.0043 5466
## (Intercept)-Sylvilagus_floridanus 1.0119 1505
## (Intercept)-Sciurus_carolinensis 1.0029 4807
## (Intercept)-Vulpes_vulpes 1.0051 436
## (Intercept)-Sus_scrofa 1.0000 3069
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0050 0.0594 -0.1118 0.0049 0.1183
## (Intercept)-Canis_latrans -2.6122 0.1688 -2.9472 -2.6104 -2.2899
## (Intercept)-Sciurus_niger -3.8524 0.5887 -5.0736 -3.8083 -2.8192
## (Intercept)-Procyon_lotor -2.2625 0.1297 -2.5265 -2.2601 -2.0187
## (Intercept)-Dasypus_novemcinctus -1.5749 0.1329 -1.8363 -1.5742 -1.3139
## (Intercept)-Lynx_rufus -3.5823 0.3460 -4.2660 -3.5766 -2.9293
## (Intercept)-Didelphis_virginiana -2.3095 0.2480 -2.8225 -2.2937 -1.8461
## (Intercept)-Sylvilagus_floridanus -3.1947 0.3139 -3.8596 -3.1772 -2.6319
## (Intercept)-Sciurus_carolinensis -2.4311 0.2597 -2.9744 -2.4217 -1.9600
## (Intercept)-Vulpes_vulpes -3.9127 0.7291 -5.5065 -3.8422 -2.6686
## (Intercept)-Sus_scrofa -2.9403 0.4789 -3.9721 -2.9091 -2.1002
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0027 4987
## (Intercept)-Canis_latrans 1.0017 3118
## (Intercept)-Sciurus_niger 1.0378 504
## (Intercept)-Procyon_lotor 1.0014 4086
## (Intercept)-Dasypus_novemcinctus 1.0040 5250
## (Intercept)-Lynx_rufus 1.0057 880
## (Intercept)-Didelphis_virginiana 1.0011 4016
## (Intercept)-Sylvilagus_floridanus 1.0036 1446
## (Intercept)-Sciurus_carolinensis 1.0002 3635
## (Intercept)-Vulpes_vulpes 1.0026 393
## (Intercept)-Sus_scrofa 1.0000 1866
# 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): 2.0597
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0240 1.0734 -2.0382 -0.0600 2.2106 1.0036 1444
## Cogon_Patch_Size -0.7757 0.6734 -2.1950 -0.7467 0.5150 1.0020 851
## Veg_shannon_index 0.8745 0.4981 -0.0891 0.8598 1.8748 1.0044 765
## total_shrub_cover -0.3138 0.4762 -1.2713 -0.3078 0.6141 1.0382 924
## Avg_Cogongrass_Cover 2.0838 0.6904 0.8073 2.0524 3.5278 1.0039 580
## Tree_Density -1.8910 0.7222 -3.3392 -1.8751 -0.4716 1.0042 814
## Avg_Canopy_Cover 1.9221 0.6271 0.7725 1.8857 3.2628 1.0016 946
## avg_veg_height -0.5087 0.4647 -1.4423 -0.5110 0.4319 1.0057 813
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.7075 19.9250 3.6463 14.9457 74.1948 1.0014 325
## Cogon_Patch_Size 2.7643 4.5964 0.1115 1.4668 13.4281 1.0333 860
## Veg_shannon_index 0.9771 1.4050 0.0532 0.5006 4.8541 1.0050 918
## total_shrub_cover 0.8327 1.3341 0.0556 0.4366 3.9360 1.1081 305
## Avg_Cogongrass_Cover 1.0164 1.7105 0.0514 0.4540 5.4807 1.0068 942
## Tree_Density 3.0687 5.2880 0.0765 1.2845 17.2145 1.0076 464
## Avg_Canopy_Cover 2.3229 3.1400 0.1409 1.4369 9.7640 1.0084 416
## avg_veg_height 0.3866 0.5251 0.0397 0.2266 1.7122 1.0082 2212
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9453 3.209 0.0665 0.9936 8.3258 1.1144 190
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6683 0.4607 -3.5571 -2.6737 -1.7192 1.0077 3492
## shrub_cover 0.2706 0.2519 -0.2131 0.2633 0.7793 1.0126 2230
## veg_height 0.0015 0.1549 -0.3067 0.0046 0.3152 1.0013 3175
## week -0.0401 0.1196 -0.2885 -0.0344 0.1849 1.0054 2777
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3559 1.4722 0.8181 1.9939 5.8929 1.0296 2946
## shrub_cover 0.4861 0.3855 0.0981 0.3855 1.4816 1.0029 1468
## veg_height 0.1982 0.1409 0.0569 0.1620 0.5648 1.0062 3170
## week 0.0987 0.0872 0.0263 0.0773 0.2880 1.0181 2400
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.5499 3.8353 3.3088 7.7708
## (Intercept)-Canis_latrans 0.9575 1.1612 -0.9271 0.8301
## (Intercept)-Sciurus_niger 1.9650 2.7571 -2.2282 1.5758
## (Intercept)-Procyon_lotor 0.9004 1.0465 -1.3086 0.9186
## (Intercept)-Dasypus_novemcinctus -1.4105 1.0390 -3.6559 -1.3366
## (Intercept)-Lynx_rufus 2.8292 3.2328 -1.9033 2.2578
## (Intercept)-Didelphis_virginiana -2.8755 1.2307 -5.6630 -2.7651
## (Intercept)-Sylvilagus_floridanus -1.2034 1.3228 -3.9783 -1.1775
## (Intercept)-Sciurus_carolinensis -3.1008 1.3537 -6.1122 -2.9642
## (Intercept)-Vulpes_vulpes -1.9101 2.5318 -5.8486 -2.2305
## (Intercept)-Sus_scrofa -4.3211 1.9670 -8.6194 -4.1653
## Cogon_Patch_Size-Odocoileus_virginianus -0.6287 1.2947 -3.1433 -0.6621
## Cogon_Patch_Size-Canis_latrans 0.6101 1.2074 -1.0991 0.3962
## Cogon_Patch_Size-Sciurus_niger -1.2761 1.7534 -5.2670 -1.0937
## Cogon_Patch_Size-Procyon_lotor -1.0674 0.7220 -2.5604 -1.0423
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6266 0.7385 -2.0698 -0.6414
## Cogon_Patch_Size-Lynx_rufus -0.8375 1.3863 -3.5478 -0.8536
## Cogon_Patch_Size-Didelphis_virginiana 0.7096 0.9092 -0.7783 0.6110
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9584 1.5558 -5.7641 -1.6529
## Cogon_Patch_Size-Sciurus_carolinensis -1.6881 1.3730 -5.2007 -1.4191
## Cogon_Patch_Size-Vulpes_vulpes -1.2877 1.5972 -5.0168 -1.0888
## Cogon_Patch_Size-Sus_scrofa -1.3164 1.4270 -4.7241 -1.0936
## Veg_shannon_index-Odocoileus_virginianus 0.7121 0.9375 -1.3669 0.7584
## Veg_shannon_index-Canis_latrans 1.2959 0.6918 0.0920 1.2413
## Veg_shannon_index-Sciurus_niger 1.0436 1.1238 -1.1566 0.9728
## Veg_shannon_index-Procyon_lotor 1.2209 0.6361 0.1429 1.1811
## Veg_shannon_index-Dasypus_novemcinctus 0.6005 0.5615 -0.5593 0.6046
## Veg_shannon_index-Lynx_rufus 0.8360 0.9583 -1.2712 0.8572
## Veg_shannon_index-Didelphis_virginiana 1.1108 0.7085 -0.1902 1.0640
## Veg_shannon_index-Sylvilagus_floridanus 1.0472 0.7335 -0.2692 0.9930
## Veg_shannon_index-Sciurus_carolinensis 0.1457 0.8070 -1.6671 0.2288
## Veg_shannon_index-Vulpes_vulpes 0.3240 0.9630 -1.8962 0.4204
## Veg_shannon_index-Sus_scrofa 1.6020 1.0427 0.0693 1.4200
## total_shrub_cover-Odocoileus_virginianus -0.0920 0.8568 -1.6936 -0.1378
## total_shrub_cover-Canis_latrans 0.3633 0.7981 -0.8482 0.2629
## total_shrub_cover-Sciurus_niger -0.4525 0.9573 -2.4728 -0.4016
## total_shrub_cover-Procyon_lotor -0.8367 0.5981 -2.1147 -0.7956
## total_shrub_cover-Dasypus_novemcinctus -0.1211 0.6161 -1.4218 -0.1086
## total_shrub_cover-Lynx_rufus -0.4778 1.0313 -2.7179 -0.4335
## total_shrub_cover-Didelphis_virginiana -0.5884 0.7374 -2.2466 -0.5231
## total_shrub_cover-Sylvilagus_floridanus -0.4109 0.8026 -2.1702 -0.3786
## total_shrub_cover-Sciurus_carolinensis -0.2958 0.7270 -1.8908 -0.2846
## total_shrub_cover-Vulpes_vulpes -0.5276 0.9627 -2.6653 -0.4536
## total_shrub_cover-Sus_scrofa -0.1224 0.8601 -1.7890 -0.1370
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0217 1.0797 -0.1208 2.0077
## Avg_Cogongrass_Cover-Canis_latrans 2.4569 0.9485 0.8853 2.3622
## Avg_Cogongrass_Cover-Sciurus_niger 1.6713 1.3014 -1.5437 1.7856
## Avg_Cogongrass_Cover-Procyon_lotor 2.2625 0.8443 0.7395 2.2093
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5964 0.9713 0.9910 2.4946
## Avg_Cogongrass_Cover-Lynx_rufus 2.3642 1.0092 0.6179 2.2744
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1624 0.8681 0.5834 2.1160
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5379 0.9728 -0.4667 1.5701
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3490 0.9379 0.7548 2.2746
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4783 1.0295 0.7676 2.3824
## Avg_Cogongrass_Cover-Sus_scrofa 1.7052 1.1642 -0.9408 1.7617
## Tree_Density-Odocoileus_virginianus -0.8889 1.3112 -2.9669 -1.0545
## Tree_Density-Canis_latrans -2.6943 1.3321 -5.9375 -2.4784
## Tree_Density-Sciurus_niger -2.0369 1.6757 -5.7149 -1.9548
## Tree_Density-Procyon_lotor -1.5332 0.7748 -2.9879 -1.5466
## Tree_Density-Dasypus_novemcinctus -3.5250 1.8211 -8.2016 -3.0963
## Tree_Density-Lynx_rufus -0.7796 1.5278 -3.1533 -0.9989
## Tree_Density-Didelphis_virginiana -2.2534 1.1905 -5.0646 -2.0998
## Tree_Density-Sylvilagus_floridanus -2.3872 1.3601 -5.4951 -2.2061
## Tree_Density-Sciurus_carolinensis -2.4688 1.3974 -5.8411 -2.2727
## Tree_Density-Vulpes_vulpes -1.9397 1.6182 -5.2844 -1.8843
## Tree_Density-Sus_scrofa -2.3724 1.5632 -6.2745 -2.1648
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3919 1.3338 -1.3441 1.4301
## Avg_Canopy_Cover-Canis_latrans 0.3320 0.6996 -1.0209 0.3150
## Avg_Canopy_Cover-Sciurus_niger 2.1240 1.6973 -0.9845 2.0304
## Avg_Canopy_Cover-Procyon_lotor 1.7803 0.7411 0.4671 1.7386
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1121 0.7433 0.8721 2.0496
## Avg_Canopy_Cover-Lynx_rufus 1.6329 1.4740 -1.1377 1.5833
## Avg_Canopy_Cover-Didelphis_virginiana 2.9165 1.1843 1.2951 2.6934
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3645 1.5210 1.2752 3.0827
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6203 1.0681 1.0567 2.4419
## Avg_Canopy_Cover-Vulpes_vulpes 2.3006 1.2223 0.3573 2.1239
## Avg_Canopy_Cover-Sus_scrofa 2.1797 0.9546 0.6122 2.0738
## avg_veg_height-Odocoileus_virginianus -0.5548 0.7196 -2.0859 -0.5286
## avg_veg_height-Canis_latrans -0.5483 0.5976 -1.7432 -0.5509
## avg_veg_height-Sciurus_niger -0.6302 0.7667 -2.2329 -0.6138
## avg_veg_height-Procyon_lotor -0.4384 0.5699 -1.5653 -0.4375
## avg_veg_height-Dasypus_novemcinctus -0.2990 0.5650 -1.3783 -0.3131
## avg_veg_height-Lynx_rufus -0.5965 0.7488 -2.1269 -0.5771
## avg_veg_height-Didelphis_virginiana -0.6449 0.6463 -2.0019 -0.6180
## avg_veg_height-Sylvilagus_floridanus -0.7009 0.6614 -2.0934 -0.6737
## avg_veg_height-Sciurus_carolinensis -0.2292 0.6287 -1.3856 -0.2691
## avg_veg_height-Vulpes_vulpes -0.5124 0.6960 -1.9099 -0.5216
## avg_veg_height-Sus_scrofa -0.5632 0.6796 -1.9471 -0.5537
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 18.2847 1.0031 256
## (Intercept)-Canis_latrans 3.5809 1.0205 693
## (Intercept)-Sciurus_niger 8.5414 1.0582 225
## (Intercept)-Procyon_lotor 2.9919 1.0047 1380
## (Intercept)-Dasypus_novemcinctus 0.4259 1.0047 1021
## (Intercept)-Lynx_rufus 10.8942 1.0123 198
## (Intercept)-Didelphis_virginiana -0.7324 1.0047 1108
## (Intercept)-Sylvilagus_floridanus 1.3752 1.0026 996
## (Intercept)-Sciurus_carolinensis -0.8309 1.0097 601
## (Intercept)-Vulpes_vulpes 4.1368 1.1181 225
## (Intercept)-Sus_scrofa -1.0300 1.0127 567
## Cogon_Patch_Size-Odocoileus_virginianus 2.1882 1.0092 2022
## Cogon_Patch_Size-Canis_latrans 3.6018 1.0031 1182
## Cogon_Patch_Size-Sciurus_niger 1.9181 1.0197 577
## Cogon_Patch_Size-Procyon_lotor 0.2846 1.0006 608
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9311 1.0028 1416
## Cogon_Patch_Size-Lynx_rufus 2.1033 1.0029 936
## Cogon_Patch_Size-Didelphis_virginiana 2.8150 1.0026 1067
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2102 1.0038 812
## Cogon_Patch_Size-Sciurus_carolinensis 0.2142 1.0031 775
## Cogon_Patch_Size-Vulpes_vulpes 1.5877 1.0158 705
## Cogon_Patch_Size-Sus_scrofa 0.8899 1.0032 1093
## Veg_shannon_index-Odocoileus_virginianus 2.5283 1.0029 1757
## Veg_shannon_index-Canis_latrans 2.8274 1.0023 1182
## Veg_shannon_index-Sciurus_niger 3.6686 1.0054 928
## Veg_shannon_index-Procyon_lotor 2.6641 1.0035 596
## Veg_shannon_index-Dasypus_novemcinctus 1.6853 1.0048 1542
## Veg_shannon_index-Lynx_rufus 2.7060 1.0080 1106
## Veg_shannon_index-Didelphis_virginiana 2.6985 1.0099 1562
## Veg_shannon_index-Sylvilagus_floridanus 2.6611 1.0040 1039
## Veg_shannon_index-Sciurus_carolinensis 1.5030 1.0061 1372
## Veg_shannon_index-Vulpes_vulpes 1.9298 1.0082 773
## Veg_shannon_index-Sus_scrofa 4.1914 1.0063 1043
## total_shrub_cover-Odocoileus_virginianus 1.7294 1.0207 1769
## total_shrub_cover-Canis_latrans 2.1966 1.0230 793
## total_shrub_cover-Sciurus_niger 1.3516 1.0171 1211
## total_shrub_cover-Procyon_lotor 0.2281 1.0075 1777
## total_shrub_cover-Dasypus_novemcinctus 1.0398 1.0192 1910
## total_shrub_cover-Lynx_rufus 1.5675 1.0599 683
## total_shrub_cover-Didelphis_virginiana 0.7044 1.0322 1785
## total_shrub_cover-Sylvilagus_floridanus 1.0950 1.0136 1376
## total_shrub_cover-Sciurus_carolinensis 1.1339 1.0133 1770
## total_shrub_cover-Vulpes_vulpes 1.1662 1.0289 974
## total_shrub_cover-Sus_scrofa 1.5774 1.0170 1744
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.2221 1.0030 1213
## Avg_Cogongrass_Cover-Canis_latrans 4.6229 1.0091 819
## Avg_Cogongrass_Cover-Sciurus_niger 3.8664 1.0040 718
## Avg_Cogongrass_Cover-Procyon_lotor 4.0459 1.0033 826
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8225 1.0051 640
## Avg_Cogongrass_Cover-Lynx_rufus 4.5979 1.0065 853
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0175 1.0057 844
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4161 1.0023 958
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4073 1.0066 750
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.8198 1.0085 648
## Avg_Cogongrass_Cover-Sus_scrofa 3.8149 1.0038 851
## Tree_Density-Odocoileus_virginianus 2.1600 1.0005 1110
## Tree_Density-Canis_latrans -0.7608 1.0092 718
## Tree_Density-Sciurus_niger 1.2700 1.0150 591
## Tree_Density-Procyon_lotor 0.0627 1.0024 1622
## Tree_Density-Dasypus_novemcinctus -1.2098 1.0231 449
## Tree_Density-Lynx_rufus 2.9295 1.0051 525
## Tree_Density-Didelphis_virginiana -0.2189 1.0089 918
## Tree_Density-Sylvilagus_floridanus -0.0925 1.0032 1154
## Tree_Density-Sciurus_carolinensis -0.2518 1.0072 936
## Tree_Density-Vulpes_vulpes 1.0922 1.0156 652
## Tree_Density-Sus_scrofa 0.1635 1.0037 930
## Avg_Canopy_Cover-Odocoileus_virginianus 4.1207 1.0012 1769
## Avg_Canopy_Cover-Canis_latrans 1.7284 1.0022 1626
## Avg_Canopy_Cover-Sciurus_niger 5.8041 1.0077 706
## Avg_Canopy_Cover-Procyon_lotor 3.3784 1.0046 1416
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7771 1.0104 844
## Avg_Canopy_Cover-Lynx_rufus 4.9548 1.0059 712
## Avg_Canopy_Cover-Didelphis_virginiana 5.7385 1.0105 427
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.0876 1.0242 387
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2055 1.0168 585
## Avg_Canopy_Cover-Vulpes_vulpes 5.2094 1.0072 805
## Avg_Canopy_Cover-Sus_scrofa 4.3458 1.0056 1324
## avg_veg_height-Odocoileus_virginianus 0.7924 1.0044 1681
## avg_veg_height-Canis_latrans 0.6193 1.0047 1129
## avg_veg_height-Sciurus_niger 0.8129 1.0065 1251
## avg_veg_height-Procyon_lotor 0.6985 1.0021 1201
## avg_veg_height-Dasypus_novemcinctus 0.8590 1.0011 1403
## avg_veg_height-Lynx_rufus 0.8516 1.0006 1382
## avg_veg_height-Didelphis_virginiana 0.6020 1.0007 1342
## avg_veg_height-Sylvilagus_floridanus 0.5392 1.0017 1191
## avg_veg_height-Sciurus_carolinensis 1.0756 1.0047 1454
## avg_veg_height-Vulpes_vulpes 0.8739 1.0010 1164
## avg_veg_height-Sus_scrofa 0.7617 1.0174 1408
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0080 0.0594 -0.1065 0.0084 0.1250
## (Intercept)-Canis_latrans -2.7568 0.1851 -3.1298 -2.7550 -2.4143
## (Intercept)-Sciurus_niger -4.7680 0.5027 -5.7119 -4.7785 -3.7465
## (Intercept)-Procyon_lotor -2.2981 0.1430 -2.5914 -2.2928 -2.0305
## (Intercept)-Dasypus_novemcinctus -1.7427 0.1600 -2.0566 -1.7344 -1.4460
## (Intercept)-Lynx_rufus -3.9713 0.3852 -4.6908 -3.9883 -3.1747
## (Intercept)-Didelphis_virginiana -2.5368 0.2813 -3.1362 -2.5242 -2.0228
## (Intercept)-Sylvilagus_floridanus -3.1963 0.2744 -3.7418 -3.1874 -2.6895
## (Intercept)-Sciurus_carolinensis -2.6593 0.3281 -3.3238 -2.6422 -2.0650
## (Intercept)-Vulpes_vulpes -4.3250 0.7058 -5.7767 -4.2932 -3.0624
## (Intercept)-Sus_scrofa -3.2867 0.6087 -4.5519 -3.2652 -2.1389
## shrub_cover-Odocoileus_virginianus -0.0521 0.0648 -0.1781 -0.0516 0.0740
## shrub_cover-Canis_latrans -0.3248 0.2210 -0.7505 -0.3242 0.1133
## shrub_cover-Sciurus_niger -0.3435 0.4364 -1.2652 -0.3262 0.4832
## shrub_cover-Procyon_lotor 0.2611 0.1607 -0.0640 0.2643 0.5661
## shrub_cover-Dasypus_novemcinctus 0.8532 0.2977 0.2811 0.8467 1.4362
## shrub_cover-Lynx_rufus -0.2188 0.3570 -0.9129 -0.2235 0.5033
## shrub_cover-Didelphis_virginiana 0.8994 0.3576 0.2562 0.8827 1.6439
## shrub_cover-Sylvilagus_floridanus 0.4187 0.3906 -0.3317 0.4131 1.1866
## shrub_cover-Sciurus_carolinensis 0.8444 0.4086 0.0740 0.8291 1.6703
## shrub_cover-Vulpes_vulpes 0.1015 0.5497 -1.0771 0.1200 1.1894
## shrub_cover-Sus_scrofa 0.5752 0.7385 -0.8659 0.5465 2.1215
## veg_height-Odocoileus_virginianus -0.2978 0.0644 -0.4227 -0.2970 -0.1729
## veg_height-Canis_latrans -0.5839 0.1803 -0.9576 -0.5805 -0.2453
## veg_height-Sciurus_niger -0.0758 0.3381 -0.7347 -0.0811 0.6148
## veg_height-Procyon_lotor 0.3407 0.1237 0.0985 0.3418 0.5854
## veg_height-Dasypus_novemcinctus 0.2386 0.1329 -0.0192 0.2381 0.4991
## veg_height-Lynx_rufus 0.0916 0.2382 -0.3899 0.0909 0.5544
## veg_height-Didelphis_virginiana 0.4199 0.2327 -0.0169 0.4124 0.8942
## veg_height-Sylvilagus_floridanus 0.1542 0.2419 -0.3307 0.1576 0.6242
## veg_height-Sciurus_carolinensis 0.0864 0.2086 -0.3149 0.0805 0.5106
## veg_height-Vulpes_vulpes -0.1893 0.3270 -0.8828 -0.1758 0.4117
## veg_height-Sus_scrofa -0.1580 0.3240 -0.8297 -0.1454 0.4386
## week-Odocoileus_virginianus 0.2104 0.0614 0.0925 0.2099 0.3309
## week-Canis_latrans 0.0743 0.1315 -0.1913 0.0776 0.3240
## week-Sciurus_niger -0.2881 0.2971 -0.9361 -0.2600 0.2125
## week-Procyon_lotor -0.0460 0.1174 -0.2852 -0.0442 0.1770
## week-Dasypus_novemcinctus -0.1588 0.1359 -0.4436 -0.1530 0.0911
## week-Lynx_rufus -0.0279 0.1913 -0.4282 -0.0208 0.3226
## week-Didelphis_virginiana -0.1959 0.2163 -0.6624 -0.1796 0.1776
## week-Sylvilagus_floridanus -0.1391 0.1998 -0.5625 -0.1295 0.2203
## week-Sciurus_carolinensis 0.1457 0.1772 -0.2102 0.1509 0.4794
## week-Vulpes_vulpes -0.1065 0.2772 -0.7085 -0.0888 0.3911
## week-Sus_scrofa 0.1054 0.2324 -0.3575 0.1060 0.5577
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0024 2151
## (Intercept)-Sciurus_niger 1.0237 476
## (Intercept)-Procyon_lotor 1.0005 3337
## (Intercept)-Dasypus_novemcinctus 1.0053 3282
## (Intercept)-Lynx_rufus 1.0388 365
## (Intercept)-Didelphis_virginiana 1.0084 2152
## (Intercept)-Sylvilagus_floridanus 1.0084 2097
## (Intercept)-Sciurus_carolinensis 1.0120 1627
## (Intercept)-Vulpes_vulpes 1.1021 368
## (Intercept)-Sus_scrofa 1.0065 1013
## shrub_cover-Odocoileus_virginianus 0.9998 5250
## shrub_cover-Canis_latrans 1.0072 1877
## shrub_cover-Sciurus_niger 1.0049 924
## shrub_cover-Procyon_lotor 0.9999 3330
## shrub_cover-Dasypus_novemcinctus 1.0110 2647
## shrub_cover-Lynx_rufus 1.0248 712
## shrub_cover-Didelphis_virginiana 1.0019 2026
## shrub_cover-Sylvilagus_floridanus 1.0171 1331
## shrub_cover-Sciurus_carolinensis 1.0218 1567
## shrub_cover-Vulpes_vulpes 1.0007 1336
## shrub_cover-Sus_scrofa 1.0063 1355
## veg_height-Odocoileus_virginianus 1.0023 5931
## veg_height-Canis_latrans 1.0066 2228
## veg_height-Sciurus_niger 1.0002 1454
## veg_height-Procyon_lotor 1.0059 3985
## veg_height-Dasypus_novemcinctus 1.0045 4636
## veg_height-Lynx_rufus 1.0028 1513
## veg_height-Didelphis_virginiana 1.0016 3774
## veg_height-Sylvilagus_floridanus 1.0040 3072
## veg_height-Sciurus_carolinensis 1.0052 3193
## veg_height-Vulpes_vulpes 1.0020 1734
## veg_height-Sus_scrofa 1.0009 2808
## week-Odocoileus_virginianus 0.9998 5208
## week-Canis_latrans 1.0002 4369
## week-Sciurus_niger 1.0008 1167
## week-Procyon_lotor 1.0015 4442
## week-Dasypus_novemcinctus 1.0033 4724
## week-Lynx_rufus 1.0001 2657
## week-Didelphis_virginiana 1.0021 3282
## week-Sylvilagus_floridanus 1.0003 2932
## week-Sciurus_carolinensis 1.0018 4547
## week-Vulpes_vulpes 1.0041 2500
## week-Sus_scrofa 1.0048 4292
#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): 1.8655
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1274 0.5512 -1.1975 -0.1455 1.003 1.0007 2421
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1772 2.5381 0.7658 2.5028 9.8485 1.0245 1351
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5881 0.4180 -3.3975 -2.5920 -1.7210 1.0050 4354
## shrub_cover 0.2114 0.2393 -0.2706 0.2078 0.6882 1.0009 3459
## veg_height -0.0020 0.1533 -0.3009 -0.0036 0.3016 1.0010 3733
## week -0.0374 0.1188 -0.2833 -0.0306 0.1859 1.0026 3409
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9964 1.3675 0.6738 1.6583 5.1271 1.0102 2807
## shrub_cover 0.4532 0.3894 0.0861 0.3551 1.3818 1.0030 2065
## veg_height 0.1874 0.1314 0.0546 0.1526 0.5251 1.0008 4157
## week 0.0972 0.0738 0.0264 0.0774 0.2920 1.0083 3071
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4536 1.2203 1.7612 3.2248 6.6455
## (Intercept)-Canis_latrans 0.3952 0.4179 -0.3837 0.3837 1.2527
## (Intercept)-Sciurus_niger -0.4669 1.1796 -1.9819 -0.6728 2.4701
## (Intercept)-Procyon_lotor 0.7431 0.4045 -0.0097 0.7261 1.6045
## (Intercept)-Dasypus_novemcinctus -0.5866 0.3829 -1.3230 -0.5796 0.1646
## (Intercept)-Lynx_rufus 0.5394 0.9046 -0.7248 0.3934 2.7032
## (Intercept)-Didelphis_virginiana -1.2266 0.4700 -2.1931 -1.2163 -0.3644
## (Intercept)-Sylvilagus_floridanus -0.3232 0.5241 -1.2235 -0.3564 0.7564
## (Intercept)-Sciurus_carolinensis -1.2131 0.4647 -2.1645 -1.2049 -0.3265
## (Intercept)-Vulpes_vulpes -1.0428 1.1172 -2.7858 -1.1866 1.6454
## (Intercept)-Sus_scrofa -1.6781 0.6651 -3.0128 -1.6719 -0.3928
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0262 1254
## (Intercept)-Canis_latrans 1.0009 4221
## (Intercept)-Sciurus_niger 1.0514 282
## (Intercept)-Procyon_lotor 1.0029 4999
## (Intercept)-Dasypus_novemcinctus 1.0045 4930
## (Intercept)-Lynx_rufus 1.0099 808
## (Intercept)-Didelphis_virginiana 1.0008 4436
## (Intercept)-Sylvilagus_floridanus 1.0009 2329
## (Intercept)-Sciurus_carolinensis 1.0016 4648
## (Intercept)-Vulpes_vulpes 1.0208 423
## (Intercept)-Sus_scrofa 0.9998 2054
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0049 0.0600 -0.1132 0.0036 0.1200
## (Intercept)-Canis_latrans -2.7447 0.1868 -3.1293 -2.7425 -2.3883
## (Intercept)-Sciurus_niger -4.0958 0.6486 -5.3629 -4.0838 -2.8473
## (Intercept)-Procyon_lotor -2.2954 0.1440 -2.5956 -2.2900 -2.0298
## (Intercept)-Dasypus_novemcinctus -1.7180 0.1559 -2.0327 -1.7149 -1.4253
## (Intercept)-Lynx_rufus -3.7705 0.3584 -4.4756 -3.7658 -3.0646
## (Intercept)-Didelphis_virginiana -2.5349 0.2823 -3.1185 -2.5247 -2.0325
## (Intercept)-Sylvilagus_floridanus -3.1862 0.3027 -3.8273 -3.1691 -2.6529
## (Intercept)-Sciurus_carolinensis -2.5918 0.3104 -3.2292 -2.5768 -2.0205
## (Intercept)-Vulpes_vulpes -4.1556 0.7490 -5.7038 -4.0981 -2.8298
## (Intercept)-Sus_scrofa -3.2781 0.5942 -4.4571 -3.2689 -2.1464
## shrub_cover-Odocoileus_virginianus -0.0533 0.0641 -0.1778 -0.0541 0.0751
## shrub_cover-Canis_latrans -0.2767 0.2196 -0.7123 -0.2777 0.1527
## shrub_cover-Sciurus_niger -0.3161 0.4609 -1.2236 -0.3074 0.5722
## shrub_cover-Procyon_lotor 0.2490 0.1608 -0.0794 0.2539 0.5453
## shrub_cover-Dasypus_novemcinctus 0.7757 0.2881 0.2335 0.7692 1.3611
## shrub_cover-Lynx_rufus -0.3035 0.3429 -1.0146 -0.2950 0.3487
## shrub_cover-Didelphis_virginiana 0.8748 0.3493 0.2476 0.8595 1.6211
## shrub_cover-Sylvilagus_floridanus 0.2286 0.3940 -0.4973 0.2143 1.0246
## shrub_cover-Sciurus_carolinensis 0.7453 0.3870 0.0207 0.7292 1.5340
## shrub_cover-Vulpes_vulpes -0.0826 0.5410 -1.1965 -0.0671 0.9898
## shrub_cover-Sus_scrofa 0.4865 0.6903 -0.9038 0.4747 1.9135
## veg_height-Odocoileus_virginianus -0.2958 0.0653 -0.4232 -0.2969 -0.1693
## veg_height-Canis_latrans -0.5691 0.1800 -0.9261 -0.5666 -0.2178
## veg_height-Sciurus_niger -0.0506 0.3871 -0.7785 -0.0574 0.7756
## veg_height-Procyon_lotor 0.3346 0.1224 0.0978 0.3346 0.5724
## veg_height-Dasypus_novemcinctus 0.2242 0.1316 -0.0283 0.2232 0.4813
## veg_height-Lynx_rufus 0.0399 0.2395 -0.4357 0.0430 0.5047
## veg_height-Didelphis_virginiana 0.4056 0.2307 -0.0262 0.3995 0.8828
## veg_height-Sylvilagus_floridanus 0.1059 0.2399 -0.3603 0.1014 0.5880
## veg_height-Sciurus_carolinensis 0.0492 0.2064 -0.3431 0.0444 0.4696
## veg_height-Vulpes_vulpes -0.1144 0.3033 -0.7531 -0.0973 0.4487
## veg_height-Sus_scrofa -0.1278 0.3195 -0.7745 -0.1268 0.4879
## week-Odocoileus_virginianus 0.2129 0.0607 0.0939 0.2129 0.3324
## week-Canis_latrans 0.0747 0.1298 -0.1881 0.0781 0.3207
## week-Sciurus_niger -0.2871 0.2955 -0.9761 -0.2502 0.2079
## week-Procyon_lotor -0.0472 0.1170 -0.2901 -0.0444 0.1698
## week-Dasypus_novemcinctus -0.1584 0.1338 -0.4400 -0.1545 0.0911
## week-Lynx_rufus -0.0312 0.1887 -0.4183 -0.0252 0.3242
## week-Didelphis_virginiana -0.1969 0.2175 -0.6824 -0.1829 0.1796
## week-Sylvilagus_floridanus -0.1392 0.2028 -0.5766 -0.1236 0.2232
## week-Sciurus_carolinensis 0.1432 0.1753 -0.2047 0.1420 0.4834
## week-Vulpes_vulpes -0.1078 0.2765 -0.7136 -0.0939 0.3906
## week-Sus_scrofa 0.1073 0.2292 -0.3421 0.1035 0.5606
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0017 2344
## (Intercept)-Sciurus_niger 1.0238 469
## (Intercept)-Procyon_lotor 1.0003 3668
## (Intercept)-Dasypus_novemcinctus 1.0000 4502
## (Intercept)-Lynx_rufus 1.0013 890
## (Intercept)-Didelphis_virginiana 1.0013 2796
## (Intercept)-Sylvilagus_floridanus 1.0040 1644
## (Intercept)-Sciurus_carolinensis 1.0057 2997
## (Intercept)-Vulpes_vulpes 1.0138 511
## (Intercept)-Sus_scrofa 1.0004 1791
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0038 2713
## shrub_cover-Sciurus_niger 1.0122 1370
## shrub_cover-Procyon_lotor 1.0010 4158
## shrub_cover-Dasypus_novemcinctus 1.0004 4123
## shrub_cover-Lynx_rufus 1.0001 1456
## shrub_cover-Didelphis_virginiana 1.0056 2133
## shrub_cover-Sylvilagus_floridanus 1.0089 1949
## shrub_cover-Sciurus_carolinensis 1.0025 2590
## shrub_cover-Vulpes_vulpes 1.0154 1962
## shrub_cover-Sus_scrofa 1.0018 2509
## veg_height-Odocoileus_virginianus 1.0010 5250
## veg_height-Canis_latrans 1.0055 2707
## veg_height-Sciurus_niger 1.0054 2573
## veg_height-Procyon_lotor 1.0027 4222
## veg_height-Dasypus_novemcinctus 1.0006 5120
## veg_height-Lynx_rufus 1.0006 2401
## veg_height-Didelphis_virginiana 1.0000 3263
## veg_height-Sylvilagus_floridanus 1.0012 2492
## veg_height-Sciurus_carolinensis 1.0015 3618
## veg_height-Vulpes_vulpes 1.0002 2309
## veg_height-Sus_scrofa 0.9999 3730
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0077 4500
## week-Sciurus_niger 1.0118 2034
## week-Procyon_lotor 1.0002 4424
## week-Dasypus_novemcinctus 1.0008 4776
## week-Lynx_rufus 0.9998 3019
## week-Didelphis_virginiana 1.0167 3411
## week-Sylvilagus_floridanus 1.0082 2876
## week-Sciurus_carolinensis 1.0002 5132
## week-Vulpes_vulpes 1.0044 2855
## week-Sus_scrofa 1.0014 5199
#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): 2.2118
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0608 0.6400 -1.1138 0.0406 1.4109 1.0015 1177
## Avg_Cogongrass_Cover 0.0250 0.3288 -0.6250 0.0271 0.6789 1.0029 787
## total_shrub_cover -0.6511 0.4412 -1.6331 -0.6092 0.1012 1.0030 488
## avg_veg_height 0.1693 0.3234 -0.4487 0.1669 0.8079 1.0278 725
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5782 3.2996 0.5323 2.7086 11.7759 1.0057 1041
## Avg_Cogongrass_Cover 0.3405 0.4280 0.0395 0.2086 1.4920 1.0224 2378
## total_shrub_cover 0.7868 0.9869 0.0566 0.4728 3.6183 1.0053 715
## avg_veg_height 0.2571 0.2979 0.0345 0.1686 1.0295 1.0018 2221
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.259 1.2327 0.0693 0.8766 4.6306 1.007 442
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6772 0.4477 -3.5350 -2.6887 -1.7754 1.0035 3790
## shrub_cover 0.4531 0.2849 -0.1007 0.4458 1.0305 1.0008 821
## veg_height -0.0136 0.1623 -0.3366 -0.0131 0.3143 1.0013 2575
## week -0.0375 0.1193 -0.2909 -0.0321 0.1892 1.0044 2553
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0920 1.3391 0.7021 1.7575 5.2755 1.0028 2529
## shrub_cover 0.5981 0.4960 0.1138 0.4615 1.8904 1.0051 925
## veg_height 0.2032 0.1434 0.0557 0.1636 0.5758 1.0080 2950
## week 0.0981 0.0797 0.0256 0.0769 0.2990 1.0076 2465
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6266 1.5281 0.9677 3.4806
## (Intercept)-Canis_latrans 0.6260 0.7780 -0.8357 0.6001
## (Intercept)-Sciurus_niger -0.2888 1.3342 -2.4165 -0.4349
## (Intercept)-Procyon_lotor 0.8108 0.7699 -0.6523 0.7960
## (Intercept)-Dasypus_novemcinctus -0.4335 0.7578 -1.8940 -0.4396
## (Intercept)-Lynx_rufus 0.2069 1.1451 -1.6279 0.0850
## (Intercept)-Didelphis_virginiana -0.9997 0.8357 -2.5978 -1.0318
## (Intercept)-Sylvilagus_floridanus 0.2497 0.9759 -1.5065 0.1694
## (Intercept)-Sciurus_carolinensis -1.0298 0.8398 -2.6520 -1.0324
## (Intercept)-Vulpes_vulpes -0.6460 1.4791 -3.0964 -0.8300
## (Intercept)-Sus_scrofa -1.3271 1.0880 -3.4787 -1.3475
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0040 0.5563 -1.1500 0.0058
## Avg_Cogongrass_Cover-Canis_latrans 0.3233 0.4939 -0.5424 0.2834
## Avg_Cogongrass_Cover-Sciurus_niger -0.3081 0.6549 -1.8096 -0.2468
## Avg_Cogongrass_Cover-Procyon_lotor -0.0704 0.4452 -0.9624 -0.0644
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1431 0.4274 -0.6934 0.1453
## Avg_Cogongrass_Cover-Lynx_rufus 0.3124 0.5279 -0.6250 0.2761
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1733 0.4682 -0.7179 0.1657
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3217 0.5418 -1.4767 -0.2917
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0792 0.4557 -0.8259 0.0814
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1460 0.5323 -0.8552 0.1247
## Avg_Cogongrass_Cover-Sus_scrofa -0.1945 0.5994 -1.5590 -0.1366
## total_shrub_cover-Odocoileus_virginianus -0.3610 0.6877 -1.7233 -0.3872
## total_shrub_cover-Canis_latrans 0.1694 0.6507 -0.9570 0.1063
## total_shrub_cover-Sciurus_niger -0.8109 0.7929 -2.5746 -0.7525
## total_shrub_cover-Procyon_lotor -1.1507 0.6163 -2.6394 -1.0592
## total_shrub_cover-Dasypus_novemcinctus -0.3726 0.6034 -1.8805 -0.3008
## total_shrub_cover-Lynx_rufus -1.0618 0.8283 -2.9161 -0.9768
## total_shrub_cover-Didelphis_virginiana -0.6978 0.6276 -2.1854 -0.6276
## total_shrub_cover-Sylvilagus_floridanus -1.1698 0.9122 -3.3697 -1.0221
## total_shrub_cover-Sciurus_carolinensis -0.6882 0.6880 -2.3087 -0.5964
## total_shrub_cover-Vulpes_vulpes -0.8065 0.9396 -2.9443 -0.7102
## total_shrub_cover-Sus_scrofa -0.5009 0.8025 -2.2592 -0.4618
## avg_veg_height-Odocoileus_virginianus 0.1343 0.5179 -0.9285 0.1350
## avg_veg_height-Canis_latrans 0.1832 0.4516 -0.6962 0.1745
## avg_veg_height-Sciurus_niger -0.0455 0.5777 -1.2848 -0.0116
## avg_veg_height-Procyon_lotor 0.1845 0.4320 -0.6677 0.1822
## avg_veg_height-Dasypus_novemcinctus 0.3557 0.4461 -0.4585 0.3351
## avg_veg_height-Lynx_rufus 0.1517 0.5221 -0.8849 0.1444
## avg_veg_height-Didelphis_virginiana 0.0830 0.4652 -0.8722 0.0904
## avg_veg_height-Sylvilagus_floridanus 0.1064 0.4963 -0.8782 0.1030
## avg_veg_height-Sciurus_carolinensis 0.4582 0.4702 -0.3901 0.4277
## avg_veg_height-Vulpes_vulpes 0.1295 0.5142 -0.9082 0.1316
## avg_veg_height-Sus_scrofa 0.1776 0.4937 -0.8123 0.1762
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0540 1.0032 977
## (Intercept)-Canis_latrans 2.2927 1.0028 1884
## (Intercept)-Sciurus_niger 2.8066 1.0312 482
## (Intercept)-Procyon_lotor 2.3890 1.0007 1509
## (Intercept)-Dasypus_novemcinctus 1.1365 1.0031 1360
## (Intercept)-Lynx_rufus 2.7653 1.0129 625
## (Intercept)-Didelphis_virginiana 0.7435 1.0119 1346
## (Intercept)-Sylvilagus_floridanus 2.3493 1.0093 926
## (Intercept)-Sciurus_carolinensis 0.6197 1.0009 1462
## (Intercept)-Vulpes_vulpes 2.7934 1.0056 352
## (Intercept)-Sus_scrofa 0.8050 1.0078 783
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1024 0.9999 2507
## Avg_Cogongrass_Cover-Canis_latrans 1.4041 1.0024 2607
## Avg_Cogongrass_Cover-Sciurus_niger 0.8462 1.0002 1736
## Avg_Cogongrass_Cover-Procyon_lotor 0.7987 1.0026 1873
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0057 1.0005 1635
## Avg_Cogongrass_Cover-Lynx_rufus 1.4531 1.0022 2137
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1411 1.0021 1632
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6392 1.0012 1430
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9647 1.0021 1866
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2383 1.0032 2329
## Avg_Cogongrass_Cover-Sus_scrofa 0.8476 1.0014 1694
## total_shrub_cover-Odocoileus_virginianus 1.1235 1.0008 2616
## total_shrub_cover-Canis_latrans 1.6761 1.0062 1332
## total_shrub_cover-Sciurus_niger 0.6878 1.0012 901
## total_shrub_cover-Procyon_lotor -0.1735 1.0069 896
## total_shrub_cover-Dasypus_novemcinctus 0.5793 1.0101 984
## total_shrub_cover-Lynx_rufus 0.3574 1.0022 976
## total_shrub_cover-Didelphis_virginiana 0.3601 1.0100 882
## total_shrub_cover-Sylvilagus_floridanus 0.2789 1.0076 465
## total_shrub_cover-Sciurus_carolinensis 0.4497 1.0048 983
## total_shrub_cover-Vulpes_vulpes 0.8477 1.0022 603
## total_shrub_cover-Sus_scrofa 1.0509 1.0170 657
## avg_veg_height-Odocoileus_virginianus 1.1765 1.0074 2029
## avg_veg_height-Canis_latrans 1.0969 1.0045 1674
## avg_veg_height-Sciurus_niger 1.0001 1.0135 1293
## avg_veg_height-Procyon_lotor 1.0667 1.0059 1584
## avg_veg_height-Dasypus_novemcinctus 1.2783 1.0076 1288
## avg_veg_height-Lynx_rufus 1.2251 1.0131 1927
## avg_veg_height-Didelphis_virginiana 0.9838 1.0137 1903
## avg_veg_height-Sylvilagus_floridanus 1.0757 1.0187 951
## avg_veg_height-Sciurus_carolinensis 1.4581 1.0042 1520
## avg_veg_height-Vulpes_vulpes 1.1602 1.0110 1546
## avg_veg_height-Sus_scrofa 1.1627 1.0083 1448
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0591 -0.1105 0.0057 0.1242
## (Intercept)-Canis_latrans -2.8026 0.1983 -3.2009 -2.7962 -2.4305
## (Intercept)-Sciurus_niger -4.1499 0.6728 -5.4394 -4.1638 -2.8250
## (Intercept)-Procyon_lotor -2.2980 0.1396 -2.5792 -2.2935 -2.0316
## (Intercept)-Dasypus_novemcinctus -1.7915 0.1785 -2.1633 -1.7822 -1.4651
## (Intercept)-Lynx_rufus -3.6282 0.3729 -4.4206 -3.6086 -2.9596
## (Intercept)-Didelphis_virginiana -2.6789 0.3194 -3.3199 -2.6650 -2.0892
## (Intercept)-Sylvilagus_floridanus -3.3391 0.3004 -3.9382 -3.3290 -2.7754
## (Intercept)-Sciurus_carolinensis -2.7548 0.3487 -3.4651 -2.7479 -2.1098
## (Intercept)-Vulpes_vulpes -4.3495 0.7723 -5.9219 -4.3192 -2.9797
## (Intercept)-Sus_scrofa -3.5750 0.6090 -4.7677 -3.5818 -2.3552
## shrub_cover-Odocoileus_virginianus -0.0508 0.0644 -0.1793 -0.0507 0.0697
## shrub_cover-Canis_latrans -0.2769 0.2418 -0.7455 -0.2804 0.2151
## shrub_cover-Sciurus_niger -0.1192 0.5522 -1.2348 -0.1115 0.9645
## shrub_cover-Procyon_lotor 0.3174 0.1601 -0.0016 0.3193 0.6244
## shrub_cover-Dasypus_novemcinctus 0.9976 0.3621 0.3400 0.9751 1.7449
## shrub_cover-Lynx_rufus 0.0470 0.3860 -0.7374 0.0539 0.7645
## shrub_cover-Didelphis_virginiana 1.1375 0.4224 0.3783 1.1188 2.0265
## shrub_cover-Sylvilagus_floridanus 0.7085 0.4516 -0.2441 0.7256 1.5583
## shrub_cover-Sciurus_carolinensis 1.0631 0.4328 0.2374 1.0619 1.9364
## shrub_cover-Vulpes_vulpes 0.2610 0.6294 -0.9851 0.2508 1.5097
## shrub_cover-Sus_scrofa 0.9851 0.7987 -0.6162 0.9836 2.5987
## veg_height-Odocoileus_virginianus -0.2985 0.0646 -0.4296 -0.2983 -0.1728
## veg_height-Canis_latrans -0.6007 0.1869 -0.9792 -0.5937 -0.2565
## veg_height-Sciurus_niger -0.0109 0.4314 -0.8227 -0.0238 0.9143
## veg_height-Procyon_lotor 0.3363 0.1223 0.1017 0.3341 0.5819
## veg_height-Dasypus_novemcinctus 0.2419 0.1373 -0.0249 0.2399 0.5093
## veg_height-Lynx_rufus 0.0198 0.2529 -0.4806 0.0204 0.5103
## veg_height-Didelphis_virginiana 0.4006 0.2443 -0.0485 0.3923 0.9030
## veg_height-Sylvilagus_floridanus 0.0387 0.2497 -0.4403 0.0344 0.5400
## veg_height-Sciurus_carolinensis 0.0736 0.2224 -0.3481 0.0676 0.5253
## veg_height-Vulpes_vulpes -0.1534 0.3329 -0.8449 -0.1355 0.4502
## veg_height-Sus_scrofa -0.1763 0.3341 -0.8541 -0.1667 0.4501
## week-Odocoileus_virginianus 0.2103 0.0621 0.0908 0.2098 0.3353
## week-Canis_latrans 0.0726 0.1295 -0.1929 0.0752 0.3160
## week-Sciurus_niger -0.2947 0.2939 -0.9603 -0.2632 0.1829
## week-Procyon_lotor -0.0453 0.1199 -0.2932 -0.0391 0.1775
## week-Dasypus_novemcinctus -0.1559 0.1366 -0.4413 -0.1499 0.0980
## week-Lynx_rufus -0.0191 0.1927 -0.4288 -0.0105 0.3391
## week-Didelphis_virginiana -0.1917 0.2087 -0.6332 -0.1754 0.1756
## week-Sylvilagus_floridanus -0.1386 0.2048 -0.5746 -0.1261 0.2278
## week-Sciurus_carolinensis 0.1408 0.1786 -0.2170 0.1421 0.4856
## week-Vulpes_vulpes -0.0979 0.2682 -0.6819 -0.0790 0.3965
## week-Sus_scrofa 0.1078 0.2292 -0.3359 0.1085 0.5594
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0042 1893
## (Intercept)-Sciurus_niger 1.0322 475
## (Intercept)-Procyon_lotor 1.0023 3811
## (Intercept)-Dasypus_novemcinctus 1.0004 1652
## (Intercept)-Lynx_rufus 1.0118 823
## (Intercept)-Didelphis_virginiana 1.0037 1226
## (Intercept)-Sylvilagus_floridanus 1.0058 1097
## (Intercept)-Sciurus_carolinensis 1.0002 1235
## (Intercept)-Vulpes_vulpes 1.0017 349
## (Intercept)-Sus_scrofa 1.0385 660
## shrub_cover-Odocoileus_virginianus 1.0011 5015
## shrub_cover-Canis_latrans 1.0050 1573
## shrub_cover-Sciurus_niger 1.0003 770
## shrub_cover-Procyon_lotor 1.0012 3915
## shrub_cover-Dasypus_novemcinctus 1.0065 1097
## shrub_cover-Lynx_rufus 1.0038 973
## shrub_cover-Didelphis_virginiana 1.0152 807
## shrub_cover-Sylvilagus_floridanus 1.0044 734
## shrub_cover-Sciurus_carolinensis 1.0042 1109
## shrub_cover-Vulpes_vulpes 1.0007 1125
## shrub_cover-Sus_scrofa 1.0067 658
## veg_height-Odocoileus_virginianus 1.0011 5250
## veg_height-Canis_latrans 1.0015 1843
## veg_height-Sciurus_niger 1.0158 1368
## veg_height-Procyon_lotor 1.0019 3923
## veg_height-Dasypus_novemcinctus 1.0008 4342
## veg_height-Lynx_rufus 1.0079 2029
## veg_height-Didelphis_virginiana 1.0004 2859
## veg_height-Sylvilagus_floridanus 1.0041 1610
## veg_height-Sciurus_carolinensis 1.0004 2718
## veg_height-Vulpes_vulpes 1.0122 1711
## veg_height-Sus_scrofa 1.0049 2474
## week-Odocoileus_virginianus 1.0027 5643
## week-Canis_latrans 1.0021 4466
## week-Sciurus_niger 1.0082 1668
## week-Procyon_lotor 1.0019 4352
## week-Dasypus_novemcinctus 1.0029 3541
## week-Lynx_rufus 1.0026 2945
## week-Didelphis_virginiana 1.0007 3815
## week-Sylvilagus_floridanus 1.0077 2575
## week-Sciurus_carolinensis 1.0006 4652
## week-Vulpes_vulpes 1.0017 2240
## week-Sus_scrofa 1.0000 3879
#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): 2.0903
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1095 0.7461 -1.5369 -0.1346 1.4419 1.0106 1383
## Tree_Density -0.7437 0.4049 -1.6105 -0.7094 -0.0496 1.0052 1341
## Avg_Canopy_Cover 1.1041 0.3796 0.3930 1.0831 1.9024 1.0003 2046
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.3627 5.0524 1.4132 4.9802 19.7679 1.0257 964
## Tree_Density 0.7443 1.5385 0.0417 0.3195 4.2590 1.0286 1056
## Avg_Canopy_Cover 0.7842 0.8818 0.0733 0.5275 3.1262 1.0117 1321
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4102 0.4353 0.0427 0.2638 1.6484 1.0023 550
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6277 0.4500 -3.5031 -2.6349 -1.6890 1.0042 4962
## shrub_cover 0.2371 0.2435 -0.2322 0.2350 0.7251 1.0030 3555
## veg_height 0.0145 0.1564 -0.3021 0.0155 0.3233 1.0023 3304
## week -0.0379 0.1193 -0.2941 -0.0338 0.1849 1.0003 3270
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1674 1.3563 0.7618 1.8389 5.3859 1.0021 2236
## shrub_cover 0.4786 0.3893 0.1011 0.3703 1.4832 1.0015 1950
## veg_height 0.1973 0.1329 0.0578 0.1613 0.5407 1.0010 3501
## week 0.1003 0.0820 0.0256 0.0766 0.3092 1.0096 2408
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6204 1.6715 2.0873 4.3548 8.6476
## (Intercept)-Canis_latrans 0.4191 0.6292 -0.7187 0.3948 1.7313
## (Intercept)-Sciurus_niger 0.1768 1.5837 -2.1818 -0.0646 4.2282
## (Intercept)-Procyon_lotor 0.8239 0.6266 -0.4159 0.8313 2.1249
## (Intercept)-Dasypus_novemcinctus -0.9359 0.6153 -2.2549 -0.9126 0.2114
## (Intercept)-Lynx_rufus 1.6271 1.9444 -1.0394 1.2475 6.4314
## (Intercept)-Didelphis_virginiana -1.7229 0.7338 -3.2284 -1.6891 -0.3801
## (Intercept)-Sylvilagus_floridanus -0.6134 0.7028 -1.9578 -0.6108 0.8169
## (Intercept)-Sciurus_carolinensis -1.7713 0.7517 -3.3619 -1.7425 -0.3581
## (Intercept)-Vulpes_vulpes -1.2872 1.5905 -3.6439 -1.5131 2.7456
## (Intercept)-Sus_scrofa -2.5294 1.0079 -4.6807 -2.4753 -0.6535
## Tree_Density-Odocoileus_virginianus -0.3742 0.6644 -1.4760 -0.4487 1.2229
## Tree_Density-Canis_latrans -0.9071 0.5608 -2.2341 -0.8383 -0.0058
## Tree_Density-Sciurus_niger -0.7791 0.7560 -2.4221 -0.7351 0.6131
## Tree_Density-Procyon_lotor -0.4885 0.4018 -1.2941 -0.4932 0.3077
## Tree_Density-Dasypus_novemcinctus -1.3011 0.8812 -3.5972 -1.1125 -0.1667
## Tree_Density-Lynx_rufus -0.0819 0.8040 -1.3889 -0.1811 1.9006
## Tree_Density-Didelphis_virginiana -0.9663 0.7404 -2.7979 -0.8481 0.1581
## Tree_Density-Sylvilagus_floridanus -1.0012 0.7342 -2.7784 -0.8979 0.1350
## Tree_Density-Sciurus_carolinensis -0.9007 0.7106 -2.6698 -0.8037 0.2220
## Tree_Density-Vulpes_vulpes -0.6786 0.7864 -2.3102 -0.6296 0.7447
## Tree_Density-Sus_scrofa -0.9477 0.8458 -3.1183 -0.8155 0.3104
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8057 0.7309 -0.6888 0.7999 2.2839
## Avg_Canopy_Cover-Canis_latrans 0.0300 0.4822 -0.9205 0.0315 0.9626
## Avg_Canopy_Cover-Sciurus_niger 1.1062 0.8846 -0.4462 1.0303 3.1556
## Avg_Canopy_Cover-Procyon_lotor 1.0946 0.4992 0.2124 1.0565 2.2160
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0847 0.4585 0.2485 1.0595 2.0669
## Avg_Canopy_Cover-Lynx_rufus 1.0284 0.8186 -0.4660 0.9845 2.7816
## Avg_Canopy_Cover-Didelphis_virginiana 1.4795 0.6307 0.4773 1.4024 2.9762
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9238 0.8830 0.6712 1.7674 4.0175
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4069 0.5837 0.4314 1.3472 2.7567
## Avg_Canopy_Cover-Vulpes_vulpes 1.1738 0.6856 -0.0025 1.1138 2.7921
## Avg_Canopy_Cover-Sus_scrofa 1.3444 0.5895 0.3363 1.2890 2.6427
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0125 852
## (Intercept)-Canis_latrans 1.0000 3197
## (Intercept)-Sciurus_niger 1.0124 433
## (Intercept)-Procyon_lotor 1.0032 2972
## (Intercept)-Dasypus_novemcinctus 1.0005 2657
## (Intercept)-Lynx_rufus 1.0570 348
## (Intercept)-Didelphis_virginiana 1.0001 2473
## (Intercept)-Sylvilagus_floridanus 1.0001 3028
## (Intercept)-Sciurus_carolinensis 1.0002 2847
## (Intercept)-Vulpes_vulpes 1.0534 240
## (Intercept)-Sus_scrofa 1.0019 1922
## Tree_Density-Odocoileus_virginianus 1.0019 2064
## Tree_Density-Canis_latrans 1.0031 2039
## Tree_Density-Sciurus_niger 1.0007 1604
## Tree_Density-Procyon_lotor 1.0011 3185
## Tree_Density-Dasypus_novemcinctus 1.0075 1128
## Tree_Density-Lynx_rufus 1.0058 873
## Tree_Density-Didelphis_virginiana 1.0011 1353
## Tree_Density-Sylvilagus_floridanus 1.0013 1528
## Tree_Density-Sciurus_carolinensis 1.0033 1614
## Tree_Density-Vulpes_vulpes 1.0063 1487
## Tree_Density-Sus_scrofa 1.0028 1550
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0052 3320
## Avg_Canopy_Cover-Canis_latrans 1.0025 2830
## Avg_Canopy_Cover-Sciurus_niger 1.0085 1315
## Avg_Canopy_Cover-Procyon_lotor 1.0031 3769
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0025 3918
## Avg_Canopy_Cover-Lynx_rufus 1.0068 1410
## Avg_Canopy_Cover-Didelphis_virginiana 1.0019 2118
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0057 1236
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0004 2916
## Avg_Canopy_Cover-Vulpes_vulpes 1.0054 1947
## Avg_Canopy_Cover-Sus_scrofa 0.9999 3156
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0604 -0.1140 0.0068 0.1222
## (Intercept)-Canis_latrans -2.7639 0.1925 -3.1583 -2.7574 -2.4037
## (Intercept)-Sciurus_niger -4.4334 0.6098 -5.5861 -4.4625 -3.1587
## (Intercept)-Procyon_lotor -2.2999 0.1434 -2.5962 -2.2946 -2.0257
## (Intercept)-Dasypus_novemcinctus -1.7374 0.1570 -2.0520 -1.7350 -1.4322
## (Intercept)-Lynx_rufus -3.9501 0.3600 -4.6491 -3.9538 -3.2274
## (Intercept)-Didelphis_virginiana -2.5791 0.2950 -3.1853 -2.5725 -2.0274
## (Intercept)-Sylvilagus_floridanus -3.1440 0.2722 -3.6933 -3.1361 -2.6319
## (Intercept)-Sciurus_carolinensis -2.6475 0.3196 -3.3148 -2.6324 -2.0613
## (Intercept)-Vulpes_vulpes -4.2499 0.7831 -5.9201 -4.1851 -2.8988
## (Intercept)-Sus_scrofa -3.2037 0.5865 -4.3999 -3.1897 -2.0916
## shrub_cover-Odocoileus_virginianus -0.0533 0.0644 -0.1777 -0.0538 0.0763
## shrub_cover-Canis_latrans -0.2775 0.2177 -0.7010 -0.2761 0.1474
## shrub_cover-Sciurus_niger -0.3557 0.4257 -1.2318 -0.3392 0.4581
## shrub_cover-Procyon_lotor 0.2499 0.1598 -0.0707 0.2529 0.5560
## shrub_cover-Dasypus_novemcinctus 0.8192 0.2879 0.2843 0.8134 1.4024
## shrub_cover-Lynx_rufus -0.3178 0.3209 -0.9855 -0.3069 0.3110
## shrub_cover-Didelphis_virginiana 0.9224 0.3522 0.2778 0.9129 1.6417
## shrub_cover-Sylvilagus_floridanus 0.3968 0.3768 -0.3404 0.3929 1.1473
## shrub_cover-Sciurus_carolinensis 0.8077 0.3927 0.0688 0.7945 1.6102
## shrub_cover-Vulpes_vulpes -0.0372 0.5184 -1.1147 -0.0314 0.9630
## shrub_cover-Sus_scrofa 0.5013 0.7213 -0.9097 0.4789 1.9577
## veg_height-Odocoileus_virginianus -0.2975 0.0643 -0.4245 -0.2966 -0.1742
## veg_height-Canis_latrans -0.5768 0.1856 -0.9538 -0.5735 -0.2299
## veg_height-Sciurus_niger -0.0566 0.3637 -0.7917 -0.0593 0.6800
## veg_height-Procyon_lotor 0.3408 0.1212 0.1029 0.3401 0.5770
## veg_height-Dasypus_novemcinctus 0.2402 0.1332 -0.0155 0.2400 0.5002
## veg_height-Lynx_rufus 0.0895 0.2352 -0.3746 0.0894 0.5492
## veg_height-Didelphis_virginiana 0.4481 0.2380 0.0123 0.4403 0.9440
## veg_height-Sylvilagus_floridanus 0.1477 0.2366 -0.3198 0.1500 0.6103
## veg_height-Sciurus_carolinensis 0.0884 0.2145 -0.3070 0.0830 0.5336
## veg_height-Vulpes_vulpes -0.1266 0.3161 -0.7835 -0.1122 0.4451
## veg_height-Sus_scrofa -0.1085 0.3181 -0.7823 -0.1002 0.4852
## week-Odocoileus_virginianus 0.2097 0.0616 0.0890 0.2096 0.3294
## week-Canis_latrans 0.0754 0.1311 -0.1955 0.0783 0.3158
## week-Sciurus_niger -0.2877 0.3018 -0.9881 -0.2592 0.2109
## week-Procyon_lotor -0.0430 0.1164 -0.2769 -0.0407 0.1753
## week-Dasypus_novemcinctus -0.1582 0.1372 -0.4399 -0.1543 0.0971
## week-Lynx_rufus -0.0266 0.1914 -0.4257 -0.0197 0.3285
## week-Didelphis_virginiana -0.1992 0.2136 -0.6526 -0.1829 0.1830
## week-Sylvilagus_floridanus -0.1393 0.2045 -0.5580 -0.1275 0.2251
## week-Sciurus_carolinensis 0.1424 0.1798 -0.2095 0.1422 0.4893
## week-Vulpes_vulpes -0.0996 0.2712 -0.6927 -0.0823 0.3887
## week-Sus_scrofa 0.1025 0.2310 -0.3496 0.1026 0.5670
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5810
## (Intercept)-Canis_latrans 1.0038 2140
## (Intercept)-Sciurus_niger 1.0042 439
## (Intercept)-Procyon_lotor 1.0024 3856
## (Intercept)-Dasypus_novemcinctus 1.0009 4291
## (Intercept)-Lynx_rufus 1.0314 527
## (Intercept)-Didelphis_virginiana 1.0005 2214
## (Intercept)-Sylvilagus_floridanus 1.0025 2244
## (Intercept)-Sciurus_carolinensis 1.0015 2148
## (Intercept)-Vulpes_vulpes 1.0112 274
## (Intercept)-Sus_scrofa 1.0028 1856
## shrub_cover-Odocoileus_virginianus 1.0000 4766
## shrub_cover-Canis_latrans 1.0024 2772
## shrub_cover-Sciurus_niger 1.0004 1318
## shrub_cover-Procyon_lotor 1.0018 4290
## shrub_cover-Dasypus_novemcinctus 1.0017 3474
## shrub_cover-Lynx_rufus 1.0104 1318
## shrub_cover-Didelphis_virginiana 1.0010 2054
## shrub_cover-Sylvilagus_floridanus 1.0002 2036
## shrub_cover-Sciurus_carolinensis 1.0041 2336
## shrub_cover-Vulpes_vulpes 1.0036 2236
## shrub_cover-Sus_scrofa 1.0018 2032
## veg_height-Odocoileus_virginianus 1.0029 5250
## veg_height-Canis_latrans 1.0033 2246
## veg_height-Sciurus_niger 1.0006 2080
## veg_height-Procyon_lotor 1.0015 4384
## veg_height-Dasypus_novemcinctus 1.0015 4718
## veg_height-Lynx_rufus 1.0014 2202
## veg_height-Didelphis_virginiana 1.0013 3183
## veg_height-Sylvilagus_floridanus 1.0085 3310
## veg_height-Sciurus_carolinensis 1.0036 3152
## veg_height-Vulpes_vulpes 1.0073 2049
## veg_height-Sus_scrofa 1.0016 3587
## week-Odocoileus_virginianus 1.0028 5469
## week-Canis_latrans 1.0003 4601
## week-Sciurus_niger 1.0063 1516
## week-Procyon_lotor 1.0012 4327
## week-Dasypus_novemcinctus 1.0013 4427
## week-Lynx_rufus 1.0007 2577
## week-Didelphis_virginiana 1.0002 3543
## week-Sylvilagus_floridanus 1.0007 3214
## week-Sciurus_carolinensis 1.0007 4624
## week-Vulpes_vulpes 1.0055 2715
## week-Sus_scrofa 1.0011 4452
#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): 2.0618
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0756 0.6619 -1.3571 -0.0865 1.2811 1.0011 1640
## Cogon_Patch_Size -0.2621 0.4264 -1.1850 -0.2319 0.5114 1.0027 1634
## Avg_Cogongrass_Cover 0.2373 0.2996 -0.3473 0.2365 0.8356 1.0155 1280
## total_shrub_cover -0.5579 0.3981 -1.4296 -0.5251 0.1767 1.0052 552
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0787 3.8395 0.5818 3.0597 13.3231 1.0051 1015
## Cogon_Patch_Size 0.9544 1.3778 0.0610 0.5413 4.5021 1.0241 1315
## Avg_Cogongrass_Cover 0.3008 0.3949 0.0362 0.1854 1.2223 1.0020 2248
## total_shrub_cover 0.5845 0.7769 0.0474 0.3199 2.7378 1.0016 818
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6056 1.6383 0.0924 1.1428 6.2092 1.0608 317
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6462 0.4211 -3.4355 -2.6584 -1.7857 1.0012 2897
## shrub_cover 0.4078 0.2664 -0.1194 0.3990 0.9555 1.0091 1520
## veg_height -0.0074 0.1559 -0.3248 -0.0056 0.2939 1.0024 2901
## week -0.0391 0.1187 -0.2815 -0.0350 0.1875 1.0000 3339
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0110 1.2342 0.7045 1.6944 5.2508 1.0061 2283
## shrub_cover 0.5226 0.5128 0.1065 0.4035 1.6424 1.0166 1906
## veg_height 0.1877 0.1344 0.0520 0.1533 0.5178 1.0076 3469
## week 0.0977 0.0804 0.0245 0.0754 0.3021 1.0267 1973
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7424 1.7013 0.9521 3.5284
## (Intercept)-Canis_latrans 0.6240 0.8243 -0.8835 0.5722
## (Intercept)-Sciurus_niger -0.4138 1.4772 -2.7118 -0.6094
## (Intercept)-Procyon_lotor 0.7076 0.8314 -0.9282 0.7084
## (Intercept)-Dasypus_novemcinctus -0.5586 0.7623 -2.1035 -0.5485
## (Intercept)-Lynx_rufus 0.0402 1.1407 -1.8505 -0.0592
## (Intercept)-Didelphis_virginiana -1.1459 0.8425 -2.8017 -1.1431
## (Intercept)-Sylvilagus_floridanus -0.0515 0.9521 -1.7971 -0.1096
## (Intercept)-Sciurus_carolinensis -1.2599 0.8881 -3.0263 -1.2592
## (Intercept)-Vulpes_vulpes -1.0056 1.3757 -3.4369 -1.0942
## (Intercept)-Sus_scrofa -1.6278 1.1448 -4.0386 -1.6095
## Cogon_Patch_Size-Odocoileus_virginianus -0.0741 0.7337 -1.3928 -0.1134
## Cogon_Patch_Size-Canis_latrans 0.6598 0.7286 -0.4044 0.5424
## Cogon_Patch_Size-Sciurus_niger -0.6392 0.9354 -2.8251 -0.5065
## Cogon_Patch_Size-Procyon_lotor -0.2951 0.4731 -1.2476 -0.2824
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1091 0.4442 -0.9989 -0.1034
## Cogon_Patch_Size-Lynx_rufus -0.2230 0.7987 -1.7245 -0.2490
## Cogon_Patch_Size-Didelphis_virginiana 0.5451 0.5195 -0.3744 0.5078
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9119 0.8821 -3.1080 -0.7523
## Cogon_Patch_Size-Sciurus_carolinensis -0.7601 0.7723 -2.6290 -0.6262
## Cogon_Patch_Size-Vulpes_vulpes -0.5790 0.9243 -2.6862 -0.4815
## Cogon_Patch_Size-Sus_scrofa -0.5435 0.8628 -2.6824 -0.4101
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2303 0.5395 -0.8478 0.2255
## Avg_Cogongrass_Cover-Canis_latrans 0.3728 0.4271 -0.3926 0.3445
## Avg_Cogongrass_Cover-Sciurus_niger -0.0558 0.6184 -1.4397 -0.0089
## Avg_Cogongrass_Cover-Procyon_lotor 0.2159 0.4251 -0.6404 0.2084
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3813 0.3792 -0.3194 0.3661
## Avg_Cogongrass_Cover-Lynx_rufus 0.4939 0.4847 -0.3852 0.4686
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2026 0.4257 -0.6762 0.2071
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0147 0.5215 -1.1086 0.0110
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4385 0.4246 -0.3453 0.4175
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3551 0.4940 -0.5918 0.3366
## Avg_Cogongrass_Cover-Sus_scrofa 0.0436 0.5704 -1.1833 0.0870
## total_shrub_cover-Odocoileus_virginianus -0.3353 0.6327 -1.5599 -0.3506
## total_shrub_cover-Canis_latrans 0.0506 0.5659 -0.9238 -0.0025
## total_shrub_cover-Sciurus_niger -0.7173 0.7427 -2.3881 -0.6550
## total_shrub_cover-Procyon_lotor -1.0165 0.5855 -2.4424 -0.9380
## total_shrub_cover-Dasypus_novemcinctus -0.3039 0.5000 -1.3998 -0.2725
## total_shrub_cover-Lynx_rufus -0.9105 0.7857 -2.7373 -0.8063
## total_shrub_cover-Didelphis_virginiana -0.6411 0.5566 -1.9149 -0.5863
## total_shrub_cover-Sylvilagus_floridanus -0.8967 0.7930 -2.8210 -0.7853
## total_shrub_cover-Sciurus_carolinensis -0.5128 0.6016 -1.8670 -0.4641
## total_shrub_cover-Vulpes_vulpes -0.6553 0.7853 -2.5284 -0.5893
## total_shrub_cover-Sus_scrofa -0.3618 0.7206 -1.8600 -0.3539
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.7644 1.0126 698
## (Intercept)-Canis_latrans 2.3663 1.0008 1627
## (Intercept)-Sciurus_niger 3.1878 1.0070 428
## (Intercept)-Procyon_lotor 2.3523 1.0027 1882
## (Intercept)-Dasypus_novemcinctus 1.0102 1.0019 1643
## (Intercept)-Lynx_rufus 2.5196 1.0099 696
## (Intercept)-Didelphis_virginiana 0.5167 1.0140 1775
## (Intercept)-Sylvilagus_floridanus 1.9830 1.0009 1366
## (Intercept)-Sciurus_carolinensis 0.4800 1.0071 1475
## (Intercept)-Vulpes_vulpes 2.0400 1.0183 533
## (Intercept)-Sus_scrofa 0.5912 1.0030 1131
## Cogon_Patch_Size-Odocoileus_virginianus 1.5365 1.0012 3674
## Cogon_Patch_Size-Canis_latrans 2.4735 1.0025 2141
## Cogon_Patch_Size-Sciurus_niger 0.8955 1.0036 1341
## Cogon_Patch_Size-Procyon_lotor 0.6219 1.0083 2651
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7719 1.0044 3936
## Cogon_Patch_Size-Lynx_rufus 1.5299 1.0040 1937
## Cogon_Patch_Size-Didelphis_virginiana 1.6816 1.0014 2278
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3707 1.0143 1416
## Cogon_Patch_Size-Sciurus_carolinensis 0.3740 1.0087 1542
## Cogon_Patch_Size-Vulpes_vulpes 0.9179 1.0046 1317
## Cogon_Patch_Size-Sus_scrofa 0.8001 1.0132 1678
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3532 1.0067 2961
## Avg_Cogongrass_Cover-Canis_latrans 1.3170 1.0017 2556
## Avg_Cogongrass_Cover-Sciurus_niger 1.0471 1.0077 1271
## Avg_Cogongrass_Cover-Procyon_lotor 1.0581 1.0021 2271
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1758 1.0030 2398
## Avg_Cogongrass_Cover-Lynx_rufus 1.5712 1.0046 2598
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0478 1.0070 2224
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9690 1.0007 1741
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3477 1.0002 2436
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3983 1.0039 2339
## Avg_Cogongrass_Cover-Sus_scrofa 1.0541 1.0049 2016
## total_shrub_cover-Odocoileus_virginianus 1.0083 1.0043 2459
## total_shrub_cover-Canis_latrans 1.3415 1.0042 1508
## total_shrub_cover-Sciurus_niger 0.5784 1.0051 931
## total_shrub_cover-Procyon_lotor -0.1047 1.0087 953
## total_shrub_cover-Dasypus_novemcinctus 0.5788 1.0147 1240
## total_shrub_cover-Lynx_rufus 0.3378 1.0112 963
## total_shrub_cover-Didelphis_virginiana 0.2986 1.0030 1078
## total_shrub_cover-Sylvilagus_floridanus 0.3143 1.0020 701
## total_shrub_cover-Sciurus_carolinensis 0.5597 1.0015 1205
## total_shrub_cover-Vulpes_vulpes 0.7765 1.0032 856
## total_shrub_cover-Sus_scrofa 1.0512 1.0054 1078
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0596 -0.1101 0.0069 0.1205
## (Intercept)-Canis_latrans -2.7641 0.1952 -3.1582 -2.7545 -2.4085
## (Intercept)-Sciurus_niger -4.1350 0.6893 -5.5036 -4.1088 -2.8613
## (Intercept)-Procyon_lotor -2.3011 0.1408 -2.5797 -2.2972 -2.0258
## (Intercept)-Dasypus_novemcinctus -1.7710 0.1671 -2.1182 -1.7629 -1.4672
## (Intercept)-Lynx_rufus -3.6152 0.3652 -4.3488 -3.6013 -2.9451
## (Intercept)-Didelphis_virginiana -2.5920 0.3020 -3.2271 -2.5743 -2.0381
## (Intercept)-Sylvilagus_floridanus -3.3148 0.2953 -3.9165 -3.3082 -2.7579
## (Intercept)-Sciurus_carolinensis -2.7313 0.3477 -3.4444 -2.7221 -2.1028
## (Intercept)-Vulpes_vulpes -4.2101 0.7402 -5.7385 -4.1685 -2.8726
## (Intercept)-Sus_scrofa -3.4761 0.6098 -4.6831 -3.4758 -2.2884
## shrub_cover-Odocoileus_virginianus -0.0517 0.0646 -0.1796 -0.0512 0.0718
## shrub_cover-Canis_latrans -0.2550 0.2299 -0.7085 -0.2564 0.1897
## shrub_cover-Sciurus_niger -0.1095 0.5118 -1.1644 -0.0987 0.8845
## shrub_cover-Procyon_lotor 0.3084 0.1600 -0.0046 0.3124 0.6205
## shrub_cover-Dasypus_novemcinctus 0.9312 0.3293 0.3233 0.9117 1.5983
## shrub_cover-Lynx_rufus 0.0393 0.3724 -0.7122 0.0517 0.7488
## shrub_cover-Didelphis_virginiana 1.0123 0.3822 0.3382 0.9900 1.8018
## shrub_cover-Sylvilagus_floridanus 0.6566 0.4179 -0.1739 0.6654 1.4667
## shrub_cover-Sciurus_carolinensis 0.9806 0.4250 0.1960 0.9773 1.8305
## shrub_cover-Vulpes_vulpes 0.2023 0.5676 -0.9392 0.2032 1.3050
## shrub_cover-Sus_scrofa 0.8481 0.7502 -0.6335 0.8219 2.3072
## veg_height-Odocoileus_virginianus -0.2947 0.0646 -0.4268 -0.2939 -0.1730
## veg_height-Canis_latrans -0.5728 0.1877 -0.9627 -0.5670 -0.2169
## veg_height-Sciurus_niger -0.0361 0.3879 -0.7786 -0.0469 0.7671
## veg_height-Procyon_lotor 0.3331 0.1236 0.0899 0.3334 0.5705
## veg_height-Dasypus_novemcinctus 0.2388 0.1343 -0.0162 0.2351 0.5071
## veg_height-Lynx_rufus 0.0394 0.2315 -0.4327 0.0448 0.4895
## veg_height-Didelphis_virginiana 0.3896 0.2330 -0.0385 0.3816 0.8592
## veg_height-Sylvilagus_floridanus 0.0514 0.2436 -0.4196 0.0513 0.5369
## veg_height-Sciurus_carolinensis 0.0874 0.2208 -0.3227 0.0788 0.5360
## veg_height-Vulpes_vulpes -0.1359 0.3142 -0.7974 -0.1182 0.4435
## veg_height-Sus_scrofa -0.1521 0.3166 -0.7824 -0.1443 0.4589
## week-Odocoileus_virginianus 0.2140 0.0608 0.0964 0.2138 0.3336
## week-Canis_latrans 0.0711 0.1308 -0.1934 0.0728 0.3184
## week-Sciurus_niger -0.2855 0.3058 -0.9632 -0.2534 0.1995
## week-Procyon_lotor -0.0453 0.1164 -0.2751 -0.0412 0.1726
## week-Dasypus_novemcinctus -0.1583 0.1349 -0.4347 -0.1551 0.0936
## week-Lynx_rufus -0.0327 0.1947 -0.4266 -0.0278 0.3290
## week-Didelphis_virginiana -0.1973 0.2153 -0.6597 -0.1827 0.1750
## week-Sylvilagus_floridanus -0.1395 0.2023 -0.5622 -0.1282 0.2235
## week-Sciurus_carolinensis 0.1396 0.1761 -0.2103 0.1403 0.4851
## week-Vulpes_vulpes -0.1013 0.2680 -0.6848 -0.0848 0.3771
## week-Sus_scrofa 0.1004 0.2301 -0.3544 0.1050 0.5503
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 5250
## (Intercept)-Canis_latrans 1.0041 2077
## (Intercept)-Sciurus_niger 1.0138 471
## (Intercept)-Procyon_lotor 1.0016 3778
## (Intercept)-Dasypus_novemcinctus 1.0009 2762
## (Intercept)-Lynx_rufus 1.0146 949
## (Intercept)-Didelphis_virginiana 1.0064 1598
## (Intercept)-Sylvilagus_floridanus 1.0029 1440
## (Intercept)-Sciurus_carolinensis 1.0025 1137
## (Intercept)-Vulpes_vulpes 1.0110 495
## (Intercept)-Sus_scrofa 1.0142 854
## shrub_cover-Odocoileus_virginianus 0.9999 4437
## shrub_cover-Canis_latrans 1.0003 1988
## shrub_cover-Sciurus_niger 1.0015 1169
## shrub_cover-Procyon_lotor 1.0034 3239
## shrub_cover-Dasypus_novemcinctus 1.0090 1360
## shrub_cover-Lynx_rufus 1.0133 1180
## shrub_cover-Didelphis_virginiana 1.0070 1292
## shrub_cover-Sylvilagus_floridanus 1.0047 900
## shrub_cover-Sciurus_carolinensis 1.0056 942
## shrub_cover-Vulpes_vulpes 1.0079 1173
## shrub_cover-Sus_scrofa 1.0181 802
## veg_height-Odocoileus_virginianus 1.0007 5250
## veg_height-Canis_latrans 1.0006 2381
## veg_height-Sciurus_niger 1.0038 1499
## veg_height-Procyon_lotor 1.0026 4067
## veg_height-Dasypus_novemcinctus 1.0037 4503
## veg_height-Lynx_rufus 1.0032 2206
## veg_height-Didelphis_virginiana 1.0044 3082
## veg_height-Sylvilagus_floridanus 1.0012 2017
## veg_height-Sciurus_carolinensis 1.0003 2751
## veg_height-Vulpes_vulpes 1.0021 1906
## veg_height-Sus_scrofa 1.0046 3134
## week-Odocoileus_virginianus 1.0008 4896
## week-Canis_latrans 1.0034 4373
## week-Sciurus_niger 1.0127 1508
## week-Procyon_lotor 1.0012 4857
## week-Dasypus_novemcinctus 1.0009 4315
## week-Lynx_rufus 1.0028 3043
## week-Didelphis_virginiana 1.0017 2903
## week-Sylvilagus_floridanus 1.0007 2708
## week-Sciurus_carolinensis 1.0001 4445
## week-Vulpes_vulpes 1.0004 2852
## week-Sus_scrofa 1.0049 4255
#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): 1.8953
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1787 0.6245 -1.3437 -0.2083 1.1431 1.0017 1953
## Veg_shannon_index 0.3661 0.2645 -0.1731 0.3637 0.9014 1.0060 2151
## Avg_Cogongrass_Cover 0.3239 0.2678 -0.2165 0.3251 0.8576 1.0004 1802
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8632 3.3014 0.7730 2.9708 12.3842 1.0089 991
## Veg_shannon_index 0.3047 0.3633 0.0382 0.1959 1.2453 1.0042 1909
## Avg_Cogongrass_Cover 0.3124 0.4289 0.0388 0.1947 1.2336 1.0200 1798
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8217 0.8991 0.0589 0.5498 3.165 1.0017 457
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6028 0.4391 -3.4583 -2.6048 -1.7142 1.0039 4700
## shrub_cover 0.2094 0.2389 -0.2640 0.2092 0.6910 1.0032 2668
## veg_height -0.0086 0.1574 -0.3318 -0.0081 0.3002 1.0011 3410
## week -0.0337 0.1213 -0.2848 -0.0278 0.1894 1.0083 2808
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0993 1.3350 0.6882 1.7648 5.3941 1.0083 1722
## shrub_cover 0.4358 0.3665 0.0882 0.3350 1.3909 1.0151 1317
## veg_height 0.1891 0.1337 0.0536 0.1543 0.5183 1.0014 3887
## week 0.0974 0.0820 0.0252 0.0762 0.2991 1.0105 2584
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6405 1.4863 1.2823 3.4484
## (Intercept)-Canis_latrans 0.4034 0.6433 -0.8931 0.4001
## (Intercept)-Sciurus_niger -0.1736 1.3425 -2.2134 -0.3697
## (Intercept)-Procyon_lotor 0.5433 0.6578 -0.7454 0.5424
## (Intercept)-Dasypus_novemcinctus -0.6793 0.6011 -1.9020 -0.6649
## (Intercept)-Lynx_rufus 0.3147 1.2492 -1.5375 0.1332
## (Intercept)-Didelphis_virginiana -1.3718 0.6757 -2.7686 -1.3610
## (Intercept)-Sylvilagus_floridanus -0.3486 0.7477 -1.7499 -0.3807
## (Intercept)-Sciurus_carolinensis -1.3482 0.6945 -2.7328 -1.3414
## (Intercept)-Vulpes_vulpes -0.9401 1.3785 -3.1618 -1.1149
## (Intercept)-Sus_scrofa -2.0033 0.8937 -3.8428 -1.9758
## Veg_shannon_index-Odocoileus_virginianus 0.3093 0.4987 -0.7255 0.3214
## Veg_shannon_index-Canis_latrans 0.6346 0.3989 -0.0967 0.6087
## Veg_shannon_index-Sciurus_niger 0.4137 0.5466 -0.6108 0.3902
## Veg_shannon_index-Procyon_lotor 0.4561 0.3740 -0.2523 0.4496
## Veg_shannon_index-Dasypus_novemcinctus 0.1981 0.3452 -0.4977 0.2050
## Veg_shannon_index-Lynx_rufus 0.2398 0.5257 -0.9163 0.2541
## Veg_shannon_index-Didelphis_virginiana 0.5078 0.3990 -0.2049 0.4845
## Veg_shannon_index-Sylvilagus_floridanus 0.4532 0.4316 -0.3468 0.4338
## Veg_shannon_index-Sciurus_carolinensis -0.0005 0.4036 -0.8627 0.0276
## Veg_shannon_index-Vulpes_vulpes 0.1364 0.4845 -0.9075 0.1597
## Veg_shannon_index-Sus_scrofa 0.7348 0.5549 -0.1548 0.6682
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3152 0.5171 -0.6825 0.3079
## Avg_Cogongrass_Cover-Canis_latrans 0.6043 0.4153 -0.1128 0.5717
## Avg_Cogongrass_Cover-Sciurus_niger -0.0090 0.6074 -1.3023 0.0357
## Avg_Cogongrass_Cover-Procyon_lotor 0.3731 0.3766 -0.3502 0.3633
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4476 0.3436 -0.2142 0.4384
## Avg_Cogongrass_Cover-Lynx_rufus 0.5631 0.4677 -0.2476 0.5236
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4550 0.3830 -0.2630 0.4402
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0689 0.4633 -1.0672 -0.0403
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4133 0.3632 -0.2636 0.4055
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4233 0.5009 -0.5126 0.4065
## Avg_Cogongrass_Cover-Sus_scrofa 0.0424 0.5631 -1.2079 0.1024
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0992 1.0049 1163
## (Intercept)-Canis_latrans 1.7026 1.0014 2627
## (Intercept)-Sciurus_niger 3.2049 1.0098 477
## (Intercept)-Procyon_lotor 1.8741 1.0040 2314
## (Intercept)-Dasypus_novemcinctus 0.4994 1.0047 3455
## (Intercept)-Lynx_rufus 3.3202 1.0054 556
## (Intercept)-Didelphis_virginiana -0.0766 1.0008 3150
## (Intercept)-Sylvilagus_floridanus 1.2243 1.0045 1946
## (Intercept)-Sciurus_carolinensis 0.0212 1.0025 2198
## (Intercept)-Vulpes_vulpes 2.3408 1.0014 436
## (Intercept)-Sus_scrofa -0.3347 1.0045 2249
## Veg_shannon_index-Odocoileus_virginianus 1.2737 1.0024 3478
## Veg_shannon_index-Canis_latrans 1.4787 1.0014 3327
## Veg_shannon_index-Sciurus_niger 1.6173 1.0046 2423
## Veg_shannon_index-Procyon_lotor 1.2210 1.0030 3095
## Veg_shannon_index-Dasypus_novemcinctus 0.8590 1.0011 4591
## Veg_shannon_index-Lynx_rufus 1.2720 1.0065 2143
## Veg_shannon_index-Didelphis_virginiana 1.3846 1.0002 3936
## Veg_shannon_index-Sylvilagus_floridanus 1.3722 1.0018 2913
## Veg_shannon_index-Sciurus_carolinensis 0.7259 1.0012 3305
## Veg_shannon_index-Vulpes_vulpes 1.0599 1.0018 2290
## Veg_shannon_index-Sus_scrofa 2.0403 1.0027 2528
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.4102 1.0019 2986
## Avg_Cogongrass_Cover-Canis_latrans 1.5188 1.0014 3000
## Avg_Cogongrass_Cover-Sciurus_niger 1.0546 1.0006 1639
## Avg_Cogongrass_Cover-Procyon_lotor 1.1414 1.0023 3520
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1614 1.0015 3580
## Avg_Cogongrass_Cover-Lynx_rufus 1.5741 1.0011 2281
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2621 1.0004 3071
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7588 1.0031 2441
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1537 1.0013 4064
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4814 1.0017 2386
## Avg_Cogongrass_Cover-Sus_scrofa 0.9968 1.0020 2252
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0047 0.0602 -0.1142 0.0061 0.1221
## (Intercept)-Canis_latrans -2.7488 0.1881 -3.1294 -2.7444 -2.3957
## (Intercept)-Sciurus_niger -4.2564 0.6415 -5.5064 -4.2541 -3.0187
## (Intercept)-Procyon_lotor -2.3014 0.1466 -2.6111 -2.2985 -2.0258
## (Intercept)-Dasypus_novemcinctus -1.7232 0.1578 -2.0537 -1.7177 -1.4238
## (Intercept)-Lynx_rufus -3.7346 0.3766 -4.4901 -3.7327 -3.0138
## (Intercept)-Didelphis_virginiana -2.5233 0.2851 -3.1091 -2.5122 -2.0055
## (Intercept)-Sylvilagus_floridanus -3.2321 0.3122 -3.8972 -3.2113 -2.6733
## (Intercept)-Sciurus_carolinensis -2.5977 0.3170 -3.2600 -2.5835 -2.0277
## (Intercept)-Vulpes_vulpes -4.2625 0.7748 -5.8091 -4.2237 -2.8532
## (Intercept)-Sus_scrofa -3.1991 0.5641 -4.3460 -3.1971 -2.1144
## shrub_cover-Odocoileus_virginianus -0.0541 0.0634 -0.1820 -0.0530 0.0698
## shrub_cover-Canis_latrans -0.2614 0.2146 -0.6790 -0.2614 0.1566
## shrub_cover-Sciurus_niger -0.3633 0.4495 -1.2926 -0.3427 0.4669
## shrub_cover-Procyon_lotor 0.2338 0.1710 -0.1137 0.2388 0.5587
## shrub_cover-Dasypus_novemcinctus 0.7856 0.2919 0.2450 0.7761 1.3857
## shrub_cover-Lynx_rufus -0.2425 0.3352 -0.9191 -0.2396 0.4026
## shrub_cover-Didelphis_virginiana 0.8613 0.3484 0.2125 0.8480 1.5909
## shrub_cover-Sylvilagus_floridanus 0.2133 0.3822 -0.4971 0.2044 0.9896
## shrub_cover-Sciurus_carolinensis 0.7508 0.3889 0.0345 0.7398 1.5389
## shrub_cover-Vulpes_vulpes -0.0743 0.5045 -1.1456 -0.0601 0.8912
## shrub_cover-Sus_scrofa 0.4303 0.6757 -0.8643 0.4212 1.8360
## veg_height-Odocoileus_virginianus -0.2980 0.0642 -0.4247 -0.2980 -0.1706
## veg_height-Canis_latrans -0.5790 0.1837 -0.9617 -0.5730 -0.2297
## veg_height-Sciurus_niger -0.0589 0.3779 -0.7764 -0.0653 0.7289
## veg_height-Procyon_lotor 0.3303 0.1220 0.0933 0.3297 0.5666
## veg_height-Dasypus_novemcinctus 0.2234 0.1304 -0.0267 0.2214 0.4774
## veg_height-Lynx_rufus 0.0102 0.2388 -0.4730 0.0151 0.4757
## veg_height-Didelphis_virginiana 0.3927 0.2339 -0.0487 0.3861 0.8715
## veg_height-Sylvilagus_floridanus 0.1147 0.2393 -0.3570 0.1186 0.5778
## veg_height-Sciurus_carolinensis 0.0466 0.2047 -0.3459 0.0446 0.4514
## veg_height-Vulpes_vulpes -0.1545 0.3083 -0.8135 -0.1368 0.4050
## veg_height-Sus_scrofa -0.1280 0.3268 -0.7987 -0.1212 0.5134
## week-Odocoileus_virginianus 0.2119 0.0612 0.0963 0.2111 0.3361
## week-Canis_latrans 0.0752 0.1307 -0.1891 0.0772 0.3234
## week-Sciurus_niger -0.2739 0.2910 -0.9371 -0.2390 0.2089
## week-Procyon_lotor -0.0467 0.1169 -0.2909 -0.0438 0.1717
## week-Dasypus_novemcinctus -0.1585 0.1368 -0.4467 -0.1518 0.0915
## week-Lynx_rufus -0.0231 0.1896 -0.4297 -0.0142 0.3235
## week-Didelphis_virginiana -0.1948 0.2149 -0.6651 -0.1837 0.1929
## week-Sylvilagus_floridanus -0.1395 0.2090 -0.5983 -0.1287 0.2375
## week-Sciurus_carolinensis 0.1456 0.1776 -0.2061 0.1449 0.4955
## week-Vulpes_vulpes -0.0992 0.2689 -0.6842 -0.0850 0.3955
## week-Sus_scrofa 0.1123 0.2373 -0.3687 0.1129 0.5809
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 4930
## (Intercept)-Canis_latrans 1.0008 2221
## (Intercept)-Sciurus_niger 1.0194 421
## (Intercept)-Procyon_lotor 1.0027 3708
## (Intercept)-Dasypus_novemcinctus 1.0062 3976
## (Intercept)-Lynx_rufus 1.0006 728
## (Intercept)-Didelphis_virginiana 1.0003 2829
## (Intercept)-Sylvilagus_floridanus 1.0033 1475
## (Intercept)-Sciurus_carolinensis 1.0005 2429
## (Intercept)-Vulpes_vulpes 1.0047 415
## (Intercept)-Sus_scrofa 1.0018 2059
## shrub_cover-Odocoileus_virginianus 1.0007 5250
## shrub_cover-Canis_latrans 1.0015 2597
## shrub_cover-Sciurus_niger 1.0293 1037
## shrub_cover-Procyon_lotor 1.0043 3770
## shrub_cover-Dasypus_novemcinctus 1.0060 3423
## shrub_cover-Lynx_rufus 1.0067 1428
## shrub_cover-Didelphis_virginiana 1.0003 2301
## shrub_cover-Sylvilagus_floridanus 0.9999 1781
## shrub_cover-Sciurus_carolinensis 1.0011 2366
## shrub_cover-Vulpes_vulpes 1.0022 1674
## shrub_cover-Sus_scrofa 1.0046 2509
## veg_height-Odocoileus_virginianus 1.0015 5250
## veg_height-Canis_latrans 1.0077 2275
## veg_height-Sciurus_niger 1.0008 1776
## veg_height-Procyon_lotor 1.0010 4339
## veg_height-Dasypus_novemcinctus 1.0023 5426
## veg_height-Lynx_rufus 0.9999 2409
## veg_height-Didelphis_virginiana 1.0002 3622
## veg_height-Sylvilagus_floridanus 1.0039 2816
## veg_height-Sciurus_carolinensis 0.9999 3311
## veg_height-Vulpes_vulpes 1.0014 2044
## veg_height-Sus_scrofa 1.0017 3583
## week-Odocoileus_virginianus 1.0001 4719
## week-Canis_latrans 0.9999 4306
## week-Sciurus_niger 1.0013 1940
## week-Procyon_lotor 1.0005 4674
## week-Dasypus_novemcinctus 0.9998 4850
## week-Lynx_rufus 1.0127 2693
## week-Didelphis_virginiana 1.0013 3419
## week-Sylvilagus_floridanus 1.0018 2695
## week-Sciurus_carolinensis 1.0031 4562
## week-Vulpes_vulpes 1.0028 2538
## week-Sus_scrofa 1.0034 4134
#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): 1.8577
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8785 0.6232 -2.0645 -0.8992 0.3945 1.0057 2918
## Avg_Cogongrass_Cover -0.7853 0.3944 -1.5756 -0.7770 -0.0312 1.0224 1180
## I(Avg_Cogongrass_Cover^2) 0.8809 0.3526 0.2581 0.8555 1.6458 1.0074 1041
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7197 2.9555 0.7436 2.8690 11.8686 1.0286 1686
## Avg_Cogongrass_Cover 0.4146 0.5226 0.0406 0.2436 1.7809 1.0136 1771
## I(Avg_Cogongrass_Cover^2) 0.4824 0.9649 0.0381 0.2199 2.6191 1.0563 699
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5308 0.5368 0.0496 0.364 2.0242 1.0097 371
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5874 0.4265 -3.3992 -2.6007 -1.7123 1.0005 3942
## shrub_cover 0.2283 0.2402 -0.2453 0.2283 0.7049 1.0006 2926
## veg_height 0.0191 0.1571 -0.2956 0.0208 0.3360 1.0030 2762
## week -0.0384 0.1176 -0.2864 -0.0343 0.1756 1.0020 3611
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9374 1.2475 0.6699 1.6282 5.0729 1.0088 2009
## shrub_cover 0.4392 0.3772 0.0841 0.3399 1.4094 1.0130 2131
## veg_height 0.1845 0.1189 0.0541 0.1528 0.5022 1.0009 3203
## week 0.0963 0.0741 0.0252 0.0761 0.2911 1.0035 3203
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8601 1.4335 0.6053 2.6840
## (Intercept)-Canis_latrans -0.4504 0.7026 -1.8933 -0.4386
## (Intercept)-Sciurus_niger -0.8033 1.2595 -2.8147 -0.9663
## (Intercept)-Procyon_lotor -0.1565 0.6588 -1.4961 -0.1461
## (Intercept)-Dasypus_novemcinctus -1.3612 0.6499 -2.6877 -1.3448
## (Intercept)-Lynx_rufus -0.9790 1.0257 -2.7613 -1.0576
## (Intercept)-Didelphis_virginiana -1.8772 0.7139 -3.3564 -1.8556
## (Intercept)-Sylvilagus_floridanus -1.0899 0.7516 -2.5829 -1.0859
## (Intercept)-Sciurus_carolinensis -2.3701 0.7704 -3.9721 -2.3310
## (Intercept)-Vulpes_vulpes -2.2158 1.1984 -4.4397 -2.2669
## (Intercept)-Sus_scrofa -2.3968 0.9312 -4.3211 -2.3608
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7831 0.6520 -2.1226 -0.7740
## Avg_Cogongrass_Cover-Canis_latrans -0.3811 0.5539 -1.3855 -0.4181
## Avg_Cogongrass_Cover-Sciurus_niger -1.0801 0.7233 -2.7167 -1.0007
## Avg_Cogongrass_Cover-Procyon_lotor -0.7079 0.5048 -1.7171 -0.7025
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5846 0.4948 -1.5591 -0.5916
## Avg_Cogongrass_Cover-Lynx_rufus -0.7197 0.5855 -1.8817 -0.7036
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5292 0.5454 -1.5330 -0.5553
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2159 0.6470 -2.7276 -1.1454
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8443 0.5574 -1.9978 -0.8197
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8002 0.6249 -2.1231 -0.7798
## Avg_Cogongrass_Cover-Sus_scrofa -1.0482 0.6882 -2.6203 -0.9886
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1836 0.8268 0.1152 1.0330
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2557 0.7561 0.2571 1.0982
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4343 0.7422 -1.2733 0.4819
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0780 0.6282 0.2286 0.9835
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7748 0.3648 0.0891 0.7675
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2066 0.5456 0.3610 1.1297
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6296 0.4248 -0.1498 0.6140
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7815 0.4841 -0.0332 0.7366
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0260 0.4256 0.3011 0.9904
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9847 0.5272 0.1376 0.9154
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5040 0.6143 -0.9105 0.5403
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.1411 1.0177 1251
## (Intercept)-Canis_latrans 0.9290 1.0023 2428
## (Intercept)-Sciurus_niger 2.3015 1.0097 528
## (Intercept)-Procyon_lotor 1.1357 1.0032 2594
## (Intercept)-Dasypus_novemcinctus -0.0733 1.0081 3159
## (Intercept)-Lynx_rufus 1.3555 1.0285 986
## (Intercept)-Didelphis_virginiana -0.4923 1.0035 2830
## (Intercept)-Sylvilagus_floridanus 0.3738 1.0000 2141
## (Intercept)-Sciurus_carolinensis -0.9557 1.0028 2490
## (Intercept)-Vulpes_vulpes 0.1786 1.0142 717
## (Intercept)-Sus_scrofa -0.6512 1.0018 1961
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5082 1.0081 2466
## Avg_Cogongrass_Cover-Canis_latrans 0.8242 1.0053 2350
## Avg_Cogongrass_Cover-Sciurus_niger 0.1648 1.0177 1485
## Avg_Cogongrass_Cover-Procyon_lotor 0.2956 1.0149 2217
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4049 1.0109 2241
## Avg_Cogongrass_Cover-Lynx_rufus 0.4385 1.0077 1822
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6138 1.0090 2383
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1618 1.0087 1472
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2033 1.0067 1850
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3967 1.0143 1730
## Avg_Cogongrass_Cover-Sus_scrofa 0.1023 1.0225 1611
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2626 1.0234 977
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2969 1.0079 880
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.7801 1.0073 771
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6117 1.0102 823
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5243 1.0107 2463
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4987 1.0095 1395
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5281 1.0052 1933
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8141 1.0191 1539
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9914 1.0052 1845
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2214 1.0063 1242
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.6210 1.0048 1406
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0601 -0.1114 0.0046 0.1233
## (Intercept)-Canis_latrans -2.7546 0.1850 -3.1379 -2.7440 -2.4178
## (Intercept)-Sciurus_niger -4.1715 0.6733 -5.4566 -4.1788 -2.8960
## (Intercept)-Procyon_lotor -2.3154 0.1504 -2.6265 -2.3084 -2.0427
## (Intercept)-Dasypus_novemcinctus -1.7236 0.1566 -2.0397 -1.7197 -1.4241
## (Intercept)-Lynx_rufus -3.6136 0.3663 -4.3497 -3.6001 -2.9135
## (Intercept)-Didelphis_virginiana -2.5532 0.2904 -3.1830 -2.5423 -2.0099
## (Intercept)-Sylvilagus_floridanus -3.2108 0.3026 -3.8684 -3.1908 -2.6594
## (Intercept)-Sciurus_carolinensis -2.5826 0.3034 -3.2188 -2.5716 -2.0297
## (Intercept)-Vulpes_vulpes -4.0723 0.7367 -5.5847 -4.0207 -2.7911
## (Intercept)-Sus_scrofa -3.2677 0.5868 -4.4603 -3.2503 -2.1287
## shrub_cover-Odocoileus_virginianus -0.0526 0.0644 -0.1811 -0.0522 0.0746
## shrub_cover-Canis_latrans -0.2364 0.2140 -0.6613 -0.2338 0.1660
## shrub_cover-Sciurus_niger -0.3040 0.4678 -1.2826 -0.2841 0.5641
## shrub_cover-Procyon_lotor 0.2276 0.1707 -0.1179 0.2299 0.5474
## shrub_cover-Dasypus_novemcinctus 0.7792 0.2897 0.2313 0.7656 1.3730
## shrub_cover-Lynx_rufus -0.2111 0.3472 -0.9168 -0.2021 0.4610
## shrub_cover-Didelphis_virginiana 0.8964 0.3649 0.2445 0.8701 1.6718
## shrub_cover-Sylvilagus_floridanus 0.2539 0.3914 -0.4713 0.2449 1.0530
## shrub_cover-Sciurus_carolinensis 0.7371 0.3814 0.0329 0.7227 1.5034
## shrub_cover-Vulpes_vulpes -0.0417 0.5300 -1.1262 -0.0275 0.9851
## shrub_cover-Sus_scrofa 0.4740 0.6896 -0.9108 0.4603 1.8513
## veg_height-Odocoileus_virginianus -0.2975 0.0654 -0.4278 -0.2963 -0.1717
## veg_height-Canis_latrans -0.5670 0.1821 -0.9405 -0.5648 -0.2207
## veg_height-Sciurus_niger 0.0366 0.3916 -0.7147 0.0192 0.8792
## veg_height-Procyon_lotor 0.3424 0.1247 0.1025 0.3406 0.5908
## veg_height-Dasypus_novemcinctus 0.2291 0.1323 -0.0349 0.2292 0.4850
## veg_height-Lynx_rufus 0.0841 0.2340 -0.3717 0.0902 0.5377
## veg_height-Didelphis_virginiana 0.3853 0.2440 -0.0755 0.3735 0.8750
## veg_height-Sylvilagus_floridanus 0.1453 0.2439 -0.3221 0.1438 0.6329
## veg_height-Sciurus_carolinensis 0.0560 0.2036 -0.3345 0.0508 0.4636
## veg_height-Vulpes_vulpes -0.1095 0.3010 -0.7289 -0.0962 0.4526
## veg_height-Sus_scrofa -0.1109 0.3274 -0.7786 -0.1081 0.5213
## week-Odocoileus_virginianus 0.2121 0.0612 0.0947 0.2124 0.3315
## week-Canis_latrans 0.0726 0.1298 -0.1953 0.0751 0.3164
## week-Sciurus_niger -0.2802 0.2877 -0.9289 -0.2529 0.2133
## week-Procyon_lotor -0.0456 0.1172 -0.2907 -0.0417 0.1699
## week-Dasypus_novemcinctus -0.1572 0.1353 -0.4335 -0.1535 0.1008
## week-Lynx_rufus -0.0297 0.1948 -0.4481 -0.0182 0.3271
## week-Didelphis_virginiana -0.1951 0.2164 -0.6565 -0.1774 0.1853
## week-Sylvilagus_floridanus -0.1345 0.2041 -0.5754 -0.1236 0.2277
## week-Sciurus_carolinensis 0.1406 0.1764 -0.2088 0.1438 0.4907
## week-Vulpes_vulpes -0.1047 0.2711 -0.7003 -0.0870 0.3934
## week-Sus_scrofa 0.1049 0.2291 -0.3480 0.1055 0.5518
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5094
## (Intercept)-Canis_latrans 1.0008 2177
## (Intercept)-Sciurus_niger 1.0374 443
## (Intercept)-Procyon_lotor 1.0038 3356
## (Intercept)-Dasypus_novemcinctus 0.9998 4384
## (Intercept)-Lynx_rufus 1.0172 885
## (Intercept)-Didelphis_virginiana 1.0022 2409
## (Intercept)-Sylvilagus_floridanus 1.0130 1477
## (Intercept)-Sciurus_carolinensis 1.0006 2621
## (Intercept)-Vulpes_vulpes 1.0145 479
## (Intercept)-Sus_scrofa 1.0002 1529
## shrub_cover-Odocoileus_virginianus 1.0000 5250
## shrub_cover-Canis_latrans 1.0011 3043
## shrub_cover-Sciurus_niger 1.0216 1042
## shrub_cover-Procyon_lotor 1.0016 3511
## shrub_cover-Dasypus_novemcinctus 1.0049 3141
## shrub_cover-Lynx_rufus 1.0065 1545
## shrub_cover-Didelphis_virginiana 1.0042 1827
## shrub_cover-Sylvilagus_floridanus 1.0099 1796
## shrub_cover-Sciurus_carolinensis 1.0046 2557
## shrub_cover-Vulpes_vulpes 1.0029 1966
## shrub_cover-Sus_scrofa 1.0047 2572
## veg_height-Odocoileus_virginianus 1.0029 5250
## veg_height-Canis_latrans 1.0032 2426
## veg_height-Sciurus_niger 1.0069 1340
## veg_height-Procyon_lotor 1.0048 4018
## veg_height-Dasypus_novemcinctus 1.0032 4891
## veg_height-Lynx_rufus 1.0153 2141
## veg_height-Didelphis_virginiana 1.0004 3102
## veg_height-Sylvilagus_floridanus 1.0172 2353
## veg_height-Sciurus_carolinensis 1.0019 3463
## veg_height-Vulpes_vulpes 1.0045 2286
## veg_height-Sus_scrofa 1.0046 2931
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0028 4366
## week-Sciurus_niger 1.0068 1891
## week-Procyon_lotor 1.0011 4535
## week-Dasypus_novemcinctus 1.0008 4633
## week-Lynx_rufus 1.0079 2716
## week-Didelphis_virginiana 1.0000 3837
## week-Sylvilagus_floridanus 1.0011 3057
## week-Sciurus_carolinensis 1.0009 4447
## week-Vulpes_vulpes 1.0024 3106
## week-Sus_scrofa 1.0013 4399
## 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): 2.0405
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9044 1.1565 -3.0255 -0.9713 1.5788 1.0044 1659
## Cogon_Patch_Size -0.2315 0.7963 -1.9268 -0.1808 1.2546 1.0006 639
## Veg_shannon_index 0.9804 0.5263 0.0066 0.9617 2.0969 1.0018 717
## total_shrub_cover -0.5501 0.5305 -1.6594 -0.5213 0.4472 1.0122 737
## Avg_Cogongrass_Cover -0.1320 0.9920 -2.0512 -0.1401 1.9175 1.0152 435
## Tree_Density -2.0344 0.8512 -3.7295 -2.0195 -0.3912 1.0133 981
## Avg_Canopy_Cover 2.0008 0.7109 0.6938 1.9584 3.5187 1.0035 882
## I(Avg_Cogongrass_Cover^2) 1.6435 0.6043 0.5547 1.6139 2.9341 1.0057 443
## avg_veg_height -0.1498 0.5325 -1.1999 -0.1466 0.9246 1.0227 758
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.5272 24.5066 4.3077 17.5548 87.5871 1.0303 327
## Cogon_Patch_Size 4.3096 7.0791 0.1480 2.2198 21.9587 1.0259 336
## Veg_shannon_index 1.0030 1.6830 0.0504 0.4742 5.4178 1.0397 714
## total_shrub_cover 0.9373 1.3183 0.0548 0.4983 4.4476 1.0487 752
## Avg_Cogongrass_Cover 1.5386 3.3264 0.0504 0.5857 8.4063 1.0316 801
## Tree_Density 5.2713 10.6314 0.0796 2.0756 32.6521 1.0101 258
## Avg_Canopy_Cover 3.8595 5.3351 0.1918 2.2252 17.9718 1.0383 344
## I(Avg_Cogongrass_Cover^2) 1.0165 1.7738 0.0464 0.4422 5.5813 1.0281 616
## avg_veg_height 0.5866 0.9443 0.0437 0.2960 2.9487 1.0149 1431
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0687 3.1091 0.0733 0.976 11.3427 1.0129 175
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6658 0.4641 -3.5386 -2.6770 -1.7093 1.0028 4925
## shrub_cover 0.3133 0.2587 -0.1781 0.3085 0.8300 1.0019 1622
## veg_height 0.0257 0.1544 -0.2765 0.0257 0.3297 1.0000 2793
## week -0.0420 0.1195 -0.2991 -0.0352 0.1775 1.0011 2766
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3081 1.3999 0.8390 1.9246 5.9451 1.0007 2551
## shrub_cover 0.5089 0.4159 0.1032 0.3974 1.5406 1.0027 1615
## veg_height 0.1932 0.1330 0.0561 0.1585 0.5422 1.0023 3657
## week 0.0970 0.0726 0.0251 0.0763 0.2958 1.0032 2610
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.1549 4.2140 2.6332
## (Intercept)-Canis_latrans -0.8519 1.3116 -3.3763
## (Intercept)-Sciurus_niger 0.9974 2.7812 -3.3127
## (Intercept)-Procyon_lotor -0.3096 1.2334 -2.7874
## (Intercept)-Dasypus_novemcinctus -2.7289 1.3344 -5.8517
## (Intercept)-Lynx_rufus 0.6182 2.9136 -3.8165
## (Intercept)-Didelphis_virginiana -4.2371 1.5540 -7.6927
## (Intercept)-Sylvilagus_floridanus -2.4622 1.6571 -6.0552
## (Intercept)-Sciurus_carolinensis -4.9416 1.8197 -9.2086
## (Intercept)-Vulpes_vulpes -4.2835 2.4982 -9.2231
## (Intercept)-Sus_scrofa -5.9490 2.4229 -11.6916
## Cogon_Patch_Size-Odocoileus_virginianus 0.0061 1.5418 -2.8959
## Cogon_Patch_Size-Canis_latrans 1.7700 1.6550 -0.4143
## Cogon_Patch_Size-Sciurus_niger -1.0285 2.1062 -6.0180
## Cogon_Patch_Size-Procyon_lotor -0.5987 0.8346 -2.3779
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0454 0.8492 -1.6939
## Cogon_Patch_Size-Lynx_rufus -0.4544 1.5823 -3.5981
## Cogon_Patch_Size-Didelphis_virginiana 1.6950 1.1548 -0.1294
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6148 1.9982 -6.5262
## Cogon_Patch_Size-Sciurus_carolinensis -1.2880 1.6400 -5.3167
## Cogon_Patch_Size-Vulpes_vulpes -0.8073 1.9425 -5.3496
## Cogon_Patch_Size-Sus_scrofa -0.9558 1.8424 -5.2118
## Veg_shannon_index-Odocoileus_virginianus 0.7697 1.0111 -1.6306
## Veg_shannon_index-Canis_latrans 1.4024 0.8116 0.1288
## Veg_shannon_index-Sciurus_niger 1.1043 1.0493 -0.9048
## Veg_shannon_index-Procyon_lotor 1.2256 0.6735 0.0670
## Veg_shannon_index-Dasypus_novemcinctus 0.6219 0.6292 -0.6686
## Veg_shannon_index-Lynx_rufus 1.1241 1.0701 -0.9041
## Veg_shannon_index-Didelphis_virginiana 1.1735 0.7413 -0.1680
## Veg_shannon_index-Sylvilagus_floridanus 1.0846 0.7776 -0.3246
## Veg_shannon_index-Sciurus_carolinensis 0.3340 0.9315 -1.8134
## Veg_shannon_index-Vulpes_vulpes 0.6340 0.9725 -1.6187
## Veg_shannon_index-Sus_scrofa 1.6588 1.1032 0.0884
## total_shrub_cover-Odocoileus_virginianus -0.3463 0.9309 -2.1484
## total_shrub_cover-Canis_latrans 0.1347 0.7995 -1.1879
## total_shrub_cover-Sciurus_niger -0.7638 1.0511 -3.2061
## total_shrub_cover-Procyon_lotor -1.1269 0.6951 -2.6934
## total_shrub_cover-Dasypus_novemcinctus -0.2695 0.6877 -1.7399
## total_shrub_cover-Lynx_rufus -0.8116 1.0788 -3.2330
## total_shrub_cover-Didelphis_virginiana -0.8537 0.8539 -2.8561
## total_shrub_cover-Sylvilagus_floridanus -0.6639 0.8853 -2.6309
## total_shrub_cover-Sciurus_carolinensis -0.4612 0.8515 -2.2933
## total_shrub_cover-Vulpes_vulpes -0.7566 1.0160 -3.1030
## total_shrub_cover-Sus_scrofa -0.2865 0.9153 -2.0632
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1725 1.4468 -3.0942
## Avg_Cogongrass_Cover-Canis_latrans 0.1168 1.2924 -2.3142
## Avg_Cogongrass_Cover-Sciurus_niger -0.6772 1.7786 -4.7757
## Avg_Cogongrass_Cover-Procyon_lotor 0.0088 1.2086 -2.3029
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5631 1.3391 -1.7613
## Avg_Cogongrass_Cover-Lynx_rufus -0.0181 1.3902 -2.7631
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0816 1.2868 -2.5877
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7611 1.4126 -3.9418
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1231 1.2912 -2.7288
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0752 1.4055 -2.5205
## Avg_Cogongrass_Cover-Sus_scrofa -0.5369 1.4471 -3.6859
## Tree_Density-Odocoileus_virginianus -0.8515 1.6620 -3.4258
## Tree_Density-Canis_latrans -3.1519 1.7440 -7.4333
## Tree_Density-Sciurus_niger -2.0267 1.9263 -5.9551
## Tree_Density-Procyon_lotor -1.9916 1.0068 -4.1088
## Tree_Density-Dasypus_novemcinctus -4.3969 2.5333 -11.2493
## Tree_Density-Lynx_rufus -0.6413 1.9496 -3.5038
## Tree_Density-Didelphis_virginiana -2.4452 1.4177 -5.7081
## Tree_Density-Sylvilagus_floridanus -2.7154 1.7302 -6.7773
## Tree_Density-Sciurus_carolinensis -2.8618 1.8425 -7.4583
## Tree_Density-Vulpes_vulpes -2.0008 1.9527 -6.0349
## Tree_Density-Sus_scrofa -2.6576 2.0328 -7.6924
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2111 1.5758 -2.1611
## Avg_Canopy_Cover-Canis_latrans 0.1183 0.7467 -1.4485
## Avg_Canopy_Cover-Sciurus_niger 2.6081 2.0352 -1.0150
## Avg_Canopy_Cover-Procyon_lotor 1.7351 0.8623 0.2458
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2674 0.9440 0.7713
## Avg_Canopy_Cover-Lynx_rufus 1.7115 1.6799 -1.3180
## Avg_Canopy_Cover-Didelphis_virginiana 3.2749 1.4288 1.2673
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0532 2.0539 1.2752
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0528 1.5128 1.0225
## Avg_Canopy_Cover-Vulpes_vulpes 2.6946 1.5640 0.4389
## Avg_Canopy_Cover-Sus_scrofa 2.2745 1.1010 0.5390
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.9247 1.1349 0.1285
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0732 0.9660 0.6339
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2601 1.2460 -1.5289
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9421 0.8533 0.5277
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5895 0.7649 0.2265
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1911 1.0567 0.6020
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2805 0.7407 -0.1502
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3912 0.8549 -0.1898
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8454 0.8378 0.4931
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9865 0.9507 0.5180
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2240 1.1506 -1.2247
## avg_veg_height-Odocoileus_virginianus -0.1326 0.8751 -1.8932
## avg_veg_height-Canis_latrans -0.2645 0.6599 -1.6332
## avg_veg_height-Sciurus_niger -0.3427 0.9479 -2.4531
## avg_veg_height-Procyon_lotor 0.0649 0.6853 -1.2230
## avg_veg_height-Dasypus_novemcinctus 0.1946 0.6614 -1.0313
## avg_veg_height-Lynx_rufus -0.3379 0.9119 -2.3465
## avg_veg_height-Didelphis_virginiana -0.3479 0.7738 -1.9851
## avg_veg_height-Sylvilagus_floridanus -0.2699 0.7698 -1.8759
## avg_veg_height-Sciurus_carolinensis 0.1782 0.7560 -1.2216
## avg_veg_height-Vulpes_vulpes -0.2923 0.8751 -2.1568
## avg_veg_height-Sus_scrofa -0.2066 0.7990 -1.8551
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3042 18.5678 1.0347 284
## (Intercept)-Canis_latrans -0.8921 1.9560 1.0029 1105
## (Intercept)-Sciurus_niger 0.6058 7.8370 1.0587 271
## (Intercept)-Procyon_lotor -0.2758 2.1203 1.0047 920
## (Intercept)-Dasypus_novemcinctus -2.5977 -0.4454 1.0042 704
## (Intercept)-Lynx_rufus 0.2066 7.7370 1.0799 309
## (Intercept)-Didelphis_virginiana -4.1066 -1.5284 1.0022 888
## (Intercept)-Sylvilagus_floridanus -2.3724 0.5059 1.0079 1028
## (Intercept)-Sciurus_carolinensis -4.7456 -1.8996 1.0062 401
## (Intercept)-Vulpes_vulpes -4.3093 0.9850 1.0191 392
## (Intercept)-Sus_scrofa -5.6238 -2.1558 1.0313 285
## Cogon_Patch_Size-Odocoileus_virginianus -0.0546 3.4626 1.0011 1599
## Cogon_Patch_Size-Canis_latrans 1.4389 5.9135 1.0110 471
## Cogon_Patch_Size-Sciurus_niger -0.7904 2.6520 1.0046 506
## Cogon_Patch_Size-Procyon_lotor -0.5690 0.9581 1.0064 544
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0488 1.7053 1.0076 986
## Cogon_Patch_Size-Lynx_rufus -0.4486 2.7931 1.0073 697
## Cogon_Patch_Size-Didelphis_virginiana 1.5489 4.3695 1.0088 449
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2152 0.9852 1.0132 491
## Cogon_Patch_Size-Sciurus_carolinensis -0.9666 0.9383 1.0106 526
## Cogon_Patch_Size-Vulpes_vulpes -0.5831 2.4361 1.0094 679
## Cogon_Patch_Size-Sus_scrofa -0.6436 1.7493 1.0063 683
## Veg_shannon_index-Odocoileus_virginianus 0.8232 2.6819 1.0060 1651
## Veg_shannon_index-Canis_latrans 1.3010 3.3320 1.0066 850
## Veg_shannon_index-Sciurus_niger 1.0527 3.3888 1.0175 836
## Veg_shannon_index-Procyon_lotor 1.1561 2.7394 1.0059 621
## Veg_shannon_index-Dasypus_novemcinctus 0.6232 1.8192 1.0030 1206
## Veg_shannon_index-Lynx_rufus 1.0923 3.3822 1.0099 925
## Veg_shannon_index-Didelphis_virginiana 1.1213 2.8262 1.0082 1243
## Veg_shannon_index-Sylvilagus_floridanus 1.0374 2.7641 1.0035 1184
## Veg_shannon_index-Sciurus_carolinensis 0.4511 1.8817 1.0091 1048
## Veg_shannon_index-Vulpes_vulpes 0.7036 2.3999 1.0030 1157
## Veg_shannon_index-Sus_scrofa 1.4581 4.3986 1.0161 728
## total_shrub_cover-Odocoileus_virginianus -0.3699 1.6201 1.0069 1907
## total_shrub_cover-Canis_latrans 0.0540 1.9906 1.0135 884
## total_shrub_cover-Sciurus_niger -0.6753 1.0393 1.0400 939
## total_shrub_cover-Procyon_lotor -1.0705 0.0639 1.0324 1334
## total_shrub_cover-Dasypus_novemcinctus -0.2423 1.0122 1.0010 1662
## total_shrub_cover-Lynx_rufus -0.7503 1.1713 1.0150 786
## total_shrub_cover-Didelphis_virginiana -0.7602 0.5497 1.0250 1021
## total_shrub_cover-Sylvilagus_floridanus -0.5810 0.8949 1.0162 1058
## total_shrub_cover-Sciurus_carolinensis -0.4183 1.1758 1.0032 1289
## total_shrub_cover-Vulpes_vulpes -0.6615 1.0029 1.0120 1305
## total_shrub_cover-Sus_scrofa -0.3062 1.6424 1.0062 1237
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1391 2.6722 1.0044 705
## Avg_Cogongrass_Cover-Canis_latrans 0.0999 2.8256 1.0142 540
## Avg_Cogongrass_Cover-Sciurus_niger -0.4937 2.1691 1.0122 604
## Avg_Cogongrass_Cover-Procyon_lotor -0.0112 2.4759 1.0085 683
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4390 3.5084 1.0142 508
## Avg_Cogongrass_Cover-Lynx_rufus -0.0436 2.8073 1.0108 690
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1324 2.5054 1.0097 640
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6515 1.8104 1.0052 654
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1254 2.5263 1.0050 594
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0427 3.0622 1.0110 687
## Avg_Cogongrass_Cover-Sus_scrofa -0.4354 2.0970 1.0023 731
## Tree_Density-Odocoileus_virginianus -1.0875 3.3608 1.0064 574
## Tree_Density-Canis_latrans -2.8175 -0.7730 1.0210 393
## Tree_Density-Sciurus_niger -1.9979 2.1221 1.0272 625
## Tree_Density-Procyon_lotor -1.9444 -0.1382 1.0129 1424
## Tree_Density-Dasypus_novemcinctus -3.8099 -1.3885 1.0161 205
## Tree_Density-Lynx_rufus -0.9613 3.9431 1.0200 378
## Tree_Density-Didelphis_virginiana -2.2664 -0.0354 1.0164 808
## Tree_Density-Sylvilagus_floridanus -2.4665 -0.0203 1.0070 672
## Tree_Density-Sciurus_carolinensis -2.5564 -0.0927 1.0255 605
## Tree_Density-Vulpes_vulpes -1.9776 2.1643 1.0080 749
## Tree_Density-Sus_scrofa -2.3476 0.3806 1.0160 689
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3070 4.3207 1.0033 1320
## Avg_Canopy_Cover-Canis_latrans 0.1300 1.5338 1.0048 1156
## Avg_Canopy_Cover-Sciurus_niger 2.3784 7.3739 1.0015 429
## Avg_Canopy_Cover-Procyon_lotor 1.6669 3.6338 1.0066 1210
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1375 4.4279 1.0087 538
## Avg_Canopy_Cover-Lynx_rufus 1.5880 5.4754 1.0025 554
## Avg_Canopy_Cover-Didelphis_virginiana 3.0216 6.7847 1.0085 440
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6427 9.3087 1.0436 361
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7349 6.8123 1.0203 559
## Avg_Canopy_Cover-Vulpes_vulpes 2.4118 6.5140 1.0060 576
## Avg_Canopy_Cover-Sus_scrofa 2.1047 4.8830 1.0201 893
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7833 4.6008 1.0096 905
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9212 4.3883 1.0108 651
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3559 3.4722 1.0189 448
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8483 3.8300 1.0053 795
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5332 3.3105 1.0045 747
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0249 4.7772 1.0112 900
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2651 2.8005 1.0056 544
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3344 3.2079 1.0037 555
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7572 3.8334 1.0043 736
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8616 4.2668 1.0149 749
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2870 3.2930 1.0185 561
## avg_veg_height-Odocoileus_virginianus -0.1311 1.6240 1.0187 1457
## avg_veg_height-Canis_latrans -0.2585 1.0321 1.0235 1090
## avg_veg_height-Sciurus_niger -0.2690 1.2743 1.0035 1049
## avg_veg_height-Procyon_lotor 0.0548 1.4362 1.0094 1153
## avg_veg_height-Dasypus_novemcinctus 0.1645 1.5806 1.0106 1007
## avg_veg_height-Lynx_rufus -0.2739 1.2591 1.0082 1014
## avg_veg_height-Didelphis_virginiana -0.3090 1.0718 1.0129 1137
## avg_veg_height-Sylvilagus_floridanus -0.2455 1.1857 1.0246 1146
## avg_veg_height-Sciurus_carolinensis 0.1340 1.7991 1.0157 1175
## avg_veg_height-Vulpes_vulpes -0.2684 1.3341 1.0304 1028
## avg_veg_height-Sus_scrofa -0.1568 1.3380 1.0189 1334
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0065 0.0590 -0.1117 0.0055 0.1226
## (Intercept)-Canis_latrans -2.7162 0.1844 -3.0957 -2.7094 -2.3757
## (Intercept)-Sciurus_niger -4.7562 0.5097 -5.7004 -4.7739 -3.6921
## (Intercept)-Procyon_lotor -2.3091 0.1449 -2.6037 -2.3070 -2.0315
## (Intercept)-Dasypus_novemcinctus -1.7602 0.1652 -2.0984 -1.7542 -1.4526
## (Intercept)-Lynx_rufus -3.9046 0.3687 -4.6338 -3.9121 -3.1724
## (Intercept)-Didelphis_virginiana -2.5651 0.2951 -3.1714 -2.5478 -2.0214
## (Intercept)-Sylvilagus_floridanus -3.2116 0.2691 -3.7541 -3.1998 -2.6999
## (Intercept)-Sciurus_carolinensis -2.7059 0.3304 -3.3774 -2.6975 -2.0876
## (Intercept)-Vulpes_vulpes -4.2883 0.6703 -5.6784 -4.2512 -3.0453
## (Intercept)-Sus_scrofa -3.3388 0.6016 -4.5061 -3.3357 -2.1817
## shrub_cover-Odocoileus_virginianus -0.0517 0.0652 -0.1787 -0.0519 0.0767
## shrub_cover-Canis_latrans -0.2824 0.2245 -0.7260 -0.2841 0.1469
## shrub_cover-Sciurus_niger -0.3102 0.4468 -1.2019 -0.3057 0.5559
## shrub_cover-Procyon_lotor 0.2676 0.1596 -0.0490 0.2699 0.5720
## shrub_cover-Dasypus_novemcinctus 0.9024 0.3137 0.3117 0.8964 1.5264
## shrub_cover-Lynx_rufus -0.1894 0.3542 -0.8948 -0.1952 0.5285
## shrub_cover-Didelphis_virginiana 0.9590 0.3619 0.3002 0.9427 1.7099
## shrub_cover-Sylvilagus_floridanus 0.4891 0.3809 -0.2437 0.4852 1.2561
## shrub_cover-Sciurus_carolinensis 0.8995 0.4087 0.1567 0.8829 1.7321
## shrub_cover-Vulpes_vulpes 0.1587 0.5394 -0.8812 0.1561 1.2109
## shrub_cover-Sus_scrofa 0.7057 0.7455 -0.7276 0.6784 2.2201
## veg_height-Odocoileus_virginianus -0.2963 0.0661 -0.4249 -0.2962 -0.1678
## veg_height-Canis_latrans -0.5418 0.1814 -0.9119 -0.5363 -0.2010
## veg_height-Sciurus_niger 0.0067 0.3343 -0.6469 -0.0011 0.6799
## veg_height-Procyon_lotor 0.3554 0.1245 0.1141 0.3547 0.5987
## veg_height-Dasypus_novemcinctus 0.2490 0.1359 -0.0185 0.2448 0.5216
## veg_height-Lynx_rufus 0.1440 0.2314 -0.3257 0.1448 0.5726
## veg_height-Didelphis_virginiana 0.4240 0.2372 -0.0206 0.4137 0.9272
## veg_height-Sylvilagus_floridanus 0.1378 0.2419 -0.3426 0.1393 0.6155
## veg_height-Sciurus_carolinensis 0.1071 0.2181 -0.3056 0.1008 0.5483
## veg_height-Vulpes_vulpes -0.1599 0.3239 -0.8435 -0.1480 0.4286
## veg_height-Sus_scrofa -0.1430 0.3229 -0.8268 -0.1310 0.4688
## week-Odocoileus_virginianus 0.2110 0.0602 0.0977 0.2087 0.3323
## week-Canis_latrans 0.0740 0.1303 -0.1874 0.0756 0.3221
## week-Sciurus_niger -0.2896 0.2901 -0.9434 -0.2591 0.1873
## week-Procyon_lotor -0.0475 0.1169 -0.2917 -0.0460 0.1711
## week-Dasypus_novemcinctus -0.1571 0.1355 -0.4430 -0.1510 0.0931
## week-Lynx_rufus -0.0308 0.1947 -0.4446 -0.0244 0.3223
## week-Didelphis_virginiana -0.1965 0.2121 -0.6455 -0.1841 0.1832
## week-Sylvilagus_floridanus -0.1438 0.2045 -0.5795 -0.1325 0.2245
## week-Sciurus_carolinensis 0.1440 0.1789 -0.2133 0.1448 0.4876
## week-Vulpes_vulpes -0.1036 0.2672 -0.6640 -0.0894 0.3865
## week-Sus_scrofa 0.1071 0.2323 -0.3567 0.1077 0.5566
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0025 4976
## (Intercept)-Canis_latrans 1.0078 2327
## (Intercept)-Sciurus_niger 1.0350 412
## (Intercept)-Procyon_lotor 1.0008 3222
## (Intercept)-Dasypus_novemcinctus 1.0000 3000
## (Intercept)-Lynx_rufus 1.0095 465
## (Intercept)-Didelphis_virginiana 1.0058 1559
## (Intercept)-Sylvilagus_floridanus 1.0062 2062
## (Intercept)-Sciurus_carolinensis 1.0091 1536
## (Intercept)-Vulpes_vulpes 1.0161 471
## (Intercept)-Sus_scrofa 1.0395 870
## shrub_cover-Odocoileus_virginianus 1.0006 5722
## shrub_cover-Canis_latrans 1.0010 1677
## shrub_cover-Sciurus_niger 1.0097 979
## shrub_cover-Procyon_lotor 1.0021 4188
## shrub_cover-Dasypus_novemcinctus 1.0022 1866
## shrub_cover-Lynx_rufus 1.0031 829
## shrub_cover-Didelphis_virginiana 1.0002 1294
## shrub_cover-Sylvilagus_floridanus 1.0001 1591
## shrub_cover-Sciurus_carolinensis 1.0051 1523
## shrub_cover-Vulpes_vulpes 1.0022 1465
## shrub_cover-Sus_scrofa 1.0220 782
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0044 2474
## veg_height-Sciurus_niger 1.0021 988
## veg_height-Procyon_lotor 1.0028 4049
## veg_height-Dasypus_novemcinctus 1.0003 3558
## veg_height-Lynx_rufus 1.0024 2164
## veg_height-Didelphis_virginiana 0.9999 2663
## veg_height-Sylvilagus_floridanus 1.0038 1900
## veg_height-Sciurus_carolinensis 1.0033 2484
## veg_height-Vulpes_vulpes 1.0007 1718
## veg_height-Sus_scrofa 1.0103 2838
## week-Odocoileus_virginianus 1.0004 5250
## week-Canis_latrans 1.0011 4739
## week-Sciurus_niger 1.0031 1409
## week-Procyon_lotor 1.0066 4581
## week-Dasypus_novemcinctus 1.0012 4183
## week-Lynx_rufus 1.0050 2379
## week-Didelphis_virginiana 1.0010 3683
## week-Sylvilagus_floridanus 1.0043 3048
## week-Sciurus_carolinensis 1.0014 4434
## week-Vulpes_vulpes 1.0014 2647
## week-Sus_scrofa 1.0008 4218
# 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): 1.472
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1941 1.0438 -2.2384 -0.2206 1.9201 1.0195 1718
## Cogon_Patch_Size -0.8059 0.6305 -2.1919 -0.7661 0.3431 1.0025 1278
## Veg_shannon_index 0.8254 0.4512 -0.0368 0.8088 1.7408 1.0029 682
## total_shrub_cover -0.1647 0.3763 -0.9095 -0.1623 0.5776 1.0098 1352
## Avg_Cogongrass_Cover 2.0221 0.6357 0.8434 2.0005 3.3097 1.0315 504
## Tree_Density -1.7823 0.6477 -3.0542 -1.7688 -0.5718 1.0100 958
## Avg_Canopy_Cover 1.7533 0.5341 0.7699 1.7273 2.8845 1.0184 783
## avg_veg_height -0.5141 0.4303 -1.3919 -0.4926 0.3042 1.0113 779
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.7338 18.5032 3.4936 13.5207 62.1863 1.0378 307
## Cogon_Patch_Size 2.7029 4.8782 0.1192 1.3817 13.5530 1.1681 513
## Veg_shannon_index 0.8531 1.3372 0.0548 0.4450 4.0936 1.0091 797
## total_shrub_cover 0.5161 0.7073 0.0419 0.2950 2.2973 1.0069 1506
## Avg_Cogongrass_Cover 0.7845 1.1364 0.0475 0.3975 3.7982 1.0159 1284
## Tree_Density 2.3431 5.2807 0.0624 0.9934 12.3823 1.0581 782
## Avg_Canopy_Cover 1.4596 1.9101 0.0913 0.8954 6.3846 1.0157 884
## avg_veg_height 0.3466 0.4430 0.0386 0.2059 1.5511 1.0126 2157
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3771 2.0218 0.0588 0.7267 7.0585 1.0241 208
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5163 0.4387 -3.3519 -2.5251 -1.6262 1.0015 5250
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1462 1.2875 0.7478 1.816 5.5196 1.0062 2224
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.0383 3.3666 3.1680 7.3928
## (Intercept)-Canis_latrans 0.6733 1.0471 -1.1262 0.5820
## (Intercept)-Sciurus_niger 1.5668 2.8837 -2.4628 1.0899
## (Intercept)-Procyon_lotor 0.8198 0.9317 -1.1472 0.8362
## (Intercept)-Dasypus_novemcinctus -1.5085 0.9522 -3.6727 -1.4077
## (Intercept)-Lynx_rufus 2.1366 3.1527 -1.8668 1.4900
## (Intercept)-Didelphis_virginiana -2.9412 1.0795 -5.2824 -2.8713
## (Intercept)-Sylvilagus_floridanus -1.3111 1.1509 -3.7455 -1.2763
## (Intercept)-Sciurus_carolinensis -3.1710 1.2072 -5.9351 -3.0547
## (Intercept)-Vulpes_vulpes -1.8504 2.4861 -5.5131 -2.2233
## (Intercept)-Sus_scrofa -4.6277 1.6070 -8.1617 -4.4797
## Cogon_Patch_Size-Odocoileus_virginianus -0.6068 1.2651 -2.9776 -0.6612
## Cogon_Patch_Size-Canis_latrans 0.6161 1.1701 -1.0336 0.4158
## Cogon_Patch_Size-Sciurus_niger -1.4702 1.7012 -5.5867 -1.2217
## Cogon_Patch_Size-Procyon_lotor -0.9690 0.7049 -2.3701 -0.9570
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7849 0.6007 -2.0368 -0.7523
## Cogon_Patch_Size-Lynx_rufus -0.8648 1.3164 -3.3640 -0.9089
## Cogon_Patch_Size-Didelphis_virginiana 0.7267 0.8479 -0.7237 0.6628
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9630 1.5195 -5.9809 -1.6523
## Cogon_Patch_Size-Sciurus_carolinensis -1.7083 1.2608 -4.8897 -1.4741
## Cogon_Patch_Size-Vulpes_vulpes -1.3047 1.4735 -4.7609 -1.1294
## Cogon_Patch_Size-Sus_scrofa -1.4068 1.5363 -4.8702 -1.1331
## Veg_shannon_index-Odocoileus_virginianus 0.6794 0.9041 -1.2739 0.7219
## Veg_shannon_index-Canis_latrans 1.2170 0.6356 0.1265 1.1527
## Veg_shannon_index-Sciurus_niger 0.9394 0.9544 -0.9568 0.8943
## Veg_shannon_index-Procyon_lotor 1.1246 0.5772 0.0502 1.0979
## Veg_shannon_index-Dasypus_novemcinctus 0.6239 0.5050 -0.4010 0.6252
## Veg_shannon_index-Lynx_rufus 0.7843 0.9148 -1.1579 0.7940
## Veg_shannon_index-Didelphis_virginiana 0.9944 0.6286 -0.1535 0.9665
## Veg_shannon_index-Sylvilagus_floridanus 0.9916 0.6657 -0.2037 0.9442
## Veg_shannon_index-Sciurus_carolinensis 0.1633 0.7237 -1.4037 0.2306
## Veg_shannon_index-Vulpes_vulpes 0.2876 0.9196 -1.7455 0.3781
## Veg_shannon_index-Sus_scrofa 1.5647 0.9855 0.1153 1.3884
## total_shrub_cover-Odocoileus_virginianus -0.0102 0.7060 -1.3248 -0.0375
## total_shrub_cover-Canis_latrans 0.1465 0.5865 -0.8334 0.0956
## total_shrub_cover-Sciurus_niger -0.3389 0.7421 -1.9506 -0.2955
## total_shrub_cover-Procyon_lotor -0.6328 0.5425 -1.8254 -0.5862
## total_shrub_cover-Dasypus_novemcinctus 0.0814 0.4732 -0.7948 0.0612
## total_shrub_cover-Lynx_rufus -0.4579 0.7533 -2.2144 -0.3901
## total_shrub_cover-Didelphis_virginiana -0.3208 0.5823 -1.5709 -0.2888
## total_shrub_cover-Sylvilagus_floridanus -0.1133 0.6201 -1.3953 -0.1097
## total_shrub_cover-Sciurus_carolinensis -0.0046 0.5602 -1.0566 -0.0208
## total_shrub_cover-Vulpes_vulpes -0.2921 0.7268 -1.9086 -0.2630
## total_shrub_cover-Sus_scrofa 0.0955 0.6716 -1.1444 0.0553
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9825 0.9652 0.0567 1.9651
## Avg_Cogongrass_Cover-Canis_latrans 2.2605 0.8249 0.8149 2.2143
## Avg_Cogongrass_Cover-Sciurus_niger 1.7139 1.1367 -0.9376 1.7835
## Avg_Cogongrass_Cover-Procyon_lotor 2.2239 0.8239 0.7989 2.1656
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4748 0.8776 0.9698 2.3984
## Avg_Cogongrass_Cover-Lynx_rufus 2.3186 0.9343 0.6933 2.2369
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1287 0.7933 0.7046 2.0816
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5590 0.8677 -0.2308 1.5663
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2824 0.8378 0.8159 2.2152
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3313 0.9326 0.6783 2.2616
## Avg_Cogongrass_Cover-Sus_scrofa 1.5528 0.9823 -0.6251 1.5950
## Tree_Density-Odocoileus_virginianus -0.9025 1.1336 -2.7397 -1.0365
## Tree_Density-Canis_latrans -2.3164 1.0662 -4.8725 -2.1390
## Tree_Density-Sciurus_niger -1.9299 1.3570 -5.0276 -1.8324
## Tree_Density-Procyon_lotor -1.4360 0.7239 -2.8498 -1.4422
## Tree_Density-Dasypus_novemcinctus -3.1163 1.5996 -7.1971 -2.7295
## Tree_Density-Lynx_rufus -0.7213 1.3668 -2.8328 -0.9060
## Tree_Density-Didelphis_virginiana -2.1466 1.0302 -4.6707 -2.0041
## Tree_Density-Sylvilagus_floridanus -2.2614 1.2187 -5.2694 -2.0927
## Tree_Density-Sciurus_carolinensis -2.3271 1.2319 -5.4120 -2.1164
## Tree_Density-Vulpes_vulpes -1.7529 1.4515 -4.5743 -1.7430
## Tree_Density-Sus_scrofa -2.2015 1.4067 -5.6383 -1.9819
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3375 1.1318 -1.0727 1.3844
## Avg_Canopy_Cover-Canis_latrans 0.4830 0.6985 -0.8861 0.4800
## Avg_Canopy_Cover-Sciurus_niger 1.9792 1.3333 -0.6107 1.8876
## Avg_Canopy_Cover-Procyon_lotor 1.7040 0.6467 0.5390 1.6680
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8798 0.6328 0.8139 1.8326
## Avg_Canopy_Cover-Lynx_rufus 1.3802 1.1879 -0.8702 1.3592
## Avg_Canopy_Cover-Didelphis_virginiana 2.4713 0.8535 1.1402 2.3516
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8006 1.1850 1.0951 2.5909
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1728 0.7543 0.9365 2.0828
## Avg_Canopy_Cover-Vulpes_vulpes 1.9984 1.0034 0.3457 1.8803
## Avg_Canopy_Cover-Sus_scrofa 1.9671 0.7748 0.6499 1.8958
## avg_veg_height-Odocoileus_virginianus -0.5244 0.6923 -1.9414 -0.4971
## avg_veg_height-Canis_latrans -0.6879 0.5533 -1.8419 -0.6614
## avg_veg_height-Sciurus_niger -0.6214 0.7022 -2.0632 -0.5916
## avg_veg_height-Procyon_lotor -0.3775 0.5283 -1.4351 -0.3769
## avg_veg_height-Dasypus_novemcinctus -0.3177 0.5256 -1.3294 -0.3266
## avg_veg_height-Lynx_rufus -0.5389 0.6998 -1.9251 -0.5266
## avg_veg_height-Didelphis_virginiana -0.6006 0.5864 -1.8087 -0.5912
## avg_veg_height-Sylvilagus_floridanus -0.6886 0.5960 -1.9415 -0.6578
## avg_veg_height-Sciurus_carolinensis -0.2393 0.5957 -1.3510 -0.2545
## avg_veg_height-Vulpes_vulpes -0.5361 0.6587 -1.8752 -0.5185
## avg_veg_height-Sus_scrofa -0.6091 0.6209 -1.9197 -0.5887
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.4460 1.0497 313
## (Intercept)-Canis_latrans 3.0334 1.0096 1267
## (Intercept)-Sciurus_niger 9.3161 1.0773 224
## (Intercept)-Procyon_lotor 2.6419 1.0038 1021
## (Intercept)-Dasypus_novemcinctus 0.0614 1.0058 886
## (Intercept)-Lynx_rufus 9.5904 1.1181 199
## (Intercept)-Didelphis_virginiana -1.0534 1.0021 1691
## (Intercept)-Sylvilagus_floridanus 0.8810 1.0090 1370
## (Intercept)-Sciurus_carolinensis -1.1446 1.0146 605
## (Intercept)-Vulpes_vulpes 4.1814 1.0555 172
## (Intercept)-Sus_scrofa -1.9329 1.0340 625
## Cogon_Patch_Size-Odocoileus_virginianus 2.1392 1.0019 2582
## Cogon_Patch_Size-Canis_latrans 3.5632 1.0314 1078
## Cogon_Patch_Size-Sciurus_niger 1.2845 1.0202 639
## Cogon_Patch_Size-Procyon_lotor 0.3656 1.0035 1148
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3353 1.0032 1300
## Cogon_Patch_Size-Lynx_rufus 1.8879 1.0225 1085
## Cogon_Patch_Size-Didelphis_virginiana 2.6006 1.0108 1151
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0305 1.0149 695
## Cogon_Patch_Size-Sciurus_carolinensis 0.0029 1.0271 779
## Cogon_Patch_Size-Vulpes_vulpes 1.2349 1.0022 830
## Cogon_Patch_Size-Sus_scrofa 0.6473 1.0992 595
## Veg_shannon_index-Odocoileus_virginianus 2.3400 1.0030 1736
## Veg_shannon_index-Canis_latrans 2.6667 1.0039 854
## Veg_shannon_index-Sciurus_niger 2.8978 1.0041 1185
## Veg_shannon_index-Procyon_lotor 2.2997 1.0032 565
## Veg_shannon_index-Dasypus_novemcinctus 1.5939 1.0074 1795
## Veg_shannon_index-Lynx_rufus 2.5345 1.0117 1456
## Veg_shannon_index-Didelphis_virginiana 2.3375 1.0033 2122
## Veg_shannon_index-Sylvilagus_floridanus 2.4730 1.0031 1087
## Veg_shannon_index-Sciurus_carolinensis 1.3800 1.0001 1322
## Veg_shannon_index-Vulpes_vulpes 1.8069 1.0114 1032
## Veg_shannon_index-Sus_scrofa 3.9843 1.0077 935
## total_shrub_cover-Odocoileus_virginianus 1.4714 1.0050 2592
## total_shrub_cover-Canis_latrans 1.4380 1.0112 1890
## total_shrub_cover-Sciurus_niger 1.0388 1.0209 1602
## total_shrub_cover-Procyon_lotor 0.3077 1.0051 2005
## total_shrub_cover-Dasypus_novemcinctus 1.0565 1.0089 2407
## total_shrub_cover-Lynx_rufus 0.8810 1.0219 1328
## total_shrub_cover-Didelphis_virginiana 0.7572 1.0022 2672
## total_shrub_cover-Sylvilagus_floridanus 1.1618 1.0028 2287
## total_shrub_cover-Sciurus_carolinensis 1.1553 1.0032 2469
## total_shrub_cover-Vulpes_vulpes 1.0714 1.0015 1929
## total_shrub_cover-Sus_scrofa 1.5780 1.0050 2622
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.9392 1.0162 992
## Avg_Cogongrass_Cover-Canis_latrans 4.0735 1.0186 668
## Avg_Cogongrass_Cover-Sciurus_niger 3.7743 1.0081 695
## Avg_Cogongrass_Cover-Procyon_lotor 3.9620 1.0215 693
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.3885 1.0165 603
## Avg_Cogongrass_Cover-Lynx_rufus 4.3809 1.0210 761
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7996 1.0124 795
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1981 1.0210 962
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0923 1.0234 672
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.3820 1.0153 782
## Avg_Cogongrass_Cover-Sus_scrofa 3.3717 1.0160 818
## Tree_Density-Odocoileus_virginianus 1.8395 1.0264 730
## Tree_Density-Canis_latrans -0.6715 1.0111 838
## Tree_Density-Sciurus_niger 0.5737 1.0047 966
## Tree_Density-Procyon_lotor 0.0183 1.0145 1646
## Tree_Density-Dasypus_novemcinctus -1.1076 1.0129 488
## Tree_Density-Lynx_rufus 2.4329 1.0114 505
## Tree_Density-Didelphis_virginiana -0.5519 1.0079 926
## Tree_Density-Sylvilagus_floridanus -0.3167 1.0084 1003
## Tree_Density-Sciurus_carolinensis -0.5400 1.0271 850
## Tree_Density-Vulpes_vulpes 0.9371 1.0093 858
## Tree_Density-Sus_scrofa -0.0738 1.0138 1105
## Avg_Canopy_Cover-Odocoileus_virginianus 3.4650 1.0063 1763
## Avg_Canopy_Cover-Canis_latrans 1.8674 1.0004 1627
## Avg_Canopy_Cover-Sciurus_niger 4.8236 1.0148 873
## Avg_Canopy_Cover-Procyon_lotor 3.1078 1.0088 1394
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3199 1.0116 894
## Avg_Canopy_Cover-Lynx_rufus 3.9029 1.0223 935
## Avg_Canopy_Cover-Didelphis_virginiana 4.4727 1.0144 607
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.6878 1.0075 764
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9498 1.0040 1166
## Avg_Canopy_Cover-Vulpes_vulpes 4.3205 1.0243 1254
## Avg_Canopy_Cover-Sus_scrofa 3.7445 1.0020 1172
## avg_veg_height-Odocoileus_virginianus 0.7929 1.0012 1538
## avg_veg_height-Canis_latrans 0.3599 1.0089 1304
## avg_veg_height-Sciurus_niger 0.6678 1.0085 1228
## avg_veg_height-Procyon_lotor 0.6714 1.0055 1514
## avg_veg_height-Dasypus_novemcinctus 0.7791 1.0081 1315
## avg_veg_height-Lynx_rufus 0.8534 1.0058 1237
## avg_veg_height-Didelphis_virginiana 0.5298 1.0048 1148
## avg_veg_height-Sylvilagus_floridanus 0.3942 1.0034 1301
## avg_veg_height-Sciurus_carolinensis 1.0504 1.0022 1606
## avg_veg_height-Vulpes_vulpes 0.6810 1.0062 1414
## avg_veg_height-Sus_scrofa 0.5576 1.0031 1512
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0045 0.0587 -0.1107 0.0039 0.1186
## (Intercept)-Canis_latrans -2.6423 0.1810 -3.0098 -2.6346 -2.3085
## (Intercept)-Sciurus_niger -4.5263 0.4629 -5.4557 -4.5286 -3.5660
## (Intercept)-Procyon_lotor -2.2683 0.1296 -2.5288 -2.2656 -2.0231
## (Intercept)-Dasypus_novemcinctus -1.5687 0.1320 -1.8277 -1.5685 -1.3199
## (Intercept)-Lynx_rufus -3.7846 0.3547 -4.4535 -3.7913 -3.0849
## (Intercept)-Didelphis_virginiana -2.2891 0.2391 -2.7693 -2.2753 -1.8523
## (Intercept)-Sylvilagus_floridanus -3.1590 0.2724 -3.7320 -3.1459 -2.6532
## (Intercept)-Sciurus_carolinensis -2.4200 0.2573 -2.9597 -2.4077 -1.9480
## (Intercept)-Vulpes_vulpes -4.1956 0.7171 -5.7164 -4.1605 -2.9233
## (Intercept)-Sus_scrofa -2.8564 0.4495 -3.8372 -2.8322 -2.0659
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0022 2323
## (Intercept)-Sciurus_niger 1.0701 438
## (Intercept)-Procyon_lotor 1.0002 4053
## (Intercept)-Dasypus_novemcinctus 1.0009 5006
## (Intercept)-Lynx_rufus 1.0305 380
## (Intercept)-Didelphis_virginiana 1.0012 3967
## (Intercept)-Sylvilagus_floridanus 1.0081 1968
## (Intercept)-Sciurus_carolinensis 1.0031 3754
## (Intercept)-Vulpes_vulpes 1.0032 317
## (Intercept)-Sus_scrofa 1.0022 2294
# 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): 1.3395
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2178 0.6400 -1.4191 -0.2409 1.1658 1.0037 1802
## Avg_Cogongrass_Cover 0.1328 0.2998 -0.4574 0.1339 0.7308 1.0023 1340
## total_shrub_cover -0.2578 0.2714 -0.8214 -0.2512 0.2574 1.0078 2209
## avg_veg_height 0.0258 0.2810 -0.5232 0.0243 0.5894 1.0007 1344
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0587 3.4389 0.8200 3.1432 12.6881 1.0023 1095
## Avg_Cogongrass_Cover 0.2968 0.3460 0.0386 0.1885 1.1929 1.0018 2257
## total_shrub_cover 0.3455 0.3908 0.0431 0.2220 1.3364 1.0080 1905
## avg_veg_height 0.2004 0.2184 0.0332 0.1369 0.7563 1.0150 2505
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8587 0.8293 0.0774 0.6 2.985 1.0056 486
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4843 0.4121 -3.296 -2.4821 -1.6795 1.0004 4143
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8637 1.1872 0.6413 1.5512 4.951 1.0164 1603
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7131 1.4974 1.3571 3.5058
## (Intercept)-Canis_latrans 0.3235 0.6635 -0.9295 0.3089
## (Intercept)-Sciurus_niger -0.4991 1.3828 -2.4978 -0.7143
## (Intercept)-Procyon_lotor 0.6458 0.6772 -0.7170 0.6469
## (Intercept)-Dasypus_novemcinctus -0.7437 0.5979 -1.9691 -0.7351
## (Intercept)-Lynx_rufus 0.0063 1.0307 -1.6880 -0.0802
## (Intercept)-Didelphis_virginiana -1.4305 0.6691 -2.8364 -1.4308
## (Intercept)-Sylvilagus_floridanus -0.1680 0.9491 -1.6742 -0.2659
## (Intercept)-Sciurus_carolinensis -1.5404 0.6884 -2.9941 -1.5147
## (Intercept)-Vulpes_vulpes -1.0058 1.5124 -3.2737 -1.2269
## (Intercept)-Sus_scrofa -2.0305 0.8614 -3.7959 -2.0035
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1317 0.5238 -0.8941 0.1241
## Avg_Cogongrass_Cover-Canis_latrans 0.3610 0.4206 -0.4104 0.3422
## Avg_Cogongrass_Cover-Sciurus_niger -0.1851 0.6003 -1.5506 -0.1252
## Avg_Cogongrass_Cover-Procyon_lotor 0.1048 0.4170 -0.7059 0.0947
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2428 0.3796 -0.5031 0.2455
## Avg_Cogongrass_Cover-Lynx_rufus 0.4037 0.4709 -0.4197 0.3700
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3108 0.4178 -0.4990 0.3058
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2003 0.4824 -1.2103 -0.1726
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2304 0.4115 -0.5721 0.2282
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2345 0.4889 -0.7139 0.2236
## Avg_Cogongrass_Cover-Sus_scrofa -0.1550 0.5560 -1.3915 -0.1013
## total_shrub_cover-Odocoileus_virginianus -0.1910 0.5242 -1.1752 -0.2065
## total_shrub_cover-Canis_latrans 0.0884 0.3972 -0.6583 0.0773
## total_shrub_cover-Sciurus_niger -0.4958 0.5365 -1.6632 -0.4536
## total_shrub_cover-Procyon_lotor -0.7259 0.4438 -1.7408 -0.6837
## total_shrub_cover-Dasypus_novemcinctus -0.0578 0.3457 -0.7159 -0.0641
## total_shrub_cover-Lynx_rufus -0.6309 0.5490 -1.8959 -0.5707
## total_shrub_cover-Didelphis_virginiana -0.2170 0.3875 -0.9792 -0.2209
## total_shrub_cover-Sylvilagus_floridanus -0.3121 0.4742 -1.3123 -0.2908
## total_shrub_cover-Sciurus_carolinensis -0.1051 0.3888 -0.8590 -0.1174
## total_shrub_cover-Vulpes_vulpes -0.3062 0.5562 -1.4806 -0.2911
## total_shrub_cover-Sus_scrofa 0.0376 0.4731 -0.7914 -0.0032
## avg_veg_height-Odocoileus_virginianus 0.0102 0.4766 -0.9901 0.0203
## avg_veg_height-Canis_latrans -0.0467 0.3849 -0.8305 -0.0415
## avg_veg_height-Sciurus_niger -0.1331 0.4691 -1.1528 -0.1187
## avg_veg_height-Procyon_lotor 0.1129 0.3936 -0.6492 0.1015
## avg_veg_height-Dasypus_novemcinctus 0.1859 0.3712 -0.5180 0.1728
## avg_veg_height-Lynx_rufus 0.0445 0.4551 -0.8443 0.0327
## avg_veg_height-Didelphis_virginiana -0.0024 0.3967 -0.7731 -0.0073
## avg_veg_height-Sylvilagus_floridanus -0.1077 0.4145 -0.9440 -0.0951
## avg_veg_height-Sciurus_carolinensis 0.2552 0.4100 -0.5050 0.2361
## avg_veg_height-Vulpes_vulpes -0.0247 0.4510 -0.9279 -0.0188
## avg_veg_height-Sus_scrofa -0.0177 0.4242 -0.8835 -0.0045
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3212 1.0061 1072
## (Intercept)-Canis_latrans 1.6546 1.0024 2799
## (Intercept)-Sciurus_niger 2.9088 1.0320 376
## (Intercept)-Procyon_lotor 1.9938 1.0010 2124
## (Intercept)-Dasypus_novemcinctus 0.4374 1.0023 2775
## (Intercept)-Lynx_rufus 2.3639 1.0026 1013
## (Intercept)-Didelphis_virginiana -0.1304 1.0007 2132
## (Intercept)-Sylvilagus_floridanus 2.0080 1.0211 776
## (Intercept)-Sciurus_carolinensis -0.2322 1.0010 2955
## (Intercept)-Vulpes_vulpes 2.5011 1.0937 285
## (Intercept)-Sus_scrofa -0.4137 1.0019 2049
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2146 1.0005 2957
## Avg_Cogongrass_Cover-Canis_latrans 1.2504 1.0007 2445
## Avg_Cogongrass_Cover-Sciurus_niger 0.8549 1.0023 1743
## Avg_Cogongrass_Cover-Procyon_lotor 0.9711 1.0000 2841
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9977 1.0005 2545
## Avg_Cogongrass_Cover-Lynx_rufus 1.4305 1.0047 2262
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1712 1.0003 2607
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6947 1.0020 1531
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0614 1.0005 2569
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2610 1.0076 2493
## Avg_Cogongrass_Cover-Sus_scrofa 0.8028 1.0019 1829
## total_shrub_cover-Odocoileus_virginianus 0.9224 1.0013 4116
## total_shrub_cover-Canis_latrans 0.9346 1.0005 3513
## total_shrub_cover-Sciurus_niger 0.4663 1.0043 2035
## total_shrub_cover-Procyon_lotor 0.0176 1.0063 2718
## total_shrub_cover-Dasypus_novemcinctus 0.6331 1.0015 4510
## total_shrub_cover-Lynx_rufus 0.2977 1.0034 1453
## total_shrub_cover-Didelphis_virginiana 0.5643 1.0015 4264
## total_shrub_cover-Sylvilagus_floridanus 0.5863 1.0115 2952
## total_shrub_cover-Sciurus_carolinensis 0.6877 1.0003 4658
## total_shrub_cover-Vulpes_vulpes 0.7497 1.0060 2269
## total_shrub_cover-Sus_scrofa 1.0425 1.0022 2732
## avg_veg_height-Odocoileus_virginianus 0.9536 1.0006 2300
## avg_veg_height-Canis_latrans 0.7173 1.0003 2578
## avg_veg_height-Sciurus_niger 0.7390 1.0018 2116
## avg_veg_height-Procyon_lotor 0.9105 1.0001 2530
## avg_veg_height-Dasypus_novemcinctus 0.9431 1.0003 2322
## avg_veg_height-Lynx_rufus 0.9523 1.0053 2047
## avg_veg_height-Didelphis_virginiana 0.7843 1.0016 2899
## avg_veg_height-Sylvilagus_floridanus 0.6686 1.0010 2280
## avg_veg_height-Sciurus_carolinensis 1.1221 1.0003 2345
## avg_veg_height-Vulpes_vulpes 0.8551 1.0012 2367
## avg_veg_height-Sus_scrofa 0.7967 1.0012 2796
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0051 0.0596 -0.1093 0.0044 0.1228
## (Intercept)-Canis_latrans -2.6319 0.1784 -3.0004 -2.6259 -2.2996
## (Intercept)-Sciurus_niger -3.9389 0.5877 -5.1512 -3.8985 -2.8961
## (Intercept)-Procyon_lotor -2.2740 0.1320 -2.5360 -2.2713 -2.0263
## (Intercept)-Dasypus_novemcinctus -1.5725 0.1341 -1.8449 -1.5697 -1.3145
## (Intercept)-Lynx_rufus -3.5535 0.3253 -4.2340 -3.5330 -2.9565
## (Intercept)-Didelphis_virginiana -2.3139 0.2560 -2.8437 -2.3032 -1.8484
## (Intercept)-Sylvilagus_floridanus -3.2495 0.3350 -3.9769 -3.2281 -2.6604
## (Intercept)-Sciurus_carolinensis -2.4350 0.2607 -2.9635 -2.4268 -1.9531
## (Intercept)-Vulpes_vulpes -4.1105 0.7790 -5.6419 -4.0624 -2.7365
## (Intercept)-Sus_scrofa -2.9181 0.4762 -3.9607 -2.8832 -2.0955
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0021 2554
## (Intercept)-Sciurus_niger 1.0353 402
## (Intercept)-Procyon_lotor 1.0019 3676
## (Intercept)-Dasypus_novemcinctus 1.0027 5250
## (Intercept)-Lynx_rufus 1.0012 962
## (Intercept)-Didelphis_virginiana 1.0015 3906
## (Intercept)-Sylvilagus_floridanus 1.0048 1069
## (Intercept)-Sciurus_carolinensis 1.0021 3656
## (Intercept)-Vulpes_vulpes 1.0616 314
## (Intercept)-Sus_scrofa 1.0008 1918
# 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): 1.367
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2281 0.7316 -1.6238 -0.2528 1.3269 1.0051 1974
## Tree_Density -0.7354 0.3896 -1.6270 -0.7038 -0.0544 1.0011 1455
## Avg_Canopy_Cover 0.9918 0.3296 0.3738 0.9782 1.6760 1.0002 2182
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.4321 5.8493 1.4715 4.8547 20.6398 1.0384 567
## Tree_Density 0.6957 1.1007 0.0461 0.3346 3.6659 1.0130 1371
## Avg_Canopy_Cover 0.5357 0.5898 0.0575 0.3544 2.0029 1.0182 2150
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.3823 0.4074 0.0436 0.248 1.5516 1.0297 576
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4955 0.4176 -3.2812 -2.5082 -1.6151 1.0002 3658
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.896 1.1658 0.6885 1.6005 4.8106 1.0012 2119
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6198 1.8006 2.1093 4.3224 9.0099
## (Intercept)-Canis_latrans 0.3294 0.6427 -0.8281 0.2957 1.6687
## (Intercept)-Sciurus_niger -0.0600 1.5653 -2.3099 -0.2934 3.7911
## (Intercept)-Procyon_lotor 0.7473 0.6021 -0.4448 0.7474 1.9273
## (Intercept)-Dasypus_novemcinctus -1.0133 0.6025 -2.2646 -0.9839 0.1306
## (Intercept)-Lynx_rufus 1.2119 1.9058 -1.2463 0.8002 5.9622
## (Intercept)-Didelphis_virginiana -1.9105 0.6791 -3.3230 -1.8814 -0.6833
## (Intercept)-Sylvilagus_floridanus -0.6505 0.7066 -2.0164 -0.6603 0.8169
## (Intercept)-Sciurus_carolinensis -1.9676 0.6892 -3.4112 -1.9336 -0.7090
## (Intercept)-Vulpes_vulpes -1.4610 1.3643 -3.6539 -1.6187 1.7106
## (Intercept)-Sus_scrofa -2.6863 0.8958 -4.5935 -2.6337 -1.0947
## Tree_Density-Odocoileus_virginianus -0.3871 0.6434 -1.4759 -0.4438 1.0878
## Tree_Density-Canis_latrans -0.8654 0.5419 -2.0908 -0.8059 0.0298
## Tree_Density-Sciurus_niger -0.7593 0.7416 -2.3997 -0.7091 0.6149
## Tree_Density-Procyon_lotor -0.4736 0.4006 -1.2649 -0.4799 0.3080
## Tree_Density-Dasypus_novemcinctus -1.2903 0.8418 -3.4633 -1.0996 -0.1720
## Tree_Density-Lynx_rufus -0.0521 0.7519 -1.3228 -0.1396 1.7121
## Tree_Density-Didelphis_virginiana -0.9939 0.7295 -2.8673 -0.8625 0.0704
## Tree_Density-Sylvilagus_floridanus -0.9973 0.7003 -2.6872 -0.8943 0.0627
## Tree_Density-Sciurus_carolinensis -0.9119 0.7076 -2.6637 -0.8064 0.1738
## Tree_Density-Vulpes_vulpes -0.6570 0.7799 -2.2778 -0.6218 0.7583
## Tree_Density-Sus_scrofa -0.9404 0.8250 -3.0626 -0.8146 0.3026
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8016 0.6596 -0.5654 0.8020 2.1455
## Avg_Canopy_Cover-Canis_latrans 0.1617 0.4713 -0.7926 0.1662 1.0818
## Avg_Canopy_Cover-Sciurus_niger 0.9961 0.7346 -0.3364 0.9457 2.6510
## Avg_Canopy_Cover-Procyon_lotor 1.0103 0.4526 0.1842 0.9917 1.9687
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0014 0.4111 0.2592 0.9839 1.8834
## Avg_Canopy_Cover-Lynx_rufus 0.9017 0.7036 -0.3899 0.8763 2.4398
## Avg_Canopy_Cover-Didelphis_virginiana 1.2426 0.4754 0.4222 1.1970 2.3345
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6056 0.7177 0.5560 1.4937 3.2567
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2161 0.4847 0.3580 1.1815 2.2854
## Avg_Canopy_Cover-Vulpes_vulpes 1.0393 0.5800 0.0365 0.9895 2.3144
## Avg_Canopy_Cover-Sus_scrofa 1.2191 0.5155 0.2979 1.1751 2.3610
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0114 814
## (Intercept)-Canis_latrans 1.0007 2402
## (Intercept)-Sciurus_niger 1.0260 350
## (Intercept)-Procyon_lotor 1.0002 3705
## (Intercept)-Dasypus_novemcinctus 1.0032 2821
## (Intercept)-Lynx_rufus 1.0550 313
## (Intercept)-Didelphis_virginiana 1.0005 3195
## (Intercept)-Sylvilagus_floridanus 1.0016 2685
## (Intercept)-Sciurus_carolinensis 1.0029 3143
## (Intercept)-Vulpes_vulpes 1.0054 548
## (Intercept)-Sus_scrofa 1.0004 2335
## Tree_Density-Odocoileus_virginianus 1.0110 1914
## Tree_Density-Canis_latrans 1.0019 2718
## Tree_Density-Sciurus_niger 1.0038 2114
## Tree_Density-Procyon_lotor 1.0029 3534
## Tree_Density-Dasypus_novemcinctus 1.0067 1432
## Tree_Density-Lynx_rufus 1.0296 756
## Tree_Density-Didelphis_virginiana 1.0025 2149
## Tree_Density-Sylvilagus_floridanus 1.0035 1805
## Tree_Density-Sciurus_carolinensis 1.0007 2355
## Tree_Density-Vulpes_vulpes 1.0064 1624
## Tree_Density-Sus_scrofa 1.0008 1931
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0030 3303
## Avg_Canopy_Cover-Canis_latrans 1.0000 2372
## Avg_Canopy_Cover-Sciurus_niger 1.0023 1426
## Avg_Canopy_Cover-Procyon_lotor 1.0030 3566
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0000 4399
## Avg_Canopy_Cover-Lynx_rufus 1.0071 1529
## Avg_Canopy_Cover-Didelphis_virginiana 1.0009 3198
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0042 1998
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0003 3343
## Avg_Canopy_Cover-Vulpes_vulpes 1.0013 2156
## Avg_Canopy_Cover-Sus_scrofa 1.0009 2006
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0036 0.0590 -0.1093 0.0046 0.1177
## (Intercept)-Canis_latrans -2.6461 0.1804 -3.0239 -2.6414 -2.3140
## (Intercept)-Sciurus_niger -4.1594 0.5701 -5.2220 -4.1652 -3.0687
## (Intercept)-Procyon_lotor -2.2650 0.1298 -2.5226 -2.2622 -2.0173
## (Intercept)-Dasypus_novemcinctus -1.5720 0.1344 -1.8401 -1.5719 -1.3143
## (Intercept)-Lynx_rufus -3.7560 0.3513 -4.4300 -3.7551 -3.0852
## (Intercept)-Didelphis_virginiana -2.3031 0.2424 -2.8068 -2.2924 -1.8556
## (Intercept)-Sylvilagus_floridanus -3.1238 0.2782 -3.7082 -3.1117 -2.6025
## (Intercept)-Sciurus_carolinensis -2.4257 0.2666 -2.9903 -2.4122 -1.9440
## (Intercept)-Vulpes_vulpes -4.0154 0.7188 -5.5113 -3.9928 -2.7485
## (Intercept)-Sus_scrofa -2.8639 0.4433 -3.8451 -2.8308 -2.0816
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0025 5250
## (Intercept)-Canis_latrans 1.0030 2329
## (Intercept)-Sciurus_niger 1.0051 404
## (Intercept)-Procyon_lotor 1.0024 4006
## (Intercept)-Dasypus_novemcinctus 1.0012 5250
## (Intercept)-Lynx_rufus 1.0070 520
## (Intercept)-Didelphis_virginiana 1.0004 3981
## (Intercept)-Sylvilagus_floridanus 1.0002 1928
## (Intercept)-Sciurus_carolinensis 1.0001 3431
## (Intercept)-Vulpes_vulpes 1.0034 462
## (Intercept)-Sus_scrofa 1.0051 2626
# 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): 1.3345
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2797 0.6434 -1.5066 -0.3098 1.0925 1.0027 2882
## Cogon_Patch_Size -0.2837 0.4061 -1.1690 -0.2561 0.4366 1.0050 2110
## Avg_Cogongrass_Cover 0.2647 0.2802 -0.2724 0.2576 0.8329 1.0038 1531
## total_shrub_cover -0.2169 0.2709 -0.7375 -0.2170 0.3194 1.0058 2390
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.1786 3.6311 0.8046 3.2661 13.0929 1.0225 1817
## Cogon_Patch_Size 0.9643 1.2998 0.0672 0.5422 4.4964 1.0340 1100
## Avg_Cogongrass_Cover 0.2725 0.3327 0.0349 0.1720 1.1574 1.0109 1722
## total_shrub_cover 0.3255 0.3385 0.0398 0.2160 1.2748 1.0099 2118
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2344 1.1154 0.0938 0.9326 4.0987 1.0032 520
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.469 0.4226 -3.2852 -2.4711 -1.602 1.0061 4901
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8779 1.2705 0.6209 1.5588 5.0153 1.0144 2537
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7296 1.5600 1.0636 3.5824
## (Intercept)-Canis_latrans 0.3923 0.7325 -0.9777 0.3650
## (Intercept)-Sciurus_niger -0.5522 1.2322 -2.6702 -0.6750
## (Intercept)-Procyon_lotor 0.5667 0.7250 -0.9296 0.5728
## (Intercept)-Dasypus_novemcinctus -0.7610 0.6814 -2.1355 -0.7499
## (Intercept)-Lynx_rufus -0.1819 0.9952 -2.0053 -0.2197
## (Intercept)-Didelphis_virginiana -1.4460 0.7320 -2.9566 -1.4405
## (Intercept)-Sylvilagus_floridanus -0.4082 0.8958 -2.0456 -0.4689
## (Intercept)-Sciurus_carolinensis -1.6670 0.7763 -3.2734 -1.6425
## (Intercept)-Vulpes_vulpes -1.2415 1.3405 -3.5875 -1.3593
## (Intercept)-Sus_scrofa -2.0983 0.9342 -3.9448 -2.0697
## Cogon_Patch_Size-Odocoileus_virginianus -0.0919 0.7224 -1.3672 -0.1439
## Cogon_Patch_Size-Canis_latrans 0.6765 0.7170 -0.3584 0.5485
## Cogon_Patch_Size-Sciurus_niger -0.6851 0.9189 -2.8052 -0.5626
## Cogon_Patch_Size-Procyon_lotor -0.2810 0.4606 -1.1985 -0.2832
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1686 0.4209 -1.0324 -0.1559
## Cogon_Patch_Size-Lynx_rufus -0.3209 0.7633 -1.7059 -0.3625
## Cogon_Patch_Size-Didelphis_virginiana 0.5703 0.5067 -0.3309 0.5321
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9646 0.9035 -3.1105 -0.7945
## Cogon_Patch_Size-Sciurus_carolinensis -0.7851 0.7365 -2.5855 -0.6612
## Cogon_Patch_Size-Vulpes_vulpes -0.6299 0.9534 -2.9034 -0.5131
## Cogon_Patch_Size-Sus_scrofa -0.5480 0.8162 -2.5000 -0.4375
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2548 0.5083 -0.7391 0.2468
## Avg_Cogongrass_Cover-Canis_latrans 0.3275 0.3975 -0.4121 0.3155
## Avg_Cogongrass_Cover-Sciurus_niger -0.0072 0.5851 -1.3449 0.0388
## Avg_Cogongrass_Cover-Procyon_lotor 0.3003 0.4141 -0.4766 0.2814
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4107 0.3560 -0.2632 0.3990
## Avg_Cogongrass_Cover-Lynx_rufus 0.5345 0.4527 -0.2336 0.5011
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2767 0.3936 -0.5102 0.2772
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0095 0.4699 -0.9604 0.0130
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4841 0.3936 -0.2415 0.4680
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3500 0.4535 -0.5375 0.3393
## Avg_Cogongrass_Cover-Sus_scrofa -0.0003 0.5168 -1.2258 0.0426
## total_shrub_cover-Odocoileus_virginianus -0.1363 0.5292 -1.1580 -0.1522
## total_shrub_cover-Canis_latrans 0.0782 0.4131 -0.6601 0.0524
## total_shrub_cover-Sciurus_niger -0.4271 0.5131 -1.5146 -0.3991
## total_shrub_cover-Procyon_lotor -0.6606 0.4483 -1.6639 -0.6173
## total_shrub_cover-Dasypus_novemcinctus -0.0438 0.3432 -0.7032 -0.0513
## total_shrub_cover-Lynx_rufus -0.5414 0.5380 -1.7732 -0.5045
## total_shrub_cover-Didelphis_virginiana -0.2558 0.4001 -1.0557 -0.2466
## total_shrub_cover-Sylvilagus_floridanus -0.2236 0.4756 -1.1940 -0.2140
## total_shrub_cover-Sciurus_carolinensis -0.0535 0.4080 -0.8475 -0.0632
## total_shrub_cover-Vulpes_vulpes -0.2586 0.5438 -1.3812 -0.2471
## total_shrub_cover-Sus_scrofa 0.0602 0.5057 -0.8354 0.0228
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.2578 1.0179 1067
## (Intercept)-Canis_latrans 1.9250 1.0059 2345
## (Intercept)-Sciurus_niger 2.1991 1.0044 581
## (Intercept)-Procyon_lotor 1.9903 1.0013 2334
## (Intercept)-Dasypus_novemcinctus 0.5954 1.0021 2326
## (Intercept)-Lynx_rufus 1.9277 1.0091 1180
## (Intercept)-Didelphis_virginiana -0.0056 1.0039 2818
## (Intercept)-Sylvilagus_floridanus 1.5772 1.0120 1488
## (Intercept)-Sciurus_carolinensis -0.2185 1.0071 2406
## (Intercept)-Vulpes_vulpes 1.7726 1.0863 449
## (Intercept)-Sus_scrofa -0.2952 1.0096 1794
## Cogon_Patch_Size-Odocoileus_virginianus 1.5612 1.0049 3154
## Cogon_Patch_Size-Canis_latrans 2.4797 1.0024 1549
## Cogon_Patch_Size-Sciurus_niger 0.8508 1.0073 1725
## Cogon_Patch_Size-Procyon_lotor 0.6179 1.0013 3698
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6433 1.0021 4002
## Cogon_Patch_Size-Lynx_rufus 1.3215 1.0027 2254
## Cogon_Patch_Size-Didelphis_virginiana 1.6225 1.0036 2695
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2952 1.0197 1241
## Cogon_Patch_Size-Sciurus_carolinensis 0.2966 1.0033 2026
## Cogon_Patch_Size-Vulpes_vulpes 0.8965 1.0091 1255
## Cogon_Patch_Size-Sus_scrofa 0.7595 1.0245 1915
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3306 1.0017 2983
## Avg_Cogongrass_Cover-Canis_latrans 1.1719 1.0006 3422
## Avg_Cogongrass_Cover-Sciurus_niger 1.0341 1.0043 1508
## Avg_Cogongrass_Cover-Procyon_lotor 1.1672 1.0034 3488
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1511 1.0009 3405
## Avg_Cogongrass_Cover-Lynx_rufus 1.5607 1.0022 2741
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0614 1.0003 2893
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8509 1.0031 2024
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3235 1.0038 2609
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3054 1.0043 2799
## Avg_Cogongrass_Cover-Sus_scrofa 0.9055 1.0058 1994
## total_shrub_cover-Odocoileus_virginianus 0.9379 1.0024 3825
## total_shrub_cover-Canis_latrans 0.9532 1.0036 3128
## total_shrub_cover-Sciurus_niger 0.5232 1.0019 2696
## total_shrub_cover-Procyon_lotor 0.0906 1.0022 2923
## total_shrub_cover-Dasypus_novemcinctus 0.6579 1.0003 3816
## total_shrub_cover-Lynx_rufus 0.3753 1.0100 2771
## total_shrub_cover-Didelphis_virginiana 0.5207 1.0030 3412
## total_shrub_cover-Sylvilagus_floridanus 0.7032 1.0042 2667
## total_shrub_cover-Sciurus_carolinensis 0.7688 1.0009 3674
## total_shrub_cover-Vulpes_vulpes 0.7810 1.0025 2856
## total_shrub_cover-Sus_scrofa 1.1957 1.0013 2711
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0055 0.0585 -0.1079 0.0047 0.1221
## (Intercept)-Canis_latrans -2.6154 0.1749 -2.9608 -2.6111 -2.2831
## (Intercept)-Sciurus_niger -3.9901 0.5615 -5.0657 -3.9904 -2.9239
## (Intercept)-Procyon_lotor -2.2743 0.1324 -2.5384 -2.2699 -2.0221
## (Intercept)-Dasypus_novemcinctus -1.5723 0.1338 -1.8439 -1.5716 -1.3183
## (Intercept)-Lynx_rufus -3.5177 0.3181 -4.1499 -3.5063 -2.9214
## (Intercept)-Didelphis_virginiana -2.3102 0.2525 -2.8330 -2.2991 -1.8405
## (Intercept)-Sylvilagus_floridanus -3.2390 0.3196 -3.9105 -3.2248 -2.6686
## (Intercept)-Sciurus_carolinensis -2.4418 0.2632 -3.0046 -2.4248 -1.9613
## (Intercept)-Vulpes_vulpes -4.0418 0.7585 -5.6051 -4.0005 -2.6948
## (Intercept)-Sus_scrofa -2.9342 0.4804 -3.9584 -2.9023 -2.0987
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 4735
## (Intercept)-Canis_latrans 1.0020 3163
## (Intercept)-Sciurus_niger 1.0088 490
## (Intercept)-Procyon_lotor 1.0104 3895
## (Intercept)-Dasypus_novemcinctus 1.0011 5065
## (Intercept)-Lynx_rufus 1.0222 1119
## (Intercept)-Didelphis_virginiana 1.0048 3703
## (Intercept)-Sylvilagus_floridanus 1.0090 1258
## (Intercept)-Sciurus_carolinensis 1.0012 3147
## (Intercept)-Vulpes_vulpes 1.0716 411
## (Intercept)-Sus_scrofa 1.0011 1852
# 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): 1.336
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2491 0.6175 -1.4334 -0.2657 1.0576 1.0059 1806
## Veg_shannon_index 0.3762 0.2619 -0.1394 0.3726 0.9181 1.0012 2106
## Avg_Cogongrass_Cover 0.3159 0.2661 -0.1992 0.3150 0.8501 1.0003 1919
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9802 3.4101 0.8096 3.0552 12.4703 1.0025 813
## Veg_shannon_index 0.2905 0.3304 0.0386 0.1928 1.1527 1.0099 2339
## Avg_Cogongrass_Cover 0.2806 0.3284 0.0348 0.1781 1.1224 1.0143 2120
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7392 0.7875 0.0619 0.5076 2.7869 1.0222 523
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.495 0.4214 -3.3159 -2.4999 -1.6177 1.0053 4556
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8791 1.2044 0.6324 1.5789 4.8797 1.0069 1767
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6288 1.4238 1.2523 3.4891
## (Intercept)-Canis_latrans 0.2413 0.6229 -0.9835 0.2382
## (Intercept)-Sciurus_niger -0.3442 1.4374 -2.3537 -0.5904
## (Intercept)-Procyon_lotor 0.5502 0.6418 -0.7229 0.5628
## (Intercept)-Dasypus_novemcinctus -0.7431 0.5755 -1.9385 -0.7280
## (Intercept)-Lynx_rufus 0.0977 1.0997 -1.6080 -0.0346
## (Intercept)-Didelphis_virginiana -1.4924 0.6475 -2.8676 -1.4727
## (Intercept)-Sylvilagus_floridanus -0.3119 0.8065 -1.7257 -0.3629
## (Intercept)-Sciurus_carolinensis -1.4997 0.6665 -2.9063 -1.4758
## (Intercept)-Vulpes_vulpes -0.9348 1.5355 -3.1915 -1.1762
## (Intercept)-Sus_scrofa -2.1618 0.8553 -3.9506 -2.1136
## Veg_shannon_index-Odocoileus_virginianus 0.3009 0.4954 -0.7438 0.3165
## Veg_shannon_index-Canis_latrans 0.6472 0.3739 -0.0136 0.6207
## Veg_shannon_index-Sciurus_niger 0.3658 0.5204 -0.6361 0.3560
## Veg_shannon_index-Procyon_lotor 0.4909 0.3935 -0.2327 0.4661
## Veg_shannon_index-Dasypus_novemcinctus 0.2197 0.3357 -0.4709 0.2299
## Veg_shannon_index-Lynx_rufus 0.1927 0.5036 -0.9114 0.2225
## Veg_shannon_index-Didelphis_virginiana 0.5150 0.3928 -0.2142 0.4972
## Veg_shannon_index-Sylvilagus_floridanus 0.4870 0.4443 -0.3317 0.4558
## Veg_shannon_index-Sciurus_carolinensis 0.0397 0.3850 -0.7754 0.0657
## Veg_shannon_index-Vulpes_vulpes 0.1353 0.4840 -0.8917 0.1496
## Veg_shannon_index-Sus_scrofa 0.7358 0.5290 -0.1383 0.6760
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3169 0.4987 -0.6755 0.3074
## Avg_Cogongrass_Cover-Canis_latrans 0.5354 0.3793 -0.1360 0.5064
## Avg_Cogongrass_Cover-Sciurus_niger -0.0032 0.5647 -1.2589 0.0452
## Avg_Cogongrass_Cover-Procyon_lotor 0.4268 0.3998 -0.3027 0.4055
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4298 0.3296 -0.2110 0.4191
## Avg_Cogongrass_Cover-Lynx_rufus 0.5658 0.4300 -0.1909 0.5309
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4533 0.3688 -0.2684 0.4463
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0314 0.4606 -1.0336 -0.0042
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4104 0.3623 -0.2941 0.4083
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3773 0.4558 -0.4811 0.3704
## Avg_Cogongrass_Cover-Sus_scrofa 0.0147 0.5160 -1.1649 0.0718
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8051 1.0196 839
## (Intercept)-Canis_latrans 1.5101 1.0031 3596
## (Intercept)-Sciurus_niger 3.2973 1.0545 253
## (Intercept)-Procyon_lotor 1.7882 1.0100 2555
## (Intercept)-Dasypus_novemcinctus 0.3612 1.0002 3294
## (Intercept)-Lynx_rufus 2.7051 1.0096 682
## (Intercept)-Didelphis_virginiana -0.2570 1.0031 3553
## (Intercept)-Sylvilagus_floridanus 1.4217 1.0175 1464
## (Intercept)-Sciurus_carolinensis -0.2528 1.0019 3153
## (Intercept)-Vulpes_vulpes 2.8192 1.0066 330
## (Intercept)-Sus_scrofa -0.5473 1.0048 2348
## Veg_shannon_index-Odocoileus_virginianus 1.2398 1.0011 2817
## Veg_shannon_index-Canis_latrans 1.4555 1.0000 3313
## Veg_shannon_index-Sciurus_niger 1.4643 1.0042 2112
## Veg_shannon_index-Procyon_lotor 1.3337 1.0015 2643
## Veg_shannon_index-Dasypus_novemcinctus 0.8824 1.0006 4218
## Veg_shannon_index-Lynx_rufus 1.0979 1.0040 2825
## Veg_shannon_index-Didelphis_virginiana 1.3803 1.0017 3655
## Veg_shannon_index-Sylvilagus_floridanus 1.4552 1.0007 3224
## Veg_shannon_index-Sciurus_carolinensis 0.7460 1.0014 3431
## Veg_shannon_index-Vulpes_vulpes 1.0627 1.0015 1890
## Veg_shannon_index-Sus_scrofa 1.9617 1.0021 2710
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3410 1.0013 3468
## Avg_Cogongrass_Cover-Canis_latrans 1.3356 1.0047 3303
## Avg_Cogongrass_Cover-Sciurus_niger 1.0096 1.0041 1769
## Avg_Cogongrass_Cover-Procyon_lotor 1.3048 1.0049 3196
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1090 1.0019 3805
## Avg_Cogongrass_Cover-Lynx_rufus 1.5231 1.0025 2527
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1942 1.0007 3362
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7879 1.0029 2544
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1400 1.0053 3533
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3282 1.0005 2623
## Avg_Cogongrass_Cover-Sus_scrofa 0.8788 1.0016 2271
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0039 0.0589 -0.1105 0.0037 0.1188
## (Intercept)-Canis_latrans -2.6083 0.1727 -2.9605 -2.6054 -2.2828
## (Intercept)-Sciurus_niger -4.0117 0.6049 -5.2344 -3.9863 -2.9019
## (Intercept)-Procyon_lotor -2.2763 0.1317 -2.5370 -2.2706 -2.0247
## (Intercept)-Dasypus_novemcinctus -1.5769 0.1330 -1.8399 -1.5743 -1.3304
## (Intercept)-Lynx_rufus -3.5695 0.3363 -4.2649 -3.5576 -2.9422
## (Intercept)-Didelphis_virginiana -2.3136 0.2482 -2.8226 -2.3046 -1.8498
## (Intercept)-Sylvilagus_floridanus -3.2341 0.3198 -3.9176 -3.2179 -2.6527
## (Intercept)-Sciurus_carolinensis -2.4361 0.2614 -2.9708 -2.4272 -1.9663
## (Intercept)-Vulpes_vulpes -4.1148 0.7692 -5.6698 -4.0540 -2.8035
## (Intercept)-Sus_scrofa -2.8994 0.4568 -3.8714 -2.8591 -2.0946
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0009 3180
## (Intercept)-Sciurus_niger 1.0123 348
## (Intercept)-Procyon_lotor 1.0045 3758
## (Intercept)-Dasypus_novemcinctus 1.0012 5022
## (Intercept)-Lynx_rufus 1.0098 928
## (Intercept)-Didelphis_virginiana 0.9999 3664
## (Intercept)-Sylvilagus_floridanus 1.0113 1173
## (Intercept)-Sciurus_carolinensis 1.0050 3655
## (Intercept)-Vulpes_vulpes 1.0025 371
## (Intercept)-Sus_scrofa 1.0047 2157
# 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): 1.2977
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9682 0.6284 -2.1687 -0.9786 0.3312 1.0042 2054
## Avg_Cogongrass_Cover -0.7554 0.3690 -1.4959 -0.7480 -0.0616 1.0073 1486
## I(Avg_Cogongrass_Cover^2) 0.8490 0.3379 0.2187 0.8284 1.5771 1.0202 1136
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7525 3.2068 0.765 2.9072 11.6139 1.0211 1833
## Avg_Cogongrass_Cover 0.3762 0.4574 0.040 0.2300 1.5702 1.0047 1711
## I(Avg_Cogongrass_Cover^2) 0.4307 0.7989 0.037 0.2092 2.2859 1.0202 749
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4947 0.5224 0.0474 0.334 1.8694 1.0161 637
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4686 0.4021 -3.2612 -2.4714 -1.6495 1.0014 3714
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7744 1.1261 0.5966 1.4884 4.6044 1.0007 1410
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8164 1.3871 0.6015 2.6543
## (Intercept)-Canis_latrans -0.4994 0.6600 -1.8144 -0.4960
## (Intercept)-Sciurus_niger -0.9965 1.1969 -2.9443 -1.1366
## (Intercept)-Procyon_lotor -0.1926 0.6319 -1.4360 -0.1874
## (Intercept)-Dasypus_novemcinctus -1.3944 0.6024 -2.5987 -1.3820
## (Intercept)-Lynx_rufus -1.2222 0.8819 -2.8283 -1.2480
## (Intercept)-Didelphis_virginiana -2.0122 0.7039 -3.4664 -1.9933
## (Intercept)-Sylvilagus_floridanus -1.0802 0.7610 -2.5830 -1.0932
## (Intercept)-Sciurus_carolinensis -2.4826 0.7503 -4.0662 -2.4548
## (Intercept)-Vulpes_vulpes -2.2272 1.2555 -4.4284 -2.3245
## (Intercept)-Sus_scrofa -2.5444 0.8881 -4.3828 -2.5119
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7628 0.6189 -2.0289 -0.7624
## Avg_Cogongrass_Cover-Canis_latrans -0.4770 0.5041 -1.4101 -0.5005
## Avg_Cogongrass_Cover-Sciurus_niger -1.0045 0.6675 -2.5186 -0.9542
## Avg_Cogongrass_Cover-Procyon_lotor -0.6232 0.4950 -1.5714 -0.6354
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5704 0.4714 -1.4783 -0.5766
## Avg_Cogongrass_Cover-Lynx_rufus -0.6468 0.5433 -1.7049 -0.6624
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4973 0.5326 -1.4977 -0.5213
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1564 0.6178 -2.5231 -1.1011
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8293 0.5310 -1.9544 -0.8088
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8141 0.6020 -2.0961 -0.7985
## Avg_Cogongrass_Cover-Sus_scrofa -1.0733 0.6569 -2.5295 -1.0148
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1213 0.7484 0.0996 0.9934
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2021 0.7207 0.2196 1.0563
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4384 0.6661 -1.0185 0.4607
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0801 0.6246 0.2266 0.9754
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7320 0.3441 0.0682 0.7273
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1887 0.5175 0.3438 1.1218
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6150 0.4080 -0.1520 0.6011
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7785 0.4793 -0.0372 0.7411
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9866 0.3867 0.3228 0.9554
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9571 0.5029 0.1537 0.9091
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4095 0.5836 -0.9604 0.4717
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.0278 1.0064 1370
## (Intercept)-Canis_latrans 0.8433 1.0079 2914
## (Intercept)-Sciurus_niger 1.8693 1.0052 570
## (Intercept)-Procyon_lotor 1.0658 1.0061 2428
## (Intercept)-Dasypus_novemcinctus -0.2218 1.0002 3318
## (Intercept)-Lynx_rufus 0.5934 1.0209 1416
## (Intercept)-Didelphis_virginiana -0.6699 1.0024 3041
## (Intercept)-Sylvilagus_floridanus 0.5081 1.0060 2340
## (Intercept)-Sciurus_carolinensis -1.0879 1.0062 2545
## (Intercept)-Vulpes_vulpes 0.5325 1.0292 561
## (Intercept)-Sus_scrofa -0.8561 1.0008 2027
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4629 1.0029 2708
## Avg_Cogongrass_Cover-Canis_latrans 0.5758 1.0011 2895
## Avg_Cogongrass_Cover-Sciurus_niger 0.1573 1.0079 1967
## Avg_Cogongrass_Cover-Procyon_lotor 0.3940 1.0007 2793
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3972 1.0033 2646
## Avg_Cogongrass_Cover-Lynx_rufus 0.4796 1.0019 1910
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6746 1.0031 2339
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1050 1.0049 2030
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1655 1.0042 2091
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3446 1.0042 1781
## Avg_Cogongrass_Cover-Sus_scrofa 0.0434 1.0018 1754
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.0296 1.0087 900
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0971 1.0220 946
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.6691 1.0027 873
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6919 1.0311 976
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4255 1.0036 2525
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3948 1.0092 1391
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4614 1.0097 2131
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8061 1.0056 1852
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8568 1.0098 1967
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1452 1.0156 1261
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3546 1.0064 1506
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0038 0.0589 -0.1117 0.0029 0.1209
## (Intercept)-Canis_latrans -2.6300 0.1732 -2.9774 -2.6235 -2.3096
## (Intercept)-Sciurus_niger -3.9435 0.6120 -5.1638 -3.9203 -2.8469
## (Intercept)-Procyon_lotor -2.2724 0.1328 -2.5382 -2.2716 -2.0195
## (Intercept)-Dasypus_novemcinctus -1.5753 0.1312 -1.8331 -1.5769 -1.3178
## (Intercept)-Lynx_rufus -3.4232 0.3150 -4.0838 -3.4081 -2.8406
## (Intercept)-Didelphis_virginiana -2.3324 0.2634 -2.8803 -2.3170 -1.8607
## (Intercept)-Sylvilagus_floridanus -3.2093 0.3138 -3.8585 -3.1962 -2.6333
## (Intercept)-Sciurus_carolinensis -2.4296 0.2600 -2.9669 -2.4193 -1.9516
## (Intercept)-Vulpes_vulpes -3.9176 0.7326 -5.4598 -3.8577 -2.6606
## (Intercept)-Sus_scrofa -2.9217 0.4771 -3.9625 -2.8797 -2.0887
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0019 5250
## (Intercept)-Canis_latrans 0.9998 2931
## (Intercept)-Sciurus_niger 1.0016 375
## (Intercept)-Procyon_lotor 1.0006 3897
## (Intercept)-Dasypus_novemcinctus 1.0010 5250
## (Intercept)-Lynx_rufus 1.0104 1256
## (Intercept)-Didelphis_virginiana 1.0029 3557
## (Intercept)-Sylvilagus_floridanus 1.0000 1423
## (Intercept)-Sciurus_carolinensis 1.0010 3854
## (Intercept)-Vulpes_vulpes 1.0287 411
## (Intercept)-Sus_scrofa 1.0023 1550
# 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): 1.4643
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0144 1.1301 -3.0926 -1.0787 1.3830 1.0070 1481
## Cogon_Patch_Size -0.2574 0.7261 -1.8052 -0.2171 1.0821 1.0086 1258
## Veg_shannon_index 0.9210 0.4829 -0.0296 0.9077 1.9399 1.0040 986
## total_shrub_cover -0.2843 0.4191 -1.1414 -0.2733 0.5344 1.0031 1414
## Avg_Cogongrass_Cover 0.0220 0.9095 -1.7543 -0.0012 1.8924 1.0264 466
## Tree_Density -2.0061 0.7840 -3.6647 -1.9612 -0.5418 1.0020 613
## Avg_Canopy_Cover 1.8076 0.6110 0.6587 1.7783 3.0927 1.0018 813
## I(Avg_Cogongrass_Cover^2) 1.4994 0.5437 0.4870 1.4662 2.6354 1.0171 617
## avg_veg_height -0.1845 0.4762 -1.1431 -0.1705 0.7025 1.0116 652
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.9661 20.6903 4.3293 16.2853 73.0907 1.0657 256
## Cogon_Patch_Size 3.7578 6.3049 0.1568 2.0739 17.6954 1.0426 632
## Veg_shannon_index 0.8756 1.4296 0.0484 0.4215 4.5549 1.0062 780
## total_shrub_cover 0.6431 0.8337 0.0510 0.3829 2.7626 1.0133 1714
## Avg_Cogongrass_Cover 1.1860 2.3572 0.0520 0.5164 6.3740 1.0195 1248
## Tree_Density 3.5643 7.1474 0.0716 1.2965 22.4082 1.0033 438
## Avg_Canopy_Cover 2.1956 3.0680 0.1104 1.2539 10.0068 1.0562 779
## I(Avg_Cogongrass_Cover^2) 0.9673 1.8954 0.0485 0.3989 5.5676 1.1368 362
## avg_veg_height 0.4463 0.6340 0.0401 0.2459 2.0485 1.0059 1916
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7011 2.0234 0.0695 1.0204 7.061 1.0031 290
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5127 0.4479 -3.3533 -2.525 -1.5791 1.0054 4377
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1551 1.3325 0.763 1.8167 5.6148 1.0072 2183
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.5872 3.6286 2.5808
## (Intercept)-Canis_latrans -0.9560 1.2157 -3.3349
## (Intercept)-Sciurus_niger 1.0262 2.8559 -2.9635
## (Intercept)-Procyon_lotor -0.4407 1.0731 -2.6950
## (Intercept)-Dasypus_novemcinctus -2.8006 1.2266 -5.6155
## (Intercept)-Lynx_rufus 0.2884 2.7846 -3.8116
## (Intercept)-Didelphis_virginiana -4.3020 1.4372 -7.3840
## (Intercept)-Sylvilagus_floridanus -2.4412 1.4297 -5.4264
## (Intercept)-Sciurus_carolinensis -5.0557 1.5933 -8.6947
## (Intercept)-Vulpes_vulpes -4.1397 2.4853 -8.9101
## (Intercept)-Sus_scrofa -6.0005 2.0059 -10.5357
## Cogon_Patch_Size-Odocoileus_virginianus -0.0606 1.5800 -2.8778
## Cogon_Patch_Size-Canis_latrans 1.6246 1.4495 -0.3125
## Cogon_Patch_Size-Sciurus_niger -0.9504 1.9514 -5.2676
## Cogon_Patch_Size-Procyon_lotor -0.5166 0.8183 -2.1342
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2413 0.7093 -1.7447
## Cogon_Patch_Size-Lynx_rufus -0.3664 1.6642 -3.4877
## Cogon_Patch_Size-Didelphis_virginiana 1.6693 1.0495 -0.0397
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5168 1.5996 -5.4869
## Cogon_Patch_Size-Sciurus_carolinensis -1.2493 1.4073 -4.6535
## Cogon_Patch_Size-Vulpes_vulpes -0.8083 1.7851 -4.9692
## Cogon_Patch_Size-Sus_scrofa -0.8927 1.6266 -4.9422
## Veg_shannon_index-Odocoileus_virginianus 0.7501 0.9264 -1.3840
## Veg_shannon_index-Canis_latrans 1.2973 0.7055 0.1164
## Veg_shannon_index-Sciurus_niger 1.0297 1.0270 -1.0194
## Veg_shannon_index-Procyon_lotor 1.1218 0.6063 0.0383
## Veg_shannon_index-Dasypus_novemcinctus 0.6356 0.5565 -0.4586
## Veg_shannon_index-Lynx_rufus 0.9801 0.9435 -0.8902
## Veg_shannon_index-Didelphis_virginiana 1.0445 0.6773 -0.2362
## Veg_shannon_index-Sylvilagus_floridanus 1.0137 0.7234 -0.3319
## Veg_shannon_index-Sciurus_carolinensis 0.3170 0.8125 -1.5221
## Veg_shannon_index-Vulpes_vulpes 0.5924 0.9076 -1.5559
## Veg_shannon_index-Sus_scrofa 1.5981 1.0470 0.1397
## total_shrub_cover-Odocoileus_virginianus -0.1336 0.7683 -1.6123
## total_shrub_cover-Canis_latrans -0.0165 0.5773 -1.1054
## total_shrub_cover-Sciurus_niger -0.5194 0.8541 -2.4580
## total_shrub_cover-Procyon_lotor -0.8375 0.6021 -2.1772
## total_shrub_cover-Dasypus_novemcinctus 0.0641 0.5172 -0.9321
## total_shrub_cover-Lynx_rufus -0.6759 0.8522 -2.5768
## total_shrub_cover-Didelphis_virginiana -0.4623 0.6464 -1.8412
## total_shrub_cover-Sylvilagus_floridanus -0.2112 0.6867 -1.5804
## total_shrub_cover-Sciurus_carolinensis -0.0222 0.6352 -1.2412
## total_shrub_cover-Vulpes_vulpes -0.4052 0.7845 -2.0782
## total_shrub_cover-Sus_scrofa 0.0512 0.7411 -1.2933
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0104 1.2880 -2.6157
## Avg_Cogongrass_Cover-Canis_latrans 0.0604 1.1300 -2.1451
## Avg_Cogongrass_Cover-Sciurus_niger -0.3049 1.4595 -3.5104
## Avg_Cogongrass_Cover-Procyon_lotor 0.2965 1.1444 -1.7826
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6403 1.2308 -1.4846
## Avg_Cogongrass_Cover-Lynx_rufus 0.2022 1.2332 -2.1415
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1084 1.1428 -2.0688
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4879 1.2828 -3.2785
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1046 1.1747 -2.1778
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1423 1.2659 -2.3010
## Avg_Cogongrass_Cover-Sus_scrofa -0.4569 1.3404 -3.4708
## Tree_Density-Odocoileus_virginianus -1.0173 1.4796 -3.3499
## Tree_Density-Canis_latrans -2.7776 1.3900 -6.1511
## Tree_Density-Sciurus_niger -1.9601 1.6680 -5.2425
## Tree_Density-Procyon_lotor -1.8467 0.9814 -3.9408
## Tree_Density-Dasypus_novemcinctus -3.7836 2.0473 -9.0563
## Tree_Density-Lynx_rufus -0.7996 1.6813 -3.4479
## Tree_Density-Didelphis_virginiana -2.3569 1.2216 -5.2053
## Tree_Density-Sylvilagus_floridanus -2.5356 1.4292 -5.9562
## Tree_Density-Sciurus_carolinensis -2.7868 1.6104 -7.0158
## Tree_Density-Vulpes_vulpes -2.1281 1.6354 -5.6552
## Tree_Density-Sus_scrofa -2.4156 1.6236 -6.3482
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2827 1.3267 -1.4626
## Avg_Canopy_Cover-Canis_latrans 0.3137 0.7563 -1.1763
## Avg_Canopy_Cover-Sciurus_niger 2.2149 1.6046 -0.6563
## Avg_Canopy_Cover-Procyon_lotor 1.6971 0.7340 0.3957
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9766 0.7558 0.7586
## Avg_Canopy_Cover-Lynx_rufus 1.5225 1.3419 -0.9758
## Avg_Canopy_Cover-Didelphis_virginiana 2.6394 1.0084 1.1037
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2529 1.5821 1.1051
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2525 0.8787 0.8382
## Avg_Canopy_Cover-Vulpes_vulpes 2.2748 1.3019 0.3501
## Avg_Canopy_Cover-Sus_scrofa 2.0233 0.8813 0.5701
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7657 1.0989 0.0460
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0175 0.9882 0.6186
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1997 1.1091 -1.2810
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8269 0.9056 0.4665
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4627 0.6950 0.2797
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9973 0.9985 0.5257
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1515 0.6738 -0.1819
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2644 0.7890 -0.1946
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6510 0.7194 0.4169
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8280 0.8642 0.4445
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9027 0.9899 -1.3408
## avg_veg_height-Odocoileus_virginianus -0.1869 0.7581 -1.7308
## avg_veg_height-Canis_latrans -0.4124 0.6281 -1.7646
## avg_veg_height-Sciurus_niger -0.2749 0.7841 -1.9655
## avg_veg_height-Procyon_lotor 0.0991 0.6068 -1.0311
## avg_veg_height-Dasypus_novemcinctus 0.0937 0.6043 -1.0455
## avg_veg_height-Lynx_rufus -0.2373 0.7539 -1.7851
## avg_veg_height-Didelphis_virginiana -0.2955 0.6741 -1.7421
## avg_veg_height-Sylvilagus_floridanus -0.3024 0.6774 -1.7849
## avg_veg_height-Sciurus_carolinensis 0.0554 0.6504 -1.1585
## avg_veg_height-Vulpes_vulpes -0.3203 0.7717 -2.0216
## avg_veg_height-Sus_scrofa -0.2844 0.6955 -1.7270
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.9238 16.6652 1.0514 216
## (Intercept)-Canis_latrans -0.9527 1.5152 1.0028 1505
## (Intercept)-Sciurus_niger 0.5699 7.5411 1.1132 228
## (Intercept)-Procyon_lotor -0.3953 1.5986 1.0063 1332
## (Intercept)-Dasypus_novemcinctus -2.6786 -0.7342 1.0036 676
## (Intercept)-Lynx_rufus -0.2387 7.4714 1.0383 255
## (Intercept)-Didelphis_virginiana -4.1978 -1.8210 1.0054 794
## (Intercept)-Sylvilagus_floridanus -2.3824 0.2945 1.0019 1028
## (Intercept)-Sciurus_carolinensis -4.8514 -2.4949 1.0019 637
## (Intercept)-Vulpes_vulpes -4.2005 1.2700 1.0215 324
## (Intercept)-Sus_scrofa -5.7812 -2.6757 1.0053 637
## Cogon_Patch_Size-Odocoileus_virginianus -0.1356 3.4470 1.0072 1948
## Cogon_Patch_Size-Canis_latrans 1.3399 5.3216 1.0020 828
## Cogon_Patch_Size-Sciurus_niger -0.7904 2.6568 1.0146 612
## Cogon_Patch_Size-Procyon_lotor -0.5024 1.0885 1.0011 996
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2114 1.0825 1.0176 1179
## Cogon_Patch_Size-Lynx_rufus -0.4321 3.3850 1.0054 758
## Cogon_Patch_Size-Didelphis_virginiana 1.5500 4.0382 1.0067 740
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2414 0.8220 1.0061 698
## Cogon_Patch_Size-Sciurus_carolinensis -0.9761 0.7144 1.0243 816
## Cogon_Patch_Size-Vulpes_vulpes -0.6684 2.4939 1.0069 679
## Cogon_Patch_Size-Sus_scrofa -0.5946 1.5202 1.0147 854
## Veg_shannon_index-Odocoileus_virginianus 0.7841 2.5328 1.0018 1483
## Veg_shannon_index-Canis_latrans 1.2386 2.8864 1.0024 1036
## Veg_shannon_index-Sciurus_niger 0.9917 3.2986 1.0070 994
## Veg_shannon_index-Procyon_lotor 1.0818 2.4537 1.0121 1185
## Veg_shannon_index-Dasypus_novemcinctus 0.6414 1.7238 1.0020 1965
## Veg_shannon_index-Lynx_rufus 0.9547 3.0184 1.0029 1545
## Veg_shannon_index-Didelphis_virginiana 1.0078 2.5295 1.0016 2368
## Veg_shannon_index-Sylvilagus_floridanus 0.9731 2.5963 1.0092 1871
## Veg_shannon_index-Sciurus_carolinensis 0.4142 1.6720 1.0048 1263
## Veg_shannon_index-Vulpes_vulpes 0.6809 2.1577 1.0029 1331
## Veg_shannon_index-Sus_scrofa 1.4040 4.2582 1.0030 944
## total_shrub_cover-Odocoileus_virginianus -0.1480 1.4877 1.0053 2570
## total_shrub_cover-Canis_latrans -0.0228 1.1882 1.0044 2374
## total_shrub_cover-Sciurus_niger -0.4610 1.0152 1.0063 1560
## total_shrub_cover-Procyon_lotor -0.7781 0.2007 1.0020 1774
## total_shrub_cover-Dasypus_novemcinctus 0.0615 1.1347 1.0054 2690
## total_shrub_cover-Lynx_rufus -0.6001 0.8337 1.0032 1236
## total_shrub_cover-Didelphis_virginiana -0.4206 0.7327 1.0016 2560
## total_shrub_cover-Sylvilagus_floridanus -0.2263 1.1967 1.0053 2118
## total_shrub_cover-Sciurus_carolinensis -0.0313 1.3048 1.0070 2357
## total_shrub_cover-Vulpes_vulpes -0.3660 1.1268 1.0054 2212
## total_shrub_cover-Sus_scrofa -0.0020 1.6342 1.0000 1929
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0039 2.6611 1.0154 730
## Avg_Cogongrass_Cover-Canis_latrans 0.0438 2.3330 1.0144 684
## Avg_Cogongrass_Cover-Sciurus_niger -0.2257 2.2605 1.0098 739
## Avg_Cogongrass_Cover-Procyon_lotor 0.2229 2.7836 1.0151 593
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5346 3.4330 1.0252 653
## Avg_Cogongrass_Cover-Lynx_rufus 0.1427 2.8882 1.0237 775
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0875 2.4363 1.0235 728
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4104 1.8530 1.0129 815
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0777 2.5223 1.0266 725
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1072 2.6866 1.0167 707
## Avg_Cogongrass_Cover-Sus_scrofa -0.3432 1.8851 1.0119 744
## Tree_Density-Odocoileus_virginianus -1.1846 2.3991 1.0052 606
## Tree_Density-Canis_latrans -2.5526 -0.7219 1.0068 623
## Tree_Density-Sciurus_niger -1.9227 1.5255 1.0086 656
## Tree_Density-Procyon_lotor -1.8103 0.0183 1.0015 857
## Tree_Density-Dasypus_novemcinctus -3.2995 -1.2376 1.0084 359
## Tree_Density-Lynx_rufus -1.0313 3.4489 1.0145 467
## Tree_Density-Didelphis_virginiana -2.1986 -0.4398 1.0055 820
## Tree_Density-Sylvilagus_floridanus -2.3521 -0.2338 1.0085 472
## Tree_Density-Sciurus_carolinensis -2.4835 -0.6453 1.0035 552
## Tree_Density-Vulpes_vulpes -2.0478 0.9197 1.0086 942
## Tree_Density-Sus_scrofa -2.2194 0.1462 1.0085 945
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3330 3.9085 1.0032 1561
## Avg_Canopy_Cover-Canis_latrans 0.3159 1.7999 1.0160 1434
## Avg_Canopy_Cover-Sciurus_niger 2.0450 6.0358 1.0085 685
## Avg_Canopy_Cover-Procyon_lotor 1.6546 3.2725 1.0011 1164
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8950 3.7653 1.0046 587
## Avg_Canopy_Cover-Lynx_rufus 1.4982 4.3233 1.0103 889
## Avg_Canopy_Cover-Didelphis_virginiana 2.4733 5.0365 1.0028 710
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9430 7.2225 1.0282 613
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1282 4.3264 1.0028 845
## Avg_Canopy_Cover-Vulpes_vulpes 2.0608 5.4718 1.0204 739
## Avg_Canopy_Cover-Sus_scrofa 1.9338 4.0446 1.0029 773
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6232 4.3968 1.0631 827
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8438 4.4856 1.0209 508
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2576 3.2987 1.0092 542
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7173 3.9057 1.0398 688
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4043 2.9776 1.0119 857
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.8441 4.4165 1.0144 744
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1541 2.5010 1.0074 868
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2186 2.9220 1.0161 1075
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5913 3.3066 1.0092 980
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7296 3.8409 1.0093 737
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9942 2.5369 1.0130 693
## avg_veg_height-Odocoileus_virginianus -0.1615 1.2553 1.0061 1353
## avg_veg_height-Canis_latrans -0.3716 0.7000 1.0061 948
## avg_veg_height-Sciurus_niger -0.2357 1.1780 1.0120 1107
## avg_veg_height-Procyon_lotor 0.0797 1.3490 1.0093 1359
## avg_veg_height-Dasypus_novemcinctus 0.0838 1.3246 1.0138 1213
## avg_veg_height-Lynx_rufus -0.2266 1.2174 1.0132 1076
## avg_veg_height-Didelphis_virginiana -0.2601 0.9442 1.0052 1175
## avg_veg_height-Sylvilagus_floridanus -0.2700 0.9571 1.0114 1391
## avg_veg_height-Sciurus_carolinensis 0.0311 1.4208 1.0076 1307
## avg_veg_height-Vulpes_vulpes -0.2813 1.0947 1.0053 1053
## avg_veg_height-Sus_scrofa -0.2520 1.0064 1.0050 1341
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0039 0.0589 -0.1107 0.0042 0.1224
## (Intercept)-Canis_latrans -2.6157 0.1720 -2.9645 -2.6109 -2.2910
## (Intercept)-Sciurus_niger -4.5845 0.4714 -5.5326 -4.5854 -3.6421
## (Intercept)-Procyon_lotor -2.2666 0.1319 -2.5387 -2.2635 -2.0160
## (Intercept)-Dasypus_novemcinctus -1.5762 0.1330 -1.8425 -1.5735 -1.3252
## (Intercept)-Lynx_rufus -3.7555 0.3359 -4.4127 -3.7588 -3.1120
## (Intercept)-Didelphis_virginiana -2.2905 0.2403 -2.7805 -2.2830 -1.8485
## (Intercept)-Sylvilagus_floridanus -3.2035 0.2782 -3.7742 -3.1971 -2.6902
## (Intercept)-Sciurus_carolinensis -2.4242 0.2606 -2.9749 -2.4111 -1.9455
## (Intercept)-Vulpes_vulpes -4.1572 0.6567 -5.5102 -4.1288 -2.9496
## (Intercept)-Sus_scrofa -2.8679 0.4553 -3.8578 -2.8388 -2.0565
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0013 5250
## (Intercept)-Canis_latrans 1.0011 3010
## (Intercept)-Sciurus_niger 1.0373 387
## (Intercept)-Procyon_lotor 1.0010 4045
## (Intercept)-Dasypus_novemcinctus 1.0013 5250
## (Intercept)-Lynx_rufus 1.0079 535
## (Intercept)-Didelphis_virginiana 0.9999 3512
## (Intercept)-Sylvilagus_floridanus 1.0019 1564
## (Intercept)-Sciurus_carolinensis 1.0050 3492
## (Intercept)-Vulpes_vulpes 1.0067 408
## (Intercept)-Sus_scrofa 1.0051 1779
# 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): 1.8613
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1738 0.5842 -1.2841 -0.1928 1.0547 1.0075 1791
## Avg_Cogongrass_Cover 0.2013 0.2406 -0.2881 0.1994 0.6655 1.0078 2379
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2557 2.6873 0.6405 2.5374 10.2443 1.0165 1759
## Avg_Cogongrass_Cover 0.2640 0.3147 0.0363 0.1703 1.0635 1.0194 2415
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7588 0.7311 0.0619 0.5384 2.7399 1.0235 536
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5950 0.4331 -3.4159 -2.6044 -1.6897 1.0006 4555
## shrub_cover 0.2214 0.2359 -0.2346 0.2225 0.6969 1.0032 3228
## veg_height -0.0016 0.1541 -0.3122 0.0001 0.3047 1.0060 2760
## week -0.0390 0.1199 -0.2887 -0.0326 0.1792 1.0015 3129
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9989 1.3193 0.6657 1.6737 5.2385 1.0029 1675
## shrub_cover 0.4363 0.3651 0.0895 0.3356 1.3780 1.0103 2105
## veg_height 0.1869 0.1246 0.0539 0.1536 0.5115 1.0003 3802
## week 0.1012 0.0851 0.0259 0.0774 0.3267 1.0224 2558
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3260 1.3199 1.0892 3.1846
## (Intercept)-Canis_latrans 0.4074 0.6362 -0.7847 0.3878
## (Intercept)-Sciurus_niger -0.3735 1.2062 -2.2662 -0.5251
## (Intercept)-Procyon_lotor 0.5352 0.6018 -0.6792 0.5412
## (Intercept)-Dasypus_novemcinctus -0.6146 0.5820 -1.7841 -0.6041
## (Intercept)-Lynx_rufus 0.1108 1.0293 -1.5264 -0.0010
## (Intercept)-Didelphis_virginiana -1.2366 0.6434 -2.5308 -1.2274
## (Intercept)-Sylvilagus_floridanus -0.3371 0.6971 -1.6530 -0.3577
## (Intercept)-Sciurus_carolinensis -1.2956 0.6703 -2.6821 -1.2778
## (Intercept)-Vulpes_vulpes -0.9463 1.2715 -3.0289 -1.1047
## (Intercept)-Sus_scrofa -1.6912 0.8271 -3.3151 -1.6747
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1815 0.4638 -0.7729 0.1786
## Avg_Cogongrass_Cover-Canis_latrans 0.4134 0.3674 -0.2316 0.3894
## Avg_Cogongrass_Cover-Sciurus_niger -0.1017 0.5577 -1.3672 -0.0415
## Avg_Cogongrass_Cover-Procyon_lotor 0.2261 0.3322 -0.4086 0.2223
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3459 0.3180 -0.2568 0.3355
## Avg_Cogongrass_Cover-Lynx_rufus 0.4217 0.4029 -0.2866 0.3969
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3240 0.3561 -0.3695 0.3170
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1590 0.4357 -1.1762 -0.1200
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3437 0.3458 -0.3166 0.3356
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2842 0.4362 -0.5451 0.2757
## Avg_Cogongrass_Cover-Sus_scrofa -0.0344 0.4990 -1.2177 0.0155
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.2869 1.0086 1418
## (Intercept)-Canis_latrans 1.7243 1.0004 3013
## (Intercept)-Sciurus_niger 2.4771 1.0374 437
## (Intercept)-Procyon_lotor 1.7373 1.0061 2548
## (Intercept)-Dasypus_novemcinctus 0.4970 1.0010 3217
## (Intercept)-Lynx_rufus 2.3833 1.0258 825
## (Intercept)-Didelphis_virginiana 0.0180 1.0016 3081
## (Intercept)-Sylvilagus_floridanus 1.1077 1.0087 2259
## (Intercept)-Sciurus_carolinensis -0.0229 1.0000 2908
## (Intercept)-Vulpes_vulpes 2.0929 1.0139 440
## (Intercept)-Sus_scrofa -0.0922 1.0107 2184
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.0786 1.0026 4183
## Avg_Cogongrass_Cover-Canis_latrans 1.2247 1.0004 3799
## Avg_Cogongrass_Cover-Sciurus_niger 0.8407 1.0159 1905
## Avg_Cogongrass_Cover-Procyon_lotor 0.9086 1.0026 4675
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0160 1.0020 3647
## Avg_Cogongrass_Cover-Lynx_rufus 1.2867 1.0089 3545
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0445 1.0008 3574
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6011 1.0097 1892
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0569 1.0027 4528
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1759 1.0024 3165
## Avg_Cogongrass_Cover-Sus_scrofa 0.8132 1.0066 2694
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0057 0.0608 -0.1131 0.0053 0.1247
## (Intercept)-Canis_latrans -2.7565 0.1921 -3.1491 -2.7475 -2.3998
## (Intercept)-Sciurus_niger -4.1696 0.6442 -5.4046 -4.1537 -2.9318
## (Intercept)-Procyon_lotor -2.2967 0.1444 -2.5856 -2.2963 -2.0196
## (Intercept)-Dasypus_novemcinctus -1.7251 0.1563 -2.0495 -1.7189 -1.4237
## (Intercept)-Lynx_rufus -3.6672 0.3597 -4.4037 -3.6638 -3.0033
## (Intercept)-Didelphis_virginiana -2.5382 0.2898 -3.1513 -2.5159 -2.0179
## (Intercept)-Sylvilagus_floridanus -3.1962 0.3082 -3.8539 -3.1790 -2.6305
## (Intercept)-Sciurus_carolinensis -2.5918 0.3116 -3.2532 -2.5749 -2.0260
## (Intercept)-Vulpes_vulpes -4.2343 0.8051 -5.9167 -4.1810 -2.8166
## (Intercept)-Sus_scrofa -3.2492 0.5766 -4.4194 -3.2447 -2.1476
## shrub_cover-Odocoileus_virginianus -0.0525 0.0639 -0.1771 -0.0528 0.0692
## shrub_cover-Canis_latrans -0.2631 0.2199 -0.6975 -0.2614 0.1532
## shrub_cover-Sciurus_niger -0.3008 0.4486 -1.2279 -0.2891 0.5739
## shrub_cover-Procyon_lotor 0.2483 0.1644 -0.0804 0.2499 0.5633
## shrub_cover-Dasypus_novemcinctus 0.7769 0.2897 0.2265 0.7670 1.3644
## shrub_cover-Lynx_rufus -0.2158 0.3365 -0.8831 -0.2098 0.4445
## shrub_cover-Didelphis_virginiana 0.8698 0.3558 0.2173 0.8501 1.6360
## shrub_cover-Sylvilagus_floridanus 0.2585 0.3914 -0.4866 0.2502 1.0434
## shrub_cover-Sciurus_carolinensis 0.7434 0.3874 0.0229 0.7305 1.5401
## shrub_cover-Vulpes_vulpes -0.0586 0.5183 -1.1465 -0.0492 0.9479
## shrub_cover-Sus_scrofa 0.4800 0.6804 -0.8068 0.4517 1.8993
## veg_height-Odocoileus_virginianus -0.2976 0.0643 -0.4250 -0.2972 -0.1720
## veg_height-Canis_latrans -0.5778 0.1860 -0.9454 -0.5765 -0.2284
## veg_height-Sciurus_niger -0.0548 0.3895 -0.8054 -0.0631 0.7552
## veg_height-Procyon_lotor 0.3326 0.1229 0.0905 0.3303 0.5761
## veg_height-Dasypus_novemcinctus 0.2214 0.1325 -0.0385 0.2197 0.4854
## veg_height-Lynx_rufus 0.0162 0.2386 -0.4701 0.0230 0.4669
## veg_height-Didelphis_virginiana 0.3924 0.2406 -0.0557 0.3861 0.8933
## veg_height-Sylvilagus_floridanus 0.1212 0.2429 -0.3513 0.1219 0.5948
## veg_height-Sciurus_carolinensis 0.0510 0.2023 -0.3333 0.0477 0.4609
## veg_height-Vulpes_vulpes -0.1271 0.3071 -0.7702 -0.1113 0.4341
## veg_height-Sus_scrofa -0.1154 0.3186 -0.7646 -0.1062 0.5035
## week-Odocoileus_virginianus 0.2125 0.0610 0.0965 0.2121 0.3326
## week-Canis_latrans 0.0730 0.1302 -0.1920 0.0755 0.3180
## week-Sciurus_niger -0.2939 0.3072 -1.0364 -0.2587 0.2020
## week-Procyon_lotor -0.0443 0.1162 -0.2858 -0.0424 0.1735
## week-Dasypus_novemcinctus -0.1610 0.1358 -0.4428 -0.1548 0.0816
## week-Lynx_rufus -0.0280 0.1916 -0.4247 -0.0206 0.3258
## week-Didelphis_virginiana -0.1986 0.2162 -0.6627 -0.1839 0.1818
## week-Sylvilagus_floridanus -0.1403 0.1999 -0.5685 -0.1316 0.2291
## week-Sciurus_carolinensis 0.1438 0.1761 -0.2076 0.1470 0.4841
## week-Vulpes_vulpes -0.1016 0.2749 -0.6953 -0.0825 0.3985
## week-Sus_scrofa 0.1076 0.2294 -0.3377 0.1050 0.5618
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0166 2203
## (Intercept)-Sciurus_niger 1.0598 418
## (Intercept)-Procyon_lotor 1.0026 4533
## (Intercept)-Dasypus_novemcinctus 1.0019 4272
## (Intercept)-Lynx_rufus 1.0366 883
## (Intercept)-Didelphis_virginiana 1.0004 2756
## (Intercept)-Sylvilagus_floridanus 1.0023 1521
## (Intercept)-Sciurus_carolinensis 1.0005 2624
## (Intercept)-Vulpes_vulpes 1.0155 362
## (Intercept)-Sus_scrofa 1.0064 1655
## shrub_cover-Odocoileus_virginianus 1.0010 5250
## shrub_cover-Canis_latrans 0.9998 2766
## shrub_cover-Sciurus_niger 1.0203 1148
## shrub_cover-Procyon_lotor 1.0007 4580
## shrub_cover-Dasypus_novemcinctus 1.0065 3137
## shrub_cover-Lynx_rufus 1.0159 1334
## shrub_cover-Didelphis_virginiana 1.0061 2387
## shrub_cover-Sylvilagus_floridanus 1.0059 1943
## shrub_cover-Sciurus_carolinensis 1.0020 2424
## shrub_cover-Vulpes_vulpes 1.0117 1540
## shrub_cover-Sus_scrofa 1.0020 2322
## veg_height-Odocoileus_virginianus 0.9999 4440
## veg_height-Canis_latrans 1.0185 2400
## veg_height-Sciurus_niger 1.0113 1919
## veg_height-Procyon_lotor 1.0042 4167
## veg_height-Dasypus_novemcinctus 1.0002 4985
## veg_height-Lynx_rufus 1.0059 2353
## veg_height-Didelphis_virginiana 1.0000 3441
## veg_height-Sylvilagus_floridanus 1.0011 2527
## veg_height-Sciurus_carolinensis 1.0015 3620
## veg_height-Vulpes_vulpes 1.0026 1806
## veg_height-Sus_scrofa 1.0075 3537
## week-Odocoileus_virginianus 1.0002 5647
## week-Canis_latrans 1.0009 4234
## week-Sciurus_niger 1.0056 1640
## week-Procyon_lotor 1.0027 4414
## week-Dasypus_novemcinctus 1.0006 4370
## week-Lynx_rufus 1.0007 3174
## week-Didelphis_virginiana 1.0002 3544
## week-Sylvilagus_floridanus 1.0053 3071
## week-Sciurus_carolinensis 1.0012 4642
## week-Vulpes_vulpes 1.0042 2876
## week-Sus_scrofa 1.0008 4178
# 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): 1.3108
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2835 0.5892 -1.4237 -0.3018 0.9236 1.0077 1876
## Avg_Cogongrass_Cover 0.1968 0.2289 -0.2767 0.1987 0.6395 1.0098 2357
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4679 3.0828 0.6944 2.6837 11.1889 1.0184 1041
## Avg_Cogongrass_Cover 0.2374 0.2426 0.0348 0.1628 0.9184 1.0001 2922
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7444 0.7604 0.0622 0.5244 2.8037 1.0075 518
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4589 0.4083 -3.2501 -2.4677 -1.6273 1.0004 4297
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8307 1.1778 0.6053 1.532 4.7541 1.0008 2189
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4267 1.4576 1.0335 3.2557
## (Intercept)-Canis_latrans 0.2844 0.6199 -0.8992 0.2712
## (Intercept)-Sciurus_niger -0.5387 1.1499 -2.3152 -0.7078
## (Intercept)-Procyon_lotor 0.5125 0.6140 -0.6967 0.5134
## (Intercept)-Dasypus_novemcinctus -0.6897 0.5724 -1.8593 -0.6810
## (Intercept)-Lynx_rufus -0.0445 1.0726 -1.6979 -0.1595
## (Intercept)-Didelphis_virginiana -1.3721 0.6397 -2.6976 -1.3577
## (Intercept)-Sylvilagus_floridanus -0.3662 0.6989 -1.7091 -0.3850
## (Intercept)-Sciurus_carolinensis -1.4573 0.6337 -2.7833 -1.4368
## (Intercept)-Vulpes_vulpes -1.0972 1.2451 -3.1290 -1.2338
## (Intercept)-Sus_scrofa -1.8892 0.8038 -3.5649 -1.8683
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1739 0.4509 -0.7328 0.1738
## Avg_Cogongrass_Cover-Canis_latrans 0.3552 0.3442 -0.2763 0.3342
## Avg_Cogongrass_Cover-Sciurus_niger -0.0959 0.4995 -1.1964 -0.0602
## Avg_Cogongrass_Cover-Procyon_lotor 0.2539 0.3409 -0.3668 0.2437
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3382 0.3066 -0.2599 0.3346
## Avg_Cogongrass_Cover-Lynx_rufus 0.4254 0.3872 -0.2543 0.3977
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3321 0.3387 -0.3247 0.3239
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1356 0.4123 -1.0296 -0.1047
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3424 0.3374 -0.3029 0.3343
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2640 0.4099 -0.5485 0.2635
## Avg_Cogongrass_Cover-Sus_scrofa -0.0720 0.4656 -1.1571 -0.0307
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8081 1.0092 896
## (Intercept)-Canis_latrans 1.5976 1.0009 3153
## (Intercept)-Sciurus_niger 2.3355 1.0254 544
## (Intercept)-Procyon_lotor 1.6976 1.0004 2584
## (Intercept)-Dasypus_novemcinctus 0.3946 1.0031 3685
## (Intercept)-Lynx_rufus 2.2141 1.0845 440
## (Intercept)-Didelphis_virginiana -0.1454 1.0014 2904
## (Intercept)-Sylvilagus_floridanus 1.0672 1.0005 2283
## (Intercept)-Sciurus_carolinensis -0.2688 1.0024 3084
## (Intercept)-Vulpes_vulpes 1.8199 1.0064 463
## (Intercept)-Sus_scrofa -0.3649 1.0020 2105
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.0786 1.0024 3724
## Avg_Cogongrass_Cover-Canis_latrans 1.0861 1.0001 4262
## Avg_Cogongrass_Cover-Sciurus_niger 0.7876 1.0008 2389
## Avg_Cogongrass_Cover-Procyon_lotor 0.9622 1.0009 4331
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9681 1.0026 4020
## Avg_Cogongrass_Cover-Lynx_rufus 1.2528 1.0010 3353
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0226 0.9999 4464
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5865 1.0043 3293
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0334 1.0033 4128
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0558 1.0032 3924
## Avg_Cogongrass_Cover-Sus_scrofa 0.7131 1.0056 2860
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0048 0.0589 -0.1133 0.0051 0.1206
## (Intercept)-Canis_latrans -2.6219 0.1767 -2.9911 -2.6174 -2.2894
## (Intercept)-Sciurus_niger -3.9123 0.5793 -5.1011 -3.8777 -2.8684
## (Intercept)-Procyon_lotor -2.2716 0.1304 -2.5356 -2.2669 -2.0288
## (Intercept)-Dasypus_novemcinctus -1.5758 0.1347 -1.8443 -1.5745 -1.3194
## (Intercept)-Lynx_rufus -3.5288 0.3336 -4.2052 -3.5189 -2.9088
## (Intercept)-Didelphis_virginiana -2.3085 0.2433 -2.8109 -2.3020 -1.8479
## (Intercept)-Sylvilagus_floridanus -3.1794 0.3089 -3.8247 -3.1639 -2.6225
## (Intercept)-Sciurus_carolinensis -2.4282 0.2617 -2.9794 -2.4187 -1.9456
## (Intercept)-Vulpes_vulpes -4.0187 0.7575 -5.6096 -3.9796 -2.7039
## (Intercept)-Sus_scrofa -2.9224 0.4878 -3.9902 -2.8834 -2.0685
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0009 5250
## (Intercept)-Canis_latrans 1.0005 2979
## (Intercept)-Sciurus_niger 1.0115 430
## (Intercept)-Procyon_lotor 1.0027 4234
## (Intercept)-Dasypus_novemcinctus 1.0027 5250
## (Intercept)-Lynx_rufus 1.0146 872
## (Intercept)-Didelphis_virginiana 1.0006 4078
## (Intercept)-Sylvilagus_floridanus 1.0108 1489
## (Intercept)-Sciurus_carolinensis 1.0001 3663
## (Intercept)-Vulpes_vulpes 1.0026 398
## (Intercept)-Sus_scrofa 1.0010 1750
# 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): 1.6943
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2694 0.5780 -1.4300 -0.2780 0.9125 1.0005 2112
## Avg_Cogongrass_Cover 0.2095 0.2299 -0.2581 0.2122 0.6636 1.0022 2476
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3065 2.5292 0.7219 2.6335 9.5439 1.0036 1567
## Avg_Cogongrass_Cover 0.2448 0.2984 0.0353 0.1590 0.9369 1.0040 2807
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7238 0.6975 0.0678 0.5232 2.5117 1.0051 571
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4859 0.4084 -3.2746 -2.4881 -1.6503 1.0011 3696
## week -0.0380 0.1190 -0.2861 -0.0318 0.1758 1.0013 3282
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7852 1.0870 0.6323 1.5162 4.6661 1.0025 1833
## week 0.0981 0.0798 0.0249 0.0756 0.3066 1.0034 2940
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3691 1.2942 1.2829 3.2244
## (Intercept)-Canis_latrans 0.2634 0.6175 -0.9943 0.2555
## (Intercept)-Sciurus_niger -0.6047 1.1032 -2.4245 -0.7254
## (Intercept)-Procyon_lotor 0.5315 0.6054 -0.7403 0.5612
## (Intercept)-Dasypus_novemcinctus -0.6840 0.5682 -1.8257 -0.6744
## (Intercept)-Lynx_rufus -0.1004 0.9430 -1.7199 -0.1777
## (Intercept)-Didelphis_virginiana -1.3873 0.6360 -2.6513 -1.3858
## (Intercept)-Sylvilagus_floridanus -0.3109 0.7477 -1.6739 -0.3424
## (Intercept)-Sciurus_carolinensis -1.4372 0.6448 -2.7461 -1.4205
## (Intercept)-Vulpes_vulpes -1.1940 1.1804 -3.2062 -1.3185
## (Intercept)-Sus_scrofa -1.8677 0.7894 -3.4496 -1.8364
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1917 0.4562 -0.7078 0.1901
## Avg_Cogongrass_Cover-Canis_latrans 0.3595 0.3468 -0.2622 0.3341
## Avg_Cogongrass_Cover-Sciurus_niger -0.0833 0.5081 -1.2496 -0.0448
## Avg_Cogongrass_Cover-Procyon_lotor 0.2588 0.3476 -0.3890 0.2433
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3463 0.3101 -0.2446 0.3402
## Avg_Cogongrass_Cover-Lynx_rufus 0.4447 0.3818 -0.2441 0.4222
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3412 0.3289 -0.2809 0.3318
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1262 0.4052 -1.0044 -0.0947
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3474 0.3321 -0.3054 0.3446
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2737 0.3961 -0.4901 0.2608
## Avg_Cogongrass_Cover-Sus_scrofa -0.0635 0.4620 -1.1586 -0.0193
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.2940 1.0003 1508
## (Intercept)-Canis_latrans 1.5091 1.0015 2912
## (Intercept)-Sciurus_niger 2.0705 1.0051 541
## (Intercept)-Procyon_lotor 1.6687 1.0012 2249
## (Intercept)-Dasypus_novemcinctus 0.4028 1.0011 3737
## (Intercept)-Lynx_rufus 1.9736 1.0093 937
## (Intercept)-Didelphis_virginiana -0.1113 1.0052 3476
## (Intercept)-Sylvilagus_floridanus 1.3321 1.0041 1530
## (Intercept)-Sciurus_carolinensis -0.2302 1.0006 2766
## (Intercept)-Vulpes_vulpes 1.5998 1.0137 596
## (Intercept)-Sus_scrofa -0.3194 0.9999 2585
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1116 1.0037 3799
## Avg_Cogongrass_Cover-Canis_latrans 1.1302 1.0009 4049
## Avg_Cogongrass_Cover-Sciurus_niger 0.8212 1.0031 2221
## Avg_Cogongrass_Cover-Procyon_lotor 0.9786 1.0003 4312
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9769 1.0035 4009
## Avg_Cogongrass_Cover-Lynx_rufus 1.2859 1.0096 3834
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0214 1.0055 4122
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6036 0.9999 2899
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0318 1.0007 4348
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0734 1.0021 3610
## Avg_Cogongrass_Cover-Sus_scrofa 0.7281 1.0006 2438
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0053 0.0598 -0.1121 0.0052 0.1233
## (Intercept)-Canis_latrans -2.6185 0.1709 -2.9688 -2.6121 -2.2985
## (Intercept)-Sciurus_niger -3.9330 0.5836 -5.1106 -3.9096 -2.8862
## (Intercept)-Procyon_lotor -2.2763 0.1326 -2.5467 -2.2738 -2.0286
## (Intercept)-Dasypus_novemcinctus -1.5892 0.1375 -1.8635 -1.5874 -1.3323
## (Intercept)-Lynx_rufus -3.5222 0.3396 -4.2312 -3.5024 -2.9206
## (Intercept)-Didelphis_virginiana -2.3391 0.2515 -2.8652 -2.3304 -1.8788
## (Intercept)-Sylvilagus_floridanus -3.2239 0.3157 -3.8912 -3.2048 -2.6550
## (Intercept)-Sciurus_carolinensis -2.4662 0.2720 -3.0367 -2.4507 -1.9733
## (Intercept)-Vulpes_vulpes -3.9848 0.7584 -5.6793 -3.9240 -2.6926
## (Intercept)-Sus_scrofa -2.9407 0.4793 -4.0191 -2.9020 -2.1021
## week-Odocoileus_virginianus 0.2064 0.0598 0.0927 0.2061 0.3250
## week-Canis_latrans 0.0721 0.1301 -0.1899 0.0740 0.3140
## week-Sciurus_niger -0.2901 0.2982 -0.9873 -0.2583 0.2110
## week-Procyon_lotor -0.0449 0.1173 -0.2771 -0.0421 0.1752
## week-Dasypus_novemcinctus -0.1558 0.1336 -0.4306 -0.1503 0.0932
## week-Lynx_rufus -0.0235 0.1929 -0.4205 -0.0183 0.3440
## week-Didelphis_virginiana -0.1944 0.2108 -0.6423 -0.1841 0.1821
## week-Sylvilagus_floridanus -0.1388 0.2043 -0.5878 -0.1233 0.2248
## week-Sciurus_carolinensis 0.1434 0.1773 -0.2121 0.1435 0.4946
## week-Vulpes_vulpes -0.1039 0.2692 -0.6686 -0.0873 0.3859
## week-Sus_scrofa 0.1021 0.2314 -0.3530 0.1012 0.5574
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5143
## (Intercept)-Canis_latrans 1.0026 3554
## (Intercept)-Sciurus_niger 1.0033 483
## (Intercept)-Procyon_lotor 1.0012 4238
## (Intercept)-Dasypus_novemcinctus 1.0013 5250
## (Intercept)-Lynx_rufus 1.0037 869
## (Intercept)-Didelphis_virginiana 1.0031 3567
## (Intercept)-Sylvilagus_floridanus 1.0105 1331
## (Intercept)-Sciurus_carolinensis 1.0011 3460
## (Intercept)-Vulpes_vulpes 1.0097 363
## (Intercept)-Sus_scrofa 1.0093 1807
## week-Odocoileus_virginianus 0.9999 5250
## week-Canis_latrans 1.0016 4618
## week-Sciurus_niger 1.0085 2074
## week-Procyon_lotor 1.0030 4029
## week-Dasypus_novemcinctus 1.0009 5250
## week-Lynx_rufus 1.0037 2974
## week-Didelphis_virginiana 1.0041 4114
## week-Sylvilagus_floridanus 1.0009 2965
## week-Sciurus_carolinensis 1.0001 4427
## week-Vulpes_vulpes 1.0061 2741
## week-Sus_scrofa 1.0023 4735
# 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): 1.8625
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1213 1.0755 -2.1817 -0.1412 2.1241 1.0115 1671
## Cogon_Patch_Size -0.8086 0.6596 -2.2063 -0.7819 0.4524 1.0051 1194
## Veg_shannon_index 0.8424 0.4473 -0.0139 0.8248 1.7565 1.0161 1132
## total_shrub_cover -0.1515 0.3702 -0.8782 -0.1517 0.6003 1.0082 1526
## Avg_Cogongrass_Cover 2.0783 0.6695 0.7898 2.0663 3.4705 1.0036 553
## Tree_Density -1.8316 0.6765 -3.2308 -1.8004 -0.5868 1.0034 779
## Avg_Canopy_Cover 1.7810 0.5368 0.7717 1.7504 2.9083 1.0024 1166
## avg_veg_height -0.5402 0.4362 -1.3881 -0.5410 0.2971 1.0144 716
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 19.6051 17.4538 3.8420 14.7887 63.5229 1.0261 494
## Cogon_Patch_Size 2.9961 4.9059 0.1314 1.5468 15.0459 1.0291 663
## Veg_shannon_index 0.8788 1.4501 0.0504 0.4399 4.6293 1.0060 927
## total_shrub_cover 0.4913 0.6245 0.0430 0.2879 2.1659 1.0152 1403
## Avg_Cogongrass_Cover 0.8854 1.5887 0.0489 0.4304 4.5924 1.0431 919
## Tree_Density 2.3410 3.9392 0.0641 0.9916 12.5708 1.0348 754
## Avg_Canopy_Cover 1.5440 1.9939 0.0886 0.9222 6.9110 1.0018 808
## avg_veg_height 0.3438 0.4158 0.0394 0.2102 1.4177 1.0011 2214
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.445 1.8052 0.0621 0.844 6.5205 1.0166 292
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5557 0.4482 -3.4192 -2.5652 -1.6296 1.0016 4826
## week -0.0373 0.1168 -0.2839 -0.0326 0.1774 1.0014 3043
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2167 1.3988 0.7794 1.8676 5.8402 1.011 2790
## week 0.0962 0.0796 0.0253 0.0747 0.2968 1.003 2738
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.3564 3.3937 3.4002 7.7974
## (Intercept)-Canis_latrans 0.6342 1.0344 -1.1958 0.5542
## (Intercept)-Sciurus_niger 1.8771 2.7362 -2.1813 1.4388
## (Intercept)-Procyon_lotor 0.8000 0.9298 -1.1451 0.8196
## (Intercept)-Dasypus_novemcinctus -1.5152 0.9380 -3.5641 -1.4358
## (Intercept)-Lynx_rufus 2.4862 3.2413 -1.8575 1.7099
## (Intercept)-Didelphis_virginiana -2.9765 1.0785 -5.3936 -2.9035
## (Intercept)-Sylvilagus_floridanus -1.3135 1.2345 -3.8581 -1.2743
## (Intercept)-Sciurus_carolinensis -3.2303 1.1898 -5.9532 -3.1161
## (Intercept)-Vulpes_vulpes -2.0552 2.2112 -5.6374 -2.2886
## (Intercept)-Sus_scrofa -4.6552 1.6626 -8.4632 -4.5257
## Cogon_Patch_Size-Odocoileus_virginianus -0.6138 1.3160 -3.0555 -0.6567
## Cogon_Patch_Size-Canis_latrans 0.6546 1.1863 -1.0290 0.4365
## Cogon_Patch_Size-Sciurus_niger -1.4422 1.8603 -5.7513 -1.2488
## Cogon_Patch_Size-Procyon_lotor -1.0120 0.7122 -2.4090 -1.0060
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7952 0.6151 -2.1176 -0.7549
## Cogon_Patch_Size-Lynx_rufus -0.8174 1.5535 -3.4536 -0.8723
## Cogon_Patch_Size-Didelphis_virginiana 0.7833 0.8694 -0.6292 0.6865
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0419 1.5979 -6.1764 -1.6994
## Cogon_Patch_Size-Sciurus_carolinensis -1.7557 1.2453 -4.9347 -1.5065
## Cogon_Patch_Size-Vulpes_vulpes -1.3084 1.6107 -5.0030 -1.1525
## Cogon_Patch_Size-Sus_scrofa -1.4285 1.3887 -4.8612 -1.2055
## Veg_shannon_index-Odocoileus_virginianus 0.6823 0.9294 -1.3479 0.7163
## Veg_shannon_index-Canis_latrans 1.2284 0.6353 0.1395 1.1657
## Veg_shannon_index-Sciurus_niger 0.9852 1.0244 -0.9616 0.9400
## Veg_shannon_index-Procyon_lotor 1.1401 0.5995 0.0906 1.1012
## Veg_shannon_index-Dasypus_novemcinctus 0.6364 0.5081 -0.3960 0.6493
## Veg_shannon_index-Lynx_rufus 0.7815 0.9500 -1.2925 0.8156
## Veg_shannon_index-Didelphis_virginiana 0.9989 0.6441 -0.1659 0.9639
## Veg_shannon_index-Sylvilagus_floridanus 1.0025 0.6792 -0.2388 0.9634
## Veg_shannon_index-Sciurus_carolinensis 0.1923 0.7205 -1.4436 0.2549
## Veg_shannon_index-Vulpes_vulpes 0.3131 0.8402 -1.6784 0.4005
## Veg_shannon_index-Sus_scrofa 1.5601 1.0042 0.1289 1.3660
## total_shrub_cover-Odocoileus_virginianus 0.0143 0.7193 -1.3599 -0.0238
## total_shrub_cover-Canis_latrans 0.1485 0.5454 -0.8136 0.1039
## total_shrub_cover-Sciurus_niger -0.3151 0.7607 -1.9401 -0.2859
## total_shrub_cover-Procyon_lotor -0.6131 0.5288 -1.7646 -0.5792
## total_shrub_cover-Dasypus_novemcinctus 0.0909 0.4807 -0.8081 0.0750
## total_shrub_cover-Lynx_rufus -0.4303 0.7761 -2.1983 -0.3689
## total_shrub_cover-Didelphis_virginiana -0.3099 0.5760 -1.5243 -0.2794
## total_shrub_cover-Sylvilagus_floridanus -0.0941 0.5959 -1.2619 -0.1050
## total_shrub_cover-Sciurus_carolinensis 0.0046 0.5607 -1.0470 -0.0147
## total_shrub_cover-Vulpes_vulpes -0.2838 0.7100 -1.7799 -0.2562
## total_shrub_cover-Sus_scrofa 0.0965 0.6720 -1.1350 0.0482
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.0426 1.0208 0.0789 2.0234
## Avg_Cogongrass_Cover-Canis_latrans 2.3466 0.8601 0.7904 2.2957
## Avg_Cogongrass_Cover-Sciurus_niger 1.7244 1.2058 -1.0470 1.8230
## Avg_Cogongrass_Cover-Procyon_lotor 2.2960 0.8515 0.7409 2.2508
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5607 0.9286 0.9604 2.4876
## Avg_Cogongrass_Cover-Lynx_rufus 2.3993 1.0390 0.6577 2.3061
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1700 0.8139 0.7211 2.1378
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6142 0.9071 -0.2007 1.6225
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3385 0.8691 0.8067 2.2752
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4352 1.0023 0.7007 2.3459
## Avg_Cogongrass_Cover-Sus_scrofa 1.6089 1.0451 -0.7185 1.6750
## Tree_Density-Odocoileus_virginianus -0.9200 1.2071 -2.9283 -1.0489
## Tree_Density-Canis_latrans -2.3843 1.1167 -5.0591 -2.2132
## Tree_Density-Sciurus_niger -1.9370 1.4923 -5.1804 -1.8779
## Tree_Density-Procyon_lotor -1.4914 0.7442 -2.9492 -1.4978
## Tree_Density-Dasypus_novemcinctus -3.1961 1.5942 -7.2487 -2.8280
## Tree_Density-Lynx_rufus -0.8358 1.3392 -3.0220 -0.9903
## Tree_Density-Didelphis_virginiana -2.1953 1.0424 -4.6374 -2.0597
## Tree_Density-Sylvilagus_floridanus -2.3604 1.2468 -5.4634 -2.1637
## Tree_Density-Sciurus_carolinensis -2.3913 1.2745 -5.5973 -2.1508
## Tree_Density-Vulpes_vulpes -1.8415 1.3727 -4.8323 -1.7904
## Tree_Density-Sus_scrofa -2.2126 1.4314 -5.9441 -1.9888
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3470 1.1432 -1.1803 1.4189
## Avg_Canopy_Cover-Canis_latrans 0.4927 0.6832 -0.8588 0.5003
## Avg_Canopy_Cover-Sciurus_niger 2.0025 1.4045 -0.5873 1.9126
## Avg_Canopy_Cover-Procyon_lotor 1.7412 0.6657 0.5425 1.6973
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9196 0.6433 0.8100 1.8726
## Avg_Canopy_Cover-Lynx_rufus 1.4639 1.1862 -0.8862 1.4495
## Avg_Canopy_Cover-Didelphis_virginiana 2.5119 0.8448 1.1667 2.4161
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8576 1.2562 1.0961 2.6151
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1997 0.7697 0.9609 2.1147
## Avg_Canopy_Cover-Vulpes_vulpes 2.0862 0.9982 0.4936 1.9672
## Avg_Canopy_Cover-Sus_scrofa 2.0149 0.7851 0.6561 1.9501
## avg_veg_height-Odocoileus_virginianus -0.5659 0.6917 -1.9806 -0.5640
## avg_veg_height-Canis_latrans -0.7212 0.5606 -1.8836 -0.7004
## avg_veg_height-Sciurus_niger -0.6353 0.6944 -2.0875 -0.6181
## avg_veg_height-Procyon_lotor -0.3894 0.5355 -1.4207 -0.3934
## avg_veg_height-Dasypus_novemcinctus -0.3447 0.5360 -1.3589 -0.3592
## avg_veg_height-Lynx_rufus -0.5659 0.6924 -1.9614 -0.5679
## avg_veg_height-Didelphis_virginiana -0.6269 0.5961 -1.8436 -0.6139
## avg_veg_height-Sylvilagus_floridanus -0.7110 0.6124 -1.9939 -0.6804
## avg_veg_height-Sciurus_carolinensis -0.2603 0.5925 -1.3623 -0.2911
## avg_veg_height-Vulpes_vulpes -0.5698 0.6523 -1.8769 -0.5545
## avg_veg_height-Sus_scrofa -0.6400 0.6197 -1.9234 -0.6195
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.7058 1.0145 367
## (Intercept)-Canis_latrans 2.9331 1.0092 1521
## (Intercept)-Sciurus_niger 8.2425 1.1248 297
## (Intercept)-Procyon_lotor 2.5754 1.0148 1138
## (Intercept)-Dasypus_novemcinctus 0.1670 1.0033 1057
## (Intercept)-Lynx_rufus 10.6042 1.0599 163
## (Intercept)-Didelphis_virginiana -1.0833 1.0002 1582
## (Intercept)-Sylvilagus_floridanus 1.1288 1.0060 1143
## (Intercept)-Sciurus_carolinensis -1.2128 1.0034 914
## (Intercept)-Vulpes_vulpes 3.0125 1.1339 260
## (Intercept)-Sus_scrofa -1.8056 1.0042 657
## Cogon_Patch_Size-Odocoileus_virginianus 2.3036 1.0124 1690
## Cogon_Patch_Size-Canis_latrans 3.6073 1.0042 1313
## Cogon_Patch_Size-Sciurus_niger 1.8119 1.0052 580
## Cogon_Patch_Size-Procyon_lotor 0.3098 1.0060 920
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3360 1.0008 1348
## Cogon_Patch_Size-Lynx_rufus 2.4077 1.0147 733
## Cogon_Patch_Size-Didelphis_virginiana 2.7040 1.0065 1229
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0137 1.0015 712
## Cogon_Patch_Size-Sciurus_carolinensis -0.0189 1.0047 776
## Cogon_Patch_Size-Vulpes_vulpes 1.6171 1.0117 703
## Cogon_Patch_Size-Sus_scrofa 0.6110 1.0050 1329
## Veg_shannon_index-Odocoileus_virginianus 2.4451 1.0136 1537
## Veg_shannon_index-Canis_latrans 2.6606 1.0045 1256
## Veg_shannon_index-Sciurus_niger 3.2830 1.0060 1154
## Veg_shannon_index-Procyon_lotor 2.3918 1.0153 808
## Veg_shannon_index-Dasypus_novemcinctus 1.6278 1.0116 1963
## Veg_shannon_index-Lynx_rufus 2.5563 1.0163 1183
## Veg_shannon_index-Didelphis_virginiana 2.4303 1.0022 2047
## Veg_shannon_index-Sylvilagus_floridanus 2.4609 1.0083 1581
## Veg_shannon_index-Sciurus_carolinensis 1.4375 1.0039 1699
## Veg_shannon_index-Vulpes_vulpes 1.7272 1.0026 1319
## Veg_shannon_index-Sus_scrofa 4.1585 1.0022 951
## total_shrub_cover-Odocoileus_virginianus 1.5880 1.0013 2561
## total_shrub_cover-Canis_latrans 1.3428 1.0001 2377
## total_shrub_cover-Sciurus_niger 1.1295 1.0103 1850
## total_shrub_cover-Procyon_lotor 0.3080 1.0088 2148
## total_shrub_cover-Dasypus_novemcinctus 1.0513 1.0018 2456
## total_shrub_cover-Lynx_rufus 0.9623 1.0100 1574
## total_shrub_cover-Didelphis_virginiana 0.7605 1.0020 2699
## total_shrub_cover-Sylvilagus_floridanus 1.1271 1.0015 2481
## total_shrub_cover-Sciurus_carolinensis 1.1393 1.0005 2783
## total_shrub_cover-Vulpes_vulpes 1.0854 1.0020 2253
## total_shrub_cover-Sus_scrofa 1.6221 1.0008 2589
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.1871 1.0022 976
## Avg_Cogongrass_Cover-Canis_latrans 4.1983 1.0026 715
## Avg_Cogongrass_Cover-Sciurus_niger 3.8811 1.0141 760
## Avg_Cogongrass_Cover-Procyon_lotor 4.1214 1.0090 742
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6253 1.0123 632
## Avg_Cogongrass_Cover-Lynx_rufus 4.6324 1.0061 963
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9039 1.0059 908
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4069 1.0016 1031
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2070 1.0072 762
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.6383 1.0143 709
## Avg_Cogongrass_Cover-Sus_scrofa 3.4766 1.0021 954
## Tree_Density-Odocoileus_virginianus 2.0235 1.0073 720
## Tree_Density-Canis_latrans -0.6775 1.0036 838
## Tree_Density-Sciurus_niger 1.0007 1.0039 922
## Tree_Density-Procyon_lotor -0.0384 1.0041 1453
## Tree_Density-Dasypus_novemcinctus -1.1846 1.0018 544
## Tree_Density-Lynx_rufus 2.2676 1.0224 458
## Tree_Density-Didelphis_virginiana -0.5223 1.0031 1053
## Tree_Density-Sylvilagus_floridanus -0.4199 1.0084 830
## Tree_Density-Sciurus_carolinensis -0.5859 1.0075 788
## Tree_Density-Vulpes_vulpes 0.8091 1.0063 1103
## Tree_Density-Sus_scrofa -0.0355 1.0044 745
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5534 1.0032 1768
## Avg_Canopy_Cover-Canis_latrans 1.8170 1.0025 1391
## Avg_Canopy_Cover-Sciurus_niger 5.0888 1.0068 804
## Avg_Canopy_Cover-Procyon_lotor 3.2028 1.0039 1422
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3598 1.0075 1113
## Avg_Canopy_Cover-Lynx_rufus 3.9622 1.0034 831
## Avg_Canopy_Cover-Didelphis_virginiana 4.4629 1.0006 753
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.0149 1.0010 791
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9989 1.0017 1233
## Avg_Canopy_Cover-Vulpes_vulpes 4.4220 1.0012 1173
## Avg_Canopy_Cover-Sus_scrofa 3.7687 1.0027 1381
## avg_veg_height-Odocoileus_virginianus 0.7648 1.0080 1412
## avg_veg_height-Canis_latrans 0.3526 1.0128 1275
## avg_veg_height-Sciurus_niger 0.7004 1.0057 1505
## avg_veg_height-Procyon_lotor 0.7068 1.0089 1259
## avg_veg_height-Dasypus_novemcinctus 0.7671 1.0072 1163
## avg_veg_height-Lynx_rufus 0.8172 1.0069 1243
## avg_veg_height-Didelphis_virginiana 0.5319 1.0032 1370
## avg_veg_height-Sylvilagus_floridanus 0.4528 1.0111 1216
## avg_veg_height-Sciurus_carolinensis 0.9896 1.0021 1266
## avg_veg_height-Vulpes_vulpes 0.7267 1.0059 1201
## avg_veg_height-Sus_scrofa 0.5724 1.0129 1298
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0073 0.0600 -0.1114 0.0071 0.1257
## (Intercept)-Canis_latrans -2.6448 0.1829 -3.0264 -2.6375 -2.3113
## (Intercept)-Sciurus_niger -4.6168 0.4770 -5.5639 -4.6261 -3.6865
## (Intercept)-Procyon_lotor -2.2703 0.1312 -2.5388 -2.2683 -2.0281
## (Intercept)-Dasypus_novemcinctus -1.5889 0.1352 -1.8619 -1.5867 -1.3306
## (Intercept)-Lynx_rufus -3.8406 0.3551 -4.5008 -3.8531 -3.1164
## (Intercept)-Didelphis_virginiana -2.3110 0.2427 -2.8194 -2.2978 -1.8646
## (Intercept)-Sylvilagus_floridanus -3.1923 0.2872 -3.7900 -3.1847 -2.6580
## (Intercept)-Sciurus_carolinensis -2.4492 0.2641 -3.0107 -2.4347 -1.9601
## (Intercept)-Vulpes_vulpes -4.2078 0.6864 -5.5918 -4.1816 -2.9575
## (Intercept)-Sus_scrofa -2.9071 0.4587 -3.9035 -2.8736 -2.1066
## week-Odocoileus_virginianus 0.2081 0.0612 0.0900 0.2073 0.3283
## week-Canis_latrans 0.0716 0.1299 -0.1945 0.0754 0.3141
## week-Sciurus_niger -0.2789 0.2846 -0.9301 -0.2526 0.1885
## week-Procyon_lotor -0.0456 0.1158 -0.2818 -0.0437 0.1731
## week-Dasypus_novemcinctus -0.1556 0.1355 -0.4320 -0.1502 0.0951
## week-Lynx_rufus -0.0267 0.1912 -0.4232 -0.0172 0.3194
## week-Didelphis_virginiana -0.1931 0.2085 -0.6316 -0.1794 0.1754
## week-Sylvilagus_floridanus -0.1361 0.1972 -0.5637 -0.1249 0.2174
## week-Sciurus_carolinensis 0.1441 0.1807 -0.2158 0.1454 0.4927
## week-Vulpes_vulpes -0.0938 0.2640 -0.6548 -0.0784 0.3831
## week-Sus_scrofa 0.1056 0.2346 -0.3577 0.0975 0.5785
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0093 2369
## (Intercept)-Sciurus_niger 1.0652 375
## (Intercept)-Procyon_lotor 1.0000 3775
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## (Intercept)-Lynx_rufus 1.0219 375
## (Intercept)-Didelphis_virginiana 1.0003 4140
## (Intercept)-Sylvilagus_floridanus 1.0021 1696
## (Intercept)-Sciurus_carolinensis 1.0011 3552
## (Intercept)-Vulpes_vulpes 1.0665 422
## (Intercept)-Sus_scrofa 1.0013 1882
## week-Odocoileus_virginianus 1.0005 5250
## week-Canis_latrans 1.0016 4292
## week-Sciurus_niger 1.0016 1385
## week-Procyon_lotor 1.0005 4456
## week-Dasypus_novemcinctus 1.0016 4835
## week-Lynx_rufus 1.0029 2572
## week-Didelphis_virginiana 1.0020 4126
## week-Sylvilagus_floridanus 1.0064 3343
## week-Sciurus_carolinensis 1.0041 4668
## week-Vulpes_vulpes 1.0014 2894
## week-Sus_scrofa 1.0005 4474
# 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): 1.7318
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2438 0.6092 -1.3991 -0.2575 0.9824 1.0026 1640
## Avg_Cogongrass_Cover 0.1168 0.3070 -0.4893 0.1170 0.7113 1.0054 1470
## total_shrub_cover -0.2594 0.2722 -0.8129 -0.2476 0.2541 1.0024 2420
## avg_veg_height 0.0326 0.2860 -0.5283 0.0380 0.5775 1.0057 1270
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7972 2.9718 0.7610 3.0032 11.6427 1.0127 1908
## Avg_Cogongrass_Cover 0.3076 0.3716 0.0381 0.1922 1.2917 1.0045 2077
## total_shrub_cover 0.3440 0.4062 0.0403 0.2181 1.3738 1.0437 2057
## avg_veg_height 0.1963 0.2057 0.0321 0.1337 0.7013 1.0041 3279
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.857 0.8852 0.0628 0.5832 3.2788 1.0253 415
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5048 0.4104 -3.3000 -2.5149 -1.6563 1.0018 4770
## week -0.0401 0.1211 -0.2877 -0.0331 0.1832 1.0024 2991
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8757 1.1922 0.6263 1.5820 4.8034 1.0102 1229
## week 0.0974 0.0775 0.0258 0.0742 0.2965 1.0026 2910
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6074 1.3944 1.1380 3.4801
## (Intercept)-Canis_latrans 0.3443 0.6782 -0.9384 0.3330
## (Intercept)-Sciurus_niger -0.7013 1.0608 -2.5124 -0.8070
## (Intercept)-Procyon_lotor 0.6685 0.6529 -0.6246 0.6689
## (Intercept)-Dasypus_novemcinctus -0.7391 0.6025 -1.9691 -0.7323
## (Intercept)-Lynx_rufus -0.0472 0.9417 -1.7506 -0.0991
## (Intercept)-Didelphis_virginiana -1.4392 0.6627 -2.7816 -1.4282
## (Intercept)-Sylvilagus_floridanus -0.2496 0.8057 -1.6769 -0.3065
## (Intercept)-Sciurus_carolinensis -1.5461 0.7050 -3.0200 -1.5137
## (Intercept)-Vulpes_vulpes -0.9163 1.4799 -3.1948 -1.1383
## (Intercept)-Sus_scrofa -2.0114 0.8427 -3.7567 -1.9921
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1003 0.5411 -0.9737 0.1031
## Avg_Cogongrass_Cover-Canis_latrans 0.3550 0.4388 -0.4413 0.3369
## Avg_Cogongrass_Cover-Sciurus_niger -0.2274 0.6068 -1.6200 -0.1801
## Avg_Cogongrass_Cover-Procyon_lotor 0.0933 0.4148 -0.7266 0.0893
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2378 0.3819 -0.5140 0.2383
## Avg_Cogongrass_Cover-Lynx_rufus 0.4010 0.4715 -0.4385 0.3709
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2978 0.4113 -0.4924 0.2917
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2305 0.4916 -1.3024 -0.1915
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2239 0.4034 -0.5496 0.2169
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2158 0.5023 -0.7813 0.2072
## Avg_Cogongrass_Cover-Sus_scrofa -0.1900 0.5861 -1.5277 -0.1274
## total_shrub_cover-Odocoileus_virginianus -0.1840 0.5243 -1.2331 -0.1856
## total_shrub_cover-Canis_latrans 0.0790 0.4082 -0.6496 0.0527
## total_shrub_cover-Sciurus_niger -0.5045 0.5221 -1.6809 -0.4656
## total_shrub_cover-Procyon_lotor -0.7247 0.4598 -1.7382 -0.6839
## total_shrub_cover-Dasypus_novemcinctus -0.0507 0.3505 -0.7180 -0.0584
## total_shrub_cover-Lynx_rufus -0.6374 0.5409 -1.9036 -0.5748
## total_shrub_cover-Didelphis_virginiana -0.2080 0.3844 -0.9716 -0.2031
## total_shrub_cover-Sylvilagus_floridanus -0.3146 0.4721 -1.3159 -0.2942
## total_shrub_cover-Sciurus_carolinensis -0.1088 0.3829 -0.8627 -0.1113
## total_shrub_cover-Vulpes_vulpes -0.2925 0.5486 -1.4857 -0.2698
## total_shrub_cover-Sus_scrofa 0.0133 0.4768 -0.8586 -0.0112
## avg_veg_height-Odocoileus_virginianus 0.0309 0.4768 -0.9269 0.0323
## avg_veg_height-Canis_latrans -0.0435 0.3898 -0.8515 -0.0324
## avg_veg_height-Sciurus_niger -0.1112 0.4656 -1.0944 -0.0844
## avg_veg_height-Procyon_lotor 0.1150 0.3919 -0.6329 0.1094
## avg_veg_height-Dasypus_novemcinctus 0.1924 0.3738 -0.5275 0.1842
## avg_veg_height-Lynx_rufus 0.0387 0.4518 -0.8697 0.0360
## avg_veg_height-Didelphis_virginiana 0.0030 0.3947 -0.8060 0.0112
## avg_veg_height-Sylvilagus_floridanus -0.1001 0.4278 -0.9622 -0.0929
## avg_veg_height-Sciurus_carolinensis 0.2691 0.4110 -0.4938 0.2592
## avg_veg_height-Vulpes_vulpes -0.0184 0.4647 -0.9577 -0.0100
## avg_veg_height-Sus_scrofa -0.0058 0.4325 -0.8911 -0.0025
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.7847 1.0039 1369
## (Intercept)-Canis_latrans 1.7828 1.0015 2607
## (Intercept)-Sciurus_niger 1.7542 1.0106 822
## (Intercept)-Procyon_lotor 1.9418 1.0018 2728
## (Intercept)-Dasypus_novemcinctus 0.4043 1.0037 3300
## (Intercept)-Lynx_rufus 1.9475 1.0035 1305
## (Intercept)-Didelphis_virginiana -0.1375 1.0085 2477
## (Intercept)-Sylvilagus_floridanus 1.5348 1.0147 1465
## (Intercept)-Sciurus_carolinensis -0.2549 1.0022 2770
## (Intercept)-Vulpes_vulpes 2.8311 1.0150 251
## (Intercept)-Sus_scrofa -0.3680 1.0000 1587
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1858 1.0009 2892
## Avg_Cogongrass_Cover-Canis_latrans 1.2969 1.0040 2580
## Avg_Cogongrass_Cover-Sciurus_niger 0.8178 1.0019 1877
## Avg_Cogongrass_Cover-Procyon_lotor 0.9150 1.0027 2615
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0030 1.0004 2860
## Avg_Cogongrass_Cover-Lynx_rufus 1.4096 1.0021 2671
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1622 1.0038 2599
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6509 1.0010 2422
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0350 1.0010 2466
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2176 1.0011 2387
## Avg_Cogongrass_Cover-Sus_scrofa 0.7782 1.0044 2030
## total_shrub_cover-Odocoileus_virginianus 0.8881 1.0015 3879
## total_shrub_cover-Canis_latrans 0.9660 1.0086 3129
## total_shrub_cover-Sciurus_niger 0.4301 1.0114 2520
## total_shrub_cover-Procyon_lotor 0.0570 1.0183 2440
## total_shrub_cover-Dasypus_novemcinctus 0.6660 1.0021 4771
## total_shrub_cover-Lynx_rufus 0.2603 1.0140 2139
## total_shrub_cover-Didelphis_virginiana 0.5457 1.0010 4212
## total_shrub_cover-Sylvilagus_floridanus 0.5577 1.0071 2225
## total_shrub_cover-Sciurus_carolinensis 0.6674 1.0002 4004
## total_shrub_cover-Vulpes_vulpes 0.7281 1.0134 2475
## total_shrub_cover-Sus_scrofa 1.0276 1.0061 3722
## avg_veg_height-Odocoileus_virginianus 0.9825 1.0090 2564
## avg_veg_height-Canis_latrans 0.7066 1.0047 2407
## avg_veg_height-Sciurus_niger 0.7416 1.0001 2254
## avg_veg_height-Procyon_lotor 0.9254 1.0046 2473
## avg_veg_height-Dasypus_novemcinctus 0.9551 1.0029 2342
## avg_veg_height-Lynx_rufus 0.9679 1.0044 2024
## avg_veg_height-Didelphis_virginiana 0.7677 1.0072 2189
## avg_veg_height-Sylvilagus_floridanus 0.7156 1.0039 2292
## avg_veg_height-Sciurus_carolinensis 1.1204 1.0012 2252
## avg_veg_height-Vulpes_vulpes 0.8906 1.0012 2011
## avg_veg_height-Sus_scrofa 0.8287 1.0016 2662
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0070 0.0587 -0.1065 0.0059 0.1231
## (Intercept)-Canis_latrans -2.6478 0.1816 -3.0209 -2.6386 -2.3127
## (Intercept)-Sciurus_niger -3.9288 0.5612 -5.0597 -3.8967 -2.9198
## (Intercept)-Procyon_lotor -2.2787 0.1304 -2.5359 -2.2774 -2.0343
## (Intercept)-Dasypus_novemcinctus -1.5899 0.1365 -1.8696 -1.5861 -1.3226
## (Intercept)-Lynx_rufus -3.5590 0.3100 -4.1927 -3.5470 -2.9733
## (Intercept)-Didelphis_virginiana -2.3407 0.2533 -2.8665 -2.3273 -1.8794
## (Intercept)-Sylvilagus_floridanus -3.2751 0.3209 -3.9413 -3.2550 -2.6949
## (Intercept)-Sciurus_carolinensis -2.4633 0.2683 -3.0264 -2.4477 -1.9783
## (Intercept)-Vulpes_vulpes -4.1675 0.7933 -5.7679 -4.1297 -2.7717
## (Intercept)-Sus_scrofa -2.9695 0.4792 -4.0081 -2.9253 -2.1224
## week-Odocoileus_virginianus 0.2078 0.0600 0.0936 0.2072 0.3250
## week-Canis_latrans 0.0699 0.1306 -0.1991 0.0727 0.3163
## week-Sciurus_niger -0.2931 0.2987 -0.9588 -0.2617 0.1979
## week-Procyon_lotor -0.0429 0.1148 -0.2776 -0.0393 0.1756
## week-Dasypus_novemcinctus -0.1590 0.1347 -0.4365 -0.1508 0.0840
## week-Lynx_rufus -0.0301 0.1922 -0.4315 -0.0174 0.3240
## week-Didelphis_virginiana -0.1981 0.2125 -0.6535 -0.1858 0.1837
## week-Sylvilagus_floridanus -0.1448 0.2061 -0.5783 -0.1315 0.2278
## week-Sciurus_carolinensis 0.1410 0.1773 -0.2220 0.1422 0.4792
## week-Vulpes_vulpes -0.0998 0.2669 -0.6812 -0.0861 0.3809
## week-Sus_scrofa 0.1038 0.2319 -0.3628 0.1084 0.5511
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 5250
## (Intercept)-Canis_latrans 1.0034 2490
## (Intercept)-Sciurus_niger 1.0010 580
## (Intercept)-Procyon_lotor 1.0017 4041
## (Intercept)-Dasypus_novemcinctus 1.0024 5250
## (Intercept)-Lynx_rufus 1.0014 1135
## (Intercept)-Didelphis_virginiana 1.0023 3698
## (Intercept)-Sylvilagus_floridanus 1.0079 1238
## (Intercept)-Sciurus_carolinensis 1.0088 3716
## (Intercept)-Vulpes_vulpes 1.0262 214
## (Intercept)-Sus_scrofa 1.0025 1876
## week-Odocoileus_virginianus 1.0026 5526
## week-Canis_latrans 1.0007 4136
## week-Sciurus_niger 1.0095 1933
## week-Procyon_lotor 1.0005 4195
## week-Dasypus_novemcinctus 1.0113 4803
## week-Lynx_rufus 1.0005 2980
## week-Didelphis_virginiana 1.0003 3970
## week-Sylvilagus_floridanus 0.9998 3085
## week-Sciurus_carolinensis 1.0026 4131
## week-Vulpes_vulpes 1.0027 2654
## week-Sus_scrofa 1.0016 4652
# 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): 1.6872
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1995 0.5467 -1.2207 -0.2148 0.9617 1.0005 2716
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1914 2.3044 0.8678 2.5597 9.1157 1.0115 2518
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4827 0.411 -3.2676 -2.4878 -1.6177 1.0001 4859
## week -0.0391 0.118 -0.2842 -0.0336 0.1792 1.0058 3446
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7760 1.0807 0.6121 1.5031 4.6052 1.0004 2881
## week 0.0963 0.0743 0.0246 0.0754 0.2940 1.0160 3641
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4345 1.0921 1.8034 3.2821 5.9754
## (Intercept)-Canis_latrans 0.3102 0.4088 -0.4416 0.2976 1.1766
## (Intercept)-Sciurus_niger -0.6714 0.9849 -2.1125 -0.8448 1.7395
## (Intercept)-Procyon_lotor 0.7100 0.3935 -0.0333 0.7033 1.5113
## (Intercept)-Dasypus_novemcinctus -0.6326 0.3687 -1.3531 -0.6334 0.0687
## (Intercept)-Lynx_rufus 0.3638 0.9325 -0.9165 0.1791 2.8137
## (Intercept)-Didelphis_virginiana -1.3664 0.4488 -2.3092 -1.3570 -0.5506
## (Intercept)-Sylvilagus_floridanus -0.3108 0.5453 -1.2556 -0.3504 0.8981
## (Intercept)-Sciurus_carolinensis -1.3566 0.4460 -2.2910 -1.3407 -0.4964
## (Intercept)-Vulpes_vulpes -1.1825 1.0303 -2.9023 -1.3026 1.2815
## (Intercept)-Sus_scrofa -1.8850 0.6331 -3.2005 -1.8648 -0.6835
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0148 1871
## (Intercept)-Canis_latrans 1.0002 4371
## (Intercept)-Sciurus_niger 1.0044 551
## (Intercept)-Procyon_lotor 1.0006 5250
## (Intercept)-Dasypus_novemcinctus 1.0006 5250
## (Intercept)-Lynx_rufus 1.0025 811
## (Intercept)-Didelphis_virginiana 1.0004 5250
## (Intercept)-Sylvilagus_floridanus 1.0044 1960
## (Intercept)-Sciurus_carolinensis 1.0004 4951
## (Intercept)-Vulpes_vulpes 1.0024 611
## (Intercept)-Sus_scrofa 1.0029 2607
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0049 0.0597 -0.1149 0.0048 0.1230
## (Intercept)-Canis_latrans -2.6132 0.1747 -2.9688 -2.6100 -2.2767
## (Intercept)-Sciurus_niger -3.8881 0.5883 -5.1227 -3.8382 -2.8689
## (Intercept)-Procyon_lotor -2.2677 0.1302 -2.5326 -2.2649 -2.0248
## (Intercept)-Dasypus_novemcinctus -1.5879 0.1339 -1.8537 -1.5895 -1.3308
## (Intercept)-Lynx_rufus -3.5966 0.3478 -4.3024 -3.5862 -2.9519
## (Intercept)-Didelphis_virginiana -2.3287 0.2507 -2.8467 -2.3161 -1.8672
## (Intercept)-Sylvilagus_floridanus -3.2055 0.3135 -3.8780 -3.1908 -2.6389
## (Intercept)-Sciurus_carolinensis -2.4565 0.2650 -3.0047 -2.4460 -1.9727
## (Intercept)-Vulpes_vulpes -3.9625 0.7202 -5.4236 -3.9088 -2.7238
## (Intercept)-Sus_scrofa -2.9642 0.4877 -4.0350 -2.9164 -2.1334
## week-Odocoileus_virginianus 0.2078 0.0610 0.0869 0.2071 0.3253
## week-Canis_latrans 0.0718 0.1299 -0.1918 0.0757 0.3141
## week-Sciurus_niger -0.2844 0.2967 -0.9878 -0.2538 0.2103
## week-Procyon_lotor -0.0422 0.1159 -0.2780 -0.0398 0.1802
## week-Dasypus_novemcinctus -0.1548 0.1332 -0.4253 -0.1494 0.0966
## week-Lynx_rufus -0.0168 0.1891 -0.4121 -0.0135 0.3337
## week-Didelphis_virginiana -0.1938 0.2128 -0.6469 -0.1807 0.1828
## week-Sylvilagus_floridanus -0.1438 0.2032 -0.5722 -0.1303 0.2206
## week-Sciurus_carolinensis 0.1395 0.1750 -0.2131 0.1402 0.4781
## week-Vulpes_vulpes -0.1132 0.2712 -0.7277 -0.0917 0.3631
## week-Sus_scrofa 0.0993 0.2323 -0.3679 0.0994 0.5506
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0019 5250
## (Intercept)-Canis_latrans 1.0000 3011
## (Intercept)-Sciurus_niger 1.0090 497
## (Intercept)-Procyon_lotor 1.0016 3456
## (Intercept)-Dasypus_novemcinctus 1.0014 5250
## (Intercept)-Lynx_rufus 1.0109 886
## (Intercept)-Didelphis_virginiana 1.0004 4059
## (Intercept)-Sylvilagus_floridanus 1.0073 1270
## (Intercept)-Sciurus_carolinensis 1.0015 3618
## (Intercept)-Vulpes_vulpes 1.0064 532
## (Intercept)-Sus_scrofa 1.0043 1813
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0007 6115
## week-Sciurus_niger 1.0108 2039
## week-Procyon_lotor 1.0001 4751
## week-Dasypus_novemcinctus 1.0076 4755
## week-Lynx_rufus 1.0015 3074
## week-Didelphis_virginiana 1.0028 3832
## week-Sylvilagus_floridanus 1.0050 2815
## week-Sciurus_carolinensis 1.0002 4745
## week-Vulpes_vulpes 1.0003 2860
## week-Sus_scrofa 1.0005 4631
#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): 1.743
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2604 0.6198 -1.4745 -0.2699 1.0242 1.0121 1597
## Veg_shannon_index 0.3721 0.2668 -0.1492 0.3686 0.9124 1.0207 2371
## Avg_Cogongrass_Cover 0.3220 0.2607 -0.2092 0.3202 0.8346 1.0211 2106
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0295 3.4054 0.7927 3.1402 12.7449 1.0208 1346
## Veg_shannon_index 0.3057 0.3575 0.0377 0.1927 1.2248 1.0092 1644
## Avg_Cogongrass_Cover 0.2915 0.4123 0.0378 0.1854 1.1880 1.0736 2414
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7678 0.752 0.0544 0.5458 2.7968 1.0141 404
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4961 0.4158 -3.3125 -2.4967 -1.6546 1.0015 4977
## week -0.0441 0.1208 -0.2972 -0.0395 0.1772 1.0045 3217
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9001 1.2520 0.6421 1.5912 4.9558 1.0057 1893
## week 0.0957 0.0727 0.0244 0.0761 0.2859 1.0014 3084
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6626 1.5224 1.2949 3.4633
## (Intercept)-Canis_latrans 0.2437 0.6332 -0.9919 0.2385
## (Intercept)-Sciurus_niger -0.3737 1.2684 -2.4073 -0.5372
## (Intercept)-Procyon_lotor 0.5688 0.6403 -0.6968 0.5723
## (Intercept)-Dasypus_novemcinctus -0.7430 0.5957 -1.9343 -0.7326
## (Intercept)-Lynx_rufus 0.0696 1.0547 -1.6430 -0.0415
## (Intercept)-Didelphis_virginiana -1.4997 0.6636 -2.8738 -1.4804
## (Intercept)-Sylvilagus_floridanus -0.2720 0.8521 -1.7686 -0.3327
## (Intercept)-Sciurus_carolinensis -1.5032 0.6817 -2.9080 -1.4755
## (Intercept)-Vulpes_vulpes -1.0639 1.4370 -3.2947 -1.2669
## (Intercept)-Sus_scrofa -2.2000 0.8811 -4.0711 -2.1658
## Veg_shannon_index-Odocoileus_virginianus 0.3021 0.5171 -0.7793 0.3163
## Veg_shannon_index-Canis_latrans 0.6499 0.3841 -0.0312 0.6262
## Veg_shannon_index-Sciurus_niger 0.3711 0.5494 -0.6873 0.3649
## Veg_shannon_index-Procyon_lotor 0.4894 0.3892 -0.2186 0.4655
## Veg_shannon_index-Dasypus_novemcinctus 0.2179 0.3423 -0.4916 0.2284
## Veg_shannon_index-Lynx_rufus 0.1861 0.5171 -0.9456 0.2128
## Veg_shannon_index-Didelphis_virginiana 0.5270 0.3899 -0.1970 0.5109
## Veg_shannon_index-Sylvilagus_floridanus 0.4998 0.4399 -0.3168 0.4759
## Veg_shannon_index-Sciurus_carolinensis 0.0253 0.4061 -0.8267 0.0413
## Veg_shannon_index-Vulpes_vulpes 0.1140 0.4875 -0.9329 0.1388
## Veg_shannon_index-Sus_scrofa 0.7407 0.5414 -0.1633 0.6744
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3167 0.4878 -0.6303 0.3167
## Avg_Cogongrass_Cover-Canis_latrans 0.5408 0.3869 -0.1341 0.5180
## Avg_Cogongrass_Cover-Sciurus_niger 0.0056 0.5838 -1.2534 0.0355
## Avg_Cogongrass_Cover-Procyon_lotor 0.4262 0.3789 -0.2690 0.3984
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4350 0.3341 -0.2134 0.4265
## Avg_Cogongrass_Cover-Lynx_rufus 0.5690 0.4402 -0.2101 0.5440
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4758 0.3691 -0.2532 0.4637
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0319 0.4465 -1.0033 -0.0072
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4159 0.3589 -0.2685 0.4089
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4051 0.4714 -0.4695 0.3826
## Avg_Cogongrass_Cover-Sus_scrofa 0.0089 0.5391 -1.2207 0.0747
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0819 1.0181 1003
## (Intercept)-Canis_latrans 1.5522 1.0001 3114
## (Intercept)-Sciurus_niger 2.6556 1.0739 451
## (Intercept)-Procyon_lotor 1.8539 1.0045 2970
## (Intercept)-Dasypus_novemcinctus 0.3798 1.0037 3342
## (Intercept)-Lynx_rufus 2.4712 1.0080 932
## (Intercept)-Didelphis_virginiana -0.2254 1.0019 2808
## (Intercept)-Sylvilagus_floridanus 1.7031 1.0039 1013
## (Intercept)-Sciurus_carolinensis -0.2312 1.0010 2450
## (Intercept)-Vulpes_vulpes 2.1861 1.0505 274
## (Intercept)-Sus_scrofa -0.5064 1.0067 1984
## Veg_shannon_index-Odocoileus_virginianus 1.2851 1.0038 3426
## Veg_shannon_index-Canis_latrans 1.4870 1.0056 2957
## Veg_shannon_index-Sciurus_niger 1.4722 1.0063 2441
## Veg_shannon_index-Procyon_lotor 1.3186 1.0088 3125
## Veg_shannon_index-Dasypus_novemcinctus 0.8683 1.0013 3734
## Veg_shannon_index-Lynx_rufus 1.1333 1.0047 2385
## Veg_shannon_index-Didelphis_virginiana 1.3790 1.0084 3352
## Veg_shannon_index-Sylvilagus_floridanus 1.4258 1.0134 3190
## Veg_shannon_index-Sciurus_carolinensis 0.7784 1.0103 3624
## Veg_shannon_index-Vulpes_vulpes 1.0114 1.0117 2431
## Veg_shannon_index-Sus_scrofa 2.0108 1.0061 2542
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3402 1.0082 3364
## Avg_Cogongrass_Cover-Canis_latrans 1.3931 1.0031 3238
## Avg_Cogongrass_Cover-Sciurus_niger 1.0742 1.0381 1483
## Avg_Cogongrass_Cover-Procyon_lotor 1.2572 1.0089 3554
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1223 1.0040 4056
## Avg_Cogongrass_Cover-Lynx_rufus 1.5189 1.0015 2833
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2117 1.0028 3682
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7691 1.0216 2690
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1424 1.0058 3850
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4152 1.0069 3069
## Avg_Cogongrass_Cover-Sus_scrofa 0.8853 1.0057 2332
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0065 0.0589 -0.1082 0.0071 0.1227
## (Intercept)-Canis_latrans -2.6183 0.1714 -2.9692 -2.6119 -2.3037
## (Intercept)-Sciurus_niger -4.0956 0.5989 -5.2537 -4.0878 -2.9792
## (Intercept)-Procyon_lotor -2.2846 0.1320 -2.5585 -2.2834 -2.0332
## (Intercept)-Dasypus_novemcinctus -1.5869 0.1338 -1.8553 -1.5860 -1.3291
## (Intercept)-Lynx_rufus -3.5856 0.3352 -4.2847 -3.5696 -2.9741
## (Intercept)-Didelphis_virginiana -2.3473 0.2541 -2.8766 -2.3356 -1.8867
## (Intercept)-Sylvilagus_floridanus -3.2602 0.3341 -3.9667 -3.2363 -2.6552
## (Intercept)-Sciurus_carolinensis -2.4575 0.2702 -3.0113 -2.4489 -1.9463
## (Intercept)-Vulpes_vulpes -4.0767 0.7556 -5.6254 -4.0235 -2.7659
## (Intercept)-Sus_scrofa -2.9276 0.4657 -3.9496 -2.8943 -2.1132
## week-Odocoileus_virginianus 0.2079 0.0613 0.0853 0.2073 0.3297
## week-Canis_latrans 0.0711 0.1287 -0.1919 0.0734 0.3120
## week-Sciurus_niger -0.2857 0.2988 -0.9799 -0.2509 0.1989
## week-Procyon_lotor -0.0455 0.1156 -0.2813 -0.0423 0.1709
## week-Dasypus_novemcinctus -0.1560 0.1331 -0.4304 -0.1509 0.0883
## week-Lynx_rufus -0.0282 0.1888 -0.4344 -0.0183 0.3234
## week-Didelphis_virginiana -0.2010 0.2123 -0.6790 -0.1847 0.1702
## week-Sylvilagus_floridanus -0.1437 0.2042 -0.5822 -0.1290 0.2217
## week-Sciurus_carolinensis 0.1390 0.1778 -0.2267 0.1431 0.4850
## week-Vulpes_vulpes -0.0977 0.2678 -0.6562 -0.0877 0.4000
## week-Sus_scrofa 0.0983 0.2319 -0.3607 0.1003 0.5466
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0002 5250
## (Intercept)-Canis_latrans 1.0047 3209
## (Intercept)-Sciurus_niger 1.0662 342
## (Intercept)-Procyon_lotor 1.0014 4074
## (Intercept)-Dasypus_novemcinctus 1.0004 4973
## (Intercept)-Lynx_rufus 1.0138 830
## (Intercept)-Didelphis_virginiana 0.9999 3809
## (Intercept)-Sylvilagus_floridanus 1.0062 1119
## (Intercept)-Sciurus_carolinensis 1.0002 3556
## (Intercept)-Vulpes_vulpes 1.0479 363
## (Intercept)-Sus_scrofa 1.0039 1901
## week-Odocoileus_virginianus 1.0001 5522
## week-Canis_latrans 0.9999 4290
## week-Sciurus_niger 1.0006 1852
## week-Procyon_lotor 1.0011 4617
## week-Dasypus_novemcinctus 1.0001 4938
## week-Lynx_rufus 1.0013 2790
## week-Didelphis_virginiana 1.0072 3742
## week-Sylvilagus_floridanus 1.0034 2897
## week-Sciurus_carolinensis 1.0003 4849
## week-Vulpes_vulpes 1.0004 3066
## week-Sus_scrofa 1.0010 4754
# 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): 1.7375
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2704 0.6617 -1.4920 -0.3038 1.0980 1.0351 1834
## Cogon_Patch_Size -0.2841 0.4075 -1.1623 -0.2643 0.4398 1.0118 2038
## Avg_Cogongrass_Cover 0.2543 0.2750 -0.2750 0.2507 0.8069 1.0055 1715
## total_shrub_cover -0.2172 0.2713 -0.7766 -0.2153 0.2990 1.0008 2294
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.4652 3.6814 0.8741 3.4420 14.1394 1.0181 1446
## Cogon_Patch_Size 0.9510 1.2301 0.0647 0.5622 4.1971 1.0123 1211
## Avg_Cogongrass_Cover 0.2756 0.3601 0.0363 0.1699 1.1972 1.0485 1574
## total_shrub_cover 0.3172 0.3597 0.0406 0.2067 1.2527 1.0026 2052
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1555 1.2051 0.0755 0.8143 4.2186 1.0978 241
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5076 0.4198 -3.3359 -2.5121 -1.6366 1.0095 3733
## week -0.0380 0.1209 -0.2953 -0.0343 0.1908 1.0038 3181
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8972 1.1906 0.6412 1.5978 4.9077 1.0405 1615
## week 0.0963 0.0800 0.0246 0.0746 0.2899 1.0149 2796
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.8316 1.6406 1.2139 3.6133
## (Intercept)-Canis_latrans 0.4132 0.7355 -0.9961 0.3887
## (Intercept)-Sciurus_niger -0.5551 1.4423 -2.7640 -0.7902
## (Intercept)-Procyon_lotor 0.5960 0.7388 -0.8921 0.6058
## (Intercept)-Dasypus_novemcinctus -0.7715 0.6609 -2.1691 -0.7497
## (Intercept)-Lynx_rufus -0.1071 1.0550 -1.9062 -0.1883
## (Intercept)-Didelphis_virginiana -1.4840 0.7409 -3.0033 -1.4684
## (Intercept)-Sylvilagus_floridanus -0.3804 0.9450 -2.0603 -0.4436
## (Intercept)-Sciurus_carolinensis -1.6644 0.7685 -3.2806 -1.6291
## (Intercept)-Vulpes_vulpes -1.0546 1.5534 -3.4962 -1.2843
## (Intercept)-Sus_scrofa -2.1137 0.9833 -4.1659 -2.0985
## Cogon_Patch_Size-Odocoileus_virginianus -0.0711 0.7325 -1.3547 -0.1265
## Cogon_Patch_Size-Canis_latrans 0.6651 0.7187 -0.3975 0.5523
## Cogon_Patch_Size-Sciurus_niger -0.6885 0.8977 -2.7881 -0.5588
## Cogon_Patch_Size-Procyon_lotor -0.2712 0.4654 -1.1645 -0.2744
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1763 0.4241 -1.0561 -0.1549
## Cogon_Patch_Size-Lynx_rufus -0.3110 0.7719 -1.7304 -0.3468
## Cogon_Patch_Size-Didelphis_virginiana 0.5847 0.5296 -0.3142 0.5451
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9603 0.8931 -3.1320 -0.8103
## Cogon_Patch_Size-Sciurus_carolinensis -0.7893 0.7194 -2.5772 -0.6653
## Cogon_Patch_Size-Vulpes_vulpes -0.6185 0.9195 -2.7671 -0.5159
## Cogon_Patch_Size-Sus_scrofa -0.5489 0.8147 -2.5264 -0.4315
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2413 0.5149 -0.7514 0.2315
## Avg_Cogongrass_Cover-Canis_latrans 0.3122 0.3930 -0.4248 0.3024
## Avg_Cogongrass_Cover-Sciurus_niger -0.0302 0.5792 -1.3042 0.0199
## Avg_Cogongrass_Cover-Procyon_lotor 0.2850 0.4143 -0.4701 0.2656
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4063 0.3561 -0.2467 0.3921
## Avg_Cogongrass_Cover-Lynx_rufus 0.5334 0.4659 -0.2638 0.4902
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2602 0.3853 -0.5092 0.2662
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0138 0.4572 -1.0326 0.0124
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4772 0.3877 -0.2403 0.4590
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3387 0.4576 -0.5162 0.3300
## Avg_Cogongrass_Cover-Sus_scrofa 0.0096 0.5391 -1.1943 0.0693
## total_shrub_cover-Odocoileus_virginianus -0.1363 0.5170 -1.1524 -0.1445
## total_shrub_cover-Canis_latrans 0.0686 0.4108 -0.6587 0.0376
## total_shrub_cover-Sciurus_niger -0.4219 0.5086 -1.5549 -0.3829
## total_shrub_cover-Procyon_lotor -0.6568 0.4548 -1.6611 -0.6119
## total_shrub_cover-Dasypus_novemcinctus -0.0428 0.3492 -0.7155 -0.0486
## total_shrub_cover-Lynx_rufus -0.5372 0.5416 -1.8155 -0.4741
## total_shrub_cover-Didelphis_virginiana -0.2526 0.3968 -1.0706 -0.2368
## total_shrub_cover-Sylvilagus_floridanus -0.2272 0.4854 -1.2361 -0.2077
## total_shrub_cover-Sciurus_carolinensis -0.0607 0.3943 -0.8125 -0.0705
## total_shrub_cover-Vulpes_vulpes -0.2372 0.5418 -1.3170 -0.2228
## total_shrub_cover-Sus_scrofa 0.0649 0.4762 -0.8091 0.0345
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.6428 1.0244 1060
## (Intercept)-Canis_latrans 1.9716 1.0025 2119
## (Intercept)-Sciurus_niger 3.0257 1.0841 390
## (Intercept)-Procyon_lotor 2.0519 1.0071 1243
## (Intercept)-Dasypus_novemcinctus 0.5337 1.0032 2577
## (Intercept)-Lynx_rufus 2.1969 1.0162 973
## (Intercept)-Didelphis_virginiana -0.0291 1.0002 2822
## (Intercept)-Sylvilagus_floridanus 1.6657 1.0122 1291
## (Intercept)-Sciurus_carolinensis -0.2229 1.0080 2424
## (Intercept)-Vulpes_vulpes 2.7567 1.1135 351
## (Intercept)-Sus_scrofa -0.2682 1.0064 1543
## Cogon_Patch_Size-Odocoileus_virginianus 1.6235 1.0020 3142
## Cogon_Patch_Size-Canis_latrans 2.4130 1.0023 2046
## Cogon_Patch_Size-Sciurus_niger 0.7753 1.0122 1616
## Cogon_Patch_Size-Procyon_lotor 0.6500 1.0015 3803
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6219 1.0029 4048
## Cogon_Patch_Size-Lynx_rufus 1.3549 1.0072 1986
## Cogon_Patch_Size-Didelphis_virginiana 1.6761 1.0003 2145
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3241 1.0066 1390
## Cogon_Patch_Size-Sciurus_carolinensis 0.2681 1.0018 1960
## Cogon_Patch_Size-Vulpes_vulpes 0.9615 1.0099 1435
## Cogon_Patch_Size-Sus_scrofa 0.7408 1.0129 2241
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3044 1.0022 3291
## Avg_Cogongrass_Cover-Canis_latrans 1.1149 1.0000 3147
## Avg_Cogongrass_Cover-Sciurus_niger 0.9903 1.0237 1848
## Avg_Cogongrass_Cover-Procyon_lotor 1.1410 1.0018 3202
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1578 1.0017 3345
## Avg_Cogongrass_Cover-Lynx_rufus 1.5982 1.0044 2683
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0167 1.0005 3047
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8090 1.0021 2534
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3043 1.0003 2544
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2804 1.0028 2210
## Avg_Cogongrass_Cover-Sus_scrofa 0.9190 1.0063 1995
## total_shrub_cover-Odocoileus_virginianus 0.9591 1.0021 3143
## total_shrub_cover-Canis_latrans 0.9368 1.0030 2837
## total_shrub_cover-Sciurus_niger 0.4808 1.0014 2470
## total_shrub_cover-Procyon_lotor 0.1192 1.0048 2556
## total_shrub_cover-Dasypus_novemcinctus 0.6927 1.0006 3932
## total_shrub_cover-Lynx_rufus 0.3652 1.0008 2265
## total_shrub_cover-Didelphis_virginiana 0.5340 1.0030 3628
## total_shrub_cover-Sylvilagus_floridanus 0.6925 1.0020 2762
## total_shrub_cover-Sciurus_carolinensis 0.7542 1.0014 3921
## total_shrub_cover-Vulpes_vulpes 0.8562 1.0022 2418
## total_shrub_cover-Sus_scrofa 1.0951 1.0032 3342
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0081 0.0586 -0.1047 0.0097 0.1202
## (Intercept)-Canis_latrans -2.6272 0.1775 -2.9971 -2.6207 -2.2997
## (Intercept)-Sciurus_niger -3.9981 0.5854 -5.1478 -3.9731 -2.9399
## (Intercept)-Procyon_lotor -2.2812 0.1332 -2.5477 -2.2803 -2.0295
## (Intercept)-Dasypus_novemcinctus -1.5901 0.1350 -1.8633 -1.5881 -1.3325
## (Intercept)-Lynx_rufus -3.5480 0.3231 -4.2198 -3.5361 -2.9446
## (Intercept)-Didelphis_virginiana -2.3377 0.2504 -2.8549 -2.3289 -1.8805
## (Intercept)-Sylvilagus_floridanus -3.2694 0.3274 -3.9749 -3.2504 -2.6796
## (Intercept)-Sciurus_carolinensis -2.4594 0.2692 -3.0065 -2.4489 -1.9573
## (Intercept)-Vulpes_vulpes -4.1606 0.7820 -5.7155 -4.1085 -2.7587
## (Intercept)-Sus_scrofa -2.9675 0.5012 -4.0599 -2.9314 -2.1058
## week-Odocoileus_virginianus 0.2065 0.0588 0.0917 0.2065 0.3223
## week-Canis_latrans 0.0720 0.1283 -0.1794 0.0748 0.3156
## week-Sciurus_niger -0.2759 0.2942 -0.9687 -0.2378 0.2006
## week-Procyon_lotor -0.0456 0.1158 -0.2815 -0.0413 0.1752
## week-Dasypus_novemcinctus -0.1553 0.1374 -0.4386 -0.1517 0.1023
## week-Lynx_rufus -0.0286 0.1883 -0.4086 -0.0215 0.3263
## week-Didelphis_virginiana -0.1937 0.2106 -0.6626 -0.1773 0.1685
## week-Sylvilagus_floridanus -0.1389 0.2022 -0.5674 -0.1245 0.2235
## week-Sciurus_carolinensis 0.1424 0.1754 -0.2052 0.1424 0.4781
## week-Vulpes_vulpes -0.0974 0.2628 -0.6665 -0.0834 0.3736
## week-Sus_scrofa 0.1007 0.2260 -0.3522 0.1014 0.5531
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0018 2734
## (Intercept)-Sciurus_niger 1.0997 417
## (Intercept)-Procyon_lotor 1.0009 4071
## (Intercept)-Dasypus_novemcinctus 1.0023 4988
## (Intercept)-Lynx_rufus 1.0124 1014
## (Intercept)-Didelphis_virginiana 1.0004 3573
## (Intercept)-Sylvilagus_floridanus 1.0028 1019
## (Intercept)-Sciurus_carolinensis 1.0007 3586
## (Intercept)-Vulpes_vulpes 1.1083 378
## (Intercept)-Sus_scrofa 1.0008 1371
## week-Odocoileus_virginianus 1.0006 5250
## week-Canis_latrans 1.0012 4359
## week-Sciurus_niger 1.0029 1951
## week-Procyon_lotor 1.0012 4343
## week-Dasypus_novemcinctus 1.0017 5113
## week-Lynx_rufus 1.0012 3021
## week-Didelphis_virginiana 1.0008 3744
## week-Sylvilagus_floridanus 1.0040 3058
## week-Sciurus_carolinensis 1.0029 4684
## week-Vulpes_vulpes 1.0018 2980
## week-Sus_scrofa 1.0011 4577
#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): 1.7592
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2422 0.7333 -1.5995 -0.2720 1.3078 1.0067 2448
## Tree_Density -0.7395 0.3870 -1.6045 -0.7040 -0.0741 1.0073 1217
## Avg_Canopy_Cover 0.9953 0.3225 0.3917 0.9882 1.6738 1.0026 1893
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.4138 5.8266 1.4193 4.8828 20.1446 1.0270 905
## Tree_Density 0.6984 1.2961 0.0441 0.3288 3.4699 1.0235 1117
## Avg_Canopy_Cover 0.4986 0.5549 0.0536 0.3367 1.8809 1.0075 2093
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4034 0.4756 0.0421 0.2405 1.7706 1.0221 443
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4963 0.4198 -3.2920 -2.5089 -1.6252 1.0005 4095
## week -0.0409 0.1207 -0.2939 -0.0346 0.1882 1.0010 3311
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9650 1.2010 0.6708 1.6667 5.0360 1.0073 2818
## week 0.0974 0.0749 0.0250 0.0764 0.2946 1.0063 3485
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.5896 1.7526 2.1083 4.2659 8.9132
## (Intercept)-Canis_latrans 0.3122 0.6491 -0.8713 0.2850 1.6999
## (Intercept)-Sciurus_niger -0.1301 1.4228 -2.3245 -0.3333 3.2478
## (Intercept)-Procyon_lotor 0.7391 0.6038 -0.4694 0.7460 1.9268
## (Intercept)-Dasypus_novemcinctus -1.0157 0.6072 -2.2830 -0.9907 0.1041
## (Intercept)-Lynx_rufus 1.1541 1.8189 -1.2076 0.7385 5.8198
## (Intercept)-Didelphis_virginiana -1.9035 0.6792 -3.3714 -1.8663 -0.6439
## (Intercept)-Sylvilagus_floridanus -0.7097 0.7055 -2.0737 -0.7227 0.6971
## (Intercept)-Sciurus_carolinensis -1.9803 0.7002 -3.4659 -1.9518 -0.7496
## (Intercept)-Vulpes_vulpes -1.4589 1.4521 -3.7373 -1.6618 2.0757
## (Intercept)-Sus_scrofa -2.7188 0.9067 -4.6691 -2.6916 -1.0684
## Tree_Density-Odocoileus_virginianus -0.3931 0.6199 -1.4640 -0.4491 1.0702
## Tree_Density-Canis_latrans -0.8628 0.5406 -2.0827 -0.7950 0.0334
## Tree_Density-Sciurus_niger -0.7656 0.7488 -2.4494 -0.6968 0.4924
## Tree_Density-Procyon_lotor -0.4709 0.4011 -1.2644 -0.4774 0.3206
## Tree_Density-Dasypus_novemcinctus -1.2945 0.8819 -3.5332 -1.1047 -0.1621
## Tree_Density-Lynx_rufus -0.0410 0.7445 -1.2618 -0.1310 1.7515
## Tree_Density-Didelphis_virginiana -0.9703 0.7124 -2.7462 -0.8476 0.1105
## Tree_Density-Sylvilagus_floridanus -1.0195 0.7091 -2.7798 -0.9135 0.0936
## Tree_Density-Sciurus_carolinensis -0.9224 0.7243 -2.7234 -0.8121 0.2063
## Tree_Density-Vulpes_vulpes -0.6774 0.8246 -2.3417 -0.6353 0.7550
## Tree_Density-Sus_scrofa -0.9486 0.8036 -2.9605 -0.8215 0.2752
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8060 0.6489 -0.5633 0.8175 2.1058
## Avg_Canopy_Cover-Canis_latrans 0.1721 0.4649 -0.7700 0.1796 1.0635
## Avg_Canopy_Cover-Sciurus_niger 0.9942 0.7455 -0.3299 0.9432 2.6228
## Avg_Canopy_Cover-Procyon_lotor 1.0139 0.4384 0.2154 0.9924 1.9754
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0011 0.4104 0.2403 0.9902 1.8465
## Avg_Canopy_Cover-Lynx_rufus 0.9015 0.6818 -0.3906 0.8837 2.3570
## Avg_Canopy_Cover-Didelphis_virginiana 1.2198 0.4740 0.3929 1.1805 2.2687
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5679 0.6872 0.5713 1.4479 3.2797
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2184 0.4766 0.3896 1.1829 2.2537
## Avg_Canopy_Cover-Vulpes_vulpes 1.0313 0.5669 -0.0231 0.9940 2.2710
## Avg_Canopy_Cover-Sus_scrofa 1.2276 0.5116 0.3302 1.1847 2.3550
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0217 916
## (Intercept)-Canis_latrans 1.0080 2223
## (Intercept)-Sciurus_niger 1.0136 489
## (Intercept)-Procyon_lotor 1.0023 1980
## (Intercept)-Dasypus_novemcinctus 1.0006 3017
## (Intercept)-Lynx_rufus 1.0270 322
## (Intercept)-Didelphis_virginiana 1.0069 3118
## (Intercept)-Sylvilagus_floridanus 1.0023 2408
## (Intercept)-Sciurus_carolinensis 1.0005 3049
## (Intercept)-Vulpes_vulpes 1.0785 412
## (Intercept)-Sus_scrofa 1.0015 2009
## Tree_Density-Odocoileus_virginianus 1.0034 1919
## Tree_Density-Canis_latrans 1.0033 2871
## Tree_Density-Sciurus_niger 1.0075 1913
## Tree_Density-Procyon_lotor 1.0026 3965
## Tree_Density-Dasypus_novemcinctus 1.0086 1239
## Tree_Density-Lynx_rufus 1.0136 860
## Tree_Density-Didelphis_virginiana 1.0018 2118
## Tree_Density-Sylvilagus_floridanus 1.0085 1475
## Tree_Density-Sciurus_carolinensis 1.0038 1959
## Tree_Density-Vulpes_vulpes 1.0234 1340
## Tree_Density-Sus_scrofa 1.0021 1823
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0001 3161
## Avg_Canopy_Cover-Canis_latrans 1.0001 2410
## Avg_Canopy_Cover-Sciurus_niger 1.0030 1436
## Avg_Canopy_Cover-Procyon_lotor 1.0003 3830
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0013 4001
## Avg_Canopy_Cover-Lynx_rufus 1.0033 1674
## Avg_Canopy_Cover-Didelphis_virginiana 1.0049 2654
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0045 1915
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0016 3191
## Avg_Canopy_Cover-Vulpes_vulpes 1.0021 2732
## Avg_Canopy_Cover-Sus_scrofa 1.0032 2833
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0074 0.0594 -0.1098 0.0072 0.1263
## (Intercept)-Canis_latrans -2.6480 0.1852 -3.0300 -2.6356 -2.3034
## (Intercept)-Sciurus_niger -4.1981 0.5740 -5.2689 -4.2150 -3.0858
## (Intercept)-Procyon_lotor -2.2713 0.1313 -2.5348 -2.2693 -2.0233
## (Intercept)-Dasypus_novemcinctus -1.5868 0.1346 -1.8558 -1.5833 -1.3321
## (Intercept)-Lynx_rufus -3.7579 0.3530 -4.4261 -3.7592 -3.0543
## (Intercept)-Didelphis_virginiana -2.3265 0.2464 -2.8409 -2.3157 -1.8748
## (Intercept)-Sylvilagus_floridanus -3.1353 0.2830 -3.7339 -3.1223 -2.6257
## (Intercept)-Sciurus_carolinensis -2.4517 0.2675 -3.0035 -2.4341 -1.9668
## (Intercept)-Vulpes_vulpes -4.0520 0.7471 -5.6366 -3.9982 -2.7706
## (Intercept)-Sus_scrofa -2.8791 0.4528 -3.8484 -2.8389 -2.0869
## week-Odocoileus_virginianus 0.2070 0.0603 0.0900 0.2061 0.3268
## week-Canis_latrans 0.0724 0.1292 -0.1919 0.0770 0.3156
## week-Sciurus_niger -0.2868 0.2889 -0.9561 -0.2591 0.1997
## week-Procyon_lotor -0.0439 0.1163 -0.2816 -0.0387 0.1717
## week-Dasypus_novemcinctus -0.1558 0.1368 -0.4332 -0.1522 0.0986
## week-Lynx_rufus -0.0342 0.1932 -0.4396 -0.0305 0.3211
## week-Didelphis_virginiana -0.1967 0.2091 -0.6393 -0.1804 0.1679
## week-Sylvilagus_floridanus -0.1415 0.2050 -0.5674 -0.1299 0.2407
## week-Sciurus_carolinensis 0.1442 0.1779 -0.2167 0.1449 0.4909
## week-Vulpes_vulpes -0.1081 0.2734 -0.7049 -0.0921 0.3817
## week-Sus_scrofa 0.1045 0.2331 -0.3602 0.1068 0.5566
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0043 5250
## (Intercept)-Canis_latrans 1.0001 2409
## (Intercept)-Sciurus_niger 1.0192 455
## (Intercept)-Procyon_lotor 1.0014 3962
## (Intercept)-Dasypus_novemcinctus 1.0028 4588
## (Intercept)-Lynx_rufus 1.0336 672
## (Intercept)-Didelphis_virginiana 1.0017 3856
## (Intercept)-Sylvilagus_floridanus 1.0030 1872
## (Intercept)-Sciurus_carolinensis 1.0058 3765
## (Intercept)-Vulpes_vulpes 1.0667 350
## (Intercept)-Sus_scrofa 1.0038 2534
## week-Odocoileus_virginianus 1.0002 5250
## week-Canis_latrans 1.0026 4574
## week-Sciurus_niger 1.0011 1909
## week-Procyon_lotor 1.0012 4685
## week-Dasypus_novemcinctus 1.0043 4018
## week-Lynx_rufus 1.0067 2430
## week-Didelphis_virginiana 1.0020 4058
## week-Sylvilagus_floridanus 1.0012 3053
## week-Sciurus_carolinensis 1.0003 4868
## week-Vulpes_vulpes 1.0009 2940
## week-Sus_scrofa 1.0004 5060
# 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): 1.6808
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9730 0.6167 -2.1477 -0.9942 0.3466 1.0009 3207
## Avg_Cogongrass_Cover -0.7448 0.3636 -1.4646 -0.7368 -0.0662 1.0065 1349
## I(Avg_Cogongrass_Cover^2) 0.8399 0.3420 0.2361 0.8097 1.5983 1.0068 1179
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7288 3.1830 0.7683 2.9280 11.3714 1.0291 1794
## Avg_Cogongrass_Cover 0.3740 0.4677 0.0419 0.2258 1.5809 1.0174 1951
## I(Avg_Cogongrass_Cover^2) 0.5316 1.2588 0.0372 0.2114 3.1022 1.0265 720
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5019 0.5148 0.0475 0.3345 1.9341 1.0032 605
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4761 0.4034 -3.2672 -2.4813 -1.6672 1.0016 4595
## week -0.0398 0.1225 -0.3034 -0.0331 0.1908 1.0006 3094
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7696 1.1361 0.6266 1.4772 4.631 1.0007 2417
## week 0.0978 0.0760 0.0255 0.0765 0.294 1.0041 2436
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.8384 1.4236 0.5964 2.6731
## (Intercept)-Canis_latrans -0.5164 0.6815 -1.8986 -0.5155
## (Intercept)-Sciurus_niger -1.0585 1.1350 -2.9533 -1.1712
## (Intercept)-Procyon_lotor -0.2036 0.6480 -1.5341 -0.2017
## (Intercept)-Dasypus_novemcinctus -1.4094 0.6198 -2.6677 -1.3911
## (Intercept)-Lynx_rufus -1.2326 0.8597 -2.8826 -1.2442
## (Intercept)-Didelphis_virginiana -2.0183 0.6837 -3.3990 -2.0114
## (Intercept)-Sylvilagus_floridanus -1.0717 0.7708 -2.5734 -1.0836
## (Intercept)-Sciurus_carolinensis -2.4675 0.7658 -4.1378 -2.4283
## (Intercept)-Vulpes_vulpes -2.2670 1.1664 -4.4774 -2.3060
## (Intercept)-Sus_scrofa -2.5302 0.8776 -4.3509 -2.4981
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7347 0.6310 -1.9732 -0.7283
## Avg_Cogongrass_Cover-Canis_latrans -0.4746 0.5136 -1.4013 -0.4975
## Avg_Cogongrass_Cover-Sciurus_niger -1.0015 0.6637 -2.4644 -0.9448
## Avg_Cogongrass_Cover-Procyon_lotor -0.6135 0.4904 -1.5477 -0.6199
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5609 0.4682 -1.4740 -0.5695
## Avg_Cogongrass_Cover-Lynx_rufus -0.6382 0.5423 -1.6799 -0.6404
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4979 0.5090 -1.4780 -0.5057
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1485 0.6062 -2.5416 -1.0906
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8147 0.5319 -1.9604 -0.7880
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7964 0.6078 -2.0771 -0.7721
## Avg_Cogongrass_Cover-Sus_scrofa -1.0350 0.6390 -2.4686 -0.9751
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1610 0.8564 0.0719 1.0033
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2439 0.8123 0.2546 1.0494
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3548 0.7237 -1.4091 0.4238
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0931 0.6760 0.2320 0.9647
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7311 0.3463 0.0699 0.7180
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1812 0.5576 0.3347 1.0951
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5974 0.3921 -0.1319 0.5917
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7605 0.4812 -0.0519 0.7196
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9769 0.3971 0.2855 0.9428
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9721 0.5298 0.1841 0.9017
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3755 0.6025 -1.1398 0.4597
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.1747 1.0083 1449
## (Intercept)-Canis_latrans 0.8376 1.0001 2401
## (Intercept)-Sciurus_niger 1.5581 1.0067 597
## (Intercept)-Procyon_lotor 1.0582 1.0003 2503
## (Intercept)-Dasypus_novemcinctus -0.2315 1.0023 3064
## (Intercept)-Lynx_rufus 0.5478 1.0025 1544
## (Intercept)-Didelphis_virginiana -0.6940 1.0008 3461
## (Intercept)-Sylvilagus_floridanus 0.4990 1.0063 2213
## (Intercept)-Sciurus_carolinensis -1.0561 1.0071 2792
## (Intercept)-Vulpes_vulpes 0.3160 1.0034 645
## (Intercept)-Sus_scrofa -0.8981 1.0041 2218
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4954 1.0018 2537
## Avg_Cogongrass_Cover-Canis_latrans 0.6065 1.0007 2750
## Avg_Cogongrass_Cover-Sciurus_niger 0.1453 1.0002 1722
## Avg_Cogongrass_Cover-Procyon_lotor 0.3835 1.0010 2599
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3638 0.9999 2498
## Avg_Cogongrass_Cover-Lynx_rufus 0.4529 1.0017 2508
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5474 1.0029 2400
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0985 1.0124 1955
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1544 1.0019 2060
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3305 1.0031 2169
## Avg_Cogongrass_Cover-Sus_scrofa 0.0315 1.0024 1761
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2252 1.0157 672
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4324 1.0012 655
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5746 1.0063 717
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.9653 1.0034 1061
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4183 1.0005 2628
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5292 1.0007 1396
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3562 1.0110 2161
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7794 1.0140 1703
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8659 1.0010 2158
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2253 1.0031 1397
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3134 1.0002 1153
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0040 0.0591 -0.1116 0.0036 0.1213
## (Intercept)-Canis_latrans -2.6458 0.1792 -3.0122 -2.6435 -2.3124
## (Intercept)-Sciurus_niger -3.9484 0.5782 -5.0807 -3.9246 -2.9030
## (Intercept)-Procyon_lotor -2.2760 0.1295 -2.5363 -2.2724 -2.0263
## (Intercept)-Dasypus_novemcinctus -1.5911 0.1359 -1.8643 -1.5894 -1.3281
## (Intercept)-Lynx_rufus -3.4351 0.3228 -4.1414 -3.4173 -2.8517
## (Intercept)-Didelphis_virginiana -2.3558 0.2598 -2.8869 -2.3439 -1.8825
## (Intercept)-Sylvilagus_floridanus -3.2242 0.3131 -3.8746 -3.2047 -2.6536
## (Intercept)-Sciurus_carolinensis -2.4466 0.2707 -3.0043 -2.4338 -1.9583
## (Intercept)-Vulpes_vulpes -3.9736 0.7043 -5.4788 -3.9158 -2.7510
## (Intercept)-Sus_scrofa -2.9499 0.4768 -3.9831 -2.9171 -2.1064
## week-Odocoileus_virginianus 0.2069 0.0596 0.0907 0.2066 0.3257
## week-Canis_latrans 0.0706 0.1298 -0.1928 0.0754 0.3107
## week-Sciurus_niger -0.2818 0.2918 -0.9618 -0.2517 0.2029
## week-Procyon_lotor -0.0460 0.1186 -0.2927 -0.0430 0.1730
## week-Dasypus_novemcinctus -0.1602 0.1387 -0.4446 -0.1540 0.0930
## week-Lynx_rufus -0.0332 0.1932 -0.4319 -0.0255 0.3196
## week-Didelphis_virginiana -0.1936 0.2104 -0.6567 -0.1791 0.1732
## week-Sylvilagus_floridanus -0.1470 0.2063 -0.6011 -0.1363 0.2207
## week-Sciurus_carolinensis 0.1377 0.1756 -0.2103 0.1388 0.4795
## week-Vulpes_vulpes -0.1044 0.2680 -0.6621 -0.0845 0.3786
## week-Sus_scrofa 0.0995 0.2361 -0.3751 0.1000 0.5642
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0000 5250
## (Intercept)-Canis_latrans 1.0006 3052
## (Intercept)-Sciurus_niger 1.0017 480
## (Intercept)-Procyon_lotor 1.0005 4013
## (Intercept)-Dasypus_novemcinctus 1.0003 5250
## (Intercept)-Lynx_rufus 1.0061 1212
## (Intercept)-Didelphis_virginiana 1.0042 3538
## (Intercept)-Sylvilagus_floridanus 1.0034 1293
## (Intercept)-Sciurus_carolinensis 0.9998 3511
## (Intercept)-Vulpes_vulpes 1.0036 532
## (Intercept)-Sus_scrofa 1.0094 1884
## week-Odocoileus_virginianus 1.0032 4972
## week-Canis_latrans 1.0001 4301
## week-Sciurus_niger 1.0014 1975
## week-Procyon_lotor 1.0001 4564
## week-Dasypus_novemcinctus 1.0006 4226
## week-Lynx_rufus 1.0005 3165
## week-Didelphis_virginiana 1.0006 4165
## week-Sylvilagus_floridanus 1.0004 3018
## week-Sciurus_carolinensis 1.0003 4644
## week-Vulpes_vulpes 1.0060 2636
## week-Sus_scrofa 1.0007 4603
# 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): 1.859
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9384 1.1638 -3.0658 -1.0129 1.4928 1.0001 1342
## Cogon_Patch_Size -0.2713 0.7382 -1.8254 -0.2340 1.1156 1.0034 946
## Veg_shannon_index 0.9227 0.4826 -0.0049 0.9115 1.9022 1.0048 874
## total_shrub_cover -0.2719 0.4075 -1.0718 -0.2628 0.5204 1.0010 1383
## Avg_Cogongrass_Cover 0.0722 0.9399 -1.7449 0.0556 1.9325 1.0123 405
## Tree_Density -2.0649 0.8022 -3.6826 -2.0416 -0.5033 1.0093 652
## Avg_Canopy_Cover 1.8522 0.6023 0.7423 1.8183 3.1073 1.0107 772
## I(Avg_Cogongrass_Cover^2) 1.4986 0.5564 0.4680 1.4846 2.6086 1.0050 680
## avg_veg_height -0.1747 0.5078 -1.1514 -0.1795 0.8317 1.0033 725
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.0143 21.4841 4.3671 17.6799 81.9391 1.0287 355
## Cogon_Patch_Size 3.4792 4.3737 0.1862 2.1441 15.7319 1.0183 694
## Veg_shannon_index 0.9833 2.0281 0.0480 0.4539 4.9134 1.1290 835
## total_shrub_cover 0.6208 0.8294 0.0477 0.3584 2.7760 1.0144 1081
## Avg_Cogongrass_Cover 1.2325 2.1055 0.0505 0.5411 6.6536 1.0281 1142
## Tree_Density 3.2222 5.6667 0.0703 1.2816 18.0606 1.0172 460
## Avg_Canopy_Cover 2.1471 3.1080 0.1215 1.2569 9.3507 1.0423 389
## I(Avg_Cogongrass_Cover^2) 1.0142 2.2564 0.0489 0.4164 5.9823 1.0310 559
## avg_veg_height 0.4695 0.6435 0.0442 0.2728 2.1379 1.0037 1734
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8212 2.4362 0.07 1.0325 8.3683 1.1648 212
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5489 0.4354 -3.3807 -2.5570 -1.6758 1.0009 5250
## week -0.0363 0.1203 -0.2924 -0.0307 0.1808 1.0035 2978
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1911 1.3008 0.8185 1.8706 5.5897 1.0021 2876
## week 0.0981 0.0792 0.0253 0.0772 0.3036 1.0150 2294
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.0174 3.8268 2.5729
## (Intercept)-Canis_latrans -0.9548 1.2198 -3.3279
## (Intercept)-Sciurus_niger 1.4050 3.1741 -2.9539
## (Intercept)-Procyon_lotor -0.4334 1.0613 -2.7218
## (Intercept)-Dasypus_novemcinctus -2.8060 1.1970 -5.5244
## (Intercept)-Lynx_rufus 0.5872 3.3496 -3.9938
## (Intercept)-Didelphis_virginiana -4.3187 1.4709 -7.5580
## (Intercept)-Sylvilagus_floridanus -2.4526 1.4199 -5.5052
## (Intercept)-Sciurus_carolinensis -5.0752 1.5925 -8.6510
## (Intercept)-Vulpes_vulpes -4.1350 2.3681 -8.7783
## (Intercept)-Sus_scrofa -6.1095 2.0887 -10.6258
## Cogon_Patch_Size-Odocoileus_virginianus -0.1091 1.4897 -2.9504
## Cogon_Patch_Size-Canis_latrans 1.5921 1.4148 -0.3451
## Cogon_Patch_Size-Sciurus_niger -0.9303 1.9421 -5.2861
## Cogon_Patch_Size-Procyon_lotor -0.5358 0.8354 -2.1153
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2469 0.7095 -1.7568
## Cogon_Patch_Size-Lynx_rufus -0.4606 1.5809 -3.5720
## Cogon_Patch_Size-Didelphis_virginiana 1.6833 1.0400 -0.0759
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5222 1.6395 -5.5734
## Cogon_Patch_Size-Sciurus_carolinensis -1.2315 1.4007 -4.5994
## Cogon_Patch_Size-Vulpes_vulpes -0.8132 1.7179 -4.5483
## Cogon_Patch_Size-Sus_scrofa -0.9096 1.5982 -4.7441
## Veg_shannon_index-Odocoileus_virginianus 0.7593 0.9781 -1.3065
## Veg_shannon_index-Canis_latrans 1.3530 0.7356 0.1684
## Veg_shannon_index-Sciurus_niger 1.0358 1.1153 -0.8754
## Veg_shannon_index-Procyon_lotor 1.1453 0.6342 0.0401
## Veg_shannon_index-Dasypus_novemcinctus 0.6507 0.5640 -0.5265
## Veg_shannon_index-Lynx_rufus 0.9446 0.9588 -0.9876
## Veg_shannon_index-Didelphis_virginiana 1.0674 0.6968 -0.2205
## Veg_shannon_index-Sylvilagus_floridanus 1.0334 0.7505 -0.3255
## Veg_shannon_index-Sciurus_carolinensis 0.2830 0.8181 -1.5579
## Veg_shannon_index-Vulpes_vulpes 0.5611 0.9174 -1.4649
## Veg_shannon_index-Sus_scrofa 1.6468 1.1510 0.0996
## total_shrub_cover-Odocoileus_virginianus -0.1036 0.7859 -1.5925
## total_shrub_cover-Canis_latrans -0.0152 0.5723 -1.0881
## total_shrub_cover-Sciurus_niger -0.4773 0.8527 -2.4234
## total_shrub_cover-Procyon_lotor -0.8038 0.5946 -2.1488
## total_shrub_cover-Dasypus_novemcinctus 0.0537 0.5036 -0.8931
## total_shrub_cover-Lynx_rufus -0.6091 0.8620 -2.5365
## total_shrub_cover-Didelphis_virginiana -0.4450 0.6402 -1.8591
## total_shrub_cover-Sylvilagus_floridanus -0.2245 0.6795 -1.6117
## total_shrub_cover-Sciurus_carolinensis -0.0211 0.6109 -1.1753
## total_shrub_cover-Vulpes_vulpes -0.4310 0.8038 -2.1896
## total_shrub_cover-Sus_scrofa 0.0418 0.7518 -1.3210
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0070 1.3382 -2.6803
## Avg_Cogongrass_Cover-Canis_latrans 0.1311 1.1781 -2.1381
## Avg_Cogongrass_Cover-Sciurus_niger -0.2182 1.5001 -3.5008
## Avg_Cogongrass_Cover-Procyon_lotor 0.3537 1.1751 -1.8662
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6743 1.2218 -1.4559
## Avg_Cogongrass_Cover-Lynx_rufus 0.2481 1.2681 -2.2086
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1627 1.1612 -2.1120
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4587 1.2988 -3.3254
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1433 1.2293 -2.2428
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2187 1.3127 -2.2820
## Avg_Cogongrass_Cover-Sus_scrofa -0.4722 1.4321 -3.7169
## Tree_Density-Odocoileus_virginianus -1.0915 1.4105 -3.3270
## Tree_Density-Canis_latrans -2.7955 1.3426 -6.1063
## Tree_Density-Sciurus_niger -2.0613 1.7498 -5.6830
## Tree_Density-Procyon_lotor -1.9089 0.9714 -3.9437
## Tree_Density-Dasypus_novemcinctus -3.7724 1.8959 -8.5624
## Tree_Density-Lynx_rufus -0.9478 1.6361 -3.4886
## Tree_Density-Didelphis_virginiana -2.4317 1.2045 -5.3760
## Tree_Density-Sylvilagus_floridanus -2.5538 1.3945 -5.8262
## Tree_Density-Sciurus_carolinensis -2.8391 1.5187 -6.6408
## Tree_Density-Vulpes_vulpes -1.9953 1.7114 -5.2537
## Tree_Density-Sus_scrofa -2.5349 1.6233 -6.4423
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3469 1.3324 -1.5480
## Avg_Canopy_Cover-Canis_latrans 0.3262 0.7593 -1.1947
## Avg_Canopy_Cover-Sciurus_niger 2.2808 1.6057 -0.4810
## Avg_Canopy_Cover-Procyon_lotor 1.7194 0.7364 0.4120
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9682 0.7346 0.7213
## Avg_Canopy_Cover-Lynx_rufus 1.6058 1.3253 -0.8978
## Avg_Canopy_Cover-Didelphis_virginiana 2.6775 1.0154 1.1724
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2876 1.5897 1.1074
## Avg_Canopy_Cover-Sciurus_carolinensis 2.3221 0.9247 0.9477
## Avg_Canopy_Cover-Vulpes_vulpes 2.3818 1.3488 0.4879
## Avg_Canopy_Cover-Sus_scrofa 2.0623 0.8731 0.5865
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7908 1.1494 0.0935
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0034 0.9692 0.6474
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1891 1.1626 -1.5135
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8458 0.8955 0.4558
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4602 0.6889 0.1951
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.9836 0.9667 0.5226
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1587 0.6730 -0.1243
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2643 0.7790 -0.1984
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6740 0.7362 0.4295
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8588 0.9251 0.4772
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.8568 1.0468 -1.5808
## avg_veg_height-Odocoileus_virginianus -0.1766 0.8012 -1.7681
## avg_veg_height-Canis_latrans -0.4261 0.6408 -1.7655
## avg_veg_height-Sciurus_niger -0.2577 0.8178 -1.9491
## avg_veg_height-Procyon_lotor 0.1220 0.6258 -1.0357
## avg_veg_height-Dasypus_novemcinctus 0.1116 0.6148 -1.0278
## avg_veg_height-Lynx_rufus -0.2417 0.8171 -1.8776
## avg_veg_height-Didelphis_virginiana -0.3128 0.7012 -1.7784
## avg_veg_height-Sylvilagus_floridanus -0.3043 0.6931 -1.6983
## avg_veg_height-Sciurus_carolinensis 0.0580 0.6766 -1.2048
## avg_veg_height-Vulpes_vulpes -0.3207 0.7950 -1.9998
## avg_veg_height-Sus_scrofa -0.2786 0.7211 -1.7886
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3011 17.2918 1.0176 325
## (Intercept)-Canis_latrans -0.9770 1.5178 1.0020 1372
## (Intercept)-Sciurus_niger 0.7927 9.3574 1.0444 205
## (Intercept)-Procyon_lotor -0.3992 1.5681 1.0033 1603
## (Intercept)-Dasypus_novemcinctus -2.6996 -0.7806 1.0049 833
## (Intercept)-Lynx_rufus -0.1481 9.6560 1.0105 206
## (Intercept)-Didelphis_virginiana -4.2106 -1.7402 1.0067 815
## (Intercept)-Sylvilagus_floridanus -2.3838 0.1549 1.0114 1058
## (Intercept)-Sciurus_carolinensis -4.9169 -2.4417 1.0139 682
## (Intercept)-Vulpes_vulpes -4.1980 0.9838 1.0286 442
## (Intercept)-Sus_scrofa -5.9320 -2.6017 1.0103 606
## Cogon_Patch_Size-Odocoileus_virginianus -0.1619 3.0367 1.0020 2013
## Cogon_Patch_Size-Canis_latrans 1.3480 5.0698 1.0012 1014
## Cogon_Patch_Size-Sciurus_niger -0.7776 2.5920 1.0014 599
## Cogon_Patch_Size-Procyon_lotor -0.5556 1.0829 1.0076 1100
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2208 1.0616 1.0051 1256
## Cogon_Patch_Size-Lynx_rufus -0.4761 2.7564 1.0092 865
## Cogon_Patch_Size-Didelphis_virginiana 1.5934 3.9869 1.0124 692
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2521 0.8937 1.0029 897
## Cogon_Patch_Size-Sciurus_carolinensis -0.9868 0.8234 1.0027 1097
## Cogon_Patch_Size-Vulpes_vulpes -0.6858 2.5525 1.0031 861
## Cogon_Patch_Size-Sus_scrofa -0.6663 1.5008 1.0070 963
## Veg_shannon_index-Odocoileus_virginianus 0.8051 2.6254 1.0038 1816
## Veg_shannon_index-Canis_latrans 1.2678 3.1235 1.0070 1062
## Veg_shannon_index-Sciurus_niger 0.9747 3.3938 1.0127 929
## Veg_shannon_index-Procyon_lotor 1.0822 2.5643 1.0132 1064
## Veg_shannon_index-Dasypus_novemcinctus 0.6551 1.7444 1.0018 2197
## Veg_shannon_index-Lynx_rufus 0.9286 2.9065 1.0016 1138
## Veg_shannon_index-Didelphis_virginiana 1.0345 2.5863 1.0042 1528
## Veg_shannon_index-Sylvilagus_floridanus 0.9826 2.6201 1.0111 1126
## Veg_shannon_index-Sciurus_carolinensis 0.3569 1.6313 1.0028 1454
## Veg_shannon_index-Vulpes_vulpes 0.6312 2.1581 1.0112 1361
## Veg_shannon_index-Sus_scrofa 1.4221 4.5568 1.0249 823
## total_shrub_cover-Odocoileus_virginianus -0.1390 1.5726 1.0019 2594
## total_shrub_cover-Canis_latrans -0.0311 1.1703 1.0044 2220
## total_shrub_cover-Sciurus_niger -0.4240 1.1623 1.0053 1592
## total_shrub_cover-Procyon_lotor -0.7572 0.1957 1.0015 1656
## total_shrub_cover-Dasypus_novemcinctus 0.0461 1.1214 1.0016 3044
## total_shrub_cover-Lynx_rufus -0.5212 0.9144 1.0043 835
## total_shrub_cover-Didelphis_virginiana -0.3945 0.7237 1.0001 2483
## total_shrub_cover-Sylvilagus_floridanus -0.2062 1.1390 0.9999 2340
## total_shrub_cover-Sciurus_carolinensis -0.0308 1.2653 1.0011 2653
## total_shrub_cover-Vulpes_vulpes -0.3612 1.0022 1.0077 1824
## total_shrub_cover-Sus_scrofa -0.0129 1.7323 1.0017 2318
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0041 2.6620 1.0021 695
## Avg_Cogongrass_Cover-Canis_latrans 0.1102 2.5111 1.0075 634
## Avg_Cogongrass_Cover-Sciurus_niger -0.1497 2.4919 1.0097 740
## Avg_Cogongrass_Cover-Procyon_lotor 0.2987 2.8426 1.0076 696
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5937 3.2923 1.0104 595
## Avg_Cogongrass_Cover-Lynx_rufus 0.2143 2.7717 1.0046 660
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1268 2.5615 1.0100 597
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3770 1.8553 1.0097 713
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1103 2.6628 1.0201 581
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1773 2.9557 1.0053 605
## Avg_Cogongrass_Cover-Sus_scrofa -0.3571 2.0305 1.0116 725
## Tree_Density-Odocoileus_virginianus -1.2679 2.3658 1.0009 713
## Tree_Density-Canis_latrans -2.5719 -0.7913 1.0042 753
## Tree_Density-Sciurus_niger -2.0418 1.4847 1.0040 630
## Tree_Density-Procyon_lotor -1.8793 -0.0197 1.0033 981
## Tree_Density-Dasypus_novemcinctus -3.3317 -1.3382 1.0104 432
## Tree_Density-Lynx_rufus -1.1544 3.0213 1.0026 485
## Tree_Density-Didelphis_virginiana -2.2788 -0.5458 1.0025 762
## Tree_Density-Sylvilagus_floridanus -2.3883 -0.2371 1.0075 809
## Tree_Density-Sciurus_carolinensis -2.5635 -0.6907 1.0062 622
## Tree_Density-Vulpes_vulpes -2.0465 1.6044 1.0286 529
## Tree_Density-Sus_scrofa -2.3105 0.0342 1.0017 1003
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3989 3.9130 1.0050 1352
## Avg_Canopy_Cover-Canis_latrans 0.3092 1.8524 1.0296 1042
## Avg_Canopy_Cover-Sciurus_niger 2.0907 5.9341 1.0089 626
## Avg_Canopy_Cover-Procyon_lotor 1.6791 3.2854 1.0066 1187
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9065 3.6143 1.0108 834
## Avg_Canopy_Cover-Lynx_rufus 1.5646 4.4460 1.0065 682
## Avg_Canopy_Cover-Didelphis_virginiana 2.5340 5.0912 1.0353 582
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9956 7.1163 1.0749 440
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1970 4.4084 1.0459 817
## Avg_Canopy_Cover-Vulpes_vulpes 2.1276 5.7485 1.0353 417
## Avg_Canopy_Cover-Sus_scrofa 1.9734 4.0872 1.0027 1087
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6532 4.3719 1.0281 655
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8670 4.2940 1.0110 830
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2817 3.1819 1.0083 471
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7315 3.9201 1.0034 813
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4122 2.9024 1.0007 983
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.8477 4.2548 1.0015 946
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1390 2.5027 1.0113 734
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2283 2.8868 1.0014 937
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6150 3.3532 1.0034 924
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7368 3.9313 1.0106 842
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9604 2.5301 1.0122 852
## avg_veg_height-Odocoileus_virginianus -0.1614 1.3971 1.0035 1159
## avg_veg_height-Canis_latrans -0.3982 0.7669 1.0056 985
## avg_veg_height-Sciurus_niger -0.2257 1.2569 1.0022 1474
## avg_veg_height-Procyon_lotor 0.0983 1.4266 1.0083 1234
## avg_veg_height-Dasypus_novemcinctus 0.1043 1.3973 1.0037 1179
## avg_veg_height-Lynx_rufus -0.2200 1.3171 1.0022 1308
## avg_veg_height-Didelphis_virginiana -0.2902 1.0040 1.0017 920
## avg_veg_height-Sylvilagus_floridanus -0.2909 1.0245 1.0036 1149
## avg_veg_height-Sciurus_carolinensis 0.0335 1.4855 1.0031 1357
## avg_veg_height-Vulpes_vulpes -0.2999 1.1621 1.0002 1013
## avg_veg_height-Sus_scrofa -0.2584 1.0777 1.0015 1168
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0056 0.0594 -0.1097 0.0048 0.1214
## (Intercept)-Canis_latrans -2.6302 0.1754 -2.9786 -2.6254 -2.2988
## (Intercept)-Sciurus_niger -4.6751 0.4400 -5.5491 -4.6796 -3.8155
## (Intercept)-Procyon_lotor -2.2715 0.1284 -2.5269 -2.2684 -2.0276
## (Intercept)-Dasypus_novemcinctus -1.5849 0.1336 -1.8488 -1.5813 -1.3271
## (Intercept)-Lynx_rufus -3.7904 0.3424 -4.4272 -3.7973 -3.1059
## (Intercept)-Didelphis_virginiana -2.3139 0.2485 -2.8330 -2.3034 -1.8522
## (Intercept)-Sylvilagus_floridanus -3.2300 0.2756 -3.8068 -3.2203 -2.7071
## (Intercept)-Sciurus_carolinensis -2.4445 0.2626 -2.9942 -2.4313 -1.9730
## (Intercept)-Vulpes_vulpes -4.1782 0.6382 -5.4812 -4.1509 -2.9943
## (Intercept)-Sus_scrofa -2.9089 0.4588 -3.9093 -2.8736 -2.1023
## week-Odocoileus_virginianus 0.2080 0.0603 0.0904 0.2076 0.3285
## week-Canis_latrans 0.0742 0.1289 -0.1843 0.0759 0.3136
## week-Sciurus_niger -0.2961 0.3008 -0.9892 -0.2623 0.1949
## week-Procyon_lotor -0.0431 0.1186 -0.2920 -0.0398 0.1804
## week-Dasypus_novemcinctus -0.1559 0.1353 -0.4371 -0.1510 0.0927
## week-Lynx_rufus -0.0263 0.1896 -0.4166 -0.0170 0.3208
## week-Didelphis_virginiana -0.1903 0.2104 -0.6570 -0.1740 0.1810
## week-Sylvilagus_floridanus -0.1346 0.2038 -0.5740 -0.1244 0.2318
## week-Sciurus_carolinensis 0.1478 0.1761 -0.1978 0.1491 0.4828
## week-Vulpes_vulpes -0.0927 0.2701 -0.6726 -0.0773 0.4023
## week-Sus_scrofa 0.1068 0.2309 -0.3631 0.1081 0.5575
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0033 2724
## (Intercept)-Sciurus_niger 1.0096 464
## (Intercept)-Procyon_lotor 1.0038 3351
## (Intercept)-Dasypus_novemcinctus 1.0029 5250
## (Intercept)-Lynx_rufus 1.0063 485
## (Intercept)-Didelphis_virginiana 1.0009 3975
## (Intercept)-Sylvilagus_floridanus 1.0044 1647
## (Intercept)-Sciurus_carolinensis 1.0037 3359
## (Intercept)-Vulpes_vulpes 1.0329 502
## (Intercept)-Sus_scrofa 1.0013 1784
## week-Odocoileus_virginianus 1.0014 5250
## week-Canis_latrans 1.0005 4273
## week-Sciurus_niger 1.0226 1256
## week-Procyon_lotor 1.0003 4458
## week-Dasypus_novemcinctus 1.0041 5250
## week-Lynx_rufus 1.0024 2594
## week-Didelphis_virginiana 1.0025 3515
## week-Sylvilagus_floridanus 1.0037 3172
## week-Sciurus_carolinensis 1.0025 4128
## week-Vulpes_vulpes 1.0034 2568
## week-Sus_scrofa 1.0011 4747
# 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): 1.368
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1760 0.5789 -1.2650 -0.1924 1.0186 1.0026 1813
## Avg_Cogongrass_Cover 0.1984 0.2453 -0.2929 0.2011 0.6807 1.0034 1903
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3259 2.8825 0.6805 2.5778 10.7287 1.0042 1365
## Avg_Cogongrass_Cover 0.2616 0.2965 0.0374 0.1677 1.0610 1.0032 2381
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.727 0.7263 0.0565 0.505 2.7398 1.016 500
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5649 0.4345 -3.4153 -2.5725 -1.6419 1.0049 4171
## shrub_cover 0.2177 0.2375 -0.2647 0.2183 0.6802 1.0004 2838
## veg_height -0.0082 0.1578 -0.3253 -0.0094 0.3014 1.0028 3015
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9560 1.2630 0.6560 1.6283 5.1418 1.0061 2206
## shrub_cover 0.4289 0.3432 0.0869 0.3342 1.3239 1.0022 1840
## veg_height 0.1903 0.1391 0.0535 0.1562 0.5232 1.0015 3691
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.4011 1.3861 1.2069 3.2213
## (Intercept)-Canis_latrans 0.4317 0.6398 -0.7907 0.3990
## (Intercept)-Sciurus_niger -0.3835 1.1319 -2.2293 -0.5119
## (Intercept)-Procyon_lotor 0.5543 0.6219 -0.7261 0.5580
## (Intercept)-Dasypus_novemcinctus -0.6444 0.5598 -1.7546 -0.6313
## (Intercept)-Lynx_rufus 0.1653 1.0249 -1.5602 0.0619
## (Intercept)-Didelphis_virginiana -1.2448 0.6390 -2.5426 -1.2334
## (Intercept)-Sylvilagus_floridanus -0.3712 0.6726 -1.6615 -0.3893
## (Intercept)-Sciurus_carolinensis -1.2988 0.6681 -2.6727 -1.2786
## (Intercept)-Vulpes_vulpes -1.0268 1.2973 -3.0288 -1.1641
## (Intercept)-Sus_scrofa -1.7202 0.8150 -3.3969 -1.6964
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1894 0.4690 -0.7505 0.1855
## Avg_Cogongrass_Cover-Canis_latrans 0.4136 0.3713 -0.2428 0.3885
## Avg_Cogongrass_Cover-Sciurus_niger -0.1148 0.5712 -1.4104 -0.0669
## Avg_Cogongrass_Cover-Procyon_lotor 0.2240 0.3458 -0.4383 0.2153
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3328 0.3203 -0.2984 0.3322
## Avg_Cogongrass_Cover-Lynx_rufus 0.4141 0.4013 -0.3022 0.3957
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3140 0.3483 -0.3525 0.3070
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1775 0.4302 -1.1330 -0.1465
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3376 0.3461 -0.3285 0.3339
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2914 0.4226 -0.5384 0.2816
## Avg_Cogongrass_Cover-Sus_scrofa -0.0502 0.5005 -1.1858 -0.0002
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.6381 1.0014 1131
## (Intercept)-Canis_latrans 1.7864 1.0018 2717
## (Intercept)-Sciurus_niger 2.2034 1.0072 638
## (Intercept)-Procyon_lotor 1.7865 1.0035 2506
## (Intercept)-Dasypus_novemcinctus 0.4331 1.0037 3412
## (Intercept)-Lynx_rufus 2.6081 1.0132 827
## (Intercept)-Didelphis_virginiana -0.0107 1.0050 3262
## (Intercept)-Sylvilagus_floridanus 1.0037 1.0149 2386
## (Intercept)-Sciurus_carolinensis -0.0445 1.0031 3099
## (Intercept)-Vulpes_vulpes 1.9821 1.0743 335
## (Intercept)-Sus_scrofa -0.1506 1.0019 2465
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1655 1.0031 3807
## Avg_Cogongrass_Cover-Canis_latrans 1.2443 1.0013 3520
## Avg_Cogongrass_Cover-Sciurus_niger 0.8878 1.0106 1729
## Avg_Cogongrass_Cover-Procyon_lotor 0.9288 1.0027 4099
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9803 1.0016 3908
## Avg_Cogongrass_Cover-Lynx_rufus 1.2868 1.0030 3412
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0184 1.0005 3601
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5976 1.0005 2459
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0337 1.0005 4319
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1589 1.0005 3551
## Avg_Cogongrass_Cover-Sus_scrofa 0.8028 1.0008 2569
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0031 0.0592 -0.1131 0.0035 0.1162
## (Intercept)-Canis_latrans -2.7533 0.1909 -3.1420 -2.7479 -2.3866
## (Intercept)-Sciurus_niger -4.1067 0.6121 -5.2868 -4.1129 -2.9134
## (Intercept)-Procyon_lotor -2.2895 0.1434 -2.5748 -2.2847 -2.0185
## (Intercept)-Dasypus_novemcinctus -1.7151 0.1574 -2.0402 -1.7107 -1.4173
## (Intercept)-Lynx_rufus -3.6799 0.3658 -4.3927 -3.6794 -2.9739
## (Intercept)-Didelphis_virginiana -2.4935 0.2814 -3.0670 -2.4862 -1.9732
## (Intercept)-Sylvilagus_floridanus -3.1672 0.3013 -3.7975 -3.1565 -2.6216
## (Intercept)-Sciurus_carolinensis -2.5686 0.3109 -3.2262 -2.5490 -2.0133
## (Intercept)-Vulpes_vulpes -4.1389 0.7601 -5.7202 -4.0946 -2.8158
## (Intercept)-Sus_scrofa -3.2090 0.5606 -4.3392 -3.2011 -2.1543
## shrub_cover-Odocoileus_virginianus -0.0528 0.0644 -0.1804 -0.0528 0.0732
## shrub_cover-Canis_latrans -0.2592 0.2168 -0.6909 -0.2597 0.1516
## shrub_cover-Sciurus_niger -0.3212 0.4424 -1.2120 -0.3082 0.5356
## shrub_cover-Procyon_lotor 0.2458 0.1645 -0.0797 0.2492 0.5578
## shrub_cover-Dasypus_novemcinctus 0.7768 0.2895 0.2355 0.7707 1.3711
## shrub_cover-Lynx_rufus -0.2473 0.3386 -0.9437 -0.2385 0.4089
## shrub_cover-Didelphis_virginiana 0.8617 0.3505 0.2279 0.8461 1.5824
## shrub_cover-Sylvilagus_floridanus 0.2737 0.3933 -0.4618 0.2581 1.0573
## shrub_cover-Sciurus_carolinensis 0.7433 0.3835 0.0435 0.7325 1.5482
## shrub_cover-Vulpes_vulpes -0.0628 0.5287 -1.1658 -0.0488 0.9447
## shrub_cover-Sus_scrofa 0.4739 0.6680 -0.8658 0.4709 1.8372
## veg_height-Odocoileus_virginianus -0.2937 0.0647 -0.4194 -0.2937 -0.1687
## veg_height-Canis_latrans -0.5843 0.1873 -0.9634 -0.5764 -0.2391
## veg_height-Sciurus_niger -0.0532 0.3950 -0.8073 -0.0594 0.7476
## veg_height-Procyon_lotor 0.3303 0.1246 0.0887 0.3278 0.5797
## veg_height-Dasypus_novemcinctus 0.2225 0.1328 -0.0304 0.2211 0.4865
## veg_height-Lynx_rufus 0.0123 0.2374 -0.4648 0.0134 0.4671
## veg_height-Didelphis_virginiana 0.3846 0.2380 -0.0556 0.3752 0.8651
## veg_height-Sylvilagus_floridanus 0.1286 0.2464 -0.3528 0.1259 0.6183
## veg_height-Sciurus_carolinensis 0.0450 0.2061 -0.3590 0.0409 0.4700
## veg_height-Vulpes_vulpes -0.1411 0.3067 -0.7910 -0.1298 0.4403
## veg_height-Sus_scrofa -0.1319 0.3224 -0.7892 -0.1265 0.4694
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0036 5250
## (Intercept)-Canis_latrans 1.0037 2242
## (Intercept)-Sciurus_niger 1.0144 555
## (Intercept)-Procyon_lotor 1.0040 3381
## (Intercept)-Dasypus_novemcinctus 1.0002 4214
## (Intercept)-Lynx_rufus 1.0092 885
## (Intercept)-Didelphis_virginiana 1.0012 2736
## (Intercept)-Sylvilagus_floridanus 1.0327 2071
## (Intercept)-Sciurus_carolinensis 1.0085 2911
## (Intercept)-Vulpes_vulpes 1.0348 353
## (Intercept)-Sus_scrofa 1.0054 1921
## shrub_cover-Odocoileus_virginianus 1.0027 5250
## shrub_cover-Canis_latrans 1.0036 2829
## shrub_cover-Sciurus_niger 1.0028 1483
## shrub_cover-Procyon_lotor 1.0013 3756
## shrub_cover-Dasypus_novemcinctus 1.0006 3540
## shrub_cover-Lynx_rufus 1.0014 1500
## shrub_cover-Didelphis_virginiana 1.0010 2376
## shrub_cover-Sylvilagus_floridanus 1.0006 1905
## shrub_cover-Sciurus_carolinensis 1.0010 2380
## shrub_cover-Vulpes_vulpes 1.0023 2032
## shrub_cover-Sus_scrofa 1.0019 2600
## veg_height-Odocoileus_virginianus 1.0010 5250
## veg_height-Canis_latrans 1.0033 2267
## veg_height-Sciurus_niger 1.0013 1830
## veg_height-Procyon_lotor 1.0000 4425
## veg_height-Dasypus_novemcinctus 1.0023 4724
## veg_height-Lynx_rufus 1.0047 2538
## veg_height-Didelphis_virginiana 1.0023 3046
## veg_height-Sylvilagus_floridanus 0.9998 2457
## veg_height-Sciurus_carolinensis 1.0026 3642
## veg_height-Vulpes_vulpes 1.0029 2314
## veg_height-Sus_scrofa 1.0005 3632
# 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): 1.538
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0881 1.0545 -2.1504 -0.1132 2.1070 1.0170 1312
## Cogon_Patch_Size -0.8268 0.6855 -2.3294 -0.7870 0.4292 1.0144 825
## Veg_shannon_index 0.8853 0.4920 -0.0740 0.8644 1.9203 1.0032 655
## total_shrub_cover -0.3340 0.4783 -1.3076 -0.3285 0.5863 1.0200 935
## Avg_Cogongrass_Cover 2.0303 0.7121 0.6572 2.0043 3.4784 1.0047 567
## Tree_Density -1.8100 0.7356 -3.3262 -1.7699 -0.4245 1.0218 681
## Avg_Canopy_Cover 1.9087 0.6407 0.7352 1.8722 3.3163 1.0031 476
## avg_veg_height -0.5095 0.4569 -1.4220 -0.5045 0.3721 1.0099 673
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.5336 16.5034 3.3994 13.5488 64.6480 1.0099 274
## Cogon_Patch_Size 3.0044 4.9426 0.1048 1.5242 15.0299 1.0209 619
## Veg_shannon_index 0.8992 1.3528 0.0495 0.4463 4.4810 1.0125 710
## total_shrub_cover 0.8422 1.4244 0.0526 0.4185 4.1856 1.0219 577
## Avg_Cogongrass_Cover 1.2467 2.4558 0.0507 0.4999 7.1048 1.0183 477
## Tree_Density 3.1669 5.7780 0.0736 1.3558 17.2203 1.0490 450
## Avg_Canopy_Cover 2.3253 3.1274 0.1329 1.3877 9.7251 1.0551 739
## avg_veg_height 0.3915 0.5696 0.0381 0.2266 1.6914 1.0487 1711
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9142 2.6498 0.0669 1.0451 8.436 1.0184 187
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6427 0.4602 -3.5028 -2.6550 -1.6756 1.0025 4523
## shrub_cover 0.2805 0.2549 -0.2185 0.2782 0.7977 1.0077 1974
## veg_height 0.0095 0.1521 -0.2944 0.0105 0.3213 1.0013 3520
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3078 1.4823 0.8168 1.9443 5.7785 1.0116 3036
## shrub_cover 0.4885 0.4106 0.0991 0.3815 1.5365 1.0174 2202
## veg_height 0.1913 0.1387 0.0554 0.1564 0.5333 1.0041 3564
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.1551 3.4451 3.3014 7.4942
## (Intercept)-Canis_latrans 0.8640 1.1044 -1.1514 0.7750
## (Intercept)-Sciurus_niger 1.5501 2.9326 -2.5595 1.0563
## (Intercept)-Procyon_lotor 0.9230 1.0112 -1.1314 0.9446
## (Intercept)-Dasypus_novemcinctus -1.4537 1.0547 -3.8950 -1.3512
## (Intercept)-Lynx_rufus 2.3021 2.9258 -1.8374 1.7446
## (Intercept)-Didelphis_virginiana -2.8479 1.2726 -5.7440 -2.7316
## (Intercept)-Sylvilagus_floridanus -1.1628 1.3044 -3.7959 -1.1485
## (Intercept)-Sciurus_carolinensis -3.0483 1.3882 -6.2064 -2.9003
## (Intercept)-Vulpes_vulpes -1.8782 2.4651 -5.9503 -2.1218
## (Intercept)-Sus_scrofa -4.2804 1.9988 -8.8328 -4.0807
## Cogon_Patch_Size-Odocoileus_virginianus -0.6709 1.3306 -3.2392 -0.7051
## Cogon_Patch_Size-Canis_latrans 0.6307 1.2681 -1.1370 0.3995
## Cogon_Patch_Size-Sciurus_niger -1.5276 1.7296 -5.5084 -1.3202
## Cogon_Patch_Size-Procyon_lotor -1.0840 0.7346 -2.6057 -1.0490
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6841 0.7340 -2.1417 -0.6815
## Cogon_Patch_Size-Lynx_rufus -0.9413 1.3846 -3.6351 -0.9350
## Cogon_Patch_Size-Didelphis_virginiana 0.7255 0.9258 -0.8378 0.6415
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9855 1.6317 -5.9929 -1.6492
## Cogon_Patch_Size-Sciurus_carolinensis -1.7938 1.4606 -5.5409 -1.5150
## Cogon_Patch_Size-Vulpes_vulpes -1.3736 1.8142 -5.5104 -1.1619
## Cogon_Patch_Size-Sus_scrofa -1.4224 1.5494 -5.2542 -1.1638
## Veg_shannon_index-Odocoileus_virginianus 0.7316 0.9194 -1.3025 0.7694
## Veg_shannon_index-Canis_latrans 1.2877 0.6917 0.0975 1.2234
## Veg_shannon_index-Sciurus_niger 1.0276 1.0113 -0.9084 1.0030
## Veg_shannon_index-Procyon_lotor 1.2135 0.6209 0.1081 1.1574
## Veg_shannon_index-Dasypus_novemcinctus 0.6418 0.5554 -0.4723 0.6472
## Veg_shannon_index-Lynx_rufus 0.8468 0.9366 -1.1329 0.8616
## Veg_shannon_index-Didelphis_virginiana 1.1057 0.7027 -0.1264 1.0362
## Veg_shannon_index-Sylvilagus_floridanus 1.0459 0.7071 -0.2288 0.9959
## Veg_shannon_index-Sciurus_carolinensis 0.2055 0.8029 -1.5278 0.3007
## Veg_shannon_index-Vulpes_vulpes 0.3522 0.9669 -1.8831 0.4765
## Veg_shannon_index-Sus_scrofa 1.5503 0.9839 0.0976 1.3720
## total_shrub_cover-Odocoileus_virginianus -0.0901 0.8546 -1.6204 -0.1385
## total_shrub_cover-Canis_latrans 0.3615 0.7797 -0.8298 0.2482
## total_shrub_cover-Sciurus_niger -0.5005 0.9594 -2.7079 -0.4294
## total_shrub_cover-Procyon_lotor -0.8274 0.6098 -2.1989 -0.7724
## total_shrub_cover-Dasypus_novemcinctus -0.1149 0.6085 -1.2905 -0.1043
## total_shrub_cover-Lynx_rufus -0.5150 1.0168 -2.7457 -0.4582
## total_shrub_cover-Didelphis_virginiana -0.5992 0.7398 -2.2382 -0.5337
## total_shrub_cover-Sylvilagus_floridanus -0.4354 0.8100 -2.1958 -0.3962
## total_shrub_cover-Sciurus_carolinensis -0.3323 0.7755 -2.0054 -0.2838
## total_shrub_cover-Vulpes_vulpes -0.5819 0.9831 -2.9179 -0.4974
## total_shrub_cover-Sus_scrofa -0.1318 0.8409 -1.7654 -0.1542
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9785 1.1034 -0.2041 1.9602
## Avg_Cogongrass_Cover-Canis_latrans 2.4330 1.0085 0.7644 2.3361
## Avg_Cogongrass_Cover-Sciurus_niger 1.4666 1.5194 -2.4556 1.6799
## Avg_Cogongrass_Cover-Procyon_lotor 2.2396 0.9108 0.6458 2.1718
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6192 1.0306 0.9491 2.4992
## Avg_Cogongrass_Cover-Lynx_rufus 2.3643 1.0488 0.5745 2.2603
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1446 0.8863 0.5846 2.0849
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4719 1.0026 -0.6379 1.5153
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3580 0.9617 0.6861 2.2533
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5072 1.1323 0.6671 2.3878
## Avg_Cogongrass_Cover-Sus_scrofa 1.6281 1.2455 -1.2492 1.7138
## Tree_Density-Odocoileus_virginianus -0.8091 1.2985 -2.9594 -0.9600
## Tree_Density-Canis_latrans -2.6597 1.3756 -6.0185 -2.3915
## Tree_Density-Sciurus_niger -1.8680 1.6021 -5.3377 -1.8225
## Tree_Density-Procyon_lotor -1.4926 0.8044 -3.0843 -1.4957
## Tree_Density-Dasypus_novemcinctus -3.5249 1.8880 -8.5652 -3.0705
## Tree_Density-Lynx_rufus -0.6245 1.5409 -3.0335 -0.8317
## Tree_Density-Didelphis_virginiana -2.2103 1.1967 -5.0406 -2.0471
## Tree_Density-Sylvilagus_floridanus -2.3601 1.3532 -5.4984 -2.1958
## Tree_Density-Sciurus_carolinensis -2.4376 1.5058 -6.0675 -2.2026
## Tree_Density-Vulpes_vulpes -1.7523 1.6147 -5.0288 -1.7578
## Tree_Density-Sus_scrofa -2.2738 1.6360 -6.4994 -1.9912
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3338 1.3375 -1.3798 1.3579
## Avg_Canopy_Cover-Canis_latrans 0.3174 0.6848 -1.0355 0.3065
## Avg_Canopy_Cover-Sciurus_niger 2.1142 1.5721 -0.9126 2.0004
## Avg_Canopy_Cover-Procyon_lotor 1.7734 0.7530 0.4585 1.7206
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1017 0.7291 0.8660 2.0374
## Avg_Canopy_Cover-Lynx_rufus 1.5747 1.4089 -0.9879 1.5072
## Avg_Canopy_Cover-Didelphis_virginiana 2.8833 1.1036 1.2689 2.6931
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3822 1.5940 1.2240 3.0565
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6208 1.1186 1.0538 2.4066
## Avg_Canopy_Cover-Vulpes_vulpes 2.2911 1.2788 0.3802 2.0961
## Avg_Canopy_Cover-Sus_scrofa 2.1636 0.9472 0.6278 2.0385
## avg_veg_height-Odocoileus_virginianus -0.5404 0.7178 -2.0310 -0.5213
## avg_veg_height-Canis_latrans -0.5388 0.5907 -1.7136 -0.5355
## avg_veg_height-Sciurus_niger -0.6456 0.7542 -2.2692 -0.6067
## avg_veg_height-Procyon_lotor -0.4322 0.5593 -1.5483 -0.4268
## avg_veg_height-Dasypus_novemcinctus -0.2953 0.5612 -1.3728 -0.3011
## avg_veg_height-Lynx_rufus -0.6157 0.7337 -2.1611 -0.5832
## avg_veg_height-Didelphis_virginiana -0.6374 0.6278 -1.9232 -0.6109
## avg_veg_height-Sylvilagus_floridanus -0.6725 0.6356 -1.9804 -0.6449
## avg_veg_height-Sciurus_carolinensis -0.2184 0.6390 -1.3909 -0.2505
## avg_veg_height-Vulpes_vulpes -0.4963 0.7136 -1.9086 -0.4829
## avg_veg_height-Sus_scrofa -0.5662 0.6510 -1.8754 -0.5494
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 16.7175 1.0039 320
## (Intercept)-Canis_latrans 3.2981 1.0027 1302
## (Intercept)-Sciurus_niger 8.0699 1.0256 227
## (Intercept)-Procyon_lotor 2.9065 1.0098 1322
## (Intercept)-Dasypus_novemcinctus 0.3659 1.0048 993
## (Intercept)-Lynx_rufus 9.5798 1.1248 194
## (Intercept)-Didelphis_virginiana -0.6870 1.0025 768
## (Intercept)-Sylvilagus_floridanus 1.4323 1.0086 1302
## (Intercept)-Sciurus_carolinensis -0.7343 1.0012 690
## (Intercept)-Vulpes_vulpes 3.9550 1.1403 223
## (Intercept)-Sus_scrofa -0.8104 1.0004 538
## Cogon_Patch_Size-Odocoileus_virginianus 2.1579 1.0129 1733
## Cogon_Patch_Size-Canis_latrans 3.8370 1.0007 938
## Cogon_Patch_Size-Sciurus_niger 1.2883 1.0209 696
## Cogon_Patch_Size-Procyon_lotor 0.2765 1.0033 610
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7723 1.0014 1062
## Cogon_Patch_Size-Lynx_rufus 1.9078 1.0121 891
## Cogon_Patch_Size-Didelphis_virginiana 2.8023 1.0014 942
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1456 1.0041 711
## Cogon_Patch_Size-Sciurus_carolinensis 0.1682 1.0061 701
## Cogon_Patch_Size-Vulpes_vulpes 1.6579 1.0391 507
## Cogon_Patch_Size-Sus_scrofa 0.8401 1.0082 964
## Veg_shannon_index-Odocoileus_virginianus 2.4993 1.0007 1549
## Veg_shannon_index-Canis_latrans 2.8413 1.0017 1056
## Veg_shannon_index-Sciurus_niger 3.1474 1.0040 744
## Veg_shannon_index-Procyon_lotor 2.6056 1.0025 496
## Veg_shannon_index-Dasypus_novemcinctus 1.7419 1.0129 1219
## Veg_shannon_index-Lynx_rufus 2.6283 1.0036 1029
## Veg_shannon_index-Didelphis_virginiana 2.6778 1.0000 1341
## Veg_shannon_index-Sylvilagus_floridanus 2.6032 1.0041 880
## Veg_shannon_index-Sciurus_carolinensis 1.5589 1.0063 1104
## Veg_shannon_index-Vulpes_vulpes 1.9391 1.0166 916
## Veg_shannon_index-Sus_scrofa 3.9468 1.0074 1250
## total_shrub_cover-Odocoileus_virginianus 1.7714 1.0198 1742
## total_shrub_cover-Canis_latrans 2.2675 1.0008 1028
## total_shrub_cover-Sciurus_niger 1.3196 1.0074 972
## total_shrub_cover-Procyon_lotor 0.2165 1.0054 1580
## total_shrub_cover-Dasypus_novemcinctus 1.0385 1.0027 1728
## total_shrub_cover-Lynx_rufus 1.4025 1.0227 816
## total_shrub_cover-Didelphis_virginiana 0.6948 1.0106 1275
## total_shrub_cover-Sylvilagus_floridanus 1.0891 1.0085 1264
## total_shrub_cover-Sciurus_carolinensis 1.0523 1.0244 1225
## total_shrub_cover-Vulpes_vulpes 1.0779 1.0122 1044
## total_shrub_cover-Sus_scrofa 1.6428 1.0048 1484
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.2224 1.0021 1007
## Avg_Cogongrass_Cover-Canis_latrans 4.6831 1.0039 492
## Avg_Cogongrass_Cover-Sciurus_niger 3.8546 1.0144 530
## Avg_Cogongrass_Cover-Procyon_lotor 4.2041 1.0020 579
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0211 1.0024 460
## Avg_Cogongrass_Cover-Lynx_rufus 4.7640 1.0010 821
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0699 1.0001 717
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3509 1.0034 889
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5481 1.0022 689
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.0721 1.0061 546
## Avg_Cogongrass_Cover-Sus_scrofa 3.8179 1.0047 959
## Tree_Density-Odocoileus_virginianus 2.3135 1.0509 822
## Tree_Density-Canis_latrans -0.6968 1.0096 512
## Tree_Density-Sciurus_niger 1.1839 1.0260 778
## Tree_Density-Procyon_lotor 0.0971 1.0110 1022
## Tree_Density-Dasypus_novemcinctus -1.2000 1.0083 348
## Tree_Density-Lynx_rufus 3.2196 1.0787 481
## Tree_Density-Didelphis_virginiana -0.2700 1.0036 749
## Tree_Density-Sylvilagus_floridanus -0.0888 1.0058 886
## Tree_Density-Sciurus_carolinensis -0.1433 1.0053 467
## Tree_Density-Vulpes_vulpes 1.5237 1.0156 648
## Tree_Density-Sus_scrofa 0.2348 1.0014 808
## Avg_Canopy_Cover-Odocoileus_virginianus 3.9841 1.0040 1357
## Avg_Canopy_Cover-Canis_latrans 1.6940 1.0065 1836
## Avg_Canopy_Cover-Sciurus_niger 5.4895 1.0041 692
## Avg_Canopy_Cover-Procyon_lotor 3.4199 1.0010 926
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7741 1.0020 770
## Avg_Canopy_Cover-Lynx_rufus 4.6505 1.0355 552
## Avg_Canopy_Cover-Didelphis_virginiana 5.6204 1.0112 584
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.2776 1.0213 587
## Avg_Canopy_Cover-Sciurus_carolinensis 5.3918 1.0068 660
## Avg_Canopy_Cover-Vulpes_vulpes 5.3331 1.0344 733
## Avg_Canopy_Cover-Sus_scrofa 4.4060 1.0023 1389
## avg_veg_height-Odocoileus_virginianus 0.8463 1.0038 1422
## avg_veg_height-Canis_latrans 0.6018 1.0031 1118
## avg_veg_height-Sciurus_niger 0.7424 1.0078 1211
## avg_veg_height-Procyon_lotor 0.6752 1.0051 1091
## avg_veg_height-Dasypus_novemcinctus 0.8504 1.0060 1255
## avg_veg_height-Lynx_rufus 0.7588 1.0055 1158
## avg_veg_height-Didelphis_virginiana 0.5712 1.0033 964
## avg_veg_height-Sylvilagus_floridanus 0.4903 1.0039 1165
## avg_veg_height-Sciurus_carolinensis 1.1238 1.0113 1238
## avg_veg_height-Vulpes_vulpes 0.8539 1.0064 942
## avg_veg_height-Sus_scrofa 0.6874 1.0043 1412
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0041 0.0591 -0.1124 0.0036 0.1220
## (Intercept)-Canis_latrans -2.7380 0.1836 -3.1141 -2.7289 -2.3907
## (Intercept)-Sciurus_niger -4.6722 0.5271 -5.6865 -4.6695 -3.6326
## (Intercept)-Procyon_lotor -2.2981 0.1441 -2.5993 -2.2914 -2.0291
## (Intercept)-Dasypus_novemcinctus -1.7306 0.1575 -2.0524 -1.7274 -1.4396
## (Intercept)-Lynx_rufus -3.9096 0.3802 -4.6255 -3.9233 -3.1335
## (Intercept)-Didelphis_virginiana -2.5045 0.2841 -3.0875 -2.4918 -1.9838
## (Intercept)-Sylvilagus_floridanus -3.1673 0.2672 -3.7166 -3.1546 -2.6743
## (Intercept)-Sciurus_carolinensis -2.6371 0.3258 -3.3132 -2.6244 -2.0341
## (Intercept)-Vulpes_vulpes -4.3302 0.7134 -5.7548 -4.3001 -3.0208
## (Intercept)-Sus_scrofa -3.2637 0.5943 -4.4063 -3.2535 -2.1360
## shrub_cover-Odocoileus_virginianus -0.0530 0.0641 -0.1797 -0.0527 0.0696
## shrub_cover-Canis_latrans -0.3272 0.2208 -0.7629 -0.3310 0.1098
## shrub_cover-Sciurus_niger -0.3427 0.4438 -1.2740 -0.3243 0.4793
## shrub_cover-Procyon_lotor 0.2646 0.1588 -0.0567 0.2671 0.5758
## shrub_cover-Dasypus_novemcinctus 0.8455 0.3023 0.2777 0.8395 1.4387
## shrub_cover-Lynx_rufus -0.2120 0.3556 -0.9135 -0.2101 0.5092
## shrub_cover-Didelphis_virginiana 0.8977 0.3495 0.2596 0.8775 1.6315
## shrub_cover-Sylvilagus_floridanus 0.4258 0.3918 -0.3361 0.4269 1.1803
## shrub_cover-Sciurus_carolinensis 0.8508 0.4036 0.1026 0.8316 1.6595
## shrub_cover-Vulpes_vulpes 0.1202 0.5363 -0.9457 0.1207 1.1992
## shrub_cover-Sus_scrofa 0.6027 0.7247 -0.7918 0.5675 2.1200
## veg_height-Odocoileus_virginianus -0.2947 0.0656 -0.4232 -0.2946 -0.1666
## veg_height-Canis_latrans -0.5770 0.1786 -0.9460 -0.5722 -0.2382
## veg_height-Sciurus_niger -0.0454 0.3447 -0.7311 -0.0483 0.6394
## veg_height-Procyon_lotor 0.3448 0.1253 0.1048 0.3445 0.5928
## veg_height-Dasypus_novemcinctus 0.2356 0.1287 -0.0136 0.2347 0.4924
## veg_height-Lynx_rufus 0.0939 0.2333 -0.3815 0.0993 0.5362
## veg_height-Didelphis_virginiana 0.4129 0.2293 -0.0140 0.4060 0.8895
## veg_height-Sylvilagus_floridanus 0.1528 0.2386 -0.3205 0.1508 0.6194
## veg_height-Sciurus_carolinensis 0.0840 0.2139 -0.3166 0.0787 0.5217
## veg_height-Vulpes_vulpes -0.1882 0.3235 -0.8457 -0.1763 0.4153
## veg_height-Sus_scrofa -0.1382 0.3239 -0.8194 -0.1256 0.4929
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0066 5250
## (Intercept)-Canis_latrans 1.0010 2080
## (Intercept)-Sciurus_niger 1.0100 396
## (Intercept)-Procyon_lotor 1.0006 3235
## (Intercept)-Dasypus_novemcinctus 1.0015 3226
## (Intercept)-Lynx_rufus 1.0804 459
## (Intercept)-Didelphis_virginiana 1.0016 2111
## (Intercept)-Sylvilagus_floridanus 1.0050 1820
## (Intercept)-Sciurus_carolinensis 1.0034 1477
## (Intercept)-Vulpes_vulpes 1.0169 409
## (Intercept)-Sus_scrofa 1.0059 1187
## shrub_cover-Odocoileus_virginianus 1.0008 5250
## shrub_cover-Canis_latrans 1.0002 2428
## shrub_cover-Sciurus_niger 1.0021 827
## shrub_cover-Procyon_lotor 1.0000 4108
## shrub_cover-Dasypus_novemcinctus 1.0021 2440
## shrub_cover-Lynx_rufus 1.0298 588
## shrub_cover-Didelphis_virginiana 1.0005 1662
## shrub_cover-Sylvilagus_floridanus 1.0039 1499
## shrub_cover-Sciurus_carolinensis 1.0159 1329
## shrub_cover-Vulpes_vulpes 1.0045 1652
## shrub_cover-Sus_scrofa 1.0013 1286
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0034 2406
## veg_height-Sciurus_niger 1.0126 1099
## veg_height-Procyon_lotor 1.0019 4121
## veg_height-Dasypus_novemcinctus 1.0015 4439
## veg_height-Lynx_rufus 1.0023 1972
## veg_height-Didelphis_virginiana 1.0000 2912
## veg_height-Sylvilagus_floridanus 1.0036 2539
## veg_height-Sciurus_carolinensis 1.0025 2823
## veg_height-Vulpes_vulpes 1.0021 1806
## veg_height-Sus_scrofa 1.0028 2979
# 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): 1.4985
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0116 0.6216 -1.2183 0.0082 1.2763 1.0088 1930
## Avg_Cogongrass_Cover 0.0330 0.3325 -0.6523 0.0472 0.6436 1.0143 1438
## total_shrub_cover -0.6270 0.4129 -1.5162 -0.6065 0.1075 1.0107 729
## avg_veg_height 0.1489 0.3182 -0.4852 0.1415 0.8059 1.0219 1033
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5041 3.0988 0.4260 2.6969 11.7352 1.0024 1511
## Avg_Cogongrass_Cover 0.3531 0.4556 0.0396 0.2138 1.4587 1.0210 1629
## total_shrub_cover 0.7041 0.9370 0.0518 0.4089 3.1143 1.0390 717
## avg_veg_height 0.2422 0.2794 0.0342 0.1549 0.9254 1.0217 2566
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2711 1.4334 0.0763 0.8436 4.8072 1.0612 381
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6323 0.4243 -3.4470 -2.6364 -1.7681 1.0026 3914
## shrub_cover 0.4341 0.2825 -0.1194 0.4309 1.0104 1.0028 1268
## veg_height -0.0117 0.1631 -0.3402 -0.0088 0.3060 1.0024 2697
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9636 1.2295 0.6928 1.6469 5.1607 1.0005 1879
## shrub_cover 0.5812 0.4785 0.1165 0.4502 1.8181 1.0130 1106
## veg_height 0.1981 0.1403 0.0542 0.1606 0.5582 1.0060 3416
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.5856 1.5345 0.8567 3.4448
## (Intercept)-Canis_latrans 0.5947 0.7627 -0.8502 0.5670
## (Intercept)-Sciurus_niger -0.4171 1.2161 -2.4701 -0.5350
## (Intercept)-Procyon_lotor 0.7795 0.7845 -0.7740 0.7660
## (Intercept)-Dasypus_novemcinctus -0.4821 0.7321 -1.8729 -0.5023
## (Intercept)-Lynx_rufus 0.1184 1.0896 -1.7310 0.0492
## (Intercept)-Didelphis_virginiana -0.9957 0.7936 -2.5203 -1.0078
## (Intercept)-Sylvilagus_floridanus 0.1730 0.9034 -1.4329 0.1207
## (Intercept)-Sciurus_carolinensis -1.0485 0.8446 -2.6769 -1.0401
## (Intercept)-Vulpes_vulpes -0.8013 1.3086 -3.0741 -0.9019
## (Intercept)-Sus_scrofa -1.3748 1.0897 -3.5399 -1.3753
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0132 0.5672 -1.1003 0.0180
## Avg_Cogongrass_Cover-Canis_latrans 0.3340 0.4941 -0.5731 0.3105
## Avg_Cogongrass_Cover-Sciurus_niger -0.3199 0.6721 -1.8537 -0.2536
## Avg_Cogongrass_Cover-Procyon_lotor -0.0393 0.4539 -0.9573 -0.0213
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1638 0.4156 -0.6332 0.1563
## Avg_Cogongrass_Cover-Lynx_rufus 0.3422 0.5215 -0.6087 0.3122
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1832 0.4798 -0.7692 0.1714
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3242 0.5450 -1.5124 -0.2757
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0907 0.4544 -0.8334 0.0894
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1672 0.5522 -0.8954 0.1514
## Avg_Cogongrass_Cover-Sus_scrofa -0.2051 0.6122 -1.6182 -0.1359
## total_shrub_cover-Odocoileus_virginianus -0.3621 0.6488 -1.5883 -0.3874
## total_shrub_cover-Canis_latrans 0.1360 0.6352 -0.9535 0.0798
## total_shrub_cover-Sciurus_niger -0.7796 0.7653 -2.4081 -0.7271
## total_shrub_cover-Procyon_lotor -1.1256 0.6103 -2.5440 -1.0390
## total_shrub_cover-Dasypus_novemcinctus -0.3268 0.5157 -1.5092 -0.2901
## total_shrub_cover-Lynx_rufus -1.0322 0.7859 -2.8350 -0.9660
## total_shrub_cover-Didelphis_virginiana -0.6471 0.5696 -1.9359 -0.5926
## total_shrub_cover-Sylvilagus_floridanus -1.0853 0.8534 -3.1417 -0.9464
## total_shrub_cover-Sciurus_carolinensis -0.6569 0.6526 -2.1330 -0.5866
## total_shrub_cover-Vulpes_vulpes -0.7429 0.8419 -2.6084 -0.6774
## total_shrub_cover-Sus_scrofa -0.4309 0.7879 -2.1018 -0.3985
## avg_veg_height-Odocoileus_virginianus 0.1183 0.5047 -0.9327 0.1180
## avg_veg_height-Canis_latrans 0.1628 0.4372 -0.6684 0.1475
## avg_veg_height-Sciurus_niger -0.0677 0.5756 -1.3737 -0.0346
## avg_veg_height-Procyon_lotor 0.1656 0.4360 -0.6894 0.1526
## avg_veg_height-Dasypus_novemcinctus 0.3144 0.4231 -0.4712 0.2956
## avg_veg_height-Lynx_rufus 0.1333 0.5106 -0.8546 0.1258
## avg_veg_height-Didelphis_virginiana 0.0695 0.4562 -0.8635 0.0705
## avg_veg_height-Sylvilagus_floridanus 0.0809 0.4777 -0.8681 0.0766
## avg_veg_height-Sciurus_carolinensis 0.4294 0.4655 -0.4046 0.3992
## avg_veg_height-Vulpes_vulpes 0.1124 0.5040 -0.9040 0.1185
## avg_veg_height-Sus_scrofa 0.1568 0.4814 -0.7877 0.1502
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1407 1.0017 957
## (Intercept)-Canis_latrans 2.2182 1.0017 2164
## (Intercept)-Sciurus_niger 2.3103 1.0219 606
## (Intercept)-Procyon_lotor 2.3518 1.0068 1917
## (Intercept)-Dasypus_novemcinctus 1.0085 1.0014 1789
## (Intercept)-Lynx_rufus 2.5158 1.0037 835
## (Intercept)-Didelphis_virginiana 0.6727 1.0040 1698
## (Intercept)-Sylvilagus_floridanus 2.0822 1.0012 1286
## (Intercept)-Sciurus_carolinensis 0.6446 1.0011 1339
## (Intercept)-Vulpes_vulpes 2.1087 1.0023 495
## (Intercept)-Sus_scrofa 0.7282 1.0132 774
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1688 1.0042 2713
## Avg_Cogongrass_Cover-Canis_latrans 1.4103 1.0011 2263
## Avg_Cogongrass_Cover-Sciurus_niger 0.8516 1.0094 1415
## Avg_Cogongrass_Cover-Procyon_lotor 0.8286 1.0096 2439
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9974 1.0208 2415
## Avg_Cogongrass_Cover-Lynx_rufus 1.4996 1.0023 1969
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1583 1.0147 2420
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6292 1.0058 1882
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9626 1.0048 2259
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2757 1.0053 1997
## Avg_Cogongrass_Cover-Sus_scrofa 0.8262 1.0114 1731
## total_shrub_cover-Odocoileus_virginianus 0.9911 1.0029 2709
## total_shrub_cover-Canis_latrans 1.5875 1.0022 1537
## total_shrub_cover-Sciurus_niger 0.6413 1.0064 1197
## total_shrub_cover-Procyon_lotor -0.1717 1.0180 1101
## total_shrub_cover-Dasypus_novemcinctus 0.5601 1.0028 1560
## total_shrub_cover-Lynx_rufus 0.3013 1.0314 890
## total_shrub_cover-Didelphis_virginiana 0.3245 1.0067 1192
## total_shrub_cover-Sylvilagus_floridanus 0.1969 1.0101 641
## total_shrub_cover-Sciurus_carolinensis 0.4489 1.0035 1004
## total_shrub_cover-Vulpes_vulpes 0.7703 1.0206 1032
## total_shrub_cover-Sus_scrofa 1.0489 1.0110 918
## avg_veg_height-Odocoileus_virginianus 1.1385 1.0106 2489
## avg_veg_height-Canis_latrans 1.0796 1.0135 2117
## avg_veg_height-Sciurus_niger 0.9904 1.0160 1602
## avg_veg_height-Procyon_lotor 1.0539 1.0043 2198
## avg_veg_height-Dasypus_novemcinctus 1.1833 1.0141 1708
## avg_veg_height-Lynx_rufus 1.1598 1.0145 1685
## avg_veg_height-Didelphis_virginiana 0.9564 1.0109 1866
## avg_veg_height-Sylvilagus_floridanus 1.0650 1.0041 1594
## avg_veg_height-Sciurus_carolinensis 1.4336 1.0055 1891
## avg_veg_height-Vulpes_vulpes 1.1185 1.0141 1720
## avg_veg_height-Sus_scrofa 1.1489 1.0039 1834
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0028 0.0601 -0.1158 0.0019 0.1211
## (Intercept)-Canis_latrans -2.7803 0.1937 -3.1669 -2.7747 -2.4173
## (Intercept)-Sciurus_niger -4.0579 0.6680 -5.3628 -4.0476 -2.8062
## (Intercept)-Procyon_lotor -2.2882 0.1388 -2.5730 -2.2855 -2.0260
## (Intercept)-Dasypus_novemcinctus -1.7693 0.1689 -2.1092 -1.7646 -1.4544
## (Intercept)-Lynx_rufus -3.5857 0.3613 -4.3301 -3.5719 -2.9179
## (Intercept)-Didelphis_virginiana -2.6498 0.3153 -3.2986 -2.6422 -2.0670
## (Intercept)-Sylvilagus_floridanus -3.2906 0.2862 -3.8696 -3.2792 -2.7530
## (Intercept)-Sciurus_carolinensis -2.7251 0.3329 -3.4318 -2.7120 -2.1219
## (Intercept)-Vulpes_vulpes -4.2316 0.7321 -5.7663 -4.1902 -2.9076
## (Intercept)-Sus_scrofa -3.4914 0.6196 -4.6845 -3.4973 -2.2539
## shrub_cover-Odocoileus_virginianus -0.0512 0.0636 -0.1751 -0.0528 0.0729
## shrub_cover-Canis_latrans -0.2611 0.2404 -0.7240 -0.2635 0.2039
## shrub_cover-Sciurus_niger -0.1195 0.5290 -1.1580 -0.1126 0.9292
## shrub_cover-Procyon_lotor 0.3151 0.1603 0.0037 0.3153 0.6294
## shrub_cover-Dasypus_novemcinctus 0.9696 0.3445 0.3496 0.9487 1.6800
## shrub_cover-Lynx_rufus 0.0471 0.3808 -0.7559 0.0649 0.7457
## shrub_cover-Didelphis_virginiana 1.1321 0.4024 0.3909 1.1118 1.9772
## shrub_cover-Sylvilagus_floridanus 0.6794 0.4290 -0.2051 0.6885 1.4933
## shrub_cover-Sciurus_carolinensis 1.0630 0.4247 0.2375 1.0517 1.9131
## shrub_cover-Vulpes_vulpes 0.2387 0.6164 -1.0335 0.2485 1.4380
## shrub_cover-Sus_scrofa 0.8976 0.8075 -0.7817 0.8909 2.5264
## veg_height-Odocoileus_virginianus -0.2947 0.0647 -0.4195 -0.2954 -0.1669
## veg_height-Canis_latrans -0.5900 0.1851 -0.9776 -0.5817 -0.2497
## veg_height-Sciurus_niger 0.0035 0.4333 -0.7979 -0.0166 0.9410
## veg_height-Procyon_lotor 0.3316 0.1232 0.0919 0.3305 0.5738
## veg_height-Dasypus_novemcinctus 0.2401 0.1378 -0.0250 0.2390 0.5186
## veg_height-Lynx_rufus 0.0195 0.2395 -0.4610 0.0237 0.4888
## veg_height-Didelphis_virginiana 0.3976 0.2459 -0.0592 0.3897 0.8964
## veg_height-Sylvilagus_floridanus 0.0468 0.2464 -0.4231 0.0474 0.5322
## veg_height-Sciurus_carolinensis 0.0693 0.2185 -0.3450 0.0657 0.5047
## veg_height-Vulpes_vulpes -0.1492 0.3283 -0.8404 -0.1325 0.4578
## veg_height-Sus_scrofa -0.1551 0.3268 -0.8216 -0.1497 0.4599
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0099 1820
## (Intercept)-Sciurus_niger 1.0347 475
## (Intercept)-Procyon_lotor 1.0006 4200
## (Intercept)-Dasypus_novemcinctus 1.0041 2264
## (Intercept)-Lynx_rufus 1.0013 912
## (Intercept)-Didelphis_virginiana 1.0033 1428
## (Intercept)-Sylvilagus_floridanus 1.0055 1362
## (Intercept)-Sciurus_carolinensis 1.0056 1174
## (Intercept)-Vulpes_vulpes 1.0037 448
## (Intercept)-Sus_scrofa 1.0405 692
## shrub_cover-Odocoileus_virginianus 1.0005 5250
## shrub_cover-Canis_latrans 1.0011 1851
## shrub_cover-Sciurus_niger 1.0128 933
## shrub_cover-Procyon_lotor 1.0018 4068
## shrub_cover-Dasypus_novemcinctus 1.0006 1490
## shrub_cover-Lynx_rufus 1.0011 978
## shrub_cover-Didelphis_virginiana 1.0046 1047
## shrub_cover-Sylvilagus_floridanus 1.0012 861
## shrub_cover-Sciurus_carolinensis 1.0036 1181
## shrub_cover-Vulpes_vulpes 1.0088 1070
## shrub_cover-Sus_scrofa 1.0255 700
## veg_height-Odocoileus_virginianus 1.0002 5250
## veg_height-Canis_latrans 1.0032 2360
## veg_height-Sciurus_niger 1.0063 1272
## veg_height-Procyon_lotor 1.0005 4358
## veg_height-Dasypus_novemcinctus 1.0029 4409
## veg_height-Lynx_rufus 1.0025 2355
## veg_height-Didelphis_virginiana 1.0012 2761
## veg_height-Sylvilagus_floridanus 1.0025 1820
## veg_height-Sciurus_carolinensis 1.0056 2904
## veg_height-Vulpes_vulpes 1.0021 1750
## veg_height-Sus_scrofa 1.0043 3187
# 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): 1.3685
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1083 0.5397 -1.1462 -0.1223 1.0179 1.0007 2911
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0282 2.4248 0.7891 2.3804 8.872 1.0167 2218
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5854 0.4213 -3.3971 -2.5877 -1.7393 1.0024 4345
## shrub_cover 0.2072 0.2466 -0.2730 0.2023 0.7096 1.0052 3122
## veg_height 0.0017 0.1531 -0.2993 0.0053 0.3008 1.0032 3960
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9518 1.2380 0.6604 1.6331 5.0060 1.0081 2232
## shrub_cover 0.4669 0.3773 0.0907 0.3698 1.4471 1.0150 1900
## veg_height 0.1881 0.1325 0.0545 0.1523 0.5402 1.0003 4204
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.3938 1.1203 1.7852 3.2047 6.0658
## (Intercept)-Canis_latrans 0.3950 0.4182 -0.3814 0.3721 1.2700
## (Intercept)-Sciurus_niger -0.4761 0.9570 -1.9789 -0.6077 1.8683
## (Intercept)-Procyon_lotor 0.7457 0.4068 -0.0307 0.7329 1.5858
## (Intercept)-Dasypus_novemcinctus -0.5719 0.3847 -1.3394 -0.5748 0.1908
## (Intercept)-Lynx_rufus 0.5487 0.9197 -0.7780 0.3985 2.8041
## (Intercept)-Didelphis_virginiana -1.2194 0.4587 -2.1533 -1.2040 -0.3689
## (Intercept)-Sylvilagus_floridanus -0.2966 0.5281 -1.2313 -0.3341 0.8073
## (Intercept)-Sciurus_carolinensis -1.2161 0.4800 -2.1991 -1.2057 -0.3187
## (Intercept)-Vulpes_vulpes -1.0058 1.0853 -2.7411 -1.1686 1.6780
## (Intercept)-Sus_scrofa -1.6785 0.6516 -2.9704 -1.6763 -0.4327
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0115 1617
## (Intercept)-Canis_latrans 0.9998 4613
## (Intercept)-Sciurus_niger 1.0167 572
## (Intercept)-Procyon_lotor 1.0030 4780
## (Intercept)-Dasypus_novemcinctus 1.0003 5250
## (Intercept)-Lynx_rufus 1.0028 776
## (Intercept)-Didelphis_virginiana 1.0040 4748
## (Intercept)-Sylvilagus_floridanus 1.0084 2581
## (Intercept)-Sciurus_carolinensis 1.0010 4312
## (Intercept)-Vulpes_vulpes 1.0074 489
## (Intercept)-Sus_scrofa 1.0011 2300
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0039 0.0596 -0.1132 0.0035 0.1192
## (Intercept)-Canis_latrans -2.7357 0.1856 -3.1263 -2.7275 -2.3864
## (Intercept)-Sciurus_niger -4.0767 0.6439 -5.3305 -4.0796 -2.8983
## (Intercept)-Procyon_lotor -2.2911 0.1444 -2.5879 -2.2868 -2.0236
## (Intercept)-Dasypus_novemcinctus -1.7106 0.1536 -2.0175 -1.7077 -1.4173
## (Intercept)-Lynx_rufus -3.7630 0.3729 -4.4808 -3.7673 -3.0344
## (Intercept)-Didelphis_virginiana -2.5077 0.2771 -3.0754 -2.4997 -1.9871
## (Intercept)-Sylvilagus_floridanus -3.1758 0.2992 -3.8109 -3.1564 -2.6348
## (Intercept)-Sciurus_carolinensis -2.5719 0.3040 -3.2093 -2.5551 -2.0315
## (Intercept)-Vulpes_vulpes -4.1300 0.7745 -5.7286 -4.0558 -2.8008
## (Intercept)-Sus_scrofa -3.2351 0.5984 -4.4575 -3.2109 -2.1080
## shrub_cover-Odocoileus_virginianus -0.0516 0.0645 -0.1769 -0.0525 0.0758
## shrub_cover-Canis_latrans -0.2899 0.2184 -0.7095 -0.2906 0.1494
## shrub_cover-Sciurus_niger -0.3400 0.4797 -1.3094 -0.3241 0.5725
## shrub_cover-Procyon_lotor 0.2438 0.1627 -0.0788 0.2454 0.5579
## shrub_cover-Dasypus_novemcinctus 0.7801 0.2851 0.2463 0.7731 1.3474
## shrub_cover-Lynx_rufus -0.3232 0.3422 -1.0242 -0.3175 0.3254
## shrub_cover-Didelphis_virginiana 0.8724 0.3460 0.2237 0.8607 1.5845
## shrub_cover-Sylvilagus_floridanus 0.2359 0.3961 -0.5198 0.2390 1.0241
## shrub_cover-Sciurus_carolinensis 0.7417 0.3878 0.0283 0.7270 1.5171
## shrub_cover-Vulpes_vulpes -0.0886 0.5296 -1.1616 -0.0819 0.9344
## shrub_cover-Sus_scrofa 0.5060 0.7268 -0.8820 0.4810 2.0043
## veg_height-Odocoileus_virginianus -0.2927 0.0639 -0.4230 -0.2921 -0.1688
## veg_height-Canis_latrans -0.5707 0.1806 -0.9423 -0.5623 -0.2282
## veg_height-Sciurus_niger -0.0615 0.3847 -0.7887 -0.0717 0.7518
## veg_height-Procyon_lotor 0.3332 0.1225 0.0917 0.3356 0.5771
## veg_height-Dasypus_novemcinctus 0.2264 0.1306 -0.0308 0.2245 0.4870
## veg_height-Lynx_rufus 0.0407 0.2349 -0.4395 0.0469 0.4938
## veg_height-Didelphis_virginiana 0.4067 0.2362 -0.0425 0.3985 0.8855
## veg_height-Sylvilagus_floridanus 0.1148 0.2401 -0.3686 0.1121 0.5874
## veg_height-Sciurus_carolinensis 0.0535 0.1987 -0.3277 0.0499 0.4544
## veg_height-Vulpes_vulpes -0.1140 0.3051 -0.7517 -0.1040 0.4671
## veg_height-Sus_scrofa -0.1232 0.3209 -0.7838 -0.1084 0.4804
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0012 5517
## (Intercept)-Canis_latrans 1.0046 2440
## (Intercept)-Sciurus_niger 1.0130 481
## (Intercept)-Procyon_lotor 1.0058 3822
## (Intercept)-Dasypus_novemcinctus 1.0039 4437
## (Intercept)-Lynx_rufus 1.0029 859
## (Intercept)-Didelphis_virginiana 1.0013 2809
## (Intercept)-Sylvilagus_floridanus 1.0036 1664
## (Intercept)-Sciurus_carolinensis 1.0017 2979
## (Intercept)-Vulpes_vulpes 1.0131 401
## (Intercept)-Sus_scrofa 1.0060 1591
## shrub_cover-Odocoileus_virginianus 1.0014 5250
## shrub_cover-Canis_latrans 1.0007 2657
## shrub_cover-Sciurus_niger 1.0044 1102
## shrub_cover-Procyon_lotor 1.0000 4671
## shrub_cover-Dasypus_novemcinctus 1.0039 3430
## shrub_cover-Lynx_rufus 1.0010 1359
## shrub_cover-Didelphis_virginiana 1.0012 2429
## shrub_cover-Sylvilagus_floridanus 1.0001 1832
## shrub_cover-Sciurus_carolinensis 1.0017 2495
## shrub_cover-Vulpes_vulpes 1.0005 1873
## shrub_cover-Sus_scrofa 1.0048 2567
## veg_height-Odocoileus_virginianus 1.0045 4831
## veg_height-Canis_latrans 1.0007 2460
## veg_height-Sciurus_niger 1.0060 1816
## veg_height-Procyon_lotor 1.0012 3869
## veg_height-Dasypus_novemcinctus 1.0014 4743
## veg_height-Lynx_rufus 1.0013 2339
## veg_height-Didelphis_virginiana 1.0013 3706
## veg_height-Sylvilagus_floridanus 1.0009 2490
## veg_height-Sciurus_carolinensis 1.0018 3659
## veg_height-Vulpes_vulpes 1.0029 2220
## veg_height-Sus_scrofa 1.0080 3802
#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): 1.4168
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1631 0.6322 -1.3513 -0.1894 1.1464 1.0214 1153
## Veg_shannon_index 0.3585 0.2642 -0.1444 0.3538 0.8886 1.0018 2247
## Avg_Cogongrass_Cover 0.3128 0.2640 -0.2285 0.3186 0.8137 1.0023 2333
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8517 3.6115 0.7512 2.9114 12.7057 1.0866 600
## Veg_shannon_index 0.3008 0.3566 0.0382 0.1946 1.1976 1.0040 1835
## Avg_Cogongrass_Cover 0.3180 0.4013 0.0349 0.1952 1.3955 1.0029 1617
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8243 0.8853 0.0619 0.541 3.2162 1.0388 528
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5883 0.4327 -3.4374 -2.5945 -1.7152 1.0018 5250
## shrub_cover 0.2034 0.2388 -0.2716 0.2044 0.6709 1.0011 3037
## veg_height -0.0077 0.1554 -0.3174 -0.0068 0.2989 1.0018 3405
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0299 1.3744 0.6811 1.6901 5.4073 1.0294 1309
## shrub_cover 0.4335 0.3389 0.0890 0.3427 1.3214 1.0030 2154
## veg_height 0.1897 0.1356 0.0529 0.1544 0.5216 1.0112 3497
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6065 1.4881 1.2684 3.4280
## (Intercept)-Canis_latrans 0.4050 0.6804 -0.8730 0.3916
## (Intercept)-Sciurus_niger -0.2833 1.3648 -2.3502 -0.4701
## (Intercept)-Procyon_lotor 0.5448 0.6592 -0.8095 0.5544
## (Intercept)-Dasypus_novemcinctus -0.6561 0.5946 -1.8595 -0.6424
## (Intercept)-Lynx_rufus 0.2389 1.0975 -1.5624 0.1369
## (Intercept)-Didelphis_virginiana -1.3536 0.6752 -2.7014 -1.3409
## (Intercept)-Sylvilagus_floridanus -0.3124 0.8082 -1.6668 -0.3648
## (Intercept)-Sciurus_carolinensis -1.3627 0.6942 -2.7836 -1.3353
## (Intercept)-Vulpes_vulpes -0.7867 1.6519 -3.1295 -1.0385
## (Intercept)-Sus_scrofa -2.0137 0.9156 -3.9195 -1.9859
## Veg_shannon_index-Odocoileus_virginianus 0.2968 0.4915 -0.7150 0.3083
## Veg_shannon_index-Canis_latrans 0.6332 0.4012 -0.1075 0.6121
## Veg_shannon_index-Sciurus_niger 0.3982 0.5518 -0.6295 0.3845
## Veg_shannon_index-Procyon_lotor 0.4599 0.3867 -0.2487 0.4381
## Veg_shannon_index-Dasypus_novemcinctus 0.1827 0.3446 -0.5229 0.1871
## Veg_shannon_index-Lynx_rufus 0.2101 0.5257 -0.9317 0.2239
## Veg_shannon_index-Didelphis_virginiana 0.5058 0.4018 -0.2434 0.4841
## Veg_shannon_index-Sylvilagus_floridanus 0.4463 0.4314 -0.3398 0.4233
## Veg_shannon_index-Sciurus_carolinensis -0.0046 0.4093 -0.8831 0.0243
## Veg_shannon_index-Vulpes_vulpes 0.1251 0.4930 -0.8940 0.1410
## Veg_shannon_index-Sus_scrofa 0.7255 0.5454 -0.1782 0.6657
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3061 0.4993 -0.6733 0.3011
## Avg_Cogongrass_Cover-Canis_latrans 0.6080 0.4195 -0.0904 0.5639
## Avg_Cogongrass_Cover-Sciurus_niger -0.0468 0.6263 -1.5057 0.0186
## Avg_Cogongrass_Cover-Procyon_lotor 0.3774 0.3748 -0.3146 0.3603
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4450 0.3377 -0.2104 0.4336
## Avg_Cogongrass_Cover-Lynx_rufus 0.5580 0.4438 -0.2091 0.5198
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4400 0.3734 -0.2852 0.4245
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0706 0.4653 -1.1063 -0.0296
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4221 0.3659 -0.3200 0.4180
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4118 0.4864 -0.4903 0.3903
## Avg_Cogongrass_Cover-Sus_scrofa 0.0285 0.5527 -1.2375 0.0887
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0050 1.0177 1009
## (Intercept)-Canis_latrans 1.8380 1.0029 2739
## (Intercept)-Sciurus_niger 2.7068 1.0464 420
## (Intercept)-Procyon_lotor 1.8405 1.0085 2321
## (Intercept)-Dasypus_novemcinctus 0.5190 1.0039 3409
## (Intercept)-Lynx_rufus 2.7513 1.0083 803
## (Intercept)-Didelphis_virginiana -0.0148 1.0018 2933
## (Intercept)-Sylvilagus_floridanus 1.3409 1.0226 1196
## (Intercept)-Sciurus_carolinensis -0.0436 1.0028 2374
## (Intercept)-Vulpes_vulpes 2.9028 1.1563 195
## (Intercept)-Sus_scrofa -0.2635 1.0032 1899
## Veg_shannon_index-Odocoileus_virginianus 1.2365 1.0015 3584
## Veg_shannon_index-Canis_latrans 1.4994 1.0016 2717
## Veg_shannon_index-Sciurus_niger 1.5193 1.0010 2320
## Veg_shannon_index-Procyon_lotor 1.2733 1.0022 2733
## Veg_shannon_index-Dasypus_novemcinctus 0.8486 1.0000 4257
## Veg_shannon_index-Lynx_rufus 1.2135 1.0008 2318
## Veg_shannon_index-Didelphis_virginiana 1.3673 1.0036 3764
## Veg_shannon_index-Sylvilagus_floridanus 1.3633 1.0061 3468
## Veg_shannon_index-Sciurus_carolinensis 0.7170 1.0000 3648
## Veg_shannon_index-Vulpes_vulpes 1.0478 1.0029 1709
## Veg_shannon_index-Sus_scrofa 2.0086 1.0018 2413
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3550 1.0009 3123
## Avg_Cogongrass_Cover-Canis_latrans 1.5597 1.0013 3269
## Avg_Cogongrass_Cover-Sciurus_niger 0.9881 1.0036 1394
## Avg_Cogongrass_Cover-Procyon_lotor 1.1737 1.0020 3739
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1373 1.0010 3686
## Avg_Cogongrass_Cover-Lynx_rufus 1.5654 1.0023 3259
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1880 1.0024 4009
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7342 1.0098 2069
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1713 1.0004 4143
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4278 1.0008 3048
## Avg_Cogongrass_Cover-Sus_scrofa 0.9580 1.0076 2031
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0041 0.0592 -0.1146 0.0050 0.1199
## (Intercept)-Canis_latrans -2.7353 0.1828 -3.1086 -2.7299 -2.3995
## (Intercept)-Sciurus_niger -4.1671 0.6503 -5.4031 -4.1554 -2.9318
## (Intercept)-Procyon_lotor -2.2956 0.1448 -2.5946 -2.2905 -2.0241
## (Intercept)-Dasypus_novemcinctus -1.7175 0.1569 -2.0418 -1.7088 -1.4225
## (Intercept)-Lynx_rufus -3.6915 0.3656 -4.4311 -3.6900 -3.0043
## (Intercept)-Didelphis_virginiana -2.5102 0.2857 -3.1025 -2.4926 -1.9820
## (Intercept)-Sylvilagus_floridanus -3.2183 0.3213 -3.9083 -3.1994 -2.6577
## (Intercept)-Sciurus_carolinensis -2.5747 0.3097 -3.2162 -2.5558 -2.0197
## (Intercept)-Vulpes_vulpes -4.2687 0.8118 -5.9158 -4.2132 -2.8909
## (Intercept)-Sus_scrofa -3.1900 0.5589 -4.3174 -3.1752 -2.1421
## shrub_cover-Odocoileus_virginianus -0.0521 0.0649 -0.1810 -0.0521 0.0749
## shrub_cover-Canis_latrans -0.2657 0.2135 -0.6888 -0.2666 0.1607
## shrub_cover-Sciurus_niger -0.3563 0.4602 -1.3059 -0.3409 0.5358
## shrub_cover-Procyon_lotor 0.2321 0.1719 -0.1255 0.2409 0.5513
## shrub_cover-Dasypus_novemcinctus 0.7828 0.2910 0.2448 0.7764 1.3673
## shrub_cover-Lynx_rufus -0.2512 0.3301 -0.9018 -0.2519 0.3812
## shrub_cover-Didelphis_virginiana 0.8633 0.3533 0.2060 0.8493 1.5945
## shrub_cover-Sylvilagus_floridanus 0.2188 0.3966 -0.5265 0.2146 1.0200
## shrub_cover-Sciurus_carolinensis 0.7482 0.3833 0.0398 0.7377 1.5535
## shrub_cover-Vulpes_vulpes -0.0750 0.5131 -1.1251 -0.0631 0.9414
## shrub_cover-Sus_scrofa 0.4480 0.6695 -0.8720 0.4414 1.8152
## veg_height-Odocoileus_virginianus -0.2927 0.0646 -0.4178 -0.2922 -0.1662
## veg_height-Canis_latrans -0.5818 0.1809 -0.9548 -0.5766 -0.2443
## veg_height-Sciurus_niger -0.0514 0.4005 -0.8226 -0.0602 0.7700
## veg_height-Procyon_lotor 0.3259 0.1211 0.0920 0.3269 0.5660
## veg_height-Dasypus_novemcinctus 0.2245 0.1313 -0.0282 0.2220 0.4900
## veg_height-Lynx_rufus 0.0059 0.2375 -0.4769 0.0086 0.4629
## veg_height-Didelphis_virginiana 0.3906 0.2364 -0.0400 0.3818 0.8873
## veg_height-Sylvilagus_floridanus 0.1186 0.2404 -0.3407 0.1164 0.5965
## veg_height-Sciurus_carolinensis 0.0465 0.2060 -0.3358 0.0384 0.4684
## veg_height-Vulpes_vulpes -0.1508 0.3183 -0.8146 -0.1412 0.4503
## veg_height-Sus_scrofa -0.1259 0.3169 -0.7654 -0.1218 0.4754
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0032 2543
## (Intercept)-Sciurus_niger 1.0149 421
## (Intercept)-Procyon_lotor 1.0016 3645
## (Intercept)-Dasypus_novemcinctus 0.9999 4240
## (Intercept)-Lynx_rufus 1.0000 836
## (Intercept)-Didelphis_virginiana 1.0001 2767
## (Intercept)-Sylvilagus_floridanus 1.0139 1165
## (Intercept)-Sciurus_carolinensis 1.0033 3004
## (Intercept)-Vulpes_vulpes 1.0677 254
## (Intercept)-Sus_scrofa 1.0037 1586
## shrub_cover-Odocoileus_virginianus 1.0083 5250
## shrub_cover-Canis_latrans 1.0072 2638
## shrub_cover-Sciurus_niger 1.0155 905
## shrub_cover-Procyon_lotor 1.0069 3295
## shrub_cover-Dasypus_novemcinctus 1.0005 3417
## shrub_cover-Lynx_rufus 1.0007 1349
## shrub_cover-Didelphis_virginiana 1.0042 2474
## shrub_cover-Sylvilagus_floridanus 1.0023 1841
## shrub_cover-Sciurus_carolinensis 1.0011 2604
## shrub_cover-Vulpes_vulpes 1.0017 1528
## shrub_cover-Sus_scrofa 1.0012 3061
## veg_height-Odocoileus_virginianus 1.0026 5250
## veg_height-Canis_latrans 1.0033 2440
## veg_height-Sciurus_niger 1.0000 1656
## veg_height-Procyon_lotor 1.0032 4505
## veg_height-Dasypus_novemcinctus 1.0001 4439
## veg_height-Lynx_rufus 1.0019 2320
## veg_height-Didelphis_virginiana 1.0039 3532
## veg_height-Sylvilagus_floridanus 1.0025 2626
## veg_height-Sciurus_carolinensis 1.0002 3950
## veg_height-Vulpes_vulpes 1.0017 2031
## veg_height-Sus_scrofa 1.0002 3719
# 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): 1.4493
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1083 0.6447 -1.3643 -0.1234 1.2175 1.0131 1759
## Cogon_Patch_Size -0.2531 0.4064 -1.1512 -0.2299 0.4871 1.0054 1615
## Avg_Cogongrass_Cover 0.2511 0.3058 -0.3414 0.2517 0.8652 1.0111 1062
## total_shrub_cover -0.5383 0.3821 -1.3865 -0.5093 0.1430 1.0329 671
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8865 3.7130 0.4789 2.9351 13.1171 1.0092 1033
## Cogon_Patch_Size 0.8659 1.1609 0.0612 0.5078 3.9025 1.0243 1226
## Avg_Cogongrass_Cover 0.3078 0.4007 0.0361 0.1848 1.2942 1.0043 2006
## total_shrub_cover 0.5773 0.7469 0.0485 0.3333 2.6825 1.0931 833
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6271 1.6848 0.1309 1.1563 5.8688 1.0686 386
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6227 0.4300 -3.4544 -2.6280 -1.7524 1.0005 3566
## shrub_cover 0.4059 0.2605 -0.0936 0.3967 0.9434 1.0043 1465
## veg_height -0.0030 0.1579 -0.3172 -0.0017 0.3066 1.0016 3301
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9165 1.2627 0.6602 1.6093 4.9112 1.0311 2941
## shrub_cover 0.5035 0.4120 0.1004 0.3880 1.6041 1.0127 1076
## veg_height 0.1918 0.1345 0.0543 0.1546 0.5398 1.0033 3553
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6030 1.6798 0.6289 3.4427
## (Intercept)-Canis_latrans 0.5817 0.8350 -0.9440 0.5234
## (Intercept)-Sciurus_niger -0.5575 1.2642 -2.6921 -0.6860
## (Intercept)-Procyon_lotor 0.6713 0.8050 -0.9180 0.6668
## (Intercept)-Dasypus_novemcinctus -0.5714 0.7673 -2.1201 -0.5805
## (Intercept)-Lynx_rufus -0.0619 1.0355 -1.9748 -0.1253
## (Intercept)-Didelphis_virginiana -1.1478 0.8680 -2.8471 -1.1547
## (Intercept)-Sylvilagus_floridanus -0.0871 0.9836 -1.8540 -0.1405
## (Intercept)-Sciurus_carolinensis -1.2875 0.8865 -3.0993 -1.2614
## (Intercept)-Vulpes_vulpes -1.0252 1.3188 -3.4213 -1.0824
## (Intercept)-Sus_scrofa -1.5598 1.1225 -3.8233 -1.5540
## Cogon_Patch_Size-Odocoileus_virginianus -0.0999 0.6803 -1.3572 -0.1371
## Cogon_Patch_Size-Canis_latrans 0.6280 0.7163 -0.4054 0.5116
## Cogon_Patch_Size-Sciurus_niger -0.6206 0.9136 -2.7399 -0.4952
## Cogon_Patch_Size-Procyon_lotor -0.2976 0.4724 -1.2781 -0.2811
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1165 0.4440 -1.0182 -0.1174
## Cogon_Patch_Size-Lynx_rufus -0.2415 0.7625 -1.6677 -0.2831
## Cogon_Patch_Size-Didelphis_virginiana 0.5266 0.4991 -0.3610 0.4908
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8987 0.8808 -3.1147 -0.7375
## Cogon_Patch_Size-Sciurus_carolinensis -0.7316 0.7265 -2.4501 -0.6183
## Cogon_Patch_Size-Vulpes_vulpes -0.5183 0.8468 -2.5006 -0.4307
## Cogon_Patch_Size-Sus_scrofa -0.4855 0.8148 -2.4180 -0.3763
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2322 0.5449 -0.8534 0.2233
## Avg_Cogongrass_Cover-Canis_latrans 0.3902 0.4373 -0.3865 0.3648
## Avg_Cogongrass_Cover-Sciurus_niger -0.0415 0.6407 -1.4395 0.0045
## Avg_Cogongrass_Cover-Procyon_lotor 0.2142 0.4409 -0.6496 0.2078
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4080 0.3987 -0.3505 0.3958
## Avg_Cogongrass_Cover-Lynx_rufus 0.5266 0.4854 -0.3182 0.4827
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2239 0.4274 -0.6342 0.2295
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0250 0.5201 -1.1281 -0.0014
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4450 0.4268 -0.3526 0.4284
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3595 0.4859 -0.5751 0.3453
## Avg_Cogongrass_Cover-Sus_scrofa 0.0616 0.5801 -1.1948 0.0966
## total_shrub_cover-Odocoileus_virginianus -0.3067 0.6349 -1.5580 -0.3190
## total_shrub_cover-Canis_latrans 0.0652 0.5707 -0.8877 0.0102
## total_shrub_cover-Sciurus_niger -0.6868 0.6824 -2.2284 -0.6330
## total_shrub_cover-Procyon_lotor -0.9884 0.5650 -2.2877 -0.9251
## total_shrub_cover-Dasypus_novemcinctus -0.2906 0.4925 -1.3639 -0.2575
## total_shrub_cover-Lynx_rufus -0.8988 0.7445 -2.6476 -0.8187
## total_shrub_cover-Didelphis_virginiana -0.6153 0.5483 -1.8408 -0.5618
## total_shrub_cover-Sylvilagus_floridanus -0.8936 0.8117 -2.8859 -0.7658
## total_shrub_cover-Sciurus_carolinensis -0.4795 0.5759 -1.8038 -0.4428
## total_shrub_cover-Vulpes_vulpes -0.6078 0.7887 -2.3593 -0.5530
## total_shrub_cover-Sus_scrofa -0.3469 0.7080 -1.7926 -0.3272
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4555 1.0066 828
## (Intercept)-Canis_latrans 2.3664 1.0070 1604
## (Intercept)-Sciurus_niger 2.3771 1.0020 634
## (Intercept)-Procyon_lotor 2.2852 1.0025 1811
## (Intercept)-Dasypus_novemcinctus 0.9845 1.0062 1732
## (Intercept)-Lynx_rufus 2.1799 1.0056 1168
## (Intercept)-Didelphis_virginiana 0.5808 1.0045 1663
## (Intercept)-Sylvilagus_floridanus 1.9655 1.0116 1182
## (Intercept)-Sciurus_carolinensis 0.4354 1.0027 1495
## (Intercept)-Vulpes_vulpes 1.8720 1.0281 532
## (Intercept)-Sus_scrofa 0.6799 1.0071 1021
## Cogon_Patch_Size-Odocoileus_virginianus 1.4122 1.0015 3415
## Cogon_Patch_Size-Canis_latrans 2.3213 1.0015 1774
## Cogon_Patch_Size-Sciurus_niger 0.8934 1.0034 1303
## Cogon_Patch_Size-Procyon_lotor 0.6032 1.0044 2572
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7629 1.0006 3654
## Cogon_Patch_Size-Lynx_rufus 1.3889 1.0042 2043
## Cogon_Patch_Size-Didelphis_virginiana 1.5731 1.0007 2055
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3351 1.0157 1476
## Cogon_Patch_Size-Sciurus_carolinensis 0.3477 1.0033 1632
## Cogon_Patch_Size-Vulpes_vulpes 0.9625 1.0026 1507
## Cogon_Patch_Size-Sus_scrofa 0.8165 1.0108 1961
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3719 1.0057 2263
## Avg_Cogongrass_Cover-Canis_latrans 1.3456 1.0018 2528
## Avg_Cogongrass_Cover-Sciurus_niger 1.1191 1.0045 1444
## Avg_Cogongrass_Cover-Procyon_lotor 1.1015 1.0048 2297
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2382 1.0004 2450
## Avg_Cogongrass_Cover-Lynx_rufus 1.6033 1.0010 2373
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0684 1.0084 2930
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9564 1.0083 1854
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3496 1.0073 2041
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3732 1.0013 2728
## Avg_Cogongrass_Cover-Sus_scrofa 1.1237 1.0029 1994
## total_shrub_cover-Odocoileus_virginianus 1.0170 1.0041 2301
## total_shrub_cover-Canis_latrans 1.4077 1.0117 1657
## total_shrub_cover-Sciurus_niger 0.5634 1.0296 1389
## total_shrub_cover-Procyon_lotor -0.0959 1.0327 1038
## total_shrub_cover-Dasypus_novemcinctus 0.5711 1.0101 1166
## total_shrub_cover-Lynx_rufus 0.3395 1.0365 1006
## total_shrub_cover-Didelphis_virginiana 0.3398 1.0086 1452
## total_shrub_cover-Sylvilagus_floridanus 0.2986 1.0612 541
## total_shrub_cover-Sciurus_carolinensis 0.5310 1.0151 1312
## total_shrub_cover-Vulpes_vulpes 0.8318 1.0100 877
## total_shrub_cover-Sus_scrofa 1.0547 1.0183 1020
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0053 0.0602 -0.1107 0.0044 0.1230
## (Intercept)-Canis_latrans -2.7544 0.1928 -3.1545 -2.7465 -2.3954
## (Intercept)-Sciurus_niger -3.9964 0.6599 -5.2559 -3.9862 -2.7459
## (Intercept)-Procyon_lotor -2.2935 0.1416 -2.5809 -2.2894 -2.0296
## (Intercept)-Dasypus_novemcinctus -1.7537 0.1714 -2.1051 -1.7458 -1.4428
## (Intercept)-Lynx_rufus -3.5465 0.3533 -4.2769 -3.5262 -2.8965
## (Intercept)-Didelphis_virginiana -2.5696 0.3026 -3.1853 -2.5520 -2.0223
## (Intercept)-Sylvilagus_floridanus -3.2727 0.2919 -3.8660 -3.2627 -2.7235
## (Intercept)-Sciurus_carolinensis -2.6934 0.3522 -3.4522 -2.6758 -2.0746
## (Intercept)-Vulpes_vulpes -4.1649 0.7222 -5.5709 -4.1579 -2.8008
## (Intercept)-Sus_scrofa -3.4758 0.5982 -4.6655 -3.4780 -2.2883
## shrub_cover-Odocoileus_virginianus -0.0512 0.0638 -0.1775 -0.0513 0.0714
## shrub_cover-Canis_latrans -0.2555 0.2324 -0.7151 -0.2525 0.1872
## shrub_cover-Sciurus_niger -0.0736 0.4966 -1.0766 -0.0700 0.9089
## shrub_cover-Procyon_lotor 0.3060 0.1601 -0.0182 0.3097 0.6155
## shrub_cover-Dasypus_novemcinctus 0.9191 0.3310 0.3004 0.9036 1.6060
## shrub_cover-Lynx_rufus 0.0491 0.3666 -0.7096 0.0650 0.7304
## shrub_cover-Didelphis_virginiana 1.0186 0.3943 0.3244 0.9929 1.8580
## shrub_cover-Sylvilagus_floridanus 0.6539 0.4180 -0.1779 0.6594 1.4711
## shrub_cover-Sciurus_carolinensis 0.9542 0.4213 0.1659 0.9397 1.7970
## shrub_cover-Vulpes_vulpes 0.2123 0.5875 -0.9998 0.2189 1.3478
## shrub_cover-Sus_scrofa 0.8343 0.7695 -0.7066 0.8242 2.3670
## veg_height-Odocoileus_virginianus -0.2940 0.0644 -0.4198 -0.2924 -0.1711
## veg_height-Canis_latrans -0.5731 0.1811 -0.9444 -0.5686 -0.2359
## veg_height-Sciurus_niger -0.0305 0.3987 -0.8031 -0.0338 0.8101
## veg_height-Procyon_lotor 0.3310 0.1217 0.0932 0.3324 0.5659
## veg_height-Dasypus_novemcinctus 0.2368 0.1365 -0.0253 0.2319 0.5117
## veg_height-Lynx_rufus 0.0275 0.2355 -0.4466 0.0321 0.4768
## veg_height-Didelphis_virginiana 0.3932 0.2389 -0.0691 0.3856 0.8806
## veg_height-Sylvilagus_floridanus 0.0499 0.2435 -0.4222 0.0458 0.5436
## veg_height-Sciurus_carolinensis 0.0874 0.2218 -0.3248 0.0798 0.5422
## veg_height-Vulpes_vulpes -0.1314 0.3118 -0.7918 -0.1165 0.4460
## veg_height-Sus_scrofa -0.1640 0.3217 -0.8188 -0.1535 0.4471
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0016 4553
## (Intercept)-Canis_latrans 1.0015 2200
## (Intercept)-Sciurus_niger 1.0072 562
## (Intercept)-Procyon_lotor 1.0021 3829
## (Intercept)-Dasypus_novemcinctus 1.0002 2899
## (Intercept)-Lynx_rufus 1.0146 1136
## (Intercept)-Didelphis_virginiana 1.0009 1588
## (Intercept)-Sylvilagus_floridanus 1.0001 1262
## (Intercept)-Sciurus_carolinensis 1.0030 1152
## (Intercept)-Vulpes_vulpes 1.0255 525
## (Intercept)-Sus_scrofa 1.0075 841
## shrub_cover-Odocoileus_virginianus 0.9998 4975
## shrub_cover-Canis_latrans 1.0016 2044
## shrub_cover-Sciurus_niger 1.0082 1159
## shrub_cover-Procyon_lotor 1.0015 2784
## shrub_cover-Dasypus_novemcinctus 1.0034 1510
## shrub_cover-Lynx_rufus 1.0155 1469
## shrub_cover-Didelphis_virginiana 1.0030 1015
## shrub_cover-Sylvilagus_floridanus 1.0123 882
## shrub_cover-Sciurus_carolinensis 1.0052 980
## shrub_cover-Vulpes_vulpes 1.0060 959
## shrub_cover-Sus_scrofa 1.0090 837
## veg_height-Odocoileus_virginianus 1.0014 5250
## veg_height-Canis_latrans 1.0020 2421
## veg_height-Sciurus_niger 1.0080 1618
## veg_height-Procyon_lotor 1.0005 4091
## veg_height-Dasypus_novemcinctus 1.0018 4138
## veg_height-Lynx_rufus 1.0064 2526
## veg_height-Didelphis_virginiana 1.0013 3050
## veg_height-Sylvilagus_floridanus 1.0085 1867
## veg_height-Sciurus_carolinensis 1.0048 2435
## veg_height-Vulpes_vulpes 1.0064 2177
## veg_height-Sus_scrofa 1.0012 2423
#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): 1.4473
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0991 0.7387 -1.4884 -0.1372 1.4972 1.0033 1425
## Tree_Density -0.7480 0.4025 -1.6396 -0.7149 -0.0393 1.0155 1352
## Avg_Canopy_Cover 1.0831 0.3714 0.3969 1.0568 1.8854 1.0060 1655
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.7049 6.0885 1.4021 4.9822 23.0358 1.0200 740
## Tree_Density 0.7095 1.1707 0.0442 0.3463 3.6808 1.0266 1119
## Avg_Canopy_Cover 0.7608 0.8451 0.0745 0.5198 2.9055 1.0043 1365
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4208 0.5273 0.0412 0.2513 1.731 1.0177 502
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6072 0.4465 -3.4401 -2.6281 -1.6746 1.0039 4721
## shrub_cover 0.2407 0.2426 -0.2448 0.2398 0.7245 1.0003 2963
## veg_height 0.0189 0.1564 -0.3024 0.0203 0.3245 1.0012 3741
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1156 1.3792 0.7192 1.7597 5.4312 1.0278 2656
## shrub_cover 0.4900 0.3951 0.1015 0.3911 1.5229 1.0081 2392
## veg_height 0.1966 0.1361 0.0567 0.1610 0.5577 1.0001 3628
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7206 1.8300 2.1422 4.3597 9.3267
## (Intercept)-Canis_latrans 0.4256 0.6307 -0.7410 0.4049 1.7450
## (Intercept)-Sciurus_niger 0.0987 1.4994 -2.2342 -0.1037 3.6702
## (Intercept)-Procyon_lotor 0.8240 0.6329 -0.3706 0.8167 2.0929
## (Intercept)-Dasypus_novemcinctus -0.9316 0.6141 -2.2229 -0.9061 0.1751
## (Intercept)-Lynx_rufus 1.6606 2.0886 -0.9196 1.2361 6.6145
## (Intercept)-Didelphis_virginiana -1.7050 0.7288 -3.2336 -1.6747 -0.3337
## (Intercept)-Sylvilagus_floridanus -0.5947 0.7192 -2.0226 -0.5923 0.8226
## (Intercept)-Sciurus_carolinensis -1.7799 0.7417 -3.3477 -1.7546 -0.4170
## (Intercept)-Vulpes_vulpes -1.3805 1.4510 -3.6918 -1.5660 1.9976
## (Intercept)-Sus_scrofa -2.5316 0.9918 -4.6789 -2.4645 -0.7543
## Tree_Density-Odocoileus_virginianus -0.3831 0.6941 -1.5431 -0.4528 1.1706
## Tree_Density-Canis_latrans -0.9173 0.5502 -2.1757 -0.8550 -0.0035
## Tree_Density-Sciurus_niger -0.7711 0.7872 -2.4979 -0.7279 0.7412
## Tree_Density-Procyon_lotor -0.4990 0.4168 -1.3336 -0.5014 0.2892
## Tree_Density-Dasypus_novemcinctus -1.3097 0.8466 -3.5137 -1.1348 -0.1909
## Tree_Density-Lynx_rufus -0.0922 0.7826 -1.3229 -0.1821 1.7579
## Tree_Density-Didelphis_virginiana -0.9771 0.7263 -2.7405 -0.8574 0.1242
## Tree_Density-Sylvilagus_floridanus -1.0046 0.7086 -2.6866 -0.9031 0.1233
## Tree_Density-Sciurus_carolinensis -0.9121 0.7206 -2.6801 -0.8077 0.2631
## Tree_Density-Vulpes_vulpes -0.6624 0.7879 -2.2823 -0.6348 0.8615
## Tree_Density-Sus_scrofa -0.9394 0.8027 -2.8128 -0.8227 0.3270
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8104 0.7391 -0.6967 0.8256 2.2708
## Avg_Canopy_Cover-Canis_latrans 0.0199 0.4823 -0.9232 0.0138 0.9807
## Avg_Canopy_Cover-Sciurus_niger 1.0694 0.8788 -0.5320 1.0196 3.0581
## Avg_Canopy_Cover-Procyon_lotor 1.0713 0.4832 0.2015 1.0453 2.1237
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0657 0.4494 0.2540 1.0414 2.0374
## Avg_Canopy_Cover-Lynx_rufus 1.0582 0.8273 -0.4402 0.9955 2.8420
## Avg_Canopy_Cover-Didelphis_virginiana 1.4378 0.6018 0.4752 1.3606 2.8348
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8847 0.8508 0.6203 1.7288 3.9928
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4066 0.5925 0.4448 1.3440 2.7798
## Avg_Canopy_Cover-Vulpes_vulpes 1.1378 0.6596 0.0181 1.0732 2.6590
## Avg_Canopy_Cover-Sus_scrofa 1.3277 0.5873 0.2703 1.2743 2.6288
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 696
## (Intercept)-Canis_latrans 1.0046 3036
## (Intercept)-Sciurus_niger 1.0464 463
## (Intercept)-Procyon_lotor 1.0058 2451
## (Intercept)-Dasypus_novemcinctus 1.0091 2892
## (Intercept)-Lynx_rufus 1.0596 287
## (Intercept)-Didelphis_virginiana 1.0084 2501
## (Intercept)-Sylvilagus_floridanus 1.0033 2787
## (Intercept)-Sciurus_carolinensis 1.0045 2458
## (Intercept)-Vulpes_vulpes 1.0243 433
## (Intercept)-Sus_scrofa 1.0095 1771
## Tree_Density-Odocoileus_virginianus 1.0022 1650
## Tree_Density-Canis_latrans 1.0052 2498
## Tree_Density-Sciurus_niger 1.0061 1797
## Tree_Density-Procyon_lotor 1.0022 2696
## Tree_Density-Dasypus_novemcinctus 1.0073 1279
## Tree_Density-Lynx_rufus 1.0019 1044
## Tree_Density-Didelphis_virginiana 1.0141 1493
## Tree_Density-Sylvilagus_floridanus 1.0057 1763
## Tree_Density-Sciurus_carolinensis 1.0072 1775
## Tree_Density-Vulpes_vulpes 1.0057 1345
## Tree_Density-Sus_scrofa 1.0111 1775
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0002 2696
## Avg_Canopy_Cover-Canis_latrans 1.0020 2389
## Avg_Canopy_Cover-Sciurus_niger 1.0075 1292
## Avg_Canopy_Cover-Procyon_lotor 1.0039 3860
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0026 3421
## Avg_Canopy_Cover-Lynx_rufus 1.0091 1276
## Avg_Canopy_Cover-Didelphis_virginiana 1.0006 2023
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0026 1302
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0041 2071
## Avg_Canopy_Cover-Vulpes_vulpes 1.0031 2496
## Avg_Canopy_Cover-Sus_scrofa 1.0008 2106
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0034 0.0594 -0.1112 0.0036 0.1181
## (Intercept)-Canis_latrans -2.7568 0.1926 -3.1519 -2.7532 -2.4008
## (Intercept)-Sciurus_niger -4.3596 0.6054 -5.5217 -4.3871 -3.1094
## (Intercept)-Procyon_lotor -2.2953 0.1433 -2.5864 -2.2921 -2.0308
## (Intercept)-Dasypus_novemcinctus -1.7244 0.1566 -2.0436 -1.7194 -1.4289
## (Intercept)-Lynx_rufus -3.9438 0.3592 -4.6169 -3.9568 -3.1999
## (Intercept)-Didelphis_virginiana -2.5537 0.2891 -3.1576 -2.5442 -2.0153
## (Intercept)-Sylvilagus_floridanus -3.1375 0.2718 -3.7073 -3.1243 -2.6404
## (Intercept)-Sciurus_carolinensis -2.6167 0.3167 -3.2562 -2.6064 -2.0308
## (Intercept)-Vulpes_vulpes -4.1729 0.7443 -5.6725 -4.1480 -2.8628
## (Intercept)-Sus_scrofa -3.1795 0.5798 -4.3589 -3.1723 -2.0960
## shrub_cover-Odocoileus_virginianus -0.0519 0.0639 -0.1730 -0.0530 0.0768
## shrub_cover-Canis_latrans -0.2829 0.2209 -0.7110 -0.2837 0.1508
## shrub_cover-Sciurus_niger -0.3527 0.4422 -1.2801 -0.3397 0.4874
## shrub_cover-Procyon_lotor 0.2488 0.1610 -0.0707 0.2511 0.5586
## shrub_cover-Dasypus_novemcinctus 0.8179 0.2897 0.2653 0.8121 1.3989
## shrub_cover-Lynx_rufus -0.3213 0.3089 -0.9604 -0.3127 0.2521
## shrub_cover-Didelphis_virginiana 0.9199 0.3495 0.2845 0.9120 1.6308
## shrub_cover-Sylvilagus_floridanus 0.3888 0.3736 -0.3124 0.3840 1.1357
## shrub_cover-Sciurus_carolinensis 0.8181 0.3930 0.0894 0.8136 1.6208
## shrub_cover-Vulpes_vulpes -0.0270 0.5286 -1.1017 -0.0129 0.9772
## shrub_cover-Sus_scrofa 0.4926 0.7199 -0.9531 0.4808 1.9386
## veg_height-Odocoileus_virginianus -0.2927 0.0643 -0.4183 -0.2931 -0.1652
## veg_height-Canis_latrans -0.5799 0.1838 -0.9580 -0.5744 -0.2334
## veg_height-Sciurus_niger -0.0474 0.3585 -0.7603 -0.0429 0.6699
## veg_height-Procyon_lotor 0.3389 0.1245 0.0965 0.3400 0.5833
## veg_height-Dasypus_novemcinctus 0.2383 0.1314 -0.0143 0.2362 0.5015
## veg_height-Lynx_rufus 0.0960 0.2293 -0.3718 0.1010 0.5294
## veg_height-Didelphis_virginiana 0.4417 0.2387 -0.0082 0.4387 0.9343
## veg_height-Sylvilagus_floridanus 0.1498 0.2321 -0.3211 0.1494 0.6046
## veg_height-Sciurus_carolinensis 0.0848 0.2108 -0.3222 0.0835 0.5054
## veg_height-Vulpes_vulpes -0.1228 0.3202 -0.8280 -0.1005 0.4487
## veg_height-Sus_scrofa -0.0989 0.3247 -0.7669 -0.0885 0.5143
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 0.9998 5250
## (Intercept)-Canis_latrans 1.0013 2145
## (Intercept)-Sciurus_niger 1.0367 421
## (Intercept)-Procyon_lotor 1.0000 3722
## (Intercept)-Dasypus_novemcinctus 1.0033 4208
## (Intercept)-Lynx_rufus 1.0103 525
## (Intercept)-Didelphis_virginiana 1.0009 2476
## (Intercept)-Sylvilagus_floridanus 1.0062 2075
## (Intercept)-Sciurus_carolinensis 1.0007 2345
## (Intercept)-Vulpes_vulpes 1.0105 405
## (Intercept)-Sus_scrofa 1.0007 1944
## shrub_cover-Odocoileus_virginianus 1.0003 5250
## shrub_cover-Canis_latrans 1.0020 2579
## shrub_cover-Sciurus_niger 1.0000 1286
## shrub_cover-Procyon_lotor 1.0030 3867
## shrub_cover-Dasypus_novemcinctus 1.0012 2840
## shrub_cover-Lynx_rufus 1.0017 1439
## shrub_cover-Didelphis_virginiana 1.0062 2385
## shrub_cover-Sylvilagus_floridanus 1.0014 1914
## shrub_cover-Sciurus_carolinensis 1.0067 2221
## shrub_cover-Vulpes_vulpes 1.0033 2067
## shrub_cover-Sus_scrofa 1.0007 2409
## veg_height-Odocoileus_virginianus 1.0054 4596
## veg_height-Canis_latrans 1.0066 2482
## veg_height-Sciurus_niger 1.0058 2131
## veg_height-Procyon_lotor 1.0011 4206
## veg_height-Dasypus_novemcinctus 1.0063 4447
## veg_height-Lynx_rufus 1.0024 2253
## veg_height-Didelphis_virginiana 1.0004 2914
## veg_height-Sylvilagus_floridanus 1.0036 3359
## veg_height-Sciurus_carolinensis 1.0014 3433
## veg_height-Vulpes_vulpes 1.0004 2183
## veg_height-Sus_scrofa 1.0031 4000
# 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): 1.3533
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9308 0.6098 -2.0760 -0.9507 0.3250 1.0010 2963
## Avg_Cogongrass_Cover -0.7972 0.3844 -1.5747 -0.7950 -0.0409 1.0020 1354
## I(Avg_Cogongrass_Cover^2) 0.8789 0.3315 0.2858 0.8562 1.5865 1.0016 1385
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6222 2.9614 0.7327 2.8554 11.3660 1.0003 2043
## Avg_Cogongrass_Cover 0.4372 0.5592 0.0446 0.2619 1.8452 1.0172 1998
## I(Avg_Cogongrass_Cover^2) 0.5046 1.3556 0.0361 0.2228 2.5961 1.1628 639
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5365 0.5741 0.0489 0.3515 2.0717 1.011 593
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5598 0.4204 -3.3759 -2.5652 -1.7016 1.0011 3677
## shrub_cover 0.2266 0.2356 -0.2472 0.2228 0.7040 1.0022 3113
## veg_height 0.0190 0.1549 -0.2896 0.0174 0.3297 1.0083 3281
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8159 1.1479 0.6436 1.5272 4.5955 1.0022 1968
## shrub_cover 0.4285 0.3709 0.0835 0.3328 1.3793 1.0026 1799
## veg_height 0.1865 0.1367 0.0516 0.1518 0.5376 1.0013 3447
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7470 1.3555 0.5436 2.5720
## (Intercept)-Canis_latrans -0.4564 0.6864 -1.8135 -0.4613
## (Intercept)-Sciurus_niger -0.9562 1.2002 -2.8590 -1.1036
## (Intercept)-Procyon_lotor -0.1489 0.6594 -1.4309 -0.1389
## (Intercept)-Dasypus_novemcinctus -1.3715 0.6246 -2.6218 -1.3573
## (Intercept)-Lynx_rufus -1.0345 1.0211 -2.8396 -1.0998
## (Intercept)-Didelphis_virginiana -1.8910 0.7123 -3.3223 -1.8701
## (Intercept)-Sylvilagus_floridanus -1.1033 0.7521 -2.6153 -1.0924
## (Intercept)-Sciurus_carolinensis -2.3787 0.7695 -3.9519 -2.3487
## (Intercept)-Vulpes_vulpes -2.2908 1.1788 -4.4348 -2.3236
## (Intercept)-Sus_scrofa -2.4431 0.9080 -4.2911 -2.4247
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.8007 0.6576 -2.1464 -0.7931
## Avg_Cogongrass_Cover-Canis_latrans -0.3979 0.5518 -1.3787 -0.4373
## Avg_Cogongrass_Cover-Sciurus_niger -1.1133 0.7029 -2.6921 -1.0456
## Avg_Cogongrass_Cover-Procyon_lotor -0.7236 0.5162 -1.7283 -0.7314
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5863 0.4824 -1.5060 -0.5982
## Avg_Cogongrass_Cover-Lynx_rufus -0.7242 0.5864 -1.9181 -0.7195
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5341 0.5406 -1.5697 -0.5560
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2519 0.6441 -2.7387 -1.1842
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8571 0.5441 -2.0002 -0.8322
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.8346 0.6333 -2.1359 -0.8258
## Avg_Cogongrass_Cover-Sus_scrofa -1.0727 0.6701 -2.5556 -1.0132
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.2021 0.8796 0.1168 1.0453
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2592 0.7140 0.2724 1.1089
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3871 0.6993 -1.2026 0.4410
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0759 0.6166 0.2303 0.9843
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7798 0.3619 0.0962 0.7679
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2300 0.5563 0.3619 1.1591
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6390 0.4192 -0.1429 0.6260
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7704 0.4395 -0.0231 0.7481
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0221 0.4070 0.3257 0.9838
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9924 0.5006 0.1843 0.9391
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4783 0.6312 -1.0136 0.5431
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 5.7905 1.0027 1568
## (Intercept)-Canis_latrans 0.9301 1.0043 2920
## (Intercept)-Sciurus_niger 1.7881 1.0255 648
## (Intercept)-Procyon_lotor 1.1660 1.0060 2837
## (Intercept)-Dasypus_novemcinctus -0.1717 1.0000 3161
## (Intercept)-Lynx_rufus 1.1423 1.0126 922
## (Intercept)-Didelphis_virginiana -0.5517 1.0037 3463
## (Intercept)-Sylvilagus_floridanus 0.3289 1.0066 2561
## (Intercept)-Sciurus_carolinensis -0.9578 1.0107 2781
## (Intercept)-Vulpes_vulpes 0.2653 1.0159 851
## (Intercept)-Sus_scrofa -0.7423 1.0002 2106
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5045 1.0008 2560
## Avg_Cogongrass_Cover-Canis_latrans 0.7655 1.0016 2392
## Avg_Cogongrass_Cover-Sciurus_niger 0.1108 1.0099 1622
## Avg_Cogongrass_Cover-Procyon_lotor 0.3243 1.0032 2663
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3800 1.0017 2648
## Avg_Cogongrass_Cover-Lynx_rufus 0.4553 1.0014 2176
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5867 1.0003 2049
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1722 1.0073 1727
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1495 1.0046 2066
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4037 1.0019 1986
## Avg_Cogongrass_Cover-Sus_scrofa 0.0883 1.0014 1856
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2962 1.0361 751
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0530 1.0043 1121
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.5753 1.0068 938
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4649 1.0085 821
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5083 1.0012 2421
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5143 1.0027 1370
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5625 1.0024 1903
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6994 1.0024 2169
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.9376 1.0007 2058
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.1294 1.0127 1882
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5124 1.0074 999
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0038 0.0602 -0.1136 0.0052 0.1186
## (Intercept)-Canis_latrans -2.7373 0.1844 -3.1156 -2.7313 -2.3880
## (Intercept)-Sciurus_niger -4.0319 0.6543 -5.3212 -4.0222 -2.7709
## (Intercept)-Procyon_lotor -2.3096 0.1479 -2.6104 -2.3054 -2.0333
## (Intercept)-Dasypus_novemcinctus -1.7117 0.1575 -2.0269 -1.7064 -1.4162
## (Intercept)-Lynx_rufus -3.5815 0.3715 -4.3587 -3.5637 -2.8871
## (Intercept)-Didelphis_virginiana -2.5353 0.2895 -3.1231 -2.5265 -1.9947
## (Intercept)-Sylvilagus_floridanus -3.1810 0.2944 -3.8000 -3.1683 -2.6483
## (Intercept)-Sciurus_carolinensis -2.5713 0.3022 -3.1980 -2.5542 -2.0149
## (Intercept)-Vulpes_vulpes -3.9829 0.7307 -5.5281 -3.9371 -2.6898
## (Intercept)-Sus_scrofa -3.1892 0.5799 -4.3869 -3.1861 -2.0705
## shrub_cover-Odocoileus_virginianus -0.0523 0.0637 -0.1770 -0.0525 0.0727
## shrub_cover-Canis_latrans -0.2442 0.2152 -0.6701 -0.2440 0.1658
## shrub_cover-Sciurus_niger -0.2956 0.4515 -1.2193 -0.2938 0.5969
## shrub_cover-Procyon_lotor 0.2339 0.1656 -0.1026 0.2376 0.5439
## shrub_cover-Dasypus_novemcinctus 0.7713 0.2864 0.2312 0.7621 1.3672
## shrub_cover-Lynx_rufus -0.2051 0.3564 -0.9257 -0.1955 0.4729
## shrub_cover-Didelphis_virginiana 0.8867 0.3707 0.2303 0.8579 1.6834
## shrub_cover-Sylvilagus_floridanus 0.2215 0.3832 -0.5015 0.2019 0.9995
## shrub_cover-Sciurus_carolinensis 0.7318 0.3803 0.0278 0.7170 1.5193
## shrub_cover-Vulpes_vulpes -0.0258 0.5225 -1.1190 -0.0098 0.9841
## shrub_cover-Sus_scrofa 0.4562 0.6945 -0.8932 0.4187 1.9097
## veg_height-Odocoileus_virginianus -0.2921 0.0640 -0.4159 -0.2916 -0.1688
## veg_height-Canis_latrans -0.5619 0.1787 -0.9222 -0.5564 -0.2241
## veg_height-Sciurus_niger 0.0459 0.4037 -0.6943 0.0312 0.9083
## veg_height-Procyon_lotor 0.3432 0.1234 0.1035 0.3384 0.5845
## veg_height-Dasypus_novemcinctus 0.2310 0.1313 -0.0204 0.2273 0.4936
## veg_height-Lynx_rufus 0.0765 0.2380 -0.4050 0.0811 0.5335
## veg_height-Didelphis_virginiana 0.3814 0.2436 -0.0734 0.3780 0.8648
## veg_height-Sylvilagus_floridanus 0.1560 0.2462 -0.3157 0.1560 0.6504
## veg_height-Sciurus_carolinensis 0.0597 0.2069 -0.3295 0.0532 0.4807
## veg_height-Vulpes_vulpes -0.1082 0.2947 -0.7408 -0.0972 0.4235
## veg_height-Sus_scrofa -0.1074 0.3203 -0.7543 -0.1000 0.5052
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 5250
## (Intercept)-Canis_latrans 0.9998 2749
## (Intercept)-Sciurus_niger 1.0238 485
## (Intercept)-Procyon_lotor 1.0014 3074
## (Intercept)-Dasypus_novemcinctus 1.0028 4505
## (Intercept)-Lynx_rufus 1.0000 840
## (Intercept)-Didelphis_virginiana 1.0017 2400
## (Intercept)-Sylvilagus_floridanus 1.0248 1653
## (Intercept)-Sciurus_carolinensis 1.0015 2508
## (Intercept)-Vulpes_vulpes 1.0157 552
## (Intercept)-Sus_scrofa 1.0017 1699
## shrub_cover-Odocoileus_virginianus 1.0027 5697
## shrub_cover-Canis_latrans 1.0036 2606
## shrub_cover-Sciurus_niger 1.0091 1153
## shrub_cover-Procyon_lotor 1.0009 3675
## shrub_cover-Dasypus_novemcinctus 1.0013 3288
## shrub_cover-Lynx_rufus 1.0008 1247
## shrub_cover-Didelphis_virginiana 1.0016 1910
## shrub_cover-Sylvilagus_floridanus 1.0008 2019
## shrub_cover-Sciurus_carolinensis 1.0027 2583
## shrub_cover-Vulpes_vulpes 1.0026 2088
## shrub_cover-Sus_scrofa 1.0005 2158
## veg_height-Odocoileus_virginianus 1.0020 5692
## veg_height-Canis_latrans 1.0040 2753
## veg_height-Sciurus_niger 1.0110 1657
## veg_height-Procyon_lotor 1.0012 3998
## veg_height-Dasypus_novemcinctus 1.0002 4722
## veg_height-Lynx_rufus 1.0005 2449
## veg_height-Didelphis_virginiana 1.0075 2943
## veg_height-Sylvilagus_floridanus 1.0009 2198
## veg_height-Sciurus_carolinensis 1.0063 3495
## veg_height-Vulpes_vulpes 1.0080 2258
## veg_height-Sus_scrofa 1.0033 3143
# 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): 1.5355
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8713 1.1372 -2.9871 -0.9175 1.4765 1.0001 1609
## Cogon_Patch_Size -0.2817 0.7824 -1.8776 -0.2523 1.1942 1.0011 1083
## Veg_shannon_index 0.9640 0.5131 0.0381 0.9309 2.0922 1.0030 637
## total_shrub_cover -0.5325 0.5041 -1.5800 -0.5120 0.3965 1.0350 956
## Avg_Cogongrass_Cover -0.0737 0.9823 -2.0604 -0.0614 1.8053 1.0430 390
## Tree_Density -2.0372 0.8876 -3.8656 -2.0265 -0.2667 1.0191 786
## Avg_Canopy_Cover 1.9852 0.7304 0.6712 1.9415 3.5129 1.0120 747
## I(Avg_Cogongrass_Cover^2) 1.6156 0.6214 0.5322 1.5635 2.9877 1.0218 365
## avg_veg_height -0.1664 0.5190 -1.2387 -0.1501 0.8443 1.0215 702
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 24.2353 24.1254 4.1822 17.2375 85.9239 1.0165 413
## Cogon_Patch_Size 4.4053 6.8808 0.1658 2.3489 20.5053 1.0484 445
## Veg_shannon_index 0.9156 1.4549 0.0519 0.4556 4.7043 1.0331 837
## total_shrub_cover 0.9439 1.5691 0.0581 0.4989 4.4581 1.0400 694
## Avg_Cogongrass_Cover 1.4817 2.6934 0.0537 0.5779 8.5136 1.0226 654
## Tree_Density 5.1020 9.7786 0.0765 1.9649 30.7372 1.0261 261
## Avg_Canopy_Cover 3.8191 5.3183 0.1820 2.1107 18.3254 1.0753 343
## I(Avg_Cogongrass_Cover^2) 1.0481 2.0987 0.0477 0.4176 5.9475 1.0537 746
## avg_veg_height 0.5124 0.7218 0.0424 0.2740 2.4486 1.0310 1751
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7245 2.7075 0.057 0.7905 9.1524 1.0249 203
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6351 0.4603 -3.4885 -2.6454 -1.6857 1.0000 4916
## shrub_cover 0.3162 0.2510 -0.1726 0.3086 0.8208 1.0051 1909
## veg_height 0.0169 0.1570 -0.3103 0.0210 0.3233 1.0094 2463
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2430 1.4075 0.7713 1.8952 5.7187 1.0047 2764
## shrub_cover 0.4913 0.3739 0.1046 0.3904 1.4900 1.0061 1534
## veg_height 0.1921 0.1363 0.0557 0.1578 0.5478 1.0124 3426
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 8.0967 4.0299 2.6140
## (Intercept)-Canis_latrans -0.8504 1.2880 -3.4291
## (Intercept)-Sciurus_niger 1.0780 2.9066 -3.2199
## (Intercept)-Procyon_lotor -0.2557 1.2022 -2.6763
## (Intercept)-Dasypus_novemcinctus -2.7605 1.3967 -5.9206
## (Intercept)-Lynx_rufus 0.6389 2.7436 -3.7634
## (Intercept)-Didelphis_virginiana -4.2086 1.5814 -7.7695
## (Intercept)-Sylvilagus_floridanus -2.5319 1.6019 -5.9766
## (Intercept)-Sciurus_carolinensis -4.8786 1.8057 -8.9709
## (Intercept)-Vulpes_vulpes -4.2990 2.7510 -9.5910
## (Intercept)-Sus_scrofa -5.8180 2.3878 -11.6198
## Cogon_Patch_Size-Odocoileus_virginianus -0.0543 1.6077 -3.0983
## Cogon_Patch_Size-Canis_latrans 1.7863 1.7304 -0.4282
## Cogon_Patch_Size-Sciurus_niger -1.1432 2.1102 -6.0713
## Cogon_Patch_Size-Procyon_lotor -0.5596 0.8808 -2.3333
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1115 0.8667 -1.8882
## Cogon_Patch_Size-Lynx_rufus -0.4377 1.6647 -3.6726
## Cogon_Patch_Size-Didelphis_virginiana 1.6604 1.1489 -0.2010
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6523 1.8638 -6.3005
## Cogon_Patch_Size-Sciurus_carolinensis -1.3536 1.6254 -5.3346
## Cogon_Patch_Size-Vulpes_vulpes -0.8392 1.8848 -5.0846
## Cogon_Patch_Size-Sus_scrofa -1.0449 1.7785 -5.4227
## Veg_shannon_index-Odocoileus_virginianus 0.7940 0.9366 -1.1938
## Veg_shannon_index-Canis_latrans 1.3538 0.7666 0.1240
## Veg_shannon_index-Sciurus_niger 1.0867 1.0701 -0.9314
## Veg_shannon_index-Procyon_lotor 1.1959 0.6546 0.0999
## Veg_shannon_index-Dasypus_novemcinctus 0.6124 0.6160 -0.6295
## Veg_shannon_index-Lynx_rufus 1.0828 1.0091 -0.7420
## Veg_shannon_index-Didelphis_virginiana 1.1554 0.7510 -0.2186
## Veg_shannon_index-Sylvilagus_floridanus 1.0743 0.7734 -0.3477
## Veg_shannon_index-Sciurus_carolinensis 0.3141 0.8814 -1.6844
## Veg_shannon_index-Vulpes_vulpes 0.6479 0.9013 -1.3030
## Veg_shannon_index-Sus_scrofa 1.6222 1.0644 0.0870
## total_shrub_cover-Odocoileus_virginianus -0.3083 0.9177 -2.0563
## total_shrub_cover-Canis_latrans 0.1275 0.7637 -1.1667
## total_shrub_cover-Sciurus_niger -0.7465 1.0043 -2.9981
## total_shrub_cover-Procyon_lotor -1.1293 0.6736 -2.6225
## total_shrub_cover-Dasypus_novemcinctus -0.2419 0.6783 -1.6749
## total_shrub_cover-Lynx_rufus -0.7883 1.0447 -3.1495
## total_shrub_cover-Didelphis_virginiana -0.8287 0.8664 -2.8301
## total_shrub_cover-Sylvilagus_floridanus -0.6382 0.8945 -2.5910
## total_shrub_cover-Sciurus_carolinensis -0.4554 0.8201 -2.1883
## total_shrub_cover-Vulpes_vulpes -0.7711 0.9842 -3.0376
## total_shrub_cover-Sus_scrofa -0.3239 0.9386 -2.2192
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0902 1.4239 -2.8298
## Avg_Cogongrass_Cover-Canis_latrans 0.1374 1.2670 -2.3382
## Avg_Cogongrass_Cover-Sciurus_niger -0.5341 1.6905 -4.2869
## Avg_Cogongrass_Cover-Procyon_lotor 0.0526 1.2116 -2.2692
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6476 1.3874 -1.7454
## Avg_Cogongrass_Cover-Lynx_rufus 0.0273 1.3757 -2.6591
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0479 1.2778 -2.5794
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7213 1.4082 -3.9283
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0556 1.2782 -2.6523
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1410 1.3824 -2.6138
## Avg_Cogongrass_Cover-Sus_scrofa -0.4423 1.4675 -3.6281
## Tree_Density-Odocoileus_virginianus -0.8543 1.6511 -3.4802
## Tree_Density-Canis_latrans -3.0398 1.6090 -7.0165
## Tree_Density-Sciurus_niger -2.0737 1.9052 -6.2028
## Tree_Density-Procyon_lotor -1.9901 1.0259 -4.1596
## Tree_Density-Dasypus_novemcinctus -4.3301 2.6052 -10.9959
## Tree_Density-Lynx_rufus -0.5993 1.9063 -3.5248
## Tree_Density-Didelphis_virginiana -2.4051 1.4224 -5.6936
## Tree_Density-Sylvilagus_floridanus -2.7254 1.7277 -6.9178
## Tree_Density-Sciurus_carolinensis -2.8563 1.8577 -7.6849
## Tree_Density-Vulpes_vulpes -2.0025 2.0922 -6.1765
## Tree_Density-Sus_scrofa -2.6885 2.0405 -7.8213
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2131 1.5962 -2.1612
## Avg_Canopy_Cover-Canis_latrans 0.1381 0.7362 -1.3406
## Avg_Canopy_Cover-Sciurus_niger 2.5919 2.0738 -0.8928
## Avg_Canopy_Cover-Procyon_lotor 1.6855 0.8573 0.1624
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2039 0.9486 0.7470
## Avg_Canopy_Cover-Lynx_rufus 1.7221 1.7140 -1.2614
## Avg_Canopy_Cover-Didelphis_virginiana 3.2822 1.5630 1.2161
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9686 1.9912 1.2446
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9945 1.5097 0.9900
## Avg_Canopy_Cover-Vulpes_vulpes 2.7074 1.5998 0.4745
## Avg_Canopy_Cover-Sus_scrofa 2.2498 1.1051 0.5120
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8854 1.1079 0.1128
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0116 0.9523 0.5524
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2714 1.3055 -1.7025
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9672 0.9364 0.5640
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5694 0.7867 0.1927
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1739 1.0626 0.6450
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2539 0.7668 -0.1913
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3608 0.8713 -0.2519
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8332 0.8600 0.4729
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9713 0.9823 0.5070
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1903 1.1345 -1.3586
## avg_veg_height-Odocoileus_virginianus -0.1655 0.8378 -1.9191
## avg_veg_height-Canis_latrans -0.2744 0.6484 -1.6503
## avg_veg_height-Sciurus_niger -0.3024 0.8610 -2.1868
## avg_veg_height-Procyon_lotor 0.0197 0.6641 -1.3117
## avg_veg_height-Dasypus_novemcinctus 0.1587 0.6477 -1.0170
## avg_veg_height-Lynx_rufus -0.3319 0.8438 -2.1712
## avg_veg_height-Didelphis_virginiana -0.3404 0.7411 -1.9448
## avg_veg_height-Sylvilagus_floridanus -0.2700 0.7329 -1.8113
## avg_veg_height-Sciurus_carolinensis 0.1377 0.7218 -1.1651
## avg_veg_height-Vulpes_vulpes -0.2600 0.8258 -2.0153
## avg_veg_height-Sus_scrofa -0.1895 0.7524 -1.7616
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.2717 18.2773 1.0051 308
## (Intercept)-Canis_latrans -0.8919 1.7677 1.0051 1618
## (Intercept)-Sciurus_niger 0.5821 8.5089 1.0297 249
## (Intercept)-Procyon_lotor -0.2374 2.1677 1.0014 1359
## (Intercept)-Dasypus_novemcinctus -2.5919 -0.5501 1.0065 489
## (Intercept)-Lynx_rufus 0.3076 7.1669 1.0733 283
## (Intercept)-Didelphis_virginiana -4.0505 -1.5026 1.0000 686
## (Intercept)-Sylvilagus_floridanus -2.4240 0.3847 1.0097 645
## (Intercept)-Sciurus_carolinensis -4.6704 -1.9027 1.0091 297
## (Intercept)-Vulpes_vulpes -4.3662 1.2506 1.0422 199
## (Intercept)-Sus_scrofa -5.5393 -1.9905 1.0138 353
## Cogon_Patch_Size-Odocoileus_virginianus -0.1551 3.4051 1.0035 1547
## Cogon_Patch_Size-Canis_latrans 1.4615 6.0602 1.0257 561
## Cogon_Patch_Size-Sciurus_niger -0.9030 2.6342 1.0033 564
## Cogon_Patch_Size-Procyon_lotor -0.5377 1.0483 1.0069 909
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1193 1.6516 1.0245 1054
## Cogon_Patch_Size-Lynx_rufus -0.4819 3.0372 1.0144 907
## Cogon_Patch_Size-Didelphis_virginiana 1.5424 4.3486 1.0099 593
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2965 0.8442 1.0291 793
## Cogon_Patch_Size-Sciurus_carolinensis -1.0510 0.9485 1.0144 825
## Cogon_Patch_Size-Vulpes_vulpes -0.6621 2.5399 1.0094 662
## Cogon_Patch_Size-Sus_scrofa -0.7811 1.6923 1.0167 880
## Veg_shannon_index-Odocoileus_virginianus 0.8135 2.6073 1.0017 1640
## Veg_shannon_index-Canis_latrans 1.2603 3.1383 1.0042 654
## Veg_shannon_index-Sciurus_niger 1.0251 3.3868 1.0019 915
## Veg_shannon_index-Procyon_lotor 1.1395 2.7077 1.0013 631
## Veg_shannon_index-Dasypus_novemcinctus 0.6194 1.8515 1.0040 1702
## Veg_shannon_index-Lynx_rufus 1.0137 3.3430 1.0036 976
## Veg_shannon_index-Didelphis_virginiana 1.1036 2.8209 1.0139 907
## Veg_shannon_index-Sylvilagus_floridanus 1.0122 2.7964 1.0064 1356
## Veg_shannon_index-Sciurus_carolinensis 0.3976 1.8706 1.0020 1527
## Veg_shannon_index-Vulpes_vulpes 0.6810 2.3405 1.0105 1584
## Veg_shannon_index-Sus_scrofa 1.4303 4.2653 1.0027 493
## total_shrub_cover-Odocoileus_virginianus -0.3399 1.6007 1.0085 1730
## total_shrub_cover-Canis_latrans 0.0432 1.9432 1.0100 1190
## total_shrub_cover-Sciurus_niger -0.6823 1.0902 1.0182 1088
## total_shrub_cover-Procyon_lotor -1.0683 0.0091 1.0086 1274
## total_shrub_cover-Dasypus_novemcinctus -0.2126 0.9979 1.0050 1801
## total_shrub_cover-Lynx_rufus -0.7093 1.1490 1.0312 713
## total_shrub_cover-Didelphis_virginiana -0.7372 0.6134 1.0474 1034
## total_shrub_cover-Sylvilagus_floridanus -0.5889 0.9642 1.0205 1293
## total_shrub_cover-Sciurus_carolinensis -0.4182 1.1042 1.0186 1487
## total_shrub_cover-Vulpes_vulpes -0.6648 0.9028 1.0373 1111
## total_shrub_cover-Sus_scrofa -0.3227 1.5545 1.0083 1372
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.1062 2.6570 1.0167 790
## Avg_Cogongrass_Cover-Canis_latrans 0.1112 2.7544 1.0437 536
## Avg_Cogongrass_Cover-Sciurus_niger -0.3997 2.2601 1.0071 546
## Avg_Cogongrass_Cover-Procyon_lotor 0.0416 2.4856 1.0210 605
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5515 3.7778 1.0378 510
## Avg_Cogongrass_Cover-Lynx_rufus 0.0350 2.6976 1.0217 683
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0391 2.4399 1.0289 653
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6122 1.7074 1.0204 565
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0471 2.3917 1.0204 668
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0985 3.0281 1.0537 678
## Avg_Cogongrass_Cover-Sus_scrofa -0.3531 2.1519 1.0226 643
## Tree_Density-Odocoileus_virginianus -1.0640 3.0870 1.0042 764
## Tree_Density-Canis_latrans -2.7501 -0.7146 1.0409 493
## Tree_Density-Sciurus_niger -2.0451 1.8432 1.0057 628
## Tree_Density-Procyon_lotor -1.9518 -0.0947 1.0122 970
## Tree_Density-Dasypus_novemcinctus -3.6723 -1.3417 1.0578 263
## Tree_Density-Lynx_rufus -0.9044 4.0158 1.0134 352
## Tree_Density-Didelphis_virginiana -2.2370 0.0086 1.0073 949
## Tree_Density-Sylvilagus_floridanus -2.4652 -0.0043 1.0407 634
## Tree_Density-Sciurus_carolinensis -2.5575 -0.0576 1.0121 507
## Tree_Density-Vulpes_vulpes -2.0281 1.9355 1.0550 513
## Tree_Density-Sus_scrofa -2.3577 0.3608 1.0238 555
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2785 4.3094 1.0082 1545
## Avg_Canopy_Cover-Canis_latrans 0.1469 1.5715 1.0126 1501
## Avg_Canopy_Cover-Sciurus_niger 2.3060 7.6688 1.0526 463
## Avg_Canopy_Cover-Procyon_lotor 1.6262 3.5472 1.0009 1273
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0777 4.3852 1.0252 391
## Avg_Canopy_Cover-Lynx_rufus 1.6376 5.5047 1.0169 454
## Avg_Canopy_Cover-Didelphis_virginiana 2.9463 7.3496 1.0270 461
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5889 9.0381 1.0428 287
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6873 6.7977 1.0425 487
## Avg_Canopy_Cover-Vulpes_vulpes 2.3983 6.9013 1.0156 522
## Avg_Canopy_Cover-Sus_scrofa 2.1049 4.7968 1.0165 1169
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.7356 4.6311 1.0072 851
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8680 4.2830 1.0167 553
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3380 3.7831 1.0303 402
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8288 4.1746 1.0054 498
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5002 3.3410 1.0058 587
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0067 4.7310 1.0039 624
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2394 2.8220 1.0221 499
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3046 3.2880 1.0150 606
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7306 3.7844 1.0127 612
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8136 4.2693 1.0126 566
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2449 3.2609 1.0254 589
## avg_veg_height-Odocoileus_virginianus -0.1347 1.4760 1.0110 1251
## avg_veg_height-Canis_latrans -0.2550 0.9396 1.0141 1115
## avg_veg_height-Sciurus_niger -0.2576 1.2826 1.0252 1245
## avg_veg_height-Procyon_lotor 0.0072 1.3030 1.0164 970
## avg_veg_height-Dasypus_novemcinctus 0.1284 1.5384 1.0106 1001
## avg_veg_height-Lynx_rufus -0.2993 1.2343 1.0197 1105
## avg_veg_height-Didelphis_virginiana -0.2903 1.0410 1.0140 1204
## avg_veg_height-Sylvilagus_floridanus -0.2454 1.0849 1.0317 1156
## avg_veg_height-Sciurus_carolinensis 0.1053 1.6960 1.0091 1133
## avg_veg_height-Vulpes_vulpes -0.2262 1.2735 1.0094 1212
## avg_veg_height-Sus_scrofa -0.1679 1.2607 1.0122 1249
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.0037 0.0597 -0.1139 0.0034 0.1212
## (Intercept)-Canis_latrans -2.7111 0.1877 -3.0920 -2.7007 -2.3701
## (Intercept)-Sciurus_niger -4.6874 0.4831 -5.6376 -4.6865 -3.7204
## (Intercept)-Procyon_lotor -2.3073 0.1472 -2.6026 -2.3030 -2.0333
## (Intercept)-Dasypus_novemcinctus -1.7415 0.1612 -2.0667 -1.7403 -1.4338
## (Intercept)-Lynx_rufus -3.8895 0.3778 -4.6095 -3.9027 -3.1215
## (Intercept)-Didelphis_virginiana -2.5395 0.2896 -3.1327 -2.5266 -2.0120
## (Intercept)-Sylvilagus_floridanus -3.1795 0.2638 -3.7076 -3.1723 -2.6837
## (Intercept)-Sciurus_carolinensis -2.6699 0.3227 -3.3358 -2.6652 -2.0617
## (Intercept)-Vulpes_vulpes -4.2374 0.6869 -5.6942 -4.1901 -3.0181
## (Intercept)-Sus_scrofa -3.3119 0.5898 -4.5040 -3.3091 -2.1876
## shrub_cover-Odocoileus_virginianus -0.0521 0.0634 -0.1757 -0.0520 0.0714
## shrub_cover-Canis_latrans -0.2809 0.2215 -0.7098 -0.2790 0.1489
## shrub_cover-Sciurus_niger -0.2890 0.4247 -1.1673 -0.2822 0.5112
## shrub_cover-Procyon_lotor 0.2631 0.1662 -0.0735 0.2684 0.5794
## shrub_cover-Dasypus_novemcinctus 0.8824 0.3100 0.3097 0.8777 1.5177
## shrub_cover-Lynx_rufus -0.2055 0.3673 -0.9245 -0.2130 0.5206
## shrub_cover-Didelphis_virginiana 0.9477 0.3637 0.2837 0.9297 1.6975
## shrub_cover-Sylvilagus_floridanus 0.4992 0.3887 -0.2348 0.4912 1.2980
## shrub_cover-Sciurus_carolinensis 0.9033 0.3994 0.1353 0.9034 1.6793
## shrub_cover-Vulpes_vulpes 0.1604 0.5252 -0.8947 0.1639 1.1858
## shrub_cover-Sus_scrofa 0.7127 0.7188 -0.6323 0.6817 2.1491
## veg_height-Odocoileus_virginianus -0.2935 0.0634 -0.4201 -0.2935 -0.1703
## veg_height-Canis_latrans -0.5422 0.1813 -0.9145 -0.5402 -0.1933
## veg_height-Sciurus_niger -0.0288 0.3221 -0.6597 -0.0272 0.6153
## veg_height-Procyon_lotor 0.3549 0.1241 0.1167 0.3549 0.5983
## veg_height-Dasypus_novemcinctus 0.2420 0.1310 -0.0166 0.2420 0.5002
## veg_height-Lynx_rufus 0.1445 0.2341 -0.3196 0.1522 0.5962
## veg_height-Didelphis_virginiana 0.4224 0.2385 -0.0325 0.4178 0.9245
## veg_height-Sylvilagus_floridanus 0.1321 0.2385 -0.3458 0.1335 0.5961
## veg_height-Sciurus_carolinensis 0.0993 0.2135 -0.3130 0.0966 0.5356
## veg_height-Vulpes_vulpes -0.1724 0.3203 -0.8338 -0.1612 0.4169
## veg_height-Sus_scrofa -0.1550 0.3191 -0.8042 -0.1446 0.4316
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0014 5250
## (Intercept)-Canis_latrans 1.0023 2214
## (Intercept)-Sciurus_niger 1.0422 510
## (Intercept)-Procyon_lotor 1.0014 2628
## (Intercept)-Dasypus_novemcinctus 1.0017 3605
## (Intercept)-Lynx_rufus 1.0376 481
## (Intercept)-Didelphis_virginiana 1.0023 1696
## (Intercept)-Sylvilagus_floridanus 1.0005 1917
## (Intercept)-Sciurus_carolinensis 1.0114 1629
## (Intercept)-Vulpes_vulpes 1.0038 440
## (Intercept)-Sus_scrofa 1.0012 950
## shrub_cover-Odocoileus_virginianus 1.0003 4629
## shrub_cover-Canis_latrans 1.0011 2225
## shrub_cover-Sciurus_niger 1.0213 873
## shrub_cover-Procyon_lotor 1.0056 2663
## shrub_cover-Dasypus_novemcinctus 1.0037 1859
## shrub_cover-Lynx_rufus 1.0212 861
## shrub_cover-Didelphis_virginiana 1.0053 1338
## shrub_cover-Sylvilagus_floridanus 1.0018 1653
## shrub_cover-Sciurus_carolinensis 1.0039 1189
## shrub_cover-Vulpes_vulpes 1.0173 1606
## shrub_cover-Sus_scrofa 1.0028 1065
## veg_height-Odocoileus_virginianus 1.0036 5250
## veg_height-Canis_latrans 1.0031 2484
## veg_height-Sciurus_niger 1.0380 1241
## veg_height-Procyon_lotor 1.0076 3679
## veg_height-Dasypus_novemcinctus 1.0002 4673
## veg_height-Lynx_rufus 1.0032 1863
## veg_height-Didelphis_virginiana 1.0163 3044
## veg_height-Sylvilagus_floridanus 1.0046 2025
## veg_height-Sciurus_carolinensis 1.0090 2827
## veg_height-Vulpes_vulpes 1.0045 1566
## veg_height-Sus_scrofa 1.0011 2533
#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%
## -------------------------------------------------
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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_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): 1.7473
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1857 0.5544 -1.2384 -0.2054 0.9713 1.0065 2021
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3488 2.6568 0.8692 2.6378 10.257 1.0025 1605
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2802 0.4372 -3.1324 -2.2824 -1.4153 1.0003 4874
## week 0.3637 0.2337 -0.1183 0.3684 0.8128 1.0025 2695
## I(week^2) -0.2827 0.0999 -0.4851 -0.2806 -0.0915 1.0001 2951
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1344 1.2746 0.7408 1.8052 5.6334 1.0152 1805
## week 0.4117 0.3290 0.0984 0.3217 1.2739 1.0036 2180
## I(week^2) 0.0719 0.0512 0.0217 0.0578 0.2058 1.0056 2781
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.4947 1.1992 1.7912 3.2895 6.4145
## (Intercept)-Canis_latrans 0.3186 0.4210 -0.4626 0.3073 1.1966
## (Intercept)-Sciurus_niger -0.5523 1.1711 -2.0883 -0.7650 2.3480
## (Intercept)-Procyon_lotor 0.7111 0.3897 -0.0036 0.7008 1.5089
## (Intercept)-Dasypus_novemcinctus -0.6357 0.3657 -1.3682 -0.6327 0.0766
## (Intercept)-Lynx_rufus 0.4226 0.9415 -0.8744 0.2377 2.8473
## (Intercept)-Didelphis_virginiana -1.3694 0.4463 -2.2794 -1.3608 -0.5372
## (Intercept)-Sylvilagus_floridanus -0.2718 0.6778 -1.2547 -0.3357 1.0120
## (Intercept)-Sciurus_carolinensis -1.3476 0.4472 -2.2512 -1.3363 -0.5044
## (Intercept)-Vulpes_vulpes -1.0447 1.1889 -2.8353 -1.2463 1.9678
## (Intercept)-Sus_scrofa -1.8804 0.6305 -3.1662 -1.8690 -0.6939
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0043 1350
## (Intercept)-Canis_latrans 1.0021 4489
## (Intercept)-Sciurus_niger 1.0560 269
## (Intercept)-Procyon_lotor 1.0000 5250
## (Intercept)-Dasypus_novemcinctus 1.0001 5250
## (Intercept)-Lynx_rufus 1.0559 835
## (Intercept)-Didelphis_virginiana 1.0050 5250
## (Intercept)-Sylvilagus_floridanus 1.0628 573
## (Intercept)-Sciurus_carolinensis 1.0017 4852
## (Intercept)-Vulpes_vulpes 1.0327 422
## (Intercept)-Sus_scrofa 1.0038 2547
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5232 0.0798 0.3721 0.5226 0.6833
## (Intercept)-Canis_latrans -2.4324 0.1895 -2.8195 -2.4275 -2.0724
## (Intercept)-Sciurus_niger -3.8658 0.6436 -5.2415 -3.8190 -2.7512
## (Intercept)-Procyon_lotor -2.1501 0.1485 -2.4416 -2.1474 -1.8659
## (Intercept)-Dasypus_novemcinctus -1.4431 0.1557 -1.7588 -1.4403 -1.1559
## (Intercept)-Lynx_rufus -3.4448 0.3695 -4.1974 -3.4344 -2.7622
## (Intercept)-Didelphis_virginiana -2.1039 0.2697 -2.6651 -2.0915 -1.5981
## (Intercept)-Sylvilagus_floridanus -3.0946 0.3522 -3.8438 -3.0691 -2.4568
## (Intercept)-Sciurus_carolinensis -2.2581 0.2861 -2.8548 -2.2513 -1.7182
## (Intercept)-Vulpes_vulpes -3.8591 0.7951 -5.4933 -3.7945 -2.5193
## (Intercept)-Sus_scrofa -2.8349 0.5039 -3.9116 -2.8090 -1.9557
## week-Odocoileus_virginianus 1.2776 0.1234 1.0416 1.2749 1.5265
## week-Canis_latrans 0.5929 0.2603 0.1083 0.5887 1.1123
## week-Sciurus_niger -0.3442 0.5631 -1.6250 -0.2843 0.6164
## week-Procyon_lotor 0.2109 0.2102 -0.2026 0.2119 0.6275
## week-Dasypus_novemcinctus 0.1077 0.2224 -0.3222 0.1107 0.5477
## week-Lynx_rufus 0.3778 0.3373 -0.2843 0.3791 1.0372
## week-Didelphis_virginiana 0.0729 0.3675 -0.6718 0.0854 0.7669
## week-Sylvilagus_floridanus 0.0714 0.3398 -0.6168 0.0745 0.7397
## week-Sciurus_carolinensis 0.7942 0.3660 0.1108 0.7845 1.5242
## week-Vulpes_vulpes 0.2213 0.5143 -0.8264 0.2399 1.2024
## week-Sus_scrofa 0.6857 0.4364 -0.1266 0.6771 1.6036
## I(week^2)-Odocoileus_virginianus -0.5263 0.0508 -0.6283 -0.5251 -0.4302
## I(week^2)-Canis_latrans -0.2459 0.1074 -0.4673 -0.2425 -0.0408
## I(week^2)-Sciurus_niger -0.2782 0.2374 -0.7824 -0.2622 0.1613
## I(week^2)-Procyon_lotor -0.1345 0.0911 -0.3167 -0.1328 0.0415
## I(week^2)-Dasypus_novemcinctus -0.1788 0.1033 -0.3841 -0.1757 0.0139
## I(week^2)-Lynx_rufus -0.2400 0.1507 -0.5499 -0.2341 0.0448
## I(week^2)-Didelphis_virginiana -0.4046 0.2121 -0.8756 -0.3840 -0.0421
## I(week^2)-Sylvilagus_floridanus -0.1837 0.1586 -0.5015 -0.1830 0.1246
## I(week^2)-Sciurus_carolinensis -0.2817 0.1452 -0.5669 -0.2794 -0.0014
## I(week^2)-Vulpes_vulpes -0.4049 0.2414 -0.9389 -0.3904 0.0245
## I(week^2)-Sus_scrofa -0.2397 0.1771 -0.5901 -0.2389 0.1035
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0031 5250
## (Intercept)-Canis_latrans 1.0023 3440
## (Intercept)-Sciurus_niger 1.0182 400
## (Intercept)-Procyon_lotor 1.0023 4106
## (Intercept)-Dasypus_novemcinctus 1.0014 4918
## (Intercept)-Lynx_rufus 1.0131 899
## (Intercept)-Didelphis_virginiana 1.0050 4186
## (Intercept)-Sylvilagus_floridanus 1.0047 1098
## (Intercept)-Sciurus_carolinensis 1.0007 3953
## (Intercept)-Vulpes_vulpes 1.0236 390
## (Intercept)-Sus_scrofa 1.0073 1723
## week-Odocoileus_virginianus 1.0008 5250
## week-Canis_latrans 1.0023 3944
## week-Sciurus_niger 1.0115 1032
## week-Procyon_lotor 1.0017 4448
## week-Dasypus_novemcinctus 1.0005 4813
## week-Lynx_rufus 1.0038 2586
## week-Didelphis_virginiana 1.0045 2884
## week-Sylvilagus_floridanus 1.0015 2943
## week-Sciurus_carolinensis 1.0031 3804
## week-Vulpes_vulpes 1.0041 1616
## week-Sus_scrofa 1.0012 4782
## I(week^2)-Odocoileus_virginianus 1.0009 5250
## I(week^2)-Canis_latrans 1.0011 4041
## I(week^2)-Sciurus_niger 1.0021 1318
## I(week^2)-Procyon_lotor 1.0022 4073
## I(week^2)-Dasypus_novemcinctus 1.0029 4963
## I(week^2)-Lynx_rufus 1.0198 1775
## I(week^2)-Didelphis_virginiana 1.0017 1900
## I(week^2)-Sylvilagus_floridanus 1.0014 2393
## I(week^2)-Sciurus_carolinensis 1.0015 4397
## I(week^2)-Vulpes_vulpes 1.0001 1661
## I(week^2)-Sus_scrofa 1.0006 4234
#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%
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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%
## ----------------------------------------
## 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): 1.9055
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1601 1.0470 -2.2081 -0.1997 2.0231 1.0030 2746
## Cogon_Patch_Size -0.8150 0.6510 -2.1735 -0.7763 0.3835 1.0096 1190
## Veg_shannon_index 0.8269 0.4586 -0.0732 0.8166 1.7308 1.0010 1302
## total_shrub_cover -0.1605 0.3741 -0.9065 -0.1564 0.5680 1.0023 1344
## Avg_Cogongrass_Cover 2.0200 0.6517 0.7826 2.0027 3.3707 1.0042 581
## Tree_Density -1.8120 0.6580 -3.1829 -1.7928 -0.6116 1.0007 695
## Avg_Canopy_Cover 1.7796 0.5379 0.8033 1.7502 2.9271 1.0039 881
## avg_veg_height -0.4986 0.4351 -1.3443 -0.4982 0.3507 1.0068 844
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 18.3864 15.6148 3.7982 14.0396 57.3102 1.0428 560
## Cogon_Patch_Size 2.9322 5.1473 0.1331 1.6494 13.5442 1.1227 986
## Veg_shannon_index 0.8651 1.4754 0.0520 0.4409 4.0210 1.0316 809
## total_shrub_cover 0.5213 0.8299 0.0416 0.2895 2.4674 1.0773 1137
## Avg_Cogongrass_Cover 0.8012 1.1346 0.0502 0.4099 3.7835 1.0053 1244
## Tree_Density 2.3523 5.2107 0.0629 0.8965 13.1559 1.1175 326
## Avg_Canopy_Cover 1.5008 2.0396 0.0936 0.8899 6.6357 1.0232 512
## avg_veg_height 0.3424 0.4586 0.0392 0.2083 1.4200 1.0279 1735
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.471 2.2324 0.0638 0.7791 7.1282 1.2476 186
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3474 0.4581 -3.2358 -2.3619 -1.4267 1.0013 4474
## week 0.3552 0.2356 -0.1159 0.3635 0.8059 1.0029 3146
## I(week^2) -0.2817 0.1019 -0.4852 -0.2798 -0.0782 1.0021 2876
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4949 1.4093 0.9112 2.1320 6.1643 1.0122 3386
## week 0.4207 0.3324 0.1014 0.3307 1.2400 1.0041 2111
## I(week^2) 0.0709 0.0490 0.0220 0.0574 0.1997 1.0007 3249
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 8.0526 2.9933 3.4192 7.6127
## (Intercept)-Canis_latrans 0.7205 1.0707 -1.1000 0.6343
## (Intercept)-Sciurus_niger 1.4637 2.5135 -2.3218 1.0842
## (Intercept)-Procyon_lotor 0.8553 0.9545 -1.0375 0.8592
## (Intercept)-Dasypus_novemcinctus -1.4922 0.9357 -3.5814 -1.4134
## (Intercept)-Lynx_rufus 2.5064 3.2555 -2.0006 1.8507
## (Intercept)-Didelphis_virginiana -2.9518 1.1042 -5.3506 -2.8877
## (Intercept)-Sylvilagus_floridanus -1.3858 1.2023 -3.9211 -1.3416
## (Intercept)-Sciurus_carolinensis -3.2298 1.2216 -5.9830 -3.0933
## (Intercept)-Vulpes_vulpes -1.8580 2.1616 -5.4156 -2.1093
## (Intercept)-Sus_scrofa -4.6879 1.6715 -8.4591 -4.5405
## Cogon_Patch_Size-Odocoileus_virginianus -0.6287 1.3110 -3.0816 -0.6941
## Cogon_Patch_Size-Canis_latrans 0.6958 1.1941 -1.0174 0.4738
## Cogon_Patch_Size-Sciurus_niger -1.5421 1.7833 -5.7194 -1.3511
## Cogon_Patch_Size-Procyon_lotor -0.9913 0.7257 -2.3771 -1.0029
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7764 0.6073 -2.0628 -0.7448
## Cogon_Patch_Size-Lynx_rufus -0.8236 1.3868 -3.3988 -0.8827
## Cogon_Patch_Size-Didelphis_virginiana 0.7923 0.8397 -0.6582 0.7205
## Cogon_Patch_Size-Sylvilagus_floridanus -2.0361 1.5770 -6.0363 -1.7125
## Cogon_Patch_Size-Sciurus_carolinensis -1.7834 1.3132 -5.1457 -1.5012
## Cogon_Patch_Size-Vulpes_vulpes -1.3369 1.6121 -4.9974 -1.1685
## Cogon_Patch_Size-Sus_scrofa -1.4597 1.4350 -4.9555 -1.2020
## Veg_shannon_index-Odocoileus_virginianus 0.6747 0.8910 -1.3071 0.7168
## Veg_shannon_index-Canis_latrans 1.2086 0.6371 0.1371 1.1434
## Veg_shannon_index-Sciurus_niger 0.9195 1.0069 -1.1424 0.8904
## Veg_shannon_index-Procyon_lotor 1.1221 0.6000 0.0519 1.0885
## Veg_shannon_index-Dasypus_novemcinctus 0.6247 0.4987 -0.4170 0.6363
## Veg_shannon_index-Lynx_rufus 0.7702 0.9599 -1.3774 0.8067
## Veg_shannon_index-Didelphis_virginiana 0.9854 0.6456 -0.1634 0.9454
## Veg_shannon_index-Sylvilagus_floridanus 1.0176 0.6855 -0.1782 0.9571
## Veg_shannon_index-Sciurus_carolinensis 0.1651 0.7101 -1.4266 0.2416
## Veg_shannon_index-Vulpes_vulpes 0.3094 0.8765 -1.6597 0.4046
## Veg_shannon_index-Sus_scrofa 1.5803 1.0270 0.1187 1.3805
## total_shrub_cover-Odocoileus_virginianus -0.0012 0.7159 -1.3983 -0.0226
## total_shrub_cover-Canis_latrans 0.1534 0.6074 -0.8277 0.0947
## total_shrub_cover-Sciurus_niger -0.3655 0.7830 -2.1981 -0.2933
## total_shrub_cover-Procyon_lotor -0.6188 0.5423 -1.8212 -0.5746
## total_shrub_cover-Dasypus_novemcinctus 0.0936 0.4797 -0.8190 0.0717
## total_shrub_cover-Lynx_rufus -0.4393 0.7980 -2.2963 -0.3661
## total_shrub_cover-Didelphis_virginiana -0.3165 0.5799 -1.5408 -0.2943
## total_shrub_cover-Sylvilagus_floridanus -0.1064 0.6166 -1.3532 -0.1066
## total_shrub_cover-Sciurus_carolinensis -0.0113 0.5587 -1.1164 -0.0280
## total_shrub_cover-Vulpes_vulpes -0.2865 0.7242 -1.8914 -0.2431
## total_shrub_cover-Sus_scrofa 0.1107 0.6915 -1.1480 0.0622
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9597 0.9779 -0.0287 1.9391
## Avg_Cogongrass_Cover-Canis_latrans 2.2755 0.8446 0.7799 2.2202
## Avg_Cogongrass_Cover-Sciurus_niger 1.7017 1.0971 -0.7515 1.7667
## Avg_Cogongrass_Cover-Procyon_lotor 2.2338 0.8115 0.7455 2.1986
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4769 0.8767 0.9990 2.3808
## Avg_Cogongrass_Cover-Lynx_rufus 2.3248 0.9403 0.6658 2.2700
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1160 0.8139 0.5965 2.0815
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5311 0.8943 -0.2372 1.5393
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2799 0.8414 0.7951 2.2179
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.3535 0.9858 0.6213 2.2680
## Avg_Cogongrass_Cover-Sus_scrofa 1.5499 1.0143 -0.6630 1.6046
## Tree_Density-Odocoileus_virginianus -0.9278 1.2113 -2.7723 -1.1002
## Tree_Density-Canis_latrans -2.3390 1.0939 -5.0760 -2.1551
## Tree_Density-Sciurus_niger -1.9032 1.3420 -4.8027 -1.8350
## Tree_Density-Procyon_lotor -1.4802 0.7401 -2.9277 -1.4838
## Tree_Density-Dasypus_novemcinctus -3.0823 1.5374 -7.0068 -2.7111
## Tree_Density-Lynx_rufus -0.8690 1.3676 -3.0265 -1.0667
## Tree_Density-Didelphis_virginiana -2.1764 1.0439 -4.7573 -2.0089
## Tree_Density-Sylvilagus_floridanus -2.3003 1.1561 -5.1262 -2.1044
## Tree_Density-Sciurus_carolinensis -2.3751 1.2226 -5.5532 -2.1513
## Tree_Density-Vulpes_vulpes -1.6859 1.6756 -4.5425 -1.7588
## Tree_Density-Sus_scrofa -2.1768 1.3975 -5.5970 -1.9530
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3770 1.1301 -1.0844 1.4274
## Avg_Canopy_Cover-Canis_latrans 0.4930 0.6999 -0.8651 0.4818
## Avg_Canopy_Cover-Sciurus_niger 1.9830 1.3465 -0.4966 1.9116
## Avg_Canopy_Cover-Procyon_lotor 1.7248 0.6730 0.5197 1.6816
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8988 0.6277 0.7724 1.8447
## Avg_Canopy_Cover-Lynx_rufus 1.5051 1.1822 -0.7414 1.5014
## Avg_Canopy_Cover-Didelphis_virginiana 2.4965 0.8684 1.1543 2.3791
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8434 1.2682 1.0616 2.6331
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1770 0.7524 0.9537 2.1066
## Avg_Canopy_Cover-Vulpes_vulpes 2.0756 1.1046 0.3774 1.9230
## Avg_Canopy_Cover-Sus_scrofa 2.0078 0.7820 0.7096 1.9350
## avg_veg_height-Odocoileus_virginianus -0.5185 0.6945 -1.9485 -0.5058
## avg_veg_height-Canis_latrans -0.6789 0.5574 -1.8396 -0.6602
## avg_veg_height-Sciurus_niger -0.6169 0.7071 -2.1178 -0.5902
## avg_veg_height-Procyon_lotor -0.3544 0.5304 -1.3575 -0.3682
## avg_veg_height-Dasypus_novemcinctus -0.3104 0.5246 -1.3340 -0.3201
## avg_veg_height-Lynx_rufus -0.5043 0.6859 -1.8716 -0.5146
## avg_veg_height-Didelphis_virginiana -0.5819 0.5915 -1.7744 -0.5673
## avg_veg_height-Sylvilagus_floridanus -0.6660 0.5882 -1.8551 -0.6441
## avg_veg_height-Sciurus_carolinensis -0.2285 0.5927 -1.3055 -0.2570
## avg_veg_height-Vulpes_vulpes -0.5039 0.6454 -1.7816 -0.5096
## avg_veg_height-Sus_scrofa -0.5866 0.6283 -1.8954 -0.5712
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 14.9880 1.0321 451
## (Intercept)-Canis_latrans 3.1740 1.0138 1060
## (Intercept)-Sciurus_niger 7.4280 1.0592 270
## (Intercept)-Procyon_lotor 2.6503 1.0090 1113
## (Intercept)-Dasypus_novemcinctus 0.1332 1.0044 972
## (Intercept)-Lynx_rufus 10.5919 1.0116 201
## (Intercept)-Didelphis_virginiana -0.9405 1.0020 1737
## (Intercept)-Sylvilagus_floridanus 0.8900 1.0244 1035
## (Intercept)-Sciurus_carolinensis -1.1885 1.0234 693
## (Intercept)-Vulpes_vulpes 3.2921 1.0677 328
## (Intercept)-Sus_scrofa -1.8434 1.0070 527
## Cogon_Patch_Size-Odocoileus_virginianus 2.2427 1.0026 2162
## Cogon_Patch_Size-Canis_latrans 3.4722 1.0404 1377
## Cogon_Patch_Size-Sciurus_niger 1.5965 1.0250 674
## Cogon_Patch_Size-Procyon_lotor 0.3806 1.0176 1296
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3371 1.0019 1395
## Cogon_Patch_Size-Lynx_rufus 2.2284 1.0133 1091
## Cogon_Patch_Size-Didelphis_virginiana 2.6016 1.0120 1225
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0548 1.0330 679
## Cogon_Patch_Size-Sciurus_carolinensis -0.0228 1.0189 1041
## Cogon_Patch_Size-Vulpes_vulpes 1.4610 1.0311 676
## Cogon_Patch_Size-Sus_scrofa 0.6832 1.0040 1005
## Veg_shannon_index-Odocoileus_virginianus 2.3652 1.0020 1914
## Veg_shannon_index-Canis_latrans 2.6127 1.0024 1055
## Veg_shannon_index-Sciurus_niger 3.1034 1.0013 1146
## Veg_shannon_index-Procyon_lotor 2.4174 1.0054 761
## Veg_shannon_index-Dasypus_novemcinctus 1.5763 1.0004 1817
## Veg_shannon_index-Lynx_rufus 2.6072 1.0032 1235
## Veg_shannon_index-Didelphis_virginiana 2.4032 1.0007 1904
## Veg_shannon_index-Sylvilagus_floridanus 2.5277 1.0046 1310
## Veg_shannon_index-Sciurus_carolinensis 1.3606 1.0038 1495
## Veg_shannon_index-Vulpes_vulpes 1.7619 1.0025 1217
## Veg_shannon_index-Sus_scrofa 4.1312 1.0033 858
## total_shrub_cover-Odocoileus_virginianus 1.5484 1.0050 2130
## total_shrub_cover-Canis_latrans 1.4658 1.0224 1413
## total_shrub_cover-Sciurus_niger 0.9718 1.0089 1345
## total_shrub_cover-Procyon_lotor 0.3171 1.0023 1986
## total_shrub_cover-Dasypus_novemcinctus 1.0985 1.0015 2602
## total_shrub_cover-Lynx_rufus 0.8950 1.0014 1300
## total_shrub_cover-Didelphis_virginiana 0.7465 1.0018 2218
## total_shrub_cover-Sylvilagus_floridanus 1.1617 1.0027 1940
## total_shrub_cover-Sciurus_carolinensis 1.1410 1.0029 2818
## total_shrub_cover-Vulpes_vulpes 1.0332 1.0074 1581
## total_shrub_cover-Sus_scrofa 1.6687 1.0007 2254
## Avg_Cogongrass_Cover-Odocoileus_virginianus 3.8914 1.0022 1072
## Avg_Cogongrass_Cover-Canis_latrans 4.1400 1.0021 910
## Avg_Cogongrass_Cover-Sciurus_niger 3.7305 1.0030 885
## Avg_Cogongrass_Cover-Procyon_lotor 3.9865 1.0050 800
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.4497 1.0066 796
## Avg_Cogongrass_Cover-Lynx_rufus 4.3648 1.0034 830
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8639 1.0021 969
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3003 1.0061 879
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1290 1.0062 760
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.4620 1.0098 808
## Avg_Cogongrass_Cover-Sus_scrofa 3.3596 1.0046 1143
## Tree_Density-Odocoileus_virginianus 2.0197 1.0060 955
## Tree_Density-Canis_latrans -0.6626 0.9998 980
## Tree_Density-Sciurus_niger 0.6346 1.0193 917
## Tree_Density-Procyon_lotor 0.0289 1.0025 1503
## Tree_Density-Dasypus_novemcinctus -1.1006 1.0053 486
## Tree_Density-Lynx_rufus 2.3777 1.0162 447
## Tree_Density-Didelphis_virginiana -0.5768 1.0017 771
## Tree_Density-Sylvilagus_floridanus -0.4952 1.0034 912
## Tree_Density-Sciurus_carolinensis -0.6461 1.0057 738
## Tree_Density-Vulpes_vulpes 1.6820 1.0198 450
## Tree_Density-Sus_scrofa -0.0768 1.0087 967
## Avg_Canopy_Cover-Odocoileus_virginianus 3.5433 1.0002 1676
## Avg_Canopy_Cover-Canis_latrans 1.9049 1.0036 1127
## Avg_Canopy_Cover-Sciurus_niger 5.0247 1.0362 955
## Avg_Canopy_Cover-Procyon_lotor 3.1379 1.0104 936
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2540 1.0003 1216
## Avg_Canopy_Cover-Lynx_rufus 4.1121 1.0139 874
## Avg_Canopy_Cover-Didelphis_virginiana 4.4728 1.0069 554
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.9679 1.0015 607
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8676 1.0014 968
## Avg_Canopy_Cover-Vulpes_vulpes 4.5984 1.0242 636
## Avg_Canopy_Cover-Sus_scrofa 3.7313 1.0018 1280
## avg_veg_height-Odocoileus_virginianus 0.8443 1.0057 1721
## avg_veg_height-Canis_latrans 0.3732 1.0062 1276
## avg_veg_height-Sciurus_niger 0.6824 1.0079 1244
## avg_veg_height-Procyon_lotor 0.7206 1.0050 1492
## avg_veg_height-Dasypus_novemcinctus 0.7390 1.0044 1584
## avg_veg_height-Lynx_rufus 0.9223 1.0044 1130
## avg_veg_height-Didelphis_virginiana 0.5556 1.0043 1335
## avg_veg_height-Sylvilagus_floridanus 0.4383 1.0095 1213
## avg_veg_height-Sciurus_carolinensis 1.0546 1.0006 1436
## avg_veg_height-Vulpes_vulpes 0.8040 1.0197 1432
## avg_veg_height-Sus_scrofa 0.5915 1.0056 1464
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5236 0.0797 0.3697 0.5241 0.6754
## (Intercept)-Canis_latrans -2.4639 0.1996 -2.8770 -2.4588 -2.0926
## (Intercept)-Sciurus_niger -4.5294 0.5099 -5.5863 -4.5191 -3.5503
## (Intercept)-Procyon_lotor -2.1551 0.1507 -2.4617 -2.1530 -1.8584
## (Intercept)-Dasypus_novemcinctus -1.4390 0.1572 -1.7452 -1.4368 -1.1417
## (Intercept)-Lynx_rufus -3.6875 0.3609 -4.3681 -3.7033 -2.9586
## (Intercept)-Didelphis_virginiana -2.0789 0.2626 -2.6111 -2.0702 -1.5858
## (Intercept)-Sylvilagus_floridanus -3.0678 0.3025 -3.6817 -3.0588 -2.4921
## (Intercept)-Sciurus_carolinensis -2.2437 0.2787 -2.8240 -2.2334 -1.7228
## (Intercept)-Vulpes_vulpes -4.0603 0.7028 -5.5074 -4.0456 -2.7511
## (Intercept)-Sus_scrofa -2.7385 0.4705 -3.7328 -2.7152 -1.8973
## week-Odocoileus_virginianus 1.2802 0.1215 1.0436 1.2793 1.5186
## week-Canis_latrans 0.5847 0.2576 0.0930 0.5775 1.1140
## week-Sciurus_niger -0.3692 0.5452 -1.5930 -0.3222 0.5476
## week-Procyon_lotor 0.2090 0.2074 -0.1962 0.2085 0.6061
## week-Dasypus_novemcinctus 0.1109 0.2256 -0.3365 0.1139 0.5561
## week-Lynx_rufus 0.3772 0.3455 -0.2826 0.3718 1.0816
## week-Didelphis_virginiana 0.0671 0.3724 -0.7082 0.0808 0.7689
## week-Sylvilagus_floridanus 0.0588 0.3463 -0.6418 0.0706 0.7083
## week-Sciurus_carolinensis 0.8002 0.3660 0.1203 0.7840 1.5676
## week-Vulpes_vulpes 0.2137 0.5006 -0.8294 0.2403 1.1437
## week-Sus_scrofa 0.6613 0.4390 -0.1558 0.6606 1.5757
## I(week^2)-Odocoileus_virginianus -0.5272 0.0498 -0.6246 -0.5267 -0.4301
## I(week^2)-Canis_latrans -0.2414 0.1054 -0.4525 -0.2406 -0.0373
## I(week^2)-Sciurus_niger -0.2841 0.2300 -0.7740 -0.2758 0.1423
## I(week^2)-Procyon_lotor -0.1340 0.0907 -0.3078 -0.1347 0.0440
## I(week^2)-Dasypus_novemcinctus -0.1812 0.1035 -0.3929 -0.1788 0.0170
## I(week^2)-Lynx_rufus -0.2403 0.1553 -0.5642 -0.2383 0.0505
## I(week^2)-Didelphis_virginiana -0.4179 0.2064 -0.8931 -0.3957 -0.0717
## I(week^2)-Sylvilagus_floridanus -0.1791 0.1586 -0.5131 -0.1756 0.1226
## I(week^2)-Sciurus_carolinensis -0.2813 0.1444 -0.5833 -0.2756 -0.0098
## I(week^2)-Vulpes_vulpes -0.3913 0.2395 -0.9377 -0.3689 0.0134
## I(week^2)-Sus_scrofa -0.2372 0.1748 -0.5857 -0.2339 0.0961
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0018 5457
## (Intercept)-Canis_latrans 1.0006 2165
## (Intercept)-Sciurus_niger 1.0485 557
## (Intercept)-Procyon_lotor 1.0000 4252
## (Intercept)-Dasypus_novemcinctus 1.0027 5250
## (Intercept)-Lynx_rufus 1.0092 404
## (Intercept)-Didelphis_virginiana 1.0024 4414
## (Intercept)-Sylvilagus_floridanus 0.9999 1784
## (Intercept)-Sciurus_carolinensis 1.0009 3700
## (Intercept)-Vulpes_vulpes 1.0188 436
## (Intercept)-Sus_scrofa 1.0046 2491
## week-Odocoileus_virginianus 1.0059 5250
## week-Canis_latrans 1.0000 3489
## week-Sciurus_niger 1.0228 687
## week-Procyon_lotor 1.0005 3974
## week-Dasypus_novemcinctus 1.0004 5250
## week-Lynx_rufus 1.0054 2360
## week-Didelphis_virginiana 1.0000 3076
## week-Sylvilagus_floridanus 1.0085 2593
## week-Sciurus_carolinensis 1.0005 3748
## week-Vulpes_vulpes 1.0009 1964
## week-Sus_scrofa 1.0004 4323
## I(week^2)-Odocoileus_virginianus 1.0023 5250
## I(week^2)-Canis_latrans 1.0004 3854
## I(week^2)-Sciurus_niger 1.0121 800
## I(week^2)-Procyon_lotor 1.0013 4290
## I(week^2)-Dasypus_novemcinctus 1.0009 4667
## I(week^2)-Lynx_rufus 1.0032 1958
## I(week^2)-Didelphis_virginiana 1.0035 1965
## I(week^2)-Sylvilagus_floridanus 1.0040 2365
## I(week^2)-Sciurus_carolinensis 1.0004 4510
## I(week^2)-Vulpes_vulpes 1.0046 1345
## I(week^2)-Sus_scrofa 1.0005 4461
#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): 1.7872
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2317 0.6232 -1.4302 -0.2469 1.1059 1.0066 1805
## Avg_Cogongrass_Cover 0.1260 0.3068 -0.4854 0.1305 0.7159 1.0021 1341
## total_shrub_cover -0.2759 0.2687 -0.8276 -0.2722 0.2255 1.0036 2340
## avg_veg_height 0.0299 0.2805 -0.5243 0.0358 0.5700 1.0007 1411
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9158 3.1485 0.8238 3.0628 12.0697 1.0029 1595
## Avg_Cogongrass_Cover 0.2963 0.3581 0.0382 0.1887 1.1569 1.0114 2672
## total_shrub_cover 0.3593 0.5156 0.0424 0.2248 1.4569 1.0855 1440
## avg_veg_height 0.1998 0.2101 0.0321 0.1375 0.7348 1.0213 3369
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9032 0.8986 0.0712 0.6416 3.179 1.0146 559
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2929 0.4490 -3.1444 -2.3018 -1.3727 1.0030 4836
## week 0.3485 0.2356 -0.1338 0.3544 0.7942 1.0025 2935
## I(week^2) -0.2817 0.1030 -0.4884 -0.2793 -0.0831 1.0007 2445
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2245 1.3283 0.7621 1.8871 5.8052 1.0046 2442
## week 0.4307 0.3237 0.1108 0.3417 1.2903 1.0063 1943
## I(week^2) 0.0723 0.0587 0.0216 0.0575 0.2133 1.0261 2217
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6594 1.4504 1.1418 3.5058
## (Intercept)-Canis_latrans 0.3420 0.6592 -0.9050 0.3153
## (Intercept)-Sciurus_niger -0.6275 1.1742 -2.5719 -0.7664
## (Intercept)-Procyon_lotor 0.6405 0.6963 -0.7603 0.6424
## (Intercept)-Dasypus_novemcinctus -0.7342 0.6087 -1.9360 -0.7219
## (Intercept)-Lynx_rufus -0.0184 0.9842 -1.7240 -0.0861
## (Intercept)-Didelphis_virginiana -1.4329 0.6932 -2.8520 -1.4200
## (Intercept)-Sylvilagus_floridanus -0.2003 0.8983 -1.7258 -0.2823
## (Intercept)-Sciurus_carolinensis -1.5610 0.6924 -2.9949 -1.5426
## (Intercept)-Vulpes_vulpes -0.9357 1.4432 -3.2018 -1.1409
## (Intercept)-Sus_scrofa -1.9988 0.8821 -3.8408 -1.9694
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1118 0.5333 -0.9626 0.1053
## Avg_Cogongrass_Cover-Canis_latrans 0.3624 0.4236 -0.4170 0.3442
## Avg_Cogongrass_Cover-Sciurus_niger -0.2081 0.5880 -1.5555 -0.1524
## Avg_Cogongrass_Cover-Procyon_lotor 0.0867 0.4167 -0.7389 0.0933
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2441 0.3802 -0.4944 0.2365
## Avg_Cogongrass_Cover-Lynx_rufus 0.3965 0.4642 -0.4527 0.3612
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3053 0.4135 -0.4822 0.2947
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2039 0.4881 -1.2515 -0.1811
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2262 0.4115 -0.5819 0.2201
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2308 0.4988 -0.7362 0.2129
## Avg_Cogongrass_Cover-Sus_scrofa -0.1672 0.5641 -1.5515 -0.1031
## total_shrub_cover-Odocoileus_virginianus -0.1878 0.5219 -1.2165 -0.1944
## total_shrub_cover-Canis_latrans 0.0859 0.4107 -0.6306 0.0499
## total_shrub_cover-Sciurus_niger -0.5036 0.5241 -1.6446 -0.4627
## total_shrub_cover-Procyon_lotor -0.7329 0.4604 -1.7872 -0.6814
## total_shrub_cover-Dasypus_novemcinctus -0.0592 0.3438 -0.7280 -0.0624
## total_shrub_cover-Lynx_rufus -0.6389 0.5502 -1.8945 -0.5764
## total_shrub_cover-Didelphis_virginiana -0.2181 0.3903 -0.9968 -0.2142
## total_shrub_cover-Sylvilagus_floridanus -0.3577 0.5123 -1.4978 -0.3175
## total_shrub_cover-Sciurus_carolinensis -0.1073 0.3913 -0.8473 -0.1144
## total_shrub_cover-Vulpes_vulpes -0.3374 0.5740 -1.5505 -0.3033
## total_shrub_cover-Sus_scrofa 0.0272 0.4862 -0.8467 0.0003
## avg_veg_height-Odocoileus_virginianus 0.0138 0.4732 -0.9290 0.0176
## avg_veg_height-Canis_latrans -0.0422 0.3856 -0.8092 -0.0357
## avg_veg_height-Sciurus_niger -0.1266 0.4691 -1.1269 -0.1094
## avg_veg_height-Procyon_lotor 0.1229 0.3951 -0.6419 0.1217
## avg_veg_height-Dasypus_novemcinctus 0.1890 0.3685 -0.5122 0.1805
## avg_veg_height-Lynx_rufus 0.0506 0.4502 -0.8266 0.0449
## avg_veg_height-Didelphis_virginiana -0.0077 0.3893 -0.8000 -0.0024
## avg_veg_height-Sylvilagus_floridanus -0.0936 0.4159 -0.9386 -0.0891
## avg_veg_height-Sciurus_carolinensis 0.2664 0.4145 -0.5099 0.2502
## avg_veg_height-Vulpes_vulpes -0.0214 0.4530 -0.9080 -0.0225
## avg_veg_height-Sus_scrofa -0.0090 0.4263 -0.8672 -0.0099
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1026 1.0099 1315
## (Intercept)-Canis_latrans 1.7344 1.0000 2633
## (Intercept)-Sciurus_niger 2.1053 1.0075 543
## (Intercept)-Procyon_lotor 2.0401 1.0082 2168
## (Intercept)-Dasypus_novemcinctus 0.4114 1.0022 3212
## (Intercept)-Lynx_rufus 2.1156 1.0095 1045
## (Intercept)-Didelphis_virginiana -0.0789 1.0012 2929
## (Intercept)-Sylvilagus_floridanus 1.7801 1.0063 1002
## (Intercept)-Sciurus_carolinensis -0.2275 1.0011 3000
## (Intercept)-Vulpes_vulpes 2.4865 1.0005 384
## (Intercept)-Sus_scrofa -0.2546 1.0031 2315
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1704 1.0016 2816
## Avg_Cogongrass_Cover-Canis_latrans 1.2749 1.0008 2870
## Avg_Cogongrass_Cover-Sciurus_niger 0.7993 1.0129 2004
## Avg_Cogongrass_Cover-Procyon_lotor 0.8928 1.0028 2660
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0032 1.0005 2815
## Avg_Cogongrass_Cover-Lynx_rufus 1.4380 1.0012 2698
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1470 1.0019 2656
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6534 1.0071 2221
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0651 1.0011 1933
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3015 1.0002 2360
## Avg_Cogongrass_Cover-Sus_scrofa 0.7790 1.0063 2029
## total_shrub_cover-Odocoileus_virginianus 0.8722 1.0003 3808
## total_shrub_cover-Canis_latrans 0.9768 1.0035 3197
## total_shrub_cover-Sciurus_niger 0.4102 1.0043 2552
## total_shrub_cover-Procyon_lotor 0.0304 1.0027 2442
## total_shrub_cover-Dasypus_novemcinctus 0.6308 1.0013 4260
## total_shrub_cover-Lynx_rufus 0.2662 1.0011 2250
## total_shrub_cover-Didelphis_virginiana 0.5488 1.0012 4514
## total_shrub_cover-Sylvilagus_floridanus 0.4839 1.0077 1424
## total_shrub_cover-Sciurus_carolinensis 0.6917 1.0004 4606
## total_shrub_cover-Vulpes_vulpes 0.6485 1.0080 1496
## total_shrub_cover-Sus_scrofa 1.0824 1.0008 3309
## avg_veg_height-Odocoileus_virginianus 0.9697 1.0017 2378
## avg_veg_height-Canis_latrans 0.7013 1.0011 2368
## avg_veg_height-Sciurus_niger 0.7296 1.0005 2116
## avg_veg_height-Procyon_lotor 0.9350 1.0037 2626
## avg_veg_height-Dasypus_novemcinctus 0.9089 1.0012 2428
## avg_veg_height-Lynx_rufus 0.9874 1.0002 2490
## avg_veg_height-Didelphis_virginiana 0.7481 1.0032 2729
## avg_veg_height-Sylvilagus_floridanus 0.7153 1.0021 2175
## avg_veg_height-Sciurus_carolinensis 1.1295 1.0048 2419
## avg_veg_height-Vulpes_vulpes 0.8653 1.0003 2324
## avg_veg_height-Sus_scrofa 0.8239 1.0005 2474
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5262 0.0782 0.3730 0.5262 0.6772
## (Intercept)-Canis_latrans -2.4489 0.1930 -2.8419 -2.4456 -2.0849
## (Intercept)-Sciurus_niger -3.9123 0.6099 -5.1253 -3.8884 -2.8000
## (Intercept)-Procyon_lotor -2.1652 0.1521 -2.4651 -2.1604 -1.8778
## (Intercept)-Dasypus_novemcinctus -1.4406 0.1579 -1.7519 -1.4397 -1.1351
## (Intercept)-Lynx_rufus -3.3943 0.3344 -4.0807 -3.3848 -2.7611
## (Intercept)-Didelphis_virginiana -2.1180 0.2745 -2.6977 -2.1048 -1.6161
## (Intercept)-Sylvilagus_floridanus -3.1495 0.3500 -3.9082 -3.1330 -2.5352
## (Intercept)-Sciurus_carolinensis -2.2540 0.2830 -2.8273 -2.2437 -1.7330
## (Intercept)-Vulpes_vulpes -4.0025 0.7886 -5.5392 -3.9699 -2.5758
## (Intercept)-Sus_scrofa -2.8256 0.5161 -3.9389 -2.7877 -1.9223
## week-Odocoileus_virginianus 1.2787 0.1201 1.0448 1.2784 1.5243
## week-Canis_latrans 0.5894 0.2614 0.0831 0.5860 1.1243
## week-Sciurus_niger -0.4161 0.5514 -1.6125 -0.3658 0.5271
## week-Procyon_lotor 0.2079 0.2098 -0.1973 0.2075 0.6229
## week-Dasypus_novemcinctus 0.1059 0.2237 -0.3311 0.1047 0.5474
## week-Lynx_rufus 0.3819 0.3533 -0.2930 0.3764 1.1017
## week-Didelphis_virginiana 0.0502 0.3731 -0.7166 0.0575 0.7459
## week-Sylvilagus_floridanus 0.0568 0.3456 -0.6454 0.0645 0.7194
## week-Sciurus_carolinensis 0.7905 0.3606 0.1045 0.7757 1.5380
## week-Vulpes_vulpes 0.1928 0.5230 -0.9025 0.2102 1.1899
## week-Sus_scrofa 0.6786 0.4611 -0.1967 0.6663 1.6392
## I(week^2)-Odocoileus_virginianus -0.5270 0.0500 -0.6258 -0.5269 -0.4292
## I(week^2)-Canis_latrans -0.2445 0.1077 -0.4592 -0.2441 -0.0339
## I(week^2)-Sciurus_niger -0.2819 0.2355 -0.7832 -0.2693 0.1449
## I(week^2)-Procyon_lotor -0.1339 0.0914 -0.3161 -0.1344 0.0448
## I(week^2)-Dasypus_novemcinctus -0.1774 0.1021 -0.3835 -0.1756 0.0204
## I(week^2)-Lynx_rufus -0.2343 0.1505 -0.5355 -0.2311 0.0547
## I(week^2)-Didelphis_virginiana -0.4090 0.2088 -0.8714 -0.3925 -0.0484
## I(week^2)-Sylvilagus_floridanus -0.1817 0.1563 -0.4982 -0.1767 0.1209
## I(week^2)-Sciurus_carolinensis -0.2796 0.1423 -0.5683 -0.2743 -0.0089
## I(week^2)-Vulpes_vulpes -0.4027 0.2501 -0.9735 -0.3812 0.0187
## I(week^2)-Sus_scrofa -0.2417 0.1795 -0.6084 -0.2393 0.0979
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0081 5250
## (Intercept)-Canis_latrans 1.0026 3065
## (Intercept)-Sciurus_niger 1.0092 558
## (Intercept)-Procyon_lotor 1.0028 3677
## (Intercept)-Dasypus_novemcinctus 0.9998 5250
## (Intercept)-Lynx_rufus 1.0092 1257
## (Intercept)-Didelphis_virginiana 1.0010 3963
## (Intercept)-Sylvilagus_floridanus 1.0086 1141
## (Intercept)-Sciurus_carolinensis 1.0015 3874
## (Intercept)-Vulpes_vulpes 1.0014 390
## (Intercept)-Sus_scrofa 1.0011 1924
## week-Odocoileus_virginianus 1.0117 5250
## week-Canis_latrans 1.0016 3699
## week-Sciurus_niger 1.0131 1083
## week-Procyon_lotor 1.0027 4434
## week-Dasypus_novemcinctus 1.0013 4758
## week-Lynx_rufus 0.9999 2454
## week-Didelphis_virginiana 1.0013 2938
## week-Sylvilagus_floridanus 0.9999 2705
## week-Sciurus_carolinensis 1.0022 4085
## week-Vulpes_vulpes 1.0062 1638
## week-Sus_scrofa 1.0029 3959
## I(week^2)-Odocoileus_virginianus 1.0066 4946
## I(week^2)-Canis_latrans 1.0009 3746
## I(week^2)-Sciurus_niger 1.0041 1302
## I(week^2)-Procyon_lotor 1.0035 4170
## I(week^2)-Dasypus_novemcinctus 1.0000 3785
## I(week^2)-Lynx_rufus 1.0021 2405
## I(week^2)-Didelphis_virginiana 1.0012 1599
## I(week^2)-Sylvilagus_floridanus 1.0001 2548
## I(week^2)-Sciurus_carolinensis 1.0024 4466
## I(week^2)-Vulpes_vulpes 1.0052 1161
## I(week^2)-Sus_scrofa 1.0015 4277
#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): 1.823
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1805 0.7624 -1.6187 -0.2099 1.4519 1.0046 1345
## Tree_Density -0.7658 0.4079 -1.6756 -0.7335 -0.0426 1.0019 1502
## Avg_Canopy_Cover 1.0195 0.3334 0.3860 1.0005 1.7153 1.0029 1367
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.9058 6.1500 1.5036 5.1792 23.1942 1.0047 430
## Tree_Density 0.7235 1.1505 0.0439 0.3445 3.8854 1.0109 1040
## Avg_Canopy_Cover 0.5435 0.5819 0.0549 0.3656 2.0386 1.0048 1846
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.3911 0.4588 0.039 0.2464 1.6195 1.0415 620
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3276 0.4623 -3.2002 -2.3401 -1.3427 1.0002 4014
## week 0.3497 0.2343 -0.1410 0.3556 0.7937 1.0019 3000
## I(week^2) -0.2793 0.1032 -0.4892 -0.2784 -0.0778 1.0012 2818
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3708 1.4969 0.8256 1.9729 6.1614 1.0032 1879
## week 0.4176 0.3153 0.1073 0.3305 1.2122 1.0076 1848
## I(week^2) 0.0719 0.0513 0.0223 0.0584 0.2088 1.0042 3170
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.7412 1.8075 2.1323 4.4183 9.0321
## (Intercept)-Canis_latrans 0.3467 0.6599 -0.7975 0.3056 1.7804
## (Intercept)-Sciurus_niger 0.1609 1.7395 -2.2299 -0.1050 4.5894
## (Intercept)-Procyon_lotor 0.7675 0.5906 -0.3926 0.7554 1.9787
## (Intercept)-Dasypus_novemcinctus -1.0219 0.5952 -2.2608 -1.0015 0.0946
## (Intercept)-Lynx_rufus 1.4552 2.1323 -1.0822 0.9177 7.0092
## (Intercept)-Didelphis_virginiana -1.9378 0.6827 -3.3897 -1.9055 -0.7231
## (Intercept)-Sylvilagus_floridanus -0.6694 0.7053 -2.0376 -0.6774 0.8095
## (Intercept)-Sciurus_carolinensis -1.9916 0.6974 -3.4945 -1.9407 -0.7447
## (Intercept)-Vulpes_vulpes -1.2199 1.6387 -3.7838 -1.4425 2.7824
## (Intercept)-Sus_scrofa -2.7484 0.9453 -4.7494 -2.6802 -1.0740
## Tree_Density-Odocoileus_virginianus -0.4170 0.6414 -1.5468 -0.4692 1.0012
## Tree_Density-Canis_latrans -0.8758 0.5366 -2.0694 -0.8183 -0.0009
## Tree_Density-Sciurus_niger -0.7972 0.7952 -2.5655 -0.7387 0.6291
## Tree_Density-Procyon_lotor -0.4885 0.4024 -1.2855 -0.4964 0.3267
## Tree_Density-Dasypus_novemcinctus -1.3286 0.8631 -3.6210 -1.1536 -0.1953
## Tree_Density-Lynx_rufus -0.0736 0.7777 -1.3632 -0.1797 1.7691
## Tree_Density-Didelphis_virginiana -1.0209 0.7465 -2.9038 -0.8845 0.0913
## Tree_Density-Sylvilagus_floridanus -1.0405 0.7277 -2.7739 -0.9309 0.0691
## Tree_Density-Sciurus_carolinensis -0.9632 0.7314 -2.7558 -0.8490 0.1440
## Tree_Density-Vulpes_vulpes -0.6648 0.7912 -2.2989 -0.6426 0.8799
## Tree_Density-Sus_scrofa -0.9696 0.7900 -2.9572 -0.8546 0.2600
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8205 0.6582 -0.5111 0.8256 2.1334
## Avg_Canopy_Cover-Canis_latrans 0.1662 0.4801 -0.7691 0.1634 1.1053
## Avg_Canopy_Cover-Sciurus_niger 1.0452 0.7814 -0.3562 0.9859 2.7715
## Avg_Canopy_Cover-Procyon_lotor 1.0307 0.4502 0.1880 1.0069 1.9617
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0140 0.4130 0.2383 1.0067 1.8881
## Avg_Canopy_Cover-Lynx_rufus 0.9307 0.7278 -0.4640 0.9047 2.4571
## Avg_Canopy_Cover-Didelphis_virginiana 1.2642 0.4723 0.4349 1.2265 2.3041
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.6315 0.6897 0.5853 1.5299 3.2979
## Avg_Canopy_Cover-Sciurus_carolinensis 1.2382 0.4730 0.4142 1.2015 2.2968
## Avg_Canopy_Cover-Vulpes_vulpes 1.0634 0.5910 -0.0046 1.0248 2.3704
## Avg_Canopy_Cover-Sus_scrofa 1.2551 0.5243 0.3286 1.2092 2.3713
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0020 720
## (Intercept)-Canis_latrans 1.0009 2501
## (Intercept)-Sciurus_niger 1.0149 337
## (Intercept)-Procyon_lotor 1.0049 3264
## (Intercept)-Dasypus_novemcinctus 1.0035 3364
## (Intercept)-Lynx_rufus 1.0070 287
## (Intercept)-Didelphis_virginiana 1.0022 3111
## (Intercept)-Sylvilagus_floridanus 1.0005 3000
## (Intercept)-Sciurus_carolinensis 1.0006 2704
## (Intercept)-Vulpes_vulpes 1.1280 264
## (Intercept)-Sus_scrofa 1.0048 1522
## Tree_Density-Odocoileus_virginianus 1.0063 2389
## Tree_Density-Canis_latrans 1.0005 2678
## Tree_Density-Sciurus_niger 1.0014 1759
## Tree_Density-Procyon_lotor 1.0006 3214
## Tree_Density-Dasypus_novemcinctus 1.0029 1393
## Tree_Density-Lynx_rufus 1.0043 831
## Tree_Density-Didelphis_virginiana 1.0016 1651
## Tree_Density-Sylvilagus_floridanus 1.0015 1997
## Tree_Density-Sciurus_carolinensis 0.9999 2098
## Tree_Density-Vulpes_vulpes 1.0020 1737
## Tree_Density-Sus_scrofa 1.0019 2043
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0017 2899
## Avg_Canopy_Cover-Canis_latrans 1.0020 2521
## Avg_Canopy_Cover-Sciurus_niger 1.0168 1262
## Avg_Canopy_Cover-Procyon_lotor 1.0031 3059
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0000 3089
## Avg_Canopy_Cover-Lynx_rufus 1.0015 1323
## Avg_Canopy_Cover-Didelphis_virginiana 1.0011 2940
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0004 1788
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0022 2563
## Avg_Canopy_Cover-Vulpes_vulpes 1.0019 2305
## Avg_Canopy_Cover-Sus_scrofa 1.0047 2627
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5259 0.0797 0.3729 0.5247 0.6848
## (Intercept)-Canis_latrans -2.4677 0.2039 -2.9198 -2.4600 -2.0870
## (Intercept)-Sciurus_niger -4.2183 0.6374 -5.4310 -4.2256 -2.9462
## (Intercept)-Procyon_lotor -2.1592 0.1503 -2.4608 -2.1581 -1.8702
## (Intercept)-Dasypus_novemcinctus -1.4355 0.1547 -1.7482 -1.4320 -1.1351
## (Intercept)-Lynx_rufus -3.6419 0.3732 -4.3560 -3.6432 -2.9104
## (Intercept)-Didelphis_virginiana -2.1092 0.2704 -2.6596 -2.0973 -1.6129
## (Intercept)-Sylvilagus_floridanus -3.0300 0.3037 -3.6550 -3.0173 -2.4687
## (Intercept)-Sciurus_carolinensis -2.2480 0.2837 -2.8301 -2.2391 -1.7231
## (Intercept)-Vulpes_vulpes -4.0024 0.8024 -5.6577 -3.9541 -2.5732
## (Intercept)-Sus_scrofa -2.7472 0.4912 -3.8090 -2.7189 -1.8963
## week-Odocoileus_virginianus 1.2774 0.1222 1.0396 1.2769 1.5188
## week-Canis_latrans 0.5826 0.2615 0.0776 0.5783 1.1058
## week-Sciurus_niger -0.3929 0.5501 -1.5785 -0.3361 0.5596
## week-Procyon_lotor 0.2066 0.2113 -0.2061 0.2077 0.6181
## week-Dasypus_novemcinctus 0.1069 0.2215 -0.3351 0.1067 0.5364
## week-Lynx_rufus 0.3727 0.3413 -0.2891 0.3732 1.0455
## week-Didelphis_virginiana 0.0558 0.3600 -0.6620 0.0632 0.7476
## week-Sylvilagus_floridanus 0.0677 0.3422 -0.6150 0.0713 0.7434
## week-Sciurus_carolinensis 0.7830 0.3611 0.1174 0.7685 1.5373
## week-Vulpes_vulpes 0.1934 0.5267 -0.8793 0.2087 1.1827
## week-Sus_scrofa 0.6712 0.4435 -0.1676 0.6639 1.5751
## I(week^2)-Odocoileus_virginianus -0.5266 0.0507 -0.6284 -0.5270 -0.4274
## I(week^2)-Canis_latrans -0.2409 0.1079 -0.4592 -0.2389 -0.0297
## I(week^2)-Sciurus_niger -0.2794 0.2351 -0.7687 -0.2667 0.1511
## I(week^2)-Procyon_lotor -0.1312 0.0918 -0.3166 -0.1313 0.0510
## I(week^2)-Dasypus_novemcinctus -0.1765 0.1026 -0.3807 -0.1771 0.0236
## I(week^2)-Lynx_rufus -0.2340 0.1502 -0.5425 -0.2287 0.0543
## I(week^2)-Didelphis_virginiana -0.4098 0.2046 -0.8515 -0.3939 -0.0566
## I(week^2)-Sylvilagus_floridanus -0.1802 0.1573 -0.4919 -0.1782 0.1242
## I(week^2)-Sciurus_carolinensis -0.2766 0.1411 -0.5653 -0.2733 -0.0162
## I(week^2)-Vulpes_vulpes -0.3985 0.2434 -0.9315 -0.3767 0.0225
## I(week^2)-Sus_scrofa -0.2371 0.1770 -0.6004 -0.2338 0.1088
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5471
## (Intercept)-Canis_latrans 1.0027 2511
## (Intercept)-Sciurus_niger 1.0217 405
## (Intercept)-Procyon_lotor 1.0001 4346
## (Intercept)-Dasypus_novemcinctus 1.0007 5250
## (Intercept)-Lynx_rufus 1.0029 635
## (Intercept)-Didelphis_virginiana 1.0027 4088
## (Intercept)-Sylvilagus_floridanus 1.0039 2102
## (Intercept)-Sciurus_carolinensis 1.0016 3494
## (Intercept)-Vulpes_vulpes 1.0410 369
## (Intercept)-Sus_scrofa 1.0072 2339
## week-Odocoileus_virginianus 0.9998 4909
## week-Canis_latrans 1.0000 3938
## week-Sciurus_niger 1.0091 915
## week-Procyon_lotor 1.0024 4612
## week-Dasypus_novemcinctus 1.0020 4482
## week-Lynx_rufus 1.0110 2529
## week-Didelphis_virginiana 1.0072 2805
## week-Sylvilagus_floridanus 1.0002 2884
## week-Sciurus_carolinensis 1.0022 4266
## week-Vulpes_vulpes 1.0068 1873
## week-Sus_scrofa 1.0008 4263
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0013 4048
## I(week^2)-Sciurus_niger 1.0054 1058
## I(week^2)-Procyon_lotor 1.0006 4294
## I(week^2)-Dasypus_novemcinctus 1.0006 4464
## I(week^2)-Lynx_rufus 1.0000 2156
## I(week^2)-Didelphis_virginiana 1.0003 2086
## I(week^2)-Sylvilagus_floridanus 1.0011 2483
## I(week^2)-Sciurus_carolinensis 1.0002 4490
## I(week^2)-Vulpes_vulpes 1.0048 1600
## I(week^2)-Sus_scrofa 1.0012 3786
#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): 1.7935
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2890 0.6439 -1.5630 -0.2983 1.0444 1.0041 2241
## Cogon_Patch_Size -0.2833 0.4099 -1.1671 -0.2574 0.4615 1.0024 1913
## Avg_Cogongrass_Cover 0.2587 0.2797 -0.2903 0.2585 0.8083 1.0011 1320
## total_shrub_cover -0.2204 0.2871 -0.7870 -0.2135 0.3390 1.0044 2126
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.3327 3.5330 0.8491 3.3748 13.6137 1.0041 1293
## Cogon_Patch_Size 0.9884 1.5167 0.0648 0.5516 4.6038 1.0435 1118
## Avg_Cogongrass_Cover 0.2829 0.3479 0.0356 0.1793 1.1695 1.0034 2262
## total_shrub_cover 0.3304 0.3993 0.0404 0.2115 1.3575 1.0451 2107
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2365 1.1356 0.0977 0.9222 4.2467 1.0447 540
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3151 0.4429 -3.1517 -2.3263 -1.3893 1.0000 4429
## week 0.3545 0.2350 -0.1206 0.3591 0.8063 1.0030 3503
## I(week^2) -0.2807 0.1015 -0.4913 -0.2792 -0.0818 1.0039 2797
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2337 1.4518 0.7839 1.8990 5.7830 1.0058 3417
## week 0.4206 0.2968 0.1075 0.3450 1.2055 1.0005 2604
## I(week^2) 0.0723 0.0521 0.0223 0.0581 0.2056 1.0071 1977
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7959 1.5772 1.2265 3.6300
## (Intercept)-Canis_latrans 0.4129 0.7393 -1.0129 0.3987
## (Intercept)-Sciurus_niger -0.5962 1.3727 -2.7503 -0.7527
## (Intercept)-Procyon_lotor 0.5710 0.7448 -0.9443 0.5813
## (Intercept)-Dasypus_novemcinctus -0.7759 0.6667 -2.1617 -0.7567
## (Intercept)-Lynx_rufus -0.1714 0.9978 -1.9986 -0.2373
## (Intercept)-Didelphis_virginiana -1.4828 0.7445 -3.0210 -1.4552
## (Intercept)-Sylvilagus_floridanus -0.3847 0.8846 -1.9861 -0.4255
## (Intercept)-Sciurus_carolinensis -1.6659 0.7823 -3.3052 -1.6303
## (Intercept)-Vulpes_vulpes -1.0877 1.4534 -3.4581 -1.2436
## (Intercept)-Sus_scrofa -2.1419 0.9705 -4.2334 -2.1079
## Cogon_Patch_Size-Odocoileus_virginianus -0.0821 0.7328 -1.4004 -0.1331
## Cogon_Patch_Size-Canis_latrans 0.6741 0.7353 -0.4000 0.5509
## Cogon_Patch_Size-Sciurus_niger -0.7128 0.9373 -2.9728 -0.5776
## Cogon_Patch_Size-Procyon_lotor -0.2757 0.4727 -1.2130 -0.2721
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1643 0.4174 -1.0050 -0.1568
## Cogon_Patch_Size-Lynx_rufus -0.3102 0.7708 -1.7287 -0.3319
## Cogon_Patch_Size-Didelphis_virginiana 0.5723 0.5152 -0.3483 0.5376
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9619 0.8916 -3.1235 -0.8002
## Cogon_Patch_Size-Sciurus_carolinensis -0.8244 0.7571 -2.7778 -0.6808
## Cogon_Patch_Size-Vulpes_vulpes -0.6351 0.9855 -2.9442 -0.5071
## Cogon_Patch_Size-Sus_scrofa -0.5696 0.8929 -2.7271 -0.4464
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2560 0.5190 -0.7523 0.2450
## Avg_Cogongrass_Cover-Canis_latrans 0.3121 0.3886 -0.4191 0.2921
## Avg_Cogongrass_Cover-Sciurus_niger -0.0430 0.5925 -1.3872 0.0048
## Avg_Cogongrass_Cover-Procyon_lotor 0.2867 0.4110 -0.4775 0.2629
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4075 0.3685 -0.2857 0.3891
## Avg_Cogongrass_Cover-Lynx_rufus 0.5498 0.4711 -0.2401 0.4977
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2626 0.3967 -0.5503 0.2678
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0164 0.4654 -1.0273 0.0119
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4914 0.3976 -0.2591 0.4758
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3441 0.4646 -0.5281 0.3248
## Avg_Cogongrass_Cover-Sus_scrofa -0.0069 0.5539 -1.2423 0.0529
## total_shrub_cover-Odocoileus_virginianus -0.1350 0.5169 -1.1511 -0.1489
## total_shrub_cover-Canis_latrans 0.0813 0.4243 -0.6726 0.0548
## total_shrub_cover-Sciurus_niger -0.4150 0.5278 -1.5887 -0.3758
## total_shrub_cover-Procyon_lotor -0.6686 0.4698 -1.7470 -0.6178
## total_shrub_cover-Dasypus_novemcinctus -0.0440 0.3515 -0.7172 -0.0508
## total_shrub_cover-Lynx_rufus -0.5389 0.5548 -1.8381 -0.4818
## total_shrub_cover-Didelphis_virginiana -0.2577 0.4120 -1.1403 -0.2453
## total_shrub_cover-Sylvilagus_floridanus -0.2538 0.4934 -1.2834 -0.2307
## total_shrub_cover-Sciurus_carolinensis -0.0634 0.4009 -0.8220 -0.0777
## total_shrub_cover-Vulpes_vulpes -0.2517 0.5398 -1.3943 -0.2267
## total_shrub_cover-Sus_scrofa 0.0643 0.5081 -0.8543 0.0272
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.5281 1.0028 1047
## (Intercept)-Canis_latrans 1.9435 1.0044 2384
## (Intercept)-Sciurus_niger 2.5959 1.0109 422
## (Intercept)-Procyon_lotor 2.0207 1.0017 2089
## (Intercept)-Dasypus_novemcinctus 0.4864 1.0042 2748
## (Intercept)-Lynx_rufus 1.9296 1.0023 1377
## (Intercept)-Didelphis_virginiana -0.0468 1.0022 2721
## (Intercept)-Sylvilagus_floridanus 1.5145 1.0074 1494
## (Intercept)-Sciurus_carolinensis -0.1584 1.0036 2271
## (Intercept)-Vulpes_vulpes 2.3043 1.0084 481
## (Intercept)-Sus_scrofa -0.3810 1.0221 1518
## Cogon_Patch_Size-Odocoileus_virginianus 1.5513 1.0005 3620
## Cogon_Patch_Size-Canis_latrans 2.4445 1.0105 1759
## Cogon_Patch_Size-Sciurus_niger 0.7553 1.0018 1570
## Cogon_Patch_Size-Procyon_lotor 0.6844 1.0014 3189
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6377 1.0004 3898
## Cogon_Patch_Size-Lynx_rufus 1.3750 1.0339 1897
## Cogon_Patch_Size-Didelphis_virginiana 1.6533 1.0109 2640
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3115 1.0079 1237
## Cogon_Patch_Size-Sciurus_carolinensis 0.2409 1.0036 1617
## Cogon_Patch_Size-Vulpes_vulpes 0.9513 1.0091 1234
## Cogon_Patch_Size-Sus_scrofa 0.8023 1.0111 1878
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3239 1.0004 2865
## Avg_Cogongrass_Cover-Canis_latrans 1.1232 1.0015 2910
## Avg_Cogongrass_Cover-Sciurus_niger 0.9729 0.9999 1708
## Avg_Cogongrass_Cover-Procyon_lotor 1.1400 1.0009 2784
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1801 1.0023 2485
## Avg_Cogongrass_Cover-Lynx_rufus 1.6241 1.0029 2215
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0339 1.0017 2596
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8371 1.0025 2411
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3363 1.0045 2771
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3112 1.0016 2108
## Avg_Cogongrass_Cover-Sus_scrofa 0.9091 1.0018 1897
## total_shrub_cover-Odocoileus_virginianus 0.9556 1.0026 3149
## total_shrub_cover-Canis_latrans 1.0041 1.0054 2948
## total_shrub_cover-Sciurus_niger 0.5366 1.0188 2602
## total_shrub_cover-Procyon_lotor 0.1200 1.0019 2521
## total_shrub_cover-Dasypus_novemcinctus 0.6694 1.0035 3851
## total_shrub_cover-Lynx_rufus 0.3781 1.0243 2207
## total_shrub_cover-Didelphis_virginiana 0.5501 1.0075 3840
## total_shrub_cover-Sylvilagus_floridanus 0.6579 1.0009 2523
## total_shrub_cover-Sciurus_carolinensis 0.7500 1.0007 3352
## total_shrub_cover-Vulpes_vulpes 0.7538 1.0051 2532
## total_shrub_cover-Sus_scrofa 1.1637 1.0076 3183
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5261 0.0807 0.3710 0.5264 0.6858
## (Intercept)-Canis_latrans -2.4348 0.1894 -2.8149 -2.4288 -2.0752
## (Intercept)-Sciurus_niger -3.9719 0.6174 -5.2045 -3.9556 -2.8214
## (Intercept)-Procyon_lotor -2.1698 0.1506 -2.4715 -2.1678 -1.8842
## (Intercept)-Dasypus_novemcinctus -1.4381 0.1568 -1.7470 -1.4371 -1.1354
## (Intercept)-Lynx_rufus -3.3616 0.3289 -4.0292 -3.3523 -2.7415
## (Intercept)-Didelphis_virginiana -2.1112 0.2722 -2.6710 -2.1076 -1.5982
## (Intercept)-Sylvilagus_floridanus -3.1591 0.3424 -3.8718 -3.1442 -2.5204
## (Intercept)-Sciurus_carolinensis -2.2602 0.2935 -2.8699 -2.2518 -1.7212
## (Intercept)-Vulpes_vulpes -3.9943 0.7757 -5.5058 -3.9815 -2.5377
## (Intercept)-Sus_scrofa -2.8256 0.5122 -3.9224 -2.7934 -1.9204
## week-Odocoileus_virginianus 1.2786 0.1251 1.0397 1.2774 1.5249
## week-Canis_latrans 0.5784 0.2539 0.0868 0.5725 1.0918
## week-Sciurus_niger -0.4043 0.5385 -1.5751 -0.3574 0.5282
## week-Procyon_lotor 0.2075 0.2107 -0.2047 0.2106 0.6212
## week-Dasypus_novemcinctus 0.1068 0.2228 -0.3330 0.1072 0.5337
## week-Lynx_rufus 0.3847 0.3495 -0.3027 0.3871 1.0819
## week-Didelphis_virginiana 0.0645 0.3708 -0.6870 0.0730 0.7853
## week-Sylvilagus_floridanus 0.0625 0.3413 -0.6344 0.0656 0.7209
## week-Sciurus_carolinensis 0.7892 0.3732 0.1004 0.7678 1.5706
## week-Vulpes_vulpes 0.2083 0.5109 -0.8788 0.2286 1.1886
## week-Sus_scrofa 0.6783 0.4466 -0.1912 0.6612 1.5926
## I(week^2)-Odocoileus_virginianus -0.5272 0.0508 -0.6251 -0.5271 -0.4277
## I(week^2)-Canis_latrans -0.2413 0.1055 -0.4568 -0.2407 -0.0375
## I(week^2)-Sciurus_niger -0.2789 0.2328 -0.7820 -0.2695 0.1449
## I(week^2)-Procyon_lotor -0.1350 0.0897 -0.3121 -0.1353 0.0413
## I(week^2)-Dasypus_novemcinctus -0.1793 0.1024 -0.3892 -0.1778 0.0141
## I(week^2)-Lynx_rufus -0.2359 0.1501 -0.5365 -0.2356 0.0548
## I(week^2)-Didelphis_virginiana -0.4084 0.2127 -0.8971 -0.3866 -0.0519
## I(week^2)-Sylvilagus_floridanus -0.1779 0.1598 -0.5061 -0.1732 0.1316
## I(week^2)-Sciurus_carolinensis -0.2786 0.1448 -0.5740 -0.2733 -0.0024
## I(week^2)-Vulpes_vulpes -0.3993 0.2450 -0.9517 -0.3757 0.0235
## I(week^2)-Sus_scrofa -0.2419 0.1766 -0.5954 -0.2391 0.0905
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0011 5250
## (Intercept)-Canis_latrans 1.0042 3291
## (Intercept)-Sciurus_niger 1.0291 533
## (Intercept)-Procyon_lotor 1.0003 4198
## (Intercept)-Dasypus_novemcinctus 1.0001 5950
## (Intercept)-Lynx_rufus 1.0014 1176
## (Intercept)-Didelphis_virginiana 1.0000 4060
## (Intercept)-Sylvilagus_floridanus 1.0045 1371
## (Intercept)-Sciurus_carolinensis 0.9998 3772
## (Intercept)-Vulpes_vulpes 1.0030 451
## (Intercept)-Sus_scrofa 1.0040 1850
## week-Odocoileus_virginianus 1.0000 5250
## week-Canis_latrans 1.0005 3921
## week-Sciurus_niger 0.9997 1159
## week-Procyon_lotor 1.0027 4516
## week-Dasypus_novemcinctus 1.0000 5200
## week-Lynx_rufus 1.0023 2741
## week-Didelphis_virginiana 1.0073 2855
## week-Sylvilagus_floridanus 1.0023 2555
## week-Sciurus_carolinensis 0.9998 3183
## week-Vulpes_vulpes 1.0031 1774
## week-Sus_scrofa 1.0043 4023
## I(week^2)-Odocoileus_virginianus 1.0006 4745
## I(week^2)-Canis_latrans 1.0016 3957
## I(week^2)-Sciurus_niger 1.0129 1084
## I(week^2)-Procyon_lotor 1.0010 4881
## I(week^2)-Dasypus_novemcinctus 1.0000 4439
## I(week^2)-Lynx_rufus 1.0107 2586
## I(week^2)-Didelphis_virginiana 1.0013 1647
## I(week^2)-Sylvilagus_floridanus 1.0059 2422
## I(week^2)-Sciurus_carolinensis 1.0004 3351
## I(week^2)-Vulpes_vulpes 1.0055 1241
## I(week^2)-Sus_scrofa 1.0011 4499
#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): 1.793
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2326 0.6237 -1.4213 -0.2664 1.0859 1.0201 1773
## Veg_shannon_index 0.3729 0.2670 -0.1300 0.3641 0.9296 1.0011 1899
## Avg_Cogongrass_Cover 0.3298 0.2644 -0.2003 0.3339 0.8368 1.0009 1843
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0537 3.4417 0.8116 3.1009 13.1438 1.0480 1115
## Veg_shannon_index 0.2969 0.3750 0.0391 0.1879 1.2259 1.0012 1665
## Avg_Cogongrass_Cover 0.2866 0.3786 0.0378 0.1802 1.1284 1.0498 1762
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7674 0.7507 0.0628 0.5502 2.8172 1.0089 533
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3223 0.4558 -3.2109 -2.3259 -1.3941 1.0029 4309
## week 0.3606 0.2370 -0.1442 0.3679 0.8183 1.0005 3076
## I(week^2) -0.2823 0.1000 -0.4875 -0.2778 -0.0935 1.0011 2829
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2918 1.4225 0.7847 1.9242 5.7891 1.0043 2069
## week 0.4311 0.3266 0.1071 0.3406 1.2863 1.0040 1733
## I(week^2) 0.0703 0.0488 0.0221 0.0571 0.2038 1.0031 2887
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6699 1.5144 1.3355 3.4437
## (Intercept)-Canis_latrans 0.2559 0.6294 -0.9708 0.2539
## (Intercept)-Sciurus_niger -0.2295 1.4862 -2.3529 -0.4936
## (Intercept)-Procyon_lotor 0.5465 0.6367 -0.7518 0.5516
## (Intercept)-Dasypus_novemcinctus -0.7398 0.5820 -1.9163 -0.7295
## (Intercept)-Lynx_rufus 0.0912 1.1374 -1.6200 -0.0432
## (Intercept)-Didelphis_virginiana -1.5137 0.6547 -2.8838 -1.4890
## (Intercept)-Sylvilagus_floridanus -0.3121 0.7924 -1.7087 -0.3499
## (Intercept)-Sciurus_carolinensis -1.4819 0.6603 -2.8609 -1.4631
## (Intercept)-Vulpes_vulpes -0.8605 1.4224 -3.1101 -1.0571
## (Intercept)-Sus_scrofa -2.2100 0.8915 -4.0923 -2.1654
## Veg_shannon_index-Odocoileus_virginianus 0.3008 0.5009 -0.7480 0.3037
## Veg_shannon_index-Canis_latrans 0.6479 0.3848 -0.0295 0.6220
## Veg_shannon_index-Sciurus_niger 0.3733 0.5490 -0.6515 0.3549
## Veg_shannon_index-Procyon_lotor 0.4959 0.3800 -0.1811 0.4723
## Veg_shannon_index-Dasypus_novemcinctus 0.2160 0.3323 -0.4447 0.2152
## Veg_shannon_index-Lynx_rufus 0.1851 0.5216 -0.9551 0.2166
## Veg_shannon_index-Didelphis_virginiana 0.5166 0.3837 -0.1886 0.4973
## Veg_shannon_index-Sylvilagus_floridanus 0.4743 0.4354 -0.3328 0.4465
## Veg_shannon_index-Sciurus_carolinensis 0.0318 0.3829 -0.7801 0.0478
## Veg_shannon_index-Vulpes_vulpes 0.1419 0.4919 -0.9325 0.1673
## Veg_shannon_index-Sus_scrofa 0.7564 0.5670 -0.1256 0.6856
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3310 0.5018 -0.6383 0.3262
## Avg_Cogongrass_Cover-Canis_latrans 0.5472 0.3726 -0.1187 0.5234
## Avg_Cogongrass_Cover-Sciurus_niger 0.0113 0.6035 -1.3573 0.0657
## Avg_Cogongrass_Cover-Procyon_lotor 0.4280 0.3953 -0.2725 0.3981
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4389 0.3269 -0.1997 0.4340
## Avg_Cogongrass_Cover-Lynx_rufus 0.5754 0.4396 -0.2073 0.5522
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4708 0.3600 -0.2214 0.4657
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0221 0.4518 -1.0020 0.0101
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4192 0.3580 -0.2618 0.4159
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4057 0.4854 -0.5430 0.4005
## Avg_Cogongrass_Cover-Sus_scrofa 0.0332 0.5184 -1.1237 0.0920
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.1855 1.0218 958
## (Intercept)-Canis_latrans 1.5120 1.0061 2945
## (Intercept)-Sciurus_niger 3.4603 1.0914 317
## (Intercept)-Procyon_lotor 1.7884 1.0016 3023
## (Intercept)-Dasypus_novemcinctus 0.3442 1.0044 3575
## (Intercept)-Lynx_rufus 2.6112 1.0086 543
## (Intercept)-Didelphis_virginiana -0.2758 1.0044 3033
## (Intercept)-Sylvilagus_floridanus 1.3033 1.0025 1629
## (Intercept)-Sciurus_carolinensis -0.2132 1.0044 3262
## (Intercept)-Vulpes_vulpes 2.4631 1.0504 334
## (Intercept)-Sus_scrofa -0.6016 1.0069 2100
## Veg_shannon_index-Odocoileus_virginianus 1.2536 1.0002 3231
## Veg_shannon_index-Canis_latrans 1.4791 1.0002 3169
## Veg_shannon_index-Sciurus_niger 1.5133 1.0027 2197
## Veg_shannon_index-Procyon_lotor 1.2851 1.0014 3157
## Veg_shannon_index-Dasypus_novemcinctus 0.8733 1.0009 3966
## Veg_shannon_index-Lynx_rufus 1.1357 1.0019 2455
## Veg_shannon_index-Didelphis_virginiana 1.3122 1.0032 3820
## Veg_shannon_index-Sylvilagus_floridanus 1.4119 1.0020 2969
## Veg_shannon_index-Sciurus_carolinensis 0.7323 1.0008 3250
## Veg_shannon_index-Vulpes_vulpes 1.0409 1.0060 2392
## Veg_shannon_index-Sus_scrofa 2.0877 1.0005 1936
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3570 1.0021 3617
## Avg_Cogongrass_Cover-Canis_latrans 1.3709 1.0069 3908
## Avg_Cogongrass_Cover-Sciurus_niger 1.0398 1.0094 1285
## Avg_Cogongrass_Cover-Procyon_lotor 1.2893 1.0047 2811
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0978 1.0045 3352
## Avg_Cogongrass_Cover-Lynx_rufus 1.5471 1.0016 3035
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2065 1.0007 3603
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7843 1.0006 2455
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1341 1.0017 3560
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3815 1.0048 2909
## Avg_Cogongrass_Cover-Sus_scrofa 0.8896 1.0017 2255
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5246 0.0797 0.3671 0.5236 0.6791
## (Intercept)-Canis_latrans -2.4291 0.1861 -2.8052 -2.4223 -2.0855
## (Intercept)-Sciurus_niger -4.0769 0.6543 -5.3832 -4.0543 -2.8665
## (Intercept)-Procyon_lotor -2.1628 0.1522 -2.4751 -2.1584 -1.8741
## (Intercept)-Dasypus_novemcinctus -1.4395 0.1561 -1.7593 -1.4364 -1.1495
## (Intercept)-Lynx_rufus -3.4085 0.3507 -4.1211 -3.4000 -2.7552
## (Intercept)-Didelphis_virginiana -2.1207 0.2699 -2.6737 -2.1110 -1.6197
## (Intercept)-Sylvilagus_floridanus -3.1299 0.3369 -3.8252 -3.1167 -2.5137
## (Intercept)-Sciurus_carolinensis -2.2621 0.2923 -2.8714 -2.2448 -1.7217
## (Intercept)-Vulpes_vulpes -4.0592 0.7933 -5.6075 -4.0352 -2.5826
## (Intercept)-Sus_scrofa -2.7950 0.4991 -3.8569 -2.7532 -1.9178
## week-Odocoileus_virginianus 1.2786 0.1203 1.0450 1.2775 1.5216
## week-Canis_latrans 0.5835 0.2592 0.0864 0.5830 1.1016
## week-Sciurus_niger -0.4027 0.5590 -1.6866 -0.3474 0.5378
## week-Procyon_lotor 0.2041 0.2056 -0.2038 0.2060 0.6084
## week-Dasypus_novemcinctus 0.1121 0.2216 -0.3265 0.1129 0.5400
## week-Lynx_rufus 0.3893 0.3470 -0.2958 0.3898 1.0673
## week-Didelphis_virginiana 0.0533 0.3790 -0.7277 0.0721 0.7672
## week-Sylvilagus_floridanus 0.0574 0.3445 -0.6386 0.0653 0.7091
## week-Sciurus_carolinensis 0.7914 0.3695 0.1020 0.7798 1.5482
## week-Vulpes_vulpes 0.2016 0.5156 -0.8583 0.2297 1.1423
## week-Sus_scrofa 0.6836 0.4407 -0.1514 0.6748 1.5978
## I(week^2)-Odocoileus_virginianus -0.5273 0.0505 -0.6287 -0.5277 -0.4288
## I(week^2)-Canis_latrans -0.2425 0.1076 -0.4573 -0.2406 -0.0362
## I(week^2)-Sciurus_niger -0.2819 0.2340 -0.7877 -0.2640 0.1299
## I(week^2)-Procyon_lotor -0.1323 0.0897 -0.3149 -0.1297 0.0401
## I(week^2)-Dasypus_novemcinctus -0.1815 0.1027 -0.3922 -0.1787 0.0116
## I(week^2)-Lynx_rufus -0.2420 0.1532 -0.5569 -0.2393 0.0525
## I(week^2)-Didelphis_virginiana -0.4065 0.2094 -0.8767 -0.3902 -0.0557
## I(week^2)-Sylvilagus_floridanus -0.1790 0.1552 -0.4911 -0.1751 0.1152
## I(week^2)-Sciurus_carolinensis -0.2800 0.1418 -0.5640 -0.2773 -0.0073
## I(week^2)-Vulpes_vulpes -0.3994 0.2366 -0.9352 -0.3808 0.0256
## I(week^2)-Sus_scrofa -0.2398 0.1775 -0.6051 -0.2331 0.1004
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0010 5250
## (Intercept)-Canis_latrans 1.0008 3525
## (Intercept)-Sciurus_niger 1.0246 383
## (Intercept)-Procyon_lotor 1.0038 4188
## (Intercept)-Dasypus_novemcinctus 1.0023 4930
## (Intercept)-Lynx_rufus 1.0003 979
## (Intercept)-Didelphis_virginiana 1.0023 4304
## (Intercept)-Sylvilagus_floridanus 1.0016 1313
## (Intercept)-Sciurus_carolinensis 1.0098 3385
## (Intercept)-Vulpes_vulpes 1.0186 358
## (Intercept)-Sus_scrofa 1.0010 2203
## week-Odocoileus_virginianus 1.0025 5250
## week-Canis_latrans 1.0019 3962
## week-Sciurus_niger 1.0328 785
## week-Procyon_lotor 1.0034 4408
## week-Dasypus_novemcinctus 1.0001 4849
## week-Lynx_rufus 1.0014 2883
## week-Didelphis_virginiana 1.0008 2914
## week-Sylvilagus_floridanus 1.0006 3019
## week-Sciurus_carolinensis 1.0004 4022
## week-Vulpes_vulpes 1.0015 1825
## week-Sus_scrofa 1.0013 3697
## I(week^2)-Odocoileus_virginianus 1.0027 5009
## I(week^2)-Canis_latrans 1.0000 4051
## I(week^2)-Sciurus_niger 1.0243 1010
## I(week^2)-Procyon_lotor 1.0021 4142
## I(week^2)-Dasypus_novemcinctus 1.0035 4446
## I(week^2)-Lynx_rufus 1.0008 2608
## I(week^2)-Didelphis_virginiana 1.0002 1693
## I(week^2)-Sylvilagus_floridanus 0.9999 2548
## I(week^2)-Sciurus_carolinensis 1.0003 4383
## I(week^2)-Vulpes_vulpes 1.0016 1690
## I(week^2)-Sus_scrofa 1.0005 4713
#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): 1.7735
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2370 0.5711 -1.3479 -0.2464 0.8922 1.0055 1646
## Avg_Cogongrass_Cover 0.1916 0.2356 -0.2810 0.1973 0.6466 1.0040 2582
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3729 2.7375 0.7085 2.6388 10.4573 1.0046 1374
## Avg_Cogongrass_Cover 0.2528 0.2941 0.0353 0.1645 1.0054 1.0096 2528
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6916 0.7424 0.0531 0.4699 2.6913 1.0226 425
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3111 0.4508 -3.1900 -2.3189 -1.3918 1.0010 3899
## week 0.3530 0.2376 -0.1461 0.3614 0.8041 1.0012 2945
## I(week^2) -0.2847 0.1030 -0.4896 -0.2832 -0.0885 1.0013 2607
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2379 1.4648 0.7722 1.8828 5.6600 1.0150 1636
## week 0.4321 0.4105 0.1058 0.3403 1.2839 1.0360 2254
## I(week^2) 0.0736 0.0559 0.0226 0.0596 0.2067 1.0182 2245
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3574 1.2955 1.2137 3.2048
## (Intercept)-Canis_latrans 0.2876 0.5989 -0.8666 0.2781
## (Intercept)-Sciurus_niger -0.4569 1.2850 -2.3649 -0.6513
## (Intercept)-Procyon_lotor 0.5174 0.5954 -0.6876 0.5224
## (Intercept)-Dasypus_novemcinctus -0.6829 0.5700 -1.8455 -0.6806
## (Intercept)-Lynx_rufus -0.0114 0.9096 -1.5267 -0.1059
## (Intercept)-Didelphis_virginiana -1.3700 0.6146 -2.5947 -1.3606
## (Intercept)-Sylvilagus_floridanus -0.3252 0.7459 -1.6621 -0.3577
## (Intercept)-Sciurus_carolinensis -1.4378 0.6331 -2.7296 -1.4215
## (Intercept)-Vulpes_vulpes -0.8878 1.3833 -3.0686 -1.0939
## (Intercept)-Sus_scrofa -1.8751 0.8133 -3.5283 -1.8578
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1684 0.4552 -0.7193 0.1670
## Avg_Cogongrass_Cover-Canis_latrans 0.3603 0.3483 -0.2764 0.3441
## Avg_Cogongrass_Cover-Sciurus_niger -0.1037 0.5287 -1.2617 -0.0592
## Avg_Cogongrass_Cover-Procyon_lotor 0.2517 0.3504 -0.3962 0.2406
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3277 0.3066 -0.2570 0.3215
## Avg_Cogongrass_Cover-Lynx_rufus 0.4446 0.3869 -0.2111 0.4140
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3349 0.3367 -0.3220 0.3280
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1463 0.4150 -1.0753 -0.1092
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3353 0.3335 -0.3001 0.3266
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2698 0.4335 -0.5933 0.2650
## Avg_Cogongrass_Cover-Sus_scrofa -0.0798 0.4861 -1.1973 -0.0255
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.2589 1.0107 1240
## (Intercept)-Canis_latrans 1.4869 1.0001 2997
## (Intercept)-Sciurus_niger 2.6091 1.0208 390
## (Intercept)-Procyon_lotor 1.6601 1.0078 2395
## (Intercept)-Dasypus_novemcinctus 0.4446 0.9999 3387
## (Intercept)-Lynx_rufus 2.1162 1.0041 1112
## (Intercept)-Didelphis_virginiana -0.1647 1.0066 2862
## (Intercept)-Sylvilagus_floridanus 1.1943 1.0061 1612
## (Intercept)-Sciurus_carolinensis -0.2272 1.0084 2624
## (Intercept)-Vulpes_vulpes 2.4023 1.0100 342
## (Intercept)-Sus_scrofa -0.3159 1.0026 1771
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1061 0.9999 4049
## Avg_Cogongrass_Cover-Canis_latrans 1.1116 1.0032 4362
## Avg_Cogongrass_Cover-Sciurus_niger 0.8111 1.0088 1915
## Avg_Cogongrass_Cover-Procyon_lotor 0.9721 1.0004 4587
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9540 1.0096 4466
## Avg_Cogongrass_Cover-Lynx_rufus 1.3183 1.0015 3972
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0393 1.0019 4295
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5654 1.0002 3061
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0221 1.0013 4097
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1774 1.0068 3536
## Avg_Cogongrass_Cover-Sus_scrofa 0.7328 1.0012 2748
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5247 0.0812 0.3660 0.5253 0.6842
## (Intercept)-Canis_latrans -2.4340 0.1905 -2.8230 -2.4286 -2.0818
## (Intercept)-Sciurus_niger -3.9565 0.6450 -5.2571 -3.9322 -2.7949
## (Intercept)-Procyon_lotor -2.1605 0.1515 -2.4712 -2.1581 -1.8703
## (Intercept)-Dasypus_novemcinctus -1.4373 0.1547 -1.7405 -1.4364 -1.1356
## (Intercept)-Lynx_rufus -3.3765 0.3461 -4.0699 -3.3594 -2.7363
## (Intercept)-Didelphis_virginiana -2.1129 0.2701 -2.6794 -2.1107 -1.6065
## (Intercept)-Sylvilagus_floridanus -3.1127 0.3319 -3.7983 -3.0989 -2.5064
## (Intercept)-Sciurus_carolinensis -2.2632 0.2831 -2.8352 -2.2554 -1.7497
## (Intercept)-Vulpes_vulpes -4.0247 0.8337 -5.7075 -3.9836 -2.5741
## (Intercept)-Sus_scrofa -2.8396 0.5206 -4.0023 -2.8028 -1.9406
## week-Odocoileus_virginianus 1.2797 0.1204 1.0462 1.2781 1.5123
## week-Canis_latrans 0.5791 0.2679 0.0623 0.5718 1.1253
## week-Sciurus_niger -0.4049 0.5501 -1.6002 -0.3607 0.5124
## week-Procyon_lotor 0.1965 0.2095 -0.2087 0.1944 0.6043
## week-Dasypus_novemcinctus 0.1040 0.2265 -0.3389 0.1075 0.5514
## week-Lynx_rufus 0.3864 0.3523 -0.2986 0.3870 1.0710
## week-Didelphis_virginiana 0.0446 0.3733 -0.7192 0.0567 0.7523
## week-Sylvilagus_floridanus 0.0612 0.3432 -0.6302 0.0654 0.7253
## week-Sciurus_carolinensis 0.7970 0.3638 0.1292 0.7846 1.5688
## week-Vulpes_vulpes 0.2037 0.5155 -0.8629 0.2152 1.1665
## week-Sus_scrofa 0.6775 0.4442 -0.1610 0.6596 1.5979
## I(week^2)-Odocoileus_virginianus -0.5273 0.0496 -0.6242 -0.5277 -0.4295
## I(week^2)-Canis_latrans -0.2430 0.1091 -0.4652 -0.2413 -0.0374
## I(week^2)-Sciurus_niger -0.2816 0.2428 -0.8067 -0.2699 0.1564
## I(week^2)-Procyon_lotor -0.1295 0.0903 -0.3078 -0.1277 0.0475
## I(week^2)-Dasypus_novemcinctus -0.1771 0.1028 -0.3804 -0.1769 0.0137
## I(week^2)-Lynx_rufus -0.2438 0.1522 -0.5535 -0.2404 0.0504
## I(week^2)-Didelphis_virginiana -0.4244 0.2180 -0.9289 -0.4025 -0.0623
## I(week^2)-Sylvilagus_floridanus -0.1816 0.1577 -0.5008 -0.1762 0.1142
## I(week^2)-Sciurus_carolinensis -0.2820 0.1438 -0.5749 -0.2783 -0.0042
## I(week^2)-Vulpes_vulpes -0.4141 0.2513 -0.9828 -0.3920 0.0123
## I(week^2)-Sus_scrofa -0.2370 0.1756 -0.5892 -0.2313 0.1038
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 4073
## (Intercept)-Canis_latrans 1.0029 3519
## (Intercept)-Sciurus_niger 1.0191 477
## (Intercept)-Procyon_lotor 1.0008 4502
## (Intercept)-Dasypus_novemcinctus 1.0008 5250
## (Intercept)-Lynx_rufus 1.0010 1019
## (Intercept)-Didelphis_virginiana 1.0042 4036
## (Intercept)-Sylvilagus_floridanus 1.0011 1229
## (Intercept)-Sciurus_carolinensis 1.0004 3617
## (Intercept)-Vulpes_vulpes 1.0153 313
## (Intercept)-Sus_scrofa 1.0013 1683
## week-Odocoileus_virginianus 1.0027 4578
## week-Canis_latrans 1.0048 2783
## week-Sciurus_niger 1.0083 912
## week-Procyon_lotor 1.0002 4678
## week-Dasypus_novemcinctus 1.0009 4759
## week-Lynx_rufus 1.0001 2602
## week-Didelphis_virginiana 1.0016 3284
## week-Sylvilagus_floridanus 1.0023 3158
## week-Sciurus_carolinensis 1.0002 3901
## week-Vulpes_vulpes 1.0044 2139
## week-Sus_scrofa 1.0005 3644
## I(week^2)-Odocoileus_virginianus 1.0013 4865
## I(week^2)-Canis_latrans 1.0057 3365
## I(week^2)-Sciurus_niger 1.0027 1260
## I(week^2)-Procyon_lotor 1.0001 4205
## I(week^2)-Dasypus_novemcinctus 1.0026 4656
## I(week^2)-Lynx_rufus 1.0026 2505
## I(week^2)-Didelphis_virginiana 1.0021 1855
## I(week^2)-Sylvilagus_floridanus 1.0010 2664
## I(week^2)-Sciurus_carolinensis 1.0010 4171
## I(week^2)-Vulpes_vulpes 1.0012 1482
## I(week^2)-Sus_scrofa 1.0002 4604
# 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): 2.0223
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9630 0.6285 -2.1672 -0.9859 0.3521 0.9997 2241
## Avg_Cogongrass_Cover -0.7340 0.3674 -1.4892 -0.7245 -0.0401 1.0033 1474
## I(Avg_Cogongrass_Cover^2) 0.8532 0.3480 0.2604 0.8302 1.6452 1.0071 1325
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6667 3.0708 0.7064 2.9006 11.1561 1.0092 1810
## Avg_Cogongrass_Cover 0.3894 0.5350 0.0395 0.2294 1.6410 1.0194 1721
## I(Avg_Cogongrass_Cover^2) 0.5577 1.6151 0.0358 0.2219 3.0290 1.1042 267
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5797 0.6416 0.0515 0.3761 2.3456 1.047 418
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2756 0.4372 -3.1021 -2.2878 -1.3463 1.0014 4758
## week 0.3524 0.2350 -0.1308 0.3583 0.7987 1.0039 3428
## I(week^2) -0.2851 0.1027 -0.4975 -0.2834 -0.0869 1.0036 2669
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1012 1.4885 0.7324 1.7675 5.5153 1.0164 2643
## week 0.4288 0.3305 0.0997 0.3384 1.3060 1.0007 2060
## I(week^2) 0.0718 0.0561 0.0223 0.0579 0.1976 1.0052 2430
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7873 1.4264 0.4411 2.6393
## (Intercept)-Canis_latrans -0.5303 0.6745 -1.9097 -0.5132
## (Intercept)-Sciurus_niger -0.9936 1.1323 -2.9120 -1.1160
## (Intercept)-Procyon_lotor -0.2331 0.6615 -1.6094 -0.2034
## (Intercept)-Dasypus_novemcinctus -1.4223 0.6290 -2.6998 -1.4083
## (Intercept)-Lynx_rufus -1.2341 0.8964 -2.9585 -1.2475
## (Intercept)-Didelphis_virginiana -2.0244 0.7138 -3.4917 -1.9905
## (Intercept)-Sylvilagus_floridanus -1.0706 0.7807 -2.5246 -1.0891
## (Intercept)-Sciurus_carolinensis -2.4880 0.7636 -4.1728 -2.4441
## (Intercept)-Vulpes_vulpes -2.1863 1.2328 -4.4790 -2.2578
## (Intercept)-Sus_scrofa -2.5143 0.9073 -4.3923 -2.4927
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7280 0.6243 -1.9916 -0.7181
## Avg_Cogongrass_Cover-Canis_latrans -0.4571 0.5134 -1.4097 -0.4848
## Avg_Cogongrass_Cover-Sciurus_niger -0.9894 0.6807 -2.4431 -0.9383
## Avg_Cogongrass_Cover-Procyon_lotor -0.6005 0.5118 -1.5555 -0.6112
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5706 0.4661 -1.4590 -0.5675
## Avg_Cogongrass_Cover-Lynx_rufus -0.6272 0.5536 -1.7161 -0.6296
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4763 0.5171 -1.4644 -0.5033
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1454 0.5968 -2.4730 -1.0768
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8119 0.5345 -1.9251 -0.7914
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7995 0.6077 -2.0947 -0.7775
## Avg_Cogongrass_Cover-Sus_scrofa -1.0350 0.6477 -2.4868 -0.9720
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1869 1.0583 0.1033 1.0057
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2307 0.8047 0.2453 1.0493
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3973 0.7228 -1.2779 0.4512
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1228 0.7302 0.2344 0.9787
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7345 0.3400 0.0855 0.7269
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1869 0.5669 0.3301 1.1073
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5979 0.4036 -0.1690 0.5958
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7643 0.4773 -0.0399 0.7141
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9842 0.3885 0.2885 0.9573
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.9720 0.5367 0.1622 0.8994
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3864 0.6207 -1.0658 0.4627
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.1126 1.0033 1171
## (Intercept)-Canis_latrans 0.7679 1.0042 2177
## (Intercept)-Sciurus_niger 1.6312 1.0079 545
## (Intercept)-Procyon_lotor 1.0026 1.0020 2448
## (Intercept)-Dasypus_novemcinctus -0.2161 1.0068 3405
## (Intercept)-Lynx_rufus 0.6467 1.0066 1282
## (Intercept)-Didelphis_virginiana -0.7095 1.0014 3668
## (Intercept)-Sylvilagus_floridanus 0.5227 1.0056 2001
## (Intercept)-Sciurus_carolinensis -1.1283 1.0055 2533
## (Intercept)-Vulpes_vulpes 0.4948 1.0042 642
## (Intercept)-Sus_scrofa -0.8075 1.0020 1834
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.4870 1.0051 2509
## Avg_Cogongrass_Cover-Canis_latrans 0.6473 1.0022 2512
## Avg_Cogongrass_Cover-Sciurus_niger 0.1561 1.0054 1630
## Avg_Cogongrass_Cover-Procyon_lotor 0.4714 1.0055 2655
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3498 1.0023 2638
## Avg_Cogongrass_Cover-Lynx_rufus 0.4885 1.0033 2340
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6184 1.0003 2092
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1453 1.0125 1970
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1750 1.0016 2017
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3239 1.0038 2464
## Avg_Cogongrass_Cover-Sus_scrofa 0.0703 1.0067 2100
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.2466 1.0557 412
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2749 1.0237 435
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.7286 1.0076 616
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0577 1.0332 443
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4179 1.0008 2715
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5444 1.0213 1272
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4119 0.9999 2507
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8274 1.0039 1659
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7968 1.0041 2161
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2663 1.0167 1076
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3548 1.0079 842
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5255 0.0804 0.3682 0.5258 0.6816
## (Intercept)-Canis_latrans -2.4478 0.1902 -2.8320 -2.4412 -2.0839
## (Intercept)-Sciurus_niger -3.9653 0.6246 -5.2325 -3.9455 -2.8273
## (Intercept)-Procyon_lotor -2.1665 0.1521 -2.4658 -2.1641 -1.8808
## (Intercept)-Dasypus_novemcinctus -1.4363 0.1576 -1.7591 -1.4324 -1.1276
## (Intercept)-Lynx_rufus -3.2584 0.3290 -3.9499 -3.2490 -2.6580
## (Intercept)-Didelphis_virginiana -2.1304 0.2784 -2.7039 -2.1200 -1.6215
## (Intercept)-Sylvilagus_floridanus -3.0977 0.3280 -3.7693 -3.0769 -2.5073
## (Intercept)-Sciurus_carolinensis -2.2538 0.2803 -2.8320 -2.2391 -1.7364
## (Intercept)-Vulpes_vulpes -3.7903 0.7209 -5.3023 -3.7377 -2.5027
## (Intercept)-Sus_scrofa -2.8248 0.5065 -3.9365 -2.7837 -1.9443
## week-Odocoileus_virginianus 1.2770 0.1205 1.0447 1.2757 1.5215
## week-Canis_latrans 0.5857 0.2619 0.0876 0.5826 1.1097
## week-Sciurus_niger -0.4200 0.5580 -1.6239 -0.3801 0.5503
## week-Procyon_lotor 0.2054 0.2115 -0.2143 0.2069 0.6116
## week-Dasypus_novemcinctus 0.1107 0.2256 -0.3218 0.1069 0.5590
## week-Lynx_rufus 0.3900 0.3481 -0.2995 0.3905 1.1013
## week-Didelphis_virginiana 0.0685 0.3729 -0.6879 0.0746 0.7607
## week-Sylvilagus_floridanus 0.0638 0.3442 -0.6305 0.0682 0.7252
## week-Sciurus_carolinensis 0.7879 0.3641 0.1093 0.7726 1.5360
## week-Vulpes_vulpes 0.1996 0.5061 -0.8766 0.2270 1.1501
## week-Sus_scrofa 0.6830 0.4507 -0.1555 0.6686 1.6009
## I(week^2)-Odocoileus_virginianus -0.5266 0.0494 -0.6257 -0.5255 -0.4327
## I(week^2)-Canis_latrans -0.2425 0.1058 -0.4531 -0.2405 -0.0402
## I(week^2)-Sciurus_niger -0.2915 0.2324 -0.7758 -0.2794 0.1323
## I(week^2)-Procyon_lotor -0.1333 0.0907 -0.3107 -0.1330 0.0444
## I(week^2)-Dasypus_novemcinctus -0.1802 0.1040 -0.3925 -0.1801 0.0169
## I(week^2)-Lynx_rufus -0.2368 0.1542 -0.5574 -0.2303 0.0583
## I(week^2)-Didelphis_virginiana -0.4118 0.2115 -0.8861 -0.3886 -0.0523
## I(week^2)-Sylvilagus_floridanus -0.1879 0.1582 -0.5079 -0.1842 0.1061
## I(week^2)-Sciurus_carolinensis -0.2799 0.1429 -0.5748 -0.2748 -0.0156
## I(week^2)-Vulpes_vulpes -0.4005 0.2494 -0.9745 -0.3769 0.0192
## I(week^2)-Sus_scrofa -0.2421 0.1776 -0.6095 -0.2388 0.0931
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5481
## (Intercept)-Canis_latrans 1.0002 3510
## (Intercept)-Sciurus_niger 1.0120 455
## (Intercept)-Procyon_lotor 1.0015 4270
## (Intercept)-Dasypus_novemcinctus 1.0001 5250
## (Intercept)-Lynx_rufus 1.0019 1428
## (Intercept)-Didelphis_virginiana 1.0002 3886
## (Intercept)-Sylvilagus_floridanus 1.0010 1346
## (Intercept)-Sciurus_carolinensis 1.0024 3913
## (Intercept)-Vulpes_vulpes 1.0097 508
## (Intercept)-Sus_scrofa 1.0076 1823
## week-Odocoileus_virginianus 1.0003 5250
## week-Canis_latrans 1.0032 3681
## week-Sciurus_niger 1.0041 895
## week-Procyon_lotor 1.0017 4483
## week-Dasypus_novemcinctus 1.0018 4702
## week-Lynx_rufus 1.0112 3020
## week-Didelphis_virginiana 1.0010 2820
## week-Sylvilagus_floridanus 1.0021 2822
## week-Sciurus_carolinensis 1.0035 4269
## week-Vulpes_vulpes 1.0026 1991
## week-Sus_scrofa 1.0004 4072
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0003 4003
## I(week^2)-Sciurus_niger 1.0048 1379
## I(week^2)-Procyon_lotor 1.0014 4158
## I(week^2)-Dasypus_novemcinctus 1.0001 4818
## I(week^2)-Lynx_rufus 1.0010 2712
## I(week^2)-Didelphis_virginiana 1.0042 1794
## I(week^2)-Sylvilagus_floridanus 1.0051 2445
## I(week^2)-Sciurus_carolinensis 1.0001 4431
## I(week^2)-Vulpes_vulpes 1.0063 1495
## I(week^2)-Sus_scrofa 1.0003 4411
# 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): 1.9078
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9436 1.1272 -3.0706 -0.9734 1.4497 1.0018 1780
## Cogon_Patch_Size -0.2740 0.7441 -1.8804 -0.2386 1.1265 1.0059 989
## Veg_shannon_index 0.9140 0.4722 0.0139 0.8985 1.8935 1.0022 1228
## total_shrub_cover -0.2783 0.4047 -1.1243 -0.2677 0.4959 1.0040 1371
## Avg_Cogongrass_Cover 0.0526 0.9703 -1.8606 0.0753 1.8592 1.0314 413
## Tree_Density -2.0260 0.7775 -3.5999 -2.0110 -0.4781 1.0015 773
## Avg_Canopy_Cover 1.8143 0.5960 0.7552 1.7834 3.1046 1.0055 843
## I(Avg_Cogongrass_Cover^2) 1.4989 0.5495 0.5036 1.4707 2.6639 1.0282 541
## avg_veg_height -0.1786 0.4966 -1.1314 -0.1841 0.8078 1.0168 614
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 22.1429 18.8824 4.4190 16.9068 70.5754 1.0374 518
## Cogon_Patch_Size 3.9785 6.0240 0.1663 2.2657 18.6779 1.0885 622
## Veg_shannon_index 0.8366 1.4032 0.0506 0.4141 3.9678 1.1627 658
## total_shrub_cover 0.6332 0.8985 0.0465 0.3564 2.9012 1.0463 1039
## Avg_Cogongrass_Cover 1.1524 1.8435 0.0526 0.5325 6.1833 1.0281 1112
## Tree_Density 3.6771 7.3703 0.0706 1.4310 20.6312 1.0879 368
## Avg_Canopy_Cover 2.0769 3.2413 0.1114 1.2071 9.4478 1.1450 414
## I(Avg_Cogongrass_Cover^2) 1.0165 2.2109 0.0489 0.3936 5.5400 1.1078 511
## avg_veg_height 0.4696 0.6323 0.0422 0.2669 2.0610 1.0195 2177
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7218 2.1604 0.0632 1.0276 7.5996 1.204 236
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3421 0.4808 -3.2387 -2.3560 -1.3462 1.0024 5250
## week 0.3534 0.2368 -0.1290 0.3596 0.8173 1.0057 3442
## I(week^2) -0.2854 0.0994 -0.4860 -0.2842 -0.0881 1.0020 2690
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5421 1.5125 0.9037 2.1793 6.3462 1.0025 2641
## week 0.4247 0.3227 0.1046 0.3379 1.2629 1.0146 1619
## I(week^2) 0.0697 0.0477 0.0217 0.0568 0.1959 1.0086 2920
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5%
## (Intercept)-Odocoileus_virginianus 7.5525 3.3957 2.5204
## (Intercept)-Canis_latrans -0.8713 1.2292 -3.2482
## (Intercept)-Sciurus_niger 1.1977 2.8393 -3.0109
## (Intercept)-Procyon_lotor -0.4275 1.1048 -2.7727
## (Intercept)-Dasypus_novemcinctus -2.8175 1.2518 -5.6110
## (Intercept)-Lynx_rufus 0.2868 2.4975 -3.5332
## (Intercept)-Didelphis_virginiana -4.2527 1.4308 -7.2828
## (Intercept)-Sylvilagus_floridanus -2.4405 1.4937 -5.5851
## (Intercept)-Sciurus_carolinensis -5.0536 1.6216 -8.7773
## (Intercept)-Vulpes_vulpes -3.9077 2.3983 -8.4153
## (Intercept)-Sus_scrofa -6.0545 2.1332 -10.9260
## Cogon_Patch_Size-Odocoileus_virginianus -0.0039 1.5441 -2.7520
## Cogon_Patch_Size-Canis_latrans 1.6566 1.4767 -0.3174
## Cogon_Patch_Size-Sciurus_niger -1.0940 2.0982 -5.9979
## Cogon_Patch_Size-Procyon_lotor -0.5149 0.8277 -2.0766
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2313 0.7281 -1.7256
## Cogon_Patch_Size-Lynx_rufus -0.3160 1.7002 -3.4516
## Cogon_Patch_Size-Didelphis_virginiana 1.6878 1.0652 -0.0778
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6506 1.6878 -5.8441
## Cogon_Patch_Size-Sciurus_carolinensis -1.3112 1.4449 -5.0055
## Cogon_Patch_Size-Vulpes_vulpes -0.8222 1.8264 -5.0343
## Cogon_Patch_Size-Sus_scrofa -0.9464 1.6564 -5.0841
## Veg_shannon_index-Odocoileus_virginianus 0.7457 0.8889 -1.1530
## Veg_shannon_index-Canis_latrans 1.3226 0.7284 0.1457
## Veg_shannon_index-Sciurus_niger 1.0255 0.9886 -0.8187
## Veg_shannon_index-Procyon_lotor 1.1290 0.6018 0.0648
## Veg_shannon_index-Dasypus_novemcinctus 0.6348 0.5556 -0.4960
## Veg_shannon_index-Lynx_rufus 0.9600 0.9140 -0.9219
## Veg_shannon_index-Didelphis_virginiana 1.0333 0.6781 -0.2120
## Veg_shannon_index-Sylvilagus_floridanus 0.9924 0.7285 -0.3782
## Veg_shannon_index-Sciurus_carolinensis 0.2952 0.8115 -1.5782
## Veg_shannon_index-Vulpes_vulpes 0.6022 0.9155 -1.5047
## Veg_shannon_index-Sus_scrofa 1.5836 1.0548 0.0930
## total_shrub_cover-Odocoileus_virginianus -0.1339 0.7515 -1.6098
## total_shrub_cover-Canis_latrans 0.0052 0.5937 -1.0849
## total_shrub_cover-Sciurus_niger -0.4961 0.8388 -2.3392
## total_shrub_cover-Procyon_lotor -0.7828 0.5866 -2.0673
## total_shrub_cover-Dasypus_novemcinctus 0.0579 0.5022 -0.8678
## total_shrub_cover-Lynx_rufus -0.6802 0.8826 -2.7837
## total_shrub_cover-Didelphis_virginiana -0.4552 0.6416 -1.8598
## total_shrub_cover-Sylvilagus_floridanus -0.2121 0.6724 -1.5920
## total_shrub_cover-Sciurus_carolinensis -0.0225 0.6254 -1.2224
## total_shrub_cover-Vulpes_vulpes -0.4271 0.7875 -2.1576
## total_shrub_cover-Sus_scrofa 0.0500 0.7474 -1.2792
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.0017 1.3422 -2.7762
## Avg_Cogongrass_Cover-Canis_latrans 0.0974 1.1721 -2.3135
## Avg_Cogongrass_Cover-Sciurus_niger -0.2377 1.4751 -3.5402
## Avg_Cogongrass_Cover-Procyon_lotor 0.3315 1.1713 -1.9357
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6529 1.2299 -1.6251
## Avg_Cogongrass_Cover-Lynx_rufus 0.2427 1.2535 -2.1249
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1420 1.2162 -2.2080
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4409 1.3222 -3.3186
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1172 1.2190 -2.3569
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1758 1.3108 -2.3985
## Avg_Cogongrass_Cover-Sus_scrofa -0.4390 1.4006 -3.5963
## Tree_Density-Odocoileus_virginianus -0.9449 1.4556 -3.1300
## Tree_Density-Canis_latrans -2.8168 1.3724 -6.2565
## Tree_Density-Sciurus_niger -1.9533 1.7073 -5.3378
## Tree_Density-Procyon_lotor -1.8509 0.9301 -3.7343
## Tree_Density-Dasypus_novemcinctus -3.8257 2.0853 -9.3633
## Tree_Density-Lynx_rufus -0.8341 1.7242 -3.4393
## Tree_Density-Didelphis_virginiana -2.4163 1.2393 -5.3572
## Tree_Density-Sylvilagus_floridanus -2.6277 1.5009 -6.1898
## Tree_Density-Sciurus_carolinensis -2.8141 1.5396 -6.6425
## Tree_Density-Vulpes_vulpes -2.0419 1.8738 -5.6213
## Tree_Density-Sus_scrofa -2.4836 1.6220 -6.4125
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3152 1.2595 -1.3382
## Avg_Canopy_Cover-Canis_latrans 0.3111 0.7649 -1.2100
## Avg_Canopy_Cover-Sciurus_niger 2.1645 1.5949 -0.6583
## Avg_Canopy_Cover-Procyon_lotor 1.7028 0.7267 0.3797
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9600 0.7375 0.7514
## Avg_Canopy_Cover-Lynx_rufus 1.5354 1.3214 -0.8830
## Avg_Canopy_Cover-Didelphis_virginiana 2.6347 0.9787 1.1217
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.1922 1.5380 1.0853
## Avg_Canopy_Cover-Sciurus_carolinensis 2.2818 0.9046 0.9022
## Avg_Canopy_Cover-Vulpes_vulpes 2.2738 1.2618 0.3352
## Avg_Canopy_Cover-Sus_scrofa 2.0437 0.8704 0.6014
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8131 1.1696 0.1120
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9935 0.9973 0.6213
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2513 1.1692 -1.3532
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8087 0.9004 0.4743
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4642 0.7002 0.1999
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0164 1.0330 0.5440
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1311 0.6954 -0.1972
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2680 0.8059 -0.2544
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6549 0.7344 0.3866
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8105 0.8966 0.4346
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.8663 1.0248 -1.5708
## avg_veg_height-Odocoileus_virginianus -0.1894 0.7714 -1.8030
## avg_veg_height-Canis_latrans -0.4084 0.6402 -1.7724
## avg_veg_height-Sciurus_niger -0.2610 0.8295 -2.0168
## avg_veg_height-Procyon_lotor 0.1185 0.6336 -1.0550
## avg_veg_height-Dasypus_novemcinctus 0.1087 0.6107 -1.0446
## avg_veg_height-Lynx_rufus -0.2408 0.7781 -1.7974
## avg_veg_height-Didelphis_virginiana -0.2945 0.6808 -1.7143
## avg_veg_height-Sylvilagus_floridanus -0.2875 0.7094 -1.7351
## avg_veg_height-Sciurus_carolinensis 0.0756 0.6699 -1.1845
## avg_veg_height-Vulpes_vulpes -0.3125 0.7786 -1.9679
## avg_veg_height-Sus_scrofa -0.2793 0.7318 -1.8202
## 50% 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.0428 15.9159 1.0278 421
## (Intercept)-Canis_latrans -0.9039 1.6834 1.0020 1512
## (Intercept)-Sciurus_niger 0.7334 8.4215 1.0148 326
## (Intercept)-Procyon_lotor -0.3945 1.6575 1.0057 1668
## (Intercept)-Dasypus_novemcinctus -2.6749 -0.7698 1.0401 725
## (Intercept)-Lynx_rufus -0.1184 6.4799 1.0047 351
## (Intercept)-Didelphis_virginiana -4.1646 -1.6725 1.0259 1074
## (Intercept)-Sylvilagus_floridanus -2.3863 0.3205 1.0152 1072
## (Intercept)-Sciurus_carolinensis -4.8838 -2.3836 1.0266 757
## (Intercept)-Vulpes_vulpes -4.0177 1.4211 1.0307 425
## (Intercept)-Sus_scrofa -5.7965 -2.6216 1.1006 489
## Cogon_Patch_Size-Odocoileus_virginianus -0.0990 3.5117 1.0137 1675
## Cogon_Patch_Size-Canis_latrans 1.3697 5.4079 1.0198 812
## Cogon_Patch_Size-Sciurus_niger -0.8609 2.6066 1.0088 459
## Cogon_Patch_Size-Procyon_lotor -0.5391 1.0982 1.0002 975
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2117 1.1263 1.0040 759
## Cogon_Patch_Size-Lynx_rufus -0.4317 3.6551 1.0110 650
## Cogon_Patch_Size-Didelphis_virginiana 1.5688 3.9961 1.0570 646
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3383 0.7424 1.0071 725
## Cogon_Patch_Size-Sciurus_carolinensis -1.0217 0.7428 1.0017 950
## Cogon_Patch_Size-Vulpes_vulpes -0.6535 2.3951 1.0143 796
## Cogon_Patch_Size-Sus_scrofa -0.6614 1.5457 1.0016 903
## Veg_shannon_index-Odocoileus_virginianus 0.7755 2.5124 1.0044 2211
## Veg_shannon_index-Canis_latrans 1.2356 2.9894 1.0091 1213
## Veg_shannon_index-Sciurus_niger 0.9809 3.2112 1.0079 1558
## Veg_shannon_index-Procyon_lotor 1.0920 2.4567 1.0193 935
## Veg_shannon_index-Dasypus_novemcinctus 0.6546 1.6870 1.0025 2429
## Veg_shannon_index-Lynx_rufus 0.9546 2.8644 1.0100 1375
## Veg_shannon_index-Didelphis_virginiana 0.9834 2.4973 1.0024 2350
## Veg_shannon_index-Sylvilagus_floridanus 0.9578 2.5512 1.0055 1523
## Veg_shannon_index-Sciurus_carolinensis 0.3960 1.6479 1.0107 1379
## Veg_shannon_index-Vulpes_vulpes 0.6728 2.2178 1.0140 1140
## Veg_shannon_index-Sus_scrofa 1.3922 4.1996 1.0468 798
## total_shrub_cover-Odocoileus_virginianus -0.1588 1.4911 1.0042 2532
## total_shrub_cover-Canis_latrans -0.0254 1.2944 1.0059 1899
## total_shrub_cover-Sciurus_niger -0.4311 0.9769 1.0010 1538
## total_shrub_cover-Procyon_lotor -0.7252 0.2263 1.0063 1967
## total_shrub_cover-Dasypus_novemcinctus 0.0319 1.0997 1.0054 2912
## total_shrub_cover-Lynx_rufus -0.5834 0.8040 1.0028 1157
## total_shrub_cover-Didelphis_virginiana -0.4109 0.6920 1.0071 1784
## total_shrub_cover-Sylvilagus_floridanus -0.2051 1.1186 1.0056 2222
## total_shrub_cover-Sciurus_carolinensis -0.0530 1.3157 1.0069 2503
## total_shrub_cover-Vulpes_vulpes -0.3757 1.0164 1.0017 2089
## total_shrub_cover-Sus_scrofa -0.0076 1.7161 1.0035 2136
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0222 2.5782 1.0248 619
## Avg_Cogongrass_Cover-Canis_latrans 0.1283 2.3385 1.0170 550
## Avg_Cogongrass_Cover-Sciurus_niger -0.1528 2.3807 1.0145 606
## Avg_Cogongrass_Cover-Procyon_lotor 0.3137 2.7055 1.0238 608
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6064 3.3032 1.0065 557
## Avg_Cogongrass_Cover-Lynx_rufus 0.2349 2.7548 1.0212 635
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1311 2.5104 1.0345 505
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3352 1.9122 1.0218 602
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1483 2.4521 1.0169 597
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1851 2.8077 1.0243 601
## Avg_Cogongrass_Cover-Sus_scrofa -0.3366 2.0365 1.0305 604
## Tree_Density-Odocoileus_virginianus -1.1697 2.6717 1.0094 795
## Tree_Density-Canis_latrans -2.5865 -0.7840 1.0113 742
## Tree_Density-Sciurus_niger -1.9787 1.7889 1.0079 692
## Tree_Density-Procyon_lotor -1.8413 -0.0159 1.0024 1619
## Tree_Density-Dasypus_novemcinctus -3.2979 -1.3290 1.0220 388
## Tree_Density-Lynx_rufus -1.0840 3.3105 1.0161 494
## Tree_Density-Didelphis_virginiana -2.2583 -0.4984 1.0023 900
## Tree_Density-Sylvilagus_floridanus -2.3666 -0.3471 1.0156 674
## Tree_Density-Sciurus_carolinensis -2.5270 -0.6998 1.0061 762
## Tree_Density-Vulpes_vulpes -2.0301 1.4752 1.0303 468
## Tree_Density-Sus_scrofa -2.2493 0.0566 1.0029 1110
## Avg_Canopy_Cover-Odocoileus_virginianus 1.3511 3.7587 1.0058 2066
## Avg_Canopy_Cover-Canis_latrans 0.3089 1.8158 1.0080 1180
## Avg_Canopy_Cover-Sciurus_niger 2.0387 5.7480 1.0253 698
## Avg_Canopy_Cover-Procyon_lotor 1.6520 3.2892 1.0140 1284
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8797 3.6421 1.0227 521
## Avg_Canopy_Cover-Lynx_rufus 1.5059 4.4044 1.0006 783
## Avg_Canopy_Cover-Didelphis_virginiana 2.5016 4.8981 1.0312 487
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9090 6.8457 1.0640 429
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1584 4.3687 1.0073 796
## Avg_Canopy_Cover-Vulpes_vulpes 2.0949 5.3186 1.0016 538
## Avg_Canopy_Cover-Sus_scrofa 1.9513 4.0859 1.0274 1068
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.6614 4.5821 1.0200 725
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8276 4.4421 1.0173 633
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3042 3.4942 1.0519 453
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.6846 3.9089 1.0032 783
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4263 2.9915 1.0123 954
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.8641 4.4215 1.0167 607
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1280 2.5066 1.0578 583
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2261 2.9526 1.0316 794
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5945 3.2823 1.0179 910
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6991 3.8217 1.0121 709
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.9727 2.5825 1.0615 561
## avg_veg_height-Odocoileus_virginianus -0.1887 1.3054 1.0089 1071
## avg_veg_height-Canis_latrans -0.3820 0.7740 1.0031 974
## avg_veg_height-Sciurus_niger -0.2395 1.3225 1.0084 1130
## avg_veg_height-Procyon_lotor 0.0958 1.4072 1.0210 951
## avg_veg_height-Dasypus_novemcinctus 0.0911 1.3799 1.0096 949
## avg_veg_height-Lynx_rufus -0.2245 1.2878 1.0149 1062
## avg_veg_height-Didelphis_virginiana -0.2666 0.9691 1.0028 1092
## avg_veg_height-Sylvilagus_floridanus -0.2646 1.0731 1.0047 991
## avg_veg_height-Sciurus_carolinensis 0.0369 1.4902 1.0081 1239
## avg_veg_height-Vulpes_vulpes -0.2863 1.1800 1.0046 995
## avg_veg_height-Sus_scrofa -0.2612 1.1166 1.0023 1170
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5290 0.0797 0.3746 0.5274 0.6855
## (Intercept)-Canis_latrans -2.4392 0.1921 -2.8365 -2.4321 -2.0824
## (Intercept)-Sciurus_niger -4.6267 0.4751 -5.5805 -4.6222 -3.7164
## (Intercept)-Procyon_lotor -2.1616 0.1512 -2.4640 -2.1607 -1.8663
## (Intercept)-Dasypus_novemcinctus -1.4360 0.1577 -1.7575 -1.4349 -1.1311
## (Intercept)-Lynx_rufus -3.6125 0.3436 -4.2570 -3.6176 -2.9252
## (Intercept)-Didelphis_virginiana -2.0961 0.2629 -2.6427 -2.0848 -1.6046
## (Intercept)-Sylvilagus_floridanus -3.1151 0.2996 -3.7337 -3.1053 -2.5649
## (Intercept)-Sciurus_carolinensis -2.2430 0.2803 -2.8280 -2.2315 -1.7283
## (Intercept)-Vulpes_vulpes -4.0369 0.6739 -5.3925 -4.0280 -2.7736
## (Intercept)-Sus_scrofa -2.7626 0.4871 -3.8112 -2.7298 -1.9103
## week-Odocoileus_virginianus 1.2824 0.1216 1.0516 1.2816 1.5236
## week-Canis_latrans 0.5883 0.2580 0.0960 0.5796 1.1054
## week-Sciurus_niger -0.4070 0.5541 -1.6650 -0.3591 0.5389
## week-Procyon_lotor 0.2069 0.2091 -0.1913 0.2023 0.6167
## week-Dasypus_novemcinctus 0.1097 0.2277 -0.3392 0.1119 0.5473
## week-Lynx_rufus 0.3838 0.3464 -0.2925 0.3858 1.0545
## week-Didelphis_virginiana 0.0628 0.3710 -0.7145 0.0742 0.7689
## week-Sylvilagus_floridanus 0.0663 0.3419 -0.6435 0.0754 0.7180
## week-Sciurus_carolinensis 0.7942 0.3648 0.1058 0.7860 1.5596
## week-Vulpes_vulpes 0.1734 0.5073 -0.8966 0.1984 1.1153
## week-Sus_scrofa 0.6846 0.4485 -0.1617 0.6736 1.6208
## I(week^2)-Odocoileus_virginianus -0.5288 0.0501 -0.6292 -0.5281 -0.4323
## I(week^2)-Canis_latrans -0.2439 0.1064 -0.4535 -0.2430 -0.0399
## I(week^2)-Sciurus_niger -0.2968 0.2287 -0.7714 -0.2906 0.1284
## I(week^2)-Procyon_lotor -0.1331 0.0907 -0.3194 -0.1321 0.0427
## I(week^2)-Dasypus_novemcinctus -0.1798 0.1017 -0.3822 -0.1787 0.0148
## I(week^2)-Lynx_rufus -0.2375 0.1532 -0.5477 -0.2351 0.0497
## I(week^2)-Didelphis_virginiana -0.4051 0.2108 -0.8765 -0.3847 -0.0523
## I(week^2)-Sylvilagus_floridanus -0.1855 0.1584 -0.5172 -0.1794 0.1088
## I(week^2)-Sciurus_carolinensis -0.2837 0.1443 -0.5833 -0.2794 -0.0116
## I(week^2)-Vulpes_vulpes -0.3969 0.2333 -0.9082 -0.3774 0.0075
## I(week^2)-Sus_scrofa -0.2414 0.1776 -0.6006 -0.2363 0.0949
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0018 5250
## (Intercept)-Canis_latrans 1.0052 3063
## (Intercept)-Sciurus_niger 1.0087 503
## (Intercept)-Procyon_lotor 1.0017 4288
## (Intercept)-Dasypus_novemcinctus 1.0004 5250
## (Intercept)-Lynx_rufus 1.0077 649
## (Intercept)-Didelphis_virginiana 1.0005 4072
## (Intercept)-Sylvilagus_floridanus 1.0163 1863
## (Intercept)-Sciurus_carolinensis 1.0076 3625
## (Intercept)-Vulpes_vulpes 1.0331 495
## (Intercept)-Sus_scrofa 1.0113 2270
## week-Odocoileus_virginianus 1.0010 5042
## week-Canis_latrans 1.0009 4071
## week-Sciurus_niger 1.0121 626
## week-Procyon_lotor 1.0016 4380
## week-Dasypus_novemcinctus 1.0020 4797
## week-Lynx_rufus 1.0016 2637
## week-Didelphis_virginiana 1.0014 2719
## week-Sylvilagus_floridanus 1.0113 2960
## week-Sciurus_carolinensis 1.0025 4006
## week-Vulpes_vulpes 1.0136 1935
## week-Sus_scrofa 1.0003 4283
## I(week^2)-Odocoileus_virginianus 1.0010 5296
## I(week^2)-Canis_latrans 1.0025 4110
## I(week^2)-Sciurus_niger 1.0076 972
## I(week^2)-Procyon_lotor 1.0014 4351
## I(week^2)-Dasypus_novemcinctus 1.0022 4634
## I(week^2)-Lynx_rufus 1.0000 2093
## I(week^2)-Didelphis_virginiana 1.0020 1693
## I(week^2)-Sylvilagus_floridanus 1.0016 2646
## I(week^2)-Sciurus_carolinensis 1.0071 4042
## I(week^2)-Vulpes_vulpes 1.0045 1550
## I(week^2)-Sus_scrofa 1.0003 4024
#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): 1.9362
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.113 0.5398 -1.1725 -0.1318 0.9848 1.0101 3011
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.0368 2.2544 0.8123 2.4587 8.7448 1.0021 2224
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3982 0.4575 -3.2981 -2.4070 -1.4603 1.0032 4156
## shrub_cover 0.2061 0.2437 -0.2716 0.2015 0.7090 1.0073 3359
## veg_height -0.0061 0.1554 -0.3184 -0.0064 0.2998 1.0015 3363
## week 0.3568 0.2392 -0.1294 0.3626 0.8052 1.0011 2943
## I(week^2) -0.2879 0.1035 -0.4972 -0.2854 -0.0855 1.0033 3026
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3319 1.5071 0.8054 1.9294 6.2471 1.0127 2031
## shrub_cover 0.4778 0.4012 0.0937 0.3665 1.5435 1.0321 2145
## veg_height 0.1970 0.1366 0.0552 0.1613 0.5636 1.0042 3912
## week 0.4437 0.3513 0.1052 0.3481 1.3316 1.0028 1640
## I(week^2) 0.0740 0.0545 0.0228 0.0595 0.2099 1.0177 2616
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 3.3936 1.0669 1.7772 3.2539 5.9124
## (Intercept)-Canis_latrans 0.4084 0.4319 -0.3951 0.3938 1.3399
## (Intercept)-Sciurus_niger -0.4833 1.0083 -2.0298 -0.6455 1.9283
## (Intercept)-Procyon_lotor 0.7362 0.4049 0.0014 0.7108 1.5873
## (Intercept)-Dasypus_novemcinctus -0.5783 0.3723 -1.3361 -0.5748 0.1331
## (Intercept)-Lynx_rufus 0.5770 0.9748 -0.7659 0.3992 3.1058
## (Intercept)-Didelphis_virginiana -1.2265 0.4676 -2.1963 -1.2064 -0.3794
## (Intercept)-Sylvilagus_floridanus -0.3020 0.5077 -1.2098 -0.3269 0.7690
## (Intercept)-Sciurus_carolinensis -1.2013 0.4678 -2.1447 -1.1946 -0.3007
## (Intercept)-Vulpes_vulpes -1.0062 1.0983 -2.6949 -1.1695 1.7389
## (Intercept)-Sus_scrofa -1.6811 0.6634 -2.9927 -1.6756 -0.4009
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0015 1763
## (Intercept)-Canis_latrans 1.0011 4904
## (Intercept)-Sciurus_niger 1.0341 560
## (Intercept)-Procyon_lotor 1.0011 4967
## (Intercept)-Dasypus_novemcinctus 1.0011 5250
## (Intercept)-Lynx_rufus 1.0094 881
## (Intercept)-Didelphis_virginiana 1.0045 4344
## (Intercept)-Sylvilagus_floridanus 1.0006 2597
## (Intercept)-Sciurus_carolinensis 0.9998 3873
## (Intercept)-Vulpes_vulpes 1.0559 532
## (Intercept)-Sus_scrofa 1.0076 2108
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5397 0.0810 0.3786 0.5390 0.7014
## (Intercept)-Canis_latrans -2.5611 0.2066 -2.9708 -2.5577 -2.1759
## (Intercept)-Sciurus_niger -4.0597 0.6844 -5.4399 -4.0372 -2.8100
## (Intercept)-Procyon_lotor -2.1734 0.1628 -2.5001 -2.1687 -1.8647
## (Intercept)-Dasypus_novemcinctus -1.5716 0.1740 -1.9227 -1.5667 -1.2316
## (Intercept)-Lynx_rufus -3.6110 0.3887 -4.3710 -3.6067 -2.8595
## (Intercept)-Didelphis_virginiana -2.3050 0.3002 -2.9248 -2.2920 -1.7519
## (Intercept)-Sylvilagus_floridanus -3.0837 0.3303 -3.7971 -3.0644 -2.4889
## (Intercept)-Sciurus_carolinensis -2.3878 0.3304 -3.0941 -2.3680 -1.7800
## (Intercept)-Vulpes_vulpes -3.9777 0.7802 -5.6135 -3.9324 -2.6221
## (Intercept)-Sus_scrofa -3.1288 0.6107 -4.3555 -3.1343 -1.9648
## shrub_cover-Odocoileus_virginianus -0.0612 0.0684 -0.1971 -0.0608 0.0732
## shrub_cover-Canis_latrans -0.2830 0.2178 -0.7115 -0.2803 0.1459
## shrub_cover-Sciurus_niger -0.3386 0.4730 -1.3160 -0.3214 0.5781
## shrub_cover-Procyon_lotor 0.2484 0.1590 -0.0776 0.2501 0.5435
## shrub_cover-Dasypus_novemcinctus 0.7882 0.2887 0.2422 0.7827 1.3853
## shrub_cover-Lynx_rufus -0.3107 0.3369 -1.0062 -0.3006 0.3250
## shrub_cover-Didelphis_virginiana 0.8772 0.3517 0.2363 0.8565 1.6095
## shrub_cover-Sylvilagus_floridanus 0.2348 0.3946 -0.5150 0.2279 1.0385
## shrub_cover-Sciurus_carolinensis 0.7457 0.3909 0.0174 0.7355 1.5612
## shrub_cover-Vulpes_vulpes -0.0962 0.5362 -1.1962 -0.0870 0.9650
## shrub_cover-Sus_scrofa 0.5060 0.7292 -0.9166 0.4819 2.0112
## veg_height-Odocoileus_virginianus -0.3302 0.0683 -0.4636 -0.3298 -0.1975
## veg_height-Canis_latrans -0.5825 0.1822 -0.9643 -0.5759 -0.2402
## veg_height-Sciurus_niger -0.0735 0.3974 -0.8502 -0.0782 0.7365
## veg_height-Procyon_lotor 0.3316 0.1214 0.0969 0.3323 0.5689
## veg_height-Dasypus_novemcinctus 0.2275 0.1302 -0.0245 0.2252 0.4925
## veg_height-Lynx_rufus 0.0433 0.2359 -0.4316 0.0457 0.4989
## veg_height-Didelphis_virginiana 0.4134 0.2379 -0.0199 0.4074 0.9028
## veg_height-Sylvilagus_floridanus 0.1144 0.2450 -0.3629 0.1132 0.6079
## veg_height-Sciurus_carolinensis 0.0471 0.2077 -0.3463 0.0394 0.4727
## veg_height-Vulpes_vulpes -0.1160 0.3122 -0.7455 -0.1052 0.4736
## veg_height-Sus_scrofa -0.1432 0.3364 -0.8111 -0.1347 0.4937
## week-Odocoileus_virginianus 1.3151 0.1269 1.0738 1.3145 1.5654
## week-Canis_latrans 0.5954 0.2647 0.0882 0.5896 1.1273
## week-Sciurus_niger -0.4113 0.5769 -1.6471 -0.3586 0.5617
## week-Procyon_lotor 0.2044 0.2132 -0.2061 0.1969 0.6302
## week-Dasypus_novemcinctus 0.1070 0.2261 -0.3357 0.1046 0.5593
## week-Lynx_rufus 0.3866 0.3514 -0.2941 0.3844 1.0770
## week-Didelphis_virginiana 0.0684 0.3785 -0.7023 0.0777 0.7780
## week-Sylvilagus_floridanus 0.0664 0.3486 -0.6127 0.0682 0.7449
## week-Sciurus_carolinensis 0.8076 0.3664 0.1282 0.7915 1.5621
## week-Vulpes_vulpes 0.1872 0.5202 -0.8926 0.2108 1.1481
## week-Sus_scrofa 0.6803 0.4460 -0.1766 0.6655 1.6158
## I(week^2)-Odocoileus_virginianus -0.5424 0.0516 -0.6429 -0.5425 -0.4446
## I(week^2)-Canis_latrans -0.2465 0.1071 -0.4637 -0.2452 -0.0394
## I(week^2)-Sciurus_niger -0.2912 0.2405 -0.8250 -0.2778 0.1470
## I(week^2)-Procyon_lotor -0.1322 0.0906 -0.3111 -0.1309 0.0439
## I(week^2)-Dasypus_novemcinctus -0.1816 0.1034 -0.3928 -0.1802 0.0180
## I(week^2)-Lynx_rufus -0.2391 0.1515 -0.5424 -0.2374 0.0531
## I(week^2)-Didelphis_virginiana -0.4140 0.2071 -0.8681 -0.3964 -0.0646
## I(week^2)-Sylvilagus_floridanus -0.1825 0.1587 -0.5009 -0.1800 0.1238
## I(week^2)-Sciurus_carolinensis -0.2861 0.1431 -0.5809 -0.2817 -0.0114
## I(week^2)-Vulpes_vulpes -0.4167 0.2641 -1.0077 -0.3889 0.0297
## I(week^2)-Sus_scrofa -0.2453 0.1796 -0.5982 -0.2428 0.0960
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0001 5250
## (Intercept)-Canis_latrans 1.0002 2551
## (Intercept)-Sciurus_niger 1.0141 600
## (Intercept)-Procyon_lotor 1.0007 3986
## (Intercept)-Dasypus_novemcinctus 1.0008 4562
## (Intercept)-Lynx_rufus 1.0141 951
## (Intercept)-Didelphis_virginiana 1.0014 3111
## (Intercept)-Sylvilagus_floridanus 1.0012 1806
## (Intercept)-Sciurus_carolinensis 1.0017 2708
## (Intercept)-Vulpes_vulpes 1.0309 425
## (Intercept)-Sus_scrofa 1.0091 1608
## shrub_cover-Odocoileus_virginianus 1.0016 5250
## shrub_cover-Canis_latrans 1.0050 2702
## shrub_cover-Sciurus_niger 1.0013 1365
## shrub_cover-Procyon_lotor 1.0005 3522
## shrub_cover-Dasypus_novemcinctus 1.0018 4006
## shrub_cover-Lynx_rufus 1.0012 1498
## shrub_cover-Didelphis_virginiana 1.0018 2505
## shrub_cover-Sylvilagus_floridanus 1.0019 1917
## shrub_cover-Sciurus_carolinensis 1.0099 2342
## shrub_cover-Vulpes_vulpes 1.0037 1728
## shrub_cover-Sus_scrofa 1.0065 2053
## veg_height-Odocoileus_virginianus 1.0002 5250
## veg_height-Canis_latrans 1.0019 2496
## veg_height-Sciurus_niger 1.0001 2341
## veg_height-Procyon_lotor 1.0017 4052
## veg_height-Dasypus_novemcinctus 1.0027 4662
## veg_height-Lynx_rufus 0.9999 2460
## veg_height-Didelphis_virginiana 1.0018 3622
## veg_height-Sylvilagus_floridanus 1.0072 2724
## veg_height-Sciurus_carolinensis 0.9998 3691
## veg_height-Vulpes_vulpes 1.0124 2051
## veg_height-Sus_scrofa 1.0027 3190
## week-Odocoileus_virginianus 1.0040 4924
## week-Canis_latrans 1.0013 4171
## week-Sciurus_niger 1.0025 864
## week-Procyon_lotor 1.0041 4327
## week-Dasypus_novemcinctus 1.0006 4918
## week-Lynx_rufus 1.0098 2841
## week-Didelphis_virginiana 1.0010 2736
## week-Sylvilagus_floridanus 1.0013 2628
## week-Sciurus_carolinensis 1.0019 3949
## week-Vulpes_vulpes 1.0077 1996
## week-Sus_scrofa 1.0016 4133
## I(week^2)-Odocoileus_virginianus 1.0053 4864
## I(week^2)-Canis_latrans 1.0034 3953
## I(week^2)-Sciurus_niger 1.0057 1072
## I(week^2)-Procyon_lotor 1.0075 4651
## I(week^2)-Dasypus_novemcinctus 1.0031 4176
## I(week^2)-Lynx_rufus 1.0045 2559
## I(week^2)-Didelphis_virginiana 1.0117 1886
## I(week^2)-Sylvilagus_floridanus 1.0019 2573
## I(week^2)-Sciurus_carolinensis 1.0038 3809
## I(week^2)-Vulpes_vulpes 1.0113 1174
## I(week^2)-Sus_scrofa 1.0015 4219
#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
## ----------------------------------------
## 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%
## -------------------------------------------------
## 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%
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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%
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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%
## -------------------------------------------------
## Sampled: 2000 of 10000, 20.00%
## -------------------------------------------------
## Sampled: 2500 of 10000, 25.00%
## -------------------------------------------------
## Sampled: 3000 of 10000, 30.00%
## -------------------------------------------------
## Sampled: 3500 of 10000, 35.00%
## -------------------------------------------------
## Sampled: 4000 of 10000, 40.00%
## -------------------------------------------------
## Sampled: 4500 of 10000, 45.00%
## -------------------------------------------------
## Sampled: 5000 of 10000, 50.00%
## -------------------------------------------------
## Sampled: 5500 of 10000, 55.00%
## -------------------------------------------------
## Sampled: 6000 of 10000, 60.00%
## -------------------------------------------------
## Sampled: 6500 of 10000, 65.00%
## -------------------------------------------------
## Sampled: 7000 of 10000, 70.00%
## -------------------------------------------------
## Sampled: 7500 of 10000, 75.00%
## -------------------------------------------------
## Sampled: 8000 of 10000, 80.00%
## -------------------------------------------------
## Sampled: 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)
##
## 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): 2.1043
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0476 1.0335 -2.0655 -0.0588 2.0695 1.0047 1891
## Cogon_Patch_Size -0.7615 0.6721 -2.1473 -0.7308 0.4966 1.0033 1103
## Veg_shannon_index 0.8947 0.4888 -0.0396 0.8867 1.8653 1.0201 856
## total_shrub_cover -0.3451 0.4898 -1.3870 -0.3226 0.5651 1.0052 537
## Avg_Cogongrass_Cover 2.0103 0.6940 0.6888 2.0034 3.4260 1.0322 572
## Tree_Density -1.7793 0.7293 -3.2230 -1.7699 -0.3310 1.0152 1084
## Avg_Canopy_Cover 1.8776 0.6200 0.7087 1.8422 3.1760 1.0019 1154
## avg_veg_height -0.4394 0.4733 -1.3759 -0.4412 0.4995 1.0101 716
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 17.7392 16.2613 3.3617 13.2550 58.4013 1.0456 541
## Cogon_Patch_Size 2.9106 4.8389 0.0997 1.4825 14.1914 1.0459 575
## Veg_shannon_index 0.8860 1.5168 0.0511 0.4584 4.2216 1.0262 673
## total_shrub_cover 0.8752 1.3405 0.0527 0.4501 4.4646 1.0416 610
## Avg_Cogongrass_Cover 1.0581 2.0146 0.0507 0.4668 5.4104 1.0611 882
## Tree_Density 3.2080 5.6720 0.0754 1.3877 17.7154 1.0276 466
## Avg_Canopy_Cover 2.4646 3.1646 0.1612 1.4906 10.8309 1.0019 718
## avg_veg_height 0.4081 0.6937 0.0397 0.2242 1.8186 1.1305 1154
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8731 2.2919 0.0735 1.0872 8.0347 1.064 240
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4530 0.4851 -3.3717 -2.4579 -1.4115 0.9998 4831
## shrub_cover 0.2834 0.2564 -0.2162 0.2784 0.8060 1.0185 1279
## veg_height -0.0055 0.1582 -0.3237 -0.0042 0.3041 1.0009 3614
## week 0.3567 0.2429 -0.1539 0.3635 0.8229 1.0053 2968
## I(week^2) -0.2873 0.1020 -0.4898 -0.2864 -0.0894 1.0016 1951
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6806 1.6008 0.9451 2.2681 6.9300 1.0019 2168
## shrub_cover 0.5123 0.4231 0.1056 0.3990 1.6138 1.0067 1920
## veg_height 0.2068 0.1413 0.0600 0.1705 0.5729 1.0016 3027
## week 0.4469 0.3346 0.1121 0.3609 1.3543 1.0061 1732
## I(week^2) 0.0720 0.0514 0.0227 0.0577 0.1983 1.0060 3486
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.9749 3.1993 3.3888 7.4262
## (Intercept)-Canis_latrans 0.8850 1.0729 -1.0652 0.8037
## (Intercept)-Sciurus_niger 1.5624 2.5064 -2.3077 1.1962
## (Intercept)-Procyon_lotor 0.9326 1.0413 -1.1266 0.9206
## (Intercept)-Dasypus_novemcinctus -1.4083 1.0166 -3.6830 -1.3371
## (Intercept)-Lynx_rufus 2.0563 2.7129 -2.0830 1.6366
## (Intercept)-Didelphis_virginiana -2.8024 1.1778 -5.3704 -2.7120
## (Intercept)-Sylvilagus_floridanus -1.1303 1.3195 -3.8593 -1.0943
## (Intercept)-Sciurus_carolinensis -3.0124 1.3512 -6.0506 -2.9109
## (Intercept)-Vulpes_vulpes -1.5695 2.6058 -5.4121 -1.9259
## (Intercept)-Sus_scrofa -4.2097 1.8757 -8.2802 -4.0839
## Cogon_Patch_Size-Odocoileus_virginianus -0.5798 1.2540 -2.9831 -0.6198
## Cogon_Patch_Size-Canis_latrans 0.6217 1.2512 -1.1211 0.4070
## Cogon_Patch_Size-Sciurus_niger -1.5442 1.9034 -5.8772 -1.2630
## Cogon_Patch_Size-Procyon_lotor -1.0491 0.7076 -2.5067 -1.0231
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6221 0.7332 -2.0123 -0.6368
## Cogon_Patch_Size-Lynx_rufus -0.7892 1.4231 -3.4145 -0.8513
## Cogon_Patch_Size-Didelphis_virginiana 0.7217 0.8881 -0.7740 0.6305
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9584 1.6445 -6.2130 -1.6190
## Cogon_Patch_Size-Sciurus_carolinensis -1.6997 1.4147 -5.2083 -1.4381
## Cogon_Patch_Size-Vulpes_vulpes -1.1701 1.6899 -4.8889 -1.0397
## Cogon_Patch_Size-Sus_scrofa -1.3508 1.4556 -5.1842 -1.0750
## Veg_shannon_index-Odocoileus_virginianus 0.7475 0.8972 -1.1852 0.7723
## Veg_shannon_index-Canis_latrans 1.3188 0.7072 0.0851 1.2621
## Veg_shannon_index-Sciurus_niger 1.0393 1.0070 -1.0116 1.0116
## Veg_shannon_index-Procyon_lotor 1.2195 0.6206 0.1018 1.1828
## Veg_shannon_index-Dasypus_novemcinctus 0.6218 0.5631 -0.5254 0.6302
## Veg_shannon_index-Lynx_rufus 0.8304 1.0197 -1.2803 0.8878
## Veg_shannon_index-Didelphis_virginiana 1.1118 0.6994 -0.1016 1.0570
## Veg_shannon_index-Sylvilagus_floridanus 1.0543 0.7269 -0.2079 1.0018
## Veg_shannon_index-Sciurus_carolinensis 0.1964 0.8091 -1.6298 0.2746
## Veg_shannon_index-Vulpes_vulpes 0.3997 0.9220 -1.7196 0.5102
## Veg_shannon_index-Sus_scrofa 1.5613 1.0309 0.1136 1.3924
## total_shrub_cover-Odocoileus_virginianus -0.0718 0.8660 -1.7167 -0.1155
## total_shrub_cover-Canis_latrans 0.3771 0.7729 -0.8228 0.2588
## total_shrub_cover-Sciurus_niger -0.4708 1.0097 -2.7223 -0.4164
## total_shrub_cover-Procyon_lotor -0.8510 0.6235 -2.2492 -0.7880
## total_shrub_cover-Dasypus_novemcinctus -0.1233 0.6230 -1.3453 -0.1083
## total_shrub_cover-Lynx_rufus -0.6201 1.0791 -3.1105 -0.5057
## total_shrub_cover-Didelphis_virginiana -0.6236 0.7726 -2.3784 -0.5386
## total_shrub_cover-Sylvilagus_floridanus -0.4807 0.9272 -2.6337 -0.3903
## total_shrub_cover-Sciurus_carolinensis -0.3125 0.7582 -1.9485 -0.2742
## total_shrub_cover-Vulpes_vulpes -0.5916 0.9950 -2.9352 -0.4990
## total_shrub_cover-Sus_scrofa -0.1514 0.8898 -1.9715 -0.1695
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.9425 1.0518 -0.2120 1.9438
## Avg_Cogongrass_Cover-Canis_latrans 2.3940 0.9749 0.7876 2.2995
## Avg_Cogongrass_Cover-Sciurus_niger 1.5369 1.3696 -1.7809 1.6900
## Avg_Cogongrass_Cover-Procyon_lotor 2.1761 0.8513 0.6085 2.1355
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5448 0.9453 0.9358 2.4630
## Avg_Cogongrass_Cover-Lynx_rufus 2.2981 1.0201 0.4968 2.2156
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1043 0.8611 0.5040 2.0791
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4640 0.9842 -0.5758 1.5130
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2771 0.9206 0.6622 2.2099
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4298 1.0498 0.6273 2.3270
## Avg_Cogongrass_Cover-Sus_scrofa 1.6502 1.1177 -0.8998 1.6929
## Tree_Density-Odocoileus_virginianus -0.7387 1.3044 -2.7838 -0.9343
## Tree_Density-Canis_latrans -2.5858 1.2755 -5.7019 -2.3829
## Tree_Density-Sciurus_niger -1.8791 1.6236 -5.1285 -1.8570
## Tree_Density-Procyon_lotor -1.4679 0.7670 -2.9337 -1.4677
## Tree_Density-Dasypus_novemcinctus -3.4853 1.7607 -8.0392 -3.0921
## Tree_Density-Lynx_rufus -0.5591 1.5701 -2.9067 -0.7670
## Tree_Density-Didelphis_virginiana -2.1555 1.2268 -4.9851 -2.0077
## Tree_Density-Sylvilagus_floridanus -2.3610 1.4234 -5.6838 -2.1869
## Tree_Density-Sciurus_carolinensis -2.3140 1.4366 -5.8524 -2.1128
## Tree_Density-Vulpes_vulpes -1.7784 1.6352 -5.1353 -1.7459
## Tree_Density-Sus_scrofa -2.2479 1.5385 -5.9899 -2.0443
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2854 1.3371 -1.5077 1.3301
## Avg_Canopy_Cover-Canis_latrans 0.2911 0.6823 -1.0849 0.2888
## Avg_Canopy_Cover-Sciurus_niger 2.1591 1.6344 -0.9419 2.0562
## Avg_Canopy_Cover-Procyon_lotor 1.7427 0.7458 0.4409 1.6964
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1189 0.7417 0.8773 2.0288
## Avg_Canopy_Cover-Lynx_rufus 1.4070 1.4258 -1.4619 1.4014
## Avg_Canopy_Cover-Didelphis_virginiana 2.8992 1.1259 1.2713 2.7120
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.4006 1.5595 1.2425 3.1002
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6428 1.0855 1.0230 2.4462
## Avg_Canopy_Cover-Vulpes_vulpes 2.2795 1.2161 0.2909 2.1162
## Avg_Canopy_Cover-Sus_scrofa 2.1575 0.9385 0.6180 2.0591
## avg_veg_height-Odocoileus_virginianus -0.4633 0.7387 -1.9898 -0.4590
## avg_veg_height-Canis_latrans -0.4807 0.5948 -1.6558 -0.4828
## avg_veg_height-Sciurus_niger -0.5987 0.8291 -2.3699 -0.5547
## avg_veg_height-Procyon_lotor -0.3821 0.5754 -1.5080 -0.3857
## avg_veg_height-Dasypus_novemcinctus -0.2371 0.5688 -1.3588 -0.2416
## avg_veg_height-Lynx_rufus -0.5184 0.7586 -2.0306 -0.5083
## avg_veg_height-Didelphis_virginiana -0.5913 0.6577 -1.9785 -0.5552
## avg_veg_height-Sylvilagus_floridanus -0.6172 0.6662 -2.0433 -0.5860
## avg_veg_height-Sciurus_carolinensis -0.1352 0.6513 -1.3397 -0.1614
## avg_veg_height-Vulpes_vulpes -0.4072 0.7361 -1.8377 -0.4136
## avg_veg_height-Sus_scrofa -0.4930 0.6814 -1.8802 -0.4958
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8711 1.0232 442
## (Intercept)-Canis_latrans 3.2158 1.0147 1374
## (Intercept)-Sciurus_niger 7.2613 1.0428 311
## (Intercept)-Procyon_lotor 2.9956 1.0024 1478
## (Intercept)-Dasypus_novemcinctus 0.3985 1.0155 1100
## (Intercept)-Lynx_rufus 8.6993 1.0081 294
## (Intercept)-Didelphis_virginiana -0.6843 1.0041 1457
## (Intercept)-Sylvilagus_floridanus 1.4331 1.0040 1049
## (Intercept)-Sciurus_carolinensis -0.6781 1.0086 827
## (Intercept)-Vulpes_vulpes 5.0327 1.0431 217
## (Intercept)-Sus_scrofa -0.8257 1.0066 630
## Cogon_Patch_Size-Odocoileus_virginianus 2.0895 1.0095 1839
## Cogon_Patch_Size-Canis_latrans 3.6634 1.0119 1213
## Cogon_Patch_Size-Sciurus_niger 1.4604 1.0278 484
## Cogon_Patch_Size-Procyon_lotor 0.3034 1.0040 844
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9442 1.0063 1740
## Cogon_Patch_Size-Lynx_rufus 2.2939 1.0061 950
## Cogon_Patch_Size-Didelphis_virginiana 2.6775 1.0034 981
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2393 1.0058 724
## Cogon_Patch_Size-Sciurus_carolinensis 0.2815 1.0160 636
## Cogon_Patch_Size-Vulpes_vulpes 1.9820 1.0059 664
## Cogon_Patch_Size-Sus_scrofa 0.7978 1.0075 855
## Veg_shannon_index-Odocoileus_virginianus 2.4932 1.0030 1741
## Veg_shannon_index-Canis_latrans 2.8814 1.0035 1019
## Veg_shannon_index-Sciurus_niger 3.1686 1.0106 1088
## Veg_shannon_index-Procyon_lotor 2.5496 1.0014 858
## Veg_shannon_index-Dasypus_novemcinctus 1.6761 1.0062 1543
## Veg_shannon_index-Lynx_rufus 2.5946 1.0271 974
## Veg_shannon_index-Didelphis_virginiana 2.6852 1.0035 1236
## Veg_shannon_index-Sylvilagus_floridanus 2.6646 1.0057 1158
## Veg_shannon_index-Sciurus_carolinensis 1.5386 1.0127 1137
## Veg_shannon_index-Vulpes_vulpes 1.9221 1.0188 1283
## Veg_shannon_index-Sus_scrofa 3.9590 1.0101 1173
## total_shrub_cover-Odocoileus_virginianus 1.8214 1.0070 1784
## total_shrub_cover-Canis_latrans 2.2410 1.0176 1239
## total_shrub_cover-Sciurus_niger 1.3281 1.0078 1180
## total_shrub_cover-Procyon_lotor 0.2097 1.0005 1150
## total_shrub_cover-Dasypus_novemcinctus 1.0643 1.0053 1512
## total_shrub_cover-Lynx_rufus 1.2330 1.0125 639
## total_shrub_cover-Didelphis_virginiana 0.6701 1.0051 969
## total_shrub_cover-Sylvilagus_floridanus 1.1117 1.0128 901
## total_shrub_cover-Sciurus_carolinensis 1.0844 1.0074 1160
## total_shrub_cover-Vulpes_vulpes 1.1433 1.0012 936
## total_shrub_cover-Sus_scrofa 1.6375 1.0058 970
## Avg_Cogongrass_Cover-Odocoileus_virginianus 4.0850 1.0233 906
## Avg_Cogongrass_Cover-Canis_latrans 4.5990 1.0169 807
## Avg_Cogongrass_Cover-Sciurus_niger 3.7961 1.0122 634
## Avg_Cogongrass_Cover-Procyon_lotor 3.9852 1.0109 955
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6619 1.0467 681
## Avg_Cogongrass_Cover-Lynx_rufus 4.4289 1.0165 973
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9753 1.0207 890
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3074 1.0165 969
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2772 1.0137 853
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.8261 1.0388 782
## Avg_Cogongrass_Cover-Sus_scrofa 3.7438 1.0256 983
## Tree_Density-Odocoileus_virginianus 2.4282 1.0052 895
## Tree_Density-Canis_latrans -0.6266 1.0156 769
## Tree_Density-Sciurus_niger 1.4603 1.0113 819
## Tree_Density-Procyon_lotor 0.0746 1.0068 1475
## Tree_Density-Dasypus_novemcinctus -1.2177 1.0359 451
## Tree_Density-Lynx_rufus 3.3405 1.0290 433
## Tree_Density-Didelphis_virginiana -0.0833 1.0246 1248
## Tree_Density-Sylvilagus_floridanus -0.0371 1.0146 1019
## Tree_Density-Sciurus_carolinensis 0.0570 1.0351 828
## Tree_Density-Vulpes_vulpes 1.2942 1.0141 720
## Tree_Density-Sus_scrofa 0.2818 1.0584 964
## Avg_Canopy_Cover-Odocoileus_virginianus 3.9216 1.0011 1846
## Avg_Canopy_Cover-Canis_latrans 1.6206 1.0029 1590
## Avg_Canopy_Cover-Sciurus_niger 5.7979 1.0180 817
## Avg_Canopy_Cover-Procyon_lotor 3.3271 1.0089 1293
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.8345 1.0166 805
## Avg_Canopy_Cover-Lynx_rufus 4.3823 1.0031 774
## Avg_Canopy_Cover-Didelphis_virginiana 5.5068 1.0076 697
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.2189 1.0015 644
## Avg_Canopy_Cover-Sciurus_carolinensis 5.3245 1.0041 947
## Avg_Canopy_Cover-Vulpes_vulpes 5.1805 1.0087 843
## Avg_Canopy_Cover-Sus_scrofa 4.2643 1.0012 1317
## avg_veg_height-Odocoileus_virginianus 1.0085 1.0036 1480
## avg_veg_height-Canis_latrans 0.6674 1.0008 1097
## avg_veg_height-Sciurus_niger 0.9019 1.0185 907
## avg_veg_height-Procyon_lotor 0.7676 1.0032 1215
## avg_veg_height-Dasypus_novemcinctus 0.8985 1.0076 1252
## avg_veg_height-Lynx_rufus 0.9590 1.0070 1084
## avg_veg_height-Didelphis_virginiana 0.6249 1.0079 1004
## avg_veg_height-Sylvilagus_floridanus 0.6253 1.0019 1214
## avg_veg_height-Sciurus_carolinensis 1.2746 1.0053 1225
## avg_veg_height-Vulpes_vulpes 1.1056 1.0173 1217
## avg_veg_height-Sus_scrofa 0.8426 1.0036 1276
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5387 0.0807 0.3814 0.5389 0.6977
## (Intercept)-Canis_latrans -2.5665 0.1990 -2.9685 -2.5596 -2.2033
## (Intercept)-Sciurus_niger -4.7146 0.5346 -5.7752 -4.7166 -3.6484
## (Intercept)-Procyon_lotor -2.1808 0.1625 -2.5179 -2.1750 -1.8712
## (Intercept)-Dasypus_novemcinctus -1.5918 0.1791 -1.9543 -1.5890 -1.2521
## (Intercept)-Lynx_rufus -3.7306 0.3964 -4.4769 -3.7450 -2.9086
## (Intercept)-Didelphis_virginiana -2.3170 0.3033 -2.9299 -2.3100 -1.7566
## (Intercept)-Sylvilagus_floridanus -3.0785 0.2977 -3.6893 -3.0671 -2.5409
## (Intercept)-Sciurus_carolinensis -2.4568 0.3458 -3.1663 -2.4443 -1.8325
## (Intercept)-Vulpes_vulpes -4.2200 0.7409 -5.6848 -4.1990 -2.8387
## (Intercept)-Sus_scrofa -3.1526 0.6153 -4.4290 -3.1437 -2.0025
## shrub_cover-Odocoileus_virginianus -0.0585 0.0676 -0.1888 -0.0581 0.0747
## shrub_cover-Canis_latrans -0.3428 0.2255 -0.7857 -0.3455 0.0975
## shrub_cover-Sciurus_niger -0.3433 0.4475 -1.2653 -0.3287 0.4973
## shrub_cover-Procyon_lotor 0.2687 0.1629 -0.0582 0.2764 0.5808
## shrub_cover-Dasypus_novemcinctus 0.8679 0.3068 0.2884 0.8679 1.4809
## shrub_cover-Lynx_rufus -0.1854 0.3522 -0.8543 -0.1935 0.5107
## shrub_cover-Didelphis_virginiana 0.9231 0.3537 0.2864 0.9059 1.6645
## shrub_cover-Sylvilagus_floridanus 0.4475 0.4086 -0.3656 0.4521 1.2560
## shrub_cover-Sciurus_carolinensis 0.8591 0.4115 0.1057 0.8391 1.6909
## shrub_cover-Vulpes_vulpes 0.0880 0.5656 -1.0449 0.0917 1.2081
## shrub_cover-Sus_scrofa 0.6218 0.7605 -0.8347 0.5953 2.1709
## veg_height-Odocoileus_virginianus -0.3304 0.0681 -0.4642 -0.3297 -0.1989
## veg_height-Canis_latrans -0.5979 0.1799 -0.9654 -0.5935 -0.2659
## veg_height-Sciurus_niger -0.0648 0.3482 -0.7345 -0.0683 0.6367
## veg_height-Procyon_lotor 0.3427 0.1238 0.0984 0.3421 0.5868
## veg_height-Dasypus_novemcinctus 0.2382 0.1337 -0.0196 0.2380 0.5040
## veg_height-Lynx_rufus 0.0911 0.2341 -0.3673 0.0976 0.5424
## veg_height-Didelphis_virginiana 0.4276 0.2311 -0.0056 0.4226 0.8987
## veg_height-Sylvilagus_floridanus 0.1473 0.2429 -0.3351 0.1469 0.6143
## veg_height-Sciurus_carolinensis 0.0791 0.2144 -0.3282 0.0719 0.5156
## veg_height-Vulpes_vulpes -0.2098 0.3351 -0.9310 -0.1909 0.4200
## veg_height-Sus_scrofa -0.1697 0.3281 -0.8428 -0.1571 0.4430
## week-Odocoileus_virginianus 1.3113 0.1229 1.0658 1.3091 1.5506
## week-Canis_latrans 0.5956 0.2669 0.0844 0.5948 1.1281
## week-Sciurus_niger -0.4314 0.5649 -1.7016 -0.3775 0.5599
## week-Procyon_lotor 0.2065 0.2107 -0.2001 0.2054 0.6148
## week-Dasypus_novemcinctus 0.1057 0.2262 -0.3353 0.1059 0.5477
## week-Lynx_rufus 0.3810 0.3544 -0.3089 0.3819 1.0847
## week-Didelphis_virginiana 0.0525 0.3740 -0.7367 0.0645 0.7579
## week-Sylvilagus_floridanus 0.0532 0.3503 -0.6232 0.0606 0.7278
## week-Sciurus_carolinensis 0.8115 0.3670 0.1244 0.7981 1.5708
## week-Vulpes_vulpes 0.1886 0.5267 -0.8956 0.2109 1.1812
## week-Sus_scrofa 0.6864 0.4475 -0.1315 0.6660 1.6486
## I(week^2)-Odocoileus_virginianus -0.5411 0.0506 -0.6393 -0.5414 -0.4428
## I(week^2)-Canis_latrans -0.2461 0.1093 -0.4679 -0.2451 -0.0301
## I(week^2)-Sciurus_niger -0.2930 0.2371 -0.7974 -0.2819 0.1422
## I(week^2)-Procyon_lotor -0.1327 0.0910 -0.3123 -0.1328 0.0437
## I(week^2)-Dasypus_novemcinctus -0.1833 0.1037 -0.3966 -0.1807 0.0099
## I(week^2)-Lynx_rufus -0.2430 0.1561 -0.5689 -0.2413 0.0469
## I(week^2)-Didelphis_virginiana -0.4206 0.2078 -0.8799 -0.4029 -0.0575
## I(week^2)-Sylvilagus_floridanus -0.1826 0.1589 -0.4963 -0.1786 0.1226
## I(week^2)-Sciurus_carolinensis -0.2870 0.1462 -0.5816 -0.2859 -0.0109
## I(week^2)-Vulpes_vulpes -0.3875 0.2369 -0.9019 -0.3693 0.0277
## I(week^2)-Sus_scrofa -0.2432 0.1783 -0.6088 -0.2405 0.0973
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0027 5250
## (Intercept)-Canis_latrans 1.0031 2755
## (Intercept)-Sciurus_niger 1.0365 442
## (Intercept)-Procyon_lotor 1.0000 3405
## (Intercept)-Dasypus_novemcinctus 1.0018 4204
## (Intercept)-Lynx_rufus 1.0014 381
## (Intercept)-Didelphis_virginiana 1.0056 2574
## (Intercept)-Sylvilagus_floridanus 1.0040 2063
## (Intercept)-Sciurus_carolinensis 1.0099 1828
## (Intercept)-Vulpes_vulpes 1.0137 439
## (Intercept)-Sus_scrofa 1.0007 1169
## shrub_cover-Odocoileus_virginianus 1.0026 5250
## shrub_cover-Canis_latrans 1.0047 1959
## shrub_cover-Sciurus_niger 1.0267 996
## shrub_cover-Procyon_lotor 1.0006 3372
## shrub_cover-Dasypus_novemcinctus 1.0021 2232
## shrub_cover-Lynx_rufus 1.0008 653
## shrub_cover-Didelphis_virginiana 1.0015 1639
## shrub_cover-Sylvilagus_floridanus 1.0045 1475
## shrub_cover-Sciurus_carolinensis 1.0187 1260
## shrub_cover-Vulpes_vulpes 1.0194 1361
## shrub_cover-Sus_scrofa 1.0022 1234
## veg_height-Odocoileus_virginianus 1.0007 5250
## veg_height-Canis_latrans 1.0057 2308
## veg_height-Sciurus_niger 1.0091 1388
## veg_height-Procyon_lotor 0.9998 3897
## veg_height-Dasypus_novemcinctus 1.0039 4325
## veg_height-Lynx_rufus 1.0044 1928
## veg_height-Didelphis_virginiana 1.0005 3211
## veg_height-Sylvilagus_floridanus 1.0048 2525
## veg_height-Sciurus_carolinensis 1.0011 3337
## veg_height-Vulpes_vulpes 1.0091 1760
## veg_height-Sus_scrofa 1.0015 2596
## week-Odocoileus_virginianus 1.0016 4770
## week-Canis_latrans 1.0049 3459
## week-Sciurus_niger 1.0093 652
## week-Procyon_lotor 1.0024 4637
## week-Dasypus_novemcinctus 0.9999 4403
## week-Lynx_rufus 1.0120 2256
## week-Didelphis_virginiana 1.0044 2939
## week-Sylvilagus_floridanus 1.0020 2932
## week-Sciurus_carolinensis 1.0016 3877
## week-Vulpes_vulpes 1.0007 1503
## week-Sus_scrofa 1.0051 3867
## I(week^2)-Odocoileus_virginianus 1.0015 4871
## I(week^2)-Canis_latrans 1.0010 3893
## I(week^2)-Sciurus_niger 1.0003 846
## I(week^2)-Procyon_lotor 1.0037 4286
## I(week^2)-Dasypus_novemcinctus 1.0027 4056
## I(week^2)-Lynx_rufus 1.0016 1923
## I(week^2)-Didelphis_virginiana 1.0043 1952
## I(week^2)-Sylvilagus_floridanus 1.0013 2275
## I(week^2)-Sciurus_carolinensis 1.0001 4029
## I(week^2)-Vulpes_vulpes 1.0100 1432
## I(week^2)-Sus_scrofa 1.0010 3659
#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): 2.0768
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0356 0.6461 -1.2121 0.0326 1.3289 1.0008 1223
## Avg_Cogongrass_Cover 0.0211 0.3467 -0.6843 0.0297 0.6751 1.0003 1243
## total_shrub_cover -0.6372 0.4340 -1.6228 -0.5909 0.1203 1.0086 586
## avg_veg_height 0.1540 0.3420 -0.5033 0.1420 0.8400 1.0004 963
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.7680 3.1462 0.5043 2.9343 12.3816 1.0040 1534
## Avg_Cogongrass_Cover 0.3707 0.4598 0.0395 0.2238 1.6065 1.0031 1816
## total_shrub_cover 0.7984 1.1693 0.0536 0.4365 3.7085 1.0205 684
## avg_veg_height 0.2575 0.3408 0.0337 0.1658 1.0251 1.0186 2273
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3415 1.5724 0.0697 0.8591 5.7852 1.0499 175
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4538 0.4796 -3.3752 -2.4616 -1.4550 1.0016 4779
## shrub_cover 0.4403 0.2786 -0.0908 0.4383 1.0113 1.0006 1405
## veg_height -0.0107 0.1608 -0.3245 -0.0129 0.3161 1.0053 2948
## week 0.3560 0.2445 -0.1569 0.3635 0.8131 1.0028 2870
## I(week^2) -0.2891 0.1061 -0.4974 -0.2884 -0.0828 1.0075 2603
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4287 1.5399 0.8223 2.0337 6.2076 1.0117 2683
## shrub_cover 0.5708 0.4481 0.1151 0.4530 1.7772 1.0018 1424
## veg_height 0.2028 0.1446 0.0564 0.1653 0.5711 1.0125 3641
## week 0.4556 0.3603 0.1077 0.3545 1.4171 1.0123 1347
## I(week^2) 0.0763 0.0687 0.0226 0.0600 0.2253 1.0747 1429
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.7129 1.5622 0.8991 3.5631
## (Intercept)-Canis_latrans 0.6208 0.8026 -0.8245 0.5781
## (Intercept)-Sciurus_niger -0.4255 1.2243 -2.5560 -0.5407
## (Intercept)-Procyon_lotor 0.8280 0.7807 -0.7047 0.8178
## (Intercept)-Dasypus_novemcinctus -0.5246 0.7423 -1.9608 -0.5262
## (Intercept)-Lynx_rufus 0.1609 1.0749 -1.7410 0.0817
## (Intercept)-Didelphis_virginiana -1.0396 0.8189 -2.6137 -1.0450
## (Intercept)-Sylvilagus_floridanus 0.2361 0.9816 -1.4797 0.1593
## (Intercept)-Sciurus_carolinensis -1.0788 0.8877 -2.8664 -1.0813
## (Intercept)-Vulpes_vulpes -0.5785 1.4932 -3.0624 -0.7444
## (Intercept)-Sus_scrofa -1.4540 1.0814 -3.5679 -1.4646
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.0117 0.5864 -1.1335 0.0022
## Avg_Cogongrass_Cover-Canis_latrans 0.3271 0.5076 -0.6102 0.3034
## Avg_Cogongrass_Cover-Sciurus_niger -0.3443 0.6771 -1.8843 -0.2773
## Avg_Cogongrass_Cover-Procyon_lotor -0.0588 0.4609 -0.9965 -0.0493
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1605 0.4327 -0.6832 0.1601
## Avg_Cogongrass_Cover-Lynx_rufus 0.3466 0.5376 -0.5908 0.3128
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1750 0.4721 -0.7373 0.1684
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3423 0.5731 -1.6255 -0.2842
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0731 0.4645 -0.8762 0.0774
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1466 0.5634 -0.9423 0.1334
## Avg_Cogongrass_Cover-Sus_scrofa -0.2348 0.6248 -1.6459 -0.1772
## total_shrub_cover-Odocoileus_virginianus -0.3638 0.6890 -1.7088 -0.3875
## total_shrub_cover-Canis_latrans 0.1550 0.6775 -1.0051 0.0813
## total_shrub_cover-Sciurus_niger -0.7987 0.7889 -2.5629 -0.7327
## total_shrub_cover-Procyon_lotor -1.1537 0.6338 -2.6320 -1.0570
## total_shrub_cover-Dasypus_novemcinctus -0.3222 0.5373 -1.5687 -0.2820
## total_shrub_cover-Lynx_rufus -1.0510 0.8498 -3.0211 -0.9499
## total_shrub_cover-Didelphis_virginiana -0.6464 0.6090 -2.0490 -0.5809
## total_shrub_cover-Sylvilagus_floridanus -1.1433 0.9408 -3.4047 -0.9843
## total_shrub_cover-Sciurus_carolinensis -0.6677 0.6817 -2.2814 -0.5868
## total_shrub_cover-Vulpes_vulpes -0.8147 1.0001 -3.1816 -0.6985
## total_shrub_cover-Sus_scrofa -0.4200 0.7963 -2.1689 -0.3929
## avg_veg_height-Odocoileus_virginianus 0.1298 0.5329 -0.9190 0.1270
## avg_veg_height-Canis_latrans 0.1674 0.4593 -0.7032 0.1521
## avg_veg_height-Sciurus_niger -0.0781 0.5847 -1.3224 -0.0412
## avg_veg_height-Procyon_lotor 0.1801 0.4498 -0.7032 0.1816
## avg_veg_height-Dasypus_novemcinctus 0.3186 0.4286 -0.4799 0.3065
## avg_veg_height-Lynx_rufus 0.1288 0.5585 -0.9376 0.1205
## avg_veg_height-Didelphis_virginiana 0.0622 0.4638 -0.8781 0.0697
## avg_veg_height-Sylvilagus_floridanus 0.0905 0.5086 -0.9198 0.0935
## avg_veg_height-Sciurus_carolinensis 0.4446 0.4786 -0.4002 0.4098
## avg_veg_height-Vulpes_vulpes 0.1215 0.5301 -0.9313 0.1184
## avg_veg_height-Sus_scrofa 0.1524 0.5121 -0.8597 0.1508
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.3674 1.0093 992
## (Intercept)-Canis_latrans 2.3800 1.0085 1430
## (Intercept)-Sciurus_niger 2.3241 1.0008 599
## (Intercept)-Procyon_lotor 2.3956 1.0115 1822
## (Intercept)-Dasypus_novemcinctus 0.9983 1.0051 1524
## (Intercept)-Lynx_rufus 2.6202 1.0006 829
## (Intercept)-Didelphis_virginiana 0.6128 1.0010 1504
## (Intercept)-Sylvilagus_floridanus 2.4355 1.0017 994
## (Intercept)-Sciurus_carolinensis 0.7054 1.0081 1384
## (Intercept)-Vulpes_vulpes 2.7622 1.0403 365
## (Intercept)-Sus_scrofa 0.7069 1.0320 982
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2549 1.0002 2565
## Avg_Cogongrass_Cover-Canis_latrans 1.3965 1.0018 2121
## Avg_Cogongrass_Cover-Sciurus_niger 0.8327 1.0017 1530
## Avg_Cogongrass_Cover-Procyon_lotor 0.8225 1.0014 2121
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0350 1.0011 2243
## Avg_Cogongrass_Cover-Lynx_rufus 1.5973 1.0040 2323
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1308 1.0013 2217
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6435 1.0009 1872
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9761 1.0009 1832
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3218 1.0040 2325
## Avg_Cogongrass_Cover-Sus_scrofa 0.8505 1.0056 1835
## total_shrub_cover-Odocoileus_virginianus 1.0988 1.0098 2552
## total_shrub_cover-Canis_latrans 1.7109 1.0192 1008
## total_shrub_cover-Sciurus_niger 0.6444 1.0133 1096
## total_shrub_cover-Procyon_lotor -0.1700 1.0013 837
## total_shrub_cover-Dasypus_novemcinctus 0.6023 1.0208 1262
## total_shrub_cover-Lynx_rufus 0.3852 1.0042 672
## total_shrub_cover-Didelphis_virginiana 0.3801 1.0005 887
## total_shrub_cover-Sylvilagus_floridanus 0.2374 1.0081 480
## total_shrub_cover-Sciurus_carolinensis 0.4553 1.0040 984
## total_shrub_cover-Vulpes_vulpes 0.8877 1.0340 578
## total_shrub_cover-Sus_scrofa 1.0971 1.0260 804
## avg_veg_height-Odocoileus_virginianus 1.1926 1.0018 1968
## avg_veg_height-Canis_latrans 1.1433 1.0014 1629
## avg_veg_height-Sciurus_niger 0.9858 1.0034 1449
## avg_veg_height-Procyon_lotor 1.0706 1.0020 1998
## avg_veg_height-Dasypus_novemcinctus 1.2058 1.0007 1614
## avg_veg_height-Lynx_rufus 1.2563 1.0019 1420
## avg_veg_height-Didelphis_virginiana 0.9532 1.0019 1824
## avg_veg_height-Sylvilagus_floridanus 1.0900 1.0015 1404
## avg_veg_height-Sciurus_carolinensis 1.4914 1.0019 1799
## avg_veg_height-Vulpes_vulpes 1.1644 1.0005 1684
## avg_veg_height-Sus_scrofa 1.1659 1.0012 1642
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5375 0.0791 0.3826 0.5369 0.6926
## (Intercept)-Canis_latrans -2.6046 0.2177 -3.0557 -2.5973 -2.1921
## (Intercept)-Sciurus_niger -4.0531 0.7129 -5.4862 -4.0572 -2.6816
## (Intercept)-Procyon_lotor -2.1818 0.1571 -2.5040 -2.1759 -1.8865
## (Intercept)-Dasypus_novemcinctus -1.6320 0.1906 -2.0204 -1.6253 -1.2781
## (Intercept)-Lynx_rufus -3.4459 0.3858 -4.2252 -3.4310 -2.7241
## (Intercept)-Didelphis_virginiana -2.4368 0.3311 -3.1266 -2.4239 -1.8332
## (Intercept)-Sylvilagus_floridanus -3.2083 0.3180 -3.8548 -3.1991 -2.6192
## (Intercept)-Sciurus_carolinensis -2.5452 0.3577 -3.2999 -2.5309 -1.8904
## (Intercept)-Vulpes_vulpes -4.2110 0.8007 -5.8408 -4.1815 -2.7517
## (Intercept)-Sus_scrofa -3.3614 0.6281 -4.6279 -3.3601 -2.0922
## shrub_cover-Odocoileus_virginianus -0.0579 0.0689 -0.1944 -0.0574 0.0755
## shrub_cover-Canis_latrans -0.2647 0.2477 -0.7421 -0.2656 0.2251
## shrub_cover-Sciurus_niger -0.1041 0.5384 -1.1680 -0.1060 0.9682
## shrub_cover-Procyon_lotor 0.3110 0.1624 -0.0125 0.3145 0.6188
## shrub_cover-Dasypus_novemcinctus 0.9643 0.3430 0.3353 0.9454 1.6592
## shrub_cover-Lynx_rufus 0.0515 0.3792 -0.7390 0.0650 0.7518
## shrub_cover-Didelphis_virginiana 1.1139 0.4114 0.3644 1.0917 1.9671
## shrub_cover-Sylvilagus_floridanus 0.6914 0.4284 -0.1721 0.7049 1.5303
## shrub_cover-Sciurus_carolinensis 1.0565 0.4336 0.2203 1.0549 1.9363
## shrub_cover-Vulpes_vulpes 0.2299 0.5835 -0.9536 0.2414 1.3367
## shrub_cover-Sus_scrofa 0.8932 0.7878 -0.7186 0.8873 2.4977
## veg_height-Odocoileus_virginianus -0.3323 0.0676 -0.4672 -0.3319 -0.2006
## veg_height-Canis_latrans -0.5991 0.1885 -0.9912 -0.5917 -0.2452
## veg_height-Sciurus_niger 0.0112 0.4333 -0.8052 -0.0085 0.9466
## veg_height-Procyon_lotor 0.3327 0.1230 0.0932 0.3318 0.5791
## veg_height-Dasypus_novemcinctus 0.2372 0.1361 -0.0259 0.2363 0.5110
## veg_height-Lynx_rufus 0.0248 0.2505 -0.4782 0.0288 0.4937
## veg_height-Didelphis_virginiana 0.4092 0.2506 -0.0543 0.3980 0.9238
## veg_height-Sylvilagus_floridanus 0.0410 0.2510 -0.4319 0.0382 0.5471
## veg_height-Sciurus_carolinensis 0.0691 0.2189 -0.3471 0.0649 0.5088
## veg_height-Vulpes_vulpes -0.1570 0.3326 -0.8636 -0.1406 0.4582
## veg_height-Sus_scrofa -0.1574 0.3311 -0.8646 -0.1481 0.4722
## week-Odocoileus_virginianus 1.3107 0.1239 1.0702 1.3103 1.5563
## week-Canis_latrans 0.5946 0.2702 0.0761 0.5910 1.1350
## week-Sciurus_niger -0.4442 0.5969 -1.7768 -0.3901 0.5507
## week-Procyon_lotor 0.1996 0.2101 -0.2097 0.1973 0.6171
## week-Dasypus_novemcinctus 0.1037 0.2281 -0.3457 0.1065 0.5509
## week-Lynx_rufus 0.3816 0.3457 -0.2958 0.3786 1.0690
## week-Didelphis_virginiana 0.0674 0.3749 -0.6738 0.0822 0.8110
## week-Sylvilagus_floridanus 0.0461 0.3479 -0.6510 0.0614 0.6898
## week-Sciurus_carolinensis 0.8008 0.3695 0.0914 0.7876 1.5590
## week-Vulpes_vulpes 0.1778 0.5470 -0.9928 0.2095 1.1798
## week-Sus_scrofa 0.6938 0.4576 -0.1473 0.6757 1.6632
## I(week^2)-Odocoileus_virginianus -0.5407 0.0507 -0.6399 -0.5410 -0.4404
## I(week^2)-Canis_latrans -0.2449 0.1098 -0.4654 -0.2435 -0.0342
## I(week^2)-Sciurus_niger -0.2847 0.2464 -0.8035 -0.2710 0.1512
## I(week^2)-Procyon_lotor -0.1322 0.0910 -0.3116 -0.1310 0.0480
## I(week^2)-Dasypus_novemcinctus -0.1781 0.1040 -0.3868 -0.1771 0.0247
## I(week^2)-Lynx_rufus -0.2386 0.1543 -0.5575 -0.2374 0.0621
## I(week^2)-Didelphis_virginiana -0.4190 0.2153 -0.8983 -0.3969 -0.0448
## I(week^2)-Sylvilagus_floridanus -0.1847 0.1628 -0.5065 -0.1807 0.1221
## I(week^2)-Sciurus_carolinensis -0.2864 0.1457 -0.5859 -0.2841 -0.0073
## I(week^2)-Vulpes_vulpes -0.4226 0.2690 -1.0050 -0.3968 0.0266
## I(week^2)-Sus_scrofa -0.2464 0.1804 -0.6113 -0.2443 0.0898
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0030 4998
## (Intercept)-Canis_latrans 1.0041 2142
## (Intercept)-Sciurus_niger 1.0060 517
## (Intercept)-Procyon_lotor 1.0012 4183
## (Intercept)-Dasypus_novemcinctus 1.0108 2657
## (Intercept)-Lynx_rufus 1.0006 960
## (Intercept)-Didelphis_virginiana 1.0008 1649
## (Intercept)-Sylvilagus_floridanus 0.9998 1452
## (Intercept)-Sciurus_carolinensis 0.9999 1298
## (Intercept)-Vulpes_vulpes 1.0366 325
## (Intercept)-Sus_scrofa 1.0346 843
## shrub_cover-Odocoileus_virginianus 1.0001 5250
## shrub_cover-Canis_latrans 1.0136 1391
## shrub_cover-Sciurus_niger 1.0074 1075
## shrub_cover-Procyon_lotor 1.0005 3698
## shrub_cover-Dasypus_novemcinctus 1.0139 1391
## shrub_cover-Lynx_rufus 1.0014 945
## shrub_cover-Didelphis_virginiana 1.0002 1153
## shrub_cover-Sylvilagus_floridanus 1.0056 872
## shrub_cover-Sciurus_carolinensis 1.0022 1141
## shrub_cover-Vulpes_vulpes 1.0093 858
## shrub_cover-Sus_scrofa 1.0122 879
## veg_height-Odocoileus_virginianus 1.0010 5261
## veg_height-Canis_latrans 1.0054 1982
## veg_height-Sciurus_niger 1.0041 1328
## veg_height-Procyon_lotor 1.0008 4132
## veg_height-Dasypus_novemcinctus 1.0058 4447
## veg_height-Lynx_rufus 1.0076 2187
## veg_height-Didelphis_virginiana 1.0002 3007
## veg_height-Sylvilagus_floridanus 1.0062 1661
## veg_height-Sciurus_carolinensis 1.0038 2927
## veg_height-Vulpes_vulpes 1.0055 1555
## veg_height-Sus_scrofa 1.0034 2740
## week-Odocoileus_virginianus 1.0097 4244
## week-Canis_latrans 1.0019 3620
## week-Sciurus_niger 1.0277 689
## week-Procyon_lotor 1.0009 4153
## week-Dasypus_novemcinctus 1.0008 4746
## week-Lynx_rufus 1.0021 2879
## week-Didelphis_virginiana 1.0032 2363
## week-Sylvilagus_floridanus 1.0000 2643
## week-Sciurus_carolinensis 1.0025 3677
## week-Vulpes_vulpes 1.0146 1474
## week-Sus_scrofa 1.0020 3498
## I(week^2)-Odocoileus_virginianus 1.0102 4140
## I(week^2)-Canis_latrans 1.0000 3705
## I(week^2)-Sciurus_niger 1.0263 1025
## I(week^2)-Procyon_lotor 1.0013 4021
## I(week^2)-Dasypus_novemcinctus 1.0007 4292
## I(week^2)-Lynx_rufus 1.0099 2435
## I(week^2)-Didelphis_virginiana 1.0119 1213
## I(week^2)-Sylvilagus_floridanus 1.0000 2063
## I(week^2)-Sciurus_carolinensis 1.0011 3767
## I(week^2)-Vulpes_vulpes 1.0534 826
## I(week^2)-Sus_scrofa 1.0012 3803
#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): 2.0218
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0663 0.7403 -1.4334 -0.0977 1.4798 1.0061 1205
## Tree_Density -0.7783 0.4147 -1.6835 -0.7472 -0.0709 1.0116 1192
## Avg_Canopy_Cover 1.0999 0.3886 0.4014 1.0772 1.9308 1.0053 1588
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 6.5813 5.9244 1.3869 4.9774 21.1638 1.0618 644
## Tree_Density 0.7163 1.1883 0.0466 0.3389 3.7381 1.0002 967
## Avg_Canopy_Cover 0.8367 0.9636 0.0781 0.5566 3.2797 1.0113 1382
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4189 0.5594 0.0385 0.2392 1.9474 1.0587 451
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4530 0.4813 -3.3941 -2.4649 -1.4541 1.0024 4354
## shrub_cover 0.2362 0.2434 -0.2299 0.2334 0.7369 1.0009 3054
## veg_height 0.0148 0.1571 -0.3092 0.0156 0.3225 1.0013 3832
## week 0.3670 0.2360 -0.1147 0.3780 0.8159 1.0011 3466
## I(week^2) -0.2882 0.1015 -0.4942 -0.2867 -0.0970 1.0029 2413
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5396 1.5332 0.8887 2.1570 6.4956 1.0062 1420
## shrub_cover 0.5002 0.3786 0.1082 0.3992 1.5235 1.0005 2152
## veg_height 0.1973 0.1327 0.0621 0.1609 0.5576 1.0080 3745
## week 0.4316 0.3177 0.1089 0.3447 1.2540 1.0083 1958
## I(week^2) 0.0720 0.0515 0.0220 0.0583 0.2112 1.0165 2782
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 4.6795 1.8196 2.0287 4.3579 9.1540
## (Intercept)-Canis_latrans 0.4307 0.6440 -0.7312 0.4017 1.7411
## (Intercept)-Sciurus_niger 0.2524 1.5904 -2.1440 -0.0095 4.2105
## (Intercept)-Procyon_lotor 0.8531 0.6465 -0.3832 0.8356 2.1647
## (Intercept)-Dasypus_novemcinctus -0.9220 0.6132 -2.2240 -0.8883 0.1847
## (Intercept)-Lynx_rufus 1.7053 2.0027 -0.9118 1.2799 6.9768
## (Intercept)-Didelphis_virginiana -1.7349 0.7379 -3.2790 -1.7098 -0.3665
## (Intercept)-Sylvilagus_floridanus -0.5941 0.7148 -1.9799 -0.6069 0.8486
## (Intercept)-Sciurus_carolinensis -1.7917 0.7269 -3.2699 -1.7624 -0.4276
## (Intercept)-Vulpes_vulpes -1.1447 1.6245 -3.6539 -1.3820 2.8157
## (Intercept)-Sus_scrofa -2.5040 1.0044 -4.6394 -2.4483 -0.6710
## Tree_Density-Odocoileus_virginianus -0.4082 0.6507 -1.5113 -0.4735 1.0923
## Tree_Density-Canis_latrans -0.9351 0.5590 -2.2332 -0.8782 -0.0124
## Tree_Density-Sciurus_niger -0.8108 0.8034 -2.5475 -0.7589 0.6838
## Tree_Density-Procyon_lotor -0.5088 0.4077 -1.3082 -0.5073 0.2909
## Tree_Density-Dasypus_novemcinctus -1.3083 0.8672 -3.5119 -1.1309 -0.1508
## Tree_Density-Lynx_rufus -0.1026 0.7607 -1.3588 -0.1802 1.7077
## Tree_Density-Didelphis_virginiana -0.9968 0.7568 -2.8690 -0.8834 0.1727
## Tree_Density-Sylvilagus_floridanus -1.0236 0.7397 -2.7881 -0.9103 0.1272
## Tree_Density-Sciurus_carolinensis -0.9187 0.7174 -2.6088 -0.8351 0.2427
## Tree_Density-Vulpes_vulpes -0.7344 0.8047 -2.5031 -0.6986 0.6787
## Tree_Density-Sus_scrofa -0.9916 0.8399 -3.1217 -0.8573 0.2689
## Avg_Canopy_Cover-Odocoileus_virginianus 0.8014 0.7583 -0.7294 0.8152 2.3618
## Avg_Canopy_Cover-Canis_latrans -0.0057 0.4954 -0.9882 -0.0116 0.9744
## Avg_Canopy_Cover-Sciurus_niger 1.0998 0.9317 -0.5614 1.0266 3.1775
## Avg_Canopy_Cover-Procyon_lotor 1.0914 0.4976 0.2043 1.0478 2.1477
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0799 0.4592 0.2378 1.0522 2.0556
## Avg_Canopy_Cover-Lynx_rufus 1.0704 0.8826 -0.4582 0.9935 3.0770
## Avg_Canopy_Cover-Didelphis_virginiana 1.4914 0.6332 0.5037 1.4073 3.0036
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9675 0.8866 0.6685 1.8137 4.1454
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4174 0.6020 0.4399 1.3422 2.7773
## Avg_Canopy_Cover-Vulpes_vulpes 1.1592 0.7110 -0.0579 1.1026 2.7273
## Avg_Canopy_Cover-Sus_scrofa 1.3611 0.6121 0.3424 1.3001 2.6878
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0172 666
## (Intercept)-Canis_latrans 1.0049 2367
## (Intercept)-Sciurus_niger 1.0497 371
## (Intercept)-Procyon_lotor 1.0067 2829
## (Intercept)-Dasypus_novemcinctus 1.0012 2588
## (Intercept)-Lynx_rufus 1.1086 298
## (Intercept)-Didelphis_virginiana 1.0029 2713
## (Intercept)-Sylvilagus_floridanus 1.0075 3012
## (Intercept)-Sciurus_carolinensis 1.0013 2828
## (Intercept)-Vulpes_vulpes 1.0029 236
## (Intercept)-Sus_scrofa 1.0011 1520
## Tree_Density-Odocoileus_virginianus 1.0017 1758
## Tree_Density-Canis_latrans 1.0047 2520
## Tree_Density-Sciurus_niger 1.0178 1274
## Tree_Density-Procyon_lotor 1.0045 3512
## Tree_Density-Dasypus_novemcinctus 1.0007 1318
## Tree_Density-Lynx_rufus 1.0050 1054
## Tree_Density-Didelphis_virginiana 1.0064 1649
## Tree_Density-Sylvilagus_floridanus 1.0099 1515
## Tree_Density-Sciurus_carolinensis 1.0053 1967
## Tree_Density-Vulpes_vulpes 1.0075 1871
## Tree_Density-Sus_scrofa 1.0095 1584
## Avg_Canopy_Cover-Odocoileus_virginianus 1.0060 3447
## Avg_Canopy_Cover-Canis_latrans 1.0040 2498
## Avg_Canopy_Cover-Sciurus_niger 1.0024 1052
## Avg_Canopy_Cover-Procyon_lotor 1.0025 4076
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0000 3825
## Avg_Canopy_Cover-Lynx_rufus 1.0070 1136
## Avg_Canopy_Cover-Didelphis_virginiana 1.0010 2067
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0036 1127
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0032 2195
## Avg_Canopy_Cover-Vulpes_vulpes 1.0026 2241
## Avg_Canopy_Cover-Sus_scrofa 1.0005 2416
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5388 0.0806 0.3841 0.5396 0.6966
## (Intercept)-Canis_latrans -2.5749 0.2093 -3.0004 -2.5677 -2.1812
## (Intercept)-Sciurus_niger -4.4003 0.6410 -5.6399 -4.4016 -3.1302
## (Intercept)-Procyon_lotor -2.1805 0.1645 -2.5190 -2.1767 -1.8684
## (Intercept)-Dasypus_novemcinctus -1.5869 0.1751 -1.9343 -1.5859 -1.2510
## (Intercept)-Lynx_rufus -3.7954 0.3781 -4.5159 -3.8066 -3.0362
## (Intercept)-Didelphis_virginiana -2.3551 0.3113 -3.0000 -2.3424 -1.7820
## (Intercept)-Sylvilagus_floridanus -3.0322 0.2978 -3.6318 -3.0274 -2.4891
## (Intercept)-Sciurus_carolinensis -2.4288 0.3386 -3.1224 -2.4156 -1.8002
## (Intercept)-Vulpes_vulpes -4.1302 0.8194 -5.8730 -4.0623 -2.6984
## (Intercept)-Sus_scrofa -3.0875 0.6107 -4.3271 -3.0733 -1.9124
## shrub_cover-Odocoileus_virginianus -0.0589 0.0680 -0.1939 -0.0590 0.0723
## shrub_cover-Canis_latrans -0.2842 0.2225 -0.7165 -0.2813 0.1521
## shrub_cover-Sciurus_niger -0.3635 0.4371 -1.2721 -0.3464 0.4528
## shrub_cover-Procyon_lotor 0.2476 0.1602 -0.0786 0.2484 0.5524
## shrub_cover-Dasypus_novemcinctus 0.8279 0.2919 0.2868 0.8171 1.4155
## shrub_cover-Lynx_rufus -0.3318 0.3162 -0.9675 -0.3238 0.2913
## shrub_cover-Didelphis_virginiana 0.9348 0.3537 0.2879 0.9260 1.6711
## shrub_cover-Sylvilagus_floridanus 0.3980 0.3754 -0.3296 0.3965 1.1421
## shrub_cover-Sciurus_carolinensis 0.8117 0.3905 0.0772 0.7971 1.6072
## shrub_cover-Vulpes_vulpes -0.0704 0.5255 -1.1860 -0.0633 0.9369
## shrub_cover-Sus_scrofa 0.5256 0.7329 -0.8998 0.4967 2.0531
## veg_height-Odocoileus_virginianus -0.3312 0.0689 -0.4702 -0.3308 -0.1969
## veg_height-Canis_latrans -0.5875 0.1838 -0.9632 -0.5828 -0.2434
## veg_height-Sciurus_niger -0.0651 0.3614 -0.7842 -0.0679 0.6760
## veg_height-Procyon_lotor 0.3405 0.1231 0.1026 0.3390 0.5848
## veg_height-Dasypus_novemcinctus 0.2404 0.1331 -0.0179 0.2381 0.4987
## veg_height-Lynx_rufus 0.1010 0.2374 -0.3794 0.1063 0.5719
## veg_height-Didelphis_virginiana 0.4549 0.2413 0.0145 0.4485 0.9457
## veg_height-Sylvilagus_floridanus 0.1532 0.2367 -0.3188 0.1574 0.6119
## veg_height-Sciurus_carolinensis 0.0859 0.2121 -0.3143 0.0805 0.5117
## veg_height-Vulpes_vulpes -0.1232 0.3146 -0.7965 -0.1135 0.4581
## veg_height-Sus_scrofa -0.1099 0.3264 -0.7832 -0.0947 0.4996
## week-Odocoileus_virginianus 1.3094 0.1233 1.0656 1.3081 1.5447
## week-Canis_latrans 0.5971 0.2615 0.0911 0.5877 1.1198
## week-Sciurus_niger -0.4012 0.5293 -1.5378 -0.3554 0.5306
## week-Procyon_lotor 0.2167 0.2111 -0.1950 0.2173 0.6348
## week-Dasypus_novemcinctus 0.1194 0.2226 -0.3234 0.1207 0.5501
## week-Lynx_rufus 0.3880 0.3592 -0.3096 0.3840 1.0860
## week-Didelphis_virginiana 0.0630 0.3845 -0.7159 0.0742 0.7824
## week-Sylvilagus_floridanus 0.0666 0.3565 -0.6557 0.0782 0.7337
## week-Sciurus_carolinensis 0.8059 0.3628 0.1221 0.7977 1.5516
## week-Vulpes_vulpes 0.2190 0.5216 -0.8519 0.2358 1.1878
## week-Sus_scrofa 0.6995 0.4493 -0.1765 0.6832 1.6091
## I(week^2)-Odocoileus_virginianus -0.5405 0.0512 -0.6414 -0.5393 -0.4401
## I(week^2)-Canis_latrans -0.2473 0.1091 -0.4741 -0.2446 -0.0384
## I(week^2)-Sciurus_niger -0.2881 0.2277 -0.7438 -0.2834 0.1544
## I(week^2)-Procyon_lotor -0.1372 0.0913 -0.3190 -0.1357 0.0406
## I(week^2)-Dasypus_novemcinctus -0.1837 0.1018 -0.3898 -0.1816 0.0093
## I(week^2)-Lynx_rufus -0.2453 0.1574 -0.5593 -0.2417 0.0577
## I(week^2)-Didelphis_virginiana -0.4197 0.2116 -0.9014 -0.4003 -0.0594
## I(week^2)-Sylvilagus_floridanus -0.1841 0.1595 -0.4990 -0.1832 0.1191
## I(week^2)-Sciurus_carolinensis -0.2853 0.1439 -0.5639 -0.2834 -0.0028
## I(week^2)-Vulpes_vulpes -0.4043 0.2478 -0.9535 -0.3860 0.0347
## I(week^2)-Sus_scrofa -0.2486 0.1817 -0.6242 -0.2418 0.0914
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5250
## (Intercept)-Canis_latrans 1.0043 2293
## (Intercept)-Sciurus_niger 1.0332 550
## (Intercept)-Procyon_lotor 1.0025 3611
## (Intercept)-Dasypus_novemcinctus 1.0001 4571
## (Intercept)-Lynx_rufus 1.0125 546
## (Intercept)-Didelphis_virginiana 1.0047 1919
## (Intercept)-Sylvilagus_floridanus 1.0001 2520
## (Intercept)-Sciurus_carolinensis 1.0030 2323
## (Intercept)-Vulpes_vulpes 1.0066 265
## (Intercept)-Sus_scrofa 1.0060 1736
## shrub_cover-Odocoileus_virginianus 1.0021 5034
## shrub_cover-Canis_latrans 1.0018 2655
## shrub_cover-Sciurus_niger 1.0000 1238
## shrub_cover-Procyon_lotor 1.0002 4213
## shrub_cover-Dasypus_novemcinctus 1.0011 3313
## shrub_cover-Lynx_rufus 1.0072 1248
## shrub_cover-Didelphis_virginiana 1.0059 1938
## shrub_cover-Sylvilagus_floridanus 1.0012 1890
## shrub_cover-Sciurus_carolinensis 1.0071 1918
## shrub_cover-Vulpes_vulpes 1.0038 1979
## shrub_cover-Sus_scrofa 1.0037 2212
## veg_height-Odocoileus_virginianus 1.0007 5250
## veg_height-Canis_latrans 1.0017 2297
## veg_height-Sciurus_niger 1.0079 1877
## veg_height-Procyon_lotor 1.0002 4370
## veg_height-Dasypus_novemcinctus 1.0003 4586
## veg_height-Lynx_rufus 1.0014 2030
## veg_height-Didelphis_virginiana 1.0028 3237
## veg_height-Sylvilagus_floridanus 1.0025 3254
## veg_height-Sciurus_carolinensis 1.0001 3225
## veg_height-Vulpes_vulpes 1.0032 2140
## veg_height-Sus_scrofa 1.0014 4015
## week-Odocoileus_virginianus 0.9999 5250
## week-Canis_latrans 1.0005 4019
## week-Sciurus_niger 1.0121 1087
## week-Procyon_lotor 1.0016 4619
## week-Dasypus_novemcinctus 1.0014 4611
## week-Lynx_rufus 1.0006 2314
## week-Didelphis_virginiana 1.0002 2969
## week-Sylvilagus_floridanus 1.0090 2716
## week-Sciurus_carolinensis 1.0065 4368
## week-Vulpes_vulpes 1.0010 1625
## week-Sus_scrofa 1.0032 4156
## I(week^2)-Odocoileus_virginianus 1.0003 5250
## I(week^2)-Canis_latrans 1.0001 3884
## I(week^2)-Sciurus_niger 1.0074 1102
## I(week^2)-Procyon_lotor 1.0009 4458
## I(week^2)-Dasypus_novemcinctus 1.0013 4393
## I(week^2)-Lynx_rufus 1.0111 1837
## I(week^2)-Didelphis_virginiana 1.0023 1736
## I(week^2)-Sylvilagus_floridanus 1.0010 2630
## I(week^2)-Sciurus_carolinensis 1.0022 4170
## I(week^2)-Vulpes_vulpes 1.0076 1133
## I(week^2)-Sus_scrofa 1.0067 4245
#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): 2.041
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0860 0.6438 -1.3462 -0.1126 1.2499 1.0092 1213
## Cogon_Patch_Size -0.2433 0.4036 -1.1068 -0.2172 0.5060 1.0119 1741
## Avg_Cogongrass_Cover 0.2314 0.3025 -0.3551 0.2313 0.8342 1.0131 1507
## total_shrub_cover -0.5445 0.3939 -1.3988 -0.5120 0.1477 1.0173 865
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9202 3.4162 0.6160 2.9590 12.7125 1.0181 1335
## Cogon_Patch_Size 0.8602 1.1738 0.0590 0.4988 3.8196 1.0132 1386
## Avg_Cogongrass_Cover 0.3031 0.3932 0.0381 0.1845 1.2776 1.0352 1561
## total_shrub_cover 0.5517 0.7327 0.0455 0.3117 2.6086 1.0307 797
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4265 1.5171 0.0972 0.9824 5.5289 1.235 351
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4306 0.4590 -3.3262 -2.4435 -1.4706 1.0032 3340
## shrub_cover 0.4057 0.2735 -0.1223 0.3988 0.9768 1.0042 1494
## veg_height -0.0103 0.1595 -0.3268 -0.0129 0.3126 1.0008 3344
## week 0.3586 0.2437 -0.1457 0.3698 0.8274 1.0068 3289
## I(week^2) -0.2864 0.1065 -0.5037 -0.2852 -0.0827 1.0035 2686
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3798 1.5877 0.8088 1.9642 6.3744 1.0072 2604
## shrub_cover 0.5238 0.4505 0.1014 0.3995 1.7065 1.0199 751
## veg_height 0.1987 0.1503 0.0552 0.1609 0.5666 1.0110 3169
## week 0.4482 0.3504 0.1048 0.3517 1.3648 1.0129 1845
## I(week^2) 0.0762 0.0792 0.0226 0.0598 0.2215 1.1179 1179
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6929 1.6276 0.9432 3.5092
## (Intercept)-Canis_latrans 0.6076 0.7879 -0.9087 0.5905
## (Intercept)-Sciurus_niger -0.5177 1.3093 -2.6601 -0.6794
## (Intercept)-Procyon_lotor 0.7382 0.7878 -0.7866 0.7208
## (Intercept)-Dasypus_novemcinctus -0.5564 0.7223 -1.9853 -0.5683
## (Intercept)-Lynx_rufus 0.0032 1.0672 -1.8741 -0.0686
## (Intercept)-Didelphis_virginiana -1.1464 0.8279 -2.7221 -1.1627
## (Intercept)-Sylvilagus_floridanus -0.0741 0.9432 -1.8613 -0.1182
## (Intercept)-Sciurus_carolinensis -1.2593 0.8584 -2.9935 -1.2585
## (Intercept)-Vulpes_vulpes -0.7628 1.5039 -3.1618 -0.9474
## (Intercept)-Sus_scrofa -1.5740 1.0779 -3.6857 -1.5844
## Cogon_Patch_Size-Odocoileus_virginianus -0.0718 0.6882 -1.3086 -0.1179
## Cogon_Patch_Size-Canis_latrans 0.6261 0.7169 -0.4193 0.5053
## Cogon_Patch_Size-Sciurus_niger -0.5935 0.8987 -2.8059 -0.4682
## Cogon_Patch_Size-Procyon_lotor -0.2859 0.4702 -1.2271 -0.2668
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1059 0.4417 -1.0052 -0.1073
## Cogon_Patch_Size-Lynx_rufus -0.2202 0.7445 -1.5800 -0.2553
## Cogon_Patch_Size-Didelphis_virginiana 0.5136 0.4995 -0.3700 0.4775
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8621 0.8370 -2.8969 -0.7182
## Cogon_Patch_Size-Sciurus_carolinensis -0.7115 0.7180 -2.4337 -0.6019
## Cogon_Patch_Size-Vulpes_vulpes -0.5445 0.9057 -2.6191 -0.4317
## Cogon_Patch_Size-Sus_scrofa -0.5141 0.7966 -2.4169 -0.3984
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.2173 0.5272 -0.8603 0.2242
## Avg_Cogongrass_Cover-Canis_latrans 0.3744 0.4350 -0.4066 0.3501
## Avg_Cogongrass_Cover-Sciurus_niger -0.0734 0.6306 -1.5041 -0.0282
## Avg_Cogongrass_Cover-Procyon_lotor 0.2046 0.4307 -0.6340 0.2029
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3932 0.3850 -0.3285 0.3753
## Avg_Cogongrass_Cover-Lynx_rufus 0.5011 0.4875 -0.3457 0.4612
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2069 0.4254 -0.6590 0.2130
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0350 0.5139 -1.1237 -0.0059
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4195 0.4207 -0.3562 0.4070
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3532 0.4898 -0.5753 0.3419
## Avg_Cogongrass_Cover-Sus_scrofa 0.0318 0.5662 -1.2592 0.0724
## total_shrub_cover-Odocoileus_virginianus -0.3316 0.6073 -1.5124 -0.3353
## total_shrub_cover-Canis_latrans 0.0451 0.5723 -0.9297 -0.0159
## total_shrub_cover-Sciurus_niger -0.6592 0.6836 -2.1992 -0.6131
## total_shrub_cover-Procyon_lotor -1.0007 0.5854 -2.4044 -0.9223
## total_shrub_cover-Dasypus_novemcinctus -0.3021 0.4973 -1.3590 -0.2711
## total_shrub_cover-Lynx_rufus -0.8768 0.7547 -2.6754 -0.7827
## total_shrub_cover-Didelphis_virginiana -0.6173 0.5579 -1.9101 -0.5566
## total_shrub_cover-Sylvilagus_floridanus -0.8780 0.7732 -2.7604 -0.7578
## total_shrub_cover-Sciurus_carolinensis -0.4910 0.5955 -1.8667 -0.4442
## total_shrub_cover-Vulpes_vulpes -0.6147 0.8127 -2.4208 -0.5479
## total_shrub_cover-Sus_scrofa -0.3660 0.7036 -1.8329 -0.3456
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 7.4253 1.0031 939
## (Intercept)-Canis_latrans 2.2644 1.0057 2151
## (Intercept)-Sciurus_niger 2.6821 1.0188 492
## (Intercept)-Procyon_lotor 2.3302 1.0054 1930
## (Intercept)-Dasypus_novemcinctus 0.9294 1.0142 1726
## (Intercept)-Lynx_rufus 2.3824 1.0072 971
## (Intercept)-Didelphis_virginiana 0.5346 1.0190 1517
## (Intercept)-Sylvilagus_floridanus 1.9716 1.0117 1562
## (Intercept)-Sciurus_carolinensis 0.4492 1.0051 1517
## (Intercept)-Vulpes_vulpes 2.7825 1.0297 347
## (Intercept)-Sus_scrofa 0.5322 1.0109 1093
## Cogon_Patch_Size-Odocoileus_virginianus 1.4979 1.0090 3754
## Cogon_Patch_Size-Canis_latrans 2.3520 1.0026 1867
## Cogon_Patch_Size-Sciurus_niger 0.8664 1.0081 1482
## Cogon_Patch_Size-Procyon_lotor 0.5983 1.0097 2186
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7559 1.0045 3481
## Cogon_Patch_Size-Lynx_rufus 1.4172 1.0081 1900
## Cogon_Patch_Size-Didelphis_virginiana 1.6195 1.0220 2529
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3648 1.0056 1570
## Cogon_Patch_Size-Sciurus_carolinensis 0.3626 1.0110 1808
## Cogon_Patch_Size-Vulpes_vulpes 0.9677 1.0113 1460
## Cogon_Patch_Size-Sus_scrofa 0.7469 1.0078 2207
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.2767 1.0039 2924
## Avg_Cogongrass_Cover-Canis_latrans 1.3429 1.0083 2397
## Avg_Cogongrass_Cover-Sciurus_niger 1.0615 1.0056 1385
## Avg_Cogongrass_Cover-Procyon_lotor 1.0855 1.0054 2719
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1879 1.0031 2759
## Avg_Cogongrass_Cover-Lynx_rufus 1.6094 1.0063 2755
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0270 1.0034 2884
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9220 1.0048 1916
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2956 1.0097 2873
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3779 1.0051 3333
## Avg_Cogongrass_Cover-Sus_scrofa 1.0304 1.0029 2105
## total_shrub_cover-Odocoileus_virginianus 0.9426 1.0005 2715
## total_shrub_cover-Canis_latrans 1.3808 1.0114 1606
## total_shrub_cover-Sciurus_niger 0.5970 1.0062 1205
## total_shrub_cover-Procyon_lotor -0.0753 1.0461 743
## total_shrub_cover-Dasypus_novemcinctus 0.5681 1.0165 1227
## total_shrub_cover-Lynx_rufus 0.3893 1.0192 936
## total_shrub_cover-Didelphis_virginiana 0.3155 1.0377 1195
## total_shrub_cover-Sylvilagus_floridanus 0.3124 1.0475 665
## total_shrub_cover-Sciurus_carolinensis 0.5103 1.0045 938
## total_shrub_cover-Vulpes_vulpes 0.8651 1.0040 730
## total_shrub_cover-Sus_scrofa 0.9759 1.0185 928
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5390 0.0809 0.3835 0.5374 0.6991
## (Intercept)-Canis_latrans -2.5793 0.2061 -2.9989 -2.5711 -2.2031
## (Intercept)-Sciurus_niger -4.0277 0.6919 -5.3877 -4.0252 -2.7080
## (Intercept)-Procyon_lotor -2.1850 0.1611 -2.5126 -2.1824 -1.8763
## (Intercept)-Dasypus_novemcinctus -1.6166 0.1904 -2.0094 -1.6115 -1.2578
## (Intercept)-Lynx_rufus -3.4064 0.3742 -4.1803 -3.3890 -2.7255
## (Intercept)-Didelphis_virginiana -2.3799 0.3228 -3.0483 -2.3597 -1.7901
## (Intercept)-Sylvilagus_floridanus -3.1912 0.3162 -3.8380 -3.1794 -2.5764
## (Intercept)-Sciurus_carolinensis -2.5136 0.3585 -3.2744 -2.4972 -1.8591
## (Intercept)-Vulpes_vulpes -4.1456 0.8029 -5.7180 -4.1190 -2.7115
## (Intercept)-Sus_scrofa -3.3444 0.6334 -4.5854 -3.3443 -2.0958
## shrub_cover-Odocoileus_virginianus -0.0572 0.0684 -0.1951 -0.0566 0.0750
## shrub_cover-Canis_latrans -0.2544 0.2371 -0.7059 -0.2548 0.2157
## shrub_cover-Sciurus_niger -0.1119 0.5093 -1.1430 -0.0987 0.8576
## shrub_cover-Procyon_lotor 0.3089 0.1626 -0.0197 0.3132 0.6286
## shrub_cover-Dasypus_novemcinctus 0.9239 0.3372 0.3176 0.9026 1.6288
## shrub_cover-Lynx_rufus 0.0410 0.3731 -0.7156 0.0482 0.7352
## shrub_cover-Didelphis_virginiana 1.0258 0.3935 0.3426 0.9966 1.8526
## shrub_cover-Sylvilagus_floridanus 0.6446 0.4067 -0.1805 0.6497 1.4239
## shrub_cover-Sciurus_carolinensis 0.9774 0.4389 0.1517 0.9639 1.8697
## shrub_cover-Vulpes_vulpes 0.1760 0.6163 -1.1258 0.1894 1.3430
## shrub_cover-Sus_scrofa 0.8559 0.7784 -0.6394 0.8405 2.4323
## veg_height-Odocoileus_virginianus -0.3308 0.0675 -0.4639 -0.3309 -0.1999
## veg_height-Canis_latrans -0.5911 0.1820 -0.9620 -0.5856 -0.2491
## veg_height-Sciurus_niger -0.0199 0.4036 -0.8006 -0.0365 0.8391
## veg_height-Procyon_lotor 0.3368 0.1238 0.0895 0.3366 0.5761
## veg_height-Dasypus_novemcinctus 0.2412 0.1359 -0.0207 0.2375 0.5182
## veg_height-Lynx_rufus 0.0296 0.2421 -0.4544 0.0342 0.4992
## veg_height-Didelphis_virginiana 0.3998 0.2364 -0.0378 0.3941 0.8839
## veg_height-Sylvilagus_floridanus 0.0471 0.2373 -0.4142 0.0475 0.5140
## veg_height-Sciurus_carolinensis 0.0860 0.2213 -0.3235 0.0796 0.5361
## veg_height-Vulpes_vulpes -0.1287 0.3216 -0.8013 -0.1109 0.4710
## veg_height-Sus_scrofa -0.1648 0.3278 -0.8535 -0.1574 0.4508
## week-Odocoileus_virginianus 1.3119 0.1246 1.0722 1.3100 1.5545
## week-Canis_latrans 0.5971 0.2612 0.0849 0.5919 1.1041
## week-Sciurus_niger -0.4201 0.5725 -1.7156 -0.3605 0.5529
## week-Procyon_lotor 0.2071 0.2146 -0.2198 0.2078 0.6236
## week-Dasypus_novemcinctus 0.1079 0.2238 -0.3264 0.1061 0.5399
## week-Lynx_rufus 0.3837 0.3489 -0.2986 0.3833 1.0734
## week-Didelphis_virginiana 0.0626 0.3761 -0.6998 0.0700 0.7817
## week-Sylvilagus_floridanus 0.0571 0.3474 -0.6498 0.0657 0.7178
## week-Sciurus_carolinensis 0.8055 0.3733 0.1121 0.7923 1.5909
## week-Vulpes_vulpes 0.1710 0.5345 -0.9452 0.1909 1.1883
## week-Sus_scrofa 0.6746 0.4476 -0.1593 0.6560 1.5906
## I(week^2)-Odocoileus_virginianus -0.5411 0.0515 -0.6408 -0.5410 -0.4422
## I(week^2)-Canis_latrans -0.2462 0.1077 -0.4610 -0.2454 -0.0412
## I(week^2)-Sciurus_niger -0.2812 0.2397 -0.7893 -0.2688 0.1546
## I(week^2)-Procyon_lotor -0.1331 0.0914 -0.3135 -0.1336 0.0432
## I(week^2)-Dasypus_novemcinctus -0.1817 0.1035 -0.3844 -0.1818 0.0182
## I(week^2)-Lynx_rufus -0.2412 0.1511 -0.5421 -0.2419 0.0597
## I(week^2)-Didelphis_virginiana -0.4158 0.2148 -0.9067 -0.3933 -0.0599
## I(week^2)-Sylvilagus_floridanus -0.1777 0.1576 -0.4951 -0.1754 0.1280
## I(week^2)-Sciurus_carolinensis -0.2867 0.1465 -0.5880 -0.2835 -0.0068
## I(week^2)-Vulpes_vulpes -0.4156 0.2671 -0.9965 -0.3918 0.0252
## I(week^2)-Sus_scrofa -0.2435 0.1763 -0.6245 -0.2397 0.0856
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0006 5674
## (Intercept)-Canis_latrans 1.0021 2283
## (Intercept)-Sciurus_niger 1.0216 513
## (Intercept)-Procyon_lotor 1.0017 3300
## (Intercept)-Dasypus_novemcinctus 1.0020 2287
## (Intercept)-Lynx_rufus 1.0022 949
## (Intercept)-Didelphis_virginiana 1.0155 1459
## (Intercept)-Sylvilagus_floridanus 1.0161 1523
## (Intercept)-Sciurus_carolinensis 1.0042 1313
## (Intercept)-Vulpes_vulpes 1.0420 394
## (Intercept)-Sus_scrofa 1.0148 932
## shrub_cover-Odocoileus_virginianus 1.0004 5250
## shrub_cover-Canis_latrans 1.0021 1803
## shrub_cover-Sciurus_niger 1.0166 981
## shrub_cover-Procyon_lotor 1.0034 3407
## shrub_cover-Dasypus_novemcinctus 1.0131 1170
## shrub_cover-Lynx_rufus 1.0095 1258
## shrub_cover-Didelphis_virginiana 1.0150 846
## shrub_cover-Sylvilagus_floridanus 1.0299 955
## shrub_cover-Sciurus_carolinensis 1.0084 1111
## shrub_cover-Vulpes_vulpes 1.0043 955
## shrub_cover-Sus_scrofa 1.0121 846
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0040 2449
## veg_height-Sciurus_niger 1.0055 1406
## veg_height-Procyon_lotor 1.0009 3925
## veg_height-Dasypus_novemcinctus 1.0000 3973
## veg_height-Lynx_rufus 1.0004 2330
## veg_height-Didelphis_virginiana 0.9999 3142
## veg_height-Sylvilagus_floridanus 1.0071 1702
## veg_height-Sciurus_carolinensis 1.0045 2752
## veg_height-Vulpes_vulpes 1.0009 1857
## veg_height-Sus_scrofa 1.0028 2362
## week-Odocoileus_virginianus 1.0007 4886
## week-Canis_latrans 1.0012 3719
## week-Sciurus_niger 1.0181 935
## week-Procyon_lotor 1.0004 3855
## week-Dasypus_novemcinctus 1.0026 5027
## week-Lynx_rufus 1.0040 2698
## week-Didelphis_virginiana 1.0031 2779
## week-Sylvilagus_floridanus 1.0020 2394
## week-Sciurus_carolinensis 1.0004 3819
## week-Vulpes_vulpes 1.0116 1604
## week-Sus_scrofa 1.0077 3728
## I(week^2)-Odocoileus_virginianus 1.0007 5250
## I(week^2)-Canis_latrans 1.0031 3934
## I(week^2)-Sciurus_niger 1.0072 1238
## I(week^2)-Procyon_lotor 1.0031 3982
## I(week^2)-Dasypus_novemcinctus 1.0018 4492
## I(week^2)-Lynx_rufus 1.0030 2509
## I(week^2)-Didelphis_virginiana 1.0059 1370
## I(week^2)-Sylvilagus_floridanus 1.0010 2432
## I(week^2)-Sciurus_carolinensis 1.0004 4081
## I(week^2)-Vulpes_vulpes 1.0291 777
## I(week^2)-Sus_scrofa 1.0011 4072
#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): 1.9688
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1778 0.6046 -1.3682 -0.1963 1.0503 1.0040 1836
## Veg_shannon_index 0.3558 0.2634 -0.1597 0.3545 0.8872 1.0046 2088
## Avg_Cogongrass_Cover 0.3114 0.2645 -0.2001 0.3095 0.8354 1.0051 1856
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8187 3.2753 0.7647 2.9954 11.8090 1.0480 1260
## Veg_shannon_index 0.3041 0.3692 0.0382 0.1972 1.1974 1.0213 1967
## Avg_Cogongrass_Cover 0.3063 0.4766 0.0358 0.1839 1.2899 1.0345 1581
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7718 0.7853 0.0607 0.5243 2.8633 1.0219 524
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3986 0.4764 -3.3093 -2.4057 -1.4075 1.0015 4586
## shrub_cover 0.2022 0.2458 -0.2982 0.2027 0.6899 1.0050 3204
## veg_height -0.0148 0.1565 -0.3191 -0.0157 0.2970 1.0009 3552
## week 0.3580 0.2401 -0.1379 0.3647 0.8180 1.0043 3015
## I(week^2) -0.2881 0.1037 -0.4977 -0.2866 -0.0882 1.0005 2535
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4255 1.4944 0.8444 2.0518 6.3321 1.0030 2220
## shrub_cover 0.4644 0.3730 0.0929 0.3640 1.4206 1.0094 2094
## veg_height 0.1951 0.1345 0.0572 0.1588 0.5473 1.0018 3100
## week 0.4441 0.3414 0.1075 0.3521 1.3235 1.0065 2288
## I(week^2) 0.0740 0.0563 0.0224 0.0589 0.2145 1.0019 3068
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.6352 1.4451 1.2909 3.4695
## (Intercept)-Canis_latrans 0.3662 0.6476 -0.8881 0.3525
## (Intercept)-Sciurus_niger -0.1653 1.3812 -2.2712 -0.3622
## (Intercept)-Procyon_lotor 0.5438 0.6523 -0.7704 0.5531
## (Intercept)-Dasypus_novemcinctus -0.6606 0.5946 -1.9349 -0.6403
## (Intercept)-Lynx_rufus 0.2477 1.0914 -1.5291 0.1108
## (Intercept)-Didelphis_virginiana -1.3705 0.6756 -2.7278 -1.3701
## (Intercept)-Sylvilagus_floridanus -0.3476 0.7527 -1.7308 -0.3746
## (Intercept)-Sciurus_carolinensis -1.3289 0.6836 -2.7283 -1.3077
## (Intercept)-Vulpes_vulpes -0.9186 1.3035 -3.1637 -1.0604
## (Intercept)-Sus_scrofa -2.0236 0.8936 -3.9260 -1.9894
## Veg_shannon_index-Odocoileus_virginianus 0.2934 0.4984 -0.7422 0.3028
## Veg_shannon_index-Canis_latrans 0.6276 0.3989 -0.1012 0.6038
## Veg_shannon_index-Sciurus_niger 0.3864 0.5538 -0.6717 0.3725
## Veg_shannon_index-Procyon_lotor 0.4519 0.3726 -0.2312 0.4327
## Veg_shannon_index-Dasypus_novemcinctus 0.1916 0.3515 -0.5069 0.1928
## Veg_shannon_index-Lynx_rufus 0.2144 0.5229 -0.9270 0.2452
## Veg_shannon_index-Didelphis_virginiana 0.5041 0.3944 -0.2257 0.4921
## Veg_shannon_index-Sylvilagus_floridanus 0.4416 0.4204 -0.3217 0.4289
## Veg_shannon_index-Sciurus_carolinensis -0.0262 0.4113 -0.9171 0.0040
## Veg_shannon_index-Vulpes_vulpes 0.1171 0.4904 -0.9288 0.1395
## Veg_shannon_index-Sus_scrofa 0.7289 0.5541 -0.1632 0.6567
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.3056 0.5053 -0.6792 0.2989
## Avg_Cogongrass_Cover-Canis_latrans 0.5910 0.4077 -0.0957 0.5522
## Avg_Cogongrass_Cover-Sciurus_niger -0.0238 0.6312 -1.4572 0.0423
## Avg_Cogongrass_Cover-Procyon_lotor 0.3631 0.3766 -0.3463 0.3508
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4320 0.3333 -0.2227 0.4344
## Avg_Cogongrass_Cover-Lynx_rufus 0.5446 0.4430 -0.2093 0.5117
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4457 0.3706 -0.2734 0.4350
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0664 0.4577 -1.0743 -0.0365
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3971 0.3633 -0.2975 0.3880
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3973 0.4789 -0.5124 0.3801
## Avg_Cogongrass_Cover-Sus_scrofa 0.0331 0.5416 -1.2221 0.0820
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.8907 1.0144 1003
## (Intercept)-Canis_latrans 1.7247 1.0010 2915
## (Intercept)-Sciurus_niger 3.1741 1.0104 457
## (Intercept)-Procyon_lotor 1.7984 1.0037 2258
## (Intercept)-Dasypus_novemcinctus 0.4779 1.0032 3478
## (Intercept)-Lynx_rufus 2.8259 1.0010 853
## (Intercept)-Didelphis_virginiana -0.0343 1.0018 2676
## (Intercept)-Sylvilagus_floridanus 1.2521 1.0017 1466
## (Intercept)-Sciurus_carolinensis -0.0296 1.0011 2914
## (Intercept)-Vulpes_vulpes 2.0227 1.0359 514
## (Intercept)-Sus_scrofa -0.3884 1.0040 1829
## Veg_shannon_index-Odocoileus_virginianus 1.2532 1.0047 3155
## Veg_shannon_index-Canis_latrans 1.4936 1.0001 3111
## Veg_shannon_index-Sciurus_niger 1.4919 1.0027 2290
## Veg_shannon_index-Procyon_lotor 1.2225 1.0040 3487
## Veg_shannon_index-Dasypus_novemcinctus 0.8779 1.0016 3505
## Veg_shannon_index-Lynx_rufus 1.2136 1.0127 2285
## Veg_shannon_index-Didelphis_virginiana 1.3289 1.0009 3410
## Veg_shannon_index-Sylvilagus_floridanus 1.3501 1.0014 3382
## Veg_shannon_index-Sciurus_carolinensis 0.7022 1.0031 3465
## Veg_shannon_index-Vulpes_vulpes 1.0471 1.0041 2528
## Veg_shannon_index-Sus_scrofa 2.0389 1.0026 2389
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.3503 1.0028 2872
## Avg_Cogongrass_Cover-Canis_latrans 1.5240 1.0047 3162
## Avg_Cogongrass_Cover-Sciurus_niger 1.0157 1.0053 1202
## Avg_Cogongrass_Cover-Procyon_lotor 1.1595 1.0038 4275
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1024 1.0001 4081
## Avg_Cogongrass_Cover-Lynx_rufus 1.4882 1.0068 2909
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2049 1.0017 3729
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7515 1.0005 2370
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1122 1.0033 3652
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3982 1.0030 3061
## Avg_Cogongrass_Cover-Sus_scrofa 0.9191 1.0023 1814
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5387 0.0825 0.3812 0.5379 0.7044
## (Intercept)-Canis_latrans -2.5442 0.2005 -2.9522 -2.5347 -2.1774
## (Intercept)-Sciurus_niger -4.2268 0.6907 -5.5839 -4.2290 -2.8973
## (Intercept)-Procyon_lotor -2.1851 0.1650 -2.5230 -2.1759 -1.8819
## (Intercept)-Dasypus_novemcinctus -1.5733 0.1740 -1.9250 -1.5699 -1.2440
## (Intercept)-Lynx_rufus -3.5552 0.3808 -4.3114 -3.5506 -2.8338
## (Intercept)-Didelphis_virginiana -2.3086 0.2984 -2.9245 -2.2992 -1.7681
## (Intercept)-Sylvilagus_floridanus -3.1079 0.3374 -3.8186 -3.0886 -2.5060
## (Intercept)-Sciurus_carolinensis -2.4021 0.3311 -3.0896 -2.3853 -1.8034
## (Intercept)-Vulpes_vulpes -4.1109 0.7995 -5.7029 -4.0773 -2.6728
## (Intercept)-Sus_scrofa -3.0643 0.5997 -4.2812 -3.0603 -1.9099
## shrub_cover-Odocoileus_virginianus -0.0586 0.0670 -0.1921 -0.0585 0.0712
## shrub_cover-Canis_latrans -0.2779 0.2125 -0.6929 -0.2768 0.1222
## shrub_cover-Sciurus_niger -0.3864 0.4490 -1.3196 -0.3788 0.4891
## shrub_cover-Procyon_lotor 0.2325 0.1726 -0.1136 0.2369 0.5623
## shrub_cover-Dasypus_novemcinctus 0.7980 0.2920 0.2470 0.7908 1.3862
## shrub_cover-Lynx_rufus -0.2674 0.3498 -0.9806 -0.2654 0.4022
## shrub_cover-Didelphis_virginiana 0.8751 0.3506 0.2464 0.8595 1.6061
## shrub_cover-Sylvilagus_floridanus 0.2238 0.3928 -0.5081 0.2100 1.0059
## shrub_cover-Sciurus_carolinensis 0.7688 0.3902 0.0388 0.7639 1.5801
## shrub_cover-Vulpes_vulpes -0.0919 0.5217 -1.1573 -0.0777 0.9205
## shrub_cover-Sus_scrofa 0.4509 0.7002 -0.9404 0.4386 1.8812
## veg_height-Odocoileus_virginianus -0.3311 0.0691 -0.4682 -0.3306 -0.1939
## veg_height-Canis_latrans -0.5860 0.1828 -0.9601 -0.5787 -0.2446
## veg_height-Sciurus_niger -0.0553 0.3977 -0.8307 -0.0588 0.7840
## veg_height-Procyon_lotor 0.3333 0.1217 0.0937 0.3345 0.5697
## veg_height-Dasypus_novemcinctus 0.2270 0.1345 -0.0372 0.2239 0.5019
## veg_height-Lynx_rufus 0.0010 0.2421 -0.4902 0.0079 0.4591
## veg_height-Didelphis_virginiana 0.4025 0.2366 -0.0275 0.3942 0.8866
## veg_height-Sylvilagus_floridanus 0.1232 0.2445 -0.3451 0.1184 0.6155
## veg_height-Sciurus_carolinensis 0.0487 0.2054 -0.3276 0.0410 0.4734
## veg_height-Vulpes_vulpes -0.1445 0.3321 -0.8664 -0.1199 0.4522
## veg_height-Sus_scrofa -0.1362 0.3244 -0.8174 -0.1261 0.4806
## week-Odocoileus_virginianus 1.3111 0.1261 1.0673 1.3102 1.5649
## week-Canis_latrans 0.5932 0.2651 0.0848 0.5852 1.1386
## week-Sciurus_niger -0.4283 0.5500 -1.5816 -0.3876 0.5404
## week-Procyon_lotor 0.2051 0.2129 -0.2200 0.2060 0.6235
## week-Dasypus_novemcinctus 0.1120 0.2278 -0.3307 0.1100 0.5528
## week-Lynx_rufus 0.3924 0.3504 -0.2795 0.3906 1.0851
## week-Didelphis_virginiana 0.0547 0.3797 -0.7315 0.0649 0.7572
## week-Sylvilagus_floridanus 0.0741 0.3477 -0.6072 0.0765 0.7480
## week-Sciurus_carolinensis 0.8054 0.3636 0.1337 0.7950 1.5498
## week-Vulpes_vulpes 0.1871 0.5304 -0.9372 0.2136 1.1743
## week-Sus_scrofa 0.6945 0.4441 -0.1541 0.6867 1.6063
## I(week^2)-Odocoileus_virginianus -0.5408 0.0514 -0.6451 -0.5409 -0.4412
## I(week^2)-Canis_latrans -0.2473 0.1082 -0.4654 -0.2471 -0.0347
## I(week^2)-Sciurus_niger -0.2925 0.2353 -0.7908 -0.2766 0.1315
## I(week^2)-Procyon_lotor -0.1336 0.0914 -0.3094 -0.1346 0.0420
## I(week^2)-Dasypus_novemcinctus -0.1840 0.1039 -0.3908 -0.1831 0.0180
## I(week^2)-Lynx_rufus -0.2447 0.1532 -0.5575 -0.2428 0.0453
## I(week^2)-Didelphis_virginiana -0.4200 0.2159 -0.9117 -0.4021 -0.0448
## I(week^2)-Sylvilagus_floridanus -0.1795 0.1561 -0.5006 -0.1767 0.1173
## I(week^2)-Sciurus_carolinensis -0.2850 0.1446 -0.5723 -0.2821 -0.0111
## I(week^2)-Vulpes_vulpes -0.4145 0.2540 -0.9955 -0.3929 0.0142
## I(week^2)-Sus_scrofa -0.2492 0.1779 -0.6109 -0.2454 0.1013
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0032 5250
## (Intercept)-Canis_latrans 1.0023 2782
## (Intercept)-Sciurus_niger 1.0267 400
## (Intercept)-Procyon_lotor 1.0011 4008
## (Intercept)-Dasypus_novemcinctus 1.0031 4623
## (Intercept)-Lynx_rufus 0.9998 872
## (Intercept)-Didelphis_virginiana 1.0002 2965
## (Intercept)-Sylvilagus_floridanus 1.0029 1328
## (Intercept)-Sciurus_carolinensis 1.0004 2750
## (Intercept)-Vulpes_vulpes 1.0548 414
## (Intercept)-Sus_scrofa 1.0001 2194
## shrub_cover-Odocoileus_virginianus 1.0074 5250
## shrub_cover-Canis_latrans 1.0064 3015
## shrub_cover-Sciurus_niger 1.0011 934
## shrub_cover-Procyon_lotor 1.0011 3683
## shrub_cover-Dasypus_novemcinctus 1.0005 3676
## shrub_cover-Lynx_rufus 1.0184 1215
## shrub_cover-Didelphis_virginiana 1.0022 2371
## shrub_cover-Sylvilagus_floridanus 1.0093 1766
## shrub_cover-Sciurus_carolinensis 1.0041 2383
## shrub_cover-Vulpes_vulpes 1.0023 1747
## shrub_cover-Sus_scrofa 1.0019 2523
## veg_height-Odocoileus_virginianus 1.0004 5250
## veg_height-Canis_latrans 1.0030 1862
## veg_height-Sciurus_niger 1.0004 1557
## veg_height-Procyon_lotor 1.0012 4241
## veg_height-Dasypus_novemcinctus 1.0028 4879
## veg_height-Lynx_rufus 1.0001 2261
## veg_height-Didelphis_virginiana 1.0050 3499
## veg_height-Sylvilagus_floridanus 1.0009 2427
## veg_height-Sciurus_carolinensis 1.0003 3780
## veg_height-Vulpes_vulpes 1.0002 1659
## veg_height-Sus_scrofa 1.0011 3679
## week-Odocoileus_virginianus 1.0009 4984
## week-Canis_latrans 1.0055 3778
## week-Sciurus_niger 1.0042 1075
## week-Procyon_lotor 1.0016 3971
## week-Dasypus_novemcinctus 1.0026 4491
## week-Lynx_rufus 1.0000 2478
## week-Didelphis_virginiana 1.0011 2738
## week-Sylvilagus_floridanus 1.0046 2966
## week-Sciurus_carolinensis 1.0012 3741
## week-Vulpes_vulpes 1.0158 1741
## week-Sus_scrofa 1.0038 3841
## I(week^2)-Odocoileus_virginianus 1.0009 4932
## I(week^2)-Canis_latrans 1.0035 3849
## I(week^2)-Sciurus_niger 1.0003 1349
## I(week^2)-Procyon_lotor 1.0013 4446
## I(week^2)-Dasypus_novemcinctus 1.0003 4463
## I(week^2)-Lynx_rufus 1.0009 2093
## I(week^2)-Didelphis_virginiana 1.0067 1724
## I(week^2)-Sylvilagus_floridanus 1.0026 2753
## I(week^2)-Sciurus_carolinensis 1.0003 4235
## I(week^2)-Vulpes_vulpes 1.0042 1116
## I(week^2)-Sus_scrofa 1.0029 4243
#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): 1.9365
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1831 0.5734 -1.2682 -0.1966 1.0022 1.0000 2171
## Avg_Cogongrass_Cover 0.2056 0.2349 -0.2552 0.2081 0.6690 1.0058 2020
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1781 2.4705 0.6290 2.5048 9.8223 1.0015 2042
## Avg_Cogongrass_Cover 0.2593 0.2943 0.0359 0.1691 1.0107 1.0054 2633
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7865 0.7838 0.0709 0.5498 2.815 1.0175 459
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3904 0.4627 -3.2812 -2.4091 -1.4230 1.0045 5000
## shrub_cover 0.2278 0.2375 -0.2296 0.2209 0.7314 1.0004 2983
## veg_height -0.0133 0.1598 -0.3323 -0.0131 0.3021 1.0002 3015
## week 0.3655 0.2424 -0.1332 0.3695 0.8340 1.0022 3303
## I(week^2) -0.2900 0.1028 -0.5038 -0.2883 -0.0954 1.0032 2637
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3090 1.4496 0.8096 1.9443 5.9310 1.0041 2019
## shrub_cover 0.4441 0.3633 0.0870 0.3468 1.3727 1.0085 2002
## veg_height 0.2023 0.1487 0.0577 0.1615 0.5718 1.0016 3443
## week 0.4470 0.3516 0.1080 0.3528 1.3639 1.0087 2056
## I(week^2) 0.0740 0.0568 0.0220 0.0590 0.2053 1.0006 2571
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 3.3013 1.3111 1.0823 3.1477
## (Intercept)-Canis_latrans 0.4331 0.6426 -0.7625 0.4187
## (Intercept)-Sciurus_niger -0.4332 1.1460 -2.3409 -0.5589
## (Intercept)-Procyon_lotor 0.5258 0.6255 -0.7276 0.5467
## (Intercept)-Dasypus_novemcinctus -0.6093 0.5889 -1.7853 -0.5977
## (Intercept)-Lynx_rufus 0.0862 0.9691 -1.5827 -0.0141
## (Intercept)-Didelphis_virginiana -1.2330 0.6488 -2.5518 -1.2195
## (Intercept)-Sylvilagus_floridanus -0.3642 0.7025 -1.6910 -0.3829
## (Intercept)-Sciurus_carolinensis -1.3006 0.6795 -2.7160 -1.2765
## (Intercept)-Vulpes_vulpes -0.9755 1.2208 -3.0813 -1.0978
## (Intercept)-Sus_scrofa -1.6835 0.8225 -3.3854 -1.6512
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.1870 0.4700 -0.7240 0.1803
## Avg_Cogongrass_Cover-Canis_latrans 0.4307 0.3793 -0.2132 0.4003
## Avg_Cogongrass_Cover-Sciurus_niger -0.1169 0.5345 -1.3388 -0.0670
## Avg_Cogongrass_Cover-Procyon_lotor 0.2226 0.3402 -0.4206 0.2052
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3420 0.3166 -0.2637 0.3367
## Avg_Cogongrass_Cover-Lynx_rufus 0.4209 0.4059 -0.3071 0.3860
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3252 0.3553 -0.3514 0.3187
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1422 0.4163 -1.0676 -0.1114
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3408 0.3441 -0.3205 0.3313
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2985 0.4247 -0.5111 0.2865
## Avg_Cogongrass_Cover-Sus_scrofa -0.0446 0.4919 -1.1735 0.0004
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.3811 1.0016 1278
## (Intercept)-Canis_latrans 1.7600 1.0003 2846
## (Intercept)-Sciurus_niger 2.3005 1.0094 594
## (Intercept)-Procyon_lotor 1.7572 1.0009 2016
## (Intercept)-Dasypus_novemcinctus 0.5690 1.0022 3265
## (Intercept)-Lynx_rufus 2.2960 1.0081 786
## (Intercept)-Didelphis_virginiana 0.0222 1.0004 2631
## (Intercept)-Sylvilagus_floridanus 1.0932 1.0026 2036
## (Intercept)-Sciurus_carolinensis -0.0278 1.0001 2947
## (Intercept)-Vulpes_vulpes 1.8863 1.0050 472
## (Intercept)-Sus_scrofa -0.1470 1.0035 2269
## Avg_Cogongrass_Cover-Odocoileus_virginianus 1.1662 1.0012 3329
## Avg_Cogongrass_Cover-Canis_latrans 1.2824 1.0056 3797
## Avg_Cogongrass_Cover-Sciurus_niger 0.8497 1.0052 1767
## Avg_Cogongrass_Cover-Procyon_lotor 0.9229 1.0032 4477
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9884 1.0014 4111
## Avg_Cogongrass_Cover-Lynx_rufus 1.3283 1.0053 3588
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0558 1.0023 3108
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5877 1.0048 2962
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0399 1.0038 4238
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1673 1.0048 3194
## Avg_Cogongrass_Cover-Sus_scrofa 0.8140 1.0007 2600
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5348 0.0805 0.3786 0.5351 0.6960
## (Intercept)-Canis_latrans -2.5758 0.2087 -3.0046 -2.5706 -2.1893
## (Intercept)-Sciurus_niger -4.0734 0.6806 -5.4415 -4.0647 -2.7920
## (Intercept)-Procyon_lotor -2.1775 0.1644 -2.5147 -2.1730 -1.8756
## (Intercept)-Dasypus_novemcinctus -1.5759 0.1755 -1.9231 -1.5726 -1.2388
## (Intercept)-Lynx_rufus -3.5066 0.3752 -4.2378 -3.5041 -2.7776
## (Intercept)-Didelphis_virginiana -2.3048 0.2996 -2.9539 -2.2850 -1.7582
## (Intercept)-Sylvilagus_floridanus -3.0741 0.3253 -3.7616 -3.0565 -2.4888
## (Intercept)-Sciurus_carolinensis -2.3928 0.3277 -3.0742 -2.3850 -1.7858
## (Intercept)-Vulpes_vulpes -4.0353 0.7789 -5.6633 -3.9802 -2.6703
## (Intercept)-Sus_scrofa -3.0995 0.6005 -4.3178 -3.0922 -1.9710
## shrub_cover-Odocoileus_virginianus -0.0594 0.0673 -0.1918 -0.0599 0.0745
## shrub_cover-Canis_latrans -0.2596 0.2164 -0.6915 -0.2598 0.1561
## shrub_cover-Sciurus_niger -0.3126 0.4542 -1.2311 -0.2983 0.5593
## shrub_cover-Procyon_lotor 0.2462 0.1612 -0.0877 0.2494 0.5502
## shrub_cover-Dasypus_novemcinctus 0.7886 0.2926 0.2366 0.7840 1.3830
## shrub_cover-Lynx_rufus -0.2290 0.3403 -0.9413 -0.2222 0.4064
## shrub_cover-Didelphis_virginiana 0.8729 0.3488 0.2369 0.8575 1.5934
## shrub_cover-Sylvilagus_floridanus 0.2852 0.3885 -0.4515 0.2750 1.0712
## shrub_cover-Sciurus_carolinensis 0.7570 0.3923 0.0303 0.7439 1.5588
## shrub_cover-Vulpes_vulpes -0.0657 0.5297 -1.1409 -0.0569 0.9561
## shrub_cover-Sus_scrofa 0.4926 0.7006 -0.8493 0.4704 1.9624
## veg_height-Odocoileus_virginianus -0.3320 0.0690 -0.4690 -0.3314 -0.2012
## veg_height-Canis_latrans -0.5990 0.1881 -0.9840 -0.5931 -0.2537
## veg_height-Sciurus_niger -0.0529 0.4062 -0.8268 -0.0608 0.7773
## veg_height-Procyon_lotor 0.3310 0.1236 0.0942 0.3293 0.5744
## veg_height-Dasypus_novemcinctus 0.2255 0.1316 -0.0330 0.2231 0.4869
## veg_height-Lynx_rufus 0.0014 0.2467 -0.4952 0.0041 0.4668
## veg_height-Didelphis_virginiana 0.4040 0.2350 -0.0338 0.3984 0.8938
## veg_height-Sylvilagus_floridanus 0.1142 0.2428 -0.3770 0.1151 0.5895
## veg_height-Sciurus_carolinensis 0.0492 0.2086 -0.3536 0.0455 0.4738
## veg_height-Vulpes_vulpes -0.1610 0.3254 -0.8541 -0.1496 0.4298
## veg_height-Sus_scrofa -0.1374 0.3363 -0.8269 -0.1296 0.5006
## week-Odocoileus_virginianus 1.3085 0.1243 1.0651 1.3069 1.5483
## week-Canis_latrans 0.5987 0.2631 0.0810 0.5939 1.1212
## week-Sciurus_niger -0.4156 0.5590 -1.6232 -0.3708 0.5672
## week-Procyon_lotor 0.2058 0.2158 -0.2096 0.2004 0.6307
## week-Dasypus_novemcinctus 0.1122 0.2253 -0.3359 0.1111 0.5472
## week-Lynx_rufus 0.4052 0.3513 -0.3000 0.4026 1.0943
## week-Didelphis_virginiana 0.0671 0.3756 -0.7062 0.0838 0.7633
## week-Sylvilagus_floridanus 0.0582 0.3537 -0.6309 0.0663 0.7284
## week-Sciurus_carolinensis 0.8078 0.3681 0.1091 0.7962 1.5550
## week-Vulpes_vulpes 0.2089 0.5181 -0.8772 0.2248 1.1755
## week-Sus_scrofa 0.6889 0.4506 -0.1705 0.6795 1.5901
## I(week^2)-Odocoileus_virginianus -0.5392 0.0507 -0.6410 -0.5386 -0.4411
## I(week^2)-Canis_latrans -0.2472 0.1077 -0.4636 -0.2447 -0.0417
## I(week^2)-Sciurus_niger -0.2940 0.2413 -0.7931 -0.2825 0.1527
## I(week^2)-Procyon_lotor -0.1323 0.0921 -0.3145 -0.1318 0.0477
## I(week^2)-Dasypus_novemcinctus -0.1808 0.1049 -0.3911 -0.1799 0.0209
## I(week^2)-Lynx_rufus -0.2484 0.1524 -0.5580 -0.2451 0.0401
## I(week^2)-Didelphis_virginiana -0.4194 0.2083 -0.8837 -0.4045 -0.0632
## I(week^2)-Sylvilagus_floridanus -0.1821 0.1627 -0.5150 -0.1807 0.1275
## I(week^2)-Sciurus_carolinensis -0.2843 0.1454 -0.5766 -0.2816 -0.0084
## I(week^2)-Vulpes_vulpes -0.4156 0.2552 -1.0018 -0.3922 0.0265
## I(week^2)-Sus_scrofa -0.2465 0.1788 -0.6042 -0.2448 0.0987
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0007 5250
## (Intercept)-Canis_latrans 1.0110 2545
## (Intercept)-Sciurus_niger 1.0117 567
## (Intercept)-Procyon_lotor 1.0020 3892
## (Intercept)-Dasypus_novemcinctus 1.0009 4328
## (Intercept)-Lynx_rufus 1.0147 928
## (Intercept)-Didelphis_virginiana 1.0056 2945
## (Intercept)-Sylvilagus_floridanus 0.9998 1714
## (Intercept)-Sciurus_carolinensis 1.0005 2819
## (Intercept)-Vulpes_vulpes 1.0066 408
## (Intercept)-Sus_scrofa 1.0006 1891
## shrub_cover-Odocoileus_virginianus 1.0033 5250
## shrub_cover-Canis_latrans 1.0048 2621
## shrub_cover-Sciurus_niger 1.0057 1241
## shrub_cover-Procyon_lotor 1.0009 3923
## shrub_cover-Dasypus_novemcinctus 1.0001 3732
## shrub_cover-Lynx_rufus 1.0199 1470
## shrub_cover-Didelphis_virginiana 1.0049 1774
## shrub_cover-Sylvilagus_floridanus 1.0013 1638
## shrub_cover-Sciurus_carolinensis 1.0024 2320
## shrub_cover-Vulpes_vulpes 1.0019 1736
## shrub_cover-Sus_scrofa 1.0021 2101
## veg_height-Odocoileus_virginianus 1.0006 5621
## veg_height-Canis_latrans 0.9999 2350
## veg_height-Sciurus_niger 1.0014 1869
## veg_height-Procyon_lotor 1.0013 4182
## veg_height-Dasypus_novemcinctus 1.0002 4832
## veg_height-Lynx_rufus 1.0011 2265
## veg_height-Didelphis_virginiana 1.0003 3541
## veg_height-Sylvilagus_floridanus 1.0038 2890
## veg_height-Sciurus_carolinensis 1.0009 3693
## veg_height-Vulpes_vulpes 1.0013 1929
## veg_height-Sus_scrofa 1.0000 3241
## week-Odocoileus_virginianus 1.0007 4681
## week-Canis_latrans 1.0081 3732
## week-Sciurus_niger 1.0157 1091
## week-Procyon_lotor 1.0008 3933
## week-Dasypus_novemcinctus 1.0002 4784
## week-Lynx_rufus 1.0109 2639
## week-Didelphis_virginiana 1.0018 2850
## week-Sylvilagus_floridanus 1.0037 2957
## week-Sciurus_carolinensis 1.0011 3926
## week-Vulpes_vulpes 1.0061 1818
## week-Sus_scrofa 1.0043 3599
## I(week^2)-Odocoileus_virginianus 1.0010 4614
## I(week^2)-Canis_latrans 1.0153 3950
## I(week^2)-Sciurus_niger 1.0269 1139
## I(week^2)-Procyon_lotor 1.0017 4085
## I(week^2)-Dasypus_novemcinctus 1.0006 3995
## I(week^2)-Lynx_rufus 1.0045 2756
## I(week^2)-Didelphis_virginiana 1.0000 1929
## I(week^2)-Sylvilagus_floridanus 1.0023 3135
## I(week^2)-Sciurus_carolinensis 1.0003 4407
## I(week^2)-Vulpes_vulpes 1.0014 1317
## I(week^2)-Sus_scrofa 1.0042 4375
# 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): 1.924
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9037 0.6244 -2.0984 -0.9191 0.3811 1.0093 2568
## Avg_Cogongrass_Cover -0.7718 0.3828 -1.5335 -0.7689 -0.0270 1.0023 1253
## I(Avg_Cogongrass_Cover^2) 0.8671 0.3385 0.2484 0.8463 1.6175 1.0119 1340
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5692 2.9026 0.6934 2.7755 11.3881 1.0011 1611
## Avg_Cogongrass_Cover 0.4261 0.5525 0.0435 0.2596 1.8370 1.0127 2180
## I(Avg_Cogongrass_Cover^2) 0.4515 0.8119 0.0378 0.2150 2.2697 1.0128 663
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5273 0.6695 0.0497 0.3385 2.0534 1.0391 486
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3881 0.4541 -3.2847 -2.3922 -1.4744 1.0032 3496
## shrub_cover 0.2226 0.2297 -0.2272 0.2173 0.6916 1.0008 2828
## veg_height 0.0082 0.1588 -0.3027 0.0065 0.3212 1.0002 3062
## week 0.3617 0.2402 -0.1426 0.3685 0.8183 1.0027 3026
## I(week^2) -0.2868 0.1018 -0.4906 -0.2852 -0.0874 1.0108 2562
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2964 1.4680 0.7930 1.9234 6.2009 1.0086 2356
## shrub_cover 0.4485 0.3655 0.0853 0.3504 1.4284 1.0032 1989
## veg_height 0.1958 0.1393 0.0569 0.1579 0.5354 1.0088 3382
## week 0.4440 0.3375 0.1128 0.3475 1.3649 1.0100 1642
## I(week^2) 0.0737 0.0562 0.0222 0.0585 0.2137 1.0018 2910
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 2.7925 1.4226 0.5409 2.6172
## (Intercept)-Canis_latrans -0.4735 0.6799 -1.8562 -0.4689
## (Intercept)-Sciurus_niger -0.8917 1.1544 -2.8714 -0.9758
## (Intercept)-Procyon_lotor -0.1642 0.6533 -1.4891 -0.1541
## (Intercept)-Dasypus_novemcinctus -1.3576 0.6193 -2.5527 -1.3492
## (Intercept)-Lynx_rufus -1.0029 0.9466 -2.7289 -1.0566
## (Intercept)-Didelphis_virginiana -1.8870 0.7153 -3.3282 -1.8659
## (Intercept)-Sylvilagus_floridanus -1.0932 0.7579 -2.5504 -1.0993
## (Intercept)-Sciurus_carolinensis -2.3578 0.7761 -3.9835 -2.3211
## (Intercept)-Vulpes_vulpes -2.0673 1.2243 -4.2695 -2.1258
## (Intercept)-Sus_scrofa -2.4102 0.9121 -4.3173 -2.3622
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.7578 0.6472 -2.0263 -0.7574
## Avg_Cogongrass_Cover-Canis_latrans -0.3812 0.5527 -1.3777 -0.4210
## Avg_Cogongrass_Cover-Sciurus_niger -1.0909 0.7233 -2.7601 -1.0261
## Avg_Cogongrass_Cover-Procyon_lotor -0.6971 0.5123 -1.7408 -0.6952
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5669 0.4902 -1.4950 -0.5793
## Avg_Cogongrass_Cover-Lynx_rufus -0.7125 0.5833 -1.9049 -0.7025
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5365 0.5419 -1.5667 -0.5478
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2341 0.6305 -2.6150 -1.1808
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.8361 0.5645 -2.0055 -0.8089
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7954 0.6411 -2.1290 -0.7831
## Avg_Cogongrass_Cover-Sus_scrofa -1.0259 0.6534 -2.4987 -0.9713
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.1658 0.7337 0.1118 1.0356
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2394 0.7096 0.2474 1.0993
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.4008 0.6966 -1.1795 0.4510
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0681 0.6043 0.2222 0.9711
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7615 0.3619 0.0936 0.7537
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1913 0.5388 0.3463 1.1157
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6335 0.4147 -0.1500 0.6175
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7624 0.4454 -0.0565 0.7367
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.0028 0.4049 0.3024 0.9760
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.0007 0.5234 0.1683 0.9445
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.4903 0.6237 -0.9730 0.5405
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 6.1419 1.0086 1297
## (Intercept)-Canis_latrans 0.8576 1.0125 2502
## (Intercept)-Sciurus_niger 1.6659 1.0126 688
## (Intercept)-Procyon_lotor 1.0911 1.0028 2780
## (Intercept)-Dasypus_novemcinctus -0.1575 1.0005 3738
## (Intercept)-Lynx_rufus 0.9629 1.0058 1222
## (Intercept)-Didelphis_virginiana -0.5078 1.0021 3000
## (Intercept)-Sylvilagus_floridanus 0.3790 1.0010 2528
## (Intercept)-Sciurus_carolinensis -0.9189 1.0030 2433
## (Intercept)-Vulpes_vulpes 0.5696 1.0064 535
## (Intercept)-Sus_scrofa -0.7513 1.0057 1888
## Avg_Cogongrass_Cover-Odocoileus_virginianus 0.5378 1.0038 2661
## Avg_Cogongrass_Cover-Canis_latrans 0.7932 1.0024 2556
## Avg_Cogongrass_Cover-Sciurus_niger 0.1220 1.0009 1468
## Avg_Cogongrass_Cover-Procyon_lotor 0.3089 1.0035 2581
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4312 1.0011 2158
## Avg_Cogongrass_Cover-Lynx_rufus 0.4204 1.0013 1936
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5740 1.0025 2340
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1424 1.0094 1540
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1889 1.0056 1908
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4268 1.0006 1988
## Avg_Cogongrass_Cover-Sus_scrofa 0.1349 1.0041 1618
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 3.0419 1.0079 1034
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1084 1.0240 952
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.6183 1.0099 1000
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.5949 1.0109 1066
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5181 1.0044 2665
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4772 1.0092 1497
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5105 1.0046 2108
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7169 1.0071 2233
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.8951 1.0093 1967
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.2482 1.0084 1208
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.5759 1.0075 1286
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5379 0.0807 0.3782 0.5371 0.6919
## (Intercept)-Canis_latrans -2.5507 0.2005 -2.9559 -2.5475 -2.1705
## (Intercept)-Sciurus_niger -4.1081 0.6667 -5.4105 -4.0957 -2.8216
## (Intercept)-Procyon_lotor -2.1913 0.1687 -2.5305 -2.1876 -1.8677
## (Intercept)-Dasypus_novemcinctus -1.5775 0.1757 -1.9261 -1.5761 -1.2385
## (Intercept)-Lynx_rufus -3.4303 0.3741 -4.1957 -3.4130 -2.7472
## (Intercept)-Didelphis_virginiana -2.3343 0.3139 -2.9968 -2.3171 -1.7597
## (Intercept)-Sylvilagus_floridanus -3.0822 0.3153 -3.7295 -3.0707 -2.4988
## (Intercept)-Sciurus_carolinensis -2.3798 0.3248 -3.0466 -2.3646 -1.7793
## (Intercept)-Vulpes_vulpes -3.9960 0.7571 -5.5362 -3.9596 -2.6581
## (Intercept)-Sus_scrofa -3.1000 0.5974 -4.2984 -3.0888 -1.9514
## shrub_cover-Odocoileus_virginianus -0.0592 0.0676 -0.1924 -0.0592 0.0750
## shrub_cover-Canis_latrans -0.2414 0.2179 -0.6812 -0.2400 0.1849
## shrub_cover-Sciurus_niger -0.3290 0.4639 -1.2411 -0.3227 0.5765
## shrub_cover-Procyon_lotor 0.2291 0.1701 -0.1225 0.2336 0.5487
## shrub_cover-Dasypus_novemcinctus 0.7916 0.2869 0.2570 0.7809 1.3765
## shrub_cover-Lynx_rufus -0.2225 0.3497 -0.9131 -0.2129 0.4289
## shrub_cover-Didelphis_virginiana 0.9073 0.3765 0.2434 0.8887 1.7060
## shrub_cover-Sylvilagus_floridanus 0.2192 0.3910 -0.5170 0.1983 1.0379
## shrub_cover-Sciurus_carolinensis 0.7368 0.3912 0.0060 0.7239 1.5495
## shrub_cover-Vulpes_vulpes -0.0687 0.5301 -1.1384 -0.0558 0.9458
## shrub_cover-Sus_scrofa 0.4908 0.6935 -0.8506 0.4788 1.9216
## veg_height-Odocoileus_virginianus -0.3312 0.0681 -0.4673 -0.3305 -0.2007
## veg_height-Canis_latrans -0.5711 0.1829 -0.9445 -0.5650 -0.2310
## veg_height-Sciurus_niger 0.0289 0.4013 -0.7250 0.0079 0.8780
## veg_height-Procyon_lotor 0.3375 0.1244 0.1017 0.3364 0.5886
## veg_height-Dasypus_novemcinctus 0.2312 0.1284 -0.0183 0.2289 0.4844
## veg_height-Lynx_rufus 0.0691 0.2403 -0.4167 0.0741 0.5266
## veg_height-Didelphis_virginiana 0.3896 0.2488 -0.0774 0.3808 0.9032
## veg_height-Sylvilagus_floridanus 0.1457 0.2470 -0.3347 0.1462 0.6367
## veg_height-Sciurus_carolinensis 0.0546 0.2076 -0.3394 0.0488 0.4631
## veg_height-Vulpes_vulpes -0.1299 0.3134 -0.7756 -0.1221 0.4488
## veg_height-Sus_scrofa -0.1181 0.3294 -0.8016 -0.1077 0.5079
## week-Odocoileus_virginianus 1.3111 0.1234 1.0687 1.3082 1.5517
## week-Canis_latrans 0.6015 0.2599 0.1002 0.5998 1.1240
## week-Sciurus_niger -0.3967 0.5657 -1.6704 -0.3497 0.5739
## week-Procyon_lotor 0.2089 0.2132 -0.2033 0.2031 0.6399
## week-Dasypus_novemcinctus 0.1078 0.2232 -0.3362 0.1075 0.5425
## week-Lynx_rufus 0.3898 0.3524 -0.2867 0.3902 1.0803
## week-Didelphis_virginiana 0.0689 0.3756 -0.6769 0.0838 0.7814
## week-Sylvilagus_floridanus 0.0643 0.3518 -0.6446 0.0638 0.7411
## week-Sciurus_carolinensis 0.8101 0.3704 0.1079 0.8056 1.5591
## week-Vulpes_vulpes 0.2082 0.5231 -0.9120 0.2256 1.1713
## week-Sus_scrofa 0.6970 0.4460 -0.1431 0.6900 1.6090
## I(week^2)-Odocoileus_virginianus -0.5405 0.0515 -0.6405 -0.5405 -0.4401
## I(week^2)-Canis_latrans -0.2487 0.1064 -0.4622 -0.2471 -0.0386
## I(week^2)-Sciurus_niger -0.2865 0.2366 -0.8035 -0.2720 0.1485
## I(week^2)-Procyon_lotor -0.1355 0.0927 -0.3202 -0.1359 0.0441
## I(week^2)-Dasypus_novemcinctus -0.1804 0.1034 -0.3856 -0.1825 0.0177
## I(week^2)-Lynx_rufus -0.2385 0.1538 -0.5509 -0.2338 0.0529
## I(week^2)-Didelphis_virginiana -0.4170 0.2102 -0.8950 -0.3934 -0.0545
## I(week^2)-Sylvilagus_floridanus -0.1770 0.1567 -0.4842 -0.1742 0.1271
## I(week^2)-Sciurus_carolinensis -0.2849 0.1457 -0.5817 -0.2810 -0.0069
## I(week^2)-Vulpes_vulpes -0.4122 0.2520 -0.9889 -0.3880 0.0119
## I(week^2)-Sus_scrofa -0.2499 0.1820 -0.6218 -0.2475 0.0946
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0016 4689
## (Intercept)-Canis_latrans 1.0095 3055
## (Intercept)-Sciurus_niger 1.0011 514
## (Intercept)-Procyon_lotor 1.0022 3051
## (Intercept)-Dasypus_novemcinctus 1.0009 4564
## (Intercept)-Lynx_rufus 1.0118 1053
## (Intercept)-Didelphis_virginiana 1.0023 2885
## (Intercept)-Sylvilagus_floridanus 1.0027 1971
## (Intercept)-Sciurus_carolinensis 1.0025 2751
## (Intercept)-Vulpes_vulpes 1.0165 536
## (Intercept)-Sus_scrofa 1.0059 1615
## shrub_cover-Odocoileus_virginianus 1.0009 4929
## shrub_cover-Canis_latrans 1.0031 2593
## shrub_cover-Sciurus_niger 1.0024 1236
## shrub_cover-Procyon_lotor 1.0008 3536
## shrub_cover-Dasypus_novemcinctus 1.0011 3501
## shrub_cover-Lynx_rufus 1.0066 1531
## shrub_cover-Didelphis_virginiana 1.0105 1679
## shrub_cover-Sylvilagus_floridanus 1.0027 1797
## shrub_cover-Sciurus_carolinensis 1.0026 2509
## shrub_cover-Vulpes_vulpes 1.0053 1954
## shrub_cover-Sus_scrofa 1.0028 2117
## veg_height-Odocoileus_virginianus 1.0005 5250
## veg_height-Canis_latrans 1.0027 2544
## veg_height-Sciurus_niger 1.0050 1670
## veg_height-Procyon_lotor 1.0024 3946
## veg_height-Dasypus_novemcinctus 1.0000 4832
## veg_height-Lynx_rufus 1.0014 2469
## veg_height-Didelphis_virginiana 1.0004 2478
## veg_height-Sylvilagus_floridanus 1.0013 2306
## veg_height-Sciurus_carolinensis 1.0016 3410
## veg_height-Vulpes_vulpes 1.0047 2096
## veg_height-Sus_scrofa 1.0033 3048
## week-Odocoileus_virginianus 1.0073 5250
## week-Canis_latrans 1.0031 3885
## week-Sciurus_niger 1.0031 805
## week-Procyon_lotor 1.0010 4477
## week-Dasypus_novemcinctus 1.0013 4935
## week-Lynx_rufus 1.0017 2470
## week-Didelphis_virginiana 1.0062 2805
## week-Sylvilagus_floridanus 1.0001 2709
## week-Sciurus_carolinensis 1.0002 3969
## week-Vulpes_vulpes 1.0005 1939
## week-Sus_scrofa 1.0013 3930
## I(week^2)-Odocoileus_virginianus 1.0044 4771
## I(week^2)-Canis_latrans 1.0032 3751
## I(week^2)-Sciurus_niger 1.0224 1215
## I(week^2)-Procyon_lotor 1.0004 4200
## I(week^2)-Dasypus_novemcinctus 1.0020 4520
## I(week^2)-Lynx_rufus 1.0014 2738
## I(week^2)-Didelphis_virginiana 1.0139 1969
## I(week^2)-Sylvilagus_floridanus 1.0061 2367
## I(week^2)-Sciurus_carolinensis 1.0001 4327
## I(week^2)-Vulpes_vulpes 1.0133 1181
## I(week^2)-Sus_scrofa 1.0070 3995
# 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): 2.1187
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8976 1.1042 -3.0154 -0.9246 1.3888 1.0132 1987
## Cogon_Patch_Size -0.2027 0.7367 -1.7604 -0.1729 1.1688 1.0196 885
## Veg_shannon_index 0.9349 0.4826 0.0261 0.9148 1.9358 1.0325 1008
## total_shrub_cover -0.5126 0.5116 -1.5843 -0.4866 0.4641 1.0029 664
## Avg_Cogongrass_Cover -0.1766 0.9587 -2.0222 -0.1926 1.7397 1.0432 398
## Tree_Density -1.9840 0.8223 -3.6435 -1.9842 -0.3472 1.0172 778
## Avg_Canopy_Cover 1.9457 0.6897 0.6486 1.9071 3.4538 1.0278 1042
## I(Avg_Cogongrass_Cover^2) 1.5995 0.5812 0.5622 1.5675 2.8679 1.0343 458
## avg_veg_height -0.1270 0.5089 -1.1661 -0.1091 0.8455 1.0420 632
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.4818 19.1266 3.9632 16.2754 70.5914 1.1014 658
## Cogon_Patch_Size 3.8271 6.2779 0.1135 1.9021 20.6051 1.1120 290
## Veg_shannon_index 0.8876 1.4162 0.0503 0.4291 4.6894 1.0037 1184
## total_shrub_cover 0.9331 1.3250 0.0571 0.5284 4.3271 1.0060 774
## Avg_Cogongrass_Cover 1.2969 2.1984 0.0505 0.5684 7.2062 1.0228 752
## Tree_Density 4.0471 6.7117 0.0742 1.7186 22.6189 1.1065 481
## Avg_Canopy_Cover 3.1143 3.9547 0.1614 1.8856 13.5485 1.0198 487
## I(Avg_Cogongrass_Cover^2) 0.9899 2.2833 0.0475 0.3803 5.4059 1.0420 574
## avg_veg_height 0.5286 0.8158 0.0436 0.2803 2.5541 1.0221 1332
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4115 1.9464 0.0514 0.6783 6.9989 1.0309 192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4713 0.4948 -3.4270 -2.4902 -1.4436 1.0002 4604
## shrub_cover 0.3096 0.2619 -0.1821 0.3045 0.8569 1.0039 1735
## veg_height 0.0172 0.1608 -0.3059 0.0151 0.3375 1.0044 2805
## week 0.3661 0.2385 -0.1376 0.3703 0.8157 1.0065 3010
## I(week^2) -0.2853 0.1018 -0.4997 -0.2839 -0.0882 1.0008 2737
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6887 1.7123 0.9594 2.2559 6.9336 1.0017 2198
## shrub_cover 0.5370 0.4288 0.1120 0.4243 1.6295 1.0093 1484
## veg_height 0.1976 0.1370 0.0567 0.1613 0.5778 1.0032 3731
## week 0.4300 0.3223 0.1080 0.3414 1.2523 1.0137 1716
## I(week^2) 0.0722 0.0520 0.0226 0.0594 0.2003 1.0082 2338
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.7137 3.5095 2.5619 7.1003
## (Intercept)-Canis_latrans -0.8302 1.2582 -3.2554 -0.8675
## (Intercept)-Sciurus_niger 1.0088 2.5874 -3.0770 0.6411
## (Intercept)-Procyon_lotor -0.2963 1.1047 -2.5736 -0.2625
## (Intercept)-Dasypus_novemcinctus -2.6596 1.1756 -5.3539 -2.5636
## (Intercept)-Lynx_rufus 0.6122 2.6506 -3.4922 0.2549
## (Intercept)-Didelphis_virginiana -4.0929 1.4750 -7.3629 -3.9756
## (Intercept)-Sylvilagus_floridanus -2.3822 1.4799 -5.5106 -2.3294
## (Intercept)-Sciurus_carolinensis -4.7244 1.6614 -8.5772 -4.5518
## (Intercept)-Vulpes_vulpes -3.8004 3.0294 -8.8607 -4.1116
## (Intercept)-Sus_scrofa -5.5406 2.0335 -9.9106 -5.3974
## Cogon_Patch_Size-Odocoileus_virginianus 0.0893 1.5506 -2.6267 -0.0060
## Cogon_Patch_Size-Canis_latrans 1.6520 1.5608 -0.4413 1.3363
## Cogon_Patch_Size-Sciurus_niger -0.8573 2.1351 -5.7330 -0.6004
## Cogon_Patch_Size-Procyon_lotor -0.5209 0.7742 -2.1499 -0.4897
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0321 0.7913 -1.5329 -0.0514
## Cogon_Patch_Size-Lynx_rufus -0.4053 1.5782 -3.3707 -0.4202
## Cogon_Patch_Size-Didelphis_virginiana 1.5516 1.0575 -0.2033 1.4526
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4594 1.8023 -5.8843 -1.0960
## Cogon_Patch_Size-Sciurus_carolinensis -1.1838 1.6081 -5.0950 -0.8582
## Cogon_Patch_Size-Vulpes_vulpes -0.6118 1.8780 -4.7389 -0.5048
## Cogon_Patch_Size-Sus_scrofa -0.8394 1.5815 -4.6017 -0.5972
## Veg_shannon_index-Odocoileus_virginianus 0.7489 0.9047 -1.2189 0.7979
## Veg_shannon_index-Canis_latrans 1.3032 0.7109 0.1298 1.2249
## Veg_shannon_index-Sciurus_niger 1.0649 1.0131 -0.8091 0.9924
## Veg_shannon_index-Procyon_lotor 1.1548 0.6279 0.0857 1.1003
## Veg_shannon_index-Dasypus_novemcinctus 0.5923 0.6027 -0.6134 0.6049
## Veg_shannon_index-Lynx_rufus 1.0580 0.9144 -0.6352 1.0013
## Veg_shannon_index-Didelphis_virginiana 1.1192 0.7255 -0.1933 1.0649
## Veg_shannon_index-Sylvilagus_floridanus 1.0247 0.7294 -0.2834 0.9812
## Veg_shannon_index-Sciurus_carolinensis 0.3175 0.8236 -1.6352 0.4038
## Veg_shannon_index-Vulpes_vulpes 0.6324 0.9117 -1.4496 0.6828
## Veg_shannon_index-Sus_scrofa 1.5615 1.0034 0.0341 1.3865
## total_shrub_cover-Odocoileus_virginianus -0.2869 0.9262 -2.0330 -0.3363
## total_shrub_cover-Canis_latrans 0.1697 0.7581 -1.0625 0.0768
## total_shrub_cover-Sciurus_niger -0.7205 1.0599 -3.1087 -0.6362
## total_shrub_cover-Procyon_lotor -1.1313 0.6798 -2.6507 -1.0599
## total_shrub_cover-Dasypus_novemcinctus -0.2248 0.6543 -1.5951 -0.2114
## total_shrub_cover-Lynx_rufus -0.7366 1.0693 -3.0201 -0.6854
## total_shrub_cover-Didelphis_virginiana -0.8172 0.8110 -2.6489 -0.7304
## total_shrub_cover-Sylvilagus_floridanus -0.6283 0.8710 -2.6359 -0.5763
## total_shrub_cover-Sciurus_carolinensis -0.4265 0.8260 -2.2117 -0.3942
## total_shrub_cover-Vulpes_vulpes -0.7075 1.0151 -2.9254 -0.6171
## total_shrub_cover-Sus_scrofa -0.2925 0.9161 -2.1133 -0.3114
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2122 1.3494 -2.9696 -0.1917
## Avg_Cogongrass_Cover-Canis_latrans 0.0588 1.2195 -2.2863 0.0221
## Avg_Cogongrass_Cover-Sciurus_niger -0.6055 1.6019 -4.4091 -0.4499
## Avg_Cogongrass_Cover-Procyon_lotor -0.0569 1.1818 -2.3243 -0.0587
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4715 1.2809 -1.7775 0.3861
## Avg_Cogongrass_Cover-Lynx_rufus -0.1012 1.2831 -2.6589 -0.1246
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1381 1.2361 -2.5979 -0.1642
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7760 1.3496 -3.6867 -0.7012
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1542 1.2375 -2.6531 -0.1304
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0394 1.3195 -2.4753 -0.0001
## Avg_Cogongrass_Cover-Sus_scrofa -0.5223 1.3898 -3.6598 -0.4291
## Tree_Density-Odocoileus_virginianus -0.8794 1.5007 -3.2186 -1.0691
## Tree_Density-Canis_latrans -2.9117 1.4534 -6.4146 -2.6514
## Tree_Density-Sciurus_niger -1.9935 1.8096 -5.8041 -1.9504
## Tree_Density-Procyon_lotor -1.9244 0.9902 -4.0718 -1.8532
## Tree_Density-Dasypus_novemcinctus -3.9829 2.1020 -9.2399 -3.4615
## Tree_Density-Lynx_rufus -0.5949 1.8364 -3.3475 -0.9063
## Tree_Density-Didelphis_virginiana -2.2999 1.3118 -5.4019 -2.1726
## Tree_Density-Sylvilagus_floridanus -2.5514 1.6016 -6.4051 -2.3084
## Tree_Density-Sciurus_carolinensis -2.6798 1.6212 -6.6211 -2.4515
## Tree_Density-Vulpes_vulpes -2.0702 1.8383 -6.0577 -2.0048
## Tree_Density-Sus_scrofa -2.5399 1.8036 -6.9131 -2.2723
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2275 1.4750 -1.8932 1.2793
## Avg_Canopy_Cover-Canis_latrans 0.1617 0.7131 -1.2496 0.1488
## Avg_Canopy_Cover-Sciurus_niger 2.2933 1.8694 -1.2746 2.1409
## Avg_Canopy_Cover-Procyon_lotor 1.6487 0.8006 0.2106 1.6027
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1295 0.8425 0.7215 2.0506
## Avg_Canopy_Cover-Lynx_rufus 1.7046 1.5686 -1.1449 1.6498
## Avg_Canopy_Cover-Didelphis_virginiana 3.0951 1.3022 1.2281 2.8646
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7374 1.8100 1.2483 3.4287
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8340 1.3324 0.9889 2.5579
## Avg_Canopy_Cover-Vulpes_vulpes 2.5540 1.4915 0.3813 2.2939
## Avg_Canopy_Cover-Sus_scrofa 2.1852 1.0447 0.5739 2.0382
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8656 1.1154 0.1485 1.7104
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0070 0.9618 0.5333 1.8745
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2108 1.2126 -1.5414 1.3004
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9176 0.8937 0.5249 1.8072
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5357 0.7300 0.2161 1.4889
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1080 1.0322 0.5919 1.9309
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2653 0.7236 -0.1332 1.2545
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3652 0.8414 -0.2115 1.3234
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7694 0.7798 0.4421 1.6903
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9383 0.9262 0.4919 1.8159
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1822 1.0377 -1.1877 1.2538
## avg_veg_height-Odocoileus_virginianus -0.1475 0.8299 -1.8661 -0.1257
## avg_veg_height-Canis_latrans -0.2154 0.6324 -1.5303 -0.1976
## avg_veg_height-Sciurus_niger -0.2739 0.8682 -2.2353 -0.2236
## avg_veg_height-Procyon_lotor 0.0756 0.6373 -1.1556 0.0751
## avg_veg_height-Dasypus_novemcinctus 0.2099 0.6301 -0.9612 0.1908
## avg_veg_height-Lynx_rufus -0.3300 0.8560 -2.2920 -0.2533
## avg_veg_height-Didelphis_virginiana -0.2974 0.7499 -1.9432 -0.2554
## avg_veg_height-Sylvilagus_floridanus -0.2436 0.7089 -1.7228 -0.2155
## avg_veg_height-Sciurus_carolinensis 0.1866 0.7077 -1.0925 0.1540
## avg_veg_height-Vulpes_vulpes -0.2141 0.8193 -1.9807 -0.1789
## avg_veg_height-Sus_scrofa -0.1728 0.7463 -1.7538 -0.1475
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8796 1.0935 486
## (Intercept)-Canis_latrans 1.8478 1.0069 1124
## (Intercept)-Sciurus_niger 7.2837 1.0790 412
## (Intercept)-Procyon_lotor 1.7911 1.0011 1144
## (Intercept)-Dasypus_novemcinctus -0.5907 1.0173 747
## (Intercept)-Lynx_rufus 7.1247 1.0241 337
## (Intercept)-Didelphis_virginiana -1.5281 1.0433 862
## (Intercept)-Sylvilagus_floridanus 0.4158 1.0363 802
## (Intercept)-Sciurus_carolinensis -1.8861 1.0581 551
## (Intercept)-Vulpes_vulpes 3.5005 1.1663 132
## (Intercept)-Sus_scrofa -1.9978 1.0212 646
## Cogon_Patch_Size-Odocoileus_virginianus 3.7199 1.0085 1581
## Cogon_Patch_Size-Canis_latrans 5.8344 1.0361 622
## Cogon_Patch_Size-Sciurus_niger 3.0217 1.0305 465
## Cogon_Patch_Size-Procyon_lotor 0.8999 1.0215 780
## Cogon_Patch_Size-Dasypus_novemcinctus 1.5694 1.0121 1460
## Cogon_Patch_Size-Lynx_rufus 2.9288 1.0260 762
## Cogon_Patch_Size-Didelphis_virginiana 3.9155 1.0215 694
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9391 1.0719 352
## Cogon_Patch_Size-Sciurus_carolinensis 0.9597 1.0697 459
## Cogon_Patch_Size-Vulpes_vulpes 2.9974 1.0434 536
## Cogon_Patch_Size-Sus_scrofa 1.6406 1.0304 787
## Veg_shannon_index-Odocoileus_virginianus 2.5060 1.0152 2017
## Veg_shannon_index-Canis_latrans 2.9267 1.0180 964
## Veg_shannon_index-Sciurus_niger 3.2697 1.0160 1064
## Veg_shannon_index-Procyon_lotor 2.5759 1.0111 954
## Veg_shannon_index-Dasypus_novemcinctus 1.7494 1.0113 2224
## Veg_shannon_index-Lynx_rufus 2.9987 1.0284 935
## Veg_shannon_index-Didelphis_virginiana 2.7272 1.0092 1489
## Veg_shannon_index-Sylvilagus_floridanus 2.5938 1.0102 1481
## Veg_shannon_index-Sciurus_carolinensis 1.7238 1.0055 1650
## Veg_shannon_index-Vulpes_vulpes 2.3298 1.0095 1594
## Veg_shannon_index-Sus_scrofa 4.0399 1.0177 902
## total_shrub_cover-Odocoileus_virginianus 1.7056 1.0017 1849
## total_shrub_cover-Canis_latrans 1.8710 1.0020 1216
## total_shrub_cover-Sciurus_niger 1.1636 1.0059 929
## total_shrub_cover-Procyon_lotor 0.0218 1.0018 1150
## total_shrub_cover-Dasypus_novemcinctus 1.0062 1.0003 1580
## total_shrub_cover-Lynx_rufus 1.2741 1.0036 692
## total_shrub_cover-Didelphis_virginiana 0.6000 1.0021 1155
## total_shrub_cover-Sylvilagus_floridanus 0.9443 1.0060 1041
## total_shrub_cover-Sciurus_carolinensis 1.2134 1.0001 1575
## total_shrub_cover-Vulpes_vulpes 1.1199 1.0013 1085
## total_shrub_cover-Sus_scrofa 1.6006 1.0019 916
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.5393 1.0179 657
## Avg_Cogongrass_Cover-Canis_latrans 2.6029 1.0334 622
## Avg_Cogongrass_Cover-Sciurus_niger 2.1548 1.0228 502
## Avg_Cogongrass_Cover-Procyon_lotor 2.2804 1.0301 551
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3202 1.0607 594
## Avg_Cogongrass_Cover-Lynx_rufus 2.4771 1.0207 775
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3417 1.0204 641
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6302 1.0135 510
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3062 1.0273 652
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8449 1.0269 672
## Avg_Cogongrass_Cover-Sus_scrofa 1.9218 1.0232 646
## Tree_Density-Odocoileus_virginianus 2.6860 1.0141 910
## Tree_Density-Canis_latrans -0.7638 1.0810 486
## Tree_Density-Sciurus_niger 1.8193 1.0287 627
## Tree_Density-Procyon_lotor -0.1212 1.0203 893
## Tree_Density-Dasypus_novemcinctus -1.2777 1.0892 368
## Tree_Density-Lynx_rufus 3.9077 1.0473 448
## Tree_Density-Didelphis_virginiana -0.0944 1.0230 1073
## Tree_Density-Sylvilagus_floridanus 0.0439 1.0285 702
## Tree_Density-Sciurus_carolinensis 0.0200 1.0474 1034
## Tree_Density-Vulpes_vulpes 1.6574 1.0227 700
## Tree_Density-Sus_scrofa 0.3508 1.0259 915
## Avg_Canopy_Cover-Odocoileus_virginianus 4.1292 1.0248 1421
## Avg_Canopy_Cover-Canis_latrans 1.6107 1.0191 1460
## Avg_Canopy_Cover-Sciurus_niger 6.5229 1.0251 746
## Avg_Canopy_Cover-Procyon_lotor 3.3717 1.0102 1070
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0373 1.0483 841
## Avg_Canopy_Cover-Lynx_rufus 5.0250 1.0034 732
## Avg_Canopy_Cover-Didelphis_virginiana 6.3404 1.0272 717
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1874 1.0304 424
## Avg_Canopy_Cover-Sciurus_carolinensis 6.1889 1.0166 633
## Avg_Canopy_Cover-Vulpes_vulpes 6.2893 1.0042 744
## Avg_Canopy_Cover-Sus_scrofa 4.6216 1.0461 1156
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 4.5817 1.0212 652
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.3155 1.0242 586
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.4089 1.0355 424
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.9767 1.0051 516
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1000 1.0274 699
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.5595 1.0090 519
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.7468 1.0154 713
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1633 1.0017 840
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5786 1.0175 567
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.0721 1.0229 608
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0588 1.0329 639
## avg_veg_height-Odocoileus_virginianus 1.4647 1.0200 1250
## avg_veg_height-Canis_latrans 1.0070 1.0483 1130
## avg_veg_height-Sciurus_niger 1.2249 1.0151 1113
## avg_veg_height-Procyon_lotor 1.3437 1.0191 1189
## avg_veg_height-Dasypus_novemcinctus 1.5149 1.0174 1239
## avg_veg_height-Lynx_rufus 1.1835 1.0314 960
## avg_veg_height-Didelphis_virginiana 1.0356 1.0241 1115
## avg_veg_height-Sylvilagus_floridanus 1.0518 1.0203 1087
## avg_veg_height-Sciurus_carolinensis 1.6931 1.0126 1440
## avg_veg_height-Vulpes_vulpes 1.4162 1.0294 1078
## avg_veg_height-Sus_scrofa 1.2292 1.0326 1045
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5363 0.0800 0.3782 0.5372 0.6955
## (Intercept)-Canis_latrans -2.5287 0.2023 -2.9473 -2.5245 -2.1505
## (Intercept)-Sciurus_niger -4.7096 0.5184 -5.7127 -4.7122 -3.6891
## (Intercept)-Procyon_lotor -2.1881 0.1608 -2.5206 -2.1861 -1.8912
## (Intercept)-Dasypus_novemcinctus -1.6024 0.1809 -1.9773 -1.5953 -1.2611
## (Intercept)-Lynx_rufus -3.7446 0.3713 -4.4524 -3.7518 -2.9868
## (Intercept)-Didelphis_virginiana -2.3454 0.3174 -3.0146 -2.3295 -1.7727
## (Intercept)-Sylvilagus_floridanus -3.0956 0.2934 -3.7049 -3.0864 -2.5468
## (Intercept)-Sciurus_carolinensis -2.4802 0.3480 -3.2066 -2.4703 -1.8412
## (Intercept)-Vulpes_vulpes -4.1721 0.7418 -5.7174 -4.1147 -2.8740
## (Intercept)-Sus_scrofa -3.2324 0.6106 -4.4489 -3.2370 -2.0332
## shrub_cover-Odocoileus_virginianus -0.0591 0.0675 -0.1897 -0.0594 0.0731
## shrub_cover-Canis_latrans -0.2919 0.2288 -0.7526 -0.2913 0.1516
## shrub_cover-Sciurus_niger -0.3302 0.4429 -1.2401 -0.3145 0.4974
## shrub_cover-Procyon_lotor 0.2715 0.1615 -0.0483 0.2730 0.5850
## shrub_cover-Dasypus_novemcinctus 0.8885 0.3035 0.3207 0.8843 1.4892
## shrub_cover-Lynx_rufus -0.2113 0.3556 -0.8838 -0.2212 0.5130
## shrub_cover-Didelphis_virginiana 0.9693 0.3838 0.2977 0.9379 1.7883
## shrub_cover-Sylvilagus_floridanus 0.4824 0.4024 -0.3096 0.4722 1.2969
## shrub_cover-Sciurus_carolinensis 0.9122 0.4101 0.1359 0.9102 1.7308
## shrub_cover-Vulpes_vulpes 0.1141 0.5473 -1.0141 0.1292 1.1886
## shrub_cover-Sus_scrofa 0.7261 0.7634 -0.7445 0.6958 2.3249
## veg_height-Odocoileus_virginianus -0.3304 0.0687 -0.4683 -0.3296 -0.1962
## veg_height-Canis_latrans -0.5473 0.1821 -0.9194 -0.5423 -0.1947
## veg_height-Sciurus_niger -0.0340 0.3373 -0.6927 -0.0421 0.6482
## veg_height-Procyon_lotor 0.3563 0.1208 0.1207 0.3569 0.5947
## veg_height-Dasypus_novemcinctus 0.2461 0.1361 -0.0151 0.2434 0.5190
## veg_height-Lynx_rufus 0.1477 0.2317 -0.3113 0.1485 0.5972
## veg_height-Didelphis_virginiana 0.4242 0.2398 -0.0252 0.4137 0.9106
## veg_height-Sylvilagus_floridanus 0.1337 0.2477 -0.3478 0.1274 0.6245
## veg_height-Sciurus_carolinensis 0.0999 0.2154 -0.3018 0.0957 0.5318
## veg_height-Vulpes_vulpes -0.1685 0.3174 -0.8284 -0.1565 0.4379
## veg_height-Sus_scrofa -0.1554 0.3270 -0.8294 -0.1476 0.4749
## week-Odocoileus_virginianus 1.3077 0.1242 1.0650 1.3084 1.5504
## week-Canis_latrans 0.5960 0.2634 0.1014 0.5887 1.1168
## week-Sciurus_niger -0.3929 0.5500 -1.6228 -0.3473 0.5396
## week-Procyon_lotor 0.2074 0.2124 -0.2065 0.2096 0.6251
## week-Dasypus_novemcinctus 0.1126 0.2268 -0.3298 0.1152 0.5507
## week-Lynx_rufus 0.3828 0.3547 -0.3035 0.3789 1.1005
## week-Didelphis_virginiana 0.0744 0.3689 -0.6835 0.0790 0.7778
## week-Sylvilagus_floridanus 0.0689 0.3457 -0.6247 0.0738 0.7288
## week-Sciurus_carolinensis 0.8096 0.3709 0.1224 0.7953 1.5910
## week-Vulpes_vulpes 0.1996 0.5267 -0.9188 0.2138 1.1953
## week-Sus_scrofa 0.6943 0.4542 -0.1553 0.6813 1.6346
## I(week^2)-Odocoileus_virginianus -0.5390 0.0515 -0.6394 -0.5391 -0.4355
## I(week^2)-Canis_latrans -0.2438 0.1096 -0.4700 -0.2423 -0.0301
## I(week^2)-Sciurus_niger -0.2742 0.2293 -0.7393 -0.2651 0.1482
## I(week^2)-Procyon_lotor -0.1333 0.0925 -0.3168 -0.1338 0.0473
## I(week^2)-Dasypus_novemcinctus -0.1823 0.1041 -0.3904 -0.1799 0.0196
## I(week^2)-Lynx_rufus -0.2376 0.1533 -0.5515 -0.2378 0.0482
## I(week^2)-Didelphis_virginiana -0.4133 0.2099 -0.8838 -0.3936 -0.0616
## I(week^2)-Sylvilagus_floridanus -0.1801 0.1611 -0.5142 -0.1750 0.1285
## I(week^2)-Sciurus_carolinensis -0.2877 0.1451 -0.5835 -0.2838 -0.0081
## I(week^2)-Vulpes_vulpes -0.4019 0.2409 -0.9208 -0.3867 0.0271
## I(week^2)-Sus_scrofa -0.2441 0.1812 -0.6101 -0.2413 0.1038
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0032 2505
## (Intercept)-Sciurus_niger 1.0306 470
## (Intercept)-Procyon_lotor 1.0022 3856
## (Intercept)-Dasypus_novemcinctus 0.9998 3298
## (Intercept)-Lynx_rufus 1.0011 573
## (Intercept)-Didelphis_virginiana 1.0033 1518
## (Intercept)-Sylvilagus_floridanus 1.0023 2144
## (Intercept)-Sciurus_carolinensis 1.0016 1651
## (Intercept)-Vulpes_vulpes 1.0292 298
## (Intercept)-Sus_scrofa 1.0018 976
## shrub_cover-Odocoileus_virginianus 1.0003 5250
## shrub_cover-Canis_latrans 1.0047 1957
## shrub_cover-Sciurus_niger 1.0088 866
## shrub_cover-Procyon_lotor 1.0008 3673
## shrub_cover-Dasypus_novemcinctus 1.0032 2341
## shrub_cover-Lynx_rufus 1.0044 817
## shrub_cover-Didelphis_virginiana 1.0016 1111
## shrub_cover-Sylvilagus_floridanus 1.0019 1417
## shrub_cover-Sciurus_carolinensis 1.0038 1272
## shrub_cover-Vulpes_vulpes 1.0091 1524
## shrub_cover-Sus_scrofa 1.0056 1007
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0087 2129
## veg_height-Sciurus_niger 1.0076 1044
## veg_height-Procyon_lotor 1.0010 3775
## veg_height-Dasypus_novemcinctus 1.0017 4152
## veg_height-Lynx_rufus 1.0047 1883
## veg_height-Didelphis_virginiana 1.0002 2696
## veg_height-Sylvilagus_floridanus 1.0015 2333
## veg_height-Sciurus_carolinensis 1.0000 2918
## veg_height-Vulpes_vulpes 1.0104 1900
## veg_height-Sus_scrofa 1.0019 2836
## week-Odocoileus_virginianus 1.0002 5017
## week-Canis_latrans 1.0004 3644
## week-Sciurus_niger 1.0656 585
## week-Procyon_lotor 1.0038 4243
## week-Dasypus_novemcinctus 1.0003 4701
## week-Lynx_rufus 1.0013 2552
## week-Didelphis_virginiana 1.0046 2482
## week-Sylvilagus_floridanus 1.0029 2807
## week-Sciurus_carolinensis 1.0004 3801
## week-Vulpes_vulpes 1.0008 1535
## week-Sus_scrofa 1.0001 3627
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0007 3598
## I(week^2)-Sciurus_niger 1.0064 983
## I(week^2)-Procyon_lotor 1.0014 3999
## I(week^2)-Dasypus_novemcinctus 1.0031 3788
## I(week^2)-Lynx_rufus 1.0014 2360
## I(week^2)-Didelphis_virginiana 1.0009 1808
## I(week^2)-Sylvilagus_floridanus 1.0017 2419
## I(week^2)-Sciurus_carolinensis 0.9998 4284
## I(week^2)-Vulpes_vulpes 1.0017 1383
## I(week^2)-Sus_scrofa 1.0006 3998
waicOcc(ms_full_full, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1766.2827 121.4186 3775.4026
waicOcc(ms_full_cover, by.sp = FALSE)
## elpd pD WAIC
## -1805.920 121.358 3854.555
waicOcc(ms_full_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1798.7714 103.2904 3804.1236
waicOcc(ms_full_move, by.sp = FALSE)
## elpd pD WAIC
## -1802.7778 118.0353 3841.6263
waicOcc(ms_full_forage, by.sp = FALSE)
## elpd pD WAIC
## -1812.3783 109.6322 3844.0210
waicOcc(ms_full_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1816.790 104.174 3841.928
waicOcc(ms_full_null, by.sp = FALSE)
## elpd pD WAIC
## -1829.51699 89.91932 3838.87261
waicOcc(ms_full_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1809.6809 107.4021 3834.1661
waicOcc(ms_full_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1758.8314 125.6446 3768.9520
waicOcc(ms_null_null, by.sp = FALSE)
## elpd pD WAIC
## -1888.44094 39.38549 3855.65286
waicOcc(ms_null_full, by.sp = FALSE)
## elpd pD WAIC
## -1827.03759 70.68286 3795.44090
waicOcc(ms_null_cover, by.sp = FALSE)
## elpd pD WAIC
## -1869.45697 62.98627 3864.88647
waicOcc(ms_null_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1859.52266 52.86301 3824.77134
waicOcc(ms_null_move, by.sp = FALSE)
## elpd pD WAIC
## -1863.96876 63.26597 3854.46946
waicOcc(ms_null_forage, by.sp = FALSE)
## elpd pD WAIC
## -1870.9744 57.3181 3856.5850
waicOcc(ms_null_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1875.26384 53.03695 3856.60158
waicOcc(ms_null_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1868.22711 55.26485 3846.98392
waicOcc(ms_null_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1819.09680 73.27532 3784.74425
waicOcc(ms_week_full, by.sp = FALSE)
## elpd pD WAIC
## -1815.2991 80.7632 3792.1247
waicOcc(ms_week_cover, by.sp = FALSE)
## elpd pD WAIC
## -1858.75711 71.25497 3860.02417
waicOcc(ms_week_null, by.sp = FALSE)
## elpd pD WAIC
## -1877.16899 48.28967 3850.91733
waicOcc(ms_week_forage, by.sp = FALSE)
## elpd pD WAIC
## -1859.61493 66.69133 3852.61250
waicOcc(ms_week_move, by.sp = FALSE)
## elpd pD WAIC
## -1853.47275 71.83243 3850.61035
waicOcc(ms_week_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1848.31692 62.05088 3820.73560
waicOcc(ms_week_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1864.11857 62.51627 3853.26967
waicOcc(ms_week_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1857.05787 64.49734 3843.11042
waicOcc(ms_week_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1808.09488 81.54546 3779.28069
waicOcc(ms_cover_full, by.sp = FALSE)
## elpd pD WAIC
## -1777.0942 112.9516 3780.0916
waicOcc(ms_cover_cover, by.sp = FALSE)
## elpd pD WAIC
## -1817.8384 109.9249 3855.5265
waicOcc(ms_cover_null, by.sp = FALSE)
## elpd pD WAIC
## -1840.844 80.896 3843.480
waicOcc(ms_cover_forage, by.sp = FALSE)
## elpd pD WAIC
## -1823.3763 100.9373 3848.6272
waicOcc(ms_cover_move, by.sp = FALSE)
## elpd pD WAIC
## -1813.8325 109.7956 3847.2563
waicOcc(ms_cover_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1810.44721 94.13222 3809.15884
waicOcc(ms_cover_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1828.26464 95.06811 3846.66552
waicOcc(ms_cover_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1821.42217 97.50914 3837.86262
waicOcc(ms_cover_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1771.1266 115.0318 3772.3168
waicOcc(ms_weekQ_full, by.sp = FALSE)
## elpd pD WAIC
## -1741.56573 93.45323 3670.03793
waicOcc(ms_weekQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -1784.1604 85.1543 3738.6294
waicOcc(ms_weekQ_null, by.sp = FALSE)
## elpd pD WAIC
## -1803.27853 62.45605 3731.46917
waicOcc(ms_weekQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -1785.50359 80.04349 3731.09417
waicOcc(ms_weekQ_move, by.sp = FALSE)
## elpd pD WAIC
## -1778.44547 86.40752 3729.70599
waicOcc(ms_weekQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1774.19557 75.69673 3699.78460
waicOcc(ms_weekQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1790.25579 75.57139 3731.65437
waicOcc(ms_weekQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1782.50381 78.50978 3722.02718
waicOcc(ms_weekQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1734.03632 95.08583 3658.24430
waicOcc(ms_fullQ_full, by.sp = FALSE)
## elpd pD WAIC
## -1689.0817 138.6509 3655.4652
waicOcc(ms_fullQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -1729.8746 136.3791 3732.5074
waicOcc(ms_fullQ_null, by.sp = FALSE)
## elpd pD WAIC
## -1752.2732 106.2938 3717.1341
waicOcc(ms_fullQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -1735.7258 125.3486 3722.1489
waicOcc(ms_fullQ_move, by.sp = FALSE)
## elpd pD WAIC
## -1727.2742 133.5679 3721.6843
waicOcc(ms_fullQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1722.2687 119.8378 3684.2130
waicOcc(ms_fullQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1740.1839 119.4358 3719.2393
waicOcc(ms_fullQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1733.6753 122.1671 3711.6849
waicOcc(ms_fullQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1684.3534 138.7447 3646.1960
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: 0.2917
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Odocoileus_virginianus Bayesian p-value: 0
## Canis_latrans Bayesian p-value: 0.6478
## Sciurus_niger Bayesian p-value: 0.12
## Procyon_lotor Bayesian p-value: 0.0653
## Dasypus_novemcinctus Bayesian p-value: 4e-04
## Lynx_rufus Bayesian p-value: 0.2939
## Didelphis_virginiana Bayesian p-value: 0.4213
## Sylvilagus_floridanus Bayesian p-value: 0.412
## Sciurus_carolinensis Bayesian p-value: 0.3958
## Vulpes_vulpes Bayesian p-value: 0.2796
## Sus_scrofa Bayesian p-value: 0.5726
## 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): 2.1187
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8976 1.1042 -3.0154 -0.9246 1.3888 1.0132 1987
## Cogon_Patch_Size -0.2027 0.7367 -1.7604 -0.1729 1.1688 1.0196 885
## Veg_shannon_index 0.9349 0.4826 0.0261 0.9148 1.9358 1.0325 1008
## total_shrub_cover -0.5126 0.5116 -1.5843 -0.4866 0.4641 1.0029 664
## Avg_Cogongrass_Cover -0.1766 0.9587 -2.0222 -0.1926 1.7397 1.0432 398
## Tree_Density -1.9840 0.8223 -3.6435 -1.9842 -0.3472 1.0172 778
## Avg_Canopy_Cover 1.9457 0.6897 0.6486 1.9071 3.4538 1.0278 1042
## I(Avg_Cogongrass_Cover^2) 1.5995 0.5812 0.5622 1.5675 2.8679 1.0343 458
## avg_veg_height -0.1270 0.5089 -1.1661 -0.1091 0.8455 1.0420 632
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.4818 19.1266 3.9632 16.2754 70.5914 1.1014 658
## Cogon_Patch_Size 3.8271 6.2779 0.1135 1.9021 20.6051 1.1120 290
## Veg_shannon_index 0.8876 1.4162 0.0503 0.4291 4.6894 1.0037 1184
## total_shrub_cover 0.9331 1.3250 0.0571 0.5284 4.3271 1.0060 774
## Avg_Cogongrass_Cover 1.2969 2.1984 0.0505 0.5684 7.2062 1.0228 752
## Tree_Density 4.0471 6.7117 0.0742 1.7186 22.6189 1.1065 481
## Avg_Canopy_Cover 3.1143 3.9547 0.1614 1.8856 13.5485 1.0198 487
## I(Avg_Cogongrass_Cover^2) 0.9899 2.2833 0.0475 0.3803 5.4059 1.0420 574
## avg_veg_height 0.5286 0.8158 0.0436 0.2803 2.5541 1.0221 1332
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4115 1.9464 0.0514 0.6783 6.9989 1.0309 192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4713 0.4948 -3.4270 -2.4902 -1.4436 1.0002 4604
## shrub_cover 0.3096 0.2619 -0.1821 0.3045 0.8569 1.0039 1735
## veg_height 0.0172 0.1608 -0.3059 0.0151 0.3375 1.0044 2805
## week 0.3661 0.2385 -0.1376 0.3703 0.8157 1.0065 3010
## I(week^2) -0.2853 0.1018 -0.4997 -0.2839 -0.0882 1.0008 2737
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6887 1.7123 0.9594 2.2559 6.9336 1.0017 2198
## shrub_cover 0.5370 0.4288 0.1120 0.4243 1.6295 1.0093 1484
## veg_height 0.1976 0.1370 0.0567 0.1613 0.5778 1.0032 3731
## week 0.4300 0.3223 0.1080 0.3414 1.2523 1.0137 1716
## I(week^2) 0.0722 0.0520 0.0226 0.0594 0.2003 1.0082 2338
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.7137 3.5095 2.5619 7.1003
## (Intercept)-Canis_latrans -0.8302 1.2582 -3.2554 -0.8675
## (Intercept)-Sciurus_niger 1.0088 2.5874 -3.0770 0.6411
## (Intercept)-Procyon_lotor -0.2963 1.1047 -2.5736 -0.2625
## (Intercept)-Dasypus_novemcinctus -2.6596 1.1756 -5.3539 -2.5636
## (Intercept)-Lynx_rufus 0.6122 2.6506 -3.4922 0.2549
## (Intercept)-Didelphis_virginiana -4.0929 1.4750 -7.3629 -3.9756
## (Intercept)-Sylvilagus_floridanus -2.3822 1.4799 -5.5106 -2.3294
## (Intercept)-Sciurus_carolinensis -4.7244 1.6614 -8.5772 -4.5518
## (Intercept)-Vulpes_vulpes -3.8004 3.0294 -8.8607 -4.1116
## (Intercept)-Sus_scrofa -5.5406 2.0335 -9.9106 -5.3974
## Cogon_Patch_Size-Odocoileus_virginianus 0.0893 1.5506 -2.6267 -0.0060
## Cogon_Patch_Size-Canis_latrans 1.6520 1.5608 -0.4413 1.3363
## Cogon_Patch_Size-Sciurus_niger -0.8573 2.1351 -5.7330 -0.6004
## Cogon_Patch_Size-Procyon_lotor -0.5209 0.7742 -2.1499 -0.4897
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0321 0.7913 -1.5329 -0.0514
## Cogon_Patch_Size-Lynx_rufus -0.4053 1.5782 -3.3707 -0.4202
## Cogon_Patch_Size-Didelphis_virginiana 1.5516 1.0575 -0.2033 1.4526
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4594 1.8023 -5.8843 -1.0960
## Cogon_Patch_Size-Sciurus_carolinensis -1.1838 1.6081 -5.0950 -0.8582
## Cogon_Patch_Size-Vulpes_vulpes -0.6118 1.8780 -4.7389 -0.5048
## Cogon_Patch_Size-Sus_scrofa -0.8394 1.5815 -4.6017 -0.5972
## Veg_shannon_index-Odocoileus_virginianus 0.7489 0.9047 -1.2189 0.7979
## Veg_shannon_index-Canis_latrans 1.3032 0.7109 0.1298 1.2249
## Veg_shannon_index-Sciurus_niger 1.0649 1.0131 -0.8091 0.9924
## Veg_shannon_index-Procyon_lotor 1.1548 0.6279 0.0857 1.1003
## Veg_shannon_index-Dasypus_novemcinctus 0.5923 0.6027 -0.6134 0.6049
## Veg_shannon_index-Lynx_rufus 1.0580 0.9144 -0.6352 1.0013
## Veg_shannon_index-Didelphis_virginiana 1.1192 0.7255 -0.1933 1.0649
## Veg_shannon_index-Sylvilagus_floridanus 1.0247 0.7294 -0.2834 0.9812
## Veg_shannon_index-Sciurus_carolinensis 0.3175 0.8236 -1.6352 0.4038
## Veg_shannon_index-Vulpes_vulpes 0.6324 0.9117 -1.4496 0.6828
## Veg_shannon_index-Sus_scrofa 1.5615 1.0034 0.0341 1.3865
## total_shrub_cover-Odocoileus_virginianus -0.2869 0.9262 -2.0330 -0.3363
## total_shrub_cover-Canis_latrans 0.1697 0.7581 -1.0625 0.0768
## total_shrub_cover-Sciurus_niger -0.7205 1.0599 -3.1087 -0.6362
## total_shrub_cover-Procyon_lotor -1.1313 0.6798 -2.6507 -1.0599
## total_shrub_cover-Dasypus_novemcinctus -0.2248 0.6543 -1.5951 -0.2114
## total_shrub_cover-Lynx_rufus -0.7366 1.0693 -3.0201 -0.6854
## total_shrub_cover-Didelphis_virginiana -0.8172 0.8110 -2.6489 -0.7304
## total_shrub_cover-Sylvilagus_floridanus -0.6283 0.8710 -2.6359 -0.5763
## total_shrub_cover-Sciurus_carolinensis -0.4265 0.8260 -2.2117 -0.3942
## total_shrub_cover-Vulpes_vulpes -0.7075 1.0151 -2.9254 -0.6171
## total_shrub_cover-Sus_scrofa -0.2925 0.9161 -2.1133 -0.3114
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2122 1.3494 -2.9696 -0.1917
## Avg_Cogongrass_Cover-Canis_latrans 0.0588 1.2195 -2.2863 0.0221
## Avg_Cogongrass_Cover-Sciurus_niger -0.6055 1.6019 -4.4091 -0.4499
## Avg_Cogongrass_Cover-Procyon_lotor -0.0569 1.1818 -2.3243 -0.0587
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4715 1.2809 -1.7775 0.3861
## Avg_Cogongrass_Cover-Lynx_rufus -0.1012 1.2831 -2.6589 -0.1246
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1381 1.2361 -2.5979 -0.1642
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7760 1.3496 -3.6867 -0.7012
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1542 1.2375 -2.6531 -0.1304
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0394 1.3195 -2.4753 -0.0001
## Avg_Cogongrass_Cover-Sus_scrofa -0.5223 1.3898 -3.6598 -0.4291
## Tree_Density-Odocoileus_virginianus -0.8794 1.5007 -3.2186 -1.0691
## Tree_Density-Canis_latrans -2.9117 1.4534 -6.4146 -2.6514
## Tree_Density-Sciurus_niger -1.9935 1.8096 -5.8041 -1.9504
## Tree_Density-Procyon_lotor -1.9244 0.9902 -4.0718 -1.8532
## Tree_Density-Dasypus_novemcinctus -3.9829 2.1020 -9.2399 -3.4615
## Tree_Density-Lynx_rufus -0.5949 1.8364 -3.3475 -0.9063
## Tree_Density-Didelphis_virginiana -2.2999 1.3118 -5.4019 -2.1726
## Tree_Density-Sylvilagus_floridanus -2.5514 1.6016 -6.4051 -2.3084
## Tree_Density-Sciurus_carolinensis -2.6798 1.6212 -6.6211 -2.4515
## Tree_Density-Vulpes_vulpes -2.0702 1.8383 -6.0577 -2.0048
## Tree_Density-Sus_scrofa -2.5399 1.8036 -6.9131 -2.2723
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2275 1.4750 -1.8932 1.2793
## Avg_Canopy_Cover-Canis_latrans 0.1617 0.7131 -1.2496 0.1488
## Avg_Canopy_Cover-Sciurus_niger 2.2933 1.8694 -1.2746 2.1409
## Avg_Canopy_Cover-Procyon_lotor 1.6487 0.8006 0.2106 1.6027
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1295 0.8425 0.7215 2.0506
## Avg_Canopy_Cover-Lynx_rufus 1.7046 1.5686 -1.1449 1.6498
## Avg_Canopy_Cover-Didelphis_virginiana 3.0951 1.3022 1.2281 2.8646
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7374 1.8100 1.2483 3.4287
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8340 1.3324 0.9889 2.5579
## Avg_Canopy_Cover-Vulpes_vulpes 2.5540 1.4915 0.3813 2.2939
## Avg_Canopy_Cover-Sus_scrofa 2.1852 1.0447 0.5739 2.0382
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8656 1.1154 0.1485 1.7104
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0070 0.9618 0.5333 1.8745
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2108 1.2126 -1.5414 1.3004
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9176 0.8937 0.5249 1.8072
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5357 0.7300 0.2161 1.4889
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1080 1.0322 0.5919 1.9309
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2653 0.7236 -0.1332 1.2545
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3652 0.8414 -0.2115 1.3234
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7694 0.7798 0.4421 1.6903
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9383 0.9262 0.4919 1.8159
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1822 1.0377 -1.1877 1.2538
## avg_veg_height-Odocoileus_virginianus -0.1475 0.8299 -1.8661 -0.1257
## avg_veg_height-Canis_latrans -0.2154 0.6324 -1.5303 -0.1976
## avg_veg_height-Sciurus_niger -0.2739 0.8682 -2.2353 -0.2236
## avg_veg_height-Procyon_lotor 0.0756 0.6373 -1.1556 0.0751
## avg_veg_height-Dasypus_novemcinctus 0.2099 0.6301 -0.9612 0.1908
## avg_veg_height-Lynx_rufus -0.3300 0.8560 -2.2920 -0.2533
## avg_veg_height-Didelphis_virginiana -0.2974 0.7499 -1.9432 -0.2554
## avg_veg_height-Sylvilagus_floridanus -0.2436 0.7089 -1.7228 -0.2155
## avg_veg_height-Sciurus_carolinensis 0.1866 0.7077 -1.0925 0.1540
## avg_veg_height-Vulpes_vulpes -0.2141 0.8193 -1.9807 -0.1789
## avg_veg_height-Sus_scrofa -0.1728 0.7463 -1.7538 -0.1475
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8796 1.0935 486
## (Intercept)-Canis_latrans 1.8478 1.0069 1124
## (Intercept)-Sciurus_niger 7.2837 1.0790 412
## (Intercept)-Procyon_lotor 1.7911 1.0011 1144
## (Intercept)-Dasypus_novemcinctus -0.5907 1.0173 747
## (Intercept)-Lynx_rufus 7.1247 1.0241 337
## (Intercept)-Didelphis_virginiana -1.5281 1.0433 862
## (Intercept)-Sylvilagus_floridanus 0.4158 1.0363 802
## (Intercept)-Sciurus_carolinensis -1.8861 1.0581 551
## (Intercept)-Vulpes_vulpes 3.5005 1.1663 132
## (Intercept)-Sus_scrofa -1.9978 1.0212 646
## Cogon_Patch_Size-Odocoileus_virginianus 3.7199 1.0085 1581
## Cogon_Patch_Size-Canis_latrans 5.8344 1.0361 622
## Cogon_Patch_Size-Sciurus_niger 3.0217 1.0305 465
## Cogon_Patch_Size-Procyon_lotor 0.8999 1.0215 780
## Cogon_Patch_Size-Dasypus_novemcinctus 1.5694 1.0121 1460
## Cogon_Patch_Size-Lynx_rufus 2.9288 1.0260 762
## Cogon_Patch_Size-Didelphis_virginiana 3.9155 1.0215 694
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9391 1.0719 352
## Cogon_Patch_Size-Sciurus_carolinensis 0.9597 1.0697 459
## Cogon_Patch_Size-Vulpes_vulpes 2.9974 1.0434 536
## Cogon_Patch_Size-Sus_scrofa 1.6406 1.0304 787
## Veg_shannon_index-Odocoileus_virginianus 2.5060 1.0152 2017
## Veg_shannon_index-Canis_latrans 2.9267 1.0180 964
## Veg_shannon_index-Sciurus_niger 3.2697 1.0160 1064
## Veg_shannon_index-Procyon_lotor 2.5759 1.0111 954
## Veg_shannon_index-Dasypus_novemcinctus 1.7494 1.0113 2224
## Veg_shannon_index-Lynx_rufus 2.9987 1.0284 935
## Veg_shannon_index-Didelphis_virginiana 2.7272 1.0092 1489
## Veg_shannon_index-Sylvilagus_floridanus 2.5938 1.0102 1481
## Veg_shannon_index-Sciurus_carolinensis 1.7238 1.0055 1650
## Veg_shannon_index-Vulpes_vulpes 2.3298 1.0095 1594
## Veg_shannon_index-Sus_scrofa 4.0399 1.0177 902
## total_shrub_cover-Odocoileus_virginianus 1.7056 1.0017 1849
## total_shrub_cover-Canis_latrans 1.8710 1.0020 1216
## total_shrub_cover-Sciurus_niger 1.1636 1.0059 929
## total_shrub_cover-Procyon_lotor 0.0218 1.0018 1150
## total_shrub_cover-Dasypus_novemcinctus 1.0062 1.0003 1580
## total_shrub_cover-Lynx_rufus 1.2741 1.0036 692
## total_shrub_cover-Didelphis_virginiana 0.6000 1.0021 1155
## total_shrub_cover-Sylvilagus_floridanus 0.9443 1.0060 1041
## total_shrub_cover-Sciurus_carolinensis 1.2134 1.0001 1575
## total_shrub_cover-Vulpes_vulpes 1.1199 1.0013 1085
## total_shrub_cover-Sus_scrofa 1.6006 1.0019 916
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.5393 1.0179 657
## Avg_Cogongrass_Cover-Canis_latrans 2.6029 1.0334 622
## Avg_Cogongrass_Cover-Sciurus_niger 2.1548 1.0228 502
## Avg_Cogongrass_Cover-Procyon_lotor 2.2804 1.0301 551
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3202 1.0607 594
## Avg_Cogongrass_Cover-Lynx_rufus 2.4771 1.0207 775
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3417 1.0204 641
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6302 1.0135 510
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3062 1.0273 652
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8449 1.0269 672
## Avg_Cogongrass_Cover-Sus_scrofa 1.9218 1.0232 646
## Tree_Density-Odocoileus_virginianus 2.6860 1.0141 910
## Tree_Density-Canis_latrans -0.7638 1.0810 486
## Tree_Density-Sciurus_niger 1.8193 1.0287 627
## Tree_Density-Procyon_lotor -0.1212 1.0203 893
## Tree_Density-Dasypus_novemcinctus -1.2777 1.0892 368
## Tree_Density-Lynx_rufus 3.9077 1.0473 448
## Tree_Density-Didelphis_virginiana -0.0944 1.0230 1073
## Tree_Density-Sylvilagus_floridanus 0.0439 1.0285 702
## Tree_Density-Sciurus_carolinensis 0.0200 1.0474 1034
## Tree_Density-Vulpes_vulpes 1.6574 1.0227 700
## Tree_Density-Sus_scrofa 0.3508 1.0259 915
## Avg_Canopy_Cover-Odocoileus_virginianus 4.1292 1.0248 1421
## Avg_Canopy_Cover-Canis_latrans 1.6107 1.0191 1460
## Avg_Canopy_Cover-Sciurus_niger 6.5229 1.0251 746
## Avg_Canopy_Cover-Procyon_lotor 3.3717 1.0102 1070
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0373 1.0483 841
## Avg_Canopy_Cover-Lynx_rufus 5.0250 1.0034 732
## Avg_Canopy_Cover-Didelphis_virginiana 6.3404 1.0272 717
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1874 1.0304 424
## Avg_Canopy_Cover-Sciurus_carolinensis 6.1889 1.0166 633
## Avg_Canopy_Cover-Vulpes_vulpes 6.2893 1.0042 744
## Avg_Canopy_Cover-Sus_scrofa 4.6216 1.0461 1156
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 4.5817 1.0212 652
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.3155 1.0242 586
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.4089 1.0355 424
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.9767 1.0051 516
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1000 1.0274 699
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.5595 1.0090 519
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.7468 1.0154 713
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1633 1.0017 840
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5786 1.0175 567
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.0721 1.0229 608
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0588 1.0329 639
## avg_veg_height-Odocoileus_virginianus 1.4647 1.0200 1250
## avg_veg_height-Canis_latrans 1.0070 1.0483 1130
## avg_veg_height-Sciurus_niger 1.2249 1.0151 1113
## avg_veg_height-Procyon_lotor 1.3437 1.0191 1189
## avg_veg_height-Dasypus_novemcinctus 1.5149 1.0174 1239
## avg_veg_height-Lynx_rufus 1.1835 1.0314 960
## avg_veg_height-Didelphis_virginiana 1.0356 1.0241 1115
## avg_veg_height-Sylvilagus_floridanus 1.0518 1.0203 1087
## avg_veg_height-Sciurus_carolinensis 1.6931 1.0126 1440
## avg_veg_height-Vulpes_vulpes 1.4162 1.0294 1078
## avg_veg_height-Sus_scrofa 1.2292 1.0326 1045
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5363 0.0800 0.3782 0.5372 0.6955
## (Intercept)-Canis_latrans -2.5287 0.2023 -2.9473 -2.5245 -2.1505
## (Intercept)-Sciurus_niger -4.7096 0.5184 -5.7127 -4.7122 -3.6891
## (Intercept)-Procyon_lotor -2.1881 0.1608 -2.5206 -2.1861 -1.8912
## (Intercept)-Dasypus_novemcinctus -1.6024 0.1809 -1.9773 -1.5953 -1.2611
## (Intercept)-Lynx_rufus -3.7446 0.3713 -4.4524 -3.7518 -2.9868
## (Intercept)-Didelphis_virginiana -2.3454 0.3174 -3.0146 -2.3295 -1.7727
## (Intercept)-Sylvilagus_floridanus -3.0956 0.2934 -3.7049 -3.0864 -2.5468
## (Intercept)-Sciurus_carolinensis -2.4802 0.3480 -3.2066 -2.4703 -1.8412
## (Intercept)-Vulpes_vulpes -4.1721 0.7418 -5.7174 -4.1147 -2.8740
## (Intercept)-Sus_scrofa -3.2324 0.6106 -4.4489 -3.2370 -2.0332
## shrub_cover-Odocoileus_virginianus -0.0591 0.0675 -0.1897 -0.0594 0.0731
## shrub_cover-Canis_latrans -0.2919 0.2288 -0.7526 -0.2913 0.1516
## shrub_cover-Sciurus_niger -0.3302 0.4429 -1.2401 -0.3145 0.4974
## shrub_cover-Procyon_lotor 0.2715 0.1615 -0.0483 0.2730 0.5850
## shrub_cover-Dasypus_novemcinctus 0.8885 0.3035 0.3207 0.8843 1.4892
## shrub_cover-Lynx_rufus -0.2113 0.3556 -0.8838 -0.2212 0.5130
## shrub_cover-Didelphis_virginiana 0.9693 0.3838 0.2977 0.9379 1.7883
## shrub_cover-Sylvilagus_floridanus 0.4824 0.4024 -0.3096 0.4722 1.2969
## shrub_cover-Sciurus_carolinensis 0.9122 0.4101 0.1359 0.9102 1.7308
## shrub_cover-Vulpes_vulpes 0.1141 0.5473 -1.0141 0.1292 1.1886
## shrub_cover-Sus_scrofa 0.7261 0.7634 -0.7445 0.6958 2.3249
## veg_height-Odocoileus_virginianus -0.3304 0.0687 -0.4683 -0.3296 -0.1962
## veg_height-Canis_latrans -0.5473 0.1821 -0.9194 -0.5423 -0.1947
## veg_height-Sciurus_niger -0.0340 0.3373 -0.6927 -0.0421 0.6482
## veg_height-Procyon_lotor 0.3563 0.1208 0.1207 0.3569 0.5947
## veg_height-Dasypus_novemcinctus 0.2461 0.1361 -0.0151 0.2434 0.5190
## veg_height-Lynx_rufus 0.1477 0.2317 -0.3113 0.1485 0.5972
## veg_height-Didelphis_virginiana 0.4242 0.2398 -0.0252 0.4137 0.9106
## veg_height-Sylvilagus_floridanus 0.1337 0.2477 -0.3478 0.1274 0.6245
## veg_height-Sciurus_carolinensis 0.0999 0.2154 -0.3018 0.0957 0.5318
## veg_height-Vulpes_vulpes -0.1685 0.3174 -0.8284 -0.1565 0.4379
## veg_height-Sus_scrofa -0.1554 0.3270 -0.8294 -0.1476 0.4749
## week-Odocoileus_virginianus 1.3077 0.1242 1.0650 1.3084 1.5504
## week-Canis_latrans 0.5960 0.2634 0.1014 0.5887 1.1168
## week-Sciurus_niger -0.3929 0.5500 -1.6228 -0.3473 0.5396
## week-Procyon_lotor 0.2074 0.2124 -0.2065 0.2096 0.6251
## week-Dasypus_novemcinctus 0.1126 0.2268 -0.3298 0.1152 0.5507
## week-Lynx_rufus 0.3828 0.3547 -0.3035 0.3789 1.1005
## week-Didelphis_virginiana 0.0744 0.3689 -0.6835 0.0790 0.7778
## week-Sylvilagus_floridanus 0.0689 0.3457 -0.6247 0.0738 0.7288
## week-Sciurus_carolinensis 0.8096 0.3709 0.1224 0.7953 1.5910
## week-Vulpes_vulpes 0.1996 0.5267 -0.9188 0.2138 1.1953
## week-Sus_scrofa 0.6943 0.4542 -0.1553 0.6813 1.6346
## I(week^2)-Odocoileus_virginianus -0.5390 0.0515 -0.6394 -0.5391 -0.4355
## I(week^2)-Canis_latrans -0.2438 0.1096 -0.4700 -0.2423 -0.0301
## I(week^2)-Sciurus_niger -0.2742 0.2293 -0.7393 -0.2651 0.1482
## I(week^2)-Procyon_lotor -0.1333 0.0925 -0.3168 -0.1338 0.0473
## I(week^2)-Dasypus_novemcinctus -0.1823 0.1041 -0.3904 -0.1799 0.0196
## I(week^2)-Lynx_rufus -0.2376 0.1533 -0.5515 -0.2378 0.0482
## I(week^2)-Didelphis_virginiana -0.4133 0.2099 -0.8838 -0.3936 -0.0616
## I(week^2)-Sylvilagus_floridanus -0.1801 0.1611 -0.5142 -0.1750 0.1285
## I(week^2)-Sciurus_carolinensis -0.2877 0.1451 -0.5835 -0.2838 -0.0081
## I(week^2)-Vulpes_vulpes -0.4019 0.2409 -0.9208 -0.3867 0.0271
## I(week^2)-Sus_scrofa -0.2441 0.1812 -0.6101 -0.2413 0.1038
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0032 2505
## (Intercept)-Sciurus_niger 1.0306 470
## (Intercept)-Procyon_lotor 1.0022 3856
## (Intercept)-Dasypus_novemcinctus 0.9998 3298
## (Intercept)-Lynx_rufus 1.0011 573
## (Intercept)-Didelphis_virginiana 1.0033 1518
## (Intercept)-Sylvilagus_floridanus 1.0023 2144
## (Intercept)-Sciurus_carolinensis 1.0016 1651
## (Intercept)-Vulpes_vulpes 1.0292 298
## (Intercept)-Sus_scrofa 1.0018 976
## shrub_cover-Odocoileus_virginianus 1.0003 5250
## shrub_cover-Canis_latrans 1.0047 1957
## shrub_cover-Sciurus_niger 1.0088 866
## shrub_cover-Procyon_lotor 1.0008 3673
## shrub_cover-Dasypus_novemcinctus 1.0032 2341
## shrub_cover-Lynx_rufus 1.0044 817
## shrub_cover-Didelphis_virginiana 1.0016 1111
## shrub_cover-Sylvilagus_floridanus 1.0019 1417
## shrub_cover-Sciurus_carolinensis 1.0038 1272
## shrub_cover-Vulpes_vulpes 1.0091 1524
## shrub_cover-Sus_scrofa 1.0056 1007
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0087 2129
## veg_height-Sciurus_niger 1.0076 1044
## veg_height-Procyon_lotor 1.0010 3775
## veg_height-Dasypus_novemcinctus 1.0017 4152
## veg_height-Lynx_rufus 1.0047 1883
## veg_height-Didelphis_virginiana 1.0002 2696
## veg_height-Sylvilagus_floridanus 1.0015 2333
## veg_height-Sciurus_carolinensis 1.0000 2918
## veg_height-Vulpes_vulpes 1.0104 1900
## veg_height-Sus_scrofa 1.0019 2836
## week-Odocoileus_virginianus 1.0002 5017
## week-Canis_latrans 1.0004 3644
## week-Sciurus_niger 1.0656 585
## week-Procyon_lotor 1.0038 4243
## week-Dasypus_novemcinctus 1.0003 4701
## week-Lynx_rufus 1.0013 2552
## week-Didelphis_virginiana 1.0046 2482
## week-Sylvilagus_floridanus 1.0029 2807
## week-Sciurus_carolinensis 1.0004 3801
## week-Vulpes_vulpes 1.0008 1535
## week-Sus_scrofa 1.0001 3627
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0007 3598
## I(week^2)-Sciurus_niger 1.0064 983
## I(week^2)-Procyon_lotor 1.0014 3999
## I(week^2)-Dasypus_novemcinctus 1.0031 3788
## I(week^2)-Lynx_rufus 1.0014 2360
## I(week^2)-Didelphis_virginiana 1.0009 1808
## I(week^2)-Sylvilagus_floridanus 1.0017 2419
## I(week^2)-Sciurus_carolinensis 0.9998 4284
## I(week^2)-Vulpes_vulpes 1.0017 1383
## I(week^2)-Sus_scrofa 1.0006 3998
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$beta.samples)
## 'mcmc' num [1:5250, 1:99] 3.3 1.89 5 7 8.31 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:99] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.005142857
MCMCplot(ms_fullQ_fullQ$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] 0.989 0.983 0.886 0.996 1 ...
## $ z.0.samples : int [1:5250, 1:11, 1:100] 1 1 1 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$psi.0.samples)
## num [1:5250, 1:11, 1:100] 0.989 0.983 0.886 0.996 1 ...
psi.0.quants <- apply(out.pred$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] 0.000303 0.139834 0.999956 0.000303 0.141995 ...
## - 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 0.14 0.142 0.139 0.139 0.14 ...
## $ psi.low : num 0.000303 0.000303 0.000315 0.000312 0.000323 ...
## $ 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): 2.1187
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8976 1.1042 -3.0154 -0.9246 1.3888 1.0132 1987
## Cogon_Patch_Size -0.2027 0.7367 -1.7604 -0.1729 1.1688 1.0196 885
## Veg_shannon_index 0.9349 0.4826 0.0261 0.9148 1.9358 1.0325 1008
## total_shrub_cover -0.5126 0.5116 -1.5843 -0.4866 0.4641 1.0029 664
## Avg_Cogongrass_Cover -0.1766 0.9587 -2.0222 -0.1926 1.7397 1.0432 398
## Tree_Density -1.9840 0.8223 -3.6435 -1.9842 -0.3472 1.0172 778
## Avg_Canopy_Cover 1.9457 0.6897 0.6486 1.9071 3.4538 1.0278 1042
## I(Avg_Cogongrass_Cover^2) 1.5995 0.5812 0.5622 1.5675 2.8679 1.0343 458
## avg_veg_height -0.1270 0.5089 -1.1661 -0.1091 0.8455 1.0420 632
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 21.4818 19.1266 3.9632 16.2754 70.5914 1.1014 658
## Cogon_Patch_Size 3.8271 6.2779 0.1135 1.9021 20.6051 1.1120 290
## Veg_shannon_index 0.8876 1.4162 0.0503 0.4291 4.6894 1.0037 1184
## total_shrub_cover 0.9331 1.3250 0.0571 0.5284 4.3271 1.0060 774
## Avg_Cogongrass_Cover 1.2969 2.1984 0.0505 0.5684 7.2062 1.0228 752
## Tree_Density 4.0471 6.7117 0.0742 1.7186 22.6189 1.1065 481
## Avg_Canopy_Cover 3.1143 3.9547 0.1614 1.8856 13.5485 1.0198 487
## I(Avg_Cogongrass_Cover^2) 0.9899 2.2833 0.0475 0.3803 5.4059 1.0420 574
## avg_veg_height 0.5286 0.8158 0.0436 0.2803 2.5541 1.0221 1332
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4115 1.9464 0.0514 0.6783 6.9989 1.0309 192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4713 0.4948 -3.4270 -2.4902 -1.4436 1.0002 4604
## shrub_cover 0.3096 0.2619 -0.1821 0.3045 0.8569 1.0039 1735
## veg_height 0.0172 0.1608 -0.3059 0.0151 0.3375 1.0044 2805
## week 0.3661 0.2385 -0.1376 0.3703 0.8157 1.0065 3010
## I(week^2) -0.2853 0.1018 -0.4997 -0.2839 -0.0882 1.0008 2737
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6887 1.7123 0.9594 2.2559 6.9336 1.0017 2198
## shrub_cover 0.5370 0.4288 0.1120 0.4243 1.6295 1.0093 1484
## veg_height 0.1976 0.1370 0.0567 0.1613 0.5778 1.0032 3731
## week 0.4300 0.3223 0.1080 0.3414 1.2523 1.0137 1716
## I(week^2) 0.0722 0.0520 0.0226 0.0594 0.2003 1.0082 2338
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Odocoileus_virginianus 7.7137 3.5095 2.5619 7.1003
## (Intercept)-Canis_latrans -0.8302 1.2582 -3.2554 -0.8675
## (Intercept)-Sciurus_niger 1.0088 2.5874 -3.0770 0.6411
## (Intercept)-Procyon_lotor -0.2963 1.1047 -2.5736 -0.2625
## (Intercept)-Dasypus_novemcinctus -2.6596 1.1756 -5.3539 -2.5636
## (Intercept)-Lynx_rufus 0.6122 2.6506 -3.4922 0.2549
## (Intercept)-Didelphis_virginiana -4.0929 1.4750 -7.3629 -3.9756
## (Intercept)-Sylvilagus_floridanus -2.3822 1.4799 -5.5106 -2.3294
## (Intercept)-Sciurus_carolinensis -4.7244 1.6614 -8.5772 -4.5518
## (Intercept)-Vulpes_vulpes -3.8004 3.0294 -8.8607 -4.1116
## (Intercept)-Sus_scrofa -5.5406 2.0335 -9.9106 -5.3974
## Cogon_Patch_Size-Odocoileus_virginianus 0.0893 1.5506 -2.6267 -0.0060
## Cogon_Patch_Size-Canis_latrans 1.6520 1.5608 -0.4413 1.3363
## Cogon_Patch_Size-Sciurus_niger -0.8573 2.1351 -5.7330 -0.6004
## Cogon_Patch_Size-Procyon_lotor -0.5209 0.7742 -2.1499 -0.4897
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0321 0.7913 -1.5329 -0.0514
## Cogon_Patch_Size-Lynx_rufus -0.4053 1.5782 -3.3707 -0.4202
## Cogon_Patch_Size-Didelphis_virginiana 1.5516 1.0575 -0.2033 1.4526
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4594 1.8023 -5.8843 -1.0960
## Cogon_Patch_Size-Sciurus_carolinensis -1.1838 1.6081 -5.0950 -0.8582
## Cogon_Patch_Size-Vulpes_vulpes -0.6118 1.8780 -4.7389 -0.5048
## Cogon_Patch_Size-Sus_scrofa -0.8394 1.5815 -4.6017 -0.5972
## Veg_shannon_index-Odocoileus_virginianus 0.7489 0.9047 -1.2189 0.7979
## Veg_shannon_index-Canis_latrans 1.3032 0.7109 0.1298 1.2249
## Veg_shannon_index-Sciurus_niger 1.0649 1.0131 -0.8091 0.9924
## Veg_shannon_index-Procyon_lotor 1.1548 0.6279 0.0857 1.1003
## Veg_shannon_index-Dasypus_novemcinctus 0.5923 0.6027 -0.6134 0.6049
## Veg_shannon_index-Lynx_rufus 1.0580 0.9144 -0.6352 1.0013
## Veg_shannon_index-Didelphis_virginiana 1.1192 0.7255 -0.1933 1.0649
## Veg_shannon_index-Sylvilagus_floridanus 1.0247 0.7294 -0.2834 0.9812
## Veg_shannon_index-Sciurus_carolinensis 0.3175 0.8236 -1.6352 0.4038
## Veg_shannon_index-Vulpes_vulpes 0.6324 0.9117 -1.4496 0.6828
## Veg_shannon_index-Sus_scrofa 1.5615 1.0034 0.0341 1.3865
## total_shrub_cover-Odocoileus_virginianus -0.2869 0.9262 -2.0330 -0.3363
## total_shrub_cover-Canis_latrans 0.1697 0.7581 -1.0625 0.0768
## total_shrub_cover-Sciurus_niger -0.7205 1.0599 -3.1087 -0.6362
## total_shrub_cover-Procyon_lotor -1.1313 0.6798 -2.6507 -1.0599
## total_shrub_cover-Dasypus_novemcinctus -0.2248 0.6543 -1.5951 -0.2114
## total_shrub_cover-Lynx_rufus -0.7366 1.0693 -3.0201 -0.6854
## total_shrub_cover-Didelphis_virginiana -0.8172 0.8110 -2.6489 -0.7304
## total_shrub_cover-Sylvilagus_floridanus -0.6283 0.8710 -2.6359 -0.5763
## total_shrub_cover-Sciurus_carolinensis -0.4265 0.8260 -2.2117 -0.3942
## total_shrub_cover-Vulpes_vulpes -0.7075 1.0151 -2.9254 -0.6171
## total_shrub_cover-Sus_scrofa -0.2925 0.9161 -2.1133 -0.3114
## Avg_Cogongrass_Cover-Odocoileus_virginianus -0.2122 1.3494 -2.9696 -0.1917
## Avg_Cogongrass_Cover-Canis_latrans 0.0588 1.2195 -2.2863 0.0221
## Avg_Cogongrass_Cover-Sciurus_niger -0.6055 1.6019 -4.4091 -0.4499
## Avg_Cogongrass_Cover-Procyon_lotor -0.0569 1.1818 -2.3243 -0.0587
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4715 1.2809 -1.7775 0.3861
## Avg_Cogongrass_Cover-Lynx_rufus -0.1012 1.2831 -2.6589 -0.1246
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.1381 1.2361 -2.5979 -0.1642
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7760 1.3496 -3.6867 -0.7012
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.1542 1.2375 -2.6531 -0.1304
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.0394 1.3195 -2.4753 -0.0001
## Avg_Cogongrass_Cover-Sus_scrofa -0.5223 1.3898 -3.6598 -0.4291
## Tree_Density-Odocoileus_virginianus -0.8794 1.5007 -3.2186 -1.0691
## Tree_Density-Canis_latrans -2.9117 1.4534 -6.4146 -2.6514
## Tree_Density-Sciurus_niger -1.9935 1.8096 -5.8041 -1.9504
## Tree_Density-Procyon_lotor -1.9244 0.9902 -4.0718 -1.8532
## Tree_Density-Dasypus_novemcinctus -3.9829 2.1020 -9.2399 -3.4615
## Tree_Density-Lynx_rufus -0.5949 1.8364 -3.3475 -0.9063
## Tree_Density-Didelphis_virginiana -2.2999 1.3118 -5.4019 -2.1726
## Tree_Density-Sylvilagus_floridanus -2.5514 1.6016 -6.4051 -2.3084
## Tree_Density-Sciurus_carolinensis -2.6798 1.6212 -6.6211 -2.4515
## Tree_Density-Vulpes_vulpes -2.0702 1.8383 -6.0577 -2.0048
## Tree_Density-Sus_scrofa -2.5399 1.8036 -6.9131 -2.2723
## Avg_Canopy_Cover-Odocoileus_virginianus 1.2275 1.4750 -1.8932 1.2793
## Avg_Canopy_Cover-Canis_latrans 0.1617 0.7131 -1.2496 0.1488
## Avg_Canopy_Cover-Sciurus_niger 2.2933 1.8694 -1.2746 2.1409
## Avg_Canopy_Cover-Procyon_lotor 1.6487 0.8006 0.2106 1.6027
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1295 0.8425 0.7215 2.0506
## Avg_Canopy_Cover-Lynx_rufus 1.7046 1.5686 -1.1449 1.6498
## Avg_Canopy_Cover-Didelphis_virginiana 3.0951 1.3022 1.2281 2.8646
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7374 1.8100 1.2483 3.4287
## Avg_Canopy_Cover-Sciurus_carolinensis 2.8340 1.3324 0.9889 2.5579
## Avg_Canopy_Cover-Vulpes_vulpes 2.5540 1.4915 0.3813 2.2939
## Avg_Canopy_Cover-Sus_scrofa 2.1852 1.0447 0.5739 2.0382
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 1.8656 1.1154 0.1485 1.7104
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0070 0.9618 0.5333 1.8745
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2108 1.2126 -1.5414 1.3004
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9176 0.8937 0.5249 1.8072
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5357 0.7300 0.2161 1.4889
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1080 1.0322 0.5919 1.9309
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2653 0.7236 -0.1332 1.2545
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3652 0.8414 -0.2115 1.3234
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7694 0.7798 0.4421 1.6903
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9383 0.9262 0.4919 1.8159
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1822 1.0377 -1.1877 1.2538
## avg_veg_height-Odocoileus_virginianus -0.1475 0.8299 -1.8661 -0.1257
## avg_veg_height-Canis_latrans -0.2154 0.6324 -1.5303 -0.1976
## avg_veg_height-Sciurus_niger -0.2739 0.8682 -2.2353 -0.2236
## avg_veg_height-Procyon_lotor 0.0756 0.6373 -1.1556 0.0751
## avg_veg_height-Dasypus_novemcinctus 0.2099 0.6301 -0.9612 0.1908
## avg_veg_height-Lynx_rufus -0.3300 0.8560 -2.2920 -0.2533
## avg_veg_height-Didelphis_virginiana -0.2974 0.7499 -1.9432 -0.2554
## avg_veg_height-Sylvilagus_floridanus -0.2436 0.7089 -1.7228 -0.2155
## avg_veg_height-Sciurus_carolinensis 0.1866 0.7077 -1.0925 0.1540
## avg_veg_height-Vulpes_vulpes -0.2141 0.8193 -1.9807 -0.1789
## avg_veg_height-Sus_scrofa -0.1728 0.7463 -1.7538 -0.1475
## 97.5% Rhat ESS
## (Intercept)-Odocoileus_virginianus 15.8796 1.0935 486
## (Intercept)-Canis_latrans 1.8478 1.0069 1124
## (Intercept)-Sciurus_niger 7.2837 1.0790 412
## (Intercept)-Procyon_lotor 1.7911 1.0011 1144
## (Intercept)-Dasypus_novemcinctus -0.5907 1.0173 747
## (Intercept)-Lynx_rufus 7.1247 1.0241 337
## (Intercept)-Didelphis_virginiana -1.5281 1.0433 862
## (Intercept)-Sylvilagus_floridanus 0.4158 1.0363 802
## (Intercept)-Sciurus_carolinensis -1.8861 1.0581 551
## (Intercept)-Vulpes_vulpes 3.5005 1.1663 132
## (Intercept)-Sus_scrofa -1.9978 1.0212 646
## Cogon_Patch_Size-Odocoileus_virginianus 3.7199 1.0085 1581
## Cogon_Patch_Size-Canis_latrans 5.8344 1.0361 622
## Cogon_Patch_Size-Sciurus_niger 3.0217 1.0305 465
## Cogon_Patch_Size-Procyon_lotor 0.8999 1.0215 780
## Cogon_Patch_Size-Dasypus_novemcinctus 1.5694 1.0121 1460
## Cogon_Patch_Size-Lynx_rufus 2.9288 1.0260 762
## Cogon_Patch_Size-Didelphis_virginiana 3.9155 1.0215 694
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9391 1.0719 352
## Cogon_Patch_Size-Sciurus_carolinensis 0.9597 1.0697 459
## Cogon_Patch_Size-Vulpes_vulpes 2.9974 1.0434 536
## Cogon_Patch_Size-Sus_scrofa 1.6406 1.0304 787
## Veg_shannon_index-Odocoileus_virginianus 2.5060 1.0152 2017
## Veg_shannon_index-Canis_latrans 2.9267 1.0180 964
## Veg_shannon_index-Sciurus_niger 3.2697 1.0160 1064
## Veg_shannon_index-Procyon_lotor 2.5759 1.0111 954
## Veg_shannon_index-Dasypus_novemcinctus 1.7494 1.0113 2224
## Veg_shannon_index-Lynx_rufus 2.9987 1.0284 935
## Veg_shannon_index-Didelphis_virginiana 2.7272 1.0092 1489
## Veg_shannon_index-Sylvilagus_floridanus 2.5938 1.0102 1481
## Veg_shannon_index-Sciurus_carolinensis 1.7238 1.0055 1650
## Veg_shannon_index-Vulpes_vulpes 2.3298 1.0095 1594
## Veg_shannon_index-Sus_scrofa 4.0399 1.0177 902
## total_shrub_cover-Odocoileus_virginianus 1.7056 1.0017 1849
## total_shrub_cover-Canis_latrans 1.8710 1.0020 1216
## total_shrub_cover-Sciurus_niger 1.1636 1.0059 929
## total_shrub_cover-Procyon_lotor 0.0218 1.0018 1150
## total_shrub_cover-Dasypus_novemcinctus 1.0062 1.0003 1580
## total_shrub_cover-Lynx_rufus 1.2741 1.0036 692
## total_shrub_cover-Didelphis_virginiana 0.6000 1.0021 1155
## total_shrub_cover-Sylvilagus_floridanus 0.9443 1.0060 1041
## total_shrub_cover-Sciurus_carolinensis 1.2134 1.0001 1575
## total_shrub_cover-Vulpes_vulpes 1.1199 1.0013 1085
## total_shrub_cover-Sus_scrofa 1.6006 1.0019 916
## Avg_Cogongrass_Cover-Odocoileus_virginianus 2.5393 1.0179 657
## Avg_Cogongrass_Cover-Canis_latrans 2.6029 1.0334 622
## Avg_Cogongrass_Cover-Sciurus_niger 2.1548 1.0228 502
## Avg_Cogongrass_Cover-Procyon_lotor 2.2804 1.0301 551
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3202 1.0607 594
## Avg_Cogongrass_Cover-Lynx_rufus 2.4771 1.0207 775
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3417 1.0204 641
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6302 1.0135 510
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3062 1.0273 652
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8449 1.0269 672
## Avg_Cogongrass_Cover-Sus_scrofa 1.9218 1.0232 646
## Tree_Density-Odocoileus_virginianus 2.6860 1.0141 910
## Tree_Density-Canis_latrans -0.7638 1.0810 486
## Tree_Density-Sciurus_niger 1.8193 1.0287 627
## Tree_Density-Procyon_lotor -0.1212 1.0203 893
## Tree_Density-Dasypus_novemcinctus -1.2777 1.0892 368
## Tree_Density-Lynx_rufus 3.9077 1.0473 448
## Tree_Density-Didelphis_virginiana -0.0944 1.0230 1073
## Tree_Density-Sylvilagus_floridanus 0.0439 1.0285 702
## Tree_Density-Sciurus_carolinensis 0.0200 1.0474 1034
## Tree_Density-Vulpes_vulpes 1.6574 1.0227 700
## Tree_Density-Sus_scrofa 0.3508 1.0259 915
## Avg_Canopy_Cover-Odocoileus_virginianus 4.1292 1.0248 1421
## Avg_Canopy_Cover-Canis_latrans 1.6107 1.0191 1460
## Avg_Canopy_Cover-Sciurus_niger 6.5229 1.0251 746
## Avg_Canopy_Cover-Procyon_lotor 3.3717 1.0102 1070
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0373 1.0483 841
## Avg_Canopy_Cover-Lynx_rufus 5.0250 1.0034 732
## Avg_Canopy_Cover-Didelphis_virginiana 6.3404 1.0272 717
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1874 1.0304 424
## Avg_Canopy_Cover-Sciurus_carolinensis 6.1889 1.0166 633
## Avg_Canopy_Cover-Vulpes_vulpes 6.2893 1.0042 744
## Avg_Canopy_Cover-Sus_scrofa 4.6216 1.0461 1156
## I(Avg_Cogongrass_Cover^2)-Odocoileus_virginianus 4.5817 1.0212 652
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.3155 1.0242 586
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.4089 1.0355 424
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.9767 1.0051 516
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1000 1.0274 699
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.5595 1.0090 519
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.7468 1.0154 713
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1633 1.0017 840
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5786 1.0175 567
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.0721 1.0229 608
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0588 1.0329 639
## avg_veg_height-Odocoileus_virginianus 1.4647 1.0200 1250
## avg_veg_height-Canis_latrans 1.0070 1.0483 1130
## avg_veg_height-Sciurus_niger 1.2249 1.0151 1113
## avg_veg_height-Procyon_lotor 1.3437 1.0191 1189
## avg_veg_height-Dasypus_novemcinctus 1.5149 1.0174 1239
## avg_veg_height-Lynx_rufus 1.1835 1.0314 960
## avg_veg_height-Didelphis_virginiana 1.0356 1.0241 1115
## avg_veg_height-Sylvilagus_floridanus 1.0518 1.0203 1087
## avg_veg_height-Sciurus_carolinensis 1.6931 1.0126 1440
## avg_veg_height-Vulpes_vulpes 1.4162 1.0294 1078
## avg_veg_height-Sus_scrofa 1.2292 1.0326 1045
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Odocoileus_virginianus 0.5363 0.0800 0.3782 0.5372 0.6955
## (Intercept)-Canis_latrans -2.5287 0.2023 -2.9473 -2.5245 -2.1505
## (Intercept)-Sciurus_niger -4.7096 0.5184 -5.7127 -4.7122 -3.6891
## (Intercept)-Procyon_lotor -2.1881 0.1608 -2.5206 -2.1861 -1.8912
## (Intercept)-Dasypus_novemcinctus -1.6024 0.1809 -1.9773 -1.5953 -1.2611
## (Intercept)-Lynx_rufus -3.7446 0.3713 -4.4524 -3.7518 -2.9868
## (Intercept)-Didelphis_virginiana -2.3454 0.3174 -3.0146 -2.3295 -1.7727
## (Intercept)-Sylvilagus_floridanus -3.0956 0.2934 -3.7049 -3.0864 -2.5468
## (Intercept)-Sciurus_carolinensis -2.4802 0.3480 -3.2066 -2.4703 -1.8412
## (Intercept)-Vulpes_vulpes -4.1721 0.7418 -5.7174 -4.1147 -2.8740
## (Intercept)-Sus_scrofa -3.2324 0.6106 -4.4489 -3.2370 -2.0332
## shrub_cover-Odocoileus_virginianus -0.0591 0.0675 -0.1897 -0.0594 0.0731
## shrub_cover-Canis_latrans -0.2919 0.2288 -0.7526 -0.2913 0.1516
## shrub_cover-Sciurus_niger -0.3302 0.4429 -1.2401 -0.3145 0.4974
## shrub_cover-Procyon_lotor 0.2715 0.1615 -0.0483 0.2730 0.5850
## shrub_cover-Dasypus_novemcinctus 0.8885 0.3035 0.3207 0.8843 1.4892
## shrub_cover-Lynx_rufus -0.2113 0.3556 -0.8838 -0.2212 0.5130
## shrub_cover-Didelphis_virginiana 0.9693 0.3838 0.2977 0.9379 1.7883
## shrub_cover-Sylvilagus_floridanus 0.4824 0.4024 -0.3096 0.4722 1.2969
## shrub_cover-Sciurus_carolinensis 0.9122 0.4101 0.1359 0.9102 1.7308
## shrub_cover-Vulpes_vulpes 0.1141 0.5473 -1.0141 0.1292 1.1886
## shrub_cover-Sus_scrofa 0.7261 0.7634 -0.7445 0.6958 2.3249
## veg_height-Odocoileus_virginianus -0.3304 0.0687 -0.4683 -0.3296 -0.1962
## veg_height-Canis_latrans -0.5473 0.1821 -0.9194 -0.5423 -0.1947
## veg_height-Sciurus_niger -0.0340 0.3373 -0.6927 -0.0421 0.6482
## veg_height-Procyon_lotor 0.3563 0.1208 0.1207 0.3569 0.5947
## veg_height-Dasypus_novemcinctus 0.2461 0.1361 -0.0151 0.2434 0.5190
## veg_height-Lynx_rufus 0.1477 0.2317 -0.3113 0.1485 0.5972
## veg_height-Didelphis_virginiana 0.4242 0.2398 -0.0252 0.4137 0.9106
## veg_height-Sylvilagus_floridanus 0.1337 0.2477 -0.3478 0.1274 0.6245
## veg_height-Sciurus_carolinensis 0.0999 0.2154 -0.3018 0.0957 0.5318
## veg_height-Vulpes_vulpes -0.1685 0.3174 -0.8284 -0.1565 0.4379
## veg_height-Sus_scrofa -0.1554 0.3270 -0.8294 -0.1476 0.4749
## week-Odocoileus_virginianus 1.3077 0.1242 1.0650 1.3084 1.5504
## week-Canis_latrans 0.5960 0.2634 0.1014 0.5887 1.1168
## week-Sciurus_niger -0.3929 0.5500 -1.6228 -0.3473 0.5396
## week-Procyon_lotor 0.2074 0.2124 -0.2065 0.2096 0.6251
## week-Dasypus_novemcinctus 0.1126 0.2268 -0.3298 0.1152 0.5507
## week-Lynx_rufus 0.3828 0.3547 -0.3035 0.3789 1.1005
## week-Didelphis_virginiana 0.0744 0.3689 -0.6835 0.0790 0.7778
## week-Sylvilagus_floridanus 0.0689 0.3457 -0.6247 0.0738 0.7288
## week-Sciurus_carolinensis 0.8096 0.3709 0.1224 0.7953 1.5910
## week-Vulpes_vulpes 0.1996 0.5267 -0.9188 0.2138 1.1953
## week-Sus_scrofa 0.6943 0.4542 -0.1553 0.6813 1.6346
## I(week^2)-Odocoileus_virginianus -0.5390 0.0515 -0.6394 -0.5391 -0.4355
## I(week^2)-Canis_latrans -0.2438 0.1096 -0.4700 -0.2423 -0.0301
## I(week^2)-Sciurus_niger -0.2742 0.2293 -0.7393 -0.2651 0.1482
## I(week^2)-Procyon_lotor -0.1333 0.0925 -0.3168 -0.1338 0.0473
## I(week^2)-Dasypus_novemcinctus -0.1823 0.1041 -0.3904 -0.1799 0.0196
## I(week^2)-Lynx_rufus -0.2376 0.1533 -0.5515 -0.2378 0.0482
## I(week^2)-Didelphis_virginiana -0.4133 0.2099 -0.8838 -0.3936 -0.0616
## I(week^2)-Sylvilagus_floridanus -0.1801 0.1611 -0.5142 -0.1750 0.1285
## I(week^2)-Sciurus_carolinensis -0.2877 0.1451 -0.5835 -0.2838 -0.0081
## I(week^2)-Vulpes_vulpes -0.4019 0.2409 -0.9208 -0.3867 0.0271
## I(week^2)-Sus_scrofa -0.2441 0.1812 -0.6101 -0.2413 0.1038
## Rhat ESS
## (Intercept)-Odocoileus_virginianus 1.0004 5250
## (Intercept)-Canis_latrans 1.0032 2505
## (Intercept)-Sciurus_niger 1.0306 470
## (Intercept)-Procyon_lotor 1.0022 3856
## (Intercept)-Dasypus_novemcinctus 0.9998 3298
## (Intercept)-Lynx_rufus 1.0011 573
## (Intercept)-Didelphis_virginiana 1.0033 1518
## (Intercept)-Sylvilagus_floridanus 1.0023 2144
## (Intercept)-Sciurus_carolinensis 1.0016 1651
## (Intercept)-Vulpes_vulpes 1.0292 298
## (Intercept)-Sus_scrofa 1.0018 976
## shrub_cover-Odocoileus_virginianus 1.0003 5250
## shrub_cover-Canis_latrans 1.0047 1957
## shrub_cover-Sciurus_niger 1.0088 866
## shrub_cover-Procyon_lotor 1.0008 3673
## shrub_cover-Dasypus_novemcinctus 1.0032 2341
## shrub_cover-Lynx_rufus 1.0044 817
## shrub_cover-Didelphis_virginiana 1.0016 1111
## shrub_cover-Sylvilagus_floridanus 1.0019 1417
## shrub_cover-Sciurus_carolinensis 1.0038 1272
## shrub_cover-Vulpes_vulpes 1.0091 1524
## shrub_cover-Sus_scrofa 1.0056 1007
## veg_height-Odocoileus_virginianus 1.0000 5250
## veg_height-Canis_latrans 1.0087 2129
## veg_height-Sciurus_niger 1.0076 1044
## veg_height-Procyon_lotor 1.0010 3775
## veg_height-Dasypus_novemcinctus 1.0017 4152
## veg_height-Lynx_rufus 1.0047 1883
## veg_height-Didelphis_virginiana 1.0002 2696
## veg_height-Sylvilagus_floridanus 1.0015 2333
## veg_height-Sciurus_carolinensis 1.0000 2918
## veg_height-Vulpes_vulpes 1.0104 1900
## veg_height-Sus_scrofa 1.0019 2836
## week-Odocoileus_virginianus 1.0002 5017
## week-Canis_latrans 1.0004 3644
## week-Sciurus_niger 1.0656 585
## week-Procyon_lotor 1.0038 4243
## week-Dasypus_novemcinctus 1.0003 4701
## week-Lynx_rufus 1.0013 2552
## week-Didelphis_virginiana 1.0046 2482
## week-Sylvilagus_floridanus 1.0029 2807
## week-Sciurus_carolinensis 1.0004 3801
## week-Vulpes_vulpes 1.0008 1535
## week-Sus_scrofa 1.0001 3627
## I(week^2)-Odocoileus_virginianus 1.0001 5250
## I(week^2)-Canis_latrans 1.0007 3598
## I(week^2)-Sciurus_niger 1.0064 983
## I(week^2)-Procyon_lotor 1.0014 3999
## I(week^2)-Dasypus_novemcinctus 1.0031 3788
## I(week^2)-Lynx_rufus 1.0014 2360
## I(week^2)-Didelphis_virginiana 1.0009 1808
## I(week^2)-Sylvilagus_floridanus 1.0017 2419
## I(week^2)-Sciurus_carolinensis 0.9998 4284
## I(week^2)-Vulpes_vulpes 1.0017 1383
## I(week^2)-Sus_scrofa 1.0006 3998
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$beta.samples)
## 'mcmc' num [1:5250, 1:99] 3.3 1.89 5 7 8.31 ...
## - attr(*, "mcpar")= num [1:3] 1 5250 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:99] "(Intercept)-Odocoileus_virginianus" "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.005142857
MCMCplot(ms_fullQ_fullQ$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$alpha.samples[, param_names],
ref_ovl = TRUE,
ci = c(50, 95),
main = paste0("Detection Parameters: ", i, " to ", end))
dev.off()
}
CameraData <- CameraData%>%
dplyr::select(-Status)
O2_data <- CameraData %>%
left_join(CameraLoc_O2, by = "Plot")
# Creating proportions of observations for each behavior type
behavior_proportions <- O2_data %>%
group_by(Plot, Name, Behavior, Status, Camera.Type, BehLoc) %>%
summarise(ObservationCount = n()) %>%
ungroup() %>%
group_by(Plot, Name, Status, Camera.Type, BehLoc) %>%
mutate(Proportion = ObservationCount / sum(ObservationCount)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot', 'Name', 'Behavior', 'Status',
## 'Camera.Type'. You can override using the `.groups` argument.
This model is out of data, compared to the one below, because I wanted local search to be the reference variable. That is because local search was a catch-all for non-foraging or transit behaviors.
#formula <- bf(Proportion ~ Status * Behavior + Status * Name + (1 | Plot),
# family = Beta(link = "logit"))
# Adjust Proportion for Beta distribution
#n <- nrow(behavior_proportions)
#behavior_proportions$Proportion <- (behavior_proportions$Proportion * (n - 1) + 0.5) / n
# Fit the model
#brms_model <- brm(formula = formula,
# data = behavior_proportions,
# iter = 4000, warmup = 1000, chains = 4, cores = 4,
# control = list(adapt_delta = 0.95))
# Diagnostic Plots
#plot(brms_model)
# Summary Statistics
#summary(brms_model)
Behavior Model- Switched to local search and dasypus novemcinctus being the reference variables
# Ensure Behavior is an unordered factor
if(!is.factor(behavior_proportions$Behavior) || is.ordered(behavior_proportions$Behavior)) {
behavior_proportions$Behavior <- factor(behavior_proportions$Behavior, ordered = FALSE)
}
# Make Local_Search the reference category
behavior_proportions$Behavior <- relevel(behavior_proportions$Behavior, ref = "Local_Search")
# Ensure Name is an unordered factor
if(!is.factor(behavior_proportions$Name) || is.ordered(behavior_proportions$Name)) {
behavior_proportions$Name <- factor(behavior_proportions$Name, ordered = FALSE)
}
# Make Dasypus_novemcinctus the reference category
behavior_proportions$Name <- relevel(behavior_proportions$Name, ref = "Dasypus_novemcinctus")
# Ensure Status is an unordered factor
if(!is.factor(behavior_proportions$Status) || is.ordered(behavior_proportions$Status)) {
behavior_proportions$Status <- factor(behavior_proportions$Status, ordered = FALSE)
}
# Make "Non_Invaded" the reference category
behavior_proportions$Status <- relevel(behavior_proportions$Status, ref = "Non_Invaded")
# Adjust Proportion for Beta distribution
n <- nrow(behavior_proportions)
behavior_proportions$Proportion <- (behavior_proportions$Proportion * (n - 1) + 0.5) / n
# Fit the Bayesian GLMM with new reference levels
brms_model_releveled <- brm(
formula = bf(Proportion ~ Status * Behavior + Status * Name + (1 | Plot),
family = Beta(link = "logit")),
data = behavior_proportions,
iter = 10000, warmup = 3000, chains = 4, cores = 4,
control = list(adapt_delta = 0.95)
)
## Compiling Stan program...
## Start sampling
plot(brms_model_releveled)
# Summary
summary(brms_model_releveled)
## Family: beta
## Links: mu = logit; phi = identity
## Formula: Proportion ~ Status * Behavior + Status * Name + (1 | Plot)
## Data: behavior_proportions (Number of observations: 323)
## Draws: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~Plot (Number of levels: 32)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.19 0.11 0.01 0.42 1.00 6538 8122
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI
## Intercept -0.02 0.43 -0.84 0.84
## StatusInvaded 0.02 0.52 -1.00 1.04
## BehaviorForaging 0.58 0.30 0.00 1.16
## BehaviorTransit 1.32 0.27 0.80 1.84
## NameCanis_latrans 0.17 0.47 -0.77 1.08
## NameDidelphis_virginiana 0.67 0.70 -0.66 2.10
## NameLynx_rufus 1.14 0.63 -0.06 2.39
## NameOdocoileus_virginianus -1.02 0.40 -1.84 -0.26
## NameProcyon_lotor 0.37 0.47 -0.56 1.27
## NameSciurus_carolinensis -0.15 0.65 -1.43 1.13
## NameSciurus_niger 1.50 0.78 0.05 3.11
## NameSus_scrofa 0.12 0.63 -1.09 1.38
## NameSylvilagus_floridanus 1.11 0.53 0.08 2.16
## NameVulpes_vulpes 1.28 0.92 -0.37 3.27
## StatusInvaded:BehaviorForaging -0.70 0.38 -1.44 0.05
## StatusInvaded:BehaviorTransit 0.03 0.33 -0.61 0.68
## StatusInvaded:NameCanis_latrans 1.03 0.63 -0.19 2.27
## StatusInvaded:NameDidelphis_virginiana 0.02 0.82 -1.62 1.60
## StatusInvaded:NameLynx_rufus 0.11 0.79 -1.46 1.66
## StatusInvaded:NameOdocoileus_virginianus 0.38 0.48 -0.55 1.33
## StatusInvaded:NameProcyon_lotor 0.13 0.56 -0.98 1.25
## StatusInvaded:NameSciurus_carolinensis 0.11 0.78 -1.44 1.65
## StatusInvaded:NameSciurus_niger -0.99 1.01 -3.00 0.95
## StatusInvaded:NameSus_scrofa 1.63 1.54 -0.96 5.15
## StatusInvaded:NameSylvilagus_floridanus 0.14 0.69 -1.21 1.48
## StatusInvaded:NameVulpes_vulpes 0.27 1.65 -2.70 3.84
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 4600 7845
## StatusInvaded 1.00 4603 7952
## BehaviorForaging 1.00 13454 19291
## BehaviorTransit 1.00 14608 19504
## NameCanis_latrans 1.00 5536 10161
## NameDidelphis_virginiana 1.00 8408 14518
## NameLynx_rufus 1.00 7918 13089
## NameOdocoileus_virginianus 1.00 4594 8458
## NameProcyon_lotor 1.00 5453 8953
## NameSciurus_carolinensis 1.00 7855 13517
## NameSciurus_niger 1.00 9604 16223
## NameSus_scrofa 1.00 8459 14427
## NameSylvilagus_floridanus 1.00 7046 11367
## NameVulpes_vulpes 1.00 13508 17550
## StatusInvaded:BehaviorForaging 1.00 13588 18960
## StatusInvaded:BehaviorTransit 1.00 14301 19628
## StatusInvaded:NameCanis_latrans 1.00 6451 11972
## StatusInvaded:NameDidelphis_virginiana 1.00 7842 12898
## StatusInvaded:NameLynx_rufus 1.00 8387 14058
## StatusInvaded:NameOdocoileus_virginianus 1.00 4706 8169
## StatusInvaded:NameProcyon_lotor 1.00 5543 9369
## StatusInvaded:NameSciurus_carolinensis 1.00 8359 14687
## StatusInvaded:NameSciurus_niger 1.00 10874 16076
## StatusInvaded:NameSus_scrofa 1.00 16620 15825
## StatusInvaded:NameSylvilagus_floridanus 1.00 7699 12175
## StatusInvaded:NameVulpes_vulpes 1.00 17888 17130
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 1.89 0.15 1.61 2.20 1.00 23309 21410
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
# Conditional Effects for interactions
ce_behavior <- conditional_effects(brms_model_releveled, effects = "Status:Behavior")
plot(ce_behavior)
ce_name <- conditional_effects(brms_model_releveled, effects = "Status:Name")
plot(ce_name)
# Filter data for invaded status
invaded_data <- behavior_proportions %>%
filter(Status == "Invaded")
# Adjust Proportion for Beta distribution
n <- nrow(invaded_data)
invaded_data$Proportion <- (invaded_data$Proportion * (n - 1) + 0.5) / n
Loc_model <- brm(
Proportion ~ BehLoc * Behavior + BehLoc * Name + (1 | Plot) + (1 | Camera.Type),
data = invaded_data,
family = Beta(link = "logit"),
iter = 10000,
warmup = 3000,
chains = 4,
cores = 4,
control = list(adapt_delta = 0.99, max_treedepth = 15)
)
## Compiling Stan program...
## Start sampling
## Warning: There were 1 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: There were 27999 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 15. See
## https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
## Warning: Examine the pairs() plot to diagnose sampling problems
## Warning: The largest R-hat is 2.99, indicating chains have not mixed.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#r-hat
## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#bulk-ess
## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## https://mc-stan.org/misc/warnings.html#tail-ess
# Summarize model
summary(Loc_model)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be
## careful when analysing the results! We recommend running more iterations and/or
## setting stronger priors.
## Warning: There were 1 divergent transitions after warmup. Increasing
## adapt_delta above 0.99 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: beta
## Links: mu = logit; phi = identity
## Formula: Proportion ~ BehLoc * Behavior + BehLoc * Name + (1 | Plot) + (1 | Camera.Type)
## Data: invaded_data (Number of observations: 193)
## Draws: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~Camera.Type (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.61 0.33 0.15 1.36 1.18 18 75
##
## ~Plot (Number of levels: 17)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.43 0.20 0.13 0.89 1.27 13 40
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI
## Intercept -0.02 0.41 -0.75
## BehLocPatch -0.35 0.53 -1.27
## BehaviorForaging 0.23 0.28 -0.31
## BehaviorTransit 0.66 0.27 0.03
## NameCanis_latrans 1.48 0.48 0.52
## NameDidelphis_virginiana 0.82 0.46 -0.08
## NameLynx_rufus 1.89 0.47 1.18
## NameOdocoileus_virginianus -0.78 0.28 -1.28
## NameProcyon_lotor 0.54 0.34 -0.08
## NameSciurus_carolinensis 0.05 0.37 -0.69
## NameSciurus_niger 0.45 0.68 -0.95
## NameSus_scrofa -163517.21 411291.23 -972432.68
## NameSylvilagus_floridanus 1.34 0.42 0.47
## NameVulpes_vulpes 2.84 1.61 0.38
## BehLocPatch:BehaviorForaging -0.98 0.54 -2.11
## BehLocPatch:BehaviorTransit 2.08 0.48 1.26
## BehLocPatch:NameCanis_latrans -1.11 0.71 -2.54
## BehLocPatch:NameDidelphis_virginiana -0.55 0.84 -2.54
## BehLocPatch:NameLynx_rufus -1.23 1.33 -3.50
## BehLocPatch:NameOdocoileus_virginianus -0.01 0.50 -0.97
## BehLocPatch:NameProcyon_lotor -0.52 0.67 -1.89
## BehLocPatch:NameSciurus_carolinensis 1.26 1.43 -1.25
## BehLocPatch:NameSciurus_niger 0.75 1.99 -2.64
## BehLocPatch:NameSus_scrofa 163518.52 411291.16 -521882.80
## BehLocPatch:NameSylvilagus_floridanus -0.79 0.80 -2.34
## BehLocPatch:NameVulpes_vulpes 221498769.57 416566857.87 -49104752.71
## u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.82 1.10 29 111
## BehLocPatch 0.66 1.22 14 64
## BehaviorForaging 0.76 1.16 19 46
## BehaviorTransit 1.12 1.31 11 15
## NameCanis_latrans 2.41 1.09 43 153
## NameDidelphis_virginiana 1.78 1.07 48 44
## NameLynx_rufus 2.95 1.05 78 81
## NameOdocoileus_virginianus -0.22 1.04 47 160
## NameProcyon_lotor 1.19 1.12 24 106
## NameSciurus_carolinensis 0.75 1.10 65 138
## NameSciurus_niger 1.73 1.08 45 99
## NameSus_scrofa 521882.22 2.88 5 11
## NameSylvilagus_floridanus 2.17 1.09 44 93
## NameVulpes_vulpes 6.83 1.33 10 16
## BehLocPatch:BehaviorForaging -0.04 1.14 23 82
## BehLocPatch:BehaviorTransit 3.08 1.24 14 52
## BehLocPatch:NameCanis_latrans 0.22 1.05 67 136
## BehLocPatch:NameDidelphis_virginiana 1.17 1.13 22 19
## BehLocPatch:NameLynx_rufus 1.81 1.05 40 98
## BehLocPatch:NameOdocoileus_virginianus 1.01 1.16 19 50
## BehLocPatch:NameProcyon_lotor 0.82 1.12 24 50
## BehLocPatch:NameSciurus_carolinensis 4.64 1.11 33 90
## BehLocPatch:NameSciurus_niger 5.28 1.20 16 57
## BehLocPatch:NameSus_scrofa 972435.20 2.88 5 11
## BehLocPatch:NameSylvilagus_floridanus 0.95 1.06 56 61
## BehLocPatch:NameVulpes_vulpes 1177831277.90 2.99 5 14
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## phi 2.85 0.32 2.29 3.53 1.16 18 91
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
# Diagnostics and posterior checks
plot(Loc_model)
pp_check(Loc_model, type = 'dens_overlay')
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
pp_check(Loc_model, type = 'hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pp_check(Loc_model, type = 'boxplot')
## Using 10 posterior draws for ppc type 'boxplot' by default.
pp_check(Loc_model, type = 'intervals')
## Using all posterior draws for ppc type 'intervals' by default.
pp_check(Loc_model, type = 'scatter_avg')
## Using all posterior draws for ppc type 'scatter_avg' by default.
# Compute marginal effects
marginal_effects_data <- conditional_effects(Loc_model)
# Plot marginal effects
plot(marginal_effects_data)
# Obtain predictions
predicted_values <- posterior_epred(Loc_model, newdata = invaded_data)
# Plot predicted vs. observed
plot(invaded_data$Proportion, colMeans(predicted_values))
abline(a = 0, b = 1, col = "red")
# Example interaction plot for BehLoc and Behavior
interaction_plot_data <- conditional_effects(Loc_model, effects = "BehLoc:Behavior")
# Convert to data frame for ggplot
df <- as.data.frame(interaction_plot_data$`BehLoc:Behavior`)
ggplot(df, aes(x = BehLoc, y = estimate__, color = Behavior, group = Behavior)) +
geom_line() +
geom_ribbon(aes(ymin = lower__, ymax = upper__), alpha = 0.2) +
theme_minimal() +
labs(title = "Interaction Plot of BehLoc and Behavior",
y = "Proportion",
x = "BehLoc")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
CameraLoc <- CameraLoc %>%
left_join(avg_cogongrass_cover, by = "Plot")
# Observation per species, per site
site_species_counts <- CameraData %>%
group_by(Plot, Name) %>%
summarize(count = n(), .groups = "drop")
# Shannon Diversity per site
shannon_diversity_wildlife <- site_species_counts %>%
group_by(Plot) %>%
mutate(proportion = count / sum(count)) %>%
summarize(Wild_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
# View results
print(shannon_diversity_wildlife)
## # A tibble: 32 × 2
## Plot Wild_shannon_index
## <chr> <dbl>
## 1 BI201 0.305
## 2 BN211 0.303
## 3 EI100 0.292
## 4 EI102 0.412
## 5 EI104 0.310
## 6 EI106 0.446
## 7 EN101 0.117
## 8 EN103 0.194
## 9 EN107 0.861
## 10 JI07 0.764
## # ℹ 22 more rows
# Merge wildlife and vegetation diversity indices with invasion data
diversity_data <- shannon_diversity_wildlife %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(CameraLoc, by = "Plot")
# View merged data
print(diversity_data)
## # A tibble: 32 × 14
## Plot Wild_shannon_index Veg_shannon_index Lat Long Status
## <chr> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 BI201 0.305 2.70 30.8 -86.9 Invaded
## 2 BN211 0.303 2.43 30.8 -86.9 Non_Invaded
## 3 EI100 0.292 2.72 31.0 -87.1 Invaded
## 4 EI102 0.412 2.15 31.0 -87.1 Invaded
## 5 EI104 0.310 2.90 31.0 -87.1 Invaded
## 6 EI106 0.446 1.63 31.0 -87.1 Invaded
## 7 EN101 0.117 2.07 31.0 -87.1 Non_Invaded
## 8 EN103 0.194 2.43 31.0 -87.1 Non_Invaded
## 9 EN107 0.861 2.69 31.0 -87.1 Non_Invaded
## 10 JI07 0.764 2.94 30.8 -87.1 Invaded
## # ℹ 22 more rows
## # ℹ 8 more variables: Start_Date <dttm>, Camera <chr>, Cogon_Patch_Size <dbl>,
## # VegetationDiversity <dbl>, PostTreatmentDensities <dbl>, Authority <chr>,
## # Auth <dbl>, Avg_Cogongrass_Cover <dbl>
summary(diversity_data)
## Plot Wild_shannon_index Veg_shannon_index Lat
## Length:32 Min. :0.04539 Min. :1.183 Min. :28.68
## Class :character 1st Qu.:0.29022 1st Qu.:2.230 1st Qu.:30.76
## Mode :character Median :0.44221 Median :2.446 Median :30.77
## Mean :0.56021 Mean :2.411 Mean :30.45
## 3rd Qu.:0.75653 3rd Qu.:2.713 3rd Qu.:30.90
## Max. :1.72721 Max. :3.160 Max. :31.01
## Long Status Start_Date
## Min. :-87.15 Length:32 Min. :2024-05-09 00:00:00
## 1st Qu.:-87.14 Class :character 1st Qu.:2024-06-03 18:00:00
## Median :-87.09 Mode :character Median :2024-06-06 12:00:00
## Mean :-86.21 Mean :2024-06-26 04:30:00
## 3rd Qu.:-86.89 3rd Qu.:2024-06-27 00:00:00
## Max. :-82.41 Max. :2024-11-04 00:00:00
## Camera Cogon_Patch_Size VegetationDiversity
## Length:32 Min. : 0.00 Min. :11.00
## Class :character 1st Qu.: 0.00 1st Qu.:18.00
## Mode :character Median : 30.07 Median :20.50
## Mean : 458.39 Mean :21.88
## 3rd Qu.: 233.84 3rd Qu.:25.00
## Max. :4168.92 Max. :42.00
## PostTreatmentDensities Authority Auth Avg_Cogongrass_Cover
## Min. :0.0000 Length:32 Min. :1.000 Min. : 0.000
## 1st Qu.:0.0000 Class :character 1st Qu.:2.000 1st Qu.: 0.000
## Median :0.0000 Mode :character Median :3.000 Median : 2.143
## Mean :0.8978 Mean :2.844 Mean :13.683
## 3rd Qu.:1.4650 3rd Qu.:3.000 3rd Qu.:20.089
## Max. :3.7100 Max. :4.000 Max. :63.571
diversity_data <- diversity_data %>%
mutate(Status = as.factor(Status))
# Standardizing the continuous variables
diversity_data <- diversity_data %>%
mutate(
Avg_Cogongrass_Cover = scale(Avg_Cogongrass_Cover),
Veg_shannon_index = scale(Veg_shannon_index),
Cogon_Patch_Size = scale(Cogon_Patch_Size)
)
glmm_model <- lmer(
Wild_shannon_index ~ Avg_Cogongrass_Cover * Veg_shannon_index + Cogon_Patch_Size +
(1 | Authority) + (1 | Camera),
data = diversity_data
)
# Summarize the model
summary(glmm_model)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Wild_shannon_index ~ Avg_Cogongrass_Cover * Veg_shannon_index +
## Cogon_Patch_Size + (1 | Authority) + (1 | Camera)
## Data: diversity_data
##
## REML criterion at convergence: 44.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4432 -0.5454 -0.2552 0.3598 2.7706
##
## Random effects:
## Groups Name Variance Std.Dev.
## Authority (Intercept) 0.006203 0.07876
## Camera (Intercept) 0.010002 0.10001
## Residual 0.151297 0.38897
## Number of obs: 32, groups: Authority, 5; Camera, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.54514 0.10252 2.49580 5.317
## Avg_Cogongrass_Cover 0.04079 0.09692 25.48601 0.421
## Veg_shannon_index 0.04254 0.08810 19.36180 0.483
## Cogon_Patch_Size -0.05781 0.09147 23.50451 -0.632
## Avg_Cogongrass_Cover:Veg_shannon_index -0.10232 0.06567 20.69791 -1.558
## Pr(>|t|)
## (Intercept) 0.0204 *
## Avg_Cogongrass_Cover 0.6774
## Veg_shannon_index 0.6346
## Cogon_Patch_Size 0.5335
## Avg_Cogongrass_Cover:Veg_shannon_index 0.1344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Av_C_C Vg_sh_ Cg_P_S
## Avg_Cgngr_C 0.066
## Vg_shnnn_nd -0.099 0.221
## Cgn_Ptch_Sz -0.005 -0.594 -0.215
## Avg_C_C:V__ 0.204 0.414 -0.375 -0.321
# Extract the fixed effects
fixed_effects <- fixef(glmm_model)
# Extract the random effects
random_effects <- ranef(glmm_model)
# Plot the fixed effects
plot_model(glmm_model, type = "pred", terms = c("Avg_Cogongrass_Cover", "Veg_shannon_index"))
# Plot the random effects
plot_model(glmm_model, type = "re")
## [[1]]
##
## [[2]]
bayesian_model <- brm(
Wild_shannon_index ~ scale(Avg_Cogongrass_Cover) * scale(Veg_shannon_index) + scale(Cogon_Patch_Size) +
(1 | Authority) + (1 | Camera),
data = diversity_data,
family = gaussian(),
chains = 4,
iter = 10000,
warmup = 3000,
cores = parallel::detectCores(),
control = list(adapt_delta = 0.99, max_treedepth = 15), # Further tuned parameters
prior = c(
prior(normal(0, 1), class = "b"), # Regularizing prior for coefficients
prior(cauchy(0, 1), class = "Intercept"), # Weakly informative cauchy prior for intercept
prior(cauchy(0, 1), class = "sd") # For random effect standard deviations
)
)
## Compiling Stan program...
## Start sampling
## Warning: There were 9 divergent transitions after warmup. See
## https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.
## Warning: Examine the pairs() plot to diagnose sampling problems
summary(bayesian_model)
## Warning: There were 9 divergent transitions after warmup. Increasing
## adapt_delta above 0.99 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: Wild_shannon_index ~ scale(Avg_Cogongrass_Cover) * scale(Veg_shannon_index) + scale(Cogon_Patch_Size) + (1 | Authority) + (1 | Camera)
## Data: diversity_data (Number of observations: 32)
## Draws: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup draws = 28000
##
## Multilevel Hyperparameters:
## ~Authority (Number of levels: 5)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.20 0.17 0.01 0.64 1.00 10132 12670
##
## ~Camera (Number of levels: 4)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.22 0.21 0.01 0.79 1.00 9455 12044
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI
## Intercept 0.51 0.21 0.06
## scaleAvg_Cogongrass_Cover 0.05 0.11 -0.15
## scaleVeg_shannon_index 0.04 0.10 -0.15
## scaleCogon_Patch_Size -0.07 0.10 -0.27
## scaleAvg_Cogongrass_Cover:scaleVeg_shannon_index -0.10 0.07 -0.24
## u-95% CI Rhat Bulk_ESS
## Intercept 0.91 1.00 11666
## scaleAvg_Cogongrass_Cover 0.27 1.00 17174
## scaleVeg_shannon_index 0.24 1.00 22196
## scaleCogon_Patch_Size 0.13 1.00 17396
## scaleAvg_Cogongrass_Cover:scaleVeg_shannon_index 0.05 1.00 19306
## Tail_ESS
## Intercept 8653
## scaleAvg_Cogongrass_Cover 17875
## scaleVeg_shannon_index 19408
## scaleCogon_Patch_Size 18892
## scaleAvg_Cogongrass_Cover:scaleVeg_shannon_index 18484
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.41 0.06 0.31 0.55 1.00 21872 18820
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
plot(bayesian_model)
# Plot the fixed effects
plot(bayesian_model, "Avg_Cogongrass_Cover")
## Warning: Argument 'pars' is deprecated. Please use 'variable' instead.
# Plot the random effects
plot(bayesian_model, "Camera")
## Warning: Argument 'pars' is deprecated. Please use 'variable' instead.
plot(bayesian_model, "Authority")
## Warning: Argument 'pars' is deprecated. Please use 'variable' instead.
# Get unique levels of Authority and Camera from original data
authority_levels <- levels(as.factor(diversity_data$Authority))
camera_levels <- levels(as.factor(diversity_data$Camera))
# Create a new data frame for predictions
new_data <- expand.grid(
Avg_Cogongrass_Cover = seq(min(diversity_data$Avg_Cogongrass_Cover), max(diversity_data$Avg_Cogongrass_Cover), length.out = 100),
Veg_shannon_index = quantile(diversity_data$Veg_shannon_index, probs = c(0.25, 0.5, 0.75)),
Cogon_Patch_Size = mean(diversity_data$Cogon_Patch_Size), # Use mean value for this predictor
Authority = authority_levels[1], # Assign a representative level for Authority
Camera = camera_levels[1] # Assign a representative level for Camera
)
# Repeat new_data to match for each combination of Camera levels
new_data <- as.data.frame(new_data)
new_data <- new_data[rep(seq_len(nrow(new_data)), each = length(authority_levels) * length(camera_levels)), ]
new_data$Authority <- rep(authority_levels, each = 100 * length(camera_levels))
new_data$Camera <- rep(camera_levels, times = 100 * length(authority_levels))
# Generate predictions including random effects
predictions <- fitted(bayesian_model, newdata = new_data, re_formula = NULL)
new_data$Wild_shannon_index <- rowMeans(predictions)
ggplot(new_data, aes(x = Avg_Cogongrass_Cover, y = Wild_shannon_index, color = factor(Veg_shannon_index))) +
geom_line() +
labs(x = "Average Cogongrass Cover", y = "Wild Shannon Index", color = "Vegetation Shannon Index") +
theme_minimal() +
ggtitle("Effect of Cogongrass Cover and Veg. Shannon Index on Wild Shannon Index")