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: 206 × 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 196 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$Avg_Canopy_Cover)
## [1] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter((Class == "Mammalia" & Name != "Odocoileus_virginianus")) %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 2) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$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 10
# 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, 1:10] 0 0 0 0 0 0 0 0 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 [1:10] "Canis_latrans" "Sciurus_niger" "Procyon_lotor" "Dasypus_novemcinctus" ...
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:10, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:10] "Canis_latrans" "Sciurus_niger" "Procyon_lotor" "Dasypus_novemcinctus" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
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## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
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## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5295
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6611 0.3454 -1.3382 -0.6647 0.0447 1.0154 1009
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.951 0.7461 0.1933 0.7463 3.0132 1.02 1072
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.74 0.2757 -3.2928 -2.7343 -2.1891 1.0237 1576
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7125 0.5392 0.182 0.5662 2.0942 1.0179 694
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1549 0.3872 -0.5797 0.1466 0.9304 1.0001
## (Intercept)-Sciurus_niger -0.9625 0.5960 -2.0674 -0.9864 0.3167 1.0390
## (Intercept)-Procyon_lotor 0.4972 0.3846 -0.1909 0.4790 1.3033 1.0056
## (Intercept)-Dasypus_novemcinctus -0.6678 0.3427 -1.3736 -0.6596 -0.0051 1.0003
## (Intercept)-Lynx_rufus -0.1110 0.5744 -1.0307 -0.1686 1.1947 1.0027
## (Intercept)-Didelphis_virginiana -1.2645 0.4112 -2.1282 -1.2497 -0.5105 1.0014
## (Intercept)-Sylvilagus_floridanus -0.4407 0.4520 -1.2810 -0.4581 0.5145 1.0035
## (Intercept)-Sciurus_carolinensis -1.2575 0.4207 -2.1052 -1.2457 -0.4924 1.0020
## (Intercept)-Vulpes_vulpes -1.2790 0.6972 -2.5965 -1.3000 0.1488 1.0865
## (Intercept)-Sus_scrofa -1.6205 0.5541 -2.7914 -1.6072 -0.5877 1.0432
## ESS
## (Intercept)-Canis_latrans 2114
## (Intercept)-Sciurus_niger 397
## (Intercept)-Procyon_lotor 1855
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 463
## (Intercept)-Didelphis_virginiana 2023
## (Intercept)-Sylvilagus_floridanus 1158
## (Intercept)-Sciurus_carolinensis 2370
## (Intercept)-Vulpes_vulpes 332
## (Intercept)-Sus_scrofa 969
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6091 0.1683 -2.9646 -2.6023 -2.2975 1.0085
## (Intercept)-Sciurus_niger -3.5634 0.4858 -4.6429 -3.5289 -2.7121 1.0735
## (Intercept)-Procyon_lotor -2.2682 0.1240 -2.5174 -2.2668 -2.0336 1.0052
## (Intercept)-Dasypus_novemcinctus -1.6037 0.1343 -1.8692 -1.6017 -1.3448 1.0003
## (Intercept)-Lynx_rufus -3.4100 0.3118 -4.0628 -3.3934 -2.8478 1.0027
## (Intercept)-Didelphis_virginiana -2.3650 0.2448 -2.8536 -2.3600 -1.9142 1.0105
## (Intercept)-Sylvilagus_floridanus -3.1233 0.2874 -3.7041 -3.1214 -2.5831 1.0047
## (Intercept)-Sciurus_carolinensis -2.4655 0.2587 -3.0138 -2.4544 -1.9914 1.0086
## (Intercept)-Vulpes_vulpes -3.6861 0.5980 -4.9042 -3.6627 -2.6146 1.1245
## (Intercept)-Sus_scrofa -2.9678 0.4560 -3.9760 -2.9335 -2.1860 1.0618
## ESS
## (Intercept)-Canis_latrans 1066
## (Intercept)-Sciurus_niger 285
## (Intercept)-Procyon_lotor 1582
## (Intercept)-Dasypus_novemcinctus 2116
## (Intercept)-Lynx_rufus 348
## (Intercept)-Didelphis_virginiana 1363
## (Intercept)-Sylvilagus_floridanus 546
## (Intercept)-Sciurus_carolinensis 1229
## (Intercept)-Vulpes_vulpes 269
## (Intercept)-Sus_scrofa 599
# Includes all covariates of detection and occupancy
ms_full_full <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8802
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1100 0.7007 -2.5284 -1.1098 0.2613 1.0451 313
## Cogon_Patch_Size -0.8632 0.6632 -2.1983 -0.8481 0.4417 1.0191 338
## Veg_shannon_index 0.9646 0.4767 0.0746 0.9379 1.9913 1.0714 233
## total_shrub_cover -0.5545 0.4527 -1.5104 -0.5295 0.2808 1.0292 417
## Avg_Cogongrass_Cover 2.1760 0.8133 0.6754 2.1225 3.9101 1.0097 124
## Tree_Density -1.9697 0.7714 -3.5880 -1.9284 -0.5098 1.0178 276
## Avg_Canopy_Cover 1.9024 0.6219 0.7986 1.8582 3.2316 1.0382 204
## avg_veg_height -0.5505 0.4840 -1.5405 -0.5518 0.3441 1.0248 162
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3957 3.9932 0.1028 2.2026 13.1838 1.1050 133
## Cogon_Patch_Size 2.6062 4.4064 0.1290 1.3813 12.1493 1.1713 180
## Veg_shannon_index 0.6724 0.8578 0.0521 0.3918 3.0476 1.0328 544
## total_shrub_cover 0.7192 0.9210 0.0552 0.4224 3.2282 1.0736 284
## Avg_Cogongrass_Cover 1.0983 1.7889 0.0511 0.5086 5.8556 1.1257 364
## Tree_Density 2.8940 5.3932 0.0857 1.3800 14.8380 1.1818 194
## Avg_Canopy_Cover 2.1799 2.4667 0.1468 1.4404 8.7862 1.0492 271
## avg_veg_height 0.3914 0.5654 0.0396 0.2243 1.6737 1.0265 682
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2553 2.3399 0.134 1.5186 8.8441 1.1599 82
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9980 0.3319 -3.6691 -2.9963 -2.3510 1.0067 1835
## shrub_cover 0.4248 0.2732 -0.1113 0.4206 0.9721 1.0072 853
## veg_height 0.0698 0.1690 -0.2655 0.0705 0.3975 1.0096 1163
## week -0.0900 0.1282 -0.3603 -0.0879 0.1469 1.0089 937
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0712 0.7875 0.2617 0.8672 3.0844 1.0039 514
## shrub_cover 0.5214 0.4568 0.1007 0.4030 1.6553 1.0306 549
## veg_height 0.2075 0.1579 0.0511 0.1673 0.6344 1.0044 1468
## week 0.1064 0.0942 0.0240 0.0789 0.3455 1.0324 753
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1818 1.0464 -1.7601 0.1577
## (Intercept)-Sciurus_niger -0.3236 1.4156 -2.7617 -0.5133
## (Intercept)-Procyon_lotor 0.2093 1.0747 -1.9739 0.2450
## (Intercept)-Dasypus_novemcinctus -1.4034 0.8858 -3.3035 -1.3408
## (Intercept)-Lynx_rufus -0.0560 1.5142 -2.4888 -0.2489
## (Intercept)-Didelphis_virginiana -2.3619 1.1500 -5.0131 -2.2465
## (Intercept)-Sylvilagus_floridanus -1.3377 1.0917 -3.6949 -1.2599
## (Intercept)-Sciurus_carolinensis -2.3454 1.2078 -5.1283 -2.2215
## (Intercept)-Vulpes_vulpes -1.8682 1.4981 -5.0636 -1.7554
## (Intercept)-Sus_scrofa -2.8860 1.6027 -6.5417 -2.7179
## Cogon_Patch_Size-Canis_latrans 0.4374 1.0513 -1.1292 0.2618
## Cogon_Patch_Size-Sciurus_niger -1.6004 1.6874 -5.2640 -1.3867
## Cogon_Patch_Size-Procyon_lotor -1.1756 0.7240 -2.6750 -1.1404
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6838 0.7613 -2.1320 -0.7121
## Cogon_Patch_Size-Lynx_rufus -1.1496 1.2131 -3.3809 -1.1659
## Cogon_Patch_Size-Didelphis_virginiana 0.6491 0.9162 -0.8524 0.5492
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9938 1.5632 -5.8782 -1.6863
## Cogon_Patch_Size-Sciurus_carolinensis -1.6915 1.2476 -4.6812 -1.4892
## Cogon_Patch_Size-Vulpes_vulpes -1.2760 1.5927 -4.4477 -1.1711
## Cogon_Patch_Size-Sus_scrofa -1.2600 1.5927 -4.3752 -1.0793
## Veg_shannon_index-Canis_latrans 1.3456 0.6707 0.2049 1.2899
## Veg_shannon_index-Sciurus_niger 1.1001 0.8281 -0.4557 1.0603
## Veg_shannon_index-Procyon_lotor 1.2939 0.6131 0.2460 1.2497
## Veg_shannon_index-Dasypus_novemcinctus 0.7017 0.5607 -0.4051 0.7095
## Veg_shannon_index-Lynx_rufus 0.9728 0.7780 -0.5932 0.9450
## Veg_shannon_index-Didelphis_virginiana 1.1468 0.6603 -0.0357 1.0900
## Veg_shannon_index-Sylvilagus_floridanus 1.0813 0.6899 -0.2231 1.0510
## Veg_shannon_index-Sciurus_carolinensis 0.3650 0.7422 -1.2937 0.4381
## Veg_shannon_index-Vulpes_vulpes 0.5227 0.7950 -1.2659 0.5879
## Veg_shannon_index-Sus_scrofa 1.4032 0.8239 0.0527 1.3011
## total_shrub_cover-Canis_latrans 0.1777 0.6899 -0.9203 0.0776
## total_shrub_cover-Sciurus_niger -0.7929 0.8020 -2.6213 -0.7015
## total_shrub_cover-Procyon_lotor -0.8984 0.5616 -2.1337 -0.8514
## total_shrub_cover-Dasypus_novemcinctus -0.2481 0.5949 -1.4383 -0.2435
## total_shrub_cover-Lynx_rufus -0.8758 0.8486 -2.9309 -0.7915
## total_shrub_cover-Didelphis_virginiana -0.7486 0.6875 -2.3375 -0.6825
## total_shrub_cover-Sylvilagus_floridanus -0.5968 0.7831 -2.4002 -0.5464
## total_shrub_cover-Sciurus_carolinensis -0.5135 0.7356 -2.1379 -0.4627
## total_shrub_cover-Vulpes_vulpes -0.8090 0.9224 -3.1024 -0.6871
## total_shrub_cover-Sus_scrofa -0.4069 0.8288 -2.1222 -0.4050
## Avg_Cogongrass_Cover-Canis_latrans 2.5209 0.9669 0.8675 2.4585
## Avg_Cogongrass_Cover-Sciurus_niger 1.6260 1.4200 -1.5876 1.7366
## Avg_Cogongrass_Cover-Procyon_lotor 2.3489 0.9467 0.6651 2.2732
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7083 1.0298 0.9299 2.6150
## Avg_Cogongrass_Cover-Lynx_rufus 2.5568 1.0442 0.6638 2.4694
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2391 0.9404 0.5150 2.2011
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5728 1.1123 -0.6483 1.6112
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4178 0.9947 0.6135 2.3652
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6565 1.1183 0.6834 2.5674
## Avg_Cogongrass_Cover-Sus_scrofa 1.9273 1.1492 -0.5393 1.9665
## Tree_Density-Canis_latrans -2.8122 1.3903 -6.2552 -2.5441
## Tree_Density-Sciurus_niger -2.0981 1.5565 -5.5119 -1.9795
## Tree_Density-Procyon_lotor -1.6024 0.7825 -3.1752 -1.6151
## Tree_Density-Dasypus_novemcinctus -3.5759 1.7977 -8.1346 -3.1883
## Tree_Density-Lynx_rufus -0.4513 1.2907 -2.5085 -0.5914
## Tree_Density-Didelphis_virginiana -2.1761 1.1346 -4.7424 -2.0863
## Tree_Density-Sylvilagus_floridanus -2.5370 1.4346 -5.9794 -2.2993
## Tree_Density-Sciurus_carolinensis -2.3192 1.2893 -5.2618 -2.2087
## Tree_Density-Vulpes_vulpes -1.9207 1.4519 -5.1473 -1.9172
## Tree_Density-Sus_scrofa -2.2238 1.4698 -5.7604 -2.0358
## Avg_Canopy_Cover-Canis_latrans 0.3370 0.6339 -0.8816 0.3305
## Avg_Canopy_Cover-Sciurus_niger 2.0042 1.4456 -0.5873 1.8824
## Avg_Canopy_Cover-Procyon_lotor 1.7556 0.7155 0.4690 1.7052
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1633 0.7705 0.9229 2.0768
## Avg_Canopy_Cover-Lynx_rufus 1.0561 1.1942 -0.9720 0.9558
## Avg_Canopy_Cover-Didelphis_virginiana 2.9091 1.0922 1.2413 2.7465
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3451 1.4566 1.2881 3.1269
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6221 1.0878 1.0024 2.4483
## Avg_Canopy_Cover-Vulpes_vulpes 2.2637 1.1730 0.4546 2.0884
## Avg_Canopy_Cover-Sus_scrofa 2.1342 1.0366 0.4863 1.9869
## avg_veg_height-Canis_latrans -0.6070 0.5891 -1.7810 -0.5983
## avg_veg_height-Sciurus_niger -0.7730 0.7916 -2.5562 -0.6986
## avg_veg_height-Procyon_lotor -0.4749 0.5642 -1.6237 -0.4773
## avg_veg_height-Dasypus_novemcinctus -0.3407 0.5981 -1.4635 -0.3449
## avg_veg_height-Lynx_rufus -0.6640 0.7018 -2.1563 -0.6242
## avg_veg_height-Didelphis_virginiana -0.6888 0.6555 -2.1070 -0.6648
## avg_veg_height-Sylvilagus_floridanus -0.7135 0.6718 -2.1866 -0.6894
## avg_veg_height-Sciurus_carolinensis -0.2311 0.6519 -1.4303 -0.2546
## avg_veg_height-Vulpes_vulpes -0.5335 0.7100 -1.9140 -0.5365
## avg_veg_height-Sus_scrofa -0.5755 0.6751 -1.9449 -0.5563
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3169 1.0151 235
## (Intercept)-Sciurus_niger 2.8577 1.0085 202
## (Intercept)-Procyon_lotor 2.3015 1.0204 221
## (Intercept)-Dasypus_novemcinctus 0.1353 1.1607 230
## (Intercept)-Lynx_rufus 3.4401 1.0201 125
## (Intercept)-Didelphis_virginiana -0.4708 1.0405 162
## (Intercept)-Sylvilagus_floridanus 0.6357 1.0436 319
## (Intercept)-Sciurus_carolinensis -0.4269 1.0904 159
## (Intercept)-Vulpes_vulpes 0.8617 1.0720 201
## (Intercept)-Sus_scrofa -0.4589 1.1401 113
## Cogon_Patch_Size-Canis_latrans 2.9066 1.0170 538
## Cogon_Patch_Size-Sciurus_niger 1.0648 1.0128 183
## Cogon_Patch_Size-Procyon_lotor 0.1596 1.0399 251
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9136 1.0065 525
## Cogon_Patch_Size-Lynx_rufus 1.4228 1.0934 336
## Cogon_Patch_Size-Didelphis_virginiana 2.8156 1.0787 461
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0096 1.1321 217
## Cogon_Patch_Size-Sciurus_carolinensis 0.1769 1.0207 291
## Cogon_Patch_Size-Vulpes_vulpes 1.5716 1.0578 291
## Cogon_Patch_Size-Sus_scrofa 0.9372 1.1332 225
## Veg_shannon_index-Canis_latrans 2.8874 1.0611 270
## Veg_shannon_index-Sciurus_niger 2.9569 1.0475 439
## Veg_shannon_index-Procyon_lotor 2.6153 1.0718 226
## Veg_shannon_index-Dasypus_novemcinctus 1.8004 1.0392 375
## Veg_shannon_index-Lynx_rufus 2.6152 1.0294 298
## Veg_shannon_index-Didelphis_virginiana 2.6528 1.0362 450
## Veg_shannon_index-Sylvilagus_floridanus 2.5505 1.0407 394
## Veg_shannon_index-Sciurus_carolinensis 1.6864 1.0099 590
## Veg_shannon_index-Vulpes_vulpes 1.9752 1.0230 575
## Veg_shannon_index-Sus_scrofa 3.4400 1.0401 510
## total_shrub_cover-Canis_latrans 1.9234 1.0338 582
## total_shrub_cover-Sciurus_niger 0.6093 1.0297 421
## total_shrub_cover-Procyon_lotor 0.0620 1.0142 644
## total_shrub_cover-Dasypus_novemcinctus 0.9124 1.0094 888
## total_shrub_cover-Lynx_rufus 0.5364 1.0332 323
## total_shrub_cover-Didelphis_virginiana 0.4397 1.0058 464
## total_shrub_cover-Sylvilagus_floridanus 0.8490 1.0424 499
## total_shrub_cover-Sciurus_carolinensis 0.8579 1.0075 589
## total_shrub_cover-Vulpes_vulpes 0.7045 1.0362 407
## total_shrub_cover-Sus_scrofa 1.3330 1.0266 667
## Avg_Cogongrass_Cover-Canis_latrans 4.5606 1.0227 203
## Avg_Cogongrass_Cover-Sciurus_niger 4.2340 1.0273 172
## Avg_Cogongrass_Cover-Procyon_lotor 4.4736 1.0152 177
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0119 1.0465 204
## Avg_Cogongrass_Cover-Lynx_rufus 4.7377 1.0119 207
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.2409 1.0073 203
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.7758 1.0208 219
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.6016 1.0161 179
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.1838 1.0277 212
## Avg_Cogongrass_Cover-Sus_scrofa 4.1884 1.0171 232
## Tree_Density-Canis_latrans -0.8444 1.0606 190
## Tree_Density-Sciurus_niger 0.8059 1.0865 257
## Tree_Density-Procyon_lotor -0.0788 1.0058 513
## Tree_Density-Dasypus_novemcinctus -1.3075 1.1086 141
## Tree_Density-Lynx_rufus 2.4894 1.0719 220
## Tree_Density-Didelphis_virginiana 0.0204 1.0187 424
## Tree_Density-Sylvilagus_floridanus -0.1739 1.0228 282
## Tree_Density-Sciurus_carolinensis 0.0597 1.0368 392
## Tree_Density-Vulpes_vulpes 0.9995 1.0377 307
## Tree_Density-Sus_scrofa 0.4336 1.0202 234
## Avg_Canopy_Cover-Canis_latrans 1.6206 1.0096 666
## Avg_Canopy_Cover-Sciurus_niger 5.0061 1.0494 159
## Avg_Canopy_Cover-Procyon_lotor 3.3349 1.0364 348
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9241 1.0746 245
## Avg_Canopy_Cover-Lynx_rufus 3.6516 1.0164 266
## Avg_Canopy_Cover-Didelphis_virginiana 5.4157 1.0783 203
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.8104 1.0474 166
## Avg_Canopy_Cover-Sciurus_carolinensis 5.3025 1.0242 225
## Avg_Canopy_Cover-Vulpes_vulpes 5.1933 1.0578 300
## Avg_Canopy_Cover-Sus_scrofa 4.6445 1.0144 467
## avg_veg_height-Canis_latrans 0.5212 1.0188 315
## avg_veg_height-Sciurus_niger 0.5634 1.0553 362
## avg_veg_height-Procyon_lotor 0.6426 1.0046 336
## avg_veg_height-Dasypus_novemcinctus 0.8685 1.0076 359
## avg_veg_height-Lynx_rufus 0.6330 1.0475 304
## avg_veg_height-Didelphis_virginiana 0.4982 1.0073 407
## avg_veg_height-Sylvilagus_floridanus 0.5192 1.0306 248
## avg_veg_height-Sciurus_carolinensis 1.1246 1.0027 316
## avg_veg_height-Vulpes_vulpes 0.8908 1.0314 297
## avg_veg_height-Sus_scrofa 0.7229 1.0327 304
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7517 0.1802 -3.1188 -2.7511 -2.4040 1.0074
## (Intercept)-Sciurus_niger -4.4122 0.5285 -5.4095 -4.4304 -3.2443 1.0186
## (Intercept)-Procyon_lotor -2.3036 0.1418 -2.5871 -2.2979 -2.0396 1.0205
## (Intercept)-Dasypus_novemcinctus -1.7796 0.1615 -2.1152 -1.7749 -1.4729 1.0000
## (Intercept)-Lynx_rufus -3.6635 0.3861 -4.4580 -3.6436 -2.9514 1.0173
## (Intercept)-Didelphis_virginiana -2.6143 0.2918 -3.2027 -2.6040 -2.0620 1.0088
## (Intercept)-Sylvilagus_floridanus -3.2026 0.2656 -3.7460 -3.2006 -2.6857 1.0184
## (Intercept)-Sciurus_carolinensis -2.7617 0.3181 -3.4149 -2.7525 -2.1618 1.0074
## (Intercept)-Vulpes_vulpes -4.1828 0.6560 -5.5315 -4.1519 -2.9673 1.0020
## (Intercept)-Sus_scrofa -3.4718 0.5190 -4.4629 -3.4727 -2.4436 1.0177
## shrub_cover-Canis_latrans -0.2802 0.2207 -0.7171 -0.2775 0.1520 1.0096
## shrub_cover-Sciurus_niger -0.1710 0.4762 -1.1333 -0.1583 0.7273 1.0373
## shrub_cover-Procyon_lotor 0.2764 0.1578 -0.0425 0.2781 0.5714 1.0048
## shrub_cover-Dasypus_novemcinctus 0.8998 0.3021 0.3272 0.8936 1.5180 1.0024
## shrub_cover-Lynx_rufus -0.0086 0.3791 -0.7854 0.0063 0.6968 1.0245
## shrub_cover-Didelphis_virginiana 0.9840 0.3582 0.3437 0.9600 1.7466 1.0054
## shrub_cover-Sylvilagus_floridanus 0.5099 0.3886 -0.2630 0.5123 1.2721 1.0205
## shrub_cover-Sciurus_carolinensis 0.9723 0.4089 0.1993 0.9680 1.7659 1.0008
## shrub_cover-Vulpes_vulpes 0.2687 0.5297 -0.7986 0.2766 1.3286 1.0054
## shrub_cover-Sus_scrofa 0.8701 0.6851 -0.5243 0.8561 2.2330 1.0084
## veg_height-Canis_latrans -0.5628 0.1833 -0.9248 -0.5584 -0.2140 1.0058
## veg_height-Sciurus_niger 0.0553 0.3826 -0.6730 0.0449 0.8300 1.0407
## veg_height-Procyon_lotor 0.3522 0.1212 0.1238 0.3498 0.5934 0.9999
## veg_height-Dasypus_novemcinctus 0.2570 0.1344 -0.0072 0.2578 0.5120 1.0034
## veg_height-Lynx_rufus 0.1117 0.2477 -0.3998 0.1196 0.5727 1.0445
## veg_height-Didelphis_virginiana 0.4655 0.2365 0.0187 0.4654 0.9436 1.0042
## veg_height-Sylvilagus_floridanus 0.1682 0.2397 -0.3007 0.1685 0.6277 1.0054
## veg_height-Sciurus_carolinensis 0.1327 0.2194 -0.2853 0.1259 0.5648 1.0059
## veg_height-Vulpes_vulpes -0.1246 0.3080 -0.7276 -0.1080 0.4483 1.0096
## veg_height-Sus_scrofa -0.1439 0.3174 -0.7727 -0.1386 0.4708 1.0153
## week-Canis_latrans 0.0649 0.1337 -0.2118 0.0637 0.3245 1.0164
## week-Sciurus_niger -0.3421 0.3213 -1.1230 -0.2922 0.1664 1.0368
## week-Procyon_lotor -0.0545 0.1161 -0.2943 -0.0504 0.1650 1.0003
## week-Dasypus_novemcinctus -0.1744 0.1385 -0.4650 -0.1668 0.0792 1.0021
## week-Lynx_rufus -0.0428 0.1966 -0.4548 -0.0367 0.3329 1.0023
## week-Didelphis_virginiana -0.2270 0.2259 -0.7245 -0.2125 0.1656 1.0065
## week-Sylvilagus_floridanus -0.1684 0.2053 -0.6029 -0.1598 0.2192 1.0012
## week-Sciurus_carolinensis 0.1275 0.1822 -0.2377 0.1306 0.4805 1.0080
## week-Vulpes_vulpes -0.1534 0.2770 -0.7511 -0.1316 0.3597 1.0120
## week-Sus_scrofa 0.0795 0.2350 -0.3705 0.0788 0.5712 1.0043
## ESS
## (Intercept)-Canis_latrans 872
## (Intercept)-Sciurus_niger 195
## (Intercept)-Procyon_lotor 1090
## (Intercept)-Dasypus_novemcinctus 1106
## (Intercept)-Lynx_rufus 151
## (Intercept)-Didelphis_virginiana 769
## (Intercept)-Sylvilagus_floridanus 608
## (Intercept)-Sciurus_carolinensis 514
## (Intercept)-Vulpes_vulpes 215
## (Intercept)-Sus_scrofa 408
## shrub_cover-Canis_latrans 786
## shrub_cover-Sciurus_niger 289
## shrub_cover-Procyon_lotor 1503
## shrub_cover-Dasypus_novemcinctus 751
## shrub_cover-Lynx_rufus 311
## shrub_cover-Didelphis_virginiana 521
## shrub_cover-Sylvilagus_floridanus 465
## shrub_cover-Sciurus_carolinensis 455
## shrub_cover-Vulpes_vulpes 505
## shrub_cover-Sus_scrofa 486
## veg_height-Canis_latrans 768
## veg_height-Sciurus_niger 354
## veg_height-Procyon_lotor 1338
## veg_height-Dasypus_novemcinctus 1936
## veg_height-Lynx_rufus 615
## veg_height-Didelphis_virginiana 1305
## veg_height-Sylvilagus_floridanus 721
## veg_height-Sciurus_carolinensis 965
## veg_height-Vulpes_vulpes 632
## veg_height-Sus_scrofa 962
## week-Canis_latrans 1707
## week-Sciurus_niger 437
## week-Procyon_lotor 1638
## week-Dasypus_novemcinctus 2075
## week-Lynx_rufus 912
## week-Didelphis_virginiana 998
## week-Sylvilagus_floridanus 848
## week-Sciurus_carolinensis 1675
## week-Vulpes_vulpes 874
## week-Sus_scrofa 1577
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7767
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5732 0.3269 -1.2214 -0.569 0.0958 1.031 1353
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8279 0.6754 0.161 0.6547 2.4945 1.0074 1481
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8924 0.2981 -3.4897 -2.8886 -2.3076 1.0032 1329
## shrub_cover 0.2931 0.2732 -0.2752 0.2938 0.8394 1.0032 1210
## veg_height 0.0574 0.1710 -0.2741 0.0613 0.4113 1.0024 1087
## week -0.0900 0.1325 -0.3586 -0.0840 0.1595 1.0079 1097
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7735 0.6802 0.1888 0.6009 2.3407 1.0396 977
## shrub_cover 0.5436 0.4860 0.0946 0.4115 1.7825 1.0162 650
## veg_height 0.2046 0.1539 0.0550 0.1625 0.5865 1.0079 1861
## week 0.1026 0.0871 0.0247 0.0774 0.3218 1.0144 1209
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2034 0.3758 -0.5070 0.1914 0.9708 1.0027
## (Intercept)-Sciurus_niger -0.8506 0.5891 -1.9766 -0.8698 0.3127 1.0294
## (Intercept)-Procyon_lotor 0.5014 0.3723 -0.2251 0.4882 1.2237 1.0081
## (Intercept)-Dasypus_novemcinctus -0.5817 0.3550 -1.2943 -0.5777 0.1077 1.0001
## (Intercept)-Lynx_rufus -0.0102 0.5439 -0.9532 -0.0524 1.1855 1.0250
## (Intercept)-Didelphis_virginiana -1.1117 0.4319 -2.0068 -1.0968 -0.3034 1.0026
## (Intercept)-Sylvilagus_floridanus -0.4195 0.4322 -1.2294 -0.4386 0.4614 1.0054
## (Intercept)-Sciurus_carolinensis -1.0738 0.4208 -1.9401 -1.0487 -0.3015 1.0086
## (Intercept)-Vulpes_vulpes -1.1808 0.6472 -2.4215 -1.1884 0.1839 1.0145
## (Intercept)-Sus_scrofa -1.4122 0.5716 -2.5897 -1.3782 -0.3796 1.0046
## ESS
## (Intercept)-Canis_latrans 1906
## (Intercept)-Sciurus_niger 476
## (Intercept)-Procyon_lotor 2172
## (Intercept)-Dasypus_novemcinctus 2708
## (Intercept)-Lynx_rufus 677
## (Intercept)-Didelphis_virginiana 1843
## (Intercept)-Sylvilagus_floridanus 1578
## (Intercept)-Sciurus_carolinensis 1699
## (Intercept)-Vulpes_vulpes 455
## (Intercept)-Sus_scrofa 811
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7377 0.1819 -3.1246 -2.7327 -2.3992 1.0061
## (Intercept)-Sciurus_niger -3.7483 0.5498 -4.8596 -3.7289 -2.7071 1.0338
## (Intercept)-Procyon_lotor -2.3008 0.1371 -2.5701 -2.2957 -2.0421 1.0009
## (Intercept)-Dasypus_novemcinctus -1.7718 0.1608 -2.0960 -1.7679 -1.4688 1.0138
## (Intercept)-Lynx_rufus -3.5716 0.3414 -4.2373 -3.5792 -2.9098 1.0503
## (Intercept)-Didelphis_virginiana -2.6090 0.2842 -3.1891 -2.5945 -2.0790 1.0052
## (Intercept)-Sylvilagus_floridanus -3.1557 0.2798 -3.7373 -3.1453 -2.6264 1.0193
## (Intercept)-Sciurus_carolinensis -2.6693 0.3044 -3.2950 -2.6552 -2.0862 1.0004
## (Intercept)-Vulpes_vulpes -3.8258 0.6091 -5.1383 -3.7807 -2.7590 1.0408
## (Intercept)-Sus_scrofa -3.3184 0.5233 -4.3215 -3.3095 -2.2880 1.0025
## shrub_cover-Canis_latrans -0.2814 0.2130 -0.7178 -0.2794 0.1293 1.0099
## shrub_cover-Sciurus_niger -0.2087 0.4960 -1.1634 -0.2051 0.7798 1.0026
## shrub_cover-Procyon_lotor 0.2598 0.1628 -0.0634 0.2624 0.5864 1.0082
## shrub_cover-Dasypus_novemcinctus 0.8348 0.2982 0.2760 0.8240 1.4364 1.0011
## shrub_cover-Lynx_rufus -0.2368 0.3585 -0.9587 -0.2265 0.4316 1.0121
## shrub_cover-Didelphis_virginiana 0.9380 0.3575 0.2827 0.9295 1.6593 1.0127
## shrub_cover-Sylvilagus_floridanus 0.2940 0.4152 -0.4931 0.2794 1.1742 1.0076
## shrub_cover-Sciurus_carolinensis 0.8332 0.4014 0.0450 0.8278 1.6441 1.0028
## shrub_cover-Vulpes_vulpes -0.0398 0.5847 -1.2657 -0.0249 1.0321 1.0066
## shrub_cover-Sus_scrofa 0.6236 0.7188 -0.7821 0.6047 2.0760 1.0022
## veg_height-Canis_latrans -0.5647 0.1787 -0.9204 -0.5558 -0.2328 1.0058
## veg_height-Sciurus_niger 0.0504 0.4165 -0.7498 0.0398 0.9162 1.0007
## veg_height-Procyon_lotor 0.3398 0.1196 0.1012 0.3398 0.5812 1.0000
## veg_height-Dasypus_novemcinctus 0.2491 0.1354 -0.0136 0.2479 0.5200 1.0082
## veg_height-Lynx_rufus 0.0550 0.2475 -0.4402 0.0676 0.5273 1.0012
## veg_height-Didelphis_virginiana 0.4399 0.2440 -0.0203 0.4309 0.9367 1.0119
## veg_height-Sylvilagus_floridanus 0.1289 0.2371 -0.3339 0.1287 0.5983 1.0048
## veg_height-Sciurus_carolinensis 0.0900 0.2140 -0.3235 0.0873 0.5290 1.0007
## veg_height-Vulpes_vulpes -0.0893 0.3005 -0.7171 -0.0740 0.4615 1.0004
## veg_height-Sus_scrofa -0.1297 0.3337 -0.8337 -0.1213 0.4893 1.0026
## week-Canis_latrans 0.0612 0.1307 -0.1982 0.0634 0.3062 1.0004
## week-Sciurus_niger -0.3235 0.2912 -0.9614 -0.3018 0.1694 1.0061
## week-Procyon_lotor -0.0544 0.1210 -0.3103 -0.0504 0.1733 1.0167
## week-Dasypus_novemcinctus -0.1726 0.1403 -0.4629 -0.1678 0.0877 1.0020
## week-Lynx_rufus -0.0560 0.1951 -0.4505 -0.0503 0.3062 1.0009
## week-Didelphis_virginiana -0.2352 0.2213 -0.7285 -0.2209 0.1602 1.0066
## week-Sylvilagus_floridanus -0.1717 0.2124 -0.6321 -0.1567 0.2016 1.0034
## week-Sciurus_carolinensis 0.1260 0.1871 -0.2393 0.1239 0.4984 1.0026
## week-Vulpes_vulpes -0.1445 0.2663 -0.7102 -0.1303 0.3477 1.0023
## week-Sus_scrofa 0.0748 0.2328 -0.3914 0.0754 0.5434 1.0011
## ESS
## (Intercept)-Canis_latrans 908
## (Intercept)-Sciurus_niger 277
## (Intercept)-Procyon_lotor 1357
## (Intercept)-Dasypus_novemcinctus 1570
## (Intercept)-Lynx_rufus 363
## (Intercept)-Didelphis_virginiana 837
## (Intercept)-Sylvilagus_floridanus 675
## (Intercept)-Sciurus_carolinensis 894
## (Intercept)-Vulpes_vulpes 296
## (Intercept)-Sus_scrofa 562
## shrub_cover-Canis_latrans 944
## shrub_cover-Sciurus_niger 402
## shrub_cover-Procyon_lotor 1535
## shrub_cover-Dasypus_novemcinctus 895
## shrub_cover-Lynx_rufus 459
## shrub_cover-Didelphis_virginiana 634
## shrub_cover-Sylvilagus_floridanus 559
## shrub_cover-Sciurus_carolinensis 840
## shrub_cover-Vulpes_vulpes 562
## shrub_cover-Sus_scrofa 724
## veg_height-Canis_latrans 787
## veg_height-Sciurus_niger 890
## veg_height-Procyon_lotor 2135
## veg_height-Dasypus_novemcinctus 2022
## veg_height-Lynx_rufus 766
## veg_height-Didelphis_virginiana 1118
## veg_height-Sylvilagus_floridanus 881
## veg_height-Sciurus_carolinensis 1161
## veg_height-Vulpes_vulpes 688
## veg_height-Sus_scrofa 1017
## week-Canis_latrans 1681
## week-Sciurus_niger 685
## week-Procyon_lotor 1607
## week-Dasypus_novemcinctus 1865
## week-Lynx_rufus 961
## week-Didelphis_virginiana 1139
## week-Sylvilagus_floridanus 1009
## week-Sciurus_carolinensis 1836
## week-Vulpes_vulpes 1188
## week-Sus_scrofa 1600
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8687
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4450 0.4272 -1.2762 -0.4514 0.3968 1.0868 249
## Avg_Cogongrass_Cover 0.0127 0.3376 -0.6897 0.0219 0.6377 1.0232 372
## total_shrub_cover -0.7704 0.4349 -1.7297 -0.7257 -0.0457 1.0655 118
## avg_veg_height 0.0981 0.3281 -0.5326 0.1018 0.7645 1.0128 349
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7052 0.8647 0.0564 0.4370 3.0477 1.0038 432
## Avg_Cogongrass_Cover 0.3671 0.4973 0.0389 0.2148 1.5946 1.0691 733
## total_shrub_cover 0.5475 0.6520 0.0554 0.3472 2.2403 1.0301 238
## avg_veg_height 0.2763 0.3139 0.0347 0.1729 1.1163 1.0351 794
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2106 1.0966 0.1008 0.9115 4.2602 1.1634 185
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9957 0.3142 -3.6256 -2.9882 -2.3883 1.0292 647
## shrub_cover 0.6078 0.3089 -0.0014 0.6002 1.2263 1.0105 258
## veg_height 0.0677 0.1818 -0.2962 0.0703 0.4114 1.0022 837
## week -0.0877 0.1320 -0.3694 -0.0827 0.1595 1.0002 1006
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8090 0.7487 0.1897 0.6179 2.5331 1.0816 577
## shrub_cover 0.5934 0.4894 0.1188 0.4635 1.8756 1.0484 366
## veg_height 0.2276 0.1835 0.0581 0.1762 0.6775 1.0331 1043
## week 0.1025 0.0852 0.0253 0.0773 0.3401 1.0056 1293
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0806 0.6294 -1.0626 0.0310
## (Intercept)-Sciurus_niger -0.6780 0.7234 -2.1322 -0.6906
## (Intercept)-Procyon_lotor 0.2094 0.6539 -1.0402 0.1899
## (Intercept)-Dasypus_novemcinctus -0.4584 0.5953 -1.5786 -0.4789
## (Intercept)-Lynx_rufus -0.2810 0.6722 -1.5151 -0.3051
## (Intercept)-Didelphis_virginiana -0.7465 0.6340 -2.0604 -0.7322
## (Intercept)-Sylvilagus_floridanus -0.2097 0.6464 -1.4008 -0.2405
## (Intercept)-Sciurus_carolinensis -0.7620 0.6691 -2.2006 -0.7503
## (Intercept)-Vulpes_vulpes -0.7771 0.8057 -2.4331 -0.7377
## (Intercept)-Sus_scrofa -0.9517 0.7787 -2.6354 -0.9083
## Avg_Cogongrass_Cover-Canis_latrans 0.3185 0.4789 -0.5399 0.2898
## Avg_Cogongrass_Cover-Sciurus_niger -0.3657 0.6198 -1.8358 -0.2931
## Avg_Cogongrass_Cover-Procyon_lotor -0.0557 0.4378 -0.9500 -0.0532
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1371 0.4431 -0.7502 0.1235
## Avg_Cogongrass_Cover-Lynx_rufus 0.3392 0.5091 -0.5687 0.3000
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1352 0.4869 -0.8520 0.1211
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3487 0.5507 -1.6161 -0.3049
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0577 0.4672 -0.9080 0.0719
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1678 0.5263 -0.8611 0.1547
## Avg_Cogongrass_Cover-Sus_scrofa -0.2043 0.5945 -1.5728 -0.1487
## total_shrub_cover-Canis_latrans -0.0050 0.5597 -1.0406 -0.0347
## total_shrub_cover-Sciurus_niger -0.8967 0.6581 -2.2698 -0.8513
## total_shrub_cover-Procyon_lotor -1.0683 0.5331 -2.2480 -1.0126
## total_shrub_cover-Dasypus_novemcinctus -0.5243 0.6034 -2.0121 -0.4509
## total_shrub_cover-Lynx_rufus -1.1068 0.7134 -2.7954 -1.0331
## total_shrub_cover-Didelphis_virginiana -0.8104 0.6146 -2.2394 -0.7495
## total_shrub_cover-Sylvilagus_floridanus -1.0163 0.6971 -2.6525 -0.9446
## total_shrub_cover-Sciurus_carolinensis -0.8315 0.7004 -2.5225 -0.7471
## total_shrub_cover-Vulpes_vulpes -0.8940 0.8073 -2.7565 -0.8176
## total_shrub_cover-Sus_scrofa -0.6957 0.7312 -2.4079 -0.6366
## avg_veg_height-Canis_latrans 0.0926 0.4347 -0.7498 0.0865
## avg_veg_height-Sciurus_niger -0.1895 0.5828 -1.5171 -0.1416
## avg_veg_height-Procyon_lotor 0.1310 0.4235 -0.6823 0.1289
## avg_veg_height-Dasypus_novemcinctus 0.3195 0.4565 -0.5124 0.2858
## avg_veg_height-Lynx_rufus 0.0574 0.5257 -1.0182 0.0638
## avg_veg_height-Didelphis_virginiana 0.0154 0.4668 -0.8881 0.0222
## avg_veg_height-Sylvilagus_floridanus -0.0030 0.4855 -0.9419 -0.0035
## avg_veg_height-Sciurus_carolinensis 0.4214 0.4970 -0.4399 0.3809
## avg_veg_height-Vulpes_vulpes 0.0757 0.5035 -0.9397 0.0726
## avg_veg_height-Sus_scrofa 0.1345 0.4918 -0.8184 0.1244
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3852 1.0366 460
## (Intercept)-Sciurus_niger 0.8386 1.0950 360
## (Intercept)-Procyon_lotor 1.4912 1.0163 376
## (Intercept)-Dasypus_novemcinctus 0.8231 1.0090 390
## (Intercept)-Lynx_rufus 1.1189 1.0342 478
## (Intercept)-Didelphis_virginiana 0.4762 1.0509 354
## (Intercept)-Sylvilagus_floridanus 1.2207 1.0303 530
## (Intercept)-Sciurus_carolinensis 0.4964 1.0698 320
## (Intercept)-Vulpes_vulpes 0.6901 1.1059 297
## (Intercept)-Sus_scrofa 0.4261 1.0251 261
## Avg_Cogongrass_Cover-Canis_latrans 1.3776 1.0048 797
## Avg_Cogongrass_Cover-Sciurus_niger 0.6598 1.0178 492
## Avg_Cogongrass_Cover-Procyon_lotor 0.7840 1.0146 655
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0227 1.0143 806
## Avg_Cogongrass_Cover-Lynx_rufus 1.4611 1.0084 692
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1687 1.0063 647
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6067 1.0126 564
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9630 1.0277 580
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2724 1.0048 694
## Avg_Cogongrass_Cover-Sus_scrofa 0.8495 1.0349 610
## total_shrub_cover-Canis_latrans 1.2319 1.0106 625
## total_shrub_cover-Sciurus_niger 0.2647 1.0399 390
## total_shrub_cover-Procyon_lotor -0.1690 1.0445 267
## total_shrub_cover-Dasypus_novemcinctus 0.4792 1.0186 202
## total_shrub_cover-Lynx_rufus 0.1326 1.0496 272
## total_shrub_cover-Didelphis_virginiana 0.2105 1.0746 235
## total_shrub_cover-Sylvilagus_floridanus 0.1379 1.0574 232
## total_shrub_cover-Sciurus_carolinensis 0.3516 1.0410 184
## total_shrub_cover-Vulpes_vulpes 0.4970 1.0635 152
## total_shrub_cover-Sus_scrofa 0.5564 1.0565 200
## avg_veg_height-Canis_latrans 0.9677 1.0057 714
## avg_veg_height-Sciurus_niger 0.8058 1.0118 503
## avg_veg_height-Procyon_lotor 0.9879 1.0147 936
## avg_veg_height-Dasypus_novemcinctus 1.3630 1.0228 612
## avg_veg_height-Lynx_rufus 1.1043 1.0132 552
## avg_veg_height-Didelphis_virginiana 0.9837 1.0061 767
## avg_veg_height-Sylvilagus_floridanus 0.9627 1.0066 568
## avg_veg_height-Sciurus_carolinensis 1.5152 1.0373 705
## avg_veg_height-Vulpes_vulpes 1.0746 1.0034 621
## avg_veg_height-Sus_scrofa 1.1500 1.0010 602
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7793 0.1940 -3.1749 -2.7715 -2.4137 1.0004
## (Intercept)-Sciurus_niger -3.7575 0.5632 -4.9039 -3.7394 -2.7206 1.0418
## (Intercept)-Procyon_lotor -2.3055 0.1382 -2.5780 -2.3026 -2.0445 1.0079
## (Intercept)-Dasypus_novemcinctus -1.8591 0.1750 -2.2255 -1.8544 -1.5267 1.0055
## (Intercept)-Lynx_rufus -3.5019 0.3353 -4.1694 -3.4951 -2.8843 1.1098
## (Intercept)-Didelphis_virginiana -2.7921 0.3209 -3.4506 -2.7838 -2.1874 1.0429
## (Intercept)-Sylvilagus_floridanus -3.2658 0.2673 -3.8044 -3.2606 -2.7572 1.0052
## (Intercept)-Sciurus_carolinensis -2.8671 0.3376 -3.5504 -2.8499 -2.2376 1.0147
## (Intercept)-Vulpes_vulpes -4.0209 0.6609 -5.3899 -3.9964 -2.8287 1.0490
## (Intercept)-Sus_scrofa -3.6040 0.5242 -4.6040 -3.5995 -2.5929 1.0711
## shrub_cover-Canis_latrans -0.2275 0.2399 -0.6963 -0.2324 0.2477 1.0022
## shrub_cover-Sciurus_niger 0.1231 0.5388 -0.9134 0.1142 1.1796 1.0156
## shrub_cover-Procyon_lotor 0.3202 0.1614 0.0001 0.3243 0.6304 1.0153
## shrub_cover-Dasypus_novemcinctus 1.0941 0.3598 0.4281 1.0942 1.8101 1.0211
## shrub_cover-Lynx_rufus 0.1554 0.3855 -0.6961 0.1787 0.8239 1.0089
## shrub_cover-Didelphis_virginiana 1.2419 0.4129 0.4875 1.2237 2.1054 1.0663
## shrub_cover-Sylvilagus_floridanus 0.7272 0.4294 -0.1615 0.7523 1.5503 1.0099
## shrub_cover-Sciurus_carolinensis 1.1840 0.4264 0.3564 1.1772 2.0220 1.0397
## shrub_cover-Vulpes_vulpes 0.4373 0.6359 -0.8500 0.4383 1.6886 1.0149
## shrub_cover-Sus_scrofa 1.1222 0.7087 -0.2213 1.0925 2.5533 1.0470
## veg_height-Canis_latrans -0.5911 0.1875 -0.9683 -0.5827 -0.2513 1.0006
## veg_height-Sciurus_niger 0.1460 0.4728 -0.7316 0.1325 1.1394 1.0019
## veg_height-Procyon_lotor 0.3456 0.1245 0.0932 0.3439 0.5913 1.0013
## veg_height-Dasypus_novemcinctus 0.2785 0.1415 0.0069 0.2762 0.5608 1.0066
## veg_height-Lynx_rufus 0.0621 0.2489 -0.4348 0.0675 0.5372 1.0153
## veg_height-Didelphis_virginiana 0.4553 0.2614 -0.0382 0.4410 0.9892 1.0042
## veg_height-Sylvilagus_floridanus 0.0859 0.2516 -0.4137 0.0852 0.5988 1.0028
## veg_height-Sciurus_carolinensis 0.1342 0.2189 -0.2869 0.1353 0.5615 1.0146
## veg_height-Vulpes_vulpes -0.0951 0.3259 -0.7919 -0.0796 0.5057 1.0055
## veg_height-Sus_scrofa -0.1559 0.3390 -0.8631 -0.1467 0.4785 1.0105
## week-Canis_latrans 0.0629 0.1343 -0.2015 0.0655 0.3251 0.9998
## week-Sciurus_niger -0.3217 0.2900 -0.9782 -0.2914 0.1627 1.0219
## week-Procyon_lotor -0.0565 0.1170 -0.2882 -0.0558 0.1753 1.0027
## week-Dasypus_novemcinctus -0.1688 0.1394 -0.4550 -0.1619 0.0899 1.0013
## week-Lynx_rufus -0.0524 0.1993 -0.4760 -0.0485 0.3205 1.0042
## week-Didelphis_virginiana -0.2273 0.2174 -0.6930 -0.2105 0.1607 1.0043
## week-Sylvilagus_floridanus -0.1655 0.2015 -0.6016 -0.1508 0.1890 1.0028
## week-Sciurus_carolinensis 0.1226 0.1863 -0.2288 0.1228 0.4977 1.0008
## week-Vulpes_vulpes -0.1339 0.2714 -0.7093 -0.1195 0.3777 1.0007
## week-Sus_scrofa 0.0660 0.2357 -0.3950 0.0617 0.5345 1.0082
## ESS
## (Intercept)-Canis_latrans 391
## (Intercept)-Sciurus_niger 247
## (Intercept)-Procyon_lotor 1392
## (Intercept)-Dasypus_novemcinctus 472
## (Intercept)-Lynx_rufus 405
## (Intercept)-Didelphis_virginiana 418
## (Intercept)-Sylvilagus_floridanus 539
## (Intercept)-Sciurus_carolinensis 434
## (Intercept)-Vulpes_vulpes 172
## (Intercept)-Sus_scrofa 198
## shrub_cover-Canis_latrans 648
## shrub_cover-Sciurus_niger 328
## shrub_cover-Procyon_lotor 1235
## shrub_cover-Dasypus_novemcinctus 268
## shrub_cover-Lynx_rufus 246
## shrub_cover-Didelphis_virginiana 240
## shrub_cover-Sylvilagus_floridanus 270
## shrub_cover-Sciurus_carolinensis 345
## shrub_cover-Vulpes_vulpes 231
## shrub_cover-Sus_scrofa 183
## veg_height-Canis_latrans 633
## veg_height-Sciurus_niger 472
## veg_height-Procyon_lotor 1605
## veg_height-Dasypus_novemcinctus 1543
## veg_height-Lynx_rufus 720
## veg_height-Didelphis_virginiana 860
## veg_height-Sylvilagus_floridanus 618
## veg_height-Sciurus_carolinensis 905
## veg_height-Vulpes_vulpes 529
## veg_height-Sus_scrofa 712
## week-Canis_latrans 1511
## week-Sciurus_niger 783
## week-Procyon_lotor 1488
## week-Dasypus_novemcinctus 1820
## week-Lynx_rufus 912
## week-Didelphis_virginiana 1287
## week-Sylvilagus_floridanus 955
## week-Sciurus_carolinensis 1344
## week-Vulpes_vulpes 1050
## week-Sus_scrofa 1282
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8073
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8068 0.4748 -1.7006 -0.8101 0.1537 1.0118 474
## Tree_Density -0.7097 0.4108 -1.6053 -0.6825 0.0439 1.0218 670
## Avg_Canopy_Cover 1.0324 0.3730 0.3655 1.0068 1.8512 1.0300 610
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4539 1.6472 0.1224 1.0265 5.2824 1.0656 515
## Tree_Density 0.8002 1.2225 0.0484 0.3931 4.2772 1.0277 417
## Avg_Canopy_Cover 0.7762 0.9170 0.0713 0.5050 3.0108 1.0205 554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5646 0.5947 0.0528 0.3681 2.3091 1.1711 144
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9262 0.3170 -3.5638 -2.9278 -2.3033 1.0051 1516
## shrub_cover 0.3512 0.2749 -0.1953 0.3480 0.9024 1.0011 1155
## veg_height 0.0764 0.1690 -0.2625 0.0805 0.4154 1.0129 1618
## week -0.0892 0.1312 -0.3583 -0.0852 0.1536 1.0199 1193
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8659 0.7031 0.1973 0.6768 2.7131 1.0046 352
## shrub_cover 0.5369 0.4459 0.1115 0.4157 1.6258 1.0074 548
## veg_height 0.2077 0.1552 0.0497 0.1669 0.6224 1.0031 1427
## week 0.1106 0.1056 0.0247 0.0800 0.4105 1.0310 771
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0706 0.6201 -1.1711 0.0747 1.3454
## (Intercept)-Sciurus_niger -0.8120 0.8455 -2.3206 -0.8832 1.0858
## (Intercept)-Procyon_lotor 0.3538 0.6725 -1.0171 0.3644 1.6306
## (Intercept)-Dasypus_novemcinctus -0.9346 0.5792 -2.1121 -0.9141 0.1145
## (Intercept)-Lynx_rufus 0.0469 1.0469 -1.5220 -0.1131 2.5338
## (Intercept)-Didelphis_virginiana -1.4707 0.6565 -2.8417 -1.4411 -0.2766
## (Intercept)-Sylvilagus_floridanus -0.7180 0.6014 -1.8951 -0.7057 0.4690
## (Intercept)-Sciurus_carolinensis -1.5635 0.6895 -3.0158 -1.5255 -0.3631
## (Intercept)-Vulpes_vulpes -1.4823 0.8773 -3.1576 -1.4429 0.2680
## (Intercept)-Sus_scrofa -1.8842 0.8397 -3.7392 -1.8206 -0.3786
## Tree_Density-Canis_latrans -0.9125 0.5799 -2.2448 -0.8384 -0.0020
## Tree_Density-Sciurus_niger -0.7155 0.7652 -2.4032 -0.6748 0.7580
## Tree_Density-Procyon_lotor -0.4776 0.4129 -1.3401 -0.4679 0.3225
## Tree_Density-Dasypus_novemcinctus -1.3559 0.8881 -3.5864 -1.1653 -0.1637
## Tree_Density-Lynx_rufus 0.1821 0.7534 -0.9272 0.0490 2.0123
## Tree_Density-Didelphis_virginiana -0.9155 0.7175 -2.6022 -0.8129 0.2353
## Tree_Density-Sylvilagus_floridanus -1.0054 0.7419 -2.8623 -0.8879 0.0977
## Tree_Density-Sciurus_carolinensis -0.8163 0.6989 -2.3949 -0.7391 0.3981
## Tree_Density-Vulpes_vulpes -0.5884 0.7615 -2.1552 -0.5789 0.8446
## Tree_Density-Sus_scrofa -0.8230 0.7832 -2.7656 -0.7122 0.4315
## Avg_Canopy_Cover-Canis_latrans 0.0090 0.4760 -0.9504 0.0166 0.9289
## Avg_Canopy_Cover-Sciurus_niger 0.8516 0.7720 -0.4517 0.7742 2.6358
## Avg_Canopy_Cover-Procyon_lotor 0.9886 0.4680 0.1385 0.9589 1.9790
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0596 0.4465 0.2614 1.0294 2.0259
## Avg_Canopy_Cover-Lynx_rufus 0.7192 0.6641 -0.5311 0.6817 2.1685
## Avg_Canopy_Cover-Didelphis_virginiana 1.4031 0.6129 0.4471 1.3194 2.8552
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8668 0.8907 0.6392 1.6959 4.0744
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4053 0.6127 0.4609 1.3248 2.8898
## Avg_Canopy_Cover-Vulpes_vulpes 1.0587 0.6363 -0.0407 0.9884 2.5237
## Avg_Canopy_Cover-Sus_scrofa 1.2101 0.5847 0.2353 1.1603 2.4966
## Rhat ESS
## (Intercept)-Canis_latrans 1.0382 512
## (Intercept)-Sciurus_niger 1.0129 277
## (Intercept)-Procyon_lotor 1.0510 385
## (Intercept)-Dasypus_novemcinctus 1.0324 1020
## (Intercept)-Lynx_rufus 1.0545 209
## (Intercept)-Didelphis_virginiana 1.0582 700
## (Intercept)-Sylvilagus_floridanus 1.0173 1189
## (Intercept)-Sciurus_carolinensis 1.0242 679
## (Intercept)-Vulpes_vulpes 1.0258 302
## (Intercept)-Sus_scrofa 1.0211 528
## Tree_Density-Canis_latrans 1.0116 751
## Tree_Density-Sciurus_niger 1.0045 563
## Tree_Density-Procyon_lotor 1.0120 2068
## Tree_Density-Dasypus_novemcinctus 1.0484 447
## Tree_Density-Lynx_rufus 1.0047 478
## Tree_Density-Didelphis_virginiana 1.0370 670
## Tree_Density-Sylvilagus_floridanus 1.0293 681
## Tree_Density-Sciurus_carolinensis 1.0143 927
## Tree_Density-Vulpes_vulpes 1.0331 716
## Tree_Density-Sus_scrofa 1.0440 543
## Avg_Canopy_Cover-Canis_latrans 1.0054 947
## Avg_Canopy_Cover-Sciurus_niger 1.0104 443
## Avg_Canopy_Cover-Procyon_lotor 1.0056 1101
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0029 1631
## Avg_Canopy_Cover-Lynx_rufus 1.0055 717
## Avg_Canopy_Cover-Didelphis_virginiana 1.0582 729
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0347 417
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0136 723
## Avg_Canopy_Cover-Vulpes_vulpes 1.0237 678
## Avg_Canopy_Cover-Sus_scrofa 1.0575 846
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7600 0.1832 -3.1418 -2.7530 -2.4217 1.0030
## (Intercept)-Sciurus_niger -3.9485 0.6038 -5.2560 -3.9307 -2.8448 1.0009
## (Intercept)-Procyon_lotor -2.3099 0.1413 -2.5944 -2.3090 -2.0454 1.0000
## (Intercept)-Dasypus_novemcinctus -1.7915 0.1662 -2.1161 -1.7858 -1.4792 1.0136
## (Intercept)-Lynx_rufus -3.6573 0.3587 -4.3939 -3.6365 -3.0069 1.0014
## (Intercept)-Didelphis_virginiana -2.6319 0.2801 -3.2023 -2.6210 -2.1121 1.0122
## (Intercept)-Sylvilagus_floridanus -3.1364 0.2592 -3.6620 -3.1301 -2.6471 1.0141
## (Intercept)-Sciurus_carolinensis -2.7182 0.3237 -3.3962 -2.7014 -2.1350 1.0025
## (Intercept)-Vulpes_vulpes -3.9688 0.6699 -5.4422 -3.8999 -2.8290 1.0147
## (Intercept)-Sus_scrofa -3.2985 0.5592 -4.3759 -3.2916 -2.1899 1.0046
## shrub_cover-Canis_latrans -0.2697 0.2233 -0.7221 -0.2666 0.1625 1.0014
## shrub_cover-Sciurus_niger -0.2127 0.4632 -1.0975 -0.2069 0.6892 1.0055
## shrub_cover-Procyon_lotor 0.2667 0.1592 -0.0500 0.2679 0.5728 1.0052
## shrub_cover-Dasypus_novemcinctus 0.8837 0.2929 0.3288 0.8775 1.4661 1.0082
## shrub_cover-Lynx_rufus -0.2269 0.3460 -0.9225 -0.2202 0.4440 1.0095
## shrub_cover-Didelphis_virginiana 0.9787 0.3552 0.3136 0.9754 1.6996 1.0004
## shrub_cover-Sylvilagus_floridanus 0.4121 0.3860 -0.3354 0.4127 1.1829 1.0107
## shrub_cover-Sciurus_carolinensis 0.9019 0.4158 0.1227 0.8885 1.7255 1.0056
## shrub_cover-Vulpes_vulpes 0.0576 0.5519 -1.0930 0.0765 1.1309 1.0137
## shrub_cover-Sus_scrofa 0.6633 0.6879 -0.7325 0.6728 2.0168 1.0089
## veg_height-Canis_latrans -0.5630 0.1876 -0.9468 -0.5614 -0.2024 1.0026
## veg_height-Sciurus_niger 0.0628 0.4025 -0.7325 0.0603 0.8589 1.0336
## veg_height-Procyon_lotor 0.3497 0.1234 0.1113 0.3511 0.5995 1.0055
## veg_height-Dasypus_novemcinctus 0.2711 0.1354 0.0080 0.2722 0.5379 1.0023
## veg_height-Lynx_rufus 0.0846 0.2406 -0.4025 0.0942 0.5483 1.0177
## veg_height-Didelphis_virginiana 0.4723 0.2303 0.0326 0.4699 0.9412 1.0015
## veg_height-Sylvilagus_floridanus 0.1651 0.2288 -0.2857 0.1657 0.6024 1.0213
## veg_height-Sciurus_carolinensis 0.1185 0.2164 -0.2967 0.1137 0.5624 1.0019
## veg_height-Vulpes_vulpes -0.0911 0.3106 -0.7351 -0.0807 0.4935 1.0056
## veg_height-Sus_scrofa -0.0756 0.3306 -0.7461 -0.0604 0.5319 1.0070
## week-Canis_latrans 0.0571 0.1330 -0.2178 0.0606 0.3096 1.0008
## week-Sciurus_niger -0.3427 0.3302 -1.1535 -0.2953 0.1621 1.0373
## week-Procyon_lotor -0.0562 0.1184 -0.2950 -0.0534 0.1714 1.0015
## week-Dasypus_novemcinctus -0.1756 0.1418 -0.4778 -0.1719 0.0818 1.0062
## week-Lynx_rufus -0.0454 0.1900 -0.4310 -0.0361 0.3068 1.0001
## week-Didelphis_virginiana -0.2275 0.2170 -0.6915 -0.2157 0.1702 1.0262
## week-Sylvilagus_floridanus -0.1656 0.2017 -0.5909 -0.1562 0.2069 1.0089
## week-Sciurus_carolinensis 0.1241 0.1838 -0.2460 0.1259 0.4696 1.0037
## week-Vulpes_vulpes -0.1381 0.2868 -0.7499 -0.1128 0.3891 1.0073
## week-Sus_scrofa 0.0806 0.2306 -0.3767 0.0774 0.5337 1.0043
## ESS
## (Intercept)-Canis_latrans 927
## (Intercept)-Sciurus_niger 159
## (Intercept)-Procyon_lotor 1258
## (Intercept)-Dasypus_novemcinctus 1421
## (Intercept)-Lynx_rufus 212
## (Intercept)-Didelphis_virginiana 762
## (Intercept)-Sylvilagus_floridanus 738
## (Intercept)-Sciurus_carolinensis 619
## (Intercept)-Vulpes_vulpes 200
## (Intercept)-Sus_scrofa 442
## shrub_cover-Canis_latrans 674
## shrub_cover-Sciurus_niger 538
## shrub_cover-Procyon_lotor 1368
## shrub_cover-Dasypus_novemcinctus 1220
## shrub_cover-Lynx_rufus 435
## shrub_cover-Didelphis_virginiana 679
## shrub_cover-Sylvilagus_floridanus 566
## shrub_cover-Sciurus_carolinensis 548
## shrub_cover-Vulpes_vulpes 703
## shrub_cover-Sus_scrofa 730
## veg_height-Canis_latrans 643
## veg_height-Sciurus_niger 686
## veg_height-Procyon_lotor 1552
## veg_height-Dasypus_novemcinctus 1973
## veg_height-Lynx_rufus 639
## veg_height-Didelphis_virginiana 1096
## veg_height-Sylvilagus_floridanus 1103
## veg_height-Sciurus_carolinensis 1107
## veg_height-Vulpes_vulpes 724
## veg_height-Sus_scrofa 1078
## week-Canis_latrans 1564
## week-Sciurus_niger 475
## week-Procyon_lotor 1576
## week-Dasypus_novemcinctus 1675
## week-Lynx_rufus 868
## week-Didelphis_virginiana 1439
## week-Sylvilagus_floridanus 1111
## week-Sciurus_carolinensis 1842
## week-Vulpes_vulpes 1091
## week-Sus_scrofa 1704
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8402
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6433 0.4442 -1.5823 -0.6258 0.2129 1.0097 287
## Cogon_Patch_Size -0.2775 0.3845 -1.1452 -0.2427 0.3889 1.0008 673
## Avg_Cogongrass_Cover 0.2003 0.2976 -0.3960 0.1955 0.7901 1.0041 665
## total_shrub_cover -0.5964 0.3538 -1.3693 -0.5799 0.0626 1.0129 283
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7337 0.9007 0.0617 0.4698 3.0890 1.0477 490
## Cogon_Patch_Size 0.8153 1.1302 0.0559 0.4591 3.8185 1.0399 493
## Avg_Cogongrass_Cover 0.3532 0.7416 0.0414 0.2147 1.4021 1.2131 1215
## total_shrub_cover 0.4419 0.6770 0.0468 0.2786 1.7325 1.0344 818
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3522 1.1586 0.1064 1.0398 4.4099 1.0422 150
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9364 0.2964 -3.5243 -2.9337 -2.3408 1.0031 1322
## shrub_cover 0.5343 0.2746 -0.0199 0.5351 1.0741 1.0136 563
## veg_height 0.0701 0.1765 -0.2764 0.0715 0.4293 1.0007 809
## week -0.0825 0.1279 -0.3584 -0.0803 0.1609 1.0050 1099
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7456 0.6304 0.1755 0.5935 2.1430 1.0039 729
## shrub_cover 0.5186 0.4158 0.0986 0.4062 1.6441 1.0079 658
## veg_height 0.2018 0.1582 0.0510 0.1576 0.5807 1.0010 1488
## week 0.1046 0.0948 0.0260 0.0784 0.3328 1.0188 959
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0574 0.6658 -1.3079 -0.0743
## (Intercept)-Sciurus_niger -0.8465 0.7861 -2.3518 -0.8533
## (Intercept)-Procyon_lotor 0.0837 0.6998 -1.2173 0.0712
## (Intercept)-Dasypus_novemcinctus -0.6355 0.5744 -1.7624 -0.6262
## (Intercept)-Lynx_rufus -0.5020 0.6753 -1.8317 -0.5204
## (Intercept)-Didelphis_virginiana -0.9580 0.6214 -2.2285 -0.9298
## (Intercept)-Sylvilagus_floridanus -0.4732 0.6390 -1.7609 -0.4721
## (Intercept)-Sciurus_carolinensis -0.9758 0.6580 -2.3753 -0.9406
## (Intercept)-Vulpes_vulpes -1.0822 0.7864 -2.7998 -1.0289
## (Intercept)-Sus_scrofa -1.1556 0.7696 -2.8706 -1.0944
## Cogon_Patch_Size-Canis_latrans 0.5310 0.6002 -0.4127 0.4388
## Cogon_Patch_Size-Sciurus_niger -0.5847 0.7894 -2.5606 -0.4724
## Cogon_Patch_Size-Procyon_lotor -0.2726 0.4521 -1.2516 -0.2554
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1208 0.4347 -1.0050 -0.1180
## Cogon_Patch_Size-Lynx_rufus -0.2938 0.6753 -1.5458 -0.3097
## Cogon_Patch_Size-Didelphis_virginiana 0.4789 0.4894 -0.4032 0.4406
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8870 0.8669 -3.0657 -0.7039
## Cogon_Patch_Size-Sciurus_carolinensis -0.7145 0.7167 -2.4143 -0.5852
## Cogon_Patch_Size-Vulpes_vulpes -0.5754 0.7958 -2.5769 -0.4686
## Cogon_Patch_Size-Sus_scrofa -0.4643 0.7620 -2.2883 -0.3568
## Avg_Cogongrass_Cover-Canis_latrans 0.3297 0.4125 -0.4031 0.3040
## Avg_Cogongrass_Cover-Sciurus_niger -0.2251 0.6287 -1.6770 -0.1750
## Avg_Cogongrass_Cover-Procyon_lotor 0.1758 0.4126 -0.6210 0.1652
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3650 0.3879 -0.3560 0.3490
## Avg_Cogongrass_Cover-Lynx_rufus 0.5144 0.5032 -0.3431 0.4598
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1902 0.4316 -0.6586 0.1915
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1196 0.4971 -1.1823 -0.0890
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4019 0.4261 -0.4015 0.3755
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3276 0.4743 -0.5677 0.2989
## Avg_Cogongrass_Cover-Sus_scrofa -0.0057 0.5823 -1.2859 0.0326
## total_shrub_cover-Canis_latrans -0.0393 0.4959 -0.9275 -0.0725
## total_shrub_cover-Sciurus_niger -0.7537 0.5958 -2.0829 -0.7168
## total_shrub_cover-Procyon_lotor -0.9192 0.4998 -2.0808 -0.8632
## total_shrub_cover-Dasypus_novemcinctus -0.3531 0.4554 -1.3224 -0.3317
## total_shrub_cover-Lynx_rufus -0.8893 0.6057 -2.2627 -0.8259
## total_shrub_cover-Didelphis_virginiana -0.6476 0.4823 -1.7122 -0.6130
## total_shrub_cover-Sylvilagus_floridanus -0.7737 0.6233 -2.1905 -0.7146
## total_shrub_cover-Sciurus_carolinensis -0.5923 0.5594 -1.8677 -0.5579
## total_shrub_cover-Vulpes_vulpes -0.6812 0.7138 -2.2533 -0.6222
## total_shrub_cover-Sus_scrofa -0.4765 0.5939 -1.6881 -0.4656
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2902 1.0078 423
## (Intercept)-Sciurus_niger 0.7115 1.0330 282
## (Intercept)-Procyon_lotor 1.5275 1.0017 362
## (Intercept)-Dasypus_novemcinctus 0.5269 1.0059 794
## (Intercept)-Lynx_rufus 0.8360 1.0029 454
## (Intercept)-Didelphis_virginiana 0.2316 1.0158 583
## (Intercept)-Sylvilagus_floridanus 0.8204 1.0064 497
## (Intercept)-Sciurus_carolinensis 0.2588 1.0172 480
## (Intercept)-Vulpes_vulpes 0.3710 1.0216 294
## (Intercept)-Sus_scrofa 0.2238 1.0388 394
## Cogon_Patch_Size-Canis_latrans 1.8999 1.0207 1050
## Cogon_Patch_Size-Sciurus_niger 0.6659 1.0113 601
## Cogon_Patch_Size-Procyon_lotor 0.5823 1.0021 1101
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7720 1.0006 2000
## Cogon_Patch_Size-Lynx_rufus 1.2314 1.0068 729
## Cogon_Patch_Size-Didelphis_virginiana 1.5318 1.0062 851
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3188 1.0049 397
## Cogon_Patch_Size-Sciurus_carolinensis 0.3368 1.0012 655
## Cogon_Patch_Size-Vulpes_vulpes 0.6603 1.0038 571
## Cogon_Patch_Size-Sus_scrofa 0.7506 1.0074 775
## Avg_Cogongrass_Cover-Canis_latrans 1.2073 1.0097 1460
## Avg_Cogongrass_Cover-Sciurus_niger 0.8493 1.0173 631
## Avg_Cogongrass_Cover-Procyon_lotor 1.0036 1.0019 1364
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1802 1.0016 1311
## Avg_Cogongrass_Cover-Lynx_rufus 1.5832 1.0203 859
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0293 1.0096 1043
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8026 1.0106 839
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2837 1.0073 1102
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2794 1.0039 1209
## Avg_Cogongrass_Cover-Sus_scrofa 1.0781 1.0127 810
## total_shrub_cover-Canis_latrans 1.0733 1.0034 838
## total_shrub_cover-Sciurus_niger 0.2751 1.0055 497
## total_shrub_cover-Procyon_lotor -0.0931 1.0031 736
## total_shrub_cover-Dasypus_novemcinctus 0.4877 1.0072 714
## total_shrub_cover-Lynx_rufus 0.1570 1.0028 533
## total_shrub_cover-Didelphis_virginiana 0.2197 1.0035 592
## total_shrub_cover-Sylvilagus_floridanus 0.3379 1.0180 354
## total_shrub_cover-Sciurus_carolinensis 0.3820 1.0186 483
## total_shrub_cover-Vulpes_vulpes 0.4853 1.0157 355
## total_shrub_cover-Sus_scrofa 0.6915 1.0515 458
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7451 0.1895 -3.1381 -2.7375 -2.3853 1.0002
## (Intercept)-Sciurus_niger -3.7541 0.5941 -4.9246 -3.7171 -2.6743 1.0118
## (Intercept)-Procyon_lotor -2.3058 0.1365 -2.5800 -2.2993 -2.0515 1.0033
## (Intercept)-Dasypus_novemcinctus -1.8229 0.1722 -2.1717 -1.8193 -1.5069 1.0007
## (Intercept)-Lynx_rufus -3.4422 0.3009 -4.0526 -3.4332 -2.8893 1.0018
## (Intercept)-Didelphis_virginiana -2.6745 0.2858 -3.2424 -2.6615 -2.1441 1.0064
## (Intercept)-Sylvilagus_floridanus -3.2453 0.2636 -3.7690 -3.2338 -2.7494 1.0053
## (Intercept)-Sciurus_carolinensis -2.8080 0.3210 -3.4432 -2.7983 -2.2040 1.0218
## (Intercept)-Vulpes_vulpes -3.8809 0.6058 -5.1563 -3.8472 -2.8037 1.0045
## (Intercept)-Sus_scrofa -3.5242 0.5209 -4.5427 -3.5323 -2.5101 1.0887
## shrub_cover-Canis_latrans -0.2185 0.2284 -0.6686 -0.2182 0.2192 1.0088
## shrub_cover-Sciurus_niger 0.1167 0.5010 -0.8824 0.1235 1.0995 1.0007
## shrub_cover-Procyon_lotor 0.3134 0.1594 -0.0077 0.3165 0.6202 1.0012
## shrub_cover-Dasypus_novemcinctus 0.9858 0.3310 0.3613 0.9768 1.6404 1.0030
## shrub_cover-Lynx_rufus 0.1474 0.3469 -0.5557 0.1547 0.8057 1.0083
## shrub_cover-Didelphis_virginiana 1.0987 0.3821 0.3812 1.0936 1.8684 1.0026
## shrub_cover-Sylvilagus_floridanus 0.6458 0.4189 -0.1710 0.6520 1.4561 1.0279
## shrub_cover-Sciurus_carolinensis 1.0663 0.4164 0.2576 1.0640 1.8932 1.0202
## shrub_cover-Vulpes_vulpes 0.3818 0.5971 -0.9080 0.3967 1.5235 1.0103
## shrub_cover-Sus_scrofa 0.9812 0.6760 -0.3817 0.9728 2.3009 1.1016
## veg_height-Canis_latrans -0.5578 0.1814 -0.9381 -0.5526 -0.2223 1.0085
## veg_height-Sciurus_niger 0.1474 0.4178 -0.6084 0.1249 1.0498 1.0113
## veg_height-Procyon_lotor 0.3423 0.1244 0.0929 0.3395 0.5852 1.0013
## veg_height-Dasypus_novemcinctus 0.2677 0.1404 -0.0040 0.2662 0.5512 1.0037
## veg_height-Lynx_rufus 0.0565 0.2479 -0.4447 0.0579 0.5409 1.0061
## veg_height-Didelphis_virginiana 0.4238 0.2385 -0.0170 0.4187 0.9018 1.0038
## veg_height-Sylvilagus_floridanus 0.0963 0.2405 -0.3817 0.0959 0.5735 1.0131
## veg_height-Sciurus_carolinensis 0.1317 0.2240 -0.2947 0.1265 0.5771 1.0033
## veg_height-Vulpes_vulpes -0.0769 0.3051 -0.7032 -0.0601 0.4904 1.0064
## veg_height-Sus_scrofa -0.1028 0.3243 -0.7725 -0.0906 0.5166 1.0079
## week-Canis_latrans 0.0637 0.1329 -0.1951 0.0665 0.3196 1.0012
## week-Sciurus_niger -0.3241 0.3012 -0.9866 -0.2964 0.1798 1.0061
## week-Procyon_lotor -0.0584 0.1187 -0.3058 -0.0526 0.1624 1.0047
## week-Dasypus_novemcinctus -0.1694 0.1392 -0.4486 -0.1658 0.0888 1.0004
## week-Lynx_rufus -0.0567 0.1908 -0.4592 -0.0454 0.2954 1.0065
## week-Didelphis_virginiana -0.2238 0.2172 -0.7015 -0.2086 0.1614 1.0116
## week-Sylvilagus_floridanus -0.1668 0.2073 -0.6068 -0.1543 0.2188 1.0054
## week-Sciurus_carolinensis 0.1347 0.1844 -0.2139 0.1364 0.5006 1.0125
## week-Vulpes_vulpes -0.1213 0.2721 -0.7228 -0.1072 0.3898 1.0031
## week-Sus_scrofa 0.0736 0.2406 -0.3919 0.0695 0.5489 1.0062
## ESS
## (Intercept)-Canis_latrans 712
## (Intercept)-Sciurus_niger 203
## (Intercept)-Procyon_lotor 1480
## (Intercept)-Dasypus_novemcinctus 865
## (Intercept)-Lynx_rufus 533
## (Intercept)-Didelphis_virginiana 668
## (Intercept)-Sylvilagus_floridanus 567
## (Intercept)-Sciurus_carolinensis 475
## (Intercept)-Vulpes_vulpes 223
## (Intercept)-Sus_scrofa 283
## shrub_cover-Canis_latrans 762
## shrub_cover-Sciurus_niger 545
## shrub_cover-Procyon_lotor 1476
## shrub_cover-Dasypus_novemcinctus 674
## shrub_cover-Lynx_rufus 537
## shrub_cover-Didelphis_virginiana 467
## shrub_cover-Sylvilagus_floridanus 277
## shrub_cover-Sciurus_carolinensis 362
## shrub_cover-Vulpes_vulpes 401
## shrub_cover-Sus_scrofa 371
## veg_height-Canis_latrans 668
## veg_height-Sciurus_niger 469
## veg_height-Procyon_lotor 1603
## veg_height-Dasypus_novemcinctus 1689
## veg_height-Lynx_rufus 726
## veg_height-Didelphis_virginiana 1031
## veg_height-Sylvilagus_floridanus 653
## veg_height-Sciurus_carolinensis 978
## veg_height-Vulpes_vulpes 657
## veg_height-Sus_scrofa 901
## week-Canis_latrans 1435
## week-Sciurus_niger 612
## week-Procyon_lotor 1563
## week-Dasypus_novemcinctus 1963
## week-Lynx_rufus 998
## week-Didelphis_virginiana 1314
## week-Sylvilagus_floridanus 865
## week-Sciurus_carolinensis 1684
## week-Vulpes_vulpes 953
## week-Sus_scrofa 1290
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.807
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7494 0.3637 -1.4901 -0.7484 -0.0386 1.0221 394
## Veg_shannon_index 0.3193 0.2458 -0.1477 0.3122 0.8112 1.0038 900
## Avg_Cogongrass_Cover 0.2973 0.2589 -0.2365 0.3027 0.7804 1.0159 862
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6913 0.7834 0.0565 0.4640 2.6994 1.0055 410
## Veg_shannon_index 0.2617 0.2800 0.0360 0.1771 0.9831 1.0236 1192
## Avg_Cogongrass_Cover 0.2997 0.3524 0.0399 0.1903 1.2586 1.0306 759
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9437 0.8184 0.0822 0.7173 3.1076 1.1299 183
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9013 0.3248 -3.5447 -2.8940 -2.2668 1.0039 1512
## shrub_cover 0.3076 0.2638 -0.2221 0.3036 0.8365 1.0071 980
## veg_height 0.0631 0.1697 -0.2642 0.0618 0.4000 1.0063 1406
## week -0.0834 0.1329 -0.3567 -0.0752 0.1545 1.0043 1082
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8320 0.6581 0.1901 0.6436 2.5118 1.0064 556
## shrub_cover 0.5146 0.4434 0.0974 0.4008 1.5570 1.0092 743
## veg_height 0.2044 0.1813 0.0522 0.1579 0.6114 1.0137 1700
## week 0.1070 0.0978 0.0246 0.0800 0.3473 1.0316 1046
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1637 0.5853 -1.2533 -0.1830
## (Intercept)-Sciurus_niger -0.8408 0.6622 -2.1568 -0.8482
## (Intercept)-Procyon_lotor -0.0461 0.6209 -1.2221 -0.0589
## (Intercept)-Dasypus_novemcinctus -0.7497 0.5052 -1.7681 -0.7313
## (Intercept)-Lynx_rufus -0.4604 0.6623 -1.6394 -0.5094
## (Intercept)-Didelphis_virginiana -1.1329 0.5405 -2.2889 -1.1000
## (Intercept)-Sylvilagus_floridanus -0.6273 0.5407 -1.6760 -0.6335
## (Intercept)-Sciurus_carolinensis -1.1474 0.5547 -2.3444 -1.1038
## (Intercept)-Vulpes_vulpes -1.1094 0.7390 -2.6218 -1.0826
## (Intercept)-Sus_scrofa -1.4068 0.6683 -2.8698 -1.3410
## Veg_shannon_index-Canis_latrans 0.6134 0.3832 -0.0885 0.5930
## Veg_shannon_index-Sciurus_niger 0.3327 0.4652 -0.5630 0.3280
## Veg_shannon_index-Procyon_lotor 0.4333 0.3512 -0.2508 0.4202
## Veg_shannon_index-Dasypus_novemcinctus 0.1828 0.3417 -0.5328 0.1915
## Veg_shannon_index-Lynx_rufus 0.1915 0.4564 -0.8171 0.2201
## Veg_shannon_index-Didelphis_virginiana 0.4560 0.3723 -0.2419 0.4487
## Veg_shannon_index-Sylvilagus_floridanus 0.4101 0.3986 -0.3554 0.3848
## Veg_shannon_index-Sciurus_carolinensis -0.0293 0.4041 -0.8841 -0.0055
## Veg_shannon_index-Vulpes_vulpes 0.0828 0.4458 -0.8747 0.0986
## Veg_shannon_index-Sus_scrofa 0.5803 0.4662 -0.2670 0.5447
## Avg_Cogongrass_Cover-Canis_latrans 0.5561 0.3765 -0.1319 0.5401
## Avg_Cogongrass_Cover-Sciurus_niger -0.0952 0.5503 -1.3265 -0.0285
## Avg_Cogongrass_Cover-Procyon_lotor 0.3271 0.3607 -0.3523 0.3194
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4268 0.3346 -0.2113 0.4179
## Avg_Cogongrass_Cover-Lynx_rufus 0.5414 0.4096 -0.1915 0.5178
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4318 0.3746 -0.2801 0.4235
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0958 0.4295 -1.0550 -0.0640
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3930 0.3494 -0.2950 0.3964
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3738 0.4608 -0.5356 0.3588
## Avg_Cogongrass_Cover-Sus_scrofa 0.0825 0.4979 -1.0421 0.1215
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0181 1.0377 394
## (Intercept)-Sciurus_niger 0.5523 1.0257 510
## (Intercept)-Procyon_lotor 1.1293 1.0557 291
## (Intercept)-Dasypus_novemcinctus 0.2423 1.0017 1017
## (Intercept)-Lynx_rufus 1.0408 1.0201 389
## (Intercept)-Didelphis_virginiana -0.1644 1.0057 1059
## (Intercept)-Sylvilagus_floridanus 0.4917 1.0145 900
## (Intercept)-Sciurus_carolinensis -0.1657 1.0010 846
## (Intercept)-Vulpes_vulpes 0.3611 1.0069 316
## (Intercept)-Sus_scrofa -0.3112 1.0055 558
## Veg_shannon_index-Canis_latrans 1.4230 1.0092 1305
## Veg_shannon_index-Sciurus_niger 1.2851 1.0279 940
## Veg_shannon_index-Procyon_lotor 1.1576 1.0057 1467
## Veg_shannon_index-Dasypus_novemcinctus 0.8350 1.0065 1851
## Veg_shannon_index-Lynx_rufus 1.0496 1.0032 1239
## Veg_shannon_index-Didelphis_virginiana 1.2501 1.0007 1402
## Veg_shannon_index-Sylvilagus_floridanus 1.2547 1.0082 1299
## Veg_shannon_index-Sciurus_carolinensis 0.7122 1.0083 1262
## Veg_shannon_index-Vulpes_vulpes 0.9331 1.0035 1005
## Veg_shannon_index-Sus_scrofa 1.6085 1.0064 1174
## Avg_Cogongrass_Cover-Canis_latrans 1.3648 1.0049 1233
## Avg_Cogongrass_Cover-Sciurus_niger 0.8081 1.0319 683
## Avg_Cogongrass_Cover-Procyon_lotor 1.0654 1.0059 1840
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1052 1.0000 2119
## Avg_Cogongrass_Cover-Lynx_rufus 1.4379 1.0029 1414
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2188 1.0076 1623
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6689 1.0308 1049
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0963 1.0002 1891
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3213 1.0011 1161
## Avg_Cogongrass_Cover-Sus_scrofa 0.9303 1.0270 1001
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7205 0.1823 -3.0856 -2.7204 -2.3768 1.0049
## (Intercept)-Sciurus_niger -3.8696 0.5518 -4.9199 -3.8779 -2.7729 1.0281
## (Intercept)-Procyon_lotor -2.3020 0.1437 -2.6059 -2.2966 -2.0403 1.0020
## (Intercept)-Dasypus_novemcinctus -1.7702 0.1649 -2.1081 -1.7644 -1.4617 1.0000
## (Intercept)-Lynx_rufus -3.5693 0.3367 -4.2426 -3.5625 -2.9374 1.0101
## (Intercept)-Didelphis_virginiana -2.6213 0.2919 -3.2236 -2.6097 -2.0942 1.0055
## (Intercept)-Sylvilagus_floridanus -3.1729 0.2776 -3.7145 -3.1599 -2.6640 1.0010
## (Intercept)-Sciurus_carolinensis -2.6666 0.3083 -3.2935 -2.6561 -2.0865 1.0017
## (Intercept)-Vulpes_vulpes -3.9552 0.6642 -5.3614 -3.9101 -2.7888 1.0044
## (Intercept)-Sus_scrofa -3.3058 0.5247 -4.3556 -3.2995 -2.2631 1.0048
## shrub_cover-Canis_latrans -0.2673 0.2184 -0.7079 -0.2691 0.1572 1.0042
## shrub_cover-Sciurus_niger -0.2603 0.4667 -1.1853 -0.2640 0.6725 1.0038
## shrub_cover-Procyon_lotor 0.2564 0.1611 -0.0597 0.2601 0.5603 1.0065
## shrub_cover-Dasypus_novemcinctus 0.8428 0.2927 0.2840 0.8353 1.4214 1.0021
## shrub_cover-Lynx_rufus -0.1647 0.3550 -0.8852 -0.1592 0.5082 1.0108
## shrub_cover-Didelphis_virginiana 0.9537 0.3692 0.2855 0.9347 1.7233 1.0133
## shrub_cover-Sylvilagus_floridanus 0.3072 0.4047 -0.4330 0.2938 1.1438 1.0149
## shrub_cover-Sciurus_carolinensis 0.8372 0.3843 0.1135 0.8301 1.5994 1.0008
## shrub_cover-Vulpes_vulpes 0.0091 0.5456 -1.1035 0.0104 1.0654 1.0156
## shrub_cover-Sus_scrofa 0.6278 0.6832 -0.8127 0.6312 1.9844 1.0159
## veg_height-Canis_latrans -0.5527 0.1853 -0.9398 -0.5415 -0.2135 1.0028
## veg_height-Sciurus_niger 0.0782 0.4104 -0.6723 0.0644 0.9848 1.0030
## veg_height-Procyon_lotor 0.3383 0.1229 0.0981 0.3367 0.5768 1.0008
## veg_height-Dasypus_novemcinctus 0.2481 0.1341 -0.0174 0.2483 0.5159 1.0027
## veg_height-Lynx_rufus 0.0367 0.2341 -0.4284 0.0383 0.4879 1.0070
## veg_height-Didelphis_virginiana 0.4399 0.2437 -0.0192 0.4303 0.9511 1.0008
## veg_height-Sylvilagus_floridanus 0.1525 0.2357 -0.3111 0.1582 0.6195 1.0048
## veg_height-Sciurus_carolinensis 0.0810 0.2133 -0.3282 0.0790 0.5049 1.0033
## veg_height-Vulpes_vulpes -0.1229 0.3056 -0.7718 -0.1100 0.4405 1.0033
## veg_height-Sus_scrofa -0.1303 0.3389 -0.8572 -0.1126 0.5137 1.0015
## week-Canis_latrans 0.0651 0.1356 -0.2104 0.0666 0.3172 1.0047
## week-Sciurus_niger -0.3212 0.3101 -1.0646 -0.2806 0.1785 1.0010
## week-Procyon_lotor -0.0562 0.1149 -0.2885 -0.0540 0.1549 1.0043
## week-Dasypus_novemcinctus -0.1709 0.1407 -0.4645 -0.1639 0.0915 0.9997
## week-Lynx_rufus -0.0471 0.1985 -0.4646 -0.0402 0.3286 1.0232
## week-Didelphis_virginiana -0.2274 0.2206 -0.7078 -0.2097 0.1472 1.0052
## week-Sylvilagus_floridanus -0.1628 0.2093 -0.6067 -0.1498 0.2160 1.0091
## week-Sciurus_carolinensis 0.1271 0.1821 -0.2328 0.1319 0.4768 1.0078
## week-Vulpes_vulpes -0.1401 0.2804 -0.7300 -0.1228 0.3642 1.0014
## week-Sus_scrofa 0.0756 0.2360 -0.3815 0.0713 0.5531 1.0041
## ESS
## (Intercept)-Canis_latrans 746
## (Intercept)-Sciurus_niger 277
## (Intercept)-Procyon_lotor 1360
## (Intercept)-Dasypus_novemcinctus 1456
## (Intercept)-Lynx_rufus 387
## (Intercept)-Didelphis_virginiana 792
## (Intercept)-Sylvilagus_floridanus 664
## (Intercept)-Sciurus_carolinensis 804
## (Intercept)-Vulpes_vulpes 199
## (Intercept)-Sus_scrofa 685
## shrub_cover-Canis_latrans 947
## shrub_cover-Sciurus_niger 430
## shrub_cover-Procyon_lotor 1438
## shrub_cover-Dasypus_novemcinctus 1356
## shrub_cover-Lynx_rufus 478
## shrub_cover-Didelphis_virginiana 655
## shrub_cover-Sylvilagus_floridanus 549
## shrub_cover-Sciurus_carolinensis 775
## shrub_cover-Vulpes_vulpes 561
## shrub_cover-Sus_scrofa 846
## veg_height-Canis_latrans 789
## veg_height-Sciurus_niger 702
## veg_height-Procyon_lotor 1607
## veg_height-Dasypus_novemcinctus 2151
## veg_height-Lynx_rufus 894
## veg_height-Didelphis_virginiana 1164
## veg_height-Sylvilagus_floridanus 986
## veg_height-Sciurus_carolinensis 1212
## veg_height-Vulpes_vulpes 726
## veg_height-Sus_scrofa 1015
## week-Canis_latrans 1510
## week-Sciurus_niger 570
## week-Procyon_lotor 1823
## week-Dasypus_novemcinctus 1991
## week-Lynx_rufus 922
## week-Didelphis_virginiana 1038
## week-Sylvilagus_floridanus 999
## week-Sciurus_carolinensis 1665
## week-Vulpes_vulpes 677
## week-Sus_scrofa 1629
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8352
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4396 0.3961 -2.2400 -1.4247 -0.6865 1.0036 414
## Avg_Cogongrass_Cover -0.7667 0.3745 -1.5187 -0.7607 -0.0542 1.0010 408
## I(Avg_Cogongrass_Cover^2) 0.7998 0.3282 0.1957 0.7862 1.4888 1.0101 467
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6819 0.7014 0.0654 0.4785 2.4891 1.0160 901
## Avg_Cogongrass_Cover 0.3565 0.4450 0.0415 0.2219 1.4389 1.1196 697
## I(Avg_Cogongrass_Cover^2) 0.4705 0.7532 0.0385 0.2404 2.2694 1.1213 346
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5312 0.5254 0.0458 0.3631 1.9475 1.0212 148
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8716 0.2922 -3.4756 -2.8663 -2.3027 1.0021 1107
## shrub_cover 0.3301 0.2694 -0.2060 0.3308 0.8572 1.0021 1077
## veg_height 0.0913 0.1701 -0.2473 0.0905 0.4262 1.0047 1177
## week -0.0821 0.1316 -0.3522 -0.0770 0.1686 1.0034 1286
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6611 0.4983 0.1631 0.5264 1.9353 1.0177 671
## shrub_cover 0.4780 0.4369 0.0867 0.3599 1.5771 1.0210 847
## veg_height 0.1967 0.1520 0.0511 0.1579 0.5917 1.0184 1108
## week 0.1043 0.0896 0.0247 0.0802 0.3382 1.0141 1410
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8988 0.5953 -2.0204 -0.9109
## (Intercept)-Sciurus_niger -1.4919 0.6267 -2.7303 -1.4748
## (Intercept)-Procyon_lotor -0.6775 0.6104 -1.8084 -0.6946
## (Intercept)-Dasypus_novemcinctus -1.4174 0.5321 -2.5133 -1.3964
## (Intercept)-Lynx_rufus -1.3581 0.6212 -2.5756 -1.3622
## (Intercept)-Didelphis_virginiana -1.7180 0.5802 -2.9099 -1.6798
## (Intercept)-Sylvilagus_floridanus -1.2973 0.5593 -2.3800 -1.3039
## (Intercept)-Sciurus_carolinensis -1.9805 0.6252 -3.3146 -1.9433
## (Intercept)-Vulpes_vulpes -2.0019 0.7902 -3.7039 -1.9399
## (Intercept)-Sus_scrofa -2.0001 0.7039 -3.5656 -1.9448
## Avg_Cogongrass_Cover-Canis_latrans -0.4960 0.5043 -1.4353 -0.5320
## Avg_Cogongrass_Cover-Sciurus_niger -1.0369 0.6288 -2.4201 -1.0047
## Avg_Cogongrass_Cover-Procyon_lotor -0.7700 0.4764 -1.7007 -0.7812
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5790 0.4913 -1.5295 -0.5944
## Avg_Cogongrass_Cover-Lynx_rufus -0.7065 0.5383 -1.8126 -0.6956
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5164 0.5199 -1.4619 -0.5386
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1997 0.6001 -2.5994 -1.1474
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7681 0.5152 -1.7836 -0.7688
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7448 0.5852 -1.9077 -0.7439
## Avg_Cogongrass_Cover-Sus_scrofa -0.9434 0.5974 -2.2633 -0.9144
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2860 0.6936 0.3121 1.1458
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2446 0.6314 -1.3329 0.3158
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1576 0.6234 0.3165 1.0469
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7607 0.3640 0.0784 0.7419
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2002 0.5056 0.3976 1.1276
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5903 0.4088 -0.1986 0.5706
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7451 0.4352 -0.0104 0.7067
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9215 0.3932 0.2097 0.8983
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8352 0.4468 0.0655 0.8063
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3719 0.6225 -1.0173 0.4415
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2917 1.0037 582
## (Intercept)-Sciurus_niger -0.2621 1.0012 839
## (Intercept)-Procyon_lotor 0.5059 1.0008 525
## (Intercept)-Dasypus_novemcinctus -0.4039 1.0007 781
## (Intercept)-Lynx_rufus -0.1156 1.0030 464
## (Intercept)-Didelphis_virginiana -0.6495 1.0045 912
## (Intercept)-Sylvilagus_floridanus -0.1838 1.0054 830
## (Intercept)-Sciurus_carolinensis -0.8680 1.0013 722
## (Intercept)-Vulpes_vulpes -0.5987 1.0116 348
## (Intercept)-Sus_scrofa -0.8030 1.0001 680
## Avg_Cogongrass_Cover-Canis_latrans 0.5615 1.0159 846
## Avg_Cogongrass_Cover-Sciurus_niger 0.0453 1.0106 552
## Avg_Cogongrass_Cover-Procyon_lotor 0.1789 1.0100 861
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4123 1.0028 715
## Avg_Cogongrass_Cover-Lynx_rufus 0.3339 1.0026 657
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5846 1.0039 816
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1576 1.0091 580
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2460 1.0006 641
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4382 1.0047 758
## Avg_Cogongrass_Cover-Sus_scrofa 0.1858 1.0060 716
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9628 1.0323 339
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2755 1.0313 444
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6745 1.0899 324
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5147 1.0041 910
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3180 1.0107 656
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4747 1.0106 660
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7150 1.0098 723
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7652 1.0028 680
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8490 1.0105 573
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.4389 1.0156 468
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7290 0.1761 -3.0803 -2.7227 -2.3812 1.0152
## (Intercept)-Sciurus_niger -3.6369 0.5359 -4.7836 -3.6141 -2.6588 1.0041
## (Intercept)-Procyon_lotor -2.3181 0.1440 -2.6148 -2.3166 -2.0436 1.0257
## (Intercept)-Dasypus_novemcinctus -1.7830 0.1628 -2.1149 -1.7828 -1.4715 1.0098
## (Intercept)-Lynx_rufus -3.4641 0.3315 -4.1458 -3.4521 -2.8463 1.0096
## (Intercept)-Didelphis_virginiana -2.6184 0.2760 -3.1849 -2.6102 -2.1017 1.0036
## (Intercept)-Sylvilagus_floridanus -3.1507 0.2584 -3.7087 -3.1409 -2.6729 1.0005
## (Intercept)-Sciurus_carolinensis -2.6881 0.3062 -3.3151 -2.6801 -2.1267 1.0105
## (Intercept)-Vulpes_vulpes -3.7669 0.6158 -5.1348 -3.7046 -2.7282 1.0046
## (Intercept)-Sus_scrofa -3.2412 0.5167 -4.2595 -3.2348 -2.2229 1.0216
## shrub_cover-Canis_latrans -0.2217 0.2118 -0.6502 -0.2173 0.1836 1.0071
## shrub_cover-Sciurus_niger -0.0882 0.4700 -1.0168 -0.0936 0.8665 1.0140
## shrub_cover-Procyon_lotor 0.2441 0.1627 -0.0737 0.2432 0.5494 1.0019
## shrub_cover-Dasypus_novemcinctus 0.8326 0.2890 0.2775 0.8364 1.4095 1.0066
## shrub_cover-Lynx_rufus -0.1110 0.3589 -0.8325 -0.0975 0.5650 1.0088
## shrub_cover-Didelphis_virginiana 0.9525 0.3753 0.2599 0.9285 1.7211 1.0128
## shrub_cover-Sylvilagus_floridanus 0.2782 0.4053 -0.4992 0.2623 1.1001 1.0039
## shrub_cover-Sciurus_carolinensis 0.8266 0.3902 0.1070 0.8168 1.6145 1.0069
## shrub_cover-Vulpes_vulpes 0.0725 0.5604 -1.1129 0.0922 1.1328 1.0063
## shrub_cover-Sus_scrofa 0.5941 0.6854 -0.7645 0.5693 1.9942 1.0020
## veg_height-Canis_latrans -0.5402 0.1790 -0.9193 -0.5373 -0.2039 1.0276
## veg_height-Sciurus_niger 0.1785 0.4275 -0.6210 0.1605 1.0520 1.0243
## veg_height-Procyon_lotor 0.3495 0.1222 0.1047 0.3456 0.5907 1.0129
## veg_height-Dasypus_novemcinctus 0.2549 0.1330 -0.0042 0.2546 0.5235 1.0050
## veg_height-Lynx_rufus 0.1090 0.2353 -0.3744 0.1107 0.5611 1.0025
## veg_height-Didelphis_virginiana 0.4326 0.2449 -0.0429 0.4331 0.9114 1.0178
## veg_height-Sylvilagus_floridanus 0.1763 0.2455 -0.3059 0.1763 0.6594 1.0049
## veg_height-Sciurus_carolinensis 0.1079 0.2130 -0.3003 0.1091 0.5432 1.0131
## veg_height-Vulpes_vulpes -0.0700 0.2851 -0.6604 -0.0588 0.4526 1.0109
## veg_height-Sus_scrofa -0.0616 0.3318 -0.7557 -0.0486 0.5722 1.0021
## week-Canis_latrans 0.0605 0.1317 -0.2089 0.0627 0.3083 1.0031
## week-Sciurus_niger -0.3234 0.2935 -0.9785 -0.2938 0.1765 1.0097
## week-Procyon_lotor -0.0542 0.1192 -0.2950 -0.0523 0.1713 1.0094
## week-Dasypus_novemcinctus -0.1717 0.1380 -0.4574 -0.1659 0.0811 1.0081
## week-Lynx_rufus -0.0455 0.1960 -0.4459 -0.0431 0.3294 1.0057
## week-Didelphis_virginiana -0.2175 0.2160 -0.6808 -0.2071 0.1775 1.0017
## week-Sylvilagus_floridanus -0.1636 0.2068 -0.5945 -0.1526 0.2083 1.0093
## week-Sciurus_carolinensis 0.1277 0.1900 -0.2495 0.1281 0.5034 1.0158
## week-Vulpes_vulpes -0.1393 0.2865 -0.7857 -0.1202 0.3749 1.0022
## week-Sus_scrofa 0.0707 0.2364 -0.4075 0.0741 0.5214 0.9999
## ESS
## (Intercept)-Canis_latrans 940
## (Intercept)-Sciurus_niger 336
## (Intercept)-Procyon_lotor 1262
## (Intercept)-Dasypus_novemcinctus 1491
## (Intercept)-Lynx_rufus 376
## (Intercept)-Didelphis_virginiana 754
## (Intercept)-Sylvilagus_floridanus 671
## (Intercept)-Sciurus_carolinensis 869
## (Intercept)-Vulpes_vulpes 242
## (Intercept)-Sus_scrofa 625
## shrub_cover-Canis_latrans 967
## shrub_cover-Sciurus_niger 572
## shrub_cover-Procyon_lotor 1423
## shrub_cover-Dasypus_novemcinctus 1229
## shrub_cover-Lynx_rufus 512
## shrub_cover-Didelphis_virginiana 528
## shrub_cover-Sylvilagus_floridanus 480
## shrub_cover-Sciurus_carolinensis 925
## shrub_cover-Vulpes_vulpes 662
## shrub_cover-Sus_scrofa 780
## veg_height-Canis_latrans 832
## veg_height-Sciurus_niger 659
## veg_height-Procyon_lotor 1491
## veg_height-Dasypus_novemcinctus 2100
## veg_height-Lynx_rufus 853
## veg_height-Didelphis_virginiana 1029
## veg_height-Sylvilagus_floridanus 656
## veg_height-Sciurus_carolinensis 1116
## veg_height-Vulpes_vulpes 746
## veg_height-Sus_scrofa 992
## week-Canis_latrans 1783
## week-Sciurus_niger 778
## week-Procyon_lotor 1557
## week-Dasypus_novemcinctus 2086
## week-Lynx_rufus 1024
## week-Didelphis_virginiana 1432
## week-Sylvilagus_floridanus 1046
## week-Sciurus_carolinensis 1718
## week-Vulpes_vulpes 1115
## week-Sus_scrofa 1633
## 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.956
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1666 0.7454 -3.7478 -2.1583 -0.7562 1.0333 217
## Cogon_Patch_Size -0.4085 0.7396 -1.9841 -0.3901 0.9597 1.0628 306
## Veg_shannon_index 1.0135 0.4861 0.0694 1.0079 1.9709 1.0056 263
## total_shrub_cover -0.7335 0.5629 -2.0247 -0.6865 0.1985 1.2405 137
## Avg_Cogongrass_Cover 0.2156 0.9668 -1.7314 0.2405 2.0243 1.1075 129
## Tree_Density -2.1312 0.8506 -3.8231 -2.1066 -0.4159 1.0246 219
## Avg_Canopy_Cover 1.9954 0.6996 0.7121 1.9530 3.4596 1.0799 201
## I(Avg_Cogongrass_Cover^2) 1.4381 0.6629 0.2042 1.4133 2.8125 1.0604 229
## avg_veg_height -0.1529 0.5390 -1.1830 -0.1720 0.9216 1.0668 167
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.7896 4.0196 0.0782 1.5121 13.5366 1.1031 234
## Cogon_Patch_Size 3.0764 3.5989 0.1285 1.8872 12.9458 1.0720 314
## Veg_shannon_index 0.6935 1.0563 0.0440 0.3493 3.5323 1.0225 401
## total_shrub_cover 0.9856 1.8506 0.0515 0.4888 4.5673 1.3643 231
## Avg_Cogongrass_Cover 1.2588 2.0481 0.0566 0.5598 6.6446 1.0810 234
## Tree_Density 3.1152 4.4616 0.0883 1.6113 16.4555 1.0793 164
## Avg_Canopy_Cover 3.2607 4.3021 0.1553 1.9518 14.9406 1.0641 159
## I(Avg_Cogongrass_Cover^2) 1.8758 3.5590 0.0670 0.7774 10.0955 1.1422 106
## avg_veg_height 0.5813 1.5595 0.0423 0.2895 2.9443 1.3839 748
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2776 2.9178 0.1071 1.3884 9.5576 1.1139 93
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0189 0.3484 -3.7115 -3.0174 -2.3295 1.0269 1192
## shrub_cover 0.4708 0.2910 -0.1201 0.4600 1.0541 1.0301 458
## veg_height 0.0803 0.1686 -0.2567 0.0811 0.4073 1.0050 1273
## week -0.0864 0.1279 -0.3481 -0.0856 0.1536 1.0050 1245
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0646 0.8750 0.2572 0.8450 3.0811 1.1132 692
## shrub_cover 0.5931 0.5828 0.1141 0.4399 1.9753 1.0490 556
## veg_height 0.2054 0.1551 0.0511 0.1635 0.6097 1.0188 1178
## week 0.1019 0.0932 0.0249 0.0768 0.3326 1.0154 1040
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5015 1.0690 -3.5713 -1.5589
## (Intercept)-Sciurus_niger -1.3939 1.4388 -3.7501 -1.5826
## (Intercept)-Procyon_lotor -1.2256 1.0944 -3.3139 -1.2450
## (Intercept)-Dasypus_novemcinctus -2.4427 0.9714 -4.5089 -2.3612
## (Intercept)-Lynx_rufus -1.5759 1.3423 -3.9598 -1.7163
## (Intercept)-Didelphis_virginiana -3.1190 1.2278 -6.0324 -2.9673
## (Intercept)-Sylvilagus_floridanus -2.3381 1.1285 -4.7216 -2.2837
## (Intercept)-Sciurus_carolinensis -3.3823 1.3456 -6.4838 -3.2065
## (Intercept)-Vulpes_vulpes -3.1791 1.4615 -6.4039 -3.0294
## (Intercept)-Sus_scrofa -3.5197 1.5193 -6.9540 -3.2655
## Cogon_Patch_Size-Canis_latrans 1.2565 1.1883 -0.5853 1.0648
## Cogon_Patch_Size-Sciurus_niger -1.3794 1.6676 -5.3449 -1.1813
## Cogon_Patch_Size-Procyon_lotor -0.7737 0.8107 -2.4068 -0.7497
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1537 0.8105 -1.7223 -0.1575
## Cogon_Patch_Size-Lynx_rufus -0.6880 1.2884 -3.2558 -0.6790
## Cogon_Patch_Size-Didelphis_virginiana 1.3628 1.0457 -0.3732 1.2587
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5741 1.5624 -5.4539 -1.3188
## Cogon_Patch_Size-Sciurus_carolinensis -1.2040 1.3508 -4.3306 -1.0330
## Cogon_Patch_Size-Vulpes_vulpes -0.8404 1.5588 -4.2552 -0.7377
## Cogon_Patch_Size-Sus_scrofa -0.7201 1.4582 -4.2625 -0.5220
## Veg_shannon_index-Canis_latrans 1.3432 0.6751 0.1827 1.2941
## Veg_shannon_index-Sciurus_niger 1.1452 0.9255 -0.4863 1.1020
## Veg_shannon_index-Procyon_lotor 1.2386 0.6076 0.1353 1.1918
## Veg_shannon_index-Dasypus_novemcinctus 0.7023 0.6045 -0.5243 0.7115
## Veg_shannon_index-Lynx_rufus 1.0519 0.8787 -0.6389 1.0459
## Veg_shannon_index-Didelphis_virginiana 1.1569 0.6738 -0.0573 1.1278
## Veg_shannon_index-Sylvilagus_floridanus 1.0674 0.6957 -0.2728 1.0484
## Veg_shannon_index-Sciurus_carolinensis 0.4720 0.8163 -1.3277 0.5407
## Veg_shannon_index-Vulpes_vulpes 0.7429 0.8136 -1.0104 0.7964
## Veg_shannon_index-Sus_scrofa 1.3779 0.8146 -0.0393 1.3274
## total_shrub_cover-Canis_latrans 0.0300 0.7153 -1.3169 -0.0353
## total_shrub_cover-Sciurus_niger -1.0662 1.0282 -3.5944 -0.9408
## total_shrub_cover-Procyon_lotor -1.1146 0.6434 -2.5673 -1.0589
## total_shrub_cover-Dasypus_novemcinctus -0.3409 0.6703 -1.7659 -0.3234
## total_shrub_cover-Lynx_rufus -1.1255 1.0449 -3.8136 -0.9771
## total_shrub_cover-Didelphis_virginiana -0.9690 0.8965 -3.0004 -0.8589
## total_shrub_cover-Sylvilagus_floridanus -0.8232 0.9106 -3.0297 -0.7041
## total_shrub_cover-Sciurus_carolinensis -0.7924 1.0499 -3.2905 -0.6580
## total_shrub_cover-Vulpes_vulpes -0.8954 0.9951 -3.2253 -0.7912
## total_shrub_cover-Sus_scrofa -0.6395 0.9852 -2.9163 -0.5706
## Avg_Cogongrass_Cover-Canis_latrans 0.2999 1.2460 -2.1366 0.2984
## Avg_Cogongrass_Cover-Sciurus_niger -0.3223 1.5030 -3.8243 -0.2097
## Avg_Cogongrass_Cover-Procyon_lotor 0.2630 1.2066 -2.1694 0.2548
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8277 1.2919 -1.4732 0.7583
## Avg_Cogongrass_Cover-Lynx_rufus 0.2999 1.2702 -2.1608 0.3055
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3114 1.2524 -2.1510 0.3309
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3950 1.3558 -3.4742 -0.2783
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3158 1.3070 -2.2570 0.3374
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4733 1.2928 -1.9544 0.4496
## Avg_Cogongrass_Cover-Sus_scrofa 0.1006 1.2941 -2.5636 0.1289
## Tree_Density-Canis_latrans -3.0964 1.3935 -6.1384 -2.9227
## Tree_Density-Sciurus_niger -1.9773 1.6290 -5.2091 -1.9657
## Tree_Density-Procyon_lotor -2.2311 1.0532 -4.4958 -2.1743
## Tree_Density-Dasypus_novemcinctus -3.9750 1.7963 -8.3353 -3.6500
## Tree_Density-Lynx_rufus -0.9121 1.4694 -3.5108 -0.9841
## Tree_Density-Didelphis_virginiana -2.2262 1.2897 -4.8618 -2.2054
## Tree_Density-Sylvilagus_floridanus -2.6028 1.5008 -5.9022 -2.4827
## Tree_Density-Sciurus_carolinensis -2.4627 1.3924 -5.3658 -2.4049
## Tree_Density-Vulpes_vulpes -1.8885 1.7224 -4.9761 -1.9534
## Tree_Density-Sus_scrofa -2.3964 1.5061 -5.7154 -2.3436
## Avg_Canopy_Cover-Canis_latrans 0.2586 0.7258 -1.1145 0.2522
## Avg_Canopy_Cover-Sciurus_niger 2.1548 1.8495 -1.0108 2.0293
## Avg_Canopy_Cover-Procyon_lotor 1.6877 0.7688 0.2712 1.6462
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2392 0.8355 0.8308 2.1562
## Avg_Canopy_Cover-Lynx_rufus 1.2939 1.3064 -1.1110 1.2558
## Avg_Canopy_Cover-Didelphis_virginiana 3.0747 1.3382 1.1221 2.8171
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8092 1.8672 1.2145 3.4543
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0224 1.4927 1.0087 2.7305
## Avg_Canopy_Cover-Vulpes_vulpes 2.5554 1.5011 0.4640 2.2682
## Avg_Canopy_Cover-Sus_scrofa 2.1604 1.2586 0.3151 1.9836
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3025 1.2591 0.5624 2.0619
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.9528 1.5919 -2.2339 1.0226
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1994 1.0476 0.5707 2.0365
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4277 0.7558 0.0876 1.3605
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3871 1.3110 0.5756 2.1650
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9302 0.7478 -0.5600 0.9307
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2364 0.9120 -0.3191 1.1674
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5441 0.8449 0.1025 1.4436
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7271 0.9016 0.2945 1.6226
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5184 1.6008 -3.1842 0.7747
## avg_veg_height-Canis_latrans -0.2939 0.6500 -1.5717 -0.2855
## avg_veg_height-Sciurus_niger -0.4212 0.9695 -2.6832 -0.3255
## avg_veg_height-Procyon_lotor 0.0551 0.6714 -1.1923 0.0282
## avg_veg_height-Dasypus_novemcinctus 0.1632 0.6697 -1.0391 0.1380
## avg_veg_height-Lynx_rufus -0.3485 0.8268 -2.2311 -0.3101
## avg_veg_height-Didelphis_virginiana -0.2936 0.7501 -1.8636 -0.2674
## avg_veg_height-Sylvilagus_floridanus -0.2750 0.7542 -1.8866 -0.2578
## avg_veg_height-Sciurus_carolinensis 0.2231 0.7679 -1.0894 0.1716
## avg_veg_height-Vulpes_vulpes -0.1984 0.8221 -1.8504 -0.1847
## avg_veg_height-Sus_scrofa -0.1502 0.7997 -1.7760 -0.1494
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6899 1.0102 367
## (Intercept)-Sciurus_niger 2.1518 1.0817 161
## (Intercept)-Procyon_lotor 0.9344 1.0568 233
## (Intercept)-Dasypus_novemcinctus -0.7028 1.0266 377
## (Intercept)-Lynx_rufus 1.5113 1.0250 224
## (Intercept)-Didelphis_virginiana -1.1313 1.0589 275
## (Intercept)-Sylvilagus_floridanus -0.2712 1.0514 347
## (Intercept)-Sciurus_carolinensis -1.3022 1.0719 287
## (Intercept)-Vulpes_vulpes -0.6982 1.0079 257
## (Intercept)-Sus_scrofa -1.1319 1.0952 177
## Cogon_Patch_Size-Canis_latrans 4.2084 1.1619 359
## Cogon_Patch_Size-Sciurus_niger 1.4018 1.0127 223
## Cogon_Patch_Size-Procyon_lotor 0.7560 1.0229 283
## Cogon_Patch_Size-Dasypus_novemcinctus 1.5204 1.0451 529
## Cogon_Patch_Size-Lynx_rufus 1.9388 1.0392 319
## Cogon_Patch_Size-Didelphis_virginiana 3.6904 1.1151 237
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7768 1.0353 316
## Cogon_Patch_Size-Sciurus_carolinensis 0.8565 1.0048 271
## Cogon_Patch_Size-Vulpes_vulpes 2.0605 1.0358 266
## Cogon_Patch_Size-Sus_scrofa 1.6068 1.0116 435
## Veg_shannon_index-Canis_latrans 2.8476 1.0141 416
## Veg_shannon_index-Sciurus_niger 3.2377 1.0267 374
## Veg_shannon_index-Procyon_lotor 2.5765 1.0067 292
## Veg_shannon_index-Dasypus_novemcinctus 1.7996 1.0044 601
## Veg_shannon_index-Lynx_rufus 2.7678 1.0102 407
## Veg_shannon_index-Didelphis_virginiana 2.6040 1.0116 436
## Veg_shannon_index-Sylvilagus_floridanus 2.5284 0.9998 595
## Veg_shannon_index-Sciurus_carolinensis 1.8513 1.0219 443
## Veg_shannon_index-Vulpes_vulpes 2.2196 1.0024 460
## Veg_shannon_index-Sus_scrofa 3.2539 1.0233 441
## total_shrub_cover-Canis_latrans 1.6111 1.0198 535
## total_shrub_cover-Sciurus_niger 0.6203 1.1798 187
## total_shrub_cover-Procyon_lotor -0.0252 1.0874 423
## total_shrub_cover-Dasypus_novemcinctus 0.9072 1.0240 691
## total_shrub_cover-Lynx_rufus 0.4479 1.2016 188
## total_shrub_cover-Didelphis_virginiana 0.4431 1.0965 280
## total_shrub_cover-Sylvilagus_floridanus 0.6163 1.2108 274
## total_shrub_cover-Sciurus_carolinensis 0.8262 1.2318 152
## total_shrub_cover-Vulpes_vulpes 0.8173 1.1021 275
## total_shrub_cover-Sus_scrofa 1.1679 1.2312 226
## Avg_Cogongrass_Cover-Canis_latrans 2.7886 1.0639 195
## Avg_Cogongrass_Cover-Sciurus_niger 2.2270 1.0997 162
## Avg_Cogongrass_Cover-Procyon_lotor 2.6714 1.0711 205
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.6133 1.0592 200
## Avg_Cogongrass_Cover-Lynx_rufus 2.8617 1.0381 181
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7572 1.0925 162
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9125 1.1047 219
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8731 1.0821 195
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.1647 1.0236 231
## Avg_Cogongrass_Cover-Sus_scrofa 2.5696 1.0804 158
## Tree_Density-Canis_latrans -0.8510 1.0134 424
## Tree_Density-Sciurus_niger 1.4626 1.0729 187
## Tree_Density-Procyon_lotor -0.2678 1.0170 420
## Tree_Density-Dasypus_novemcinctus -1.4449 1.1029 193
## Tree_Density-Lynx_rufus 2.1449 1.0011 207
## Tree_Density-Didelphis_virginiana 0.3863 1.0245 462
## Tree_Density-Sylvilagus_floridanus 0.1152 1.0486 306
## Tree_Density-Sciurus_carolinensis 0.1830 1.0574 309
## Tree_Density-Vulpes_vulpes 1.7090 1.0422 240
## Tree_Density-Sus_scrofa 0.3457 1.0235 421
## Avg_Canopy_Cover-Canis_latrans 1.7498 1.0324 424
## Avg_Canopy_Cover-Sciurus_niger 6.1531 1.0174 132
## Avg_Canopy_Cover-Procyon_lotor 3.2447 1.0173 542
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0992 1.0598 248
## Avg_Canopy_Cover-Lynx_rufus 3.9670 1.0136 251
## Avg_Canopy_Cover-Didelphis_virginiana 6.3013 1.0788 184
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.3653 1.0663 173
## Avg_Canopy_Cover-Sciurus_carolinensis 6.6758 1.1440 216
## Avg_Canopy_Cover-Vulpes_vulpes 6.3521 1.0367 245
## Avg_Canopy_Cover-Sus_scrofa 4.9910 1.1797 257
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6453 1.0282 122
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.7916 1.0606 87
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.8099 1.0155 269
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0562 1.0398 240
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6476 1.0457 168
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4547 1.0729 270
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3010 1.0227 268
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.4272 1.0337 364
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.8470 1.0329 258
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.8143 1.1208 88
## avg_veg_height-Canis_latrans 0.9655 1.0339 276
## avg_veg_height-Sciurus_niger 1.2212 1.0464 283
## avg_veg_height-Procyon_lotor 1.4060 1.0313 251
## avg_veg_height-Dasypus_novemcinctus 1.5517 1.0366 281
## avg_veg_height-Lynx_rufus 1.1153 1.0508 275
## avg_veg_height-Didelphis_virginiana 1.1046 1.0404 261
## avg_veg_height-Sylvilagus_floridanus 1.1201 1.0359 336
## avg_veg_height-Sciurus_carolinensis 1.9378 1.1025 239
## avg_veg_height-Vulpes_vulpes 1.4284 1.0311 292
## avg_veg_height-Sus_scrofa 1.3434 1.0353 272
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7249 0.1800 -3.0953 -2.7150 -2.3889 1.0053
## (Intercept)-Sciurus_niger -4.3262 0.5651 -5.4721 -4.3482 -3.1180 1.1075
## (Intercept)-Procyon_lotor -2.3138 0.1409 -2.6030 -2.3076 -2.0516 1.0040
## (Intercept)-Dasypus_novemcinctus -1.7955 0.1651 -2.1359 -1.7925 -1.4872 1.0154
## (Intercept)-Lynx_rufus -3.6747 0.3277 -4.3244 -3.6719 -3.0330 1.0871
## (Intercept)-Didelphis_virginiana -2.6448 0.2934 -3.2309 -2.6317 -2.0988 1.0044
## (Intercept)-Sylvilagus_floridanus -3.2238 0.2724 -3.7777 -3.2162 -2.7157 1.0429
## (Intercept)-Sciurus_carolinensis -2.8407 0.3196 -3.4882 -2.8353 -2.2240 1.0277
## (Intercept)-Vulpes_vulpes -4.2127 0.5954 -5.4175 -4.1999 -3.1007 1.0654
## (Intercept)-Sus_scrofa -3.5496 0.5657 -4.6518 -3.5483 -2.4525 1.1328
## shrub_cover-Canis_latrans -0.2523 0.2352 -0.7044 -0.2548 0.2161 1.0160
## shrub_cover-Sciurus_niger -0.1558 0.4919 -1.0872 -0.1698 0.8390 1.0503
## shrub_cover-Procyon_lotor 0.2772 0.1589 -0.0436 0.2778 0.5743 1.0038
## shrub_cover-Dasypus_novemcinctus 0.9480 0.3052 0.3432 0.9453 1.5494 1.0380
## shrub_cover-Lynx_rufus -0.0213 0.3557 -0.7312 -0.0247 0.6563 1.0384
## shrub_cover-Didelphis_virginiana 1.0477 0.3763 0.3437 1.0436 1.8013 1.0097
## shrub_cover-Sylvilagus_floridanus 0.5824 0.3877 -0.1943 0.5840 1.3478 1.0187
## shrub_cover-Sciurus_carolinensis 1.0742 0.4144 0.2779 1.0719 1.9317 1.0913
## shrub_cover-Vulpes_vulpes 0.3080 0.5435 -0.7622 0.3064 1.3584 1.0031
## shrub_cover-Sus_scrofa 0.9881 0.7566 -0.5231 0.9695 2.4846 1.0839
## veg_height-Canis_latrans -0.5278 0.1850 -0.9076 -0.5244 -0.1697 1.0174
## veg_height-Sciurus_niger 0.0563 0.3892 -0.6396 0.0325 0.8733 1.0050
## veg_height-Procyon_lotor 0.3556 0.1229 0.1203 0.3553 0.5968 1.0031
## veg_height-Dasypus_novemcinctus 0.2674 0.1392 -0.0028 0.2667 0.5488 1.0130
## veg_height-Lynx_rufus 0.1563 0.2350 -0.3125 0.1612 0.5904 1.0414
## veg_height-Didelphis_virginiana 0.4563 0.2328 0.0292 0.4455 0.9353 1.0017
## veg_height-Sylvilagus_floridanus 0.1448 0.2395 -0.3240 0.1492 0.6119 1.0036
## veg_height-Sciurus_carolinensis 0.1598 0.2191 -0.2716 0.1592 0.5939 1.0015
## veg_height-Vulpes_vulpes -0.1132 0.3189 -0.8078 -0.0934 0.4608 1.0038
## veg_height-Sus_scrofa -0.1333 0.3230 -0.8177 -0.1180 0.4679 1.0378
## week-Canis_latrans 0.0618 0.1324 -0.2060 0.0645 0.3092 1.0002
## week-Sciurus_niger -0.3167 0.2945 -0.9832 -0.2909 0.1750 1.0079
## week-Procyon_lotor -0.0571 0.1193 -0.3047 -0.0519 0.1684 1.0037
## week-Dasypus_novemcinctus -0.1736 0.1378 -0.4527 -0.1692 0.0887 1.0074
## week-Lynx_rufus -0.0582 0.1956 -0.4555 -0.0528 0.3002 1.0009
## week-Didelphis_virginiana -0.2221 0.2126 -0.6766 -0.2071 0.1472 1.0100
## week-Sylvilagus_floridanus -0.1659 0.2109 -0.6133 -0.1500 0.2157 1.0253
## week-Sciurus_carolinensis 0.1277 0.1834 -0.2275 0.1260 0.4910 1.0024
## week-Vulpes_vulpes -0.1369 0.2702 -0.7236 -0.1306 0.3537 1.0047
## week-Sus_scrofa 0.0691 0.2373 -0.3978 0.0672 0.5317 1.0057
## ESS
## (Intercept)-Canis_latrans 719
## (Intercept)-Sciurus_niger 120
## (Intercept)-Procyon_lotor 1061
## (Intercept)-Dasypus_novemcinctus 1111
## (Intercept)-Lynx_rufus 317
## (Intercept)-Didelphis_virginiana 485
## (Intercept)-Sylvilagus_floridanus 520
## (Intercept)-Sciurus_carolinensis 484
## (Intercept)-Vulpes_vulpes 189
## (Intercept)-Sus_scrofa 267
## shrub_cover-Canis_latrans 534
## shrub_cover-Sciurus_niger 281
## shrub_cover-Procyon_lotor 1233
## shrub_cover-Dasypus_novemcinctus 834
## shrub_cover-Lynx_rufus 397
## shrub_cover-Didelphis_virginiana 485
## shrub_cover-Sylvilagus_floridanus 382
## shrub_cover-Sciurus_carolinensis 235
## shrub_cover-Vulpes_vulpes 458
## shrub_cover-Sus_scrofa 236
## veg_height-Canis_latrans 781
## veg_height-Sciurus_niger 333
## veg_height-Procyon_lotor 1447
## veg_height-Dasypus_novemcinctus 1543
## veg_height-Lynx_rufus 550
## veg_height-Didelphis_virginiana 916
## veg_height-Sylvilagus_floridanus 855
## veg_height-Sciurus_carolinensis 828
## veg_height-Vulpes_vulpes 613
## veg_height-Sus_scrofa 570
## week-Canis_latrans 1665
## week-Sciurus_niger 444
## week-Procyon_lotor 1673
## week-Dasypus_novemcinctus 2000
## week-Lynx_rufus 763
## week-Didelphis_virginiana 1352
## week-Sylvilagus_floridanus 911
## week-Sciurus_carolinensis 1493
## week-Vulpes_vulpes 900
## week-Sus_scrofa 1482
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6327
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2355 0.6790 -2.5417 -1.2450 0.1284 1.0310 327
## Cogon_Patch_Size -0.8684 0.5851 -2.1481 -0.8534 0.2427 1.0030 387
## Veg_shannon_index 0.8613 0.4059 0.1024 0.8462 1.6662 1.0094 260
## total_shrub_cover -0.2527 0.3587 -0.9429 -0.2563 0.4638 1.0136 622
## Avg_Cogongrass_Cover 2.1079 0.6406 0.9561 2.0891 3.4455 1.0432 195
## Tree_Density -1.8965 0.6125 -3.1252 -1.8948 -0.7242 1.0143 350
## Avg_Canopy_Cover 1.6905 0.4779 0.7666 1.6779 2.6666 1.0065 630
## avg_veg_height -0.6211 0.3965 -1.4347 -0.6163 0.0998 1.0118 234
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5918 3.5713 0.1802 2.6809 12.4839 1.0206 390
## Cogon_Patch_Size 2.2030 2.8329 0.1368 1.3009 9.5136 1.1036 358
## Veg_shannon_index 0.5717 0.7626 0.0489 0.3411 2.3525 1.0389 670
## total_shrub_cover 0.4848 0.7218 0.0435 0.2848 2.1059 1.0086 903
## Avg_Cogongrass_Cover 0.8715 1.2802 0.0444 0.4412 4.2767 1.0523 387
## Tree_Density 1.6744 2.8350 0.0575 0.7683 9.1085 1.0761 316
## Avg_Canopy_Cover 1.2116 1.6275 0.0849 0.7495 5.0495 1.0719 412
## avg_veg_height 0.3428 0.4410 0.0340 0.1963 1.4958 1.0014 733
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7124 1.7787 0.0853 1.2014 6.8737 1.0979 57
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8134 0.319 -3.4352 -2.8139 -2.1749 1.0069 1834
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9669 0.6612 0.294 0.8126 2.5955 1.0458 1391
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0972 0.9491 -1.8444 0.1050
## (Intercept)-Sciurus_niger -0.5324 1.3631 -2.9374 -0.6346
## (Intercept)-Procyon_lotor 0.2180 1.0019 -1.9499 0.3119
## (Intercept)-Dasypus_novemcinctus -1.4680 0.7671 -3.0734 -1.4284
## (Intercept)-Lynx_rufus 0.0270 1.4743 -2.4246 -0.1212
## (Intercept)-Didelphis_virginiana -2.4954 0.9270 -4.4926 -2.4485
## (Intercept)-Sylvilagus_floridanus -1.3928 0.9513 -3.3088 -1.3797
## (Intercept)-Sciurus_carolinensis -2.6714 0.9757 -4.7748 -2.5885
## (Intercept)-Vulpes_vulpes -2.2875 1.2469 -4.8876 -2.2328
## (Intercept)-Sus_scrofa -3.3644 1.2545 -5.9891 -3.2690
## Cogon_Patch_Size-Canis_latrans 0.4596 0.9810 -1.0218 0.3126
## Cogon_Patch_Size-Sciurus_niger -1.7245 1.4395 -5.3962 -1.5016
## Cogon_Patch_Size-Procyon_lotor -1.0648 0.6497 -2.3354 -1.0385
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8073 0.5811 -2.0648 -0.7858
## Cogon_Patch_Size-Lynx_rufus -1.0365 1.1277 -3.0846 -1.0806
## Cogon_Patch_Size-Didelphis_virginiana 0.6324 0.8093 -0.7646 0.5777
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8850 1.2898 -5.0308 -1.6735
## Cogon_Patch_Size-Sciurus_carolinensis -1.6150 1.0545 -4.2505 -1.4376
## Cogon_Patch_Size-Vulpes_vulpes -1.3412 1.3507 -4.5200 -1.1975
## Cogon_Patch_Size-Sus_scrofa -1.0913 1.0927 -3.6833 -0.9788
## Veg_shannon_index-Canis_latrans 1.1675 0.5398 0.1781 1.1384
## Veg_shannon_index-Sciurus_niger 0.9680 0.7766 -0.5269 0.9378
## Veg_shannon_index-Procyon_lotor 1.1402 0.5431 0.1439 1.1158
## Veg_shannon_index-Dasypus_novemcinctus 0.6693 0.4920 -0.3238 0.6822
## Veg_shannon_index-Lynx_rufus 0.8071 0.7023 -0.6030 0.8125
## Veg_shannon_index-Didelphis_virginiana 0.9600 0.5635 -0.0798 0.9312
## Veg_shannon_index-Sylvilagus_floridanus 0.9672 0.6044 -0.1010 0.9392
## Veg_shannon_index-Sciurus_carolinensis 0.3136 0.6514 -1.1041 0.3782
## Veg_shannon_index-Vulpes_vulpes 0.4042 0.6956 -1.1210 0.4662
## Veg_shannon_index-Sus_scrofa 1.3152 0.7799 0.0891 1.2104
## total_shrub_cover-Canis_latrans 0.0754 0.5183 -0.8394 0.0379
## total_shrub_cover-Sciurus_niger -0.5297 0.6751 -2.0367 -0.4849
## total_shrub_cover-Procyon_lotor -0.6657 0.5096 -1.7555 -0.6240
## total_shrub_cover-Dasypus_novemcinctus 0.0145 0.4724 -0.8717 -0.0131
## total_shrub_cover-Lynx_rufus -0.5924 0.6475 -2.1280 -0.5309
## total_shrub_cover-Didelphis_virginiana -0.3625 0.5609 -1.5665 -0.3380
## total_shrub_cover-Sylvilagus_floridanus -0.1897 0.5798 -1.3591 -0.1839
## total_shrub_cover-Sciurus_carolinensis -0.0624 0.5451 -1.0605 -0.0894
## total_shrub_cover-Vulpes_vulpes -0.3668 0.6342 -1.7381 -0.3386
## total_shrub_cover-Sus_scrofa 0.0238 0.6308 -1.0804 -0.0246
## Avg_Cogongrass_Cover-Canis_latrans 2.3471 0.7852 1.0176 2.3061
## Avg_Cogongrass_Cover-Sciurus_niger 1.6296 1.1410 -1.1013 1.7183
## Avg_Cogongrass_Cover-Procyon_lotor 2.3227 0.8294 0.8116 2.2769
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5665 0.8661 1.0736 2.5017
## Avg_Cogongrass_Cover-Lynx_rufus 2.5125 0.9358 0.9104 2.4355
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2160 0.7989 0.7644 2.1781
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5864 0.8917 -0.2417 1.6009
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3606 0.7983 0.9646 2.3249
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4814 0.9279 0.8815 2.4264
## Avg_Cogongrass_Cover-Sus_scrofa 1.6793 0.9889 -0.4334 1.6975
## Tree_Density-Canis_latrans -2.3387 0.9623 -4.5740 -2.2036
## Tree_Density-Sciurus_niger -1.8993 1.0973 -4.3029 -1.8627
## Tree_Density-Procyon_lotor -1.5566 0.6836 -2.8101 -1.5635
## Tree_Density-Dasypus_novemcinctus -3.0004 1.3131 -6.2696 -2.7216
## Tree_Density-Lynx_rufus -0.7481 1.1389 -2.5067 -0.9075
## Tree_Density-Didelphis_virginiana -2.1718 0.9455 -4.4331 -2.0623
## Tree_Density-Sylvilagus_floridanus -2.3581 1.1545 -5.0290 -2.1852
## Tree_Density-Sciurus_carolinensis -2.2408 1.0434 -4.6856 -2.1240
## Tree_Density-Vulpes_vulpes -1.8567 1.1915 -4.2557 -1.8495
## Tree_Density-Sus_scrofa -2.0692 1.1404 -4.8005 -1.9468
## Avg_Canopy_Cover-Canis_latrans 0.5190 0.6240 -0.7425 0.5091
## Avg_Canopy_Cover-Sciurus_niger 1.7684 1.1504 -0.3971 1.7187
## Avg_Canopy_Cover-Procyon_lotor 1.6458 0.6006 0.5037 1.6226
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8347 0.5895 0.8312 1.7949
## Avg_Canopy_Cover-Lynx_rufus 1.2113 0.9174 -0.5250 1.1983
## Avg_Canopy_Cover-Didelphis_virginiana 2.3553 0.7750 1.1418 2.2520
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.6125 1.0915 1.0192 2.4337
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0283 0.6812 0.9212 1.9479
## Avg_Canopy_Cover-Vulpes_vulpes 1.8707 0.8710 0.4302 1.7940
## Avg_Canopy_Cover-Sus_scrofa 1.8367 0.7482 0.5621 1.7959
## avg_veg_height-Canis_latrans -0.7972 0.5264 -1.9194 -0.7759
## avg_veg_height-Sciurus_niger -0.7732 0.6974 -2.4031 -0.7130
## avg_veg_height-Procyon_lotor -0.4663 0.5110 -1.4389 -0.4726
## avg_veg_height-Dasypus_novemcinctus -0.4000 0.5170 -1.3865 -0.4116
## avg_veg_height-Lynx_rufus -0.6742 0.6205 -1.9260 -0.6616
## avg_veg_height-Didelphis_virginiana -0.6797 0.5494 -1.8004 -0.6793
## avg_veg_height-Sylvilagus_floridanus -0.7846 0.5689 -2.0095 -0.7520
## avg_veg_height-Sciurus_carolinensis -0.3424 0.5724 -1.4115 -0.3569
## avg_veg_height-Vulpes_vulpes -0.6545 0.5849 -1.8402 -0.6459
## avg_veg_height-Sus_scrofa -0.6931 0.5761 -1.8979 -0.6687
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0124 1.0171 294
## (Intercept)-Sciurus_niger 2.5041 1.0765 268
## (Intercept)-Procyon_lotor 2.0723 1.0422 126
## (Intercept)-Dasypus_novemcinctus -0.0689 1.0267 422
## (Intercept)-Lynx_rufus 3.3947 1.0214 171
## (Intercept)-Didelphis_virginiana -0.7798 1.0118 759
## (Intercept)-Sylvilagus_floridanus 0.5151 1.0021 708
## (Intercept)-Sciurus_carolinensis -1.0167 1.0058 520
## (Intercept)-Vulpes_vulpes 0.1207 1.0630 326
## (Intercept)-Sus_scrofa -1.2256 1.0066 353
## Cogon_Patch_Size-Canis_latrans 2.8581 1.0204 520
## Cogon_Patch_Size-Sciurus_niger 0.5115 1.0475 286
## Cogon_Patch_Size-Procyon_lotor 0.1914 1.0067 441
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3140 1.0050 714
## Cogon_Patch_Size-Lynx_rufus 1.3028 1.0057 412
## Cogon_Patch_Size-Didelphis_virginiana 2.3995 1.0072 562
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0212 1.0193 490
## Cogon_Patch_Size-Sciurus_carolinensis -0.0722 1.0168 488
## Cogon_Patch_Size-Vulpes_vulpes 0.9992 1.0144 298
## Cogon_Patch_Size-Sus_scrofa 0.8357 1.0062 637
## Veg_shannon_index-Canis_latrans 2.3313 1.0144 632
## Veg_shannon_index-Sciurus_niger 2.5925 1.0137 611
## Veg_shannon_index-Procyon_lotor 2.3038 1.0192 406
## Veg_shannon_index-Dasypus_novemcinctus 1.6635 1.0023 681
## Veg_shannon_index-Lynx_rufus 2.1528 1.0286 596
## Veg_shannon_index-Didelphis_virginiana 2.1301 1.0152 845
## Veg_shannon_index-Sylvilagus_floridanus 2.2455 1.0047 480
## Veg_shannon_index-Sciurus_carolinensis 1.4212 1.0088 588
## Veg_shannon_index-Vulpes_vulpes 1.6419 1.0089 502
## Veg_shannon_index-Sus_scrofa 3.1551 1.0362 541
## total_shrub_cover-Canis_latrans 1.2122 1.0022 1054
## total_shrub_cover-Sciurus_niger 0.6900 1.0075 758
## total_shrub_cover-Procyon_lotor 0.2290 1.0040 999
## total_shrub_cover-Dasypus_novemcinctus 1.0146 1.0105 1105
## total_shrub_cover-Lynx_rufus 0.5364 1.0009 646
## total_shrub_cover-Didelphis_virginiana 0.6726 1.0027 1172
## total_shrub_cover-Sylvilagus_floridanus 1.0354 1.0114 947
## total_shrub_cover-Sciurus_carolinensis 1.0687 1.0011 1119
## total_shrub_cover-Vulpes_vulpes 0.8007 1.0088 876
## total_shrub_cover-Sus_scrofa 1.4440 1.0001 995
## Avg_Cogongrass_Cover-Canis_latrans 4.0444 1.0103 332
## Avg_Cogongrass_Cover-Sciurus_niger 3.6354 1.0318 301
## Avg_Cogongrass_Cover-Procyon_lotor 4.1027 1.0083 307
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5088 1.0036 276
## Avg_Cogongrass_Cover-Lynx_rufus 4.6434 1.0048 325
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8991 1.0211 311
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3258 1.0262 347
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0739 1.0038 309
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.5513 1.0133 299
## Avg_Cogongrass_Cover-Sus_scrofa 3.5950 1.0864 368
## Tree_Density-Canis_latrans -0.7924 1.0156 333
## Tree_Density-Sciurus_niger 0.2526 1.0142 455
## Tree_Density-Procyon_lotor -0.1493 1.0064 503
## Tree_Density-Dasypus_novemcinctus -1.1876 1.0450 149
## Tree_Density-Lynx_rufus 1.8394 1.0214 260
## Tree_Density-Didelphis_virginiana -0.6304 1.0631 594
## Tree_Density-Sylvilagus_floridanus -0.6719 1.0227 401
## Tree_Density-Sciurus_carolinensis -0.5473 1.0258 405
## Tree_Density-Vulpes_vulpes 0.6500 1.0381 426
## Tree_Density-Sus_scrofa -0.1347 1.0208 467
## Avg_Canopy_Cover-Canis_latrans 1.7339 1.0191 669
## Avg_Canopy_Cover-Sciurus_niger 4.2213 1.0237 413
## Avg_Canopy_Cover-Procyon_lotor 2.9002 1.0086 689
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.0871 1.0029 665
## Avg_Canopy_Cover-Lynx_rufus 3.0144 1.0081 420
## Avg_Canopy_Cover-Didelphis_virginiana 4.0941 1.0149 470
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.3374 1.0329 419
## Avg_Canopy_Cover-Sciurus_carolinensis 3.5524 1.0326 790
## Avg_Canopy_Cover-Vulpes_vulpes 3.8995 1.0057 552
## Avg_Canopy_Cover-Sus_scrofa 3.4675 1.0257 834
## avg_veg_height-Canis_latrans 0.1913 1.0050 422
## avg_veg_height-Sciurus_niger 0.4257 1.0102 360
## avg_veg_height-Procyon_lotor 0.5853 1.0063 654
## avg_veg_height-Dasypus_novemcinctus 0.6463 1.0100 758
## avg_veg_height-Lynx_rufus 0.4923 1.0007 489
## avg_veg_height-Didelphis_virginiana 0.3826 1.0047 433
## avg_veg_height-Sylvilagus_floridanus 0.2224 1.0096 609
## avg_veg_height-Sciurus_carolinensis 0.9007 1.0021 574
## avg_veg_height-Vulpes_vulpes 0.4932 1.0123 522
## avg_veg_height-Sus_scrofa 0.4191 1.0114 543
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6334 0.1728 -2.9825 -2.6299 -2.3072 1.0013
## (Intercept)-Sciurus_niger -4.1937 0.4253 -5.0157 -4.2029 -3.3287 1.0644
## (Intercept)-Procyon_lotor -2.2737 0.1305 -2.5410 -2.2676 -2.0293 1.0102
## (Intercept)-Dasypus_novemcinctus -1.5949 0.1371 -1.8620 -1.5922 -1.3341 1.0024
## (Intercept)-Lynx_rufus -3.5673 0.3108 -4.1666 -3.5716 -2.9497 1.0105
## (Intercept)-Didelphis_virginiana -2.3181 0.2396 -2.8072 -2.3023 -1.8672 1.0004
## (Intercept)-Sylvilagus_floridanus -3.1458 0.2655 -3.6696 -3.1383 -2.6448 1.0017
## (Intercept)-Sciurus_carolinensis -2.4644 0.2633 -3.0228 -2.4520 -1.9862 1.0255
## (Intercept)-Vulpes_vulpes -3.9078 0.5622 -5.1008 -3.8866 -2.8791 1.0761
## (Intercept)-Sus_scrofa -2.9338 0.4355 -3.8216 -2.9100 -2.1382 1.0053
## ESS
## (Intercept)-Canis_latrans 1032
## (Intercept)-Sciurus_niger 260
## (Intercept)-Procyon_lotor 1492
## (Intercept)-Dasypus_novemcinctus 2375
## (Intercept)-Lynx_rufus 277
## (Intercept)-Didelphis_virginiana 1623
## (Intercept)-Sylvilagus_floridanus 667
## (Intercept)-Sciurus_carolinensis 1206
## (Intercept)-Vulpes_vulpes 261
## (Intercept)-Sus_scrofa 718
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5742
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8072 0.3868 -1.5834 -0.7943 -0.0549 1.0113 582
## Avg_Cogongrass_Cover 0.1501 0.3080 -0.4822 0.1499 0.7556 1.1102 461
## total_shrub_cover -0.2686 0.2673 -0.8368 -0.2555 0.2292 1.0065 903
## avg_veg_height -0.0071 0.2807 -0.5393 -0.0108 0.5371 1.0096 426
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8681 0.8606 0.0768 0.6199 2.9703 1.0357 589
## Avg_Cogongrass_Cover 0.3065 0.3468 0.0383 0.1984 1.1752 1.0758 560
## total_shrub_cover 0.3345 0.4011 0.0390 0.2119 1.3378 1.0322 719
## avg_veg_height 0.1974 0.2115 0.0317 0.1347 0.7647 1.0308 1155
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.932 0.7956 0.0841 0.7226 3.0041 1.04 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.761 0.3004 -3.3663 -2.7512 -2.1784 1.0013 1758
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8168 0.6191 0.2058 0.6436 2.4894 1.037 400
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1007 0.5998 -1.2594 -0.1132
## (Intercept)-Sciurus_niger -0.9817 0.6942 -2.3274 -1.0065
## (Intercept)-Procyon_lotor 0.0845 0.6567 -1.1526 0.0813
## (Intercept)-Dasypus_novemcinctus -0.8013 0.5034 -1.8388 -0.7791
## (Intercept)-Lynx_rufus -0.5187 0.6546 -1.7502 -0.5426
## (Intercept)-Didelphis_virginiana -1.2475 0.5713 -2.4078 -1.2186
## (Intercept)-Sylvilagus_floridanus -0.6052 0.5994 -1.7505 -0.6253
## (Intercept)-Sciurus_carolinensis -1.3213 0.5733 -2.4909 -1.2831
## (Intercept)-Vulpes_vulpes -1.2061 0.8350 -2.9141 -1.2008
## (Intercept)-Sus_scrofa -1.5815 0.7240 -3.1419 -1.5108
## Avg_Cogongrass_Cover-Canis_latrans 0.3855 0.4320 -0.4231 0.3757
## Avg_Cogongrass_Cover-Sciurus_niger -0.1990 0.5666 -1.5191 -0.1475
## Avg_Cogongrass_Cover-Procyon_lotor 0.0995 0.4137 -0.7243 0.1027
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2745 0.3781 -0.4475 0.2670
## Avg_Cogongrass_Cover-Lynx_rufus 0.4350 0.4539 -0.3922 0.4108
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3367 0.4133 -0.4326 0.3235
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2027 0.4928 -1.2907 -0.1731
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2570 0.4039 -0.5484 0.2631
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2521 0.4907 -0.6912 0.2422
## Avg_Cogongrass_Cover-Sus_scrofa -0.1091 0.5413 -1.2731 -0.0632
## total_shrub_cover-Canis_latrans 0.0943 0.4132 -0.6453 0.0620
## total_shrub_cover-Sciurus_niger -0.5234 0.5059 -1.6570 -0.4700
## total_shrub_cover-Procyon_lotor -0.6835 0.4277 -1.6342 -0.6361
## total_shrub_cover-Dasypus_novemcinctus -0.0636 0.3386 -0.7174 -0.0688
## total_shrub_cover-Lynx_rufus -0.6328 0.5479 -1.8255 -0.5581
## total_shrub_cover-Didelphis_virginiana -0.2172 0.3856 -0.9917 -0.2109
## total_shrub_cover-Sylvilagus_floridanus -0.2934 0.4341 -1.1875 -0.2779
## total_shrub_cover-Sciurus_carolinensis -0.1163 0.3831 -0.8692 -0.1221
## total_shrub_cover-Vulpes_vulpes -0.3062 0.5060 -1.3912 -0.2900
## total_shrub_cover-Sus_scrofa -0.0015 0.4616 -0.8424 -0.0277
## avg_veg_height-Canis_latrans -0.0753 0.3829 -0.8248 -0.0767
## avg_veg_height-Sciurus_niger -0.1553 0.4529 -1.0816 -0.1457
## avg_veg_height-Procyon_lotor 0.0776 0.4007 -0.6785 0.0726
## avg_veg_height-Dasypus_novemcinctus 0.1504 0.3688 -0.5372 0.1389
## avg_veg_height-Lynx_rufus -0.0136 0.4401 -0.8626 -0.0158
## avg_veg_height-Didelphis_virginiana -0.0390 0.3950 -0.8395 -0.0365
## avg_veg_height-Sylvilagus_floridanus -0.1367 0.4157 -1.0070 -0.1320
## avg_veg_height-Sciurus_carolinensis 0.2291 0.4055 -0.5309 0.2087
## avg_veg_height-Vulpes_vulpes -0.0760 0.4401 -0.9803 -0.0713
## avg_veg_height-Sus_scrofa -0.0292 0.4228 -0.8645 -0.0263
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1219 1.0113 406
## (Intercept)-Sciurus_niger 0.5024 1.0330 597
## (Intercept)-Procyon_lotor 1.4300 1.0188 332
## (Intercept)-Dasypus_novemcinctus 0.1915 1.0034 909
## (Intercept)-Lynx_rufus 0.8786 1.0041 685
## (Intercept)-Didelphis_virginiana -0.1828 1.0025 1034
## (Intercept)-Sylvilagus_floridanus 0.6405 1.0038 719
## (Intercept)-Sciurus_carolinensis -0.2827 1.0059 951
## (Intercept)-Vulpes_vulpes 0.4116 1.0517 252
## (Intercept)-Sus_scrofa -0.3268 1.0091 620
## Avg_Cogongrass_Cover-Canis_latrans 1.2960 1.0173 1020
## Avg_Cogongrass_Cover-Sciurus_niger 0.7798 1.0881 494
## Avg_Cogongrass_Cover-Procyon_lotor 0.9087 1.0264 1268
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0315 1.0099 1211
## Avg_Cogongrass_Cover-Lynx_rufus 1.4550 1.0098 973
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1941 1.0206 953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6754 1.0978 700
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0711 1.0460 937
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3013 1.0172 907
## Avg_Cogongrass_Cover-Sus_scrofa 0.7926 1.1276 799
## total_shrub_cover-Canis_latrans 0.9798 1.0026 1231
## total_shrub_cover-Sciurus_niger 0.3378 1.0012 989
## total_shrub_cover-Procyon_lotor 0.0181 1.0070 1165
## total_shrub_cover-Dasypus_novemcinctus 0.6356 1.0016 1734
## total_shrub_cover-Lynx_rufus 0.2591 1.0108 852
## total_shrub_cover-Didelphis_virginiana 0.5332 1.0012 1780
## total_shrub_cover-Sylvilagus_floridanus 0.5293 1.0036 1415
## total_shrub_cover-Sciurus_carolinensis 0.6465 1.0074 1673
## total_shrub_cover-Vulpes_vulpes 0.6789 1.0089 1015
## total_shrub_cover-Sus_scrofa 0.9964 1.0258 1290
## avg_veg_height-Canis_latrans 0.6721 1.0069 921
## avg_veg_height-Sciurus_niger 0.7117 1.0103 860
## avg_veg_height-Procyon_lotor 0.9062 1.0085 1005
## avg_veg_height-Dasypus_novemcinctus 0.9164 1.0138 812
## avg_veg_height-Lynx_rufus 0.8873 1.0009 887
## avg_veg_height-Didelphis_virginiana 0.7100 1.0037 757
## avg_veg_height-Sylvilagus_floridanus 0.6813 1.0068 875
## avg_veg_height-Sciurus_carolinensis 1.0979 1.0087 946
## avg_veg_height-Vulpes_vulpes 0.7865 1.0121 806
## avg_veg_height-Sus_scrofa 0.7918 1.0076 896
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6213 0.1691 -2.9620 -2.6153 -2.3063 1.0086
## (Intercept)-Sciurus_niger -3.6641 0.4701 -4.6318 -3.6427 -2.8202 1.0449
## (Intercept)-Procyon_lotor -2.2749 0.1282 -2.5367 -2.2731 -2.0277 1.0193
## (Intercept)-Dasypus_novemcinctus -1.5990 0.1373 -1.8742 -1.5974 -1.3371 1.0016
## (Intercept)-Lynx_rufus -3.4429 0.2915 -4.0470 -3.4353 -2.8968 1.0062
## (Intercept)-Didelphis_virginiana -2.3510 0.2588 -2.8875 -2.3398 -1.8715 1.0065
## (Intercept)-Sylvilagus_floridanus -3.1678 0.2968 -3.8000 -3.1507 -2.6320 1.0003
## (Intercept)-Sciurus_carolinensis -2.4675 0.2561 -2.9932 -2.4584 -2.0016 1.0087
## (Intercept)-Vulpes_vulpes -3.8547 0.7102 -5.2876 -3.7846 -2.6620 1.0821
## (Intercept)-Sus_scrofa -2.9624 0.4288 -3.9089 -2.9375 -2.1897 1.0008
## ESS
## (Intercept)-Canis_latrans 1120
## (Intercept)-Sciurus_niger 291
## (Intercept)-Procyon_lotor 1539
## (Intercept)-Dasypus_novemcinctus 2243
## (Intercept)-Lynx_rufus 438
## (Intercept)-Didelphis_virginiana 1179
## (Intercept)-Sylvilagus_floridanus 480
## (Intercept)-Sciurus_carolinensis 1234
## (Intercept)-Vulpes_vulpes 145
## (Intercept)-Sus_scrofa 667
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5895
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9474 0.4567 -1.8398 -0.9573 0.0077 1.0047 649
## Tree_Density -0.7455 0.3998 -1.6369 -0.7147 -0.0420 1.0084 658
## Avg_Canopy_Cover 0.9182 0.3073 0.3570 0.9061 1.5385 1.0120 718
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5197 1.3469 0.1637 1.1490 5.0714 1.0322 657
## Tree_Density 0.7223 0.9686 0.0541 0.3939 3.4380 1.0071 414
## Avg_Canopy_Cover 0.4909 0.5667 0.0515 0.3243 1.8234 1.0194 1038
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4655 0.4949 0.0506 0.3049 1.8531 1.1025 182
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7476 0.2911 -3.3202 -2.7384 -2.1594 1.0022 1375
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7713 0.5373 0.2138 0.6187 2.1863 1.018 746
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0318 0.5892 -1.2128 -0.0247 1.1569
## (Intercept)-Sciurus_niger -0.8703 0.8661 -2.3515 -0.9458 1.0975
## (Intercept)-Procyon_lotor 0.3178 0.6303 -0.9973 0.3323 1.5147
## (Intercept)-Dasypus_novemcinctus -1.0332 0.5315 -2.1049 -1.0191 -0.0205
## (Intercept)-Lynx_rufus -0.1337 0.9033 -1.6356 -0.2501 1.8925
## (Intercept)-Didelphis_virginiana -1.7149 0.6068 -2.9748 -1.6901 -0.5894
## (Intercept)-Sylvilagus_floridanus -0.8486 0.5897 -1.9960 -0.8488 0.3019
## (Intercept)-Sciurus_carolinensis -1.7675 0.6231 -3.1159 -1.7266 -0.6650
## (Intercept)-Vulpes_vulpes -1.7036 0.8385 -3.3677 -1.6765 -0.0721
## (Intercept)-Sus_scrofa -2.2064 0.8159 -3.9184 -2.1392 -0.7559
## Tree_Density-Canis_latrans -0.8865 0.5674 -2.2123 -0.8181 0.0019
## Tree_Density-Sciurus_niger -0.7143 0.7543 -2.3638 -0.6714 0.7133
## Tree_Density-Procyon_lotor -0.4484 0.4078 -1.2221 -0.4536 0.3447
## Tree_Density-Dasypus_novemcinctus -1.3217 0.8219 -3.4587 -1.1429 -0.1946
## Tree_Density-Lynx_rufus 0.1163 0.6568 -0.9309 0.0365 1.7478
## Tree_Density-Didelphis_virginiana -0.9725 0.7095 -2.6242 -0.8546 0.1094
## Tree_Density-Sylvilagus_floridanus -1.0510 0.7391 -2.8784 -0.9273 0.0534
## Tree_Density-Sciurus_carolinensis -0.8898 0.6827 -2.6048 -0.8065 0.1638
## Tree_Density-Vulpes_vulpes -0.5779 0.7165 -2.0789 -0.5478 0.8065
## Tree_Density-Sus_scrofa -0.8805 0.7304 -2.6454 -0.7848 0.3135
## Avg_Canopy_Cover-Canis_latrans 0.1328 0.4507 -0.8179 0.1474 0.9887
## Avg_Canopy_Cover-Sciurus_niger 0.8086 0.6552 -0.4153 0.7780 2.2827
## Avg_Canopy_Cover-Procyon_lotor 0.9258 0.4100 0.1622 0.9074 1.7664
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9563 0.4022 0.2150 0.9385 1.7950
## Avg_Canopy_Cover-Lynx_rufus 0.7060 0.5794 -0.3305 0.6770 1.9438
## Avg_Canopy_Cover-Didelphis_virginiana 1.1671 0.4569 0.3697 1.1242 2.1782
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.4771 0.6612 0.4865 1.3729 3.0310
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1401 0.4731 0.3162 1.1052 2.1753
## Avg_Canopy_Cover-Vulpes_vulpes 0.9306 0.5272 -0.0476 0.8995 2.0575
## Avg_Canopy_Cover-Sus_scrofa 1.0936 0.4909 0.2281 1.0572 2.1680
## Rhat ESS
## (Intercept)-Canis_latrans 1.0220 542
## (Intercept)-Sciurus_niger 1.0004 340
## (Intercept)-Procyon_lotor 1.0334 352
## (Intercept)-Dasypus_novemcinctus 1.0032 1182
## (Intercept)-Lynx_rufus 1.0437 303
## (Intercept)-Didelphis_virginiana 1.0081 1199
## (Intercept)-Sylvilagus_floridanus 1.0011 1001
## (Intercept)-Sciurus_carolinensis 1.0045 862
## (Intercept)-Vulpes_vulpes 1.0184 511
## (Intercept)-Sus_scrofa 1.0224 545
## Tree_Density-Canis_latrans 1.0020 1147
## Tree_Density-Sciurus_niger 1.0087 672
## Tree_Density-Procyon_lotor 0.9997 1975
## Tree_Density-Dasypus_novemcinctus 1.0111 567
## Tree_Density-Lynx_rufus 1.0124 430
## Tree_Density-Didelphis_virginiana 1.0050 807
## Tree_Density-Sylvilagus_floridanus 1.0022 683
## Tree_Density-Sciurus_carolinensis 1.0048 843
## Tree_Density-Vulpes_vulpes 1.0336 707
## Tree_Density-Sus_scrofa 1.0035 737
## Avg_Canopy_Cover-Canis_latrans 1.0041 1213
## Avg_Canopy_Cover-Sciurus_niger 1.0070 629
## Avg_Canopy_Cover-Procyon_lotor 1.0029 1674
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0016 2091
## Avg_Canopy_Cover-Lynx_rufus 1.0161 604
## Avg_Canopy_Cover-Didelphis_virginiana 1.0129 1238
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0151 773
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0175 1336
## Avg_Canopy_Cover-Vulpes_vulpes 1.0094 1048
## Avg_Canopy_Cover-Sus_scrofa 1.0145 1302
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6253 0.1812 -2.9993 -2.6156 -2.2930 1.0278
## (Intercept)-Sciurus_niger -3.8168 0.4973 -4.7996 -3.8011 -2.8913 1.0066
## (Intercept)-Procyon_lotor -2.2677 0.1304 -2.5277 -2.2652 -2.0226 1.0028
## (Intercept)-Dasypus_novemcinctus -1.6007 0.1346 -1.8702 -1.5997 -1.3436 1.0011
## (Intercept)-Lynx_rufus -3.4977 0.3140 -4.1421 -3.4793 -2.9245 1.0255
## (Intercept)-Didelphis_virginiana -2.3435 0.2394 -2.8484 -2.3360 -1.9095 1.0029
## (Intercept)-Sylvilagus_floridanus -3.0838 0.2635 -3.5969 -3.0860 -2.5995 1.0068
## (Intercept)-Sciurus_carolinensis -2.4695 0.2598 -2.9942 -2.4574 -1.9807 1.0010
## (Intercept)-Vulpes_vulpes -3.6778 0.5630 -4.8157 -3.6636 -2.6530 1.0218
## (Intercept)-Sus_scrofa -2.9067 0.4252 -3.8116 -2.8706 -2.1373 1.0026
## ESS
## (Intercept)-Canis_latrans 910
## (Intercept)-Sciurus_niger 242
## (Intercept)-Procyon_lotor 1508
## (Intercept)-Dasypus_novemcinctus 2151
## (Intercept)-Lynx_rufus 257
## (Intercept)-Didelphis_virginiana 1363
## (Intercept)-Sylvilagus_floridanus 718
## (Intercept)-Sciurus_carolinensis 1304
## (Intercept)-Vulpes_vulpes 324
## (Intercept)-Sus_scrofa 786
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.593
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8840 0.4131 -1.7202 -0.8743 -0.0856 1.0281 378
## Cogon_Patch_Size -0.3052 0.4009 -1.1638 -0.2936 0.4466 1.0085 698
## Avg_Cogongrass_Cover 0.2478 0.2859 -0.3102 0.2389 0.8211 1.0074 690
## total_shrub_cover -0.2307 0.2713 -0.7750 -0.2251 0.2997 1.0030 822
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8678 0.8222 0.0750 0.6330 3.0580 1.0064 635
## Cogon_Patch_Size 0.8477 0.9825 0.0635 0.5355 3.5968 1.0155 643
## Avg_Cogongrass_Cover 0.2831 0.3240 0.0364 0.1762 1.1988 1.0036 969
## total_shrub_cover 0.2967 0.3204 0.0399 0.1983 1.0838 1.0519 963
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1111 0.8814 0.1142 0.8811 3.4739 1.0343 142
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7481 0.2982 -3.3565 -2.7414 -2.1881 1.0028 1496
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7513 0.6062 0.1873 0.6017 2.1697 1.0101 538
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1372 0.6412 -1.3474 -0.1693
## (Intercept)-Sciurus_niger -1.0570 0.7248 -2.5148 -1.0588
## (Intercept)-Procyon_lotor -0.0277 0.6593 -1.3347 -0.0180
## (Intercept)-Dasypus_novemcinctus -0.8507 0.5319 -1.9514 -0.8385
## (Intercept)-Lynx_rufus -0.6379 0.6928 -1.9293 -0.6636
## (Intercept)-Didelphis_virginiana -1.3055 0.6023 -2.5633 -1.2794
## (Intercept)-Sylvilagus_floridanus -0.7504 0.6315 -1.9713 -0.7635
## (Intercept)-Sciurus_carolinensis -1.3872 0.5975 -2.6386 -1.3509
## (Intercept)-Vulpes_vulpes -1.3614 0.7819 -2.9845 -1.3207
## (Intercept)-Sus_scrofa -1.6391 0.7510 -3.3098 -1.5752
## Cogon_Patch_Size-Canis_latrans 0.5830 0.6186 -0.3736 0.5061
## Cogon_Patch_Size-Sciurus_niger -0.6638 0.7926 -2.6311 -0.5653
## Cogon_Patch_Size-Procyon_lotor -0.3107 0.4390 -1.1594 -0.3011
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1792 0.4160 -1.0321 -0.1796
## Cogon_Patch_Size-Lynx_rufus -0.3829 0.7026 -1.7777 -0.4019
## Cogon_Patch_Size-Didelphis_virginiana 0.5602 0.5110 -0.3180 0.5302
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9451 0.8058 -2.8228 -0.8295
## Cogon_Patch_Size-Sciurus_carolinensis -0.7328 0.6500 -2.2114 -0.6403
## Cogon_Patch_Size-Vulpes_vulpes -0.5932 0.8236 -2.4227 -0.5135
## Cogon_Patch_Size-Sus_scrofa -0.4479 0.7413 -2.2258 -0.3578
## Avg_Cogongrass_Cover-Canis_latrans 0.3021 0.3824 -0.4345 0.2831
## Avg_Cogongrass_Cover-Sciurus_niger -0.0905 0.5381 -1.3596 -0.0465
## Avg_Cogongrass_Cover-Procyon_lotor 0.2602 0.3971 -0.4854 0.2468
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4107 0.3558 -0.2602 0.3992
## Avg_Cogongrass_Cover-Lynx_rufus 0.5533 0.4640 -0.2057 0.5018
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2644 0.3910 -0.5288 0.2528
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0432 0.4451 -1.0139 -0.0151
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4644 0.3896 -0.2823 0.4448
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3383 0.4611 -0.5083 0.3226
## Avg_Cogongrass_Cover-Sus_scrofa -0.0124 0.5181 -1.2021 0.0241
## total_shrub_cover-Canis_latrans 0.0625 0.3912 -0.6597 0.0444
## total_shrub_cover-Sciurus_niger -0.4375 0.4757 -1.4998 -0.4130
## total_shrub_cover-Procyon_lotor -0.6139 0.4190 -1.5321 -0.5779
## total_shrub_cover-Dasypus_novemcinctus -0.0462 0.3417 -0.7096 -0.0533
## total_shrub_cover-Lynx_rufus -0.5181 0.5034 -1.6897 -0.4594
## total_shrub_cover-Didelphis_virginiana -0.2643 0.3873 -1.0576 -0.2520
## total_shrub_cover-Sylvilagus_floridanus -0.2288 0.4404 -1.1482 -0.2159
## total_shrub_cover-Sciurus_carolinensis -0.0619 0.3934 -0.8252 -0.0695
## total_shrub_cover-Vulpes_vulpes -0.2590 0.4750 -1.2264 -0.2467
## total_shrub_cover-Sus_scrofa 0.0285 0.4622 -0.8462 0.0004
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1847 1.0458 507
## (Intercept)-Sciurus_niger 0.4440 1.0340 365
## (Intercept)-Procyon_lotor 1.2243 1.0182 366
## (Intercept)-Dasypus_novemcinctus 0.1705 1.0046 930
## (Intercept)-Lynx_rufus 0.7885 1.0317 333
## (Intercept)-Didelphis_virginiana -0.1780 1.0078 805
## (Intercept)-Sylvilagus_floridanus 0.5268 1.0149 557
## (Intercept)-Sciurus_carolinensis -0.2884 1.0143 740
## (Intercept)-Vulpes_vulpes 0.1340 1.0224 331
## (Intercept)-Sus_scrofa -0.3706 1.0087 452
## Cogon_Patch_Size-Canis_latrans 2.0859 1.0051 1009
## Cogon_Patch_Size-Sciurus_niger 0.5965 1.0110 625
## Cogon_Patch_Size-Procyon_lotor 0.5340 1.0035 1111
## Cogon_Patch_Size-Dasypus_novemcinctus 0.5897 1.0042 1972
## Cogon_Patch_Size-Lynx_rufus 1.1130 1.0034 809
## Cogon_Patch_Size-Didelphis_virginiana 1.6584 1.0028 1131
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2746 1.0305 581
## Cogon_Patch_Size-Sciurus_carolinensis 0.2943 1.0127 894
## Cogon_Patch_Size-Vulpes_vulpes 0.7957 1.0028 530
## Cogon_Patch_Size-Sus_scrofa 0.7865 1.0121 822
## Avg_Cogongrass_Cover-Canis_latrans 1.0952 1.0055 985
## Avg_Cogongrass_Cover-Sciurus_niger 0.8457 1.0033 733
## Avg_Cogongrass_Cover-Procyon_lotor 1.1049 1.0010 1199
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1538 1.0028 1319
## Avg_Cogongrass_Cover-Lynx_rufus 1.6170 1.0021 979
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0453 1.0040 1389
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7777 1.0011 1081
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2693 1.0068 1111
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2876 1.0149 1260
## Avg_Cogongrass_Cover-Sus_scrofa 0.8762 1.0073 709
## total_shrub_cover-Canis_latrans 0.8573 1.0039 1146
## total_shrub_cover-Sciurus_niger 0.4221 1.0125 1159
## total_shrub_cover-Procyon_lotor 0.0805 1.0267 1040
## total_shrub_cover-Dasypus_novemcinctus 0.6396 1.0114 2055
## total_shrub_cover-Lynx_rufus 0.3335 1.0128 908
## total_shrub_cover-Didelphis_virginiana 0.4965 1.0013 1355
## total_shrub_cover-Sylvilagus_floridanus 0.6387 1.0016 1341
## total_shrub_cover-Sciurus_carolinensis 0.7375 1.0067 1193
## total_shrub_cover-Vulpes_vulpes 0.7036 1.0029 1259
## total_shrub_cover-Sus_scrofa 0.9839 1.0060 1381
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6152 0.1755 -2.9794 -2.6035 -2.2943 1.0224
## (Intercept)-Sciurus_niger -3.6605 0.4925 -4.7060 -3.6350 -2.7641 1.0098
## (Intercept)-Procyon_lotor -2.2765 0.1310 -2.5410 -2.2731 -2.0241 1.0067
## (Intercept)-Dasypus_novemcinctus -1.6053 0.1386 -1.8733 -1.6054 -1.3345 1.0001
## (Intercept)-Lynx_rufus -3.3872 0.2934 -3.9798 -3.3805 -2.8328 1.0199
## (Intercept)-Didelphis_virginiana -2.3537 0.2411 -2.8355 -2.3501 -1.9100 1.0019
## (Intercept)-Sylvilagus_floridanus -3.1573 0.2962 -3.7939 -3.1343 -2.6168 1.0156
## (Intercept)-Sciurus_carolinensis -2.4803 0.2612 -3.0070 -2.4695 -1.9973 1.0090
## (Intercept)-Vulpes_vulpes -3.7427 0.6235 -5.0170 -3.7027 -2.6163 1.0279
## (Intercept)-Sus_scrofa -2.9795 0.4518 -3.9734 -2.9322 -2.2054 1.0064
## ESS
## (Intercept)-Canis_latrans 921
## (Intercept)-Sciurus_niger 248
## (Intercept)-Procyon_lotor 1415
## (Intercept)-Dasypus_novemcinctus 1840
## (Intercept)-Lynx_rufus 354
## (Intercept)-Didelphis_virginiana 1409
## (Intercept)-Sylvilagus_floridanus 403
## (Intercept)-Sciurus_carolinensis 1246
## (Intercept)-Vulpes_vulpes 206
## (Intercept)-Sus_scrofa 607
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6032
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8501 0.3799 -1.5703 -0.8557 -0.0643 1.0220 641
## Veg_shannon_index 0.3381 0.2563 -0.1517 0.3349 0.8504 1.0248 827
## Avg_Cogongrass_Cover 0.3017 0.2464 -0.2015 0.3034 0.7660 1.0099 792
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7989 0.7995 0.0708 0.5623 2.9654 1.0237 670
## Veg_shannon_index 0.2796 0.3077 0.0391 0.1817 1.0896 1.0369 721
## Avg_Cogongrass_Cover 0.2702 0.2898 0.0376 0.1813 1.0200 1.0195 876
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.872 0.715 0.0919 0.693 2.6733 1.0273 202
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7427 0.2946 -3.3406 -2.7386 -2.1659 1.0113 1771
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7528 0.5748 0.1948 0.6033 2.1991 1.039 742
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2096 0.5960 -1.3018 -0.2479
## (Intercept)-Sciurus_niger -0.9297 0.6982 -2.2104 -0.9561
## (Intercept)-Procyon_lotor -0.0507 0.6256 -1.2217 -0.0427
## (Intercept)-Dasypus_novemcinctus -0.8233 0.4985 -1.8575 -0.8103
## (Intercept)-Lynx_rufus -0.5709 0.6324 -1.7516 -0.6017
## (Intercept)-Didelphis_virginiana -1.2970 0.5468 -2.4429 -1.2603
## (Intercept)-Sylvilagus_floridanus -0.6601 0.5834 -1.8178 -0.6713
## (Intercept)-Sciurus_carolinensis -1.3102 0.5625 -2.4775 -1.2938
## (Intercept)-Vulpes_vulpes -1.2312 0.7677 -2.7868 -1.2178
## (Intercept)-Sus_scrofa -1.6553 0.7271 -3.2758 -1.5944
## Veg_shannon_index-Canis_latrans 0.6175 0.3711 -0.0417 0.5906
## Veg_shannon_index-Sciurus_niger 0.3252 0.4801 -0.5947 0.3151
## Veg_shannon_index-Procyon_lotor 0.4744 0.3747 -0.2298 0.4563
## Veg_shannon_index-Dasypus_novemcinctus 0.1999 0.3381 -0.4983 0.2058
## Veg_shannon_index-Lynx_rufus 0.1709 0.4540 -0.7680 0.2028
## Veg_shannon_index-Didelphis_virginiana 0.4875 0.3888 -0.2468 0.4713
## Veg_shannon_index-Sylvilagus_floridanus 0.4589 0.4238 -0.3009 0.4330
## Veg_shannon_index-Sciurus_carolinensis 0.0098 0.3867 -0.8260 0.0404
## Veg_shannon_index-Vulpes_vulpes 0.0774 0.4546 -0.9014 0.0936
## Veg_shannon_index-Sus_scrofa 0.6462 0.4772 -0.1645 0.5997
## Avg_Cogongrass_Cover-Canis_latrans 0.5038 0.3581 -0.1469 0.4817
## Avg_Cogongrass_Cover-Sciurus_niger -0.0544 0.4976 -1.1742 -0.0034
## Avg_Cogongrass_Cover-Procyon_lotor 0.3953 0.3706 -0.2880 0.3722
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4213 0.3257 -0.2154 0.4109
## Avg_Cogongrass_Cover-Lynx_rufus 0.5608 0.3964 -0.1597 0.5383
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4592 0.3686 -0.2275 0.4539
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0541 0.4240 -0.9602 -0.0303
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4116 0.3436 -0.2680 0.4131
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3715 0.4377 -0.4964 0.3640
## Avg_Cogongrass_Cover-Sus_scrofa 0.0545 0.4654 -1.0112 0.0941
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0403 1.0164 517
## (Intercept)-Sciurus_niger 0.4668 1.0053 397
## (Intercept)-Procyon_lotor 1.1888 1.0052 427
## (Intercept)-Dasypus_novemcinctus 0.1201 1.0015 1184
## (Intercept)-Lynx_rufus 0.7798 1.0068 484
## (Intercept)-Didelphis_virginiana -0.3103 1.0020 1067
## (Intercept)-Sylvilagus_floridanus 0.5757 1.0087 875
## (Intercept)-Sciurus_carolinensis -0.2715 1.0072 798
## (Intercept)-Vulpes_vulpes 0.2522 1.0258 312
## (Intercept)-Sus_scrofa -0.4032 1.0042 620
## Veg_shannon_index-Canis_latrans 1.4003 1.0202 1328
## Veg_shannon_index-Sciurus_niger 1.3182 1.0105 1050
## Veg_shannon_index-Procyon_lotor 1.2592 1.0181 893
## Veg_shannon_index-Dasypus_novemcinctus 0.8468 1.0045 1856
## Veg_shannon_index-Lynx_rufus 0.9840 1.0079 1039
## Veg_shannon_index-Didelphis_virginiana 1.3195 1.0041 1631
## Veg_shannon_index-Sylvilagus_floridanus 1.3605 1.0505 966
## Veg_shannon_index-Sciurus_carolinensis 0.7227 1.0006 1276
## Veg_shannon_index-Vulpes_vulpes 0.9200 1.0072 1013
## Veg_shannon_index-Sus_scrofa 1.7200 1.0136 1001
## Avg_Cogongrass_Cover-Canis_latrans 1.2438 1.0035 1837
## Avg_Cogongrass_Cover-Sciurus_niger 0.7938 1.0052 845
## Avg_Cogongrass_Cover-Procyon_lotor 1.1844 1.0256 1964
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0710 1.0092 1636
## Avg_Cogongrass_Cover-Lynx_rufus 1.4578 1.0066 1423
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1892 1.0077 1779
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7043 1.0067 1110
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0965 1.0041 1798
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2560 1.0060 1268
## Avg_Cogongrass_Cover-Sus_scrofa 0.8644 1.0039 1145
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5877 0.1648 -2.9153 -2.5843 -2.2655 1.0145
## (Intercept)-Sciurus_niger -3.6536 0.4790 -4.5968 -3.6354 -2.7713 1.0114
## (Intercept)-Procyon_lotor -2.2736 0.1279 -2.5304 -2.2697 -2.0300 1.0062
## (Intercept)-Dasypus_novemcinctus -1.6036 0.1329 -1.8680 -1.6018 -1.3419 1.0018
## (Intercept)-Lynx_rufus -3.3996 0.2869 -3.9984 -3.3890 -2.8550 1.0209
## (Intercept)-Didelphis_virginiana -2.3576 0.2498 -2.8631 -2.3425 -1.8963 1.0018
## (Intercept)-Sylvilagus_floridanus -3.1353 0.2912 -3.7178 -3.1253 -2.5986 1.0328
## (Intercept)-Sciurus_carolinensis -2.4810 0.2693 -3.0335 -2.4813 -1.9687 1.0126
## (Intercept)-Vulpes_vulpes -3.7378 0.6397 -5.0916 -3.6991 -2.6527 1.0867
## (Intercept)-Sus_scrofa -2.9173 0.4406 -3.8873 -2.8894 -2.1386 1.0736
## ESS
## (Intercept)-Canis_latrans 1137
## (Intercept)-Sciurus_niger 267
## (Intercept)-Procyon_lotor 1468
## (Intercept)-Dasypus_novemcinctus 2680
## (Intercept)-Lynx_rufus 437
## (Intercept)-Didelphis_virginiana 1406
## (Intercept)-Sylvilagus_floridanus 528
## (Intercept)-Sciurus_carolinensis 1207
## (Intercept)-Vulpes_vulpes 203
## (Intercept)-Sus_scrofa 454
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5833
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5410 0.4051 -2.3336 -1.5446 -0.7509 1.0070 664
## Avg_Cogongrass_Cover -0.7206 0.3542 -1.4499 -0.7164 -0.0457 1.0020 387
## I(Avg_Cogongrass_Cover^2) 0.7838 0.3275 0.1988 0.7581 1.5108 1.0019 456
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7669 0.8266 0.0676 0.5309 2.8294 1.0059 881
## Avg_Cogongrass_Cover 0.3013 0.3613 0.0382 0.1881 1.2698 1.0181 840
## I(Avg_Cogongrass_Cover^2) 0.5668 0.9268 0.0405 0.2627 3.0216 1.0233 309
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.548 0.5066 0.0558 0.4182 1.7923 1.01 204
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.707 0.2742 -3.2583 -2.6999 -2.155 1.0045 1345
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6392 0.4767 0.1677 0.5095 1.8076 1.0546 785
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9407 0.6047 -2.1159 -0.9387
## (Intercept)-Sciurus_niger -1.5617 0.6711 -2.8962 -1.5768
## (Intercept)-Procyon_lotor -0.7440 0.6261 -1.9582 -0.7529
## (Intercept)-Dasypus_novemcinctus -1.4774 0.5330 -2.5543 -1.4624
## (Intercept)-Lynx_rufus -1.4546 0.6410 -2.6831 -1.4675
## (Intercept)-Didelphis_virginiana -1.8700 0.5789 -3.0861 -1.8317
## (Intercept)-Sylvilagus_floridanus -1.3451 0.5993 -2.5358 -1.3323
## (Intercept)-Sciurus_carolinensis -2.1368 0.6414 -3.5459 -2.0737
## (Intercept)-Vulpes_vulpes -2.1760 0.7514 -3.8300 -2.1383
## (Intercept)-Sus_scrofa -2.1258 0.6851 -3.5962 -2.0817
## Avg_Cogongrass_Cover-Canis_latrans -0.5283 0.4876 -1.4321 -0.5394
## Avg_Cogongrass_Cover-Sciurus_niger -0.9554 0.5758 -2.2658 -0.8984
## Avg_Cogongrass_Cover-Procyon_lotor -0.6617 0.4724 -1.5836 -0.6719
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5636 0.4653 -1.4703 -0.5734
## Avg_Cogongrass_Cover-Lynx_rufus -0.6369 0.5088 -1.6099 -0.6478
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4671 0.4924 -1.3605 -0.4906
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0857 0.5359 -2.2800 -1.0300
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7248 0.4871 -1.7278 -0.7173
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7220 0.5314 -1.8181 -0.6982
## Avg_Cogongrass_Cover-Sus_scrofa -0.9127 0.5749 -2.2088 -0.8826
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3682 0.8869 0.2858 1.1240
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.1879 0.6542 -1.4027 0.2690
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2382 0.7617 0.2937 1.0628
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7249 0.3412 0.0687 0.7136
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1929 0.5349 0.3795 1.1113
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5340 0.3808 -0.1937 0.5273
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7338 0.4355 -0.0216 0.6944
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8874 0.3655 0.2243 0.8613
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8118 0.4440 0.0634 0.7835
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2284 0.6036 -1.2616 0.3236
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2490 1.0044 742
## (Intercept)-Sciurus_niger -0.2244 1.0099 741
## (Intercept)-Procyon_lotor 0.5290 1.0141 587
## (Intercept)-Dasypus_novemcinctus -0.4635 1.0011 1106
## (Intercept)-Lynx_rufus -0.1716 1.0045 528
## (Intercept)-Didelphis_virginiana -0.8103 1.0070 887
## (Intercept)-Sylvilagus_floridanus -0.1940 1.0080 960
## (Intercept)-Sciurus_carolinensis -1.0023 1.0076 872
## (Intercept)-Vulpes_vulpes -0.8324 1.0390 649
## (Intercept)-Sus_scrofa -0.8949 1.0111 789
## Avg_Cogongrass_Cover-Canis_latrans 0.4905 1.0109 865
## Avg_Cogongrass_Cover-Sciurus_niger 0.0333 1.0022 661
## Avg_Cogongrass_Cover-Procyon_lotor 0.2638 1.0035 706
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3583 1.0005 906
## Avg_Cogongrass_Cover-Lynx_rufus 0.3572 1.0100 625
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5708 1.0038 751
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1413 1.0085 679
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2177 1.0028 709
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2940 1.0020 678
## Avg_Cogongrass_Cover-Sus_scrofa 0.1217 1.0063 665
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.8200 1.0214 270
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2388 1.0212 403
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.3906 1.0047 308
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4312 1.0015 990
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5238 1.0023 438
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3318 1.0025 866
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7336 1.0007 765
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6672 1.0045 832
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7753 1.0023 546
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1715 1.0164 497
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6268 0.1637 -2.9634 -2.6227 -2.3151 1.0004
## (Intercept)-Sciurus_niger -3.5055 0.4583 -4.5328 -3.4650 -2.7204 1.0091
## (Intercept)-Procyon_lotor -2.2778 0.1287 -2.5334 -2.2775 -2.0271 1.0101
## (Intercept)-Dasypus_novemcinctus -1.6077 0.1353 -1.8642 -1.6062 -1.3481 1.0030
## (Intercept)-Lynx_rufus -3.3333 0.2997 -3.9602 -3.3208 -2.7988 1.0425
## (Intercept)-Didelphis_virginiana -2.3678 0.2443 -2.8836 -2.3558 -1.9252 1.0018
## (Intercept)-Sylvilagus_floridanus -3.1029 0.2732 -3.6707 -3.0871 -2.6075 1.0489
## (Intercept)-Sciurus_carolinensis -2.4559 0.2629 -3.0020 -2.4444 -1.9777 0.9998
## (Intercept)-Vulpes_vulpes -3.5355 0.5368 -4.6754 -3.4977 -2.5851 1.0559
## (Intercept)-Sus_scrofa -2.9164 0.4237 -3.8078 -2.8821 -2.1632 1.0203
## ESS
## (Intercept)-Canis_latrans 1241
## (Intercept)-Sciurus_niger 314
## (Intercept)-Procyon_lotor 1540
## (Intercept)-Dasypus_novemcinctus 2239
## (Intercept)-Lynx_rufus 418
## (Intercept)-Didelphis_virginiana 1286
## (Intercept)-Sylvilagus_floridanus 620
## (Intercept)-Sciurus_carolinensis 1186
## (Intercept)-Vulpes_vulpes 260
## (Intercept)-Sus_scrofa 821
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6787
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2208 0.7826 -3.7627 -2.2224 -0.5866 1.0424 268
## Cogon_Patch_Size -0.3703 0.6746 -1.7756 -0.3377 0.9163 1.0356 381
## Veg_shannon_index 0.8859 0.4297 0.0817 0.8668 1.7977 1.0231 371
## total_shrub_cover -0.3618 0.3893 -1.1513 -0.3573 0.3970 1.0272 547
## Avg_Cogongrass_Cover 0.3071 0.9258 -1.4404 0.3074 2.1855 1.0211 141
## Tree_Density -2.1656 0.7441 -3.7327 -2.1532 -0.7216 1.0084 178
## Avg_Canopy_Cover 1.7239 0.5594 0.6967 1.6730 2.9810 1.0240 295
## I(Avg_Cogongrass_Cover^2) 1.2661 0.5798 0.2187 1.2335 2.5071 1.0372 235
## avg_veg_height -0.1933 0.4707 -1.1469 -0.1796 0.7364 1.0231 239
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6314 5.1182 0.1459 2.3687 14.1585 1.0421 327
## Cogon_Patch_Size 3.1675 4.4841 0.1637 1.7696 15.4050 1.1252 165
## Veg_shannon_index 0.6000 0.7763 0.0441 0.3372 2.8704 1.0164 534
## total_shrub_cover 0.6444 0.8687 0.0520 0.3570 3.1084 1.0015 404
## Avg_Cogongrass_Cover 0.9529 1.4642 0.0470 0.4482 4.9874 1.0102 440
## Tree_Density 2.0531 3.7151 0.0624 0.8381 11.5176 1.1368 237
## Avg_Canopy_Cover 1.6160 2.3114 0.0835 0.8924 7.5988 1.0566 250
## I(Avg_Cogongrass_Cover^2) 1.3897 2.3634 0.0669 0.6937 6.9474 1.0318 268
## avg_veg_height 0.4119 0.5302 0.0427 0.2511 1.7029 1.1152 705
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5899 2.1415 0.0666 0.9079 8.0971 1.3322 64
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8203 0.3435 -3.4711 -2.8316 -2.0733 1.0022 2406
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0846 0.8109 0.3121 0.8673 3.2342 1.0184 908
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4020 1.0774 -3.5302 -1.3820
## (Intercept)-Sciurus_niger -1.2912 1.4784 -3.6649 -1.5036
## (Intercept)-Procyon_lotor -1.0382 1.0860 -3.2717 -1.0102
## (Intercept)-Dasypus_novemcinctus -2.5027 0.9429 -4.5836 -2.4347
## (Intercept)-Lynx_rufus -1.3998 1.4334 -4.0587 -1.4889
## (Intercept)-Didelphis_virginiana -3.4427 1.1373 -6.0014 -3.2990
## (Intercept)-Sylvilagus_floridanus -2.3656 1.1315 -4.8531 -2.3020
## (Intercept)-Sciurus_carolinensis -3.8822 1.2582 -6.7736 -3.7594
## (Intercept)-Vulpes_vulpes -3.4615 1.5121 -6.7422 -3.3598
## (Intercept)-Sus_scrofa -4.1460 1.5568 -7.6250 -3.9170
## Cogon_Patch_Size-Canis_latrans 1.3178 1.2245 -0.4628 1.0990
## Cogon_Patch_Size-Sciurus_niger -1.3485 1.7952 -5.7209 -1.0888
## Cogon_Patch_Size-Procyon_lotor -0.6027 0.8108 -2.1419 -0.5939
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2816 0.6786 -1.6933 -0.2651
## Cogon_Patch_Size-Lynx_rufus -0.5961 1.3696 -3.2201 -0.6252
## Cogon_Patch_Size-Didelphis_virginiana 1.3858 0.9730 -0.2855 1.2906
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6312 1.4887 -5.4285 -1.3599
## Cogon_Patch_Size-Sciurus_carolinensis -1.1487 1.1290 -3.9291 -0.9860
## Cogon_Patch_Size-Vulpes_vulpes -0.8033 1.4856 -4.0808 -0.6724
## Cogon_Patch_Size-Sus_scrofa -0.7195 1.3474 -3.9027 -0.5223
## Veg_shannon_index-Canis_latrans 1.2536 0.6567 0.1601 1.1773
## Veg_shannon_index-Sciurus_niger 0.9474 0.7915 -0.5347 0.9098
## Veg_shannon_index-Procyon_lotor 1.0741 0.5789 0.0602 1.0455
## Veg_shannon_index-Dasypus_novemcinctus 0.6417 0.5267 -0.3870 0.6343
## Veg_shannon_index-Lynx_rufus 0.8961 0.7842 -0.6392 0.8607
## Veg_shannon_index-Didelphis_virginiana 0.9847 0.6193 -0.1119 0.9410
## Veg_shannon_index-Sylvilagus_floridanus 0.9572 0.6513 -0.2376 0.9315
## Veg_shannon_index-Sciurus_carolinensis 0.3641 0.6741 -1.1580 0.4192
## Veg_shannon_index-Vulpes_vulpes 0.6506 0.7532 -0.9102 0.6599
## Veg_shannon_index-Sus_scrofa 1.3205 0.7996 0.0117 1.2142
## total_shrub_cover-Canis_latrans -0.0302 0.5305 -1.0215 -0.0494
## total_shrub_cover-Sciurus_niger -0.7173 0.8094 -2.5635 -0.6254
## total_shrub_cover-Procyon_lotor -0.8397 0.5909 -2.1802 -0.7849
## total_shrub_cover-Dasypus_novemcinctus 0.0180 0.5021 -0.9260 -0.0064
## total_shrub_cover-Lynx_rufus -0.8452 0.8124 -2.8095 -0.7197
## total_shrub_cover-Didelphis_virginiana -0.4865 0.5942 -1.7806 -0.4463
## total_shrub_cover-Sylvilagus_floridanus -0.3068 0.6584 -1.7242 -0.3074
## total_shrub_cover-Sciurus_carolinensis -0.0950 0.5917 -1.2082 -0.1114
## total_shrub_cover-Vulpes_vulpes -0.4835 0.8105 -2.3351 -0.4066
## total_shrub_cover-Sus_scrofa -0.0512 0.6781 -1.3346 -0.0916
## Avg_Cogongrass_Cover-Canis_latrans 0.2553 1.1440 -2.0247 0.2653
## Avg_Cogongrass_Cover-Sciurus_niger -0.1032 1.3225 -2.9606 -0.0002
## Avg_Cogongrass_Cover-Procyon_lotor 0.4680 1.1289 -1.6636 0.4565
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7897 1.1985 -1.3346 0.7184
## Avg_Cogongrass_Cover-Lynx_rufus 0.5272 1.2099 -1.7992 0.4790
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4572 1.1393 -1.7837 0.4367
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1844 1.2508 -2.9275 -0.1038
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4405 1.1368 -1.7863 0.4287
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5037 1.2056 -1.7978 0.4809
## Avg_Cogongrass_Cover-Sus_scrofa 0.0302 1.2090 -2.4571 0.0634
## Tree_Density-Canis_latrans -2.8219 1.2087 -5.7107 -2.6269
## Tree_Density-Sciurus_niger -2.0949 1.4096 -4.9783 -2.0457
## Tree_Density-Procyon_lotor -2.0604 0.9354 -3.9826 -2.0402
## Tree_Density-Dasypus_novemcinctus -3.3838 1.5679 -7.5336 -3.0416
## Tree_Density-Lynx_rufus -1.2031 1.2764 -3.4470 -1.2946
## Tree_Density-Didelphis_virginiana -2.3423 1.0716 -4.7994 -2.2383
## Tree_Density-Sylvilagus_floridanus -2.5089 1.2090 -5.1881 -2.3872
## Tree_Density-Sciurus_carolinensis -2.5279 1.1038 -5.1723 -2.3970
## Tree_Density-Vulpes_vulpes -2.0680 1.4344 -4.9976 -2.1186
## Tree_Density-Sus_scrofa -2.2382 1.2933 -5.1617 -2.1347
## Avg_Canopy_Cover-Canis_latrans 0.4018 0.6825 -0.9996 0.4038
## Avg_Canopy_Cover-Sciurus_niger 2.0109 1.3595 -0.3451 1.8882
## Avg_Canopy_Cover-Procyon_lotor 1.6085 0.6547 0.4107 1.5742
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8638 0.6942 0.7011 1.7900
## Avg_Canopy_Cover-Lynx_rufus 1.2568 1.0485 -0.7802 1.2540
## Avg_Canopy_Cover-Didelphis_virginiana 2.3956 0.8930 1.0545 2.2492
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9307 1.4059 1.0322 2.6089
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0154 0.7454 0.7930 1.9363
## Avg_Canopy_Cover-Vulpes_vulpes 2.0838 1.1032 0.4206 1.8801
## Avg_Canopy_Cover-Sus_scrofa 1.8341 0.8200 0.4864 1.7422
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0679 1.0649 0.5525 1.8813
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.9271 1.3468 -1.7138 0.9009
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9208 0.9427 0.4553 1.7927
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2882 0.6931 0.0898 1.2121
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.1599 1.0430 0.6046 1.9845
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8362 0.6530 -0.3887 0.8190
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9568 0.7397 -0.3921 0.9132
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3579 0.7141 0.1383 1.2938
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.5828 0.8324 0.2505 1.4965
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2923 1.1138 -2.4234 0.4500
## avg_veg_height-Canis_latrans -0.4172 0.5854 -1.6099 -0.4034
## avg_veg_height-Sciurus_niger -0.3340 0.7795 -2.0189 -0.2857
## avg_veg_height-Procyon_lotor 0.0460 0.5836 -1.1008 0.0533
## avg_veg_height-Dasypus_novemcinctus 0.0700 0.5797 -1.0204 0.0461
## avg_veg_height-Lynx_rufus -0.2540 0.7279 -1.7516 -0.2479
## avg_veg_height-Didelphis_virginiana -0.2772 0.6406 -1.5605 -0.2710
## avg_veg_height-Sylvilagus_floridanus -0.3331 0.6473 -1.7142 -0.3005
## avg_veg_height-Sciurus_carolinensis 0.0537 0.6294 -1.1499 0.0404
## avg_veg_height-Vulpes_vulpes -0.2776 0.7160 -1.7407 -0.2619
## avg_veg_height-Sus_scrofa -0.2555 0.6698 -1.6315 -0.2468
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6488 1.0381 214
## (Intercept)-Sciurus_niger 2.2511 1.0282 209
## (Intercept)-Procyon_lotor 0.9551 1.1075 153
## (Intercept)-Dasypus_novemcinctus -0.8322 1.0305 380
## (Intercept)-Lynx_rufus 1.7814 1.0667 152
## (Intercept)-Didelphis_virginiana -1.5382 1.0240 297
## (Intercept)-Sylvilagus_floridanus -0.3153 1.0666 288
## (Intercept)-Sciurus_carolinensis -1.7869 1.0364 235
## (Intercept)-Vulpes_vulpes -0.5821 1.0214 298
## (Intercept)-Sus_scrofa -1.7326 1.0208 194
## Cogon_Patch_Size-Canis_latrans 4.3658 1.0345 349
## Cogon_Patch_Size-Sciurus_niger 1.5140 1.0247 228
## Cogon_Patch_Size-Procyon_lotor 0.9374 1.0230 499
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0326 1.0127 408
## Cogon_Patch_Size-Lynx_rufus 2.2544 1.0162 295
## Cogon_Patch_Size-Didelphis_virginiana 3.6302 1.0398 167
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4125 1.0540 349
## Cogon_Patch_Size-Sciurus_carolinensis 0.5417 1.0826 380
## Cogon_Patch_Size-Vulpes_vulpes 2.0176 1.0804 364
## Cogon_Patch_Size-Sus_scrofa 1.4415 1.0626 453
## Veg_shannon_index-Canis_latrans 2.7530 1.0195 521
## Veg_shannon_index-Sciurus_niger 2.7645 1.0050 631
## Veg_shannon_index-Procyon_lotor 2.2509 1.0502 385
## Veg_shannon_index-Dasypus_novemcinctus 1.6704 1.0136 744
## Veg_shannon_index-Lynx_rufus 2.6507 1.0098 529
## Veg_shannon_index-Didelphis_virginiana 2.3166 1.0262 612
## Veg_shannon_index-Sylvilagus_floridanus 2.3998 1.0129 621
## Veg_shannon_index-Sciurus_carolinensis 1.5477 1.0100 706
## Veg_shannon_index-Vulpes_vulpes 2.1148 1.0132 447
## Veg_shannon_index-Sus_scrofa 3.2894 1.0115 516
## total_shrub_cover-Canis_latrans 1.0838 1.0006 1108
## total_shrub_cover-Sciurus_niger 0.7081 1.0071 416
## total_shrub_cover-Procyon_lotor 0.1541 1.0031 602
## total_shrub_cover-Dasypus_novemcinctus 1.0600 1.0034 1369
## total_shrub_cover-Lynx_rufus 0.4395 1.0048 434
## total_shrub_cover-Didelphis_virginiana 0.5733 1.0244 798
## total_shrub_cover-Sylvilagus_floridanus 1.0271 1.0188 839
## total_shrub_cover-Sciurus_carolinensis 1.1461 1.0039 1181
## total_shrub_cover-Vulpes_vulpes 0.9135 1.0223 553
## total_shrub_cover-Sus_scrofa 1.4301 1.0180 1093
## Avg_Cogongrass_Cover-Canis_latrans 2.5606 1.0082 184
## Avg_Cogongrass_Cover-Sciurus_niger 2.3099 1.0209 255
## Avg_Cogongrass_Cover-Procyon_lotor 2.8261 1.0191 202
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.2940 1.0071 258
## Avg_Cogongrass_Cover-Lynx_rufus 2.9995 1.0172 178
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8390 1.0100 172
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0430 1.0150 261
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6829 1.0195 210
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.0730 1.0056 191
## Avg_Cogongrass_Cover-Sus_scrofa 2.3589 1.0203 238
## Tree_Density-Canis_latrans -0.9154 1.0822 223
## Tree_Density-Sciurus_niger 0.6781 1.0239 247
## Tree_Density-Procyon_lotor -0.2493 1.0180 380
## Tree_Density-Dasypus_novemcinctus -1.3227 1.0661 168
## Tree_Density-Lynx_rufus 1.6426 1.0267 248
## Tree_Density-Didelphis_virginiana -0.5958 1.0119 250
## Tree_Density-Sylvilagus_floridanus -0.5150 1.0216 401
## Tree_Density-Sciurus_carolinensis -0.7422 1.0480 251
## Tree_Density-Vulpes_vulpes 1.0273 1.0123 299
## Tree_Density-Sus_scrofa 0.0344 1.0172 449
## Avg_Canopy_Cover-Canis_latrans 1.6935 1.0072 456
## Avg_Canopy_Cover-Sciurus_niger 5.3150 1.0089 225
## Avg_Canopy_Cover-Procyon_lotor 3.0404 1.0251 664
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.4868 1.0303 307
## Avg_Canopy_Cover-Lynx_rufus 3.3156 1.0167 354
## Avg_Canopy_Cover-Didelphis_virginiana 4.5249 1.0615 253
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.3058 1.0159 203
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7204 1.0193 460
## Avg_Canopy_Cover-Vulpes_vulpes 4.9101 1.0412 267
## Avg_Canopy_Cover-Sus_scrofa 3.7769 1.0159 659
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7080 1.0541 183
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.7305 1.0803 163
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.1455 1.0213 266
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8629 1.0075 399
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.5514 1.0111 296
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.1893 1.0247 246
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.5451 1.0101 407
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.9482 1.0077 275
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.3915 1.0283 344
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.0886 1.0165 220
## avg_veg_height-Canis_latrans 0.7063 1.0119 324
## avg_veg_height-Sciurus_niger 1.1174 1.0208 432
## avg_veg_height-Procyon_lotor 1.2259 1.0133 372
## avg_veg_height-Dasypus_novemcinctus 1.2854 1.0231 370
## avg_veg_height-Lynx_rufus 1.2092 1.0068 372
## avg_veg_height-Didelphis_virginiana 0.9719 1.0065 475
## avg_veg_height-Sylvilagus_floridanus 0.8955 1.0086 384
## avg_veg_height-Sciurus_carolinensis 1.3400 1.0043 433
## avg_veg_height-Vulpes_vulpes 1.0583 1.0047 432
## avg_veg_height-Sus_scrofa 1.0256 1.0097 507
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6148 0.1690 -2.9576 -2.6107 -2.2983 1.0040
## (Intercept)-Sciurus_niger -4.2201 0.4556 -5.1254 -4.2274 -3.3021 1.0024
## (Intercept)-Procyon_lotor -2.2720 0.1273 -2.5313 -2.2719 -2.0187 1.0033
## (Intercept)-Dasypus_novemcinctus -1.5961 0.1342 -1.8685 -1.5925 -1.3481 1.0016
## (Intercept)-Lynx_rufus -3.6190 0.2917 -4.1645 -3.6246 -3.0243 1.0017
## (Intercept)-Didelphis_virginiana -2.3236 0.2422 -2.8185 -2.3138 -1.8747 1.0064
## (Intercept)-Sylvilagus_floridanus -3.1657 0.2645 -3.7149 -3.1643 -2.6689 1.0017
## (Intercept)-Sciurus_carolinensis -2.4652 0.2590 -2.9990 -2.4562 -1.9906 1.0005
## (Intercept)-Vulpes_vulpes -4.0391 0.5742 -5.2235 -4.0240 -2.9835 1.1220
## (Intercept)-Sus_scrofa -2.9685 0.4769 -4.0311 -2.9299 -2.1518 1.0163
## ESS
## (Intercept)-Canis_latrans 1045
## (Intercept)-Sciurus_niger 160
## (Intercept)-Procyon_lotor 1364
## (Intercept)-Dasypus_novemcinctus 2356
## (Intercept)-Lynx_rufus 389
## (Intercept)-Didelphis_virginiana 1495
## (Intercept)-Sylvilagus_floridanus 612
## (Intercept)-Sciurus_carolinensis 1197
## (Intercept)-Vulpes_vulpes 246
## (Intercept)-Sus_scrofa 513
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8998
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6792 0.3588 -1.3617 -0.6857 0.0536 1.0311 456
## Avg_Cogongrass_Cover 0.1905 0.2393 -0.2925 0.1932 0.6546 1.0068 1022
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6577 0.6621 0.0497 0.4615 2.422 1.0178 489
## Avg_Cogongrass_Cover 0.2561 0.2955 0.0326 0.1686 1.038 1.0229 694
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7868 0.6446 0.0725 0.6092 2.4509 1.0408 196
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8973 0.3038 -3.5239 -2.8882 -2.3168 1.0145 877
## shrub_cover 0.3172 0.2752 -0.2266 0.3162 0.8630 1.0030 1080
## veg_height 0.0648 0.1706 -0.2666 0.0640 0.4052 1.0022 1138
## week -0.0843 0.1329 -0.3485 -0.0842 0.1710 1.0103 1300
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7689 0.6058 0.1863 0.5897 2.3548 1.1170 157
## shrub_cover 0.5101 0.4832 0.0945 0.3733 1.7094 1.0163 584
## veg_height 0.2020 0.1487 0.0542 0.1612 0.5910 1.0036 1420
## week 0.1028 0.0915 0.0234 0.0779 0.3350 1.0565 1045
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0676 0.5434 -1.0461 -0.0807
## (Intercept)-Sciurus_niger -0.8282 0.6527 -2.0742 -0.8383
## (Intercept)-Procyon_lotor 0.0461 0.5860 -1.0666 0.0309
## (Intercept)-Dasypus_novemcinctus -0.6811 0.4752 -1.6462 -0.6761
## (Intercept)-Lynx_rufus -0.4493 0.5985 -1.5711 -0.4788
## (Intercept)-Didelphis_virginiana -1.0272 0.5208 -2.1013 -1.0008
## (Intercept)-Sylvilagus_floridanus -0.5781 0.5026 -1.5277 -0.5843
## (Intercept)-Sciurus_carolinensis -1.0816 0.5398 -2.2351 -1.0543
## (Intercept)-Vulpes_vulpes -1.0500 0.7786 -2.5023 -1.0621
## (Intercept)-Sus_scrofa -1.2906 0.6501 -2.6961 -1.2394
## Avg_Cogongrass_Cover-Canis_latrans 0.3944 0.3563 -0.2313 0.3660
## Avg_Cogongrass_Cover-Sciurus_niger -0.1568 0.5224 -1.3422 -0.1104
## Avg_Cogongrass_Cover-Procyon_lotor 0.1971 0.3357 -0.4438 0.1869
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3395 0.3166 -0.2519 0.3297
## Avg_Cogongrass_Cover-Lynx_rufus 0.4238 0.3736 -0.2434 0.4077
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3076 0.3524 -0.3594 0.3031
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1709 0.4147 -1.0888 -0.1287
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3214 0.3372 -0.3204 0.3064
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2828 0.4081 -0.5254 0.2711
## Avg_Cogongrass_Cover-Sus_scrofa -0.0177 0.4673 -1.0834 0.0178
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0813 1.0288 561
## (Intercept)-Sciurus_niger 0.4404 1.0398 453
## (Intercept)-Procyon_lotor 1.2468 1.0292 439
## (Intercept)-Dasypus_novemcinctus 0.2431 1.0019 890
## (Intercept)-Lynx_rufus 0.8207 1.0258 447
## (Intercept)-Didelphis_virginiana -0.0668 1.0139 639
## (Intercept)-Sylvilagus_floridanus 0.4287 1.0169 1069
## (Intercept)-Sciurus_carolinensis -0.0909 1.0021 650
## (Intercept)-Vulpes_vulpes 0.5935 1.0569 160
## (Intercept)-Sus_scrofa -0.1453 1.0017 590
## Avg_Cogongrass_Cover-Canis_latrans 1.1523 1.0032 1794
## Avg_Cogongrass_Cover-Sciurus_niger 0.7176 1.0201 718
## Avg_Cogongrass_Cover-Procyon_lotor 0.8932 1.0016 1846
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9699 1.0042 2116
## Avg_Cogongrass_Cover-Lynx_rufus 1.2329 1.0046 1436
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0361 1.0017 1599
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5596 1.0168 967
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0239 1.0003 1936
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1118 1.0033 1449
## Avg_Cogongrass_Cover-Sus_scrofa 0.8424 1.0120 969
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7347 0.1829 -3.1032 -2.7303 -2.3846 1.0137
## (Intercept)-Sciurus_niger -3.7734 0.5827 -4.9439 -3.7688 -2.7332 1.1539
## (Intercept)-Procyon_lotor -2.3069 0.1437 -2.5946 -2.2994 -2.0372 1.0096
## (Intercept)-Dasypus_novemcinctus -1.7856 0.1621 -2.1110 -1.7805 -1.4735 1.0009
## (Intercept)-Lynx_rufus -3.5189 0.3349 -4.1920 -3.5045 -2.9067 1.0486
## (Intercept)-Didelphis_virginiana -2.5944 0.2812 -3.1731 -2.5787 -2.0816 1.0008
## (Intercept)-Sylvilagus_floridanus -3.1569 0.2779 -3.7302 -3.1432 -2.6549 1.0088
## (Intercept)-Sciurus_carolinensis -2.6868 0.3089 -3.3378 -2.6705 -2.1054 1.0134
## (Intercept)-Vulpes_vulpes -3.9196 0.6663 -5.4950 -3.8709 -2.7851 1.0765
## (Intercept)-Sus_scrofa -3.3149 0.5289 -4.3699 -3.3106 -2.2976 1.0275
## shrub_cover-Canis_latrans -0.2390 0.2162 -0.6684 -0.2398 0.1788 1.0046
## shrub_cover-Sciurus_niger -0.2026 0.4847 -1.2109 -0.1971 0.7299 1.0251
## shrub_cover-Procyon_lotor 0.2622 0.1604 -0.0457 0.2643 0.5750 1.0163
## shrub_cover-Dasypus_novemcinctus 0.8667 0.2977 0.3051 0.8610 1.4655 1.0036
## shrub_cover-Lynx_rufus -0.1707 0.3499 -0.8767 -0.1618 0.5206 1.0281
## shrub_cover-Didelphis_virginiana 0.9262 0.3556 0.2778 0.9108 1.6667 1.0013
## shrub_cover-Sylvilagus_floridanus 0.2980 0.3868 -0.4337 0.2853 1.0556 1.0310
## shrub_cover-Sciurus_carolinensis 0.8299 0.4059 0.0606 0.8158 1.6718 1.0169
## shrub_cover-Vulpes_vulpes 0.0483 0.5742 -1.1366 0.0549 1.1495 1.0223
## shrub_cover-Sus_scrofa 0.6072 0.7065 -0.7583 0.5908 2.1136 1.0152
## veg_height-Canis_latrans -0.5591 0.1851 -0.9237 -0.5578 -0.2184 1.0059
## veg_height-Sciurus_niger 0.0814 0.4246 -0.7120 0.0594 0.9793 1.0300
## veg_height-Procyon_lotor 0.3435 0.1226 0.0983 0.3437 0.5829 1.0172
## veg_height-Dasypus_novemcinctus 0.2552 0.1326 -0.0096 0.2537 0.5127 1.0012
## veg_height-Lynx_rufus 0.0283 0.2477 -0.4805 0.0375 0.4937 1.0054
## veg_height-Didelphis_virginiana 0.4324 0.2417 -0.0215 0.4281 0.9249 1.0003
## veg_height-Sylvilagus_floridanus 0.1538 0.2443 -0.3442 0.1568 0.6167 1.0066
## veg_height-Sciurus_carolinensis 0.0934 0.2155 -0.3092 0.0900 0.5310 1.0065
## veg_height-Vulpes_vulpes -0.0941 0.2931 -0.7160 -0.0817 0.4617 1.0108
## veg_height-Sus_scrofa -0.0960 0.3276 -0.8076 -0.0883 0.5180 1.0023
## week-Canis_latrans 0.0609 0.1330 -0.2071 0.0641 0.3142 1.0044
## week-Sciurus_niger -0.3218 0.3045 -1.0440 -0.2873 0.1764 1.0291
## week-Procyon_lotor -0.0530 0.1196 -0.2960 -0.0481 0.1734 1.0014
## week-Dasypus_novemcinctus -0.1786 0.1404 -0.4635 -0.1721 0.0758 1.0034
## week-Lynx_rufus -0.0535 0.1959 -0.4637 -0.0452 0.3027 1.0050
## week-Didelphis_virginiana -0.2206 0.2179 -0.6934 -0.2049 0.1737 1.0028
## week-Sylvilagus_floridanus -0.1729 0.2133 -0.6159 -0.1637 0.2318 1.0048
## week-Sciurus_carolinensis 0.1243 0.1853 -0.2318 0.1243 0.4909 1.0076
## week-Vulpes_vulpes -0.1318 0.2683 -0.7083 -0.1152 0.3501 1.0049
## week-Sus_scrofa 0.0642 0.2436 -0.4199 0.0680 0.5467 1.0014
## ESS
## (Intercept)-Canis_latrans 893
## (Intercept)-Sciurus_niger 212
## (Intercept)-Procyon_lotor 1144
## (Intercept)-Dasypus_novemcinctus 1513
## (Intercept)-Lynx_rufus 312
## (Intercept)-Didelphis_virginiana 785
## (Intercept)-Sylvilagus_floridanus 659
## (Intercept)-Sciurus_carolinensis 731
## (Intercept)-Vulpes_vulpes 109
## (Intercept)-Sus_scrofa 480
## shrub_cover-Canis_latrans 901
## shrub_cover-Sciurus_niger 434
## shrub_cover-Procyon_lotor 1465
## shrub_cover-Dasypus_novemcinctus 880
## shrub_cover-Lynx_rufus 439
## shrub_cover-Didelphis_virginiana 902
## shrub_cover-Sylvilagus_floridanus 559
## shrub_cover-Sciurus_carolinensis 515
## shrub_cover-Vulpes_vulpes 568
## shrub_cover-Sus_scrofa 677
## veg_height-Canis_latrans 649
## veg_height-Sciurus_niger 557
## veg_height-Procyon_lotor 1723
## veg_height-Dasypus_novemcinctus 2093
## veg_height-Lynx_rufus 758
## veg_height-Didelphis_virginiana 1012
## veg_height-Sylvilagus_floridanus 929
## veg_height-Sciurus_carolinensis 1041
## veg_height-Vulpes_vulpes 742
## veg_height-Sus_scrofa 1124
## week-Canis_latrans 1521
## week-Sciurus_niger 611
## week-Procyon_lotor 1557
## week-Dasypus_novemcinctus 2185
## week-Lynx_rufus 933
## week-Didelphis_virginiana 1147
## week-Sylvilagus_floridanus 897
## week-Sciurus_carolinensis 1829
## week-Vulpes_vulpes 1167
## week-Sus_scrofa 1512
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.588
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7967 0.3445 -1.5087 -0.7952 -0.0922 1.0176 678
## Avg_Cogongrass_Cover 0.1855 0.2277 -0.2639 0.1928 0.6308 1.0101 957
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7182 0.7841 0.0619 0.5114 2.5600 1.0773 870
## Avg_Cogongrass_Cover 0.2441 0.2586 0.0365 0.1668 0.8985 1.0646 505
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7536 0.6189 0.0675 0.5876 2.4297 1.0245 225
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7415 0.2818 -3.3391 -2.7325 -2.2104 1.0056 1061
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.73 0.6053 0.1873 0.5749 2.2254 1.0529 362
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1719 0.5551 -1.2220 -0.1809
## (Intercept)-Sciurus_niger -0.9179 0.6443 -2.1716 -0.9387
## (Intercept)-Procyon_lotor 0.0076 0.5844 -1.0854 0.0060
## (Intercept)-Dasypus_novemcinctus -0.7693 0.4695 -1.6970 -0.7691
## (Intercept)-Lynx_rufus -0.5395 0.5977 -1.6233 -0.5798
## (Intercept)-Didelphis_virginiana -1.2138 0.5119 -2.2748 -1.1803
## (Intercept)-Sylvilagus_floridanus -0.6257 0.5257 -1.6597 -0.6341
## (Intercept)-Sciurus_carolinensis -1.2449 0.5178 -2.3459 -1.2209
## (Intercept)-Vulpes_vulpes -1.2694 0.7466 -2.7793 -1.2329
## (Intercept)-Sus_scrofa -1.4794 0.6342 -2.8495 -1.4476
## Avg_Cogongrass_Cover-Canis_latrans 0.3335 0.3297 -0.2956 0.3228
## Avg_Cogongrass_Cover-Sciurus_niger -0.1389 0.4716 -1.2306 -0.0992
## Avg_Cogongrass_Cover-Procyon_lotor 0.2273 0.3297 -0.3756 0.2221
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3361 0.3061 -0.2438 0.3296
## Avg_Cogongrass_Cover-Lynx_rufus 0.4434 0.3675 -0.1920 0.4118
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3234 0.3367 -0.3244 0.3227
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1639 0.4033 -1.0414 -0.1307
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3369 0.3387 -0.3145 0.3323
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2620 0.3953 -0.5006 0.2537
## Avg_Cogongrass_Cover-Sus_scrofa -0.0665 0.4470 -1.0752 -0.0177
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9542 1.0078 683
## (Intercept)-Sciurus_niger 0.4291 1.0204 464
## (Intercept)-Procyon_lotor 1.1432 1.0329 382
## (Intercept)-Dasypus_novemcinctus 0.1692 1.0026 1207
## (Intercept)-Lynx_rufus 0.7382 1.0021 568
## (Intercept)-Didelphis_virginiana -0.2794 1.0033 1382
## (Intercept)-Sylvilagus_floridanus 0.4184 1.0163 710
## (Intercept)-Sciurus_carolinensis -0.3091 1.0249 1261
## (Intercept)-Vulpes_vulpes 0.1760 1.0126 428
## (Intercept)-Sus_scrofa -0.3650 1.0183 590
## Avg_Cogongrass_Cover-Canis_latrans 1.0273 1.0010 1899
## Avg_Cogongrass_Cover-Sciurus_niger 0.7014 1.0183 919
## Avg_Cogongrass_Cover-Procyon_lotor 0.9087 1.0005 1970
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9687 1.0028 2003
## Avg_Cogongrass_Cover-Lynx_rufus 1.2738 1.0004 1512
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9909 1.0010 1724
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5389 1.0042 1189
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0369 1.0018 2230
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0701 1.0226 1555
## Avg_Cogongrass_Cover-Sus_scrofa 0.6860 1.0153 884
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6058 0.1712 -2.9486 -2.6039 -2.2782 1.0119
## (Intercept)-Sciurus_niger -3.6153 0.4895 -4.6807 -3.5874 -2.7585 1.0463
## (Intercept)-Procyon_lotor -2.2726 0.1288 -2.5276 -2.2714 -2.0288 1.0053
## (Intercept)-Dasypus_novemcinctus -1.6037 0.1364 -1.8755 -1.6011 -1.3439 1.0034
## (Intercept)-Lynx_rufus -3.4013 0.3033 -4.0467 -3.3872 -2.8677 1.0204
## (Intercept)-Didelphis_virginiana -2.3634 0.2545 -2.8852 -2.3532 -1.8905 1.0018
## (Intercept)-Sylvilagus_floridanus -3.1088 0.2736 -3.6682 -3.0969 -2.6119 1.0031
## (Intercept)-Sciurus_carolinensis -2.4820 0.2620 -3.0152 -2.4684 -1.9976 1.0052
## (Intercept)-Vulpes_vulpes -3.6908 0.6308 -5.1619 -3.6161 -2.6708 1.0453
## (Intercept)-Sus_scrofa -2.9630 0.4463 -3.9039 -2.9318 -2.1652 1.0057
## ESS
## (Intercept)-Canis_latrans 1077
## (Intercept)-Sciurus_niger 229
## (Intercept)-Procyon_lotor 1542
## (Intercept)-Dasypus_novemcinctus 2295
## (Intercept)-Lynx_rufus 443
## (Intercept)-Didelphis_virginiana 1302
## (Intercept)-Sylvilagus_floridanus 507
## (Intercept)-Sciurus_carolinensis 1219
## (Intercept)-Vulpes_vulpes 191
## (Intercept)-Sus_scrofa 622
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8053
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8273 0.3420 -1.5218 -0.8254 -0.1493 1.0023 841
## Avg_Cogongrass_Cover 0.1897 0.2411 -0.2776 0.1934 0.6503 1.0027 960
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7085 0.6953 0.0675 0.5034 2.6071 1.0478 697
## Avg_Cogongrass_Cover 0.2617 0.2776 0.0338 0.1751 0.9464 1.0028 1092
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7585 0.6571 0.0735 0.5823 2.469 1.045 172
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7413 0.2888 -3.3055 -2.7400 -2.1466 1.0008 1916
## week -0.0856 0.1326 -0.3621 -0.0777 0.1534 1.0015 1077
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7308 0.5842 0.195 0.5935 2.0378 1.0414 1016
## week 0.1029 0.0896 0.024 0.0766 0.3482 1.0070 1324
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1684 0.5570 -1.2498 -0.1759
## (Intercept)-Sciurus_niger -1.0156 0.6177 -2.2021 -1.0147
## (Intercept)-Procyon_lotor -0.0331 0.5887 -1.1816 -0.0169
## (Intercept)-Dasypus_novemcinctus -0.7968 0.4662 -1.7513 -0.7791
## (Intercept)-Lynx_rufus -0.5285 0.5756 -1.6043 -0.5665
## (Intercept)-Didelphis_virginiana -1.2307 0.5208 -2.2962 -1.2152
## (Intercept)-Sylvilagus_floridanus -0.6427 0.5092 -1.6123 -0.6548
## (Intercept)-Sciurus_carolinensis -1.2538 0.5301 -2.3662 -1.2158
## (Intercept)-Vulpes_vulpes -1.2861 0.6956 -2.7528 -1.2515
## (Intercept)-Sus_scrofa -1.4924 0.6488 -2.9003 -1.4399
## Avg_Cogongrass_Cover-Canis_latrans 0.3630 0.3405 -0.2843 0.3430
## Avg_Cogongrass_Cover-Sciurus_niger -0.1716 0.4944 -1.3058 -0.1090
## Avg_Cogongrass_Cover-Procyon_lotor 0.2378 0.3401 -0.4025 0.2335
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3518 0.3143 -0.2473 0.3370
## Avg_Cogongrass_Cover-Lynx_rufus 0.4561 0.3720 -0.1993 0.4305
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3395 0.3394 -0.3093 0.3362
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1689 0.4055 -1.0709 -0.1433
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3350 0.3371 -0.3214 0.3306
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2798 0.3927 -0.5032 0.2710
## Avg_Cogongrass_Cover-Sus_scrofa -0.0845 0.4732 -1.1654 -0.0309
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9755 1.0245 518
## (Intercept)-Sciurus_niger 0.2267 1.0012 790
## (Intercept)-Procyon_lotor 1.0621 1.0223 420
## (Intercept)-Dasypus_novemcinctus 0.1168 1.0015 1045
## (Intercept)-Lynx_rufus 0.6919 1.0024 797
## (Intercept)-Didelphis_virginiana -0.2766 1.0110 908
## (Intercept)-Sylvilagus_floridanus 0.4026 1.0106 808
## (Intercept)-Sciurus_carolinensis -0.3072 1.0081 1054
## (Intercept)-Vulpes_vulpes 0.0441 1.0137 556
## (Intercept)-Sus_scrofa -0.3217 1.0196 748
## Avg_Cogongrass_Cover-Canis_latrans 1.0826 1.0005 2294
## Avg_Cogongrass_Cover-Sciurus_niger 0.6288 1.0027 976
## Avg_Cogongrass_Cover-Procyon_lotor 0.9466 1.0007 2273
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0183 1.0052 2169
## Avg_Cogongrass_Cover-Lynx_rufus 1.2678 1.0012 1987
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0278 1.0005 1846
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5590 1.0066 1203
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0143 1.0010 1741
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0737 1.0018 1680
## Avg_Cogongrass_Cover-Sus_scrofa 0.7175 1.0024 1117
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6154 0.1669 -2.9539 -2.6087 -2.3016 1.0159
## (Intercept)-Sciurus_niger -3.6219 0.4594 -4.6111 -3.6017 -2.8119 1.0092
## (Intercept)-Procyon_lotor -2.2778 0.1271 -2.5333 -2.2783 -2.0274 1.0092
## (Intercept)-Dasypus_novemcinctus -1.6226 0.1372 -1.8966 -1.6204 -1.3565 1.0017
## (Intercept)-Lynx_rufus -3.4159 0.2870 -4.0089 -3.4138 -2.8775 1.0130
## (Intercept)-Didelphis_virginiana -2.3822 0.2531 -2.9247 -2.3705 -1.9181 1.0042
## (Intercept)-Sylvilagus_floridanus -3.1262 0.2832 -3.7157 -3.1176 -2.5967 1.0204
## (Intercept)-Sciurus_carolinensis -2.4942 0.2619 -3.0334 -2.4870 -2.0151 1.0056
## (Intercept)-Vulpes_vulpes -3.7086 0.5870 -5.0019 -3.6695 -2.6912 1.0367
## (Intercept)-Sus_scrofa -2.9691 0.4435 -3.9135 -2.9391 -2.2063 1.0182
## week-Canis_latrans 0.0624 0.1338 -0.2048 0.0652 0.3180 1.0037
## week-Sciurus_niger -0.3100 0.2943 -0.9769 -0.2789 0.1762 1.0036
## week-Procyon_lotor -0.0549 0.1183 -0.2939 -0.0504 0.1626 1.0059
## week-Dasypus_novemcinctus -0.1708 0.1396 -0.4625 -0.1669 0.0978 1.0005
## week-Lynx_rufus -0.0530 0.1963 -0.4665 -0.0464 0.3136 1.0412
## week-Didelphis_virginiana -0.2288 0.2213 -0.7177 -0.2110 0.1531 1.0036
## week-Sylvilagus_floridanus -0.1627 0.2055 -0.6073 -0.1555 0.2073 1.0258
## week-Sciurus_carolinensis 0.1234 0.1806 -0.2348 0.1271 0.4736 1.0007
## week-Vulpes_vulpes -0.1459 0.2752 -0.7174 -0.1239 0.3419 1.0181
## week-Sus_scrofa 0.0721 0.2363 -0.3904 0.0765 0.5378 1.0055
## ESS
## (Intercept)-Canis_latrans 1081
## (Intercept)-Sciurus_niger 298
## (Intercept)-Procyon_lotor 1843
## (Intercept)-Dasypus_novemcinctus 2292
## (Intercept)-Lynx_rufus 432
## (Intercept)-Didelphis_virginiana 1287
## (Intercept)-Sylvilagus_floridanus 555
## (Intercept)-Sciurus_carolinensis 1267
## (Intercept)-Vulpes_vulpes 263
## (Intercept)-Sus_scrofa 640
## week-Canis_latrans 1751
## week-Sciurus_niger 745
## week-Procyon_lotor 1764
## week-Dasypus_novemcinctus 2116
## week-Lynx_rufus 833
## week-Didelphis_virginiana 1280
## week-Sylvilagus_floridanus 1047
## week-Sciurus_carolinensis 1823
## week-Vulpes_vulpes 1070
## week-Sus_scrofa 1898
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8715
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2003 0.6809 -2.5206 -1.2156 0.2150 1.0357 626
## Cogon_Patch_Size -0.8690 0.5952 -2.1687 -0.8405 0.2412 1.0042 677
## Veg_shannon_index 0.8445 0.4026 0.0785 0.8382 1.6535 1.0142 310
## total_shrub_cover -0.2745 0.3739 -1.0684 -0.2669 0.4334 1.0049 586
## Avg_Cogongrass_Cover 2.0085 0.6479 0.7435 2.0011 3.2598 1.0635 246
## Tree_Density -1.8696 0.6486 -3.2558 -1.8283 -0.6754 1.0642 397
## Avg_Canopy_Cover 1.7116 0.5232 0.6989 1.6824 2.8383 1.1048 356
## avg_veg_height -0.5475 0.4049 -1.3636 -0.5326 0.2602 1.0291 301
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.1161 4.4620 0.2347 2.9031 15.4371 1.2115 240
## Cogon_Patch_Size 2.3163 3.0502 0.1331 1.4032 9.9305 1.0349 437
## Veg_shannon_index 0.5700 0.8069 0.0489 0.3334 2.3594 1.0359 571
## total_shrub_cover 0.5196 0.7582 0.0458 0.3043 2.1775 1.0080 608
## Avg_Cogongrass_Cover 1.0252 1.6525 0.0488 0.4625 5.2567 1.0738 377
## Tree_Density 1.9160 3.1144 0.0695 0.9177 9.4069 1.0393 220
## Avg_Canopy_Cover 1.2786 1.6508 0.0876 0.7626 5.3567 1.0519 279
## avg_veg_height 0.3479 0.4880 0.0386 0.2044 1.5358 1.0153 954
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3974 1.4015 0.0653 0.9475 5.1335 1.0029 93
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8335 0.3274 -3.4766 -2.8369 -2.1689 1.0049 1773
## week -0.0740 0.1282 -0.3516 -0.0679 0.1699 1.0095 1226
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0617 0.7274 0.3012 0.8695 2.9702 1.0351 1033
## week 0.0999 0.0871 0.0253 0.0746 0.3394 1.0332 850
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1433 0.9198 -1.7186 0.1554
## (Intercept)-Sciurus_niger -0.2465 1.4833 -2.7566 -0.4094
## (Intercept)-Procyon_lotor 0.3092 0.9341 -1.6493 0.3627
## (Intercept)-Dasypus_novemcinctus -1.4754 0.7873 -3.2046 -1.4160
## (Intercept)-Lynx_rufus 0.1784 1.5412 -2.3246 -0.0374
## (Intercept)-Didelphis_virginiana -2.5524 0.9403 -4.5540 -2.4890
## (Intercept)-Sylvilagus_floridanus -1.3762 0.9994 -3.3940 -1.3616
## (Intercept)-Sciurus_carolinensis -2.7233 0.9612 -4.7489 -2.6639
## (Intercept)-Vulpes_vulpes -2.3279 1.3262 -5.0216 -2.2931
## (Intercept)-Sus_scrofa -3.5319 1.3835 -6.5550 -3.4063
## Cogon_Patch_Size-Canis_latrans 0.4953 0.9865 -1.0025 0.3233
## Cogon_Patch_Size-Sciurus_niger -1.6702 1.5144 -5.2800 -1.4587
## Cogon_Patch_Size-Procyon_lotor -1.0184 0.6584 -2.4005 -0.9971
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7936 0.6080 -2.0684 -0.7644
## Cogon_Patch_Size-Lynx_rufus -1.0057 1.0823 -3.1315 -1.0314
## Cogon_Patch_Size-Didelphis_virginiana 0.6522 0.7830 -0.6872 0.5895
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8861 1.2700 -5.0063 -1.6760
## Cogon_Patch_Size-Sciurus_carolinensis -1.6196 1.0888 -4.3614 -1.4231
## Cogon_Patch_Size-Vulpes_vulpes -1.3108 1.3249 -4.1199 -1.2191
## Cogon_Patch_Size-Sus_scrofa -1.2129 1.1928 -3.9186 -1.0569
## Veg_shannon_index-Canis_latrans 1.1517 0.5339 0.1971 1.1121
## Veg_shannon_index-Sciurus_niger 0.9406 0.7889 -0.4794 0.8929
## Veg_shannon_index-Procyon_lotor 1.1155 0.5443 0.1305 1.0780
## Veg_shannon_index-Dasypus_novemcinctus 0.6393 0.4872 -0.3629 0.6454
## Veg_shannon_index-Lynx_rufus 0.8159 0.7001 -0.5609 0.8104
## Veg_shannon_index-Didelphis_virginiana 0.9625 0.5808 -0.1331 0.9297
## Veg_shannon_index-Sylvilagus_floridanus 0.9273 0.5827 -0.1774 0.8995
## Veg_shannon_index-Sciurus_carolinensis 0.2863 0.6413 -1.0741 0.3428
## Veg_shannon_index-Vulpes_vulpes 0.4192 0.7027 -1.1202 0.4799
## Veg_shannon_index-Sus_scrofa 1.3142 0.7630 0.0990 1.2006
## total_shrub_cover-Canis_latrans 0.0890 0.5235 -0.8204 0.0419
## total_shrub_cover-Sciurus_niger -0.5133 0.7316 -2.1931 -0.4383
## total_shrub_cover-Procyon_lotor -0.6791 0.5174 -1.8175 -0.6321
## total_shrub_cover-Dasypus_novemcinctus 0.0125 0.4720 -0.8792 -0.0024
## total_shrub_cover-Lynx_rufus -0.6409 0.7094 -2.3064 -0.5590
## total_shrub_cover-Didelphis_virginiana -0.3752 0.5576 -1.5602 -0.3522
## total_shrub_cover-Sylvilagus_floridanus -0.2054 0.5854 -1.3965 -0.1967
## total_shrub_cover-Sciurus_carolinensis -0.0804 0.5546 -1.1479 -0.1028
## total_shrub_cover-Vulpes_vulpes -0.4242 0.6899 -1.9519 -0.3721
## total_shrub_cover-Sus_scrofa -0.0080 0.6605 -1.1729 -0.0366
## Avg_Cogongrass_Cover-Canis_latrans 2.3000 0.8376 0.7914 2.2494
## Avg_Cogongrass_Cover-Sciurus_niger 1.4943 1.2831 -1.5994 1.6638
## Avg_Cogongrass_Cover-Procyon_lotor 2.2465 0.8422 0.7682 2.1991
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5111 0.8867 1.0116 2.4431
## Avg_Cogongrass_Cover-Lynx_rufus 2.4221 0.9164 0.9014 2.3476
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1418 0.8016 0.6474 2.0979
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4779 0.8947 -0.4128 1.5244
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3190 0.8577 0.8146 2.2477
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4720 0.9853 0.7495 2.3915
## Avg_Cogongrass_Cover-Sus_scrofa 1.5546 1.0149 -0.7338 1.6419
## Tree_Density-Canis_latrans -2.3285 1.0471 -4.7868 -2.1695
## Tree_Density-Sciurus_niger -1.8627 1.2189 -4.4296 -1.8056
## Tree_Density-Procyon_lotor -1.4985 0.6970 -2.9127 -1.5074
## Tree_Density-Dasypus_novemcinctus -3.0645 1.4505 -6.6515 -2.7382
## Tree_Density-Lynx_rufus -0.6283 1.1152 -2.4723 -0.7584
## Tree_Density-Didelphis_virginiana -2.1285 0.9951 -4.4821 -1.9779
## Tree_Density-Sylvilagus_floridanus -2.3053 1.2354 -5.3951 -2.0800
## Tree_Density-Sciurus_carolinensis -2.2511 1.1420 -5.0743 -2.0385
## Tree_Density-Vulpes_vulpes -1.8221 1.1763 -4.4095 -1.7630
## Tree_Density-Sus_scrofa -2.0425 1.2463 -5.0851 -1.8720
## Avg_Canopy_Cover-Canis_latrans 0.5164 0.6486 -0.7958 0.5142
## Avg_Canopy_Cover-Sciurus_niger 1.9118 1.2538 -0.4067 1.8318
## Avg_Canopy_Cover-Procyon_lotor 1.6338 0.6155 0.5204 1.6024
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8442 0.6017 0.8012 1.7974
## Avg_Canopy_Cover-Lynx_rufus 1.1790 0.9983 -0.7255 1.1765
## Avg_Canopy_Cover-Didelphis_virginiana 2.3283 0.7603 1.1075 2.2306
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.6483 1.1619 1.0050 2.4359
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0764 0.7187 0.9107 1.9921
## Avg_Canopy_Cover-Vulpes_vulpes 1.9361 0.9617 0.3980 1.7884
## Avg_Canopy_Cover-Sus_scrofa 1.8494 0.7472 0.5399 1.8066
## avg_veg_height-Canis_latrans -0.7269 0.5191 -1.7971 -0.7052
## avg_veg_height-Sciurus_niger -0.6860 0.6461 -2.0338 -0.6566
## avg_veg_height-Procyon_lotor -0.3955 0.4967 -1.3607 -0.4056
## avg_veg_height-Dasypus_novemcinctus -0.3474 0.5157 -1.3563 -0.3635
## avg_veg_height-Lynx_rufus -0.6029 0.6184 -1.8413 -0.5903
## avg_veg_height-Didelphis_virginiana -0.6281 0.5708 -1.7811 -0.6035
## avg_veg_height-Sylvilagus_floridanus -0.7084 0.5864 -1.9652 -0.6711
## avg_veg_height-Sciurus_carolinensis -0.2518 0.5717 -1.2972 -0.2780
## avg_veg_height-Vulpes_vulpes -0.5926 0.6386 -1.8885 -0.5828
## avg_veg_height-Sus_scrofa -0.6140 0.5960 -1.8776 -0.6011
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0723 1.0147 422
## (Intercept)-Sciurus_niger 3.0729 1.0682 220
## (Intercept)-Procyon_lotor 1.9468 1.0225 218
## (Intercept)-Dasypus_novemcinctus -0.0475 1.0031 524
## (Intercept)-Lynx_rufus 3.7485 1.3603 162
## (Intercept)-Didelphis_virginiana -0.8666 1.0465 519
## (Intercept)-Sylvilagus_floridanus 0.6115 1.0014 535
## (Intercept)-Sciurus_carolinensis -1.0316 1.0302 294
## (Intercept)-Vulpes_vulpes 0.2408 1.0108 253
## (Intercept)-Sus_scrofa -1.1629 1.0229 264
## Cogon_Patch_Size-Canis_latrans 2.9070 1.0028 751
## Cogon_Patch_Size-Sciurus_niger 0.8173 1.0297 317
## Cogon_Patch_Size-Procyon_lotor 0.1843 1.0024 647
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3592 1.0131 976
## Cogon_Patch_Size-Lynx_rufus 1.2462 1.0056 468
## Cogon_Patch_Size-Didelphis_virginiana 2.4245 1.0152 715
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0077 1.0271 470
## Cogon_Patch_Size-Sciurus_carolinensis -0.0245 1.0197 461
## Cogon_Patch_Size-Vulpes_vulpes 0.9913 1.0178 460
## Cogon_Patch_Size-Sus_scrofa 0.7591 1.0020 386
## Veg_shannon_index-Canis_latrans 2.3213 1.0114 659
## Veg_shannon_index-Sciurus_niger 2.6384 1.0064 468
## Veg_shannon_index-Procyon_lotor 2.2571 1.0004 385
## Veg_shannon_index-Dasypus_novemcinctus 1.5813 1.0155 839
## Veg_shannon_index-Lynx_rufus 2.2426 1.0164 574
## Veg_shannon_index-Didelphis_virginiana 2.1880 1.0204 754
## Veg_shannon_index-Sylvilagus_floridanus 2.1623 1.0031 882
## Veg_shannon_index-Sciurus_carolinensis 1.4437 1.0067 619
## Veg_shannon_index-Vulpes_vulpes 1.6932 1.0361 631
## Veg_shannon_index-Sus_scrofa 3.0663 1.0059 693
## total_shrub_cover-Canis_latrans 1.2338 1.0027 1005
## total_shrub_cover-Sciurus_niger 0.7155 1.0053 461
## total_shrub_cover-Procyon_lotor 0.2354 1.0165 741
## total_shrub_cover-Dasypus_novemcinctus 0.9767 1.0087 1162
## total_shrub_cover-Lynx_rufus 0.5217 1.0099 629
## total_shrub_cover-Didelphis_virginiana 0.6996 1.0229 1216
## total_shrub_cover-Sylvilagus_floridanus 0.9746 1.0021 920
## total_shrub_cover-Sciurus_carolinensis 1.0735 1.0035 1338
## total_shrub_cover-Vulpes_vulpes 0.7825 1.0027 748
## total_shrub_cover-Sus_scrofa 1.4452 1.0016 947
## Avg_Cogongrass_Cover-Canis_latrans 4.1564 1.0499 419
## Avg_Cogongrass_Cover-Sciurus_niger 3.4794 1.0099 281
## Avg_Cogongrass_Cover-Procyon_lotor 4.0456 1.0751 302
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.4605 1.0675 350
## Avg_Cogongrass_Cover-Lynx_rufus 4.4651 1.0669 448
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8215 1.0586 437
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1265 1.0040 349
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2541 1.0864 329
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7856 1.0739 335
## Avg_Cogongrass_Cover-Sus_scrofa 3.3778 1.0190 459
## Tree_Density-Canis_latrans -0.6892 1.0221 272
## Tree_Density-Sciurus_niger 0.5551 1.0169 451
## Tree_Density-Procyon_lotor -0.1101 1.0423 656
## Tree_Density-Dasypus_novemcinctus -1.1812 1.0087 195
## Tree_Density-Lynx_rufus 2.0355 1.1453 280
## Tree_Density-Didelphis_virginiana -0.5614 1.0671 544
## Tree_Density-Sylvilagus_floridanus -0.3811 1.0097 402
## Tree_Density-Sciurus_carolinensis -0.5099 1.0196 287
## Tree_Density-Vulpes_vulpes 0.3785 1.0252 468
## Tree_Density-Sus_scrofa -0.0280 1.0140 415
## Avg_Canopy_Cover-Canis_latrans 1.8303 1.0057 628
## Avg_Canopy_Cover-Sciurus_niger 4.7186 1.0992 290
## Avg_Canopy_Cover-Procyon_lotor 2.9649 1.0393 456
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.1139 1.0513 390
## Avg_Canopy_Cover-Lynx_rufus 3.1257 1.1118 346
## Avg_Canopy_Cover-Didelphis_virginiana 4.0519 1.0549 336
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.5776 1.0823 211
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7114 1.0523 441
## Avg_Canopy_Cover-Vulpes_vulpes 4.1519 1.0215 381
## Avg_Canopy_Cover-Sus_scrofa 3.4337 1.0182 734
## avg_veg_height-Canis_latrans 0.2159 1.0191 482
## avg_veg_height-Sciurus_niger 0.4963 1.0209 499
## avg_veg_height-Procyon_lotor 0.6036 1.0514 416
## avg_veg_height-Dasypus_novemcinctus 0.7120 1.0242 600
## avg_veg_height-Lynx_rufus 0.5738 1.0177 454
## avg_veg_height-Didelphis_virginiana 0.4562 1.0109 576
## avg_veg_height-Sylvilagus_floridanus 0.3204 1.0197 491
## avg_veg_height-Sciurus_carolinensis 0.9549 1.0202 614
## avg_veg_height-Vulpes_vulpes 0.6703 1.0071 569
## avg_veg_height-Sus_scrofa 0.5063 1.0271 626
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6339 0.1721 -3.0026 -2.6264 -2.3107 1.0082
## (Intercept)-Sciurus_niger -4.2744 0.4635 -5.1550 -4.2809 -3.3294 1.0854
## (Intercept)-Procyon_lotor -2.2770 0.1304 -2.5326 -2.2740 -2.0283 1.0017
## (Intercept)-Dasypus_novemcinctus -1.6146 0.1344 -1.8796 -1.6137 -1.3597 1.0000
## (Intercept)-Lynx_rufus -3.6147 0.3122 -4.2415 -3.6125 -3.0262 1.1899
## (Intercept)-Didelphis_virginiana -2.3513 0.2419 -2.8531 -2.3470 -1.8958 1.0065
## (Intercept)-Sylvilagus_floridanus -3.1794 0.2760 -3.7369 -3.1680 -2.6801 1.0218
## (Intercept)-Sciurus_carolinensis -2.4853 0.2552 -3.0236 -2.4692 -2.0294 1.0000
## (Intercept)-Vulpes_vulpes -3.9981 0.5974 -5.2223 -3.9596 -2.9137 1.0095
## (Intercept)-Sus_scrofa -3.0007 0.4501 -3.9278 -2.9758 -2.2036 1.0051
## week-Canis_latrans 0.0677 0.1305 -0.2014 0.0711 0.3149 1.0000
## week-Sciurus_niger -0.2887 0.2769 -0.9181 -0.2625 0.1705 1.0090
## week-Procyon_lotor -0.0547 0.1172 -0.2936 -0.0518 0.1634 1.0050
## week-Dasypus_novemcinctus -0.1726 0.1359 -0.4542 -0.1651 0.0789 1.0083
## week-Lynx_rufus -0.0281 0.1867 -0.4105 -0.0248 0.3278 1.0019
## week-Didelphis_virginiana -0.2150 0.2034 -0.6636 -0.2045 0.1495 1.0098
## week-Sylvilagus_floridanus -0.1457 0.2018 -0.5638 -0.1342 0.2219 1.0010
## week-Sciurus_carolinensis 0.1263 0.1809 -0.2326 0.1308 0.4756 0.9998
## week-Vulpes_vulpes -0.1371 0.2756 -0.6950 -0.1228 0.3620 1.0019
## week-Sus_scrofa 0.0833 0.2399 -0.3973 0.0824 0.5598 1.0037
## ESS
## (Intercept)-Canis_latrans 859
## (Intercept)-Sciurus_niger 203
## (Intercept)-Procyon_lotor 1501
## (Intercept)-Dasypus_novemcinctus 2255
## (Intercept)-Lynx_rufus 257
## (Intercept)-Didelphis_virginiana 1575
## (Intercept)-Sylvilagus_floridanus 537
## (Intercept)-Sciurus_carolinensis 1001
## (Intercept)-Vulpes_vulpes 229
## (Intercept)-Sus_scrofa 577
## week-Canis_latrans 1686
## week-Sciurus_niger 507
## week-Procyon_lotor 1624
## week-Dasypus_novemcinctus 1963
## week-Lynx_rufus 977
## week-Didelphis_virginiana 1492
## week-Sylvilagus_floridanus 1108
## week-Sciurus_carolinensis 1633
## week-Vulpes_vulpes 952
## week-Sus_scrofa 1741
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.821
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8133 0.3845 -1.6391 -0.8024 -0.0927 1.0006 669
## Avg_Cogongrass_Cover 0.1301 0.2893 -0.4375 0.1346 0.6767 1.0052 582
## total_shrub_cover -0.2632 0.2539 -0.7747 -0.2543 0.2146 1.0088 900
## avg_veg_height 0.0155 0.2769 -0.5111 0.0119 0.5690 1.0106 491
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8520 0.8650 0.0803 0.6038 3.1463 1.0303 575
## Avg_Cogongrass_Cover 0.2869 0.3361 0.0367 0.1830 1.1965 1.0291 816
## total_shrub_cover 0.3063 0.3393 0.0367 0.1999 1.2005 1.0200 763
## avg_veg_height 0.1914 0.1973 0.0337 0.1349 0.6787 1.0139 1350
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8651 0.7326 0.0973 0.6689 2.7201 1.0187 238
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7744 0.2965 -3.3702 -2.7676 -2.2054 1.0045 1929
## week -0.0824 0.1285 -0.3593 -0.0778 0.1551 1.0060 1144
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7688 0.5780 0.2108 0.6256 2.1250 1.0117 860
## week 0.1038 0.0964 0.0242 0.0759 0.3735 1.0008 1059
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1235 0.6031 -1.2921 -0.1414
## (Intercept)-Sciurus_niger -0.9615 0.7033 -2.3478 -0.9714
## (Intercept)-Procyon_lotor 0.0769 0.6556 -1.2209 0.0679
## (Intercept)-Dasypus_novemcinctus -0.8011 0.5023 -1.8395 -0.7931
## (Intercept)-Lynx_rufus -0.5046 0.6661 -1.7341 -0.5288
## (Intercept)-Didelphis_virginiana -1.2719 0.5634 -2.4236 -1.2509
## (Intercept)-Sylvilagus_floridanus -0.5963 0.5850 -1.7350 -0.6154
## (Intercept)-Sciurus_carolinensis -1.3328 0.5688 -2.5346 -1.2941
## (Intercept)-Vulpes_vulpes -1.3056 0.7421 -2.9020 -1.2637
## (Intercept)-Sus_scrofa -1.5482 0.7054 -3.0677 -1.5048
## Avg_Cogongrass_Cover-Canis_latrans 0.3608 0.4247 -0.4086 0.3365
## Avg_Cogongrass_Cover-Sciurus_niger -0.2183 0.5417 -1.4733 -0.1746
## Avg_Cogongrass_Cover-Procyon_lotor 0.0903 0.3954 -0.6694 0.0952
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2347 0.3707 -0.4945 0.2396
## Avg_Cogongrass_Cover-Lynx_rufus 0.3957 0.4345 -0.3834 0.3712
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2892 0.4039 -0.4664 0.2775
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2241 0.4610 -1.2748 -0.1757
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2150 0.3969 -0.5561 0.2070
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2328 0.4672 -0.6305 0.2222
## Avg_Cogongrass_Cover-Sus_scrofa -0.1345 0.5067 -1.2629 -0.0917
## total_shrub_cover-Canis_latrans 0.0811 0.3974 -0.6356 0.0581
## total_shrub_cover-Sciurus_niger -0.5027 0.4917 -1.5939 -0.4610
## total_shrub_cover-Procyon_lotor -0.6584 0.4247 -1.6054 -0.6245
## total_shrub_cover-Dasypus_novemcinctus -0.0677 0.3351 -0.7270 -0.0706
## total_shrub_cover-Lynx_rufus -0.6038 0.5147 -1.8261 -0.5432
## total_shrub_cover-Didelphis_virginiana -0.2187 0.3787 -0.9889 -0.2183
## total_shrub_cover-Sylvilagus_floridanus -0.2896 0.4330 -1.1950 -0.2697
## total_shrub_cover-Sciurus_carolinensis -0.1162 0.3715 -0.8069 -0.1279
## total_shrub_cover-Vulpes_vulpes -0.2807 0.4878 -1.3023 -0.2692
## total_shrub_cover-Sus_scrofa -0.0172 0.4479 -0.8522 -0.0305
## avg_veg_height-Canis_latrans -0.0635 0.3751 -0.8037 -0.0565
## avg_veg_height-Sciurus_niger -0.1412 0.4532 -1.0577 -0.1353
## avg_veg_height-Procyon_lotor 0.0969 0.3858 -0.6605 0.0886
## avg_veg_height-Dasypus_novemcinctus 0.1758 0.3770 -0.5516 0.1647
## avg_veg_height-Lynx_rufus 0.0204 0.4354 -0.8340 0.0171
## avg_veg_height-Didelphis_virginiana -0.0043 0.3906 -0.7705 -0.0092
## avg_veg_height-Sylvilagus_floridanus -0.1128 0.4019 -0.9453 -0.0997
## avg_veg_height-Sciurus_carolinensis 0.2523 0.4091 -0.5205 0.2408
## avg_veg_height-Vulpes_vulpes -0.0493 0.4365 -0.9640 -0.0357
## avg_veg_height-Sus_scrofa -0.0122 0.4149 -0.8511 -0.0142
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0801 1.0007 491
## (Intercept)-Sciurus_niger 0.4886 1.0172 430
## (Intercept)-Procyon_lotor 1.3490 1.0078 336
## (Intercept)-Dasypus_novemcinctus 0.1520 1.0029 1106
## (Intercept)-Lynx_rufus 0.8342 1.0207 536
## (Intercept)-Didelphis_virginiana -0.2314 1.0094 950
## (Intercept)-Sylvilagus_floridanus 0.6186 1.0023 735
## (Intercept)-Sciurus_carolinensis -0.3312 1.0023 975
## (Intercept)-Vulpes_vulpes 0.1105 1.0052 512
## (Intercept)-Sus_scrofa -0.2967 1.0018 586
## Avg_Cogongrass_Cover-Canis_latrans 1.2872 1.0099 792
## Avg_Cogongrass_Cover-Sciurus_niger 0.7198 1.0019 741
## Avg_Cogongrass_Cover-Procyon_lotor 0.8920 1.0010 1041
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9606 1.0061 1306
## Avg_Cogongrass_Cover-Lynx_rufus 1.3408 1.0009 1117
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1089 1.0005 947
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5748 1.0092 778
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9957 1.0021 1325
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1916 1.0103 924
## Avg_Cogongrass_Cover-Sus_scrofa 0.7501 1.0030 788
## total_shrub_cover-Canis_latrans 0.9301 1.0031 1867
## total_shrub_cover-Sciurus_niger 0.3468 1.0069 1165
## total_shrub_cover-Procyon_lotor 0.0491 1.0075 1033
## total_shrub_cover-Dasypus_novemcinctus 0.6078 1.0034 1853
## total_shrub_cover-Lynx_rufus 0.2208 1.0088 906
## total_shrub_cover-Didelphis_virginiana 0.5048 1.0012 1882
## total_shrub_cover-Sylvilagus_floridanus 0.5126 1.0016 1389
## total_shrub_cover-Sciurus_carolinensis 0.6610 1.0042 1873
## total_shrub_cover-Vulpes_vulpes 0.6451 1.0105 1001
## total_shrub_cover-Sus_scrofa 0.9163 1.0031 1395
## avg_veg_height-Canis_latrans 0.6537 1.0153 864
## avg_veg_height-Sciurus_niger 0.7415 1.0026 947
## avg_veg_height-Procyon_lotor 0.8804 1.0049 1008
## avg_veg_height-Dasypus_novemcinctus 0.9838 1.0087 1175
## avg_veg_height-Lynx_rufus 0.9181 1.0134 1014
## avg_veg_height-Didelphis_virginiana 0.7772 1.0089 1083
## avg_veg_height-Sylvilagus_floridanus 0.6398 1.0131 1147
## avg_veg_height-Sciurus_carolinensis 1.0797 1.0096 1020
## avg_veg_height-Vulpes_vulpes 0.7826 1.0054 732
## avg_veg_height-Sus_scrofa 0.7952 1.0049 1102
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6294 0.1703 -2.9665 -2.6274 -2.3071 1.0067
## (Intercept)-Sciurus_niger -3.7032 0.5192 -4.8630 -3.6535 -2.7955 1.0362
## (Intercept)-Procyon_lotor -2.2763 0.1284 -2.5367 -2.2744 -2.0433 1.0341
## (Intercept)-Dasypus_novemcinctus -1.6164 0.1354 -1.8865 -1.6160 -1.3470 1.0054
## (Intercept)-Lynx_rufus -3.4599 0.2882 -4.0338 -3.4578 -2.8981 1.0135
## (Intercept)-Didelphis_virginiana -2.3917 0.2560 -2.9211 -2.3862 -1.9088 1.0128
## (Intercept)-Sylvilagus_floridanus -3.2009 0.2873 -3.7844 -3.1922 -2.6643 1.0249
## (Intercept)-Sciurus_carolinensis -2.4943 0.2683 -3.0554 -2.4784 -1.9970 1.0008
## (Intercept)-Vulpes_vulpes -3.7495 0.6004 -4.9852 -3.7050 -2.7005 1.0114
## (Intercept)-Sus_scrofa -3.0069 0.4800 -4.0819 -2.9631 -2.1822 1.0004
## week-Canis_latrans 0.0619 0.1347 -0.2092 0.0641 0.3134 1.0046
## week-Sciurus_niger -0.3059 0.2966 -0.9835 -0.2691 0.1729 1.0158
## week-Procyon_lotor -0.0541 0.1184 -0.2989 -0.0520 0.1718 1.0026
## week-Dasypus_novemcinctus -0.1715 0.1381 -0.4539 -0.1661 0.0853 1.0026
## week-Lynx_rufus -0.0425 0.1948 -0.4589 -0.0373 0.3133 1.0049
## week-Didelphis_virginiana -0.2255 0.2222 -0.6993 -0.2086 0.1545 1.0138
## week-Sylvilagus_floridanus -0.1662 0.2065 -0.6213 -0.1545 0.2038 1.0155
## week-Sciurus_carolinensis 0.1301 0.1788 -0.2263 0.1305 0.4754 1.0024
## week-Vulpes_vulpes -0.1301 0.2748 -0.7215 -0.1131 0.3580 1.0042
## week-Sus_scrofa 0.0769 0.2387 -0.3722 0.0759 0.5449 1.0028
## ESS
## (Intercept)-Canis_latrans 1007
## (Intercept)-Sciurus_niger 214
## (Intercept)-Procyon_lotor 1556
## (Intercept)-Dasypus_novemcinctus 2154
## (Intercept)-Lynx_rufus 477
## (Intercept)-Didelphis_virginiana 1312
## (Intercept)-Sylvilagus_floridanus 538
## (Intercept)-Sciurus_carolinensis 959
## (Intercept)-Vulpes_vulpes 233
## (Intercept)-Sus_scrofa 546
## week-Canis_latrans 1565
## week-Sciurus_niger 595
## week-Procyon_lotor 1580
## week-Dasypus_novemcinctus 1989
## week-Lynx_rufus 971
## week-Didelphis_virginiana 1100
## week-Sylvilagus_floridanus 1065
## week-Sciurus_carolinensis 1694
## week-Vulpes_vulpes 1093
## week-Sus_scrofa 1588
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.828
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.687 0.336 -1.3698 -0.6916 0.0059 1.0049 1453
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9082 0.6723 0.1932 0.7217 2.6324 1.0022 1472
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7355 0.2863 -3.3289 -2.7278 -2.1730 1.0059 1811
## week -0.0882 0.1319 -0.3739 -0.0786 0.1471 1.0000 1191
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7001 0.5145 0.1821 0.5643 2.0649 1.0083 725
## week 0.1059 0.1038 0.0246 0.0778 0.3545 1.0087 849
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1388 0.3831 -0.5641 0.1262 0.9414 0.9996
## (Intercept)-Sciurus_niger -0.9727 0.5965 -2.0537 -1.0047 0.2938 1.0029
## (Intercept)-Procyon_lotor 0.4848 0.3776 -0.2440 0.4868 1.2478 1.0038
## (Intercept)-Dasypus_novemcinctus -0.6654 0.3422 -1.3729 -0.6564 -0.0129 1.0006
## (Intercept)-Lynx_rufus -0.1335 0.5188 -1.0294 -0.1716 1.0196 1.0023
## (Intercept)-Didelphis_virginiana -1.2771 0.4164 -2.1656 -1.2616 -0.5128 1.0027
## (Intercept)-Sylvilagus_floridanus -0.4471 0.4584 -1.2464 -0.4705 0.5223 1.0311
## (Intercept)-Sciurus_carolinensis -1.2455 0.4138 -2.1308 -1.2345 -0.4839 1.0010
## (Intercept)-Vulpes_vulpes -1.3479 0.6121 -2.5419 -1.3596 -0.1527 1.0123
## (Intercept)-Sus_scrofa -1.6440 0.5480 -2.8425 -1.6223 -0.5939 1.0210
## ESS
## (Intercept)-Canis_latrans 1825
## (Intercept)-Sciurus_niger 540
## (Intercept)-Procyon_lotor 1928
## (Intercept)-Dasypus_novemcinctus 2833
## (Intercept)-Lynx_rufus 696
## (Intercept)-Didelphis_virginiana 2371
## (Intercept)-Sylvilagus_floridanus 975
## (Intercept)-Sciurus_carolinensis 2306
## (Intercept)-Vulpes_vulpes 591
## (Intercept)-Sus_scrofa 1118
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6126 0.1663 -2.9464 -2.6099 -2.2932 1.0026
## (Intercept)-Sciurus_niger -3.6004 0.4761 -4.6191 -3.5662 -2.7680 1.0124
## (Intercept)-Procyon_lotor -2.2736 0.1321 -2.5373 -2.2693 -2.0231 1.0004
## (Intercept)-Dasypus_novemcinctus -1.6179 0.1359 -1.8923 -1.6118 -1.3588 1.0002
## (Intercept)-Lynx_rufus -3.4168 0.3047 -4.0248 -3.4118 -2.8465 1.0039
## (Intercept)-Didelphis_virginiana -2.3793 0.2458 -2.8816 -2.3660 -1.9285 1.0007
## (Intercept)-Sylvilagus_floridanus -3.1426 0.2878 -3.7637 -3.1249 -2.6304 1.0615
## (Intercept)-Sciurus_carolinensis -2.4878 0.2546 -3.0249 -2.4845 -2.0122 1.0038
## (Intercept)-Vulpes_vulpes -3.6332 0.5632 -4.8345 -3.5853 -2.6503 1.0286
## (Intercept)-Sus_scrofa -2.9688 0.4505 -3.9690 -2.9458 -2.2005 1.0248
## week-Canis_latrans 0.0658 0.1311 -0.1860 0.0669 0.3130 1.0006
## week-Sciurus_niger -0.3149 0.3071 -1.0403 -0.2777 0.1850 1.0046
## week-Procyon_lotor -0.0528 0.1162 -0.2929 -0.0482 0.1594 1.0021
## week-Dasypus_novemcinctus -0.1676 0.1388 -0.4444 -0.1638 0.0906 1.0019
## week-Lynx_rufus -0.0519 0.1957 -0.4639 -0.0398 0.3047 1.0077
## week-Didelphis_virginiana -0.2277 0.2184 -0.6996 -0.2102 0.1555 0.9999
## week-Sylvilagus_floridanus -0.1656 0.2049 -0.5962 -0.1537 0.2120 0.9997
## week-Sciurus_carolinensis 0.1284 0.1849 -0.2342 0.1295 0.4917 1.0012
## week-Vulpes_vulpes -0.1432 0.2774 -0.7407 -0.1175 0.3523 1.0016
## week-Sus_scrofa 0.0721 0.2322 -0.3727 0.0656 0.5291 1.0076
## ESS
## (Intercept)-Canis_latrans 1120
## (Intercept)-Sciurus_niger 279
## (Intercept)-Procyon_lotor 1489
## (Intercept)-Dasypus_novemcinctus 2374
## (Intercept)-Lynx_rufus 443
## (Intercept)-Didelphis_virginiana 1327
## (Intercept)-Sylvilagus_floridanus 518
## (Intercept)-Sciurus_carolinensis 1264
## (Intercept)-Vulpes_vulpes 266
## (Intercept)-Sus_scrofa 586
## week-Canis_latrans 1646
## week-Sciurus_niger 623
## week-Procyon_lotor 1574
## week-Dasypus_novemcinctus 1988
## week-Lynx_rufus 1078
## week-Didelphis_virginiana 1320
## week-Sylvilagus_floridanus 1006
## week-Sciurus_carolinensis 1519
## week-Vulpes_vulpes 963
## week-Sus_scrofa 1678
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8252
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8469 0.3833 -1.6096 -0.8448 -0.0843 1.0043 847
## Veg_shannon_index 0.3288 0.2488 -0.1556 0.3248 0.8360 1.0010 921
## Avg_Cogongrass_Cover 0.2935 0.2419 -0.1865 0.2963 0.7579 1.0029 753
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8349 1.0165 0.0666 0.5571 3.2148 1.0437 516
## Veg_shannon_index 0.2768 0.3145 0.0355 0.1842 1.0499 1.0012 978
## Avg_Cogongrass_Cover 0.2594 0.2820 0.0363 0.1703 0.9754 1.0156 1100
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8499 0.6955 0.0704 0.6931 2.6161 1.0349 200
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7623 0.2858 -3.3397 -2.7656 -2.1910 1.0086 1196
## week -0.0821 0.1285 -0.3535 -0.0794 0.1546 1.0064 1173
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7630 0.6476 0.1920 0.5919 2.3101 1.0583 297
## week 0.0987 0.0815 0.0238 0.0747 0.3180 1.0055 1250
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1970 0.5865 -1.3229 -0.2100
## (Intercept)-Sciurus_niger -0.9674 0.7135 -2.2427 -0.9806
## (Intercept)-Procyon_lotor -0.0164 0.6452 -1.2442 -0.0244
## (Intercept)-Dasypus_novemcinctus -0.8410 0.4883 -1.8722 -0.8265
## (Intercept)-Lynx_rufus -0.5179 0.6569 -1.6939 -0.5437
## (Intercept)-Didelphis_virginiana -1.2980 0.5537 -2.5019 -1.2697
## (Intercept)-Sylvilagus_floridanus -0.6833 0.5650 -1.8248 -0.6908
## (Intercept)-Sciurus_carolinensis -1.2909 0.5347 -2.4500 -1.2579
## (Intercept)-Vulpes_vulpes -1.2945 0.7516 -2.8950 -1.2719
## (Intercept)-Sus_scrofa -1.6156 0.7137 -3.1603 -1.5386
## Veg_shannon_index-Canis_latrans 0.6271 0.3648 -0.0492 0.6009
## Veg_shannon_index-Sciurus_niger 0.3122 0.4632 -0.6058 0.3109
## Veg_shannon_index-Procyon_lotor 0.4563 0.3567 -0.2133 0.4442
## Veg_shannon_index-Dasypus_novemcinctus 0.1857 0.3356 -0.4894 0.1949
## Veg_shannon_index-Lynx_rufus 0.1326 0.4537 -0.8566 0.1613
## Veg_shannon_index-Didelphis_virginiana 0.4831 0.3874 -0.2293 0.4644
## Veg_shannon_index-Sylvilagus_floridanus 0.4289 0.4056 -0.3113 0.4110
## Veg_shannon_index-Sciurus_carolinensis 0.0064 0.3914 -0.8126 0.0296
## Veg_shannon_index-Vulpes_vulpes 0.0656 0.4404 -0.8936 0.0891
## Veg_shannon_index-Sus_scrofa 0.6173 0.4748 -0.1989 0.5707
## Avg_Cogongrass_Cover-Canis_latrans 0.5046 0.3583 -0.1423 0.4772
## Avg_Cogongrass_Cover-Sciurus_niger -0.0393 0.5042 -1.2271 0.0206
## Avg_Cogongrass_Cover-Procyon_lotor 0.3729 0.3553 -0.2917 0.3597
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4100 0.3229 -0.2253 0.4121
## Avg_Cogongrass_Cover-Lynx_rufus 0.5543 0.3848 -0.1244 0.5297
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4485 0.3507 -0.2183 0.4347
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0597 0.4309 -1.0231 -0.0329
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3997 0.3473 -0.2532 0.3869
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3406 0.4229 -0.5005 0.3454
## Avg_Cogongrass_Cover-Sus_scrofa 0.0655 0.4526 -0.9521 0.1073
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9902 1.0101 661
## (Intercept)-Sciurus_niger 0.4239 1.0334 430
## (Intercept)-Procyon_lotor 1.2247 1.0331 413
## (Intercept)-Dasypus_novemcinctus 0.1008 1.0052 1291
## (Intercept)-Lynx_rufus 0.8498 1.0280 610
## (Intercept)-Didelphis_virginiana -0.3004 1.0008 973
## (Intercept)-Sylvilagus_floridanus 0.4513 1.0217 813
## (Intercept)-Sciurus_carolinensis -0.3084 1.0034 1067
## (Intercept)-Vulpes_vulpes 0.1091 1.0276 355
## (Intercept)-Sus_scrofa -0.3503 1.0009 613
## Veg_shannon_index-Canis_latrans 1.4085 1.0009 1390
## Veg_shannon_index-Sciurus_niger 1.2611 1.0038 1146
## Veg_shannon_index-Procyon_lotor 1.2389 1.0084 1576
## Veg_shannon_index-Dasypus_novemcinctus 0.8327 1.0010 2088
## Veg_shannon_index-Lynx_rufus 0.9184 1.0019 1173
## Veg_shannon_index-Didelphis_virginiana 1.3121 0.9999 1623
## Veg_shannon_index-Sylvilagus_floridanus 1.3033 1.0011 1474
## Veg_shannon_index-Sciurus_carolinensis 0.7264 1.0045 1686
## Veg_shannon_index-Vulpes_vulpes 0.8884 1.0107 1104
## Veg_shannon_index-Sus_scrofa 1.7091 1.0028 1219
## Avg_Cogongrass_Cover-Canis_latrans 1.2646 1.0012 1765
## Avg_Cogongrass_Cover-Sciurus_niger 0.7946 1.0021 875
## Avg_Cogongrass_Cover-Procyon_lotor 1.1170 1.0030 1986
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0528 1.0014 1849
## Avg_Cogongrass_Cover-Lynx_rufus 1.3595 1.0032 1422
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1813 1.0015 1773
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7114 1.0028 1053
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1215 1.0061 1715
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2337 1.0030 1232
## Avg_Cogongrass_Cover-Sus_scrofa 0.8439 1.0035 1105
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6067 0.1709 -2.9447 -2.6028 -2.2822 1.0015
## (Intercept)-Sciurus_niger -3.6772 0.4870 -4.6839 -3.6437 -2.8230 1.0059
## (Intercept)-Procyon_lotor -2.2813 0.1299 -2.5388 -2.2799 -2.0347 1.0022
## (Intercept)-Dasypus_novemcinctus -1.6188 0.1373 -1.8979 -1.6159 -1.3572 0.9999
## (Intercept)-Lynx_rufus -3.4345 0.2927 -4.0188 -3.4292 -2.8974 1.0042
## (Intercept)-Didelphis_virginiana -2.3766 0.2495 -2.9048 -2.3678 -1.9068 1.0020
## (Intercept)-Sylvilagus_floridanus -3.1674 0.2796 -3.7629 -3.1535 -2.6405 1.0116
## (Intercept)-Sciurus_carolinensis -2.4961 0.2622 -3.0383 -2.4798 -2.0094 1.0002
## (Intercept)-Vulpes_vulpes -3.7408 0.6594 -5.1190 -3.6694 -2.6742 1.1093
## (Intercept)-Sus_scrofa -2.9887 0.4649 -4.0343 -2.9524 -2.1781 1.0142
## week-Canis_latrans 0.0551 0.1319 -0.2104 0.0597 0.3003 1.0021
## week-Sciurus_niger -0.2988 0.2775 -0.9306 -0.2638 0.1634 1.0104
## week-Procyon_lotor -0.0497 0.1135 -0.2750 -0.0442 0.1707 1.0012
## week-Dasypus_novemcinctus -0.1694 0.1392 -0.4530 -0.1665 0.0878 1.0061
## week-Lynx_rufus -0.0577 0.1919 -0.4567 -0.0479 0.3070 1.0046
## week-Didelphis_virginiana -0.2188 0.2104 -0.6683 -0.2055 0.1511 1.0038
## week-Sylvilagus_floridanus -0.1524 0.1953 -0.5702 -0.1479 0.2171 1.0004
## week-Sciurus_carolinensis 0.1289 0.1771 -0.2161 0.1306 0.4715 1.0012
## week-Vulpes_vulpes -0.1334 0.2607 -0.6766 -0.1258 0.3488 1.0026
## week-Sus_scrofa 0.0814 0.2371 -0.3945 0.0808 0.5444 1.0045
## ESS
## (Intercept)-Canis_latrans 1129
## (Intercept)-Sciurus_niger 279
## (Intercept)-Procyon_lotor 1455
## (Intercept)-Dasypus_novemcinctus 2218
## (Intercept)-Lynx_rufus 410
## (Intercept)-Didelphis_virginiana 1351
## (Intercept)-Sylvilagus_floridanus 543
## (Intercept)-Sciurus_carolinensis 1139
## (Intercept)-Vulpes_vulpes 169
## (Intercept)-Sus_scrofa 634
## week-Canis_latrans 1678
## week-Sciurus_niger 727
## week-Procyon_lotor 2133
## week-Dasypus_novemcinctus 2024
## week-Lynx_rufus 1082
## week-Didelphis_virginiana 1455
## week-Sylvilagus_floridanus 1142
## week-Sciurus_carolinensis 1512
## week-Vulpes_vulpes 1007
## week-Sus_scrofa 1713
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8237
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9011 0.3912 -1.6494 -0.9086 -0.1125 1.0026 631
## Cogon_Patch_Size -0.3327 0.3988 -1.2110 -0.2969 0.3688 1.0104 710
## Avg_Cogongrass_Cover 0.2458 0.2660 -0.2780 0.2458 0.7714 1.0061 583
## total_shrub_cover -0.2213 0.2551 -0.7479 -0.2222 0.2807 1.0265 793
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8563 0.8843 0.0671 0.6085 3.0312 1.0375 554
## Cogon_Patch_Size 0.8525 1.1332 0.0668 0.5096 3.7826 1.0137 426
## Avg_Cogongrass_Cover 0.2627 0.2972 0.0374 0.1807 1.0348 1.0057 1165
## total_shrub_cover 0.2751 0.2865 0.0396 0.1877 1.0596 1.0134 1090
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1464 0.9203 0.1032 0.9027 3.4823 1.0122 226
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7703 0.2900 -3.3539 -2.7719 -2.1963 1.0161 1287
## week -0.0797 0.1255 -0.3565 -0.0744 0.1539 1.0014 1180
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7382 0.5495 0.1895 0.5897 2.2445 1.0236 596
## week 0.0991 0.0871 0.0250 0.0756 0.3218 1.0069 1159
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1861 0.6460 -1.4163 -0.1957
## (Intercept)-Sciurus_niger -1.1255 0.6952 -2.5173 -1.1217
## (Intercept)-Procyon_lotor -0.0463 0.6895 -1.3712 -0.0482
## (Intercept)-Dasypus_novemcinctus -0.8766 0.5311 -1.9556 -0.8731
## (Intercept)-Lynx_rufus -0.6659 0.6502 -1.8145 -0.6952
## (Intercept)-Didelphis_virginiana -1.3068 0.5660 -2.4257 -1.2915
## (Intercept)-Sylvilagus_floridanus -0.7577 0.6381 -2.0107 -0.7773
## (Intercept)-Sciurus_carolinensis -1.4029 0.6167 -2.7473 -1.3727
## (Intercept)-Vulpes_vulpes -1.3472 0.7730 -2.9320 -1.3397
## (Intercept)-Sus_scrofa -1.6609 0.7209 -3.2464 -1.5989
## Cogon_Patch_Size-Canis_latrans 0.5403 0.5877 -0.4131 0.4778
## Cogon_Patch_Size-Sciurus_niger -0.6967 0.8094 -2.6213 -0.5750
## Cogon_Patch_Size-Procyon_lotor -0.3038 0.4367 -1.1983 -0.3044
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1874 0.4088 -1.0324 -0.1699
## Cogon_Patch_Size-Lynx_rufus -0.4330 0.6548 -1.6807 -0.4350
## Cogon_Patch_Size-Didelphis_virginiana 0.5207 0.5121 -0.3660 0.4821
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9802 0.8286 -3.0049 -0.8402
## Cogon_Patch_Size-Sciurus_carolinensis -0.7514 0.6830 -2.3566 -0.6407
## Cogon_Patch_Size-Vulpes_vulpes -0.6380 0.8356 -2.6643 -0.5043
## Cogon_Patch_Size-Sus_scrofa -0.5206 0.7583 -2.3279 -0.4176
## Avg_Cogongrass_Cover-Canis_latrans 0.3087 0.3718 -0.4132 0.2953
## Avg_Cogongrass_Cover-Sciurus_niger -0.0725 0.5230 -1.2345 -0.0330
## Avg_Cogongrass_Cover-Procyon_lotor 0.2676 0.3881 -0.4509 0.2551
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4033 0.3520 -0.2751 0.3945
## Avg_Cogongrass_Cover-Lynx_rufus 0.5343 0.4176 -0.2472 0.5131
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2610 0.3836 -0.5372 0.2739
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0335 0.4478 -1.0032 -0.0081
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4698 0.3742 -0.2365 0.4569
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3413 0.4262 -0.4847 0.3333
## Avg_Cogongrass_Cover-Sus_scrofa 0.0202 0.5024 -1.1243 0.0669
## total_shrub_cover-Canis_latrans 0.0587 0.3770 -0.6341 0.0387
## total_shrub_cover-Sciurus_niger -0.4051 0.4602 -1.4024 -0.3649
## total_shrub_cover-Procyon_lotor -0.5942 0.4082 -1.5005 -0.5560
## total_shrub_cover-Dasypus_novemcinctus -0.0465 0.3412 -0.7109 -0.0603
## total_shrub_cover-Lynx_rufus -0.4919 0.4797 -1.5756 -0.4379
## total_shrub_cover-Didelphis_virginiana -0.2449 0.3786 -1.0306 -0.2440
## total_shrub_cover-Sylvilagus_floridanus -0.2026 0.4461 -1.0676 -0.2036
## total_shrub_cover-Sciurus_carolinensis -0.0757 0.3873 -0.8292 -0.0858
## total_shrub_cover-Vulpes_vulpes -0.2344 0.4884 -1.2582 -0.2228
## total_shrub_cover-Sus_scrofa 0.0266 0.4541 -0.8186 -0.0055
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1293 1.0113 456
## (Intercept)-Sciurus_niger 0.2813 1.0174 509
## (Intercept)-Procyon_lotor 1.3330 1.0155 390
## (Intercept)-Dasypus_novemcinctus 0.1600 0.9999 1166
## (Intercept)-Lynx_rufus 0.6760 1.0162 551
## (Intercept)-Didelphis_virginiana -0.2360 1.0058 1109
## (Intercept)-Sylvilagus_floridanus 0.5210 1.0213 657
## (Intercept)-Sciurus_carolinensis -0.2743 1.0095 812
## (Intercept)-Vulpes_vulpes 0.1526 1.0315 492
## (Intercept)-Sus_scrofa -0.4244 1.0172 640
## Cogon_Patch_Size-Canis_latrans 1.9358 1.0091 880
## Cogon_Patch_Size-Sciurus_niger 0.5442 1.0142 594
## Cogon_Patch_Size-Procyon_lotor 0.5543 0.9998 1722
## Cogon_Patch_Size-Dasypus_novemcinctus 0.5910 1.0000 2167
## Cogon_Patch_Size-Lynx_rufus 0.9281 1.0099 793
## Cogon_Patch_Size-Didelphis_virginiana 1.6337 1.0057 772
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2503 1.0096 526
## Cogon_Patch_Size-Sciurus_carolinensis 0.2694 1.0065 604
## Cogon_Patch_Size-Vulpes_vulpes 0.6864 1.0153 515
## Cogon_Patch_Size-Sus_scrofa 0.6817 1.0108 960
## Avg_Cogongrass_Cover-Canis_latrans 1.0613 1.0003 1476
## Avg_Cogongrass_Cover-Sciurus_niger 0.8170 1.0008 681
## Avg_Cogongrass_Cover-Procyon_lotor 1.1195 1.0021 1278
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1214 1.0034 1315
## Avg_Cogongrass_Cover-Lynx_rufus 1.4582 1.0183 1373
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0007 1.0026 1536
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7574 1.0006 1041
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2314 1.0027 1476
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2042 1.0065 1359
## Avg_Cogongrass_Cover-Sus_scrofa 0.8853 1.0000 892
## total_shrub_cover-Canis_latrans 0.8844 0.9999 1273
## total_shrub_cover-Sciurus_niger 0.4136 1.0092 1276
## total_shrub_cover-Procyon_lotor 0.1171 1.0282 1253
## total_shrub_cover-Dasypus_novemcinctus 0.6492 1.0008 1574
## total_shrub_cover-Lynx_rufus 0.3275 1.0425 939
## total_shrub_cover-Didelphis_virginiana 0.4848 1.0065 1463
## total_shrub_cover-Sylvilagus_floridanus 0.6546 1.0015 1424
## total_shrub_cover-Sciurus_carolinensis 0.7054 1.0200 1482
## total_shrub_cover-Vulpes_vulpes 0.7200 1.0254 1146
## total_shrub_cover-Sus_scrofa 1.0041 1.0002 1275
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6087 0.1650 -2.9476 -2.6081 -2.2911 1.0034
## (Intercept)-Sciurus_niger -3.6522 0.4771 -4.5733 -3.6313 -2.7880 1.0308
## (Intercept)-Procyon_lotor -2.2917 0.1292 -2.5502 -2.2874 -2.0477 1.0040
## (Intercept)-Dasypus_novemcinctus -1.6209 0.1415 -1.9070 -1.6197 -1.3490 1.0033
## (Intercept)-Lynx_rufus -3.4106 0.2887 -3.9923 -3.4014 -2.8826 1.0408
## (Intercept)-Didelphis_virginiana -2.3764 0.2519 -2.8998 -2.3655 -1.9150 1.0003
## (Intercept)-Sylvilagus_floridanus -3.1796 0.2887 -3.7814 -3.1614 -2.6708 1.0106
## (Intercept)-Sciurus_carolinensis -2.5120 0.2656 -3.0619 -2.5015 -2.0040 1.0015
## (Intercept)-Vulpes_vulpes -3.7581 0.5817 -4.9826 -3.7322 -2.7132 1.0385
## (Intercept)-Sus_scrofa -2.9802 0.4563 -4.0070 -2.9440 -2.1907 1.0250
## week-Canis_latrans 0.0637 0.1291 -0.1990 0.0673 0.3036 1.0047
## week-Sciurus_niger -0.3038 0.2898 -0.9834 -0.2735 0.1730 1.0039
## week-Procyon_lotor -0.0503 0.1159 -0.2896 -0.0456 0.1640 1.0013
## week-Dasypus_novemcinctus -0.1674 0.1366 -0.4513 -0.1624 0.0857 1.0022
## week-Lynx_rufus -0.0416 0.1859 -0.4334 -0.0337 0.2988 1.0037
## week-Didelphis_virginiana -0.2088 0.2176 -0.6884 -0.1928 0.1823 1.0048
## week-Sylvilagus_floridanus -0.1546 0.2041 -0.5967 -0.1467 0.2105 1.0096
## week-Sciurus_carolinensis 0.1269 0.1824 -0.2312 0.1297 0.4913 1.0015
## week-Vulpes_vulpes -0.1301 0.2719 -0.7222 -0.1120 0.3597 1.0113
## week-Sus_scrofa 0.0780 0.2309 -0.3754 0.0778 0.5315 1.0013
## ESS
## (Intercept)-Canis_latrans 1074
## (Intercept)-Sciurus_niger 287
## (Intercept)-Procyon_lotor 1719
## (Intercept)-Dasypus_novemcinctus 2246
## (Intercept)-Lynx_rufus 458
## (Intercept)-Didelphis_virginiana 1381
## (Intercept)-Sylvilagus_floridanus 484
## (Intercept)-Sciurus_carolinensis 1314
## (Intercept)-Vulpes_vulpes 231
## (Intercept)-Sus_scrofa 530
## week-Canis_latrans 1621
## week-Sciurus_niger 813
## week-Procyon_lotor 1767
## week-Dasypus_novemcinctus 1885
## week-Lynx_rufus 1092
## week-Didelphis_virginiana 1457
## week-Sylvilagus_floridanus 925
## week-Sciurus_carolinensis 1846
## week-Vulpes_vulpes 1068
## week-Sus_scrofa 1709
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8352
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9502 0.4820 -1.8635 -0.9643 0.0841 1.0113 414
## Tree_Density -0.7490 0.4111 -1.6874 -0.7157 -0.0605 1.0042 439
## Avg_Canopy_Cover 0.9228 0.3183 0.3435 0.9132 1.6173 1.0225 763
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6863 1.9122 0.1707 1.1509 6.5316 1.0774 199
## Tree_Density 0.7626 1.1254 0.0477 0.3886 3.9005 1.0129 330
## Avg_Canopy_Cover 0.5219 0.5889 0.0554 0.3409 2.0687 1.0342 839
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.53 0.486 0.0505 0.3902 1.8221 1.1413 204
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7723 0.3089 -3.4129 -2.7693 -2.170 1.0006 1103
## week -0.0839 0.1306 -0.3566 -0.0833 0.173 1.0005 1160
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8313 0.6578 0.2038 0.6567 2.4934 1.0131 422
## week 0.1052 0.0916 0.0258 0.0786 0.3423 1.0304 738
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0231 0.6291 -1.1947 -0.0346 1.2750
## (Intercept)-Sciurus_niger -0.8261 1.0831 -2.4914 -0.9682 1.7870
## (Intercept)-Procyon_lotor 0.3060 0.6283 -0.9678 0.3144 1.5184
## (Intercept)-Dasypus_novemcinctus -1.0577 0.5605 -2.2377 -1.0254 -0.0113
## (Intercept)-Lynx_rufus -0.0991 1.1308 -1.6724 -0.2685 2.8619
## (Intercept)-Didelphis_virginiana -1.7388 0.6259 -3.0813 -1.7146 -0.5938
## (Intercept)-Sylvilagus_floridanus -0.8260 0.6353 -2.1243 -0.8204 0.4028
## (Intercept)-Sciurus_carolinensis -1.7838 0.6342 -3.1005 -1.7474 -0.6473
## (Intercept)-Vulpes_vulpes -1.6202 0.8929 -3.3897 -1.6254 0.1750
## (Intercept)-Sus_scrofa -2.2398 0.8125 -3.9619 -2.2098 -0.7754
## Tree_Density-Canis_latrans -0.8932 0.5548 -2.2077 -0.8361 -0.0045
## Tree_Density-Sciurus_niger -0.7200 0.7863 -2.4069 -0.6603 0.6692
## Tree_Density-Procyon_lotor -0.4611 0.4085 -1.2889 -0.4679 0.3454
## Tree_Density-Dasypus_novemcinctus -1.3416 0.8925 -3.7093 -1.1490 -0.1470
## Tree_Density-Lynx_rufus 0.1416 0.7080 -0.9747 0.0577 1.8677
## Tree_Density-Didelphis_virginiana -0.9902 0.7363 -2.7349 -0.8768 0.1203
## Tree_Density-Sylvilagus_floridanus -1.0851 0.7778 -3.0183 -0.9395 0.0416
## Tree_Density-Sciurus_carolinensis -0.8922 0.7161 -2.6171 -0.8035 0.2108
## Tree_Density-Vulpes_vulpes -0.6293 0.7555 -2.1711 -0.6042 0.8052
## Tree_Density-Sus_scrofa -0.9059 0.7585 -2.7540 -0.7827 0.2638
## Avg_Canopy_Cover-Canis_latrans 0.1283 0.4449 -0.7481 0.1325 0.9612
## Avg_Canopy_Cover-Sciurus_niger 0.8210 0.6838 -0.3588 0.7593 2.3556
## Avg_Canopy_Cover-Procyon_lotor 0.9365 0.4254 0.1780 0.9132 1.8198
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9622 0.3997 0.1982 0.9525 1.8219
## Avg_Canopy_Cover-Lynx_rufus 0.6777 0.6060 -0.4421 0.6502 2.0212
## Avg_Canopy_Cover-Didelphis_virginiana 1.1726 0.4658 0.3583 1.1171 2.1684
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5415 0.6943 0.5303 1.4244 3.2269
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1248 0.4609 0.3307 1.0901 2.1297
## Avg_Canopy_Cover-Vulpes_vulpes 0.9677 0.5377 0.0198 0.9249 2.1640
## Avg_Canopy_Cover-Sus_scrofa 1.1144 0.4926 0.2377 1.0828 2.1994
## Rhat ESS
## (Intercept)-Canis_latrans 1.0002 700
## (Intercept)-Sciurus_niger 1.1918 136
## (Intercept)-Procyon_lotor 1.0075 416
## (Intercept)-Dasypus_novemcinctus 1.0050 867
## (Intercept)-Lynx_rufus 1.1270 122
## (Intercept)-Didelphis_virginiana 1.0128 799
## (Intercept)-Sylvilagus_floridanus 1.0033 950
## (Intercept)-Sciurus_carolinensis 1.0122 988
## (Intercept)-Vulpes_vulpes 1.0043 283
## (Intercept)-Sus_scrofa 1.0077 502
## Tree_Density-Canis_latrans 1.0087 954
## Tree_Density-Sciurus_niger 1.0203 488
## Tree_Density-Procyon_lotor 1.0059 1932
## Tree_Density-Dasypus_novemcinctus 1.0033 322
## Tree_Density-Lynx_rufus 1.0094 364
## Tree_Density-Didelphis_virginiana 1.0049 538
## Tree_Density-Sylvilagus_floridanus 1.0013 531
## Tree_Density-Sciurus_carolinensis 1.0133 627
## Tree_Density-Vulpes_vulpes 1.0180 653
## Tree_Density-Sus_scrofa 1.0011 591
## Avg_Canopy_Cover-Canis_latrans 1.0137 953
## Avg_Canopy_Cover-Sciurus_niger 1.0445 467
## Avg_Canopy_Cover-Procyon_lotor 1.0165 1375
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0157 1845
## Avg_Canopy_Cover-Lynx_rufus 1.0237 716
## Avg_Canopy_Cover-Didelphis_virginiana 1.0060 1138
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0452 648
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0161 1004
## Avg_Canopy_Cover-Vulpes_vulpes 1.0055 1192
## Avg_Canopy_Cover-Sus_scrofa 1.0010 1175
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6397 0.1807 -3.0157 -2.6345 -2.3090 1.0066
## (Intercept)-Sciurus_niger -3.8374 0.5212 -4.9565 -3.7977 -2.9213 1.0518
## (Intercept)-Procyon_lotor -2.2722 0.1290 -2.5387 -2.2668 -2.0339 1.0007
## (Intercept)-Dasypus_novemcinctus -1.6126 0.1368 -1.8892 -1.6095 -1.3516 1.0044
## (Intercept)-Lynx_rufus -3.5168 0.3414 -4.2652 -3.4864 -2.9234 1.0543
## (Intercept)-Didelphis_virginiana -2.3728 0.2425 -2.8759 -2.3687 -1.9209 1.0017
## (Intercept)-Sylvilagus_floridanus -3.1302 0.2691 -3.6807 -3.1155 -2.6381 1.0035
## (Intercept)-Sciurus_carolinensis -2.4899 0.2595 -3.0203 -2.4827 -2.0165 1.0226
## (Intercept)-Vulpes_vulpes -3.7741 0.6158 -5.0875 -3.7404 -2.7069 1.0216
## (Intercept)-Sus_scrofa -2.8964 0.4243 -3.7745 -2.8711 -2.1212 1.0010
## week-Canis_latrans 0.0658 0.1375 -0.2179 0.0666 0.3263 1.0003
## week-Sciurus_niger -0.3300 0.3044 -1.0389 -0.3012 0.1640 1.0131
## week-Procyon_lotor -0.0526 0.1205 -0.2970 -0.0511 0.1725 1.0003
## week-Dasypus_novemcinctus -0.1714 0.1370 -0.4581 -0.1674 0.0800 1.0042
## week-Lynx_rufus -0.0487 0.1912 -0.4317 -0.0454 0.3249 1.0000
## week-Didelphis_virginiana -0.2254 0.2165 -0.6933 -0.2151 0.1566 1.0018
## week-Sylvilagus_floridanus -0.1565 0.2066 -0.5859 -0.1486 0.2218 1.0067
## week-Sciurus_carolinensis 0.1296 0.1808 -0.2175 0.1287 0.4855 1.0054
## week-Vulpes_vulpes -0.1330 0.2783 -0.7487 -0.1167 0.3798 1.0031
## week-Sus_scrofa 0.0765 0.2404 -0.3886 0.0710 0.5534 1.0026
## ESS
## (Intercept)-Canis_latrans 853
## (Intercept)-Sciurus_niger 154
## (Intercept)-Procyon_lotor 1503
## (Intercept)-Dasypus_novemcinctus 2150
## (Intercept)-Lynx_rufus 229
## (Intercept)-Didelphis_virginiana 1469
## (Intercept)-Sylvilagus_floridanus 719
## (Intercept)-Sciurus_carolinensis 1287
## (Intercept)-Vulpes_vulpes 214
## (Intercept)-Sus_scrofa 844
## week-Canis_latrans 1473
## week-Sciurus_niger 449
## week-Procyon_lotor 1657
## week-Dasypus_novemcinctus 2123
## week-Lynx_rufus 904
## week-Didelphis_virginiana 1562
## week-Sylvilagus_floridanus 1007
## week-Sciurus_carolinensis 1864
## week-Vulpes_vulpes 863
## week-Sus_scrofa 1919
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7995
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4746 0.3786 -2.2255 -1.4665 -0.7121 1.0365 638
## Avg_Cogongrass_Cover -0.6992 0.3412 -1.3645 -0.6977 -0.0401 1.0871 386
## I(Avg_Cogongrass_Cover^2) 0.7584 0.3203 0.1739 0.7452 1.4612 1.0353 592
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6901 0.6605 0.0682 0.4946 2.4745 1.0065 1024
## Avg_Cogongrass_Cover 0.3187 0.4084 0.0392 0.1934 1.3665 1.0176 817
## I(Avg_Cogongrass_Cover^2) 0.5235 0.7685 0.0431 0.2653 2.7415 1.0201 320
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5356 0.5312 0.0489 0.371 2.0524 1.0449 232
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7591 0.2899 -3.3601 -2.7550 -2.1668 1.0010 1737
## week -0.0848 0.1295 -0.3485 -0.0829 0.1573 1.0011 1118
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7545 0.5871 0.1833 0.5896 2.3195 1.0045 609
## week 0.1043 0.0860 0.0251 0.0789 0.3280 1.0098 1135
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9076 0.5728 -2.0135 -0.9115
## (Intercept)-Sciurus_niger -1.4465 0.6575 -2.7071 -1.4686
## (Intercept)-Procyon_lotor -0.7027 0.6122 -1.8903 -0.7196
## (Intercept)-Dasypus_novemcinctus -1.4495 0.5177 -2.5112 -1.4381
## (Intercept)-Lynx_rufus -1.4074 0.6085 -2.5942 -1.4121
## (Intercept)-Didelphis_virginiana -1.8209 0.5731 -3.0422 -1.7662
## (Intercept)-Sylvilagus_floridanus -1.2938 0.5813 -2.4702 -1.2960
## (Intercept)-Sciurus_carolinensis -2.0616 0.6221 -3.3958 -2.0266
## (Intercept)-Vulpes_vulpes -1.9788 0.7534 -3.5314 -1.9331
## (Intercept)-Sus_scrofa -2.0445 0.6518 -3.4449 -1.9851
## Avg_Cogongrass_Cover-Canis_latrans -0.5236 0.4653 -1.4119 -0.5274
## Avg_Cogongrass_Cover-Sciurus_niger -0.9376 0.6021 -2.2794 -0.8872
## Avg_Cogongrass_Cover-Procyon_lotor -0.6462 0.4814 -1.5965 -0.6559
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5470 0.4415 -1.4165 -0.5434
## Avg_Cogongrass_Cover-Lynx_rufus -0.6130 0.5155 -1.6562 -0.6179
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4360 0.4928 -1.3432 -0.4615
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0716 0.5485 -2.3075 -1.0178
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7169 0.4758 -1.6996 -0.7010
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.6810 0.5403 -1.8228 -0.6688
## Avg_Cogongrass_Cover-Sus_scrofa -0.8957 0.5504 -2.0670 -0.8576
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3014 0.7721 0.3484 1.0979
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.1850 0.6017 -1.1986 0.2472
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1798 0.6683 0.2907 1.0381
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7062 0.3326 0.0985 0.6875
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1921 0.5383 0.3614 1.1090
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5264 0.3643 -0.1788 0.5206
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7441 0.4516 -0.0184 0.6924
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8642 0.3501 0.2316 0.8482
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8457 0.4603 0.1064 0.7878
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2080 0.6151 -1.2877 0.2966
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2704 1.0458 626
## (Intercept)-Sciurus_niger -0.0894 1.0265 644
## (Intercept)-Procyon_lotor 0.4904 1.0223 576
## (Intercept)-Dasypus_novemcinctus -0.4660 1.0399 1018
## (Intercept)-Lynx_rufus -0.2109 1.0129 642
## (Intercept)-Didelphis_virginiana -0.7558 1.0104 1054
## (Intercept)-Sylvilagus_floridanus -0.1580 1.0599 875
## (Intercept)-Sciurus_carolinensis -0.9591 1.0043 866
## (Intercept)-Vulpes_vulpes -0.5790 1.0046 542
## (Intercept)-Sus_scrofa -0.8757 1.0063 872
## Avg_Cogongrass_Cover-Canis_latrans 0.3998 1.0201 1095
## Avg_Cogongrass_Cover-Sciurus_niger 0.0493 1.0583 561
## Avg_Cogongrass_Cover-Procyon_lotor 0.2756 1.0222 806
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3373 1.0344 793
## Avg_Cogongrass_Cover-Lynx_rufus 0.4086 1.0260 797
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6240 1.0229 900
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1328 1.0604 655
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1832 1.0266 716
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3495 1.0460 681
## Avg_Cogongrass_Cover-Sus_scrofa 0.0860 1.0689 692
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2994 1.0352 320
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2019 1.0053 454
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.9030 1.0108 336
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3840 1.0242 765
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4418 1.0159 431
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2486 1.0358 850
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7322 1.0172 793
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5999 1.0120 771
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8948 1.0187 670
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1493 1.0115 462
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6389 0.1711 -2.9912 -2.6332 -2.3173 1.0006
## (Intercept)-Sciurus_niger -3.6310 0.4971 -4.7303 -3.6104 -2.7425 1.0108
## (Intercept)-Procyon_lotor -2.2823 0.1292 -2.5405 -2.2823 -2.0315 1.0018
## (Intercept)-Dasypus_novemcinctus -1.6144 0.1374 -1.8842 -1.6129 -1.3467 1.0065
## (Intercept)-Lynx_rufus -3.3649 0.2994 -4.0007 -3.3587 -2.8010 1.0154
## (Intercept)-Didelphis_virginiana -2.4026 0.2595 -2.9474 -2.3878 -1.9423 1.0054
## (Intercept)-Sylvilagus_floridanus -3.1469 0.2748 -3.7218 -3.1333 -2.6446 1.0131
## (Intercept)-Sciurus_carolinensis -2.4914 0.2654 -3.0473 -2.4864 -1.9934 1.0054
## (Intercept)-Vulpes_vulpes -3.7883 0.6306 -5.1801 -3.7397 -2.7121 1.0075
## (Intercept)-Sus_scrofa -2.9656 0.4357 -3.8905 -2.9338 -2.1945 1.0030
## week-Canis_latrans 0.0639 0.1308 -0.2158 0.0683 0.3163 1.0216
## week-Sciurus_niger -0.3228 0.2959 -1.0192 -0.2859 0.1639 1.0142
## week-Procyon_lotor -0.0583 0.1211 -0.3227 -0.0518 0.1586 1.0232
## week-Dasypus_novemcinctus -0.1694 0.1391 -0.4631 -0.1661 0.0948 1.0007
## week-Lynx_rufus -0.0535 0.1908 -0.4468 -0.0451 0.2985 1.0021
## week-Didelphis_virginiana -0.2315 0.2158 -0.7079 -0.2128 0.1429 1.0009
## week-Sylvilagus_floridanus -0.1588 0.2042 -0.5954 -0.1451 0.2119 1.0013
## week-Sciurus_carolinensis 0.1305 0.1827 -0.2355 0.1363 0.4803 0.9997
## week-Vulpes_vulpes -0.1416 0.2702 -0.7273 -0.1287 0.3291 1.0015
## week-Sus_scrofa 0.0721 0.2400 -0.4026 0.0729 0.5611 1.0066
## ESS
## (Intercept)-Canis_latrans 1022
## (Intercept)-Sciurus_niger 293
## (Intercept)-Procyon_lotor 1548
## (Intercept)-Dasypus_novemcinctus 2186
## (Intercept)-Lynx_rufus 480
## (Intercept)-Didelphis_virginiana 1137
## (Intercept)-Sylvilagus_floridanus 628
## (Intercept)-Sciurus_carolinensis 1433
## (Intercept)-Vulpes_vulpes 257
## (Intercept)-Sus_scrofa 628
## week-Canis_latrans 1642
## week-Sciurus_niger 661
## week-Procyon_lotor 1680
## week-Dasypus_novemcinctus 2069
## week-Lynx_rufus 1183
## week-Didelphis_virginiana 1303
## week-Sylvilagus_floridanus 1111
## week-Sciurus_carolinensis 1860
## week-Vulpes_vulpes 899
## week-Sus_scrofa 1769
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.874
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3038 0.7498 -3.7496 -2.3054 -0.7935 1.0042 272
## Cogon_Patch_Size -0.4319 0.6994 -1.9183 -0.4018 0.9024 1.0245 379
## Veg_shannon_index 0.8777 0.4369 0.0566 0.8488 1.7715 1.0156 383
## total_shrub_cover -0.3578 0.3737 -1.1324 -0.3454 0.3611 1.0446 634
## Avg_Cogongrass_Cover 0.3520 0.9201 -1.4784 0.3751 2.1313 1.0194 122
## Tree_Density -2.2527 0.7621 -3.7422 -2.2550 -0.7820 1.0174 208
## Avg_Canopy_Cover 1.7427 0.5519 0.7480 1.7116 2.8789 1.1011 308
## I(Avg_Cogongrass_Cover^2) 1.3261 0.5803 0.1933 1.3071 2.4869 1.0269 258
## avg_veg_height -0.2821 0.4482 -1.1502 -0.2788 0.5806 1.0116 197
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4854 4.0962 0.1449 2.2514 14.2095 1.0616 297
## Cogon_Patch_Size 3.1367 4.0138 0.1566 1.9209 13.9740 1.0568 283
## Veg_shannon_index 0.6004 0.7572 0.0441 0.3597 2.7186 1.0449 675
## total_shrub_cover 0.5679 0.7926 0.0460 0.3263 2.6301 1.0413 598
## Avg_Cogongrass_Cover 1.2666 2.3510 0.0544 0.5142 7.4470 1.1730 118
## Tree_Density 2.0693 3.7466 0.0598 0.9345 11.3528 1.1176 324
## Avg_Canopy_Cover 1.8985 5.1162 0.0724 0.8707 9.7207 1.8754 78
## I(Avg_Cogongrass_Cover^2) 1.6583 3.2109 0.0650 0.7500 9.1054 1.1627 115
## avg_veg_height 0.4409 0.6089 0.0394 0.2561 1.8737 1.0208 747
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4679 1.3343 0.0973 1.0658 5.0601 1.0248 114
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8284 0.3311 -3.4491 -2.8301 -2.1408 1.0049 1767
## week -0.0806 0.1311 -0.3491 -0.0737 0.1659 1.0020 1107
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0489 0.7898 0.2955 0.8520 3.0225 1.0023 648
## week 0.1020 0.0880 0.0233 0.0769 0.3257 1.0094 627
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4881 1.0391 -3.5248 -1.4787
## (Intercept)-Sciurus_niger -1.4003 1.4591 -3.7612 -1.6045
## (Intercept)-Procyon_lotor -1.0854 1.0590 -3.2323 -1.0426
## (Intercept)-Dasypus_novemcinctus -2.5696 0.9090 -4.4852 -2.5412
## (Intercept)-Lynx_rufus -1.5194 1.4582 -4.0158 -1.6696
## (Intercept)-Didelphis_virginiana -3.5006 1.1374 -6.0145 -3.4022
## (Intercept)-Sylvilagus_floridanus -2.4522 1.0913 -4.6687 -2.4452
## (Intercept)-Sciurus_carolinensis -3.9349 1.2062 -6.5922 -3.7926
## (Intercept)-Vulpes_vulpes -3.6275 1.4680 -6.8199 -3.4904
## (Intercept)-Sus_scrofa -4.2157 1.5064 -7.5263 -4.0194
## Cogon_Patch_Size-Canis_latrans 1.3521 1.2289 -0.4632 1.1686
## Cogon_Patch_Size-Sciurus_niger -1.5157 1.7460 -5.7500 -1.2730
## Cogon_Patch_Size-Procyon_lotor -0.6885 0.7649 -2.2305 -0.6658
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3346 0.7102 -1.8094 -0.3281
## Cogon_Patch_Size-Lynx_rufus -0.6775 1.4056 -3.3307 -0.6816
## Cogon_Patch_Size-Didelphis_virginiana 1.3714 0.9313 -0.2242 1.2944
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6534 1.4967 -5.1629 -1.3819
## Cogon_Patch_Size-Sciurus_carolinensis -1.1930 1.1923 -4.1046 -0.9895
## Cogon_Patch_Size-Vulpes_vulpes -0.9473 1.5681 -4.5696 -0.7814
## Cogon_Patch_Size-Sus_scrofa -0.7300 1.2768 -3.7969 -0.5709
## Veg_shannon_index-Canis_latrans 1.2776 0.6637 0.1709 1.2138
## Veg_shannon_index-Sciurus_niger 0.9264 0.8216 -0.6538 0.8956
## Veg_shannon_index-Procyon_lotor 1.0878 0.5779 0.0053 1.0708
## Veg_shannon_index-Dasypus_novemcinctus 0.6330 0.5288 -0.4362 0.6368
## Veg_shannon_index-Lynx_rufus 0.9113 0.7616 -0.5329 0.8863
## Veg_shannon_index-Didelphis_virginiana 0.9470 0.6143 -0.1646 0.9131
## Veg_shannon_index-Sylvilagus_floridanus 0.9117 0.6466 -0.3342 0.8946
## Veg_shannon_index-Sciurus_carolinensis 0.3416 0.6957 -1.2230 0.4066
## Veg_shannon_index-Vulpes_vulpes 0.6342 0.7476 -0.9415 0.6770
## Veg_shannon_index-Sus_scrofa 1.3032 0.7855 0.0331 1.2048
## total_shrub_cover-Canis_latrans -0.0657 0.5197 -1.0334 -0.0900
## total_shrub_cover-Sciurus_niger -0.7016 0.7417 -2.3678 -0.6026
## total_shrub_cover-Procyon_lotor -0.8076 0.5532 -2.0395 -0.7459
## total_shrub_cover-Dasypus_novemcinctus -0.0101 0.4841 -0.9213 -0.0298
## total_shrub_cover-Lynx_rufus -0.7707 0.7305 -2.4725 -0.6880
## total_shrub_cover-Didelphis_virginiana -0.4609 0.5627 -1.7102 -0.4314
## total_shrub_cover-Sylvilagus_floridanus -0.2797 0.6277 -1.5162 -0.2724
## total_shrub_cover-Sciurus_carolinensis -0.1019 0.5804 -1.2020 -0.1206
## total_shrub_cover-Vulpes_vulpes -0.4242 0.7136 -1.9961 -0.3889
## total_shrub_cover-Sus_scrofa -0.0550 0.6617 -1.2646 -0.1020
## Avg_Cogongrass_Cover-Canis_latrans 0.3146 1.1762 -2.1224 0.3332
## Avg_Cogongrass_Cover-Sciurus_niger -0.1853 1.6839 -3.8790 0.0618
## Avg_Cogongrass_Cover-Procyon_lotor 0.5619 1.1396 -1.5855 0.5416
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9262 1.1967 -1.3071 0.8617
## Avg_Cogongrass_Cover-Lynx_rufus 0.5418 1.2568 -1.8104 0.5310
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5367 1.1850 -1.8420 0.5375
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1887 1.2653 -2.9300 -0.0848
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4917 1.1879 -1.8341 0.4806
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5647 1.2659 -1.8847 0.5353
## Avg_Cogongrass_Cover-Sus_scrofa 0.0751 1.3081 -2.7587 0.1466
## Tree_Density-Canis_latrans -2.9329 1.2292 -5.9150 -2.7539
## Tree_Density-Sciurus_niger -2.1795 1.4043 -4.7494 -2.1695
## Tree_Density-Procyon_lotor -2.1255 0.9508 -4.0491 -2.1222
## Tree_Density-Dasypus_novemcinctus -3.5090 1.4678 -7.3364 -3.2287
## Tree_Density-Lynx_rufus -1.2369 1.3776 -3.4824 -1.3713
## Tree_Density-Didelphis_virginiana -2.3815 1.0517 -4.5945 -2.3143
## Tree_Density-Sylvilagus_floridanus -2.5884 1.1711 -5.2068 -2.5082
## Tree_Density-Sciurus_carolinensis -2.5790 1.1149 -5.0549 -2.4760
## Tree_Density-Vulpes_vulpes -2.1794 1.4700 -4.8659 -2.2314
## Tree_Density-Sus_scrofa -2.3857 1.3724 -5.4667 -2.3118
## Avg_Canopy_Cover-Canis_latrans 0.4419 0.7184 -0.9861 0.4395
## Avg_Canopy_Cover-Sciurus_niger 2.0483 1.8046 -0.2160 1.7728
## Avg_Canopy_Cover-Procyon_lotor 1.6520 0.6701 0.4244 1.6213
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8774 0.6580 0.7526 1.8194
## Avg_Canopy_Cover-Lynx_rufus 1.3830 1.0451 -0.5720 1.3554
## Avg_Canopy_Cover-Didelphis_virginiana 2.3571 0.8615 1.0212 2.2308
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8980 1.4201 1.0227 2.5821
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0733 0.7861 0.8248 1.9706
## Avg_Canopy_Cover-Vulpes_vulpes 2.1763 1.4142 0.3842 1.9214
## Avg_Canopy_Cover-Sus_scrofa 1.8398 0.7597 0.5507 1.7706
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1901 1.2011 0.5960 1.9713
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.8848 1.3376 -1.6296 0.9077
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0523 1.0799 0.5356 1.8655
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3332 0.7073 0.1118 1.2788
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3465 1.3589 0.6093 2.1035
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8424 0.7066 -0.5554 0.8543
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0931 0.8499 -0.3565 1.0361
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3996 0.7017 0.1334 1.3708
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6688 0.8752 0.2697 1.5699
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2856 1.1734 -2.6711 0.4572
## avg_veg_height-Canis_latrans -0.5244 0.5928 -1.7883 -0.5139
## avg_veg_height-Sciurus_niger -0.4310 0.7347 -1.9998 -0.4034
## avg_veg_height-Procyon_lotor -0.0119 0.5937 -1.1193 -0.0259
## avg_veg_height-Dasypus_novemcinctus -0.0228 0.5718 -1.0766 -0.0407
## avg_veg_height-Lynx_rufus -0.3555 0.7303 -1.8040 -0.3342
## avg_veg_height-Didelphis_virginiana -0.3707 0.6338 -1.6907 -0.3673
## avg_veg_height-Sylvilagus_floridanus -0.4076 0.6408 -1.7013 -0.4096
## avg_veg_height-Sciurus_carolinensis -0.0111 0.6460 -1.2130 -0.0459
## avg_veg_height-Vulpes_vulpes -0.4057 0.7147 -1.9329 -0.3759
## avg_veg_height-Sus_scrofa -0.3331 0.6580 -1.6425 -0.3226
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5508 1.0286 297
## (Intercept)-Sciurus_niger 2.1959 1.0377 259
## (Intercept)-Procyon_lotor 0.8915 1.0277 251
## (Intercept)-Dasypus_novemcinctus -0.8455 1.0010 375
## (Intercept)-Lynx_rufus 1.7912 1.0455 200
## (Intercept)-Didelphis_virginiana -1.5805 1.0409 340
## (Intercept)-Sylvilagus_floridanus -0.2841 1.0376 423
## (Intercept)-Sciurus_carolinensis -1.9138 1.0396 236
## (Intercept)-Vulpes_vulpes -0.9833 1.0650 295
## (Intercept)-Sus_scrofa -1.8763 1.0762 277
## Cogon_Patch_Size-Canis_latrans 4.3550 1.0245 373
## Cogon_Patch_Size-Sciurus_niger 1.2336 1.0491 246
## Cogon_Patch_Size-Procyon_lotor 0.7736 1.0021 400
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0394 1.0107 369
## Cogon_Patch_Size-Lynx_rufus 2.2738 1.0375 344
## Cogon_Patch_Size-Didelphis_virginiana 3.4460 1.0368 320
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4066 1.0155 391
## Cogon_Patch_Size-Sciurus_carolinensis 0.5894 1.0433 451
## Cogon_Patch_Size-Vulpes_vulpes 1.8645 1.0869 320
## Cogon_Patch_Size-Sus_scrofa 1.3931 1.0285 591
## Veg_shannon_index-Canis_latrans 2.7904 1.0178 470
## Veg_shannon_index-Sciurus_niger 2.7213 1.0508 410
## Veg_shannon_index-Procyon_lotor 2.2763 1.0050 457
## Veg_shannon_index-Dasypus_novemcinctus 1.6038 1.0078 571
## Veg_shannon_index-Lynx_rufus 2.5464 1.0119 531
## Veg_shannon_index-Didelphis_virginiana 2.2768 1.0036 1085
## Veg_shannon_index-Sylvilagus_floridanus 2.2420 1.0052 631
## Veg_shannon_index-Sciurus_carolinensis 1.5429 1.0058 768
## Veg_shannon_index-Vulpes_vulpes 2.0497 1.0020 694
## Veg_shannon_index-Sus_scrofa 3.1622 1.0205 533
## total_shrub_cover-Canis_latrans 1.0276 1.0034 1342
## total_shrub_cover-Sciurus_niger 0.5356 1.0608 525
## total_shrub_cover-Procyon_lotor 0.1006 1.0312 792
## total_shrub_cover-Dasypus_novemcinctus 0.9854 1.0038 1227
## total_shrub_cover-Lynx_rufus 0.4392 1.0308 575
## total_shrub_cover-Didelphis_virginiana 0.5907 1.0252 1090
## total_shrub_cover-Sylvilagus_floridanus 0.9582 1.0047 947
## total_shrub_cover-Sciurus_carolinensis 1.1016 1.0022 1269
## total_shrub_cover-Vulpes_vulpes 0.9430 1.0240 780
## total_shrub_cover-Sus_scrofa 1.4545 1.0063 1017
## Avg_Cogongrass_Cover-Canis_latrans 2.5568 1.0093 211
## Avg_Cogongrass_Cover-Sciurus_niger 2.2310 1.2000 136
## Avg_Cogongrass_Cover-Procyon_lotor 2.7550 1.0120 155
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.4603 1.0099 151
## Avg_Cogongrass_Cover-Lynx_rufus 3.1518 1.0312 218
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.9509 1.0134 145
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0694 1.0182 203
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8032 1.0105 181
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2016 1.0071 200
## Avg_Cogongrass_Cover-Sus_scrofa 2.4191 1.0137 189
## Tree_Density-Canis_latrans -1.0507 1.0074 325
## Tree_Density-Sciurus_niger 0.5091 1.0514 240
## Tree_Density-Procyon_lotor -0.3194 1.0088 315
## Tree_Density-Dasypus_novemcinctus -1.4285 1.0265 255
## Tree_Density-Lynx_rufus 1.6756 1.0376 183
## Tree_Density-Didelphis_virginiana -0.4995 1.0053 441
## Tree_Density-Sylvilagus_floridanus -0.5612 1.0043 416
## Tree_Density-Sciurus_carolinensis -0.6574 1.0052 410
## Tree_Density-Vulpes_vulpes 0.9702 1.0322 287
## Tree_Density-Sus_scrofa -0.0023 1.0116 450
## Avg_Canopy_Cover-Canis_latrans 1.8187 1.0142 427
## Avg_Canopy_Cover-Sciurus_niger 6.1718 1.5993 75
## Avg_Canopy_Cover-Procyon_lotor 3.0858 1.0134 362
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3507 1.0196 379
## Avg_Canopy_Cover-Lynx_rufus 3.5969 1.0393 411
## Avg_Canopy_Cover-Didelphis_virginiana 4.2793 1.0724 210
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.6323 1.1311 168
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8843 1.0378 438
## Avg_Canopy_Cover-Vulpes_vulpes 5.6967 1.2553 102
## Avg_Canopy_Cover-Sus_scrofa 3.5442 1.0355 525
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4146 1.0545 120
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.4427 1.0938 76
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.9656 1.0056 233
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8795 1.0116 538
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2321 1.0833 135
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.2240 1.0095 348
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.0203 1.0286 260
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.8441 1.0239 469
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.6661 1.0494 247
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.1964 1.0150 173
## avg_veg_height-Canis_latrans 0.5962 1.0037 412
## avg_veg_height-Sciurus_niger 0.9607 1.0173 413
## avg_veg_height-Procyon_lotor 1.1972 1.0176 430
## avg_veg_height-Dasypus_novemcinctus 1.1707 1.0056 335
## avg_veg_height-Lynx_rufus 1.0423 1.0026 380
## avg_veg_height-Didelphis_virginiana 0.8892 1.0076 342
## avg_veg_height-Sylvilagus_floridanus 0.8456 1.0004 453
## avg_veg_height-Sciurus_carolinensis 1.3741 1.0026 516
## avg_veg_height-Vulpes_vulpes 0.9657 1.0208 442
## avg_veg_height-Sus_scrofa 0.9350 1.0010 459
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6218 0.1708 -2.9657 -2.6167 -2.2935 1.0049
## (Intercept)-Sciurus_niger -4.2189 0.4739 -5.1280 -4.2151 -3.2388 1.0375
## (Intercept)-Procyon_lotor -2.2739 0.1277 -2.5281 -2.2725 -2.0279 1.0071
## (Intercept)-Dasypus_novemcinctus -1.6083 0.1325 -1.8803 -1.6045 -1.3545 1.0040
## (Intercept)-Lynx_rufus -3.6163 0.3086 -4.2198 -3.6162 -3.0270 1.0085
## (Intercept)-Didelphis_virginiana -2.3554 0.2424 -2.8492 -2.3480 -1.8994 1.0070
## (Intercept)-Sylvilagus_floridanus -3.1847 0.2625 -3.7257 -3.1766 -2.6969 1.0027
## (Intercept)-Sciurus_carolinensis -2.4982 0.2672 -3.0680 -2.4906 -1.9991 1.0051
## (Intercept)-Vulpes_vulpes -4.0626 0.5460 -5.2057 -4.0362 -3.0854 1.0133
## (Intercept)-Sus_scrofa -2.9779 0.4542 -3.9430 -2.9483 -2.1799 1.0576
## week-Canis_latrans 0.0587 0.1375 -0.2236 0.0634 0.3195 1.0022
## week-Sciurus_niger -0.3161 0.3094 -1.0729 -0.2850 0.1844 1.0014
## week-Procyon_lotor -0.0542 0.1201 -0.3013 -0.0497 0.1744 1.0024
## week-Dasypus_novemcinctus -0.1686 0.1385 -0.4482 -0.1644 0.0926 1.0119
## week-Lynx_rufus -0.0404 0.1919 -0.4492 -0.0362 0.3235 1.0239
## week-Didelphis_virginiana -0.2151 0.2125 -0.6681 -0.2022 0.1689 1.0016
## week-Sylvilagus_floridanus -0.1542 0.2057 -0.5819 -0.1454 0.2153 1.0174
## week-Sciurus_carolinensis 0.1241 0.1815 -0.2446 0.1263 0.4784 1.0071
## week-Vulpes_vulpes -0.1189 0.2581 -0.6362 -0.1084 0.3690 1.0020
## week-Sus_scrofa 0.0696 0.2431 -0.4077 0.0676 0.5507 1.0069
## ESS
## (Intercept)-Canis_latrans 908
## (Intercept)-Sciurus_niger 132
## (Intercept)-Procyon_lotor 1468
## (Intercept)-Dasypus_novemcinctus 2386
## (Intercept)-Lynx_rufus 287
## (Intercept)-Didelphis_virginiana 1527
## (Intercept)-Sylvilagus_floridanus 677
## (Intercept)-Sciurus_carolinensis 1148
## (Intercept)-Vulpes_vulpes 221
## (Intercept)-Sus_scrofa 517
## week-Canis_latrans 1534
## week-Sciurus_niger 391
## week-Procyon_lotor 1606
## week-Dasypus_novemcinctus 1885
## week-Lynx_rufus 897
## week-Didelphis_virginiana 1461
## week-Sylvilagus_floridanus 986
## week-Sciurus_carolinensis 1887
## week-Vulpes_vulpes 1069
## week-Sus_scrofa 1593
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6398
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7465 0.3605 -1.4735 -0.7505 -0.0404 1.0360 480
## Avg_Cogongrass_Cover 0.1814 0.2448 -0.3177 0.1861 0.6737 1.0029 913
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6640 0.6987 0.0594 0.4569 2.5266 1.0075 593
## Avg_Cogongrass_Cover 0.2646 0.3093 0.0379 0.1743 1.0568 1.0055 842
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8106 0.6665 0.0931 0.6278 2.6092 1.1106 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8489 0.2970 -3.4367 -2.8420 -2.2664 1.0038 934
## shrub_cover 0.3213 0.2570 -0.2042 0.3276 0.8168 1.0084 965
## veg_height 0.0687 0.1704 -0.2762 0.0725 0.4065 1.0083 1317
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7121 0.6072 0.1631 0.5500 2.2700 1.0401 553
## shrub_cover 0.4795 0.4108 0.0824 0.3671 1.5286 1.0303 851
## veg_height 0.2012 0.1592 0.0510 0.1596 0.6068 1.0063 1503
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1150 0.5668 -1.1734 -0.1372
## (Intercept)-Sciurus_niger -0.8885 0.6298 -2.0950 -0.8997
## (Intercept)-Procyon_lotor 0.0129 0.6059 -1.1139 -0.0053
## (Intercept)-Dasypus_novemcinctus -0.7012 0.4950 -1.7319 -0.6860
## (Intercept)-Lynx_rufus -0.5062 0.5946 -1.5972 -0.5365
## (Intercept)-Didelphis_virginiana -1.0797 0.5117 -2.1619 -1.0506
## (Intercept)-Sylvilagus_floridanus -0.5954 0.5122 -1.5360 -0.5985
## (Intercept)-Sciurus_carolinensis -1.1382 0.5209 -2.2459 -1.1159
## (Intercept)-Vulpes_vulpes -1.1747 0.7284 -2.6460 -1.1385
## (Intercept)-Sus_scrofa -1.3318 0.6668 -2.8388 -1.2825
## Avg_Cogongrass_Cover-Canis_latrans 0.3879 0.3528 -0.2623 0.3694
## Avg_Cogongrass_Cover-Sciurus_niger -0.1949 0.5104 -1.3726 -0.1446
## Avg_Cogongrass_Cover-Procyon_lotor 0.1985 0.3277 -0.4348 0.1960
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3412 0.3306 -0.2866 0.3289
## Avg_Cogongrass_Cover-Lynx_rufus 0.4251 0.3773 -0.2725 0.4044
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3095 0.3493 -0.3597 0.3008
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1607 0.4200 -1.0865 -0.1294
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3227 0.3464 -0.3330 0.3104
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2573 0.4098 -0.5503 0.2581
## Avg_Cogongrass_Cover-Sus_scrofa -0.0591 0.4948 -1.1666 -0.0157
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0494 1.0571 554
## (Intercept)-Sciurus_niger 0.4063 1.0276 448
## (Intercept)-Procyon_lotor 1.2383 1.0566 464
## (Intercept)-Dasypus_novemcinctus 0.2641 1.0292 928
## (Intercept)-Lynx_rufus 0.7221 1.0641 587
## (Intercept)-Didelphis_virginiana -0.1523 1.0190 1107
## (Intercept)-Sylvilagus_floridanus 0.4442 1.0025 1158
## (Intercept)-Sciurus_carolinensis -0.2146 1.0024 893
## (Intercept)-Vulpes_vulpes 0.1413 1.0251 348
## (Intercept)-Sus_scrofa -0.1959 1.0058 630
## Avg_Cogongrass_Cover-Canis_latrans 1.1705 1.0043 1898
## Avg_Cogongrass_Cover-Sciurus_niger 0.6599 1.0122 570
## Avg_Cogongrass_Cover-Procyon_lotor 0.8529 0.9999 1727
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0060 1.0007 2393
## Avg_Cogongrass_Cover-Lynx_rufus 1.2510 1.0046 1781
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0200 1.0049 1865
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5732 1.0030 852
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0281 1.0047 1857
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0993 1.0004 1491
## Avg_Cogongrass_Cover-Sus_scrofa 0.8015 1.0067 945
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7231 0.1833 -3.0894 -2.7203 -2.3843 1.0299
## (Intercept)-Sciurus_niger -3.6540 0.5568 -4.8311 -3.6231 -2.6572 1.0256
## (Intercept)-Procyon_lotor -2.2910 0.1424 -2.5865 -2.2837 -2.0185 1.0294
## (Intercept)-Dasypus_novemcinctus -1.7664 0.1642 -2.0972 -1.7624 -1.4553 1.0057
## (Intercept)-Lynx_rufus -3.4707 0.3381 -4.1870 -3.4589 -2.8288 1.0669
## (Intercept)-Didelphis_virginiana -2.5555 0.2719 -3.1019 -2.5489 -2.0249 1.0005
## (Intercept)-Sylvilagus_floridanus -3.1263 0.2764 -3.6967 -3.1151 -2.6215 1.0062
## (Intercept)-Sciurus_carolinensis -2.6334 0.2971 -3.2497 -2.6176 -2.0872 1.0049
## (Intercept)-Vulpes_vulpes -3.7566 0.6493 -5.1763 -3.6821 -2.6289 1.0500
## (Intercept)-Sus_scrofa -3.2250 0.5049 -4.2608 -3.2146 -2.2683 1.0003
## shrub_cover-Canis_latrans -0.2416 0.2184 -0.6664 -0.2485 0.1970 1.0100
## shrub_cover-Sciurus_niger -0.1792 0.4693 -1.1254 -0.1758 0.7370 1.0332
## shrub_cover-Procyon_lotor 0.2689 0.1598 -0.0539 0.2720 0.5729 1.0008
## shrub_cover-Dasypus_novemcinctus 0.8484 0.2861 0.3016 0.8392 1.4145 1.0000
## shrub_cover-Lynx_rufus -0.1321 0.3743 -0.9198 -0.1173 0.5651 1.0571
## shrub_cover-Didelphis_virginiana 0.9235 0.3564 0.2691 0.9156 1.6649 1.0041
## shrub_cover-Sylvilagus_floridanus 0.3253 0.3897 -0.4459 0.3272 1.0709 1.0021
## shrub_cover-Sciurus_carolinensis 0.8167 0.3823 0.1110 0.8002 1.5918 1.0191
## shrub_cover-Vulpes_vulpes 0.0441 0.5705 -1.1101 0.0597 1.1371 1.0156
## shrub_cover-Sus_scrofa 0.5984 0.6504 -0.7228 0.5870 1.8815 1.0124
## veg_height-Canis_latrans -0.5537 0.1861 -0.9219 -0.5506 -0.2006 1.0141
## veg_height-Sciurus_niger 0.1144 0.4295 -0.7064 0.1017 1.0204 1.0176
## veg_height-Procyon_lotor 0.3375 0.1238 0.0916 0.3409 0.5712 1.0021
## veg_height-Dasypus_novemcinctus 0.2530 0.1372 -0.0031 0.2508 0.5298 1.0112
## veg_height-Lynx_rufus 0.0287 0.2394 -0.4405 0.0351 0.4974 1.0191
## veg_height-Didelphis_virginiana 0.4253 0.2340 -0.0132 0.4211 0.9177 1.0015
## veg_height-Sylvilagus_floridanus 0.1608 0.2500 -0.3548 0.1649 0.6442 1.0091
## veg_height-Sciurus_carolinensis 0.0858 0.2106 -0.3060 0.0836 0.5226 1.0217
## veg_height-Vulpes_vulpes -0.0718 0.2930 -0.6748 -0.0616 0.4760 0.9999
## veg_height-Sus_scrofa -0.0743 0.3242 -0.7358 -0.0714 0.5443 1.0110
## ESS
## (Intercept)-Canis_latrans 864
## (Intercept)-Sciurus_niger 199
## (Intercept)-Procyon_lotor 1347
## (Intercept)-Dasypus_novemcinctus 1520
## (Intercept)-Lynx_rufus 349
## (Intercept)-Didelphis_virginiana 882
## (Intercept)-Sylvilagus_floridanus 580
## (Intercept)-Sciurus_carolinensis 910
## (Intercept)-Vulpes_vulpes 187
## (Intercept)-Sus_scrofa 692
## shrub_cover-Canis_latrans 763
## shrub_cover-Sciurus_niger 488
## shrub_cover-Procyon_lotor 1455
## shrub_cover-Dasypus_novemcinctus 1443
## shrub_cover-Lynx_rufus 423
## shrub_cover-Didelphis_virginiana 786
## shrub_cover-Sylvilagus_floridanus 594
## shrub_cover-Sciurus_carolinensis 863
## shrub_cover-Vulpes_vulpes 607
## shrub_cover-Sus_scrofa 876
## veg_height-Canis_latrans 685
## veg_height-Sciurus_niger 559
## veg_height-Procyon_lotor 1345
## veg_height-Dasypus_novemcinctus 1832
## veg_height-Lynx_rufus 848
## veg_height-Didelphis_virginiana 1258
## veg_height-Sylvilagus_floridanus 759
## veg_height-Sciurus_carolinensis 1095
## veg_height-Vulpes_vulpes 652
## veg_height-Sus_scrofa 1128
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.714
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0139 0.6844 -2.3364 -1.0003 0.3122 1.0078 486
## Cogon_Patch_Size -0.8604 0.6143 -2.1412 -0.8324 0.2915 1.0026 496
## Veg_shannon_index 0.9388 0.4472 0.0643 0.9328 1.8519 1.0096 286
## total_shrub_cover -0.5730 0.4914 -1.6510 -0.5276 0.2975 1.0147 191
## Avg_Cogongrass_Cover 2.1973 0.7387 0.7757 2.1805 3.6111 1.0145 177
## Tree_Density -2.0028 0.7203 -3.5369 -1.9752 -0.6337 1.0096 316
## Avg_Canopy_Cover 1.8862 0.5928 0.7851 1.8607 3.1571 1.0252 256
## avg_veg_height -0.6208 0.4626 -1.5243 -0.6148 0.3062 1.0052 197
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5668 4.1029 0.1470 2.3377 13.7176 1.0161 161
## Cogon_Patch_Size 2.0995 3.2917 0.0871 1.1692 9.7092 1.0201 331
## Veg_shannon_index 0.6145 0.7893 0.0491 0.3716 2.7303 1.0145 528
## total_shrub_cover 0.7776 1.0139 0.0532 0.4425 3.5920 1.0596 323
## Avg_Cogongrass_Cover 1.1237 1.8760 0.0485 0.5061 5.6272 1.0655 343
## Tree_Density 2.4935 4.0826 0.0924 1.2472 12.3374 1.1697 208
## Avg_Canopy_Cover 2.1546 3.1106 0.1583 1.3290 9.0892 1.0992 176
## avg_veg_height 0.4262 0.6218 0.0373 0.2357 2.0402 1.0215 695
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7038 1.8631 0.0711 1.1463 6.8741 1.0348 83
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9774 0.3390 -3.6444 -2.9870 -2.2988 1.0060 961
## shrub_cover 0.4239 0.2873 -0.1259 0.4142 1.0310 1.0151 685
## veg_height 0.0663 0.1742 -0.2816 0.0673 0.4147 1.0208 910
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9917 0.7700 0.2550 0.7931 3.0023 1.0103 618
## shrub_cover 0.5528 0.5165 0.0982 0.4152 1.8799 1.0549 623
## veg_height 0.2148 0.1677 0.0532 0.1670 0.6123 1.0224 879
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2408 0.9631 -1.6647 0.2198
## (Intercept)-Sciurus_niger -0.3583 1.4722 -2.8744 -0.5050
## (Intercept)-Procyon_lotor 0.3879 0.9848 -1.6432 0.4308
## (Intercept)-Dasypus_novemcinctus -1.2997 0.8484 -3.1649 -1.2418
## (Intercept)-Lynx_rufus 0.3096 1.6730 -2.1633 0.0115
## (Intercept)-Didelphis_virginiana -2.2493 1.0385 -4.4098 -2.1777
## (Intercept)-Sylvilagus_floridanus -1.1011 1.0152 -3.0948 -1.1164
## (Intercept)-Sciurus_carolinensis -2.2645 1.0960 -4.6996 -2.1594
## (Intercept)-Vulpes_vulpes -2.0734 1.3437 -4.8083 -2.0261
## (Intercept)-Sus_scrofa -2.8831 1.5907 -6.3367 -2.7327
## Cogon_Patch_Size-Canis_latrans 0.3261 1.0291 -1.1966 0.1748
## Cogon_Patch_Size-Sciurus_niger -1.5047 1.4345 -4.7057 -1.3400
## Cogon_Patch_Size-Procyon_lotor -1.1054 0.6783 -2.4644 -1.0898
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6672 0.7187 -2.0346 -0.6799
## Cogon_Patch_Size-Lynx_rufus -1.0005 1.1048 -3.2390 -1.0228
## Cogon_Patch_Size-Didelphis_virginiana 0.4869 0.8945 -0.9997 0.3948
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7272 1.3077 -4.7271 -1.5474
## Cogon_Patch_Size-Sciurus_carolinensis -1.5250 1.1058 -4.2219 -1.3785
## Cogon_Patch_Size-Vulpes_vulpes -1.2226 1.2526 -4.0250 -1.1549
## Cogon_Patch_Size-Sus_scrofa -1.2307 1.2007 -4.0506 -1.0776
## Veg_shannon_index-Canis_latrans 1.2916 0.6357 0.1762 1.2475
## Veg_shannon_index-Sciurus_niger 1.0844 0.8141 -0.4499 1.0474
## Veg_shannon_index-Procyon_lotor 1.2337 0.5802 0.2055 1.1995
## Veg_shannon_index-Dasypus_novemcinctus 0.6829 0.5456 -0.4210 0.6918
## Veg_shannon_index-Lynx_rufus 0.8911 0.7576 -0.6395 0.8978
## Veg_shannon_index-Didelphis_virginiana 1.1032 0.6257 -0.0440 1.0670
## Veg_shannon_index-Sylvilagus_floridanus 1.0098 0.6389 -0.2059 0.9899
## Veg_shannon_index-Sciurus_carolinensis 0.3794 0.6858 -1.0891 0.4127
## Veg_shannon_index-Vulpes_vulpes 0.5142 0.7591 -1.1521 0.5774
## Veg_shannon_index-Sus_scrofa 1.3581 0.7859 0.0467 1.2856
## total_shrub_cover-Canis_latrans 0.1749 0.6910 -0.9420 0.1005
## total_shrub_cover-Sciurus_niger -0.7887 0.9279 -3.0128 -0.6955
## total_shrub_cover-Procyon_lotor -0.9324 0.5688 -2.1360 -0.8929
## total_shrub_cover-Dasypus_novemcinctus -0.2645 0.6330 -1.5860 -0.2428
## total_shrub_cover-Lynx_rufus -0.8651 0.9686 -3.1233 -0.7523
## total_shrub_cover-Didelphis_virginiana -0.7911 0.7829 -2.6597 -0.7133
## total_shrub_cover-Sylvilagus_floridanus -0.6347 0.8360 -2.5095 -0.5553
## total_shrub_cover-Sciurus_carolinensis -0.5449 0.7683 -2.2623 -0.4778
## total_shrub_cover-Vulpes_vulpes -0.8045 0.9135 -3.0671 -0.6675
## total_shrub_cover-Sus_scrofa -0.4478 0.8835 -2.4149 -0.4032
## Avg_Cogongrass_Cover-Canis_latrans 2.5663 0.9393 0.8575 2.5128
## Avg_Cogongrass_Cover-Sciurus_niger 1.5405 1.3738 -1.6666 1.7308
## Avg_Cogongrass_Cover-Procyon_lotor 2.3917 0.8941 0.7164 2.3567
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7464 1.0026 1.0170 2.6656
## Avg_Cogongrass_Cover-Lynx_rufus 2.5752 0.9822 0.7923 2.5138
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2568 0.9123 0.4143 2.2271
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6142 1.0604 -0.6470 1.6822
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4448 0.9403 0.7056 2.3961
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.7074 1.0838 0.8099 2.6128
## Avg_Cogongrass_Cover-Sus_scrofa 1.9308 1.1353 -0.4780 1.9886
## Tree_Density-Canis_latrans -2.7826 1.2331 -5.8396 -2.5874
## Tree_Density-Sciurus_niger -2.1121 1.3048 -5.0942 -2.0287
## Tree_Density-Procyon_lotor -1.6262 0.7496 -3.1091 -1.6220
## Tree_Density-Dasypus_novemcinctus -3.5107 1.6438 -7.8570 -3.1267
## Tree_Density-Lynx_rufus -0.6413 1.2664 -2.6743 -0.7549
## Tree_Density-Didelphis_virginiana -2.1535 1.1321 -4.6269 -2.0984
## Tree_Density-Sylvilagus_floridanus -2.4757 1.3493 -5.8019 -2.3043
## Tree_Density-Sciurus_carolinensis -2.3141 1.3277 -5.3247 -2.1989
## Tree_Density-Vulpes_vulpes -1.8767 1.3185 -4.5541 -1.8851
## Tree_Density-Sus_scrofa -2.2754 1.4456 -5.6647 -2.1446
## Avg_Canopy_Cover-Canis_latrans 0.3643 0.6139 -0.7852 0.3439
## Avg_Canopy_Cover-Sciurus_niger 1.9715 1.4867 -0.7435 1.9127
## Avg_Canopy_Cover-Procyon_lotor 1.7177 0.7117 0.4374 1.6855
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1103 0.7518 0.8432 2.0238
## Avg_Canopy_Cover-Lynx_rufus 1.1978 1.1410 -0.9000 1.1822
## Avg_Canopy_Cover-Didelphis_virginiana 2.8412 1.0511 1.2698 2.6674
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3008 1.4578 1.2598 3.0473
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5935 1.1194 0.9994 2.3975
## Avg_Canopy_Cover-Vulpes_vulpes 2.1549 1.0886 0.3679 2.0279
## Avg_Canopy_Cover-Sus_scrofa 2.1051 0.9307 0.5290 1.9881
## avg_veg_height-Canis_latrans -0.6839 0.5635 -1.8206 -0.6844
## avg_veg_height-Sciurus_niger -0.8678 0.7808 -2.6314 -0.8082
## avg_veg_height-Procyon_lotor -0.5213 0.5521 -1.5646 -0.5363
## avg_veg_height-Dasypus_novemcinctus -0.3959 0.5766 -1.4817 -0.4146
## avg_veg_height-Lynx_rufus -0.7552 0.7215 -2.3287 -0.7286
## avg_veg_height-Didelphis_virginiana -0.7602 0.6289 -2.0926 -0.7382
## avg_veg_height-Sylvilagus_floridanus -0.7664 0.6360 -2.0645 -0.7599
## avg_veg_height-Sciurus_carolinensis -0.2842 0.6552 -1.4508 -0.3310
## avg_veg_height-Vulpes_vulpes -0.6237 0.7315 -2.1101 -0.6215
## avg_veg_height-Sus_scrofa -0.6318 0.6736 -2.0032 -0.6210
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2316 1.0350 279
## (Intercept)-Sciurus_niger 3.0031 1.0309 237
## (Intercept)-Procyon_lotor 2.2251 1.0158 237
## (Intercept)-Dasypus_novemcinctus 0.2098 1.0182 409
## (Intercept)-Lynx_rufus 4.2727 1.0737 102
## (Intercept)-Didelphis_virginiana -0.3563 1.0009 336
## (Intercept)-Sylvilagus_floridanus 0.9533 1.0038 457
## (Intercept)-Sciurus_carolinensis -0.3959 1.0313 213
## (Intercept)-Vulpes_vulpes 0.4647 1.0226 214
## (Intercept)-Sus_scrofa -0.2142 1.0356 174
## Cogon_Patch_Size-Canis_latrans 2.8495 1.0101 532
## Cogon_Patch_Size-Sciurus_niger 0.8766 1.0321 365
## Cogon_Patch_Size-Procyon_lotor 0.1823 1.0092 339
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8548 1.0060 613
## Cogon_Patch_Size-Lynx_rufus 1.2271 1.0114 394
## Cogon_Patch_Size-Didelphis_virginiana 2.4873 1.0019 492
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2228 1.0156 318
## Cogon_Patch_Size-Sciurus_carolinensis 0.1653 1.0019 315
## Cogon_Patch_Size-Vulpes_vulpes 1.1169 1.0077 376
## Cogon_Patch_Size-Sus_scrofa 0.5637 1.0095 522
## Veg_shannon_index-Canis_latrans 2.6842 1.0039 347
## Veg_shannon_index-Sciurus_niger 2.8294 1.0103 492
## Veg_shannon_index-Procyon_lotor 2.5214 1.0066 285
## Veg_shannon_index-Dasypus_novemcinctus 1.7053 1.0195 639
## Veg_shannon_index-Lynx_rufus 2.3925 1.0319 353
## Veg_shannon_index-Didelphis_virginiana 2.3741 1.0015 427
## Veg_shannon_index-Sylvilagus_floridanus 2.3020 1.0014 496
## Veg_shannon_index-Sciurus_carolinensis 1.6348 1.0111 470
## Veg_shannon_index-Vulpes_vulpes 1.8447 1.0204 467
## Veg_shannon_index-Sus_scrofa 3.1114 1.0188 420
## total_shrub_cover-Canis_latrans 1.7309 1.0189 553
## total_shrub_cover-Sciurus_niger 0.8251 1.0180 275
## total_shrub_cover-Procyon_lotor 0.0292 1.0038 550
## total_shrub_cover-Dasypus_novemcinctus 0.8929 1.0117 679
## total_shrub_cover-Lynx_rufus 0.7497 1.0448 256
## total_shrub_cover-Didelphis_virginiana 0.4861 1.0154 441
## total_shrub_cover-Sylvilagus_floridanus 0.8613 1.0098 342
## total_shrub_cover-Sciurus_carolinensis 0.8358 1.0096 325
## total_shrub_cover-Vulpes_vulpes 0.6883 1.0033 414
## total_shrub_cover-Sus_scrofa 1.2817 1.0073 372
## Avg_Cogongrass_Cover-Canis_latrans 4.6250 1.0051 217
## Avg_Cogongrass_Cover-Sciurus_niger 3.7778 1.0289 187
## Avg_Cogongrass_Cover-Procyon_lotor 4.2843 1.0031 290
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0091 1.0168 224
## Avg_Cogongrass_Cover-Lynx_rufus 4.8022 1.0120 278
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.1151 1.0037 336
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5303 1.0075 308
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4775 1.0077 304
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.0616 1.0058 247
## Avg_Cogongrass_Cover-Sus_scrofa 4.0576 1.0113 301
## Tree_Density-Canis_latrans -0.9146 1.0237 319
## Tree_Density-Sciurus_niger 0.2362 1.0204 385
## Tree_Density-Procyon_lotor -0.2016 1.0005 396
## Tree_Density-Dasypus_novemcinctus -1.3668 1.1017 173
## Tree_Density-Lynx_rufus 2.1012 1.1168 210
## Tree_Density-Didelphis_virginiana 0.1275 1.0173 545
## Tree_Density-Sylvilagus_floridanus -0.2025 1.0651 404
## Tree_Density-Sciurus_carolinensis -0.0265 1.0441 345
## Tree_Density-Vulpes_vulpes 0.8545 1.0150 417
## Tree_Density-Sus_scrofa 0.3116 1.0116 359
## Avg_Canopy_Cover-Canis_latrans 1.6021 1.0137 886
## Avg_Canopy_Cover-Sciurus_niger 5.0914 1.0710 190
## Avg_Canopy_Cover-Procyon_lotor 3.2468 1.0028 413
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7911 1.0370 457
## Avg_Canopy_Cover-Lynx_rufus 3.4952 1.0317 226
## Avg_Canopy_Cover-Didelphis_virginiana 5.3666 1.0222 348
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.8799 1.0766 275
## Avg_Canopy_Cover-Sciurus_carolinensis 5.4729 1.0789 338
## Avg_Canopy_Cover-Vulpes_vulpes 4.6336 1.0209 348
## Avg_Canopy_Cover-Sus_scrofa 4.3119 1.0520 566
## avg_veg_height-Canis_latrans 0.4098 1.0053 345
## avg_veg_height-Sciurus_niger 0.5024 1.0149 456
## avg_veg_height-Procyon_lotor 0.6307 1.0025 475
## avg_veg_height-Dasypus_novemcinctus 0.8354 1.0071 458
## avg_veg_height-Lynx_rufus 0.6314 1.0171 353
## avg_veg_height-Didelphis_virginiana 0.4389 1.0006 366
## avg_veg_height-Sylvilagus_floridanus 0.4765 1.0024 396
## avg_veg_height-Sciurus_carolinensis 1.1411 1.0020 441
## avg_veg_height-Vulpes_vulpes 0.8999 1.0178 320
## avg_veg_height-Sus_scrofa 0.7103 1.0095 387
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7422 0.1840 -3.1233 -2.7347 -2.4017 1.0405
## (Intercept)-Sciurus_niger -4.2839 0.5025 -5.2034 -4.3240 -3.1987 1.0398
## (Intercept)-Procyon_lotor -2.3027 0.1418 -2.5992 -2.2997 -2.0343 1.0029
## (Intercept)-Dasypus_novemcinctus -1.7741 0.1641 -2.1039 -1.7678 -1.4655 1.0060
## (Intercept)-Lynx_rufus -3.6986 0.3710 -4.4662 -3.6762 -3.0211 1.0799
## (Intercept)-Didelphis_virginiana -2.6236 0.2849 -3.2121 -2.6089 -2.0967 1.0034
## (Intercept)-Sylvilagus_floridanus -3.1693 0.2634 -3.7289 -3.1604 -2.6865 1.0205
## (Intercept)-Sciurus_carolinensis -2.7415 0.3309 -3.4035 -2.7374 -2.1254 1.0070
## (Intercept)-Vulpes_vulpes -4.0203 0.6245 -5.2912 -3.9880 -2.8659 1.0380
## (Intercept)-Sus_scrofa -3.4420 0.5421 -4.5115 -3.4455 -2.3818 1.0367
## shrub_cover-Canis_latrans -0.2962 0.2271 -0.7361 -0.2959 0.1485 1.0135
## shrub_cover-Sciurus_niger -0.1498 0.4764 -1.1233 -0.1427 0.8105 1.0251
## shrub_cover-Procyon_lotor 0.2765 0.1610 -0.0405 0.2790 0.5854 1.0287
## shrub_cover-Dasypus_novemcinctus 0.9120 0.3138 0.3186 0.9063 1.5320 1.0039
## shrub_cover-Lynx_rufus -0.0504 0.3666 -0.7867 -0.0387 0.6394 1.0227
## shrub_cover-Didelphis_virginiana 1.0202 0.3741 0.3245 1.0097 1.7719 1.0052
## shrub_cover-Sylvilagus_floridanus 0.4989 0.3918 -0.2783 0.5025 1.2707 1.0151
## shrub_cover-Sciurus_carolinensis 0.9682 0.4226 0.1948 0.9609 1.8360 1.0028
## shrub_cover-Vulpes_vulpes 0.2802 0.5537 -0.8390 0.2906 1.3723 1.0284
## shrub_cover-Sus_scrofa 0.8836 0.6886 -0.4588 0.8631 2.2943 1.0122
## veg_height-Canis_latrans -0.5705 0.1882 -0.9615 -0.5630 -0.2288 1.0281
## veg_height-Sciurus_niger 0.0342 0.3769 -0.7196 0.0441 0.7892 1.0376
## veg_height-Procyon_lotor 0.3502 0.1181 0.1252 0.3480 0.5876 1.0014
## veg_height-Dasypus_novemcinctus 0.2575 0.1354 -0.0083 0.2574 0.5300 1.0024
## veg_height-Lynx_rufus 0.1132 0.2431 -0.3744 0.1158 0.5612 1.0140
## veg_height-Didelphis_virginiana 0.4769 0.2325 0.0401 0.4692 0.9479 1.0012
## veg_height-Sylvilagus_floridanus 0.1557 0.2476 -0.3419 0.1547 0.6262 1.0040
## veg_height-Sciurus_carolinensis 0.1312 0.2144 -0.2697 0.1265 0.5613 1.0102
## veg_height-Vulpes_vulpes -0.1652 0.3419 -0.9255 -0.1410 0.4529 1.0385
## veg_height-Sus_scrofa -0.1434 0.3311 -0.8534 -0.1306 0.4889 1.0079
## ESS
## (Intercept)-Canis_latrans 593
## (Intercept)-Sciurus_niger 173
## (Intercept)-Procyon_lotor 1445
## (Intercept)-Dasypus_novemcinctus 1172
## (Intercept)-Lynx_rufus 183
## (Intercept)-Didelphis_virginiana 632
## (Intercept)-Sylvilagus_floridanus 590
## (Intercept)-Sciurus_carolinensis 471
## (Intercept)-Vulpes_vulpes 205
## (Intercept)-Sus_scrofa 287
## shrub_cover-Canis_latrans 804
## shrub_cover-Sciurus_niger 331
## shrub_cover-Procyon_lotor 1258
## shrub_cover-Dasypus_novemcinctus 753
## shrub_cover-Lynx_rufus 274
## shrub_cover-Didelphis_virginiana 472
## shrub_cover-Sylvilagus_floridanus 471
## shrub_cover-Sciurus_carolinensis 401
## shrub_cover-Vulpes_vulpes 429
## shrub_cover-Sus_scrofa 345
## veg_height-Canis_latrans 620
## veg_height-Sciurus_niger 336
## veg_height-Procyon_lotor 1459
## veg_height-Dasypus_novemcinctus 2049
## veg_height-Lynx_rufus 564
## veg_height-Didelphis_virginiana 1348
## veg_height-Sylvilagus_floridanus 709
## veg_height-Sciurus_carolinensis 996
## veg_height-Vulpes_vulpes 492
## veg_height-Sus_scrofa 820
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7102
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4519 0.4336 -1.3147 -0.4553 0.3901 1.0280 206
## Avg_Cogongrass_Cover 0.0124 0.3408 -0.6863 0.0275 0.6774 1.0648 427
## total_shrub_cover -0.7955 0.4319 -1.7495 -0.7557 -0.0356 1.0404 195
## avg_veg_height 0.1042 0.3216 -0.5004 0.0975 0.7398 1.0232 402
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6546 0.7647 0.0575 0.4063 2.5607 1.0105 469
## Avg_Cogongrass_Cover 0.3863 0.4983 0.0406 0.2328 1.5820 1.0127 689
## total_shrub_cover 0.5942 0.7435 0.0512 0.3550 2.5954 1.0035 395
## avg_veg_height 0.2755 0.3783 0.0342 0.1674 1.1567 1.1224 334
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.223 1.0454 0.1173 0.9267 3.9724 1.0132 131
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9616 0.2981 -3.5733 -2.9588 -2.3713 1.0011 413
## shrub_cover 0.6309 0.3094 0.0140 0.6284 1.2416 1.0268 359
## veg_height 0.0723 0.1845 -0.2919 0.0698 0.4465 1.0029 720
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7342 0.5861 0.1750 0.5682 2.2582 1.0077 398
## shrub_cover 0.5968 0.5355 0.1118 0.4453 1.9113 1.0079 289
## veg_height 0.2153 0.1602 0.0545 0.1719 0.6875 1.0187 1167
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0516 0.6222 -1.1527 0.0206
## (Intercept)-Sciurus_niger -0.6714 0.7025 -2.1032 -0.6722
## (Intercept)-Procyon_lotor 0.1998 0.6426 -0.9712 0.1845
## (Intercept)-Dasypus_novemcinctus -0.4670 0.5970 -1.6372 -0.4727
## (Intercept)-Lynx_rufus -0.3051 0.6654 -1.5482 -0.3137
## (Intercept)-Didelphis_virginiana -0.7498 0.6175 -2.0630 -0.7327
## (Intercept)-Sylvilagus_floridanus -0.2051 0.6599 -1.4725 -0.2376
## (Intercept)-Sciurus_carolinensis -0.7602 0.6528 -2.0889 -0.7274
## (Intercept)-Vulpes_vulpes -0.8255 0.7921 -2.5235 -0.7691
## (Intercept)-Sus_scrofa -0.9180 0.7815 -2.6311 -0.8646
## Avg_Cogongrass_Cover-Canis_latrans 0.3310 0.4845 -0.5498 0.3079
## Avg_Cogongrass_Cover-Sciurus_niger -0.3780 0.6242 -1.8631 -0.3084
## Avg_Cogongrass_Cover-Procyon_lotor -0.0576 0.4542 -0.9693 -0.0407
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1449 0.4368 -0.7104 0.1433
## Avg_Cogongrass_Cover-Lynx_rufus 0.3409 0.5133 -0.6105 0.3072
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1610 0.4879 -0.8084 0.1654
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4068 0.5746 -1.7944 -0.3383
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0660 0.4806 -0.8660 0.0679
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1539 0.5481 -0.8818 0.1440
## Avg_Cogongrass_Cover-Sus_scrofa -0.2255 0.6116 -1.6568 -0.1742
## total_shrub_cover-Canis_latrans -0.0185 0.5605 -1.0369 -0.0471
## total_shrub_cover-Sciurus_niger -0.9652 0.6610 -2.4567 -0.9082
## total_shrub_cover-Procyon_lotor -1.0926 0.5667 -2.3822 -1.0147
## total_shrub_cover-Dasypus_novemcinctus -0.4876 0.5870 -1.8225 -0.4249
## total_shrub_cover-Lynx_rufus -1.1635 0.6926 -2.6938 -1.0821
## total_shrub_cover-Didelphis_virginiana -0.8260 0.6002 -2.1883 -0.7574
## total_shrub_cover-Sylvilagus_floridanus -1.0648 0.7500 -2.8960 -0.9733
## total_shrub_cover-Sciurus_carolinensis -0.8628 0.6771 -2.4835 -0.7807
## total_shrub_cover-Vulpes_vulpes -0.9462 0.8024 -2.8330 -0.8653
## total_shrub_cover-Sus_scrofa -0.6681 0.7088 -2.1953 -0.6461
## avg_veg_height-Canis_latrans 0.0883 0.4409 -0.7608 0.0810
## avg_veg_height-Sciurus_niger -0.1753 0.5623 -1.3961 -0.1414
## avg_veg_height-Procyon_lotor 0.1348 0.4376 -0.7076 0.1262
## avg_veg_height-Dasypus_novemcinctus 0.3144 0.4466 -0.4716 0.2879
## avg_veg_height-Lynx_rufus 0.0619 0.5126 -0.9499 0.0570
## avg_veg_height-Didelphis_virginiana 0.0137 0.4675 -0.9377 0.0244
## avg_veg_height-Sylvilagus_floridanus 0.0155 0.4847 -0.9524 0.0184
## avg_veg_height-Sciurus_carolinensis 0.4269 0.4989 -0.4552 0.3999
## avg_veg_height-Vulpes_vulpes 0.0727 0.5016 -0.9007 0.0609
## avg_veg_height-Sus_scrofa 0.1316 0.4879 -0.8699 0.1379
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3332 1.0067 487
## (Intercept)-Sciurus_niger 0.7047 1.0568 322
## (Intercept)-Procyon_lotor 1.5129 1.0151 358
## (Intercept)-Dasypus_novemcinctus 0.7158 1.0063 399
## (Intercept)-Lynx_rufus 1.0433 1.0121 477
## (Intercept)-Didelphis_virginiana 0.4096 1.0110 404
## (Intercept)-Sylvilagus_floridanus 1.2446 1.0091 623
## (Intercept)-Sciurus_carolinensis 0.4479 1.0121 325
## (Intercept)-Vulpes_vulpes 0.5997 1.0129 269
## (Intercept)-Sus_scrofa 0.4957 1.0137 243
## Avg_Cogongrass_Cover-Canis_latrans 1.3448 1.0282 814
## Avg_Cogongrass_Cover-Sciurus_niger 0.6533 1.0010 468
## Avg_Cogongrass_Cover-Procyon_lotor 0.7833 1.0267 693
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9932 1.0301 812
## Avg_Cogongrass_Cover-Lynx_rufus 1.4685 1.0066 906
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1514 1.0100 906
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5830 1.0212 557
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9924 1.0338 662
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2847 1.0194 652
## Avg_Cogongrass_Cover-Sus_scrofa 0.7923 1.0311 701
## total_shrub_cover-Canis_latrans 1.1829 1.0276 561
## total_shrub_cover-Sciurus_niger 0.1673 1.0376 386
## total_shrub_cover-Procyon_lotor -0.1965 1.0206 369
## total_shrub_cover-Dasypus_novemcinctus 0.4685 1.0215 376
## total_shrub_cover-Lynx_rufus -0.0135 1.0092 258
## total_shrub_cover-Didelphis_virginiana 0.1656 1.0438 303
## total_shrub_cover-Sylvilagus_floridanus 0.1106 1.0329 256
## total_shrub_cover-Sciurus_carolinensis 0.2225 1.0103 211
## total_shrub_cover-Vulpes_vulpes 0.3934 1.0838 212
## total_shrub_cover-Sus_scrofa 0.7078 1.0325 236
## avg_veg_height-Canis_latrans 0.9893 1.0088 701
## avg_veg_height-Sciurus_niger 0.8349 1.0043 497
## avg_veg_height-Procyon_lotor 1.0060 1.0210 849
## avg_veg_height-Dasypus_novemcinctus 1.2722 1.0178 606
## avg_veg_height-Lynx_rufus 1.0706 1.0089 583
## avg_veg_height-Didelphis_virginiana 0.9200 1.0035 818
## avg_veg_height-Sylvilagus_floridanus 0.9797 1.0059 670
## avg_veg_height-Sciurus_carolinensis 1.5401 1.0341 489
## avg_veg_height-Vulpes_vulpes 1.0689 1.0135 908
## avg_veg_height-Sus_scrofa 1.0971 1.0046 818
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7696 0.1933 -3.1657 -2.7596 -2.4070 1.0256
## (Intercept)-Sciurus_niger -3.6394 0.5636 -4.7509 -3.6003 -2.6115 1.0290
## (Intercept)-Procyon_lotor -2.3074 0.1411 -2.5883 -2.3064 -2.0299 1.0075
## (Intercept)-Dasypus_novemcinctus -1.8454 0.1804 -2.2171 -1.8388 -1.5109 1.0064
## (Intercept)-Lynx_rufus -3.4481 0.3150 -4.1165 -3.4269 -2.8851 1.0024
## (Intercept)-Didelphis_virginiana -2.7631 0.3011 -3.3846 -2.7544 -2.1993 1.0036
## (Intercept)-Sylvilagus_floridanus -3.2454 0.2569 -3.7614 -3.2395 -2.7538 1.0095
## (Intercept)-Sciurus_carolinensis -2.8266 0.3256 -3.4861 -2.8209 -2.2122 1.0066
## (Intercept)-Vulpes_vulpes -3.9216 0.6186 -5.2953 -3.8850 -2.8637 1.0066
## (Intercept)-Sus_scrofa -3.5821 0.5656 -4.8160 -3.5503 -2.5316 1.0112
## shrub_cover-Canis_latrans -0.2178 0.2471 -0.7013 -0.2172 0.2616 1.0288
## shrub_cover-Sciurus_niger 0.2008 0.5161 -0.8087 0.2072 1.1874 1.0241
## shrub_cover-Procyon_lotor 0.3249 0.1599 0.0084 0.3286 0.6307 1.0071
## shrub_cover-Dasypus_novemcinctus 1.0864 0.3588 0.3989 1.0843 1.7666 1.0153
## shrub_cover-Lynx_rufus 0.1873 0.3581 -0.5466 0.2009 0.8323 1.0036
## shrub_cover-Didelphis_virginiana 1.2526 0.4109 0.4918 1.2313 2.0986 1.0174
## shrub_cover-Sylvilagus_floridanus 0.7273 0.4074 -0.0775 0.7257 1.5380 1.0212
## shrub_cover-Sciurus_carolinensis 1.1970 0.4173 0.4113 1.1949 2.0441 1.0025
## shrub_cover-Vulpes_vulpes 0.5219 0.6025 -0.7045 0.5275 1.6864 1.0681
## shrub_cover-Sus_scrofa 1.1679 0.7898 -0.3485 1.1267 2.8841 1.0132
## veg_height-Canis_latrans -0.5699 0.1876 -0.9603 -0.5653 -0.2193 1.0072
## veg_height-Sciurus_niger 0.1991 0.4852 -0.6398 0.1638 1.2446 1.0091
## veg_height-Procyon_lotor 0.3454 0.1247 0.0951 0.3461 0.5881 1.0002
## veg_height-Dasypus_novemcinctus 0.2749 0.1424 0.0048 0.2713 0.5589 1.0010
## veg_height-Lynx_rufus 0.0578 0.2496 -0.4525 0.0581 0.5209 1.0026
## veg_height-Didelphis_virginiana 0.4357 0.2515 -0.0549 0.4286 0.9450 1.0176
## veg_height-Sylvilagus_floridanus 0.0995 0.2517 -0.3867 0.0992 0.5938 1.0350
## veg_height-Sciurus_carolinensis 0.1108 0.2146 -0.3070 0.1116 0.5475 1.0116
## veg_height-Vulpes_vulpes -0.0820 0.3149 -0.7286 -0.0765 0.5201 1.0069
## veg_height-Sus_scrofa -0.1356 0.3231 -0.8150 -0.1256 0.4746 1.0073
## ESS
## (Intercept)-Canis_latrans 581
## (Intercept)-Sciurus_niger 217
## (Intercept)-Procyon_lotor 1351
## (Intercept)-Dasypus_novemcinctus 650
## (Intercept)-Lynx_rufus 501
## (Intercept)-Didelphis_virginiana 367
## (Intercept)-Sylvilagus_floridanus 542
## (Intercept)-Sciurus_carolinensis 420
## (Intercept)-Vulpes_vulpes 189
## (Intercept)-Sus_scrofa 149
## shrub_cover-Canis_latrans 510
## shrub_cover-Sciurus_niger 368
## shrub_cover-Procyon_lotor 1435
## shrub_cover-Dasypus_novemcinctus 364
## shrub_cover-Lynx_rufus 481
## shrub_cover-Didelphis_virginiana 280
## shrub_cover-Sylvilagus_floridanus 306
## shrub_cover-Sciurus_carolinensis 270
## shrub_cover-Vulpes_vulpes 313
## shrub_cover-Sus_scrofa 141
## veg_height-Canis_latrans 742
## veg_height-Sciurus_niger 360
## veg_height-Procyon_lotor 1572
## veg_height-Dasypus_novemcinctus 1582
## veg_height-Lynx_rufus 707
## veg_height-Didelphis_virginiana 867
## veg_height-Sylvilagus_floridanus 615
## veg_height-Sciurus_carolinensis 902
## veg_height-Vulpes_vulpes 611
## veg_height-Sus_scrofa 647
# 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 = 5000, # number of MCMC samples
n.thin = 2, # 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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5885
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5782 0.3338 -1.2749 -0.5772 0.0905 1.0171 1520
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.862 0.6993 0.169 0.681 2.5819 1.0055 1256
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8628 0.2956 -3.4560 -2.8568 -2.2655 1.0148 1121
## shrub_cover 0.3017 0.2684 -0.2356 0.2942 0.8303 1.0038 1204
## veg_height 0.0682 0.1751 -0.2749 0.0699 0.4200 1.0156 1224
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7153 0.5474 0.1854 0.5671 2.1024 1.0146 723
## shrub_cover 0.4996 0.4121 0.0997 0.3860 1.5798 1.0014 836
## veg_height 0.2069 0.1651 0.0484 0.1578 0.6391 1.0095 1550
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2212 0.3953 -0.4991 0.2016 1.0407 1.0029
## (Intercept)-Sciurus_niger -0.8298 0.5811 -1.9234 -0.8446 0.4144 1.0016
## (Intercept)-Procyon_lotor 0.5126 0.3813 -0.2013 0.5034 1.2968 1.0101
## (Intercept)-Dasypus_novemcinctus -0.6012 0.3564 -1.3624 -0.5956 0.0983 1.0004
## (Intercept)-Lynx_rufus -0.0234 0.5434 -0.9718 -0.0692 1.1943 1.0320
## (Intercept)-Didelphis_virginiana -1.1187 0.4305 -2.0062 -1.0986 -0.3056 1.0000
## (Intercept)-Sylvilagus_floridanus -0.4501 0.4246 -1.2497 -0.4558 0.4158 1.0012
## (Intercept)-Sciurus_carolinensis -1.0983 0.4284 -2.0024 -1.0801 -0.3018 1.0068
## (Intercept)-Vulpes_vulpes -1.2606 0.6532 -2.5543 -1.2734 0.0404 1.0155
## (Intercept)-Sus_scrofa -1.4398 0.5568 -2.5613 -1.4379 -0.3441 1.0077
## ESS
## (Intercept)-Canis_latrans 2348
## (Intercept)-Sciurus_niger 520
## (Intercept)-Procyon_lotor 1980
## (Intercept)-Dasypus_novemcinctus 2840
## (Intercept)-Lynx_rufus 529
## (Intercept)-Didelphis_virginiana 1763
## (Intercept)-Sylvilagus_floridanus 1499
## (Intercept)-Sciurus_carolinensis 1796
## (Intercept)-Vulpes_vulpes 460
## (Intercept)-Sus_scrofa 865
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7366 0.1812 -3.0986 -2.7310 -2.3892 1.0181
## (Intercept)-Sciurus_niger -3.6941 0.5169 -4.7181 -3.6826 -2.6975 1.0098
## (Intercept)-Procyon_lotor -2.2937 0.1413 -2.5719 -2.2898 -2.0290 1.0067
## (Intercept)-Dasypus_novemcinctus -1.7521 0.1557 -2.0637 -1.7506 -1.4575 1.0074
## (Intercept)-Lynx_rufus -3.5310 0.3126 -4.1512 -3.5277 -2.9394 1.0519
## (Intercept)-Didelphis_virginiana -2.5664 0.2738 -3.1387 -2.5592 -2.0543 1.0087
## (Intercept)-Sylvilagus_floridanus -3.1216 0.2638 -3.6474 -3.1136 -2.6159 1.0037
## (Intercept)-Sciurus_carolinensis -2.6441 0.3039 -3.2540 -2.6378 -2.0835 1.0000
## (Intercept)-Vulpes_vulpes -3.7388 0.5903 -5.0002 -3.7008 -2.6989 1.0517
## (Intercept)-Sus_scrofa -3.2842 0.5270 -4.3795 -3.2729 -2.3093 1.0275
## shrub_cover-Canis_latrans -0.2705 0.2204 -0.7108 -0.2681 0.1549 1.0023
## shrub_cover-Sciurus_niger -0.2013 0.4761 -1.1139 -0.2081 0.7787 1.0173
## shrub_cover-Procyon_lotor 0.2617 0.1608 -0.0548 0.2653 0.5594 1.0023
## shrub_cover-Dasypus_novemcinctus 0.8250 0.2832 0.2709 0.8192 1.3882 1.0068
## shrub_cover-Lynx_rufus -0.2070 0.3615 -0.9056 -0.2007 0.5060 1.0044
## shrub_cover-Didelphis_virginiana 0.9276 0.3502 0.2832 0.9101 1.6482 1.0084
## shrub_cover-Sylvilagus_floridanus 0.2661 0.3951 -0.4710 0.2487 1.0627 1.0123
## shrub_cover-Sciurus_carolinensis 0.8082 0.3921 0.0787 0.7923 1.6191 1.0008
## shrub_cover-Vulpes_vulpes 0.0152 0.5778 -1.1856 0.0366 1.1637 1.0099
## shrub_cover-Sus_scrofa 0.6028 0.6993 -0.8262 0.6146 2.0040 1.0117
## veg_height-Canis_latrans -0.5583 0.1918 -0.9422 -0.5536 -0.1894 1.0367
## veg_height-Sciurus_niger 0.0506 0.4315 -0.7888 0.0432 0.9569 1.0161
## veg_height-Procyon_lotor 0.3414 0.1249 0.0985 0.3389 0.5853 1.0029
## veg_height-Dasypus_novemcinctus 0.2500 0.1316 0.0000 0.2504 0.5059 1.0041
## veg_height-Lynx_rufus 0.0510 0.2502 -0.4727 0.0587 0.5222 1.0235
## veg_height-Didelphis_virginiana 0.4448 0.2336 0.0065 0.4366 0.9193 1.0022
## veg_height-Sylvilagus_floridanus 0.1392 0.2361 -0.3260 0.1443 0.6063 1.0096
## veg_height-Sciurus_carolinensis 0.0842 0.2110 -0.3205 0.0803 0.5031 1.0115
## veg_height-Vulpes_vulpes -0.0592 0.2892 -0.6964 -0.0434 0.4709 1.0410
## veg_height-Sus_scrofa -0.1112 0.3342 -0.8011 -0.1033 0.5166 1.0330
## ESS
## (Intercept)-Canis_latrans 838
## (Intercept)-Sciurus_niger 312
## (Intercept)-Procyon_lotor 1472
## (Intercept)-Dasypus_novemcinctus 1747
## (Intercept)-Lynx_rufus 305
## (Intercept)-Didelphis_virginiana 843
## (Intercept)-Sylvilagus_floridanus 660
## (Intercept)-Sciurus_carolinensis 957
## (Intercept)-Vulpes_vulpes 266
## (Intercept)-Sus_scrofa 471
## shrub_cover-Canis_latrans 918
## shrub_cover-Sciurus_niger 486
## shrub_cover-Procyon_lotor 1422
## shrub_cover-Dasypus_novemcinctus 1139
## shrub_cover-Lynx_rufus 504
## shrub_cover-Didelphis_virginiana 705
## shrub_cover-Sylvilagus_floridanus 638
## shrub_cover-Sciurus_carolinensis 747
## shrub_cover-Vulpes_vulpes 628
## shrub_cover-Sus_scrofa 580
## veg_height-Canis_latrans 823
## veg_height-Sciurus_niger 661
## veg_height-Procyon_lotor 1491
## veg_height-Dasypus_novemcinctus 1869
## veg_height-Lynx_rufus 745
## veg_height-Didelphis_virginiana 1348
## veg_height-Sylvilagus_floridanus 949
## veg_height-Sciurus_carolinensis 1218
## veg_height-Vulpes_vulpes 663
## veg_height-Sus_scrofa 855
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5978
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7753 0.3835 -1.5290 -0.7718 -0.0285 1.0126 634
## Veg_shannon_index 0.3129 0.2590 -0.1784 0.3092 0.8315 1.0187 790
## Avg_Cogongrass_Cover 0.2647 0.2635 -0.2794 0.2695 0.7745 1.0081 874
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7390 0.8025 0.0713 0.5165 2.8183 1.0232 697
## Veg_shannon_index 0.2725 0.2969 0.0372 0.1788 1.1192 1.0192 852
## Avg_Cogongrass_Cover 0.3072 0.4028 0.0376 0.1903 1.2177 1.1402 657
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8885 0.7923 0.0784 0.661 2.8835 1.0474 159
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8793 0.2999 -3.4713 -2.8689 -2.2902 1.0162 1244
## shrub_cover 0.3263 0.2764 -0.2148 0.3257 0.8796 1.0238 1161
## veg_height 0.0568 0.1778 -0.3008 0.0568 0.4098 1.0026 1126
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7661 0.5681 0.1797 0.6211 2.2106 1.0054 477
## shrub_cover 0.5164 0.4673 0.0930 0.3874 1.5924 1.0242 736
## veg_height 0.2020 0.1655 0.0501 0.1590 0.6039 1.0081 1363
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1621 0.5864 -1.2802 -0.1581
## (Intercept)-Sciurus_niger -0.8943 0.6684 -2.2127 -0.8889
## (Intercept)-Procyon_lotor -0.0168 0.6300 -1.2317 -0.0175
## (Intercept)-Dasypus_novemcinctus -0.7659 0.4992 -1.7776 -0.7508
## (Intercept)-Lynx_rufus -0.4770 0.6587 -1.6825 -0.5257
## (Intercept)-Didelphis_virginiana -1.1710 0.5779 -2.4207 -1.1317
## (Intercept)-Sylvilagus_floridanus -0.6481 0.5592 -1.7506 -0.6548
## (Intercept)-Sciurus_carolinensis -1.1753 0.5693 -2.4427 -1.1327
## (Intercept)-Vulpes_vulpes -1.1803 0.6878 -2.6447 -1.1566
## (Intercept)-Sus_scrofa -1.4727 0.6804 -2.9730 -1.4190
## Veg_shannon_index-Canis_latrans 0.6016 0.3844 -0.0758 0.5809
## Veg_shannon_index-Sciurus_niger 0.3077 0.4807 -0.6082 0.2940
## Veg_shannon_index-Procyon_lotor 0.4355 0.3641 -0.2542 0.4200
## Veg_shannon_index-Dasypus_novemcinctus 0.1779 0.3423 -0.5300 0.1861
## Veg_shannon_index-Lynx_rufus 0.1660 0.4764 -0.8464 0.1878
## Veg_shannon_index-Didelphis_virginiana 0.4692 0.3852 -0.2352 0.4481
## Veg_shannon_index-Sylvilagus_floridanus 0.3925 0.3988 -0.3454 0.3757
## Veg_shannon_index-Sciurus_carolinensis -0.0257 0.4048 -0.8838 -0.0041
## Veg_shannon_index-Vulpes_vulpes 0.0606 0.4600 -0.8846 0.0816
## Veg_shannon_index-Sus_scrofa 0.5979 0.4958 -0.2501 0.5607
## Avg_Cogongrass_Cover-Canis_latrans 0.5319 0.3868 -0.1710 0.5044
## Avg_Cogongrass_Cover-Sciurus_niger -0.1327 0.5558 -1.4284 -0.0549
## Avg_Cogongrass_Cover-Procyon_lotor 0.3103 0.3559 -0.3396 0.2993
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4058 0.3403 -0.2298 0.3914
## Avg_Cogongrass_Cover-Lynx_rufus 0.5349 0.4249 -0.1901 0.4998
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4120 0.3809 -0.3058 0.4014
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1228 0.4349 -1.0600 -0.0908
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3826 0.3711 -0.3189 0.3767
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3551 0.4454 -0.5104 0.3397
## Avg_Cogongrass_Cover-Sus_scrofa 0.0372 0.5202 -1.1151 0.0764
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9620 1.0293 603
## (Intercept)-Sciurus_niger 0.4595 1.0070 492
## (Intercept)-Procyon_lotor 1.2316 1.0176 391
## (Intercept)-Dasypus_novemcinctus 0.1990 1.0052 1128
## (Intercept)-Lynx_rufus 0.9090 1.0087 517
## (Intercept)-Didelphis_virginiana -0.0901 1.0046 710
## (Intercept)-Sylvilagus_floridanus 0.4478 1.0232 648
## (Intercept)-Sciurus_carolinensis -0.1564 1.0082 992
## (Intercept)-Vulpes_vulpes 0.1083 1.0064 374
## (Intercept)-Sus_scrofa -0.2894 1.0379 585
## Veg_shannon_index-Canis_latrans 1.4201 1.0042 1148
## Veg_shannon_index-Sciurus_niger 1.3135 1.0321 588
## Veg_shannon_index-Procyon_lotor 1.1806 1.0011 1290
## Veg_shannon_index-Dasypus_novemcinctus 0.8199 1.0049 1547
## Veg_shannon_index-Lynx_rufus 1.0816 1.0105 1105
## Veg_shannon_index-Didelphis_virginiana 1.2970 1.0003 1592
## Veg_shannon_index-Sylvilagus_floridanus 1.2266 1.0098 1331
## Veg_shannon_index-Sciurus_carolinensis 0.6929 1.0027 1313
## Veg_shannon_index-Vulpes_vulpes 0.9102 1.0109 1000
## Veg_shannon_index-Sus_scrofa 1.7529 1.0264 950
## Avg_Cogongrass_Cover-Canis_latrans 1.3840 1.0026 1293
## Avg_Cogongrass_Cover-Sciurus_niger 0.7672 1.0327 555
## Avg_Cogongrass_Cover-Procyon_lotor 1.0356 1.0103 1590
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1249 1.0007 1919
## Avg_Cogongrass_Cover-Lynx_rufus 1.4533 1.0067 1104
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1733 1.0032 1251
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6146 1.0104 1081
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1442 1.0054 1823
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2696 1.0156 840
## Avg_Cogongrass_Cover-Sus_scrofa 0.9731 1.0180 927
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7114 0.1783 -3.0794 -2.7037 -2.3777 1.0028
## (Intercept)-Sciurus_niger -3.7649 0.5847 -4.9826 -3.7480 -2.6539 1.0524
## (Intercept)-Procyon_lotor -2.2952 0.1439 -2.5889 -2.2939 -2.0232 1.0039
## (Intercept)-Dasypus_novemcinctus -1.7642 0.1627 -2.1036 -1.7578 -1.4633 1.0028
## (Intercept)-Lynx_rufus -3.4962 0.3177 -4.1281 -3.4929 -2.8992 1.0492
## (Intercept)-Didelphis_virginiana -2.5816 0.2733 -3.1260 -2.5773 -2.0643 1.0098
## (Intercept)-Sylvilagus_floridanus -3.1365 0.2754 -3.6886 -3.1252 -2.6218 1.0364
## (Intercept)-Sciurus_carolinensis -2.6602 0.3083 -3.2937 -2.6458 -2.0921 1.0389
## (Intercept)-Vulpes_vulpes -3.8654 0.6226 -5.1459 -3.8382 -2.7400 1.0147
## (Intercept)-Sus_scrofa -3.2953 0.5333 -4.3557 -3.2709 -2.2759 1.0231
## shrub_cover-Canis_latrans -0.2603 0.2154 -0.6853 -0.2630 0.1599 1.0072
## shrub_cover-Sciurus_niger -0.2362 0.4911 -1.2685 -0.2335 0.7040 1.0513
## shrub_cover-Procyon_lotor 0.2477 0.1675 -0.0938 0.2563 0.5506 1.0000
## shrub_cover-Dasypus_novemcinctus 0.8390 0.2964 0.2870 0.8226 1.4424 1.0077
## shrub_cover-Lynx_rufus -0.1388 0.3501 -0.8084 -0.1341 0.5489 1.0225
## shrub_cover-Didelphis_virginiana 0.9501 0.3622 0.2860 0.9446 1.6995 1.0083
## shrub_cover-Sylvilagus_floridanus 0.3048 0.3952 -0.4511 0.2985 1.0745 1.0005
## shrub_cover-Sciurus_carolinensis 0.8543 0.3967 0.1198 0.8411 1.6355 1.0087
## shrub_cover-Vulpes_vulpes 0.0409 0.5460 -1.0594 0.0508 1.0856 1.0148
## shrub_cover-Sus_scrofa 0.6569 0.7244 -0.7008 0.6154 2.2135 1.0257
## veg_height-Canis_latrans -0.5495 0.1842 -0.9270 -0.5418 -0.2123 1.0091
## veg_height-Sciurus_niger 0.0729 0.4196 -0.7326 0.0659 0.9702 1.0051
## veg_height-Procyon_lotor 0.3320 0.1189 0.0910 0.3318 0.5621 1.0034
## veg_height-Dasypus_novemcinctus 0.2508 0.1338 -0.0097 0.2471 0.5154 1.0030
## veg_height-Lynx_rufus 0.0193 0.2449 -0.4649 0.0214 0.5034 1.0050
## veg_height-Didelphis_virginiana 0.4330 0.2378 -0.0051 0.4181 0.9434 1.0067
## veg_height-Sylvilagus_floridanus 0.1444 0.2392 -0.3226 0.1424 0.6167 1.0121
## veg_height-Sciurus_carolinensis 0.0846 0.2113 -0.3190 0.0771 0.5201 1.0059
## veg_height-Vulpes_vulpes -0.1022 0.3147 -0.7622 -0.0826 0.4696 1.0044
## veg_height-Sus_scrofa -0.1100 0.3290 -0.7920 -0.0964 0.5102 1.0077
## ESS
## (Intercept)-Canis_latrans 818
## (Intercept)-Sciurus_niger 250
## (Intercept)-Procyon_lotor 1096
## (Intercept)-Dasypus_novemcinctus 1501
## (Intercept)-Lynx_rufus 401
## (Intercept)-Didelphis_virginiana 731
## (Intercept)-Sylvilagus_floridanus 606
## (Intercept)-Sciurus_carolinensis 811
## (Intercept)-Vulpes_vulpes 201
## (Intercept)-Sus_scrofa 481
## shrub_cover-Canis_latrans 948
## shrub_cover-Sciurus_niger 413
## shrub_cover-Procyon_lotor 1358
## shrub_cover-Dasypus_novemcinctus 1078
## shrub_cover-Lynx_rufus 469
## shrub_cover-Didelphis_virginiana 659
## shrub_cover-Sylvilagus_floridanus 583
## shrub_cover-Sciurus_carolinensis 694
## shrub_cover-Vulpes_vulpes 656
## shrub_cover-Sus_scrofa 566
## veg_height-Canis_latrans 629
## veg_height-Sciurus_niger 705
## veg_height-Procyon_lotor 1680
## veg_height-Dasypus_novemcinctus 2126
## veg_height-Lynx_rufus 591
## veg_height-Didelphis_virginiana 1212
## veg_height-Sylvilagus_floridanus 759
## veg_height-Sciurus_carolinensis 1193
## veg_height-Vulpes_vulpes 651
## veg_height-Sus_scrofa 1095
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6898
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6190 0.4312 -1.4908 -0.6138 0.2085 1.0574 324
## Cogon_Patch_Size -0.2780 0.3982 -1.1293 -0.2608 0.4704 1.0038 637
## Avg_Cogongrass_Cover 0.2053 0.2936 -0.3846 0.2096 0.7602 1.0095 588
## total_shrub_cover -0.5980 0.3625 -1.3817 -0.5740 0.0298 1.0083 311
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8557 0.8917 0.0717 0.5899 3.2420 1.0441 583
## Cogon_Patch_Size 0.8209 1.0575 0.0584 0.4862 3.8276 1.0080 489
## Avg_Cogongrass_Cover 0.3164 0.4261 0.0367 0.2021 1.2169 1.0561 1112
## total_shrub_cover 0.4174 0.5195 0.0440 0.2648 1.6902 1.0132 391
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1863 1.0804 0.074 0.891 4.1098 1.0403 157
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9125 0.3054 -3.5319 -2.9094 -2.3392 1.0259 629
## shrub_cover 0.5481 0.2910 -0.0171 0.5423 1.1423 1.0103 566
## veg_height 0.0622 0.1722 -0.2923 0.0620 0.4230 1.0023 882
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7317 0.5787 0.1782 0.5653 2.1504 1.0159 547
## shrub_cover 0.5451 0.5204 0.1047 0.4093 1.8111 1.0358 544
## veg_height 0.2004 0.1614 0.0506 0.1576 0.6043 1.0301 1081
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0866 0.6748 -1.2011 0.0658
## (Intercept)-Sciurus_niger -0.8942 0.7676 -2.3541 -0.8782
## (Intercept)-Procyon_lotor 0.1878 0.6777 -1.1519 0.1856
## (Intercept)-Dasypus_novemcinctus -0.5852 0.5778 -1.7066 -0.5966
## (Intercept)-Lynx_rufus -0.4115 0.6888 -1.7327 -0.4352
## (Intercept)-Didelphis_virginiana -0.9695 0.6308 -2.2747 -0.9553
## (Intercept)-Sylvilagus_floridanus -0.4130 0.6715 -1.6816 -0.4232
## (Intercept)-Sciurus_carolinensis -1.0523 0.6614 -2.4535 -1.0208
## (Intercept)-Vulpes_vulpes -1.0996 0.8326 -2.8246 -1.0586
## (Intercept)-Sus_scrofa -1.2377 0.7986 -2.9654 -1.1713
## Cogon_Patch_Size-Canis_latrans 0.5540 0.6349 -0.4317 0.4647
## Cogon_Patch_Size-Sciurus_niger -0.5893 0.8035 -2.4323 -0.4661
## Cogon_Patch_Size-Procyon_lotor -0.2852 0.4481 -1.1988 -0.2839
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0952 0.4340 -0.9929 -0.0831
## Cogon_Patch_Size-Lynx_rufus -0.3068 0.6470 -1.5410 -0.3165
## Cogon_Patch_Size-Didelphis_virginiana 0.4928 0.5036 -0.4033 0.4589
## Cogon_Patch_Size-Sylvilagus_floridanus -0.9207 0.8415 -2.9842 -0.7646
## Cogon_Patch_Size-Sciurus_carolinensis -0.6879 0.6815 -2.3182 -0.5881
## Cogon_Patch_Size-Vulpes_vulpes -0.5498 0.7962 -2.3315 -0.4486
## Cogon_Patch_Size-Sus_scrofa -0.4852 0.7733 -2.3383 -0.3975
## Avg_Cogongrass_Cover-Canis_latrans 0.3331 0.4252 -0.4617 0.3124
## Avg_Cogongrass_Cover-Sciurus_niger -0.1446 0.5936 -1.4213 -0.1070
## Avg_Cogongrass_Cover-Procyon_lotor 0.1834 0.4149 -0.6014 0.1755
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3612 0.3834 -0.3628 0.3377
## Avg_Cogongrass_Cover-Lynx_rufus 0.5072 0.4716 -0.3230 0.4638
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1798 0.4325 -0.6917 0.1894
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0763 0.5175 -1.1351 -0.0570
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4015 0.4111 -0.3362 0.3785
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3392 0.4689 -0.5346 0.3343
## Avg_Cogongrass_Cover-Sus_scrofa 0.0004 0.5637 -1.2652 0.0477
## total_shrub_cover-Canis_latrans -0.0591 0.5017 -0.9400 -0.0979
## total_shrub_cover-Sciurus_niger -0.7406 0.5812 -2.0446 -0.6838
## total_shrub_cover-Procyon_lotor -0.9114 0.4868 -2.0238 -0.8570
## total_shrub_cover-Dasypus_novemcinctus -0.3657 0.4808 -1.4482 -0.3444
## total_shrub_cover-Lynx_rufus -0.8836 0.6496 -2.4220 -0.8040
## total_shrub_cover-Didelphis_virginiana -0.6608 0.5098 -1.7892 -0.6177
## total_shrub_cover-Sylvilagus_floridanus -0.7832 0.5907 -2.1406 -0.7192
## total_shrub_cover-Sciurus_carolinensis -0.5599 0.5435 -1.7516 -0.5354
## total_shrub_cover-Vulpes_vulpes -0.6471 0.6906 -2.2243 -0.6010
## total_shrub_cover-Sus_scrofa -0.4634 0.6001 -1.7609 -0.4385
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4979 1.0024 304
## (Intercept)-Sciurus_niger 0.6019 1.0487 324
## (Intercept)-Procyon_lotor 1.5426 1.0071 401
## (Intercept)-Dasypus_novemcinctus 0.6088 1.0105 504
## (Intercept)-Lynx_rufus 1.0401 1.0037 551
## (Intercept)-Didelphis_virginiana 0.2552 1.0329 522
## (Intercept)-Sylvilagus_floridanus 1.0174 1.0283 505
## (Intercept)-Sciurus_carolinensis 0.1506 1.0100 466
## (Intercept)-Vulpes_vulpes 0.4493 1.0591 342
## (Intercept)-Sus_scrofa 0.1553 1.0344 354
## Cogon_Patch_Size-Canis_latrans 2.0875 1.0079 765
## Cogon_Patch_Size-Sciurus_niger 0.6865 1.0101 538
## Cogon_Patch_Size-Procyon_lotor 0.6136 1.0072 1385
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7403 1.0022 1400
## Cogon_Patch_Size-Lynx_rufus 1.0328 1.0068 770
## Cogon_Patch_Size-Didelphis_virginiana 1.5797 1.0025 881
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2754 1.0019 537
## Cogon_Patch_Size-Sciurus_carolinensis 0.3975 1.0111 714
## Cogon_Patch_Size-Vulpes_vulpes 0.7462 1.0106 551
## Cogon_Patch_Size-Sus_scrofa 0.7648 1.0002 838
## Avg_Cogongrass_Cover-Canis_latrans 1.2259 1.0013 1168
## Avg_Cogongrass_Cover-Sciurus_niger 0.9233 1.0113 561
## Avg_Cogongrass_Cover-Procyon_lotor 1.0358 1.0005 988
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1621 1.0033 1004
## Avg_Cogongrass_Cover-Lynx_rufus 1.5606 1.0042 1242
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9963 1.0173 1069
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8995 1.0071 535
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2862 1.0031 1389
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3290 1.0023 910
## Avg_Cogongrass_Cover-Sus_scrofa 1.0245 1.0207 594
## total_shrub_cover-Canis_latrans 1.0389 1.0004 758
## total_shrub_cover-Sciurus_niger 0.2737 1.0146 328
## total_shrub_cover-Procyon_lotor -0.1111 1.0234 473
## total_shrub_cover-Dasypus_novemcinctus 0.4978 1.0046 457
## total_shrub_cover-Lynx_rufus 0.1795 1.0138 311
## total_shrub_cover-Didelphis_virginiana 0.2446 1.0137 548
## total_shrub_cover-Sylvilagus_floridanus 0.2527 1.0078 288
## total_shrub_cover-Sciurus_carolinensis 0.4134 1.0041 467
## total_shrub_cover-Vulpes_vulpes 0.5584 1.0309 420
## total_shrub_cover-Sus_scrofa 0.6743 1.0209 452
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7396 0.1811 -3.1049 -2.7331 -2.4062 1.0025
## (Intercept)-Sciurus_niger -3.6884 0.5461 -4.7602 -3.6635 -2.6539 1.0539
## (Intercept)-Procyon_lotor -2.2976 0.1340 -2.5687 -2.2936 -2.0353 1.0064
## (Intercept)-Dasypus_novemcinctus -1.8061 0.1696 -2.1541 -1.7988 -1.4920 1.0000
## (Intercept)-Lynx_rufus -3.4396 0.3268 -4.1173 -3.4232 -2.8349 1.0213
## (Intercept)-Didelphis_virginiana -2.6566 0.3104 -3.3238 -2.6407 -2.0956 1.0214
## (Intercept)-Sylvilagus_floridanus -3.2205 0.2654 -3.7827 -3.2132 -2.7097 1.0026
## (Intercept)-Sciurus_carolinensis -2.7615 0.3313 -3.4635 -2.7490 -2.1437 1.0183
## (Intercept)-Vulpes_vulpes -3.8336 0.6161 -5.1341 -3.7911 -2.7590 1.0497
## (Intercept)-Sus_scrofa -3.4776 0.5401 -4.5443 -3.4604 -2.4612 1.0541
## shrub_cover-Canis_latrans -0.2279 0.2300 -0.6899 -0.2246 0.2165 1.0081
## shrub_cover-Sciurus_niger 0.0786 0.5094 -1.0033 0.0819 1.0676 1.0363
## shrub_cover-Procyon_lotor 0.3140 0.1562 0.0003 0.3178 0.6132 1.0038
## shrub_cover-Dasypus_novemcinctus 0.9977 0.3293 0.3763 0.9898 1.6400 1.0006
## shrub_cover-Lynx_rufus 0.1394 0.3799 -0.6781 0.1657 0.7995 1.0362
## shrub_cover-Didelphis_virginiana 1.1078 0.4043 0.3929 1.0749 1.9609 1.0365
## shrub_cover-Sylvilagus_floridanus 0.6735 0.4007 -0.1121 0.6816 1.4406 1.0113
## shrub_cover-Sciurus_carolinensis 1.0604 0.4087 0.2624 1.0534 1.8869 1.0200
## shrub_cover-Vulpes_vulpes 0.3872 0.6082 -0.8572 0.3973 1.5349 1.0198
## shrub_cover-Sus_scrofa 1.0185 0.7306 -0.2997 0.9725 2.5667 1.0404
## veg_height-Canis_latrans -0.5515 0.1833 -0.9179 -0.5456 -0.2125 1.0006
## veg_height-Sciurus_niger 0.1032 0.4351 -0.7150 0.0845 1.0230 1.0129
## veg_height-Procyon_lotor 0.3428 0.1212 0.1088 0.3433 0.5796 1.0007
## veg_height-Dasypus_novemcinctus 0.2622 0.1348 0.0117 0.2626 0.5320 1.0004
## veg_height-Lynx_rufus 0.0762 0.2241 -0.3686 0.0828 0.4944 1.0072
## veg_height-Didelphis_virginiana 0.4265 0.2434 -0.0185 0.4115 0.9360 1.0021
## veg_height-Sylvilagus_floridanus 0.0818 0.2350 -0.3733 0.0807 0.5385 1.0020
## veg_height-Sciurus_carolinensis 0.1138 0.2187 -0.3006 0.1093 0.5551 1.0014
## veg_height-Vulpes_vulpes -0.0918 0.3137 -0.7793 -0.0736 0.4696 1.0069
## veg_height-Sus_scrofa -0.1294 0.3178 -0.7659 -0.1254 0.4566 1.0128
## ESS
## (Intercept)-Canis_latrans 768
## (Intercept)-Sciurus_niger 243
## (Intercept)-Procyon_lotor 1558
## (Intercept)-Dasypus_novemcinctus 823
## (Intercept)-Lynx_rufus 389
## (Intercept)-Didelphis_virginiana 446
## (Intercept)-Sylvilagus_floridanus 515
## (Intercept)-Sciurus_carolinensis 396
## (Intercept)-Vulpes_vulpes 234
## (Intercept)-Sus_scrofa 243
## shrub_cover-Canis_latrans 746
## shrub_cover-Sciurus_niger 431
## shrub_cover-Procyon_lotor 1539
## shrub_cover-Dasypus_novemcinctus 767
## shrub_cover-Lynx_rufus 447
## shrub_cover-Didelphis_virginiana 393
## shrub_cover-Sylvilagus_floridanus 381
## shrub_cover-Sciurus_carolinensis 459
## shrub_cover-Vulpes_vulpes 362
## shrub_cover-Sus_scrofa 301
## veg_height-Canis_latrans 822
## veg_height-Sciurus_niger 535
## veg_height-Procyon_lotor 1638
## veg_height-Dasypus_novemcinctus 1803
## veg_height-Lynx_rufus 921
## veg_height-Didelphis_virginiana 1094
## veg_height-Sylvilagus_floridanus 715
## veg_height-Sciurus_carolinensis 719
## veg_height-Vulpes_vulpes 586
## veg_height-Sus_scrofa 974
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6482
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7624 0.4564 -1.6591 -0.7646 0.1247 1.0398 599
## Tree_Density -0.7472 0.4119 -1.6708 -0.7135 -0.0183 1.0491 468
## Avg_Canopy_Cover 1.0329 0.3597 0.3908 1.0137 1.7925 1.0007 766
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4692 1.4285 0.1257 1.0386 5.4457 1.0418 388
## Tree_Density 0.7225 1.0649 0.0454 0.3674 3.5331 1.0065 447
## Avg_Canopy_Cover 0.7419 0.8034 0.0746 0.4897 3.1025 1.0043 554
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5022 0.583 0.0446 0.3201 1.97 1.0438 167
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9210 0.3180 -3.5439 -2.9175 -2.2690 1.0068 1320
## shrub_cover 0.3431 0.2807 -0.2164 0.3482 0.8920 1.0186 1108
## veg_height 0.0791 0.1712 -0.2524 0.0806 0.4199 1.0055 1183
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8746 0.6856 0.2065 0.7045 2.6316 1.0331 640
## shrub_cover 0.5851 0.4851 0.1093 0.4539 1.7999 1.0281 615
## veg_height 0.2034 0.1579 0.0502 0.1625 0.6429 1.0206 1318
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0694 0.5921 -1.0805 0.0533 1.3032
## (Intercept)-Sciurus_niger -0.6618 0.8734 -2.2009 -0.7272 1.3089
## (Intercept)-Procyon_lotor 0.3901 0.6401 -0.9471 0.4019 1.6750
## (Intercept)-Dasypus_novemcinctus -0.9207 0.5403 -2.0011 -0.9047 0.0907
## (Intercept)-Lynx_rufus 0.1876 1.0158 -1.3158 0.0237 2.7409
## (Intercept)-Didelphis_virginiana -1.4297 0.6379 -2.7540 -1.3964 -0.2341
## (Intercept)-Sylvilagus_floridanus -0.7079 0.5955 -1.9026 -0.6925 0.4385
## (Intercept)-Sciurus_carolinensis -1.5053 0.6682 -2.8740 -1.4801 -0.2983
## (Intercept)-Vulpes_vulpes -1.4892 0.8606 -3.2301 -1.4666 0.1880
## (Intercept)-Sus_scrofa -1.9041 0.8664 -3.7337 -1.8526 -0.3934
## Tree_Density-Canis_latrans -0.9411 0.5659 -2.2540 -0.8706 -0.0205
## Tree_Density-Sciurus_niger -0.7546 0.7217 -2.3101 -0.6882 0.5208
## Tree_Density-Procyon_lotor -0.4707 0.3949 -1.2663 -0.4679 0.3052
## Tree_Density-Dasypus_novemcinctus -1.3226 0.8594 -3.4467 -1.1337 -0.1431
## Tree_Density-Lynx_rufus 0.1077 0.7055 -0.9818 -0.0030 1.8402
## Tree_Density-Didelphis_virginiana -0.9027 0.7174 -2.6585 -0.7967 0.2175
## Tree_Density-Sylvilagus_floridanus -1.0098 0.7006 -2.6516 -0.9099 0.1098
## Tree_Density-Sciurus_carolinensis -0.8605 0.6916 -2.5302 -0.7603 0.2250
## Tree_Density-Vulpes_vulpes -0.5962 0.8245 -2.2470 -0.5915 0.9744
## Tree_Density-Sus_scrofa -0.8702 0.7649 -2.6521 -0.7541 0.3177
## Avg_Canopy_Cover-Canis_latrans 0.0212 0.4707 -0.8958 0.0171 0.9563
## Avg_Canopy_Cover-Sciurus_niger 0.9029 0.7826 -0.5213 0.8853 2.5647
## Avg_Canopy_Cover-Procyon_lotor 0.9813 0.4537 0.1292 0.9600 1.9402
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0598 0.4436 0.2716 1.0244 1.9948
## Avg_Canopy_Cover-Lynx_rufus 0.7467 0.6745 -0.4919 0.7100 2.1714
## Avg_Canopy_Cover-Didelphis_virginiana 1.4183 0.5968 0.4803 1.3464 2.8196
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8243 0.7834 0.6248 1.6942 3.6286
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3684 0.5867 0.4341 1.3017 2.7744
## Avg_Canopy_Cover-Vulpes_vulpes 1.0365 0.6156 -0.0608 0.9963 2.4049
## Avg_Canopy_Cover-Sus_scrofa 1.2485 0.6104 0.2398 1.1875 2.7157
## Rhat ESS
## (Intercept)-Canis_latrans 1.0058 597
## (Intercept)-Sciurus_niger 1.0158 234
## (Intercept)-Procyon_lotor 1.0069 411
## (Intercept)-Dasypus_novemcinctus 1.0221 1368
## (Intercept)-Lynx_rufus 1.0231 247
## (Intercept)-Didelphis_virginiana 1.0608 674
## (Intercept)-Sylvilagus_floridanus 1.0002 950
## (Intercept)-Sciurus_carolinensis 1.0159 725
## (Intercept)-Vulpes_vulpes 1.0456 432
## (Intercept)-Sus_scrofa 1.0639 424
## Tree_Density-Canis_latrans 1.0275 939
## Tree_Density-Sciurus_niger 1.0191 694
## Tree_Density-Procyon_lotor 1.0079 1772
## Tree_Density-Dasypus_novemcinctus 1.0447 570
## Tree_Density-Lynx_rufus 1.0026 525
## Tree_Density-Didelphis_virginiana 1.0364 733
## Tree_Density-Sylvilagus_floridanus 1.0129 725
## Tree_Density-Sciurus_carolinensis 1.0287 761
## Tree_Density-Vulpes_vulpes 1.0242 555
## Tree_Density-Sus_scrofa 1.0128 757
## Avg_Canopy_Cover-Canis_latrans 1.0071 848
## Avg_Canopy_Cover-Sciurus_niger 1.0100 430
## Avg_Canopy_Cover-Procyon_lotor 1.0063 1824
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0117 1652
## Avg_Canopy_Cover-Lynx_rufus 1.0187 570
## Avg_Canopy_Cover-Didelphis_virginiana 1.0016 703
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0108 513
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0055 934
## Avg_Canopy_Cover-Vulpes_vulpes 1.0011 1002
## Avg_Canopy_Cover-Sus_scrofa 1.0116 788
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7450 0.1875 -3.1342 -2.7391 -2.3954 1.0514
## (Intercept)-Sciurus_niger -4.0105 0.5848 -5.1516 -4.0096 -2.8690 1.0388
## (Intercept)-Procyon_lotor -2.3021 0.1396 -2.5871 -2.2972 -2.0450 1.0089
## (Intercept)-Dasypus_novemcinctus -1.7648 0.1625 -2.0914 -1.7623 -1.4540 1.0038
## (Intercept)-Lynx_rufus -3.7078 0.3599 -4.4405 -3.6961 -3.0364 1.0331
## (Intercept)-Didelphis_virginiana -2.6305 0.2755 -3.1824 -2.6242 -2.1062 1.0127
## (Intercept)-Sylvilagus_floridanus -3.0990 0.2628 -3.6473 -3.0872 -2.6018 1.0037
## (Intercept)-Sciurus_carolinensis -2.7081 0.3116 -3.3387 -2.7017 -2.1223 1.0029
## (Intercept)-Vulpes_vulpes -3.8943 0.5962 -5.0530 -3.8666 -2.7776 1.0519
## (Intercept)-Sus_scrofa -3.3267 0.5543 -4.4500 -3.3182 -2.2918 1.0174
## shrub_cover-Canis_latrans -0.2858 0.2218 -0.7168 -0.2870 0.1583 1.0264
## shrub_cover-Sciurus_niger -0.2885 0.4779 -1.2299 -0.2779 0.6252 1.0367
## shrub_cover-Procyon_lotor 0.2643 0.1567 -0.0507 0.2634 0.5763 1.0085
## shrub_cover-Dasypus_novemcinctus 0.8771 0.3021 0.2950 0.8713 1.4840 1.0135
## shrub_cover-Lynx_rufus -0.2441 0.3475 -0.9212 -0.2406 0.4264 1.0060
## shrub_cover-Didelphis_virginiana 1.0107 0.3637 0.3154 1.0018 1.7562 1.0274
## shrub_cover-Sylvilagus_floridanus 0.4080 0.3865 -0.3249 0.4086 1.1996 1.0406
## shrub_cover-Sciurus_carolinensis 0.9179 0.4078 0.1265 0.9177 1.7105 1.0188
## shrub_cover-Vulpes_vulpes 0.0401 0.5667 -1.1281 0.0555 1.1165 1.0147
## shrub_cover-Sus_scrofa 0.7226 0.7435 -0.7961 0.7096 2.2688 1.0190
## veg_height-Canis_latrans -0.5701 0.1846 -0.9486 -0.5660 -0.2174 1.0092
## veg_height-Sciurus_niger 0.0357 0.3755 -0.6698 0.0277 0.8227 1.0034
## veg_height-Procyon_lotor 0.3504 0.1253 0.1113 0.3475 0.5969 1.0042
## veg_height-Dasypus_novemcinctus 0.2592 0.1375 -0.0083 0.2613 0.5232 1.0067
## veg_height-Lynx_rufus 0.0882 0.2420 -0.4099 0.0973 0.5502 1.0081
## veg_height-Didelphis_virginiana 0.4776 0.2344 0.0326 0.4783 0.9210 1.0112
## veg_height-Sylvilagus_floridanus 0.1672 0.2237 -0.2759 0.1650 0.6010 1.0163
## veg_height-Sciurus_carolinensis 0.1279 0.2134 -0.2781 0.1221 0.5590 1.0166
## veg_height-Vulpes_vulpes -0.0774 0.3046 -0.7043 -0.0661 0.5016 1.0000
## veg_height-Sus_scrofa -0.0920 0.3205 -0.7734 -0.0808 0.5016 1.0044
## ESS
## (Intercept)-Canis_latrans 656
## (Intercept)-Sciurus_niger 162
## (Intercept)-Procyon_lotor 1218
## (Intercept)-Dasypus_novemcinctus 1535
## (Intercept)-Lynx_rufus 263
## (Intercept)-Didelphis_virginiana 877
## (Intercept)-Sylvilagus_floridanus 697
## (Intercept)-Sciurus_carolinensis 674
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 461
## shrub_cover-Canis_latrans 773
## shrub_cover-Sciurus_niger 316
## shrub_cover-Procyon_lotor 1521
## shrub_cover-Dasypus_novemcinctus 1192
## shrub_cover-Lynx_rufus 419
## shrub_cover-Didelphis_virginiana 626
## shrub_cover-Sylvilagus_floridanus 607
## shrub_cover-Sciurus_carolinensis 571
## shrub_cover-Vulpes_vulpes 570
## shrub_cover-Sus_scrofa 457
## veg_height-Canis_latrans 617
## veg_height-Sciurus_niger 628
## veg_height-Procyon_lotor 1571
## veg_height-Dasypus_novemcinctus 1742
## veg_height-Lynx_rufus 540
## veg_height-Didelphis_virginiana 1430
## veg_height-Sylvilagus_floridanus 1256
## veg_height-Sciurus_carolinensis 703
## veg_height-Vulpes_vulpes 732
## veg_height-Sus_scrofa 1127
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.616
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4538 0.4016 -2.2654 -1.4540 -0.6657 1.0137 357
## Avg_Cogongrass_Cover -0.7688 0.3645 -1.4747 -0.7670 -0.0468 1.0105 428
## I(Avg_Cogongrass_Cover^2) 0.8231 0.3321 0.2370 0.8128 1.5545 1.0133 435
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6499 0.7179 0.0584 0.4570 2.4451 1.0293 891
## Avg_Cogongrass_Cover 0.3433 0.4529 0.0400 0.2112 1.3894 1.0691 666
## I(Avg_Cogongrass_Cover^2) 0.5202 0.7856 0.0388 0.2537 2.6536 1.0515 292
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5678 0.5154 0.0546 0.4132 1.9322 1.0353 210
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8613 0.2895 -3.4497 -2.8593 -2.2911 1.0013 782
## shrub_cover 0.3391 0.2738 -0.1903 0.3337 0.8875 1.0051 881
## veg_height 0.0883 0.1728 -0.2509 0.0863 0.4357 1.0015 1103
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7061 0.5427 0.1583 0.5618 2.1015 1.0009 488
## shrub_cover 0.5031 0.4591 0.0888 0.3702 1.7334 1.0338 580
## veg_height 0.2037 0.1630 0.0508 0.1576 0.6353 1.0161 1234
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9349 0.5881 -2.0725 -0.9369
## (Intercept)-Sciurus_niger -1.4262 0.6864 -2.6905 -1.4510
## (Intercept)-Procyon_lotor -0.7272 0.6207 -1.8952 -0.7366
## (Intercept)-Dasypus_novemcinctus -1.4238 0.5224 -2.4735 -1.4209
## (Intercept)-Lynx_rufus -1.3834 0.6187 -2.5962 -1.4014
## (Intercept)-Didelphis_virginiana -1.7151 0.5550 -2.8652 -1.6997
## (Intercept)-Sylvilagus_floridanus -1.2766 0.5588 -2.3867 -1.2656
## (Intercept)-Sciurus_carolinensis -1.9573 0.6177 -3.3170 -1.9295
## (Intercept)-Vulpes_vulpes -1.9721 0.7529 -3.5639 -1.9343
## (Intercept)-Sus_scrofa -1.9693 0.6928 -3.5037 -1.9148
## Avg_Cogongrass_Cover-Canis_latrans -0.5037 0.4914 -1.3942 -0.5278
## Avg_Cogongrass_Cover-Sciurus_niger -1.0589 0.6314 -2.4255 -1.0109
## Avg_Cogongrass_Cover-Procyon_lotor -0.7730 0.4754 -1.7105 -0.7621
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5802 0.4776 -1.5014 -0.5808
## Avg_Cogongrass_Cover-Lynx_rufus -0.7103 0.5341 -1.7781 -0.7002
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.5222 0.5061 -1.5101 -0.5369
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1782 0.5817 -2.4419 -1.1273
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7633 0.5065 -1.7625 -0.7630
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7602 0.5861 -1.9357 -0.7519
## Avg_Cogongrass_Cover-Sus_scrofa -0.9373 0.5836 -2.1680 -0.8980
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3994 0.8291 0.3525 1.1906
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2503 0.6311 -1.2160 0.3174
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2289 0.6822 0.3559 1.0927
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7618 0.3548 0.0723 0.7555
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2305 0.5428 0.3868 1.1483
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5946 0.4080 -0.1454 0.5828
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7668 0.4331 -0.0274 0.7433
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9362 0.3908 0.2187 0.9097
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8769 0.4812 0.0675 0.8204
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3557 0.6020 -1.0787 0.4232
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2288 1.0061 579
## (Intercept)-Sciurus_niger -0.0055 1.0065 462
## (Intercept)-Procyon_lotor 0.5281 1.0058 434
## (Intercept)-Dasypus_novemcinctus -0.4162 1.0052 811
## (Intercept)-Lynx_rufus -0.0730 1.0132 583
## (Intercept)-Didelphis_virginiana -0.6809 1.0077 856
## (Intercept)-Sylvilagus_floridanus -0.1736 1.0062 679
## (Intercept)-Sciurus_carolinensis -0.8678 1.0069 641
## (Intercept)-Vulpes_vulpes -0.6289 1.0107 372
## (Intercept)-Sus_scrofa -0.7316 1.0233 535
## Avg_Cogongrass_Cover-Canis_latrans 0.5139 1.0120 845
## Avg_Cogongrass_Cover-Sciurus_niger 0.0370 1.0033 485
## Avg_Cogongrass_Cover-Procyon_lotor 0.1411 1.0085 728
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4145 1.0030 882
## Avg_Cogongrass_Cover-Lynx_rufus 0.3237 1.0323 704
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5180 1.0044 789
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1946 1.0018 643
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2825 1.0014 767
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3808 1.0076 708
## Avg_Cogongrass_Cover-Sus_scrofa 0.1427 1.0035 630
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5571 1.0489 300
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3229 1.0449 365
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0343 1.0208 257
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4835 1.0055 731
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5701 1.0460 384
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.5031 1.0056 581
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7357 1.0100 735
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7735 1.0061 739
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0576 1.0451 493
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3870 1.0236 413
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7308 0.1805 -3.0821 -2.7234 -2.3951 1.0260
## (Intercept)-Sciurus_niger -3.6518 0.5875 -4.8798 -3.6135 -2.6228 1.0287
## (Intercept)-Procyon_lotor -2.3198 0.1457 -2.6327 -2.3137 -2.0512 1.0013
## (Intercept)-Dasypus_novemcinctus -1.7648 0.1579 -2.0835 -1.7599 -1.4703 1.0256
## (Intercept)-Lynx_rufus -3.4398 0.3295 -4.1447 -3.4282 -2.8373 1.0842
## (Intercept)-Didelphis_virginiana -2.6050 0.2924 -3.1977 -2.5957 -2.0586 1.0170
## (Intercept)-Sylvilagus_floridanus -3.1438 0.2688 -3.6955 -3.1306 -2.6343 1.0018
## (Intercept)-Sciurus_carolinensis -2.6641 0.3047 -3.2991 -2.6532 -2.1206 1.0442
## (Intercept)-Vulpes_vulpes -3.8052 0.6400 -5.1959 -3.7412 -2.7311 1.0206
## (Intercept)-Sus_scrofa -3.2504 0.5385 -4.3326 -3.2313 -2.2415 1.0265
## shrub_cover-Canis_latrans -0.2229 0.2195 -0.6596 -0.2233 0.1827 1.0164
## shrub_cover-Sciurus_niger -0.1381 0.4836 -1.1171 -0.1410 0.8187 1.0188
## shrub_cover-Procyon_lotor 0.2450 0.1621 -0.0761 0.2464 0.5568 1.0074
## shrub_cover-Dasypus_novemcinctus 0.8410 0.2905 0.3107 0.8286 1.4516 1.0392
## shrub_cover-Lynx_rufus -0.1070 0.3704 -0.8860 -0.1053 0.6016 1.0651
## shrub_cover-Didelphis_virginiana 0.9789 0.3849 0.3071 0.9557 1.8241 1.0596
## shrub_cover-Sylvilagus_floridanus 0.3350 0.3906 -0.3976 0.3179 1.1230 1.0017
## shrub_cover-Sciurus_carolinensis 0.8352 0.4094 0.0753 0.8255 1.6722 1.0298
## shrub_cover-Vulpes_vulpes 0.0361 0.5858 -1.1887 0.0587 1.1529 1.0239
## shrub_cover-Sus_scrofa 0.5974 0.7252 -0.8379 0.5690 2.0610 1.0301
## veg_height-Canis_latrans -0.5436 0.1879 -0.9196 -0.5381 -0.1928 1.0127
## veg_height-Sciurus_niger 0.1754 0.4291 -0.6009 0.1499 1.0891 1.0007
## veg_height-Procyon_lotor 0.3518 0.1236 0.1191 0.3508 0.5986 1.0014
## veg_height-Dasypus_novemcinctus 0.2546 0.1331 -0.0085 0.2521 0.5271 1.0024
## veg_height-Lynx_rufus 0.1038 0.2296 -0.3741 0.1094 0.5462 1.0055
## veg_height-Didelphis_virginiana 0.4149 0.2541 -0.0762 0.4123 0.9449 1.0151
## veg_height-Sylvilagus_floridanus 0.1543 0.2415 -0.3255 0.1537 0.6356 1.0033
## veg_height-Sciurus_carolinensis 0.1026 0.2116 -0.2986 0.0995 0.5364 1.0037
## veg_height-Vulpes_vulpes -0.0746 0.2992 -0.6954 -0.0621 0.4953 1.0015
## veg_height-Sus_scrofa -0.0679 0.3257 -0.7607 -0.0589 0.5455 1.0108
## ESS
## (Intercept)-Canis_latrans 921
## (Intercept)-Sciurus_niger 197
## (Intercept)-Procyon_lotor 1408
## (Intercept)-Dasypus_novemcinctus 1526
## (Intercept)-Lynx_rufus 398
## (Intercept)-Didelphis_virginiana 680
## (Intercept)-Sylvilagus_floridanus 636
## (Intercept)-Sciurus_carolinensis 828
## (Intercept)-Vulpes_vulpes 189
## (Intercept)-Sus_scrofa 498
## shrub_cover-Canis_latrans 908
## shrub_cover-Sciurus_niger 342
## shrub_cover-Procyon_lotor 1439
## shrub_cover-Dasypus_novemcinctus 1228
## shrub_cover-Lynx_rufus 466
## shrub_cover-Didelphis_virginiana 528
## shrub_cover-Sylvilagus_floridanus 602
## shrub_cover-Sciurus_carolinensis 647
## shrub_cover-Vulpes_vulpes 507
## shrub_cover-Sus_scrofa 604
## veg_height-Canis_latrans 826
## veg_height-Sciurus_niger 620
## veg_height-Procyon_lotor 1426
## veg_height-Dasypus_novemcinctus 1944
## veg_height-Lynx_rufus 936
## veg_height-Didelphis_virginiana 848
## veg_height-Sylvilagus_floridanus 684
## veg_height-Sciurus_carolinensis 1111
## veg_height-Vulpes_vulpes 644
## veg_height-Sus_scrofa 1096
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6748
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0364 0.7668 -3.5262 -2.0498 -0.4911 1.0401 452
## Cogon_Patch_Size -0.2625 0.6781 -1.7664 -0.2306 0.9666 1.0045 308
## Veg_shannon_index 0.9025 0.4876 0.0131 0.8760 1.9136 1.1407 257
## total_shrub_cover -0.7702 0.5402 -1.9514 -0.7402 0.2081 1.0582 250
## Avg_Cogongrass_Cover 0.1202 0.9727 -1.6384 0.0329 2.1385 1.0363 153
## Tree_Density -2.2410 0.7924 -3.8475 -2.2084 -0.6567 1.0351 214
## Avg_Canopy_Cover 1.9223 0.6727 0.7201 1.8758 3.3809 1.0804 189
## I(Avg_Cogongrass_Cover^2) 1.4346 0.6121 0.2706 1.4217 2.7701 1.0487 152
## avg_veg_height -0.2318 0.5307 -1.3166 -0.2319 0.7720 1.0296 152
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3362 4.1340 0.1235 2.0571 14.2747 1.1319 196
## Cogon_Patch_Size 2.4654 3.3823 0.1038 1.4360 11.0557 1.0267 344
## Veg_shannon_index 0.6958 0.8743 0.0497 0.3835 3.2377 1.0128 469
## total_shrub_cover 0.9439 1.3519 0.0613 0.5157 4.4437 1.0417 374
## Avg_Cogongrass_Cover 1.3959 2.3697 0.0527 0.5573 7.7301 1.0985 260
## Tree_Density 2.3086 3.7757 0.0598 1.0690 12.0100 1.1212 139
## Avg_Canopy_Cover 2.6238 3.7166 0.1379 1.5612 11.2230 1.1198 255
## I(Avg_Cogongrass_Cover^2) 1.5002 2.7100 0.0575 0.6979 7.6548 1.3915 169
## avg_veg_height 0.6105 0.9049 0.0444 0.3000 3.3544 1.0066 166
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2678 1.8333 0.0613 0.6001 6.3393 1.4729 71
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9943 0.3379 -3.6458 -2.9999 -2.3013 1.0037 1168
## shrub_cover 0.4703 0.2903 -0.0901 0.4649 1.0671 1.0386 458
## veg_height 0.0935 0.1778 -0.2685 0.0916 0.4437 1.0086 681
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0230 0.7628 0.2736 0.8200 2.9409 1.0051 764
## shrub_cover 0.5834 0.4690 0.1135 0.4573 1.8348 1.0461 465
## veg_height 0.2084 0.1629 0.0506 0.1625 0.6482 1.0375 1013
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3359 1.0427 -3.3043 -1.3893
## (Intercept)-Sciurus_niger -1.2354 1.5168 -3.8615 -1.4150
## (Intercept)-Procyon_lotor -0.9021 1.0565 -2.9815 -0.9009
## (Intercept)-Dasypus_novemcinctus -2.3278 0.9202 -4.3019 -2.2586
## (Intercept)-Lynx_rufus -1.0213 1.5821 -3.4792 -1.2908
## (Intercept)-Didelphis_virginiana -3.1799 1.2188 -6.0016 -3.0629
## (Intercept)-Sylvilagus_floridanus -2.2093 1.0857 -4.4774 -2.1705
## (Intercept)-Sciurus_carolinensis -3.4982 1.2177 -6.1361 -3.3999
## (Intercept)-Vulpes_vulpes -3.3056 1.5327 -6.5264 -3.1974
## (Intercept)-Sus_scrofa -3.7025 1.5393 -7.0686 -3.5114
## Cogon_Patch_Size-Canis_latrans 1.2165 1.1630 -0.5133 1.0431
## Cogon_Patch_Size-Sciurus_niger -1.0452 1.5952 -4.7542 -0.8304
## Cogon_Patch_Size-Procyon_lotor -0.6001 0.7958 -2.2878 -0.5633
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1107 0.8138 -1.7945 -0.1094
## Cogon_Patch_Size-Lynx_rufus -0.3939 1.2816 -2.9307 -0.3959
## Cogon_Patch_Size-Didelphis_virginiana 1.2424 0.9560 -0.3696 1.1565
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2743 1.4197 -4.7199 -1.0563
## Cogon_Patch_Size-Sciurus_carolinensis -0.9794 1.2142 -3.9334 -0.8075
## Cogon_Patch_Size-Vulpes_vulpes -0.4956 1.4490 -3.6173 -0.4262
## Cogon_Patch_Size-Sus_scrofa -0.6017 1.2206 -3.5273 -0.4961
## Veg_shannon_index-Canis_latrans 1.2430 0.7164 0.0641 1.1500
## Veg_shannon_index-Sciurus_niger 1.0462 0.9470 -0.6436 0.9616
## Veg_shannon_index-Procyon_lotor 1.1107 0.6359 0.0442 1.0462
## Veg_shannon_index-Dasypus_novemcinctus 0.5826 0.5794 -0.5738 0.5955
## Veg_shannon_index-Lynx_rufus 0.9641 0.8481 -0.6171 0.9229
## Veg_shannon_index-Didelphis_virginiana 1.0407 0.7092 -0.2493 0.9929
## Veg_shannon_index-Sylvilagus_floridanus 0.9700 0.6951 -0.3141 0.9278
## Veg_shannon_index-Sciurus_carolinensis 0.3421 0.7515 -1.2698 0.3829
## Veg_shannon_index-Vulpes_vulpes 0.6055 0.8700 -1.3010 0.6605
## Veg_shannon_index-Sus_scrofa 1.3109 0.8815 -0.0585 1.1785
## total_shrub_cover-Canis_latrans -0.0279 0.6906 -1.2633 -0.0792
## total_shrub_cover-Sciurus_niger -1.1317 1.0223 -3.4506 -1.0238
## total_shrub_cover-Procyon_lotor -1.2251 0.6655 -2.7562 -1.1710
## total_shrub_cover-Dasypus_novemcinctus -0.3957 0.6637 -1.7973 -0.3669
## total_shrub_cover-Lynx_rufus -1.1154 1.0091 -3.4291 -0.9820
## total_shrub_cover-Didelphis_virginiana -1.0160 0.8767 -3.1316 -0.9090
## total_shrub_cover-Sylvilagus_floridanus -0.8181 0.8722 -2.7105 -0.7570
## total_shrub_cover-Sciurus_carolinensis -0.7280 0.8507 -2.6343 -0.6588
## total_shrub_cover-Vulpes_vulpes -0.9611 1.0601 -3.3774 -0.8522
## total_shrub_cover-Sus_scrofa -0.6678 0.9490 -2.6823 -0.6259
## Avg_Cogongrass_Cover-Canis_latrans 0.2571 1.2098 -1.9919 0.1872
## Avg_Cogongrass_Cover-Sciurus_niger -0.5325 1.5464 -4.0850 -0.4204
## Avg_Cogongrass_Cover-Procyon_lotor 0.1807 1.1941 -2.0246 0.1002
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7544 1.3263 -1.4605 0.6257
## Avg_Cogongrass_Cover-Lynx_rufus 0.1888 1.3530 -2.3010 0.1318
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2223 1.2789 -2.1593 0.1720
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5142 1.3444 -3.5278 -0.4553
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2120 1.2573 -2.0528 0.1174
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4531 1.4322 -1.9988 0.2999
## Avg_Cogongrass_Cover-Sus_scrofa -0.0173 1.3348 -2.7020 -0.0521
## Tree_Density-Canis_latrans -3.0043 1.3120 -5.9926 -2.8340
## Tree_Density-Sciurus_niger -2.1320 1.3879 -4.8671 -2.1212
## Tree_Density-Procyon_lotor -2.2451 0.9535 -4.2575 -2.1831
## Tree_Density-Dasypus_novemcinctus -3.6654 1.6441 -8.0320 -3.3260
## Tree_Density-Lynx_rufus -1.1660 1.4793 -3.6846 -1.3373
## Tree_Density-Didelphis_virginiana -2.3150 1.1285 -4.7320 -2.2467
## Tree_Density-Sylvilagus_floridanus -2.6116 1.3079 -5.5643 -2.5068
## Tree_Density-Sciurus_carolinensis -2.4365 1.2727 -5.0971 -2.4038
## Tree_Density-Vulpes_vulpes -2.2007 1.4165 -4.9993 -2.2010
## Tree_Density-Sus_scrofa -2.3507 1.3195 -5.2068 -2.2882
## Avg_Canopy_Cover-Canis_latrans 0.2816 0.6902 -1.0017 0.2475
## Avg_Canopy_Cover-Sciurus_niger 2.0273 1.6862 -0.7581 1.8667
## Avg_Canopy_Cover-Procyon_lotor 1.6192 0.7702 0.2309 1.5768
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1181 0.8358 0.7708 2.0117
## Avg_Canopy_Cover-Lynx_rufus 1.4883 1.3046 -1.0319 1.4765
## Avg_Canopy_Cover-Didelphis_virginiana 2.8978 1.2456 1.1253 2.6648
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5327 1.6585 1.1285 3.2580
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7087 1.2278 0.9580 2.4602
## Avg_Canopy_Cover-Vulpes_vulpes 2.3776 1.3117 0.4183 2.1515
## Avg_Canopy_Cover-Sus_scrofa 2.0541 1.0353 0.4461 1.9229
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1326 1.0518 0.6149 1.9333
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.7894 1.3077 -1.9852 0.8813
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1587 1.0979 0.6481 1.9692
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3927 0.7225 0.0653 1.3486
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3905 1.1735 0.7476 2.1483
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.9585 0.7317 -0.4753 0.9616
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1927 0.8621 -0.3251 1.1472
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5107 0.7485 0.1516 1.4451
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7604 0.9096 0.2855 1.6463
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.7606 1.3044 -2.4021 0.8793
## avg_veg_height-Canis_latrans -0.3331 0.6489 -1.6939 -0.3130
## avg_veg_height-Sciurus_niger -0.5722 0.9547 -2.8070 -0.4605
## avg_veg_height-Procyon_lotor -0.0430 0.6648 -1.3781 -0.0298
## avg_veg_height-Dasypus_novemcinctus 0.1573 0.6833 -1.0830 0.1308
## avg_veg_height-Lynx_rufus -0.5435 0.9195 -2.6561 -0.4642
## avg_veg_height-Didelphis_virginiana -0.4018 0.7293 -1.9443 -0.3730
## avg_veg_height-Sylvilagus_floridanus -0.3439 0.7493 -1.9414 -0.3239
## avg_veg_height-Sciurus_carolinensis 0.1595 0.7713 -1.2069 0.1045
## avg_veg_height-Vulpes_vulpes -0.2614 0.8324 -1.9704 -0.2440
## avg_veg_height-Sus_scrofa -0.2096 0.7596 -1.7605 -0.1926
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8056 1.0073 424
## (Intercept)-Sciurus_niger 2.2560 1.0072 233
## (Intercept)-Procyon_lotor 1.1866 1.0052 242
## (Intercept)-Dasypus_novemcinctus -0.6985 1.1422 452
## (Intercept)-Lynx_rufus 2.7600 1.0093 188
## (Intercept)-Didelphis_virginiana -1.2083 1.1331 346
## (Intercept)-Sylvilagus_floridanus -0.1249 1.0560 391
## (Intercept)-Sciurus_carolinensis -1.4395 1.1403 285
## (Intercept)-Vulpes_vulpes -0.5719 1.1202 203
## (Intercept)-Sus_scrofa -1.2735 1.1192 208
## Cogon_Patch_Size-Canis_latrans 4.0399 1.0215 591
## Cogon_Patch_Size-Sciurus_niger 1.4818 1.0161 295
## Cogon_Patch_Size-Procyon_lotor 0.9101 1.0607 190
## Cogon_Patch_Size-Dasypus_novemcinctus 1.5688 1.0190 439
## Cogon_Patch_Size-Lynx_rufus 2.2724 1.0043 305
## Cogon_Patch_Size-Didelphis_virginiana 3.3758 1.0865 357
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8448 1.0081 277
## Cogon_Patch_Size-Sciurus_carolinensis 0.9127 1.0365 238
## Cogon_Patch_Size-Vulpes_vulpes 2.2912 1.0217 307
## Cogon_Patch_Size-Sus_scrofa 1.5416 1.0164 368
## Veg_shannon_index-Canis_latrans 2.9176 1.0419 379
## Veg_shannon_index-Sciurus_niger 3.1737 1.1099 311
## Veg_shannon_index-Procyon_lotor 2.5556 1.0934 202
## Veg_shannon_index-Dasypus_novemcinctus 1.7138 1.0364 573
## Veg_shannon_index-Lynx_rufus 2.8390 1.0443 384
## Veg_shannon_index-Didelphis_virginiana 2.5394 1.0506 576
## Veg_shannon_index-Sylvilagus_floridanus 2.4964 1.0781 391
## Veg_shannon_index-Sciurus_carolinensis 1.7303 1.0407 558
## Veg_shannon_index-Vulpes_vulpes 2.1719 1.0495 433
## Veg_shannon_index-Sus_scrofa 3.5150 1.0514 397
## total_shrub_cover-Canis_latrans 1.5606 1.0040 466
## total_shrub_cover-Sciurus_niger 0.5759 1.0492 269
## total_shrub_cover-Procyon_lotor -0.0847 1.0414 330
## total_shrub_cover-Dasypus_novemcinctus 0.8791 1.0062 630
## total_shrub_cover-Lynx_rufus 0.5431 1.0356 297
## total_shrub_cover-Didelphis_virginiana 0.3949 1.0295 278
## total_shrub_cover-Sylvilagus_floridanus 0.6994 1.0068 385
## total_shrub_cover-Sciurus_carolinensis 0.7665 1.0216 340
## total_shrub_cover-Vulpes_vulpes 0.8063 1.0256 215
## total_shrub_cover-Sus_scrofa 1.0361 1.0403 283
## Avg_Cogongrass_Cover-Canis_latrans 2.8067 1.0121 189
## Avg_Cogongrass_Cover-Sciurus_niger 2.1204 1.0713 220
## Avg_Cogongrass_Cover-Procyon_lotor 2.6426 1.0351 138
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.8252 1.0113 160
## Avg_Cogongrass_Cover-Lynx_rufus 3.0822 1.0178 182
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8466 1.0175 222
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9642 1.0872 203
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8657 1.0185 182
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.6917 1.0182 226
## Avg_Cogongrass_Cover-Sus_scrofa 2.6991 1.0183 241
## Tree_Density-Canis_latrans -0.9201 1.0516 275
## Tree_Density-Sciurus_niger 0.7997 1.0374 369
## Tree_Density-Procyon_lotor -0.4401 1.0108 271
## Tree_Density-Dasypus_novemcinctus -1.4541 1.1426 147
## Tree_Density-Lynx_rufus 2.2244 1.0339 135
## Tree_Density-Didelphis_virginiana -0.1711 1.0338 325
## Tree_Density-Sylvilagus_floridanus -0.2393 1.0262 455
## Tree_Density-Sciurus_carolinensis 0.0665 1.0160 365
## Tree_Density-Vulpes_vulpes 0.9069 1.0164 290
## Tree_Density-Sus_scrofa 0.1518 1.0320 427
## Avg_Canopy_Cover-Canis_latrans 1.6965 1.0042 593
## Avg_Canopy_Cover-Sciurus_niger 5.7713 1.2234 178
## Avg_Canopy_Cover-Procyon_lotor 3.2977 1.0396 360
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.1187 1.0705 263
## Avg_Canopy_Cover-Lynx_rufus 4.1861 1.0235 273
## Avg_Canopy_Cover-Didelphis_virginiana 5.9426 1.0625 212
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.5542 1.0723 218
## Avg_Canopy_Cover-Sciurus_carolinensis 5.8401 1.1064 181
## Avg_Canopy_Cover-Vulpes_vulpes 5.5598 1.1027 218
## Avg_Canopy_Cover-Sus_scrofa 4.4627 1.0753 471
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7706 1.0513 268
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.1637 1.1067 77
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6070 1.1607 222
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0297 1.0410 434
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2586 1.0849 173
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4586 1.0825 146
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.0305 1.0125 279
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.1145 1.0730 425
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.8403 1.0357 331
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.0118 1.1248 141
## avg_veg_height-Canis_latrans 0.8692 1.0284 214
## avg_veg_height-Sciurus_niger 1.0053 1.0180 226
## avg_veg_height-Procyon_lotor 1.2534 1.0221 234
## avg_veg_height-Dasypus_novemcinctus 1.5427 1.0123 316
## avg_veg_height-Lynx_rufus 1.0111 1.0290 223
## avg_veg_height-Didelphis_virginiana 0.9398 1.0188 272
## avg_veg_height-Sylvilagus_floridanus 1.0090 1.0117 352
## avg_veg_height-Sciurus_carolinensis 1.8339 1.0074 271
## avg_veg_height-Vulpes_vulpes 1.3057 1.0024 283
## avg_veg_height-Sus_scrofa 1.1905 1.0100 212
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7048 0.1864 -3.1003 -2.6939 -2.3601 1.0009
## (Intercept)-Sciurus_niger -4.2278 0.5647 -5.2628 -4.2552 -2.9862 1.1049
## (Intercept)-Procyon_lotor -2.3076 0.1450 -2.5954 -2.3049 -2.0344 1.0030
## (Intercept)-Dasypus_novemcinctus -1.7946 0.1648 -2.1300 -1.7873 -1.4773 1.0006
## (Intercept)-Lynx_rufus -3.7453 0.3395 -4.3981 -3.7446 -3.0570 1.0021
## (Intercept)-Didelphis_virginiana -2.6442 0.2953 -3.2498 -2.6327 -2.1057 1.0376
## (Intercept)-Sylvilagus_floridanus -3.1852 0.2526 -3.7050 -3.1767 -2.7029 1.0247
## (Intercept)-Sciurus_carolinensis -2.7710 0.3087 -3.4069 -2.7604 -2.1822 1.0250
## (Intercept)-Vulpes_vulpes -4.1475 0.5842 -5.3093 -4.1351 -3.0335 1.0146
## (Intercept)-Sus_scrofa -3.4784 0.5379 -4.5834 -3.4671 -2.4280 1.0597
## shrub_cover-Canis_latrans -0.2558 0.2320 -0.7056 -0.2574 0.2029 1.0096
## shrub_cover-Sciurus_niger -0.1210 0.4898 -1.0420 -0.1309 0.8753 1.0273
## shrub_cover-Procyon_lotor 0.2829 0.1583 -0.0310 0.2875 0.5912 1.0216
## shrub_cover-Dasypus_novemcinctus 0.9742 0.3081 0.3849 0.9704 1.5775 1.0049
## shrub_cover-Lynx_rufus -0.0791 0.3785 -0.8204 -0.0845 0.6708 1.0205
## shrub_cover-Didelphis_virginiana 1.0559 0.3910 0.3773 1.0298 1.8936 1.0427
## shrub_cover-Sylvilagus_floridanus 0.5545 0.4006 -0.1927 0.5505 1.3594 1.0329
## shrub_cover-Sciurus_carolinensis 1.0261 0.3979 0.2608 1.0282 1.8081 1.0140
## shrub_cover-Vulpes_vulpes 0.3095 0.5535 -0.8150 0.3093 1.3784 1.0157
## shrub_cover-Sus_scrofa 0.9948 0.7349 -0.4422 0.9577 2.5421 1.0427
## veg_height-Canis_latrans -0.5147 0.1817 -0.8914 -0.5084 -0.1787 1.0264
## veg_height-Sciurus_niger 0.1489 0.3917 -0.5574 0.1213 1.0198 1.0572
## veg_height-Procyon_lotor 0.3629 0.1263 0.1282 0.3618 0.6099 1.0027
## veg_height-Dasypus_novemcinctus 0.2713 0.1340 0.0082 0.2695 0.5377 1.0038
## veg_height-Lynx_rufus 0.1709 0.2358 -0.2981 0.1755 0.6250 1.0187
## veg_height-Didelphis_virginiana 0.4713 0.2437 0.0212 0.4664 0.9629 1.0367
## veg_height-Sylvilagus_floridanus 0.1566 0.2492 -0.3121 0.1505 0.6530 1.0120
## veg_height-Sciurus_carolinensis 0.1437 0.2212 -0.2734 0.1387 0.6014 1.0061
## veg_height-Vulpes_vulpes -0.1468 0.3242 -0.8881 -0.1262 0.4312 1.0179
## veg_height-Sus_scrofa -0.1361 0.3226 -0.8165 -0.1272 0.4694 1.0276
## ESS
## (Intercept)-Canis_latrans 682
## (Intercept)-Sciurus_niger 160
## (Intercept)-Procyon_lotor 1011
## (Intercept)-Dasypus_novemcinctus 994
## (Intercept)-Lynx_rufus 192
## (Intercept)-Didelphis_virginiana 342
## (Intercept)-Sylvilagus_floridanus 513
## (Intercept)-Sciurus_carolinensis 595
## (Intercept)-Vulpes_vulpes 185
## (Intercept)-Sus_scrofa 345
## shrub_cover-Canis_latrans 625
## shrub_cover-Sciurus_niger 331
## shrub_cover-Procyon_lotor 1457
## shrub_cover-Dasypus_novemcinctus 655
## shrub_cover-Lynx_rufus 413
## shrub_cover-Didelphis_virginiana 351
## shrub_cover-Sylvilagus_floridanus 410
## shrub_cover-Sciurus_carolinensis 485
## shrub_cover-Vulpes_vulpes 450
## shrub_cover-Sus_scrofa 283
## veg_height-Canis_latrans 615
## veg_height-Sciurus_niger 225
## veg_height-Procyon_lotor 1346
## veg_height-Dasypus_novemcinctus 1562
## veg_height-Lynx_rufus 515
## veg_height-Didelphis_virginiana 581
## veg_height-Sylvilagus_floridanus 657
## veg_height-Sciurus_carolinensis 1052
## veg_height-Vulpes_vulpes 581
## veg_height-Sus_scrofa 548
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7088
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6786 0.3386 -1.3497 -0.6791 -0.0102 1.0006 1440
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9307 0.7789 0.1852 0.7313 2.9025 1.0052 1540
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6226 0.2907 -3.2159 -2.6288 -2.0556 1.0004 1703
## week 0.1948 0.1899 -0.1828 0.1953 0.5630 1.0013 1129
## I(week^2) -0.2089 0.1030 -0.4380 -0.2025 -0.0224 1.0416 738
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7501 0.5906 0.1858 0.5877 2.3117 1.0052 807
## week 0.2016 0.2258 0.0360 0.1421 0.7025 1.0462 809
## I(week^2) 0.0665 0.0597 0.0178 0.0493 0.2258 1.0424 937
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1334 0.3753 -0.5737 0.1197 0.8948 1.0091
## (Intercept)-Sciurus_niger -0.9761 0.5618 -2.0235 -1.0012 0.1705 1.0258
## (Intercept)-Procyon_lotor 0.4813 0.3866 -0.2386 0.4738 1.2816 1.0003
## (Intercept)-Dasypus_novemcinctus -0.6691 0.3564 -1.3915 -0.6694 0.0253 0.9998
## (Intercept)-Lynx_rufus -0.0970 0.5468 -1.0314 -0.1545 1.1439 1.0148
## (Intercept)-Didelphis_virginiana -1.2697 0.4160 -2.1146 -1.2637 -0.4793 1.0021
## (Intercept)-Sylvilagus_floridanus -0.4787 0.4279 -1.3152 -0.4929 0.3894 1.0021
## (Intercept)-Sciurus_carolinensis -1.2490 0.4201 -2.1154 -1.2271 -0.4768 1.0071
## (Intercept)-Vulpes_vulpes -1.2774 0.6759 -2.5311 -1.2795 0.1480 1.0196
## (Intercept)-Sus_scrofa -1.6242 0.5463 -2.7241 -1.6090 -0.5899 1.0060
## ESS
## (Intercept)-Canis_latrans 2319
## (Intercept)-Sciurus_niger 579
## (Intercept)-Procyon_lotor 2159
## (Intercept)-Dasypus_novemcinctus 2675
## (Intercept)-Lynx_rufus 760
## (Intercept)-Didelphis_virginiana 2031
## (Intercept)-Sylvilagus_floridanus 1294
## (Intercept)-Sciurus_carolinensis 2174
## (Intercept)-Vulpes_vulpes 410
## (Intercept)-Sus_scrofa 887
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4524 0.1802 -2.8133 -2.4503 -2.1022 1.0036
## (Intercept)-Sciurus_niger -3.5034 0.4874 -4.5337 -3.4806 -2.6172 1.0486
## (Intercept)-Procyon_lotor -2.1733 0.1447 -2.4701 -2.1644 -1.9030 1.0019
## (Intercept)-Dasypus_novemcinctus -1.4945 0.1553 -1.7987 -1.4930 -1.1932 1.0013
## (Intercept)-Lynx_rufus -3.2959 0.3231 -3.9759 -3.2753 -2.7000 1.0147
## (Intercept)-Didelphis_virginiana -2.1860 0.2663 -2.7329 -2.1694 -1.6876 1.0113
## (Intercept)-Sylvilagus_floridanus -3.0128 0.2935 -3.6092 -3.0005 -2.4788 1.0016
## (Intercept)-Sciurus_carolinensis -2.3478 0.2749 -2.9062 -2.3394 -1.8363 1.0020
## (Intercept)-Vulpes_vulpes -3.5429 0.5964 -4.8493 -3.5011 -2.4964 1.0218
## (Intercept)-Sus_scrofa -2.8832 0.4881 -3.9890 -2.8449 -2.0344 1.0097
## week-Canis_latrans 0.4527 0.2416 0.0107 0.4435 0.9426 1.0126
## week-Sciurus_niger -0.1868 0.4111 -1.1441 -0.1393 0.5002 1.0315
## week-Procyon_lotor 0.1756 0.1909 -0.1930 0.1759 0.5509 1.0017
## week-Dasypus_novemcinctus 0.0691 0.2085 -0.3430 0.0714 0.4795 1.0020
## week-Lynx_rufus 0.2651 0.2975 -0.3269 0.2649 0.8451 1.0019
## week-Didelphis_virginiana 0.0327 0.3078 -0.6012 0.0462 0.6223 1.0033
## week-Sylvilagus_floridanus 0.0527 0.2936 -0.5605 0.0608 0.5985 1.0040
## week-Sciurus_carolinensis 0.5428 0.3175 -0.0293 0.5233 1.2169 1.0048
## week-Vulpes_vulpes 0.1338 0.3864 -0.6896 0.1433 0.8702 1.0063
## week-Sus_scrofa 0.4187 0.3635 -0.2534 0.3945 1.2100 1.0186
## I(week^2)-Canis_latrans -0.1951 0.1032 -0.4046 -0.1918 0.0020 1.0119
## I(week^2)-Sciurus_niger -0.2387 0.2138 -0.7308 -0.2185 0.1083 1.0601
## I(week^2)-Procyon_lotor -0.1153 0.0837 -0.2786 -0.1153 0.0458 1.0006
## I(week^2)-Dasypus_novemcinctus -0.1486 0.0987 -0.3473 -0.1449 0.0391 1.0007
## I(week^2)-Lynx_rufus -0.1953 0.1427 -0.4961 -0.1869 0.0576 1.0112
## I(week^2)-Didelphis_virginiana -0.3442 0.2021 -0.8360 -0.3231 -0.0237 1.0191
## I(week^2)-Sylvilagus_floridanus -0.1517 0.1432 -0.4591 -0.1483 0.1131 1.0005
## I(week^2)-Sciurus_carolinensis -0.1891 0.1335 -0.4693 -0.1853 0.0586 1.0086
## I(week^2)-Vulpes_vulpes -0.3422 0.2451 -0.9155 -0.3115 0.0450 1.0293
## I(week^2)-Sus_scrofa -0.1612 0.1630 -0.5081 -0.1532 0.1339 1.0174
## ESS
## (Intercept)-Canis_latrans 1205
## (Intercept)-Sciurus_niger 323
## (Intercept)-Procyon_lotor 1620
## (Intercept)-Dasypus_novemcinctus 2249
## (Intercept)-Lynx_rufus 484
## (Intercept)-Didelphis_virginiana 1633
## (Intercept)-Sylvilagus_floridanus 743
## (Intercept)-Sciurus_carolinensis 1297
## (Intercept)-Vulpes_vulpes 290
## (Intercept)-Sus_scrofa 485
## week-Canis_latrans 1282
## week-Sciurus_niger 724
## week-Procyon_lotor 1881
## week-Dasypus_novemcinctus 1919
## week-Lynx_rufus 985
## week-Didelphis_virginiana 1495
## week-Sylvilagus_floridanus 974
## week-Sciurus_carolinensis 1053
## week-Vulpes_vulpes 1103
## week-Sus_scrofa 1336
## I(week^2)-Canis_latrans 1290
## I(week^2)-Sciurus_niger 419
## I(week^2)-Procyon_lotor 1741
## I(week^2)-Dasypus_novemcinctus 1764
## I(week^2)-Lynx_rufus 776
## I(week^2)-Didelphis_virginiana 471
## I(week^2)-Sylvilagus_floridanus 1015
## I(week^2)-Sciurus_carolinensis 1064
## I(week^2)-Vulpes_vulpes 433
## I(week^2)-Sus_scrofa 1251
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.786
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1914 0.6724 -2.4750 -1.2109 0.2584 1.0179 414
## Cogon_Patch_Size -0.8314 0.5902 -2.1117 -0.8019 0.2710 1.0436 520
## Veg_shannon_index 0.8349 0.4071 0.0216 0.8304 1.6349 1.0428 350
## total_shrub_cover -0.2892 0.3667 -1.0641 -0.2891 0.4035 1.0235 555
## Avg_Cogongrass_Cover 2.0387 0.6329 0.8640 2.0131 3.3647 1.0653 223
## Tree_Density -1.8763 0.5975 -3.1585 -1.8666 -0.7745 1.0246 360
## Avg_Canopy_Cover 1.6601 0.4721 0.7602 1.6507 2.6143 1.0048 462
## avg_veg_height -0.5922 0.4229 -1.4558 -0.5772 0.2157 1.0110 309
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.8162 3.9136 0.2081 2.7195 13.9561 1.0121 300
## Cogon_Patch_Size 2.1271 3.0529 0.1100 1.1978 9.5780 1.0479 340
## Veg_shannon_index 0.7107 1.5506 0.0513 0.3368 3.5135 1.1623 169
## total_shrub_cover 0.5165 0.6531 0.0500 0.3108 2.2094 1.0418 548
## Avg_Cogongrass_Cover 0.9788 1.4615 0.0460 0.4827 5.0020 1.0135 476
## Tree_Density 1.5159 2.2415 0.0649 0.7726 7.3311 1.0727 362
## Avg_Canopy_Cover 1.1923 1.6365 0.0876 0.7241 4.9757 1.0625 319
## avg_veg_height 0.3272 0.3931 0.0382 0.2071 1.3267 1.0110 1002
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3596 1.4619 0.0691 0.8716 5.1361 1.1257 78
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6914 0.3363 -3.3615 -2.6918 -2.0253 1.0016 2316
## week 0.1961 0.1937 -0.1921 0.1976 0.5693 1.0402 913
## I(week^2) -0.2053 0.1093 -0.4286 -0.2033 0.0018 1.0096 768
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0585 0.7842 0.2822 0.8564 3.1230 1.0053 556
## week 0.1941 0.1840 0.0364 0.1370 0.7293 1.0529 440
## I(week^2) 0.0645 0.0526 0.0176 0.0496 0.2046 1.0325 649
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1286 0.9635 -1.8062 0.1138
## (Intercept)-Sciurus_niger -0.3192 1.4431 -2.8222 -0.4557
## (Intercept)-Procyon_lotor 0.3432 0.9172 -1.5907 0.4110
## (Intercept)-Dasypus_novemcinctus -1.4353 0.7641 -3.1184 -1.3888
## (Intercept)-Lynx_rufus 0.0318 1.4057 -2.3771 -0.0892
## (Intercept)-Didelphis_virginiana -2.5413 0.9207 -4.4731 -2.4975
## (Intercept)-Sylvilagus_floridanus -1.3113 0.9283 -3.1058 -1.3104
## (Intercept)-Sciurus_carolinensis -2.6288 0.9794 -4.6322 -2.5576
## (Intercept)-Vulpes_vulpes -2.1266 1.4581 -4.7668 -2.1386
## (Intercept)-Sus_scrofa -3.5405 1.3542 -6.4738 -3.4395
## Cogon_Patch_Size-Canis_latrans 0.4467 1.0008 -1.0398 0.2770
## Cogon_Patch_Size-Sciurus_niger -1.5482 1.4714 -5.0580 -1.3447
## Cogon_Patch_Size-Procyon_lotor -1.0095 0.6354 -2.2700 -0.9956
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7916 0.5745 -1.9643 -0.7805
## Cogon_Patch_Size-Lynx_rufus -0.8988 1.1000 -3.0305 -0.9450
## Cogon_Patch_Size-Didelphis_virginiana 0.6250 0.7890 -0.6934 0.5618
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7863 1.2549 -4.8658 -1.5469
## Cogon_Patch_Size-Sciurus_carolinensis -1.5349 1.0223 -4.0261 -1.3798
## Cogon_Patch_Size-Vulpes_vulpes -1.3057 1.2245 -4.2270 -1.1522
## Cogon_Patch_Size-Sus_scrofa -1.1842 1.1634 -3.9928 -1.0325
## Veg_shannon_index-Canis_latrans 1.1807 0.5683 0.1958 1.1372
## Veg_shannon_index-Sciurus_niger 0.9457 1.0226 -0.8651 0.9109
## Veg_shannon_index-Procyon_lotor 1.0992 0.5493 0.1271 1.0631
## Veg_shannon_index-Dasypus_novemcinctus 0.6597 0.4657 -0.2749 0.6648
## Veg_shannon_index-Lynx_rufus 0.7141 0.8659 -1.0140 0.7703
## Veg_shannon_index-Didelphis_virginiana 0.9507 0.5769 -0.0980 0.9319
## Veg_shannon_index-Sylvilagus_floridanus 0.9519 0.6214 -0.1650 0.9221
## Veg_shannon_index-Sciurus_carolinensis 0.2816 0.6404 -1.1664 0.3528
## Veg_shannon_index-Vulpes_vulpes 0.3541 0.7937 -1.4830 0.4564
## Veg_shannon_index-Sus_scrofa 1.3138 0.8237 0.0483 1.1927
## total_shrub_cover-Canis_latrans 0.0776 0.5400 -0.8773 0.0288
## total_shrub_cover-Sciurus_niger -0.5775 0.7145 -2.1767 -0.5108
## total_shrub_cover-Procyon_lotor -0.6888 0.5158 -1.8284 -0.6331
## total_shrub_cover-Dasypus_novemcinctus 0.0275 0.4620 -0.8631 0.0124
## total_shrub_cover-Lynx_rufus -0.6645 0.7238 -2.3319 -0.5756
## total_shrub_cover-Didelphis_virginiana -0.3849 0.5564 -1.5714 -0.3532
## total_shrub_cover-Sylvilagus_floridanus -0.2104 0.5922 -1.4081 -0.1996
## total_shrub_cover-Sciurus_carolinensis -0.0681 0.5493 -1.1047 -0.0825
## total_shrub_cover-Vulpes_vulpes -0.4370 0.7015 -2.0139 -0.3776
## total_shrub_cover-Sus_scrofa 0.0140 0.6716 -1.1877 -0.0368
## Avg_Cogongrass_Cover-Canis_latrans 2.3052 0.8213 0.8430 2.2689
## Avg_Cogongrass_Cover-Sciurus_niger 1.4508 1.2285 -1.3855 1.5669
## Avg_Cogongrass_Cover-Procyon_lotor 2.2917 0.8227 0.7829 2.2515
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5272 0.8155 1.0430 2.4803
## Avg_Cogongrass_Cover-Lynx_rufus 2.4735 0.9251 0.9474 2.3726
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1755 0.7929 0.7282 2.1387
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4957 0.9002 -0.3413 1.5033
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3295 0.8071 0.8691 2.2951
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4549 0.9768 0.7946 2.3633
## Avg_Cogongrass_Cover-Sus_scrofa 1.5791 0.9936 -0.5716 1.6202
## Tree_Density-Canis_latrans -2.2774 0.9571 -4.5212 -2.1763
## Tree_Density-Sciurus_niger -1.8843 1.1493 -4.4875 -1.8665
## Tree_Density-Procyon_lotor -1.5380 0.6857 -2.8081 -1.5516
## Tree_Density-Dasypus_novemcinctus -2.9506 1.2627 -6.1095 -2.6994
## Tree_Density-Lynx_rufus -0.8071 1.0419 -2.5467 -0.9000
## Tree_Density-Didelphis_virginiana -2.1123 0.8902 -4.2023 -2.0120
## Tree_Density-Sylvilagus_floridanus -2.2206 1.0300 -4.5979 -2.1080
## Tree_Density-Sciurus_carolinensis -2.2015 1.0015 -4.6636 -2.0638
## Tree_Density-Vulpes_vulpes -1.8237 1.1641 -4.2376 -1.7892
## Tree_Density-Sus_scrofa -1.9636 1.0349 -4.3727 -1.8950
## Avg_Canopy_Cover-Canis_latrans 0.5149 0.6319 -0.7324 0.5130
## Avg_Canopy_Cover-Sciurus_niger 1.6769 1.1738 -0.6406 1.6358
## Avg_Canopy_Cover-Procyon_lotor 1.6183 0.5906 0.5325 1.6048
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8236 0.5827 0.7830 1.7799
## Avg_Canopy_Cover-Lynx_rufus 1.1413 0.9230 -0.6472 1.1381
## Avg_Canopy_Cover-Didelphis_virginiana 2.2963 0.7340 1.0741 2.2118
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.6007 1.1074 1.0008 2.4178
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0188 0.6762 0.8955 1.9426
## Avg_Canopy_Cover-Vulpes_vulpes 1.8913 0.8560 0.4720 1.8093
## Avg_Canopy_Cover-Sus_scrofa 1.8306 0.7020 0.6039 1.7640
## avg_veg_height-Canis_latrans -0.7432 0.5345 -1.8604 -0.7287
## avg_veg_height-Sciurus_niger -0.7326 0.6683 -2.1348 -0.6889
## avg_veg_height-Procyon_lotor -0.4404 0.5214 -1.4518 -0.4376
## avg_veg_height-Dasypus_novemcinctus -0.3724 0.5241 -1.3550 -0.3716
## avg_veg_height-Lynx_rufus -0.6695 0.6355 -1.9369 -0.6418
## avg_veg_height-Didelphis_virginiana -0.6726 0.5650 -1.8723 -0.6417
## avg_veg_height-Sylvilagus_floridanus -0.7377 0.5930 -2.0040 -0.7132
## avg_veg_height-Sciurus_carolinensis -0.3031 0.5573 -1.3741 -0.3231
## avg_veg_height-Vulpes_vulpes -0.6066 0.6292 -1.8963 -0.5907
## avg_veg_height-Sus_scrofa -0.6523 0.6143 -1.9653 -0.6157
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0787 1.0611 210
## (Intercept)-Sciurus_niger 2.7335 1.0339 192
## (Intercept)-Procyon_lotor 2.0207 1.0789 172
## (Intercept)-Dasypus_novemcinctus -0.0073 1.0221 840
## (Intercept)-Lynx_rufus 2.9991 1.0149 146
## (Intercept)-Didelphis_virginiana -0.8427 1.0003 466
## (Intercept)-Sylvilagus_floridanus 0.5944 1.0133 660
## (Intercept)-Sciurus_carolinensis -0.9232 1.0028 469
## (Intercept)-Vulpes_vulpes 0.7629 1.0789 104
## (Intercept)-Sus_scrofa -1.2225 1.0010 314
## Cogon_Patch_Size-Canis_latrans 2.8449 1.0033 574
## Cogon_Patch_Size-Sciurus_niger 0.8994 1.0587 265
## Cogon_Patch_Size-Procyon_lotor 0.2291 1.0448 570
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3307 1.0169 864
## Cogon_Patch_Size-Lynx_rufus 1.4163 1.0865 344
## Cogon_Patch_Size-Didelphis_virginiana 2.2981 1.0086 629
## Cogon_Patch_Size-Sylvilagus_floridanus -0.0533 1.0296 386
## Cogon_Patch_Size-Sciurus_carolinensis 0.0205 1.0385 510
## Cogon_Patch_Size-Vulpes_vulpes 0.7299 1.0432 401
## Cogon_Patch_Size-Sus_scrofa 0.6509 1.0457 490
## Veg_shannon_index-Canis_latrans 2.3969 1.0205 540
## Veg_shannon_index-Sciurus_niger 2.6982 1.0600 218
## Veg_shannon_index-Procyon_lotor 2.3026 1.0350 510
## Veg_shannon_index-Dasypus_novemcinctus 1.5651 1.0465 552
## Veg_shannon_index-Lynx_rufus 2.2046 1.0711 254
## Veg_shannon_index-Didelphis_virginiana 2.2275 1.0169 949
## Veg_shannon_index-Sylvilagus_floridanus 2.3169 1.0266 645
## Veg_shannon_index-Sciurus_carolinensis 1.3446 1.0462 512
## Veg_shannon_index-Vulpes_vulpes 1.6052 1.0268 357
## Veg_shannon_index-Sus_scrofa 3.3922 1.0176 446
## total_shrub_cover-Canis_latrans 1.3113 1.0228 1051
## total_shrub_cover-Sciurus_niger 0.6614 1.0033 624
## total_shrub_cover-Procyon_lotor 0.2032 1.0011 838
## total_shrub_cover-Dasypus_novemcinctus 0.9720 1.0186 1205
## total_shrub_cover-Lynx_rufus 0.5146 1.0068 430
## total_shrub_cover-Didelphis_virginiana 0.6598 1.0042 986
## total_shrub_cover-Sylvilagus_floridanus 1.0144 1.0208 827
## total_shrub_cover-Sciurus_carolinensis 1.0575 1.0131 1095
## total_shrub_cover-Vulpes_vulpes 0.8106 1.0123 680
## total_shrub_cover-Sus_scrofa 1.5093 1.0155 905
## Avg_Cogongrass_Cover-Canis_latrans 4.0401 1.0256 399
## Avg_Cogongrass_Cover-Sciurus_niger 3.5037 1.0878 269
## Avg_Cogongrass_Cover-Procyon_lotor 4.0135 1.0338 371
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.2225 1.0450 312
## Avg_Cogongrass_Cover-Lynx_rufus 4.5577 1.0301 376
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8055 1.0492 390
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1899 1.0445 362
## Avg_Cogongrass_Cover-Sciurus_carolinensis 3.9782 1.0369 311
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.6076 1.0469 278
## Avg_Cogongrass_Cover-Sus_scrofa 3.4372 1.0275 369
## Tree_Density-Canis_latrans -0.7138 1.0213 408
## Tree_Density-Sciurus_niger 0.5731 1.0272 397
## Tree_Density-Procyon_lotor -0.1192 1.0243 571
## Tree_Density-Dasypus_novemcinctus -1.2148 1.0992 309
## Tree_Density-Lynx_rufus 1.4024 1.0295 346
## Tree_Density-Didelphis_virginiana -0.6065 1.0101 705
## Tree_Density-Sylvilagus_floridanus -0.5291 1.0097 598
## Tree_Density-Sciurus_carolinensis -0.6646 1.0353 533
## Tree_Density-Vulpes_vulpes 0.4944 1.0554 454
## Tree_Density-Sus_scrofa -0.0662 1.0234 816
## Avg_Canopy_Cover-Canis_latrans 1.7775 1.0084 538
## Avg_Canopy_Cover-Sciurus_niger 4.1231 1.0131 316
## Avg_Canopy_Cover-Procyon_lotor 2.8750 1.0122 1008
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.0570 1.0392 698
## Avg_Canopy_Cover-Lynx_rufus 2.9910 1.0333 342
## Avg_Canopy_Cover-Didelphis_virginiana 3.9735 1.0294 536
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.2812 1.0579 298
## Avg_Canopy_Cover-Sciurus_carolinensis 3.5771 1.0062 680
## Avg_Canopy_Cover-Vulpes_vulpes 3.8844 1.0137 662
## Avg_Canopy_Cover-Sus_scrofa 3.4165 1.0010 769
## avg_veg_height-Canis_latrans 0.2918 1.0043 583
## avg_veg_height-Sciurus_niger 0.4974 1.0068 486
## avg_veg_height-Procyon_lotor 0.5914 1.0078 675
## avg_veg_height-Dasypus_novemcinctus 0.6845 1.0022 663
## avg_veg_height-Lynx_rufus 0.5439 1.0082 573
## avg_veg_height-Didelphis_virginiana 0.3600 1.0064 578
## avg_veg_height-Sylvilagus_floridanus 0.3474 1.0059 525
## avg_veg_height-Sciurus_carolinensis 0.8781 1.0054 597
## avg_veg_height-Vulpes_vulpes 0.6379 1.0015 465
## avg_veg_height-Sus_scrofa 0.5332 1.0042 614
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4833 0.1924 -2.8866 -2.4757 -2.1229 1.0140
## (Intercept)-Sciurus_niger -4.1677 0.5128 -5.2185 -4.1481 -3.1622 1.0005
## (Intercept)-Procyon_lotor -2.1718 0.1478 -2.4702 -2.1700 -1.8974 1.0024
## (Intercept)-Dasypus_novemcinctus -1.4776 0.1535 -1.7783 -1.4739 -1.1790 1.0008
## (Intercept)-Lynx_rufus -3.4383 0.3310 -4.0928 -3.4308 -2.8071 1.0057
## (Intercept)-Didelphis_virginiana -2.1449 0.2644 -2.6733 -2.1351 -1.6450 1.0035
## (Intercept)-Sylvilagus_floridanus -3.0667 0.2942 -3.6753 -3.0600 -2.5110 1.0004
## (Intercept)-Sciurus_carolinensis -2.3403 0.2818 -2.9243 -2.3284 -1.8197 1.0022
## (Intercept)-Vulpes_vulpes -3.8124 0.6110 -5.0533 -3.7993 -2.6684 1.0417
## (Intercept)-Sus_scrofa -2.8451 0.4433 -3.7935 -2.8184 -2.0740 1.0145
## week-Canis_latrans 0.4558 0.2430 0.0065 0.4491 0.9673 1.0077
## week-Sciurus_niger -0.2002 0.4295 -1.1916 -0.1540 0.5190 1.1062
## week-Procyon_lotor 0.1616 0.1942 -0.2153 0.1611 0.5584 1.0023
## week-Dasypus_novemcinctus 0.0682 0.2121 -0.3446 0.0728 0.4812 1.0026
## week-Lynx_rufus 0.2735 0.3048 -0.3273 0.2711 0.8929 1.0248
## week-Didelphis_virginiana 0.0396 0.3207 -0.5954 0.0556 0.6323 1.0209
## week-Sylvilagus_floridanus 0.0498 0.3020 -0.5625 0.0539 0.6381 1.0067
## week-Sciurus_carolinensis 0.5288 0.3278 -0.0661 0.5109 1.2102 1.0053
## week-Vulpes_vulpes 0.1400 0.3849 -0.6592 0.1543 0.8441 1.0292
## week-Sus_scrofa 0.4088 0.3621 -0.2393 0.3881 1.2189 1.0051
## I(week^2)-Canis_latrans -0.1944 0.1036 -0.4042 -0.1913 0.0000 1.0141
## I(week^2)-Sciurus_niger -0.2537 0.2270 -0.7692 -0.2318 0.1206 1.0492
## I(week^2)-Procyon_lotor -0.1145 0.0868 -0.2867 -0.1152 0.0537 1.0045
## I(week^2)-Dasypus_novemcinctus -0.1555 0.0988 -0.3498 -0.1541 0.0382 1.0114
## I(week^2)-Lynx_rufus -0.1978 0.1433 -0.5089 -0.1932 0.0678 1.0202
## I(week^2)-Didelphis_virginiana -0.3392 0.1977 -0.7704 -0.3174 -0.0059 1.0108
## I(week^2)-Sylvilagus_floridanus -0.1483 0.1401 -0.4250 -0.1470 0.1223 1.0026
## I(week^2)-Sciurus_carolinensis -0.1913 0.1362 -0.4739 -0.1850 0.0537 1.0025
## I(week^2)-Vulpes_vulpes -0.3259 0.2342 -0.8647 -0.3011 0.0584 1.0308
## I(week^2)-Sus_scrofa -0.1460 0.1616 -0.4781 -0.1437 0.1583 1.0039
## ESS
## (Intercept)-Canis_latrans 963
## (Intercept)-Sciurus_niger 158
## (Intercept)-Procyon_lotor 1842
## (Intercept)-Dasypus_novemcinctus 2316
## (Intercept)-Lynx_rufus 251
## (Intercept)-Didelphis_virginiana 1644
## (Intercept)-Sylvilagus_floridanus 513
## (Intercept)-Sciurus_carolinensis 1330
## (Intercept)-Vulpes_vulpes 207
## (Intercept)-Sus_scrofa 674
## week-Canis_latrans 1155
## week-Sciurus_niger 393
## week-Procyon_lotor 1546
## week-Dasypus_novemcinctus 1983
## week-Lynx_rufus 891
## week-Didelphis_virginiana 1138
## week-Sylvilagus_floridanus 990
## week-Sciurus_carolinensis 1082
## week-Vulpes_vulpes 871
## week-Sus_scrofa 1291
## I(week^2)-Canis_latrans 1161
## I(week^2)-Sciurus_niger 248
## I(week^2)-Procyon_lotor 1761
## I(week^2)-Dasypus_novemcinctus 1849
## I(week^2)-Lynx_rufus 698
## I(week^2)-Didelphis_virginiana 687
## I(week^2)-Sylvilagus_floridanus 869
## I(week^2)-Sciurus_carolinensis 1261
## I(week^2)-Vulpes_vulpes 379
## I(week^2)-Sus_scrofa 1434
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7545
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8052 0.3733 -1.5447 -0.8236 -0.0637 1.0050 746
## Avg_Cogongrass_Cover 0.1252 0.2922 -0.4823 0.1309 0.6789 1.0219 492
## total_shrub_cover -0.2593 0.2640 -0.7931 -0.2544 0.2405 1.0028 726
## avg_veg_height 0.0126 0.2801 -0.5200 0.0109 0.5664 1.0028 461
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8296 0.8131 0.0794 0.5920 3.0596 1.0078 640
## Avg_Cogongrass_Cover 0.3030 0.3568 0.0365 0.1924 1.2538 1.0443 1012
## total_shrub_cover 0.3108 0.3583 0.0414 0.2051 1.2292 1.0103 808
## avg_veg_height 0.1952 0.2076 0.0320 0.1339 0.6959 1.0070 1289
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8507 0.7063 0.08 0.6633 2.6238 1.0413 139
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6332 0.3026 -3.2522 -2.6260 -2.0302 1.0007 1355
## week 0.1830 0.2012 -0.2326 0.1834 0.5900 1.0014 873
## I(week^2) -0.2136 0.1134 -0.4588 -0.2070 -0.0060 1.0337 569
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7901 0.6583 0.1982 0.6293 2.3137 1.0122 657
## week 0.2109 0.2114 0.0347 0.1497 0.7572 1.0026 779
## I(week^2) 0.0676 0.0600 0.0182 0.0510 0.2205 1.0930 386
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1294 0.5901 -1.2628 -0.1372
## (Intercept)-Sciurus_niger -0.9429 0.7012 -2.2134 -0.9712
## (Intercept)-Procyon_lotor 0.1109 0.6420 -1.0861 0.1079
## (Intercept)-Dasypus_novemcinctus -0.8123 0.5098 -1.8789 -0.7935
## (Intercept)-Lynx_rufus -0.5196 0.6319 -1.6688 -0.5389
## (Intercept)-Didelphis_virginiana -1.2407 0.5472 -2.3534 -1.2196
## (Intercept)-Sylvilagus_floridanus -0.5753 0.5736 -1.6455 -0.5907
## (Intercept)-Sciurus_carolinensis -1.3159 0.5404 -2.5075 -1.2796
## (Intercept)-Vulpes_vulpes -1.2753 0.7454 -2.7750 -1.2529
## (Intercept)-Sus_scrofa -1.5673 0.7000 -3.0615 -1.5187
## Avg_Cogongrass_Cover-Canis_latrans 0.3588 0.4121 -0.3986 0.3395
## Avg_Cogongrass_Cover-Sciurus_niger -0.2048 0.5455 -1.4088 -0.1465
## Avg_Cogongrass_Cover-Procyon_lotor 0.0820 0.4028 -0.7312 0.0861
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2488 0.3737 -0.4874 0.2513
## Avg_Cogongrass_Cover-Lynx_rufus 0.4064 0.4472 -0.4225 0.3809
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3049 0.4061 -0.4953 0.3022
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2301 0.4812 -1.2856 -0.1957
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2370 0.3974 -0.5529 0.2317
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2417 0.4739 -0.6885 0.2346
## Avg_Cogongrass_Cover-Sus_scrofa -0.1397 0.5281 -1.3410 -0.0926
## total_shrub_cover-Canis_latrans 0.0791 0.3834 -0.6124 0.0529
## total_shrub_cover-Sciurus_niger -0.4806 0.4684 -1.5094 -0.4339
## total_shrub_cover-Procyon_lotor -0.6766 0.4161 -1.5893 -0.6415
## total_shrub_cover-Dasypus_novemcinctus -0.0622 0.3451 -0.7121 -0.0733
## total_shrub_cover-Lynx_rufus -0.6070 0.5147 -1.8339 -0.5385
## total_shrub_cover-Didelphis_virginiana -0.2177 0.3741 -0.9587 -0.2160
## total_shrub_cover-Sylvilagus_floridanus -0.2810 0.4196 -1.1803 -0.2634
## total_shrub_cover-Sciurus_carolinensis -0.0967 0.3788 -0.8193 -0.1085
## total_shrub_cover-Vulpes_vulpes -0.2979 0.5136 -1.4082 -0.2780
## total_shrub_cover-Sus_scrofa 0.0146 0.4647 -0.8209 -0.0062
## avg_veg_height-Canis_latrans -0.0567 0.3821 -0.8324 -0.0486
## avg_veg_height-Sciurus_niger -0.1498 0.4364 -1.0417 -0.1342
## avg_veg_height-Procyon_lotor 0.0943 0.3862 -0.6270 0.0886
## avg_veg_height-Dasypus_novemcinctus 0.1827 0.3664 -0.5080 0.1642
## avg_veg_height-Lynx_rufus 0.0196 0.4376 -0.8250 0.0206
## avg_veg_height-Didelphis_virginiana -0.0242 0.3878 -0.7831 -0.0303
## avg_veg_height-Sylvilagus_floridanus -0.1228 0.4040 -0.9379 -0.1150
## avg_veg_height-Sciurus_carolinensis 0.2447 0.4067 -0.5035 0.2295
## avg_veg_height-Vulpes_vulpes -0.0445 0.4388 -0.9382 -0.0350
## avg_veg_height-Sus_scrofa -0.0166 0.4145 -0.8721 -0.0131
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0588 1.0065 480
## (Intercept)-Sciurus_niger 0.5787 1.0107 399
## (Intercept)-Procyon_lotor 1.3992 1.0002 417
## (Intercept)-Dasypus_novemcinctus 0.1759 1.0187 1132
## (Intercept)-Lynx_rufus 0.8065 1.0101 640
## (Intercept)-Didelphis_virginiana -0.1913 1.0012 958
## (Intercept)-Sylvilagus_floridanus 0.6216 1.0054 617
## (Intercept)-Sciurus_carolinensis -0.3586 1.0042 754
## (Intercept)-Vulpes_vulpes 0.1957 1.0310 374
## (Intercept)-Sus_scrofa -0.3327 1.0035 549
## Avg_Cogongrass_Cover-Canis_latrans 1.2423 1.0005 1078
## Avg_Cogongrass_Cover-Sciurus_niger 0.7341 1.0182 576
## Avg_Cogongrass_Cover-Procyon_lotor 0.8896 1.0064 918
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9619 1.0066 1095
## Avg_Cogongrass_Cover-Lynx_rufus 1.3533 1.0034 906
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1161 1.0016 1152
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6104 1.0141 616
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0151 1.0197 921
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2648 1.0072 994
## Avg_Cogongrass_Cover-Sus_scrofa 0.7702 1.0170 762
## total_shrub_cover-Canis_latrans 0.8807 1.0005 1449
## total_shrub_cover-Sciurus_niger 0.3257 1.0012 1030
## total_shrub_cover-Procyon_lotor 0.0331 1.0018 1203
## total_shrub_cover-Dasypus_novemcinctus 0.6499 1.0030 1932
## total_shrub_cover-Lynx_rufus 0.2428 1.0102 843
## total_shrub_cover-Didelphis_virginiana 0.5061 1.0009 1969
## total_shrub_cover-Sylvilagus_floridanus 0.5221 1.0040 1133
## total_shrub_cover-Sciurus_carolinensis 0.6987 1.0036 1985
## total_shrub_cover-Vulpes_vulpes 0.6071 1.0229 871
## total_shrub_cover-Sus_scrofa 0.9787 1.0115 1297
## avg_veg_height-Canis_latrans 0.6773 1.0008 937
## avg_veg_height-Sciurus_niger 0.6679 1.0001 831
## avg_veg_height-Procyon_lotor 0.9099 1.0002 911
## avg_veg_height-Dasypus_novemcinctus 0.9453 1.0056 1021
## avg_veg_height-Lynx_rufus 0.9278 0.9999 889
## avg_veg_height-Didelphis_virginiana 0.7472 1.0031 1030
## avg_veg_height-Sylvilagus_floridanus 0.6749 1.0062 867
## avg_veg_height-Sciurus_carolinensis 1.0869 1.0058 941
## avg_veg_height-Vulpes_vulpes 0.8482 1.0033 880
## avg_veg_height-Sus_scrofa 0.8153 1.0002 964
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4660 0.1924 -2.8438 -2.4586 -2.1067 1.0022
## (Intercept)-Sciurus_niger -3.5820 0.5281 -4.6632 -3.5336 -2.6555 1.0079
## (Intercept)-Procyon_lotor -2.1872 0.1478 -2.4851 -2.1810 -1.9107 1.0000
## (Intercept)-Dasypus_novemcinctus -1.4845 0.1593 -1.7980 -1.4819 -1.1880 1.0002
## (Intercept)-Lynx_rufus -3.2800 0.2907 -3.8664 -3.2691 -2.7312 1.0010
## (Intercept)-Didelphis_virginiana -2.1910 0.2711 -2.7486 -2.1766 -1.7012 1.0030
## (Intercept)-Sylvilagus_floridanus -3.0634 0.3127 -3.7100 -3.0377 -2.5025 1.0063
## (Intercept)-Sciurus_carolinensis -2.3610 0.2904 -2.9600 -2.3454 -1.8177 1.0141
## (Intercept)-Vulpes_vulpes -3.6166 0.6496 -4.9279 -3.5947 -2.4406 1.0274
## (Intercept)-Sus_scrofa -2.9128 0.4618 -3.9203 -2.8775 -2.1102 1.0119
## week-Canis_latrans 0.4464 0.2441 -0.0174 0.4347 0.9440 1.0032
## week-Sciurus_niger -0.2327 0.4371 -1.2511 -0.1829 0.4940 1.0039
## week-Procyon_lotor 0.1475 0.1969 -0.2399 0.1458 0.5306 1.0041
## week-Dasypus_novemcinctus 0.0631 0.2160 -0.3559 0.0632 0.4934 1.0040
## week-Lynx_rufus 0.2627 0.3024 -0.3283 0.2557 0.9026 1.0044
## week-Didelphis_virginiana 0.0280 0.3140 -0.6302 0.0402 0.6243 1.0009
## week-Sylvilagus_floridanus 0.0187 0.3025 -0.6060 0.0276 0.6016 1.0103
## week-Sciurus_carolinensis 0.5418 0.3316 -0.0452 0.5100 1.2586 1.0026
## week-Vulpes_vulpes 0.1241 0.4153 -0.7511 0.1396 0.9250 1.0049
## week-Sus_scrofa 0.4228 0.3825 -0.2798 0.3919 1.2401 1.0012
## I(week^2)-Canis_latrans -0.1932 0.1042 -0.4083 -0.1912 0.0083 1.0104
## I(week^2)-Sciurus_niger -0.2582 0.2257 -0.7511 -0.2406 0.1381 1.0086
## I(week^2)-Procyon_lotor -0.1097 0.0873 -0.2818 -0.1088 0.0581 1.0122
## I(week^2)-Dasypus_novemcinctus -0.1500 0.0990 -0.3499 -0.1470 0.0401 1.0041
## I(week^2)-Lynx_rufus -0.2025 0.1412 -0.4979 -0.1978 0.0574 1.0018
## I(week^2)-Didelphis_virginiana -0.3476 0.2119 -0.8556 -0.3192 -0.0121 1.0639
## I(week^2)-Sylvilagus_floridanus -0.1626 0.1537 -0.4958 -0.1537 0.1214 1.0058
## I(week^2)-Sciurus_carolinensis -0.1924 0.1344 -0.4690 -0.1868 0.0526 1.0010
## I(week^2)-Vulpes_vulpes -0.3527 0.2662 -0.9515 -0.3165 0.0596 1.1221
## I(week^2)-Sus_scrofa -0.1601 0.1643 -0.4949 -0.1531 0.1531 1.0004
## ESS
## (Intercept)-Canis_latrans 1044
## (Intercept)-Sciurus_niger 297
## (Intercept)-Procyon_lotor 1728
## (Intercept)-Dasypus_novemcinctus 2369
## (Intercept)-Lynx_rufus 491
## (Intercept)-Didelphis_virginiana 1487
## (Intercept)-Sylvilagus_floridanus 503
## (Intercept)-Sciurus_carolinensis 1386
## (Intercept)-Vulpes_vulpes 230
## (Intercept)-Sus_scrofa 674
## week-Canis_latrans 1072
## week-Sciurus_niger 602
## week-Procyon_lotor 1589
## week-Dasypus_novemcinctus 1607
## week-Lynx_rufus 978
## week-Didelphis_virginiana 1013
## week-Sylvilagus_floridanus 1032
## week-Sciurus_carolinensis 1054
## week-Vulpes_vulpes 765
## week-Sus_scrofa 1206
## I(week^2)-Canis_latrans 1182
## I(week^2)-Sciurus_niger 452
## I(week^2)-Procyon_lotor 1442
## I(week^2)-Dasypus_novemcinctus 1708
## I(week^2)-Lynx_rufus 874
## I(week^2)-Didelphis_virginiana 464
## I(week^2)-Sylvilagus_floridanus 779
## I(week^2)-Sciurus_carolinensis 1258
## I(week^2)-Vulpes_vulpes 269
## I(week^2)-Sus_scrofa 1598
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8088
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9485 0.4442 -1.8177 -0.9555 -0.0461 1.0262 773
## Tree_Density -0.7373 0.4168 -1.6997 -0.6882 -0.0370 1.0008 599
## Avg_Canopy_Cover 0.9262 0.3114 0.3805 0.9110 1.6042 1.0260 776
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5298 1.3563 0.1778 1.1615 5.2016 1.0011 702
## Tree_Density 0.7457 1.0488 0.0477 0.4295 3.3776 1.0114 441
## Avg_Canopy_Cover 0.5134 0.6047 0.0529 0.3361 1.9841 1.0047 962
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4504 0.4614 0.0459 0.2993 1.7141 1.0317 178
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6284 0.3009 -3.2361 -2.6190 -2.0481 1.0057 1767
## week 0.1962 0.1965 -0.1814 0.1971 0.5829 1.0064 1034
## I(week^2) -0.2082 0.1021 -0.4293 -0.2020 -0.0193 1.0089 791
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8290 0.6151 0.1984 0.6643 2.3908 1.0149 719
## week 0.2076 0.2087 0.0337 0.1461 0.8172 1.0081 604
## I(week^2) 0.0641 0.0535 0.0178 0.0496 0.1975 1.0228 628
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0126 0.5877 -1.1793 -0.0234 1.1482
## (Intercept)-Sciurus_niger -0.9550 0.7711 -2.3760 -0.9835 0.7385
## (Intercept)-Procyon_lotor 0.3549 0.6225 -0.9379 0.3674 1.5494
## (Intercept)-Dasypus_novemcinctus -1.0611 0.5444 -2.1610 -1.0404 -0.0631
## (Intercept)-Lynx_rufus -0.1194 0.9228 -1.6099 -0.2561 2.0307
## (Intercept)-Didelphis_virginiana -1.7195 0.6102 -3.0045 -1.6879 -0.5971
## (Intercept)-Sylvilagus_floridanus -0.8486 0.6179 -2.1051 -0.8505 0.3567
## (Intercept)-Sciurus_carolinensis -1.7651 0.6281 -3.0696 -1.7215 -0.6173
## (Intercept)-Vulpes_vulpes -1.5816 0.9164 -3.3475 -1.6018 0.2559
## (Intercept)-Sus_scrofa -2.2009 0.7769 -3.9206 -2.1361 -0.8528
## Tree_Density-Canis_latrans -0.9014 0.5677 -2.2450 -0.8339 0.0287
## Tree_Density-Sciurus_niger -0.7646 0.7136 -2.4045 -0.6983 0.5376
## Tree_Density-Procyon_lotor -0.4534 0.4102 -1.2883 -0.4436 0.3456
## Tree_Density-Dasypus_novemcinctus -1.3252 0.8645 -3.4300 -1.1428 -0.1441
## Tree_Density-Lynx_rufus 0.1539 0.6633 -0.9119 0.0673 1.8089
## Tree_Density-Didelphis_virginiana -0.9543 0.7003 -2.6589 -0.8521 0.1401
## Tree_Density-Sylvilagus_floridanus -1.0460 0.7391 -2.8049 -0.9499 0.0843
## Tree_Density-Sciurus_carolinensis -0.8744 0.7091 -2.5627 -0.7675 0.2436
## Tree_Density-Vulpes_vulpes -0.6403 0.7746 -2.3246 -0.5970 0.7335
## Tree_Density-Sus_scrofa -0.8847 0.7685 -2.6939 -0.7853 0.3503
## Avg_Canopy_Cover-Canis_latrans 0.1122 0.4480 -0.7612 0.1233 0.9536
## Avg_Canopy_Cover-Sciurus_niger 0.8290 0.6822 -0.3814 0.7777 2.3543
## Avg_Canopy_Cover-Procyon_lotor 0.9438 0.4287 0.1538 0.9250 1.8677
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9761 0.3972 0.2189 0.9658 1.8127
## Avg_Canopy_Cover-Lynx_rufus 0.6951 0.5831 -0.3950 0.6683 1.9205
## Avg_Canopy_Cover-Didelphis_virginiana 1.1786 0.4677 0.3577 1.1361 2.1891
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5192 0.6372 0.5428 1.4095 3.0274
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1571 0.4557 0.3584 1.1131 2.1537
## Avg_Canopy_Cover-Vulpes_vulpes 0.9843 0.5358 0.0179 0.9447 2.1852
## Avg_Canopy_Cover-Sus_scrofa 1.1144 0.4833 0.2604 1.0665 2.1469
## Rhat ESS
## (Intercept)-Canis_latrans 1.0015 844
## (Intercept)-Sciurus_niger 1.0085 574
## (Intercept)-Procyon_lotor 1.0214 551
## (Intercept)-Dasypus_novemcinctus 1.0123 857
## (Intercept)-Lynx_rufus 1.0413 284
## (Intercept)-Didelphis_virginiana 1.0003 1037
## (Intercept)-Sylvilagus_floridanus 1.0077 913
## (Intercept)-Sciurus_carolinensis 1.0061 883
## (Intercept)-Vulpes_vulpes 1.0163 400
## (Intercept)-Sus_scrofa 1.0041 626
## Tree_Density-Canis_latrans 1.0005 809
## Tree_Density-Sciurus_niger 1.0045 913
## Tree_Density-Procyon_lotor 1.0018 1820
## Tree_Density-Dasypus_novemcinctus 1.0061 484
## Tree_Density-Lynx_rufus 1.0006 540
## Tree_Density-Didelphis_virginiana 1.0021 724
## Tree_Density-Sylvilagus_floridanus 1.0004 614
## Tree_Density-Sciurus_carolinensis 1.0001 808
## Tree_Density-Vulpes_vulpes 1.0039 826
## Tree_Density-Sus_scrofa 1.0006 762
## Avg_Canopy_Cover-Canis_latrans 1.0065 1031
## Avg_Canopy_Cover-Sciurus_niger 1.0357 566
## Avg_Canopy_Cover-Procyon_lotor 1.0173 1714
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0004 1759
## Avg_Canopy_Cover-Lynx_rufus 1.0334 685
## Avg_Canopy_Cover-Didelphis_virginiana 1.0011 1368
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0051 863
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0029 1316
## Avg_Canopy_Cover-Vulpes_vulpes 1.0118 1016
## Avg_Canopy_Cover-Sus_scrofa 1.0005 1241
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4712 0.1897 -2.8650 -2.4656 -2.1134 1.0123
## (Intercept)-Sciurus_niger -3.7012 0.5140 -4.7297 -3.6798 -2.7077 1.0084
## (Intercept)-Procyon_lotor -2.1743 0.1471 -2.4721 -2.1705 -1.8980 1.0036
## (Intercept)-Dasypus_novemcinctus -1.4844 0.1542 -1.7953 -1.4814 -1.1972 1.0011
## (Intercept)-Lynx_rufus -3.3572 0.3307 -4.0306 -3.3501 -2.7364 1.0649
## (Intercept)-Didelphis_virginiana -2.1761 0.2619 -2.7081 -2.1706 -1.6710 1.0028
## (Intercept)-Sylvilagus_floridanus -2.9904 0.2812 -3.5726 -2.9857 -2.4707 1.0068
## (Intercept)-Sciurus_carolinensis -2.3460 0.2852 -2.9209 -2.3340 -1.8124 1.0017
## (Intercept)-Vulpes_vulpes -3.6216 0.6311 -4.9905 -3.5907 -2.4827 1.0370
## (Intercept)-Sus_scrofa -2.7992 0.4451 -3.7860 -2.7688 -1.9989 1.0429
## week-Canis_latrans 0.4554 0.2451 0.0004 0.4480 0.9627 1.0049
## week-Sciurus_niger -0.2072 0.4343 -1.1781 -0.1617 0.5197 1.0041
## week-Procyon_lotor 0.1665 0.1984 -0.2165 0.1658 0.5578 1.0026
## week-Dasypus_novemcinctus 0.0659 0.2106 -0.3488 0.0634 0.4710 1.0008
## week-Lynx_rufus 0.2680 0.2954 -0.2863 0.2636 0.8796 1.0036
## week-Didelphis_virginiana 0.0452 0.3159 -0.6001 0.0604 0.6524 1.0003
## week-Sylvilagus_floridanus 0.0449 0.2942 -0.5730 0.0573 0.6032 1.0033
## week-Sciurus_carolinensis 0.5390 0.3310 -0.0385 0.5151 1.2627 1.0185
## week-Vulpes_vulpes 0.1427 0.3824 -0.6144 0.1556 0.8765 1.0037
## week-Sus_scrofa 0.4190 0.3690 -0.2752 0.3983 1.1962 1.0043
## I(week^2)-Canis_latrans -0.1937 0.1034 -0.4010 -0.1897 -0.0026 1.0059
## I(week^2)-Sciurus_niger -0.2392 0.2108 -0.7060 -0.2198 0.1271 1.0090
## I(week^2)-Procyon_lotor -0.1159 0.0856 -0.2832 -0.1155 0.0528 1.0048
## I(week^2)-Dasypus_novemcinctus -0.1525 0.0998 -0.3520 -0.1492 0.0458 1.0096
## I(week^2)-Lynx_rufus -0.1984 0.1447 -0.4979 -0.1888 0.0672 1.0132
## I(week^2)-Didelphis_virginiana -0.3370 0.1877 -0.7563 -0.3230 -0.0137 1.0083
## I(week^2)-Sylvilagus_floridanus -0.1564 0.1485 -0.4615 -0.1494 0.1213 1.0001
## I(week^2)-Sciurus_carolinensis -0.1910 0.1350 -0.4731 -0.1838 0.0561 1.0192
## I(week^2)-Vulpes_vulpes -0.3400 0.2513 -0.9399 -0.3090 0.0499 1.0269
## I(week^2)-Sus_scrofa -0.1601 0.1687 -0.5037 -0.1565 0.1576 1.0177
## ESS
## (Intercept)-Canis_latrans 1058
## (Intercept)-Sciurus_niger 259
## (Intercept)-Procyon_lotor 1603
## (Intercept)-Dasypus_novemcinctus 1883
## (Intercept)-Lynx_rufus 317
## (Intercept)-Didelphis_virginiana 1555
## (Intercept)-Sylvilagus_floridanus 631
## (Intercept)-Sciurus_carolinensis 1294
## (Intercept)-Vulpes_vulpes 237
## (Intercept)-Sus_scrofa 701
## week-Canis_latrans 964
## week-Sciurus_niger 587
## week-Procyon_lotor 1671
## week-Dasypus_novemcinctus 1949
## week-Lynx_rufus 983
## week-Didelphis_virginiana 1256
## week-Sylvilagus_floridanus 808
## week-Sciurus_carolinensis 1153
## week-Vulpes_vulpes 877
## week-Sus_scrofa 1330
## I(week^2)-Canis_latrans 1063
## I(week^2)-Sciurus_niger 458
## I(week^2)-Procyon_lotor 1658
## I(week^2)-Dasypus_novemcinctus 1808
## I(week^2)-Lynx_rufus 711
## I(week^2)-Didelphis_virginiana 773
## I(week^2)-Sylvilagus_floridanus 856
## I(week^2)-Sciurus_carolinensis 1210
## I(week^2)-Vulpes_vulpes 410
## I(week^2)-Sus_scrofa 1518
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7847
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8908 0.4001 -1.7140 -0.8865 -0.1114 1.0630 588
## Cogon_Patch_Size -0.3311 0.4239 -1.2992 -0.3008 0.4202 1.0461 677
## Avg_Cogongrass_Cover 0.2554 0.2774 -0.2859 0.2505 0.8141 1.0144 642
## total_shrub_cover -0.2154 0.2650 -0.7473 -0.2078 0.2932 1.0015 848
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9371 1.0821 0.0682 0.6560 3.5931 1.0091 665
## Cogon_Patch_Size 0.9358 1.1307 0.0725 0.5676 3.9170 1.0357 597
## Avg_Cogongrass_Cover 0.2882 0.3515 0.0347 0.1844 1.2170 1.0032 863
## total_shrub_cover 0.2907 0.3348 0.0402 0.1955 1.1096 1.0003 980
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1448 0.9753 0.108 0.9013 3.7877 1.0793 224
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6285 0.2981 -3.2174 -2.6300 -2.0245 1.0094 1686
## week 0.2007 0.1987 -0.1938 0.2022 0.5866 1.0102 1001
## I(week^2) -0.2156 0.1108 -0.4563 -0.2087 -0.0177 1.0302 658
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7837 0.6254 0.1960 0.6240 2.2751 1.0758 604
## week 0.2097 0.2097 0.0346 0.1473 0.7551 1.0014 667
## I(week^2) 0.0658 0.0604 0.0163 0.0481 0.2261 1.0445 537
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1092 0.6579 -1.2996 -0.1374
## (Intercept)-Sciurus_niger -1.1034 0.7322 -2.5628 -1.0659
## (Intercept)-Procyon_lotor -0.0080 0.7086 -1.3217 -0.0064
## (Intercept)-Dasypus_novemcinctus -0.8685 0.5462 -1.9936 -0.8364
## (Intercept)-Lynx_rufus -0.6171 0.6639 -1.9495 -0.6448
## (Intercept)-Didelphis_virginiana -1.3050 0.5840 -2.5278 -1.2757
## (Intercept)-Sylvilagus_floridanus -0.7528 0.6259 -1.9638 -0.7648
## (Intercept)-Sciurus_carolinensis -1.4040 0.6143 -2.6610 -1.3661
## (Intercept)-Vulpes_vulpes -1.3795 0.7823 -3.1046 -1.3248
## (Intercept)-Sus_scrofa -1.6518 0.7305 -3.2600 -1.5895
## Cogon_Patch_Size-Canis_latrans 0.6119 0.6369 -0.3480 0.5090
## Cogon_Patch_Size-Sciurus_niger -0.7385 0.8941 -2.9241 -0.5999
## Cogon_Patch_Size-Procyon_lotor -0.3155 0.4458 -1.2743 -0.3049
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1865 0.4160 -1.0529 -0.1731
## Cogon_Patch_Size-Lynx_rufus -0.4431 0.6873 -1.7512 -0.4510
## Cogon_Patch_Size-Didelphis_virginiana 0.5612 0.5053 -0.3461 0.5319
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0451 0.9049 -3.3237 -0.8530
## Cogon_Patch_Size-Sciurus_carolinensis -0.7817 0.6856 -2.4115 -0.6666
## Cogon_Patch_Size-Vulpes_vulpes -0.6156 0.8438 -2.5696 -0.5203
## Cogon_Patch_Size-Sus_scrofa -0.4993 0.7607 -2.2334 -0.4136
## Avg_Cogongrass_Cover-Canis_latrans 0.3060 0.3823 -0.4110 0.2914
## Avg_Cogongrass_Cover-Sciurus_niger -0.0804 0.5443 -1.2847 -0.0416
## Avg_Cogongrass_Cover-Procyon_lotor 0.2779 0.3903 -0.4521 0.2677
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4099 0.3580 -0.2461 0.3942
## Avg_Cogongrass_Cover-Lynx_rufus 0.5719 0.4585 -0.1954 0.5336
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2702 0.3954 -0.5533 0.2793
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0339 0.4472 -1.0084 -0.0095
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4803 0.3879 -0.2475 0.4607
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3634 0.4499 -0.4948 0.3425
## Avg_Cogongrass_Cover-Sus_scrofa 0.0246 0.5142 -1.1219 0.0661
## total_shrub_cover-Canis_latrans 0.0777 0.3954 -0.6376 0.0543
## total_shrub_cover-Sciurus_niger -0.4225 0.4779 -1.4591 -0.3845
## total_shrub_cover-Procyon_lotor -0.6120 0.4330 -1.5791 -0.5742
## total_shrub_cover-Dasypus_novemcinctus -0.0485 0.3486 -0.7391 -0.0574
## total_shrub_cover-Lynx_rufus -0.4915 0.4886 -1.5608 -0.4520
## total_shrub_cover-Didelphis_virginiana -0.2482 0.3799 -1.0063 -0.2347
## total_shrub_cover-Sylvilagus_floridanus -0.1997 0.4387 -1.0826 -0.1969
## total_shrub_cover-Sciurus_carolinensis -0.0630 0.3916 -0.7964 -0.0694
## total_shrub_cover-Vulpes_vulpes -0.2355 0.4767 -1.2528 -0.2201
## total_shrub_cover-Sus_scrofa 0.0225 0.4552 -0.7958 0.0046
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2287 1.0180 560
## (Intercept)-Sciurus_niger 0.3089 1.0658 478
## (Intercept)-Procyon_lotor 1.3327 1.0326 350
## (Intercept)-Dasypus_novemcinctus 0.1445 1.0505 859
## (Intercept)-Lynx_rufus 0.7454 1.0427 536
## (Intercept)-Didelphis_virginiana -0.1951 1.0139 836
## (Intercept)-Sylvilagus_floridanus 0.5549 1.0113 872
## (Intercept)-Sciurus_carolinensis -0.3040 1.0351 858
## (Intercept)-Vulpes_vulpes 0.0320 1.0472 466
## (Intercept)-Sus_scrofa -0.3957 1.0184 629
## Cogon_Patch_Size-Canis_latrans 2.1820 1.0122 871
## Cogon_Patch_Size-Sciurus_niger 0.6680 1.0303 641
## Cogon_Patch_Size-Procyon_lotor 0.5260 1.0228 1379
## Cogon_Patch_Size-Dasypus_novemcinctus 0.5771 1.0048 1869
## Cogon_Patch_Size-Lynx_rufus 1.0344 1.0143 920
## Cogon_Patch_Size-Didelphis_virginiana 1.6222 1.0019 1215
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2361 1.0278 522
## Cogon_Patch_Size-Sciurus_carolinensis 0.2359 1.0491 735
## Cogon_Patch_Size-Vulpes_vulpes 0.7394 1.0275 703
## Cogon_Patch_Size-Sus_scrofa 0.7782 1.0501 860
## Avg_Cogongrass_Cover-Canis_latrans 1.1002 1.0112 1417
## Avg_Cogongrass_Cover-Sciurus_niger 0.8744 1.0050 803
## Avg_Cogongrass_Cover-Procyon_lotor 1.0690 1.0019 1493
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1572 1.0143 1185
## Avg_Cogongrass_Cover-Lynx_rufus 1.6182 1.0096 896
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0537 1.0053 1285
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7838 1.0124 962
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3063 1.0115 1514
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2993 1.0073 1203
## Avg_Cogongrass_Cover-Sus_scrofa 0.9357 1.0028 1103
## total_shrub_cover-Canis_latrans 0.9158 1.0010 1426
## total_shrub_cover-Sciurus_niger 0.4219 0.9996 1107
## total_shrub_cover-Procyon_lotor 0.1287 1.0032 1078
## total_shrub_cover-Dasypus_novemcinctus 0.6465 1.0093 1877
## total_shrub_cover-Lynx_rufus 0.3687 1.0071 730
## total_shrub_cover-Didelphis_virginiana 0.5006 1.0024 1815
## total_shrub_cover-Sylvilagus_floridanus 0.6429 1.0035 1277
## total_shrub_cover-Sciurus_carolinensis 0.7499 1.0031 1625
## total_shrub_cover-Vulpes_vulpes 0.6911 1.0003 1338
## total_shrub_cover-Sus_scrofa 0.9700 1.0098 1373
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4559 0.1830 -2.8285 -2.4519 -2.1143 1.0018
## (Intercept)-Sciurus_niger -3.5787 0.4983 -4.6631 -3.5376 -2.7081 1.0650
## (Intercept)-Procyon_lotor -2.1830 0.1472 -2.4781 -2.1834 -1.8978 1.0001
## (Intercept)-Dasypus_novemcinctus -1.4861 0.1580 -1.7978 -1.4838 -1.1909 1.0008
## (Intercept)-Lynx_rufus -3.2791 0.2994 -3.9029 -3.2740 -2.7141 1.0468
## (Intercept)-Didelphis_virginiana -2.1867 0.2674 -2.7184 -2.1804 -1.6915 1.0109
## (Intercept)-Sylvilagus_floridanus -3.0574 0.2913 -3.6387 -3.0447 -2.5214 1.0091
## (Intercept)-Sciurus_carolinensis -2.3551 0.2808 -2.9188 -2.3449 -1.8212 1.0039
## (Intercept)-Vulpes_vulpes -3.5966 0.6187 -4.8870 -3.5681 -2.5047 1.0771
## (Intercept)-Sus_scrofa -2.8618 0.4628 -3.8546 -2.8312 -2.0442 1.0682
## week-Canis_latrans 0.4672 0.2519 0.0045 0.4526 0.9871 1.0067
## week-Sciurus_niger -0.2005 0.4110 -1.1238 -0.1524 0.4861 1.0146
## week-Procyon_lotor 0.1709 0.1923 -0.1916 0.1689 0.5527 1.0051
## week-Dasypus_novemcinctus 0.0674 0.2155 -0.3421 0.0704 0.4769 1.0010
## week-Lynx_rufus 0.2685 0.3078 -0.3268 0.2597 0.9055 1.0038
## week-Didelphis_virginiana 0.0328 0.3283 -0.6454 0.0540 0.6476 1.0020
## week-Sylvilagus_floridanus 0.0641 0.3029 -0.5597 0.0725 0.6442 1.0044
## week-Sciurus_carolinensis 0.5562 0.3327 -0.0386 0.5329 1.2799 1.0010
## week-Vulpes_vulpes 0.1457 0.4029 -0.6489 0.1532 0.9268 1.0156
## week-Sus_scrofa 0.4430 0.3778 -0.2165 0.4180 1.2871 1.0025
## I(week^2)-Canis_latrans -0.1977 0.1049 -0.4108 -0.1957 0.0018 1.0165
## I(week^2)-Sciurus_niger -0.2539 0.2234 -0.7573 -0.2389 0.1329 1.0124
## I(week^2)-Procyon_lotor -0.1155 0.0850 -0.2896 -0.1157 0.0494 1.0029
## I(week^2)-Dasypus_novemcinctus -0.1533 0.1008 -0.3591 -0.1514 0.0354 1.0008
## I(week^2)-Lynx_rufus -0.1982 0.1466 -0.4975 -0.1902 0.0732 1.0007
## I(week^2)-Didelphis_virginiana -0.3435 0.2095 -0.8499 -0.3180 -0.0076 1.0602
## I(week^2)-Sylvilagus_floridanus -0.1661 0.1439 -0.4610 -0.1600 0.1048 1.0154
## I(week^2)-Sciurus_carolinensis -0.1968 0.1361 -0.4847 -0.1909 0.0495 1.0011
## I(week^2)-Vulpes_vulpes -0.3464 0.2719 -0.9298 -0.3130 0.0656 1.0597
## I(week^2)-Sus_scrofa -0.1729 0.1648 -0.5142 -0.1652 0.1385 1.0019
## ESS
## (Intercept)-Canis_latrans 1287
## (Intercept)-Sciurus_niger 293
## (Intercept)-Procyon_lotor 1642
## (Intercept)-Dasypus_novemcinctus 2209
## (Intercept)-Lynx_rufus 493
## (Intercept)-Didelphis_virginiana 1339
## (Intercept)-Sylvilagus_floridanus 639
## (Intercept)-Sciurus_carolinensis 1155
## (Intercept)-Vulpes_vulpes 253
## (Intercept)-Sus_scrofa 677
## week-Canis_latrans 1042
## week-Sciurus_niger 676
## week-Procyon_lotor 1683
## week-Dasypus_novemcinctus 2182
## week-Lynx_rufus 771
## week-Didelphis_virginiana 964
## week-Sylvilagus_floridanus 984
## week-Sciurus_carolinensis 940
## week-Vulpes_vulpes 1054
## week-Sus_scrofa 880
## I(week^2)-Canis_latrans 1142
## I(week^2)-Sciurus_niger 404
## I(week^2)-Procyon_lotor 1501
## I(week^2)-Dasypus_novemcinctus 1588
## I(week^2)-Lynx_rufus 671
## I(week^2)-Didelphis_virginiana 393
## I(week^2)-Sylvilagus_floridanus 812
## I(week^2)-Sciurus_carolinensis 1067
## I(week^2)-Vulpes_vulpes 356
## I(week^2)-Sus_scrofa 1020
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7635
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8476 0.3867 -1.6197 -0.8550 -0.0596 1.0169 561
## Veg_shannon_index 0.3480 0.2519 -0.1428 0.3433 0.8529 1.0022 926
## Avg_Cogongrass_Cover 0.3044 0.2466 -0.1808 0.3082 0.7843 1.0025 1095
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7927 0.7873 0.0664 0.5692 2.7530 1.0049 581
## Veg_shannon_index 0.2753 0.3392 0.0363 0.1795 1.1230 1.0164 909
## Avg_Cogongrass_Cover 0.2755 0.3051 0.0385 0.1797 1.0779 1.0388 1007
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8688 0.7131 0.0882 0.6968 2.736 1.03 267
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6333 0.2983 -3.2394 -2.6309 -2.0542 1.0025 1471
## week 0.2001 0.1870 -0.1664 0.2007 0.5621 1.0103 1144
## I(week^2) -0.2071 0.1047 -0.4287 -0.2025 -0.0120 1.0024 927
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7745 0.6006 0.1952 0.6103 2.1881 1.0207 663
## week 0.1911 0.1879 0.0320 0.1335 0.7021 1.0058 644
## I(week^2) 0.0627 0.0582 0.0171 0.0490 0.1937 1.0296 981
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2394 0.5849 -1.3277 -0.2445
## (Intercept)-Sciurus_niger -0.9710 0.6691 -2.2921 -0.9799
## (Intercept)-Procyon_lotor -0.0100 0.6187 -1.1761 -0.0218
## (Intercept)-Dasypus_novemcinctus -0.8260 0.4968 -1.8113 -0.8275
## (Intercept)-Lynx_rufus -0.5315 0.6458 -1.6867 -0.5790
## (Intercept)-Didelphis_virginiana -1.2700 0.5425 -2.4156 -1.2455
## (Intercept)-Sylvilagus_floridanus -0.6363 0.5946 -1.7095 -0.6661
## (Intercept)-Sciurus_carolinensis -1.3053 0.5519 -2.4715 -1.2799
## (Intercept)-Vulpes_vulpes -1.2468 0.7585 -2.7982 -1.2385
## (Intercept)-Sus_scrofa -1.6824 0.6944 -3.1382 -1.6350
## Veg_shannon_index-Canis_latrans 0.6401 0.3843 -0.0511 0.6132
## Veg_shannon_index-Sciurus_niger 0.3328 0.4540 -0.5563 0.3225
## Veg_shannon_index-Procyon_lotor 0.4644 0.3565 -0.1983 0.4511
## Veg_shannon_index-Dasypus_novemcinctus 0.2055 0.3318 -0.4668 0.2136
## Veg_shannon_index-Lynx_rufus 0.1737 0.4680 -0.8518 0.2011
## Veg_shannon_index-Didelphis_virginiana 0.4761 0.3726 -0.2201 0.4580
## Veg_shannon_index-Sylvilagus_floridanus 0.4457 0.4034 -0.3072 0.4295
## Veg_shannon_index-Sciurus_carolinensis 0.0239 0.3853 -0.7959 0.0414
## Veg_shannon_index-Vulpes_vulpes 0.0797 0.4495 -0.8807 0.1021
## Veg_shannon_index-Sus_scrofa 0.6441 0.4981 -0.1990 0.5909
## Avg_Cogongrass_Cover-Canis_latrans 0.5082 0.3722 -0.1677 0.4884
## Avg_Cogongrass_Cover-Sciurus_niger -0.0696 0.5042 -1.2187 -0.0128
## Avg_Cogongrass_Cover-Procyon_lotor 0.3842 0.3667 -0.2786 0.3674
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4296 0.3249 -0.1875 0.4217
## Avg_Cogongrass_Cover-Lynx_rufus 0.5683 0.4060 -0.1394 0.5295
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4438 0.3596 -0.2560 0.4281
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0633 0.4301 -0.9780 -0.0342
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4015 0.3497 -0.2808 0.4020
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3638 0.4286 -0.4531 0.3596
## Avg_Cogongrass_Cover-Sus_scrofa 0.0507 0.4793 -1.0559 0.1024
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8983 1.0171 461
## (Intercept)-Sciurus_niger 0.3900 1.0047 372
## (Intercept)-Procyon_lotor 1.2646 1.0080 430
## (Intercept)-Dasypus_novemcinctus 0.1626 1.0153 1025
## (Intercept)-Lynx_rufus 0.8408 1.0089 405
## (Intercept)-Didelphis_virginiana -0.2644 1.0011 987
## (Intercept)-Sylvilagus_floridanus 0.6272 1.0232 689
## (Intercept)-Sciurus_carolinensis -0.2550 1.0029 891
## (Intercept)-Vulpes_vulpes 0.2139 1.0034 369
## (Intercept)-Sus_scrofa -0.4893 1.0018 561
## Veg_shannon_index-Canis_latrans 1.5167 1.0091 1241
## Veg_shannon_index-Sciurus_niger 1.2822 1.0033 1073
## Veg_shannon_index-Procyon_lotor 1.2432 1.0021 1447
## Veg_shannon_index-Dasypus_novemcinctus 0.8594 1.0043 1586
## Veg_shannon_index-Lynx_rufus 1.0664 1.0024 1226
## Veg_shannon_index-Didelphis_virginiana 1.2471 0.9999 1839
## Veg_shannon_index-Sylvilagus_floridanus 1.3076 1.0003 1605
## Veg_shannon_index-Sciurus_carolinensis 0.7080 1.0028 1616
## Veg_shannon_index-Vulpes_vulpes 0.8928 1.0023 1218
## Veg_shannon_index-Sus_scrofa 1.8323 1.0006 1063
## Avg_Cogongrass_Cover-Canis_latrans 1.2952 1.0058 1610
## Avg_Cogongrass_Cover-Sciurus_niger 0.7712 1.0044 839
## Avg_Cogongrass_Cover-Procyon_lotor 1.1282 1.0027 1753
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0726 1.0013 2253
## Avg_Cogongrass_Cover-Lynx_rufus 1.4738 1.0067 1417
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1891 1.0007 1862
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7078 1.0122 1074
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1254 1.0027 2193
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2647 1.0049 1355
## Avg_Cogongrass_Cover-Sus_scrofa 0.8478 1.0150 987
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4465 0.1838 -2.8063 -2.4451 -2.0991 1.0062
## (Intercept)-Sciurus_niger -3.5842 0.5028 -4.6621 -3.5631 -2.6892 1.0394
## (Intercept)-Procyon_lotor -2.1825 0.1438 -2.4721 -2.1793 -1.9090 1.0147
## (Intercept)-Dasypus_novemcinctus -1.4890 0.1565 -1.7956 -1.4847 -1.1933 1.0065
## (Intercept)-Lynx_rufus -3.2791 0.3104 -3.8923 -3.2738 -2.6794 1.0033
## (Intercept)-Didelphis_virginiana -2.1826 0.2678 -2.7401 -2.1658 -1.7015 1.0019
## (Intercept)-Sylvilagus_floridanus -3.0564 0.3036 -3.6953 -3.0472 -2.5020 1.0031
## (Intercept)-Sciurus_carolinensis -2.3418 0.2793 -2.9154 -2.3288 -1.8093 1.0132
## (Intercept)-Vulpes_vulpes -3.6082 0.6213 -4.8211 -3.5692 -2.4900 1.0092
## (Intercept)-Sus_scrofa -2.8523 0.4646 -3.8649 -2.8233 -2.0527 1.0141
## week-Canis_latrans 0.4490 0.2474 -0.0147 0.4379 0.9587 1.0018
## week-Sciurus_niger -0.1685 0.4093 -1.1167 -0.1188 0.5257 1.0006
## week-Procyon_lotor 0.1643 0.1927 -0.2128 0.1631 0.5500 1.0147
## week-Dasypus_novemcinctus 0.0688 0.2095 -0.3373 0.0714 0.4880 1.0004
## week-Lynx_rufus 0.2676 0.2918 -0.2956 0.2564 0.8584 1.0148
## week-Didelphis_virginiana 0.0437 0.3139 -0.6110 0.0589 0.6395 1.0027
## week-Sylvilagus_floridanus 0.0562 0.2891 -0.5217 0.0615 0.6114 1.0149
## week-Sciurus_carolinensis 0.5198 0.3165 -0.0406 0.4936 1.2155 1.0005
## week-Vulpes_vulpes 0.1577 0.3881 -0.6581 0.1581 0.9175 1.0242
## week-Sus_scrofa 0.4222 0.3666 -0.2361 0.4038 1.1972 1.0043
## I(week^2)-Canis_latrans -0.1913 0.1039 -0.4133 -0.1884 0.0068 1.0035
## I(week^2)-Sciurus_niger -0.2395 0.2098 -0.7050 -0.2200 0.1361 1.0217
## I(week^2)-Procyon_lotor -0.1137 0.0856 -0.2882 -0.1114 0.0502 1.0067
## I(week^2)-Dasypus_novemcinctus -0.1557 0.1005 -0.3575 -0.1514 0.0328 1.0011
## I(week^2)-Lynx_rufus -0.2026 0.1390 -0.5027 -0.1957 0.0488 1.0066
## I(week^2)-Didelphis_virginiana -0.3449 0.1935 -0.7755 -0.3243 -0.0244 1.0048
## I(week^2)-Sylvilagus_floridanus -0.1628 0.1456 -0.4768 -0.1564 0.1089 1.0083
## I(week^2)-Sciurus_carolinensis -0.1862 0.1336 -0.4661 -0.1836 0.0623 1.0003
## I(week^2)-Vulpes_vulpes -0.3301 0.2400 -0.8516 -0.3075 0.0628 1.0385
## I(week^2)-Sus_scrofa -0.1534 0.1642 -0.4957 -0.1479 0.1439 1.0059
## ESS
## (Intercept)-Canis_latrans 1321
## (Intercept)-Sciurus_niger 231
## (Intercept)-Procyon_lotor 1502
## (Intercept)-Dasypus_novemcinctus 2360
## (Intercept)-Lynx_rufus 499
## (Intercept)-Didelphis_virginiana 1450
## (Intercept)-Sylvilagus_floridanus 576
## (Intercept)-Sciurus_carolinensis 1340
## (Intercept)-Vulpes_vulpes 222
## (Intercept)-Sus_scrofa 597
## week-Canis_latrans 1160
## week-Sciurus_niger 594
## week-Procyon_lotor 1659
## week-Dasypus_novemcinctus 2150
## week-Lynx_rufus 1010
## week-Didelphis_virginiana 1248
## week-Sylvilagus_floridanus 1000
## week-Sciurus_carolinensis 1206
## week-Vulpes_vulpes 1126
## week-Sus_scrofa 1338
## I(week^2)-Canis_latrans 1279
## I(week^2)-Sciurus_niger 454
## I(week^2)-Procyon_lotor 1592
## I(week^2)-Dasypus_novemcinctus 1662
## I(week^2)-Lynx_rufus 846
## I(week^2)-Didelphis_virginiana 615
## I(week^2)-Sylvilagus_floridanus 780
## I(week^2)-Sciurus_carolinensis 1416
## I(week^2)-Vulpes_vulpes 520
## I(week^2)-Sus_scrofa 1244
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7022
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8106 0.3596 -1.5216 -0.8061 -0.1204 1.0133 753
## Avg_Cogongrass_Cover 0.2003 0.2332 -0.2652 0.1993 0.6323 1.0036 1034
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7102 0.6834 0.0684 0.5196 2.5174 1.0078 872
## Avg_Cogongrass_Cover 0.2473 0.2886 0.0356 0.1597 0.9281 1.0312 965
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7553 0.6044 0.0822 0.6086 2.3992 1.0099 243
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6069 0.2949 -3.2135 -2.5997 -2.0334 1.0491 1382
## week 0.1890 0.1889 -0.1948 0.1935 0.5490 1.0005 868
## I(week^2) -0.2050 0.1038 -0.4136 -0.2006 -0.0072 1.0014 896
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7213 0.5626 0.1814 0.5739 2.1258 1.0787 764
## week 0.1999 0.1979 0.0337 0.1426 0.7037 1.0245 923
## I(week^2) 0.0636 0.0517 0.0185 0.0492 0.1962 1.0118 1159
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1604 0.5513 -1.2516 -0.1546
## (Intercept)-Sciurus_niger -0.9534 0.6213 -2.1403 -0.9566
## (Intercept)-Procyon_lotor -0.0121 0.5905 -1.1624 -0.0133
## (Intercept)-Dasypus_novemcinctus -0.7837 0.4831 -1.7698 -0.7647
## (Intercept)-Lynx_rufus -0.5726 0.5986 -1.6777 -0.6145
## (Intercept)-Didelphis_virginiana -1.2089 0.5233 -2.2988 -1.1955
## (Intercept)-Sylvilagus_floridanus -0.6029 0.5508 -1.6470 -0.6307
## (Intercept)-Sciurus_carolinensis -1.2418 0.5222 -2.3388 -1.2217
## (Intercept)-Vulpes_vulpes -1.2668 0.6927 -2.6909 -1.2484
## (Intercept)-Sus_scrofa -1.5121 0.6481 -2.8785 -1.4689
## Avg_Cogongrass_Cover-Canis_latrans 0.3519 0.3413 -0.2863 0.3371
## Avg_Cogongrass_Cover-Sciurus_niger -0.1240 0.4725 -1.2316 -0.0769
## Avg_Cogongrass_Cover-Procyon_lotor 0.2452 0.3287 -0.3766 0.2376
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3423 0.3143 -0.2607 0.3419
## Avg_Cogongrass_Cover-Lynx_rufus 0.4513 0.3697 -0.2080 0.4205
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3317 0.3320 -0.3313 0.3331
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1462 0.4002 -1.0261 -0.1133
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3329 0.3339 -0.2898 0.3204
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2744 0.3848 -0.4864 0.2635
## Avg_Cogongrass_Cover-Sus_scrofa -0.0573 0.4554 -1.0935 -0.0167
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9203 1.0011 688
## (Intercept)-Sciurus_niger 0.3242 1.0205 606
## (Intercept)-Procyon_lotor 1.1060 1.0212 454
## (Intercept)-Dasypus_novemcinctus 0.1162 1.0092 1198
## (Intercept)-Lynx_rufus 0.6898 1.0134 630
## (Intercept)-Didelphis_virginiana -0.1982 1.0048 1135
## (Intercept)-Sylvilagus_floridanus 0.4987 1.0093 1060
## (Intercept)-Sciurus_carolinensis -0.2829 1.0031 1140
## (Intercept)-Vulpes_vulpes 0.0290 1.0577 412
## (Intercept)-Sus_scrofa -0.3607 1.0082 610
## Avg_Cogongrass_Cover-Canis_latrans 1.0869 1.0002 2350
## Avg_Cogongrass_Cover-Sciurus_niger 0.6737 1.0092 894
## Avg_Cogongrass_Cover-Procyon_lotor 0.9019 1.0009 2253
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9645 1.0021 1973
## Avg_Cogongrass_Cover-Lynx_rufus 1.2918 1.0026 1554
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9926 1.0046 1523
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5663 1.0004 1329
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0353 1.0038 2056
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0321 1.0011 1806
## Avg_Cogongrass_Cover-Sus_scrofa 0.7208 1.0084 1071
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4564 0.1833 -2.8279 -2.4504 -2.1109 1.0191
## (Intercept)-Sciurus_niger -3.5375 0.4983 -4.6044 -3.5006 -2.6685 1.0330
## (Intercept)-Procyon_lotor -2.1784 0.1434 -2.4663 -2.1757 -1.9115 1.0113
## (Intercept)-Dasypus_novemcinctus -1.4921 0.1579 -1.8159 -1.4869 -1.1927 1.0098
## (Intercept)-Lynx_rufus -3.2266 0.3131 -3.8661 -3.2262 -2.6418 1.0275
## (Intercept)-Didelphis_virginiana -2.1787 0.2621 -2.7014 -2.1767 -1.6749 0.9998
## (Intercept)-Sylvilagus_floridanus -3.0211 0.2985 -3.6337 -3.0069 -2.4687 1.0342
## (Intercept)-Sciurus_carolinensis -2.3492 0.2797 -2.9278 -2.3410 -1.8135 1.0032
## (Intercept)-Vulpes_vulpes -3.4909 0.6116 -4.8306 -3.4156 -2.4680 1.1765
## (Intercept)-Sus_scrofa -2.8526 0.4626 -3.8090 -2.8189 -2.0361 1.0239
## week-Canis_latrans 0.4494 0.2419 -0.0058 0.4425 0.9354 1.0023
## week-Sciurus_niger -0.1910 0.4086 -1.1015 -0.1534 0.4961 1.0029
## week-Procyon_lotor 0.1562 0.1950 -0.2281 0.1538 0.5350 1.0046
## week-Dasypus_novemcinctus 0.0630 0.2111 -0.3590 0.0670 0.4792 1.0030
## week-Lynx_rufus 0.2591 0.2980 -0.3138 0.2517 0.8851 1.0029
## week-Didelphis_virginiana 0.0363 0.3129 -0.6147 0.0520 0.6036 1.0086
## week-Sylvilagus_floridanus 0.0566 0.2825 -0.5030 0.0666 0.5875 1.0008
## week-Sciurus_carolinensis 0.5243 0.3239 -0.0608 0.4921 1.2236 1.0019
## week-Vulpes_vulpes 0.1357 0.3914 -0.6695 0.1361 0.8987 1.0115
## week-Sus_scrofa 0.4327 0.3627 -0.2217 0.4018 1.2420 1.0006
## I(week^2)-Canis_latrans -0.1930 0.1022 -0.4009 -0.1926 0.0015 1.0031
## I(week^2)-Sciurus_niger -0.2374 0.2088 -0.7026 -0.2228 0.1232 1.0087
## I(week^2)-Procyon_lotor -0.1127 0.0845 -0.2824 -0.1120 0.0548 1.0008
## I(week^2)-Dasypus_novemcinctus -0.1520 0.0982 -0.3493 -0.1513 0.0378 1.0041
## I(week^2)-Lynx_rufus -0.2016 0.1449 -0.4863 -0.1985 0.0682 1.0189
## I(week^2)-Didelphis_virginiana -0.3450 0.2072 -0.8702 -0.3144 -0.0141 1.0100
## I(week^2)-Sylvilagus_floridanus -0.1572 0.1448 -0.4600 -0.1531 0.1143 1.0004
## I(week^2)-Sciurus_carolinensis -0.1869 0.1344 -0.4676 -0.1817 0.0588 1.0045
## I(week^2)-Vulpes_vulpes -0.3239 0.2286 -0.8240 -0.3020 0.0628 1.0217
## I(week^2)-Sus_scrofa -0.1586 0.1614 -0.4919 -0.1508 0.1391 1.0013
## ESS
## (Intercept)-Canis_latrans 1271
## (Intercept)-Sciurus_niger 253
## (Intercept)-Procyon_lotor 1785
## (Intercept)-Dasypus_novemcinctus 2358
## (Intercept)-Lynx_rufus 385
## (Intercept)-Didelphis_virginiana 1463
## (Intercept)-Sylvilagus_floridanus 705
## (Intercept)-Sciurus_carolinensis 1180
## (Intercept)-Vulpes_vulpes 181
## (Intercept)-Sus_scrofa 772
## week-Canis_latrans 1261
## week-Sciurus_niger 464
## week-Procyon_lotor 1753
## week-Dasypus_novemcinctus 2032
## week-Lynx_rufus 893
## week-Didelphis_virginiana 1216
## week-Sylvilagus_floridanus 1274
## week-Sciurus_carolinensis 1254
## week-Vulpes_vulpes 906
## week-Sus_scrofa 1317
## I(week^2)-Canis_latrans 1301
## I(week^2)-Sciurus_niger 476
## I(week^2)-Procyon_lotor 1676
## I(week^2)-Dasypus_novemcinctus 1741
## I(week^2)-Lynx_rufus 711
## I(week^2)-Didelphis_virginiana 332
## I(week^2)-Sylvilagus_floridanus 707
## I(week^2)-Sciurus_carolinensis 1281
## I(week^2)-Vulpes_vulpes 597
## I(week^2)-Sus_scrofa 1589
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.703
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.5365 0.4014 -2.3596 -1.5203 -0.7723 1.0123 621
## Avg_Cogongrass_Cover -0.7344 0.3428 -1.3995 -0.7327 -0.0763 1.0115 474
## I(Avg_Cogongrass_Cover^2) 0.7936 0.3547 0.1455 0.7714 1.5448 1.0201 610
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7026 0.7470 0.0618 0.4922 2.7686 1.0349 897
## Avg_Cogongrass_Cover 0.3139 0.4049 0.0368 0.2007 1.1812 1.0699 1142
## I(Avg_Cogongrass_Cover^2) 0.6160 1.1806 0.0402 0.2600 3.5432 1.4399 152
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5727 0.5326 0.0583 0.4016 1.9841 1.0223 236
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6115 0.2824 -3.2053 -2.5997 -2.0734 1.0078 1507
## week 0.1839 0.1950 -0.2164 0.1862 0.5695 1.0014 1010
## I(week^2) -0.2028 0.1051 -0.4173 -0.2017 -0.0062 1.0048 651
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6919 0.5228 0.1637 0.5538 1.9839 1.0231 679
## week 0.2117 0.2291 0.0337 0.1432 0.8153 1.0024 873
## I(week^2) 0.0616 0.0484 0.0179 0.0489 0.1900 1.0235 1489
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9702 0.5929 -2.1407 -0.9825
## (Intercept)-Sciurus_niger -1.5314 0.6417 -2.7878 -1.5297
## (Intercept)-Procyon_lotor -0.7787 0.6254 -1.9875 -0.7879
## (Intercept)-Dasypus_novemcinctus -1.4913 0.5260 -2.5397 -1.4912
## (Intercept)-Lynx_rufus -1.4843 0.6041 -2.6742 -1.4754
## (Intercept)-Didelphis_virginiana -1.8587 0.5892 -3.0894 -1.8258
## (Intercept)-Sylvilagus_floridanus -1.3534 0.5917 -2.5183 -1.3475
## (Intercept)-Sciurus_carolinensis -2.1178 0.6288 -3.5079 -2.0683
## (Intercept)-Vulpes_vulpes -2.0849 0.7416 -3.6406 -2.0325
## (Intercept)-Sus_scrofa -2.1076 0.6882 -3.5693 -2.0636
## Avg_Cogongrass_Cover-Canis_latrans -0.5598 0.4900 -1.4753 -0.5713
## Avg_Cogongrass_Cover-Sciurus_niger -0.9603 0.5558 -2.1333 -0.9346
## Avg_Cogongrass_Cover-Procyon_lotor -0.6888 0.4634 -1.5951 -0.6981
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5728 0.4590 -1.4376 -0.5778
## Avg_Cogongrass_Cover-Lynx_rufus -0.6482 0.5050 -1.5913 -0.6594
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4722 0.4981 -1.4047 -0.4941
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1158 0.5568 -2.3199 -1.0654
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7505 0.4900 -1.7567 -0.7404
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7308 0.5357 -1.7718 -0.7350
## Avg_Cogongrass_Cover-Sus_scrofa -0.9188 0.5672 -2.1621 -0.8837
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3851 0.8890 0.3311 1.1479
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2275 0.7300 -1.6437 0.3048
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.3020 0.8317 0.3283 1.0935
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7343 0.3341 0.1007 0.7183
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2203 0.5314 0.3858 1.1442
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5679 0.4048 -0.2005 0.5582
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7869 0.4695 0.0240 0.7395
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9027 0.3725 0.2238 0.8812
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8146 0.4262 0.0501 0.7820
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2350 0.6555 -1.3647 0.3496
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2359 1.0457 736
## (Intercept)-Sciurus_niger -0.2133 1.0024 740
## (Intercept)-Procyon_lotor 0.4671 1.0643 559
## (Intercept)-Dasypus_novemcinctus -0.4427 1.0055 1009
## (Intercept)-Lynx_rufus -0.3365 1.0205 746
## (Intercept)-Didelphis_virginiana -0.7749 1.0295 714
## (Intercept)-Sylvilagus_floridanus -0.1815 1.0089 894
## (Intercept)-Sciurus_carolinensis -0.9957 1.0044 756
## (Intercept)-Vulpes_vulpes -0.7158 1.0464 489
## (Intercept)-Sus_scrofa -0.9110 1.0207 670
## Avg_Cogongrass_Cover-Canis_latrans 0.4479 1.0088 844
## Avg_Cogongrass_Cover-Sciurus_niger 0.0444 1.0011 718
## Avg_Cogongrass_Cover-Procyon_lotor 0.2646 1.0064 970
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3502 1.0009 793
## Avg_Cogongrass_Cover-Lynx_rufus 0.3848 1.0080 863
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6194 1.0166 905
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1615 1.0017 737
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.1702 1.0017 871
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3230 1.0057 816
## Avg_Cogongrass_Cover-Sus_scrofa 0.0862 1.0006 733
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.8294 1.1465 155
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.4689 1.1456 176
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.5910 1.2113 192
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3858 1.0054 1011
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4426 1.0320 388
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3641 1.0218 626
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8849 1.0526 590
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6894 1.0071 798
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7818 1.0111 589
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2124 1.0914 319
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4806 0.1864 -2.8553 -2.4786 -2.1268 1.0074
## (Intercept)-Sciurus_niger -3.5252 0.5091 -4.5678 -3.4789 -2.6312 1.0690
## (Intercept)-Procyon_lotor -2.1931 0.1470 -2.4944 -2.1888 -1.9203 1.0043
## (Intercept)-Dasypus_novemcinctus -1.4948 0.1553 -1.7994 -1.4904 -1.2028 1.0058
## (Intercept)-Lynx_rufus -3.2057 0.2940 -3.8078 -3.2026 -2.6509 1.0086
## (Intercept)-Didelphis_virginiana -2.2104 0.2768 -2.7891 -2.1965 -1.6922 1.0038
## (Intercept)-Sylvilagus_floridanus -3.0298 0.2995 -3.6559 -3.0230 -2.4748 1.0105
## (Intercept)-Sciurus_carolinensis -2.3474 0.2769 -2.9070 -2.3437 -1.8290 1.0211
## (Intercept)-Vulpes_vulpes -3.4847 0.5717 -4.6592 -3.4524 -2.4561 1.0967
## (Intercept)-Sus_scrofa -2.8267 0.4576 -3.8365 -2.7951 -2.0171 1.0062
## week-Canis_latrans 0.4507 0.2416 -0.0012 0.4370 0.9622 1.0072
## week-Sciurus_niger -0.2145 0.4109 -1.1645 -0.1681 0.4707 1.0085
## week-Procyon_lotor 0.1563 0.1972 -0.2220 0.1556 0.5481 1.0001
## week-Dasypus_novemcinctus 0.0573 0.2055 -0.3355 0.0613 0.4589 1.0012
## week-Lynx_rufus 0.2594 0.3015 -0.3318 0.2627 0.8683 1.0214
## week-Didelphis_virginiana 0.0267 0.3081 -0.6383 0.0414 0.5886 0.9996
## week-Sylvilagus_floridanus 0.0489 0.2943 -0.5607 0.0552 0.5962 1.0051
## week-Sciurus_carolinensis 0.5427 0.3459 -0.0687 0.5199 1.2996 1.0019
## week-Vulpes_vulpes 0.1336 0.3872 -0.6796 0.1515 0.8717 1.0009
## week-Sus_scrofa 0.4239 0.3783 -0.2730 0.3940 1.2419 1.0024
## I(week^2)-Canis_latrans -0.1947 0.1013 -0.4068 -0.1901 -0.0032 1.0141
## I(week^2)-Sciurus_niger -0.2312 0.2043 -0.6703 -0.2221 0.1538 1.0067
## I(week^2)-Procyon_lotor -0.1104 0.0870 -0.2853 -0.1075 0.0545 1.0000
## I(week^2)-Dasypus_novemcinctus -0.1460 0.0983 -0.3441 -0.1449 0.0382 1.0025
## I(week^2)-Lynx_rufus -0.1982 0.1469 -0.5020 -0.1962 0.0742 1.0003
## I(week^2)-Didelphis_virginiana -0.3404 0.2018 -0.7989 -0.3183 -0.0209 1.0071
## I(week^2)-Sylvilagus_floridanus -0.1646 0.1442 -0.4594 -0.1591 0.1137 1.0017
## I(week^2)-Sciurus_carolinensis -0.1929 0.1418 -0.4940 -0.1853 0.0705 1.0022
## I(week^2)-Vulpes_vulpes -0.3145 0.2172 -0.8238 -0.2889 0.0420 1.0153
## I(week^2)-Sus_scrofa -0.1543 0.1618 -0.4927 -0.1460 0.1398 1.0018
## ESS
## (Intercept)-Canis_latrans 1141
## (Intercept)-Sciurus_niger 281
## (Intercept)-Procyon_lotor 1610
## (Intercept)-Dasypus_novemcinctus 2412
## (Intercept)-Lynx_rufus 572
## (Intercept)-Didelphis_virginiana 1063
## (Intercept)-Sylvilagus_floridanus 645
## (Intercept)-Sciurus_carolinensis 1362
## (Intercept)-Vulpes_vulpes 239
## (Intercept)-Sus_scrofa 642
## week-Canis_latrans 1233
## week-Sciurus_niger 648
## week-Procyon_lotor 1797
## week-Dasypus_novemcinctus 2099
## week-Lynx_rufus 1034
## week-Didelphis_virginiana 1099
## week-Sylvilagus_floridanus 1062
## week-Sciurus_carolinensis 998
## week-Vulpes_vulpes 1024
## week-Sus_scrofa 1193
## I(week^2)-Canis_latrans 1229
## I(week^2)-Sciurus_niger 524
## I(week^2)-Procyon_lotor 1326
## I(week^2)-Dasypus_novemcinctus 1473
## I(week^2)-Lynx_rufus 796
## I(week^2)-Didelphis_virginiana 521
## I(week^2)-Sylvilagus_floridanus 775
## I(week^2)-Sciurus_carolinensis 1378
## I(week^2)-Vulpes_vulpes 485
## I(week^2)-Sus_scrofa 1389
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2456 0.7433 -3.6700 -2.2729 -0.7524 1.0189 341
## Cogon_Patch_Size -0.4357 0.7050 -1.9447 -0.3938 0.9149 1.0043 305
## Veg_shannon_index 0.8875 0.4209 0.0468 0.8919 1.7075 1.0102 307
## total_shrub_cover -0.3826 0.3850 -1.1499 -0.3788 0.3783 1.0009 625
## Avg_Cogongrass_Cover 0.4474 0.8770 -1.2329 0.4528 2.0933 1.0164 163
## Tree_Density -2.2089 0.7569 -3.7963 -2.1975 -0.7812 1.0084 261
## Avg_Canopy_Cover 1.7183 0.5370 0.7256 1.7035 2.7988 1.0559 222
## I(Avg_Cogongrass_Cover^2) 1.2351 0.5505 0.1320 1.2374 2.3066 1.0744 322
## avg_veg_height -0.2600 0.4428 -1.1190 -0.2637 0.6372 1.0085 257
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.5490 4.2153 0.1325 2.3747 14.2241 1.0444 337
## Cogon_Patch_Size 2.9965 4.2416 0.1692 1.8480 12.2982 1.0713 431
## Veg_shannon_index 0.5745 0.7493 0.0434 0.3207 2.5007 1.0122 686
## total_shrub_cover 0.5991 0.7558 0.0481 0.3581 2.6817 1.0013 542
## Avg_Cogongrass_Cover 1.0316 1.6922 0.0532 0.4667 5.6303 1.0494 438
## Tree_Density 2.1881 3.9018 0.0604 0.9644 12.7438 1.1341 147
## Avg_Canopy_Cover 1.4072 1.7714 0.0799 0.8887 5.9153 1.0270 248
## I(Avg_Cogongrass_Cover^2) 1.4974 2.0392 0.0641 0.7466 7.5192 1.0330 206
## avg_veg_height 0.4043 0.5323 0.0407 0.2376 1.7468 1.0714 737
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6316 1.5333 0.076 1.2087 5.6832 1.045 107
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6983 0.3393 -3.3532 -2.6999 -2.0142 1.0037 1871
## week 0.1835 0.2005 -0.2245 0.1912 0.5577 1.0161 912
## I(week^2) -0.2076 0.1113 -0.4495 -0.2005 -0.0041 1.0393 459
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0693 0.7637 0.3018 0.8647 3.0203 1.0073 1021
## week 0.2204 0.2599 0.0347 0.1443 0.8937 1.1088 276
## I(week^2) 0.0676 0.0597 0.0188 0.0513 0.2287 1.0513 151
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3663 1.0786 -3.2807 -1.4163
## (Intercept)-Sciurus_niger -1.3203 1.3805 -3.6893 -1.4981
## (Intercept)-Procyon_lotor -1.0901 1.0117 -3.0194 -1.1022
## (Intercept)-Dasypus_novemcinctus -2.4839 0.8964 -4.3267 -2.4510
## (Intercept)-Lynx_rufus -1.5876 1.3264 -3.9915 -1.6806
## (Intercept)-Didelphis_virginiana -3.4350 1.1669 -5.9448 -3.3269
## (Intercept)-Sylvilagus_floridanus -2.3649 1.0686 -4.5115 -2.3495
## (Intercept)-Sciurus_carolinensis -3.8969 1.2579 -6.7349 -3.7695
## (Intercept)-Vulpes_vulpes -3.4145 1.5673 -6.5743 -3.3362
## (Intercept)-Sus_scrofa -4.1995 1.5088 -7.6699 -4.0148
## Cogon_Patch_Size-Canis_latrans 1.2576 1.1813 -0.4632 1.0633
## Cogon_Patch_Size-Sciurus_niger -1.4717 1.7032 -5.6818 -1.1924
## Cogon_Patch_Size-Procyon_lotor -0.7263 0.7923 -2.3109 -0.7087
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3395 0.6819 -1.7327 -0.3223
## Cogon_Patch_Size-Lynx_rufus -0.7757 1.3125 -3.3251 -0.7410
## Cogon_Patch_Size-Didelphis_virginiana 1.3725 0.9221 -0.1753 1.2727
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6677 1.5464 -5.6662 -1.3956
## Cogon_Patch_Size-Sciurus_carolinensis -1.2043 1.1994 -3.9000 -1.0379
## Cogon_Patch_Size-Vulpes_vulpes -0.6601 1.4986 -3.8617 -0.5828
## Cogon_Patch_Size-Sus_scrofa -0.7810 1.2835 -3.8423 -0.6146
## Veg_shannon_index-Canis_latrans 1.2547 0.6473 0.1747 1.1881
## Veg_shannon_index-Sciurus_niger 0.9172 0.8064 -0.6119 0.8759
## Veg_shannon_index-Procyon_lotor 1.0906 0.5634 0.0654 1.0562
## Veg_shannon_index-Dasypus_novemcinctus 0.6508 0.5122 -0.3807 0.6596
## Veg_shannon_index-Lynx_rufus 0.9000 0.7391 -0.5381 0.8936
## Veg_shannon_index-Didelphis_virginiana 0.9755 0.6131 -0.1681 0.9596
## Veg_shannon_index-Sylvilagus_floridanus 0.9081 0.6154 -0.2639 0.8870
## Veg_shannon_index-Sciurus_carolinensis 0.3669 0.6766 -1.1218 0.4451
## Veg_shannon_index-Vulpes_vulpes 0.6759 0.7240 -0.8229 0.7064
## Veg_shannon_index-Sus_scrofa 1.2975 0.7728 -0.0064 1.2129
## total_shrub_cover-Canis_latrans -0.0546 0.5308 -1.0091 -0.0746
## total_shrub_cover-Sciurus_niger -0.7311 0.7304 -2.3636 -0.6543
## total_shrub_cover-Procyon_lotor -0.8420 0.5592 -2.0814 -0.7997
## total_shrub_cover-Dasypus_novemcinctus -0.0163 0.5032 -0.9358 -0.0455
## total_shrub_cover-Lynx_rufus -0.8178 0.7694 -2.7451 -0.7408
## total_shrub_cover-Didelphis_virginiana -0.4962 0.5732 -1.6998 -0.4585
## total_shrub_cover-Sylvilagus_floridanus -0.3038 0.6300 -1.5856 -0.2906
## total_shrub_cover-Sciurus_carolinensis -0.1112 0.5945 -1.2000 -0.1529
## total_shrub_cover-Vulpes_vulpes -0.5175 0.7497 -2.2358 -0.4554
## total_shrub_cover-Sus_scrofa -0.0745 0.6960 -1.3244 -0.1191
## Avg_Cogongrass_Cover-Canis_latrans 0.4100 1.0770 -1.7005 0.4236
## Avg_Cogongrass_Cover-Sciurus_niger 0.0355 1.3223 -2.7600 0.1128
## Avg_Cogongrass_Cover-Procyon_lotor 0.6270 1.0782 -1.4663 0.5943
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9706 1.1235 -1.0296 0.9265
## Avg_Cogongrass_Cover-Lynx_rufus 0.6236 1.1376 -1.5160 0.6073
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6115 1.1068 -1.4775 0.6002
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0452 1.2120 -2.5817 0.0197
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6053 1.1236 -1.4337 0.5716
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.6371 1.2292 -1.7171 0.6137
## Avg_Cogongrass_Cover-Sus_scrofa 0.1809 1.2011 -2.3057 0.2441
## Tree_Density-Canis_latrans -2.8994 1.1937 -5.6269 -2.7662
## Tree_Density-Sciurus_niger -2.2587 1.3379 -5.2545 -2.2169
## Tree_Density-Procyon_lotor -2.1211 0.9465 -4.1231 -2.1074
## Tree_Density-Dasypus_novemcinctus -3.4984 1.5055 -7.1297 -3.1934
## Tree_Density-Lynx_rufus -1.0533 1.3815 -3.3289 -1.2024
## Tree_Density-Didelphis_virginiana -2.3893 1.0437 -4.7185 -2.3070
## Tree_Density-Sylvilagus_floridanus -2.6156 1.4135 -5.9405 -2.4443
## Tree_Density-Sciurus_carolinensis -2.5734 1.1505 -5.1402 -2.4208
## Tree_Density-Vulpes_vulpes -2.0870 1.4900 -4.6775 -2.1524
## Tree_Density-Sus_scrofa -2.3442 1.3067 -5.2269 -2.2767
## Avg_Canopy_Cover-Canis_latrans 0.4455 0.6893 -0.8964 0.4444
## Avg_Canopy_Cover-Sciurus_niger 1.8200 1.2415 -0.5493 1.7515
## Avg_Canopy_Cover-Procyon_lotor 1.6508 0.6736 0.4407 1.6181
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8684 0.6585 0.7428 1.8118
## Avg_Canopy_Cover-Lynx_rufus 1.1969 1.0092 -0.7857 1.1925
## Avg_Canopy_Cover-Didelphis_virginiana 2.3812 0.8300 1.0724 2.2854
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.7975 1.2487 0.9764 2.5829
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0249 0.7600 0.7533 1.9524
## Avg_Canopy_Cover-Vulpes_vulpes 2.0165 0.9877 0.3973 1.9025
## Avg_Canopy_Cover-Sus_scrofa 1.8186 0.7994 0.4875 1.7515
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.0400 0.9849 0.6195 1.8800
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.6425 1.3272 -2.5104 0.8049
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.9458 0.9409 0.5178 1.8080
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2525 0.6707 0.0298 1.2088
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.0924 1.0239 0.5166 1.9484
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7897 0.6697 -0.5730 0.8117
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9707 0.8002 -0.4949 0.9304
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3374 0.6893 0.0689 1.2990
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.5969 0.9187 0.1854 1.4662
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1876 1.2497 -2.8982 0.3969
## avg_veg_height-Canis_latrans -0.4900 0.5779 -1.6452 -0.5054
## avg_veg_height-Sciurus_niger -0.3822 0.7112 -1.8554 -0.3618
## avg_veg_height-Procyon_lotor 0.0033 0.5875 -1.0812 -0.0096
## avg_veg_height-Dasypus_novemcinctus -0.0216 0.5774 -1.1419 -0.0364
## avg_veg_height-Lynx_rufus -0.3512 0.6985 -1.7804 -0.3450
## avg_veg_height-Didelphis_virginiana -0.3476 0.6151 -1.5469 -0.3482
## avg_veg_height-Sylvilagus_floridanus -0.4005 0.6276 -1.7138 -0.3808
## avg_veg_height-Sciurus_carolinensis -0.0083 0.6054 -1.1321 -0.0366
## avg_veg_height-Vulpes_vulpes -0.3584 0.6887 -1.7746 -0.3537
## avg_veg_height-Sus_scrofa -0.2880 0.6441 -1.5571 -0.2880
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8088 1.0386 354
## (Intercept)-Sciurus_niger 1.7622 1.0095 256
## (Intercept)-Procyon_lotor 0.8449 1.0138 322
## (Intercept)-Dasypus_novemcinctus -0.8016 1.0045 625
## (Intercept)-Lynx_rufus 1.4339 1.0151 225
## (Intercept)-Didelphis_virginiana -1.5449 1.0415 344
## (Intercept)-Sylvilagus_floridanus -0.2684 1.0171 458
## (Intercept)-Sciurus_carolinensis -1.7816 1.0233 357
## (Intercept)-Vulpes_vulpes -0.4703 1.0795 217
## (Intercept)-Sus_scrofa -1.7982 1.0632 261
## Cogon_Patch_Size-Canis_latrans 4.1780 1.0073 576
## Cogon_Patch_Size-Sciurus_niger 1.1936 1.0281 252
## Cogon_Patch_Size-Procyon_lotor 0.7255 1.0041 395
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9454 1.0036 587
## Cogon_Patch_Size-Lynx_rufus 1.9665 1.0061 305
## Cogon_Patch_Size-Didelphis_virginiana 3.3915 1.0420 462
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5262 1.0290 296
## Cogon_Patch_Size-Sciurus_carolinensis 0.5457 1.0354 340
## Cogon_Patch_Size-Vulpes_vulpes 2.2182 1.0151 315
## Cogon_Patch_Size-Sus_scrofa 1.3579 1.0088 410
## Veg_shannon_index-Canis_latrans 2.7327 1.0054 610
## Veg_shannon_index-Sciurus_niger 2.6353 1.0059 517
## Veg_shannon_index-Procyon_lotor 2.2820 1.0024 430
## Veg_shannon_index-Dasypus_novemcinctus 1.6192 1.0013 1045
## Veg_shannon_index-Lynx_rufus 2.4711 1.0098 521
## Veg_shannon_index-Didelphis_virginiana 2.2488 1.0040 554
## Veg_shannon_index-Sylvilagus_floridanus 2.1678 1.0189 627
## Veg_shannon_index-Sciurus_carolinensis 1.5236 1.0328 649
## Veg_shannon_index-Vulpes_vulpes 2.0447 1.0349 758
## Veg_shannon_index-Sus_scrofa 3.1098 1.0045 591
## total_shrub_cover-Canis_latrans 1.0681 1.0089 1140
## total_shrub_cover-Sciurus_niger 0.5383 1.0051 557
## total_shrub_cover-Procyon_lotor 0.1194 1.0032 688
## total_shrub_cover-Dasypus_novemcinctus 1.0130 1.0001 1154
## total_shrub_cover-Lynx_rufus 0.4622 1.0067 375
## total_shrub_cover-Didelphis_virginiana 0.5655 1.0010 1022
## total_shrub_cover-Sylvilagus_floridanus 0.9413 1.0014 733
## total_shrub_cover-Sciurus_carolinensis 1.1589 1.0016 1177
## total_shrub_cover-Vulpes_vulpes 0.8431 1.0047 610
## total_shrub_cover-Sus_scrofa 1.4913 1.0010 851
## Avg_Cogongrass_Cover-Canis_latrans 2.4859 1.0154 240
## Avg_Cogongrass_Cover-Sciurus_niger 2.3471 1.0025 297
## Avg_Cogongrass_Cover-Procyon_lotor 2.8237 1.0065 237
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3935 1.0144 231
## Avg_Cogongrass_Cover-Lynx_rufus 3.0155 1.0167 199
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8333 1.0174 224
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.1132 1.0141 233
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9525 1.0028 242
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.1125 1.0144 202
## Avg_Cogongrass_Cover-Sus_scrofa 2.3353 1.0397 231
## Tree_Density-Canis_latrans -0.9274 1.0094 403
## Tree_Density-Sciurus_niger 0.2898 1.0189 355
## Tree_Density-Procyon_lotor -0.2616 1.0046 353
## Tree_Density-Dasypus_novemcinctus -1.3880 1.0371 239
## Tree_Density-Lynx_rufus 2.1717 1.0188 224
## Tree_Density-Didelphis_virginiana -0.5609 1.0324 393
## Tree_Density-Sylvilagus_floridanus -0.3267 1.0246 275
## Tree_Density-Sciurus_carolinensis -0.7020 1.0276 388
## Tree_Density-Vulpes_vulpes 1.0132 1.1054 204
## Tree_Density-Sus_scrofa -0.0088 1.0321 361
## Avg_Canopy_Cover-Canis_latrans 1.8199 1.0043 345
## Avg_Canopy_Cover-Sciurus_niger 4.5627 1.0921 226
## Avg_Canopy_Cover-Procyon_lotor 3.0725 1.0110 433
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3344 1.0107 545
## Avg_Canopy_Cover-Lynx_rufus 3.2513 1.0341 353
## Avg_Canopy_Cover-Didelphis_virginiana 4.3451 1.0374 209
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.8570 1.0149 289
## Avg_Canopy_Cover-Sciurus_carolinensis 3.6506 1.0319 500
## Avg_Canopy_Cover-Vulpes_vulpes 4.3744 1.0113 394
## Avg_Canopy_Cover-Sus_scrofa 3.5532 1.0295 376
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4481 1.0178 276
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 2.9447 1.0737 174
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.2141 1.0011 318
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.7228 1.0490 581
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 4.4919 1.0088 292
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0694 1.0513 355
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7182 1.0410 358
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.7628 1.0477 555
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.0386 1.0247 307
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.0157 1.0367 212
## avg_veg_height-Canis_latrans 0.6700 1.0041 437
## avg_veg_height-Sciurus_niger 0.9705 1.0136 487
## avg_veg_height-Procyon_lotor 1.2206 1.0080 463
## avg_veg_height-Dasypus_novemcinctus 1.1770 1.0122 442
## avg_veg_height-Lynx_rufus 1.0351 1.0075 397
## avg_veg_height-Didelphis_virginiana 0.8670 1.0069 458
## avg_veg_height-Sylvilagus_floridanus 0.8071 1.0101 604
## avg_veg_height-Sciurus_carolinensis 1.2338 1.0196 514
## avg_veg_height-Vulpes_vulpes 1.0105 1.0060 432
## avg_veg_height-Sus_scrofa 1.0370 1.0029 474
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4689 0.1902 -2.8650 -2.4596 -2.1236 1.0050
## (Intercept)-Sciurus_niger -4.1628 0.4729 -5.0490 -4.1926 -3.1431 1.0292
## (Intercept)-Procyon_lotor -2.1681 0.1488 -2.4725 -2.1634 -1.8825 1.0019
## (Intercept)-Dasypus_novemcinctus -1.4774 0.1559 -1.7950 -1.4733 -1.1795 1.0001
## (Intercept)-Lynx_rufus -3.4345 0.3203 -4.0608 -3.4250 -2.8115 1.0079
## (Intercept)-Didelphis_virginiana -2.1612 0.2677 -2.7056 -2.1508 -1.6644 1.0034
## (Intercept)-Sylvilagus_floridanus -3.0943 0.2904 -3.6925 -3.0896 -2.5457 1.0098
## (Intercept)-Sciurus_carolinensis -2.3327 0.2859 -2.9317 -2.3226 -1.8045 1.0291
## (Intercept)-Vulpes_vulpes -3.9028 0.5870 -5.0722 -3.8814 -2.8024 1.0063
## (Intercept)-Sus_scrofa -2.8655 0.4596 -3.8073 -2.8364 -2.0311 1.0200
## week-Canis_latrans 0.4470 0.2451 -0.0036 0.4418 0.9740 1.0070
## week-Sciurus_niger -0.2193 0.4698 -1.3799 -0.1472 0.5013 1.1286
## week-Procyon_lotor 0.1592 0.1941 -0.2146 0.1565 0.5611 1.0028
## week-Dasypus_novemcinctus 0.0712 0.2062 -0.3538 0.0740 0.4756 0.9999
## week-Lynx_rufus 0.2771 0.2926 -0.2968 0.2636 0.8646 1.0093
## week-Didelphis_virginiana 0.0230 0.3269 -0.6971 0.0352 0.6198 1.0049
## week-Sylvilagus_floridanus 0.0350 0.2885 -0.5771 0.0447 0.5985 1.0064
## week-Sciurus_carolinensis 0.5414 0.3384 -0.0499 0.5214 1.2700 1.0157
## week-Vulpes_vulpes 0.1127 0.4027 -0.7523 0.1266 0.8783 1.0420
## week-Sus_scrofa 0.4310 0.3841 -0.2615 0.4070 1.2572 1.0259
## I(week^2)-Canis_latrans -0.1948 0.1017 -0.3991 -0.1938 0.0027 1.0029
## I(week^2)-Sciurus_niger -0.2626 0.2332 -0.8035 -0.2383 0.1287 1.0472
## I(week^2)-Procyon_lotor -0.1147 0.0845 -0.2828 -0.1143 0.0501 1.0064
## I(week^2)-Dasypus_novemcinctus -0.1538 0.0984 -0.3528 -0.1528 0.0319 1.0007
## I(week^2)-Lynx_rufus -0.1961 0.1493 -0.5206 -0.1877 0.0748 1.0107
## I(week^2)-Didelphis_virginiana -0.3456 0.2110 -0.8198 -0.3203 -0.0066 1.0341
## I(week^2)-Sylvilagus_floridanus -0.1459 0.1450 -0.4447 -0.1424 0.1301 1.0233
## I(week^2)-Sciurus_carolinensis -0.1902 0.1395 -0.4911 -0.1834 0.0670 1.0136
## I(week^2)-Vulpes_vulpes -0.3517 0.2683 -0.9604 -0.3193 0.0524 1.1301
## I(week^2)-Sus_scrofa -0.1571 0.1661 -0.4996 -0.1531 0.1441 1.0055
## ESS
## (Intercept)-Canis_latrans 786
## (Intercept)-Sciurus_niger 205
## (Intercept)-Procyon_lotor 1814
## (Intercept)-Dasypus_novemcinctus 2586
## (Intercept)-Lynx_rufus 341
## (Intercept)-Didelphis_virginiana 1530
## (Intercept)-Sylvilagus_floridanus 708
## (Intercept)-Sciurus_carolinensis 1109
## (Intercept)-Vulpes_vulpes 244
## (Intercept)-Sus_scrofa 695
## week-Canis_latrans 948
## week-Sciurus_niger 238
## week-Procyon_lotor 1725
## week-Dasypus_novemcinctus 1897
## week-Lynx_rufus 1073
## week-Didelphis_virginiana 962
## week-Sylvilagus_floridanus 1083
## week-Sciurus_carolinensis 889
## week-Vulpes_vulpes 481
## week-Sus_scrofa 1285
## I(week^2)-Canis_latrans 1281
## I(week^2)-Sciurus_niger 243
## I(week^2)-Procyon_lotor 1584
## I(week^2)-Dasypus_novemcinctus 1822
## I(week^2)-Lynx_rufus 566
## I(week^2)-Didelphis_virginiana 367
## I(week^2)-Sylvilagus_floridanus 906
## I(week^2)-Sciurus_carolinensis 904
## I(week^2)-Vulpes_vulpes 131
## I(week^2)-Sus_scrofa 1416
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_null)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7988
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5815 0.3286 -1.2436 -0.5827 0.079 1.0118 1218
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8472 0.7001 0.167 0.654 2.7231 1.0419 1144
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7252 0.2924 -3.3391 -2.7204 -2.1616 1.0334 913
## shrub_cover 0.2896 0.2768 -0.2349 0.2846 0.8373 1.0081 856
## veg_height 0.0616 0.1694 -0.2776 0.0637 0.3947 0.9996 1371
## week 0.1924 0.2040 -0.2143 0.1938 0.5966 1.0054 976
## I(week^2) -0.2131 0.1103 -0.4484 -0.2098 -0.0104 1.0048 924
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7465 0.6613 0.1696 0.5870 2.2365 1.0163 535
## shrub_cover 0.5159 0.4590 0.0947 0.3929 1.7527 1.0277 801
## veg_height 0.1994 0.1491 0.0520 0.1567 0.5905 1.0009 1471
## week 0.2190 0.2173 0.0340 0.1530 0.7768 1.0094 696
## I(week^2) 0.0675 0.0779 0.0177 0.0506 0.2131 1.0889 1356
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2131 0.3803 -0.4807 0.1975 1.0157 1.0010
## (Intercept)-Sciurus_niger -0.8832 0.5847 -1.9820 -0.8891 0.3272 1.0377
## (Intercept)-Procyon_lotor 0.5028 0.3807 -0.2071 0.4984 1.2767 1.0068
## (Intercept)-Dasypus_novemcinctus -0.5901 0.3506 -1.2782 -0.5923 0.0946 1.0038
## (Intercept)-Lynx_rufus -0.0112 0.5564 -0.9675 -0.0586 1.2280 1.0197
## (Intercept)-Didelphis_virginiana -1.1288 0.4211 -1.9963 -1.1219 -0.3392 1.0086
## (Intercept)-Sylvilagus_floridanus -0.4352 0.4131 -1.2156 -0.4472 0.4289 1.0087
## (Intercept)-Sciurus_carolinensis -1.0964 0.4271 -1.9662 -1.0792 -0.2842 1.0097
## (Intercept)-Vulpes_vulpes -1.2362 0.6742 -2.5075 -1.2622 0.1390 1.0319
## (Intercept)-Sus_scrofa -1.4324 0.5577 -2.5596 -1.4192 -0.3936 1.0241
## ESS
## (Intercept)-Canis_latrans 1960
## (Intercept)-Sciurus_niger 619
## (Intercept)-Procyon_lotor 1567
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 512
## (Intercept)-Didelphis_virginiana 1419
## (Intercept)-Sylvilagus_floridanus 1700
## (Intercept)-Sciurus_carolinensis 1932
## (Intercept)-Vulpes_vulpes 386
## (Intercept)-Sus_scrofa 571
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5816 0.2030 -3.0038 -2.5780 -2.2018 1.0103
## (Intercept)-Sciurus_niger -3.6084 0.5648 -4.7938 -3.5917 -2.5613 1.1067
## (Intercept)-Procyon_lotor -2.2018 0.1555 -2.5091 -2.1999 -1.9028 1.0029
## (Intercept)-Dasypus_novemcinctus -1.6389 0.1817 -1.9978 -1.6348 -1.2962 1.0018
## (Intercept)-Lynx_rufus -3.4021 0.3590 -4.1170 -3.3965 -2.7081 1.0041
## (Intercept)-Didelphis_virginiana -2.3875 0.2892 -2.9531 -2.3768 -1.8501 1.0053
## (Intercept)-Sylvilagus_floridanus -3.0117 0.2841 -3.6040 -3.0025 -2.4724 1.0411
## (Intercept)-Sciurus_carolinensis -2.5141 0.3089 -3.1458 -2.5036 -1.9495 1.0146
## (Intercept)-Vulpes_vulpes -3.6034 0.6323 -5.0170 -3.5547 -2.5409 1.0575
## (Intercept)-Sus_scrofa -3.1721 0.5432 -4.3518 -3.1495 -2.1495 1.0514
## shrub_cover-Canis_latrans -0.2775 0.2180 -0.7145 -0.2741 0.1393 1.0077
## shrub_cover-Sciurus_niger -0.1855 0.4885 -1.1361 -0.1905 0.7935 1.0266
## shrub_cover-Procyon_lotor 0.2551 0.1556 -0.0581 0.2608 0.5462 1.0040
## shrub_cover-Dasypus_novemcinctus 0.8380 0.2878 0.3132 0.8259 1.4238 1.0022
## shrub_cover-Lynx_rufus -0.2249 0.3629 -0.9458 -0.2156 0.4785 1.0060
## shrub_cover-Didelphis_virginiana 0.9260 0.3695 0.2488 0.9075 1.7087 1.0213
## shrub_cover-Sylvilagus_floridanus 0.2686 0.4012 -0.4983 0.2601 1.0729 1.0357
## shrub_cover-Sciurus_carolinensis 0.8213 0.3895 0.0973 0.8102 1.6297 1.0079
## shrub_cover-Vulpes_vulpes -0.0147 0.5623 -1.1828 -0.0055 1.0922 1.0107
## shrub_cover-Sus_scrofa 0.5860 0.7013 -0.8229 0.5598 2.0175 1.0054
## veg_height-Canis_latrans -0.5626 0.1862 -0.9503 -0.5579 -0.2199 1.0019
## veg_height-Sciurus_niger 0.0681 0.4102 -0.6775 0.0547 0.9297 1.0250
## veg_height-Procyon_lotor 0.3363 0.1197 0.1037 0.3384 0.5630 1.0023
## veg_height-Dasypus_novemcinctus 0.2536 0.1331 -0.0012 0.2561 0.5173 1.0031
## veg_height-Lynx_rufus 0.0384 0.2363 -0.4272 0.0388 0.4937 1.0193
## veg_height-Didelphis_virginiana 0.4452 0.2360 -0.0054 0.4433 0.9233 1.0056
## veg_height-Sylvilagus_floridanus 0.1411 0.2284 -0.3056 0.1398 0.5813 1.0051
## veg_height-Sciurus_carolinensis 0.0871 0.2086 -0.3067 0.0788 0.5052 1.0144
## veg_height-Vulpes_vulpes -0.0773 0.2939 -0.7041 -0.0704 0.4690 1.0018
## veg_height-Sus_scrofa -0.1026 0.3276 -0.7887 -0.0925 0.5234 1.0028
## week-Canis_latrans 0.4566 0.2467 -0.0087 0.4528 0.9650 1.0060
## week-Sciurus_niger -0.2208 0.4326 -1.2273 -0.1623 0.4816 1.0068
## week-Procyon_lotor 0.1627 0.2036 -0.2359 0.1637 0.5641 1.0004
## week-Dasypus_novemcinctus 0.0683 0.2195 -0.3867 0.0684 0.4862 1.0020
## week-Lynx_rufus 0.2769 0.2923 -0.2722 0.2648 0.8978 1.0173
## week-Didelphis_virginiana 0.0239 0.3230 -0.6176 0.0370 0.6388 1.0106
## week-Sylvilagus_floridanus 0.0369 0.2986 -0.5660 0.0473 0.6074 1.0041
## week-Sciurus_carolinensis 0.5649 0.3410 -0.0271 0.5361 1.3053 1.0139
## week-Vulpes_vulpes 0.1315 0.4172 -0.7167 0.1455 0.9575 1.0004
## week-Sus_scrofa 0.4472 0.3845 -0.2616 0.4259 1.3112 1.0004
## I(week^2)-Canis_latrans -0.1952 0.1042 -0.4005 -0.1921 -0.0004 1.0019
## I(week^2)-Sciurus_niger -0.2421 0.2168 -0.7465 -0.2271 0.1307 1.0321
## I(week^2)-Procyon_lotor -0.1145 0.0895 -0.2916 -0.1142 0.0570 1.0035
## I(week^2)-Dasypus_novemcinctus -0.1577 0.1010 -0.3607 -0.1570 0.0375 1.0039
## I(week^2)-Lynx_rufus -0.2018 0.1422 -0.5000 -0.1946 0.0543 1.0194
## I(week^2)-Didelphis_virginiana -0.3628 0.2094 -0.8293 -0.3391 -0.0160 1.0049
## I(week^2)-Sylvilagus_floridanus -0.1571 0.1503 -0.4638 -0.1548 0.1329 1.0163
## I(week^2)-Sciurus_carolinensis -0.1951 0.1402 -0.4866 -0.1882 0.0558 1.0043
## I(week^2)-Vulpes_vulpes -0.3368 0.2347 -0.8749 -0.3149 0.0530 1.0129
## I(week^2)-Sus_scrofa -0.1725 0.1617 -0.5036 -0.1693 0.1334 1.0026
## ESS
## (Intercept)-Canis_latrans 902
## (Intercept)-Sciurus_niger 310
## (Intercept)-Procyon_lotor 1549
## (Intercept)-Dasypus_novemcinctus 1760
## (Intercept)-Lynx_rufus 336
## (Intercept)-Didelphis_virginiana 1168
## (Intercept)-Sylvilagus_floridanus 856
## (Intercept)-Sciurus_carolinensis 1089
## (Intercept)-Vulpes_vulpes 232
## (Intercept)-Sus_scrofa 469
## shrub_cover-Canis_latrans 852
## shrub_cover-Sciurus_niger 488
## shrub_cover-Procyon_lotor 1350
## shrub_cover-Dasypus_novemcinctus 1241
## shrub_cover-Lynx_rufus 462
## shrub_cover-Didelphis_virginiana 719
## shrub_cover-Sylvilagus_floridanus 609
## shrub_cover-Sciurus_carolinensis 780
## shrub_cover-Vulpes_vulpes 589
## shrub_cover-Sus_scrofa 563
## veg_height-Canis_latrans 718
## veg_height-Sciurus_niger 791
## veg_height-Procyon_lotor 1677
## veg_height-Dasypus_novemcinctus 1751
## veg_height-Lynx_rufus 826
## veg_height-Didelphis_virginiana 1266
## veg_height-Sylvilagus_floridanus 958
## veg_height-Sciurus_carolinensis 1253
## veg_height-Vulpes_vulpes 811
## veg_height-Sus_scrofa 1161
## week-Canis_latrans 1116
## week-Sciurus_niger 509
## week-Procyon_lotor 1564
## week-Dasypus_novemcinctus 2229
## week-Lynx_rufus 1004
## week-Didelphis_virginiana 1319
## week-Sylvilagus_floridanus 1142
## week-Sciurus_carolinensis 1051
## week-Vulpes_vulpes 882
## week-Sus_scrofa 1078
## I(week^2)-Canis_latrans 1134
## I(week^2)-Sciurus_niger 420
## I(week^2)-Procyon_lotor 1492
## I(week^2)-Dasypus_novemcinctus 1994
## I(week^2)-Lynx_rufus 795
## I(week^2)-Didelphis_virginiana 409
## I(week^2)-Sylvilagus_floridanus 871
## I(week^2)-Sciurus_carolinensis 1423
## I(week^2)-Vulpes_vulpes 574
## I(week^2)-Sus_scrofa 1235
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_full)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8642
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1072 0.6926 -2.4132 -1.1298 0.3629 1.0002 289
## Cogon_Patch_Size -0.8582 0.6637 -2.2399 -0.8367 0.3951 1.0140 381
## Veg_shannon_index 0.9164 0.4426 0.0724 0.9183 1.7763 1.0024 346
## total_shrub_cover -0.5603 0.5157 -1.7439 -0.5261 0.3559 1.0750 272
## Avg_Cogongrass_Cover 2.1079 0.7450 0.6452 2.1179 3.5221 1.0300 139
## Tree_Density -1.9863 0.7508 -3.4926 -1.9770 -0.5544 1.0151 282
## Avg_Canopy_Cover 1.8638 0.6213 0.7377 1.8168 3.2359 1.0146 256
## avg_veg_height -0.5907 0.4804 -1.5522 -0.5823 0.3634 1.0176 193
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.4921 4.5967 0.1107 2.2642 14.3046 1.0851 166
## Cogon_Patch_Size 2.4138 3.3244 0.1140 1.4109 10.6709 1.0591 314
## Veg_shannon_index 0.7066 1.0714 0.0487 0.3674 3.4215 1.0425 343
## total_shrub_cover 0.8413 1.2451 0.0496 0.4458 4.0663 1.0187 239
## Avg_Cogongrass_Cover 1.3621 2.6355 0.0547 0.5582 7.5178 1.1848 83
## Tree_Density 2.5374 3.7353 0.0909 1.3438 12.7275 1.0392 284
## Avg_Canopy_Cover 2.1456 2.6830 0.1429 1.3481 9.5287 1.0760 166
## avg_veg_height 0.4185 0.6568 0.0400 0.2216 2.0580 1.0071 432
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2746 2.4113 0.0795 1.5756 8.738 1.0075 65
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8347 0.3367 -3.4987 -2.8357 -2.1444 1.0014 1710
## shrub_cover 0.4339 0.2759 -0.1041 0.4244 0.9930 1.0365 491
## veg_height 0.0778 0.1720 -0.2646 0.0834 0.3977 1.0005 1292
## week 0.1973 0.2052 -0.2304 0.2006 0.5876 1.0096 899
## I(week^2) -0.2125 0.1070 -0.4329 -0.2081 -0.0184 1.0306 575
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0229 0.8181 0.2511 0.8180 2.9732 1.0115 919
## shrub_cover 0.5173 0.4386 0.1031 0.3943 1.7119 1.0414 628
## veg_height 0.2082 0.1626 0.0533 0.1656 0.6080 1.0098 1381
## week 0.2208 0.2325 0.0346 0.1531 0.8192 1.0303 490
## I(week^2) 0.0631 0.0474 0.0174 0.0503 0.1859 1.0006 901
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1008 1.0779 -1.9061 0.0860
## (Intercept)-Sciurus_niger -0.4188 1.5872 -2.8246 -0.6309
## (Intercept)-Procyon_lotor 0.1933 1.1443 -1.9893 0.2554
## (Intercept)-Dasypus_novemcinctus -1.3963 0.8731 -3.2689 -1.3612
## (Intercept)-Lynx_rufus 0.0609 1.6418 -2.4324 -0.1674
## (Intercept)-Didelphis_virginiana -2.3022 1.0560 -4.5826 -2.1808
## (Intercept)-Sylvilagus_floridanus -1.2321 1.0671 -3.3499 -1.2674
## (Intercept)-Sciurus_carolinensis -2.3492 1.1685 -4.8903 -2.2043
## (Intercept)-Vulpes_vulpes -2.1060 1.3029 -4.8541 -2.0478
## (Intercept)-Sus_scrofa -2.9148 1.4621 -6.1459 -2.7605
## Cogon_Patch_Size-Canis_latrans 0.4353 1.0963 -1.1259 0.2792
## Cogon_Patch_Size-Sciurus_niger -1.5655 1.5342 -4.9944 -1.3704
## Cogon_Patch_Size-Procyon_lotor -1.1602 0.7408 -2.6635 -1.1334
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6724 0.7514 -2.1122 -0.6883
## Cogon_Patch_Size-Lynx_rufus -1.0165 1.2397 -3.4068 -1.0103
## Cogon_Patch_Size-Didelphis_virginiana 0.6431 0.8932 -0.8815 0.5424
## Cogon_Patch_Size-Sylvilagus_floridanus -1.9422 1.3779 -5.3691 -1.7425
## Cogon_Patch_Size-Sciurus_carolinensis -1.5648 1.1743 -4.3580 -1.4110
## Cogon_Patch_Size-Vulpes_vulpes -1.3714 1.4320 -4.6978 -1.1866
## Cogon_Patch_Size-Sus_scrofa -1.2629 1.2526 -4.1575 -1.1024
## Veg_shannon_index-Canis_latrans 1.2910 0.6124 0.2117 1.2427
## Veg_shannon_index-Sciurus_niger 1.0277 0.8470 -0.5262 1.0076
## Veg_shannon_index-Procyon_lotor 1.2539 0.5868 0.1671 1.2180
## Veg_shannon_index-Dasypus_novemcinctus 0.6953 0.5400 -0.4395 0.6949
## Veg_shannon_index-Lynx_rufus 0.8328 0.8059 -0.9812 0.8793
## Veg_shannon_index-Didelphis_virginiana 1.0968 0.6341 -0.0417 1.0670
## Veg_shannon_index-Sylvilagus_floridanus 1.0435 0.6719 -0.1812 1.0200
## Veg_shannon_index-Sciurus_carolinensis 0.3545 0.7355 -1.3088 0.4081
## Veg_shannon_index-Vulpes_vulpes 0.4573 0.8053 -1.3685 0.5393
## Veg_shannon_index-Sus_scrofa 1.3229 0.8275 -0.0098 1.2196
## total_shrub_cover-Canis_latrans 0.2073 0.6903 -0.9425 0.1237
## total_shrub_cover-Sciurus_niger -0.8898 1.0011 -3.3524 -0.7686
## total_shrub_cover-Procyon_lotor -0.8995 0.5953 -2.1715 -0.8455
## total_shrub_cover-Dasypus_novemcinctus -0.2348 0.6453 -1.5787 -0.2295
## total_shrub_cover-Lynx_rufus -0.8979 1.0168 -3.3156 -0.7766
## total_shrub_cover-Didelphis_virginiana -0.7684 0.7473 -2.5257 -0.7099
## total_shrub_cover-Sylvilagus_floridanus -0.5836 0.8507 -2.5593 -0.5078
## total_shrub_cover-Sciurus_carolinensis -0.5391 0.8065 -2.4186 -0.4683
## total_shrub_cover-Vulpes_vulpes -0.7797 0.9170 -2.9022 -0.6746
## total_shrub_cover-Sus_scrofa -0.4184 0.9426 -2.4749 -0.3627
## Avg_Cogongrass_Cover-Canis_latrans 2.4932 0.9100 0.8748 2.4447
## Avg_Cogongrass_Cover-Sciurus_niger 1.3595 1.5331 -2.4223 1.5876
## Avg_Cogongrass_Cover-Procyon_lotor 2.3486 0.9303 0.5733 2.3314
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6975 1.0043 0.9528 2.6330
## Avg_Cogongrass_Cover-Lynx_rufus 2.5109 1.0408 0.6936 2.4463
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1931 0.9126 0.4838 2.1764
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5145 1.0347 -0.5354 1.5869
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4086 0.9905 0.6291 2.3347
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6296 1.0644 0.7661 2.5520
## Avg_Cogongrass_Cover-Sus_scrofa 1.7692 1.2171 -0.9251 1.8787
## Tree_Density-Canis_latrans -2.7335 1.2353 -5.6265 -2.5925
## Tree_Density-Sciurus_niger -2.0213 1.3253 -4.9162 -1.9838
## Tree_Density-Procyon_lotor -1.6296 0.7954 -3.1845 -1.6316
## Tree_Density-Dasypus_novemcinctus -3.5786 1.6915 -8.0084 -3.2309
## Tree_Density-Lynx_rufus -0.5753 1.3215 -2.8778 -0.7066
## Tree_Density-Didelphis_virginiana -2.2576 1.1957 -4.8282 -2.1734
## Tree_Density-Sylvilagus_floridanus -2.4075 1.2771 -5.1667 -2.3092
## Tree_Density-Sciurus_carolinensis -2.3489 1.2798 -5.1663 -2.2262
## Tree_Density-Vulpes_vulpes -1.8465 1.4271 -4.6943 -1.8539
## Tree_Density-Sus_scrofa -2.1461 1.3833 -5.1749 -2.0595
## Avg_Canopy_Cover-Canis_latrans 0.3643 0.6441 -0.9612 0.3748
## Avg_Canopy_Cover-Sciurus_niger 1.9083 1.4556 -0.6906 1.8212
## Avg_Canopy_Cover-Procyon_lotor 1.7314 0.7077 0.5260 1.6874
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1218 0.7475 0.8611 2.0488
## Avg_Canopy_Cover-Lynx_rufus 1.1105 1.1599 -1.0622 1.0733
## Avg_Canopy_Cover-Didelphis_virginiana 2.8873 1.2244 1.2067 2.6434
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2363 1.3967 1.1869 3.0132
## Avg_Canopy_Cover-Sciurus_carolinensis 2.5885 1.1240 0.9450 2.3929
## Avg_Canopy_Cover-Vulpes_vulpes 2.1821 1.0667 0.4310 2.0535
## Avg_Canopy_Cover-Sus_scrofa 2.0447 0.9316 0.4293 1.9685
## avg_veg_height-Canis_latrans -0.6564 0.5918 -1.8536 -0.6352
## avg_veg_height-Sciurus_niger -0.8660 0.8694 -2.8597 -0.7740
## avg_veg_height-Procyon_lotor -0.4922 0.5704 -1.5817 -0.4858
## avg_veg_height-Dasypus_novemcinctus -0.3651 0.5780 -1.4741 -0.3742
## avg_veg_height-Lynx_rufus -0.6868 0.7228 -2.1880 -0.6730
## avg_veg_height-Didelphis_virginiana -0.7274 0.6827 -2.0947 -0.6905
## avg_veg_height-Sylvilagus_floridanus -0.7497 0.6625 -2.1256 -0.7162
## avg_veg_height-Sciurus_carolinensis -0.2705 0.6520 -1.4635 -0.3173
## avg_veg_height-Vulpes_vulpes -0.6126 0.7041 -2.0795 -0.5815
## avg_veg_height-Sus_scrofa -0.5990 0.6886 -1.9541 -0.5963
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2965 1.0448 194
## (Intercept)-Sciurus_niger 3.1000 1.0298 154
## (Intercept)-Procyon_lotor 2.3278 1.0258 187
## (Intercept)-Dasypus_novemcinctus 0.1594 1.0175 593
## (Intercept)-Lynx_rufus 4.4824 1.0805 99
## (Intercept)-Didelphis_virginiana -0.4543 1.0080 345
## (Intercept)-Sylvilagus_floridanus 1.0740 1.0049 535
## (Intercept)-Sciurus_carolinensis -0.4513 1.0600 240
## (Intercept)-Vulpes_vulpes 0.5370 1.0668 275
## (Intercept)-Sus_scrofa -0.5770 1.1034 221
## Cogon_Patch_Size-Canis_latrans 2.8111 1.0648 520
## Cogon_Patch_Size-Sciurus_niger 0.9186 1.0356 340
## Cogon_Patch_Size-Procyon_lotor 0.2055 1.0339 352
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8682 1.0184 823
## Cogon_Patch_Size-Lynx_rufus 1.5178 1.0069 327
## Cogon_Patch_Size-Didelphis_virginiana 2.5602 1.0108 678
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1981 1.0367 313
## Cogon_Patch_Size-Sciurus_carolinensis 0.3280 1.0394 393
## Cogon_Patch_Size-Vulpes_vulpes 0.9874 1.0388 316
## Cogon_Patch_Size-Sus_scrofa 0.7613 1.0274 440
## Veg_shannon_index-Canis_latrans 2.6059 1.0118 457
## Veg_shannon_index-Sciurus_niger 2.7529 1.0108 477
## Veg_shannon_index-Procyon_lotor 2.4980 1.0072 272
## Veg_shannon_index-Dasypus_novemcinctus 1.7421 1.0037 541
## Veg_shannon_index-Lynx_rufus 2.3085 1.0166 436
## Veg_shannon_index-Didelphis_virginiana 2.4119 1.0096 649
## Veg_shannon_index-Sylvilagus_floridanus 2.4973 1.0123 576
## Veg_shannon_index-Sciurus_carolinensis 1.6569 1.0088 504
## Veg_shannon_index-Vulpes_vulpes 1.8132 1.0038 469
## Veg_shannon_index-Sus_scrofa 3.2649 1.0267 444
## total_shrub_cover-Canis_latrans 1.8626 1.0138 446
## total_shrub_cover-Sciurus_niger 0.7297 1.0454 227
## total_shrub_cover-Procyon_lotor 0.1311 1.0191 494
## total_shrub_cover-Dasypus_novemcinctus 1.0313 1.0195 576
## total_shrub_cover-Lynx_rufus 0.8079 1.0640 190
## total_shrub_cover-Didelphis_virginiana 0.5715 1.0256 443
## total_shrub_cover-Sylvilagus_floridanus 0.9382 1.0211 402
## total_shrub_cover-Sciurus_carolinensis 0.9254 1.0728 404
## total_shrub_cover-Vulpes_vulpes 0.7548 1.0251 391
## total_shrub_cover-Sus_scrofa 1.2886 1.0997 300
## Avg_Cogongrass_Cover-Canis_latrans 4.4219 1.0034 232
## Avg_Cogongrass_Cover-Sciurus_niger 3.6478 1.0714 144
## Avg_Cogongrass_Cover-Procyon_lotor 4.3027 1.0231 246
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8410 1.0076 188
## Avg_Cogongrass_Cover-Lynx_rufus 4.8493 1.0192 250
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0559 1.0053 213
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3613 1.0273 269
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5168 1.0101 208
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.9692 1.0066 179
## Avg_Cogongrass_Cover-Sus_scrofa 3.7950 1.0418 234
## Tree_Density-Canis_latrans -0.7291 1.0019 316
## Tree_Density-Sciurus_niger 0.4408 1.0159 432
## Tree_Density-Procyon_lotor -0.0411 1.0063 302
## Tree_Density-Dasypus_novemcinctus -1.2201 1.0367 269
## Tree_Density-Lynx_rufus 2.4538 1.0300 180
## Tree_Density-Didelphis_virginiana -0.1360 1.0224 410
## Tree_Density-Sylvilagus_floridanus 0.0072 1.0056 369
## Tree_Density-Sciurus_carolinensis -0.1651 1.0145 241
## Tree_Density-Vulpes_vulpes 1.1205 1.0337 403
## Tree_Density-Sus_scrofa 0.3926 1.0137 404
## Avg_Canopy_Cover-Canis_latrans 1.6332 1.0356 621
## Avg_Canopy_Cover-Sciurus_niger 5.0743 1.0202 235
## Avg_Canopy_Cover-Procyon_lotor 3.2780 1.0031 656
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7836 1.0129 396
## Avg_Canopy_Cover-Lynx_rufus 3.6777 1.0209 369
## Avg_Canopy_Cover-Didelphis_virginiana 6.0630 1.0552 129
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.5155 1.0105 166
## Avg_Canopy_Cover-Sciurus_carolinensis 5.4318 1.0153 213
## Avg_Canopy_Cover-Vulpes_vulpes 4.7638 1.0182 453
## Avg_Canopy_Cover-Sus_scrofa 4.1227 1.0080 521
## avg_veg_height-Canis_latrans 0.4943 1.0121 321
## avg_veg_height-Sciurus_niger 0.5798 1.0156 279
## avg_veg_height-Procyon_lotor 0.6339 1.0148 280
## avg_veg_height-Dasypus_novemcinctus 0.8024 1.0052 361
## avg_veg_height-Lynx_rufus 0.7371 1.0582 269
## avg_veg_height-Didelphis_virginiana 0.5368 1.0078 350
## avg_veg_height-Sylvilagus_floridanus 0.4782 1.0116 394
## avg_veg_height-Sciurus_carolinensis 1.0733 1.0191 438
## avg_veg_height-Vulpes_vulpes 0.7637 1.0118 320
## avg_veg_height-Sus_scrofa 0.8024 1.0067 415
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5836 0.1989 -3.0100 -2.5784 -2.2090 1.0023
## (Intercept)-Sciurus_niger -4.1885 0.5907 -5.3276 -4.2125 -2.9571 1.0320
## (Intercept)-Procyon_lotor -2.2079 0.1593 -2.5307 -2.2046 -1.9073 1.0071
## (Intercept)-Dasypus_novemcinctus -1.6594 0.1840 -2.0265 -1.6607 -1.3083 1.0192
## (Intercept)-Lynx_rufus -3.5175 0.3453 -4.2028 -3.5043 -2.8730 1.0013
## (Intercept)-Didelphis_virginiana -2.4111 0.2971 -3.0279 -2.4021 -1.8716 1.0082
## (Intercept)-Sylvilagus_floridanus -3.0864 0.2870 -3.6684 -3.0681 -2.5624 1.0087
## (Intercept)-Sciurus_carolinensis -2.6073 0.3301 -3.2684 -2.5983 -1.9877 1.0115
## (Intercept)-Vulpes_vulpes -3.8458 0.6098 -5.1246 -3.8131 -2.7637 1.0152
## (Intercept)-Sus_scrofa -3.3228 0.5405 -4.3776 -3.3102 -2.2697 1.0608
## shrub_cover-Canis_latrans -0.3046 0.2163 -0.7297 -0.3031 0.1089 1.0042
## shrub_cover-Sciurus_niger -0.1071 0.4683 -1.0416 -0.1075 0.8185 1.0036
## shrub_cover-Procyon_lotor 0.2804 0.1630 -0.0551 0.2833 0.5934 1.0019
## shrub_cover-Dasypus_novemcinctus 0.9261 0.2984 0.3479 0.9247 1.5143 1.0420
## shrub_cover-Lynx_rufus -0.0241 0.3538 -0.7122 -0.0290 0.6624 1.0155
## shrub_cover-Didelphis_virginiana 0.9919 0.3569 0.3317 0.9833 1.7480 1.0175
## shrub_cover-Sylvilagus_floridanus 0.4852 0.3745 -0.2722 0.4914 1.2076 1.0010
## shrub_cover-Sciurus_carolinensis 0.9822 0.4071 0.2000 0.9679 1.7983 1.0163
## shrub_cover-Vulpes_vulpes 0.3151 0.5417 -0.7597 0.3238 1.3532 1.0184
## shrub_cover-Sus_scrofa 0.8566 0.6952 -0.4783 0.8299 2.3380 1.0835
## veg_height-Canis_latrans -0.5621 0.1858 -0.9480 -0.5553 -0.2090 1.0068
## veg_height-Sciurus_niger 0.0828 0.3777 -0.6355 0.0683 0.8913 1.0388
## veg_height-Procyon_lotor 0.3544 0.1224 0.1181 0.3536 0.5937 1.0071
## veg_height-Dasypus_novemcinctus 0.2623 0.1360 -0.0048 0.2622 0.5411 1.0087
## veg_height-Lynx_rufus 0.1162 0.2287 -0.3386 0.1186 0.5641 1.0055
## veg_height-Didelphis_virginiana 0.4683 0.2298 0.0346 0.4650 0.9363 1.0086
## veg_height-Sylvilagus_floridanus 0.1897 0.2390 -0.2971 0.1955 0.6506 1.0006
## veg_height-Sciurus_carolinensis 0.1327 0.2174 -0.2856 0.1340 0.5714 1.0214
## veg_height-Vulpes_vulpes -0.1325 0.3104 -0.7626 -0.1235 0.4641 1.0045
## veg_height-Sus_scrofa -0.1215 0.3215 -0.8091 -0.1096 0.4811 1.0013
## week-Canis_latrans 0.4593 0.2482 -0.0015 0.4570 0.9663 1.0080
## week-Sciurus_niger -0.2264 0.4456 -1.3050 -0.1686 0.4706 1.0343
## week-Procyon_lotor 0.1634 0.1928 -0.2253 0.1608 0.5413 1.0036
## week-Dasypus_novemcinctus 0.0751 0.2152 -0.3451 0.0766 0.4991 1.0151
## week-Lynx_rufus 0.2685 0.2973 -0.3296 0.2765 0.8793 1.0372
## week-Didelphis_virginiana 0.0489 0.3266 -0.6424 0.0674 0.6476 1.0023
## week-Sylvilagus_floridanus 0.0360 0.2999 -0.5638 0.0525 0.5967 1.0044
## week-Sciurus_carolinensis 0.5547 0.3234 -0.0445 0.5357 1.2565 1.0102
## week-Vulpes_vulpes 0.1128 0.4052 -0.7340 0.1380 0.8743 1.0081
## week-Sus_scrofa 0.4456 0.3791 -0.2537 0.4242 1.2519 1.0087
## I(week^2)-Canis_latrans -0.1965 0.1064 -0.4119 -0.1935 0.0054 1.0043
## I(week^2)-Sciurus_niger -0.2634 0.2150 -0.7319 -0.2518 0.1252 1.0175
## I(week^2)-Procyon_lotor -0.1150 0.0857 -0.2852 -0.1130 0.0512 1.0060
## I(week^2)-Dasypus_novemcinctus -0.1601 0.1009 -0.3649 -0.1590 0.0240 1.0139
## I(week^2)-Lynx_rufus -0.2010 0.1463 -0.5094 -0.1919 0.0682 1.0400
## I(week^2)-Didelphis_virginiana -0.3473 0.1912 -0.7655 -0.3288 -0.0280 1.0030
## I(week^2)-Sylvilagus_floridanus -0.1539 0.1457 -0.4603 -0.1498 0.1159 1.0154
## I(week^2)-Sciurus_carolinensis -0.1971 0.1369 -0.4815 -0.1917 0.0598 1.0064
## I(week^2)-Vulpes_vulpes -0.3358 0.2307 -0.8549 -0.3160 0.0615 1.0127
## I(week^2)-Sus_scrofa -0.1709 0.1668 -0.5167 -0.1640 0.1371 1.0056
## ESS
## (Intercept)-Canis_latrans 894
## (Intercept)-Sciurus_niger 168
## (Intercept)-Procyon_lotor 1102
## (Intercept)-Dasypus_novemcinctus 1329
## (Intercept)-Lynx_rufus 315
## (Intercept)-Didelphis_virginiana 697
## (Intercept)-Sylvilagus_floridanus 608
## (Intercept)-Sciurus_carolinensis 594
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 411
## shrub_cover-Canis_latrans 764
## shrub_cover-Sciurus_niger 385
## shrub_cover-Procyon_lotor 1546
## shrub_cover-Dasypus_novemcinctus 660
## shrub_cover-Lynx_rufus 378
## shrub_cover-Didelphis_virginiana 638
## shrub_cover-Sylvilagus_floridanus 601
## shrub_cover-Sciurus_carolinensis 512
## shrub_cover-Vulpes_vulpes 602
## shrub_cover-Sus_scrofa 345
## veg_height-Canis_latrans 733
## veg_height-Sciurus_niger 361
## veg_height-Procyon_lotor 1430
## veg_height-Dasypus_novemcinctus 2056
## veg_height-Lynx_rufus 520
## veg_height-Didelphis_virginiana 1181
## veg_height-Sylvilagus_floridanus 977
## veg_height-Sciurus_carolinensis 1042
## veg_height-Vulpes_vulpes 692
## veg_height-Sus_scrofa 969
## week-Canis_latrans 1296
## week-Sciurus_niger 348
## week-Procyon_lotor 1587
## week-Dasypus_novemcinctus 1674
## week-Lynx_rufus 909
## week-Didelphis_virginiana 854
## week-Sylvilagus_floridanus 920
## week-Sciurus_carolinensis 1175
## week-Vulpes_vulpes 863
## week-Sus_scrofa 1137
## I(week^2)-Canis_latrans 1375
## I(week^2)-Sciurus_niger 334
## I(week^2)-Procyon_lotor 1423
## I(week^2)-Dasypus_novemcinctus 1763
## I(week^2)-Lynx_rufus 646
## I(week^2)-Didelphis_virginiana 568
## I(week^2)-Sylvilagus_floridanus 826
## I(week^2)-Sciurus_carolinensis 1183
## I(week^2)-Vulpes_vulpes 468
## I(week^2)-Sus_scrofa 1184
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cover)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8993
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4375 0.4301 -1.2515 -0.4438 0.4782 1.0078 311
## Avg_Cogongrass_Cover 0.0207 0.3398 -0.6472 0.0350 0.6856 1.0164 524
## total_shrub_cover -0.7757 0.4650 -1.8149 -0.7187 -0.0346 1.0332 199
## avg_veg_height 0.1082 0.3353 -0.5711 0.1190 0.7591 1.0128 447
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7042 0.8451 0.0550 0.4388 2.8451 1.0094 504
## Avg_Cogongrass_Cover 0.4402 0.9162 0.0389 0.2399 1.8438 1.2714 216
## total_shrub_cover 0.7658 1.4978 0.0531 0.3851 3.4441 1.1152 149
## avg_veg_height 0.3002 0.4453 0.0370 0.1821 1.2869 1.0812 537
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2879 1.3961 0.0806 0.8578 5.1161 1.095 114
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8596 0.3081 -3.4957 -2.8506 -2.2670 1.0012 1230
## shrub_cover 0.5925 0.3054 -0.0043 0.5973 1.1895 1.0228 316
## veg_height 0.0696 0.1841 -0.2907 0.0723 0.4275 1.0020 1053
## week 0.1678 0.1940 -0.2208 0.1728 0.5554 1.0098 1041
## I(week^2) -0.2058 0.1024 -0.4226 -0.2036 -0.0100 1.0041 792
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7836 0.6499 0.1759 0.6186 2.3507 1.0341 603
## shrub_cover 0.5975 0.4661 0.1215 0.4683 1.8004 1.0073 514
## veg_height 0.2276 0.1912 0.0545 0.1775 0.6965 1.0036 1054
## week 0.2069 0.2007 0.0356 0.1456 0.7262 1.0015 850
## I(week^2) 0.0624 0.0504 0.0178 0.0486 0.1939 1.0020 1146
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0925 0.6123 -1.0658 0.0517
## (Intercept)-Sciurus_niger -0.6565 0.7325 -2.1029 -0.6595
## (Intercept)-Procyon_lotor 0.2249 0.6433 -0.9241 0.1830
## (Intercept)-Dasypus_novemcinctus -0.4703 0.5789 -1.5698 -0.4806
## (Intercept)-Lynx_rufus -0.2776 0.6769 -1.5001 -0.3261
## (Intercept)-Didelphis_virginiana -0.7567 0.6113 -1.9865 -0.7330
## (Intercept)-Sylvilagus_floridanus -0.2528 0.6530 -1.4588 -0.2949
## (Intercept)-Sciurus_carolinensis -0.7846 0.6344 -2.1073 -0.7687
## (Intercept)-Vulpes_vulpes -0.7975 0.8062 -2.5328 -0.7577
## (Intercept)-Sus_scrofa -0.8902 0.7693 -2.5154 -0.8312
## Avg_Cogongrass_Cover-Canis_latrans 0.3300 0.4918 -0.5628 0.3001
## Avg_Cogongrass_Cover-Sciurus_niger -0.4142 0.8122 -2.0499 -0.2979
## Avg_Cogongrass_Cover-Procyon_lotor -0.0666 0.4737 -1.0376 -0.0519
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1682 0.4466 -0.6879 0.1709
## Avg_Cogongrass_Cover-Lynx_rufus 0.3589 0.5183 -0.5415 0.3107
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1606 0.4855 -0.7903 0.1598
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3994 0.6089 -1.7621 -0.3342
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0593 0.4693 -0.9033 0.0571
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1694 0.5985 -0.8795 0.1454
## Avg_Cogongrass_Cover-Sus_scrofa -0.1883 0.6097 -1.5402 -0.1531
## total_shrub_cover-Canis_latrans 0.0592 0.6050 -0.9847 0.0119
## total_shrub_cover-Sciurus_niger -0.9542 0.8059 -2.8173 -0.8556
## total_shrub_cover-Procyon_lotor -1.1049 0.5948 -2.5305 -1.0231
## total_shrub_cover-Dasypus_novemcinctus -0.4706 0.6307 -2.0303 -0.3944
## total_shrub_cover-Lynx_rufus -1.1453 0.7741 -2.9778 -1.0322
## total_shrub_cover-Didelphis_virginiana -0.7744 0.5942 -2.1394 -0.7121
## total_shrub_cover-Sylvilagus_floridanus -1.0917 0.8871 -3.1096 -0.9662
## total_shrub_cover-Sciurus_carolinensis -0.8159 0.6752 -2.3961 -0.7365
## total_shrub_cover-Vulpes_vulpes -1.0284 1.1268 -3.6996 -0.8488
## total_shrub_cover-Sus_scrofa -0.7223 0.8397 -2.5811 -0.6295
## avg_veg_height-Canis_latrans 0.1020 0.4446 -0.7581 0.1025
## avg_veg_height-Sciurus_niger -0.2177 0.6795 -1.7068 -0.1364
## avg_veg_height-Procyon_lotor 0.1333 0.4266 -0.7012 0.1364
## avg_veg_height-Dasypus_novemcinctus 0.3111 0.4466 -0.5026 0.2810
## avg_veg_height-Lynx_rufus 0.0433 0.5282 -1.0677 0.0424
## avg_veg_height-Didelphis_virginiana 0.0184 0.4700 -0.9622 0.0344
## avg_veg_height-Sylvilagus_floridanus 0.0248 0.4985 -0.9702 0.0253
## avg_veg_height-Sciurus_carolinensis 0.4453 0.5094 -0.4499 0.4040
## avg_veg_height-Vulpes_vulpes 0.0663 0.5378 -1.0183 0.0781
## avg_veg_height-Sus_scrofa 0.1496 0.4965 -0.8640 0.1545
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3859 1.0027 432
## (Intercept)-Sciurus_niger 0.8597 1.0154 339
## (Intercept)-Procyon_lotor 1.6351 1.0078 460
## (Intercept)-Dasypus_novemcinctus 0.8151 1.0015 493
## (Intercept)-Lynx_rufus 1.2247 1.0054 477
## (Intercept)-Didelphis_virginiana 0.4095 1.0191 389
## (Intercept)-Sylvilagus_floridanus 1.1730 1.0033 449
## (Intercept)-Sciurus_carolinensis 0.4036 1.0029 351
## (Intercept)-Vulpes_vulpes 0.7967 1.0055 295
## (Intercept)-Sus_scrofa 0.5013 1.0065 369
## Avg_Cogongrass_Cover-Canis_latrans 1.3747 1.0039 887
## Avg_Cogongrass_Cover-Sciurus_niger 0.6673 1.0522 266
## Avg_Cogongrass_Cover-Procyon_lotor 0.8045 1.0089 580
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0834 1.0093 1042
## Avg_Cogongrass_Cover-Lynx_rufus 1.5390 1.0049 730
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1666 1.0117 1035
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5996 1.0165 390
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9793 1.0153 989
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3348 1.0229 821
## Avg_Cogongrass_Cover-Sus_scrofa 0.8740 1.0021 718
## total_shrub_cover-Canis_latrans 1.4308 1.0413 499
## total_shrub_cover-Sciurus_niger 0.2704 1.0398 187
## total_shrub_cover-Procyon_lotor -0.1846 1.0136 287
## total_shrub_cover-Dasypus_novemcinctus 0.4918 1.0200 360
## total_shrub_cover-Lynx_rufus 0.0978 1.0817 272
## total_shrub_cover-Didelphis_virginiana 0.2265 1.0181 301
## total_shrub_cover-Sylvilagus_floridanus 0.1548 1.0773 170
## total_shrub_cover-Sciurus_carolinensis 0.3084 1.0304 300
## total_shrub_cover-Vulpes_vulpes 0.4743 1.1017 139
## total_shrub_cover-Sus_scrofa 0.6223 1.0456 112
## avg_veg_height-Canis_latrans 1.0068 1.0107 800
## avg_veg_height-Sciurus_niger 0.8597 1.0340 456
## avg_veg_height-Procyon_lotor 0.9823 1.0080 941
## avg_veg_height-Dasypus_novemcinctus 1.2503 1.0370 770
## avg_veg_height-Lynx_rufus 1.0735 1.0185 764
## avg_veg_height-Didelphis_virginiana 0.9169 1.0133 879
## avg_veg_height-Sylvilagus_floridanus 0.9889 1.0059 783
## avg_veg_height-Sciurus_carolinensis 1.5939 1.0309 731
## avg_veg_height-Vulpes_vulpes 1.0822 1.0249 619
## avg_veg_height-Sus_scrofa 1.1365 1.0100 930
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6328 0.2052 -3.0605 -2.6294 -2.2539 1.0041
## (Intercept)-Sciurus_niger -3.6850 0.6115 -4.9482 -3.6720 -2.5316 1.0046
## (Intercept)-Procyon_lotor -2.2091 0.1612 -2.5349 -2.2062 -1.9008 1.0169
## (Intercept)-Dasypus_novemcinctus -1.7175 0.1964 -2.1222 -1.7104 -1.3515 1.0033
## (Intercept)-Lynx_rufus -3.3474 0.3397 -4.0550 -3.3306 -2.7336 1.0196
## (Intercept)-Didelphis_virginiana -2.5775 0.3097 -3.1954 -2.5726 -1.9923 1.0227
## (Intercept)-Sylvilagus_floridanus -3.1490 0.2890 -3.7268 -3.1408 -2.6108 1.0094
## (Intercept)-Sciurus_carolinensis -2.7062 0.3509 -3.4243 -2.7000 -2.0651 1.0100
## (Intercept)-Vulpes_vulpes -3.8094 0.5925 -5.0048 -3.7859 -2.7096 1.0035
## (Intercept)-Sus_scrofa -3.4700 0.5117 -4.4874 -3.4602 -2.4880 1.0042
## shrub_cover-Canis_latrans -0.2307 0.2417 -0.6969 -0.2371 0.2568 1.0094
## shrub_cover-Sciurus_niger 0.1038 0.5531 -1.0372 0.1164 1.1621 1.0104
## shrub_cover-Procyon_lotor 0.3220 0.1594 -0.0005 0.3251 0.6181 1.0029
## shrub_cover-Dasypus_novemcinctus 1.0639 0.3464 0.4199 1.0527 1.7417 1.0084
## shrub_cover-Lynx_rufus 0.1379 0.3816 -0.6389 0.1631 0.8233 1.0245
## shrub_cover-Didelphis_virginiana 1.2241 0.4035 0.4807 1.2109 2.0622 1.0675
## shrub_cover-Sylvilagus_floridanus 0.6996 0.4064 -0.1610 0.7172 1.4775 1.0212
## shrub_cover-Sciurus_carolinensis 1.1921 0.4090 0.3901 1.1957 1.9993 1.0159
## shrub_cover-Vulpes_vulpes 0.4492 0.6293 -0.8810 0.4624 1.6137 1.0365
## shrub_cover-Sus_scrofa 1.1368 0.6909 -0.2936 1.1212 2.5539 1.0110
## veg_height-Canis_latrans -0.5838 0.1876 -0.9748 -0.5789 -0.2299 1.0175
## veg_height-Sciurus_niger 0.1905 0.4904 -0.7470 0.1733 1.2605 1.0062
## veg_height-Procyon_lotor 0.3455 0.1234 0.1032 0.3453 0.5852 1.0072
## veg_height-Dasypus_novemcinctus 0.2731 0.1393 0.0012 0.2721 0.5470 1.0029
## veg_height-Lynx_rufus 0.0656 0.2493 -0.4471 0.0779 0.5360 1.0313
## veg_height-Didelphis_virginiana 0.4508 0.2531 -0.0245 0.4481 0.9481 1.0076
## veg_height-Sylvilagus_floridanus 0.0878 0.2509 -0.4039 0.0819 0.5857 1.0023
## veg_height-Sciurus_carolinensis 0.1157 0.2304 -0.3143 0.1057 0.5924 1.0110
## veg_height-Vulpes_vulpes -0.0862 0.3180 -0.7636 -0.0775 0.5244 1.0195
## veg_height-Sus_scrofa -0.1564 0.3327 -0.8284 -0.1426 0.4803 1.0012
## week-Canis_latrans 0.4440 0.2433 -0.0140 0.4344 0.9336 1.0010
## week-Sciurus_niger -0.2276 0.4081 -1.1301 -0.1939 0.4563 1.0026
## week-Procyon_lotor 0.1438 0.1993 -0.2309 0.1424 0.5291 1.0035
## week-Dasypus_novemcinctus 0.0471 0.2131 -0.3589 0.0458 0.4647 1.0132
## week-Lynx_rufus 0.2552 0.2891 -0.2960 0.2451 0.8482 1.0004
## week-Didelphis_virginiana 0.0108 0.3156 -0.6391 0.0206 0.6173 1.0168
## week-Sylvilagus_floridanus 0.0126 0.2935 -0.5970 0.0171 0.5765 1.0149
## week-Sciurus_carolinensis 0.5296 0.3334 -0.0692 0.5048 1.2507 1.0093
## week-Vulpes_vulpes 0.0961 0.3883 -0.7354 0.1031 0.8501 1.0108
## week-Sus_scrofa 0.4087 0.3727 -0.2542 0.3830 1.2285 1.0048
## I(week^2)-Canis_latrans -0.1917 0.1030 -0.4033 -0.1895 0.0085 1.0028
## I(week^2)-Sciurus_niger -0.2214 0.2072 -0.6573 -0.2130 0.1478 1.0367
## I(week^2)-Procyon_lotor -0.1077 0.0888 -0.2902 -0.1060 0.0580 1.0003
## I(week^2)-Dasypus_novemcinctus -0.1480 0.1000 -0.3442 -0.1452 0.0385 1.0036
## I(week^2)-Lynx_rufus -0.1903 0.1414 -0.4897 -0.1850 0.0645 1.0018
## I(week^2)-Didelphis_virginiana -0.3382 0.1934 -0.7725 -0.3213 -0.0096 1.0028
## I(week^2)-Sylvilagus_floridanus -0.1469 0.1462 -0.4485 -0.1430 0.1210 1.0027
## I(week^2)-Sciurus_carolinensis -0.1918 0.1368 -0.4751 -0.1854 0.0594 1.0007
## I(week^2)-Vulpes_vulpes -0.3267 0.2175 -0.8238 -0.3073 0.0352 1.0203
## I(week^2)-Sus_scrofa -0.1719 0.1626 -0.5111 -0.1670 0.1210 1.0005
## ESS
## (Intercept)-Canis_latrans 842
## (Intercept)-Sciurus_niger 277
## (Intercept)-Procyon_lotor 1452
## (Intercept)-Dasypus_novemcinctus 778
## (Intercept)-Lynx_rufus 497
## (Intercept)-Didelphis_virginiana 579
## (Intercept)-Sylvilagus_floridanus 650
## (Intercept)-Sciurus_carolinensis 540
## (Intercept)-Vulpes_vulpes 315
## (Intercept)-Sus_scrofa 286
## shrub_cover-Canis_latrans 632
## shrub_cover-Sciurus_niger 393
## shrub_cover-Procyon_lotor 1289
## shrub_cover-Dasypus_novemcinctus 409
## shrub_cover-Lynx_rufus 336
## shrub_cover-Didelphis_virginiana 231
## shrub_cover-Sylvilagus_floridanus 309
## shrub_cover-Sciurus_carolinensis 303
## shrub_cover-Vulpes_vulpes 379
## shrub_cover-Sus_scrofa 270
## veg_height-Canis_latrans 799
## veg_height-Sciurus_niger 427
## veg_height-Procyon_lotor 1390
## veg_height-Dasypus_novemcinctus 1457
## veg_height-Lynx_rufus 708
## veg_height-Didelphis_virginiana 865
## veg_height-Sylvilagus_floridanus 694
## veg_height-Sciurus_carolinensis 828
## veg_height-Vulpes_vulpes 632
## veg_height-Sus_scrofa 801
## week-Canis_latrans 1230
## week-Sciurus_niger 550
## week-Procyon_lotor 1595
## week-Dasypus_novemcinctus 1791
## week-Lynx_rufus 1108
## week-Didelphis_virginiana 1042
## week-Sylvilagus_floridanus 979
## week-Sciurus_carolinensis 997
## week-Vulpes_vulpes 839
## week-Sus_scrofa 1409
## I(week^2)-Canis_latrans 1340
## I(week^2)-Sciurus_niger 511
## I(week^2)-Procyon_lotor 1525
## I(week^2)-Dasypus_novemcinctus 1576
## I(week^2)-Lynx_rufus 682
## I(week^2)-Didelphis_virginiana 531
## I(week^2)-Sylvilagus_floridanus 731
## I(week^2)-Sciurus_carolinensis 1380
## I(week^2)-Vulpes_vulpes 429
## I(week^2)-Sus_scrofa 1133
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_canopy)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8242
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8167 0.4494 -1.7015 -0.8072 0.0566 1.0084 556
## Tree_Density -0.7207 0.4028 -1.5947 -0.6865 -0.0255 1.0109 646
## Avg_Canopy_Cover 0.9950 0.3581 0.3370 0.9818 1.7444 1.0081 936
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3285 1.2383 0.1615 0.9948 4.4828 1.0211 734
## Tree_Density 0.7854 1.1591 0.0487 0.4038 3.7186 1.0163 363
## Avg_Canopy_Cover 0.6523 0.7862 0.0726 0.4265 2.4911 1.0146 500
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5036 0.5291 0.0452 0.3215 1.8738 1.0305 131
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7838 0.3252 -3.4285 -2.7824 -2.1457 1.0016 821
## shrub_cover 0.3222 0.2853 -0.2584 0.3234 0.8844 1.0128 1055
## veg_height 0.0773 0.1686 -0.2790 0.0806 0.4060 1.0085 1386
## week 0.1833 0.2041 -0.2501 0.1870 0.5880 1.0028 1015
## I(week^2) -0.2109 0.1116 -0.4523 -0.2030 -0.0124 1.0118 720
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8432 0.6311 0.1865 0.6729 2.5123 1.0132 455
## shrub_cover 0.5731 0.4900 0.1046 0.4357 1.9047 1.0176 618
## veg_height 0.2135 0.1612 0.0552 0.1672 0.6649 1.0198 971
## week 0.2250 0.2277 0.0370 0.1544 0.8150 1.0226 597
## I(week^2) 0.0668 0.0620 0.0181 0.0493 0.2331 1.0156 641
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0695 0.5830 -1.0561 0.0637 1.2332
## (Intercept)-Sciurus_niger -0.8632 0.8130 -2.3434 -0.9041 0.8222
## (Intercept)-Procyon_lotor 0.3429 0.6139 -0.9312 0.3793 1.5082
## (Intercept)-Dasypus_novemcinctus -0.9365 0.5631 -2.1326 -0.9076 0.0997
## (Intercept)-Lynx_rufus -0.0207 0.8843 -1.5792 -0.1122 1.8543
## (Intercept)-Didelphis_virginiana -1.4756 0.6421 -2.8409 -1.4412 -0.3101
## (Intercept)-Sylvilagus_floridanus -0.7447 0.6033 -1.9350 -0.7452 0.4068
## (Intercept)-Sciurus_carolinensis -1.5036 0.6446 -2.8398 -1.4807 -0.3357
## (Intercept)-Vulpes_vulpes -1.4987 0.9023 -3.3165 -1.4839 0.2590
## (Intercept)-Sus_scrofa -1.8943 0.8211 -3.6304 -1.8498 -0.4587
## Tree_Density-Canis_latrans -0.9476 0.6162 -2.4101 -0.8577 0.0150
## Tree_Density-Sciurus_niger -0.6956 0.7087 -2.2762 -0.6748 0.5871
## Tree_Density-Procyon_lotor -0.4511 0.4031 -1.2599 -0.4435 0.3374
## Tree_Density-Dasypus_novemcinctus -1.3363 0.8954 -3.6723 -1.1584 -0.1280
## Tree_Density-Lynx_rufus 0.1882 0.7112 -0.8943 0.0686 1.9173
## Tree_Density-Didelphis_virginiana -0.9302 0.7073 -2.7794 -0.8211 0.1536
## Tree_Density-Sylvilagus_floridanus -1.0258 0.7380 -2.8858 -0.8994 0.0915
## Tree_Density-Sciurus_carolinensis -0.8644 0.7241 -2.6462 -0.7651 0.3223
## Tree_Density-Vulpes_vulpes -0.6165 0.7411 -2.2237 -0.5738 0.6613
## Tree_Density-Sus_scrofa -0.8513 0.7946 -2.8355 -0.7425 0.3838
## Avg_Canopy_Cover-Canis_latrans 0.0524 0.4493 -0.8490 0.0518 0.9155
## Avg_Canopy_Cover-Sciurus_niger 0.8131 0.7021 -0.4801 0.7816 2.3849
## Avg_Canopy_Cover-Procyon_lotor 0.9700 0.4471 0.1472 0.9545 1.9187
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0466 0.4372 0.2707 1.0216 1.9653
## Avg_Canopy_Cover-Lynx_rufus 0.6922 0.6281 -0.5071 0.6826 2.0034
## Avg_Canopy_Cover-Didelphis_virginiana 1.3721 0.5871 0.4272 1.2814 2.7823
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7550 0.7946 0.6463 1.6237 3.6579
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3334 0.5640 0.3850 1.2672 2.5761
## Avg_Canopy_Cover-Vulpes_vulpes 0.9868 0.5801 -0.0553 0.9461 2.2513
## Avg_Canopy_Cover-Sus_scrofa 1.2094 0.5727 0.2820 1.1462 2.4489
## Rhat ESS
## (Intercept)-Canis_latrans 1.0017 752
## (Intercept)-Sciurus_niger 1.0132 391
## (Intercept)-Procyon_lotor 1.0474 482
## (Intercept)-Dasypus_novemcinctus 1.0037 1122
## (Intercept)-Lynx_rufus 1.0071 292
## (Intercept)-Didelphis_virginiana 1.0216 641
## (Intercept)-Sylvilagus_floridanus 1.0019 1286
## (Intercept)-Sciurus_carolinensis 1.0049 842
## (Intercept)-Vulpes_vulpes 1.0989 336
## (Intercept)-Sus_scrofa 1.0152 541
## Tree_Density-Canis_latrans 1.0006 833
## Tree_Density-Sciurus_niger 1.0173 919
## Tree_Density-Procyon_lotor 1.0052 1474
## Tree_Density-Dasypus_novemcinctus 1.0115 347
## Tree_Density-Lynx_rufus 1.0046 495
## Tree_Density-Didelphis_virginiana 1.0295 656
## Tree_Density-Sylvilagus_floridanus 1.0059 577
## Tree_Density-Sciurus_carolinensis 1.0002 745
## Tree_Density-Vulpes_vulpes 1.0024 777
## Tree_Density-Sus_scrofa 1.0204 682
## Avg_Canopy_Cover-Canis_latrans 1.0045 1168
## Avg_Canopy_Cover-Sciurus_niger 1.0068 516
## Avg_Canopy_Cover-Procyon_lotor 1.0088 1582
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0068 1802
## Avg_Canopy_Cover-Lynx_rufus 1.0028 799
## Avg_Canopy_Cover-Didelphis_virginiana 0.9996 705
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0036 477
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0003 756
## Avg_Canopy_Cover-Vulpes_vulpes 1.0104 1035
## Avg_Canopy_Cover-Sus_scrofa 1.0153 749
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6097 0.2075 -3.0271 -2.6075 -2.2260 1.0195
## (Intercept)-Sciurus_niger -3.8260 0.5901 -4.9572 -3.8423 -2.6865 1.0249
## (Intercept)-Procyon_lotor -2.2068 0.1636 -2.5419 -2.2003 -1.8948 1.0268
## (Intercept)-Dasypus_novemcinctus -1.6487 0.1747 -2.0099 -1.6457 -1.3160 1.0001
## (Intercept)-Lynx_rufus -3.5093 0.3606 -4.2385 -3.5094 -2.8220 1.0137
## (Intercept)-Didelphis_virginiana -2.4509 0.3164 -3.1119 -2.4379 -1.8623 1.0014
## (Intercept)-Sylvilagus_floridanus -3.0189 0.2862 -3.5886 -3.0081 -2.4916 1.0050
## (Intercept)-Sciurus_carolinensis -2.5323 0.3325 -3.2174 -2.5155 -1.9116 1.0007
## (Intercept)-Vulpes_vulpes -3.7384 0.6574 -5.1740 -3.6939 -2.6089 1.0744
## (Intercept)-Sus_scrofa -3.1961 0.5609 -4.3820 -3.1829 -2.1386 1.0228
## shrub_cover-Canis_latrans -0.2950 0.2280 -0.7472 -0.2929 0.1538 1.0024
## shrub_cover-Sciurus_niger -0.2479 0.5094 -1.2252 -0.2440 0.7390 1.0094
## shrub_cover-Procyon_lotor 0.2637 0.1580 -0.0513 0.2671 0.5640 1.0064
## shrub_cover-Dasypus_novemcinctus 0.8690 0.2943 0.3173 0.8642 1.4614 1.0033
## shrub_cover-Lynx_rufus -0.2324 0.3533 -0.9242 -0.2158 0.4150 1.0378
## shrub_cover-Didelphis_virginiana 0.9890 0.3604 0.3470 0.9741 1.7664 1.0081
## shrub_cover-Sylvilagus_floridanus 0.3934 0.3875 -0.3529 0.3965 1.1588 1.0188
## shrub_cover-Sciurus_carolinensis 0.8786 0.3986 0.1053 0.8698 1.6897 1.0037
## shrub_cover-Vulpes_vulpes -0.0059 0.5683 -1.1628 0.0100 1.0918 1.0029
## shrub_cover-Sus_scrofa 0.6855 0.7315 -0.7138 0.6599 2.2203 1.0028
## veg_height-Canis_latrans -0.5784 0.1958 -0.9824 -0.5730 -0.2221 1.0135
## veg_height-Sciurus_niger 0.0458 0.4113 -0.7940 0.0447 0.8478 1.0159
## veg_height-Procyon_lotor 0.3463 0.1226 0.1081 0.3400 0.5923 1.0062
## veg_height-Dasypus_novemcinctus 0.2603 0.1331 -0.0076 0.2595 0.5166 1.0000
## veg_height-Lynx_rufus 0.0912 0.2381 -0.3902 0.0971 0.5509 1.0223
## veg_height-Didelphis_virginiana 0.4926 0.2527 0.0465 0.4820 1.0272 1.0216
## veg_height-Sylvilagus_floridanus 0.1677 0.2337 -0.2957 0.1616 0.6310 1.0141
## veg_height-Sciurus_carolinensis 0.1153 0.2141 -0.2904 0.1091 0.5462 1.0026
## veg_height-Vulpes_vulpes -0.0853 0.3032 -0.7144 -0.0742 0.4763 1.0010
## veg_height-Sus_scrofa -0.0842 0.3377 -0.7917 -0.0668 0.5283 1.0110
## week-Canis_latrans 0.4559 0.2519 -0.0085 0.4493 0.9791 1.0014
## week-Sciurus_niger -0.2599 0.4486 -1.2835 -0.2018 0.4755 1.0126
## week-Procyon_lotor 0.1617 0.1985 -0.2421 0.1636 0.5482 1.0050
## week-Dasypus_novemcinctus 0.0673 0.2132 -0.3589 0.0737 0.4705 1.0008
## week-Lynx_rufus 0.2758 0.3032 -0.3182 0.2712 0.8910 1.0032
## week-Didelphis_virginiana 0.0249 0.3263 -0.6858 0.0452 0.6204 1.0147
## week-Sylvilagus_floridanus 0.0232 0.3032 -0.6139 0.0374 0.6066 1.0008
## week-Sciurus_carolinensis 0.5566 0.3291 -0.0223 0.5199 1.2856 1.0005
## week-Vulpes_vulpes 0.1264 0.4133 -0.7437 0.1403 0.9041 1.0111
## week-Sus_scrofa 0.4323 0.3857 -0.2789 0.4095 1.2772 1.0016
## I(week^2)-Canis_latrans -0.1952 0.1086 -0.4169 -0.1926 0.0125 1.0054
## I(week^2)-Sciurus_niger -0.2570 0.2263 -0.7768 -0.2356 0.1229 1.0799
## I(week^2)-Procyon_lotor -0.1144 0.0878 -0.2846 -0.1154 0.0616 1.0062
## I(week^2)-Dasypus_novemcinctus -0.1610 0.0999 -0.3670 -0.1596 0.0367 1.0026
## I(week^2)-Lynx_rufus -0.1974 0.1424 -0.5015 -0.1931 0.0644 1.0311
## I(week^2)-Didelphis_virginiana -0.3519 0.2022 -0.8218 -0.3265 -0.0234 1.0053
## I(week^2)-Sylvilagus_floridanus -0.1408 0.1439 -0.4343 -0.1382 0.1354 1.0038
## I(week^2)-Sciurus_carolinensis -0.1966 0.1358 -0.4776 -0.1931 0.0560 1.0020
## I(week^2)-Vulpes_vulpes -0.3400 0.2520 -0.9328 -0.3059 0.0452 1.0105
## I(week^2)-Sus_scrofa -0.1622 0.1637 -0.4886 -0.1595 0.1442 1.0085
## ESS
## (Intercept)-Canis_latrans 835
## (Intercept)-Sciurus_niger 191
## (Intercept)-Procyon_lotor 1462
## (Intercept)-Dasypus_novemcinctus 1483
## (Intercept)-Lynx_rufus 266
## (Intercept)-Didelphis_virginiana 686
## (Intercept)-Sylvilagus_floridanus 793
## (Intercept)-Sciurus_carolinensis 739
## (Intercept)-Vulpes_vulpes 224
## (Intercept)-Sus_scrofa 367
## shrub_cover-Canis_latrans 728
## shrub_cover-Sciurus_niger 382
## shrub_cover-Procyon_lotor 1570
## shrub_cover-Dasypus_novemcinctus 1321
## shrub_cover-Lynx_rufus 438
## shrub_cover-Didelphis_virginiana 695
## shrub_cover-Sylvilagus_floridanus 665
## shrub_cover-Sciurus_carolinensis 658
## shrub_cover-Vulpes_vulpes 601
## shrub_cover-Sus_scrofa 586
## veg_height-Canis_latrans 564
## veg_height-Sciurus_niger 747
## veg_height-Procyon_lotor 1653
## veg_height-Dasypus_novemcinctus 2068
## veg_height-Lynx_rufus 763
## veg_height-Didelphis_virginiana 768
## veg_height-Sylvilagus_floridanus 1101
## veg_height-Sciurus_carolinensis 1173
## veg_height-Vulpes_vulpes 781
## veg_height-Sus_scrofa 963
## week-Canis_latrans 1096
## week-Sciurus_niger 470
## week-Procyon_lotor 1549
## week-Dasypus_novemcinctus 1796
## week-Lynx_rufus 932
## week-Didelphis_virginiana 1116
## week-Sylvilagus_floridanus 1142
## week-Sciurus_carolinensis 895
## week-Vulpes_vulpes 920
## week-Sus_scrofa 954
## I(week^2)-Canis_latrans 1117
## I(week^2)-Sciurus_niger 259
## I(week^2)-Procyon_lotor 1708
## I(week^2)-Dasypus_novemcinctus 1585
## I(week^2)-Lynx_rufus 688
## I(week^2)-Didelphis_virginiana 491
## I(week^2)-Sylvilagus_floridanus 920
## I(week^2)-Sciurus_carolinensis 1369
## I(week^2)-Vulpes_vulpes 416
## I(week^2)-Sus_scrofa 1378
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_move)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8775
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6498 0.4049 -1.4790 -0.6462 0.1396 1.0496 373
## Cogon_Patch_Size -0.2371 0.3911 -1.0912 -0.2150 0.4788 1.0059 599
## Avg_Cogongrass_Cover 0.1686 0.2960 -0.4035 0.1659 0.7548 1.0064 522
## total_shrub_cover -0.6376 0.3707 -1.4214 -0.6216 0.0321 1.0288 342
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7263 0.8821 0.0553 0.4479 2.8092 1.0082 447
## Cogon_Patch_Size 0.7999 1.2568 0.0568 0.4515 3.5085 1.1085 525
## Avg_Cogongrass_Cover 0.3084 0.3421 0.0389 0.1955 1.3035 1.0043 638
## total_shrub_cover 0.4815 0.6101 0.0456 0.2846 2.1271 1.0753 301
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3207 1.0262 0.1176 1.0827 3.9097 1.0989 163
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7972 0.3044 -3.3999 -2.7966 -2.2258 1.0177 864
## shrub_cover 0.5622 0.2883 -0.0114 0.5551 1.1497 1.0110 588
## veg_height 0.0692 0.1703 -0.2757 0.0688 0.3865 1.0024 1212
## week 0.1892 0.2112 -0.2262 0.1880 0.6100 1.0037 956
## I(week^2) -0.2086 0.1101 -0.4454 -0.2036 -0.0086 1.0041 645
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7469 0.6388 0.1615 0.5821 2.3194 1.0099 647
## shrub_cover 0.5369 0.4929 0.1002 0.4072 1.7020 1.0253 644
## veg_height 0.2077 0.1687 0.0531 0.1631 0.6521 1.0261 1374
## week 0.2209 0.2229 0.0356 0.1500 0.8294 1.0305 519
## I(week^2) 0.0672 0.0588 0.0176 0.0506 0.2206 1.0117 802
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0386 0.6487 -1.2121 -0.0637
## (Intercept)-Sciurus_niger -0.9047 0.6822 -2.3462 -0.8781
## (Intercept)-Procyon_lotor 0.0537 0.6846 -1.1994 0.0272
## (Intercept)-Dasypus_novemcinctus -0.6414 0.5603 -1.7823 -0.6403
## (Intercept)-Lynx_rufus -0.4847 0.6381 -1.6242 -0.5171
## (Intercept)-Didelphis_virginiana -0.9261 0.6234 -2.1899 -0.8983
## (Intercept)-Sylvilagus_floridanus -0.4566 0.6392 -1.6329 -0.4845
## (Intercept)-Sciurus_carolinensis -0.9883 0.6423 -2.3815 -0.9496
## (Intercept)-Vulpes_vulpes -1.0466 0.7784 -2.7098 -0.9911
## (Intercept)-Sus_scrofa -1.1700 0.7785 -2.8913 -1.1064
## Cogon_Patch_Size-Canis_latrans 0.5517 0.6149 -0.4136 0.4738
## Cogon_Patch_Size-Sciurus_niger -0.5560 0.7996 -2.4382 -0.4436
## Cogon_Patch_Size-Procyon_lotor -0.2547 0.4432 -1.2161 -0.2445
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0750 0.4480 -0.9806 -0.0686
## Cogon_Patch_Size-Lynx_rufus -0.2412 0.6493 -1.5392 -0.2505
## Cogon_Patch_Size-Didelphis_virginiana 0.5048 0.4903 -0.3856 0.4764
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8313 0.8290 -2.9158 -0.6661
## Cogon_Patch_Size-Sciurus_carolinensis -0.6502 0.6915 -2.3699 -0.5403
## Cogon_Patch_Size-Vulpes_vulpes -0.4688 0.7747 -2.2655 -0.3945
## Cogon_Patch_Size-Sus_scrofa -0.4014 0.7387 -2.0873 -0.3200
## Avg_Cogongrass_Cover-Canis_latrans 0.2925 0.4110 -0.4489 0.2723
## Avg_Cogongrass_Cover-Sciurus_niger -0.1867 0.5748 -1.5093 -0.1473
## Avg_Cogongrass_Cover-Procyon_lotor 0.1459 0.4097 -0.6610 0.1377
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3464 0.3801 -0.3734 0.3293
## Avg_Cogongrass_Cover-Lynx_rufus 0.4749 0.4718 -0.3479 0.4433
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1682 0.4335 -0.6565 0.1640
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1337 0.4920 -1.1506 -0.1138
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3655 0.4280 -0.4124 0.3503
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3014 0.4740 -0.6327 0.2846
## Avg_Cogongrass_Cover-Sus_scrofa -0.0139 0.5672 -1.3095 0.0184
## total_shrub_cover-Canis_latrans -0.0525 0.5130 -0.9791 -0.0936
## total_shrub_cover-Sciurus_niger -0.7868 0.5935 -2.0521 -0.7480
## total_shrub_cover-Procyon_lotor -0.9542 0.5025 -2.0735 -0.8956
## total_shrub_cover-Dasypus_novemcinctus -0.3909 0.4860 -1.4014 -0.3663
## total_shrub_cover-Lynx_rufus -0.9508 0.6506 -2.3764 -0.8892
## total_shrub_cover-Didelphis_virginiana -0.7104 0.5408 -1.9217 -0.6593
## total_shrub_cover-Sylvilagus_floridanus -0.8167 0.6286 -2.2365 -0.7614
## total_shrub_cover-Sciurus_carolinensis -0.6477 0.6006 -2.0258 -0.5831
## total_shrub_cover-Vulpes_vulpes -0.6973 0.7449 -2.2741 -0.6752
## total_shrub_cover-Sus_scrofa -0.5113 0.6586 -1.8700 -0.5056
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3184 1.0300 418
## (Intercept)-Sciurus_niger 0.4226 1.0318 466
## (Intercept)-Procyon_lotor 1.4382 1.0345 392
## (Intercept)-Dasypus_novemcinctus 0.4539 1.0261 788
## (Intercept)-Lynx_rufus 0.8413 1.0284 535
## (Intercept)-Didelphis_virginiana 0.2441 1.0235 439
## (Intercept)-Sylvilagus_floridanus 0.8712 1.0242 520
## (Intercept)-Sciurus_carolinensis 0.1998 1.0407 431
## (Intercept)-Vulpes_vulpes 0.4072 1.0198 337
## (Intercept)-Sus_scrofa 0.1839 1.0232 369
## Cogon_Patch_Size-Canis_latrans 1.9552 1.0087 765
## Cogon_Patch_Size-Sciurus_niger 0.7025 1.0250 535
## Cogon_Patch_Size-Procyon_lotor 0.5896 1.0129 925
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7978 1.0037 1399
## Cogon_Patch_Size-Lynx_rufus 1.1761 1.0080 918
## Cogon_Patch_Size-Didelphis_virginiana 1.5594 1.0030 949
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3521 1.0129 473
## Cogon_Patch_Size-Sciurus_carolinensis 0.4178 1.0142 612
## Cogon_Patch_Size-Vulpes_vulpes 0.8228 1.0070 621
## Cogon_Patch_Size-Sus_scrofa 0.8665 1.0069 637
## Avg_Cogongrass_Cover-Canis_latrans 1.1408 1.0110 1219
## Avg_Cogongrass_Cover-Sciurus_niger 0.8509 1.0033 629
## Avg_Cogongrass_Cover-Procyon_lotor 0.9678 1.0050 1167
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1841 1.0039 1165
## Avg_Cogongrass_Cover-Lynx_rufus 1.5333 1.0057 933
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0096 1.0067 1057
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7763 1.0130 800
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2387 1.0204 788
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3090 1.0050 1138
## Avg_Cogongrass_Cover-Sus_scrofa 1.0247 1.0051 837
## total_shrub_cover-Canis_latrans 1.1081 1.0180 887
## total_shrub_cover-Sciurus_niger 0.2789 1.0342 723
## total_shrub_cover-Procyon_lotor -0.1057 1.0160 687
## total_shrub_cover-Dasypus_novemcinctus 0.4908 1.0022 655
## total_shrub_cover-Lynx_rufus 0.1035 1.0482 424
## total_shrub_cover-Didelphis_virginiana 0.2113 1.0096 486
## total_shrub_cover-Sylvilagus_floridanus 0.2520 1.0517 309
## total_shrub_cover-Sciurus_carolinensis 0.3971 1.0198 311
## total_shrub_cover-Vulpes_vulpes 0.7170 1.1084 307
## total_shrub_cover-Sus_scrofa 0.7104 1.0090 304
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5950 0.2015 -3.0061 -2.5909 -2.2169 1.0101
## (Intercept)-Sciurus_niger -3.5865 0.5685 -4.7604 -3.5595 -2.5455 1.0666
## (Intercept)-Procyon_lotor -2.2119 0.1506 -2.5200 -2.2069 -1.9338 1.0017
## (Intercept)-Dasypus_novemcinctus -1.7101 0.1925 -2.1043 -1.7043 -1.3494 1.0026
## (Intercept)-Lynx_rufus -3.2814 0.3313 -3.9489 -3.2678 -2.6680 1.0332
## (Intercept)-Didelphis_virginiana -2.4845 0.3133 -3.1127 -2.4674 -1.9150 1.0021
## (Intercept)-Sylvilagus_floridanus -3.1241 0.2837 -3.6938 -3.1188 -2.5926 1.0087
## (Intercept)-Sciurus_carolinensis -2.6636 0.3476 -3.3962 -2.6571 -2.0121 1.0167
## (Intercept)-Vulpes_vulpes -3.6847 0.6297 -4.9726 -3.6368 -2.5844 1.0501
## (Intercept)-Sus_scrofa -3.3804 0.5353 -4.4172 -3.3817 -2.3422 1.0070
## shrub_cover-Canis_latrans -0.2240 0.2394 -0.6944 -0.2261 0.2606 1.0060
## shrub_cover-Sciurus_niger 0.1147 0.5001 -0.8672 0.1112 1.0668 1.0374
## shrub_cover-Procyon_lotor 0.3240 0.1597 0.0093 0.3240 0.6301 1.0016
## shrub_cover-Dasypus_novemcinctus 1.0066 0.3346 0.3929 0.9928 1.6996 1.0012
## shrub_cover-Lynx_rufus 0.1671 0.3660 -0.6156 0.1890 0.8233 1.0249
## shrub_cover-Didelphis_virginiana 1.1251 0.4008 0.4134 1.0987 1.9287 1.0023
## shrub_cover-Sylvilagus_floridanus 0.6640 0.4231 -0.1652 0.6716 1.4820 1.0571
## shrub_cover-Sciurus_carolinensis 1.0820 0.4339 0.2836 1.0548 1.9630 1.0123
## shrub_cover-Vulpes_vulpes 0.4406 0.6115 -0.8443 0.4543 1.6101 1.1559
## shrub_cover-Sus_scrofa 1.0343 0.6931 -0.2755 1.0016 2.4588 1.0010
## veg_height-Canis_latrans -0.5603 0.1947 -0.9680 -0.5549 -0.1841 1.0097
## veg_height-Sciurus_niger 0.1198 0.4202 -0.7379 0.1106 0.9729 1.0330
## veg_height-Procyon_lotor 0.3491 0.1206 0.1202 0.3466 0.5903 1.0019
## veg_height-Dasypus_novemcinctus 0.2749 0.1356 0.0119 0.2735 0.5484 1.0029
## veg_height-Lynx_rufus 0.0503 0.2351 -0.4400 0.0523 0.5051 1.0033
## veg_height-Didelphis_virginiana 0.4382 0.2434 -0.0075 0.4287 0.9482 1.0028
## veg_height-Sylvilagus_floridanus 0.0899 0.2392 -0.3769 0.0917 0.5652 1.0217
## veg_height-Sciurus_carolinensis 0.1385 0.2232 -0.2948 0.1299 0.5979 1.0214
## veg_height-Vulpes_vulpes -0.0780 0.3150 -0.7466 -0.0618 0.5101 1.0158
## veg_height-Sus_scrofa -0.1196 0.3144 -0.7732 -0.1089 0.4660 1.0045
## week-Canis_latrans 0.4541 0.2511 -0.0221 0.4406 0.9718 1.0010
## week-Sciurus_niger -0.2385 0.4499 -1.2727 -0.1838 0.4887 1.0024
## week-Procyon_lotor 0.1605 0.2033 -0.2252 0.1578 0.5598 1.0044
## week-Dasypus_novemcinctus 0.0622 0.2221 -0.3739 0.0647 0.4944 1.0038
## week-Lynx_rufus 0.2696 0.3070 -0.3239 0.2691 0.9020 1.0067
## week-Didelphis_virginiana 0.0445 0.3160 -0.6168 0.0521 0.6469 1.0136
## week-Sylvilagus_floridanus 0.0468 0.3003 -0.5669 0.0522 0.6253 1.0027
## week-Sciurus_carolinensis 0.5411 0.3395 -0.0491 0.5140 1.2705 1.0126
## week-Vulpes_vulpes 0.1296 0.4163 -0.7702 0.1415 0.9296 1.0154
## week-Sus_scrofa 0.4317 0.3891 -0.2430 0.3900 1.3049 1.0030
## I(week^2)-Canis_latrans -0.1975 0.1041 -0.4136 -0.1942 0.0019 1.0014
## I(week^2)-Sciurus_niger -0.2461 0.2198 -0.7549 -0.2284 0.1276 1.0029
## I(week^2)-Procyon_lotor -0.1116 0.0901 -0.2937 -0.1090 0.0603 1.0033
## I(week^2)-Dasypus_novemcinctus -0.1539 0.1037 -0.3579 -0.1530 0.0467 1.0027
## I(week^2)-Lynx_rufus -0.1861 0.1420 -0.4751 -0.1837 0.0756 1.0053
## I(week^2)-Didelphis_virginiana -0.3506 0.2036 -0.8250 -0.3313 -0.0150 1.0379
## I(week^2)-Sylvilagus_floridanus -0.1576 0.1487 -0.4754 -0.1502 0.1177 1.0090
## I(week^2)-Sciurus_carolinensis -0.1920 0.1359 -0.4755 -0.1844 0.0595 1.0075
## I(week^2)-Vulpes_vulpes -0.3452 0.2447 -0.9215 -0.3258 0.0596 1.0056
## I(week^2)-Sus_scrofa -0.1682 0.1651 -0.5326 -0.1586 0.1371 1.0031
## ESS
## (Intercept)-Canis_latrans 877
## (Intercept)-Sciurus_niger 303
## (Intercept)-Procyon_lotor 1511
## (Intercept)-Dasypus_novemcinctus 1068
## (Intercept)-Lynx_rufus 500
## (Intercept)-Didelphis_virginiana 530
## (Intercept)-Sylvilagus_floridanus 681
## (Intercept)-Sciurus_carolinensis 555
## (Intercept)-Vulpes_vulpes 246
## (Intercept)-Sus_scrofa 318
## shrub_cover-Canis_latrans 694
## shrub_cover-Sciurus_niger 577
## shrub_cover-Procyon_lotor 1527
## shrub_cover-Dasypus_novemcinctus 560
## shrub_cover-Lynx_rufus 434
## shrub_cover-Didelphis_virginiana 427
## shrub_cover-Sylvilagus_floridanus 318
## shrub_cover-Sciurus_carolinensis 301
## shrub_cover-Vulpes_vulpes 346
## shrub_cover-Sus_scrofa 339
## veg_height-Canis_latrans 628
## veg_height-Sciurus_niger 603
## veg_height-Procyon_lotor 1543
## veg_height-Dasypus_novemcinctus 1872
## veg_height-Lynx_rufus 843
## veg_height-Didelphis_virginiana 1066
## veg_height-Sylvilagus_floridanus 714
## veg_height-Sciurus_carolinensis 975
## veg_height-Vulpes_vulpes 666
## veg_height-Sus_scrofa 921
## week-Canis_latrans 819
## week-Sciurus_niger 463
## week-Procyon_lotor 1458
## week-Dasypus_novemcinctus 1671
## week-Lynx_rufus 997
## week-Didelphis_virginiana 978
## week-Sylvilagus_floridanus 1102
## week-Sciurus_carolinensis 816
## week-Vulpes_vulpes 674
## week-Sus_scrofa 867
## I(week^2)-Canis_latrans 1252
## I(week^2)-Sciurus_niger 448
## I(week^2)-Procyon_lotor 1439
## I(week^2)-Dasypus_novemcinctus 1753
## I(week^2)-Lynx_rufus 789
## I(week^2)-Didelphis_virginiana 479
## I(week^2)-Sylvilagus_floridanus 639
## I(week^2)-Sciurus_carolinensis 931
## I(week^2)-Vulpes_vulpes 457
## I(week^2)-Sus_scrofa 1039
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_forage)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8023
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7255 0.3907 -1.4702 -0.7302 0.0507 1.0225 537
## Veg_shannon_index 0.3246 0.2439 -0.1436 0.3105 0.8225 1.0130 873
## Avg_Cogongrass_Cover 0.2883 0.2638 -0.2436 0.2902 0.8213 1.0106 906
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8188 0.9528 0.0672 0.5530 3.1961 1.0446 610
## Veg_shannon_index 0.2761 0.3197 0.0349 0.1792 1.0675 1.0426 519
## Avg_Cogongrass_Cover 0.3126 0.3654 0.0368 0.2008 1.2794 1.0628 508
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8091 0.7125 0.0681 0.5863 2.6987 1.1684 189
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7693 0.3209 -3.4265 -2.7611 -2.1293 1.0046 1257
## shrub_cover 0.2980 0.2667 -0.2694 0.2994 0.8263 1.0018 1169
## veg_height 0.0493 0.1762 -0.3101 0.0527 0.3926 1.0089 1152
## week 0.1839 0.2050 -0.2223 0.1892 0.5664 1.0214 801
## I(week^2) -0.2041 0.1070 -0.4237 -0.2000 -0.0058 1.0074 802
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8548 0.7165 0.1790 0.6549 2.7041 1.0355 410
## shrub_cover 0.5183 0.4651 0.0930 0.3973 1.6589 1.0112 972
## veg_height 0.2104 0.1649 0.0554 0.1670 0.6217 1.0212 1440
## week 0.2036 0.2184 0.0336 0.1408 0.7356 1.0321 437
## I(week^2) 0.0638 0.0512 0.0178 0.0491 0.2033 0.9996 888
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0960 0.5922 -1.2406 -0.0968
## (Intercept)-Sciurus_niger -0.8444 0.7534 -2.2002 -0.8910
## (Intercept)-Procyon_lotor 0.0566 0.6252 -1.1435 0.0690
## (Intercept)-Dasypus_novemcinctus -0.7449 0.5097 -1.8021 -0.7431
## (Intercept)-Lynx_rufus -0.3658 0.7311 -1.5829 -0.4280
## (Intercept)-Didelphis_virginiana -1.1650 0.5536 -2.3559 -1.1279
## (Intercept)-Sylvilagus_floridanus -0.5969 0.5495 -1.6728 -0.6013
## (Intercept)-Sciurus_carolinensis -1.1919 0.5798 -2.3968 -1.1592
## (Intercept)-Vulpes_vulpes -1.1069 0.7887 -2.6690 -1.0930
## (Intercept)-Sus_scrofa -1.4856 0.7020 -3.0282 -1.4268
## Veg_shannon_index-Canis_latrans 0.6098 0.3800 -0.0762 0.5785
## Veg_shannon_index-Sciurus_niger 0.3227 0.4736 -0.5600 0.3027
## Veg_shannon_index-Procyon_lotor 0.4325 0.3565 -0.2382 0.4205
## Veg_shannon_index-Dasypus_novemcinctus 0.1816 0.3400 -0.5119 0.1883
## Veg_shannon_index-Lynx_rufus 0.1665 0.4700 -0.8587 0.1841
## Veg_shannon_index-Didelphis_virginiana 0.4666 0.3809 -0.2306 0.4481
## Veg_shannon_index-Sylvilagus_floridanus 0.4106 0.4033 -0.3432 0.3931
## Veg_shannon_index-Sciurus_carolinensis -0.0232 0.3966 -0.8920 0.0070
## Veg_shannon_index-Vulpes_vulpes 0.0631 0.4490 -0.8772 0.0878
## Veg_shannon_index-Sus_scrofa 0.6052 0.4769 -0.2211 0.5571
## Avg_Cogongrass_Cover-Canis_latrans 0.5502 0.3830 -0.1157 0.5230
## Avg_Cogongrass_Cover-Sciurus_niger -0.1158 0.5733 -1.4584 -0.0522
## Avg_Cogongrass_Cover-Procyon_lotor 0.3441 0.3694 -0.3487 0.3278
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4232 0.3401 -0.2098 0.4154
## Avg_Cogongrass_Cover-Lynx_rufus 0.5632 0.4239 -0.1757 0.5355
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4117 0.3705 -0.2980 0.3946
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1078 0.4469 -1.1088 -0.0769
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3928 0.3630 -0.3207 0.3804
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3828 0.4554 -0.4558 0.3588
## Avg_Cogongrass_Cover-Sus_scrofa 0.0568 0.4990 -1.0943 0.0972
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0941 1.0451 449
## (Intercept)-Sciurus_niger 0.8787 1.0141 336
## (Intercept)-Procyon_lotor 1.2965 1.0831 369
## (Intercept)-Dasypus_novemcinctus 0.2201 1.0118 1376
## (Intercept)-Lynx_rufus 1.2921 1.0402 350
## (Intercept)-Didelphis_virginiana -0.1719 1.0018 852
## (Intercept)-Sylvilagus_floridanus 0.4948 1.0107 838
## (Intercept)-Sciurus_carolinensis -0.1394 1.0056 879
## (Intercept)-Vulpes_vulpes 0.5376 1.0021 334
## (Intercept)-Sus_scrofa -0.2237 1.0359 550
## Veg_shannon_index-Canis_latrans 1.4225 1.0152 1308
## Veg_shannon_index-Sciurus_niger 1.3661 1.0155 1123
## Veg_shannon_index-Procyon_lotor 1.1716 1.0064 1781
## Veg_shannon_index-Dasypus_novemcinctus 0.8521 1.0027 1862
## Veg_shannon_index-Lynx_rufus 1.0531 1.0081 1053
## Veg_shannon_index-Didelphis_virginiana 1.2880 1.0050 1596
## Veg_shannon_index-Sylvilagus_floridanus 1.2920 1.0023 1617
## Veg_shannon_index-Sciurus_carolinensis 0.6878 1.0024 1369
## Veg_shannon_index-Vulpes_vulpes 0.9132 1.0094 1088
## Veg_shannon_index-Sus_scrofa 1.7001 1.0006 1051
## Avg_Cogongrass_Cover-Canis_latrans 1.4064 1.0230 1416
## Avg_Cogongrass_Cover-Sciurus_niger 0.8168 1.0132 618
## Avg_Cogongrass_Cover-Procyon_lotor 1.0907 1.0056 1579
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1279 1.0009 2003
## Avg_Cogongrass_Cover-Lynx_rufus 1.5322 1.0151 1144
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1919 1.0108 1387
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6739 1.0004 954
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1358 1.0000 1631
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3783 1.0116 1113
## Avg_Cogongrass_Cover-Sus_scrofa 0.9485 1.0036 1037
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5689 0.1989 -2.9736 -2.5630 -2.1954 1.0122
## (Intercept)-Sciurus_niger -3.7055 0.6379 -5.0578 -3.6645 -2.5721 1.0195
## (Intercept)-Procyon_lotor -2.2109 0.1622 -2.5506 -2.2054 -1.9003 1.0131
## (Intercept)-Dasypus_novemcinctus -1.6374 0.1774 -1.9890 -1.6313 -1.2976 1.0087
## (Intercept)-Lynx_rufus -3.4204 0.3590 -4.1407 -3.4079 -2.7384 1.0170
## (Intercept)-Didelphis_virginiana -2.4200 0.2980 -3.0211 -2.4108 -1.8591 1.0238
## (Intercept)-Sylvilagus_floridanus -3.0681 0.2953 -3.6820 -3.0494 -2.5256 1.0267
## (Intercept)-Sciurus_carolinensis -2.5100 0.3152 -3.1558 -2.4967 -1.9345 1.0058
## (Intercept)-Vulpes_vulpes -3.8116 0.6973 -5.3308 -3.7581 -2.6490 1.0014
## (Intercept)-Sus_scrofa -3.1710 0.5301 -4.2073 -3.1710 -2.1193 1.0227
## shrub_cover-Canis_latrans -0.2508 0.2139 -0.6693 -0.2568 0.1700 1.0201
## shrub_cover-Sciurus_niger -0.2286 0.4995 -1.1763 -0.2348 0.7805 1.0119
## shrub_cover-Procyon_lotor 0.2454 0.1693 -0.1105 0.2515 0.5544 1.0148
## shrub_cover-Dasypus_novemcinctus 0.8457 0.2971 0.2852 0.8361 1.4363 1.0143
## shrub_cover-Lynx_rufus -0.1819 0.3575 -0.8878 -0.1767 0.4977 1.0075
## shrub_cover-Didelphis_virginiana 0.9797 0.3817 0.3034 0.9565 1.7654 1.0013
## shrub_cover-Sylvilagus_floridanus 0.2771 0.4037 -0.4874 0.2579 1.0835 1.0024
## shrub_cover-Sciurus_carolinensis 0.8124 0.3885 0.0930 0.8040 1.6043 1.0014
## shrub_cover-Vulpes_vulpes -0.0054 0.5738 -1.1942 -0.0035 1.1247 1.0086
## shrub_cover-Sus_scrofa 0.5771 0.6865 -0.9057 0.5905 1.9354 1.0174
## veg_height-Canis_latrans -0.5624 0.1819 -0.9200 -0.5604 -0.2265 1.0250
## veg_height-Sciurus_niger 0.0545 0.4389 -0.7712 0.0402 0.9799 1.0035
## veg_height-Procyon_lotor 0.3413 0.1209 0.1099 0.3418 0.5770 1.0018
## veg_height-Dasypus_novemcinctus 0.2508 0.1333 -0.0022 0.2486 0.5232 1.0083
## veg_height-Lynx_rufus 0.0046 0.2343 -0.4515 0.0080 0.4519 1.0026
## veg_height-Didelphis_virginiana 0.4370 0.2494 -0.0227 0.4283 0.9688 1.0035
## veg_height-Sylvilagus_floridanus 0.1572 0.2401 -0.3014 0.1569 0.6277 1.0051
## veg_height-Sciurus_carolinensis 0.0728 0.2071 -0.3248 0.0712 0.4894 1.0001
## veg_height-Vulpes_vulpes -0.1442 0.3284 -0.8601 -0.1273 0.4457 1.0349
## veg_height-Sus_scrofa -0.1053 0.3332 -0.7864 -0.0912 0.5205 1.0036
## week-Canis_latrans 0.4523 0.2448 0.0018 0.4375 0.9677 1.0071
## week-Sciurus_niger -0.2105 0.4465 -1.2527 -0.1471 0.4803 1.0358
## week-Procyon_lotor 0.1533 0.1991 -0.2403 0.1573 0.5465 1.0040
## week-Dasypus_novemcinctus 0.0707 0.2082 -0.3425 0.0664 0.4906 1.0065
## week-Lynx_rufus 0.2691 0.2953 -0.2914 0.2583 0.8715 1.0245
## week-Didelphis_virginiana 0.0374 0.3137 -0.6165 0.0505 0.6250 1.0018
## week-Sylvilagus_floridanus 0.0380 0.2963 -0.6006 0.0436 0.5917 1.0036
## week-Sciurus_carolinensis 0.5303 0.3258 -0.0535 0.5025 1.2357 1.0124
## week-Vulpes_vulpes 0.1227 0.3929 -0.6935 0.1336 0.8799 1.0205
## week-Sus_scrofa 0.4070 0.3732 -0.2552 0.3825 1.2114 1.0092
## I(week^2)-Canis_latrans -0.1942 0.1046 -0.4077 -0.1914 0.0034 1.0124
## I(week^2)-Sciurus_niger -0.2224 0.2139 -0.6710 -0.2095 0.1538 1.0190
## I(week^2)-Procyon_lotor -0.1110 0.0891 -0.2941 -0.1094 0.0618 0.9997
## I(week^2)-Dasypus_novemcinctus -0.1576 0.0990 -0.3590 -0.1572 0.0281 1.0052
## I(week^2)-Lynx_rufus -0.1929 0.1489 -0.5006 -0.1852 0.0778 1.0117
## I(week^2)-Didelphis_virginiana -0.3437 0.2008 -0.8238 -0.3198 -0.0103 1.0052
## I(week^2)-Sylvilagus_floridanus -0.1522 0.1435 -0.4503 -0.1462 0.1213 1.0003
## I(week^2)-Sciurus_carolinensis -0.1845 0.1340 -0.4622 -0.1764 0.0669 1.0095
## I(week^2)-Vulpes_vulpes -0.3267 0.2372 -0.8977 -0.3024 0.0579 0.9999
## I(week^2)-Sus_scrofa -0.1589 0.1695 -0.5126 -0.1511 0.1540 1.0007
## ESS
## (Intercept)-Canis_latrans 909
## (Intercept)-Sciurus_niger 181
## (Intercept)-Procyon_lotor 1507
## (Intercept)-Dasypus_novemcinctus 1549
## (Intercept)-Lynx_rufus 347
## (Intercept)-Didelphis_virginiana 882
## (Intercept)-Sylvilagus_floridanus 673
## (Intercept)-Sciurus_carolinensis 1538
## (Intercept)-Vulpes_vulpes 171
## (Intercept)-Sus_scrofa 736
## shrub_cover-Canis_latrans 944
## shrub_cover-Sciurus_niger 439
## shrub_cover-Procyon_lotor 1204
## shrub_cover-Dasypus_novemcinctus 1240
## shrub_cover-Lynx_rufus 466
## shrub_cover-Didelphis_virginiana 583
## shrub_cover-Sylvilagus_floridanus 570
## shrub_cover-Sciurus_carolinensis 815
## shrub_cover-Vulpes_vulpes 628
## shrub_cover-Sus_scrofa 880
## veg_height-Canis_latrans 766
## veg_height-Sciurus_niger 577
## veg_height-Procyon_lotor 1496
## veg_height-Dasypus_novemcinctus 2083
## veg_height-Lynx_rufus 892
## veg_height-Didelphis_virginiana 1086
## veg_height-Sylvilagus_floridanus 923
## veg_height-Sciurus_carolinensis 1081
## veg_height-Vulpes_vulpes 606
## veg_height-Sus_scrofa 1280
## week-Canis_latrans 1247
## week-Sciurus_niger 334
## week-Procyon_lotor 1661
## week-Dasypus_novemcinctus 1882
## week-Lynx_rufus 1014
## week-Didelphis_virginiana 1133
## week-Sylvilagus_floridanus 925
## week-Sciurus_carolinensis 1340
## week-Vulpes_vulpes 796
## week-Sus_scrofa 1247
## I(week^2)-Canis_latrans 1352
## I(week^2)-Sciurus_niger 458
## I(week^2)-Procyon_lotor 1545
## I(week^2)-Dasypus_novemcinctus 1634
## I(week^2)-Lynx_rufus 641
## I(week^2)-Didelphis_virginiana 462
## I(week^2)-Sylvilagus_floridanus 858
## I(week^2)-Sciurus_carolinensis 1362
## I(week^2)-Vulpes_vulpes 426
## I(week^2)-Sus_scrofa 1022
#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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogon)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7977
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7120 0.3652 -1.4456 -0.7084 -0.0024 1.0174 580
## Avg_Cogongrass_Cover 0.1788 0.2308 -0.2905 0.1795 0.6155 1.0094 1139
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6945 0.6965 0.067 0.4963 2.5719 1.0116 691
## Avg_Cogongrass_Cover 0.2626 0.3123 0.035 0.1702 1.0568 1.0068 982
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7916 0.7065 0.0559 0.5991 2.6682 1.0123 164
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7511 0.3031 -3.3808 -2.7413 -2.1499 1.0155 992
## shrub_cover 0.3308 0.2744 -0.2185 0.3252 0.8808 1.0285 737
## veg_height 0.0677 0.1643 -0.2554 0.0721 0.3883 1.0112 1124
## week 0.1863 0.2046 -0.2233 0.1863 0.5854 1.0065 886
## I(week^2) -0.2061 0.1061 -0.4243 -0.2018 -0.0020 1.0095 770
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7621 0.6645 0.1687 0.5896 2.4313 1.0095 486
## shrub_cover 0.5020 0.5104 0.0925 0.3643 1.7222 1.0477 583
## veg_height 0.2032 0.1571 0.0514 0.1587 0.6311 1.0025 1268
## week 0.2210 0.2138 0.0335 0.1544 0.7819 1.0027 916
## I(week^2) 0.0616 0.0467 0.0174 0.0481 0.1868 1.0077 964
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0731 0.5484 -1.0769 -0.0743
## (Intercept)-Sciurus_niger -0.8647 0.6461 -2.1101 -0.8772
## (Intercept)-Procyon_lotor 0.0474 0.5933 -1.0937 0.0397
## (Intercept)-Dasypus_novemcinctus -0.6976 0.5079 -1.7096 -0.6882
## (Intercept)-Lynx_rufus -0.4346 0.6093 -1.5937 -0.4552
## (Intercept)-Didelphis_virginiana -1.0609 0.5295 -2.1751 -1.0459
## (Intercept)-Sylvilagus_floridanus -0.5856 0.5217 -1.6433 -0.5833
## (Intercept)-Sciurus_carolinensis -1.1072 0.5580 -2.2688 -1.0647
## (Intercept)-Vulpes_vulpes -1.1452 0.6884 -2.5504 -1.1143
## (Intercept)-Sus_scrofa -1.3283 0.6764 -2.7847 -1.2803
## Avg_Cogongrass_Cover-Canis_latrans 0.3806 0.3576 -0.2676 0.3576
## Avg_Cogongrass_Cover-Sciurus_niger -0.1608 0.4906 -1.2765 -0.1184
## Avg_Cogongrass_Cover-Procyon_lotor 0.2013 0.3246 -0.4393 0.2005
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3312 0.3170 -0.2630 0.3229
## Avg_Cogongrass_Cover-Lynx_rufus 0.4144 0.3767 -0.2565 0.3871
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3026 0.3458 -0.3592 0.2925
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1896 0.4019 -1.0820 -0.1518
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3354 0.3445 -0.3155 0.3187
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2822 0.3961 -0.4575 0.2766
## Avg_Cogongrass_Cover-Sus_scrofa -0.0437 0.4746 -1.1241 -0.0084
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0244 1.0063 737
## (Intercept)-Sciurus_niger 0.5430 1.0002 455
## (Intercept)-Procyon_lotor 1.2145 1.0001 399
## (Intercept)-Dasypus_novemcinctus 0.2793 1.0218 586
## (Intercept)-Lynx_rufus 0.8276 1.0114 517
## (Intercept)-Didelphis_virginiana -0.0524 1.0063 924
## (Intercept)-Sylvilagus_floridanus 0.4306 1.0057 1251
## (Intercept)-Sciurus_carolinensis -0.0892 1.0007 873
## (Intercept)-Vulpes_vulpes 0.1515 1.0074 504
## (Intercept)-Sus_scrofa -0.1440 1.0381 518
## Avg_Cogongrass_Cover-Canis_latrans 1.1698 1.0009 1515
## Avg_Cogongrass_Cover-Sciurus_niger 0.6690 1.0025 951
## Avg_Cogongrass_Cover-Procyon_lotor 0.8763 1.0009 2122
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9982 1.0035 1757
## Avg_Cogongrass_Cover-Lynx_rufus 1.2288 1.0124 1343
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9898 1.0055 1784
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4906 1.0074 865
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0394 1.0016 1821
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1135 1.0036 1425
## Avg_Cogongrass_Cover-Sus_scrofa 0.7894 1.0035 842
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5729 0.1970 -2.9783 -2.5625 -2.2050 1.0426
## (Intercept)-Sciurus_niger -3.6556 0.6123 -4.9284 -3.6130 -2.5744 1.0046
## (Intercept)-Procyon_lotor -2.2084 0.1609 -2.5455 -2.1999 -1.9043 1.0216
## (Intercept)-Dasypus_novemcinctus -1.6544 0.1786 -2.0033 -1.6516 -1.3073 1.0037
## (Intercept)-Lynx_rufus -3.3656 0.3447 -4.0703 -3.3626 -2.7309 1.0411
## (Intercept)-Didelphis_virginiana -2.4084 0.2935 -2.9902 -2.3952 -1.8488 1.0075
## (Intercept)-Sylvilagus_floridanus -3.0211 0.2911 -3.6022 -3.0115 -2.4677 1.0052
## (Intercept)-Sciurus_carolinensis -2.5279 0.3208 -3.1953 -2.5238 -1.9257 1.0041
## (Intercept)-Vulpes_vulpes -3.6385 0.6336 -5.0333 -3.5850 -2.5434 1.0098
## (Intercept)-Sus_scrofa -3.2017 0.5511 -4.3286 -3.1646 -2.1985 1.0500
## shrub_cover-Canis_latrans -0.2391 0.2202 -0.6643 -0.2394 0.1894 1.0251
## shrub_cover-Sciurus_niger -0.1589 0.4970 -1.2094 -0.1456 0.8262 1.0113
## shrub_cover-Procyon_lotor 0.2652 0.1635 -0.0533 0.2685 0.5848 1.0050
## shrub_cover-Dasypus_novemcinctus 0.8460 0.3009 0.2744 0.8383 1.4606 1.0205
## shrub_cover-Lynx_rufus -0.1291 0.3540 -0.8388 -0.1257 0.5652 1.0016
## shrub_cover-Didelphis_virginiana 0.9378 0.3589 0.2799 0.9245 1.6744 1.0160
## shrub_cover-Sylvilagus_floridanus 0.3255 0.3932 -0.4484 0.3212 1.0970 1.0211
## shrub_cover-Sciurus_carolinensis 0.8333 0.4041 0.0759 0.8198 1.6610 1.0260
## shrub_cover-Vulpes_vulpes 0.0622 0.5576 -1.1190 0.0759 1.1286 1.0050
## shrub_cover-Sus_scrofa 0.6255 0.7212 -0.7558 0.5879 2.1809 1.0487
## veg_height-Canis_latrans -0.5516 0.1817 -0.9088 -0.5474 -0.1983 1.0192
## veg_height-Sciurus_niger 0.1052 0.4229 -0.6854 0.0889 1.0064 1.0054
## veg_height-Procyon_lotor 0.3387 0.1225 0.1013 0.3387 0.5769 0.9998
## veg_height-Dasypus_novemcinctus 0.2549 0.1366 -0.0028 0.2522 0.5331 1.0177
## veg_height-Lynx_rufus 0.0403 0.2289 -0.4262 0.0441 0.4717 1.0024
## veg_height-Didelphis_virginiana 0.4319 0.2364 -0.0175 0.4252 0.9120 1.0127
## veg_height-Sylvilagus_floridanus 0.1608 0.2348 -0.2954 0.1637 0.6232 1.0288
## veg_height-Sciurus_carolinensis 0.0933 0.2122 -0.3187 0.0901 0.5149 1.0125
## veg_height-Vulpes_vulpes -0.0865 0.3072 -0.7685 -0.0655 0.4546 1.0185
## veg_height-Sus_scrofa -0.1040 0.3282 -0.7823 -0.0979 0.5107 1.0006
## week-Canis_latrans 0.4573 0.2554 -0.0064 0.4470 0.9848 1.0042
## week-Sciurus_niger -0.2439 0.4294 -1.2282 -0.2029 0.4815 1.0077
## week-Procyon_lotor 0.1506 0.1989 -0.2360 0.1476 0.5408 1.0021
## week-Dasypus_novemcinctus 0.0580 0.2113 -0.3662 0.0599 0.4822 1.0069
## week-Lynx_rufus 0.2545 0.3030 -0.3415 0.2526 0.8675 1.0046
## week-Didelphis_virginiana 0.0210 0.3210 -0.6435 0.0297 0.6307 1.0006
## week-Sylvilagus_floridanus 0.0470 0.3079 -0.5722 0.0581 0.6384 1.0154
## week-Sciurus_carolinensis 0.5515 0.3437 -0.0548 0.5293 1.2868 1.0044
## week-Vulpes_vulpes 0.1604 0.3957 -0.6488 0.1700 0.9168 1.0033
## week-Sus_scrofa 0.4353 0.3780 -0.2562 0.4123 1.2569 1.0192
## I(week^2)-Canis_latrans -0.1959 0.1056 -0.4107 -0.1910 -0.0023 1.0153
## I(week^2)-Sciurus_niger -0.2411 0.2066 -0.6872 -0.2225 0.1311 1.0265
## I(week^2)-Procyon_lotor -0.1086 0.0872 -0.2786 -0.1077 0.0672 0.9999
## I(week^2)-Dasypus_novemcinctus -0.1532 0.1003 -0.3488 -0.1538 0.0413 0.9997
## I(week^2)-Lynx_rufus -0.1980 0.1437 -0.4928 -0.1958 0.0724 1.0240
## I(week^2)-Didelphis_virginiana -0.3391 0.1947 -0.7941 -0.3170 -0.0266 1.0022
## I(week^2)-Sylvilagus_floridanus -0.1625 0.1440 -0.4415 -0.1595 0.1168 1.0061
## I(week^2)-Sciurus_carolinensis -0.1945 0.1419 -0.4899 -0.1921 0.0699 1.0066
## I(week^2)-Vulpes_vulpes -0.3174 0.2344 -0.8340 -0.2960 0.0725 1.0137
## I(week^2)-Sus_scrofa -0.1621 0.1633 -0.4934 -0.1558 0.1498 1.0087
## ESS
## (Intercept)-Canis_latrans 759
## (Intercept)-Sciurus_niger 211
## (Intercept)-Procyon_lotor 1334
## (Intercept)-Dasypus_novemcinctus 1723
## (Intercept)-Lynx_rufus 382
## (Intercept)-Didelphis_virginiana 1073
## (Intercept)-Sylvilagus_floridanus 694
## (Intercept)-Sciurus_carolinensis 842
## (Intercept)-Vulpes_vulpes 184
## (Intercept)-Sus_scrofa 469
## shrub_cover-Canis_latrans 754
## shrub_cover-Sciurus_niger 323
## shrub_cover-Procyon_lotor 1434
## shrub_cover-Dasypus_novemcinctus 1047
## shrub_cover-Lynx_rufus 505
## shrub_cover-Didelphis_virginiana 642
## shrub_cover-Sylvilagus_floridanus 627
## shrub_cover-Sciurus_carolinensis 716
## shrub_cover-Vulpes_vulpes 572
## shrub_cover-Sus_scrofa 415
## veg_height-Canis_latrans 598
## veg_height-Sciurus_niger 613
## veg_height-Procyon_lotor 1515
## veg_height-Dasypus_novemcinctus 1977
## veg_height-Lynx_rufus 912
## veg_height-Didelphis_virginiana 1280
## veg_height-Sylvilagus_floridanus 876
## veg_height-Sciurus_carolinensis 1120
## veg_height-Vulpes_vulpes 698
## veg_height-Sus_scrofa 958
## week-Canis_latrans 1280
## week-Sciurus_niger 523
## week-Procyon_lotor 1477
## week-Dasypus_novemcinctus 1763
## week-Lynx_rufus 1108
## week-Didelphis_virginiana 1375
## week-Sylvilagus_floridanus 892
## week-Sciurus_carolinensis 1153
## week-Vulpes_vulpes 895
## week-Sus_scrofa 1092
## I(week^2)-Canis_latrans 1304
## I(week^2)-Sciurus_niger 534
## I(week^2)-Procyon_lotor 1653
## I(week^2)-Dasypus_novemcinctus 1686
## I(week^2)-Lynx_rufus 674
## I(week^2)-Didelphis_virginiana 624
## I(week^2)-Sylvilagus_floridanus 887
## I(week^2)-Sciurus_carolinensis 1194
## I(week^2)-Vulpes_vulpes 509
## I(week^2)-Sus_scrofa 1262
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogonQ)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7943
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4271 0.3911 -2.2086 -1.4259 -0.6718 1.0082 435
## Avg_Cogongrass_Cover -0.7403 0.3582 -1.4524 -0.7384 -0.0490 1.0342 455
## I(Avg_Cogongrass_Cover^2) 0.7995 0.3471 0.1942 0.7712 1.5697 1.0026 478
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6699 0.7353 0.0593 0.4671 2.5219 1.0088 859
## Avg_Cogongrass_Cover 0.3661 0.4669 0.0408 0.2199 1.4922 1.0151 634
## I(Avg_Cogongrass_Cover^2) 0.5777 0.8749 0.0415 0.2907 2.8474 1.0338 375
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5008 0.482 0.0484 0.3511 1.8624 1.1371 192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7267 0.2990 -3.3337 -2.7155 -2.1494 1.0081 1313
## shrub_cover 0.3203 0.2649 -0.2086 0.3189 0.8389 1.0153 969
## veg_height 0.0910 0.1680 -0.2436 0.0918 0.4110 1.0042 1207
## week 0.1909 0.1977 -0.2189 0.1978 0.5598 1.0037 867
## I(week^2) -0.2109 0.1037 -0.4343 -0.2053 -0.0213 1.0005 922
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7134 0.5942 0.1541 0.5553 2.1859 1.0559 489
## shrub_cover 0.4993 0.4825 0.0768 0.3747 1.7217 1.0092 822
## veg_height 0.1981 0.1484 0.0484 0.1589 0.5999 1.0086 1462
## week 0.2002 0.2041 0.0335 0.1412 0.7214 1.0135 697
## I(week^2) 0.0625 0.0520 0.0176 0.0489 0.1804 1.0224 1148
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9197 0.5762 -2.0370 -0.9231
## (Intercept)-Sciurus_niger -1.4323 0.6210 -2.6434 -1.4270
## (Intercept)-Procyon_lotor -0.7075 0.6108 -1.9191 -0.7178
## (Intercept)-Dasypus_novemcinctus -1.3870 0.5066 -2.4151 -1.3833
## (Intercept)-Lynx_rufus -1.3327 0.6156 -2.5311 -1.3436
## (Intercept)-Didelphis_virginiana -1.6743 0.5499 -2.8093 -1.6484
## (Intercept)-Sylvilagus_floridanus -1.2749 0.5762 -2.4184 -1.2691
## (Intercept)-Sciurus_carolinensis -1.9631 0.6319 -3.3481 -1.9110
## (Intercept)-Vulpes_vulpes -1.9399 0.7627 -3.5442 -1.8868
## (Intercept)-Sus_scrofa -1.9574 0.6691 -3.4530 -1.8901
## Avg_Cogongrass_Cover-Canis_latrans -0.4596 0.5034 -1.3528 -0.4910
## Avg_Cogongrass_Cover-Sciurus_niger -1.0260 0.6154 -2.3770 -0.9713
## Avg_Cogongrass_Cover-Procyon_lotor -0.7443 0.4727 -1.6898 -0.7426
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5663 0.4614 -1.4561 -0.5744
## Avg_Cogongrass_Cover-Lynx_rufus -0.6971 0.5461 -1.8325 -0.6912
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4771 0.5163 -1.4184 -0.4979
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2025 0.6124 -2.5567 -1.1377
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7377 0.5152 -1.7681 -0.7334
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7234 0.5805 -1.8755 -0.7234
## Avg_Cogongrass_Cover-Sus_scrofa -0.9042 0.5692 -2.0997 -0.8625
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.4268 0.8458 0.2989 1.2280
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2132 0.6623 -1.2255 0.2607
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2305 0.7303 0.3178 1.0785
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7581 0.3623 0.1137 0.7410
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2273 0.5574 0.3994 1.1364
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5485 0.4067 -0.2075 0.5297
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7363 0.4529 -0.0649 0.6931
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.9118 0.3882 0.2216 0.8870
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8606 0.5152 0.0916 0.8021
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.3001 0.5984 -1.0805 0.3536
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2088 1.0138 600
## (Intercept)-Sciurus_niger -0.2189 1.0040 637
## (Intercept)-Procyon_lotor 0.5232 1.0194 550
## (Intercept)-Dasypus_novemcinctus -0.4067 1.0030 1112
## (Intercept)-Lynx_rufus -0.0137 1.0027 581
## (Intercept)-Didelphis_virginiana -0.6448 1.0050 847
## (Intercept)-Sylvilagus_floridanus -0.1079 1.0045 882
## (Intercept)-Sciurus_carolinensis -0.8572 1.0362 739
## (Intercept)-Vulpes_vulpes -0.5829 1.0589 463
## (Intercept)-Sus_scrofa -0.8095 1.0218 791
## Avg_Cogongrass_Cover-Canis_latrans 0.6237 1.0179 1027
## Avg_Cogongrass_Cover-Sciurus_niger 0.0111 1.0049 666
## Avg_Cogongrass_Cover-Procyon_lotor 0.1419 1.0090 1020
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3992 1.0104 737
## Avg_Cogongrass_Cover-Lynx_rufus 0.3749 1.0349 767
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6073 1.0332 882
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1791 1.0096 619
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2823 1.0138 843
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4654 1.0163 785
## Avg_Cogongrass_Cover-Sus_scrofa 0.1273 1.0171 759
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5482 1.0089 334
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3705 1.0308 313
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.2169 1.0123 284
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5068 1.0036 999
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6692 1.0113 475
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4234 1.0106 547
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7185 1.0035 616
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7561 1.0026 695
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0432 1.0727 462
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.3422 1.0075 486
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5839 0.1944 -2.9623 -2.5841 -2.2109 1.0018
## (Intercept)-Sciurus_niger -3.5327 0.5722 -4.6775 -3.5111 -2.4849 1.0089
## (Intercept)-Procyon_lotor -2.2225 0.1601 -2.5412 -2.2176 -1.9195 1.0000
## (Intercept)-Dasypus_novemcinctus -1.6492 0.1767 -2.0033 -1.6487 -1.3104 0.9998
## (Intercept)-Lynx_rufus -3.3162 0.3539 -4.0812 -3.3058 -2.6579 1.0302
## (Intercept)-Didelphis_virginiana -2.4019 0.2880 -2.9775 -2.3957 -1.8558 1.0094
## (Intercept)-Sylvilagus_floridanus -3.0339 0.2988 -3.6585 -3.0143 -2.4851 1.0407
## (Intercept)-Sciurus_carolinensis -2.5061 0.3127 -3.1601 -2.4889 -1.9441 1.0070
## (Intercept)-Vulpes_vulpes -3.6187 0.6328 -5.0204 -3.5535 -2.5348 1.1249
## (Intercept)-Sus_scrofa -3.1173 0.5263 -4.1782 -3.1045 -2.1271 1.0131
## shrub_cover-Canis_latrans -0.2315 0.2171 -0.6560 -0.2375 0.2051 1.0081
## shrub_cover-Sciurus_niger -0.1415 0.4884 -1.1345 -0.1288 0.7927 1.0047
## shrub_cover-Procyon_lotor 0.2408 0.1627 -0.0708 0.2454 0.5432 1.0005
## shrub_cover-Dasypus_novemcinctus 0.8464 0.2987 0.2959 0.8369 1.4432 1.0069
## shrub_cover-Lynx_rufus -0.1270 0.3618 -0.8378 -0.1202 0.5557 1.0215
## shrub_cover-Didelphis_virginiana 0.9438 0.3665 0.2712 0.9193 1.7084 1.0326
## shrub_cover-Sylvilagus_floridanus 0.2617 0.4012 -0.4780 0.2524 1.1074 1.0077
## shrub_cover-Sciurus_carolinensis 0.8182 0.3994 0.0774 0.8070 1.6264 1.0024
## shrub_cover-Vulpes_vulpes 0.0576 0.5613 -1.0732 0.0604 1.1498 1.0104
## shrub_cover-Sus_scrofa 0.5954 0.7245 -0.8328 0.5716 2.0802 1.0180
## veg_height-Canis_latrans -0.5544 0.1875 -0.9455 -0.5492 -0.2048 1.0071
## veg_height-Sciurus_niger 0.1889 0.4276 -0.6034 0.1641 1.0937 1.0135
## veg_height-Procyon_lotor 0.3517 0.1235 0.1189 0.3511 0.5913 1.0032
## veg_height-Dasypus_novemcinctus 0.2567 0.1293 0.0054 0.2565 0.5172 1.0045
## veg_height-Lynx_rufus 0.0999 0.2377 -0.3765 0.1028 0.5694 1.0014
## veg_height-Didelphis_virginiana 0.4166 0.2430 -0.0289 0.4100 0.9265 1.0124
## veg_height-Sylvilagus_floridanus 0.1881 0.2459 -0.2817 0.1808 0.6863 1.0152
## veg_height-Sciurus_carolinensis 0.1019 0.2097 -0.3057 0.0946 0.5264 1.0049
## veg_height-Vulpes_vulpes -0.0499 0.3048 -0.6759 -0.0410 0.5439 1.0017
## veg_height-Sus_scrofa -0.0694 0.3292 -0.7426 -0.0634 0.5716 1.0116
## week-Canis_latrans 0.4552 0.2419 0.0051 0.4438 0.9416 1.0032
## week-Sciurus_niger -0.1985 0.4451 -1.2349 -0.1394 0.4952 1.0229
## week-Procyon_lotor 0.1640 0.1963 -0.2211 0.1616 0.5520 1.0015
## week-Dasypus_novemcinctus 0.0705 0.2130 -0.3654 0.0747 0.4811 1.0005
## week-Lynx_rufus 0.2655 0.2838 -0.2995 0.2645 0.8289 1.0032
## week-Didelphis_virginiana 0.0413 0.3142 -0.6237 0.0597 0.6264 1.0048
## week-Sylvilagus_floridanus 0.0647 0.2924 -0.5302 0.0714 0.6237 1.0007
## week-Sciurus_carolinensis 0.5313 0.3278 -0.0462 0.5140 1.2219 1.0173
## week-Vulpes_vulpes 0.1462 0.3941 -0.6499 0.1479 0.8987 1.0070
## week-Sus_scrofa 0.4174 0.3693 -0.2658 0.3910 1.2064 1.0036
## I(week^2)-Canis_latrans -0.1957 0.1019 -0.4087 -0.1924 -0.0104 1.0010
## I(week^2)-Sciurus_niger -0.2350 0.2056 -0.6794 -0.2205 0.1376 1.0077
## I(week^2)-Procyon_lotor -0.1119 0.0856 -0.2853 -0.1098 0.0561 1.0028
## I(week^2)-Dasypus_novemcinctus -0.1559 0.0983 -0.3552 -0.1575 0.0376 1.0058
## I(week^2)-Lynx_rufus -0.1963 0.1419 -0.4919 -0.1940 0.0611 1.0038
## I(week^2)-Didelphis_virginiana -0.3453 0.1938 -0.8031 -0.3248 -0.0318 1.0088
## I(week^2)-Sylvilagus_floridanus -0.1616 0.1408 -0.4563 -0.1575 0.0981 1.0107
## I(week^2)-Sciurus_carolinensis -0.1895 0.1334 -0.4596 -0.1842 0.0602 1.0060
## I(week^2)-Vulpes_vulpes -0.3358 0.2368 -0.8900 -0.3087 0.0647 1.0265
## I(week^2)-Sus_scrofa -0.1665 0.1615 -0.4944 -0.1614 0.1305 1.0120
## ESS
## (Intercept)-Canis_latrans 977
## (Intercept)-Sciurus_niger 217
## (Intercept)-Procyon_lotor 1446
## (Intercept)-Dasypus_novemcinctus 1766
## (Intercept)-Lynx_rufus 363
## (Intercept)-Didelphis_virginiana 1019
## (Intercept)-Sylvilagus_floridanus 640
## (Intercept)-Sciurus_carolinensis 954
## (Intercept)-Vulpes_vulpes 208
## (Intercept)-Sus_scrofa 653
## shrub_cover-Canis_latrans 773
## shrub_cover-Sciurus_niger 379
## shrub_cover-Procyon_lotor 1434
## shrub_cover-Dasypus_novemcinctus 1235
## shrub_cover-Lynx_rufus 502
## shrub_cover-Didelphis_virginiana 662
## shrub_cover-Sylvilagus_floridanus 524
## shrub_cover-Sciurus_carolinensis 648
## shrub_cover-Vulpes_vulpes 438
## shrub_cover-Sus_scrofa 686
## veg_height-Canis_latrans 748
## veg_height-Sciurus_niger 746
## veg_height-Procyon_lotor 1540
## veg_height-Dasypus_novemcinctus 1110
## veg_height-Lynx_rufus 897
## veg_height-Didelphis_virginiana 1078
## veg_height-Sylvilagus_floridanus 668
## veg_height-Sciurus_carolinensis 1088
## veg_height-Vulpes_vulpes 635
## veg_height-Sus_scrofa 1146
## week-Canis_latrans 1137
## week-Sciurus_niger 487
## week-Procyon_lotor 1771
## week-Dasypus_novemcinctus 1592
## week-Lynx_rufus 1161
## week-Didelphis_virginiana 946
## week-Sylvilagus_floridanus 1088
## week-Sciurus_carolinensis 1239
## week-Vulpes_vulpes 1093
## week-Sus_scrofa 1211
## I(week^2)-Canis_latrans 968
## I(week^2)-Sciurus_niger 615
## I(week^2)-Procyon_lotor 1599
## I(week^2)-Dasypus_novemcinctus 1748
## I(week^2)-Lynx_rufus 823
## I(week^2)-Didelphis_virginiana 615
## I(week^2)-Sylvilagus_floridanus 1008
## I(week^2)-Sciurus_carolinensis 1554
## I(week^2)-Vulpes_vulpes 541
## I(week^2)-Sus_scrofa 1449
# 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 = 5000,
n.thin = 2,
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 10 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_fullQ)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9032
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1617 0.7352 -3.5858 -2.1745 -0.6803 1.0244 383
## Cogon_Patch_Size -0.1809 0.7285 -1.6819 -0.1592 1.2207 1.0205 179
## Veg_shannon_index 0.9334 0.4693 0.0498 0.9131 1.9154 1.0067 345
## total_shrub_cover -0.8485 0.5302 -1.9608 -0.8223 0.0970 1.1329 179
## Avg_Cogongrass_Cover -0.1255 0.9820 -2.1650 -0.0836 1.7033 1.1051 98
## Tree_Density -2.1211 0.8483 -3.8182 -2.0819 -0.5566 1.0112 232
## Avg_Canopy_Cover 1.8635 0.6741 0.6514 1.8158 3.2513 1.0138 300
## I(Avg_Cogongrass_Cover^2) 1.5518 0.6024 0.4513 1.5177 2.8635 1.0510 155
## avg_veg_height -0.1127 0.4761 -1.0753 -0.1048 0.8176 1.0160 250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3157 4.7022 0.0755 1.8864 16.2477 1.0640 113
## Cogon_Patch_Size 2.5344 3.1349 0.1249 1.5587 11.3047 1.0313 328
## Veg_shannon_index 0.7732 1.3112 0.0444 0.3845 3.7695 1.0256 511
## total_shrub_cover 0.9019 1.1433 0.0577 0.5179 4.0571 1.0178 371
## Avg_Cogongrass_Cover 1.2584 2.5902 0.0494 0.5040 6.8756 1.0354 361
## Tree_Density 2.8088 4.2871 0.0777 1.2947 15.0764 1.1984 253
## Avg_Canopy_Cover 2.8254 3.8953 0.2069 1.7639 11.8049 1.0939 312
## I(Avg_Cogongrass_Cover^2) 1.2075 1.9003 0.0544 0.5747 6.6230 1.1049 202
## avg_veg_height 0.5942 0.9827 0.0463 0.2953 2.9382 1.0610 353
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.533 1.8306 0.066 0.9514 6.6193 1.2043 107
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8855 0.3427 -3.5500 -2.8825 -2.1883 1.0019 1223
## shrub_cover 0.4968 0.2858 -0.0683 0.4883 1.0752 1.0270 750
## veg_height 0.0896 0.1764 -0.2618 0.0883 0.4311 1.0050 991
## week 0.1908 0.2022 -0.2016 0.1903 0.5912 1.0015 897
## I(week^2) -0.2099 0.1063 -0.4283 -0.2054 -0.0055 1.0371 685
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0237 0.7921 0.2754 0.8275 2.7982 1.0156 925
## shrub_cover 0.5796 0.4480 0.1100 0.4539 1.7440 1.0505 504
## veg_height 0.2194 0.1688 0.0531 0.1714 0.6771 1.0168 724
## week 0.2193 0.2473 0.0345 0.1476 0.8402 1.0348 608
## I(week^2) 0.0622 0.0471 0.0178 0.0490 0.1804 1.0336 1201
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5205 1.0078 -3.4686 -1.5541
## (Intercept)-Sciurus_niger -1.2724 1.6432 -3.7390 -1.5335
## (Intercept)-Procyon_lotor -1.0541 1.0248 -3.0290 -1.0456
## (Intercept)-Dasypus_novemcinctus -2.4330 0.9347 -4.4426 -2.3685
## (Intercept)-Lynx_rufus -1.3879 1.4038 -3.6890 -1.6051
## (Intercept)-Didelphis_virginiana -3.2190 1.2507 -5.9632 -3.0624
## (Intercept)-Sylvilagus_floridanus -2.2365 1.1008 -4.4863 -2.2163
## (Intercept)-Sciurus_carolinensis -3.5594 1.3621 -6.7040 -3.3932
## (Intercept)-Vulpes_vulpes -3.4284 1.5698 -6.9688 -3.1957
## (Intercept)-Sus_scrofa -3.7151 1.6381 -7.5964 -3.4559
## Cogon_Patch_Size-Canis_latrans 1.3344 1.1537 -0.3496 1.1348
## Cogon_Patch_Size-Sciurus_niger -0.9697 1.6774 -5.0383 -0.7294
## Cogon_Patch_Size-Procyon_lotor -0.5311 0.7888 -2.1005 -0.5172
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0411 0.8190 -1.5295 0.0137
## Cogon_Patch_Size-Lynx_rufus -0.4250 1.3432 -3.2688 -0.3754
## Cogon_Patch_Size-Didelphis_virginiana 1.3592 0.9494 -0.2092 1.2576
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2089 1.4504 -4.8228 -1.0345
## Cogon_Patch_Size-Sciurus_carolinensis -0.8747 1.1975 -3.5315 -0.7297
## Cogon_Patch_Size-Vulpes_vulpes -0.5311 1.3606 -3.6314 -0.3874
## Cogon_Patch_Size-Sus_scrofa -0.4894 1.4039 -3.4147 -0.4398
## Veg_shannon_index-Canis_latrans 1.3016 0.6908 0.1371 1.2249
## Veg_shannon_index-Sciurus_niger 1.0367 0.9276 -0.7124 0.9823
## Veg_shannon_index-Procyon_lotor 1.1652 0.6293 0.1050 1.1170
## Veg_shannon_index-Dasypus_novemcinctus 0.6055 0.5875 -0.6112 0.6104
## Veg_shannon_index-Lynx_rufus 1.0225 0.8482 -0.5475 0.9783
## Veg_shannon_index-Didelphis_virginiana 1.1436 0.7034 -0.0607 1.0885
## Veg_shannon_index-Sylvilagus_floridanus 1.0116 0.6940 -0.2576 0.9791
## Veg_shannon_index-Sciurus_carolinensis 0.3661 0.7937 -1.3908 0.4329
## Veg_shannon_index-Vulpes_vulpes 0.6118 0.8461 -1.2561 0.6763
## Veg_shannon_index-Sus_scrofa 1.3516 0.8720 -0.0679 1.2274
## total_shrub_cover-Canis_latrans -0.0743 0.6960 -1.3159 -0.1160
## total_shrub_cover-Sciurus_niger -1.1892 0.9136 -3.2792 -1.1100
## total_shrub_cover-Procyon_lotor -1.2798 0.6486 -2.7563 -1.2144
## total_shrub_cover-Dasypus_novemcinctus -0.4798 0.7468 -2.0556 -0.4439
## total_shrub_cover-Lynx_rufus -1.2284 0.9526 -3.4843 -1.1390
## total_shrub_cover-Didelphis_virginiana -1.0650 0.8124 -2.9537 -0.9708
## total_shrub_cover-Sylvilagus_floridanus -0.9219 0.9083 -2.9989 -0.8482
## total_shrub_cover-Sciurus_carolinensis -0.8213 0.8804 -2.7481 -0.7446
## total_shrub_cover-Vulpes_vulpes -1.0312 1.0096 -3.3235 -0.9271
## total_shrub_cover-Sus_scrofa -0.7086 0.9097 -2.5035 -0.7042
## Avg_Cogongrass_Cover-Canis_latrans -0.0234 1.1759 -2.4016 -0.0209
## Avg_Cogongrass_Cover-Sciurus_niger -0.6467 1.4839 -4.0071 -0.5180
## Avg_Cogongrass_Cover-Procyon_lotor -0.0730 1.1796 -2.5192 -0.0499
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4251 1.2745 -2.0730 0.4162
## Avg_Cogongrass_Cover-Lynx_rufus -0.0066 1.2728 -2.5931 0.0141
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0459 1.2555 -2.6539 -0.0240
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6911 1.3509 -3.6346 -0.5605
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0456 1.2949 -2.6811 -0.0406
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1428 1.3599 -2.4737 0.1050
## Avg_Cogongrass_Cover-Sus_scrofa -0.2659 1.3955 -3.0478 -0.1854
## Tree_Density-Canis_latrans -2.9030 1.3181 -5.9458 -2.7429
## Tree_Density-Sciurus_niger -1.8857 1.4972 -4.8894 -1.9233
## Tree_Density-Procyon_lotor -2.1003 0.9729 -4.1573 -2.0422
## Tree_Density-Dasypus_novemcinctus -3.8118 1.7591 -8.3708 -3.4745
## Tree_Density-Lynx_rufus -0.9170 1.5028 -3.5758 -1.0244
## Tree_Density-Didelphis_virginiana -2.2172 1.2495 -4.8825 -2.1177
## Tree_Density-Sylvilagus_floridanus -2.5423 1.4376 -5.6663 -2.4192
## Tree_Density-Sciurus_carolinensis -2.4633 1.3837 -5.6925 -2.3514
## Tree_Density-Vulpes_vulpes -2.0017 1.4836 -4.9237 -2.0102
## Tree_Density-Sus_scrofa -2.1932 1.4735 -5.4701 -2.1114
## Avg_Canopy_Cover-Canis_latrans 0.1754 0.7002 -1.1718 0.1606
## Avg_Canopy_Cover-Sciurus_niger 1.8738 1.7468 -1.0439 1.7009
## Avg_Canopy_Cover-Procyon_lotor 1.5858 0.7612 0.2119 1.5523
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1594 0.8357 0.7828 2.0632
## Avg_Canopy_Cover-Lynx_rufus 1.2944 1.2586 -0.8604 1.1681
## Avg_Canopy_Cover-Didelphis_virginiana 2.9962 1.3482 1.0585 2.7454
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5185 1.5997 1.1469 3.2662
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7412 1.2508 0.9324 2.5152
## Avg_Canopy_Cover-Vulpes_vulpes 2.2955 1.2879 0.3013 2.1106
## Avg_Canopy_Cover-Sus_scrofa 2.0816 1.0255 0.3793 1.9698
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1746 1.0008 0.7262 2.0146
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1387 1.2476 -1.4179 1.1732
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1286 0.9481 0.7005 2.0050
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5006 0.7224 0.2015 1.4517
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3889 1.1434 0.7995 2.1762
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0484 0.7424 -0.3663 1.0307
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3033 0.8681 -0.2598 1.2468
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5959 0.7563 0.3046 1.5356
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7753 0.8623 0.3648 1.6859
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0593 1.1924 -1.4244 1.1575
## avg_veg_height-Canis_latrans -0.2494 0.5929 -1.4519 -0.2526
## avg_veg_height-Sciurus_niger -0.4202 0.9816 -2.6309 -0.3282
## avg_veg_height-Procyon_lotor 0.0604 0.6239 -1.0674 0.0282
## avg_veg_height-Dasypus_novemcinctus 0.2343 0.6671 -0.9769 0.1865
## avg_veg_height-Lynx_rufus -0.3610 0.8134 -2.2457 -0.3006
## avg_veg_height-Didelphis_virginiana -0.2737 0.7067 -1.8709 -0.2244
## avg_veg_height-Sylvilagus_floridanus -0.2310 0.7217 -1.7258 -0.2111
## avg_veg_height-Sciurus_carolinensis 0.2596 0.7065 -0.9966 0.1989
## avg_veg_height-Vulpes_vulpes -0.1816 0.7867 -1.8183 -0.1698
## avg_veg_height-Sus_scrofa -0.0427 0.7232 -1.4055 -0.0654
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5080 1.0372 401
## (Intercept)-Sciurus_niger 2.3467 1.0218 119
## (Intercept)-Procyon_lotor 0.9552 1.0573 331
## (Intercept)-Dasypus_novemcinctus -0.7443 1.0200 370
## (Intercept)-Lynx_rufus 1.9142 1.0367 192
## (Intercept)-Didelphis_virginiana -1.1186 1.0297 261
## (Intercept)-Sylvilagus_floridanus -0.0191 1.0166 415
## (Intercept)-Sciurus_carolinensis -1.4138 1.0342 133
## (Intercept)-Vulpes_vulpes -0.8377 1.0810 148
## (Intercept)-Sus_scrofa -1.2502 1.0041 165
## Cogon_Patch_Size-Canis_latrans 4.1312 1.0473 348
## Cogon_Patch_Size-Sciurus_niger 1.7165 1.0119 234
## Cogon_Patch_Size-Procyon_lotor 0.9525 1.0020 314
## Cogon_Patch_Size-Dasypus_novemcinctus 1.7557 1.0462 356
## Cogon_Patch_Size-Lynx_rufus 2.2174 1.0292 239
## Cogon_Patch_Size-Didelphis_virginiana 3.5110 1.0407 359
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1056 1.0139 188
## Cogon_Patch_Size-Sciurus_carolinensis 1.0895 1.0036 241
## Cogon_Patch_Size-Vulpes_vulpes 2.0172 1.0039 304
## Cogon_Patch_Size-Sus_scrofa 1.9092 1.0256 299
## Veg_shannon_index-Canis_latrans 2.8661 1.0074 602
## Veg_shannon_index-Sciurus_niger 3.1818 1.0231 428
## Veg_shannon_index-Procyon_lotor 2.5183 1.0325 365
## Veg_shannon_index-Dasypus_novemcinctus 1.7655 1.0102 504
## Veg_shannon_index-Lynx_rufus 2.7917 1.0149 478
## Veg_shannon_index-Didelphis_virginiana 2.6539 1.0117 663
## Veg_shannon_index-Sylvilagus_floridanus 2.5561 1.0088 450
## Veg_shannon_index-Sciurus_carolinensis 1.7399 1.0141 548
## Veg_shannon_index-Vulpes_vulpes 2.0909 0.9999 553
## Veg_shannon_index-Sus_scrofa 3.4785 1.0223 494
## total_shrub_cover-Canis_latrans 1.4364 1.0849 538
## total_shrub_cover-Sciurus_niger 0.4035 1.0687 462
## total_shrub_cover-Procyon_lotor -0.1670 1.0064 383
## total_shrub_cover-Dasypus_novemcinctus 0.8899 1.0793 402
## total_shrub_cover-Lynx_rufus 0.4625 1.0165 289
## total_shrub_cover-Didelphis_virginiana 0.3194 1.0265 369
## total_shrub_cover-Sylvilagus_floridanus 0.6810 1.0259 353
## total_shrub_cover-Sciurus_carolinensis 0.7272 1.1124 262
## total_shrub_cover-Vulpes_vulpes 0.7558 1.0733 293
## total_shrub_cover-Sus_scrofa 1.2132 1.0266 359
## Avg_Cogongrass_Cover-Canis_latrans 2.3158 1.0468 159
## Avg_Cogongrass_Cover-Sciurus_niger 1.8446 1.0779 198
## Avg_Cogongrass_Cover-Procyon_lotor 2.2115 1.0324 165
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.0850 1.0318 179
## Avg_Cogongrass_Cover-Lynx_rufus 2.4217 1.0439 210
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3556 1.0818 141
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6149 1.0485 167
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4456 1.0632 149
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8810 1.0460 160
## Avg_Cogongrass_Cover-Sus_scrofa 2.3033 1.0609 146
## Tree_Density-Canis_latrans -0.7330 1.0150 303
## Tree_Density-Sciurus_niger 1.2194 1.0544 272
## Tree_Density-Procyon_lotor -0.3381 1.0099 359
## Tree_Density-Dasypus_novemcinctus -1.4049 1.1876 227
## Tree_Density-Lynx_rufus 2.2953 1.1168 216
## Tree_Density-Didelphis_virginiana 0.1537 1.0102 508
## Tree_Density-Sylvilagus_floridanus 0.0384 1.0354 324
## Tree_Density-Sciurus_carolinensis 0.0371 1.0135 300
## Tree_Density-Vulpes_vulpes 1.2705 1.0200 285
## Tree_Density-Sus_scrofa 0.6071 1.0051 301
## Avg_Canopy_Cover-Canis_latrans 1.5778 1.0329 479
## Avg_Canopy_Cover-Sciurus_niger 5.7863 1.0074 248
## Avg_Canopy_Cover-Procyon_lotor 3.1682 1.0037 595
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0828 1.0483 325
## Avg_Canopy_Cover-Lynx_rufus 4.0897 1.0759 199
## Avg_Canopy_Cover-Didelphis_virginiana 6.3812 1.0866 278
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6766 1.0523 210
## Avg_Canopy_Cover-Sciurus_carolinensis 5.7029 1.0743 244
## Avg_Canopy_Cover-Vulpes_vulpes 5.3585 1.0195 288
## Avg_Canopy_Cover-Sus_scrofa 4.4637 1.0210 522
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6537 1.0254 281
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.6038 1.0952 161
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.4157 1.0160 272
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9827 1.0369 327
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2050 1.0750 183
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5810 1.1053 174
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2044 1.0388 266
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.2532 1.0472 280
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.8053 1.0327 268
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.1701 1.0560 158
## avg_veg_height-Canis_latrans 0.8662 1.0230 457
## avg_veg_height-Sciurus_niger 1.1878 1.0209 288
## avg_veg_height-Procyon_lotor 1.3865 1.0097 443
## avg_veg_height-Dasypus_novemcinctus 1.6889 1.0238 330
## avg_veg_height-Lynx_rufus 1.0139 1.0134 310
## avg_veg_height-Didelphis_virginiana 1.0006 1.0068 498
## avg_veg_height-Sylvilagus_floridanus 1.1480 1.0144 560
## avg_veg_height-Sciurus_carolinensis 1.8026 1.0476 418
## avg_veg_height-Vulpes_vulpes 1.3504 1.0010 455
## avg_veg_height-Sus_scrofa 1.4971 1.0229 511
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5623 0.1982 -2.9540 -2.5605 -2.1983 1.0080
## (Intercept)-Sciurus_niger -4.2059 0.5797 -5.3292 -4.2301 -2.9931 1.0062
## (Intercept)-Procyon_lotor -2.2124 0.1618 -2.5343 -2.2101 -1.9038 1.0045
## (Intercept)-Dasypus_novemcinctus -1.6792 0.1883 -2.0610 -1.6713 -1.3138 1.0413
## (Intercept)-Lynx_rufus -3.5385 0.3588 -4.2467 -3.5314 -2.8552 1.0400
## (Intercept)-Didelphis_virginiana -2.4791 0.3142 -3.1091 -2.4709 -1.8798 1.0062
## (Intercept)-Sylvilagus_floridanus -3.1199 0.2725 -3.6756 -3.1143 -2.5963 1.0107
## (Intercept)-Sciurus_carolinensis -2.6633 0.3308 -3.3431 -2.6610 -2.0389 1.0112
## (Intercept)-Vulpes_vulpes -3.9530 0.5875 -5.1347 -3.9372 -2.8865 1.0624
## (Intercept)-Sus_scrofa -3.4909 0.5441 -4.5346 -3.5115 -2.3342 1.0387
## shrub_cover-Canis_latrans -0.2461 0.2346 -0.6963 -0.2427 0.2001 1.0412
## shrub_cover-Sciurus_niger -0.0855 0.4972 -1.0726 -0.0773 0.9125 1.0397
## shrub_cover-Procyon_lotor 0.2861 0.1582 -0.0311 0.2910 0.5839 1.0081
## shrub_cover-Dasypus_novemcinctus 0.9964 0.3217 0.3835 0.9949 1.6414 1.0937
## shrub_cover-Lynx_rufus -0.0154 0.3582 -0.7284 -0.0151 0.6939 1.0360
## shrub_cover-Didelphis_virginiana 1.0898 0.3806 0.3743 1.0790 1.8556 1.0202
## shrub_cover-Sylvilagus_floridanus 0.5892 0.3856 -0.1734 0.6007 1.3169 1.0181
## shrub_cover-Sciurus_carolinensis 1.0568 0.4138 0.2434 1.0599 1.8941 1.0542
## shrub_cover-Vulpes_vulpes 0.3186 0.5726 -0.8455 0.3247 1.4710 1.0614
## shrub_cover-Sus_scrofa 1.0764 0.7190 -0.4038 1.0776 2.4677 1.0101
## veg_height-Canis_latrans -0.5361 0.1962 -0.9491 -0.5282 -0.1701 1.0157
## veg_height-Sciurus_niger 0.0882 0.4182 -0.6706 0.0657 1.0360 1.0124
## veg_height-Procyon_lotor 0.3648 0.1204 0.1339 0.3674 0.6066 0.9999
## veg_height-Dasypus_novemcinctus 0.2784 0.1414 0.0079 0.2764 0.5568 1.0030
## veg_height-Lynx_rufus 0.1735 0.2316 -0.2952 0.1830 0.6119 1.0262
## veg_height-Didelphis_virginiana 0.4813 0.2486 0.0152 0.4720 1.0042 1.0101
## veg_height-Sylvilagus_floridanus 0.1615 0.2451 -0.3170 0.1591 0.6534 1.0093
## veg_height-Sciurus_carolinensis 0.1536 0.2214 -0.2674 0.1464 0.6078 1.0085
## veg_height-Vulpes_vulpes -0.1333 0.3371 -0.8127 -0.1146 0.4874 1.0411
## veg_height-Sus_scrofa -0.1436 0.3264 -0.8180 -0.1351 0.4427 1.0197
## week-Canis_latrans 0.4615 0.2482 -0.0038 0.4512 0.9573 1.0089
## week-Sciurus_niger -0.2298 0.4308 -1.2397 -0.1751 0.4721 1.0441
## week-Procyon_lotor 0.1613 0.1946 -0.2275 0.1584 0.5548 0.9998
## week-Dasypus_novemcinctus 0.0751 0.2160 -0.3476 0.0779 0.4839 1.0014
## week-Lynx_rufus 0.2684 0.3029 -0.3152 0.2655 0.8943 1.0205
## week-Didelphis_virginiana 0.0297 0.3216 -0.6348 0.0451 0.6218 1.0152
## week-Sylvilagus_floridanus 0.0317 0.3039 -0.5865 0.0363 0.6044 1.0044
## week-Sciurus_carolinensis 0.5438 0.3309 -0.0578 0.5238 1.2647 1.0042
## week-Vulpes_vulpes 0.1266 0.3983 -0.7102 0.1404 0.8856 1.0283
## week-Sus_scrofa 0.4335 0.3691 -0.2435 0.4146 1.2336 1.0156
## I(week^2)-Canis_latrans -0.1955 0.1043 -0.4060 -0.1912 -0.0034 1.0095
## I(week^2)-Sciurus_niger -0.2494 0.2056 -0.7001 -0.2355 0.1245 1.0424
## I(week^2)-Procyon_lotor -0.1119 0.0880 -0.2891 -0.1116 0.0600 0.9997
## I(week^2)-Dasypus_novemcinctus -0.1612 0.1011 -0.3566 -0.1598 0.0355 1.0046
## I(week^2)-Lynx_rufus -0.1997 0.1438 -0.5002 -0.1961 0.0670 1.0722
## I(week^2)-Didelphis_virginiana -0.3512 0.1942 -0.7980 -0.3291 -0.0234 1.0235
## I(week^2)-Sylvilagus_floridanus -0.1548 0.1508 -0.4797 -0.1495 0.1187 1.0095
## I(week^2)-Sciurus_carolinensis -0.1892 0.1338 -0.4730 -0.1864 0.0579 1.0088
## I(week^2)-Vulpes_vulpes -0.3282 0.2331 -0.8557 -0.3088 0.0640 1.0542
## I(week^2)-Sus_scrofa -0.1708 0.1647 -0.5067 -0.1683 0.1349 1.0089
## ESS
## (Intercept)-Canis_latrans 878
## (Intercept)-Sciurus_niger 184
## (Intercept)-Procyon_lotor 1511
## (Intercept)-Dasypus_novemcinctus 700
## (Intercept)-Lynx_rufus 311
## (Intercept)-Didelphis_virginiana 570
## (Intercept)-Sylvilagus_floridanus 866
## (Intercept)-Sciurus_carolinensis 354
## (Intercept)-Vulpes_vulpes 258
## (Intercept)-Sus_scrofa 263
## shrub_cover-Canis_latrans 728
## shrub_cover-Sciurus_niger 288
## shrub_cover-Procyon_lotor 1420
## shrub_cover-Dasypus_novemcinctus 393
## shrub_cover-Lynx_rufus 355
## shrub_cover-Didelphis_virginiana 396
## shrub_cover-Sylvilagus_floridanus 483
## shrub_cover-Sciurus_carolinensis 436
## shrub_cover-Vulpes_vulpes 473
## shrub_cover-Sus_scrofa 295
## veg_height-Canis_latrans 701
## veg_height-Sciurus_niger 253
## veg_height-Procyon_lotor 1279
## veg_height-Dasypus_novemcinctus 1827
## veg_height-Lynx_rufus 625
## veg_height-Didelphis_virginiana 994
## veg_height-Sylvilagus_floridanus 706
## veg_height-Sciurus_carolinensis 712
## veg_height-Vulpes_vulpes 539
## veg_height-Sus_scrofa 648
## week-Canis_latrans 1187
## week-Sciurus_niger 367
## week-Procyon_lotor 1597
## week-Dasypus_novemcinctus 1352
## week-Lynx_rufus 877
## week-Didelphis_virginiana 838
## week-Sylvilagus_floridanus 905
## week-Sciurus_carolinensis 847
## week-Vulpes_vulpes 835
## week-Sus_scrofa 961
## I(week^2)-Canis_latrans 969
## I(week^2)-Sciurus_niger 414
## I(week^2)-Procyon_lotor 1484
## I(week^2)-Dasypus_novemcinctus 1586
## I(week^2)-Lynx_rufus 754
## I(week^2)-Didelphis_virginiana 625
## I(week^2)-Sylvilagus_floridanus 723
## I(week^2)-Sciurus_carolinensis 955
## I(week^2)-Vulpes_vulpes 455
## I(week^2)-Sus_scrofa 1096
waicOcc(ms_full_full, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -990.9277 102.2622 2186.3798
waicOcc(ms_full_cover, by.sp = FALSE)
## elpd pD WAIC
## -1031.8958 98.5537 2260.8991
waicOcc(ms_full_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1024.44392 84.18114 2217.25013
waicOcc(ms_full_move, by.sp = FALSE)
## elpd pD WAIC
## -1029.26625 95.93927 2250.41103
waicOcc(ms_full_forage, by.sp = FALSE)
## elpd pD WAIC
## -1037.83274 87.23433 2250.13414
waicOcc(ms_full_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1042.29824 82.92305 2250.44260
waicOcc(ms_full_null, by.sp = FALSE)
## elpd pD WAIC
## -1056.19323 68.83067 2250.04780
waicOcc(ms_full_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1036.3182 84.2992 2241.2347
waicOcc(ms_full_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -985.9672 104.0251 2179.9845
waicOcc(ms_null_null, by.sp = FALSE)
## elpd pD WAIC
## -1092.61780 29.93642 2245.10844
waicOcc(ms_null_full, by.sp = FALSE)
## elpd pD WAIC
## -1028.99458 64.36207 2186.71329
waicOcc(ms_null_cover, by.sp = FALSE)
## elpd pD WAIC
## -1072.30120 54.30205 2253.20650
waicOcc(ms_null_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1062.85419 45.44893 2216.60624
waicOcc(ms_null_move, by.sp = FALSE)
## elpd pD WAIC
## -1067.34860 54.84469 2244.38659
waicOcc(ms_null_forage, by.sp = FALSE)
## elpd pD WAIC
## -1073.46488 49.17012 2245.27000
waicOcc(ms_null_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1078.21258 44.72622 2245.87759
waicOcc(ms_null_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1070.74598 46.35031 2234.19258
waicOcc(ms_null_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1023.94819 64.50238 2176.90115
waicOcc(ms_week_full, by.sp = FALSE)
## elpd pD WAIC
## -1025.74640 70.92166 2193.33611
waicOcc(ms_week_cover, by.sp = FALSE)
## elpd pD WAIC
## -1068.85287 60.88504 2259.47582
waicOcc(ms_week_null, by.sp = FALSE)
## elpd pD WAIC
## -1088.78814 37.09412 2251.76452
waicOcc(ms_week_forage, by.sp = FALSE)
## elpd pD WAIC
## -1069.64107 56.22734 2251.73682
waicOcc(ms_week_move, by.sp = FALSE)
## elpd pD WAIC
## -1063.51516 61.20326 2249.43685
waicOcc(ms_week_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1057.80142 53.42378 2222.45040
waicOcc(ms_week_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1074.31817 51.38133 2251.39899
waicOcc(ms_week_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1067.29448 54.14823 2242.88543
waicOcc(ms_week_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1019.59094 71.43349 2182.04886
waicOcc(ms_cover_full, by.sp = FALSE)
## elpd pD WAIC
## -996.31669 95.87727 2184.38792
waicOcc(ms_cover_cover, by.sp = FALSE)
## elpd pD WAIC
## -1035.75776 93.07133 2257.65817
waicOcc(ms_cover_null, by.sp = FALSE)
## elpd pD WAIC
## -1060.75136 58.99241 2239.48755
waicOcc(ms_cover_forage, by.sp = FALSE)
## elpd pD WAIC
## -1042.05020 80.74171 2245.58381
waicOcc(ms_cover_move, by.sp = FALSE)
## elpd pD WAIC
## -1034.69689 86.95983 2243.31343
waicOcc(ms_cover_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1028.96279 76.17741 2210.28040
waicOcc(ms_cover_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1046.70328 74.68546 2242.77748
waicOcc(ms_cover_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1039.89618 77.61477 2235.02189
waicOcc(ms_cover_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -992.64220 94.71453 2174.71346
waicOcc(ms_weekQ_full, by.sp = FALSE)
## elpd pD WAIC
## -1019.68732 79.44147 2198.25758
waicOcc(ms_weekQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -1062.03731 70.61672 2265.30805
waicOcc(ms_weekQ_null, by.sp = FALSE)
## elpd pD WAIC
## -1082.14123 45.46457 2255.21161
waicOcc(ms_weekQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -1063.03522 65.14107 2256.35257
waicOcc(ms_weekQ_move, by.sp = FALSE)
## elpd pD WAIC
## -1056.22950 70.42505 2253.30910
waicOcc(ms_weekQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1052.36985 61.61989 2227.97948
waicOcc(ms_weekQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1067.73066 60.25922 2255.97975
waicOcc(ms_weekQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1060.53017 63.27645 2247.61324
waicOcc(ms_weekQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -1012.76538 80.46692 2186.46460
waicOcc(ms_fullQ_full, by.sp = FALSE)
## elpd pD WAIC
## -984.3771 111.6657 2192.0855
waicOcc(ms_fullQ_cover, by.sp = FALSE)
## elpd pD WAIC
## -1025.1231 107.8169 2265.8800
waicOcc(ms_fullQ_null, by.sp = FALSE)
## elpd pD WAIC
## -1049.9071 76.5719 2252.9579
waicOcc(ms_fullQ_forage, by.sp = FALSE)
## elpd pD WAIC
## -1031.20150 97.51852 2257.44005
waicOcc(ms_fullQ_move, by.sp = FALSE)
## elpd pD WAIC
## -1022.5579 104.8225 2254.7607
waicOcc(ms_fullQ_canopy, by.sp = FALSE)
## elpd pD WAIC
## -1018.21491 92.14626 2220.72234
waicOcc(ms_fullQ_cogon, by.sp = FALSE)
## elpd pD WAIC
## -1036.08286 91.96773 2256.10118
waicOcc(ms_fullQ_cogonQ, by.sp = FALSE)
## elpd pD WAIC
## -1029.38680 93.29075 2245.35510
waicOcc(ms_fullQ_fullQ, by.sp = FALSE)
## elpd pD WAIC
## -980.2763 112.9904 2186.5332
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 10
## Currently on species 2 out of 10
## Currently on species 3 out of 10
## Currently on species 4 out of 10
## Currently on species 5 out of 10
## Currently on species 6 out of 10
## Currently on species 7 out of 10
## Currently on species 8 out of 10
## Currently on species 9 out of 10
## Currently on species 10 out of 10
summary(ppc.ms_fullQ_fullQ)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ, fit.stat = "freeman-tukey", group = 1)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.3258
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.644
## Sciurus_niger Bayesian p-value: 0.1607
## Procyon_lotor Bayesian p-value: 0.0813
## Dasypus_novemcinctus Bayesian p-value: 0
## Lynx_rufus Bayesian p-value: 0.4017
## Didelphis_virginiana Bayesian p-value: 0.4243
## Sylvilagus_floridanus Bayesian p-value: 0.4227
## Sciurus_carolinensis Bayesian p-value: 0.348
## Vulpes_vulpes Bayesian p-value: 0.263
## Sus_scrofa Bayesian p-value: 0.512
## 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 = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9032
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1617 0.7352 -3.5858 -2.1745 -0.6803 1.0244 383
## Cogon_Patch_Size -0.1809 0.7285 -1.6819 -0.1592 1.2207 1.0205 179
## Veg_shannon_index 0.9334 0.4693 0.0498 0.9131 1.9154 1.0067 345
## total_shrub_cover -0.8485 0.5302 -1.9608 -0.8223 0.0970 1.1329 179
## Avg_Cogongrass_Cover -0.1255 0.9820 -2.1650 -0.0836 1.7033 1.1051 98
## Tree_Density -2.1211 0.8483 -3.8182 -2.0819 -0.5566 1.0112 232
## Avg_Canopy_Cover 1.8635 0.6741 0.6514 1.8158 3.2513 1.0138 300
## I(Avg_Cogongrass_Cover^2) 1.5518 0.6024 0.4513 1.5177 2.8635 1.0510 155
## avg_veg_height -0.1127 0.4761 -1.0753 -0.1048 0.8176 1.0160 250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3157 4.7022 0.0755 1.8864 16.2477 1.0640 113
## Cogon_Patch_Size 2.5344 3.1349 0.1249 1.5587 11.3047 1.0313 328
## Veg_shannon_index 0.7732 1.3112 0.0444 0.3845 3.7695 1.0256 511
## total_shrub_cover 0.9019 1.1433 0.0577 0.5179 4.0571 1.0178 371
## Avg_Cogongrass_Cover 1.2584 2.5902 0.0494 0.5040 6.8756 1.0354 361
## Tree_Density 2.8088 4.2871 0.0777 1.2947 15.0764 1.1984 253
## Avg_Canopy_Cover 2.8254 3.8953 0.2069 1.7639 11.8049 1.0939 312
## I(Avg_Cogongrass_Cover^2) 1.2075 1.9003 0.0544 0.5747 6.6230 1.1049 202
## avg_veg_height 0.5942 0.9827 0.0463 0.2953 2.9382 1.0610 353
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.533 1.8306 0.066 0.9514 6.6193 1.2043 107
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8855 0.3427 -3.5500 -2.8825 -2.1883 1.0019 1223
## shrub_cover 0.4968 0.2858 -0.0683 0.4883 1.0752 1.0270 750
## veg_height 0.0896 0.1764 -0.2618 0.0883 0.4311 1.0050 991
## week 0.1908 0.2022 -0.2016 0.1903 0.5912 1.0015 897
## I(week^2) -0.2099 0.1063 -0.4283 -0.2054 -0.0055 1.0371 685
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0237 0.7921 0.2754 0.8275 2.7982 1.0156 925
## shrub_cover 0.5796 0.4480 0.1100 0.4539 1.7440 1.0505 504
## veg_height 0.2194 0.1688 0.0531 0.1714 0.6771 1.0168 724
## week 0.2193 0.2473 0.0345 0.1476 0.8402 1.0348 608
## I(week^2) 0.0622 0.0471 0.0178 0.0490 0.1804 1.0336 1201
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5205 1.0078 -3.4686 -1.5541
## (Intercept)-Sciurus_niger -1.2724 1.6432 -3.7390 -1.5335
## (Intercept)-Procyon_lotor -1.0541 1.0248 -3.0290 -1.0456
## (Intercept)-Dasypus_novemcinctus -2.4330 0.9347 -4.4426 -2.3685
## (Intercept)-Lynx_rufus -1.3879 1.4038 -3.6890 -1.6051
## (Intercept)-Didelphis_virginiana -3.2190 1.2507 -5.9632 -3.0624
## (Intercept)-Sylvilagus_floridanus -2.2365 1.1008 -4.4863 -2.2163
## (Intercept)-Sciurus_carolinensis -3.5594 1.3621 -6.7040 -3.3932
## (Intercept)-Vulpes_vulpes -3.4284 1.5698 -6.9688 -3.1957
## (Intercept)-Sus_scrofa -3.7151 1.6381 -7.5964 -3.4559
## Cogon_Patch_Size-Canis_latrans 1.3344 1.1537 -0.3496 1.1348
## Cogon_Patch_Size-Sciurus_niger -0.9697 1.6774 -5.0383 -0.7294
## Cogon_Patch_Size-Procyon_lotor -0.5311 0.7888 -2.1005 -0.5172
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0411 0.8190 -1.5295 0.0137
## Cogon_Patch_Size-Lynx_rufus -0.4250 1.3432 -3.2688 -0.3754
## Cogon_Patch_Size-Didelphis_virginiana 1.3592 0.9494 -0.2092 1.2576
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2089 1.4504 -4.8228 -1.0345
## Cogon_Patch_Size-Sciurus_carolinensis -0.8747 1.1975 -3.5315 -0.7297
## Cogon_Patch_Size-Vulpes_vulpes -0.5311 1.3606 -3.6314 -0.3874
## Cogon_Patch_Size-Sus_scrofa -0.4894 1.4039 -3.4147 -0.4398
## Veg_shannon_index-Canis_latrans 1.3016 0.6908 0.1371 1.2249
## Veg_shannon_index-Sciurus_niger 1.0367 0.9276 -0.7124 0.9823
## Veg_shannon_index-Procyon_lotor 1.1652 0.6293 0.1050 1.1170
## Veg_shannon_index-Dasypus_novemcinctus 0.6055 0.5875 -0.6112 0.6104
## Veg_shannon_index-Lynx_rufus 1.0225 0.8482 -0.5475 0.9783
## Veg_shannon_index-Didelphis_virginiana 1.1436 0.7034 -0.0607 1.0885
## Veg_shannon_index-Sylvilagus_floridanus 1.0116 0.6940 -0.2576 0.9791
## Veg_shannon_index-Sciurus_carolinensis 0.3661 0.7937 -1.3908 0.4329
## Veg_shannon_index-Vulpes_vulpes 0.6118 0.8461 -1.2561 0.6763
## Veg_shannon_index-Sus_scrofa 1.3516 0.8720 -0.0679 1.2274
## total_shrub_cover-Canis_latrans -0.0743 0.6960 -1.3159 -0.1160
## total_shrub_cover-Sciurus_niger -1.1892 0.9136 -3.2792 -1.1100
## total_shrub_cover-Procyon_lotor -1.2798 0.6486 -2.7563 -1.2144
## total_shrub_cover-Dasypus_novemcinctus -0.4798 0.7468 -2.0556 -0.4439
## total_shrub_cover-Lynx_rufus -1.2284 0.9526 -3.4843 -1.1390
## total_shrub_cover-Didelphis_virginiana -1.0650 0.8124 -2.9537 -0.9708
## total_shrub_cover-Sylvilagus_floridanus -0.9219 0.9083 -2.9989 -0.8482
## total_shrub_cover-Sciurus_carolinensis -0.8213 0.8804 -2.7481 -0.7446
## total_shrub_cover-Vulpes_vulpes -1.0312 1.0096 -3.3235 -0.9271
## total_shrub_cover-Sus_scrofa -0.7086 0.9097 -2.5035 -0.7042
## Avg_Cogongrass_Cover-Canis_latrans -0.0234 1.1759 -2.4016 -0.0209
## Avg_Cogongrass_Cover-Sciurus_niger -0.6467 1.4839 -4.0071 -0.5180
## Avg_Cogongrass_Cover-Procyon_lotor -0.0730 1.1796 -2.5192 -0.0499
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4251 1.2745 -2.0730 0.4162
## Avg_Cogongrass_Cover-Lynx_rufus -0.0066 1.2728 -2.5931 0.0141
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0459 1.2555 -2.6539 -0.0240
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6911 1.3509 -3.6346 -0.5605
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0456 1.2949 -2.6811 -0.0406
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1428 1.3599 -2.4737 0.1050
## Avg_Cogongrass_Cover-Sus_scrofa -0.2659 1.3955 -3.0478 -0.1854
## Tree_Density-Canis_latrans -2.9030 1.3181 -5.9458 -2.7429
## Tree_Density-Sciurus_niger -1.8857 1.4972 -4.8894 -1.9233
## Tree_Density-Procyon_lotor -2.1003 0.9729 -4.1573 -2.0422
## Tree_Density-Dasypus_novemcinctus -3.8118 1.7591 -8.3708 -3.4745
## Tree_Density-Lynx_rufus -0.9170 1.5028 -3.5758 -1.0244
## Tree_Density-Didelphis_virginiana -2.2172 1.2495 -4.8825 -2.1177
## Tree_Density-Sylvilagus_floridanus -2.5423 1.4376 -5.6663 -2.4192
## Tree_Density-Sciurus_carolinensis -2.4633 1.3837 -5.6925 -2.3514
## Tree_Density-Vulpes_vulpes -2.0017 1.4836 -4.9237 -2.0102
## Tree_Density-Sus_scrofa -2.1932 1.4735 -5.4701 -2.1114
## Avg_Canopy_Cover-Canis_latrans 0.1754 0.7002 -1.1718 0.1606
## Avg_Canopy_Cover-Sciurus_niger 1.8738 1.7468 -1.0439 1.7009
## Avg_Canopy_Cover-Procyon_lotor 1.5858 0.7612 0.2119 1.5523
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1594 0.8357 0.7828 2.0632
## Avg_Canopy_Cover-Lynx_rufus 1.2944 1.2586 -0.8604 1.1681
## Avg_Canopy_Cover-Didelphis_virginiana 2.9962 1.3482 1.0585 2.7454
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5185 1.5997 1.1469 3.2662
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7412 1.2508 0.9324 2.5152
## Avg_Canopy_Cover-Vulpes_vulpes 2.2955 1.2879 0.3013 2.1106
## Avg_Canopy_Cover-Sus_scrofa 2.0816 1.0255 0.3793 1.9698
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1746 1.0008 0.7262 2.0146
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1387 1.2476 -1.4179 1.1732
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1286 0.9481 0.7005 2.0050
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5006 0.7224 0.2015 1.4517
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3889 1.1434 0.7995 2.1762
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0484 0.7424 -0.3663 1.0307
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3033 0.8681 -0.2598 1.2468
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5959 0.7563 0.3046 1.5356
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7753 0.8623 0.3648 1.6859
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0593 1.1924 -1.4244 1.1575
## avg_veg_height-Canis_latrans -0.2494 0.5929 -1.4519 -0.2526
## avg_veg_height-Sciurus_niger -0.4202 0.9816 -2.6309 -0.3282
## avg_veg_height-Procyon_lotor 0.0604 0.6239 -1.0674 0.0282
## avg_veg_height-Dasypus_novemcinctus 0.2343 0.6671 -0.9769 0.1865
## avg_veg_height-Lynx_rufus -0.3610 0.8134 -2.2457 -0.3006
## avg_veg_height-Didelphis_virginiana -0.2737 0.7067 -1.8709 -0.2244
## avg_veg_height-Sylvilagus_floridanus -0.2310 0.7217 -1.7258 -0.2111
## avg_veg_height-Sciurus_carolinensis 0.2596 0.7065 -0.9966 0.1989
## avg_veg_height-Vulpes_vulpes -0.1816 0.7867 -1.8183 -0.1698
## avg_veg_height-Sus_scrofa -0.0427 0.7232 -1.4055 -0.0654
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5080 1.0372 401
## (Intercept)-Sciurus_niger 2.3467 1.0218 119
## (Intercept)-Procyon_lotor 0.9552 1.0573 331
## (Intercept)-Dasypus_novemcinctus -0.7443 1.0200 370
## (Intercept)-Lynx_rufus 1.9142 1.0367 192
## (Intercept)-Didelphis_virginiana -1.1186 1.0297 261
## (Intercept)-Sylvilagus_floridanus -0.0191 1.0166 415
## (Intercept)-Sciurus_carolinensis -1.4138 1.0342 133
## (Intercept)-Vulpes_vulpes -0.8377 1.0810 148
## (Intercept)-Sus_scrofa -1.2502 1.0041 165
## Cogon_Patch_Size-Canis_latrans 4.1312 1.0473 348
## Cogon_Patch_Size-Sciurus_niger 1.7165 1.0119 234
## Cogon_Patch_Size-Procyon_lotor 0.9525 1.0020 314
## Cogon_Patch_Size-Dasypus_novemcinctus 1.7557 1.0462 356
## Cogon_Patch_Size-Lynx_rufus 2.2174 1.0292 239
## Cogon_Patch_Size-Didelphis_virginiana 3.5110 1.0407 359
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1056 1.0139 188
## Cogon_Patch_Size-Sciurus_carolinensis 1.0895 1.0036 241
## Cogon_Patch_Size-Vulpes_vulpes 2.0172 1.0039 304
## Cogon_Patch_Size-Sus_scrofa 1.9092 1.0256 299
## Veg_shannon_index-Canis_latrans 2.8661 1.0074 602
## Veg_shannon_index-Sciurus_niger 3.1818 1.0231 428
## Veg_shannon_index-Procyon_lotor 2.5183 1.0325 365
## Veg_shannon_index-Dasypus_novemcinctus 1.7655 1.0102 504
## Veg_shannon_index-Lynx_rufus 2.7917 1.0149 478
## Veg_shannon_index-Didelphis_virginiana 2.6539 1.0117 663
## Veg_shannon_index-Sylvilagus_floridanus 2.5561 1.0088 450
## Veg_shannon_index-Sciurus_carolinensis 1.7399 1.0141 548
## Veg_shannon_index-Vulpes_vulpes 2.0909 0.9999 553
## Veg_shannon_index-Sus_scrofa 3.4785 1.0223 494
## total_shrub_cover-Canis_latrans 1.4364 1.0849 538
## total_shrub_cover-Sciurus_niger 0.4035 1.0687 462
## total_shrub_cover-Procyon_lotor -0.1670 1.0064 383
## total_shrub_cover-Dasypus_novemcinctus 0.8899 1.0793 402
## total_shrub_cover-Lynx_rufus 0.4625 1.0165 289
## total_shrub_cover-Didelphis_virginiana 0.3194 1.0265 369
## total_shrub_cover-Sylvilagus_floridanus 0.6810 1.0259 353
## total_shrub_cover-Sciurus_carolinensis 0.7272 1.1124 262
## total_shrub_cover-Vulpes_vulpes 0.7558 1.0733 293
## total_shrub_cover-Sus_scrofa 1.2132 1.0266 359
## Avg_Cogongrass_Cover-Canis_latrans 2.3158 1.0468 159
## Avg_Cogongrass_Cover-Sciurus_niger 1.8446 1.0779 198
## Avg_Cogongrass_Cover-Procyon_lotor 2.2115 1.0324 165
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.0850 1.0318 179
## Avg_Cogongrass_Cover-Lynx_rufus 2.4217 1.0439 210
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3556 1.0818 141
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6149 1.0485 167
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4456 1.0632 149
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8810 1.0460 160
## Avg_Cogongrass_Cover-Sus_scrofa 2.3033 1.0609 146
## Tree_Density-Canis_latrans -0.7330 1.0150 303
## Tree_Density-Sciurus_niger 1.2194 1.0544 272
## Tree_Density-Procyon_lotor -0.3381 1.0099 359
## Tree_Density-Dasypus_novemcinctus -1.4049 1.1876 227
## Tree_Density-Lynx_rufus 2.2953 1.1168 216
## Tree_Density-Didelphis_virginiana 0.1537 1.0102 508
## Tree_Density-Sylvilagus_floridanus 0.0384 1.0354 324
## Tree_Density-Sciurus_carolinensis 0.0371 1.0135 300
## Tree_Density-Vulpes_vulpes 1.2705 1.0200 285
## Tree_Density-Sus_scrofa 0.6071 1.0051 301
## Avg_Canopy_Cover-Canis_latrans 1.5778 1.0329 479
## Avg_Canopy_Cover-Sciurus_niger 5.7863 1.0074 248
## Avg_Canopy_Cover-Procyon_lotor 3.1682 1.0037 595
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0828 1.0483 325
## Avg_Canopy_Cover-Lynx_rufus 4.0897 1.0759 199
## Avg_Canopy_Cover-Didelphis_virginiana 6.3812 1.0866 278
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6766 1.0523 210
## Avg_Canopy_Cover-Sciurus_carolinensis 5.7029 1.0743 244
## Avg_Canopy_Cover-Vulpes_vulpes 5.3585 1.0195 288
## Avg_Canopy_Cover-Sus_scrofa 4.4637 1.0210 522
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6537 1.0254 281
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.6038 1.0952 161
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.4157 1.0160 272
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9827 1.0369 327
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2050 1.0750 183
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5810 1.1053 174
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2044 1.0388 266
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.2532 1.0472 280
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.8053 1.0327 268
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.1701 1.0560 158
## avg_veg_height-Canis_latrans 0.8662 1.0230 457
## avg_veg_height-Sciurus_niger 1.1878 1.0209 288
## avg_veg_height-Procyon_lotor 1.3865 1.0097 443
## avg_veg_height-Dasypus_novemcinctus 1.6889 1.0238 330
## avg_veg_height-Lynx_rufus 1.0139 1.0134 310
## avg_veg_height-Didelphis_virginiana 1.0006 1.0068 498
## avg_veg_height-Sylvilagus_floridanus 1.1480 1.0144 560
## avg_veg_height-Sciurus_carolinensis 1.8026 1.0476 418
## avg_veg_height-Vulpes_vulpes 1.3504 1.0010 455
## avg_veg_height-Sus_scrofa 1.4971 1.0229 511
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5623 0.1982 -2.9540 -2.5605 -2.1983 1.0080
## (Intercept)-Sciurus_niger -4.2059 0.5797 -5.3292 -4.2301 -2.9931 1.0062
## (Intercept)-Procyon_lotor -2.2124 0.1618 -2.5343 -2.2101 -1.9038 1.0045
## (Intercept)-Dasypus_novemcinctus -1.6792 0.1883 -2.0610 -1.6713 -1.3138 1.0413
## (Intercept)-Lynx_rufus -3.5385 0.3588 -4.2467 -3.5314 -2.8552 1.0400
## (Intercept)-Didelphis_virginiana -2.4791 0.3142 -3.1091 -2.4709 -1.8798 1.0062
## (Intercept)-Sylvilagus_floridanus -3.1199 0.2725 -3.6756 -3.1143 -2.5963 1.0107
## (Intercept)-Sciurus_carolinensis -2.6633 0.3308 -3.3431 -2.6610 -2.0389 1.0112
## (Intercept)-Vulpes_vulpes -3.9530 0.5875 -5.1347 -3.9372 -2.8865 1.0624
## (Intercept)-Sus_scrofa -3.4909 0.5441 -4.5346 -3.5115 -2.3342 1.0387
## shrub_cover-Canis_latrans -0.2461 0.2346 -0.6963 -0.2427 0.2001 1.0412
## shrub_cover-Sciurus_niger -0.0855 0.4972 -1.0726 -0.0773 0.9125 1.0397
## shrub_cover-Procyon_lotor 0.2861 0.1582 -0.0311 0.2910 0.5839 1.0081
## shrub_cover-Dasypus_novemcinctus 0.9964 0.3217 0.3835 0.9949 1.6414 1.0937
## shrub_cover-Lynx_rufus -0.0154 0.3582 -0.7284 -0.0151 0.6939 1.0360
## shrub_cover-Didelphis_virginiana 1.0898 0.3806 0.3743 1.0790 1.8556 1.0202
## shrub_cover-Sylvilagus_floridanus 0.5892 0.3856 -0.1734 0.6007 1.3169 1.0181
## shrub_cover-Sciurus_carolinensis 1.0568 0.4138 0.2434 1.0599 1.8941 1.0542
## shrub_cover-Vulpes_vulpes 0.3186 0.5726 -0.8455 0.3247 1.4710 1.0614
## shrub_cover-Sus_scrofa 1.0764 0.7190 -0.4038 1.0776 2.4677 1.0101
## veg_height-Canis_latrans -0.5361 0.1962 -0.9491 -0.5282 -0.1701 1.0157
## veg_height-Sciurus_niger 0.0882 0.4182 -0.6706 0.0657 1.0360 1.0124
## veg_height-Procyon_lotor 0.3648 0.1204 0.1339 0.3674 0.6066 0.9999
## veg_height-Dasypus_novemcinctus 0.2784 0.1414 0.0079 0.2764 0.5568 1.0030
## veg_height-Lynx_rufus 0.1735 0.2316 -0.2952 0.1830 0.6119 1.0262
## veg_height-Didelphis_virginiana 0.4813 0.2486 0.0152 0.4720 1.0042 1.0101
## veg_height-Sylvilagus_floridanus 0.1615 0.2451 -0.3170 0.1591 0.6534 1.0093
## veg_height-Sciurus_carolinensis 0.1536 0.2214 -0.2674 0.1464 0.6078 1.0085
## veg_height-Vulpes_vulpes -0.1333 0.3371 -0.8127 -0.1146 0.4874 1.0411
## veg_height-Sus_scrofa -0.1436 0.3264 -0.8180 -0.1351 0.4427 1.0197
## week-Canis_latrans 0.4615 0.2482 -0.0038 0.4512 0.9573 1.0089
## week-Sciurus_niger -0.2298 0.4308 -1.2397 -0.1751 0.4721 1.0441
## week-Procyon_lotor 0.1613 0.1946 -0.2275 0.1584 0.5548 0.9998
## week-Dasypus_novemcinctus 0.0751 0.2160 -0.3476 0.0779 0.4839 1.0014
## week-Lynx_rufus 0.2684 0.3029 -0.3152 0.2655 0.8943 1.0205
## week-Didelphis_virginiana 0.0297 0.3216 -0.6348 0.0451 0.6218 1.0152
## week-Sylvilagus_floridanus 0.0317 0.3039 -0.5865 0.0363 0.6044 1.0044
## week-Sciurus_carolinensis 0.5438 0.3309 -0.0578 0.5238 1.2647 1.0042
## week-Vulpes_vulpes 0.1266 0.3983 -0.7102 0.1404 0.8856 1.0283
## week-Sus_scrofa 0.4335 0.3691 -0.2435 0.4146 1.2336 1.0156
## I(week^2)-Canis_latrans -0.1955 0.1043 -0.4060 -0.1912 -0.0034 1.0095
## I(week^2)-Sciurus_niger -0.2494 0.2056 -0.7001 -0.2355 0.1245 1.0424
## I(week^2)-Procyon_lotor -0.1119 0.0880 -0.2891 -0.1116 0.0600 0.9997
## I(week^2)-Dasypus_novemcinctus -0.1612 0.1011 -0.3566 -0.1598 0.0355 1.0046
## I(week^2)-Lynx_rufus -0.1997 0.1438 -0.5002 -0.1961 0.0670 1.0722
## I(week^2)-Didelphis_virginiana -0.3512 0.1942 -0.7980 -0.3291 -0.0234 1.0235
## I(week^2)-Sylvilagus_floridanus -0.1548 0.1508 -0.4797 -0.1495 0.1187 1.0095
## I(week^2)-Sciurus_carolinensis -0.1892 0.1338 -0.4730 -0.1864 0.0579 1.0088
## I(week^2)-Vulpes_vulpes -0.3282 0.2331 -0.8557 -0.3088 0.0640 1.0542
## I(week^2)-Sus_scrofa -0.1708 0.1647 -0.5067 -0.1683 0.1349 1.0089
## ESS
## (Intercept)-Canis_latrans 878
## (Intercept)-Sciurus_niger 184
## (Intercept)-Procyon_lotor 1511
## (Intercept)-Dasypus_novemcinctus 700
## (Intercept)-Lynx_rufus 311
## (Intercept)-Didelphis_virginiana 570
## (Intercept)-Sylvilagus_floridanus 866
## (Intercept)-Sciurus_carolinensis 354
## (Intercept)-Vulpes_vulpes 258
## (Intercept)-Sus_scrofa 263
## shrub_cover-Canis_latrans 728
## shrub_cover-Sciurus_niger 288
## shrub_cover-Procyon_lotor 1420
## shrub_cover-Dasypus_novemcinctus 393
## shrub_cover-Lynx_rufus 355
## shrub_cover-Didelphis_virginiana 396
## shrub_cover-Sylvilagus_floridanus 483
## shrub_cover-Sciurus_carolinensis 436
## shrub_cover-Vulpes_vulpes 473
## shrub_cover-Sus_scrofa 295
## veg_height-Canis_latrans 701
## veg_height-Sciurus_niger 253
## veg_height-Procyon_lotor 1279
## veg_height-Dasypus_novemcinctus 1827
## veg_height-Lynx_rufus 625
## veg_height-Didelphis_virginiana 994
## veg_height-Sylvilagus_floridanus 706
## veg_height-Sciurus_carolinensis 712
## veg_height-Vulpes_vulpes 539
## veg_height-Sus_scrofa 648
## week-Canis_latrans 1187
## week-Sciurus_niger 367
## week-Procyon_lotor 1597
## week-Dasypus_novemcinctus 1352
## week-Lynx_rufus 877
## week-Didelphis_virginiana 838
## week-Sylvilagus_floridanus 905
## week-Sciurus_carolinensis 847
## week-Vulpes_vulpes 835
## week-Sus_scrofa 961
## I(week^2)-Canis_latrans 969
## I(week^2)-Sciurus_niger 414
## I(week^2)-Procyon_lotor 1484
## I(week^2)-Dasypus_novemcinctus 1586
## I(week^2)-Lynx_rufus 754
## I(week^2)-Didelphis_virginiana 625
## I(week^2)-Sylvilagus_floridanus 723
## I(week^2)-Sciurus_carolinensis 955
## I(week^2)-Vulpes_vulpes 455
## I(week^2)-Sus_scrofa 1096
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$beta.samples)
## 'mcmc' num [1:3000, 1:90] -2.039 -2.023 -1.803 -1.818 -0.428 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:90] "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1496667
MCMCplot(ms_fullQ_fullQ$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 [1:3000, 1:10, 1:100] 0.0441 0.2313 0.1514 0.0502 0.2483 ...
## $ z.0.samples : int [1:3000, 1:10, 1:100] 0 1 0 0 1 0 0 0 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:10, 1:100] 0.0441 0.2313 0.1514 0.0502 0.2483 ...
psi.0.quants <- apply(out.pred$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] 0.000953 0.101539 0.899232 0.001023 0.101537 ...
## - 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 0.102 0.102 0.103 0.1 0.101 ...
## $ psi.low : num 0.000953 0.001023 0.000981 0.001052 0.001074 ...
## $ psi.high : num 0.899 0.891 0.886 0.882 0.873 ...
## $ 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 = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9032
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1617 0.7352 -3.5858 -2.1745 -0.6803 1.0244 383
## Cogon_Patch_Size -0.1809 0.7285 -1.6819 -0.1592 1.2207 1.0205 179
## Veg_shannon_index 0.9334 0.4693 0.0498 0.9131 1.9154 1.0067 345
## total_shrub_cover -0.8485 0.5302 -1.9608 -0.8223 0.0970 1.1329 179
## Avg_Cogongrass_Cover -0.1255 0.9820 -2.1650 -0.0836 1.7033 1.1051 98
## Tree_Density -2.1211 0.8483 -3.8182 -2.0819 -0.5566 1.0112 232
## Avg_Canopy_Cover 1.8635 0.6741 0.6514 1.8158 3.2513 1.0138 300
## I(Avg_Cogongrass_Cover^2) 1.5518 0.6024 0.4513 1.5177 2.8635 1.0510 155
## avg_veg_height -0.1127 0.4761 -1.0753 -0.1048 0.8176 1.0160 250
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3157 4.7022 0.0755 1.8864 16.2477 1.0640 113
## Cogon_Patch_Size 2.5344 3.1349 0.1249 1.5587 11.3047 1.0313 328
## Veg_shannon_index 0.7732 1.3112 0.0444 0.3845 3.7695 1.0256 511
## total_shrub_cover 0.9019 1.1433 0.0577 0.5179 4.0571 1.0178 371
## Avg_Cogongrass_Cover 1.2584 2.5902 0.0494 0.5040 6.8756 1.0354 361
## Tree_Density 2.8088 4.2871 0.0777 1.2947 15.0764 1.1984 253
## Avg_Canopy_Cover 2.8254 3.8953 0.2069 1.7639 11.8049 1.0939 312
## I(Avg_Cogongrass_Cover^2) 1.2075 1.9003 0.0544 0.5747 6.6230 1.1049 202
## avg_veg_height 0.5942 0.9827 0.0463 0.2953 2.9382 1.0610 353
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.533 1.8306 0.066 0.9514 6.6193 1.2043 107
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8855 0.3427 -3.5500 -2.8825 -2.1883 1.0019 1223
## shrub_cover 0.4968 0.2858 -0.0683 0.4883 1.0752 1.0270 750
## veg_height 0.0896 0.1764 -0.2618 0.0883 0.4311 1.0050 991
## week 0.1908 0.2022 -0.2016 0.1903 0.5912 1.0015 897
## I(week^2) -0.2099 0.1063 -0.4283 -0.2054 -0.0055 1.0371 685
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0237 0.7921 0.2754 0.8275 2.7982 1.0156 925
## shrub_cover 0.5796 0.4480 0.1100 0.4539 1.7440 1.0505 504
## veg_height 0.2194 0.1688 0.0531 0.1714 0.6771 1.0168 724
## week 0.2193 0.2473 0.0345 0.1476 0.8402 1.0348 608
## I(week^2) 0.0622 0.0471 0.0178 0.0490 0.1804 1.0336 1201
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5205 1.0078 -3.4686 -1.5541
## (Intercept)-Sciurus_niger -1.2724 1.6432 -3.7390 -1.5335
## (Intercept)-Procyon_lotor -1.0541 1.0248 -3.0290 -1.0456
## (Intercept)-Dasypus_novemcinctus -2.4330 0.9347 -4.4426 -2.3685
## (Intercept)-Lynx_rufus -1.3879 1.4038 -3.6890 -1.6051
## (Intercept)-Didelphis_virginiana -3.2190 1.2507 -5.9632 -3.0624
## (Intercept)-Sylvilagus_floridanus -2.2365 1.1008 -4.4863 -2.2163
## (Intercept)-Sciurus_carolinensis -3.5594 1.3621 -6.7040 -3.3932
## (Intercept)-Vulpes_vulpes -3.4284 1.5698 -6.9688 -3.1957
## (Intercept)-Sus_scrofa -3.7151 1.6381 -7.5964 -3.4559
## Cogon_Patch_Size-Canis_latrans 1.3344 1.1537 -0.3496 1.1348
## Cogon_Patch_Size-Sciurus_niger -0.9697 1.6774 -5.0383 -0.7294
## Cogon_Patch_Size-Procyon_lotor -0.5311 0.7888 -2.1005 -0.5172
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0411 0.8190 -1.5295 0.0137
## Cogon_Patch_Size-Lynx_rufus -0.4250 1.3432 -3.2688 -0.3754
## Cogon_Patch_Size-Didelphis_virginiana 1.3592 0.9494 -0.2092 1.2576
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2089 1.4504 -4.8228 -1.0345
## Cogon_Patch_Size-Sciurus_carolinensis -0.8747 1.1975 -3.5315 -0.7297
## Cogon_Patch_Size-Vulpes_vulpes -0.5311 1.3606 -3.6314 -0.3874
## Cogon_Patch_Size-Sus_scrofa -0.4894 1.4039 -3.4147 -0.4398
## Veg_shannon_index-Canis_latrans 1.3016 0.6908 0.1371 1.2249
## Veg_shannon_index-Sciurus_niger 1.0367 0.9276 -0.7124 0.9823
## Veg_shannon_index-Procyon_lotor 1.1652 0.6293 0.1050 1.1170
## Veg_shannon_index-Dasypus_novemcinctus 0.6055 0.5875 -0.6112 0.6104
## Veg_shannon_index-Lynx_rufus 1.0225 0.8482 -0.5475 0.9783
## Veg_shannon_index-Didelphis_virginiana 1.1436 0.7034 -0.0607 1.0885
## Veg_shannon_index-Sylvilagus_floridanus 1.0116 0.6940 -0.2576 0.9791
## Veg_shannon_index-Sciurus_carolinensis 0.3661 0.7937 -1.3908 0.4329
## Veg_shannon_index-Vulpes_vulpes 0.6118 0.8461 -1.2561 0.6763
## Veg_shannon_index-Sus_scrofa 1.3516 0.8720 -0.0679 1.2274
## total_shrub_cover-Canis_latrans -0.0743 0.6960 -1.3159 -0.1160
## total_shrub_cover-Sciurus_niger -1.1892 0.9136 -3.2792 -1.1100
## total_shrub_cover-Procyon_lotor -1.2798 0.6486 -2.7563 -1.2144
## total_shrub_cover-Dasypus_novemcinctus -0.4798 0.7468 -2.0556 -0.4439
## total_shrub_cover-Lynx_rufus -1.2284 0.9526 -3.4843 -1.1390
## total_shrub_cover-Didelphis_virginiana -1.0650 0.8124 -2.9537 -0.9708
## total_shrub_cover-Sylvilagus_floridanus -0.9219 0.9083 -2.9989 -0.8482
## total_shrub_cover-Sciurus_carolinensis -0.8213 0.8804 -2.7481 -0.7446
## total_shrub_cover-Vulpes_vulpes -1.0312 1.0096 -3.3235 -0.9271
## total_shrub_cover-Sus_scrofa -0.7086 0.9097 -2.5035 -0.7042
## Avg_Cogongrass_Cover-Canis_latrans -0.0234 1.1759 -2.4016 -0.0209
## Avg_Cogongrass_Cover-Sciurus_niger -0.6467 1.4839 -4.0071 -0.5180
## Avg_Cogongrass_Cover-Procyon_lotor -0.0730 1.1796 -2.5192 -0.0499
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4251 1.2745 -2.0730 0.4162
## Avg_Cogongrass_Cover-Lynx_rufus -0.0066 1.2728 -2.5931 0.0141
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.0459 1.2555 -2.6539 -0.0240
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6911 1.3509 -3.6346 -0.5605
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0456 1.2949 -2.6811 -0.0406
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1428 1.3599 -2.4737 0.1050
## Avg_Cogongrass_Cover-Sus_scrofa -0.2659 1.3955 -3.0478 -0.1854
## Tree_Density-Canis_latrans -2.9030 1.3181 -5.9458 -2.7429
## Tree_Density-Sciurus_niger -1.8857 1.4972 -4.8894 -1.9233
## Tree_Density-Procyon_lotor -2.1003 0.9729 -4.1573 -2.0422
## Tree_Density-Dasypus_novemcinctus -3.8118 1.7591 -8.3708 -3.4745
## Tree_Density-Lynx_rufus -0.9170 1.5028 -3.5758 -1.0244
## Tree_Density-Didelphis_virginiana -2.2172 1.2495 -4.8825 -2.1177
## Tree_Density-Sylvilagus_floridanus -2.5423 1.4376 -5.6663 -2.4192
## Tree_Density-Sciurus_carolinensis -2.4633 1.3837 -5.6925 -2.3514
## Tree_Density-Vulpes_vulpes -2.0017 1.4836 -4.9237 -2.0102
## Tree_Density-Sus_scrofa -2.1932 1.4735 -5.4701 -2.1114
## Avg_Canopy_Cover-Canis_latrans 0.1754 0.7002 -1.1718 0.1606
## Avg_Canopy_Cover-Sciurus_niger 1.8738 1.7468 -1.0439 1.7009
## Avg_Canopy_Cover-Procyon_lotor 1.5858 0.7612 0.2119 1.5523
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1594 0.8357 0.7828 2.0632
## Avg_Canopy_Cover-Lynx_rufus 1.2944 1.2586 -0.8604 1.1681
## Avg_Canopy_Cover-Didelphis_virginiana 2.9962 1.3482 1.0585 2.7454
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.5185 1.5997 1.1469 3.2662
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7412 1.2508 0.9324 2.5152
## Avg_Canopy_Cover-Vulpes_vulpes 2.2955 1.2879 0.3013 2.1106
## Avg_Canopy_Cover-Sus_scrofa 2.0816 1.0255 0.3793 1.9698
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1746 1.0008 0.7262 2.0146
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1387 1.2476 -1.4179 1.1732
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1286 0.9481 0.7005 2.0050
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5006 0.7224 0.2015 1.4517
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3889 1.1434 0.7995 2.1762
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.0484 0.7424 -0.3663 1.0307
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.3033 0.8681 -0.2598 1.2468
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5959 0.7563 0.3046 1.5356
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7753 0.8623 0.3648 1.6859
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0593 1.1924 -1.4244 1.1575
## avg_veg_height-Canis_latrans -0.2494 0.5929 -1.4519 -0.2526
## avg_veg_height-Sciurus_niger -0.4202 0.9816 -2.6309 -0.3282
## avg_veg_height-Procyon_lotor 0.0604 0.6239 -1.0674 0.0282
## avg_veg_height-Dasypus_novemcinctus 0.2343 0.6671 -0.9769 0.1865
## avg_veg_height-Lynx_rufus -0.3610 0.8134 -2.2457 -0.3006
## avg_veg_height-Didelphis_virginiana -0.2737 0.7067 -1.8709 -0.2244
## avg_veg_height-Sylvilagus_floridanus -0.2310 0.7217 -1.7258 -0.2111
## avg_veg_height-Sciurus_carolinensis 0.2596 0.7065 -0.9966 0.1989
## avg_veg_height-Vulpes_vulpes -0.1816 0.7867 -1.8183 -0.1698
## avg_veg_height-Sus_scrofa -0.0427 0.7232 -1.4055 -0.0654
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5080 1.0372 401
## (Intercept)-Sciurus_niger 2.3467 1.0218 119
## (Intercept)-Procyon_lotor 0.9552 1.0573 331
## (Intercept)-Dasypus_novemcinctus -0.7443 1.0200 370
## (Intercept)-Lynx_rufus 1.9142 1.0367 192
## (Intercept)-Didelphis_virginiana -1.1186 1.0297 261
## (Intercept)-Sylvilagus_floridanus -0.0191 1.0166 415
## (Intercept)-Sciurus_carolinensis -1.4138 1.0342 133
## (Intercept)-Vulpes_vulpes -0.8377 1.0810 148
## (Intercept)-Sus_scrofa -1.2502 1.0041 165
## Cogon_Patch_Size-Canis_latrans 4.1312 1.0473 348
## Cogon_Patch_Size-Sciurus_niger 1.7165 1.0119 234
## Cogon_Patch_Size-Procyon_lotor 0.9525 1.0020 314
## Cogon_Patch_Size-Dasypus_novemcinctus 1.7557 1.0462 356
## Cogon_Patch_Size-Lynx_rufus 2.2174 1.0292 239
## Cogon_Patch_Size-Didelphis_virginiana 3.5110 1.0407 359
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1056 1.0139 188
## Cogon_Patch_Size-Sciurus_carolinensis 1.0895 1.0036 241
## Cogon_Patch_Size-Vulpes_vulpes 2.0172 1.0039 304
## Cogon_Patch_Size-Sus_scrofa 1.9092 1.0256 299
## Veg_shannon_index-Canis_latrans 2.8661 1.0074 602
## Veg_shannon_index-Sciurus_niger 3.1818 1.0231 428
## Veg_shannon_index-Procyon_lotor 2.5183 1.0325 365
## Veg_shannon_index-Dasypus_novemcinctus 1.7655 1.0102 504
## Veg_shannon_index-Lynx_rufus 2.7917 1.0149 478
## Veg_shannon_index-Didelphis_virginiana 2.6539 1.0117 663
## Veg_shannon_index-Sylvilagus_floridanus 2.5561 1.0088 450
## Veg_shannon_index-Sciurus_carolinensis 1.7399 1.0141 548
## Veg_shannon_index-Vulpes_vulpes 2.0909 0.9999 553
## Veg_shannon_index-Sus_scrofa 3.4785 1.0223 494
## total_shrub_cover-Canis_latrans 1.4364 1.0849 538
## total_shrub_cover-Sciurus_niger 0.4035 1.0687 462
## total_shrub_cover-Procyon_lotor -0.1670 1.0064 383
## total_shrub_cover-Dasypus_novemcinctus 0.8899 1.0793 402
## total_shrub_cover-Lynx_rufus 0.4625 1.0165 289
## total_shrub_cover-Didelphis_virginiana 0.3194 1.0265 369
## total_shrub_cover-Sylvilagus_floridanus 0.6810 1.0259 353
## total_shrub_cover-Sciurus_carolinensis 0.7272 1.1124 262
## total_shrub_cover-Vulpes_vulpes 0.7558 1.0733 293
## total_shrub_cover-Sus_scrofa 1.2132 1.0266 359
## Avg_Cogongrass_Cover-Canis_latrans 2.3158 1.0468 159
## Avg_Cogongrass_Cover-Sciurus_niger 1.8446 1.0779 198
## Avg_Cogongrass_Cover-Procyon_lotor 2.2115 1.0324 165
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.0850 1.0318 179
## Avg_Cogongrass_Cover-Lynx_rufus 2.4217 1.0439 210
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.3556 1.0818 141
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6149 1.0485 167
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4456 1.0632 149
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8810 1.0460 160
## Avg_Cogongrass_Cover-Sus_scrofa 2.3033 1.0609 146
## Tree_Density-Canis_latrans -0.7330 1.0150 303
## Tree_Density-Sciurus_niger 1.2194 1.0544 272
## Tree_Density-Procyon_lotor -0.3381 1.0099 359
## Tree_Density-Dasypus_novemcinctus -1.4049 1.1876 227
## Tree_Density-Lynx_rufus 2.2953 1.1168 216
## Tree_Density-Didelphis_virginiana 0.1537 1.0102 508
## Tree_Density-Sylvilagus_floridanus 0.0384 1.0354 324
## Tree_Density-Sciurus_carolinensis 0.0371 1.0135 300
## Tree_Density-Vulpes_vulpes 1.2705 1.0200 285
## Tree_Density-Sus_scrofa 0.6071 1.0051 301
## Avg_Canopy_Cover-Canis_latrans 1.5778 1.0329 479
## Avg_Canopy_Cover-Sciurus_niger 5.7863 1.0074 248
## Avg_Canopy_Cover-Procyon_lotor 3.1682 1.0037 595
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0828 1.0483 325
## Avg_Canopy_Cover-Lynx_rufus 4.0897 1.0759 199
## Avg_Canopy_Cover-Didelphis_virginiana 6.3812 1.0866 278
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.6766 1.0523 210
## Avg_Canopy_Cover-Sciurus_carolinensis 5.7029 1.0743 244
## Avg_Canopy_Cover-Vulpes_vulpes 5.3585 1.0195 288
## Avg_Canopy_Cover-Sus_scrofa 4.4637 1.0210 522
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.6537 1.0254 281
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.6038 1.0952 161
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.4157 1.0160 272
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9827 1.0369 327
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.2050 1.0750 183
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.5810 1.1053 174
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2044 1.0388 266
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.2532 1.0472 280
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.8053 1.0327 268
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.1701 1.0560 158
## avg_veg_height-Canis_latrans 0.8662 1.0230 457
## avg_veg_height-Sciurus_niger 1.1878 1.0209 288
## avg_veg_height-Procyon_lotor 1.3865 1.0097 443
## avg_veg_height-Dasypus_novemcinctus 1.6889 1.0238 330
## avg_veg_height-Lynx_rufus 1.0139 1.0134 310
## avg_veg_height-Didelphis_virginiana 1.0006 1.0068 498
## avg_veg_height-Sylvilagus_floridanus 1.1480 1.0144 560
## avg_veg_height-Sciurus_carolinensis 1.8026 1.0476 418
## avg_veg_height-Vulpes_vulpes 1.3504 1.0010 455
## avg_veg_height-Sus_scrofa 1.4971 1.0229 511
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5623 0.1982 -2.9540 -2.5605 -2.1983 1.0080
## (Intercept)-Sciurus_niger -4.2059 0.5797 -5.3292 -4.2301 -2.9931 1.0062
## (Intercept)-Procyon_lotor -2.2124 0.1618 -2.5343 -2.2101 -1.9038 1.0045
## (Intercept)-Dasypus_novemcinctus -1.6792 0.1883 -2.0610 -1.6713 -1.3138 1.0413
## (Intercept)-Lynx_rufus -3.5385 0.3588 -4.2467 -3.5314 -2.8552 1.0400
## (Intercept)-Didelphis_virginiana -2.4791 0.3142 -3.1091 -2.4709 -1.8798 1.0062
## (Intercept)-Sylvilagus_floridanus -3.1199 0.2725 -3.6756 -3.1143 -2.5963 1.0107
## (Intercept)-Sciurus_carolinensis -2.6633 0.3308 -3.3431 -2.6610 -2.0389 1.0112
## (Intercept)-Vulpes_vulpes -3.9530 0.5875 -5.1347 -3.9372 -2.8865 1.0624
## (Intercept)-Sus_scrofa -3.4909 0.5441 -4.5346 -3.5115 -2.3342 1.0387
## shrub_cover-Canis_latrans -0.2461 0.2346 -0.6963 -0.2427 0.2001 1.0412
## shrub_cover-Sciurus_niger -0.0855 0.4972 -1.0726 -0.0773 0.9125 1.0397
## shrub_cover-Procyon_lotor 0.2861 0.1582 -0.0311 0.2910 0.5839 1.0081
## shrub_cover-Dasypus_novemcinctus 0.9964 0.3217 0.3835 0.9949 1.6414 1.0937
## shrub_cover-Lynx_rufus -0.0154 0.3582 -0.7284 -0.0151 0.6939 1.0360
## shrub_cover-Didelphis_virginiana 1.0898 0.3806 0.3743 1.0790 1.8556 1.0202
## shrub_cover-Sylvilagus_floridanus 0.5892 0.3856 -0.1734 0.6007 1.3169 1.0181
## shrub_cover-Sciurus_carolinensis 1.0568 0.4138 0.2434 1.0599 1.8941 1.0542
## shrub_cover-Vulpes_vulpes 0.3186 0.5726 -0.8455 0.3247 1.4710 1.0614
## shrub_cover-Sus_scrofa 1.0764 0.7190 -0.4038 1.0776 2.4677 1.0101
## veg_height-Canis_latrans -0.5361 0.1962 -0.9491 -0.5282 -0.1701 1.0157
## veg_height-Sciurus_niger 0.0882 0.4182 -0.6706 0.0657 1.0360 1.0124
## veg_height-Procyon_lotor 0.3648 0.1204 0.1339 0.3674 0.6066 0.9999
## veg_height-Dasypus_novemcinctus 0.2784 0.1414 0.0079 0.2764 0.5568 1.0030
## veg_height-Lynx_rufus 0.1735 0.2316 -0.2952 0.1830 0.6119 1.0262
## veg_height-Didelphis_virginiana 0.4813 0.2486 0.0152 0.4720 1.0042 1.0101
## veg_height-Sylvilagus_floridanus 0.1615 0.2451 -0.3170 0.1591 0.6534 1.0093
## veg_height-Sciurus_carolinensis 0.1536 0.2214 -0.2674 0.1464 0.6078 1.0085
## veg_height-Vulpes_vulpes -0.1333 0.3371 -0.8127 -0.1146 0.4874 1.0411
## veg_height-Sus_scrofa -0.1436 0.3264 -0.8180 -0.1351 0.4427 1.0197
## week-Canis_latrans 0.4615 0.2482 -0.0038 0.4512 0.9573 1.0089
## week-Sciurus_niger -0.2298 0.4308 -1.2397 -0.1751 0.4721 1.0441
## week-Procyon_lotor 0.1613 0.1946 -0.2275 0.1584 0.5548 0.9998
## week-Dasypus_novemcinctus 0.0751 0.2160 -0.3476 0.0779 0.4839 1.0014
## week-Lynx_rufus 0.2684 0.3029 -0.3152 0.2655 0.8943 1.0205
## week-Didelphis_virginiana 0.0297 0.3216 -0.6348 0.0451 0.6218 1.0152
## week-Sylvilagus_floridanus 0.0317 0.3039 -0.5865 0.0363 0.6044 1.0044
## week-Sciurus_carolinensis 0.5438 0.3309 -0.0578 0.5238 1.2647 1.0042
## week-Vulpes_vulpes 0.1266 0.3983 -0.7102 0.1404 0.8856 1.0283
## week-Sus_scrofa 0.4335 0.3691 -0.2435 0.4146 1.2336 1.0156
## I(week^2)-Canis_latrans -0.1955 0.1043 -0.4060 -0.1912 -0.0034 1.0095
## I(week^2)-Sciurus_niger -0.2494 0.2056 -0.7001 -0.2355 0.1245 1.0424
## I(week^2)-Procyon_lotor -0.1119 0.0880 -0.2891 -0.1116 0.0600 0.9997
## I(week^2)-Dasypus_novemcinctus -0.1612 0.1011 -0.3566 -0.1598 0.0355 1.0046
## I(week^2)-Lynx_rufus -0.1997 0.1438 -0.5002 -0.1961 0.0670 1.0722
## I(week^2)-Didelphis_virginiana -0.3512 0.1942 -0.7980 -0.3291 -0.0234 1.0235
## I(week^2)-Sylvilagus_floridanus -0.1548 0.1508 -0.4797 -0.1495 0.1187 1.0095
## I(week^2)-Sciurus_carolinensis -0.1892 0.1338 -0.4730 -0.1864 0.0579 1.0088
## I(week^2)-Vulpes_vulpes -0.3282 0.2331 -0.8557 -0.3088 0.0640 1.0542
## I(week^2)-Sus_scrofa -0.1708 0.1647 -0.5067 -0.1683 0.1349 1.0089
## ESS
## (Intercept)-Canis_latrans 878
## (Intercept)-Sciurus_niger 184
## (Intercept)-Procyon_lotor 1511
## (Intercept)-Dasypus_novemcinctus 700
## (Intercept)-Lynx_rufus 311
## (Intercept)-Didelphis_virginiana 570
## (Intercept)-Sylvilagus_floridanus 866
## (Intercept)-Sciurus_carolinensis 354
## (Intercept)-Vulpes_vulpes 258
## (Intercept)-Sus_scrofa 263
## shrub_cover-Canis_latrans 728
## shrub_cover-Sciurus_niger 288
## shrub_cover-Procyon_lotor 1420
## shrub_cover-Dasypus_novemcinctus 393
## shrub_cover-Lynx_rufus 355
## shrub_cover-Didelphis_virginiana 396
## shrub_cover-Sylvilagus_floridanus 483
## shrub_cover-Sciurus_carolinensis 436
## shrub_cover-Vulpes_vulpes 473
## shrub_cover-Sus_scrofa 295
## veg_height-Canis_latrans 701
## veg_height-Sciurus_niger 253
## veg_height-Procyon_lotor 1279
## veg_height-Dasypus_novemcinctus 1827
## veg_height-Lynx_rufus 625
## veg_height-Didelphis_virginiana 994
## veg_height-Sylvilagus_floridanus 706
## veg_height-Sciurus_carolinensis 712
## veg_height-Vulpes_vulpes 539
## veg_height-Sus_scrofa 648
## week-Canis_latrans 1187
## week-Sciurus_niger 367
## week-Procyon_lotor 1597
## week-Dasypus_novemcinctus 1352
## week-Lynx_rufus 877
## week-Didelphis_virginiana 838
## week-Sylvilagus_floridanus 905
## week-Sciurus_carolinensis 847
## week-Vulpes_vulpes 835
## week-Sus_scrofa 961
## I(week^2)-Canis_latrans 969
## I(week^2)-Sciurus_niger 414
## I(week^2)-Procyon_lotor 1484
## I(week^2)-Dasypus_novemcinctus 1586
## I(week^2)-Lynx_rufus 754
## I(week^2)-Didelphis_virginiana 625
## I(week^2)-Sylvilagus_floridanus 723
## I(week^2)-Sciurus_carolinensis 955
## I(week^2)-Vulpes_vulpes 455
## I(week^2)-Sus_scrofa 1096
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$beta.samples)
## 'mcmc' num [1:3000, 1:90] -2.039 -2.023 -1.803 -1.818 -0.428 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:90] "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1496667
MCMCplot(ms_fullQ_fullQ$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
for (i in seq(1, n_params, by = chunk_size)) {
end <- min(i + chunk_size - 1, n_params)
param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
# Set filename
file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
# Save plot to PNG
png(filename = file_name, width = 1200, height = 800, res = 150)
MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
ref_ovl = TRUE,
ci = c(50, 95),
main = paste0("Occupancy Parameters: ", i, " to ", end))
dev.off()
}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
for (i in seq(1, n_params, by = chunk_size)) {
end <- min(i + chunk_size - 1, n_params)
param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
# Set filename
file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
# Save plot to PNG
png(filename = file_name, width = 1200, height = 800, res = 150)
MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
ref_ovl = TRUE,
ci = c(50, 95),
main = paste0("Detection Parameters: ", i, " to ", end))
dev.off()
}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 206 × 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 196 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] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter((Class == "Mammalia" | Name == "Meleagris_gallopavo") & Name != "Odocoileus_virginianus") %>%
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] 0 0 0 0 0 0 0 0 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] "Canis_latrans" "Sciurus_niger" "Procyon_lotor" "Dasypus_novemcinctus" ...
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:11, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:11] "Canis_latrans" "Sciurus_niger" "Procyon_lotor" "Dasypus_novemcinctus" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
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## Sampled: 2500 of 5000, 50.00%
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## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5852
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6524 0.307 -1.2749 -0.6483 -0.0478 1.0088 1323
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7844 0.5874 0.1677 0.6307 2.3948 1.0004 1215
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.797 0.2648 -3.3216 -2.7972 -2.2495 1.0033 1436
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6763 0.4564 0.1882 0.554 1.8595 1.0201 823
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1217 0.3693 -0.5408 0.1054 0.8816 1.0032
## (Intercept)-Sciurus_niger -0.9112 0.5594 -1.9579 -0.9278 0.2577 1.0323
## (Intercept)-Procyon_lotor 0.4709 0.3708 -0.2107 0.4621 1.2261 1.0011
## (Intercept)-Dasypus_novemcinctus -0.6558 0.3389 -1.3206 -0.6594 -0.0010 1.0024
## (Intercept)-Lynx_rufus -0.1439 0.5069 -0.9939 -0.1984 0.9708 1.0033
## (Intercept)-Didelphis_virginiana -1.2385 0.4103 -2.0743 -1.2290 -0.4889 1.0032
## (Intercept)-Sylvilagus_floridanus -0.4580 0.4403 -1.2939 -0.4696 0.4450 1.0037
## (Intercept)-Meleagris_gallopavo -0.4781 0.4630 -1.3281 -0.4974 0.5078 1.0021
## (Intercept)-Sciurus_carolinensis -1.2228 0.4073 -2.0841 -1.2085 -0.4560 1.0046
## (Intercept)-Vulpes_vulpes -1.2287 0.6650 -2.4987 -1.2404 0.1271 1.0149
## (Intercept)-Sus_scrofa -1.5899 0.5384 -2.7327 -1.5678 -0.6088 1.0010
## ESS
## (Intercept)-Canis_latrans 2316
## (Intercept)-Sciurus_niger 568
## (Intercept)-Procyon_lotor 2006
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 864
## (Intercept)-Didelphis_virginiana 2146
## (Intercept)-Sylvilagus_floridanus 1237
## (Intercept)-Meleagris_gallopavo 1028
## (Intercept)-Sciurus_carolinensis 2013
## (Intercept)-Vulpes_vulpes 426
## (Intercept)-Sus_scrofa 1166
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6075 0.1636 -2.9396 -2.6059 -2.2910 1.0022
## (Intercept)-Sciurus_niger -3.6237 0.4621 -4.6071 -3.5949 -2.8028 1.0532
## (Intercept)-Procyon_lotor -2.2655 0.1300 -2.5219 -2.2643 -2.0171 1.0043
## (Intercept)-Dasypus_novemcinctus -1.6064 0.1343 -1.8728 -1.5999 -1.3475 1.0050
## (Intercept)-Lynx_rufus -3.3993 0.3006 -4.0184 -3.3842 -2.8613 1.0015
## (Intercept)-Didelphis_virginiana -2.3632 0.2496 -2.8902 -2.3549 -1.9093 0.9997
## (Intercept)-Sylvilagus_floridanus -3.1187 0.2798 -3.6647 -3.1128 -2.5959 1.0186
## (Intercept)-Meleagris_gallopavo -3.3211 0.3036 -3.9441 -3.3186 -2.7577 1.0149
## (Intercept)-Sciurus_carolinensis -2.4712 0.2527 -2.9951 -2.4557 -2.0147 1.0146
## (Intercept)-Vulpes_vulpes -3.7185 0.5973 -4.9503 -3.6695 -2.6479 1.0098
## (Intercept)-Sus_scrofa -2.9857 0.4445 -3.9556 -2.9629 -2.1761 1.0060
## ESS
## (Intercept)-Canis_latrans 1133
## (Intercept)-Sciurus_niger 313
## (Intercept)-Procyon_lotor 1516
## (Intercept)-Dasypus_novemcinctus 2500
## (Intercept)-Lynx_rufus 384
## (Intercept)-Didelphis_virginiana 1457
## (Intercept)-Sylvilagus_floridanus 653
## (Intercept)-Meleagris_gallopavo 442
## (Intercept)-Sciurus_carolinensis 1261
## (Intercept)-Vulpes_vulpes 277
## (Intercept)-Sus_scrofa 621
# Includes all covariates of detection and occupancy
ms_full_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9828
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0051 0.6551 -2.3159 -0.9933 0.2816 1.0175 268
## Cogon_Patch_Size -0.7172 0.5942 -1.9470 -0.6954 0.3886 1.0157 348
## Veg_shannon_index 1.0196 0.4643 0.1339 1.0038 1.9507 1.0176 237
## total_shrub_cover -0.8418 0.5461 -2.0300 -0.8001 0.1214 1.0167 197
## Avg_Cogongrass_Cover 1.9920 0.7604 0.5043 1.9778 3.5709 1.1022 159
## Tree_Density -1.9696 0.7085 -3.4490 -1.9471 -0.6393 1.0174 213
## Avg_Canopy_Cover 1.9522 0.6383 0.8249 1.9103 3.2934 1.0305 222
## avg_veg_height -0.5463 0.4495 -1.4873 -0.5213 0.3302 1.0065 258
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1856 3.7063 0.1220 2.1374 12.6612 1.0069 269
## Cogon_Patch_Size 1.7874 2.2095 0.0835 1.0793 7.9389 1.0409 304
## Veg_shannon_index 0.8344 1.6248 0.0497 0.3748 4.5832 1.3854 224
## total_shrub_cover 1.5144 1.9365 0.0781 0.8662 6.8136 1.0683 241
## Avg_Cogongrass_Cover 1.6330 3.2168 0.0520 0.7006 8.9884 1.1167 221
## Tree_Density 2.6795 4.0867 0.0699 1.3950 13.4230 1.1168 159
## Avg_Canopy_Cover 2.3231 3.3019 0.1510 1.3914 9.8679 1.1770 202
## avg_veg_height 0.4190 0.5588 0.0360 0.2403 1.9385 1.0850 487
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2944 2.5589 0.0792 1.3622 9.4565 1.3921 63
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0994 0.3224 -3.7323 -3.1098 -2.4424 1.0038 1033
## shrub_cover 0.3483 0.3017 -0.2464 0.3418 0.9600 1.0002 582
## veg_height 0.0299 0.1738 -0.3075 0.0323 0.3832 1.0160 894
## week -0.1200 0.1287 -0.3797 -0.1158 0.1270 1.0078 1100
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0185 0.6963 0.2931 0.8325 2.8890 1.0013 536
## shrub_cover 0.6943 0.5480 0.1591 0.5479 2.1696 1.0318 433
## veg_height 0.2243 0.1653 0.0582 0.1816 0.6426 1.0026 1010
## week 0.1052 0.0872 0.0259 0.0818 0.3233 0.9999 690
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1450 1.0322 -1.8972 0.1435
## (Intercept)-Sciurus_niger -0.3619 1.3971 -2.7899 -0.4737
## (Intercept)-Procyon_lotor 0.2620 1.0605 -1.9526 0.3152
## (Intercept)-Dasypus_novemcinctus -1.3377 0.8876 -3.3260 -1.2852
## (Intercept)-Lynx_rufus 0.2062 1.6582 -2.3813 -0.0167
## (Intercept)-Didelphis_virginiana -2.1910 1.0535 -4.3537 -2.1471
## (Intercept)-Sylvilagus_floridanus -0.9947 1.0049 -2.8692 -1.0183
## (Intercept)-Meleagris_gallopavo -0.8995 1.3426 -3.6380 -0.9433
## (Intercept)-Sciurus_carolinensis -2.1901 1.1928 -4.8117 -2.0887
## (Intercept)-Vulpes_vulpes -1.9038 1.2783 -4.4584 -1.8849
## (Intercept)-Sus_scrofa -2.6999 1.5347 -6.1285 -2.5331
## Cogon_Patch_Size-Canis_latrans 0.3614 1.0090 -1.1893 0.2230
## Cogon_Patch_Size-Sciurus_niger -1.3048 1.3115 -4.3208 -1.1831
## Cogon_Patch_Size-Procyon_lotor -1.0252 0.7114 -2.5148 -0.9846
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6067 0.7459 -2.0713 -0.6146
## Cogon_Patch_Size-Lynx_rufus -0.8105 1.1681 -3.1641 -0.8241
## Cogon_Patch_Size-Didelphis_virginiana 0.5437 0.8945 -0.9509 0.4446
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5824 1.2856 -4.7581 -1.4138
## Cogon_Patch_Size-Meleagris_gallopavo -0.3574 1.0746 -2.3302 -0.4372
## Cogon_Patch_Size-Sciurus_carolinensis -1.4206 1.1128 -3.9604 -1.2664
## Cogon_Patch_Size-Vulpes_vulpes -1.1084 1.2758 -3.8066 -1.0401
## Cogon_Patch_Size-Sus_scrofa -1.0402 1.1437 -3.5994 -0.9391
## Veg_shannon_index-Canis_latrans 1.3581 0.6903 0.1807 1.2860
## Veg_shannon_index-Sciurus_niger 1.1689 1.0132 -0.5309 1.1349
## Veg_shannon_index-Procyon_lotor 1.2986 0.6279 0.1507 1.2501
## Veg_shannon_index-Dasypus_novemcinctus 0.7201 0.5697 -0.4253 0.7296
## Veg_shannon_index-Lynx_rufus 0.9563 0.8818 -0.8202 0.9763
## Veg_shannon_index-Didelphis_virginiana 1.2125 0.6989 0.0008 1.1677
## Veg_shannon_index-Sylvilagus_floridanus 1.1003 0.6874 -0.1151 1.0692
## Veg_shannon_index-Meleagris_gallopavo 1.3566 0.8447 -0.0080 1.2728
## Veg_shannon_index-Sciurus_carolinensis 0.3679 0.7756 -1.3333 0.4375
## Veg_shannon_index-Vulpes_vulpes 0.4755 0.9036 -1.7063 0.5839
## Veg_shannon_index-Sus_scrofa 1.4829 0.9293 0.0988 1.3385
## total_shrub_cover-Canis_latrans 0.3669 0.8240 -0.8982 0.2495
## total_shrub_cover-Sciurus_niger -1.0869 1.0638 -3.4708 -0.9853
## total_shrub_cover-Procyon_lotor -1.1351 0.6403 -2.5358 -1.0806
## total_shrub_cover-Dasypus_novemcinctus -0.3306 0.7369 -1.9726 -0.3001
## total_shrub_cover-Lynx_rufus -1.2414 1.1392 -3.9440 -1.0751
## total_shrub_cover-Didelphis_virginiana -1.0479 0.9095 -3.1575 -0.9179
## total_shrub_cover-Sylvilagus_floridanus -0.9491 1.0978 -3.5985 -0.7957
## total_shrub_cover-Meleagris_gallopavo -1.9725 1.3635 -5.2778 -1.7100
## total_shrub_cover-Sciurus_carolinensis -0.7745 0.9900 -3.0276 -0.6679
## total_shrub_cover-Vulpes_vulpes -1.1088 1.1662 -3.8716 -0.9752
## total_shrub_cover-Sus_scrofa -0.6540 1.0865 -2.9193 -0.5852
## Avg_Cogongrass_Cover-Canis_latrans 2.5225 0.9964 0.8990 2.4067
## Avg_Cogongrass_Cover-Sciurus_niger 1.1632 1.6226 -2.6262 1.4611
## Avg_Cogongrass_Cover-Procyon_lotor 2.2193 0.9039 0.5490 2.1643
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7053 1.0491 1.0275 2.5736
## Avg_Cogongrass_Cover-Lynx_rufus 2.5058 1.0910 0.6940 2.3737
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1266 0.9471 0.3769 2.0844
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3293 1.1112 -1.0671 1.3711
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5057 1.4078 -1.9448 1.6918
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3629 1.0041 0.5837 2.2585
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.6385 1.1185 0.7578 2.4990
## Avg_Cogongrass_Cover-Sus_scrofa 1.6999 1.2861 -1.1402 1.7868
## Tree_Density-Canis_latrans -2.7905 1.3070 -6.0194 -2.5738
## Tree_Density-Sciurus_niger -2.1051 1.4404 -5.2358 -2.0247
## Tree_Density-Procyon_lotor -1.5855 0.8038 -3.2064 -1.5811
## Tree_Density-Dasypus_novemcinctus -3.5489 1.6987 -7.9630 -3.2120
## Tree_Density-Lynx_rufus -0.5790 1.2956 -2.7236 -0.6985
## Tree_Density-Didelphis_virginiana -2.2335 1.2205 -4.9888 -2.1123
## Tree_Density-Sylvilagus_floridanus -2.3640 1.4045 -5.7928 -2.2013
## Tree_Density-Meleagris_gallopavo -2.2692 1.3642 -5.2379 -2.1847
## Tree_Density-Sciurus_carolinensis -2.1532 1.2654 -5.0665 -2.0684
## Tree_Density-Vulpes_vulpes -1.7942 1.5508 -4.8318 -1.8641
## Tree_Density-Sus_scrofa -2.0669 1.4431 -5.1378 -1.9631
## Avg_Canopy_Cover-Canis_latrans 0.3416 0.6546 -0.9701 0.3501
## Avg_Canopy_Cover-Sciurus_niger 1.9972 1.7984 -0.8791 1.8265
## Avg_Canopy_Cover-Procyon_lotor 1.7466 0.7226 0.4423 1.7008
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2046 0.7704 0.9194 2.1166
## Avg_Canopy_Cover-Lynx_rufus 1.0849 1.1435 -1.1450 1.0504
## Avg_Canopy_Cover-Didelphis_virginiana 2.9188 1.1837 1.2133 2.7233
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3526 1.5912 1.1163 3.0317
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4853 1.1932 0.6706 2.2888
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7558 1.2042 1.0303 2.5141
## Avg_Canopy_Cover-Vulpes_vulpes 2.2647 1.2146 0.3789 2.0887
## Avg_Canopy_Cover-Sus_scrofa 2.1166 1.0306 0.4275 1.9865
## avg_veg_height-Canis_latrans -0.5885 0.5842 -1.7996 -0.5601
## avg_veg_height-Sciurus_niger -0.7725 0.7595 -2.5331 -0.7020
## avg_veg_height-Procyon_lotor -0.4760 0.5584 -1.5639 -0.4781
## avg_veg_height-Dasypus_novemcinctus -0.3220 0.5795 -1.4592 -0.3327
## avg_veg_height-Lynx_rufus -0.6890 0.7294 -2.2214 -0.6433
## avg_veg_height-Didelphis_virginiana -0.7143 0.6470 -2.0825 -0.6710
## avg_veg_height-Sylvilagus_floridanus -0.6710 0.6651 -2.0557 -0.6261
## avg_veg_height-Meleagris_gallopavo -0.5321 0.7067 -1.9710 -0.5310
## avg_veg_height-Sciurus_carolinensis -0.2298 0.6575 -1.4783 -0.2556
## avg_veg_height-Vulpes_vulpes -0.5340 0.7204 -2.0118 -0.5206
## avg_veg_height-Sus_scrofa -0.5551 0.6766 -1.9327 -0.5435
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3027 1.0369 279
## (Intercept)-Sciurus_niger 2.8537 1.0302 214
## (Intercept)-Procyon_lotor 2.2819 1.0449 111
## (Intercept)-Dasypus_novemcinctus 0.2510 1.0812 217
## (Intercept)-Lynx_rufus 4.0063 1.0155 119
## (Intercept)-Didelphis_virginiana -0.2144 1.0061 327
## (Intercept)-Sylvilagus_floridanus 1.1012 1.0641 608
## (Intercept)-Meleagris_gallopavo 1.8395 1.0595 201
## (Intercept)-Sciurus_carolinensis -0.1368 1.0754 306
## (Intercept)-Vulpes_vulpes 0.6246 1.0114 244
## (Intercept)-Sus_scrofa -0.2392 1.0453 183
## Cogon_Patch_Size-Canis_latrans 2.7161 1.0314 424
## Cogon_Patch_Size-Sciurus_niger 0.9233 1.0163 353
## Cogon_Patch_Size-Procyon_lotor 0.3292 1.0478 234
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9129 1.0201 549
## Cogon_Patch_Size-Lynx_rufus 1.5185 1.0419 330
## Cogon_Patch_Size-Didelphis_virginiana 2.5088 1.0405 362
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4677 1.0284 309
## Cogon_Patch_Size-Meleagris_gallopavo 2.1181 1.0423 498
## Cogon_Patch_Size-Sciurus_carolinensis 0.4130 1.1013 387
## Cogon_Patch_Size-Vulpes_vulpes 1.2255 1.0255 372
## Cogon_Patch_Size-Sus_scrofa 0.9813 1.0122 483
## Veg_shannon_index-Canis_latrans 2.9479 1.0305 268
## Veg_shannon_index-Sciurus_niger 3.2040 1.0718 264
## Veg_shannon_index-Procyon_lotor 2.6873 1.0383 189
## Veg_shannon_index-Dasypus_novemcinctus 1.8097 1.0142 448
## Veg_shannon_index-Lynx_rufus 2.5699 1.0251 250
## Veg_shannon_index-Didelphis_virginiana 2.8048 1.0281 306
## Veg_shannon_index-Sylvilagus_floridanus 2.5645 1.0266 306
## Veg_shannon_index-Meleagris_gallopavo 3.2671 1.0533 381
## Veg_shannon_index-Sciurus_carolinensis 1.6484 1.0147 498
## Veg_shannon_index-Vulpes_vulpes 1.8839 1.0323 331
## Veg_shannon_index-Sus_scrofa 3.7887 1.0807 420
## total_shrub_cover-Canis_latrans 2.2458 1.0506 313
## total_shrub_cover-Sciurus_niger 0.8016 1.0185 244
## total_shrub_cover-Procyon_lotor -0.0314 1.0206 800
## total_shrub_cover-Dasypus_novemcinctus 1.0359 1.0350 567
## total_shrub_cover-Lynx_rufus 0.6272 1.0499 205
## total_shrub_cover-Didelphis_virginiana 0.3901 1.0076 356
## total_shrub_cover-Sylvilagus_floridanus 0.8987 1.0480 364
## total_shrub_cover-Meleagris_gallopavo -0.0208 1.0697 157
## total_shrub_cover-Sciurus_carolinensis 0.9417 1.0374 289
## total_shrub_cover-Vulpes_vulpes 1.0589 1.1162 338
## total_shrub_cover-Sus_scrofa 1.3986 1.0168 322
## Avg_Cogongrass_Cover-Canis_latrans 4.7452 1.0323 181
## Avg_Cogongrass_Cover-Sciurus_niger 3.7279 1.1134 140
## Avg_Cogongrass_Cover-Procyon_lotor 4.1672 1.0265 284
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0549 1.0383 202
## Avg_Cogongrass_Cover-Lynx_rufus 4.8463 1.0377 210
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.1733 1.0394 308
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5107 1.0494 241
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.9311 1.0990 211
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5650 1.0231 276
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.1424 1.0194 321
## Avg_Cogongrass_Cover-Sus_scrofa 4.0811 1.0541 242
## Tree_Density-Canis_latrans -0.8808 1.0454 281
## Tree_Density-Sciurus_niger 0.5101 1.0315 309
## Tree_Density-Procyon_lotor 0.0129 1.0046 465
## Tree_Density-Dasypus_novemcinctus -1.2660 1.0908 135
## Tree_Density-Lynx_rufus 2.2993 1.0423 271
## Tree_Density-Didelphis_virginiana -0.0987 1.0471 229
## Tree_Density-Sylvilagus_floridanus 0.1467 1.0415 346
## Tree_Density-Meleagris_gallopavo 0.3001 1.0339 350
## Tree_Density-Sciurus_carolinensis 0.1793 1.0136 369
## Tree_Density-Vulpes_vulpes 1.5050 1.0175 272
## Tree_Density-Sus_scrofa 0.6156 1.0112 274
## Avg_Canopy_Cover-Canis_latrans 1.6651 1.0406 681
## Avg_Canopy_Cover-Sciurus_niger 5.6192 1.1522 136
## Avg_Canopy_Cover-Procyon_lotor 3.2823 1.0385 317
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9864 1.0364 270
## Avg_Canopy_Cover-Lynx_rufus 3.4809 1.0232 256
## Avg_Canopy_Cover-Didelphis_virginiana 5.7943 1.0981 153
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.4602 1.0971 161
## Avg_Canopy_Cover-Meleagris_gallopavo 5.4328 1.0456 347
## Avg_Canopy_Cover-Sciurus_carolinensis 5.7394 1.0831 159
## Avg_Canopy_Cover-Vulpes_vulpes 5.0552 1.0876 252
## Avg_Canopy_Cover-Sus_scrofa 4.4878 1.0362 537
## avg_veg_height-Canis_latrans 0.5479 1.0118 421
## avg_veg_height-Sciurus_niger 0.5172 1.0256 395
## avg_veg_height-Procyon_lotor 0.6400 1.0020 508
## avg_veg_height-Dasypus_novemcinctus 0.8051 1.0026 461
## avg_veg_height-Lynx_rufus 0.6703 1.0101 380
## avg_veg_height-Didelphis_virginiana 0.4670 1.0164 514
## avg_veg_height-Sylvilagus_floridanus 0.5770 1.0040 452
## avg_veg_height-Meleagris_gallopavo 0.8386 1.0053 518
## avg_veg_height-Sciurus_carolinensis 1.1586 1.0056 422
## avg_veg_height-Vulpes_vulpes 0.9519 1.0058 459
## avg_veg_height-Sus_scrofa 0.7550 1.0118 477
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7614 0.1794 -3.1310 -2.7575 -2.4180 1.0011
## (Intercept)-Sciurus_niger -4.3925 0.5136 -5.3256 -4.4112 -3.3155 1.0350
## (Intercept)-Procyon_lotor -2.3049 0.1388 -2.5754 -2.3043 -2.0366 1.0061
## (Intercept)-Dasypus_novemcinctus -1.8066 0.1679 -2.1489 -1.8008 -1.4939 1.0012
## (Intercept)-Lynx_rufus -3.7135 0.3531 -4.4070 -3.7156 -3.0674 1.0026
## (Intercept)-Didelphis_virginiana -2.6680 0.2960 -3.2675 -2.6587 -2.1149 1.0213
## (Intercept)-Sylvilagus_floridanus -3.2411 0.2645 -3.7882 -3.2324 -2.7453 1.0125
## (Intercept)-Meleagris_gallopavo -3.9020 0.4647 -4.8504 -3.9035 -3.0064 1.0105
## (Intercept)-Sciurus_carolinensis -2.8089 0.3335 -3.4922 -2.7986 -2.1897 1.0022
## (Intercept)-Vulpes_vulpes -4.1503 0.6290 -5.6013 -4.0768 -3.0867 1.0041
## (Intercept)-Sus_scrofa -3.5388 0.5462 -4.5714 -3.5479 -2.3862 1.0355
## shrub_cover-Canis_latrans -0.3454 0.2272 -0.7690 -0.3500 0.1069 1.0294
## shrub_cover-Sciurus_niger -0.2176 0.5046 -1.2349 -0.2101 0.7584 1.0096
## shrub_cover-Procyon_lotor 0.2768 0.1587 -0.0326 0.2772 0.5844 1.0011
## shrub_cover-Dasypus_novemcinctus 0.9607 0.3161 0.3651 0.9622 1.5922 1.0031
## shrub_cover-Lynx_rufus -0.0507 0.3914 -0.8494 -0.0361 0.6925 1.0044
## shrub_cover-Didelphis_virginiana 1.0373 0.3724 0.3331 1.0255 1.8046 1.0087
## shrub_cover-Sylvilagus_floridanus 0.5589 0.4141 -0.2889 0.5653 1.3603 1.0230
## shrub_cover-Meleagris_gallopavo -0.5594 0.4489 -1.4759 -0.5555 0.3087 1.0143
## shrub_cover-Sciurus_carolinensis 1.0381 0.4261 0.2157 1.0384 1.8575 1.0176
## shrub_cover-Vulpes_vulpes 0.2729 0.6195 -0.9294 0.2692 1.5190 1.0786
## shrub_cover-Sus_scrofa 0.9443 0.7513 -0.5960 0.9708 2.3826 1.0063
## veg_height-Canis_latrans -0.5862 0.1839 -0.9582 -0.5798 -0.2445 1.0009
## veg_height-Sciurus_niger 0.0581 0.4097 -0.7043 0.0466 0.9567 1.0172
## veg_height-Procyon_lotor 0.3464 0.1230 0.1000 0.3489 0.5867 1.0064
## veg_height-Dasypus_novemcinctus 0.2661 0.1428 -0.0136 0.2655 0.5516 1.0106
## veg_height-Lynx_rufus 0.0939 0.2325 -0.3658 0.0933 0.5577 1.0147
## veg_height-Didelphis_virginiana 0.4735 0.2462 0.0100 0.4647 0.9678 1.0039
## veg_height-Sylvilagus_floridanus 0.1451 0.2523 -0.3383 0.1457 0.6485 1.0263
## veg_height-Meleagris_gallopavo -0.2092 0.3719 -0.9405 -0.2106 0.5193 1.0083
## veg_height-Sciurus_carolinensis 0.1308 0.2265 -0.2964 0.1257 0.6049 1.0065
## veg_height-Vulpes_vulpes -0.1726 0.3316 -0.8579 -0.1550 0.4456 1.0007
## veg_height-Sus_scrofa -0.1642 0.3337 -0.8442 -0.1497 0.4773 1.0061
## week-Canis_latrans 0.0536 0.1356 -0.2143 0.0516 0.3183 1.0015
## week-Sciurus_niger -0.3309 0.2984 -1.0349 -0.3005 0.1582 1.0091
## week-Procyon_lotor -0.0633 0.1191 -0.3060 -0.0595 0.1605 1.0011
## week-Dasypus_novemcinctus -0.1868 0.1421 -0.4823 -0.1791 0.0749 1.0059
## week-Lynx_rufus -0.0664 0.1980 -0.4672 -0.0588 0.2972 1.0078
## week-Didelphis_virginiana -0.2525 0.2256 -0.7322 -0.2416 0.1633 1.0041
## week-Sylvilagus_floridanus -0.1933 0.2074 -0.6389 -0.1795 0.1830 1.0021
## week-Meleagris_gallopavo -0.3192 0.2426 -0.8624 -0.2993 0.1035 1.0168
## week-Sciurus_carolinensis 0.1172 0.1889 -0.2613 0.1190 0.4769 1.0059
## week-Vulpes_vulpes -0.1603 0.2729 -0.7453 -0.1425 0.3510 1.0120
## week-Sus_scrofa 0.0622 0.2369 -0.4003 0.0612 0.5223 1.0003
## ESS
## (Intercept)-Canis_latrans 733
## (Intercept)-Sciurus_niger 155
## (Intercept)-Procyon_lotor 1146
## (Intercept)-Dasypus_novemcinctus 930
## (Intercept)-Lynx_rufus 250
## (Intercept)-Didelphis_virginiana 542
## (Intercept)-Sylvilagus_floridanus 644
## (Intercept)-Meleagris_gallopavo 218
## (Intercept)-Sciurus_carolinensis 296
## (Intercept)-Vulpes_vulpes 183
## (Intercept)-Sus_scrofa 333
## shrub_cover-Canis_latrans 684
## shrub_cover-Sciurus_niger 314
## shrub_cover-Procyon_lotor 1581
## shrub_cover-Dasypus_novemcinctus 740
## shrub_cover-Lynx_rufus 229
## shrub_cover-Didelphis_virginiana 563
## shrub_cover-Sylvilagus_floridanus 490
## shrub_cover-Meleagris_gallopavo 257
## shrub_cover-Sciurus_carolinensis 406
## shrub_cover-Vulpes_vulpes 372
## shrub_cover-Sus_scrofa 407
## veg_height-Canis_latrans 756
## veg_height-Sciurus_niger 284
## veg_height-Procyon_lotor 1507
## veg_height-Dasypus_novemcinctus 1810
## veg_height-Lynx_rufus 670
## veg_height-Didelphis_virginiana 780
## veg_height-Sylvilagus_floridanus 825
## veg_height-Meleagris_gallopavo 349
## veg_height-Sciurus_carolinensis 806
## veg_height-Vulpes_vulpes 509
## veg_height-Sus_scrofa 940
## week-Canis_latrans 1398
## week-Sciurus_niger 336
## week-Procyon_lotor 1750
## week-Dasypus_novemcinctus 1947
## week-Lynx_rufus 892
## week-Didelphis_virginiana 1235
## week-Sylvilagus_floridanus 973
## week-Meleagris_gallopavo 724
## week-Sciurus_carolinensis 1530
## week-Vulpes_vulpes 886
## week-Sus_scrofa 1468
#Includes all covariates of detection and only null for occupancy
ms_full_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9277
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4368 0.3295 -1.0749 -0.4469 0.2349 1.0748 406
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8077 0.653 0.1392 0.6266 2.5156 1.0242 899
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0436 0.3354 -3.7113 -3.0339 -2.3920 1.0239 1050
## shrub_cover 0.1606 0.2898 -0.4074 0.1588 0.7253 1.0237 1247
## veg_height -0.0017 0.1714 -0.3450 0.0073 0.3228 1.0206 920
## week -0.1165 0.1268 -0.3811 -0.1110 0.1179 1.0141 998
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9911 0.7633 0.2471 0.7906 2.8286 1.1121 390
## shrub_cover 0.7305 0.7282 0.1585 0.5730 2.2252 1.0947 845
## veg_height 0.2166 0.1647 0.0552 0.1729 0.6441 1.0074 1101
## week 0.1047 0.0942 0.0246 0.0798 0.3347 1.0078 852
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2580 0.3948 -0.4720 0.2420 1.0634 1.0091
## (Intercept)-Sciurus_niger -0.6608 0.6374 -1.7765 -0.6970 0.6774 1.0607
## (Intercept)-Procyon_lotor 0.5263 0.3854 -0.1677 0.5204 1.3339 1.0072
## (Intercept)-Dasypus_novemcinctus -0.5718 0.3552 -1.2846 -0.5700 0.1109 1.0066
## (Intercept)-Lynx_rufus 0.1266 0.5913 -0.8638 0.0632 1.4960 1.0586
## (Intercept)-Didelphis_virginiana -1.0592 0.4317 -1.9402 -1.0448 -0.2516 1.0053
## (Intercept)-Sylvilagus_floridanus -0.3856 0.4223 -1.1645 -0.4019 0.5122 1.0314
## (Intercept)-Meleagris_gallopavo 0.1263 0.6792 -0.9866 0.0412 1.7416 1.1011
## (Intercept)-Sciurus_carolinensis -1.0472 0.4343 -1.9479 -1.0323 -0.2364 1.0109
## (Intercept)-Vulpes_vulpes -0.9584 0.7507 -2.2834 -1.0075 0.6453 1.0509
## (Intercept)-Sus_scrofa -1.2997 0.5968 -2.5285 -1.2722 -0.1813 1.0127
## ESS
## (Intercept)-Canis_latrans 1929
## (Intercept)-Sciurus_niger 322
## (Intercept)-Procyon_lotor 1730
## (Intercept)-Dasypus_novemcinctus 2634
## (Intercept)-Lynx_rufus 517
## (Intercept)-Didelphis_virginiana 1259
## (Intercept)-Sylvilagus_floridanus 967
## (Intercept)-Meleagris_gallopavo 272
## (Intercept)-Sciurus_carolinensis 1178
## (Intercept)-Vulpes_vulpes 216
## (Intercept)-Sus_scrofa 637
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7635 0.1854 -3.1477 -2.7603 -2.4183 1.0176
## (Intercept)-Sciurus_niger -4.0025 0.5596 -5.1468 -3.9900 -2.9580 1.1035
## (Intercept)-Procyon_lotor -2.3014 0.1441 -2.5888 -2.3024 -2.0317 1.0157
## (Intercept)-Dasypus_novemcinctus -1.7639 0.1608 -2.0894 -1.7594 -1.4574 1.0048
## (Intercept)-Lynx_rufus -3.6731 0.3553 -4.3986 -3.6674 -2.9866 1.0994
## (Intercept)-Didelphis_virginiana -2.6217 0.2820 -3.2022 -2.6118 -2.1038 1.0009
## (Intercept)-Sylvilagus_floridanus -3.1897 0.2826 -3.7704 -3.1722 -2.6909 1.0376
## (Intercept)-Meleagris_gallopavo -4.0799 0.5035 -5.0827 -4.0836 -3.0810 1.1725
## (Intercept)-Sciurus_carolinensis -2.6661 0.3142 -3.2950 -2.6565 -2.0739 1.0156
## (Intercept)-Vulpes_vulpes -4.0914 0.6852 -5.5067 -4.0237 -2.9155 1.1070
## (Intercept)-Sus_scrofa -3.4737 0.5849 -4.6012 -3.4639 -2.3588 1.0211
## shrub_cover-Canis_latrans -0.3202 0.2232 -0.7548 -0.3154 0.1129 1.0090
## shrub_cover-Sciurus_niger -0.4127 0.4831 -1.3820 -0.4094 0.5348 1.0248
## shrub_cover-Procyon_lotor 0.2383 0.1665 -0.0927 0.2396 0.5520 1.0047
## shrub_cover-Dasypus_novemcinctus 0.8363 0.2932 0.2736 0.8297 1.4431 1.0022
## shrub_cover-Lynx_rufus -0.3261 0.3617 -1.0713 -0.3109 0.3451 1.0308
## shrub_cover-Didelphis_virginiana 0.9851 0.3677 0.3034 0.9737 1.7404 1.0114
## shrub_cover-Sylvilagus_floridanus 0.2499 0.4126 -0.5240 0.2460 1.0837 1.0181
## shrub_cover-Meleagris_gallopavo -0.7924 0.4342 -1.6635 -0.7847 0.0590 1.1591
## shrub_cover-Sciurus_carolinensis 0.8346 0.4152 0.0354 0.8323 1.6397 1.0198
## shrub_cover-Vulpes_vulpes -0.1431 0.6049 -1.3449 -0.1340 1.0543 1.0555
## shrub_cover-Sus_scrofa 0.7362 0.8140 -0.7737 0.7151 2.4085 1.0297
## veg_height-Canis_latrans -0.5913 0.1844 -0.9540 -0.5863 -0.2410 1.0396
## veg_height-Sciurus_niger -0.0826 0.4239 -0.9405 -0.0633 0.7646 1.0269
## veg_height-Procyon_lotor 0.3321 0.1207 0.0861 0.3316 0.5659 1.0083
## veg_height-Dasypus_novemcinctus 0.2463 0.1345 -0.0076 0.2458 0.5106 1.0018
## veg_height-Lynx_rufus 0.0302 0.2496 -0.4772 0.0376 0.5170 1.0282
## veg_height-Didelphis_virginiana 0.4243 0.2448 -0.0330 0.4227 0.9015 1.0060
## veg_height-Sylvilagus_floridanus 0.1163 0.2530 -0.3728 0.1147 0.6185 1.0326
## veg_height-Meleagris_gallopavo -0.2668 0.3635 -1.0140 -0.2582 0.4453 1.0435
## veg_height-Sciurus_carolinensis 0.0844 0.2173 -0.3234 0.0770 0.5310 1.0102
## veg_height-Vulpes_vulpes -0.1359 0.3216 -0.8394 -0.1184 0.4564 1.0236
## veg_height-Sus_scrofa -0.1770 0.3420 -0.8925 -0.1638 0.4730 1.0132
## week-Canis_latrans 0.0521 0.1346 -0.2221 0.0591 0.2976 1.0092
## week-Sciurus_niger -0.3513 0.3071 -1.0398 -0.3167 0.1601 1.0091
## week-Procyon_lotor -0.0600 0.1184 -0.2945 -0.0557 0.1637 1.0001
## week-Dasypus_novemcinctus -0.1848 0.1418 -0.4761 -0.1786 0.0755 1.0036
## week-Lynx_rufus -0.0652 0.1887 -0.4627 -0.0537 0.2798 1.0033
## week-Didelphis_virginiana -0.2447 0.2168 -0.6941 -0.2351 0.1521 1.0036
## week-Sylvilagus_floridanus -0.1801 0.2086 -0.6350 -0.1713 0.1879 1.0154
## week-Meleagris_gallopavo -0.2939 0.2438 -0.8392 -0.2752 0.1148 1.0083
## week-Sciurus_carolinensis 0.1200 0.1868 -0.2495 0.1207 0.4782 0.9999
## week-Vulpes_vulpes -0.1630 0.2785 -0.7569 -0.1436 0.3247 1.0136
## week-Sus_scrofa 0.0537 0.2358 -0.3979 0.0531 0.5276 1.0088
## ESS
## (Intercept)-Canis_latrans 787
## (Intercept)-Sciurus_niger 219
## (Intercept)-Procyon_lotor 1205
## (Intercept)-Dasypus_novemcinctus 1890
## (Intercept)-Lynx_rufus 297
## (Intercept)-Didelphis_virginiana 904
## (Intercept)-Sylvilagus_floridanus 509
## (Intercept)-Meleagris_gallopavo 170
## (Intercept)-Sciurus_carolinensis 715
## (Intercept)-Vulpes_vulpes 178
## (Intercept)-Sus_scrofa 346
## shrub_cover-Canis_latrans 783
## shrub_cover-Sciurus_niger 496
## shrub_cover-Procyon_lotor 1434
## shrub_cover-Dasypus_novemcinctus 1366
## shrub_cover-Lynx_rufus 366
## shrub_cover-Didelphis_virginiana 817
## shrub_cover-Sylvilagus_floridanus 477
## shrub_cover-Meleagris_gallopavo 171
## shrub_cover-Sciurus_carolinensis 768
## shrub_cover-Vulpes_vulpes 423
## shrub_cover-Sus_scrofa 433
## veg_height-Canis_latrans 760
## veg_height-Sciurus_niger 700
## veg_height-Procyon_lotor 1766
## veg_height-Dasypus_novemcinctus 1827
## veg_height-Lynx_rufus 724
## veg_height-Didelphis_virginiana 1161
## veg_height-Sylvilagus_floridanus 787
## veg_height-Meleagris_gallopavo 458
## veg_height-Sciurus_carolinensis 1199
## veg_height-Vulpes_vulpes 645
## veg_height-Sus_scrofa 954
## week-Canis_latrans 1542
## week-Sciurus_niger 593
## week-Procyon_lotor 1676
## week-Dasypus_novemcinctus 2100
## week-Lynx_rufus 1071
## week-Didelphis_virginiana 1319
## week-Sylvilagus_floridanus 974
## week-Meleagris_gallopavo 708
## week-Sciurus_carolinensis 1510
## week-Vulpes_vulpes 830
## week-Sus_scrofa 1658
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9448
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4588 0.4159 -1.2663 -0.4555 0.3474 1.0076 175
## Avg_Cogongrass_Cover -0.0793 0.3401 -0.7735 -0.0813 0.5803 1.0063 588
## total_shrub_cover -0.8920 0.3909 -1.7061 -0.8757 -0.1656 1.0058 317
## avg_veg_height 0.0814 0.3644 -0.6405 0.0805 0.8081 1.0056 336
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6253 0.6646 0.0522 0.4116 2.4518 1.0043 489
## Avg_Cogongrass_Cover 0.4816 0.6358 0.0465 0.2894 1.9738 1.0292 555
## total_shrub_cover 0.7302 0.7668 0.0750 0.4891 2.7309 1.1006 301
## avg_veg_height 0.3570 0.4688 0.0374 0.2028 1.5913 1.0231 431
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3917 1.2941 0.0717 1.0263 4.6279 1.0128 129
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0648 0.3007 -3.6774 -3.0501 -2.5006 1.0015 646
## shrub_cover 0.5088 0.3103 -0.1235 0.5109 1.1132 1.0060 584
## veg_height 0.0405 0.1759 -0.3157 0.0444 0.3756 1.0084 726
## week -0.1265 0.1331 -0.4054 -0.1191 0.1208 1.0033 746
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7756 0.5752 0.1800 0.6229 2.2696 1.0143 488
## shrub_cover 0.7454 0.6479 0.1457 0.5817 2.2445 1.0355 373
## veg_height 0.2168 0.1546 0.0566 0.1767 0.6223 1.0041 1175
## week 0.1147 0.1065 0.0245 0.0847 0.3758 1.0086 806
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0420 0.6590 -1.1883 0.0258
## (Intercept)-Sciurus_niger -0.7040 0.6931 -2.1194 -0.6973
## (Intercept)-Procyon_lotor 0.1873 0.6729 -1.0655 0.1593
## (Intercept)-Dasypus_novemcinctus -0.4777 0.5865 -1.6426 -0.4765
## (Intercept)-Lynx_rufus -0.3424 0.6295 -1.5904 -0.3462
## (Intercept)-Didelphis_virginiana -0.7495 0.6179 -2.0112 -0.7373
## (Intercept)-Sylvilagus_floridanus -0.2283 0.6448 -1.4275 -0.2611
## (Intercept)-Meleagris_gallopavo -0.4853 0.7038 -1.7406 -0.5102
## (Intercept)-Sciurus_carolinensis -0.7384 0.6466 -2.0700 -0.7113
## (Intercept)-Vulpes_vulpes -0.7715 0.7769 -2.4004 -0.7575
## (Intercept)-Sus_scrofa -0.9192 0.7566 -2.5270 -0.8721
## Avg_Cogongrass_Cover-Canis_latrans 0.3272 0.5214 -0.5727 0.2774
## Avg_Cogongrass_Cover-Sciurus_niger -0.5256 0.6940 -2.1925 -0.4514
## Avg_Cogongrass_Cover-Procyon_lotor -0.1092 0.4732 -1.0636 -0.1068
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1152 0.4496 -0.7711 0.1100
## Avg_Cogongrass_Cover-Lynx_rufus 0.3508 0.5490 -0.5835 0.2950
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1337 0.5010 -0.8641 0.1205
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4951 0.5888 -1.8431 -0.4396
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5223 0.7026 -2.0657 -0.4417
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0166 0.4950 -1.0413 -0.0100
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1530 0.5709 -0.8854 0.1125
## Avg_Cogongrass_Cover-Sus_scrofa -0.3267 0.6762 -1.8422 -0.2754
## total_shrub_cover-Canis_latrans 0.0569 0.6059 -1.0488 0.0109
## total_shrub_cover-Sciurus_niger -1.0250 0.7426 -2.5703 -0.9732
## total_shrub_cover-Procyon_lotor -1.1898 0.5260 -2.2971 -1.1363
## total_shrub_cover-Dasypus_novemcinctus -0.5120 0.6031 -1.8500 -0.4625
## total_shrub_cover-Lynx_rufus -1.2772 0.7351 -2.9274 -1.2097
## total_shrub_cover-Didelphis_virginiana -0.9079 0.5845 -2.2237 -0.8615
## total_shrub_cover-Sylvilagus_floridanus -1.2030 0.7307 -2.8823 -1.1141
## total_shrub_cover-Meleagris_gallopavo -1.4206 0.7305 -3.0097 -1.3461
## total_shrub_cover-Sciurus_carolinensis -0.9386 0.6740 -2.4404 -0.8731
## total_shrub_cover-Vulpes_vulpes -0.9296 0.8384 -2.5995 -0.8964
## total_shrub_cover-Sus_scrofa -0.7326 0.7411 -2.1699 -0.7292
## avg_veg_height-Canis_latrans 0.0953 0.4593 -0.8019 0.0988
## avg_veg_height-Sciurus_niger -0.2636 0.6983 -1.9564 -0.1964
## avg_veg_height-Procyon_lotor 0.1401 0.4704 -0.7482 0.1206
## avg_veg_height-Dasypus_novemcinctus 0.3306 0.4648 -0.5226 0.2995
## avg_veg_height-Lynx_rufus 0.0644 0.5776 -1.0975 0.0649
## avg_veg_height-Didelphis_virginiana -0.0037 0.4873 -0.9570 -0.0032
## avg_veg_height-Sylvilagus_floridanus 0.0204 0.4965 -0.9726 0.0127
## avg_veg_height-Meleagris_gallopavo -0.1981 0.7345 -1.9045 -0.1536
## avg_veg_height-Sciurus_carolinensis 0.4803 0.5282 -0.4616 0.4430
## avg_veg_height-Vulpes_vulpes 0.0605 0.5767 -1.1232 0.0694
## avg_veg_height-Sus_scrofa 0.1408 0.5601 -0.9163 0.1303
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4410 1.0118 451
## (Intercept)-Sciurus_niger 0.6216 1.0032 465
## (Intercept)-Procyon_lotor 1.5966 1.0091 233
## (Intercept)-Dasypus_novemcinctus 0.7002 1.0090 416
## (Intercept)-Lynx_rufus 0.9469 1.0011 426
## (Intercept)-Didelphis_virginiana 0.4309 1.0107 373
## (Intercept)-Sylvilagus_floridanus 1.1777 1.0029 384
## (Intercept)-Meleagris_gallopavo 1.0545 1.0067 269
## (Intercept)-Sciurus_carolinensis 0.4982 1.0063 407
## (Intercept)-Vulpes_vulpes 0.7510 1.0216 181
## (Intercept)-Sus_scrofa 0.4404 1.0033 293
## Avg_Cogongrass_Cover-Canis_latrans 1.4911 1.0052 819
## Avg_Cogongrass_Cover-Sciurus_niger 0.6455 1.0124 448
## Avg_Cogongrass_Cover-Procyon_lotor 0.8562 1.0006 939
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9948 1.0139 1084
## Avg_Cogongrass_Cover-Lynx_rufus 1.5828 1.0055 800
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1824 1.0044 910
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5135 1.0200 590
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6050 1.0309 530
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9221 1.0293 915
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3975 1.0018 968
## Avg_Cogongrass_Cover-Sus_scrofa 0.8317 1.0119 622
## total_shrub_cover-Canis_latrans 1.3809 1.0779 519
## total_shrub_cover-Sciurus_niger 0.3701 1.0341 318
## total_shrub_cover-Procyon_lotor -0.3016 1.0178 859
## total_shrub_cover-Dasypus_novemcinctus 0.4872 1.0065 481
## total_shrub_cover-Lynx_rufus 0.0097 1.0119 459
## total_shrub_cover-Didelphis_virginiana 0.0547 1.0528 504
## total_shrub_cover-Sylvilagus_floridanus 0.0010 1.0063 387
## total_shrub_cover-Meleagris_gallopavo -0.1636 1.0076 516
## total_shrub_cover-Sciurus_carolinensis 0.2187 1.0266 321
## total_shrub_cover-Vulpes_vulpes 0.6837 1.0368 392
## total_shrub_cover-Sus_scrofa 0.6923 1.0019 519
## avg_veg_height-Canis_latrans 1.0303 1.0038 798
## avg_veg_height-Sciurus_niger 0.9306 1.0144 429
## avg_veg_height-Procyon_lotor 1.1079 1.0007 696
## avg_veg_height-Dasypus_novemcinctus 1.2599 1.0071 642
## avg_veg_height-Lynx_rufus 1.2244 1.0088 698
## avg_veg_height-Didelphis_virginiana 0.9747 1.0122 705
## avg_veg_height-Sylvilagus_floridanus 0.9876 1.0153 583
## avg_veg_height-Meleagris_gallopavo 1.1067 1.0286 345
## avg_veg_height-Sciurus_carolinensis 1.6486 1.0124 425
## avg_veg_height-Vulpes_vulpes 1.1492 1.0036 676
## avg_veg_height-Sus_scrofa 1.2551 1.0022 625
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7885 0.1915 -3.1845 -2.7849 -2.4272 1.0145
## (Intercept)-Sciurus_niger -3.8439 0.6189 -5.1328 -3.8198 -2.7136 1.0159
## (Intercept)-Procyon_lotor -2.3095 0.1449 -2.5986 -2.3025 -2.0341 1.0109
## (Intercept)-Dasypus_novemcinctus -1.8598 0.1853 -2.2396 -1.8569 -1.5125 1.0089
## (Intercept)-Lynx_rufus -3.4719 0.3189 -4.1302 -3.4672 -2.8833 1.0055
## (Intercept)-Didelphis_virginiana -2.7969 0.3053 -3.4213 -2.7845 -2.2284 1.0698
## (Intercept)-Sylvilagus_floridanus -3.2728 0.2617 -3.8052 -3.2658 -2.7757 1.0023
## (Intercept)-Meleagris_gallopavo -3.6771 0.5300 -4.7690 -3.6635 -2.7074 1.0013
## (Intercept)-Sciurus_carolinensis -2.8688 0.3243 -3.5343 -2.8673 -2.2527 1.0056
## (Intercept)-Vulpes_vulpes -4.0505 0.6623 -5.5857 -4.0052 -2.9208 1.0040
## (Intercept)-Sus_scrofa -3.6194 0.5250 -4.6185 -3.6316 -2.5870 1.0024
## shrub_cover-Canis_latrans -0.2557 0.2543 -0.7689 -0.2531 0.2418 1.0508
## shrub_cover-Sciurus_niger 0.0606 0.5921 -1.1879 0.0894 1.1673 1.0267
## shrub_cover-Procyon_lotor 0.3103 0.1643 -0.0090 0.3087 0.6231 1.0053
## shrub_cover-Dasypus_novemcinctus 1.0920 0.3635 0.3896 1.0923 1.7766 1.0095
## shrub_cover-Lynx_rufus 0.1576 0.3739 -0.6495 0.1758 0.8173 1.0138
## shrub_cover-Didelphis_virginiana 1.2842 0.4093 0.5244 1.2746 2.0847 1.0712
## shrub_cover-Sylvilagus_floridanus 0.7356 0.4073 -0.1234 0.7469 1.4950 1.0145
## shrub_cover-Meleagris_gallopavo -0.3703 0.4943 -1.3408 -0.3647 0.5598 1.0022
## shrub_cover-Sciurus_carolinensis 1.2185 0.4127 0.4019 1.2125 2.0099 1.0243
## shrub_cover-Vulpes_vulpes 0.3347 0.6901 -1.1470 0.3754 1.6020 1.0231
## shrub_cover-Sus_scrofa 1.1796 0.7738 -0.3697 1.1701 2.8270 1.0078
## veg_height-Canis_latrans -0.5969 0.1909 -0.9635 -0.5940 -0.2355 1.0070
## veg_height-Sciurus_niger 0.1758 0.4493 -0.6637 0.1628 1.1376 1.0195
## veg_height-Procyon_lotor 0.3387 0.1277 0.0964 0.3362 0.5908 1.0022
## veg_height-Dasypus_novemcinctus 0.2740 0.1416 0.0009 0.2738 0.5566 1.0124
## veg_height-Lynx_rufus 0.0361 0.2524 -0.4763 0.0374 0.5184 1.0032
## veg_height-Didelphis_virginiana 0.4356 0.2501 -0.0352 0.4284 0.9297 1.0291
## veg_height-Sylvilagus_floridanus 0.0658 0.2446 -0.4017 0.0693 0.5361 1.0092
## veg_height-Meleagris_gallopavo -0.1050 0.4445 -1.0084 -0.1040 0.7323 1.0072
## veg_height-Sciurus_carolinensis 0.1112 0.2268 -0.3218 0.1061 0.5791 1.0016
## veg_height-Vulpes_vulpes -0.1268 0.3320 -0.8230 -0.1136 0.5025 1.0081
## veg_height-Sus_scrofa -0.1638 0.3226 -0.8168 -0.1623 0.4550 1.0080
## week-Canis_latrans 0.0531 0.1353 -0.2215 0.0546 0.3068 1.0157
## week-Sciurus_niger -0.3842 0.3360 -1.1623 -0.3356 0.1566 1.0129
## week-Procyon_lotor -0.0619 0.1200 -0.3073 -0.0588 0.1647 1.0016
## week-Dasypus_novemcinctus -0.1844 0.1385 -0.4556 -0.1819 0.0814 1.0075
## week-Lynx_rufus -0.0702 0.2015 -0.4868 -0.0645 0.3094 1.0046
## week-Didelphis_virginiana -0.2500 0.2294 -0.7483 -0.2321 0.1629 1.0010
## week-Sylvilagus_floridanus -0.1875 0.2149 -0.6518 -0.1719 0.1933 1.0066
## week-Meleagris_gallopavo -0.3164 0.2582 -0.8835 -0.2939 0.1237 1.0030
## week-Sciurus_carolinensis 0.1191 0.1855 -0.2539 0.1230 0.4741 1.0234
## week-Vulpes_vulpes -0.1707 0.2844 -0.8078 -0.1491 0.3393 1.0067
## week-Sus_scrofa 0.0562 0.2374 -0.4023 0.0547 0.5235 1.0014
## ESS
## (Intercept)-Canis_latrans 497
## (Intercept)-Sciurus_niger 201
## (Intercept)-Procyon_lotor 1333
## (Intercept)-Dasypus_novemcinctus 723
## (Intercept)-Lynx_rufus 478
## (Intercept)-Didelphis_virginiana 304
## (Intercept)-Sylvilagus_floridanus 634
## (Intercept)-Meleagris_gallopavo 201
## (Intercept)-Sciurus_carolinensis 511
## (Intercept)-Vulpes_vulpes 183
## (Intercept)-Sus_scrofa 299
## shrub_cover-Canis_latrans 542
## shrub_cover-Sciurus_niger 256
## shrub_cover-Procyon_lotor 1280
## shrub_cover-Dasypus_novemcinctus 417
## shrub_cover-Lynx_rufus 399
## shrub_cover-Didelphis_virginiana 272
## shrub_cover-Sylvilagus_floridanus 342
## shrub_cover-Meleagris_gallopavo 212
## shrub_cover-Sciurus_carolinensis 345
## shrub_cover-Vulpes_vulpes 292
## shrub_cover-Sus_scrofa 238
## veg_height-Canis_latrans 554
## veg_height-Sciurus_niger 525
## veg_height-Procyon_lotor 1451
## veg_height-Dasypus_novemcinctus 1653
## veg_height-Lynx_rufus 631
## veg_height-Didelphis_virginiana 669
## veg_height-Sylvilagus_floridanus 772
## veg_height-Meleagris_gallopavo 322
## veg_height-Sciurus_carolinensis 637
## veg_height-Vulpes_vulpes 484
## veg_height-Sus_scrofa 713
## week-Canis_latrans 1295
## week-Sciurus_niger 553
## week-Procyon_lotor 1632
## week-Dasypus_novemcinctus 1788
## week-Lynx_rufus 954
## week-Didelphis_virginiana 981
## week-Sylvilagus_floridanus 803
## week-Meleagris_gallopavo 717
## week-Sciurus_carolinensis 1454
## week-Vulpes_vulpes 728
## week-Sus_scrofa 1168
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9102
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6755 0.4451 -1.5626 -0.6695 0.1816 1.0226 751
## Tree_Density -0.7806 0.3898 -1.6100 -0.7534 -0.0972 1.0218 475
## Avg_Canopy_Cover 1.1026 0.3542 0.4898 1.0738 1.9036 1.0014 532
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3862 1.4791 0.1075 1.0083 4.7502 1.0419 365
## Tree_Density 0.7516 1.2519 0.0471 0.3547 3.8170 1.0936 249
## Avg_Canopy_Cover 0.7227 0.8723 0.0746 0.4642 2.8827 1.0241 490
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6273 0.6835 0.052 0.4006 2.5701 1.1013 112
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0699 0.3131 -3.7120 -3.0720 -2.4520 1.0035 1349
## shrub_cover 0.2099 0.2848 -0.3630 0.2118 0.7733 1.0075 1107
## veg_height 0.0367 0.1650 -0.2877 0.0387 0.3640 1.0013 984
## week -0.1199 0.1262 -0.3719 -0.1140 0.1101 1.0084 1006
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9399 0.6999 0.2635 0.7861 2.4493 1.0413 758
## shrub_cover 0.6790 0.4839 0.1540 0.5615 1.9783 1.0296 860
## veg_height 0.2060 0.1512 0.0521 0.1658 0.6036 1.0062 1147
## week 0.1034 0.0848 0.0244 0.0798 0.3248 1.0248 827
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0878 0.6125 -1.1214 0.0655 1.3087
## (Intercept)-Sciurus_niger -0.5684 0.8798 -2.1158 -0.6127 1.4246
## (Intercept)-Procyon_lotor 0.3812 0.6484 -0.8855 0.3816 1.7033
## (Intercept)-Dasypus_novemcinctus -0.8989 0.5715 -2.1118 -0.8767 0.1772
## (Intercept)-Lynx_rufus 0.1482 0.9337 -1.3839 0.0244 2.2615
## (Intercept)-Didelphis_virginiana -1.4017 0.6622 -2.7581 -1.3638 -0.1789
## (Intercept)-Sylvilagus_floridanus -0.6849 0.6183 -1.8911 -0.6822 0.5450
## (Intercept)-Meleagris_gallopavo -0.1977 0.8416 -1.6040 -0.2619 1.4598
## (Intercept)-Sciurus_carolinensis -1.4539 0.6845 -2.9307 -1.3950 -0.2098
## (Intercept)-Vulpes_vulpes -1.3521 0.8751 -3.0669 -1.3359 0.3489
## (Intercept)-Sus_scrofa -1.8897 0.8842 -3.7267 -1.8468 -0.3486
## Tree_Density-Canis_latrans -0.9439 0.5668 -2.2801 -0.8706 -0.0361
## Tree_Density-Sciurus_niger -0.7776 0.8649 -2.5047 -0.7469 0.7548
## Tree_Density-Procyon_lotor -0.4987 0.4138 -1.3351 -0.4964 0.3141
## Tree_Density-Dasypus_novemcinctus -1.3474 0.9113 -3.7885 -1.1412 -0.1722
## Tree_Density-Lynx_rufus 0.0624 0.6867 -1.0215 -0.0284 1.7387
## Tree_Density-Didelphis_virginiana -0.9310 0.6867 -2.5188 -0.8392 0.1562
## Tree_Density-Sylvilagus_floridanus -1.0228 0.7167 -2.7120 -0.9227 0.0739
## Tree_Density-Meleagris_gallopavo -1.0250 0.8183 -3.1104 -0.9124 0.1864
## Tree_Density-Sciurus_carolinensis -0.8437 0.6971 -2.4157 -0.7680 0.3376
## Tree_Density-Vulpes_vulpes -0.6671 0.7381 -2.2918 -0.6306 0.7369
## Tree_Density-Sus_scrofa -0.8880 0.7989 -2.8831 -0.7865 0.4075
## Avg_Canopy_Cover-Canis_latrans 0.0466 0.4637 -0.8580 0.0491 0.9193
## Avg_Canopy_Cover-Sciurus_niger 0.9762 0.7975 -0.4304 0.9135 2.8055
## Avg_Canopy_Cover-Procyon_lotor 1.0379 0.4552 0.2134 1.0149 2.0179
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0874 0.4567 0.2715 1.0581 2.0920
## Avg_Canopy_Cover-Lynx_rufus 0.8068 0.6718 -0.4482 0.7890 2.1790
## Avg_Canopy_Cover-Didelphis_virginiana 1.4746 0.6327 0.4682 1.3968 2.8670
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8737 0.8630 0.6614 1.7003 3.9053
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4209 0.7004 0.3038 1.3330 3.0601
## Avg_Canopy_Cover-Sciurus_carolinensis 1.3835 0.5910 0.4365 1.3053 2.7743
## Avg_Canopy_Cover-Vulpes_vulpes 1.1156 0.6437 0.0510 1.0595 2.6132
## Avg_Canopy_Cover-Sus_scrofa 1.2269 0.5653 0.2868 1.1763 2.5309
## Rhat ESS
## (Intercept)-Canis_latrans 1.0315 589
## (Intercept)-Sciurus_niger 1.0238 321
## (Intercept)-Procyon_lotor 1.0327 412
## (Intercept)-Dasypus_novemcinctus 1.0060 1137
## (Intercept)-Lynx_rufus 1.0220 277
## (Intercept)-Didelphis_virginiana 1.0063 739
## (Intercept)-Sylvilagus_floridanus 1.0085 894
## (Intercept)-Meleagris_gallopavo 1.0769 384
## (Intercept)-Sciurus_carolinensis 1.0203 483
## (Intercept)-Vulpes_vulpes 1.0063 338
## (Intercept)-Sus_scrofa 1.0385 390
## Tree_Density-Canis_latrans 1.0142 909
## Tree_Density-Sciurus_niger 1.0358 286
## Tree_Density-Procyon_lotor 1.0068 1871
## Tree_Density-Dasypus_novemcinctus 1.0668 436
## Tree_Density-Lynx_rufus 1.0566 558
## Tree_Density-Didelphis_virginiana 1.0260 545
## Tree_Density-Sylvilagus_floridanus 1.0456 563
## Tree_Density-Meleagris_gallopavo 1.0349 566
## Tree_Density-Sciurus_carolinensis 1.0229 782
## Tree_Density-Vulpes_vulpes 1.0141 735
## Tree_Density-Sus_scrofa 1.0190 594
## Avg_Canopy_Cover-Canis_latrans 1.0072 852
## Avg_Canopy_Cover-Sciurus_niger 1.0013 420
## Avg_Canopy_Cover-Procyon_lotor 1.0061 1165
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0064 1522
## Avg_Canopy_Cover-Lynx_rufus 1.0416 586
## Avg_Canopy_Cover-Didelphis_virginiana 1.0076 442
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0018 417
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0198 535
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0000 835
## Avg_Canopy_Cover-Vulpes_vulpes 1.0151 632
## Avg_Canopy_Cover-Sus_scrofa 1.0029 1094
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7686 0.1869 -3.1560 -2.7626 -2.4304 1.0017
## (Intercept)-Sciurus_niger -4.1561 0.5523 -5.1989 -4.1662 -3.0765 1.0306
## (Intercept)-Procyon_lotor -2.3052 0.1412 -2.5977 -2.3021 -2.0440 1.0178
## (Intercept)-Dasypus_novemcinctus -1.7820 0.1668 -2.1219 -1.7762 -1.4639 1.0106
## (Intercept)-Lynx_rufus -3.7688 0.3432 -4.4402 -3.7746 -3.1043 1.0026
## (Intercept)-Didelphis_virginiana -2.6802 0.3003 -3.2827 -2.6735 -2.1263 1.0070
## (Intercept)-Sylvilagus_floridanus -3.1577 0.2697 -3.7244 -3.1454 -2.6606 1.0128
## (Intercept)-Meleagris_gallopavo -3.9812 0.4211 -4.7888 -3.9701 -3.1670 1.0363
## (Intercept)-Sciurus_carolinensis -2.7380 0.3078 -3.3676 -2.7315 -2.1645 1.0070
## (Intercept)-Vulpes_vulpes -4.1097 0.6282 -5.3340 -4.1109 -2.8971 1.0122
## (Intercept)-Sus_scrofa -3.3102 0.5592 -4.2940 -3.3306 -2.1849 1.0238
## shrub_cover-Canis_latrans -0.2905 0.2227 -0.7343 -0.2861 0.1398 1.0058
## shrub_cover-Sciurus_niger -0.3558 0.4542 -1.2747 -0.3460 0.5131 1.0097
## shrub_cover-Procyon_lotor 0.2609 0.1592 -0.0496 0.2612 0.5743 1.0056
## shrub_cover-Dasypus_novemcinctus 0.8751 0.3007 0.3042 0.8728 1.4761 1.0106
## shrub_cover-Lynx_rufus -0.3078 0.3314 -0.9680 -0.3057 0.3178 1.0101
## shrub_cover-Didelphis_virginiana 1.0312 0.3822 0.3528 1.0151 1.8265 1.0061
## shrub_cover-Sylvilagus_floridanus 0.4153 0.3992 -0.3423 0.3952 1.1974 1.0239
## shrub_cover-Meleagris_gallopavo -0.7218 0.3864 -1.5162 -0.7052 0.0048 1.0388
## shrub_cover-Sciurus_carolinensis 0.9150 0.4039 0.1514 0.9134 1.7319 1.0271
## shrub_cover-Vulpes_vulpes -0.0824 0.6107 -1.3704 -0.0607 1.0743 1.0151
## shrub_cover-Sus_scrofa 0.6314 0.7849 -0.9779 0.6332 2.1910 1.0254
## veg_height-Canis_latrans -0.5779 0.1915 -0.9574 -0.5754 -0.2130 1.0242
## veg_height-Sciurus_niger -0.0213 0.3591 -0.7500 -0.0110 0.6836 1.0017
## veg_height-Procyon_lotor 0.3413 0.1215 0.1051 0.3395 0.5825 1.0041
## veg_height-Dasypus_novemcinctus 0.2559 0.1357 -0.0001 0.2507 0.5323 1.0051
## veg_height-Lynx_rufus 0.0750 0.2378 -0.4145 0.0807 0.5321 1.0070
## veg_height-Didelphis_virginiana 0.4824 0.2473 0.0303 0.4683 0.9865 1.0196
## veg_height-Sylvilagus_floridanus 0.1596 0.2395 -0.3274 0.1650 0.6090 1.0017
## veg_height-Meleagris_gallopavo -0.2073 0.3474 -0.9469 -0.1927 0.4559 0.9997
## veg_height-Sciurus_carolinensis 0.1147 0.2126 -0.2921 0.1085 0.5456 1.0034
## veg_height-Vulpes_vulpes -0.1224 0.3166 -0.7852 -0.1017 0.4562 1.0084
## veg_height-Sus_scrofa -0.0844 0.3322 -0.7732 -0.0820 0.5380 1.0163
## week-Canis_latrans 0.0573 0.1321 -0.2005 0.0555 0.3146 1.0068
## week-Sciurus_niger -0.3396 0.2886 -0.9808 -0.3084 0.1435 1.0160
## week-Procyon_lotor -0.0651 0.1200 -0.3014 -0.0616 0.1616 1.0024
## week-Dasypus_novemcinctus -0.1822 0.1380 -0.4668 -0.1767 0.0706 1.0005
## week-Lynx_rufus -0.0643 0.1914 -0.4481 -0.0620 0.2985 1.0010
## week-Didelphis_virginiana -0.2396 0.2162 -0.7026 -0.2173 0.1525 1.0197
## week-Sylvilagus_floridanus -0.1853 0.2107 -0.6360 -0.1730 0.1908 1.0068
## week-Meleagris_gallopavo -0.3064 0.2481 -0.8759 -0.2847 0.1169 1.0167
## week-Sciurus_carolinensis 0.1143 0.1861 -0.2399 0.1119 0.4798 1.0055
## week-Vulpes_vulpes -0.1654 0.2644 -0.7421 -0.1556 0.3202 1.0008
## week-Sus_scrofa 0.0644 0.2368 -0.4014 0.0670 0.5332 1.0005
## ESS
## (Intercept)-Canis_latrans 742
## (Intercept)-Sciurus_niger 159
## (Intercept)-Procyon_lotor 1365
## (Intercept)-Dasypus_novemcinctus 1576
## (Intercept)-Lynx_rufus 314
## (Intercept)-Didelphis_virginiana 689
## (Intercept)-Sylvilagus_floridanus 611
## (Intercept)-Meleagris_gallopavo 257
## (Intercept)-Sciurus_carolinensis 704
## (Intercept)-Vulpes_vulpes 252
## (Intercept)-Sus_scrofa 538
## shrub_cover-Canis_latrans 854
## shrub_cover-Sciurus_niger 356
## shrub_cover-Procyon_lotor 1673
## shrub_cover-Dasypus_novemcinctus 1416
## shrub_cover-Lynx_rufus 491
## shrub_cover-Didelphis_virginiana 715
## shrub_cover-Sylvilagus_floridanus 612
## shrub_cover-Meleagris_gallopavo 319
## shrub_cover-Sciurus_carolinensis 699
## shrub_cover-Vulpes_vulpes 436
## shrub_cover-Sus_scrofa 644
## veg_height-Canis_latrans 730
## veg_height-Sciurus_niger 692
## veg_height-Procyon_lotor 1561
## veg_height-Dasypus_novemcinctus 1998
## veg_height-Lynx_rufus 775
## veg_height-Didelphis_virginiana 962
## veg_height-Sylvilagus_floridanus 1043
## veg_height-Meleagris_gallopavo 590
## veg_height-Sciurus_carolinensis 986
## veg_height-Vulpes_vulpes 653
## veg_height-Sus_scrofa 1298
## week-Canis_latrans 1507
## week-Sciurus_niger 641
## week-Procyon_lotor 1513
## week-Dasypus_novemcinctus 1695
## week-Lynx_rufus 1234
## week-Didelphis_virginiana 1078
## week-Sylvilagus_floridanus 859
## week-Meleagris_gallopavo 594
## week-Sciurus_carolinensis 1552
## week-Vulpes_vulpes 1052
## week-Sus_scrofa 1648
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.976
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5090 0.4156 -1.2925 -0.5241 0.3516 1.0388 163
## Cogon_Patch_Size -0.1511 0.3674 -0.9420 -0.1321 0.5137 1.0218 446
## Avg_Cogongrass_Cover 0.0529 0.3421 -0.6600 0.0646 0.6967 1.0097 429
## total_shrub_cover -0.8587 0.4177 -1.7483 -0.8191 -0.1181 1.0043 187
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5626 0.6932 0.0533 0.3516 2.2952 1.0214 424
## Cogon_Patch_Size 0.6514 0.9930 0.0509 0.3934 2.7889 1.1211 433
## Avg_Cogongrass_Cover 0.4303 0.5234 0.0412 0.2645 1.7471 1.0152 582
## total_shrub_cover 0.6139 0.6752 0.0584 0.4050 2.3380 1.0293 393
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.686 1.36 0.1543 1.3378 5.2326 1.0234 171
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0738 0.3059 -3.6720 -3.0659 -2.4868 1.0087 586
## shrub_cover 0.4989 0.3006 -0.0864 0.4943 1.1053 1.0065 622
## veg_height 0.0403 0.1755 -0.3061 0.0387 0.3841 1.0182 643
## week -0.1233 0.1259 -0.3853 -0.1176 0.1156 1.0094 871
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7816 0.5413 0.1969 0.6490 2.2236 1.0080 364
## shrub_cover 0.6761 0.5492 0.1208 0.5404 1.9655 1.0224 415
## veg_height 0.2102 0.1565 0.0513 0.1697 0.6255 1.0068 1440
## week 0.1010 0.0782 0.0268 0.0793 0.3046 1.0089 824
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0370 0.6094 -1.1403 -0.0826
## (Intercept)-Sciurus_niger -0.6817 0.7114 -2.1342 -0.6848
## (Intercept)-Procyon_lotor 0.0529 0.6763 -1.1427 0.0024
## (Intercept)-Dasypus_novemcinctus -0.5369 0.5868 -1.7136 -0.5431
## (Intercept)-Lynx_rufus -0.4074 0.6544 -1.6384 -0.4333
## (Intercept)-Didelphis_virginiana -0.7447 0.6290 -2.0218 -0.7372
## (Intercept)-Sylvilagus_floridanus -0.3580 0.6457 -1.6271 -0.3766
## (Intercept)-Meleagris_gallopavo -0.5531 0.6659 -1.8664 -0.5710
## (Intercept)-Sciurus_carolinensis -0.8114 0.6832 -2.2399 -0.7789
## (Intercept)-Vulpes_vulpes -0.7841 0.7651 -2.4067 -0.7512
## (Intercept)-Sus_scrofa -0.9105 0.7867 -2.6849 -0.8627
## Cogon_Patch_Size-Canis_latrans 0.5796 0.6196 -0.3362 0.4834
## Cogon_Patch_Size-Sciurus_niger -0.4337 0.7494 -2.2714 -0.3416
## Cogon_Patch_Size-Procyon_lotor -0.2160 0.4561 -1.1353 -0.2095
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0343 0.4389 -0.9267 -0.0432
## Cogon_Patch_Size-Lynx_rufus -0.1574 0.6438 -1.4387 -0.1546
## Cogon_Patch_Size-Didelphis_virginiana 0.5207 0.5053 -0.3677 0.4891
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6959 0.7869 -2.6122 -0.5553
## Cogon_Patch_Size-Meleagris_gallopavo -0.0299 0.6496 -1.2696 -0.0344
## Cogon_Patch_Size-Sciurus_carolinensis -0.5187 0.6672 -2.1032 -0.4213
## Cogon_Patch_Size-Vulpes_vulpes -0.3742 0.7419 -2.1651 -0.2860
## Cogon_Patch_Size-Sus_scrofa -0.2900 0.7082 -1.9184 -0.2190
## Avg_Cogongrass_Cover-Canis_latrans 0.2887 0.4476 -0.5145 0.2598
## Avg_Cogongrass_Cover-Sciurus_niger -0.3638 0.7104 -2.0187 -0.2863
## Avg_Cogongrass_Cover-Procyon_lotor 0.0601 0.4430 -0.8539 0.0655
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3100 0.4235 -0.5263 0.3019
## Avg_Cogongrass_Cover-Lynx_rufus 0.4349 0.5283 -0.4894 0.3970
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0910 0.4736 -0.8795 0.0957
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2768 0.5655 -1.5712 -0.2372
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3937 0.6957 -1.9578 -0.3234
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3172 0.4667 -0.5949 0.3044
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2550 0.5205 -0.7881 0.2507
## Avg_Cogongrass_Cover-Sus_scrofa -0.1229 0.6580 -1.6701 -0.0581
## total_shrub_cover-Canis_latrans -0.0438 0.5466 -1.0201 -0.0832
## total_shrub_cover-Sciurus_niger -0.8948 0.6880 -2.3411 -0.8576
## total_shrub_cover-Procyon_lotor -1.1327 0.5543 -2.3557 -1.0624
## total_shrub_cover-Dasypus_novemcinctus -0.5035 0.5469 -1.7589 -0.4584
## total_shrub_cover-Lynx_rufus -1.1588 0.7511 -2.8528 -1.0840
## total_shrub_cover-Didelphis_virginiana -0.8905 0.6223 -2.2588 -0.8195
## total_shrub_cover-Sylvilagus_floridanus -1.0568 0.7143 -2.6464 -0.9714
## total_shrub_cover-Meleagris_gallopavo -1.3484 0.7018 -2.9587 -1.2593
## total_shrub_cover-Sciurus_carolinensis -0.8481 0.6818 -2.3820 -0.7752
## total_shrub_cover-Vulpes_vulpes -1.0216 0.8109 -2.8768 -0.9431
## total_shrub_cover-Sus_scrofa -0.7099 0.7567 -2.3454 -0.6798
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2387 1.0234 419
## (Intercept)-Sciurus_niger 0.7549 1.0082 324
## (Intercept)-Procyon_lotor 1.4635 1.0307 426
## (Intercept)-Dasypus_novemcinctus 0.6346 1.0147 451
## (Intercept)-Lynx_rufus 0.9634 1.0104 434
## (Intercept)-Didelphis_virginiana 0.4740 1.0160 264
## (Intercept)-Sylvilagus_floridanus 1.0058 1.0376 463
## (Intercept)-Meleagris_gallopavo 0.7940 1.0468 368
## (Intercept)-Sciurus_carolinensis 0.4686 1.0110 317
## (Intercept)-Vulpes_vulpes 0.6976 1.0032 282
## (Intercept)-Sus_scrofa 0.4565 1.0129 273
## Cogon_Patch_Size-Canis_latrans 2.0366 1.0072 663
## Cogon_Patch_Size-Sciurus_niger 0.7658 1.0083 724
## Cogon_Patch_Size-Procyon_lotor 0.6655 1.0023 995
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8236 1.0065 1332
## Cogon_Patch_Size-Lynx_rufus 1.2218 1.0056 802
## Cogon_Patch_Size-Didelphis_virginiana 1.5933 1.0100 983
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4316 1.0096 462
## Cogon_Patch_Size-Meleagris_gallopavo 1.4120 1.0146 853
## Cogon_Patch_Size-Sciurus_carolinensis 0.5261 1.0051 674
## Cogon_Patch_Size-Vulpes_vulpes 0.8595 1.0223 606
## Cogon_Patch_Size-Sus_scrofa 0.9157 1.0236 888
## Avg_Cogongrass_Cover-Canis_latrans 1.2795 1.0077 1045
## Avg_Cogongrass_Cover-Sciurus_niger 0.8042 1.0126 416
## Avg_Cogongrass_Cover-Procyon_lotor 0.9489 1.0008 1003
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1638 1.0017 1170
## Avg_Cogongrass_Cover-Lynx_rufus 1.6689 1.0002 991
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0153 1.0063 991
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7462 1.0114 548
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7998 1.0078 454
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2656 1.0088 700
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3255 1.0010 980
## Avg_Cogongrass_Cover-Sus_scrofa 0.9992 1.0059 507
## total_shrub_cover-Canis_latrans 1.1358 1.0068 612
## total_shrub_cover-Sciurus_niger 0.4178 1.0231 381
## total_shrub_cover-Procyon_lotor -0.2168 1.0026 421
## total_shrub_cover-Dasypus_novemcinctus 0.4896 1.0076 586
## total_shrub_cover-Lynx_rufus 0.1694 1.0036 306
## total_shrub_cover-Didelphis_virginiana 0.1688 1.0023 305
## total_shrub_cover-Sylvilagus_floridanus 0.1495 1.0030 383
## total_shrub_cover-Meleagris_gallopavo -0.2196 1.0048 367
## total_shrub_cover-Sciurus_carolinensis 0.2985 1.0090 233
## total_shrub_cover-Vulpes_vulpes 0.3832 1.0064 322
## total_shrub_cover-Sus_scrofa 0.7116 1.0045 271
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7561 0.1884 -3.1458 -2.7468 -2.4088 1.0180
## (Intercept)-Sciurus_niger -3.8618 0.5818 -5.1012 -3.8278 -2.7922 1.0175
## (Intercept)-Procyon_lotor -2.3201 0.1412 -2.6171 -2.3157 -2.0533 1.0027
## (Intercept)-Dasypus_novemcinctus -1.8454 0.1780 -2.2004 -1.8394 -1.5165 1.0159
## (Intercept)-Lynx_rufus -3.5253 0.3398 -4.2432 -3.5007 -2.9088 1.0429
## (Intercept)-Didelphis_virginiana -2.7517 0.3247 -3.4267 -2.7334 -2.1649 1.0104
## (Intercept)-Sylvilagus_floridanus -3.2853 0.2638 -3.8196 -3.2783 -2.7897 1.0012
## (Intercept)-Meleagris_gallopavo -3.6762 0.4863 -4.6368 -3.6758 -2.7545 1.0390
## (Intercept)-Sciurus_carolinensis -2.8824 0.3707 -3.6647 -2.8649 -2.2073 1.0090
## (Intercept)-Vulpes_vulpes -4.0813 0.6177 -5.2987 -4.0494 -2.9312 1.0132
## (Intercept)-Sus_scrofa -3.6361 0.5279 -4.6902 -3.6366 -2.5766 1.0024
## shrub_cover-Canis_latrans -0.2525 0.2319 -0.7108 -0.2474 0.1993 1.0035
## shrub_cover-Sciurus_niger 0.0048 0.5345 -1.0281 0.0176 1.0075 1.0044
## shrub_cover-Procyon_lotor 0.3213 0.1592 0.0133 0.3247 0.6317 1.0005
## shrub_cover-Dasypus_novemcinctus 1.0542 0.3408 0.4110 1.0517 1.7020 1.0364
## shrub_cover-Lynx_rufus 0.1238 0.3733 -0.6679 0.1428 0.7945 1.0273
## shrub_cover-Didelphis_virginiana 1.1983 0.4242 0.4388 1.1750 2.0897 1.0069
## shrub_cover-Sylvilagus_floridanus 0.7220 0.4013 -0.0932 0.7359 1.5006 1.0204
## shrub_cover-Meleagris_gallopavo -0.3643 0.4573 -1.2734 -0.3630 0.4997 1.0514
## shrub_cover-Sciurus_carolinensis 1.1566 0.4462 0.3068 1.1448 2.0438 1.0042
## shrub_cover-Vulpes_vulpes 0.4566 0.6324 -0.8495 0.4682 1.6834 1.0029
## shrub_cover-Sus_scrofa 1.1298 0.7526 -0.3318 1.1035 2.6837 1.0116
## veg_height-Canis_latrans -0.5679 0.1905 -0.9628 -0.5611 -0.2067 1.0114
## veg_height-Sciurus_niger 0.0738 0.4249 -0.7123 0.0595 0.9669 1.0107
## veg_height-Procyon_lotor 0.3498 0.1239 0.1105 0.3502 0.5869 1.0006
## veg_height-Dasypus_novemcinctus 0.2758 0.1410 -0.0034 0.2776 0.5581 1.0114
## veg_height-Lynx_rufus 0.0413 0.2434 -0.4680 0.0534 0.4873 1.0056
## veg_height-Didelphis_virginiana 0.4376 0.2486 -0.0114 0.4301 0.9444 1.0244
## veg_height-Sylvilagus_floridanus 0.0902 0.2406 -0.3697 0.0942 0.5597 1.0276
## veg_height-Meleagris_gallopavo -0.1770 0.4058 -0.9785 -0.1813 0.6524 1.0080
## veg_height-Sciurus_carolinensis 0.1407 0.2300 -0.2948 0.1312 0.6260 1.0030
## veg_height-Vulpes_vulpes -0.1094 0.3347 -0.7868 -0.0958 0.4960 1.0114
## veg_height-Sus_scrofa -0.1409 0.3270 -0.7973 -0.1304 0.4833 1.0087
## week-Canis_latrans 0.0533 0.1352 -0.2231 0.0550 0.3165 1.0081
## week-Sciurus_niger -0.3518 0.2941 -1.0102 -0.3213 0.1623 1.0071
## week-Procyon_lotor -0.0666 0.1173 -0.3053 -0.0631 0.1592 1.0040
## week-Dasypus_novemcinctus -0.1814 0.1373 -0.4662 -0.1788 0.0718 0.9999
## week-Lynx_rufus -0.0628 0.2034 -0.4857 -0.0596 0.3215 1.0038
## week-Didelphis_virginiana -0.2424 0.2179 -0.7064 -0.2298 0.1492 1.0013
## week-Sylvilagus_floridanus -0.1878 0.2084 -0.6356 -0.1767 0.2052 1.0026
## week-Meleagris_gallopavo -0.3040 0.2382 -0.8017 -0.2912 0.1086 1.0031
## week-Sciurus_carolinensis 0.1034 0.1794 -0.2601 0.1030 0.4542 1.0040
## week-Vulpes_vulpes -0.1681 0.2797 -0.7684 -0.1523 0.3475 1.0020
## week-Sus_scrofa 0.0437 0.2370 -0.4262 0.0432 0.4991 1.0121
## ESS
## (Intercept)-Canis_latrans 676
## (Intercept)-Sciurus_niger 168
## (Intercept)-Procyon_lotor 1357
## (Intercept)-Dasypus_novemcinctus 875
## (Intercept)-Lynx_rufus 282
## (Intercept)-Didelphis_virginiana 392
## (Intercept)-Sylvilagus_floridanus 580
## (Intercept)-Meleagris_gallopavo 276
## (Intercept)-Sciurus_carolinensis 252
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 332
## shrub_cover-Canis_latrans 764
## shrub_cover-Sciurus_niger 263
## shrub_cover-Procyon_lotor 1570
## shrub_cover-Dasypus_novemcinctus 541
## shrub_cover-Lynx_rufus 364
## shrub_cover-Didelphis_virginiana 300
## shrub_cover-Sylvilagus_floridanus 419
## shrub_cover-Meleagris_gallopavo 371
## shrub_cover-Sciurus_carolinensis 224
## shrub_cover-Vulpes_vulpes 331
## shrub_cover-Sus_scrofa 264
## veg_height-Canis_latrans 742
## veg_height-Sciurus_niger 572
## veg_height-Procyon_lotor 1483
## veg_height-Dasypus_novemcinctus 1624
## veg_height-Lynx_rufus 718
## veg_height-Didelphis_virginiana 934
## veg_height-Sylvilagus_floridanus 623
## veg_height-Meleagris_gallopavo 436
## veg_height-Sciurus_carolinensis 742
## veg_height-Vulpes_vulpes 541
## veg_height-Sus_scrofa 683
## week-Canis_latrans 1544
## week-Sciurus_niger 542
## week-Procyon_lotor 1737
## week-Dasypus_novemcinctus 1566
## week-Lynx_rufus 904
## week-Didelphis_virginiana 1325
## week-Sylvilagus_floridanus 925
## week-Meleagris_gallopavo 716
## week-Sciurus_carolinensis 1460
## week-Vulpes_vulpes 750
## week-Sus_scrofa 1323
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9075
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6037 0.3887 -1.3227 -0.6116 0.2038 1.0537 322
## Veg_shannon_index 0.3612 0.2655 -0.1328 0.3541 0.8885 1.0099 713
## Avg_Cogongrass_Cover 0.2321 0.2682 -0.2964 0.2335 0.7474 1.0016 604
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7288 0.9120 0.0599 0.4779 2.9068 1.0901 157
## Veg_shannon_index 0.2611 0.3496 0.0372 0.1722 0.9788 1.1169 913
## Avg_Cogongrass_Cover 0.3205 0.3445 0.0385 0.2120 1.2634 1.0105 726
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0332 0.9323 0.0901 0.7627 3.5793 1.0582 181
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0507 0.3219 -3.7204 -3.0498 -2.4145 1.0318 962
## shrub_cover 0.1776 0.2811 -0.3662 0.1688 0.7543 1.0014 862
## veg_height 0.0137 0.1669 -0.3162 0.0173 0.3518 1.0129 862
## week -0.1173 0.1276 -0.3908 -0.1120 0.1312 1.0105 995
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9885 0.7314 0.2957 0.8068 2.7571 1.0719 524
## shrub_cover 0.6800 0.5206 0.1617 0.5430 2.0474 1.0205 443
## veg_height 0.1983 0.1394 0.0531 0.1600 0.5705 1.0020 1155
## week 0.1038 0.0868 0.0241 0.0792 0.3161 1.0297 1046
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1029 0.5705 -1.1625 -0.1121
## (Intercept)-Sciurus_niger -0.6310 0.7658 -1.9518 -0.6655
## (Intercept)-Procyon_lotor 0.0112 0.6096 -1.1450 -0.0070
## (Intercept)-Dasypus_novemcinctus -0.6762 0.5032 -1.6984 -0.6691
## (Intercept)-Lynx_rufus -0.3206 0.6672 -1.4810 -0.3798
## (Intercept)-Didelphis_virginiana -1.0344 0.5651 -2.2212 -1.0058
## (Intercept)-Sylvilagus_floridanus -0.5375 0.5724 -1.6075 -0.5527
## (Intercept)-Meleagris_gallopavo -0.2683 0.7540 -1.5273 -0.3391
## (Intercept)-Sciurus_carolinensis -1.0678 0.5832 -2.3077 -1.0290
## (Intercept)-Vulpes_vulpes -0.8648 0.9284 -2.3969 -0.9094
## (Intercept)-Sus_scrofa -1.3303 0.7119 -2.9239 -1.2641
## Veg_shannon_index-Canis_latrans 0.6229 0.3865 -0.0802 0.6056
## Veg_shannon_index-Sciurus_niger 0.3780 0.4819 -0.5769 0.3694
## Veg_shannon_index-Procyon_lotor 0.4584 0.3651 -0.2247 0.4474
## Veg_shannon_index-Dasypus_novemcinctus 0.1892 0.3436 -0.5043 0.1838
## Veg_shannon_index-Lynx_rufus 0.2248 0.4545 -0.7185 0.2464
## Veg_shannon_index-Didelphis_virginiana 0.4808 0.3861 -0.2294 0.4583
## Veg_shannon_index-Sylvilagus_floridanus 0.4205 0.4112 -0.3987 0.4094
## Veg_shannon_index-Meleagris_gallopavo 0.5169 0.4759 -0.3490 0.4701
## Veg_shannon_index-Sciurus_carolinensis 0.0023 0.4158 -0.9147 0.0277
## Veg_shannon_index-Vulpes_vulpes 0.1276 0.4587 -0.8465 0.1475
## Veg_shannon_index-Sus_scrofa 0.5942 0.4801 -0.2444 0.5600
## Avg_Cogongrass_Cover-Canis_latrans 0.5258 0.3909 -0.1531 0.4998
## Avg_Cogongrass_Cover-Sciurus_niger -0.1373 0.5904 -1.5062 -0.0736
## Avg_Cogongrass_Cover-Procyon_lotor 0.3107 0.3692 -0.3843 0.2974
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4102 0.3455 -0.2244 0.3936
## Avg_Cogongrass_Cover-Lynx_rufus 0.5334 0.4255 -0.2260 0.5045
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3999 0.3819 -0.3626 0.3810
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1259 0.4471 -1.0823 -0.0991
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1021 0.5811 -1.4174 -0.0643
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3776 0.3828 -0.3470 0.3604
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3519 0.4807 -0.5519 0.3404
## Avg_Cogongrass_Cover-Sus_scrofa 0.0214 0.5084 -1.1034 0.0609
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0806 1.0034 488
## (Intercept)-Sciurus_niger 0.9466 1.0835 254
## (Intercept)-Procyon_lotor 1.2632 1.0069 401
## (Intercept)-Dasypus_novemcinctus 0.2738 1.0120 782
## (Intercept)-Lynx_rufus 1.2324 1.0100 460
## (Intercept)-Didelphis_virginiana -0.0049 1.0124 751
## (Intercept)-Sylvilagus_floridanus 0.6636 1.0007 840
## (Intercept)-Meleagris_gallopavo 1.4184 1.0336 259
## (Intercept)-Sciurus_carolinensis -0.0166 1.0112 802
## (Intercept)-Vulpes_vulpes 1.1792 1.3842 131
## (Intercept)-Sus_scrofa -0.1028 1.0134 483
## Veg_shannon_index-Canis_latrans 1.4258 1.0012 1188
## Veg_shannon_index-Sciurus_niger 1.3594 1.0029 1074
## Veg_shannon_index-Procyon_lotor 1.2171 1.0001 1464
## Veg_shannon_index-Dasypus_novemcinctus 0.8693 0.9999 1412
## Veg_shannon_index-Lynx_rufus 1.0629 1.0110 1083
## Veg_shannon_index-Didelphis_virginiana 1.2985 1.0036 1504
## Veg_shannon_index-Sylvilagus_floridanus 1.2725 1.0036 1063
## Veg_shannon_index-Meleagris_gallopavo 1.5758 1.0045 1009
## Veg_shannon_index-Sciurus_carolinensis 0.7417 1.0113 1038
## Veg_shannon_index-Vulpes_vulpes 0.9829 1.0058 788
## Veg_shannon_index-Sus_scrofa 1.6683 1.0008 970
## Avg_Cogongrass_Cover-Canis_latrans 1.3755 1.0030 1500
## Avg_Cogongrass_Cover-Sciurus_niger 0.8750 1.0047 707
## Avg_Cogongrass_Cover-Procyon_lotor 1.0779 1.0003 1768
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1320 1.0009 1478
## Avg_Cogongrass_Cover-Lynx_rufus 1.4638 1.0017 1125
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1734 1.0007 1031
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6823 1.0015 974
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9162 1.0016 509
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1568 1.0017 1463
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.4249 1.0008 908
## Avg_Cogongrass_Cover-Sus_scrofa 0.9186 1.0019 917
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7364 0.1809 -3.1056 -2.7277 -2.3969 1.0002
## (Intercept)-Sciurus_niger -4.0802 0.5542 -5.1139 -4.0978 -2.9682 1.0829
## (Intercept)-Procyon_lotor -2.3114 0.1439 -2.5998 -2.3067 -2.0435 1.0032
## (Intercept)-Dasypus_novemcinctus -1.7659 0.1599 -2.0876 -1.7614 -1.4520 1.0020
## (Intercept)-Lynx_rufus -3.6385 0.3340 -4.2940 -3.6376 -2.9982 1.0307
## (Intercept)-Didelphis_virginiana -2.6467 0.2877 -3.2301 -2.6445 -2.1111 1.0046
## (Intercept)-Sylvilagus_floridanus -3.2208 0.2922 -3.8380 -3.2024 -2.6892 1.0091
## (Intercept)-Meleagris_gallopavo -4.0315 0.4783 -4.9641 -4.0339 -3.0893 1.1147
## (Intercept)-Sciurus_carolinensis -2.6861 0.3255 -3.3632 -2.6698 -2.0841 1.0246
## (Intercept)-Vulpes_vulpes -4.1852 0.7067 -5.7573 -4.1539 -2.9598 1.1722
## (Intercept)-Sus_scrofa -3.3655 0.5242 -4.3857 -3.3769 -2.2760 1.0434
## shrub_cover-Canis_latrans -0.2948 0.2188 -0.7226 -0.2942 0.1434 1.0000
## shrub_cover-Sciurus_niger -0.4181 0.4516 -1.2824 -0.4299 0.5003 1.0189
## shrub_cover-Procyon_lotor 0.2261 0.1651 -0.0916 0.2288 0.5404 1.0058
## shrub_cover-Dasypus_novemcinctus 0.8578 0.3049 0.2883 0.8521 1.4601 1.0140
## shrub_cover-Lynx_rufus -0.2741 0.3620 -0.9702 -0.2777 0.4622 1.0480
## shrub_cover-Didelphis_virginiana 1.0012 0.3967 0.2579 0.9865 1.8074 1.0089
## shrub_cover-Sylvilagus_floridanus 0.2392 0.4129 -0.5321 0.2294 1.0681 1.0002
## shrub_cover-Meleagris_gallopavo -0.7514 0.4140 -1.5746 -0.7537 0.0784 1.0690
## shrub_cover-Sciurus_carolinensis 0.8493 0.4209 0.0416 0.8281 1.7176 1.0224
## shrub_cover-Vulpes_vulpes -0.1372 0.5932 -1.3025 -0.1305 1.0616 1.0113
## shrub_cover-Sus_scrofa 0.6379 0.7888 -0.9802 0.6540 2.1754 1.0560
## veg_height-Canis_latrans -0.5687 0.1812 -0.9313 -0.5644 -0.2282 1.0062
## veg_height-Sciurus_niger -0.0290 0.4104 -0.8658 -0.0338 0.7744 1.0013
## veg_height-Procyon_lotor 0.3345 0.1223 0.0944 0.3347 0.5792 1.0144
## veg_height-Dasypus_novemcinctus 0.2440 0.1331 -0.0107 0.2416 0.5164 1.0005
## veg_height-Lynx_rufus 0.0081 0.2437 -0.4849 0.0185 0.4662 1.0041
## veg_height-Didelphis_virginiana 0.4212 0.2372 -0.0237 0.4179 0.8920 1.0023
## veg_height-Sylvilagus_floridanus 0.1345 0.2404 -0.3239 0.1308 0.6077 1.0012
## veg_height-Meleagris_gallopavo -0.2028 0.3682 -0.9616 -0.1892 0.5067 1.0087
## veg_height-Sciurus_carolinensis 0.0857 0.2153 -0.3208 0.0809 0.5223 1.0071
## veg_height-Vulpes_vulpes -0.1205 0.3088 -0.7598 -0.1085 0.4605 1.0044
## veg_height-Sus_scrofa -0.1434 0.3328 -0.8553 -0.1302 0.4844 1.0203
## week-Canis_latrans 0.0605 0.1333 -0.2091 0.0637 0.3146 1.0006
## week-Sciurus_niger -0.3282 0.2963 -1.0140 -0.2979 0.1541 1.0027
## week-Procyon_lotor -0.0587 0.1207 -0.3015 -0.0539 0.1672 1.0048
## week-Dasypus_novemcinctus -0.1835 0.1387 -0.4804 -0.1797 0.0660 1.0048
## week-Lynx_rufus -0.0513 0.1926 -0.4621 -0.0418 0.2983 1.0033
## week-Didelphis_virginiana -0.2418 0.2171 -0.7144 -0.2259 0.1383 1.0217
## week-Sylvilagus_floridanus -0.1866 0.2087 -0.6383 -0.1730 0.1925 1.0454
## week-Meleagris_gallopavo -0.3013 0.2503 -0.8871 -0.2757 0.1266 1.0041
## week-Sciurus_carolinensis 0.1187 0.1840 -0.2582 0.1217 0.4691 1.0067
## week-Vulpes_vulpes -0.1527 0.2826 -0.7537 -0.1390 0.3513 1.0083
## week-Sus_scrofa 0.0503 0.2399 -0.4159 0.0509 0.5249 1.0003
## ESS
## (Intercept)-Canis_latrans 753
## (Intercept)-Sciurus_niger 208
## (Intercept)-Procyon_lotor 1238
## (Intercept)-Dasypus_novemcinctus 1604
## (Intercept)-Lynx_rufus 360
## (Intercept)-Didelphis_virginiana 777
## (Intercept)-Sylvilagus_floridanus 509
## (Intercept)-Meleagris_gallopavo 171
## (Intercept)-Sciurus_carolinensis 757
## (Intercept)-Vulpes_vulpes 135
## (Intercept)-Sus_scrofa 552
## shrub_cover-Canis_latrans 951
## shrub_cover-Sciurus_niger 392
## shrub_cover-Procyon_lotor 1146
## shrub_cover-Dasypus_novemcinctus 1277
## shrub_cover-Lynx_rufus 416
## shrub_cover-Didelphis_virginiana 579
## shrub_cover-Sylvilagus_floridanus 563
## shrub_cover-Meleagris_gallopavo 255
## shrub_cover-Sciurus_carolinensis 680
## shrub_cover-Vulpes_vulpes 441
## shrub_cover-Sus_scrofa 401
## veg_height-Canis_latrans 799
## veg_height-Sciurus_niger 583
## veg_height-Procyon_lotor 1466
## veg_height-Dasypus_novemcinctus 1682
## veg_height-Lynx_rufus 723
## veg_height-Didelphis_virginiana 1230
## veg_height-Sylvilagus_floridanus 899
## veg_height-Meleagris_gallopavo 401
## veg_height-Sciurus_carolinensis 978
## veg_height-Vulpes_vulpes 707
## veg_height-Sus_scrofa 845
## week-Canis_latrans 1618
## week-Sciurus_niger 666
## week-Procyon_lotor 1420
## week-Dasypus_novemcinctus 1944
## week-Lynx_rufus 1029
## week-Didelphis_virginiana 1269
## week-Sylvilagus_floridanus 875
## week-Meleagris_gallopavo 565
## week-Sciurus_carolinensis 1760
## week-Vulpes_vulpes 811
## week-Sus_scrofa 1675
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8878
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2971 0.4025 -2.0875 -1.2935 -0.5271 1.0561 374
## Avg_Cogongrass_Cover -0.7931 0.3766 -1.5562 -0.7943 -0.0783 1.0065 422
## I(Avg_Cogongrass_Cover^2) 0.7223 0.3190 0.1125 0.7166 1.3735 1.0350 529
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6842 0.6931 0.0638 0.4756 2.5740 1.0265 540
## Avg_Cogongrass_Cover 0.3984 0.4499 0.0436 0.2535 1.5297 1.0118 701
## I(Avg_Cogongrass_Cover^2) 0.5604 0.8784 0.0407 0.2955 2.8628 1.0124 302
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.656 0.651 0.0643 0.4497 2.4325 1.1271 150
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0074 0.2978 -3.6018 -2.9974 -2.4371 1.0064 933
## shrub_cover 0.2025 0.2732 -0.3580 0.2044 0.7579 1.0114 1282
## veg_height 0.0615 0.1697 -0.2697 0.0671 0.4001 1.0168 908
## week -0.1143 0.1236 -0.3733 -0.1102 0.1169 1.0045 908
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8259 0.6122 0.2445 0.6758 2.4050 1.0018 755
## shrub_cover 0.6221 0.4951 0.1203 0.4970 1.8490 1.0026 556
## veg_height 0.1950 0.1418 0.0536 0.1581 0.5378 1.0142 1193
## week 0.1005 0.0789 0.0238 0.0790 0.3034 1.0115 1395
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8267 0.6008 -1.9615 -0.8432
## (Intercept)-Sciurus_niger -1.3017 0.6768 -2.6266 -1.3139
## (Intercept)-Procyon_lotor -0.6567 0.6165 -1.8774 -0.6655
## (Intercept)-Dasypus_novemcinctus -1.3317 0.5313 -2.4290 -1.3173
## (Intercept)-Lynx_rufus -1.2379 0.6390 -2.4480 -1.2432
## (Intercept)-Didelphis_virginiana -1.6231 0.5867 -2.8684 -1.5874
## (Intercept)-Sylvilagus_floridanus -1.1947 0.5844 -2.3377 -1.1930
## (Intercept)-Meleagris_gallopavo -0.7900 0.7346 -2.1201 -0.8395
## (Intercept)-Sciurus_carolinensis -1.8695 0.6228 -3.1519 -1.8370
## (Intercept)-Vulpes_vulpes -1.8214 0.7650 -3.4525 -1.7837
## (Intercept)-Sus_scrofa -1.8555 0.6902 -3.3422 -1.8119
## Avg_Cogongrass_Cover-Canis_latrans -0.4503 0.5147 -1.3676 -0.4827
## Avg_Cogongrass_Cover-Sciurus_niger -1.0980 0.6388 -2.5590 -1.0383
## Avg_Cogongrass_Cover-Procyon_lotor -0.7495 0.5040 -1.7732 -0.7496
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5531 0.4827 -1.4870 -0.5724
## Avg_Cogongrass_Cover-Lynx_rufus -0.7001 0.5581 -1.8152 -0.7042
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4784 0.5586 -1.5116 -0.4990
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2364 0.6183 -2.6979 -1.1640
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0969 0.6684 -2.6388 -1.0329
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7381 0.5053 -1.7423 -0.7448
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7556 0.6079 -2.0307 -0.7481
## Avg_Cogongrass_Cover-Sus_scrofa -0.9377 0.6090 -2.2285 -0.9139
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2965 0.7521 0.2682 1.1323
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.1295 0.6612 -1.4130 0.2225
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1897 0.7137 0.2758 1.0405
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7271 0.3631 0.0463 0.7165
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1992 0.5319 0.3467 1.1401
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5251 0.4126 -0.2640 0.5221
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7120 0.4422 -0.0900 0.6846
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3355 0.6261 -0.9651 0.3590
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8916 0.3874 0.2081 0.8696
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8663 0.4882 0.0921 0.8116
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2290 0.6802 -1.4229 0.3389
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4135 1.0371 596
## (Intercept)-Sciurus_niger 0.0655 1.0534 511
## (Intercept)-Procyon_lotor 0.5674 1.0322 463
## (Intercept)-Dasypus_novemcinctus -0.3095 1.0405 889
## (Intercept)-Lynx_rufus 0.0643 1.0325 531
## (Intercept)-Didelphis_virginiana -0.5520 1.0123 632
## (Intercept)-Sylvilagus_floridanus -0.0655 1.0227 751
## (Intercept)-Meleagris_gallopavo 0.7932 1.0379 477
## (Intercept)-Sciurus_carolinensis -0.7367 1.0014 661
## (Intercept)-Vulpes_vulpes -0.4475 1.0248 340
## (Intercept)-Sus_scrofa -0.6466 1.0113 656
## Avg_Cogongrass_Cover-Canis_latrans 0.6346 1.0063 959
## Avg_Cogongrass_Cover-Sciurus_niger -0.0049 1.0071 591
## Avg_Cogongrass_Cover-Procyon_lotor 0.2205 1.0193 1166
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4301 1.0208 1007
## Avg_Cogongrass_Cover-Lynx_rufus 0.3969 1.0072 667
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6703 1.0238 876
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1808 1.0082 572
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0770 1.0005 503
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2642 1.0082 832
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4365 1.0163 777
## Avg_Cogongrass_Cover-Sus_scrofa 0.1864 1.0099 779
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2261 1.0002 324
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1533 1.0250 466
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.2785 1.0087 329
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5014 1.0259 1072
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4076 1.0086 650
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3912 1.0500 812
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7094 1.0178 749
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5770 1.0787 363
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6887 1.0057 889
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0383 1.0256 440
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2836 1.0250 399
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7527 0.1777 -3.1055 -2.7470 -2.4146 1.0023
## (Intercept)-Sciurus_niger -3.8375 0.5144 -4.8853 -3.8461 -2.8154 1.0113
## (Intercept)-Procyon_lotor -2.3249 0.1497 -2.6341 -2.3184 -2.0462 1.0043
## (Intercept)-Dasypus_novemcinctus -1.7815 0.1571 -2.1006 -1.7775 -1.4799 1.0101
## (Intercept)-Lynx_rufus -3.5311 0.3409 -4.2438 -3.5259 -2.9006 1.0046
## (Intercept)-Didelphis_virginiana -2.6399 0.2849 -3.2262 -2.6309 -2.1047 1.0025
## (Intercept)-Sylvilagus_floridanus -3.1805 0.2853 -3.7547 -3.1729 -2.6218 1.0194
## (Intercept)-Meleagris_gallopavo -3.9116 0.4501 -4.8212 -3.9028 -3.0624 1.0137
## (Intercept)-Sciurus_carolinensis -2.6891 0.3174 -3.3691 -2.6717 -2.1319 1.0095
## (Intercept)-Vulpes_vulpes -4.0197 0.6486 -5.4143 -3.9714 -2.8983 1.0442
## (Intercept)-Sus_scrofa -3.3198 0.5532 -4.4007 -3.3127 -2.2396 1.0090
## shrub_cover-Canis_latrans -0.2556 0.2118 -0.6678 -0.2571 0.1548 1.0143
## shrub_cover-Sciurus_niger -0.2964 0.4710 -1.2358 -0.2946 0.6496 1.0126
## shrub_cover-Procyon_lotor 0.2265 0.1696 -0.1169 0.2322 0.5417 1.0093
## shrub_cover-Dasypus_novemcinctus 0.8482 0.2930 0.2779 0.8409 1.4321 1.0000
## shrub_cover-Lynx_rufus -0.1976 0.3620 -0.9122 -0.2049 0.5009 1.0143
## shrub_cover-Didelphis_virginiana 0.9701 0.3774 0.2458 0.9590 1.7377 1.0002
## shrub_cover-Sylvilagus_floridanus 0.2333 0.4019 -0.5256 0.2258 1.0432 1.0145
## shrub_cover-Meleagris_gallopavo -0.6594 0.4050 -1.4748 -0.6553 0.1292 1.0024
## shrub_cover-Sciurus_carolinensis 0.8478 0.4094 0.0782 0.8274 1.7053 1.0030
## shrub_cover-Vulpes_vulpes -0.0777 0.5940 -1.2373 -0.0803 1.0812 1.0288
## shrub_cover-Sus_scrofa 0.6013 0.7914 -0.9778 0.5901 2.1971 1.0021
## veg_height-Canis_latrans -0.5642 0.1831 -0.9329 -0.5647 -0.2186 0.9999
## veg_height-Sciurus_niger 0.1405 0.4043 -0.6259 0.1279 0.9463 1.0070
## veg_height-Procyon_lotor 0.3421 0.1219 0.1061 0.3431 0.5790 1.0004
## veg_height-Dasypus_novemcinctus 0.2547 0.1346 -0.0084 0.2534 0.5179 1.0011
## veg_height-Lynx_rufus 0.0789 0.2389 -0.3990 0.0830 0.5572 1.0020
## veg_height-Didelphis_virginiana 0.4360 0.2453 -0.0304 0.4315 0.9354 1.0024
## veg_height-Sylvilagus_floridanus 0.1759 0.2453 -0.2951 0.1741 0.6619 1.0243
## veg_height-Meleagris_gallopavo -0.1070 0.3862 -0.9215 -0.0937 0.6392 1.0270
## veg_height-Sciurus_carolinensis 0.1017 0.2131 -0.2939 0.0972 0.5351 1.0093
## veg_height-Vulpes_vulpes -0.0759 0.3133 -0.7654 -0.0586 0.4748 1.0107
## veg_height-Sus_scrofa -0.0842 0.3336 -0.7851 -0.0757 0.5556 1.0009
## week-Canis_latrans 0.0607 0.1330 -0.2048 0.0628 0.3057 1.0010
## week-Sciurus_niger -0.3235 0.2754 -0.9570 -0.3006 0.1408 1.0043
## week-Procyon_lotor -0.0607 0.1209 -0.3059 -0.0605 0.1741 1.0006
## week-Dasypus_novemcinctus -0.1816 0.1378 -0.4677 -0.1770 0.0727 1.0155
## week-Lynx_rufus -0.0623 0.1890 -0.4425 -0.0545 0.2933 1.0098
## week-Didelphis_virginiana -0.2342 0.2087 -0.6813 -0.2244 0.1376 1.0034
## week-Sylvilagus_floridanus -0.1796 0.2051 -0.6081 -0.1711 0.1859 1.0006
## week-Meleagris_gallopavo -0.2861 0.2306 -0.7952 -0.2696 0.1200 1.0098
## week-Sciurus_carolinensis 0.1151 0.1801 -0.2484 0.1182 0.4610 1.0028
## week-Vulpes_vulpes -0.1505 0.2687 -0.7281 -0.1341 0.3335 1.0059
## week-Sus_scrofa 0.0573 0.2368 -0.4027 0.0496 0.5225 1.0050
## ESS
## (Intercept)-Canis_latrans 901
## (Intercept)-Sciurus_niger 318
## (Intercept)-Procyon_lotor 1048
## (Intercept)-Dasypus_novemcinctus 1803
## (Intercept)-Lynx_rufus 340
## (Intercept)-Didelphis_virginiana 866
## (Intercept)-Sylvilagus_floridanus 633
## (Intercept)-Meleagris_gallopavo 224
## (Intercept)-Sciurus_carolinensis 748
## (Intercept)-Vulpes_vulpes 219
## (Intercept)-Sus_scrofa 491
## shrub_cover-Canis_latrans 954
## shrub_cover-Sciurus_niger 533
## shrub_cover-Procyon_lotor 1152
## shrub_cover-Dasypus_novemcinctus 1494
## shrub_cover-Lynx_rufus 497
## shrub_cover-Didelphis_virginiana 584
## shrub_cover-Sylvilagus_floridanus 576
## shrub_cover-Meleagris_gallopavo 333
## shrub_cover-Sciurus_carolinensis 784
## shrub_cover-Vulpes_vulpes 598
## shrub_cover-Sus_scrofa 640
## veg_height-Canis_latrans 715
## veg_height-Sciurus_niger 881
## veg_height-Procyon_lotor 1769
## veg_height-Dasypus_novemcinctus 1807
## veg_height-Lynx_rufus 849
## veg_height-Didelphis_virginiana 896
## veg_height-Sylvilagus_floridanus 693
## veg_height-Meleagris_gallopavo 462
## veg_height-Sciurus_carolinensis 1080
## veg_height-Vulpes_vulpes 569
## veg_height-Sus_scrofa 1122
## week-Canis_latrans 1583
## week-Sciurus_niger 799
## week-Procyon_lotor 1947
## week-Dasypus_novemcinctus 2005
## week-Lynx_rufus 1098
## week-Didelphis_virginiana 1358
## week-Sylvilagus_floridanus 830
## week-Meleagris_gallopavo 800
## week-Sciurus_carolinensis 1743
## week-Vulpes_vulpes 1151
## week-Sus_scrofa 1721
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9635
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9646 0.7356 -3.3515 -1.9781 -0.4295 1.0056 277
## Cogon_Patch_Size -0.1959 0.6494 -1.4607 -0.2083 1.1159 1.0224 287
## Veg_shannon_index 1.0861 0.4760 0.2588 1.0510 2.1666 1.0931 198
## total_shrub_cover -1.0873 0.5870 -2.3069 -1.0449 -0.0038 1.2455 187
## Avg_Cogongrass_Cover 0.1322 0.9228 -1.6569 0.1268 1.9789 1.0167 133
## Tree_Density -2.2508 0.7876 -3.7476 -2.2896 -0.6437 1.0208 243
## Avg_Canopy_Cover 2.0537 0.6984 0.8401 1.9889 3.6188 1.0601 190
## I(Avg_Cogongrass_Cover^2) 1.3044 0.6844 0.0284 1.2577 2.7132 1.0297 190
## avg_veg_height -0.1929 0.5239 -1.2382 -0.1956 0.8213 1.0275 247
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2099 4.7000 0.0906 1.6657 15.5834 1.0327 126
## Cogon_Patch_Size 2.3631 3.7754 0.0935 1.2483 10.3190 1.0739 227
## Veg_shannon_index 0.6173 0.8859 0.0433 0.3274 3.0280 1.0364 461
## total_shrub_cover 1.6141 2.0841 0.0837 1.0100 6.7794 1.0520 348
## Avg_Cogongrass_Cover 1.3155 2.1887 0.0546 0.5811 7.1107 1.0288 273
## Tree_Density 2.9295 5.7639 0.0583 1.1554 17.2082 1.2361 183
## Avg_Canopy_Cover 3.2081 4.8013 0.1709 1.7792 14.2343 1.0483 140
## I(Avg_Cogongrass_Cover^2) 2.0142 2.9090 0.0669 1.1137 9.1432 1.1826 158
## avg_veg_height 0.5954 1.0699 0.0416 0.2997 3.0132 1.1055 471
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6851 2.3151 0.0637 0.8641 7.9593 1.0388 69
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1283 0.3287 -3.7769 -3.1246 -2.4658 1.0023 965
## shrub_cover 0.3788 0.2970 -0.2139 0.3769 0.9845 1.0345 642
## veg_height 0.0586 0.1807 -0.2965 0.0611 0.4166 1.0281 707
## week -0.1245 0.1364 -0.4075 -0.1189 0.1163 1.0028 931
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0641 0.7655 0.3105 0.8643 2.9630 1.0230 536
## shrub_cover 0.7372 0.5777 0.1540 0.5898 2.1795 1.0086 509
## veg_height 0.2142 0.1516 0.0573 0.1726 0.6216 1.0017 923
## week 0.1125 0.0958 0.0267 0.0861 0.3646 1.0402 824
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3507 1.0525 -3.2674 -1.4067
## (Intercept)-Sciurus_niger -1.1319 1.5487 -3.5660 -1.3461
## (Intercept)-Procyon_lotor -0.9441 1.1074 -3.1105 -0.9587
## (Intercept)-Dasypus_novemcinctus -2.2933 1.0067 -4.4959 -2.2499
## (Intercept)-Lynx_rufus -1.0901 1.6080 -3.5386 -1.3310
## (Intercept)-Didelphis_virginiana -3.0374 1.2855 -6.1362 -2.8816
## (Intercept)-Sylvilagus_floridanus -2.0223 1.1188 -4.2829 -2.0018
## (Intercept)-Meleagris_gallopavo -1.6828 1.3401 -4.1341 -1.8091
## (Intercept)-Sciurus_carolinensis -3.2914 1.3901 -6.3313 -3.1608
## (Intercept)-Vulpes_vulpes -3.0856 1.6330 -6.6061 -2.9050
## (Intercept)-Sus_scrofa -3.5104 1.6773 -7.6659 -3.2123
## Cogon_Patch_Size-Canis_latrans 1.2214 1.3632 -0.5518 0.9528
## Cogon_Patch_Size-Sciurus_niger -0.8910 1.4204 -4.0978 -0.7426
## Cogon_Patch_Size-Procyon_lotor -0.5839 0.8013 -2.2427 -0.5488
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0694 0.8062 -1.5594 -0.0984
## Cogon_Patch_Size-Lynx_rufus -0.3790 1.2915 -3.0156 -0.3816
## Cogon_Patch_Size-Didelphis_virginiana 1.1732 1.0233 -0.4496 1.0221
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1847 1.4059 -4.4775 -0.9653
## Cogon_Patch_Size-Meleagris_gallopavo 0.2125 1.1379 -1.7983 0.1181
## Cogon_Patch_Size-Sciurus_carolinensis -0.8922 1.1645 -3.5913 -0.7102
## Cogon_Patch_Size-Vulpes_vulpes -0.3330 1.4678 -3.2978 -0.3009
## Cogon_Patch_Size-Sus_scrofa -0.5810 1.2828 -3.7336 -0.4282
## Veg_shannon_index-Canis_latrans 1.3588 0.6953 0.2440 1.2823
## Veg_shannon_index-Sciurus_niger 1.1423 0.8655 -0.3649 1.0752
## Veg_shannon_index-Procyon_lotor 1.2372 0.6245 0.1351 1.1913
## Veg_shannon_index-Dasypus_novemcinctus 0.7413 0.5723 -0.4098 0.7470
## Veg_shannon_index-Lynx_rufus 1.1605 0.7799 -0.3224 1.1075
## Veg_shannon_index-Didelphis_virginiana 1.2256 0.6961 0.0204 1.1809
## Veg_shannon_index-Sylvilagus_floridanus 1.1241 0.7182 -0.1772 1.0831
## Veg_shannon_index-Meleagris_gallopavo 1.3259 0.7621 0.0321 1.2426
## Veg_shannon_index-Sciurus_carolinensis 0.5474 0.7617 -1.0806 0.5852
## Veg_shannon_index-Vulpes_vulpes 0.8254 0.8292 -0.9684 0.8525
## Veg_shannon_index-Sus_scrofa 1.4232 0.8478 0.0879 1.3136
## total_shrub_cover-Canis_latrans 0.1307 0.8134 -1.2510 0.0438
## total_shrub_cover-Sciurus_niger -1.4499 1.1084 -3.9287 -1.3605
## total_shrub_cover-Procyon_lotor -1.4431 0.7247 -3.0652 -1.3751
## total_shrub_cover-Dasypus_novemcinctus -0.5243 0.8544 -2.4837 -0.4693
## total_shrub_cover-Lynx_rufus -1.4798 1.1251 -4.0914 -1.3637
## total_shrub_cover-Didelphis_virginiana -1.3225 0.9831 -3.6948 -1.1882
## total_shrub_cover-Sylvilagus_floridanus -1.1066 1.1046 -3.6464 -0.9822
## total_shrub_cover-Meleagris_gallopavo -2.2385 1.2964 -5.0506 -2.0965
## total_shrub_cover-Sciurus_carolinensis -0.9994 1.0803 -3.4004 -0.9040
## total_shrub_cover-Vulpes_vulpes -1.2539 1.2418 -4.0814 -1.1150
## total_shrub_cover-Sus_scrofa -0.9702 1.1579 -3.5642 -0.8753
## Avg_Cogongrass_Cover-Canis_latrans 0.3228 1.1647 -1.8782 0.2931
## Avg_Cogongrass_Cover-Sciurus_niger -0.4781 1.5599 -4.4336 -0.2951
## Avg_Cogongrass_Cover-Procyon_lotor 0.1328 1.1609 -2.1495 0.1260
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7925 1.2691 -1.4372 0.6836
## Avg_Cogongrass_Cover-Lynx_rufus 0.1856 1.2687 -2.3482 0.1895
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3910 1.2253 -1.9334 0.3469
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4412 1.3570 -3.5109 -0.3032
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0763 1.3757 -3.0928 -0.0166
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2243 1.2211 -2.1328 0.2310
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4335 1.3217 -1.9585 0.3953
## Avg_Cogongrass_Cover-Sus_scrofa 0.0604 1.2683 -2.5966 0.0611
## Tree_Density-Canis_latrans -3.0629 1.3404 -6.2288 -2.8698
## Tree_Density-Sciurus_niger -2.0984 1.5217 -5.1065 -2.1416
## Tree_Density-Procyon_lotor -2.2754 0.9856 -4.3086 -2.2505
## Tree_Density-Dasypus_novemcinctus -3.8005 1.7947 -8.2369 -3.3966
## Tree_Density-Lynx_rufus -1.0463 1.6963 -3.4804 -1.3077
## Tree_Density-Didelphis_virginiana -2.2885 1.2590 -4.8845 -2.2558
## Tree_Density-Sylvilagus_floridanus -2.6604 1.4341 -5.8428 -2.5382
## Tree_Density-Meleagris_gallopavo -2.3925 1.4444 -5.4952 -2.3826
## Tree_Density-Sciurus_carolinensis -2.5286 1.4714 -5.8465 -2.4426
## Tree_Density-Vulpes_vulpes -2.1252 1.6615 -5.3602 -2.1373
## Tree_Density-Sus_scrofa -2.4349 1.4577 -5.6382 -2.3681
## Avg_Canopy_Cover-Canis_latrans 0.2038 0.7041 -1.1785 0.2123
## Avg_Canopy_Cover-Sciurus_niger 1.9729 1.7010 -1.2508 1.8801
## Avg_Canopy_Cover-Procyon_lotor 1.7024 0.8170 0.2881 1.6369
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2658 0.8578 0.8745 2.1629
## Avg_Canopy_Cover-Lynx_rufus 1.4872 1.4324 -1.2014 1.3988
## Avg_Canopy_Cover-Didelphis_virginiana 3.1030 1.3662 1.2092 2.8143
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8434 1.9293 1.3150 3.4238
## Avg_Canopy_Cover-Meleagris_gallopavo 2.6513 1.5183 0.5788 2.3411
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0358 1.5282 1.0977 2.7068
## Avg_Canopy_Cover-Vulpes_vulpes 2.6705 1.5719 0.4566 2.3667
## Avg_Canopy_Cover-Sus_scrofa 2.2976 1.1679 0.4610 2.1266
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2188 1.1122 0.5891 2.0540
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.6784 1.5364 -2.4698 0.7216
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2583 1.2190 0.5832 2.0804
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3711 0.7633 0.0614 1.3173
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5543 1.2638 0.6679 2.3593
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7721 0.7792 -0.7354 0.7413
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1310 0.9417 -0.4619 1.0305
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4026 1.3838 -2.7250 0.5401
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5688 0.8848 0.1199 1.4662
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7588 0.9667 0.1353 1.6489
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.5630 1.4705 -3.0704 0.7403
## avg_veg_height-Canis_latrans -0.2901 0.6447 -1.5824 -0.2821
## avg_veg_height-Sciurus_niger -0.5154 0.9387 -2.7345 -0.4212
## avg_veg_height-Procyon_lotor -0.0181 0.6388 -1.2788 -0.0280
## avg_veg_height-Dasypus_novemcinctus 0.1618 0.6768 -1.0948 0.1272
## avg_veg_height-Lynx_rufus -0.4613 0.9051 -2.5138 -0.3918
## avg_veg_height-Didelphis_virginiana -0.3338 0.7350 -1.8740 -0.3029
## avg_veg_height-Sylvilagus_floridanus -0.2945 0.7477 -1.8428 -0.2788
## avg_veg_height-Meleagris_gallopavo -0.1796 0.8694 -2.0378 -0.1666
## avg_veg_height-Sciurus_carolinensis 0.2039 0.7492 -1.1168 0.1355
## avg_veg_height-Vulpes_vulpes -0.2231 0.8174 -1.8772 -0.2236
## avg_veg_height-Sus_scrofa -0.1671 0.7572 -1.6863 -0.1666
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0080 1.0072 383
## (Intercept)-Sciurus_niger 2.6492 1.0187 150
## (Intercept)-Procyon_lotor 1.2441 1.0192 236
## (Intercept)-Dasypus_novemcinctus -0.5633 1.0113 351
## (Intercept)-Lynx_rufus 2.8830 1.0314 162
## (Intercept)-Didelphis_virginiana -0.9718 1.0057 179
## (Intercept)-Sylvilagus_floridanus 0.1295 1.0090 352
## (Intercept)-Meleagris_gallopavo 1.4297 1.0727 220
## (Intercept)-Sciurus_carolinensis -0.9386 1.0086 249
## (Intercept)-Vulpes_vulpes -0.2971 1.0379 167
## (Intercept)-Sus_scrofa -0.9996 1.0421 196
## Cogon_Patch_Size-Canis_latrans 4.5288 1.0287 230
## Cogon_Patch_Size-Sciurus_niger 1.7624 1.0549 321
## Cogon_Patch_Size-Procyon_lotor 0.8825 1.0125 298
## Cogon_Patch_Size-Dasypus_novemcinctus 1.7013 1.0044 470
## Cogon_Patch_Size-Lynx_rufus 2.3655 1.0126 380
## Cogon_Patch_Size-Didelphis_virginiana 3.5241 1.0115 245
## Cogon_Patch_Size-Sylvilagus_floridanus 1.0275 1.0308 324
## Cogon_Patch_Size-Meleagris_gallopavo 2.8382 1.0027 428
## Cogon_Patch_Size-Sciurus_carolinensis 0.9932 1.0215 401
## Cogon_Patch_Size-Vulpes_vulpes 2.8683 1.0293 334
## Cogon_Patch_Size-Sus_scrofa 1.4778 1.0362 266
## Veg_shannon_index-Canis_latrans 2.9581 1.0269 410
## Veg_shannon_index-Sciurus_niger 3.1008 1.0366 427
## Veg_shannon_index-Procyon_lotor 2.6046 1.0478 149
## Veg_shannon_index-Dasypus_novemcinctus 1.8227 1.0232 553
## Veg_shannon_index-Lynx_rufus 2.8535 1.0411 351
## Veg_shannon_index-Didelphis_virginiana 2.7715 1.0524 335
## Veg_shannon_index-Sylvilagus_floridanus 2.6978 1.0535 215
## Veg_shannon_index-Meleagris_gallopavo 3.1670 1.0507 319
## Veg_shannon_index-Sciurus_carolinensis 1.9343 1.0544 465
## Veg_shannon_index-Vulpes_vulpes 2.4456 1.0402 320
## Veg_shannon_index-Sus_scrofa 3.5619 1.0189 363
## total_shrub_cover-Canis_latrans 2.0565 1.0066 438
## total_shrub_cover-Sciurus_niger 0.5398 1.1507 366
## total_shrub_cover-Procyon_lotor -0.2742 1.0753 297
## total_shrub_cover-Dasypus_novemcinctus 0.9466 1.0606 354
## total_shrub_cover-Lynx_rufus 0.4795 1.1147 201
## total_shrub_cover-Didelphis_virginiana 0.1959 1.2056 333
## total_shrub_cover-Sylvilagus_floridanus 0.8352 1.1186 305
## total_shrub_cover-Meleagris_gallopavo -0.1127 1.1338 213
## total_shrub_cover-Sciurus_carolinensis 0.8605 1.1380 237
## total_shrub_cover-Vulpes_vulpes 0.9579 1.0230 324
## total_shrub_cover-Sus_scrofa 1.1434 1.1293 320
## Avg_Cogongrass_Cover-Canis_latrans 2.6784 1.0072 203
## Avg_Cogongrass_Cover-Sciurus_niger 2.0426 1.0266 222
## Avg_Cogongrass_Cover-Procyon_lotor 2.4352 1.0018 226
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5487 1.0077 182
## Avg_Cogongrass_Cover-Lynx_rufus 2.6975 1.0169 202
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8974 1.0086 219
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0031 1.0141 236
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.4676 1.0227 225
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6882 1.0229 188
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2396 1.0125 237
## Avg_Cogongrass_Cover-Sus_scrofa 2.5872 1.0243 246
## Tree_Density-Canis_latrans -0.9988 1.0208 182
## Tree_Density-Sciurus_niger 1.2253 1.0302 288
## Tree_Density-Procyon_lotor -0.3732 1.0038 393
## Tree_Density-Dasypus_novemcinctus -1.3855 1.0452 128
## Tree_Density-Lynx_rufus 2.9778 1.0385 149
## Tree_Density-Didelphis_virginiana 0.2168 1.0370 351
## Tree_Density-Sylvilagus_floridanus -0.1338 1.0427 333
## Tree_Density-Meleagris_gallopavo 0.4361 1.0107 164
## Tree_Density-Sciurus_carolinensis 0.1409 1.0585 205
## Tree_Density-Vulpes_vulpes 1.0251 1.0460 206
## Tree_Density-Sus_scrofa 0.3469 1.0161 337
## Avg_Canopy_Cover-Canis_latrans 1.6347 1.0015 578
## Avg_Canopy_Cover-Sciurus_niger 5.7351 1.0461 222
## Avg_Canopy_Cover-Procyon_lotor 3.5711 1.0124 485
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2009 1.0420 200
## Avg_Canopy_Cover-Lynx_rufus 4.5611 1.0038 169
## Avg_Canopy_Cover-Didelphis_virginiana 6.6067 1.0817 115
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.7809 1.0140 144
## Avg_Canopy_Cover-Meleagris_gallopavo 6.4627 1.0862 210
## Avg_Canopy_Cover-Sciurus_carolinensis 6.7561 1.0433 117
## Avg_Canopy_Cover-Vulpes_vulpes 6.5407 1.0326 226
## Avg_Canopy_Cover-Sus_scrofa 5.1684 1.0374 280
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.9356 1.0122 302
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.4741 1.1002 115
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.9805 1.0792 161
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0997 1.0117 376
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.5409 1.0331 225
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4463 1.0350 221
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.4212 1.0499 214
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.8799 1.0829 153
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.5072 1.0093 276
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.9462 1.0227 283
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.1213 1.0973 145
## avg_veg_height-Canis_latrans 1.0065 1.0089 378
## avg_veg_height-Sciurus_niger 1.1288 1.0205 354
## avg_veg_height-Procyon_lotor 1.2699 1.0269 464
## avg_veg_height-Dasypus_novemcinctus 1.6187 1.0112 363
## avg_veg_height-Lynx_rufus 1.1312 1.0320 287
## avg_veg_height-Didelphis_virginiana 1.0193 1.0100 455
## avg_veg_height-Sylvilagus_floridanus 1.1399 1.0091 450
## avg_veg_height-Meleagris_gallopavo 1.4911 1.0173 352
## avg_veg_height-Sciurus_carolinensis 1.8152 1.0106 377
## avg_veg_height-Vulpes_vulpes 1.3953 1.0066 394
## avg_veg_height-Sus_scrofa 1.2985 1.0249 412
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7324 0.1839 -3.1095 -2.7233 -2.3984 1.0112
## (Intercept)-Sciurus_niger -4.3688 0.5342 -5.3713 -4.3839 -3.2178 1.0177
## (Intercept)-Procyon_lotor -2.3202 0.1411 -2.6113 -2.3156 -2.0575 1.0125
## (Intercept)-Dasypus_novemcinctus -1.8163 0.1671 -2.1522 -1.8129 -1.5030 1.0399
## (Intercept)-Lynx_rufus -3.7646 0.3456 -4.4548 -3.7548 -3.1028 1.0110
## (Intercept)-Didelphis_virginiana -2.7011 0.2924 -3.2860 -2.6979 -2.1310 1.0415
## (Intercept)-Sylvilagus_floridanus -3.2244 0.2653 -3.7759 -3.2178 -2.7056 1.0159
## (Intercept)-Meleagris_gallopavo -3.8298 0.5121 -4.9251 -3.8032 -2.8463 1.0477
## (Intercept)-Sciurus_carolinensis -2.8645 0.3456 -3.6031 -2.8565 -2.2233 1.0436
## (Intercept)-Vulpes_vulpes -4.2998 0.6314 -5.6220 -4.2643 -3.1793 1.0092
## (Intercept)-Sus_scrofa -3.6371 0.5573 -4.6700 -3.6731 -2.4484 1.0627
## shrub_cover-Canis_latrans -0.2989 0.2336 -0.7515 -0.2986 0.1554 1.0084
## shrub_cover-Sciurus_niger -0.1908 0.4920 -1.1596 -0.1997 0.7682 1.0060
## shrub_cover-Procyon_lotor 0.2792 0.1628 -0.0491 0.2804 0.6063 1.0048
## shrub_cover-Dasypus_novemcinctus 0.9996 0.3181 0.3490 1.0059 1.5810 1.0400
## shrub_cover-Lynx_rufus -0.0803 0.3589 -0.7865 -0.0890 0.6213 1.0449
## shrub_cover-Didelphis_virginiana 1.1066 0.3635 0.4239 1.0981 1.8197 1.0529
## shrub_cover-Sylvilagus_floridanus 0.5958 0.3941 -0.1840 0.5960 1.3779 1.0088
## shrub_cover-Meleagris_gallopavo -0.5042 0.4662 -1.4873 -0.4816 0.3674 1.0097
## shrub_cover-Sciurus_carolinensis 1.0989 0.4310 0.2808 1.0991 1.9582 1.0428
## shrub_cover-Vulpes_vulpes 0.1923 0.5894 -1.0572 0.2213 1.3156 1.0394
## shrub_cover-Sus_scrofa 1.1136 0.7828 -0.4754 1.1217 2.6495 1.0683
## veg_height-Canis_latrans -0.5511 0.1848 -0.9161 -0.5474 -0.1826 1.0086
## veg_height-Sciurus_niger 0.1014 0.4079 -0.6313 0.0738 1.0165 1.0739
## veg_height-Procyon_lotor 0.3635 0.1239 0.1161 0.3651 0.6031 1.0005
## veg_height-Dasypus_novemcinctus 0.2704 0.1383 0.0083 0.2689 0.5349 1.0172
## veg_height-Lynx_rufus 0.1638 0.2373 -0.2944 0.1645 0.6409 1.0134
## veg_height-Didelphis_virginiana 0.4792 0.2421 0.0414 0.4704 0.9652 1.0170
## veg_height-Sylvilagus_floridanus 0.1426 0.2519 -0.3636 0.1445 0.6259 1.0051
## veg_height-Meleagris_gallopavo -0.1554 0.3838 -0.9141 -0.1576 0.5678 1.0323
## veg_height-Sciurus_carolinensis 0.1693 0.2274 -0.2773 0.1707 0.6230 1.0149
## veg_height-Vulpes_vulpes -0.1439 0.3299 -0.8377 -0.1334 0.4649 1.0344
## veg_height-Sus_scrofa -0.1592 0.3382 -0.8364 -0.1490 0.4669 1.0127
## week-Canis_latrans 0.0578 0.1355 -0.2050 0.0606 0.3221 1.0006
## week-Sciurus_niger -0.3667 0.3045 -1.0361 -0.3402 0.1553 1.0190
## week-Procyon_lotor -0.0636 0.1224 -0.3060 -0.0608 0.1698 1.0058
## week-Dasypus_novemcinctus -0.1821 0.1413 -0.4672 -0.1784 0.0862 1.0002
## week-Lynx_rufus -0.0665 0.1947 -0.4648 -0.0577 0.3019 1.0156
## week-Didelphis_virginiana -0.2539 0.2252 -0.7495 -0.2423 0.1473 1.0100
## week-Sylvilagus_floridanus -0.1908 0.2123 -0.6459 -0.1777 0.1863 1.0022
## week-Meleagris_gallopavo -0.3206 0.2541 -0.8938 -0.2929 0.1226 1.0369
## week-Sciurus_carolinensis 0.1172 0.1875 -0.2520 0.1232 0.4792 1.0109
## week-Vulpes_vulpes -0.1662 0.2769 -0.7441 -0.1518 0.3283 1.0162
## week-Sus_scrofa 0.0607 0.2428 -0.4316 0.0622 0.5314 1.0125
## ESS
## (Intercept)-Canis_latrans 796
## (Intercept)-Sciurus_niger 166
## (Intercept)-Procyon_lotor 985
## (Intercept)-Dasypus_novemcinctus 1007
## (Intercept)-Lynx_rufus 264
## (Intercept)-Didelphis_virginiana 552
## (Intercept)-Sylvilagus_floridanus 535
## (Intercept)-Meleagris_gallopavo 224
## (Intercept)-Sciurus_carolinensis 355
## (Intercept)-Vulpes_vulpes 160
## (Intercept)-Sus_scrofa 240
## shrub_cover-Canis_latrans 613
## shrub_cover-Sciurus_niger 313
## shrub_cover-Procyon_lotor 1213
## shrub_cover-Dasypus_novemcinctus 635
## shrub_cover-Lynx_rufus 204
## shrub_cover-Didelphis_virginiana 547
## shrub_cover-Sylvilagus_floridanus 461
## shrub_cover-Meleagris_gallopavo 227
## shrub_cover-Sciurus_carolinensis 331
## shrub_cover-Vulpes_vulpes 333
## shrub_cover-Sus_scrofa 170
## veg_height-Canis_latrans 730
## veg_height-Sciurus_niger 298
## veg_height-Procyon_lotor 1390
## veg_height-Dasypus_novemcinctus 1692
## veg_height-Lynx_rufus 485
## veg_height-Didelphis_virginiana 793
## veg_height-Sylvilagus_floridanus 528
## veg_height-Meleagris_gallopavo 293
## veg_height-Sciurus_carolinensis 749
## veg_height-Vulpes_vulpes 551
## veg_height-Sus_scrofa 607
## week-Canis_latrans 1540
## week-Sciurus_niger 466
## week-Procyon_lotor 1496
## week-Dasypus_novemcinctus 2080
## week-Lynx_rufus 872
## week-Didelphis_virginiana 852
## week-Sylvilagus_floridanus 890
## week-Meleagris_gallopavo 654
## week-Sciurus_carolinensis 1312
## week-Vulpes_vulpes 731
## week-Sus_scrofa 1238
# Includes all covariates of occupancy and null for detection
ms_null_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6478
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2872 0.6458 -2.5355 -1.2998 0.0677 1.0045 333
## Cogon_Patch_Size -0.7588 0.5512 -1.9274 -0.7259 0.2534 1.0222 641
## Veg_shannon_index 0.9706 0.4032 0.1950 0.9585 1.8128 1.0572 245
## total_shrub_cover -0.5814 0.4559 -1.5658 -0.5545 0.2763 1.0014 816
## Avg_Cogongrass_Cover 1.9999 0.7010 0.6855 1.9713 3.5595 1.0330 169
## Tree_Density -1.9893 0.6008 -3.2033 -1.9674 -0.8197 1.0302 291
## Avg_Canopy_Cover 1.8007 0.5210 0.8591 1.7664 2.9316 1.0358 247
## avg_veg_height -0.5839 0.4183 -1.4342 -0.5833 0.2436 1.0073 230
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.2392 3.6325 0.1013 2.2087 12.5005 1.0197 244
## Cogon_Patch_Size 1.9917 2.6962 0.0986 1.2403 8.2705 1.1253 210
## Veg_shannon_index 0.5796 0.7906 0.0483 0.3472 2.4145 1.0440 512
## total_shrub_cover 1.3684 1.5911 0.0743 0.8884 5.3452 1.0333 383
## Avg_Cogongrass_Cover 1.1776 2.0028 0.0534 0.5440 5.6702 1.1264 399
## Tree_Density 1.5686 2.2646 0.0607 0.7831 7.8478 1.0058 297
## Avg_Canopy_Cover 1.3708 1.7040 0.0795 0.8365 5.6635 1.1117 338
## avg_veg_height 0.3513 0.5282 0.0389 0.2118 1.4597 1.0245 507
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2646 2.2147 0.0813 1.5341 8.1273 1.1428 66
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8744 0.3023 -3.4664 -2.8771 -2.258 1.0006 2361
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9692 0.6892 0.2916 0.7953 2.6782 1.0029 686
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0761 1.0271 -2.0890 -0.0681
## (Intercept)-Sciurus_niger -0.6318 1.4102 -3.0191 -0.7753
## (Intercept)-Procyon_lotor 0.0876 1.0913 -2.1080 0.1864
## (Intercept)-Dasypus_novemcinctus -1.5378 0.8052 -3.2818 -1.4942
## (Intercept)-Lynx_rufus -0.1440 1.5285 -2.5873 -0.3404
## (Intercept)-Didelphis_virginiana -2.4693 0.9892 -4.6889 -2.4010
## (Intercept)-Sylvilagus_floridanus -1.3888 0.9824 -3.3707 -1.3635
## (Intercept)-Meleagris_gallopavo -1.4071 1.0887 -3.5714 -1.4135
## (Intercept)-Sciurus_carolinensis -2.6060 1.0241 -4.7740 -2.5114
## (Intercept)-Vulpes_vulpes -2.1719 1.2900 -4.8457 -2.1294
## (Intercept)-Sus_scrofa -3.3397 1.4946 -6.7530 -3.1615
## Cogon_Patch_Size-Canis_latrans 0.4152 0.9505 -1.1385 0.2834
## Cogon_Patch_Size-Sciurus_niger -1.4798 1.3923 -4.8156 -1.2799
## Cogon_Patch_Size-Procyon_lotor -1.0171 0.7114 -2.4701 -0.9950
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7960 0.6068 -2.1112 -0.7649
## Cogon_Patch_Size-Lynx_rufus -0.8183 1.0869 -2.9678 -0.8329
## Cogon_Patch_Size-Didelphis_virginiana 0.7151 0.8509 -0.7235 0.6352
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7645 1.2531 -4.8501 -1.5523
## Cogon_Patch_Size-Meleagris_gallopavo -0.4544 0.9738 -2.2089 -0.5376
## Cogon_Patch_Size-Sciurus_carolinensis -1.5567 1.0181 -4.1150 -1.4002
## Cogon_Patch_Size-Vulpes_vulpes -1.2507 1.2431 -4.1012 -1.1336
## Cogon_Patch_Size-Sus_scrofa -0.9622 1.2386 -3.5540 -0.8787
## Veg_shannon_index-Canis_latrans 1.2490 0.5987 0.2109 1.1932
## Veg_shannon_index-Sciurus_niger 1.0932 0.7700 -0.3814 1.0450
## Veg_shannon_index-Procyon_lotor 1.2433 0.5622 0.2312 1.2058
## Veg_shannon_index-Dasypus_novemcinctus 0.7331 0.4946 -0.2749 0.7372
## Veg_shannon_index-Lynx_rufus 0.9191 0.7346 -0.5982 0.9141
## Veg_shannon_index-Didelphis_virginiana 1.0606 0.5965 -0.0452 1.0367
## Veg_shannon_index-Sylvilagus_floridanus 1.0493 0.6458 -0.0983 1.0139
## Veg_shannon_index-Meleagris_gallopavo 1.2310 0.6958 0.0356 1.1615
## Veg_shannon_index-Sciurus_carolinensis 0.3607 0.6541 -1.1090 0.4243
## Veg_shannon_index-Vulpes_vulpes 0.5149 0.7525 -1.2201 0.5735
## Veg_shannon_index-Sus_scrofa 1.3675 0.7793 0.0773 1.2603
## total_shrub_cover-Canis_latrans 0.1714 0.6914 -0.9997 0.1195
## total_shrub_cover-Sciurus_niger -1.0341 0.9818 -3.3302 -0.9062
## total_shrub_cover-Procyon_lotor -0.9816 0.5927 -2.2584 -0.9363
## total_shrub_cover-Dasypus_novemcinctus 0.0575 0.5522 -0.9584 0.0360
## total_shrub_cover-Lynx_rufus -1.2282 1.0322 -3.7220 -1.0531
## total_shrub_cover-Didelphis_virginiana -0.6434 0.7301 -2.2980 -0.5909
## total_shrub_cover-Sylvilagus_floridanus -0.3145 0.7880 -1.9486 -0.3177
## total_shrub_cover-Meleagris_gallopavo -2.0116 1.1777 -4.6436 -1.8471
## total_shrub_cover-Sciurus_carolinensis -0.0790 0.6982 -1.3682 -0.0967
## total_shrub_cover-Vulpes_vulpes -0.7006 0.9532 -2.8395 -0.6347
## total_shrub_cover-Sus_scrofa 0.0145 0.8821 -1.6198 -0.0669
## Avg_Cogongrass_Cover-Canis_latrans 2.3435 0.8823 0.7765 2.2860
## Avg_Cogongrass_Cover-Sciurus_niger 1.3555 1.3682 -1.8477 1.5222
## Avg_Cogongrass_Cover-Procyon_lotor 2.2506 0.8608 0.7486 2.1784
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5920 0.9213 1.0818 2.4904
## Avg_Cogongrass_Cover-Lynx_rufus 2.5504 1.0259 0.8628 2.4157
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1826 0.8394 0.7040 2.1127
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4375 0.9658 -0.6253 1.4793
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5239 1.1853 -1.1901 1.5995
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3914 0.9210 0.8635 2.2792
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5296 1.0347 0.7775 2.4241
## Avg_Cogongrass_Cover-Sus_scrofa 1.5364 1.0996 -0.8088 1.5974
## Tree_Density-Canis_latrans -2.4660 1.0243 -4.8276 -2.3297
## Tree_Density-Sciurus_niger -1.9863 1.1725 -4.4448 -1.9500
## Tree_Density-Procyon_lotor -1.6162 0.7182 -3.0019 -1.6305
## Tree_Density-Dasypus_novemcinctus -3.0775 1.3051 -6.5274 -2.8152
## Tree_Density-Lynx_rufus -0.8593 1.1022 -2.7173 -0.9892
## Tree_Density-Didelphis_virginiana -2.2075 0.9428 -4.2925 -2.1216
## Tree_Density-Sylvilagus_floridanus -2.3683 1.1188 -5.0622 -2.2321
## Tree_Density-Meleagris_gallopavo -2.1603 1.0354 -4.4762 -2.0969
## Tree_Density-Sciurus_carolinensis -2.2961 1.0562 -4.7846 -2.1585
## Tree_Density-Vulpes_vulpes -1.9013 1.2035 -4.1600 -1.9280
## Tree_Density-Sus_scrofa -2.0951 1.1342 -4.6503 -1.9982
## Avg_Canopy_Cover-Canis_latrans 0.4999 0.6930 -0.8881 0.5170
## Avg_Canopy_Cover-Sciurus_niger 1.8498 1.2834 -0.6402 1.7390
## Avg_Canopy_Cover-Procyon_lotor 1.7301 0.6426 0.5968 1.6807
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9298 0.6285 0.8329 1.8735
## Avg_Canopy_Cover-Lynx_rufus 1.2000 1.0405 -0.7505 1.1724
## Avg_Canopy_Cover-Didelphis_virginiana 2.4382 0.8365 1.1005 2.3314
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8369 1.2640 1.0552 2.6102
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1667 0.9863 0.6420 2.0113
## Avg_Canopy_Cover-Sciurus_carolinensis 2.1110 0.7390 0.9034 2.0204
## Avg_Canopy_Cover-Vulpes_vulpes 2.0081 0.9192 0.5043 1.8823
## Avg_Canopy_Cover-Sus_scrofa 1.9275 0.7883 0.6027 1.8484
## avg_veg_height-Canis_latrans -0.7345 0.5586 -1.8935 -0.7092
## avg_veg_height-Sciurus_niger -0.7952 0.6802 -2.2803 -0.7555
## avg_veg_height-Procyon_lotor -0.4565 0.5174 -1.4405 -0.4528
## avg_veg_height-Dasypus_novemcinctus -0.3428 0.5336 -1.3956 -0.3487
## avg_veg_height-Lynx_rufus -0.6727 0.6646 -2.0865 -0.6459
## avg_veg_height-Didelphis_virginiana -0.6577 0.5963 -1.8896 -0.6326
## avg_veg_height-Sylvilagus_floridanus -0.7312 0.5820 -1.9130 -0.7078
## avg_veg_height-Meleagris_gallopavo -0.6533 0.6737 -2.1025 -0.6261
## avg_veg_height-Sciurus_carolinensis -0.2977 0.5805 -1.4191 -0.3186
## avg_veg_height-Vulpes_vulpes -0.5949 0.6351 -1.9098 -0.5676
## avg_veg_height-Sus_scrofa -0.6352 0.6019 -1.8948 -0.6241
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9868 1.0128 129
## (Intercept)-Sciurus_niger 2.4681 1.0214 167
## (Intercept)-Procyon_lotor 2.0585 1.0035 118
## (Intercept)-Dasypus_novemcinctus -0.0461 1.0120 327
## (Intercept)-Lynx_rufus 3.3681 1.0064 164
## (Intercept)-Didelphis_virginiana -0.7443 1.0481 307
## (Intercept)-Sylvilagus_floridanus 0.6359 1.0251 374
## (Intercept)-Meleagris_gallopavo 0.7840 1.0009 316
## (Intercept)-Sciurus_carolinensis -0.8558 1.0295 385
## (Intercept)-Vulpes_vulpes 0.4458 1.0041 216
## (Intercept)-Sus_scrofa -1.0438 1.0119 184
## Cogon_Patch_Size-Canis_latrans 2.6935 1.0160 767
## Cogon_Patch_Size-Sciurus_niger 0.7246 1.0158 286
## Cogon_Patch_Size-Procyon_lotor 0.3207 1.0562 277
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3317 1.0054 601
## Cogon_Patch_Size-Lynx_rufus 1.4026 1.0137 357
## Cogon_Patch_Size-Didelphis_virginiana 2.6837 1.0329 450
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0773 1.0592 270
## Cogon_Patch_Size-Meleagris_gallopavo 1.8337 1.0096 635
## Cogon_Patch_Size-Sciurus_carolinensis -0.0434 1.0721 430
## Cogon_Patch_Size-Vulpes_vulpes 1.1164 1.0143 433
## Cogon_Patch_Size-Sus_scrofa 1.0328 1.0486 313
## Veg_shannon_index-Canis_latrans 2.6068 1.0445 376
## Veg_shannon_index-Sciurus_niger 2.7532 1.0247 530
## Veg_shannon_index-Procyon_lotor 2.4441 1.0544 284
## Veg_shannon_index-Dasypus_novemcinctus 1.7112 1.0175 758
## Veg_shannon_index-Lynx_rufus 2.4297 1.0217 417
## Veg_shannon_index-Didelphis_virginiana 2.3044 1.0189 671
## Veg_shannon_index-Sylvilagus_floridanus 2.4288 1.0149 725
## Veg_shannon_index-Meleagris_gallopavo 2.8063 1.0250 545
## Veg_shannon_index-Sciurus_carolinensis 1.4927 1.0212 717
## Veg_shannon_index-Vulpes_vulpes 1.8595 1.0189 569
## Veg_shannon_index-Sus_scrofa 3.1863 1.0149 481
## total_shrub_cover-Canis_latrans 1.7169 1.0339 830
## total_shrub_cover-Sciurus_niger 0.6861 1.0128 497
## total_shrub_cover-Procyon_lotor 0.0763 1.0071 1283
## total_shrub_cover-Dasypus_novemcinctus 1.2216 1.0126 962
## total_shrub_cover-Lynx_rufus 0.4001 1.0134 407
## total_shrub_cover-Didelphis_virginiana 0.6803 1.0031 841
## total_shrub_cover-Sylvilagus_floridanus 1.3494 1.0127 858
## total_shrub_cover-Meleagris_gallopavo -0.2791 1.0244 318
## total_shrub_cover-Sciurus_carolinensis 1.3422 1.0053 1011
## total_shrub_cover-Vulpes_vulpes 1.0054 1.0151 622
## total_shrub_cover-Sus_scrofa 1.8988 1.0037 854
## Avg_Cogongrass_Cover-Canis_latrans 4.2555 1.0251 298
## Avg_Cogongrass_Cover-Sciurus_niger 3.7078 1.0363 230
## Avg_Cogongrass_Cover-Procyon_lotor 4.1103 1.0300 236
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.7134 1.0118 270
## Avg_Cogongrass_Cover-Lynx_rufus 4.9320 1.0093 270
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0736 1.0127 252
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3296 1.0118 290
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.7881 1.0203 244
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.4729 1.0249 266
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7648 1.0625 294
## Avg_Cogongrass_Cover-Sus_scrofa 3.6278 1.0295 293
## Tree_Density-Canis_latrans -0.8728 1.0240 460
## Tree_Density-Sciurus_niger 0.4109 1.0041 507
## Tree_Density-Procyon_lotor -0.1742 1.0048 453
## Tree_Density-Dasypus_novemcinctus -1.2974 1.0162 249
## Tree_Density-Lynx_rufus 1.5479 1.0029 267
## Tree_Density-Didelphis_virginiana -0.6096 1.0189 480
## Tree_Density-Sylvilagus_floridanus -0.4581 1.0433 414
## Tree_Density-Meleagris_gallopavo -0.2279 1.0155 547
## Tree_Density-Sciurus_carolinensis -0.6007 1.0178 395
## Tree_Density-Vulpes_vulpes 0.7303 1.0041 438
## Tree_Density-Sus_scrofa -0.0739 1.0210 439
## Avg_Canopy_Cover-Canis_latrans 1.8393 1.0135 508
## Avg_Canopy_Cover-Sciurus_niger 4.7208 1.0048 268
## Avg_Canopy_Cover-Procyon_lotor 3.1068 1.0303 471
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3298 1.0461 328
## Avg_Canopy_Cover-Lynx_rufus 3.3815 1.0083 261
## Avg_Canopy_Cover-Didelphis_virginiana 4.3758 1.0863 259
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.0577 1.0970 297
## Avg_Canopy_Cover-Meleagris_gallopavo 4.5707 1.1041 419
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9226 1.0458 597
## Avg_Canopy_Cover-Vulpes_vulpes 4.2398 1.0305 530
## Avg_Canopy_Cover-Sus_scrofa 3.7390 1.0037 496
## avg_veg_height-Canis_latrans 0.3491 1.0059 385
## avg_veg_height-Sciurus_niger 0.3969 1.0017 437
## avg_veg_height-Procyon_lotor 0.5867 1.0019 498
## avg_veg_height-Dasypus_novemcinctus 0.7184 1.0000 501
## avg_veg_height-Lynx_rufus 0.5739 1.0155 460
## avg_veg_height-Didelphis_virginiana 0.4312 1.0007 528
## avg_veg_height-Sylvilagus_floridanus 0.3580 1.0041 432
## avg_veg_height-Meleagris_gallopavo 0.6344 1.0118 459
## avg_veg_height-Sciurus_carolinensis 0.9219 1.0080 545
## avg_veg_height-Vulpes_vulpes 0.6466 1.0029 445
## avg_veg_height-Sus_scrofa 0.5418 1.0059 391
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6314 0.1772 -2.9865 -2.6254 -2.2935 1.0044
## (Intercept)-Sciurus_niger -4.2158 0.4462 -5.1122 -4.2157 -3.3761 1.0168
## (Intercept)-Procyon_lotor -2.2651 0.1262 -2.5269 -2.2603 -2.0292 1.0062
## (Intercept)-Dasypus_novemcinctus -1.6013 0.1348 -1.8748 -1.5969 -1.3357 1.0002
## (Intercept)-Lynx_rufus -3.6057 0.2947 -4.1802 -3.6012 -3.0453 1.0042
## (Intercept)-Didelphis_virginiana -2.3283 0.2454 -2.8478 -2.3161 -1.8760 1.0015
## (Intercept)-Sylvilagus_floridanus -3.1726 0.2721 -3.7206 -3.1616 -2.6608 1.0072
## (Intercept)-Meleagris_gallopavo -3.3896 0.2902 -3.9755 -3.3796 -2.8384 1.0111
## (Intercept)-Sciurus_carolinensis -2.4709 0.2632 -3.0065 -2.4645 -1.9707 1.0005
## (Intercept)-Vulpes_vulpes -3.9802 0.5329 -5.0576 -3.9843 -2.9679 1.0275
## (Intercept)-Sus_scrofa -2.9800 0.4501 -3.9559 -2.9487 -2.1734 1.0045
## ESS
## (Intercept)-Canis_latrans 866
## (Intercept)-Sciurus_niger 232
## (Intercept)-Procyon_lotor 1540
## (Intercept)-Dasypus_novemcinctus 2519
## (Intercept)-Lynx_rufus 323
## (Intercept)-Didelphis_virginiana 1535
## (Intercept)-Sylvilagus_floridanus 626
## (Intercept)-Meleagris_gallopavo 431
## (Intercept)-Sciurus_carolinensis 1181
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 531
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5817
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8427 0.3826 -1.5976 -0.8454 -0.0696 1.0041 457
## Avg_Cogongrass_Cover 0.0386 0.3069 -0.5767 0.0423 0.6184 1.0256 621
## total_shrub_cover -0.4516 0.2962 -1.0730 -0.4401 0.1119 1.0109 780
## avg_veg_height -0.0258 0.2816 -0.5753 -0.0262 0.5443 1.0265 521
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7132 0.7684 0.0678 0.5000 2.7742 1.0209 563
## Avg_Cogongrass_Cover 0.3739 0.4306 0.0443 0.2361 1.4997 1.0059 743
## total_shrub_cover 0.5115 0.5075 0.0578 0.3481 1.9036 1.0031 536
## avg_veg_height 0.1982 0.1915 0.0312 0.1396 0.6923 1.0046 896
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1615 0.9286 0.1558 0.9377 3.4634 1.044 189
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8178 0.2711 -3.3595 -2.8153 -2.2859 1.0005 1396
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7025 0.4809 0.1957 0.5732 1.9134 1.0065 523
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2371 0.6168 -1.4207 -0.2692
## (Intercept)-Sciurus_niger -0.9629 0.6952 -2.3015 -0.9839
## (Intercept)-Procyon_lotor -0.0650 0.6667 -1.3268 -0.0836
## (Intercept)-Dasypus_novemcinctus -0.8407 0.5348 -1.9168 -0.8321
## (Intercept)-Lynx_rufus -0.5770 0.6597 -1.8377 -0.6057
## (Intercept)-Didelphis_virginiana -1.2382 0.5487 -2.3242 -1.2202
## (Intercept)-Sylvilagus_floridanus -0.6248 0.6465 -1.7873 -0.6625
## (Intercept)-Meleagris_gallopavo -0.9003 0.5763 -2.0698 -0.8993
## (Intercept)-Sciurus_carolinensis -1.2975 0.5879 -2.4600 -1.2678
## (Intercept)-Vulpes_vulpes -1.2307 0.7501 -2.6998 -1.2171
## (Intercept)-Sus_scrofa -1.5181 0.6852 -2.9519 -1.4839
## Avg_Cogongrass_Cover-Canis_latrans 0.3499 0.4351 -0.4343 0.3338
## Avg_Cogongrass_Cover-Sciurus_niger -0.3392 0.5989 -1.7508 -0.2676
## Avg_Cogongrass_Cover-Procyon_lotor 0.0155 0.4312 -0.8644 0.0269
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2294 0.3913 -0.5291 0.2241
## Avg_Cogongrass_Cover-Lynx_rufus 0.3779 0.4821 -0.5008 0.3509
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2890 0.4266 -0.5178 0.2787
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3171 0.5132 -1.4898 -0.2648
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4059 0.5688 -1.7097 -0.3323
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2149 0.4182 -0.5930 0.2064
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2039 0.5071 -0.7655 0.1857
## Avg_Cogongrass_Cover-Sus_scrofa -0.2114 0.5511 -1.4492 -0.1687
## total_shrub_cover-Canis_latrans 0.0865 0.4366 -0.6957 0.0600
## total_shrub_cover-Sciurus_niger -0.7401 0.5866 -2.0953 -0.6751
## total_shrub_cover-Procyon_lotor -0.8552 0.4552 -1.8192 -0.8250
## total_shrub_cover-Dasypus_novemcinctus -0.0866 0.3661 -0.7881 -0.1021
## total_shrub_cover-Lynx_rufus -0.8774 0.5986 -2.2437 -0.8090
## total_shrub_cover-Didelphis_virginiana -0.2956 0.4144 -1.1502 -0.2922
## total_shrub_cover-Sylvilagus_floridanus -0.4199 0.4997 -1.4675 -0.4029
## total_shrub_cover-Meleagris_gallopavo -1.1918 0.6212 -2.6203 -1.1089
## total_shrub_cover-Sciurus_carolinensis -0.1777 0.4199 -1.0125 -0.1806
## total_shrub_cover-Vulpes_vulpes -0.4632 0.5836 -1.7849 -0.4135
## total_shrub_cover-Sus_scrofa -0.0351 0.5384 -1.0094 -0.0691
## avg_veg_height-Canis_latrans -0.0712 0.3871 -0.8222 -0.0727
## avg_veg_height-Sciurus_niger -0.1792 0.4748 -1.2188 -0.1517
## avg_veg_height-Procyon_lotor 0.0788 0.3945 -0.6834 0.0774
## avg_veg_height-Dasypus_novemcinctus 0.1729 0.3748 -0.5519 0.1590
## avg_veg_height-Lynx_rufus -0.0099 0.4468 -0.9158 0.0005
## avg_veg_height-Didelphis_virginiana -0.0369 0.3848 -0.8081 -0.0296
## avg_veg_height-Sylvilagus_floridanus -0.1430 0.4212 -1.0330 -0.1273
## avg_veg_height-Meleagris_gallopavo -0.2248 0.4526 -1.1883 -0.2023
## avg_veg_height-Sciurus_carolinensis 0.2310 0.4087 -0.5206 0.2180
## avg_veg_height-Vulpes_vulpes -0.0649 0.4379 -0.9641 -0.0646
## avg_veg_height-Sus_scrofa -0.0177 0.4310 -0.8794 -0.0032
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0401 1.0271 482
## (Intercept)-Sciurus_niger 0.4285 1.0033 551
## (Intercept)-Procyon_lotor 1.3364 1.0201 396
## (Intercept)-Dasypus_novemcinctus 0.2133 1.0122 882
## (Intercept)-Lynx_rufus 0.8318 1.0012 587
## (Intercept)-Didelphis_virginiana -0.1924 1.0022 818
## (Intercept)-Sylvilagus_floridanus 0.7727 1.0315 615
## (Intercept)-Meleagris_gallopavo 0.2215 1.0058 879
## (Intercept)-Sciurus_carolinensis -0.2385 1.0022 877
## (Intercept)-Vulpes_vulpes 0.2564 1.0236 332
## (Intercept)-Sus_scrofa -0.3212 1.0021 484
## Avg_Cogongrass_Cover-Canis_latrans 1.3087 1.0111 969
## Avg_Cogongrass_Cover-Sciurus_niger 0.6463 1.0141 706
## Avg_Cogongrass_Cover-Procyon_lotor 0.8328 1.0158 1171
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0500 1.0101 1397
## Avg_Cogongrass_Cover-Lynx_rufus 1.4406 1.0017 1125
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1652 1.0100 1030
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5663 1.0184 799
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5266 1.0255 710
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0546 1.0083 1215
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2622 1.0087 1050
## Avg_Cogongrass_Cover-Sus_scrofa 0.7912 1.0165 894
## total_shrub_cover-Canis_latrans 1.0097 1.0095 1664
## total_shrub_cover-Sciurus_niger 0.2548 1.0017 656
## total_shrub_cover-Procyon_lotor -0.0662 1.0023 1143
## total_shrub_cover-Dasypus_novemcinctus 0.6642 1.0080 1784
## total_shrub_cover-Lynx_rufus 0.1391 1.0159 674
## total_shrub_cover-Didelphis_virginiana 0.5280 1.0059 2355
## total_shrub_cover-Sylvilagus_floridanus 0.5254 1.0018 1230
## total_shrub_cover-Meleagris_gallopavo -0.2105 1.0120 567
## total_shrub_cover-Sciurus_carolinensis 0.6694 1.0080 2057
## total_shrub_cover-Vulpes_vulpes 0.5961 1.0029 993
## total_shrub_cover-Sus_scrofa 1.1032 1.0168 1185
## avg_veg_height-Canis_latrans 0.7156 1.0187 873
## avg_veg_height-Sciurus_niger 0.6944 1.0152 753
## avg_veg_height-Procyon_lotor 0.8885 1.0102 1088
## avg_veg_height-Dasypus_novemcinctus 0.9768 1.0069 1386
## avg_veg_height-Lynx_rufus 0.8786 1.0060 920
## avg_veg_height-Didelphis_virginiana 0.6942 1.0220 1006
## avg_veg_height-Sylvilagus_floridanus 0.6260 1.0054 905
## avg_veg_height-Meleagris_gallopavo 0.6244 1.0032 782
## avg_veg_height-Sciurus_carolinensis 1.0884 1.0125 1012
## avg_veg_height-Vulpes_vulpes 0.7736 1.0185 930
## avg_veg_height-Sus_scrofa 0.8170 1.0113 865
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6285 0.1721 -2.9847 -2.6231 -2.3005 1.0183
## (Intercept)-Sciurus_niger -3.6889 0.4807 -4.6873 -3.6641 -2.8159 1.0063
## (Intercept)-Procyon_lotor -2.2849 0.1330 -2.5517 -2.2831 -2.0351 1.0027
## (Intercept)-Dasypus_novemcinctus -1.6037 0.1357 -1.8746 -1.6027 -1.3460 1.0093
## (Intercept)-Lynx_rufus -3.4456 0.2928 -4.0406 -3.4383 -2.9084 1.0059
## (Intercept)-Didelphis_virginiana -2.3694 0.2542 -2.9004 -2.3611 -1.8911 1.0208
## (Intercept)-Sylvilagus_floridanus -3.1789 0.2930 -3.7825 -3.1645 -2.6427 1.0313
## (Intercept)-Meleagris_gallopavo -3.2484 0.2988 -3.8502 -3.2397 -2.6797 1.0201
## (Intercept)-Sciurus_carolinensis -2.5038 0.2757 -3.1005 -2.4872 -2.0151 1.0008
## (Intercept)-Vulpes_vulpes -3.7658 0.5897 -4.9759 -3.7232 -2.7042 1.0599
## (Intercept)-Sus_scrofa -3.0047 0.4696 -4.0439 -2.9614 -2.1706 1.0094
## ESS
## (Intercept)-Canis_latrans 1034
## (Intercept)-Sciurus_niger 232
## (Intercept)-Procyon_lotor 1467
## (Intercept)-Dasypus_novemcinctus 2246
## (Intercept)-Lynx_rufus 433
## (Intercept)-Didelphis_virginiana 1241
## (Intercept)-Sylvilagus_floridanus 450
## (Intercept)-Meleagris_gallopavo 564
## (Intercept)-Sciurus_carolinensis 958
## (Intercept)-Vulpes_vulpes 217
## (Intercept)-Sus_scrofa 333
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5823
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9226 0.4120 -1.7088 -0.9275 -0.1080 1.0050 568
## Tree_Density -0.7691 0.3889 -1.6435 -0.7363 -0.1070 1.0040 521
## Avg_Canopy_Cover 0.9939 0.3076 0.4376 0.9725 1.6607 1.0033 614
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2055 1.1753 0.1183 0.8942 4.0539 1.0202 642
## Tree_Density 0.6567 0.8790 0.0504 0.3605 3.1130 1.0080 337
## Avg_Canopy_Cover 0.5023 0.5462 0.0551 0.3436 1.8489 1.0420 414
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6316 0.6584 0.044 0.4292 2.319 1.0243 176
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8272 0.2795 -3.3868 -2.8251 -2.2904 1.0005 1526
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7856 0.6037 0.225 0.6367 2.1837 1.0147 800
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0845 0.5932 -1.2241 -0.0932 1.0864
## (Intercept)-Sciurus_niger -0.8987 0.7725 -2.3339 -0.9395 0.7294
## (Intercept)-Procyon_lotor 0.1872 0.6531 -1.1269 0.2235 1.3836
## (Intercept)-Dasypus_novemcinctus -1.0395 0.5390 -2.1588 -1.0155 -0.0320
## (Intercept)-Lynx_rufus -0.2562 0.8664 -1.6793 -0.3478 1.7014
## (Intercept)-Didelphis_virginiana -1.6450 0.6229 -2.9517 -1.6117 -0.5834
## (Intercept)-Sylvilagus_floridanus -0.8346 0.6136 -2.0650 -0.8158 0.3863
## (Intercept)-Meleagris_gallopavo -0.7483 0.6461 -2.0115 -0.7575 0.5687
## (Intercept)-Sciurus_carolinensis -1.6936 0.6297 -3.0138 -1.6475 -0.5879
## (Intercept)-Vulpes_vulpes -1.4917 0.8317 -3.1240 -1.4862 0.1834
## (Intercept)-Sus_scrofa -2.0570 0.8001 -3.7936 -2.0009 -0.6351
## Tree_Density-Canis_latrans -0.9119 0.5505 -2.1698 -0.8488 -0.0022
## Tree_Density-Sciurus_niger -0.7779 0.6657 -2.2831 -0.7160 0.3868
## Tree_Density-Procyon_lotor -0.4943 0.3966 -1.2893 -0.4896 0.2726
## Tree_Density-Dasypus_novemcinctus -1.3247 0.8162 -3.4732 -1.1631 -0.2101
## Tree_Density-Lynx_rufus 0.1112 0.6374 -0.8975 0.0438 1.6508
## Tree_Density-Didelphis_virginiana -0.9686 0.7257 -2.6818 -0.8513 0.1263
## Tree_Density-Sylvilagus_floridanus -1.0576 0.7390 -2.8913 -0.9342 0.0433
## Tree_Density-Meleagris_gallopavo -0.8689 0.6707 -2.4061 -0.7914 0.1942
## Tree_Density-Sciurus_carolinensis -0.8861 0.6695 -2.4505 -0.7934 0.1577
## Tree_Density-Vulpes_vulpes -0.6020 0.7655 -2.1209 -0.5920 0.9810
## Tree_Density-Sus_scrofa -0.8909 0.7362 -2.6358 -0.7853 0.2720
## Avg_Canopy_Cover-Canis_latrans 0.1521 0.4575 -0.7568 0.1601 1.0623
## Avg_Canopy_Cover-Sciurus_niger 0.8851 0.6558 -0.2867 0.8496 2.3033
## Avg_Canopy_Cover-Procyon_lotor 0.9746 0.4298 0.1887 0.9598 1.8807
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0043 0.4097 0.2494 0.9858 1.8434
## Avg_Canopy_Cover-Lynx_rufus 0.7249 0.6060 -0.3631 0.7047 2.0712
## Avg_Canopy_Cover-Didelphis_virginiana 1.2103 0.4859 0.3706 1.1721 2.2772
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5848 0.6788 0.5723 1.4673 3.2868
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3231 0.6167 0.3467 1.2311 2.8159
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1963 0.4626 0.3638 1.1677 2.2173
## Avg_Canopy_Cover-Vulpes_vulpes 0.9976 0.5405 0.0094 0.9706 2.1925
## Avg_Canopy_Cover-Sus_scrofa 1.1115 0.4747 0.2530 1.0838 2.0978
## Rhat ESS
## (Intercept)-Canis_latrans 1.0137 558
## (Intercept)-Sciurus_niger 1.0119 468
## (Intercept)-Procyon_lotor 1.0137 323
## (Intercept)-Dasypus_novemcinctus 1.0029 1386
## (Intercept)-Lynx_rufus 1.0063 282
## (Intercept)-Didelphis_virginiana 1.0250 900
## (Intercept)-Sylvilagus_floridanus 1.0041 828
## (Intercept)-Meleagris_gallopavo 1.0096 831
## (Intercept)-Sciurus_carolinensis 1.0187 704
## (Intercept)-Vulpes_vulpes 1.0076 387
## (Intercept)-Sus_scrofa 1.0108 530
## Tree_Density-Canis_latrans 1.0042 1262
## Tree_Density-Sciurus_niger 1.0251 687
## Tree_Density-Procyon_lotor 1.0020 1778
## Tree_Density-Dasypus_novemcinctus 1.0109 430
## Tree_Density-Lynx_rufus 1.0002 493
## Tree_Density-Didelphis_virginiana 1.0029 755
## Tree_Density-Sylvilagus_floridanus 1.0063 410
## Tree_Density-Meleagris_gallopavo 1.0014 766
## Tree_Density-Sciurus_carolinensis 1.0086 684
## Tree_Density-Vulpes_vulpes 1.0024 620
## Tree_Density-Sus_scrofa 1.0008 587
## Avg_Canopy_Cover-Canis_latrans 1.0086 843
## Avg_Canopy_Cover-Sciurus_niger 1.0091 581
## Avg_Canopy_Cover-Procyon_lotor 1.0074 1759
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0017 1211
## Avg_Canopy_Cover-Lynx_rufus 0.9996 736
## Avg_Canopy_Cover-Didelphis_virginiana 1.0054 1077
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0077 499
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0013 709
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0032 1077
## Avg_Canopy_Cover-Vulpes_vulpes 1.0137 1225
## Avg_Canopy_Cover-Sus_scrofa 1.0056 1334
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6334 0.1790 -3.0085 -2.6270 -2.3020 1.0024
## (Intercept)-Sciurus_niger -3.8133 0.5050 -4.8773 -3.8058 -2.8520 1.0050
## (Intercept)-Procyon_lotor -2.2674 0.1253 -2.5164 -2.2635 -2.0277 1.0037
## (Intercept)-Dasypus_novemcinctus -1.6036 0.1344 -1.8719 -1.6000 -1.3437 1.0114
## (Intercept)-Lynx_rufus -3.5095 0.3128 -4.1420 -3.5021 -2.9354 1.0193
## (Intercept)-Didelphis_virginiana -2.3502 0.2507 -2.8796 -2.3330 -1.8823 1.0165
## (Intercept)-Sylvilagus_floridanus -3.0959 0.2629 -3.6549 -3.0889 -2.6131 1.0110
## (Intercept)-Meleagris_gallopavo -3.3484 0.3006 -3.9675 -3.3395 -2.7861 1.0048
## (Intercept)-Sciurus_carolinensis -2.4814 0.2680 -3.0163 -2.4735 -2.0004 1.0019
## (Intercept)-Vulpes_vulpes -3.8148 0.6185 -5.0929 -3.7765 -2.7022 1.0153
## (Intercept)-Sus_scrofa -2.9544 0.4661 -4.0138 -2.9175 -2.1495 1.0073
## ESS
## (Intercept)-Canis_latrans 957
## (Intercept)-Sciurus_niger 232
## (Intercept)-Procyon_lotor 1549
## (Intercept)-Dasypus_novemcinctus 2250
## (Intercept)-Lynx_rufus 400
## (Intercept)-Didelphis_virginiana 1304
## (Intercept)-Sylvilagus_floridanus 613
## (Intercept)-Meleagris_gallopavo 500
## (Intercept)-Sciurus_carolinensis 1509
## (Intercept)-Vulpes_vulpes 185
## (Intercept)-Sus_scrofa 527
# Includes movement covariates of occupancy and null for detection
ms_null_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5835
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8562 0.3756 -1.6169 -0.8479 -0.1518 1.0147 536
## Cogon_Patch_Size -0.1906 0.3436 -0.9186 -0.1711 0.4233 1.0026 520
## Avg_Cogongrass_Cover 0.0872 0.2783 -0.4949 0.0957 0.6077 1.0090 643
## total_shrub_cover -0.4077 0.2820 -0.9850 -0.3996 0.1115 1.0006 1032
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7321 0.7119 0.0653 0.5447 2.4962 1.0203 691
## Cogon_Patch_Size 0.6071 0.7578 0.0537 0.3670 2.5515 1.0357 427
## Avg_Cogongrass_Cover 0.3519 0.4564 0.0395 0.2164 1.4938 1.0189 662
## total_shrub_cover 0.4655 0.5297 0.0524 0.3061 1.7857 1.0526 567
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2037 0.9782 0.1282 0.958 3.7079 1.0519 161
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8165 0.2693 -3.3731 -2.8065 -2.2911 1.0017 1473
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7236 0.5314 0.2072 0.5851 2.0531 1.0052 526
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2064 0.6175 -1.3856 -0.2167
## (Intercept)-Sciurus_niger -1.0021 0.7085 -2.4214 -1.0074
## (Intercept)-Procyon_lotor -0.0209 0.6679 -1.2319 -0.0370
## (Intercept)-Dasypus_novemcinctus -0.8453 0.5279 -1.9348 -0.8228
## (Intercept)-Lynx_rufus -0.5884 0.6382 -1.7761 -0.6022
## (Intercept)-Didelphis_virginiana -1.2181 0.5700 -2.4270 -1.1874
## (Intercept)-Sylvilagus_floridanus -0.6886 0.6144 -1.9094 -0.6996
## (Intercept)-Meleagris_gallopavo -0.8654 0.6029 -2.1010 -0.8727
## (Intercept)-Sciurus_carolinensis -1.3260 0.5809 -2.5768 -1.2870
## (Intercept)-Vulpes_vulpes -1.2761 0.7792 -2.8454 -1.2470
## (Intercept)-Sus_scrofa -1.5855 0.7222 -3.1588 -1.5093
## Cogon_Patch_Size-Canis_latrans 0.5452 0.5838 -0.3617 0.4552
## Cogon_Patch_Size-Sciurus_niger -0.4897 0.7568 -2.2356 -0.3998
## Cogon_Patch_Size-Procyon_lotor -0.2157 0.4166 -1.0917 -0.2149
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1215 0.3947 -0.9552 -0.1083
## Cogon_Patch_Size-Lynx_rufus -0.2643 0.6393 -1.5229 -0.2775
## Cogon_Patch_Size-Didelphis_virginiana 0.5409 0.4773 -0.2925 0.4956
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7100 0.7302 -2.4650 -0.6024
## Cogon_Patch_Size-Meleagris_gallopavo -0.1239 0.5434 -1.2130 -0.1176
## Cogon_Patch_Size-Sciurus_carolinensis -0.5651 0.5935 -1.9797 -0.4853
## Cogon_Patch_Size-Vulpes_vulpes -0.4427 0.7449 -2.1321 -0.3718
## Cogon_Patch_Size-Sus_scrofa -0.3017 0.6717 -1.9141 -0.2179
## Avg_Cogongrass_Cover-Canis_latrans 0.2367 0.3815 -0.4824 0.2198
## Avg_Cogongrass_Cover-Sciurus_niger -0.2718 0.6106 -1.7117 -0.1913
## Avg_Cogongrass_Cover-Procyon_lotor 0.1477 0.4071 -0.6077 0.1299
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3282 0.3612 -0.3613 0.3228
## Avg_Cogongrass_Cover-Lynx_rufus 0.4608 0.4788 -0.3486 0.4150
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1757 0.4063 -0.6587 0.1748
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1963 0.4631 -1.1910 -0.1669
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3801 0.5636 -1.6511 -0.3233
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3898 0.3971 -0.3531 0.3810
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2384 0.4733 -0.6419 0.2245
## Avg_Cogongrass_Cover-Sus_scrofa -0.1508 0.5384 -1.3989 -0.0969
## total_shrub_cover-Canis_latrans 0.0328 0.4221 -0.7478 0.0130
## total_shrub_cover-Sciurus_niger -0.6366 0.5466 -1.8583 -0.5908
## total_shrub_cover-Procyon_lotor -0.7957 0.4514 -1.7977 -0.7483
## total_shrub_cover-Dasypus_novemcinctus -0.1007 0.3675 -0.8034 -0.1027
## total_shrub_cover-Lynx_rufus -0.7400 0.5750 -2.0381 -0.6668
## total_shrub_cover-Didelphis_virginiana -0.3551 0.4201 -1.1864 -0.3500
## total_shrub_cover-Sylvilagus_floridanus -0.3505 0.4912 -1.3643 -0.3359
## total_shrub_cover-Meleagris_gallopavo -1.1088 0.6075 -2.4952 -1.0397
## total_shrub_cover-Sciurus_carolinensis -0.1452 0.4251 -0.9895 -0.1550
## total_shrub_cover-Vulpes_vulpes -0.3651 0.5625 -1.4965 -0.3541
## total_shrub_cover-Sus_scrofa -0.0254 0.5167 -1.0092 -0.0502
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0653 1.0068 497
## (Intercept)-Sciurus_niger 0.4355 1.0076 486
## (Intercept)-Procyon_lotor 1.3180 1.0211 430
## (Intercept)-Dasypus_novemcinctus 0.0879 1.0174 1019
## (Intercept)-Lynx_rufus 0.7423 1.0117 560
## (Intercept)-Didelphis_virginiana -0.1750 1.0030 1130
## (Intercept)-Sylvilagus_floridanus 0.5641 1.0120 738
## (Intercept)-Meleagris_gallopavo 0.3557 1.0373 880
## (Intercept)-Sciurus_carolinensis -0.2835 1.0072 971
## (Intercept)-Vulpes_vulpes 0.2083 1.0367 365
## (Intercept)-Sus_scrofa -0.3247 1.0112 553
## Cogon_Patch_Size-Canis_latrans 1.9150 1.0017 919
## Cogon_Patch_Size-Sciurus_niger 0.7438 1.0290 477
## Cogon_Patch_Size-Procyon_lotor 0.5915 1.0037 1543
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6185 1.0062 1755
## Cogon_Patch_Size-Lynx_rufus 1.0842 1.0083 855
## Cogon_Patch_Size-Didelphis_virginiana 1.5580 1.0006 914
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4521 1.0031 427
## Cogon_Patch_Size-Meleagris_gallopavo 0.9353 1.0034 1252
## Cogon_Patch_Size-Sciurus_carolinensis 0.3719 1.0029 680
## Cogon_Patch_Size-Vulpes_vulpes 0.7679 1.0020 526
## Cogon_Patch_Size-Sus_scrofa 0.7892 1.0046 772
## Avg_Cogongrass_Cover-Canis_latrans 1.0257 1.0021 1099
## Avg_Cogongrass_Cover-Sciurus_niger 0.7260 1.0080 656
## Avg_Cogongrass_Cover-Procyon_lotor 0.9955 1.0016 1417
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0535 1.0014 1408
## Avg_Cogongrass_Cover-Lynx_rufus 1.5510 1.0084 1207
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9685 1.0096 1241
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6231 1.0116 945
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5716 1.0004 710
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1848 1.0014 1431
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2441 1.0063 1234
## Avg_Cogongrass_Cover-Sus_scrofa 0.7442 1.0040 858
## total_shrub_cover-Canis_latrans 0.9253 1.0012 865
## total_shrub_cover-Sciurus_niger 0.3242 1.0249 918
## total_shrub_cover-Procyon_lotor -0.0284 1.0003 1087
## total_shrub_cover-Dasypus_novemcinctus 0.6358 1.0056 2210
## total_shrub_cover-Lynx_rufus 0.2178 1.0083 788
## total_shrub_cover-Didelphis_virginiana 0.4916 0.9999 1949
## total_shrub_cover-Sylvilagus_floridanus 0.5814 1.0029 1133
## total_shrub_cover-Meleagris_gallopavo -0.1403 1.0163 620
## total_shrub_cover-Sciurus_carolinensis 0.7368 1.0085 1731
## total_shrub_cover-Vulpes_vulpes 0.7720 1.0040 1126
## total_shrub_cover-Sus_scrofa 1.0430 1.0044 1359
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6074 0.1711 -2.9535 -2.6018 -2.2913 1.0048
## (Intercept)-Sciurus_niger -3.7378 0.5258 -4.7931 -3.7146 -2.7849 1.0039
## (Intercept)-Procyon_lotor -2.2778 0.1284 -2.5363 -2.2744 -2.0357 1.0154
## (Intercept)-Dasypus_novemcinctus -1.6064 0.1368 -1.8793 -1.6053 -1.3453 0.9999
## (Intercept)-Lynx_rufus -3.4197 0.2768 -3.9789 -3.4215 -2.8913 1.0062
## (Intercept)-Didelphis_virginiana -2.3760 0.2480 -2.9103 -2.3707 -1.9109 1.0160
## (Intercept)-Sylvilagus_floridanus -3.1825 0.2848 -3.7522 -3.1721 -2.6543 1.0370
## (Intercept)-Meleagris_gallopavo -3.2594 0.3084 -3.8895 -3.2472 -2.7000 1.0014
## (Intercept)-Sciurus_carolinensis -2.4893 0.2635 -3.0414 -2.4763 -2.0012 1.0037
## (Intercept)-Vulpes_vulpes -3.7913 0.5957 -5.0455 -3.7738 -2.7172 1.0232
## (Intercept)-Sus_scrofa -2.9694 0.4388 -3.9260 -2.9373 -2.2061 1.0526
## ESS
## (Intercept)-Canis_latrans 1035
## (Intercept)-Sciurus_niger 222
## (Intercept)-Procyon_lotor 1548
## (Intercept)-Dasypus_novemcinctus 2376
## (Intercept)-Lynx_rufus 518
## (Intercept)-Didelphis_virginiana 1242
## (Intercept)-Sylvilagus_floridanus 462
## (Intercept)-Meleagris_gallopavo 479
## (Intercept)-Sciurus_carolinensis 1307
## (Intercept)-Vulpes_vulpes 223
## (Intercept)-Sus_scrofa 417
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5693
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8261 0.3542 -1.5314 -0.8316 -0.1304 1.0164 457
## Veg_shannon_index 0.3347 0.2400 -0.1308 0.3302 0.8332 1.0182 1051
## Avg_Cogongrass_Cover 0.2083 0.2408 -0.2818 0.2091 0.6842 1.0038 917
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6117 0.5979 0.0579 0.4436 2.1941 1.0460 605
## Veg_shannon_index 0.2407 0.2726 0.0344 0.1664 0.8345 1.0412 910
## Avg_Cogongrass_Cover 0.3020 0.4017 0.0407 0.2005 1.1250 1.0665 906
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9781 0.7451 0.117 0.7844 2.9115 1.0583 236
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8201 0.2764 -3.387 -2.8082 -2.2772 1.0005 986
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7098 0.5005 0.1969 0.5769 2.0031 1.0025 577
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2683 0.5664 -1.3118 -0.2809
## (Intercept)-Sciurus_niger -0.9260 0.6498 -2.1789 -0.9490
## (Intercept)-Procyon_lotor -0.1381 0.6336 -1.3072 -0.1610
## (Intercept)-Dasypus_novemcinctus -0.8225 0.4992 -1.8372 -0.8191
## (Intercept)-Lynx_rufus -0.6022 0.6092 -1.6937 -0.6337
## (Intercept)-Didelphis_virginiana -1.2360 0.5441 -2.3991 -1.2088
## (Intercept)-Sylvilagus_floridanus -0.6616 0.5712 -1.7251 -0.6796
## (Intercept)-Meleagris_gallopavo -0.7289 0.6054 -1.9013 -0.7513
## (Intercept)-Sciurus_carolinensis -1.2288 0.5344 -2.3522 -1.2005
## (Intercept)-Vulpes_vulpes -1.2014 0.6941 -2.6033 -1.1875
## (Intercept)-Sus_scrofa -1.5165 0.6569 -2.9272 -1.4653
## Veg_shannon_index-Canis_latrans 0.6098 0.3622 -0.0564 0.5924
## Veg_shannon_index-Sciurus_niger 0.3307 0.4460 -0.5294 0.3219
## Veg_shannon_index-Procyon_lotor 0.4719 0.3630 -0.1928 0.4530
## Veg_shannon_index-Dasypus_novemcinctus 0.2008 0.3251 -0.4594 0.2043
## Veg_shannon_index-Lynx_rufus 0.1932 0.4283 -0.6870 0.2118
## Veg_shannon_index-Didelphis_virginiana 0.4530 0.3696 -0.2624 0.4404
## Veg_shannon_index-Sylvilagus_floridanus 0.4181 0.4080 -0.3396 0.4190
## Veg_shannon_index-Meleagris_gallopavo 0.4294 0.4225 -0.3601 0.4135
## Veg_shannon_index-Sciurus_carolinensis 0.0139 0.3789 -0.8050 0.0391
## Veg_shannon_index-Vulpes_vulpes 0.0676 0.4321 -0.8343 0.0865
## Veg_shannon_index-Sus_scrofa 0.5777 0.4485 -0.2104 0.5367
## Avg_Cogongrass_Cover-Canis_latrans 0.4700 0.3583 -0.1863 0.4506
## Avg_Cogongrass_Cover-Sciurus_niger -0.1301 0.5219 -1.3256 -0.0725
## Avg_Cogongrass_Cover-Procyon_lotor 0.3469 0.3721 -0.3415 0.3211
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3935 0.3230 -0.2227 0.3830
## Avg_Cogongrass_Cover-Lynx_rufus 0.5162 0.4018 -0.2035 0.4885
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4047 0.3642 -0.3005 0.3908
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1317 0.4464 -1.1813 -0.0912
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1781 0.5026 -1.3460 -0.1226
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3732 0.3541 -0.2907 0.3615
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3031 0.4376 -0.5272 0.2896
## Avg_Cogongrass_Cover-Sus_scrofa -0.0311 0.4881 -1.2122 0.0217
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8467 1.0495 550
## (Intercept)-Sciurus_niger 0.3974 1.0073 528
## (Intercept)-Procyon_lotor 1.1153 1.0411 360
## (Intercept)-Dasypus_novemcinctus 0.1644 1.0093 1280
## (Intercept)-Lynx_rufus 0.6836 1.0200 587
## (Intercept)-Didelphis_virginiana -0.2622 1.0101 1047
## (Intercept)-Sylvilagus_floridanus 0.5152 1.0030 591
## (Intercept)-Meleagris_gallopavo 0.5426 1.0094 635
## (Intercept)-Sciurus_carolinensis -0.2804 1.0129 744
## (Intercept)-Vulpes_vulpes 0.1766 1.0210 391
## (Intercept)-Sus_scrofa -0.3390 1.0156 730
## Veg_shannon_index-Canis_latrans 1.3673 1.0166 1357
## Veg_shannon_index-Sciurus_niger 1.2819 1.0159 1211
## Veg_shannon_index-Procyon_lotor 1.2269 1.0205 1241
## Veg_shannon_index-Dasypus_novemcinctus 0.8410 1.0013 1984
## Veg_shannon_index-Lynx_rufus 1.0146 1.0114 1235
## Veg_shannon_index-Didelphis_virginiana 1.2060 1.0087 1697
## Veg_shannon_index-Sylvilagus_floridanus 1.2553 1.0102 1379
## Veg_shannon_index-Meleagris_gallopavo 1.3056 1.0176 1311
## Veg_shannon_index-Sciurus_carolinensis 0.6973 1.0078 1611
## Veg_shannon_index-Vulpes_vulpes 0.8359 1.0046 1242
## Veg_shannon_index-Sus_scrofa 1.5564 1.0288 1126
## Avg_Cogongrass_Cover-Canis_latrans 1.2147 1.0070 1453
## Avg_Cogongrass_Cover-Sciurus_niger 0.7551 1.0019 870
## Avg_Cogongrass_Cover-Procyon_lotor 1.1254 1.0043 1625
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0628 1.0071 2193
## Avg_Cogongrass_Cover-Lynx_rufus 1.4276 1.0063 1543
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1543 1.0019 1682
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6181 1.0032 1115
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6865 1.0009 931
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1241 1.0116 1821
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2218 1.0053 1085
## Avg_Cogongrass_Cover-Sus_scrofa 0.7840 1.0007 1127
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6000 0.1666 -2.9362 -2.5947 -2.2834 1.0010
## (Intercept)-Sciurus_niger -3.7021 0.4768 -4.7059 -3.6828 -2.8322 1.0064
## (Intercept)-Procyon_lotor -2.2854 0.1290 -2.5493 -2.2833 -2.0426 1.0125
## (Intercept)-Dasypus_novemcinctus -1.6078 0.1351 -1.8823 -1.6039 -1.3522 1.0009
## (Intercept)-Lynx_rufus -3.4175 0.2883 -4.0166 -3.4085 -2.8882 1.0068
## (Intercept)-Didelphis_virginiana -2.3591 0.2476 -2.8863 -2.3448 -1.9073 1.0012
## (Intercept)-Sylvilagus_floridanus -3.1616 0.2840 -3.7584 -3.1471 -2.6233 1.0229
## (Intercept)-Meleagris_gallopavo -3.3545 0.3292 -4.0859 -3.3296 -2.7717 1.0022
## (Intercept)-Sciurus_carolinensis -2.4893 0.2626 -3.0258 -2.4788 -1.9944 1.0010
## (Intercept)-Vulpes_vulpes -3.7287 0.6092 -4.9599 -3.6817 -2.6344 1.0055
## (Intercept)-Sus_scrofa -2.9794 0.4391 -3.9007 -2.9565 -2.1778 1.0126
## ESS
## (Intercept)-Canis_latrans 1140
## (Intercept)-Sciurus_niger 260
## (Intercept)-Procyon_lotor 1470
## (Intercept)-Dasypus_novemcinctus 2581
## (Intercept)-Lynx_rufus 447
## (Intercept)-Didelphis_virginiana 1220
## (Intercept)-Sylvilagus_floridanus 491
## (Intercept)-Meleagris_gallopavo 383
## (Intercept)-Sciurus_carolinensis 977
## (Intercept)-Vulpes_vulpes 193
## (Intercept)-Sus_scrofa 670
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5677
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4079 0.3820 -2.1596 -1.3980 -0.6533 1.0148 451
## Avg_Cogongrass_Cover -0.6762 0.3404 -1.3637 -0.6626 -0.0137 1.0302 358
## I(Avg_Cogongrass_Cover^2) 0.6579 0.3245 0.0338 0.6461 1.3545 1.0158 446
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5848 0.5966 0.0563 0.4217 2.0482 1.0034 733
## Avg_Cogongrass_Cover 0.3082 0.3445 0.0356 0.2065 1.2355 1.0212 828
## I(Avg_Cogongrass_Cover^2) 0.5949 0.8900 0.0428 0.3064 2.9100 1.2075 289
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6415 0.6041 0.0608 0.4587 2.292 1.0393 139
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7862 0.2554 -3.3262 -2.7765 -2.2975 1.0114 1373
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6262 0.4386 0.1726 0.5081 1.7203 1.0242 833
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9284 0.6027 -2.0719 -0.9334
## (Intercept)-Sciurus_niger -1.4570 0.6247 -2.6739 -1.4674
## (Intercept)-Procyon_lotor -0.7474 0.6277 -1.9567 -0.7591
## (Intercept)-Dasypus_novemcinctus -1.4056 0.5090 -2.4439 -1.3973
## (Intercept)-Lynx_rufus -1.3945 0.6059 -2.6318 -1.3937
## (Intercept)-Didelphis_virginiana -1.7123 0.5463 -2.8646 -1.6740
## (Intercept)-Sylvilagus_floridanus -1.2664 0.5534 -2.3314 -1.2634
## (Intercept)-Meleagris_gallopavo -1.1145 0.6017 -2.2715 -1.1333
## (Intercept)-Sciurus_carolinensis -1.9450 0.5975 -3.2077 -1.9105
## (Intercept)-Vulpes_vulpes -1.9148 0.7198 -3.4642 -1.8726
## (Intercept)-Sus_scrofa -1.9274 0.6415 -3.3072 -1.8827
## Avg_Cogongrass_Cover-Canis_latrans -0.4892 0.4891 -1.4006 -0.5013
## Avg_Cogongrass_Cover-Sciurus_niger -0.8751 0.5517 -2.0994 -0.8485
## Avg_Cogongrass_Cover-Procyon_lotor -0.6258 0.4841 -1.5619 -0.6307
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5010 0.4537 -1.3958 -0.5091
## Avg_Cogongrass_Cover-Lynx_rufus -0.5723 0.4981 -1.5440 -0.5723
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3693 0.5027 -1.2960 -0.3971
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0390 0.5528 -2.2557 -0.9891
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9051 0.5247 -2.0060 -0.8669
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6548 0.4871 -1.6009 -0.6666
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.6467 0.5227 -1.6979 -0.6433
## Avg_Cogongrass_Cover-Sus_scrofa -0.8288 0.5477 -2.0117 -0.8133
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2809 0.8050 0.2411 1.0760
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.0881 0.6472 -1.4457 0.1606
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1919 0.7523 0.2053 1.0250
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6661 0.3344 0.0343 0.6540
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1518 0.5279 0.2807 1.0930
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4690 0.4012 -0.2831 0.4568
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6847 0.5242 -0.1307 0.6325
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1333 0.5521 -1.1063 0.1747
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8275 0.3631 0.1485 0.8041
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.7791 0.4949 -0.0056 0.7137
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.0897 0.6379 -1.5172 0.1960
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2751 1.0039 488
## (Intercept)-Sciurus_niger -0.1886 1.0339 498
## (Intercept)-Procyon_lotor 0.4944 1.0015 482
## (Intercept)-Dasypus_novemcinctus -0.4149 1.0053 897
## (Intercept)-Lynx_rufus -0.1843 1.0122 729
## (Intercept)-Didelphis_virginiana -0.6989 1.0057 838
## (Intercept)-Sylvilagus_floridanus -0.1466 1.0053 788
## (Intercept)-Meleagris_gallopavo 0.1178 1.0017 534
## (Intercept)-Sciurus_carolinensis -0.8791 1.0139 639
## (Intercept)-Vulpes_vulpes -0.6211 1.0029 446
## (Intercept)-Sus_scrofa -0.7682 1.0068 663
## Avg_Cogongrass_Cover-Canis_latrans 0.4827 1.0078 890
## Avg_Cogongrass_Cover-Sciurus_niger 0.0945 1.0174 669
## Avg_Cogongrass_Cover-Procyon_lotor 0.3713 1.0086 733
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4204 1.0057 773
## Avg_Cogongrass_Cover-Lynx_rufus 0.4480 1.0085 499
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7229 1.0123 762
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1045 1.0094 444
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0619 1.0080 609
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3119 1.0144 755
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3777 1.0215 543
## Avg_Cogongrass_Cover-Sus_scrofa 0.1786 1.0188 647
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.4321 1.0827 237
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1344 1.0843 428
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.1400 1.1295 190
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3454 1.0044 755
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3507 1.0159 363
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2987 1.0006 801
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8158 1.0085 396
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0682 1.0366 472
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5929 1.0135 857
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9894 1.0320 458
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0665 1.0464 411
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6271 0.1650 -2.9433 -2.6198 -2.3079 1.0002
## (Intercept)-Sciurus_niger -3.5156 0.4634 -4.5280 -3.4820 -2.7133 1.2106
## (Intercept)-Procyon_lotor -2.2814 0.1293 -2.5471 -2.2768 -2.0396 1.0036
## (Intercept)-Dasypus_novemcinctus -1.6140 0.1348 -1.8856 -1.6063 -1.3664 1.0005
## (Intercept)-Lynx_rufus -3.3436 0.2787 -3.9283 -3.3352 -2.8280 1.0334
## (Intercept)-Didelphis_virginiana -2.3790 0.2568 -2.8908 -2.3676 -1.8993 1.0046
## (Intercept)-Sylvilagus_floridanus -3.1273 0.2781 -3.7228 -3.1195 -2.6257 1.0128
## (Intercept)-Meleagris_gallopavo -3.3032 0.3171 -3.9690 -3.2870 -2.7149 1.0152
## (Intercept)-Sciurus_carolinensis -2.4858 0.2624 -3.0212 -2.4775 -2.0143 1.0051
## (Intercept)-Vulpes_vulpes -3.6572 0.5537 -4.7699 -3.6189 -2.6378 1.0034
## (Intercept)-Sus_scrofa -2.9411 0.4248 -3.8423 -2.9119 -2.1926 1.0061
## ESS
## (Intercept)-Canis_latrans 922
## (Intercept)-Sciurus_niger 320
## (Intercept)-Procyon_lotor 1314
## (Intercept)-Dasypus_novemcinctus 2418
## (Intercept)-Lynx_rufus 513
## (Intercept)-Didelphis_virginiana 1215
## (Intercept)-Sylvilagus_floridanus 515
## (Intercept)-Meleagris_gallopavo 450
## (Intercept)-Sciurus_carolinensis 1234
## (Intercept)-Vulpes_vulpes 261
## (Intercept)-Sus_scrofa 785
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6393
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1926 0.6315 -3.4392 -2.1820 -0.9319 1.0564 325
## Cogon_Patch_Size -0.2155 0.6278 -1.5272 -0.2039 0.9365 1.0177 365
## Veg_shannon_index 0.9245 0.4099 0.1518 0.9234 1.7424 1.0063 364
## total_shrub_cover -0.6962 0.4926 -1.7228 -0.6741 0.2232 1.0129 570
## Avg_Cogongrass_Cover 0.2973 0.8914 -1.5281 0.3059 2.0050 1.0270 123
## Tree_Density -2.1824 0.6821 -3.5790 -2.1693 -0.8899 1.0474 181
## Avg_Canopy_Cover 1.7103 0.5352 0.7919 1.6739 2.8977 1.0321 219
## I(Avg_Cogongrass_Cover^2) 1.0933 0.5964 -0.0439 1.0677 2.3528 1.0019 394
## avg_veg_height -0.2712 0.4727 -1.2123 -0.2798 0.6239 1.0071 189
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3186 2.6689 0.0880 1.5269 9.1740 1.0114 214
## Cogon_Patch_Size 1.9940 2.8099 0.1202 1.1530 9.0304 1.4034 251
## Veg_shannon_index 0.5503 0.7400 0.0433 0.3021 2.5171 1.0370 535
## total_shrub_cover 1.6882 2.0645 0.1026 1.0626 6.8037 1.0561 192
## Avg_Cogongrass_Cover 0.9815 1.6128 0.0535 0.4811 4.6630 1.1194 312
## Tree_Density 1.3422 2.4372 0.0568 0.5724 7.5412 1.0306 308
## Avg_Canopy_Cover 1.3192 1.8718 0.0771 0.7633 6.2411 1.0271 332
## I(Avg_Cogongrass_Cover^2) 2.4833 3.7816 0.0982 1.3363 11.7217 1.0140 121
## avg_veg_height 0.4024 0.5811 0.0406 0.2378 1.7662 1.0328 475
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5825 1.7449 0.0663 1.0048 6.1779 1.5566 81
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8722 0.3103 -3.5007 -2.8683 -2.2499 1.0029 1509
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9733 0.7321 0.3064 0.7886 2.733 1.0392 899
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5690 0.9729 -3.4440 -1.5807
## (Intercept)-Sciurus_niger -1.3828 1.2737 -3.5302 -1.5334
## (Intercept)-Procyon_lotor -1.2155 0.9997 -3.1509 -1.2258
## (Intercept)-Dasypus_novemcinctus -2.3846 0.8421 -4.1695 -2.3424
## (Intercept)-Lynx_rufus -1.6666 1.2333 -3.7870 -1.7912
## (Intercept)-Didelphis_virginiana -3.1322 1.0708 -5.4934 -3.0055
## (Intercept)-Sylvilagus_floridanus -2.1974 0.9833 -4.2708 -2.1693
## (Intercept)-Meleagris_gallopavo -2.0781 1.0592 -4.3098 -2.0640
## (Intercept)-Sciurus_carolinensis -3.5258 1.1889 -6.2411 -3.3831
## (Intercept)-Vulpes_vulpes -3.1346 1.3689 -6.2459 -2.9600
## (Intercept)-Sus_scrofa -3.6444 1.3705 -6.8509 -3.4733
## Cogon_Patch_Size-Canis_latrans 1.0800 1.0145 -0.5145 0.9181
## Cogon_Patch_Size-Sciurus_niger -0.8888 1.4907 -4.3081 -0.7402
## Cogon_Patch_Size-Procyon_lotor -0.5479 0.7667 -2.0680 -0.5307
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2530 0.6552 -1.5563 -0.2423
## Cogon_Patch_Size-Lynx_rufus -0.2847 1.2190 -2.6318 -0.2930
## Cogon_Patch_Size-Didelphis_virginiana 1.2375 0.9125 -0.3209 1.1421
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2412 1.4182 -4.8768 -1.0044
## Cogon_Patch_Size-Meleagris_gallopavo 0.0160 0.9485 -1.8222 0.0040
## Cogon_Patch_Size-Sciurus_carolinensis -0.8836 1.0295 -3.4423 -0.7440
## Cogon_Patch_Size-Vulpes_vulpes -0.4962 1.2966 -3.2729 -0.4337
## Cogon_Patch_Size-Sus_scrofa -0.3851 1.0809 -2.7722 -0.3109
## Veg_shannon_index-Canis_latrans 1.2859 0.6520 0.1837 1.2235
## Veg_shannon_index-Sciurus_niger 1.0007 0.7901 -0.4988 0.9457
## Veg_shannon_index-Procyon_lotor 1.1035 0.5698 0.0966 1.0649
## Veg_shannon_index-Dasypus_novemcinctus 0.6746 0.4992 -0.3415 0.6889
## Veg_shannon_index-Lynx_rufus 0.9240 0.7722 -0.5643 0.8832
## Veg_shannon_index-Didelphis_virginiana 0.9744 0.6043 -0.2080 0.9626
## Veg_shannon_index-Sylvilagus_floridanus 0.9682 0.6182 -0.1841 0.9504
## Veg_shannon_index-Meleagris_gallopavo 1.1575 0.6758 -0.0418 1.1072
## Veg_shannon_index-Sciurus_carolinensis 0.3806 0.6752 -1.1692 0.4602
## Veg_shannon_index-Vulpes_vulpes 0.6999 0.7529 -0.9261 0.7381
## Veg_shannon_index-Sus_scrofa 1.2925 0.7209 0.0809 1.2081
## total_shrub_cover-Canis_latrans 0.0102 0.6572 -1.1342 -0.0263
## total_shrub_cover-Sciurus_niger -1.2637 1.0268 -3.5811 -1.1333
## total_shrub_cover-Procyon_lotor -1.1663 0.6474 -2.6016 -1.1045
## total_shrub_cover-Dasypus_novemcinctus 0.0548 0.5714 -1.0251 0.0362
## total_shrub_cover-Lynx_rufus -1.5209 1.2012 -4.3341 -1.3425
## total_shrub_cover-Didelphis_virginiana -0.6634 0.6946 -2.1350 -0.6410
## total_shrub_cover-Sylvilagus_floridanus -0.3939 0.8509 -2.0871 -0.4103
## total_shrub_cover-Meleagris_gallopavo -2.4015 1.3174 -5.3513 -2.2058
## total_shrub_cover-Sciurus_carolinensis -0.0663 0.7035 -1.3522 -0.1067
## total_shrub_cover-Vulpes_vulpes -0.8294 1.0664 -3.0391 -0.7684
## total_shrub_cover-Sus_scrofa -0.0125 0.9278 -1.6557 -0.0836
## Avg_Cogongrass_Cover-Canis_latrans 0.2959 1.1152 -1.9644 0.2922
## Avg_Cogongrass_Cover-Sciurus_niger -0.1276 1.3038 -2.9931 -0.0425
## Avg_Cogongrass_Cover-Procyon_lotor 0.4105 1.0827 -1.7074 0.3758
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8279 1.1378 -1.2707 0.7839
## Avg_Cogongrass_Cover-Lynx_rufus 0.4353 1.1975 -1.8711 0.3886
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6086 1.1542 -1.5683 0.5550
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1719 1.1848 -2.7155 -0.1073
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0148 1.2305 -2.6125 0.0583
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4546 1.1393 -1.7287 0.4279
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.5226 1.2301 -1.8065 0.4706
## Avg_Cogongrass_Cover-Sus_scrofa 0.1591 1.1620 -2.1815 0.1653
## Tree_Density-Canis_latrans -2.7683 1.1271 -5.4147 -2.6284
## Tree_Density-Sciurus_niger -2.0532 1.1761 -4.3830 -2.0723
## Tree_Density-Procyon_lotor -2.1477 0.8910 -3.9993 -2.1246
## Tree_Density-Dasypus_novemcinctus -3.0705 1.2056 -6.1570 -2.8702
## Tree_Density-Lynx_rufus -1.4096 1.3082 -3.5167 -1.5673
## Tree_Density-Didelphis_virginiana -2.2373 0.9204 -4.2350 -2.1787
## Tree_Density-Sylvilagus_floridanus -2.4616 1.1018 -4.9727 -2.3613
## Tree_Density-Meleagris_gallopavo -2.1868 1.0763 -4.4927 -2.1809
## Tree_Density-Sciurus_carolinensis -2.3845 0.9691 -4.5019 -2.3066
## Tree_Density-Vulpes_vulpes -2.0502 1.1750 -4.3458 -2.0606
## Tree_Density-Sus_scrofa -2.2578 1.0819 -4.6483 -2.2029
## Avg_Canopy_Cover-Canis_latrans 0.4483 0.7176 -0.9875 0.4563
## Avg_Canopy_Cover-Sciurus_niger 1.7433 1.2428 -0.4125 1.6278
## Avg_Canopy_Cover-Procyon_lotor 1.5995 0.6645 0.3772 1.5677
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8382 0.6349 0.7108 1.7880
## Avg_Canopy_Cover-Lynx_rufus 1.2279 1.0094 -0.8292 1.2311
## Avg_Canopy_Cover-Didelphis_virginiana 2.2550 0.8401 0.9747 2.1357
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.6910 1.2421 0.9441 2.4490
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0064 0.9095 0.5662 1.8632
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0153 0.7683 0.8021 1.9022
## Avg_Canopy_Cover-Vulpes_vulpes 1.9964 1.0714 0.2885 1.8437
## Avg_Canopy_Cover-Sus_scrofa 1.8277 0.7861 0.5257 1.7502
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2800 1.3345 0.5845 1.9976
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.6071 1.5207 -2.1906 0.5981
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1908 1.2663 0.5011 1.9597
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.1939 0.6813 -0.0280 1.1480
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5444 1.2842 0.6466 2.3341
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6262 0.6569 -0.7182 0.6332
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9703 0.9046 -0.5209 0.8704
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2298 1.2196 -2.9189 -0.1091
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2824 0.6877 0.0117 1.2397
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6961 1.0264 0.1615 1.5320
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.2490 1.3375 -3.7450 -0.0399
## avg_veg_height-Canis_latrans -0.4690 0.5945 -1.7423 -0.4447
## avg_veg_height-Sciurus_niger -0.4833 0.7764 -2.1761 -0.4320
## avg_veg_height-Procyon_lotor -0.0237 0.6021 -1.1920 -0.0392
## avg_veg_height-Dasypus_novemcinctus 0.0212 0.5821 -1.0683 -0.0084
## avg_veg_height-Lynx_rufus -0.3903 0.7285 -1.9097 -0.3657
## avg_veg_height-Didelphis_virginiana -0.3139 0.6426 -1.6640 -0.3082
## avg_veg_height-Sylvilagus_floridanus -0.3897 0.6467 -1.8124 -0.3658
## avg_veg_height-Meleagris_gallopavo -0.3259 0.7330 -1.8831 -0.3126
## avg_veg_height-Sciurus_carolinensis 0.0135 0.6271 -1.1057 -0.0228
## avg_veg_height-Vulpes_vulpes -0.3424 0.7034 -1.7909 -0.3246
## avg_veg_height-Sus_scrofa -0.2830 0.6562 -1.6028 -0.2748
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3905 1.0108 379
## (Intercept)-Sciurus_niger 1.5282 1.0031 256
## (Intercept)-Procyon_lotor 0.7189 1.0481 212
## (Intercept)-Dasypus_novemcinctus -0.8113 1.0285 572
## (Intercept)-Lynx_rufus 1.1303 1.0386 284
## (Intercept)-Didelphis_virginiana -1.4173 1.0086 352
## (Intercept)-Sylvilagus_floridanus -0.1731 1.0135 556
## (Intercept)-Meleagris_gallopavo 0.0013 1.0491 397
## (Intercept)-Sciurus_carolinensis -1.5486 1.0042 316
## (Intercept)-Vulpes_vulpes -0.7465 1.0438 250
## (Intercept)-Sus_scrofa -1.4850 1.0076 242
## Cogon_Patch_Size-Canis_latrans 3.4489 1.0730 405
## Cogon_Patch_Size-Sciurus_niger 1.5363 1.0578 245
## Cogon_Patch_Size-Procyon_lotor 0.9137 1.0239 359
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9898 1.0356 334
## Cogon_Patch_Size-Lynx_rufus 2.2928 1.0513 382
## Cogon_Patch_Size-Didelphis_virginiana 3.2795 1.0940 338
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7845 1.1529 254
## Cogon_Patch_Size-Meleagris_gallopavo 1.9821 1.0019 520
## Cogon_Patch_Size-Sciurus_carolinensis 0.6883 1.0732 478
## Cogon_Patch_Size-Vulpes_vulpes 1.9183 1.0529 295
## Cogon_Patch_Size-Sus_scrofa 1.4891 1.0275 513
## Veg_shannon_index-Canis_latrans 2.8419 1.0394 569
## Veg_shannon_index-Sciurus_niger 2.6900 1.0150 471
## Veg_shannon_index-Procyon_lotor 2.3286 1.0444 250
## Veg_shannon_index-Dasypus_novemcinctus 1.6084 1.0020 853
## Veg_shannon_index-Lynx_rufus 2.6142 1.0118 629
## Veg_shannon_index-Didelphis_virginiana 2.2597 1.0023 789
## Veg_shannon_index-Sylvilagus_floridanus 2.3094 1.0141 699
## Veg_shannon_index-Meleagris_gallopavo 2.6066 1.0133 773
## Veg_shannon_index-Sciurus_carolinensis 1.5042 1.0046 745
## Veg_shannon_index-Vulpes_vulpes 2.0590 1.0015 635
## Veg_shannon_index-Sus_scrofa 2.9789 1.0116 701
## total_shrub_cover-Canis_latrans 1.4108 1.0042 1094
## total_shrub_cover-Sciurus_niger 0.4702 1.0151 357
## total_shrub_cover-Procyon_lotor -0.0474 1.0031 822
## total_shrub_cover-Dasypus_novemcinctus 1.2150 1.0084 1342
## total_shrub_cover-Lynx_rufus 0.2510 1.0280 187
## total_shrub_cover-Didelphis_virginiana 0.6280 1.0046 1166
## total_shrub_cover-Sylvilagus_floridanus 1.3758 1.0141 751
## total_shrub_cover-Meleagris_gallopavo -0.4153 1.0470 237
## total_shrub_cover-Sciurus_carolinensis 1.3577 1.0080 1214
## total_shrub_cover-Vulpes_vulpes 1.0875 1.0109 399
## total_shrub_cover-Sus_scrofa 2.0231 1.0167 570
## Avg_Cogongrass_Cover-Canis_latrans 2.5391 1.0167 205
## Avg_Cogongrass_Cover-Sciurus_niger 2.2305 1.0112 152
## Avg_Cogongrass_Cover-Procyon_lotor 2.5693 1.0288 169
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.2583 1.0337 148
## Avg_Cogongrass_Cover-Lynx_rufus 2.8824 1.0230 181
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0727 1.0375 166
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9248 1.0240 180
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1976 1.0159 230
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7997 1.0260 151
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2358 1.0397 205
## Avg_Cogongrass_Cover-Sus_scrofa 2.4287 1.0062 141
## Tree_Density-Canis_latrans -0.9917 1.0054 245
## Tree_Density-Sciurus_niger 0.4615 1.0550 315
## Tree_Density-Procyon_lotor -0.4393 1.0124 251
## Tree_Density-Dasypus_novemcinctus -1.3599 1.0174 283
## Tree_Density-Lynx_rufus 1.4059 1.0134 175
## Tree_Density-Didelphis_virginiana -0.6382 1.0387 301
## Tree_Density-Sylvilagus_floridanus -0.5718 1.0243 346
## Tree_Density-Meleagris_gallopavo -0.0459 1.0260 433
## Tree_Density-Sciurus_carolinensis -0.7314 1.0552 264
## Tree_Density-Vulpes_vulpes 0.5849 1.0451 309
## Tree_Density-Sus_scrofa -0.2464 1.0097 442
## Avg_Canopy_Cover-Canis_latrans 1.8334 1.0084 512
## Avg_Canopy_Cover-Sciurus_niger 4.7160 1.0033 244
## Avg_Canopy_Cover-Procyon_lotor 3.0574 1.0477 378
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2092 1.0425 485
## Avg_Canopy_Cover-Lynx_rufus 3.1835 1.0139 396
## Avg_Canopy_Cover-Didelphis_virginiana 4.1825 1.1120 245
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.7837 1.0416 227
## Avg_Canopy_Cover-Meleagris_gallopavo 4.1816 1.0229 407
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7937 1.0229 320
## Avg_Canopy_Cover-Vulpes_vulpes 4.7623 1.0099 280
## Avg_Canopy_Cover-Sus_scrofa 3.6453 1.0427 278
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.7275 1.0369 110
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.9283 1.0579 125
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.6594 1.0870 152
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.6438 1.0316 481
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6274 1.0088 194
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.8967 1.0477 271
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1727 1.0399 301
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.7127 1.0084 157
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.7661 1.0122 510
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.4902 1.0321 184
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.7552 1.0115 206
## avg_veg_height-Canis_latrans 0.6018 1.0126 347
## avg_veg_height-Sciurus_niger 0.8821 1.0071 254
## avg_veg_height-Procyon_lotor 1.1673 1.0016 417
## avg_veg_height-Dasypus_novemcinctus 1.2296 1.0015 339
## avg_veg_height-Lynx_rufus 1.0005 1.0054 345
## avg_veg_height-Didelphis_virginiana 0.8865 1.0219 309
## avg_veg_height-Sylvilagus_floridanus 0.7917 1.0039 338
## avg_veg_height-Meleagris_gallopavo 1.1307 1.0096 233
## avg_veg_height-Sciurus_carolinensis 1.3554 1.0023 456
## avg_veg_height-Vulpes_vulpes 0.9702 1.0060 399
## avg_veg_height-Sus_scrofa 0.9666 1.0145 318
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6152 0.1740 -2.9799 -2.6103 -2.2878 1.0182
## (Intercept)-Sciurus_niger -4.1430 0.4501 -4.9999 -4.1484 -3.2279 1.0197
## (Intercept)-Procyon_lotor -2.2733 0.1249 -2.5264 -2.2745 -2.0388 1.0049
## (Intercept)-Dasypus_novemcinctus -1.5944 0.1313 -1.8587 -1.5900 -1.3500 1.0004
## (Intercept)-Lynx_rufus -3.6013 0.2798 -4.1625 -3.5985 -3.0542 1.0011
## (Intercept)-Didelphis_virginiana -2.3382 0.2463 -2.8549 -2.3281 -1.8848 1.0044
## (Intercept)-Sylvilagus_floridanus -3.2052 0.2786 -3.7872 -3.1979 -2.6892 1.0273
## (Intercept)-Meleagris_gallopavo -3.3136 0.2947 -3.9251 -3.2996 -2.7687 1.0163
## (Intercept)-Sciurus_carolinensis -2.4688 0.2684 -3.0487 -2.4592 -1.9892 0.9996
## (Intercept)-Vulpes_vulpes -4.1391 0.5808 -5.3233 -4.1321 -3.0723 1.0302
## (Intercept)-Sus_scrofa -2.9425 0.4526 -3.9167 -2.9112 -2.1466 1.0178
## ESS
## (Intercept)-Canis_latrans 975
## (Intercept)-Sciurus_niger 176
## (Intercept)-Procyon_lotor 1688
## (Intercept)-Dasypus_novemcinctus 2402
## (Intercept)-Lynx_rufus 380
## (Intercept)-Didelphis_virginiana 1307
## (Intercept)-Sylvilagus_floridanus 440
## (Intercept)-Meleagris_gallopavo 493
## (Intercept)-Sciurus_carolinensis 1075
## (Intercept)-Vulpes_vulpes 192
## (Intercept)-Sus_scrofa 500
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8912
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5953 0.3596 -1.3221 -0.5969 0.1219 0.9999 393
## Avg_Cogongrass_Cover 0.1065 0.2537 -0.4069 0.1085 0.5879 1.0156 766
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6655 0.6917 0.0556 0.4524 2.4653 1.0198 517
## Avg_Cogongrass_Cover 0.3124 0.3517 0.0392 0.1955 1.2736 1.0541 508
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8454 0.8093 0.0692 0.624 2.7936 1.0935 135
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0286 0.3151 -3.6778 -3.0265 -2.4048 1.0118 1139
## shrub_cover 0.1773 0.2701 -0.3743 0.1796 0.7053 1.0075 961
## veg_height 0.0169 0.1696 -0.3181 0.0174 0.3451 1.0156 843
## week -0.1141 0.1258 -0.3772 -0.1085 0.1252 1.0028 1059
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9228 0.6638 0.2520 0.7530 2.6895 1.0025 637
## shrub_cover 0.6455 0.5035 0.1340 0.5196 1.9228 1.0309 457
## veg_height 0.2058 0.1545 0.0518 0.1642 0.6039 1.0165 1039
## week 0.1027 0.0817 0.0257 0.0806 0.3195 1.0096 1026
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0357 0.5609 -1.1055 -0.0455
## (Intercept)-Sciurus_niger -0.6969 0.6424 -1.9826 -0.7096
## (Intercept)-Procyon_lotor 0.0783 0.6156 -1.1299 0.0775
## (Intercept)-Dasypus_novemcinctus -0.6767 0.4699 -1.6266 -0.6782
## (Intercept)-Lynx_rufus -0.3120 0.6544 -1.4945 -0.3712
## (Intercept)-Didelphis_virginiana -0.9925 0.5484 -2.1663 -0.9619
## (Intercept)-Sylvilagus_floridanus -0.5210 0.5275 -1.5433 -0.5414
## (Intercept)-Meleagris_gallopavo -0.2540 0.6795 -1.4251 -0.3306
## (Intercept)-Sciurus_carolinensis -1.0195 0.5376 -2.1790 -0.9983
## (Intercept)-Vulpes_vulpes -0.9897 0.7287 -2.4793 -0.9682
## (Intercept)-Sus_scrofa -1.2476 0.6575 -2.7029 -1.1878
## Avg_Cogongrass_Cover-Canis_latrans 0.3580 0.3786 -0.3120 0.3217
## Avg_Cogongrass_Cover-Sciurus_niger -0.2628 0.5558 -1.5624 -0.1964
## Avg_Cogongrass_Cover-Procyon_lotor 0.1710 0.3399 -0.4831 0.1633
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3177 0.3350 -0.3114 0.2979
## Avg_Cogongrass_Cover-Lynx_rufus 0.3959 0.4121 -0.3462 0.3629
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2873 0.3721 -0.4270 0.2811
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2403 0.4325 -1.1689 -0.2107
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3000 0.5775 -1.6521 -0.2369
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3039 0.3565 -0.3595 0.2877
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2242 0.4312 -0.5950 0.2019
## Avg_Cogongrass_Cover-Sus_scrofa -0.0935 0.5152 -1.2639 -0.0472
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1096 1.0073 404
## (Intercept)-Sciurus_niger 0.6206 1.0108 519
## (Intercept)-Procyon_lotor 1.3006 1.0464 292
## (Intercept)-Dasypus_novemcinctus 0.2344 1.0007 1093
## (Intercept)-Lynx_rufus 1.1993 1.0045 414
## (Intercept)-Didelphis_virginiana 0.0269 1.0022 1093
## (Intercept)-Sylvilagus_floridanus 0.5639 1.0010 646
## (Intercept)-Meleagris_gallopavo 1.2583 1.0063 402
## (Intercept)-Sciurus_carolinensis -0.0251 1.0276 734
## (Intercept)-Vulpes_vulpes 0.4691 1.0075 420
## (Intercept)-Sus_scrofa -0.1128 1.0402 488
## Avg_Cogongrass_Cover-Canis_latrans 1.1960 1.0150 1406
## Avg_Cogongrass_Cover-Sciurus_niger 0.6513 1.0305 563
## Avg_Cogongrass_Cover-Procyon_lotor 0.8777 1.0011 2059
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0154 1.0019 1522
## Avg_Cogongrass_Cover-Lynx_rufus 1.3239 1.0060 1141
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0580 1.0120 1613
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5037 1.0158 899
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6789 1.0139 579
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0515 1.0033 1433
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1509 1.0060 1295
## Avg_Cogongrass_Cover-Sus_scrofa 0.7995 1.0125 957
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7593 0.1839 -3.1472 -2.7562 -2.4251 1.0622
## (Intercept)-Sciurus_niger -4.0032 0.5456 -5.0699 -4.0039 -2.9423 1.0233
## (Intercept)-Procyon_lotor -2.2990 0.1475 -2.6032 -2.2935 -2.0300 1.0020
## (Intercept)-Dasypus_novemcinctus -1.7688 0.1575 -2.1037 -1.7682 -1.4702 1.0034
## (Intercept)-Lynx_rufus -3.6224 0.3547 -4.3419 -3.6146 -2.9563 1.0421
## (Intercept)-Didelphis_virginiana -2.6205 0.2943 -3.2326 -2.6042 -2.0738 0.9998
## (Intercept)-Sylvilagus_floridanus -3.1809 0.2851 -3.7920 -3.1607 -2.6663 1.0082
## (Intercept)-Meleagris_gallopavo -3.9724 0.4476 -4.8411 -3.9663 -3.0787 1.0425
## (Intercept)-Sciurus_carolinensis -2.6888 0.3188 -3.3592 -2.6663 -2.1025 1.0486
## (Intercept)-Vulpes_vulpes -4.0268 0.6358 -5.3872 -3.9808 -2.9229 1.0060
## (Intercept)-Sus_scrofa -3.3648 0.5491 -4.4503 -3.3469 -2.2974 1.0125
## shrub_cover-Canis_latrans -0.2948 0.2178 -0.7138 -0.2939 0.1146 1.0028
## shrub_cover-Sciurus_niger -0.3973 0.4681 -1.3430 -0.3919 0.5427 1.0219
## shrub_cover-Procyon_lotor 0.2444 0.1649 -0.0889 0.2436 0.5581 1.0035
## shrub_cover-Dasypus_novemcinctus 0.8490 0.2930 0.2819 0.8449 1.4050 1.0061
## shrub_cover-Lynx_rufus -0.2411 0.3553 -0.9560 -0.2406 0.4536 1.0630
## shrub_cover-Didelphis_virginiana 0.9887 0.3746 0.3173 0.9663 1.7964 1.0103
## shrub_cover-Sylvilagus_floridanus 0.2473 0.4034 -0.5158 0.2401 1.0543 1.0266
## shrub_cover-Meleagris_gallopavo -0.7033 0.4040 -1.4962 -0.6989 0.0766 1.0246
## shrub_cover-Sciurus_carolinensis 0.8378 0.4106 0.0569 0.8273 1.6708 1.0182
## shrub_cover-Vulpes_vulpes -0.0885 0.5587 -1.2550 -0.0868 1.0256 1.0188
## shrub_cover-Sus_scrofa 0.6201 0.7683 -0.8862 0.6018 2.1853 1.0152
## veg_height-Canis_latrans -0.5863 0.1861 -0.9732 -0.5812 -0.2249 1.0169
## veg_height-Sciurus_niger -0.0049 0.4199 -0.8405 -0.0083 0.8541 1.0126
## veg_height-Procyon_lotor 0.3312 0.1259 0.0840 0.3293 0.5845 1.0048
## veg_height-Dasypus_novemcinctus 0.2451 0.1338 -0.0184 0.2439 0.5037 1.0004
## veg_height-Lynx_rufus 0.0047 0.2400 -0.4743 0.0040 0.4682 1.0260
## veg_height-Didelphis_virginiana 0.4168 0.2358 -0.0171 0.4079 0.8871 1.0036
## veg_height-Sylvilagus_floridanus 0.1417 0.2386 -0.3298 0.1399 0.6159 1.0285
## veg_height-Meleagris_gallopavo -0.2170 0.3784 -1.0084 -0.2030 0.5093 1.0245
## veg_height-Sciurus_carolinensis 0.0830 0.2136 -0.3190 0.0820 0.5021 1.0124
## veg_height-Vulpes_vulpes -0.1191 0.3122 -0.7601 -0.1035 0.4639 1.0069
## veg_height-Sus_scrofa -0.1321 0.3395 -0.8373 -0.1172 0.4948 1.0062
## week-Canis_latrans 0.0564 0.1366 -0.2173 0.0600 0.3190 1.0101
## week-Sciurus_niger -0.3452 0.2934 -1.0158 -0.3116 0.1406 1.0220
## week-Procyon_lotor -0.0605 0.1187 -0.3001 -0.0581 0.1581 1.0043
## week-Dasypus_novemcinctus -0.1842 0.1382 -0.4665 -0.1797 0.0715 1.0043
## week-Lynx_rufus -0.0661 0.2033 -0.4823 -0.0580 0.3022 1.0018
## week-Didelphis_virginiana -0.2289 0.2148 -0.6833 -0.2209 0.1471 0.9998
## week-Sylvilagus_floridanus -0.1806 0.2047 -0.6310 -0.1745 0.1918 1.0247
## week-Meleagris_gallopavo -0.2957 0.2456 -0.8307 -0.2706 0.1309 1.0003
## week-Sciurus_carolinensis 0.1258 0.1842 -0.2378 0.1272 0.4875 1.0003
## week-Vulpes_vulpes -0.1609 0.2799 -0.7496 -0.1457 0.3460 1.0030
## week-Sus_scrofa 0.0637 0.2343 -0.3947 0.0663 0.5170 1.0012
## ESS
## (Intercept)-Canis_latrans 847
## (Intercept)-Sciurus_niger 308
## (Intercept)-Procyon_lotor 1215
## (Intercept)-Dasypus_novemcinctus 1519
## (Intercept)-Lynx_rufus 311
## (Intercept)-Didelphis_virginiana 733
## (Intercept)-Sylvilagus_floridanus 601
## (Intercept)-Meleagris_gallopavo 258
## (Intercept)-Sciurus_carolinensis 661
## (Intercept)-Vulpes_vulpes 216
## (Intercept)-Sus_scrofa 559
## shrub_cover-Canis_latrans 861
## shrub_cover-Sciurus_niger 468
## shrub_cover-Procyon_lotor 1299
## shrub_cover-Dasypus_novemcinctus 1437
## shrub_cover-Lynx_rufus 440
## shrub_cover-Didelphis_virginiana 627
## shrub_cover-Sylvilagus_floridanus 575
## shrub_cover-Meleagris_gallopavo 301
## shrub_cover-Sciurus_carolinensis 715
## shrub_cover-Vulpes_vulpes 500
## shrub_cover-Sus_scrofa 592
## veg_height-Canis_latrans 921
## veg_height-Sciurus_niger 664
## veg_height-Procyon_lotor 1623
## veg_height-Dasypus_novemcinctus 1994
## veg_height-Lynx_rufus 790
## veg_height-Didelphis_virginiana 1192
## veg_height-Sylvilagus_floridanus 938
## veg_height-Meleagris_gallopavo 403
## veg_height-Sciurus_carolinensis 953
## veg_height-Vulpes_vulpes 699
## veg_height-Sus_scrofa 1157
## week-Canis_latrans 1417
## week-Sciurus_niger 783
## week-Procyon_lotor 1499
## week-Dasypus_novemcinctus 2036
## week-Lynx_rufus 911
## week-Didelphis_virginiana 1405
## week-Sylvilagus_floridanus 1045
## week-Meleagris_gallopavo 608
## week-Sciurus_carolinensis 1575
## week-Vulpes_vulpes 935
## week-Sus_scrofa 1580
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.575
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7577 0.3381 -1.4123 -0.7556 -0.1157 1.0376 498
## Avg_Cogongrass_Cover 0.1101 0.2309 -0.3328 0.1131 0.5477 1.0002 1122
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5865 0.6094 0.0587 0.4165 2.1808 1.0304 666
## Avg_Cogongrass_Cover 0.2758 0.2956 0.0382 0.1806 1.0903 1.0236 775
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9038 0.7464 0.1022 0.7311 2.8491 1.0206 217
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8142 0.2808 -3.3589 -2.8138 -2.2461 1.0138 1967
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7439 0.541 0.2035 0.5995 2.1498 1.0419 513
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2017 0.5388 -1.2087 -0.2170
## (Intercept)-Sciurus_niger -0.8427 0.6314 -2.0497 -0.8602
## (Intercept)-Procyon_lotor -0.0660 0.5685 -1.1278 -0.0821
## (Intercept)-Dasypus_novemcinctus -0.7526 0.4678 -1.6812 -0.7455
## (Intercept)-Lynx_rufus -0.5342 0.5959 -1.6766 -0.5452
## (Intercept)-Didelphis_virginiana -1.1172 0.5285 -2.2270 -1.0924
## (Intercept)-Sylvilagus_floridanus -0.6002 0.5723 -1.6237 -0.6149
## (Intercept)-Meleagris_gallopavo -0.6768 0.5485 -1.6896 -0.7057
## (Intercept)-Sciurus_carolinensis -1.1891 0.5329 -2.2977 -1.1564
## (Intercept)-Vulpes_vulpes -1.1003 0.6571 -2.4713 -1.0751
## (Intercept)-Sus_scrofa -1.3831 0.6445 -2.8263 -1.3256
## Avg_Cogongrass_Cover-Canis_latrans 0.3060 0.3453 -0.3193 0.2871
## Avg_Cogongrass_Cover-Sciurus_niger -0.2123 0.5091 -1.3895 -0.1625
## Avg_Cogongrass_Cover-Procyon_lotor 0.1929 0.3340 -0.4333 0.1840
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2925 0.3184 -0.2946 0.2878
## Avg_Cogongrass_Cover-Lynx_rufus 0.4073 0.3942 -0.2771 0.3815
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2976 0.3402 -0.3138 0.2843
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2229 0.4212 -1.1230 -0.1912
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2578 0.4590 -1.2929 -0.2163
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3050 0.3617 -0.3740 0.2873
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2290 0.4118 -0.5543 0.2209
## Avg_Cogongrass_Cover-Sus_scrofa -0.1160 0.4684 -1.2129 -0.0706
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8978 1.0037 780
## (Intercept)-Sciurus_niger 0.4255 1.0442 440
## (Intercept)-Procyon_lotor 1.0743 1.0032 553
## (Intercept)-Dasypus_novemcinctus 0.1563 1.0117 1195
## (Intercept)-Lynx_rufus 0.6933 1.0141 507
## (Intercept)-Didelphis_virginiana -0.1172 1.0209 795
## (Intercept)-Sylvilagus_floridanus 0.5793 1.0226 509
## (Intercept)-Meleagris_gallopavo 0.4181 1.0013 823
## (Intercept)-Sciurus_carolinensis -0.2341 1.0058 792
## (Intercept)-Vulpes_vulpes 0.1610 1.0154 414
## (Intercept)-Sus_scrofa -0.2593 1.0214 705
## Avg_Cogongrass_Cover-Canis_latrans 1.0689 1.0047 2092
## Avg_Cogongrass_Cover-Sciurus_niger 0.6303 1.0148 900
## Avg_Cogongrass_Cover-Procyon_lotor 0.8799 1.0011 2418
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9427 1.0037 1944
## Avg_Cogongrass_Cover-Lynx_rufus 1.2823 1.0037 1656
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0122 1.0022 1954
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5344 1.0014 1335
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5517 1.0029 942
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0703 1.0023 2132
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0645 1.0011 1406
## Avg_Cogongrass_Cover-Sus_scrofa 0.7241 1.0033 1053
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6038 0.1662 -2.9469 -2.5984 -2.2979 1.0039
## (Intercept)-Sciurus_niger -3.7074 0.4836 -4.7272 -3.6670 -2.8586 1.1008
## (Intercept)-Procyon_lotor -2.2738 0.1286 -2.5379 -2.2706 -2.0341 1.0001
## (Intercept)-Dasypus_novemcinctus -1.6139 0.1363 -1.8814 -1.6091 -1.3556 1.0068
## (Intercept)-Lynx_rufus -3.4418 0.3053 -4.0522 -3.4349 -2.8746 1.0796
## (Intercept)-Didelphis_virginiana -2.3662 0.2527 -2.8993 -2.3580 -1.9041 1.0196
## (Intercept)-Sylvilagus_floridanus -3.1394 0.2920 -3.7596 -3.1241 -2.6145 1.0093
## (Intercept)-Meleagris_gallopavo -3.3266 0.3110 -3.9705 -3.3144 -2.7403 1.0264
## (Intercept)-Sciurus_carolinensis -2.4784 0.2659 -3.0290 -2.4648 -1.9813 1.0099
## (Intercept)-Vulpes_vulpes -3.8158 0.6172 -5.0265 -3.7744 -2.7252 1.0414
## (Intercept)-Sus_scrofa -2.9754 0.4385 -3.9012 -2.9579 -2.1878 1.0166
## ESS
## (Intercept)-Canis_latrans 1066
## (Intercept)-Sciurus_niger 244
## (Intercept)-Procyon_lotor 1443
## (Intercept)-Dasypus_novemcinctus 2330
## (Intercept)-Lynx_rufus 381
## (Intercept)-Didelphis_virginiana 1186
## (Intercept)-Sylvilagus_floridanus 525
## (Intercept)-Meleagris_gallopavo 447
## (Intercept)-Sciurus_carolinensis 1049
## (Intercept)-Vulpes_vulpes 187
## (Intercept)-Sus_scrofa 748
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7708
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7937 0.3387 -1.4916 -0.7897 -0.1355 1.0182 558
## Avg_Cogongrass_Cover 0.1106 0.2301 -0.3528 0.1162 0.5482 1.0022 859
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6205 0.6108 0.0608 0.4574 2.2109 1.0122 721
## Avg_Cogongrass_Cover 0.2835 0.2849 0.0410 0.1970 1.0795 1.0179 829
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8565 0.6794 0.0777 0.6831 2.5199 1.0245 248
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8226 0.2685 -3.3685 -2.8224 -2.2924 1.0187 1184
## week -0.1121 0.1260 -0.3751 -0.1069 0.1153 1.0033 970
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6914 0.4470 0.2047 0.5791 1.8810 1.0393 489
## week 0.1030 0.0842 0.0239 0.0798 0.3254 1.0001 1038
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1909 0.5457 -1.2136 -0.2040
## (Intercept)-Sciurus_niger -0.9187 0.6232 -2.1619 -0.9115
## (Intercept)-Procyon_lotor -0.0518 0.5960 -1.2001 -0.0527
## (Intercept)-Dasypus_novemcinctus -0.7781 0.4668 -1.7280 -0.7690
## (Intercept)-Lynx_rufus -0.5639 0.5906 -1.6480 -0.5827
## (Intercept)-Didelphis_virginiana -1.1624 0.5084 -2.2146 -1.1402
## (Intercept)-Sylvilagus_floridanus -0.6176 0.5423 -1.6502 -0.6214
## (Intercept)-Meleagris_gallopavo -0.7088 0.5528 -1.7506 -0.7162
## (Intercept)-Sciurus_carolinensis -1.2090 0.5290 -2.2961 -1.1913
## (Intercept)-Vulpes_vulpes -1.2261 0.6977 -2.7740 -1.1768
## (Intercept)-Sus_scrofa -1.4179 0.6198 -2.6816 -1.3748
## Avg_Cogongrass_Cover-Canis_latrans 0.3068 0.3428 -0.3245 0.2913
## Avg_Cogongrass_Cover-Sciurus_niger -0.2120 0.5079 -1.3862 -0.1608
## Avg_Cogongrass_Cover-Procyon_lotor 0.1985 0.3381 -0.4175 0.1901
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3023 0.3325 -0.3184 0.2879
## Avg_Cogongrass_Cover-Lynx_rufus 0.4245 0.3904 -0.2575 0.3959
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2958 0.3429 -0.3890 0.2933
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2320 0.4220 -1.1602 -0.1910
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2993 0.4690 -1.3465 -0.2517
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3120 0.3475 -0.3582 0.3171
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2081 0.4074 -0.5839 0.2113
## Avg_Cogongrass_Cover-Sus_scrofa -0.1317 0.4594 -1.1348 -0.0933
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9736 1.0076 501
## (Intercept)-Sciurus_niger 0.3327 1.0185 648
## (Intercept)-Procyon_lotor 1.1321 1.0114 330
## (Intercept)-Dasypus_novemcinctus 0.1325 1.0104 1015
## (Intercept)-Lynx_rufus 0.6764 1.0023 641
## (Intercept)-Didelphis_virginiana -0.2363 1.0094 1089
## (Intercept)-Sylvilagus_floridanus 0.4915 1.0040 933
## (Intercept)-Meleagris_gallopavo 0.4176 1.0063 843
## (Intercept)-Sciurus_carolinensis -0.1806 1.0072 886
## (Intercept)-Vulpes_vulpes 0.0398 1.0080 450
## (Intercept)-Sus_scrofa -0.3350 1.0195 561
## Avg_Cogongrass_Cover-Canis_latrans 1.0024 1.0008 1908
## Avg_Cogongrass_Cover-Sciurus_niger 0.6324 1.0041 836
## Avg_Cogongrass_Cover-Procyon_lotor 0.9222 1.0030 2035
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9945 1.0072 2144
## Avg_Cogongrass_Cover-Lynx_rufus 1.3227 1.0053 1636
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9713 1.0022 1605
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4844 1.0019 1113
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4946 1.0057 1086
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0184 1.0018 1862
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.9945 1.0012 1692
## Avg_Cogongrass_Cover-Sus_scrofa 0.6738 1.0202 1172
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6168 0.1755 -2.9775 -2.6108 -2.2808 1.0188
## (Intercept)-Sciurus_niger -3.7088 0.4969 -4.7598 -3.6699 -2.8443 1.0495
## (Intercept)-Procyon_lotor -2.2836 0.1294 -2.5470 -2.2798 -2.0448 1.0018
## (Intercept)-Dasypus_novemcinctus -1.6185 0.1368 -1.8875 -1.6196 -1.3526 1.0033
## (Intercept)-Lynx_rufus -3.4184 0.2942 -4.0077 -3.4005 -2.8667 1.0128
## (Intercept)-Didelphis_virginiana -2.3887 0.2471 -2.8862 -2.3834 -1.9229 1.0039
## (Intercept)-Sylvilagus_floridanus -3.1706 0.2846 -3.7699 -3.1624 -2.6369 1.0134
## (Intercept)-Meleagris_gallopavo -3.3637 0.3269 -4.0425 -3.3435 -2.7813 1.0194
## (Intercept)-Sciurus_carolinensis -2.5155 0.2716 -3.1061 -2.5000 -2.0239 1.0052
## (Intercept)-Vulpes_vulpes -3.7289 0.5881 -4.9365 -3.6970 -2.6692 1.0401
## (Intercept)-Sus_scrofa -3.0202 0.4311 -3.9419 -2.9929 -2.2497 1.0224
## week-Canis_latrans 0.0593 0.1340 -0.2156 0.0610 0.3193 1.0039
## week-Sciurus_niger -0.3329 0.2915 -0.9931 -0.3038 0.1573 1.0018
## week-Procyon_lotor -0.0629 0.1179 -0.3033 -0.0605 0.1549 1.0090
## week-Dasypus_novemcinctus -0.1769 0.1370 -0.4638 -0.1709 0.0744 1.0065
## week-Lynx_rufus -0.0564 0.1924 -0.4510 -0.0488 0.3007 1.0096
## week-Didelphis_virginiana -0.2313 0.2108 -0.6799 -0.2178 0.1522 1.0000
## week-Sylvilagus_floridanus -0.1729 0.2125 -0.6192 -0.1622 0.2202 1.0046
## week-Meleagris_gallopavo -0.3062 0.2385 -0.8207 -0.2886 0.1207 1.0011
## week-Sciurus_carolinensis 0.1191 0.1833 -0.2413 0.1228 0.4691 1.0010
## week-Vulpes_vulpes -0.1518 0.2696 -0.7176 -0.1334 0.3541 1.0007
## week-Sus_scrofa 0.0625 0.2456 -0.4329 0.0600 0.5574 1.0037
## ESS
## (Intercept)-Canis_latrans 913
## (Intercept)-Sciurus_niger 295
## (Intercept)-Procyon_lotor 1506
## (Intercept)-Dasypus_novemcinctus 2277
## (Intercept)-Lynx_rufus 460
## (Intercept)-Didelphis_virginiana 1331
## (Intercept)-Sylvilagus_floridanus 573
## (Intercept)-Meleagris_gallopavo 426
## (Intercept)-Sciurus_carolinensis 1138
## (Intercept)-Vulpes_vulpes 288
## (Intercept)-Sus_scrofa 764
## week-Canis_latrans 1529
## week-Sciurus_niger 723
## week-Procyon_lotor 1886
## week-Dasypus_novemcinctus 1954
## week-Lynx_rufus 962
## week-Didelphis_virginiana 1518
## week-Sylvilagus_floridanus 893
## week-Meleagris_gallopavo 853
## week-Sciurus_carolinensis 1632
## week-Vulpes_vulpes 1231
## week-Sus_scrofa 1769
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8433
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3104 0.6546 -2.5740 -1.3221 0.0059 1.0487 322
## Cogon_Patch_Size -0.7758 0.5585 -1.9842 -0.7446 0.2518 1.0090 429
## Veg_shannon_index 0.9722 0.3776 0.2574 0.9688 1.7451 1.0027 490
## total_shrub_cover -0.5644 0.4553 -1.5588 -0.5305 0.2488 1.0054 690
## Avg_Cogongrass_Cover 2.0276 0.6444 0.8066 2.0150 3.3394 1.0038 185
## Tree_Density -1.9494 0.6241 -3.2670 -1.9231 -0.7729 1.0413 237
## Avg_Canopy_Cover 1.7550 0.4906 0.8432 1.7277 2.8126 1.0070 292
## avg_veg_height -0.6264 0.4090 -1.4266 -0.6333 0.1931 1.0186 253
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1507 3.1698 0.2152 2.2574 11.1892 1.0398 370
## Cogon_Patch_Size 1.8201 2.4081 0.0739 1.0259 8.0243 1.0593 266
## Veg_shannon_index 0.5636 0.7829 0.0455 0.3396 2.4275 1.0411 563
## total_shrub_cover 1.2887 1.6385 0.0675 0.7671 5.3697 1.0280 253
## Avg_Cogongrass_Cover 1.0898 1.6046 0.0529 0.5710 5.1470 1.0666 367
## Tree_Density 1.4901 2.8961 0.0551 0.7168 7.2399 1.0882 289
## Avg_Canopy_Cover 1.2738 1.6254 0.0787 0.8079 5.3388 1.0707 329
## avg_veg_height 0.3243 0.3786 0.0399 0.2013 1.3715 1.0136 802
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9357 1.7752 0.1117 1.4417 6.7121 1.0177 78
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8993 0.3090 -3.4985 -2.9009 -2.2958 1.0072 1881
## week -0.1139 0.1282 -0.3753 -0.1084 0.1275 1.0201 1011
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9460 0.6291 0.2834 0.7872 2.6607 1.0323 828
## week 0.1001 0.0868 0.0260 0.0762 0.3184 1.0097 789
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0493 0.9926 -1.9786 -0.0753
## (Intercept)-Sciurus_niger -0.6096 1.3432 -3.0306 -0.7074
## (Intercept)-Procyon_lotor 0.1040 1.0110 -1.8862 0.1351
## (Intercept)-Dasypus_novemcinctus -1.5096 0.7920 -3.1240 -1.4706
## (Intercept)-Lynx_rufus -0.1549 1.4905 -2.5369 -0.3257
## (Intercept)-Didelphis_virginiana -2.5657 0.9608 -4.6668 -2.4994
## (Intercept)-Sylvilagus_floridanus -1.4277 0.9673 -3.2927 -1.4251
## (Intercept)-Meleagris_gallopavo -1.3636 1.1125 -3.4994 -1.3596
## (Intercept)-Sciurus_carolinensis -2.6639 0.9711 -4.7462 -2.5878
## (Intercept)-Vulpes_vulpes -2.2160 1.2768 -4.8598 -2.1818
## (Intercept)-Sus_scrofa -3.2988 1.2648 -6.0246 -3.2215
## Cogon_Patch_Size-Canis_latrans 0.3493 0.9754 -1.0614 0.1868
## Cogon_Patch_Size-Sciurus_niger -1.4246 1.3956 -4.7551 -1.2309
## Cogon_Patch_Size-Procyon_lotor -1.0278 0.6650 -2.3777 -0.9953
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8133 0.5960 -2.0943 -0.7813
## Cogon_Patch_Size-Lynx_rufus -0.8425 1.0822 -2.9844 -0.8225
## Cogon_Patch_Size-Didelphis_virginiana 0.5911 0.8502 -0.7937 0.4862
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6821 1.2585 -4.7560 -1.4422
## Cogon_Patch_Size-Meleagris_gallopavo -0.5270 0.8981 -2.2223 -0.5843
## Cogon_Patch_Size-Sciurus_carolinensis -1.5123 1.0043 -3.9267 -1.3456
## Cogon_Patch_Size-Vulpes_vulpes -1.2003 1.2102 -3.9060 -1.0504
## Cogon_Patch_Size-Sus_scrofa -1.0130 1.0605 -3.5360 -0.8744
## Veg_shannon_index-Canis_latrans 1.2475 0.5687 0.2503 1.1906
## Veg_shannon_index-Sciurus_niger 1.0845 0.7646 -0.3141 1.0289
## Veg_shannon_index-Procyon_lotor 1.2342 0.5422 0.2347 1.1824
## Veg_shannon_index-Dasypus_novemcinctus 0.7304 0.4842 -0.2426 0.7402
## Veg_shannon_index-Lynx_rufus 0.9307 0.7041 -0.5142 0.9356
## Veg_shannon_index-Didelphis_virginiana 1.0713 0.5786 0.0359 1.0199
## Veg_shannon_index-Sylvilagus_floridanus 1.0630 0.6051 -0.0358 1.0179
## Veg_shannon_index-Meleagris_gallopavo 1.2199 0.6704 0.0666 1.1588
## Veg_shannon_index-Sciurus_carolinensis 0.3686 0.6408 -1.0888 0.4510
## Veg_shannon_index-Vulpes_vulpes 0.5002 0.7188 -1.1091 0.5968
## Veg_shannon_index-Sus_scrofa 1.3640 0.7222 0.2064 1.2615
## total_shrub_cover-Canis_latrans 0.1463 0.6457 -0.9312 0.0825
## total_shrub_cover-Sciurus_niger -0.9772 0.9298 -3.1074 -0.8701
## total_shrub_cover-Procyon_lotor -0.9462 0.5774 -2.2400 -0.9022
## total_shrub_cover-Dasypus_novemcinctus 0.0545 0.5383 -0.9552 0.0200
## total_shrub_cover-Lynx_rufus -1.1555 1.0117 -3.6427 -0.9819
## total_shrub_cover-Didelphis_virginiana -0.6045 0.6940 -2.2079 -0.5555
## total_shrub_cover-Sylvilagus_floridanus -0.3097 0.7544 -1.8780 -0.3084
## total_shrub_cover-Meleagris_gallopavo -1.9265 1.1954 -4.7865 -1.7590
## total_shrub_cover-Sciurus_carolinensis -0.0951 0.6544 -1.3575 -0.1004
## total_shrub_cover-Vulpes_vulpes -0.7350 0.9793 -3.0725 -0.6072
## total_shrub_cover-Sus_scrofa -0.0111 0.8320 -1.5870 -0.0525
## Avg_Cogongrass_Cover-Canis_latrans 2.3764 0.8550 0.8843 2.3102
## Avg_Cogongrass_Cover-Sciurus_niger 1.4057 1.2407 -1.6937 1.5608
## Avg_Cogongrass_Cover-Procyon_lotor 2.3181 0.8558 0.7839 2.2696
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6043 0.9191 1.1234 2.5065
## Avg_Cogongrass_Cover-Lynx_rufus 2.5790 1.0214 0.8683 2.4468
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.2352 0.8345 0.7553 2.1627
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4499 0.9265 -0.4810 1.4901
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5906 1.1319 -1.0608 1.6831
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3938 0.8489 0.8806 2.3226
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5300 1.0188 0.7897 2.4369
## Avg_Cogongrass_Cover-Sus_scrofa 1.5928 1.0011 -0.5757 1.6724
## Tree_Density-Canis_latrans -2.3745 1.0238 -4.8832 -2.2353
## Tree_Density-Sciurus_niger -2.0132 1.1180 -4.5122 -1.9485
## Tree_Density-Procyon_lotor -1.6050 0.7272 -2.9948 -1.6109
## Tree_Density-Dasypus_novemcinctus -2.9707 1.3086 -6.3478 -2.6854
## Tree_Density-Lynx_rufus -0.8573 1.0593 -2.6949 -0.9575
## Tree_Density-Didelphis_virginiana -2.1656 0.9073 -4.2840 -2.0717
## Tree_Density-Sylvilagus_floridanus -2.2830 1.0329 -4.7512 -2.1753
## Tree_Density-Meleagris_gallopavo -2.0686 1.0498 -4.3391 -2.0062
## Tree_Density-Sciurus_carolinensis -2.2831 1.0735 -4.7815 -2.1406
## Tree_Density-Vulpes_vulpes -1.8665 1.1400 -4.1559 -1.8573
## Tree_Density-Sus_scrofa -2.0077 1.0887 -4.4051 -1.9379
## Avg_Canopy_Cover-Canis_latrans 0.5104 0.6640 -0.7723 0.5106
## Avg_Canopy_Cover-Sciurus_niger 1.7870 1.1760 -0.3531 1.6687
## Avg_Canopy_Cover-Procyon_lotor 1.6908 0.6265 0.5675 1.6501
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8807 0.6161 0.8071 1.8318
## Avg_Canopy_Cover-Lynx_rufus 1.1253 0.9616 -0.8576 1.1617
## Avg_Canopy_Cover-Didelphis_virginiana 2.3763 0.7843 1.1154 2.2852
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.7162 1.1553 1.0717 2.5338
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0979 0.9195 0.5994 1.9506
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0912 0.7027 0.9391 2.0131
## Avg_Canopy_Cover-Vulpes_vulpes 1.9751 0.9002 0.4847 1.8556
## Avg_Canopy_Cover-Sus_scrofa 1.8487 0.7331 0.5258 1.7955
## avg_veg_height-Canis_latrans -0.7680 0.5222 -1.8165 -0.7612
## avg_veg_height-Sciurus_niger -0.8144 0.6752 -2.2944 -0.7947
## avg_veg_height-Procyon_lotor -0.4797 0.5141 -1.4626 -0.4790
## avg_veg_height-Dasypus_novemcinctus -0.3940 0.5469 -1.4099 -0.4001
## avg_veg_height-Lynx_rufus -0.7070 0.6282 -1.9444 -0.7034
## avg_veg_height-Didelphis_virginiana -0.6902 0.5687 -1.8183 -0.6900
## avg_veg_height-Sylvilagus_floridanus -0.7618 0.5814 -1.9260 -0.7610
## avg_veg_height-Meleagris_gallopavo -0.6744 0.6400 -1.9825 -0.6466
## avg_veg_height-Sciurus_carolinensis -0.3332 0.5854 -1.4214 -0.3554
## avg_veg_height-Vulpes_vulpes -0.6265 0.6483 -1.8798 -0.6293
## avg_veg_height-Sus_scrofa -0.6880 0.5866 -1.8470 -0.6906
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9811 1.0152 275
## (Intercept)-Sciurus_niger 2.3422 1.0863 221
## (Intercept)-Procyon_lotor 2.0093 1.0203 340
## (Intercept)-Dasypus_novemcinctus -0.0557 1.0319 588
## (Intercept)-Lynx_rufus 3.2401 1.1049 157
## (Intercept)-Didelphis_virginiana -0.8644 1.0071 714
## (Intercept)-Sylvilagus_floridanus 0.3676 1.0110 613
## (Intercept)-Meleagris_gallopavo 0.8160 1.0344 288
## (Intercept)-Sciurus_carolinensis -0.9165 1.0182 439
## (Intercept)-Vulpes_vulpes 0.3623 1.0022 316
## (Intercept)-Sus_scrofa -1.0389 1.0062 309
## Cogon_Patch_Size-Canis_latrans 2.7134 1.0316 477
## Cogon_Patch_Size-Sciurus_niger 0.8657 1.0370 228
## Cogon_Patch_Size-Procyon_lotor 0.2616 1.0122 346
## Cogon_Patch_Size-Dasypus_novemcinctus 0.2752 1.0211 666
## Cogon_Patch_Size-Lynx_rufus 1.3451 1.0162 265
## Cogon_Patch_Size-Didelphis_virginiana 2.5671 1.0094 355
## Cogon_Patch_Size-Sylvilagus_floridanus 0.0592 1.0190 302
## Cogon_Patch_Size-Meleagris_gallopavo 1.4616 1.0057 727
## Cogon_Patch_Size-Sciurus_carolinensis -0.0212 1.0454 415
## Cogon_Patch_Size-Vulpes_vulpes 0.9187 1.0051 367
## Cogon_Patch_Size-Sus_scrofa 0.7694 1.0062 541
## Veg_shannon_index-Canis_latrans 2.4740 1.0030 654
## Veg_shannon_index-Sciurus_niger 2.8287 1.0014 551
## Veg_shannon_index-Procyon_lotor 2.3931 0.9998 419
## Veg_shannon_index-Dasypus_novemcinctus 1.6966 1.0055 800
## Veg_shannon_index-Lynx_rufus 2.3326 1.0053 631
## Veg_shannon_index-Didelphis_virginiana 2.3063 1.0030 873
## Veg_shannon_index-Sylvilagus_floridanus 2.3973 1.0085 668
## Veg_shannon_index-Meleagris_gallopavo 2.6772 1.0078 706
## Veg_shannon_index-Sciurus_carolinensis 1.4688 1.0122 672
## Veg_shannon_index-Vulpes_vulpes 1.7057 1.0079 578
## Veg_shannon_index-Sus_scrofa 3.0427 1.0047 623
## total_shrub_cover-Canis_latrans 1.6535 1.0016 673
## total_shrub_cover-Sciurus_niger 0.5827 1.0029 446
## total_shrub_cover-Procyon_lotor 0.0516 1.0036 1067
## total_shrub_cover-Dasypus_novemcinctus 1.1779 1.0018 848
## total_shrub_cover-Lynx_rufus 0.3597 1.0053 346
## total_shrub_cover-Didelphis_virginiana 0.6629 1.0045 711
## total_shrub_cover-Sylvilagus_floridanus 1.2384 1.0028 873
## total_shrub_cover-Meleagris_gallopavo -0.1549 1.0097 244
## total_shrub_cover-Sciurus_carolinensis 1.2503 1.0096 1255
## total_shrub_cover-Vulpes_vulpes 0.9198 1.0076 431
## total_shrub_cover-Sus_scrofa 1.7432 1.0003 850
## Avg_Cogongrass_Cover-Canis_latrans 4.3122 1.0130 309
## Avg_Cogongrass_Cover-Sciurus_niger 3.4824 1.0065 315
## Avg_Cogongrass_Cover-Procyon_lotor 4.1392 1.0253 240
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6972 1.0399 203
## Avg_Cogongrass_Cover-Lynx_rufus 4.8878 1.0071 191
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0486 1.0164 299
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.1063 1.0057 372
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.5532 1.0074 296
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2477 1.0314 233
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.8121 1.0351 218
## Avg_Cogongrass_Cover-Sus_scrofa 3.4402 0.9998 328
## Tree_Density-Canis_latrans -0.7855 1.0834 224
## Tree_Density-Sciurus_niger 0.1604 1.0306 314
## Tree_Density-Procyon_lotor -0.1528 1.0148 291
## Tree_Density-Dasypus_novemcinctus -1.2443 1.0298 222
## Tree_Density-Lynx_rufus 1.4052 1.0160 212
## Tree_Density-Didelphis_virginiana -0.6359 1.0207 372
## Tree_Density-Sylvilagus_floridanus -0.5477 1.0058 511
## Tree_Density-Meleagris_gallopavo 0.0057 1.0356 489
## Tree_Density-Sciurus_carolinensis -0.6747 1.0269 315
## Tree_Density-Vulpes_vulpes 0.4293 1.0388 407
## Tree_Density-Sus_scrofa -0.1200 1.0131 532
## Avg_Canopy_Cover-Canis_latrans 1.8499 1.0419 494
## Avg_Canopy_Cover-Sciurus_niger 4.4028 1.0271 232
## Avg_Canopy_Cover-Procyon_lotor 3.0863 1.0061 609
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2273 1.0098 365
## Avg_Canopy_Cover-Lynx_rufus 3.0240 1.0346 420
## Avg_Canopy_Cover-Didelphis_virginiana 4.1775 1.0018 256
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.4371 1.0174 246
## Avg_Canopy_Cover-Meleagris_gallopavo 4.3377 1.0063 383
## Avg_Canopy_Cover-Sciurus_carolinensis 3.6990 1.0015 377
## Avg_Canopy_Cover-Vulpes_vulpes 4.0511 1.0009 395
## Avg_Canopy_Cover-Sus_scrofa 3.4221 1.0011 575
## avg_veg_height-Canis_latrans 0.2262 1.0117 500
## avg_veg_height-Sciurus_niger 0.4464 1.0157 437
## avg_veg_height-Procyon_lotor 0.5198 1.0182 508
## avg_veg_height-Dasypus_novemcinctus 0.7376 1.0109 603
## avg_veg_height-Lynx_rufus 0.5323 1.0250 464
## avg_veg_height-Didelphis_virginiana 0.4244 1.0097 502
## avg_veg_height-Sylvilagus_floridanus 0.3409 1.0036 529
## avg_veg_height-Meleagris_gallopavo 0.5398 1.0124 530
## avg_veg_height-Sciurus_carolinensis 0.9265 1.0086 638
## avg_veg_height-Vulpes_vulpes 0.7151 1.0243 454
## avg_veg_height-Sus_scrofa 0.4868 1.0077 586
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6446 0.1688 -2.9935 -2.6411 -2.3225 1.0031
## (Intercept)-Sciurus_niger -4.2304 0.4431 -5.1063 -4.2243 -3.3464 1.0833
## (Intercept)-Procyon_lotor -2.2760 0.1274 -2.5303 -2.2739 -2.0339 1.0038
## (Intercept)-Dasypus_novemcinctus -1.6126 0.1371 -1.8907 -1.6131 -1.3570 1.0062
## (Intercept)-Lynx_rufus -3.6255 0.3133 -4.2234 -3.6262 -3.0238 1.0241
## (Intercept)-Didelphis_virginiana -2.3601 0.2489 -2.8743 -2.3524 -1.9016 1.0019
## (Intercept)-Sylvilagus_floridanus -3.1983 0.2664 -3.7589 -3.1878 -2.6845 1.0023
## (Intercept)-Meleagris_gallopavo -3.4454 0.3075 -4.0501 -3.4411 -2.8801 1.0288
## (Intercept)-Sciurus_carolinensis -2.4866 0.2610 -3.0442 -2.4765 -2.0217 1.0009
## (Intercept)-Vulpes_vulpes -4.0330 0.5842 -5.2566 -4.0035 -2.9705 1.0429
## (Intercept)-Sus_scrofa -3.0132 0.4402 -3.9557 -2.9923 -2.2168 1.0338
## week-Canis_latrans 0.0582 0.1343 -0.2077 0.0620 0.3123 1.0038
## week-Sciurus_niger -0.3165 0.2918 -0.9818 -0.2854 0.1615 1.0250
## week-Procyon_lotor -0.0636 0.1206 -0.3041 -0.0627 0.1642 1.0051
## week-Dasypus_novemcinctus -0.1748 0.1403 -0.4643 -0.1703 0.0917 1.0010
## week-Lynx_rufus -0.0701 0.1888 -0.4528 -0.0617 0.2911 1.0084
## week-Didelphis_virginiana -0.2326 0.2167 -0.7083 -0.2181 0.1595 1.0010
## week-Sylvilagus_floridanus -0.1722 0.2065 -0.6298 -0.1600 0.2033 1.0063
## week-Meleagris_gallopavo -0.2909 0.2397 -0.8245 -0.2702 0.1239 1.0071
## week-Sciurus_carolinensis 0.1127 0.1869 -0.2660 0.1107 0.4705 1.0075
## week-Vulpes_vulpes -0.1681 0.2706 -0.7969 -0.1469 0.3181 1.0029
## week-Sus_scrofa 0.0544 0.2382 -0.4095 0.0539 0.5143 1.0106
## ESS
## (Intercept)-Canis_latrans 1077
## (Intercept)-Sciurus_niger 199
## (Intercept)-Procyon_lotor 1509
## (Intercept)-Dasypus_novemcinctus 2345
## (Intercept)-Lynx_rufus 248
## (Intercept)-Didelphis_virginiana 1288
## (Intercept)-Sylvilagus_floridanus 644
## (Intercept)-Meleagris_gallopavo 475
## (Intercept)-Sciurus_carolinensis 1349
## (Intercept)-Vulpes_vulpes 210
## (Intercept)-Sus_scrofa 612
## week-Canis_latrans 1401
## week-Sciurus_niger 497
## week-Procyon_lotor 1752
## week-Dasypus_novemcinctus 2093
## week-Lynx_rufus 943
## week-Didelphis_virginiana 1368
## week-Sylvilagus_floridanus 1022
## week-Meleagris_gallopavo 584
## week-Sciurus_carolinensis 1801
## week-Vulpes_vulpes 1067
## week-Sus_scrofa 1786
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8175
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8259 0.3689 -1.5674 -0.8266 -0.1069 1.0248 274
## Avg_Cogongrass_Cover 0.0402 0.3090 -0.5993 0.0521 0.6472 1.0082 667
## total_shrub_cover -0.4373 0.2827 -1.0522 -0.4304 0.1068 1.0062 638
## avg_veg_height -0.0275 0.2749 -0.5649 -0.0270 0.4958 1.0113 499
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6636 0.7026 0.0578 0.4586 2.6020 1.0341 356
## Avg_Cogongrass_Cover 0.3839 0.4169 0.0427 0.2526 1.4506 1.0416 574
## total_shrub_cover 0.4730 0.5203 0.0504 0.3272 1.7629 1.0823 408
## avg_veg_height 0.1909 0.1947 0.0336 0.1289 0.7270 1.0020 1167
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.116 1.085 0.0879 0.8497 3.9031 1.1709 101
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8377 0.2691 -3.3933 -2.8315 -2.3179 1.0050 1619
## week -0.1172 0.1250 -0.3754 -0.1133 0.1194 1.0016 973
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7203 0.4919 0.2189 0.5982 1.9187 1.0594 607
## week 0.1006 0.0813 0.0249 0.0785 0.3213 1.0087 984
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2407 0.6190 -1.4537 -0.2517
## (Intercept)-Sciurus_niger -0.9616 0.6958 -2.2594 -0.9857
## (Intercept)-Procyon_lotor -0.0792 0.6869 -1.4280 -0.0899
## (Intercept)-Dasypus_novemcinctus -0.8279 0.5022 -1.8560 -0.8165
## (Intercept)-Lynx_rufus -0.5798 0.6172 -1.7040 -0.6174
## (Intercept)-Didelphis_virginiana -1.2010 0.5434 -2.3452 -1.1709
## (Intercept)-Sylvilagus_floridanus -0.5955 0.6286 -1.7330 -0.6133
## (Intercept)-Meleagris_gallopavo -0.8619 0.5883 -1.9931 -0.8647
## (Intercept)-Sciurus_carolinensis -1.2682 0.5468 -2.4421 -1.2415
## (Intercept)-Vulpes_vulpes -1.1777 0.7270 -2.6449 -1.1503
## (Intercept)-Sus_scrofa -1.4824 0.6662 -2.9635 -1.4230
## Avg_Cogongrass_Cover-Canis_latrans 0.3468 0.4309 -0.4262 0.3248
## Avg_Cogongrass_Cover-Sciurus_niger -0.3625 0.6270 -1.8078 -0.2822
## Avg_Cogongrass_Cover-Procyon_lotor 0.0282 0.4342 -0.8358 0.0340
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2308 0.3860 -0.4874 0.2141
## Avg_Cogongrass_Cover-Lynx_rufus 0.4230 0.4757 -0.4132 0.3840
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2917 0.4379 -0.5589 0.2806
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3162 0.5110 -1.4903 -0.2752
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4276 0.5605 -1.6744 -0.3605
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2201 0.4113 -0.5519 0.2096
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2113 0.5170 -0.7743 0.2026
## Avg_Cogongrass_Cover-Sus_scrofa -0.2364 0.5786 -1.5651 -0.1753
## total_shrub_cover-Canis_latrans 0.0787 0.4374 -0.6768 0.0432
## total_shrub_cover-Sciurus_niger -0.6891 0.5496 -1.8716 -0.6337
## total_shrub_cover-Procyon_lotor -0.8396 0.4593 -1.8754 -0.8045
## total_shrub_cover-Dasypus_novemcinctus -0.1069 0.3642 -0.8161 -0.1163
## total_shrub_cover-Lynx_rufus -0.8172 0.5582 -2.1063 -0.7497
## total_shrub_cover-Didelphis_virginiana -0.2961 0.4121 -1.0973 -0.2989
## total_shrub_cover-Sylvilagus_floridanus -0.4289 0.4786 -1.4490 -0.4125
## total_shrub_cover-Meleagris_gallopavo -1.1357 0.6105 -2.5568 -1.0544
## total_shrub_cover-Sciurus_carolinensis -0.1809 0.4089 -0.9768 -0.1945
## total_shrub_cover-Vulpes_vulpes -0.4172 0.5410 -1.5309 -0.4117
## total_shrub_cover-Sus_scrofa -0.0587 0.5136 -1.0155 -0.0905
## avg_veg_height-Canis_latrans -0.0714 0.3757 -0.8367 -0.0673
## avg_veg_height-Sciurus_niger -0.1683 0.4523 -1.1325 -0.1570
## avg_veg_height-Procyon_lotor 0.0657 0.3891 -0.6845 0.0553
## avg_veg_height-Dasypus_novemcinctus 0.1531 0.3790 -0.5708 0.1541
## avg_veg_height-Lynx_rufus -0.0157 0.4305 -0.8535 -0.0269
## avg_veg_height-Didelphis_virginiana -0.0386 0.3873 -0.7758 -0.0353
## avg_veg_height-Sylvilagus_floridanus -0.1274 0.3917 -0.9335 -0.1177
## avg_veg_height-Meleagris_gallopavo -0.1929 0.4384 -1.1059 -0.1722
## avg_veg_height-Sciurus_carolinensis 0.2145 0.4026 -0.5477 0.2021
## avg_veg_height-Vulpes_vulpes -0.0603 0.4335 -0.9147 -0.0524
## avg_veg_height-Sus_scrofa -0.0352 0.4196 -0.9034 -0.0327
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0163 1.0131 302
## (Intercept)-Sciurus_niger 0.5507 1.0364 445
## (Intercept)-Procyon_lotor 1.2473 1.0344 186
## (Intercept)-Dasypus_novemcinctus 0.1194 1.0044 476
## (Intercept)-Lynx_rufus 0.7255 1.0087 426
## (Intercept)-Didelphis_virginiana -0.2252 1.0081 778
## (Intercept)-Sylvilagus_floridanus 0.7329 1.0206 374
## (Intercept)-Meleagris_gallopavo 0.3241 1.0136 632
## (Intercept)-Sciurus_carolinensis -0.2687 1.0098 712
## (Intercept)-Vulpes_vulpes 0.2070 1.0204 257
## (Intercept)-Sus_scrofa -0.3109 1.0487 503
## Avg_Cogongrass_Cover-Canis_latrans 1.3066 1.0035 1060
## Avg_Cogongrass_Cover-Sciurus_niger 0.6811 1.0182 677
## Avg_Cogongrass_Cover-Procyon_lotor 0.8787 1.0071 1076
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0222 1.0047 1402
## Avg_Cogongrass_Cover-Lynx_rufus 1.4898 1.0108 1198
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1990 1.0038 1339
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5682 1.0033 954
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5301 1.0019 745
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0883 1.0027 1234
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2810 1.0051 1108
## Avg_Cogongrass_Cover-Sus_scrofa 0.7205 1.0296 724
## total_shrub_cover-Canis_latrans 1.0188 1.0150 1036
## total_shrub_cover-Sciurus_niger 0.2515 1.0171 630
## total_shrub_cover-Procyon_lotor -0.0357 1.0233 839
## total_shrub_cover-Dasypus_novemcinctus 0.6311 1.0013 2216
## total_shrub_cover-Lynx_rufus 0.0931 1.0148 551
## total_shrub_cover-Didelphis_virginiana 0.5472 1.0012 2076
## total_shrub_cover-Sylvilagus_floridanus 0.4880 1.0210 1187
## total_shrub_cover-Meleagris_gallopavo -0.1632 1.0232 531
## total_shrub_cover-Sciurus_carolinensis 0.6341 1.0015 1815
## total_shrub_cover-Vulpes_vulpes 0.6588 1.0120 1108
## total_shrub_cover-Sus_scrofa 1.0094 1.0102 1293
## avg_veg_height-Canis_latrans 0.6664 1.0111 1049
## avg_veg_height-Sciurus_niger 0.6834 1.0201 755
## avg_veg_height-Procyon_lotor 0.8471 1.0034 912
## avg_veg_height-Dasypus_novemcinctus 0.9396 1.0103 915
## avg_veg_height-Lynx_rufus 0.8664 1.0042 787
## avg_veg_height-Didelphis_virginiana 0.7101 1.0012 962
## avg_veg_height-Sylvilagus_floridanus 0.5989 1.0028 1142
## avg_veg_height-Meleagris_gallopavo 0.6046 1.0019 807
## avg_veg_height-Sciurus_carolinensis 1.0473 1.0122 821
## avg_veg_height-Vulpes_vulpes 0.8068 1.0112 1050
## avg_veg_height-Sus_scrofa 0.7994 1.0177 822
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6427 0.1739 -3.0014 -2.6369 -2.3144 1.0017
## (Intercept)-Sciurus_niger -3.6999 0.4717 -4.6757 -3.6754 -2.8477 1.0728
## (Intercept)-Procyon_lotor -2.2856 0.1295 -2.5389 -2.2865 -2.0318 1.0037
## (Intercept)-Dasypus_novemcinctus -1.6206 0.1333 -1.8833 -1.6175 -1.3575 1.0121
## (Intercept)-Lynx_rufus -3.4310 0.2740 -3.9739 -3.4260 -2.9044 1.0216
## (Intercept)-Didelphis_virginiana -2.3889 0.2529 -2.9153 -2.3793 -1.9218 1.0192
## (Intercept)-Sylvilagus_floridanus -3.2230 0.2976 -3.8527 -3.2065 -2.6758 1.0256
## (Intercept)-Meleagris_gallopavo -3.2950 0.3041 -3.9287 -3.2865 -2.7470 1.0154
## (Intercept)-Sciurus_carolinensis -2.5096 0.2658 -3.0684 -2.4975 -2.0190 1.0110
## (Intercept)-Vulpes_vulpes -3.8391 0.5858 -5.1106 -3.8169 -2.7749 1.0646
## (Intercept)-Sus_scrofa -3.0445 0.4569 -4.0365 -3.0134 -2.2522 1.0200
## week-Canis_latrans 0.0578 0.1333 -0.2081 0.0616 0.3086 1.0041
## week-Sciurus_niger -0.3285 0.2954 -0.9909 -0.2987 0.1685 1.0032
## week-Procyon_lotor -0.0675 0.1217 -0.3108 -0.0672 0.1615 1.0014
## week-Dasypus_novemcinctus -0.1777 0.1371 -0.4627 -0.1700 0.0704 1.0105
## week-Lynx_rufus -0.0613 0.1916 -0.4552 -0.0500 0.2930 1.0067
## week-Didelphis_virginiana -0.2428 0.2163 -0.7143 -0.2261 0.1364 1.0016
## week-Sylvilagus_floridanus -0.1846 0.2070 -0.6099 -0.1753 0.1979 1.0000
## week-Meleagris_gallopavo -0.3066 0.2394 -0.8379 -0.2798 0.1076 1.0005
## week-Sciurus_carolinensis 0.1096 0.1859 -0.2575 0.1132 0.4612 0.9998
## week-Vulpes_vulpes -0.1535 0.2729 -0.7468 -0.1475 0.3472 1.0132
## week-Sus_scrofa 0.0491 0.2457 -0.4521 0.0495 0.5302 1.0043
## ESS
## (Intercept)-Canis_latrans 960
## (Intercept)-Sciurus_niger 253
## (Intercept)-Procyon_lotor 1399
## (Intercept)-Dasypus_novemcinctus 2417
## (Intercept)-Lynx_rufus 553
## (Intercept)-Didelphis_virginiana 1228
## (Intercept)-Sylvilagus_floridanus 500
## (Intercept)-Meleagris_gallopavo 496
## (Intercept)-Sciurus_carolinensis 1131
## (Intercept)-Vulpes_vulpes 204
## (Intercept)-Sus_scrofa 587
## week-Canis_latrans 1526
## week-Sciurus_niger 614
## week-Procyon_lotor 1587
## week-Dasypus_novemcinctus 1777
## week-Lynx_rufus 816
## week-Didelphis_virginiana 1129
## week-Sylvilagus_floridanus 1125
## week-Meleagris_gallopavo 685
## week-Sciurus_carolinensis 1647
## week-Vulpes_vulpes 1050
## week-Sus_scrofa 1741
# Includes week covariate for detection and none for occupancy
ms_week_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.783
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6373 0.3094 -1.2534 -0.6377 -0.0294 1.0005 1299
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7902 0.5894 0.1709 0.6479 2.3297 1.0065 1169
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8297 0.2709 -3.3895 -2.8250 -2.2868 1.0033 1664
## week -0.1155 0.1269 -0.3841 -0.1106 0.1125 1.0028 926
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7100 0.5070 0.2032 0.5755 1.9919 1.0063 687
## week 0.1066 0.0878 0.0245 0.0808 0.3468 1.0097 840
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1324 0.3794 -0.5507 0.1143 0.9210 1.0001
## (Intercept)-Sciurus_niger -0.9063 0.5713 -1.9638 -0.9274 0.3255 1.0069
## (Intercept)-Procyon_lotor 0.4731 0.3801 -0.2138 0.4528 1.2760 1.0009
## (Intercept)-Dasypus_novemcinctus -0.6529 0.3469 -1.3368 -0.6483 0.0185 1.0002
## (Intercept)-Lynx_rufus -0.1419 0.5053 -1.0158 -0.1779 0.9726 1.0032
## (Intercept)-Didelphis_virginiana -1.2378 0.4227 -2.0991 -1.2179 -0.4568 1.0005
## (Intercept)-Sylvilagus_floridanus -0.4394 0.4424 -1.2498 -0.4658 0.4907 1.0030
## (Intercept)-Meleagris_gallopavo -0.4373 0.4872 -1.3038 -0.4650 0.6208 1.0018
## (Intercept)-Sciurus_carolinensis -1.2119 0.4133 -2.0785 -1.1960 -0.4420 1.0030
## (Intercept)-Vulpes_vulpes -1.2075 0.6939 -2.4986 -1.2422 0.2480 1.0179
## (Intercept)-Sus_scrofa -1.5677 0.5425 -2.6867 -1.5633 -0.5493 1.0015
## ESS
## (Intercept)-Canis_latrans 1866
## (Intercept)-Sciurus_niger 595
## (Intercept)-Procyon_lotor 1946
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 642
## (Intercept)-Didelphis_virginiana 2225
## (Intercept)-Sylvilagus_floridanus 911
## (Intercept)-Meleagris_gallopavo 811
## (Intercept)-Sciurus_carolinensis 1972
## (Intercept)-Vulpes_vulpes 359
## (Intercept)-Sus_scrofa 1071
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6224 0.1726 -2.9810 -2.6176 -2.3036 1.0004
## (Intercept)-Sciurus_niger -3.6737 0.4547 -4.6010 -3.6599 -2.8268 1.0081
## (Intercept)-Procyon_lotor -2.2789 0.1280 -2.5333 -2.2772 -2.0296 1.0016
## (Intercept)-Dasypus_novemcinctus -1.6194 0.1357 -1.8979 -1.6191 -1.3596 1.0035
## (Intercept)-Lynx_rufus -3.4380 0.3018 -4.0616 -3.4283 -2.8745 1.0074
## (Intercept)-Didelphis_virginiana -2.3927 0.2572 -2.9249 -2.3801 -1.9246 1.0025
## (Intercept)-Sylvilagus_floridanus -3.1543 0.2769 -3.7092 -3.1426 -2.6563 1.0053
## (Intercept)-Meleagris_gallopavo -3.4090 0.3353 -4.0932 -3.4000 -2.7918 1.0016
## (Intercept)-Sciurus_carolinensis -2.5013 0.2620 -3.0722 -2.4917 -2.0178 1.0036
## (Intercept)-Vulpes_vulpes -3.7806 0.6205 -5.1283 -3.7190 -2.7246 1.0201
## (Intercept)-Sus_scrofa -3.0219 0.4401 -3.9490 -2.9953 -2.2452 1.0031
## week-Canis_latrans 0.0574 0.1329 -0.2120 0.0589 0.3080 1.0085
## week-Sciurus_niger -0.3523 0.3023 -1.0782 -0.3164 0.1335 1.0086
## week-Procyon_lotor -0.0601 0.1178 -0.2976 -0.0555 0.1642 1.0070
## week-Dasypus_novemcinctus -0.1776 0.1375 -0.4753 -0.1720 0.0782 1.0059
## week-Lynx_rufus -0.0515 0.1906 -0.4189 -0.0529 0.3208 1.0011
## week-Didelphis_virginiana -0.2439 0.2210 -0.7109 -0.2301 0.1432 1.0009
## week-Sylvilagus_floridanus -0.1869 0.2065 -0.6276 -0.1730 0.1888 1.0019
## week-Meleagris_gallopavo -0.3062 0.2420 -0.8445 -0.2885 0.1157 1.0033
## week-Sciurus_carolinensis 0.1230 0.1813 -0.2367 0.1226 0.4693 1.0012
## week-Vulpes_vulpes -0.1521 0.2757 -0.7177 -0.1345 0.3545 1.0124
## week-Sus_scrofa 0.0721 0.2376 -0.3869 0.0654 0.5588 1.0004
## ESS
## (Intercept)-Canis_latrans 1081
## (Intercept)-Sciurus_niger 317
## (Intercept)-Procyon_lotor 1412
## (Intercept)-Dasypus_novemcinctus 2413
## (Intercept)-Lynx_rufus 389
## (Intercept)-Didelphis_virginiana 1317
## (Intercept)-Sylvilagus_floridanus 524
## (Intercept)-Meleagris_gallopavo 410
## (Intercept)-Sciurus_carolinensis 1347
## (Intercept)-Vulpes_vulpes 218
## (Intercept)-Sus_scrofa 644
## week-Canis_latrans 1246
## week-Sciurus_niger 617
## week-Procyon_lotor 1721
## week-Dasypus_novemcinctus 1936
## week-Lynx_rufus 1142
## week-Didelphis_virginiana 1284
## week-Sylvilagus_floridanus 1039
## week-Meleagris_gallopavo 847
## week-Sciurus_carolinensis 1903
## week-Vulpes_vulpes 1104
## week-Sus_scrofa 1781
#Includes week for detection and only foraging for occupancy
ms_week_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8138
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8596 0.3740 -1.6133 -0.8517 -0.1339 1.0042 421
## Veg_shannon_index 0.3565 0.2348 -0.0857 0.3509 0.8387 1.0116 821
## Avg_Cogongrass_Cover 0.2252 0.2513 -0.2701 0.2302 0.7051 1.0206 916
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6598 0.6784 0.0543 0.4625 2.4534 1.0299 567
## Veg_shannon_index 0.2420 0.2652 0.0364 0.1640 0.8564 1.0263 791
## Avg_Cogongrass_Cover 0.3053 0.3362 0.0384 0.2032 1.2086 1.0248 729
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0401 0.8324 0.114 0.8399 3.1273 1.2073 104
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8313 0.2753 -3.4079 -2.8270 -2.3018 1.005 1396
## week -0.1110 0.1282 -0.3729 -0.1076 0.1316 1.004 1023
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7076 0.4897 0.2087 0.5832 1.9473 1.0360 537
## week 0.1006 0.0833 0.0254 0.0775 0.3118 1.0118 1233
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2846 0.6004 -1.4190 -0.2940
## (Intercept)-Sciurus_niger -0.9538 0.6479 -2.2752 -0.9554
## (Intercept)-Procyon_lotor -0.1295 0.6386 -1.3298 -0.1475
## (Intercept)-Dasypus_novemcinctus -0.8477 0.5069 -1.8953 -0.8274
## (Intercept)-Lynx_rufus -0.6281 0.6398 -1.8451 -0.6484
## (Intercept)-Didelphis_virginiana -1.2549 0.5391 -2.3630 -1.2153
## (Intercept)-Sylvilagus_floridanus -0.7106 0.5661 -1.8133 -0.7222
## (Intercept)-Meleagris_gallopavo -0.7576 0.6229 -1.9982 -0.7795
## (Intercept)-Sciurus_carolinensis -1.2749 0.5714 -2.4830 -1.2337
## (Intercept)-Vulpes_vulpes -1.2323 0.7194 -2.8201 -1.1814
## (Intercept)-Sus_scrofa -1.5705 0.7212 -3.2006 -1.4946
## Veg_shannon_index-Canis_latrans 0.6203 0.3591 -0.0338 0.5919
## Veg_shannon_index-Sciurus_niger 0.3062 0.4306 -0.5424 0.2988
## Veg_shannon_index-Procyon_lotor 0.4665 0.3641 -0.2049 0.4499
## Veg_shannon_index-Dasypus_novemcinctus 0.2076 0.3310 -0.4598 0.2093
## Veg_shannon_index-Lynx_rufus 0.1936 0.4203 -0.6638 0.2027
## Veg_shannon_index-Didelphis_virginiana 0.4749 0.3674 -0.2113 0.4639
## Veg_shannon_index-Sylvilagus_floridanus 0.4510 0.3921 -0.2717 0.4286
## Veg_shannon_index-Meleagris_gallopavo 0.4508 0.4206 -0.3418 0.4368
## Veg_shannon_index-Sciurus_carolinensis 0.0246 0.3794 -0.7557 0.0492
## Veg_shannon_index-Vulpes_vulpes 0.1017 0.4357 -0.8278 0.1128
## Veg_shannon_index-Sus_scrofa 0.6064 0.4785 -0.2136 0.5524
## Avg_Cogongrass_Cover-Canis_latrans 0.4776 0.3591 -0.1870 0.4666
## Avg_Cogongrass_Cover-Sciurus_niger -0.1244 0.5397 -1.3077 -0.0658
## Avg_Cogongrass_Cover-Procyon_lotor 0.3499 0.3659 -0.3174 0.3401
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4003 0.3285 -0.2177 0.3903
## Avg_Cogongrass_Cover-Lynx_rufus 0.5486 0.3987 -0.1523 0.5112
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4301 0.3592 -0.2553 0.4192
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1143 0.4244 -1.0828 -0.0720
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1620 0.5010 -1.2931 -0.1228
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3749 0.3543 -0.3359 0.3710
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3238 0.4634 -0.5614 0.3125
## Avg_Cogongrass_Cover-Sus_scrofa -0.0137 0.4958 -1.1745 0.0296
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9452 1.0198 369
## (Intercept)-Sciurus_niger 0.3140 1.0043 452
## (Intercept)-Procyon_lotor 1.1183 1.0171 374
## (Intercept)-Dasypus_novemcinctus 0.1251 1.0000 734
## (Intercept)-Lynx_rufus 0.7311 1.0087 525
## (Intercept)-Didelphis_virginiana -0.2763 1.0166 910
## (Intercept)-Sylvilagus_floridanus 0.4462 1.0179 706
## (Intercept)-Meleagris_gallopavo 0.5049 1.0106 418
## (Intercept)-Sciurus_carolinensis -0.2400 1.0036 684
## (Intercept)-Vulpes_vulpes 0.1197 1.0054 324
## (Intercept)-Sus_scrofa -0.3802 1.0241 459
## Veg_shannon_index-Canis_latrans 1.4022 1.0015 1602
## Veg_shannon_index-Sciurus_niger 1.1936 1.0047 1196
## Veg_shannon_index-Procyon_lotor 1.2374 1.0093 1318
## Veg_shannon_index-Dasypus_novemcinctus 0.8514 1.0085 1822
## Veg_shannon_index-Lynx_rufus 1.0073 1.0079 1162
## Veg_shannon_index-Didelphis_virginiana 1.2252 1.0032 1691
## Veg_shannon_index-Sylvilagus_floridanus 1.2607 1.0006 1497
## Veg_shannon_index-Meleagris_gallopavo 1.3703 1.0019 1355
## Veg_shannon_index-Sciurus_carolinensis 0.6911 1.0052 1320
## Veg_shannon_index-Vulpes_vulpes 0.9265 1.0058 1137
## Veg_shannon_index-Sus_scrofa 1.7157 1.0071 1005
## Avg_Cogongrass_Cover-Canis_latrans 1.2030 1.0059 1589
## Avg_Cogongrass_Cover-Sciurus_niger 0.8086 1.0096 790
## Avg_Cogongrass_Cover-Procyon_lotor 1.1330 1.0053 1697
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0687 1.0035 1991
## Avg_Cogongrass_Cover-Lynx_rufus 1.4115 1.0055 1305
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1722 1.0071 2065
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6291 1.0093 1236
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6915 1.0129 875
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1114 1.0016 1687
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2921 1.0017 1096
## Avg_Cogongrass_Cover-Sus_scrofa 0.8516 1.0197 1085
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6183 0.1674 -2.9613 -2.6103 -2.3078 1.0068
## (Intercept)-Sciurus_niger -3.7308 0.4844 -4.7510 -3.7114 -2.8685 1.0167
## (Intercept)-Procyon_lotor -2.2907 0.1267 -2.5448 -2.2894 -2.0504 1.0050
## (Intercept)-Dasypus_novemcinctus -1.6215 0.1383 -1.9028 -1.6220 -1.3450 1.0013
## (Intercept)-Lynx_rufus -3.4064 0.2885 -3.9840 -3.3911 -2.8677 1.0215
## (Intercept)-Didelphis_virginiana -2.4034 0.2474 -2.9136 -2.3956 -1.9521 1.0015
## (Intercept)-Sylvilagus_floridanus -3.1815 0.2803 -3.7692 -3.1706 -2.6729 1.0145
## (Intercept)-Meleagris_gallopavo -3.3836 0.3177 -4.0657 -3.3701 -2.8158 1.0334
## (Intercept)-Sciurus_carolinensis -2.5079 0.2651 -3.0644 -2.5030 -2.0277 1.0128
## (Intercept)-Vulpes_vulpes -3.8042 0.6148 -5.1440 -3.7439 -2.7736 1.0363
## (Intercept)-Sus_scrofa -3.0013 0.4437 -3.9459 -2.9609 -2.2250 1.0031
## week-Canis_latrans 0.0605 0.1302 -0.1990 0.0627 0.3100 1.0006
## week-Sciurus_niger -0.3203 0.2810 -0.9469 -0.2953 0.1654 1.0122
## week-Procyon_lotor -0.0563 0.1198 -0.2960 -0.0533 0.1716 1.0009
## week-Dasypus_novemcinctus -0.1818 0.1384 -0.4618 -0.1761 0.0796 1.0036
## week-Lynx_rufus -0.0577 0.1958 -0.4618 -0.0502 0.3038 1.0037
## week-Didelphis_virginiana -0.2518 0.2215 -0.7263 -0.2365 0.1407 1.0040
## week-Sylvilagus_floridanus -0.1628 0.2055 -0.6108 -0.1499 0.2133 1.0027
## week-Meleagris_gallopavo -0.3005 0.2392 -0.8240 -0.2833 0.1179 1.0052
## week-Sciurus_carolinensis 0.1187 0.1849 -0.2553 0.1220 0.4793 1.0040
## week-Vulpes_vulpes -0.1466 0.2662 -0.7373 -0.1305 0.3265 1.0020
## week-Sus_scrofa 0.0655 0.2438 -0.4234 0.0656 0.5562 1.0106
## ESS
## (Intercept)-Canis_latrans 978
## (Intercept)-Sciurus_niger 283
## (Intercept)-Procyon_lotor 1673
## (Intercept)-Dasypus_novemcinctus 2214
## (Intercept)-Lynx_rufus 361
## (Intercept)-Didelphis_virginiana 1280
## (Intercept)-Sylvilagus_floridanus 530
## (Intercept)-Meleagris_gallopavo 333
## (Intercept)-Sciurus_carolinensis 1169
## (Intercept)-Vulpes_vulpes 194
## (Intercept)-Sus_scrofa 554
## week-Canis_latrans 1649
## week-Sciurus_niger 809
## week-Procyon_lotor 1660
## week-Dasypus_novemcinctus 1981
## week-Lynx_rufus 995
## week-Didelphis_virginiana 1294
## week-Sylvilagus_floridanus 1051
## week-Meleagris_gallopavo 709
## week-Sciurus_carolinensis 1965
## week-Vulpes_vulpes 974
## week-Sus_scrofa 1682
# Includes movement covariates of occupancy and week for detection
ms_week_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7918
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8778 0.3761 -1.6862 -0.8670 -0.1679 1.0135 574
## Cogon_Patch_Size -0.2025 0.3674 -1.0310 -0.1772 0.4506 1.0086 562
## Avg_Cogongrass_Cover 0.0967 0.2778 -0.4624 0.0964 0.6507 1.0111 555
## total_shrub_cover -0.4187 0.2815 -1.0017 -0.4128 0.1253 1.0112 726
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7451 0.8378 0.0618 0.5208 2.6037 1.0188 579
## Cogon_Patch_Size 0.6569 0.8154 0.0579 0.4046 2.9272 1.0256 445
## Avg_Cogongrass_Cover 0.3639 0.4379 0.0400 0.2219 1.5645 1.0103 651
## total_shrub_cover 0.4522 0.5126 0.0502 0.2989 1.8055 1.0134 660
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2399 0.9383 0.1532 1.0299 3.5945 1.0185 211
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8439 0.2724 -3.4022 -2.8415 -2.3132 1.0030 1235
## week -0.1110 0.1267 -0.3741 -0.1033 0.1251 1.0147 686
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7293 0.4974 0.2043 0.5973 2.0198 1.0132 444
## week 0.0998 0.0840 0.0243 0.0769 0.3219 1.0090 934
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2280 0.6379 -1.4257 -0.2351
## (Intercept)-Sciurus_niger -1.0358 0.6853 -2.4372 -1.0202
## (Intercept)-Procyon_lotor -0.0854 0.6898 -1.3602 -0.1050
## (Intercept)-Dasypus_novemcinctus -0.8591 0.5220 -1.9178 -0.8359
## (Intercept)-Lynx_rufus -0.6308 0.6811 -1.8372 -0.6940
## (Intercept)-Didelphis_virginiana -1.2573 0.5606 -2.4281 -1.2308
## (Intercept)-Sylvilagus_floridanus -0.7515 0.5973 -1.9167 -0.7745
## (Intercept)-Meleagris_gallopavo -0.9192 0.6054 -2.1369 -0.9158
## (Intercept)-Sciurus_carolinensis -1.3593 0.5984 -2.6401 -1.3313
## (Intercept)-Vulpes_vulpes -1.2742 0.7701 -2.8596 -1.2465
## (Intercept)-Sus_scrofa -1.5957 0.7055 -3.0795 -1.5431
## Cogon_Patch_Size-Canis_latrans 0.5469 0.5694 -0.3570 0.4689
## Cogon_Patch_Size-Sciurus_niger -0.4917 0.7735 -2.2854 -0.3883
## Cogon_Patch_Size-Procyon_lotor -0.2291 0.4320 -1.1094 -0.2143
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1368 0.4087 -0.9912 -0.1327
## Cogon_Patch_Size-Lynx_rufus -0.2850 0.6565 -1.5882 -0.2973
## Cogon_Patch_Size-Didelphis_virginiana 0.5582 0.4825 -0.3025 0.5261
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7897 0.8103 -2.7498 -0.6379
## Cogon_Patch_Size-Meleagris_gallopavo -0.1329 0.5652 -1.3316 -0.1085
## Cogon_Patch_Size-Sciurus_carolinensis -0.5963 0.5893 -1.9696 -0.5026
## Cogon_Patch_Size-Vulpes_vulpes -0.4673 0.7373 -2.1789 -0.3932
## Cogon_Patch_Size-Sus_scrofa -0.3114 0.6560 -1.7826 -0.2495
## Avg_Cogongrass_Cover-Canis_latrans 0.2404 0.3845 -0.5025 0.2320
## Avg_Cogongrass_Cover-Sciurus_niger -0.2606 0.6225 -1.7279 -0.2018
## Avg_Cogongrass_Cover-Procyon_lotor 0.1542 0.4131 -0.6510 0.1451
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3514 0.3738 -0.3516 0.3322
## Avg_Cogongrass_Cover-Lynx_rufus 0.4814 0.4745 -0.3249 0.4251
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1888 0.3947 -0.5741 0.1873
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2024 0.4905 -1.2709 -0.1699
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3880 0.6012 -1.8049 -0.3219
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4117 0.3954 -0.3232 0.3974
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2394 0.4727 -0.7037 0.2246
## Avg_Cogongrass_Cover-Sus_scrofa -0.1232 0.5348 -1.3531 -0.0746
## total_shrub_cover-Canis_latrans 0.0312 0.4421 -0.7495 -0.0026
## total_shrub_cover-Sciurus_niger -0.6317 0.5357 -1.8221 -0.5955
## total_shrub_cover-Procyon_lotor -0.8001 0.4513 -1.7431 -0.7686
## total_shrub_cover-Dasypus_novemcinctus -0.1055 0.3728 -0.7929 -0.1077
## total_shrub_cover-Lynx_rufus -0.7399 0.5743 -2.0776 -0.6869
## total_shrub_cover-Didelphis_virginiana -0.3620 0.4075 -1.1979 -0.3582
## total_shrub_cover-Sylvilagus_floridanus -0.3656 0.4966 -1.3960 -0.3582
## total_shrub_cover-Meleagris_gallopavo -1.1065 0.6163 -2.5446 -1.0194
## total_shrub_cover-Sciurus_carolinensis -0.1382 0.4238 -0.9469 -0.1540
## total_shrub_cover-Vulpes_vulpes -0.4001 0.5778 -1.5965 -0.3717
## total_shrub_cover-Sus_scrofa -0.0528 0.5066 -0.9509 -0.0883
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0704 1.0467 431
## (Intercept)-Sciurus_niger 0.3269 1.0172 680
## (Intercept)-Procyon_lotor 1.2852 1.0102 373
## (Intercept)-Dasypus_novemcinctus 0.1537 1.0087 1108
## (Intercept)-Lynx_rufus 0.8233 1.0759 572
## (Intercept)-Didelphis_virginiana -0.2399 1.0008 1010
## (Intercept)-Sylvilagus_floridanus 0.4796 1.0302 940
## (Intercept)-Meleagris_gallopavo 0.2702 1.0130 843
## (Intercept)-Sciurus_carolinensis -0.3092 1.0155 769
## (Intercept)-Vulpes_vulpes 0.1980 1.0174 403
## (Intercept)-Sus_scrofa -0.3898 1.0013 633
## Cogon_Patch_Size-Canis_latrans 1.9239 1.0040 747
## Cogon_Patch_Size-Sciurus_niger 0.7781 1.0448 530
## Cogon_Patch_Size-Procyon_lotor 0.5893 1.0020 1184
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6320 1.0063 1894
## Cogon_Patch_Size-Lynx_rufus 1.0877 1.0299 675
## Cogon_Patch_Size-Didelphis_virginiana 1.5917 1.0014 859
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3615 1.0192 508
## Cogon_Patch_Size-Meleagris_gallopavo 0.9524 1.0135 1349
## Cogon_Patch_Size-Sciurus_carolinensis 0.3520 1.0102 762
## Cogon_Patch_Size-Vulpes_vulpes 0.8057 1.0065 737
## Cogon_Patch_Size-Sus_scrofa 0.8031 1.0154 974
## Avg_Cogongrass_Cover-Canis_latrans 1.0510 1.0047 1621
## Avg_Cogongrass_Cover-Sciurus_niger 0.7953 1.0021 569
## Avg_Cogongrass_Cover-Procyon_lotor 0.9726 1.0075 1314
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1337 1.0079 1618
## Avg_Cogongrass_Cover-Lynx_rufus 1.5679 1.0014 1055
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9635 1.0117 1388
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6583 1.0071 815
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6529 1.0067 783
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2354 1.0044 1300
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2199 1.0048 1032
## Avg_Cogongrass_Cover-Sus_scrofa 0.7931 1.0085 866
## total_shrub_cover-Canis_latrans 1.0128 1.0030 1219
## total_shrub_cover-Sciurus_niger 0.3051 1.0008 1070
## total_shrub_cover-Procyon_lotor 0.0087 1.0216 1063
## total_shrub_cover-Dasypus_novemcinctus 0.6537 1.0015 1915
## total_shrub_cover-Lynx_rufus 0.2324 1.0149 680
## total_shrub_cover-Didelphis_virginiana 0.4194 1.0020 1900
## total_shrub_cover-Sylvilagus_floridanus 0.5708 1.0150 1152
## total_shrub_cover-Meleagris_gallopavo -0.1557 1.0087 531
## total_shrub_cover-Sciurus_carolinensis 0.7287 1.0026 1556
## total_shrub_cover-Vulpes_vulpes 0.6459 1.0347 582
## total_shrub_cover-Sus_scrofa 1.0238 1.0034 1327
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6236 0.1660 -2.9685 -2.6147 -2.3191 1.0075
## (Intercept)-Sciurus_niger -3.7750 0.5000 -4.7939 -3.7579 -2.8814 1.0039
## (Intercept)-Procyon_lotor -2.2894 0.1304 -2.5504 -2.2883 -2.0361 1.0012
## (Intercept)-Dasypus_novemcinctus -1.6197 0.1389 -1.8970 -1.6175 -1.3564 1.0059
## (Intercept)-Lynx_rufus -3.4456 0.2900 -4.0275 -3.4353 -2.9188 1.0860
## (Intercept)-Didelphis_virginiana -2.3856 0.2525 -2.8965 -2.3768 -1.9142 1.0055
## (Intercept)-Sylvilagus_floridanus -3.1959 0.2926 -3.8201 -3.1843 -2.6552 1.0064
## (Intercept)-Meleagris_gallopavo -3.3066 0.3125 -3.9794 -3.2903 -2.7382 1.0127
## (Intercept)-Sciurus_carolinensis -2.5254 0.2654 -3.0791 -2.5142 -2.0265 1.0065
## (Intercept)-Vulpes_vulpes -3.8356 0.6201 -5.1318 -3.7938 -2.7413 1.0171
## (Intercept)-Sus_scrofa -2.9670 0.4445 -3.9237 -2.9403 -2.1715 1.0076
## week-Canis_latrans 0.0563 0.1311 -0.2147 0.0587 0.3078 1.0156
## week-Sciurus_niger -0.3294 0.2892 -0.9898 -0.3045 0.1592 1.0038
## week-Procyon_lotor -0.0604 0.1184 -0.3035 -0.0557 0.1680 1.0026
## week-Dasypus_novemcinctus -0.1791 0.1357 -0.4635 -0.1746 0.0760 1.0011
## week-Lynx_rufus -0.0523 0.1893 -0.4478 -0.0448 0.3060 1.0138
## week-Didelphis_virginiana -0.2315 0.2134 -0.6885 -0.2171 0.1489 1.0030
## week-Sylvilagus_floridanus -0.1695 0.2032 -0.5989 -0.1587 0.1866 1.0044
## week-Meleagris_gallopavo -0.2966 0.2385 -0.8215 -0.2741 0.1082 1.0013
## week-Sciurus_carolinensis 0.1112 0.1868 -0.2609 0.1130 0.4740 1.0021
## week-Vulpes_vulpes -0.1479 0.2731 -0.7103 -0.1366 0.3423 1.0063
## week-Sus_scrofa 0.0607 0.2365 -0.4148 0.0623 0.5049 1.0104
## ESS
## (Intercept)-Canis_latrans 1083
## (Intercept)-Sciurus_niger 222
## (Intercept)-Procyon_lotor 1468
## (Intercept)-Dasypus_novemcinctus 2262
## (Intercept)-Lynx_rufus 441
## (Intercept)-Didelphis_virginiana 1290
## (Intercept)-Sylvilagus_floridanus 500
## (Intercept)-Meleagris_gallopavo 466
## (Intercept)-Sciurus_carolinensis 1185
## (Intercept)-Vulpes_vulpes 188
## (Intercept)-Sus_scrofa 632
## week-Canis_latrans 1615
## week-Sciurus_niger 626
## week-Procyon_lotor 1762
## week-Dasypus_novemcinctus 1682
## week-Lynx_rufus 1070
## week-Didelphis_virginiana 1433
## week-Sylvilagus_floridanus 951
## week-Meleagris_gallopavo 767
## week-Sciurus_carolinensis 1766
## week-Vulpes_vulpes 911
## week-Sus_scrofa 1571
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7938
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9073 0.4238 -1.7465 -0.9147 -0.0673 1.0303 635
## Tree_Density -0.7570 0.3682 -1.5777 -0.7340 -0.1432 1.0169 532
## Avg_Canopy_Cover 0.9763 0.2998 0.4346 0.9599 1.6097 1.0010 783
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2834 1.1254 0.1408 0.9792 4.2021 1.0242 731
## Tree_Density 0.5779 0.7617 0.0498 0.3347 2.6209 1.0624 418
## Avg_Canopy_Cover 0.4648 0.4815 0.0555 0.3164 1.7533 1.0107 628
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5779 0.6138 0.0502 0.4022 2.3415 1.4008 120
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8403 0.2870 -3.4050 -2.8375 -2.2655 1.0028 1112
## week -0.1134 0.1253 -0.3789 -0.1093 0.1215 1.0073 1086
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7804 0.5534 0.2191 0.6381 2.1209 1.0139 512
## week 0.1022 0.0870 0.0246 0.0789 0.3212 1.0066 1180
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0696 0.6150 -1.3003 -0.0648 1.1404
## (Intercept)-Sciurus_niger -0.8764 0.8186 -2.2691 -0.9456 0.9523
## (Intercept)-Procyon_lotor 0.2715 0.6450 -1.0406 0.3068 1.5246
## (Intercept)-Dasypus_novemcinctus -1.0313 0.5252 -2.1171 -1.0236 -0.0366
## (Intercept)-Lynx_rufus -0.2571 0.8642 -1.7377 -0.3202 1.6705
## (Intercept)-Didelphis_virginiana -1.6623 0.6188 -2.9809 -1.6362 -0.5349
## (Intercept)-Sylvilagus_floridanus -0.8252 0.5960 -1.9926 -0.8402 0.3713
## (Intercept)-Meleagris_gallopavo -0.6823 0.6429 -1.9537 -0.6886 0.6107
## (Intercept)-Sciurus_carolinensis -1.6952 0.6158 -2.9906 -1.6637 -0.5537
## (Intercept)-Vulpes_vulpes -1.5448 0.8605 -3.2637 -1.5389 0.1169
## (Intercept)-Sus_scrofa -2.1188 0.8086 -3.8236 -2.0749 -0.6810
## Tree_Density-Canis_latrans -0.8988 0.5325 -2.1495 -0.8220 -0.0176
## Tree_Density-Sciurus_niger -0.7595 0.6613 -2.2007 -0.7036 0.3722
## Tree_Density-Procyon_lotor -0.4824 0.3982 -1.2471 -0.4770 0.3019
## Tree_Density-Dasypus_novemcinctus -1.2520 0.7598 -3.1785 -1.1134 -0.1875
## Tree_Density-Lynx_rufus 0.0639 0.6172 -0.9161 -0.0055 1.5676
## Tree_Density-Didelphis_virginiana -0.9343 0.6722 -2.5426 -0.8403 0.1110
## Tree_Density-Sylvilagus_floridanus -1.0127 0.6728 -2.5829 -0.9184 0.0459
## Tree_Density-Meleagris_gallopavo -0.8564 0.6458 -2.3242 -0.7818 0.2303
## Tree_Density-Sciurus_carolinensis -0.8599 0.6402 -2.3585 -0.7828 0.1707
## Tree_Density-Vulpes_vulpes -0.6742 0.6508 -2.0370 -0.6468 0.5685
## Tree_Density-Sus_scrofa -0.8637 0.6895 -2.5316 -0.8022 0.2999
## Avg_Canopy_Cover-Canis_latrans 0.1580 0.4523 -0.7466 0.1797 1.0015
## Avg_Canopy_Cover-Sciurus_niger 0.8455 0.6695 -0.3622 0.8047 2.4023
## Avg_Canopy_Cover-Procyon_lotor 0.9678 0.4219 0.1721 0.9381 1.8388
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9994 0.3935 0.2793 0.9857 1.8387
## Avg_Canopy_Cover-Lynx_rufus 0.7295 0.5716 -0.3325 0.7049 1.9726
## Avg_Canopy_Cover-Didelphis_virginiana 1.1827 0.4618 0.3881 1.1354 2.1904
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5087 0.6512 0.5532 1.4014 3.0013
## Avg_Canopy_Cover-Meleagris_gallopavo 1.2647 0.5839 0.2787 1.1974 2.6053
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1667 0.4661 0.3698 1.1271 2.2053
## Avg_Canopy_Cover-Vulpes_vulpes 0.9843 0.5349 0.0104 0.9443 2.1487
## Avg_Canopy_Cover-Sus_scrofa 1.1004 0.4887 0.2405 1.0560 2.1992
## Rhat ESS
## (Intercept)-Canis_latrans 1.0395 768
## (Intercept)-Sciurus_niger 1.0062 403
## (Intercept)-Procyon_lotor 1.0734 449
## (Intercept)-Dasypus_novemcinctus 1.0067 1412
## (Intercept)-Lynx_rufus 1.1031 322
## (Intercept)-Didelphis_virginiana 1.0100 1133
## (Intercept)-Sylvilagus_floridanus 1.0246 1363
## (Intercept)-Meleagris_gallopavo 1.0451 628
## (Intercept)-Sciurus_carolinensis 1.0095 1037
## (Intercept)-Vulpes_vulpes 1.0125 363
## (Intercept)-Sus_scrofa 1.0215 549
## Tree_Density-Canis_latrans 1.0093 871
## Tree_Density-Sciurus_niger 1.0111 681
## Tree_Density-Procyon_lotor 1.0022 2227
## Tree_Density-Dasypus_novemcinctus 1.0053 508
## Tree_Density-Lynx_rufus 1.0179 592
## Tree_Density-Didelphis_virginiana 1.0012 705
## Tree_Density-Sylvilagus_floridanus 1.0018 715
## Tree_Density-Meleagris_gallopavo 1.0156 769
## Tree_Density-Sciurus_carolinensis 1.0046 929
## Tree_Density-Vulpes_vulpes 1.0099 837
## Tree_Density-Sus_scrofa 1.0075 784
## Avg_Canopy_Cover-Canis_latrans 1.0256 1175
## Avg_Canopy_Cover-Sciurus_niger 1.0198 583
## Avg_Canopy_Cover-Procyon_lotor 1.0010 1432
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0015 1663
## Avg_Canopy_Cover-Lynx_rufus 1.0058 919
## Avg_Canopy_Cover-Didelphis_virginiana 1.0001 1204
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0057 634
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0009 908
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0008 1528
## Avg_Canopy_Cover-Vulpes_vulpes 1.0039 873
## Avg_Canopy_Cover-Sus_scrofa 1.0009 1366
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6437 0.1724 -2.9976 -2.6403 -2.3188 1.0128
## (Intercept)-Sciurus_niger -3.8637 0.5195 -4.9002 -3.8590 -2.9087 1.0190
## (Intercept)-Procyon_lotor -2.2822 0.1305 -2.5448 -2.2789 -2.0457 1.0006
## (Intercept)-Dasypus_novemcinctus -1.6181 0.1374 -1.8967 -1.6134 -1.3522 1.0031
## (Intercept)-Lynx_rufus -3.5127 0.3072 -4.1224 -3.5000 -2.9235 1.0380
## (Intercept)-Didelphis_virginiana -2.3817 0.2481 -2.8921 -2.3704 -1.9183 1.0089
## (Intercept)-Sylvilagus_floridanus -3.1014 0.2724 -3.6634 -3.0874 -2.5986 1.0162
## (Intercept)-Meleagris_gallopavo -3.3958 0.3189 -4.0700 -3.3765 -2.8373 1.0748
## (Intercept)-Sciurus_carolinensis -2.5085 0.2602 -3.0704 -2.4951 -2.0260 1.0062
## (Intercept)-Vulpes_vulpes -3.8152 0.6037 -4.9837 -3.7996 -2.7032 1.0530
## (Intercept)-Sus_scrofa -2.9681 0.4296 -3.8997 -2.9417 -2.2032 1.0036
## week-Canis_latrans 0.0587 0.1313 -0.2106 0.0613 0.3044 1.0013
## week-Sciurus_niger -0.3261 0.2872 -0.9982 -0.3016 0.1741 1.0136
## week-Procyon_lotor -0.0589 0.1212 -0.3088 -0.0564 0.1716 1.0138
## week-Dasypus_novemcinctus -0.1751 0.1376 -0.4671 -0.1668 0.0704 1.0039
## week-Lynx_rufus -0.0543 0.1905 -0.4583 -0.0480 0.2881 1.0027
## week-Didelphis_virginiana -0.2351 0.2228 -0.7183 -0.2188 0.1663 1.0047
## week-Sylvilagus_floridanus -0.1662 0.2010 -0.5874 -0.1574 0.1990 1.0007
## week-Meleagris_gallopavo -0.2910 0.2353 -0.8042 -0.2726 0.1111 1.0085
## week-Sciurus_carolinensis 0.1096 0.1815 -0.2532 0.1162 0.4599 1.0033
## week-Vulpes_vulpes -0.1602 0.2687 -0.7289 -0.1462 0.3282 1.0024
## week-Sus_scrofa 0.0586 0.2428 -0.4188 0.0567 0.5333 1.0016
## ESS
## (Intercept)-Canis_latrans 1050
## (Intercept)-Sciurus_niger 197
## (Intercept)-Procyon_lotor 1406
## (Intercept)-Dasypus_novemcinctus 1975
## (Intercept)-Lynx_rufus 331
## (Intercept)-Didelphis_virginiana 1369
## (Intercept)-Sylvilagus_floridanus 644
## (Intercept)-Meleagris_gallopavo 334
## (Intercept)-Sciurus_carolinensis 1176
## (Intercept)-Vulpes_vulpes 183
## (Intercept)-Sus_scrofa 650
## week-Canis_latrans 1519
## week-Sciurus_niger 604
## week-Procyon_lotor 1736
## week-Dasypus_novemcinctus 1955
## week-Lynx_rufus 1040
## week-Didelphis_virginiana 1307
## week-Sylvilagus_floridanus 1019
## week-Meleagris_gallopavo 832
## week-Sciurus_carolinensis 1960
## week-Vulpes_vulpes 1129
## week-Sus_scrofa 1714
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
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## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
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## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
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## Sampled: 3000 of 5000, 60.00%
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## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8008
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4362 0.3909 -2.2108 -1.4373 -0.6868 1.0180 356
## Avg_Cogongrass_Cover -0.7109 0.3355 -1.4034 -0.7037 -0.0819 1.0284 418
## I(Avg_Cogongrass_Cover^2) 0.6816 0.3423 0.0682 0.6608 1.4205 1.0139 452
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5727 0.5715 0.0645 0.3963 2.1352 1.0018 630
## Avg_Cogongrass_Cover 0.3248 0.4274 0.0384 0.1958 1.4667 1.0024 754
## I(Avg_Cogongrass_Cover^2) 0.6514 0.9425 0.0478 0.3270 3.1442 1.1030 281
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6541 0.5961 0.0649 0.4763 2.253 1.0374 191
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8241 0.2595 -3.3603 -2.821 -2.3189 1.0000 1763
## week -0.1117 0.1264 -0.3736 -0.106 0.1210 1.0073 1219
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6436 0.4496 0.1817 0.5245 1.7750 1.0143 690
## week 0.1009 0.0799 0.0253 0.0789 0.3151 1.0170 1160
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9655 0.5951 -2.1212 -0.9720
## (Intercept)-Sciurus_niger -1.4609 0.6215 -2.7143 -1.4554
## (Intercept)-Procyon_lotor -0.7997 0.6221 -2.0558 -0.8035
## (Intercept)-Dasypus_novemcinctus -1.4310 0.5211 -2.4813 -1.4181
## (Intercept)-Lynx_rufus -1.4299 0.6030 -2.5833 -1.4339
## (Intercept)-Didelphis_virginiana -1.7504 0.5509 -2.8915 -1.7355
## (Intercept)-Sylvilagus_floridanus -1.2986 0.5932 -2.4954 -1.2913
## (Intercept)-Meleagris_gallopavo -1.1712 0.6213 -2.3714 -1.1893
## (Intercept)-Sciurus_carolinensis -1.9656 0.6045 -3.2808 -1.9154
## (Intercept)-Vulpes_vulpes -1.9183 0.7189 -3.5163 -1.8774
## (Intercept)-Sus_scrofa -1.9412 0.6592 -3.3245 -1.8713
## Avg_Cogongrass_Cover-Canis_latrans -0.5043 0.4792 -1.4392 -0.5077
## Avg_Cogongrass_Cover-Sciurus_niger -0.9272 0.5733 -2.1982 -0.8872
## Avg_Cogongrass_Cover-Procyon_lotor -0.6413 0.4745 -1.5202 -0.6433
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5403 0.4446 -1.4020 -0.5490
## Avg_Cogongrass_Cover-Lynx_rufus -0.6118 0.5261 -1.6610 -0.6088
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4098 0.4933 -1.3351 -0.4307
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0779 0.5604 -2.2919 -1.0360
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9374 0.5401 -2.1270 -0.8976
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6951 0.4856 -1.6628 -0.6877
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.6904 0.5433 -1.7841 -0.6743
## Avg_Cogongrass_Cover-Sus_scrofa -0.8641 0.5698 -2.1189 -0.8274
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3351 0.8673 0.2433 1.1017
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.0667 0.6606 -1.5463 0.1572
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2656 0.8155 0.2681 1.0639
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6948 0.3329 0.0670 0.6903
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1807 0.5581 0.3449 1.0853
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4871 0.3756 -0.2503 0.4786
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7303 0.4966 -0.1026 0.6841
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1804 0.5917 -1.0301 0.2002
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8613 0.3659 0.2081 0.8394
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.7910 0.4706 0.0271 0.7421
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.0763 0.6537 -1.5724 0.1796
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2358 1.0018 467
## (Intercept)-Sciurus_niger -0.2420 1.0240 655
## (Intercept)-Procyon_lotor 0.4353 1.0038 537
## (Intercept)-Dasypus_novemcinctus -0.4268 1.0129 551
## (Intercept)-Lynx_rufus -0.1940 1.0053 704
## (Intercept)-Didelphis_virginiana -0.7066 1.0093 549
## (Intercept)-Sylvilagus_floridanus -0.1507 1.0059 708
## (Intercept)-Meleagris_gallopavo 0.0955 1.0051 581
## (Intercept)-Sciurus_carolinensis -0.9070 1.0183 578
## (Intercept)-Vulpes_vulpes -0.6292 1.0025 451
## (Intercept)-Sus_scrofa -0.8116 1.0041 415
## Avg_Cogongrass_Cover-Canis_latrans 0.4713 1.0048 982
## Avg_Cogongrass_Cover-Sciurus_niger 0.0793 1.0058 742
## Avg_Cogongrass_Cover-Procyon_lotor 0.3011 1.0190 937
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3813 1.0116 936
## Avg_Cogongrass_Cover-Lynx_rufus 0.4085 1.0209 730
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6296 1.0072 791
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1311 1.0204 627
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0189 1.0091 861
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2173 1.0096 646
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3717 1.0172 714
## Avg_Cogongrass_Cover-Sus_scrofa 0.1910 1.0101 772
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5898 1.0674 233
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1648 1.0197 407
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.5562 1.0803 297
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3571 1.0026 1013
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4582 1.0451 390
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2610 1.0067 932
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9114 1.0044 463
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3036 1.0488 321
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6783 1.0101 706
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8853 1.0184 549
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.0458 1.0618 407
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6448 0.1672 -2.9905 -2.6371 -2.3393 1.0054
## (Intercept)-Sciurus_niger -3.6089 0.4359 -4.4986 -3.5797 -2.8426 1.0092
## (Intercept)-Procyon_lotor -2.2937 0.1300 -2.5516 -2.2915 -2.0531 1.0018
## (Intercept)-Dasypus_novemcinctus -1.6237 0.1395 -1.9101 -1.6181 -1.3615 1.0008
## (Intercept)-Lynx_rufus -3.3546 0.2836 -3.9463 -3.3454 -2.8285 1.0118
## (Intercept)-Didelphis_virginiana -2.4095 0.2569 -2.9350 -2.4002 -1.9325 1.0041
## (Intercept)-Sylvilagus_floridanus -3.1856 0.2865 -3.7630 -3.1864 -2.6454 1.0338
## (Intercept)-Meleagris_gallopavo -3.3597 0.3192 -4.0566 -3.3407 -2.7976 1.0144
## (Intercept)-Sciurus_carolinensis -2.5332 0.2672 -3.1041 -2.5213 -2.0494 1.0055
## (Intercept)-Vulpes_vulpes -3.7015 0.5785 -4.9526 -3.6779 -2.6768 1.0016
## (Intercept)-Sus_scrofa -2.9829 0.4338 -3.9145 -2.9537 -2.2088 1.0042
## week-Canis_latrans 0.0529 0.1304 -0.2103 0.0548 0.2969 1.0047
## week-Sciurus_niger -0.3235 0.2917 -0.9657 -0.2905 0.1666 1.0020
## week-Procyon_lotor -0.0599 0.1193 -0.3032 -0.0571 0.1717 1.0002
## week-Dasypus_novemcinctus -0.1752 0.1376 -0.4536 -0.1710 0.0871 1.0003
## week-Lynx_rufus -0.0555 0.1940 -0.4764 -0.0464 0.2961 1.0034
## week-Didelphis_virginiana -0.2348 0.2172 -0.7015 -0.2215 0.1478 1.0045
## week-Sylvilagus_floridanus -0.1781 0.2040 -0.5986 -0.1718 0.1986 1.0239
## week-Meleagris_gallopavo -0.2941 0.2355 -0.8058 -0.2769 0.1291 1.0019
## week-Sciurus_carolinensis 0.1154 0.1872 -0.2540 0.1156 0.4744 1.0105
## week-Vulpes_vulpes -0.1522 0.2738 -0.7376 -0.1325 0.3373 1.0033
## week-Sus_scrofa 0.0641 0.2403 -0.4169 0.0613 0.5455 1.0059
## ESS
## (Intercept)-Canis_latrans 1085
## (Intercept)-Sciurus_niger 345
## (Intercept)-Procyon_lotor 1227
## (Intercept)-Dasypus_novemcinctus 2287
## (Intercept)-Lynx_rufus 521
## (Intercept)-Didelphis_virginiana 1143
## (Intercept)-Sylvilagus_floridanus 558
## (Intercept)-Meleagris_gallopavo 407
## (Intercept)-Sciurus_carolinensis 1181
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 664
## week-Canis_latrans 1205
## week-Sciurus_niger 743
## week-Procyon_lotor 1359
## week-Dasypus_novemcinctus 1818
## week-Lynx_rufus 941
## week-Didelphis_virginiana 1413
## week-Sylvilagus_floridanus 1058
## week-Meleagris_gallopavo 873
## week-Sciurus_carolinensis 1861
## week-Vulpes_vulpes 1051
## week-Sus_scrofa 1731
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8517
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2377 0.7427 -3.6849 -2.2224 -0.7357 1.0590 165
## Cogon_Patch_Size -0.2179 0.6144 -1.5903 -0.1824 0.9204 1.0062 361
## Veg_shannon_index 0.8879 0.3993 0.1665 0.8679 1.6946 1.0074 432
## total_shrub_cover -0.7434 0.4971 -1.8146 -0.7288 0.1564 1.0169 763
## Avg_Cogongrass_Cover 0.1740 0.7952 -1.3143 0.1468 1.8118 1.0591 120
## Tree_Density -2.2558 0.7278 -3.6890 -2.2378 -0.9254 1.0014 151
## Avg_Canopy_Cover 1.6876 0.5311 0.7332 1.6541 2.8339 1.0103 173
## I(Avg_Cogongrass_Cover^2) 1.0872 0.6442 -0.2337 1.0861 2.3685 1.0398 240
## avg_veg_height -0.2535 0.4609 -1.1584 -0.2494 0.6513 1.0488 210
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.9406 4.0834 0.1208 1.8004 12.7721 1.0195 213
## Cogon_Patch_Size 2.4109 3.4494 0.1281 1.3468 11.3967 1.0515 145
## Veg_shannon_index 0.5498 0.7873 0.0463 0.3092 2.6005 1.0471 339
## total_shrub_cover 1.6436 1.9535 0.0959 1.1070 6.6156 1.0480 283
## Avg_Cogongrass_Cover 0.9723 1.4996 0.0486 0.4465 5.0274 1.0624 306
## Tree_Density 1.6311 3.0265 0.0568 0.6810 8.4645 1.2046 149
## Avg_Canopy_Cover 1.2817 1.6488 0.0715 0.7614 5.7265 1.1004 218
## I(Avg_Cogongrass_Cover^2) 3.0218 4.2325 0.0954 1.6534 14.0016 1.2441 107
## avg_veg_height 0.3888 0.5267 0.0417 0.2364 1.6315 1.0867 773
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3972 1.8284 0.0542 0.7891 5.7571 1.075 69
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8946 0.3072 -3.5021 -2.9002 -2.2707 1.0032 1893
## week -0.1080 0.1285 -0.3721 -0.1030 0.1322 1.0105 880
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9458 0.6133 0.2874 0.7769 2.530 1.0072 587
## week 0.1013 0.0856 0.0231 0.0760 0.329 1.0075 945
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5706 1.0319 -3.5565 -1.5651
## (Intercept)-Sciurus_niger -1.3526 1.5508 -3.7799 -1.5269
## (Intercept)-Procyon_lotor -1.1261 1.0700 -3.2564 -1.0983
## (Intercept)-Dasypus_novemcinctus -2.5209 0.9120 -4.4543 -2.4667
## (Intercept)-Lynx_rufus -1.5744 1.4621 -4.0478 -1.6602
## (Intercept)-Didelphis_virginiana -3.3111 1.0635 -5.5399 -3.2295
## (Intercept)-Sylvilagus_floridanus -2.2246 1.0898 -4.4532 -2.1808
## (Intercept)-Meleagris_gallopavo -1.9842 1.1653 -4.1690 -2.0083
## (Intercept)-Sciurus_carolinensis -3.7214 1.1566 -6.3163 -3.6133
## (Intercept)-Vulpes_vulpes -3.4902 1.5782 -7.0101 -3.3722
## (Intercept)-Sus_scrofa -3.7974 1.3813 -6.9327 -3.6285
## Cogon_Patch_Size-Canis_latrans 1.2678 1.1284 -0.3420 1.0854
## Cogon_Patch_Size-Sciurus_niger -1.1165 1.7268 -5.2960 -0.8650
## Cogon_Patch_Size-Procyon_lotor -0.5152 0.7795 -2.1506 -0.4668
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2290 0.6544 -1.5802 -0.1940
## Cogon_Patch_Size-Lynx_rufus -0.2968 1.2758 -2.8474 -0.2899
## Cogon_Patch_Size-Didelphis_virginiana 1.3472 0.9539 -0.1504 1.2436
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2896 1.4237 -4.7379 -1.0085
## Cogon_Patch_Size-Meleagris_gallopavo 0.0612 0.9409 -1.7584 0.0305
## Cogon_Patch_Size-Sciurus_carolinensis -0.9354 1.1468 -3.6497 -0.7495
## Cogon_Patch_Size-Vulpes_vulpes -0.6391 1.3273 -3.7152 -0.5267
## Cogon_Patch_Size-Sus_scrofa -0.4681 1.1140 -3.2331 -0.3467
## Veg_shannon_index-Canis_latrans 1.2066 0.6183 0.1970 1.1472
## Veg_shannon_index-Sciurus_niger 0.8776 0.7740 -0.6214 0.8597
## Veg_shannon_index-Procyon_lotor 1.0602 0.5657 0.1131 1.0059
## Veg_shannon_index-Dasypus_novemcinctus 0.6308 0.5194 -0.4120 0.6380
## Veg_shannon_index-Lynx_rufus 0.8672 0.7572 -0.6521 0.8390
## Veg_shannon_index-Didelphis_virginiana 0.9440 0.5912 -0.1663 0.9171
## Veg_shannon_index-Sylvilagus_floridanus 0.9007 0.6356 -0.2898 0.8713
## Veg_shannon_index-Meleagris_gallopavo 1.0976 0.6713 -0.0522 1.0386
## Veg_shannon_index-Sciurus_carolinensis 0.3812 0.6553 -1.0449 0.4364
## Veg_shannon_index-Vulpes_vulpes 0.6000 0.7442 -1.1219 0.6389
## Veg_shannon_index-Sus_scrofa 1.2968 0.7658 0.1050 1.1847
## total_shrub_cover-Canis_latrans 0.0026 0.6521 -1.2308 -0.0212
## total_shrub_cover-Sciurus_niger -1.2798 1.0406 -3.5436 -1.1449
## total_shrub_cover-Procyon_lotor -1.2369 0.6759 -2.7546 -1.1760
## total_shrub_cover-Dasypus_novemcinctus 0.0421 0.5629 -1.0322 0.0476
## total_shrub_cover-Lynx_rufus -1.5338 1.0936 -4.0620 -1.3759
## total_shrub_cover-Didelphis_virginiana -0.6992 0.7282 -2.2499 -0.6755
## total_shrub_cover-Sylvilagus_floridanus -0.4990 0.8609 -2.4328 -0.4580
## total_shrub_cover-Meleagris_gallopavo -2.3683 1.2410 -5.1701 -2.2148
## total_shrub_cover-Sciurus_carolinensis -0.0948 0.7035 -1.4238 -0.1115
## total_shrub_cover-Vulpes_vulpes -0.9094 1.0295 -3.2971 -0.8002
## total_shrub_cover-Sus_scrofa -0.0841 0.8791 -1.7448 -0.1318
## Avg_Cogongrass_Cover-Canis_latrans 0.1453 1.0404 -1.8888 0.1382
## Avg_Cogongrass_Cover-Sciurus_niger -0.2214 1.2096 -2.9224 -0.1520
## Avg_Cogongrass_Cover-Procyon_lotor 0.2293 1.0333 -1.6885 0.2052
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6959 1.1011 -1.1569 0.5934
## Avg_Cogongrass_Cover-Lynx_rufus 0.2866 1.0591 -1.6747 0.2656
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4944 1.1006 -1.4750 0.4261
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2889 1.1115 -2.7139 -0.2448
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0808 1.0974 -2.2973 -0.0646
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3346 1.0929 -1.6394 0.2660
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3650 1.1458 -1.6868 0.2870
## Avg_Cogongrass_Cover-Sus_scrofa 0.0021 1.1125 -2.1769 0.0049
## Tree_Density-Canis_latrans -2.8620 1.2708 -5.9400 -2.6934
## Tree_Density-Sciurus_niger -2.1306 1.2197 -4.6261 -2.1260
## Tree_Density-Procyon_lotor -2.1934 0.9747 -4.2489 -2.1576
## Tree_Density-Dasypus_novemcinctus -3.3070 1.4342 -7.0136 -3.0356
## Tree_Density-Lynx_rufus -1.4555 1.2172 -3.6052 -1.5436
## Tree_Density-Didelphis_virginiana -2.2988 0.9965 -4.5435 -2.2376
## Tree_Density-Sylvilagus_floridanus -2.5996 1.1826 -5.3761 -2.4731
## Tree_Density-Meleagris_gallopavo -2.2266 1.1340 -4.6577 -2.1807
## Tree_Density-Sciurus_carolinensis -2.5034 1.1137 -5.1118 -2.4093
## Tree_Density-Vulpes_vulpes -2.1214 1.4366 -4.6476 -2.1521
## Tree_Density-Sus_scrofa -2.2954 1.2343 -4.9526 -2.2419
## Avg_Canopy_Cover-Canis_latrans 0.4670 0.6976 -0.9061 0.4622
## Avg_Canopy_Cover-Sciurus_niger 1.6770 1.2549 -0.6441 1.5908
## Avg_Canopy_Cover-Procyon_lotor 1.5921 0.6693 0.3748 1.5560
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8372 0.6529 0.7244 1.7616
## Avg_Canopy_Cover-Lynx_rufus 1.2630 1.0668 -0.8460 1.2575
## Avg_Canopy_Cover-Didelphis_virginiana 2.2606 0.8268 0.9368 2.1656
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.7497 1.3305 0.9349 2.4699
## Avg_Canopy_Cover-Meleagris_gallopavo 1.9855 0.9343 0.5780 1.8370
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0031 0.8035 0.7622 1.8870
## Avg_Canopy_Cover-Vulpes_vulpes 1.9038 0.9667 0.3643 1.7789
## Avg_Canopy_Cover-Sus_scrofa 1.7749 0.7581 0.4852 1.7034
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4712 1.4201 0.6490 2.1672
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.5163 1.6178 -2.6694 0.5180
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2721 1.2513 0.5745 2.0370
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2850 0.7385 0.0280 1.1991
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.7108 1.5035 0.7182 2.3888
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7072 0.6792 -0.6815 0.7125
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9170 0.8337 -0.4943 0.8578
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2944 1.3952 -3.3835 -0.1946
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3802 0.7133 0.1586 1.3110
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7429 1.1153 0.2352 1.5136
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.4370 1.5370 -4.3468 -0.1321
## avg_veg_height-Canis_latrans -0.4351 0.5971 -1.6764 -0.4189
## avg_veg_height-Sciurus_niger -0.4733 0.7841 -2.2951 -0.4086
## avg_veg_height-Procyon_lotor -0.0176 0.5787 -1.1374 -0.0101
## avg_veg_height-Dasypus_novemcinctus 0.0417 0.5675 -1.0061 0.0195
## avg_veg_height-Lynx_rufus -0.3857 0.7207 -1.8972 -0.3412
## avg_veg_height-Didelphis_virginiana -0.3001 0.6277 -1.6276 -0.2691
## avg_veg_height-Sylvilagus_floridanus -0.3604 0.6332 -1.7019 -0.3367
## avg_veg_height-Meleagris_gallopavo -0.3141 0.7059 -1.7883 -0.2907
## avg_veg_height-Sciurus_carolinensis 0.0126 0.6043 -1.1339 -0.0065
## avg_veg_height-Vulpes_vulpes -0.3424 0.7283 -1.8652 -0.2892
## avg_veg_height-Sus_scrofa -0.2481 0.6410 -1.5303 -0.2453
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4160 1.1128 168
## (Intercept)-Sciurus_niger 2.2475 1.0616 166
## (Intercept)-Procyon_lotor 0.8688 1.0940 132
## (Intercept)-Dasypus_novemcinctus -0.9121 1.0155 290
## (Intercept)-Lynx_rufus 1.9077 1.1199 176
## (Intercept)-Didelphis_virginiana -1.4753 0.9996 358
## (Intercept)-Sylvilagus_floridanus -0.2211 1.0013 330
## (Intercept)-Meleagris_gallopavo 0.3859 1.0577 242
## (Intercept)-Sciurus_carolinensis -1.8200 1.0351 300
## (Intercept)-Vulpes_vulpes -0.6905 1.0306 166
## (Intercept)-Sus_scrofa -1.5821 1.0258 269
## Cogon_Patch_Size-Canis_latrans 4.0566 1.0093 419
## Cogon_Patch_Size-Sciurus_niger 1.5795 1.0374 160
## Cogon_Patch_Size-Procyon_lotor 0.9523 1.0278 418
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0009 1.0018 414
## Cogon_Patch_Size-Lynx_rufus 2.3136 1.0349 375
## Cogon_Patch_Size-Didelphis_virginiana 3.4996 1.0209 371
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8332 1.0082 245
## Cogon_Patch_Size-Meleagris_gallopavo 2.1585 1.0125 565
## Cogon_Patch_Size-Sciurus_carolinensis 0.7524 1.0106 373
## Cogon_Patch_Size-Vulpes_vulpes 1.6919 1.0289 251
## Cogon_Patch_Size-Sus_scrofa 1.3884 1.0032 541
## Veg_shannon_index-Canis_latrans 2.6402 1.0011 603
## Veg_shannon_index-Sciurus_niger 2.4304 1.0055 503
## Veg_shannon_index-Procyon_lotor 2.3160 1.0065 695
## Veg_shannon_index-Dasypus_novemcinctus 1.6247 1.0012 1048
## Veg_shannon_index-Lynx_rufus 2.4403 1.0086 506
## Veg_shannon_index-Didelphis_virginiana 2.1635 1.0104 837
## Veg_shannon_index-Sylvilagus_floridanus 2.2403 1.0159 752
## Veg_shannon_index-Meleagris_gallopavo 2.6009 1.0030 787
## Veg_shannon_index-Sciurus_carolinensis 1.5669 1.0027 609
## Veg_shannon_index-Vulpes_vulpes 1.9523 1.0065 628
## Veg_shannon_index-Sus_scrofa 3.0426 1.0233 412
## total_shrub_cover-Canis_latrans 1.3810 1.0067 904
## total_shrub_cover-Sciurus_niger 0.4595 1.0250 407
## total_shrub_cover-Procyon_lotor -0.0811 1.0239 558
## total_shrub_cover-Dasypus_novemcinctus 1.1305 1.0043 1128
## total_shrub_cover-Lynx_rufus 0.1956 1.0262 382
## total_shrub_cover-Didelphis_virginiana 0.6826 1.0032 1383
## total_shrub_cover-Sylvilagus_floridanus 1.0931 1.0047 794
## total_shrub_cover-Meleagris_gallopavo -0.4435 1.0125 255
## total_shrub_cover-Sciurus_carolinensis 1.3362 1.0048 1073
## total_shrub_cover-Vulpes_vulpes 0.8776 1.0192 468
## total_shrub_cover-Sus_scrofa 1.7457 1.0131 904
## Avg_Cogongrass_Cover-Canis_latrans 2.1719 1.0755 196
## Avg_Cogongrass_Cover-Sciurus_niger 1.9849 1.0476 248
## Avg_Cogongrass_Cover-Procyon_lotor 2.3232 1.0504 177
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.1589 1.0178 167
## Avg_Cogongrass_Cover-Lynx_rufus 2.4585 1.0692 248
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8953 1.0088 250
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7630 1.0351 205
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0453 1.0298 224
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6579 1.0458 203
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.8846 1.0252 188
## Avg_Cogongrass_Cover-Sus_scrofa 2.2082 1.0418 231
## Tree_Density-Canis_latrans -0.9221 1.0270 168
## Tree_Density-Sciurus_niger 0.3074 1.0100 374
## Tree_Density-Procyon_lotor -0.3594 1.0057 183
## Tree_Density-Dasypus_novemcinctus -1.2989 1.0456 134
## Tree_Density-Lynx_rufus 1.1806 1.0327 268
## Tree_Density-Didelphis_virginiana -0.5185 1.0028 257
## Tree_Density-Sylvilagus_floridanus -0.6320 1.0423 269
## Tree_Density-Meleagris_gallopavo -0.0639 1.0020 280
## Tree_Density-Sciurus_carolinensis -0.6398 1.0259 221
## Tree_Density-Vulpes_vulpes 0.7343 1.0820 171
## Tree_Density-Sus_scrofa 0.0153 1.0223 263
## Avg_Canopy_Cover-Canis_latrans 1.8133 1.0259 422
## Avg_Canopy_Cover-Sciurus_niger 4.5508 1.0077 275
## Avg_Canopy_Cover-Procyon_lotor 3.0347 1.0034 324
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3552 1.0002 241
## Avg_Canopy_Cover-Lynx_rufus 3.3970 1.0107 380
## Avg_Canopy_Cover-Didelphis_virginiana 4.1022 1.0501 206
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.2265 1.0532 259
## Avg_Canopy_Cover-Meleagris_gallopavo 4.1759 1.0545 286
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8991 1.0232 385
## Avg_Canopy_Cover-Vulpes_vulpes 4.1244 1.0154 271
## Avg_Canopy_Cover-Sus_scrofa 3.6183 1.0077 582
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.0312 1.1228 94
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 4.1331 1.0382 94
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.3928 1.0548 212
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0013 1.0458 649
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.5264 1.0536 170
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.9995 1.0051 432
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7947 1.0818 283
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.1717 1.0619 192
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.9924 1.0713 344
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 4.7338 1.2280 171
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.6727 1.1997 132
## avg_veg_height-Canis_latrans 0.6330 1.0350 327
## avg_veg_height-Sciurus_niger 0.8781 1.0356 296
## avg_veg_height-Procyon_lotor 1.1434 1.0414 370
## avg_veg_height-Dasypus_novemcinctus 1.2544 1.0192 285
## avg_veg_height-Lynx_rufus 0.8934 1.1007 291
## avg_veg_height-Didelphis_virginiana 0.8869 1.0208 388
## avg_veg_height-Sylvilagus_floridanus 0.8241 1.0116 443
## avg_veg_height-Meleagris_gallopavo 1.0467 1.0446 329
## avg_veg_height-Sciurus_carolinensis 1.2709 1.0090 374
## avg_veg_height-Vulpes_vulpes 0.9940 1.0269 430
## avg_veg_height-Sus_scrofa 1.0498 1.0127 454
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6334 0.1690 -2.9855 -2.6253 -2.3194 1.0021
## (Intercept)-Sciurus_niger -4.2169 0.4770 -5.1685 -4.2112 -3.2806 1.0424
## (Intercept)-Procyon_lotor -2.2848 0.1317 -2.5511 -2.2834 -2.0356 1.0003
## (Intercept)-Dasypus_novemcinctus -1.6103 0.1346 -1.8800 -1.6064 -1.3573 1.0018
## (Intercept)-Lynx_rufus -3.6512 0.2869 -4.2241 -3.6584 -3.0790 1.0211
## (Intercept)-Didelphis_virginiana -2.3720 0.2452 -2.8701 -2.3694 -1.9127 1.0030
## (Intercept)-Sylvilagus_floridanus -3.2180 0.2828 -3.7975 -3.2148 -2.6898 1.0399
## (Intercept)-Meleagris_gallopavo -3.3700 0.3122 -4.0208 -3.3575 -2.8084 1.0044
## (Intercept)-Sciurus_carolinensis -2.5002 0.2722 -3.0804 -2.4849 -2.0078 1.0050
## (Intercept)-Vulpes_vulpes -4.0550 0.5739 -5.2547 -4.0352 -2.9550 1.0176
## (Intercept)-Sus_scrofa -2.9516 0.4303 -3.8602 -2.9269 -2.1761 1.0110
## week-Canis_latrans 0.0511 0.1330 -0.2198 0.0517 0.3069 1.0016
## week-Sciurus_niger -0.3089 0.2880 -0.9887 -0.2788 0.1671 1.0126
## week-Procyon_lotor -0.0565 0.1188 -0.2961 -0.0562 0.1703 1.0030
## week-Dasypus_novemcinctus -0.1741 0.1367 -0.4584 -0.1689 0.0745 1.0070
## week-Lynx_rufus -0.0423 0.1930 -0.4273 -0.0388 0.3143 1.0026
## week-Didelphis_virginiana -0.2322 0.2151 -0.7143 -0.2181 0.1549 1.0021
## week-Sylvilagus_floridanus -0.1781 0.2012 -0.5993 -0.1669 0.2053 1.0073
## week-Meleagris_gallopavo -0.2897 0.2462 -0.8138 -0.2645 0.1280 1.0398
## week-Sciurus_carolinensis 0.1159 0.1877 -0.2610 0.1174 0.4743 1.0016
## week-Vulpes_vulpes -0.1466 0.2657 -0.7451 -0.1310 0.3411 1.0028
## week-Sus_scrofa 0.0548 0.2366 -0.4246 0.0522 0.5111 1.0038
## ESS
## (Intercept)-Canis_latrans 846
## (Intercept)-Sciurus_niger 197
## (Intercept)-Procyon_lotor 1282
## (Intercept)-Dasypus_novemcinctus 2068
## (Intercept)-Lynx_rufus 285
## (Intercept)-Didelphis_virginiana 1393
## (Intercept)-Sylvilagus_floridanus 478
## (Intercept)-Meleagris_gallopavo 395
## (Intercept)-Sciurus_carolinensis 918
## (Intercept)-Vulpes_vulpes 188
## (Intercept)-Sus_scrofa 625
## week-Canis_latrans 1506
## week-Sciurus_niger 535
## week-Procyon_lotor 1789
## week-Dasypus_novemcinctus 2006
## week-Lynx_rufus 882
## week-Didelphis_virginiana 1353
## week-Sylvilagus_floridanus 938
## week-Meleagris_gallopavo 598
## week-Sciurus_carolinensis 1698
## week-Vulpes_vulpes 888
## week-Sus_scrofa 1830
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6333
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6147 0.3591 -1.3266 -0.6116 0.1085 1.0053 337
## Avg_Cogongrass_Cover 0.1040 0.2581 -0.4144 0.1114 0.5844 1.0129 723
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6658 0.6680 0.0609 0.4829 2.2936 1.0117 548
## Avg_Cogongrass_Cover 0.3308 0.3484 0.0415 0.2221 1.2885 1.0039 463
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8835 0.6972 0.0819 0.715 2.5881 1.0068 209
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9877 0.3038 -3.5831 -2.9813 -2.3935 1.0051 1047
## shrub_cover 0.1856 0.2825 -0.3909 0.1898 0.7512 1.0013 905
## veg_height 0.0121 0.1675 -0.3164 0.0122 0.3507 1.0101 1084
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8480 0.6603 0.2213 0.6771 2.5331 1.0021 415
## shrub_cover 0.6429 0.4829 0.1327 0.5113 1.9419 1.0065 600
## veg_height 0.2079 0.1968 0.0562 0.1652 0.5735 1.0823 1402
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0238 0.5677 -1.0451 -0.0576
## (Intercept)-Sciurus_niger -0.7411 0.6663 -2.0239 -0.7526
## (Intercept)-Procyon_lotor 0.0748 0.6160 -1.0837 0.0679
## (Intercept)-Dasypus_novemcinctus -0.6417 0.5079 -1.6869 -0.6367
## (Intercept)-Lynx_rufus -0.3900 0.6198 -1.5397 -0.4259
## (Intercept)-Didelphis_virginiana -1.0258 0.5409 -2.1637 -0.9898
## (Intercept)-Sylvilagus_floridanus -0.5214 0.5523 -1.5884 -0.5361
## (Intercept)-Meleagris_gallopavo -0.2882 0.6763 -1.4952 -0.3299
## (Intercept)-Sciurus_carolinensis -1.0463 0.5646 -2.2560 -1.0088
## (Intercept)-Vulpes_vulpes -1.0380 0.7126 -2.4466 -1.0335
## (Intercept)-Sus_scrofa -1.2722 0.6691 -2.6980 -1.2270
## Avg_Cogongrass_Cover-Canis_latrans 0.3824 0.3849 -0.2928 0.3586
## Avg_Cogongrass_Cover-Sciurus_niger -0.2977 0.5843 -1.6899 -0.2261
## Avg_Cogongrass_Cover-Procyon_lotor 0.1656 0.3433 -0.5030 0.1552
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3186 0.3346 -0.3031 0.3100
## Avg_Cogongrass_Cover-Lynx_rufus 0.4315 0.4043 -0.2468 0.3999
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2923 0.3700 -0.4083 0.2823
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2778 0.4496 -1.3049 -0.2436
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3166 0.5608 -1.5161 -0.2758
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3226 0.3647 -0.3690 0.3077
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2445 0.4464 -0.6078 0.2316
## Avg_Cogongrass_Cover-Sus_scrofa -0.1170 0.5030 -1.2269 -0.0721
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1747 1.0007 419
## (Intercept)-Sciurus_niger 0.6174 1.0278 361
## (Intercept)-Procyon_lotor 1.3025 1.0023 313
## (Intercept)-Dasypus_novemcinctus 0.3411 1.0099 946
## (Intercept)-Lynx_rufus 0.9635 1.0079 362
## (Intercept)-Didelphis_virginiana -0.0352 1.0049 901
## (Intercept)-Sylvilagus_floridanus 0.6019 1.0076 764
## (Intercept)-Meleagris_gallopavo 1.2400 1.0094 272
## (Intercept)-Sciurus_carolinensis -0.0336 0.9997 748
## (Intercept)-Vulpes_vulpes 0.3861 1.0306 305
## (Intercept)-Sus_scrofa -0.0607 1.0008 554
## Avg_Cogongrass_Cover-Canis_latrans 1.2280 1.0034 1358
## Avg_Cogongrass_Cover-Sciurus_niger 0.6763 1.0231 505
## Avg_Cogongrass_Cover-Procyon_lotor 0.8680 1.0029 1982
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9995 1.0023 2414
## Avg_Cogongrass_Cover-Lynx_rufus 1.3535 1.0147 1396
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0723 1.0027 1672
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5000 1.0032 826
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6846 1.0024 557
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0558 1.0017 1682
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1511 1.0052 1240
## Avg_Cogongrass_Cover-Sus_scrofa 0.7451 1.0043 850
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7479 0.1791 -3.1165 -2.7427 -2.4046 1.0107
## (Intercept)-Sciurus_niger -3.9140 0.5942 -5.2114 -3.8721 -2.8439 1.0245
## (Intercept)-Procyon_lotor -2.2986 0.1440 -2.5974 -2.2939 -2.0247 1.0138
## (Intercept)-Dasypus_novemcinctus -1.7569 0.1577 -2.0728 -1.7507 -1.4602 1.0015
## (Intercept)-Lynx_rufus -3.5379 0.3240 -4.1671 -3.5343 -2.8976 1.0006
## (Intercept)-Didelphis_virginiana -2.5887 0.2854 -3.1920 -2.5719 -2.0622 1.0156
## (Intercept)-Sylvilagus_floridanus -3.1666 0.2802 -3.7347 -3.1551 -2.6546 1.0354
## (Intercept)-Meleagris_gallopavo -3.8792 0.4628 -4.8593 -3.8716 -2.9592 1.0093
## (Intercept)-Sciurus_carolinensis -2.6431 0.2973 -3.2677 -2.6271 -2.0912 1.0092
## (Intercept)-Vulpes_vulpes -3.9516 0.6461 -5.2999 -3.9134 -2.8427 1.0101
## (Intercept)-Sus_scrofa -3.2893 0.5275 -4.3014 -3.2880 -2.2684 1.0335
## shrub_cover-Canis_latrans -0.2878 0.2190 -0.7192 -0.2873 0.1336 1.0034
## shrub_cover-Sciurus_niger -0.3781 0.4870 -1.3559 -0.3607 0.5600 1.0160
## shrub_cover-Procyon_lotor 0.2400 0.1618 -0.0894 0.2447 0.5465 1.0085
## shrub_cover-Dasypus_novemcinctus 0.8545 0.2948 0.2901 0.8521 1.4352 1.0053
## shrub_cover-Lynx_rufus -0.2361 0.3642 -0.9836 -0.2222 0.4436 1.0028
## shrub_cover-Didelphis_virginiana 0.9648 0.3737 0.2860 0.9467 1.7625 1.0061
## shrub_cover-Sylvilagus_floridanus 0.2772 0.4236 -0.4994 0.2644 1.1517 1.0107
## shrub_cover-Meleagris_gallopavo -0.6787 0.4084 -1.5152 -0.6713 0.1316 1.0079
## shrub_cover-Sciurus_carolinensis 0.8339 0.4186 0.0363 0.8293 1.6774 1.0072
## shrub_cover-Vulpes_vulpes -0.1315 0.5729 -1.3438 -0.1230 0.9688 1.0303
## shrub_cover-Sus_scrofa 0.5932 0.7576 -0.9260 0.5848 2.0769 1.0091
## veg_height-Canis_latrans -0.5886 0.1832 -0.9568 -0.5748 -0.2499 1.0658
## veg_height-Sciurus_niger 0.0158 0.4202 -0.7842 -0.0043 0.8494 1.0145
## veg_height-Procyon_lotor 0.3333 0.1216 0.0981 0.3325 0.5743 1.0178
## veg_height-Dasypus_novemcinctus 0.2493 0.1336 -0.0133 0.2498 0.5084 1.0097
## veg_height-Lynx_rufus -0.0232 0.2401 -0.5163 -0.0215 0.4268 1.0032
## veg_height-Didelphis_virginiana 0.4258 0.2416 -0.0177 0.4148 0.9093 1.0147
## veg_height-Sylvilagus_floridanus 0.1416 0.2411 -0.3203 0.1387 0.6285 1.0088
## veg_height-Meleagris_gallopavo -0.1917 0.3852 -0.9975 -0.1830 0.5211 1.0379
## veg_height-Sciurus_carolinensis 0.0648 0.2147 -0.3529 0.0620 0.4867 1.0060
## veg_height-Vulpes_vulpes -0.1314 0.3132 -0.7936 -0.1207 0.4442 1.0100
## veg_height-Sus_scrofa -0.1236 0.3263 -0.7850 -0.1243 0.4794 1.0018
## ESS
## (Intercept)-Canis_latrans 955
## (Intercept)-Sciurus_niger 229
## (Intercept)-Procyon_lotor 1085
## (Intercept)-Dasypus_novemcinctus 1705
## (Intercept)-Lynx_rufus 374
## (Intercept)-Didelphis_virginiana 810
## (Intercept)-Sylvilagus_floridanus 555
## (Intercept)-Meleagris_gallopavo 216
## (Intercept)-Sciurus_carolinensis 934
## (Intercept)-Vulpes_vulpes 186
## (Intercept)-Sus_scrofa 666
## shrub_cover-Canis_latrans 853
## shrub_cover-Sciurus_niger 348
## shrub_cover-Procyon_lotor 1324
## shrub_cover-Dasypus_novemcinctus 1253
## shrub_cover-Lynx_rufus 304
## shrub_cover-Didelphis_virginiana 790
## shrub_cover-Sylvilagus_floridanus 535
## shrub_cover-Meleagris_gallopavo 250
## shrub_cover-Sciurus_carolinensis 714
## shrub_cover-Vulpes_vulpes 620
## shrub_cover-Sus_scrofa 839
## veg_height-Canis_latrans 821
## veg_height-Sciurus_niger 563
## veg_height-Procyon_lotor 1720
## veg_height-Dasypus_novemcinctus 1731
## veg_height-Lynx_rufus 857
## veg_height-Didelphis_virginiana 1323
## veg_height-Sylvilagus_floridanus 741
## veg_height-Meleagris_gallopavo 514
## veg_height-Sciurus_carolinensis 1305
## veg_height-Vulpes_vulpes 623
## veg_height-Sus_scrofa 1210
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7128
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0057 0.6455 -2.2604 -1.0190 0.2976 1.0690 264
## Cogon_Patch_Size -0.7332 0.5501 -1.9307 -0.7168 0.2676 1.0575 413
## Veg_shannon_index 1.0292 0.4480 0.2296 1.0034 2.0274 1.0417 247
## total_shrub_cover -0.8813 0.5504 -2.0851 -0.8442 0.1378 1.1132 171
## Avg_Cogongrass_Cover 1.9461 0.7319 0.5807 1.9058 3.4731 1.0555 144
## Tree_Density -1.9979 0.7160 -3.4782 -1.9764 -0.6889 1.0644 270
## Avg_Canopy_Cover 1.9838 0.6083 0.9163 1.9412 3.3327 1.0084 317
## avg_veg_height -0.4873 0.4595 -1.4191 -0.4826 0.3626 1.0239 235
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6899 3.3305 0.0897 1.7695 11.1812 1.3472 205
## Cogon_Patch_Size 1.7021 2.2642 0.0829 1.0045 7.6016 1.2307 194
## Veg_shannon_index 0.6451 0.9155 0.0507 0.3600 2.7614 1.1196 453
## total_shrub_cover 1.3476 1.6946 0.0789 0.7682 6.0203 1.0060 175
## Avg_Cogongrass_Cover 1.4944 2.2695 0.0619 0.7212 7.4767 1.4409 200
## Tree_Density 2.4298 4.3314 0.0730 1.2029 12.4167 1.1255 146
## Avg_Canopy_Cover 2.1664 2.5300 0.1762 1.3507 8.7591 1.0535 233
## avg_veg_height 0.4207 0.5729 0.0425 0.2394 1.8674 1.0386 639
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0946 2.0595 0.1024 1.5128 7.7189 1.1823 92
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0751 0.3200 -3.7042 -3.0731 -2.4323 1.0100 1141
## shrub_cover 0.3661 0.3083 -0.2322 0.3588 1.0068 1.0668 444
## veg_height 0.0295 0.1702 -0.3219 0.0328 0.3479 1.0014 899
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0297 0.7686 0.2875 0.8462 2.8852 1.0478 556
## shrub_cover 0.7125 0.5915 0.1434 0.5552 2.0914 1.0585 524
## veg_height 0.2248 0.1765 0.0562 0.1811 0.6788 1.0168 817
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0755 1.0060 -1.7993 0.0710
## (Intercept)-Sciurus_niger -0.3945 1.4032 -2.6926 -0.5616
## (Intercept)-Procyon_lotor 0.2229 0.9722 -1.6878 0.2554
## (Intercept)-Dasypus_novemcinctus -1.3105 0.8305 -3.0917 -1.2590
## (Intercept)-Lynx_rufus 0.0054 1.3931 -2.1987 -0.1940
## (Intercept)-Didelphis_virginiana -2.0409 1.0393 -4.3197 -1.9639
## (Intercept)-Sylvilagus_floridanus -1.1446 0.9425 -3.1432 -1.1280
## (Intercept)-Meleagris_gallopavo -0.8951 1.1948 -3.1876 -0.9227
## (Intercept)-Sciurus_carolinensis -2.0689 1.0879 -4.4674 -1.9569
## (Intercept)-Vulpes_vulpes -1.7921 1.2903 -4.5639 -1.7083
## (Intercept)-Sus_scrofa -2.5097 1.4705 -5.8785 -2.3294
## Cogon_Patch_Size-Canis_latrans 0.3049 0.9748 -1.1651 0.1697
## Cogon_Patch_Size-Sciurus_niger -1.3055 1.2901 -4.0560 -1.1734
## Cogon_Patch_Size-Procyon_lotor -1.0202 0.6833 -2.4434 -0.9914
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6202 0.7272 -2.0296 -0.6182
## Cogon_Patch_Size-Lynx_rufus -0.8082 1.0632 -2.9553 -0.7866
## Cogon_Patch_Size-Didelphis_virginiana 0.5186 0.8818 -0.9401 0.4209
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6073 1.2193 -4.5432 -1.4134
## Cogon_Patch_Size-Meleagris_gallopavo -0.4297 0.9960 -2.2973 -0.4864
## Cogon_Patch_Size-Sciurus_carolinensis -1.4327 1.1165 -4.1325 -1.2741
## Cogon_Patch_Size-Vulpes_vulpes -1.0850 1.1870 -3.6946 -0.9918
## Cogon_Patch_Size-Sus_scrofa -1.0414 1.1301 -3.7246 -0.9031
## Veg_shannon_index-Canis_latrans 1.3736 0.6239 0.2902 1.3110
## Veg_shannon_index-Sciurus_niger 1.1571 0.8908 -0.4597 1.0870
## Veg_shannon_index-Procyon_lotor 1.3140 0.5816 0.2797 1.2862
## Veg_shannon_index-Dasypus_novemcinctus 0.7171 0.5542 -0.4164 0.7237
## Veg_shannon_index-Lynx_rufus 0.9712 0.7148 -0.4980 0.9682
## Veg_shannon_index-Didelphis_virginiana 1.2082 0.6611 0.0415 1.1549
## Veg_shannon_index-Sylvilagus_floridanus 1.0837 0.6427 -0.1007 1.0690
## Veg_shannon_index-Meleagris_gallopavo 1.3014 0.8109 -0.1129 1.2267
## Veg_shannon_index-Sciurus_carolinensis 0.4304 0.7306 -1.1676 0.4766
## Veg_shannon_index-Vulpes_vulpes 0.5832 0.7750 -1.1772 0.6453
## Veg_shannon_index-Sus_scrofa 1.4197 0.8148 0.0824 1.3133
## total_shrub_cover-Canis_latrans 0.2540 0.7455 -0.9665 0.1630
## total_shrub_cover-Sciurus_niger -1.1201 1.0340 -3.4833 -1.0155
## total_shrub_cover-Procyon_lotor -1.1091 0.6212 -2.4272 -1.0664
## total_shrub_cover-Dasypus_novemcinctus -0.3049 0.6815 -1.6831 -0.2980
## total_shrub_cover-Lynx_rufus -1.2545 1.0415 -3.6497 -1.1435
## total_shrub_cover-Didelphis_virginiana -1.0795 0.9055 -3.3201 -0.9494
## total_shrub_cover-Sylvilagus_floridanus -0.9276 1.0029 -3.5132 -0.8166
## total_shrub_cover-Meleagris_gallopavo -1.8725 1.2154 -4.8294 -1.6731
## total_shrub_cover-Sciurus_carolinensis -0.8897 1.0767 -3.6000 -0.7354
## total_shrub_cover-Vulpes_vulpes -1.2018 1.2285 -4.1224 -1.0428
## total_shrub_cover-Sus_scrofa -0.7192 1.0016 -2.9853 -0.6738
## Avg_Cogongrass_Cover-Canis_latrans 2.4569 0.9473 0.8610 2.3717
## Avg_Cogongrass_Cover-Sciurus_niger 1.0933 1.5860 -2.8146 1.3532
## Avg_Cogongrass_Cover-Procyon_lotor 2.1799 0.8982 0.5420 2.1260
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6410 1.0098 0.9903 2.5456
## Avg_Cogongrass_Cover-Lynx_rufus 2.4621 1.0604 0.7510 2.3528
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0687 0.9156 0.3933 1.9931
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2484 1.0649 -1.0060 1.3037
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5018 1.4078 -2.0345 1.6459
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2979 1.0219 0.5212 2.2078
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5957 1.0924 0.7808 2.4701
## Avg_Cogongrass_Cover-Sus_scrofa 1.7133 1.1838 -0.8300 1.7468
## Tree_Density-Canis_latrans -2.6707 1.1757 -5.3927 -2.4729
## Tree_Density-Sciurus_niger -2.0844 1.4252 -5.3102 -2.0128
## Tree_Density-Procyon_lotor -1.5824 0.7717 -3.1348 -1.5837
## Tree_Density-Dasypus_novemcinctus -3.4881 1.6397 -7.8459 -3.1702
## Tree_Density-Lynx_rufus -0.6319 1.2850 -2.6961 -0.7448
## Tree_Density-Didelphis_virginiana -2.1935 1.1132 -4.6047 -2.1288
## Tree_Density-Sylvilagus_floridanus -2.4600 1.3894 -5.8937 -2.2793
## Tree_Density-Meleagris_gallopavo -2.2957 1.3049 -5.1586 -2.1748
## Tree_Density-Sciurus_carolinensis -2.2449 1.3172 -5.1625 -2.1161
## Tree_Density-Vulpes_vulpes -1.8686 1.4419 -4.7091 -1.8643
## Tree_Density-Sus_scrofa -2.1935 1.4382 -5.3559 -2.0501
## Avg_Canopy_Cover-Canis_latrans 0.3508 0.6306 -0.8745 0.3349
## Avg_Canopy_Cover-Sciurus_niger 2.0100 1.5534 -0.7627 1.8791
## Avg_Canopy_Cover-Procyon_lotor 1.7433 0.7204 0.4153 1.6930
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1983 0.7470 0.9048 2.1214
## Avg_Canopy_Cover-Lynx_rufus 1.1403 1.1799 -1.0789 1.0825
## Avg_Canopy_Cover-Didelphis_virginiana 2.8736 1.0770 1.2607 2.6981
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3930 1.5138 1.2984 3.0898
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4574 1.1874 0.6429 2.2773
## Avg_Canopy_Cover-Sciurus_carolinensis 2.7220 1.1549 1.0536 2.5087
## Avg_Canopy_Cover-Vulpes_vulpes 2.3713 1.1713 0.5349 2.1932
## Avg_Canopy_Cover-Sus_scrofa 2.1006 0.9794 0.5534 1.9882
## avg_veg_height-Canis_latrans -0.5594 0.5797 -1.6851 -0.5532
## avg_veg_height-Sciurus_niger -0.7612 0.8457 -2.7923 -0.6845
## avg_veg_height-Procyon_lotor -0.4081 0.5694 -1.5574 -0.4013
## avg_veg_height-Dasypus_novemcinctus -0.2681 0.5850 -1.3619 -0.2796
## avg_veg_height-Lynx_rufus -0.6354 0.7226 -2.2162 -0.5932
## avg_veg_height-Didelphis_virginiana -0.6323 0.6299 -2.0041 -0.5981
## avg_veg_height-Sylvilagus_floridanus -0.6370 0.6682 -2.0153 -0.6106
## avg_veg_height-Meleagris_gallopavo -0.4171 0.7385 -1.9245 -0.3988
## avg_veg_height-Sciurus_carolinensis -0.1436 0.6595 -1.3217 -0.1777
## avg_veg_height-Vulpes_vulpes -0.4462 0.7126 -1.8884 -0.4297
## avg_veg_height-Sus_scrofa -0.4662 0.6740 -1.7881 -0.4571
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1943 1.0932 252
## (Intercept)-Sciurus_niger 2.6586 1.0909 168
## (Intercept)-Procyon_lotor 2.1366 1.1055 178
## (Intercept)-Dasypus_novemcinctus 0.2026 1.1072 256
## (Intercept)-Lynx_rufus 3.2587 1.0884 202
## (Intercept)-Didelphis_virginiana -0.1862 1.2271 281
## (Intercept)-Sylvilagus_floridanus 0.7599 1.0964 351
## (Intercept)-Meleagris_gallopavo 1.6359 1.0423 257
## (Intercept)-Sciurus_carolinensis -0.2130 1.2283 220
## (Intercept)-Vulpes_vulpes 0.5390 1.1325 174
## (Intercept)-Sus_scrofa -0.2077 1.3103 194
## Cogon_Patch_Size-Canis_latrans 2.6501 1.0527 527
## Cogon_Patch_Size-Sciurus_niger 0.8134 1.0739 251
## Cogon_Patch_Size-Procyon_lotor 0.2555 1.0529 255
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8845 1.0166 781
## Cogon_Patch_Size-Lynx_rufus 1.3592 1.0539 344
## Cogon_Patch_Size-Didelphis_virginiana 2.5814 1.1532 365
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2165 1.2082 303
## Cogon_Patch_Size-Meleagris_gallopavo 1.7054 1.0110 539
## Cogon_Patch_Size-Sciurus_carolinensis 0.3041 1.1807 308
## Cogon_Patch_Size-Vulpes_vulpes 1.0880 1.0692 461
## Cogon_Patch_Size-Sus_scrofa 0.7970 1.0956 542
## Veg_shannon_index-Canis_latrans 2.7684 1.0336 350
## Veg_shannon_index-Sciurus_niger 3.2210 1.0715 397
## Veg_shannon_index-Procyon_lotor 2.5735 1.0473 285
## Veg_shannon_index-Dasypus_novemcinctus 1.7716 1.0057 335
## Veg_shannon_index-Lynx_rufus 2.3953 1.0340 406
## Veg_shannon_index-Didelphis_virginiana 2.6604 1.0182 328
## Veg_shannon_index-Sylvilagus_floridanus 2.4419 1.0359 368
## Veg_shannon_index-Meleagris_gallopavo 3.1734 1.0200 380
## Veg_shannon_index-Sciurus_carolinensis 1.7181 1.0049 467
## Veg_shannon_index-Vulpes_vulpes 1.9114 1.0098 349
## Veg_shannon_index-Sus_scrofa 3.3643 1.0449 379
## total_shrub_cover-Canis_latrans 1.9126 1.0011 220
## total_shrub_cover-Sciurus_niger 0.7466 1.0124 328
## total_shrub_cover-Procyon_lotor -0.0093 1.0169 784
## total_shrub_cover-Dasypus_novemcinctus 0.9885 1.0437 530
## total_shrub_cover-Lynx_rufus 0.5878 1.0388 231
## total_shrub_cover-Didelphis_virginiana 0.3548 1.0331 263
## total_shrub_cover-Sylvilagus_floridanus 0.7310 1.0488 341
## total_shrub_cover-Meleagris_gallopavo -0.0174 1.0369 143
## total_shrub_cover-Sciurus_carolinensis 0.7911 1.1414 159
## total_shrub_cover-Vulpes_vulpes 0.7544 1.0249 278
## total_shrub_cover-Sus_scrofa 1.1270 1.1000 245
## Avg_Cogongrass_Cover-Canis_latrans 4.5428 1.0190 286
## Avg_Cogongrass_Cover-Sciurus_niger 3.6156 1.1705 157
## Avg_Cogongrass_Cover-Procyon_lotor 4.1277 1.0744 263
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.9371 1.0530 233
## Avg_Cogongrass_Cover-Lynx_rufus 4.7584 1.0753 341
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9704 1.0233 373
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2574 1.0392 334
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.8481 1.0460 190
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.5889 1.0646 293
## Avg_Cogongrass_Cover-Vulpes_vulpes 5.1679 1.0912 267
## Avg_Cogongrass_Cover-Sus_scrofa 4.0425 1.0403 210
## Tree_Density-Canis_latrans -0.8023 1.0305 348
## Tree_Density-Sciurus_niger 0.5729 1.0816 184
## Tree_Density-Procyon_lotor -0.0156 1.0353 428
## Tree_Density-Dasypus_novemcinctus -1.3745 1.0793 145
## Tree_Density-Lynx_rufus 2.2262 1.0340 213
## Tree_Density-Didelphis_virginiana -0.0735 1.0331 469
## Tree_Density-Sylvilagus_floridanus -0.0859 1.0603 341
## Tree_Density-Meleagris_gallopavo 0.0784 1.0529 370
## Tree_Density-Sciurus_carolinensis 0.0280 1.0391 447
## Tree_Density-Vulpes_vulpes 1.2310 1.0663 336
## Tree_Density-Sus_scrofa 0.2934 1.1098 214
## Avg_Canopy_Cover-Canis_latrans 1.6175 1.0018 709
## Avg_Canopy_Cover-Sciurus_niger 5.5282 1.0582 200
## Avg_Canopy_Cover-Procyon_lotor 3.3059 1.0093 520
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9189 1.0162 353
## Avg_Canopy_Cover-Lynx_rufus 3.7458 1.0059 290
## Avg_Canopy_Cover-Didelphis_virginiana 5.3204 1.0214 162
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1403 1.0666 204
## Avg_Canopy_Cover-Meleagris_gallopavo 5.2522 1.0209 254
## Avg_Canopy_Cover-Sciurus_carolinensis 5.6752 1.0405 263
## Avg_Canopy_Cover-Vulpes_vulpes 5.3522 1.0296 367
## Avg_Canopy_Cover-Sus_scrofa 4.3124 1.0267 547
## avg_veg_height-Canis_latrans 0.6023 1.0074 446
## avg_veg_height-Sciurus_niger 0.7400 1.0030 353
## avg_veg_height-Procyon_lotor 0.6848 1.0136 482
## avg_veg_height-Dasypus_novemcinctus 0.9444 1.0044 486
## avg_veg_height-Lynx_rufus 0.7273 1.0464 433
## avg_veg_height-Didelphis_virginiana 0.5066 1.0104 541
## avg_veg_height-Sylvilagus_floridanus 0.6095 1.0153 400
## avg_veg_height-Meleagris_gallopavo 1.0068 1.0206 313
## avg_veg_height-Sciurus_carolinensis 1.3351 1.0168 501
## avg_veg_height-Vulpes_vulpes 0.9838 1.0367 287
## avg_veg_height-Sus_scrofa 0.9019 1.0133 479
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7546 0.1819 -3.1311 -2.7519 -2.4081 1.0096
## (Intercept)-Sciurus_niger -4.3327 0.5430 -5.4123 -4.3323 -3.2163 1.0845
## (Intercept)-Procyon_lotor -2.2995 0.1401 -2.5888 -2.2947 -2.0391 1.0024
## (Intercept)-Dasypus_novemcinctus -1.7807 0.1609 -2.1093 -1.7756 -1.4780 1.0028
## (Intercept)-Lynx_rufus -3.6695 0.3496 -4.4014 -3.6584 -3.0318 1.0050
## (Intercept)-Didelphis_virginiana -2.6337 0.2963 -3.2483 -2.6211 -2.0924 1.0393
## (Intercept)-Sylvilagus_floridanus -3.1959 0.2635 -3.7386 -3.1897 -2.6926 1.0114
## (Intercept)-Meleagris_gallopavo -3.8266 0.4502 -4.7210 -3.8251 -2.9678 1.0844
## (Intercept)-Sciurus_carolinensis -2.8034 0.3259 -3.4655 -2.7994 -2.1813 1.0914
## (Intercept)-Vulpes_vulpes -4.1485 0.6352 -5.4910 -4.1147 -3.0297 1.0855
## (Intercept)-Sus_scrofa -3.5677 0.5547 -4.6621 -3.5764 -2.5217 1.1271
## shrub_cover-Canis_latrans -0.3378 0.2237 -0.7571 -0.3383 0.1068 1.0163
## shrub_cover-Sciurus_niger -0.2119 0.4842 -1.2158 -0.1976 0.7336 1.0249
## shrub_cover-Procyon_lotor 0.2779 0.1603 -0.0307 0.2780 0.5975 1.0036
## shrub_cover-Dasypus_novemcinctus 0.9454 0.3066 0.3537 0.9404 1.5449 1.0231
## shrub_cover-Lynx_rufus -0.0536 0.3921 -0.8365 -0.0478 0.7027 1.0230
## shrub_cover-Didelphis_virginiana 1.0726 0.3854 0.3899 1.0544 1.8906 1.0856
## shrub_cover-Sylvilagus_floridanus 0.5529 0.4162 -0.3030 0.5512 1.3629 1.0336
## shrub_cover-Meleagris_gallopavo -0.5298 0.4262 -1.3877 -0.5160 0.3198 1.0365
## shrub_cover-Sciurus_carolinensis 1.0737 0.4556 0.1950 1.0614 2.0163 1.1271
## shrub_cover-Vulpes_vulpes 0.2778 0.6223 -1.0535 0.2860 1.4883 1.0387
## shrub_cover-Sus_scrofa 1.0205 0.7621 -0.4556 1.0182 2.5531 1.1792
## veg_height-Canis_latrans -0.5905 0.1864 -0.9764 -0.5843 -0.2371 1.0034
## veg_height-Sciurus_niger 0.0468 0.3927 -0.6972 0.0286 0.8678 1.0759
## veg_height-Procyon_lotor 0.3476 0.1220 0.1153 0.3488 0.5897 1.0065
## veg_height-Dasypus_novemcinctus 0.2610 0.1364 -0.0043 0.2595 0.5280 1.0023
## veg_height-Lynx_rufus 0.0713 0.2449 -0.4342 0.0780 0.5311 1.0982
## veg_height-Didelphis_virginiana 0.4638 0.2458 0.0147 0.4511 0.9553 1.0143
## veg_height-Sylvilagus_floridanus 0.1578 0.2406 -0.3288 0.1608 0.6240 1.0410
## veg_height-Meleagris_gallopavo -0.2710 0.3581 -1.0090 -0.2483 0.3888 1.0063
## veg_height-Sciurus_carolinensis 0.1326 0.2155 -0.2722 0.1288 0.5653 1.0256
## veg_height-Vulpes_vulpes -0.1838 0.3263 -0.8659 -0.1722 0.4153 1.0170
## veg_height-Sus_scrofa -0.1713 0.3349 -0.8571 -0.1595 0.4710 1.0122
## ESS
## (Intercept)-Canis_latrans 705
## (Intercept)-Sciurus_niger 163
## (Intercept)-Procyon_lotor 1318
## (Intercept)-Dasypus_novemcinctus 999
## (Intercept)-Lynx_rufus 271
## (Intercept)-Didelphis_virginiana 553
## (Intercept)-Sylvilagus_floridanus 588
## (Intercept)-Meleagris_gallopavo 242
## (Intercept)-Sciurus_carolinensis 285
## (Intercept)-Vulpes_vulpes 191
## (Intercept)-Sus_scrofa 231
## shrub_cover-Canis_latrans 831
## shrub_cover-Sciurus_niger 259
## shrub_cover-Procyon_lotor 1454
## shrub_cover-Dasypus_novemcinctus 659
## shrub_cover-Lynx_rufus 263
## shrub_cover-Didelphis_virginiana 358
## shrub_cover-Sylvilagus_floridanus 468
## shrub_cover-Meleagris_gallopavo 318
## shrub_cover-Sciurus_carolinensis 242
## shrub_cover-Vulpes_vulpes 285
## shrub_cover-Sus_scrofa 183
## veg_height-Canis_latrans 774
## veg_height-Sciurus_niger 297
## veg_height-Procyon_lotor 1609
## veg_height-Dasypus_novemcinctus 1511
## veg_height-Lynx_rufus 564
## veg_height-Didelphis_virginiana 927
## veg_height-Sylvilagus_floridanus 929
## veg_height-Meleagris_gallopavo 388
## veg_height-Sciurus_carolinensis 1029
## veg_height-Vulpes_vulpes 520
## veg_height-Sus_scrofa 633
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7253
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4686 0.4145 -1.3260 -0.4610 0.3293 1.0701 250
## Avg_Cogongrass_Cover -0.0749 0.3400 -0.7491 -0.0677 0.5509 1.0284 594
## total_shrub_cover -0.9324 0.4305 -1.8696 -0.8983 -0.1937 1.0302 214
## avg_veg_height 0.0826 0.3508 -0.5963 0.0821 0.7803 1.1136 320
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5782 0.6690 0.0500 0.3667 2.3241 1.0183 352
## Avg_Cogongrass_Cover 0.4934 0.6837 0.0469 0.2818 2.1624 1.0975 351
## total_shrub_cover 0.7840 0.9681 0.0643 0.4860 3.4136 1.0700 155
## avg_veg_height 0.3228 0.4287 0.0365 0.1903 1.4332 1.0166 662
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6224 1.302 0.1071 1.3352 4.9364 1.0449 84
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0456 0.2918 -3.6594 -3.0316 -2.4996 1.0528 611
## shrub_cover 0.5256 0.3091 -0.0585 0.5214 1.1335 1.0489 426
## veg_height 0.0484 0.1822 -0.3031 0.0442 0.4152 1.0330 578
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7236 0.5199 0.1808 0.5909 2.0292 1.1055 480
## shrub_cover 0.7391 0.6107 0.1567 0.5639 2.2889 1.0828 288
## veg_height 0.2144 0.1563 0.0528 0.1733 0.6194 1.0079 997
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0140 0.6250 -1.1620 -0.0434
## (Intercept)-Sciurus_niger -0.6814 0.6756 -2.1755 -0.6348
## (Intercept)-Procyon_lotor 0.0966 0.6669 -1.1179 0.0610
## (Intercept)-Dasypus_novemcinctus -0.4606 0.6050 -1.6498 -0.4690
## (Intercept)-Lynx_rufus -0.3243 0.6600 -1.6064 -0.3355
## (Intercept)-Didelphis_virginiana -0.7207 0.6561 -2.1152 -0.6903
## (Intercept)-Sylvilagus_floridanus -0.2405 0.6575 -1.4628 -0.2662
## (Intercept)-Meleagris_gallopavo -0.5198 0.6684 -1.8577 -0.5078
## (Intercept)-Sciurus_carolinensis -0.7138 0.6330 -2.0332 -0.6769
## (Intercept)-Vulpes_vulpes -0.7587 0.7602 -2.3780 -0.7096
## (Intercept)-Sus_scrofa -0.8788 0.7617 -2.5468 -0.8150
## Avg_Cogongrass_Cover-Canis_latrans 0.3159 0.5150 -0.6048 0.2765
## Avg_Cogongrass_Cover-Sciurus_niger -0.5372 0.7260 -2.2689 -0.4186
## Avg_Cogongrass_Cover-Procyon_lotor -0.1279 0.4593 -1.0640 -0.1131
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1329 0.4651 -0.7977 0.1303
## Avg_Cogongrass_Cover-Lynx_rufus 0.3546 0.5546 -0.5725 0.3032
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1457 0.5161 -0.7939 0.1298
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4854 0.6009 -1.8630 -0.4114
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5065 0.6797 -2.0543 -0.4378
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0042 0.4918 -1.0197 0.0166
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1376 0.5659 -0.9592 0.1197
## Avg_Cogongrass_Cover-Sus_scrofa -0.3121 0.6598 -1.7759 -0.2526
## total_shrub_cover-Canis_latrans 0.0686 0.6233 -1.0148 0.0152
## total_shrub_cover-Sciurus_niger -1.0463 0.7377 -2.7520 -1.0017
## total_shrub_cover-Procyon_lotor -1.1983 0.5723 -2.5054 -1.1356
## total_shrub_cover-Dasypus_novemcinctus -0.5728 0.6401 -2.0930 -0.5015
## total_shrub_cover-Lynx_rufus -1.2893 0.7909 -3.0786 -1.2102
## total_shrub_cover-Didelphis_virginiana -0.9252 0.6428 -2.4279 -0.8691
## total_shrub_cover-Sylvilagus_floridanus -1.2922 0.8331 -3.3051 -1.1870
## total_shrub_cover-Meleagris_gallopavo -1.5045 0.8211 -3.4232 -1.3925
## total_shrub_cover-Sciurus_carolinensis -1.0072 0.7499 -2.7697 -0.9283
## total_shrub_cover-Vulpes_vulpes -1.0406 0.8976 -3.1398 -0.9609
## total_shrub_cover-Sus_scrofa -0.7696 0.7888 -2.5360 -0.7451
## avg_veg_height-Canis_latrans 0.0969 0.4503 -0.7655 0.0888
## avg_veg_height-Sciurus_niger -0.2037 0.6304 -1.5579 -0.1497
## avg_veg_height-Procyon_lotor 0.1584 0.4563 -0.7003 0.1433
## avg_veg_height-Dasypus_novemcinctus 0.3329 0.4579 -0.4878 0.3046
## avg_veg_height-Lynx_rufus 0.0519 0.5709 -1.1124 0.0438
## avg_veg_height-Didelphis_virginiana 0.0091 0.4970 -1.0519 0.0180
## avg_veg_height-Sylvilagus_floridanus 0.0228 0.5112 -0.9942 0.0210
## avg_veg_height-Meleagris_gallopavo -0.1562 0.6562 -1.5604 -0.1189
## avg_veg_height-Sciurus_carolinensis 0.4559 0.5129 -0.4188 0.4075
## avg_veg_height-Vulpes_vulpes 0.0573 0.5282 -1.0099 0.0676
## avg_veg_height-Sus_scrofa 0.1197 0.5321 -0.9300 0.1138
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3763 1.0187 408
## (Intercept)-Sciurus_niger 0.5745 1.0335 395
## (Intercept)-Procyon_lotor 1.4192 1.0571 426
## (Intercept)-Dasypus_novemcinctus 0.8311 1.0252 263
## (Intercept)-Lynx_rufus 1.0420 1.0406 393
## (Intercept)-Didelphis_virginiana 0.5161 1.0051 480
## (Intercept)-Sylvilagus_floridanus 1.1895 1.0098 464
## (Intercept)-Meleagris_gallopavo 0.7856 1.0518 513
## (Intercept)-Sciurus_carolinensis 0.4361 1.0060 408
## (Intercept)-Vulpes_vulpes 0.6581 1.0819 310
## (Intercept)-Sus_scrofa 0.4830 1.0279 264
## Avg_Cogongrass_Cover-Canis_latrans 1.4537 1.0145 840
## Avg_Cogongrass_Cover-Sciurus_niger 0.5868 1.0295 466
## Avg_Cogongrass_Cover-Procyon_lotor 0.7424 1.0065 1103
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0894 1.0071 1078
## Avg_Cogongrass_Cover-Lynx_rufus 1.5987 1.0014 709
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2365 1.0086 664
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5106 1.0254 474
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6777 1.0261 595
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9313 1.0095 989
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3751 1.0116 984
## Avg_Cogongrass_Cover-Sus_scrofa 0.8344 1.0461 641
## total_shrub_cover-Canis_latrans 1.5116 1.0293 387
## total_shrub_cover-Sciurus_niger 0.2708 1.0112 345
## total_shrub_cover-Procyon_lotor -0.2397 1.0021 375
## total_shrub_cover-Dasypus_novemcinctus 0.4871 1.0474 292
## total_shrub_cover-Lynx_rufus 0.0773 1.0220 308
## total_shrub_cover-Didelphis_virginiana 0.1624 1.0105 372
## total_shrub_cover-Sylvilagus_floridanus 0.0084 1.0058 160
## total_shrub_cover-Meleagris_gallopavo -0.2299 1.0247 251
## total_shrub_cover-Sciurus_carolinensis 0.2103 1.0012 356
## total_shrub_cover-Vulpes_vulpes 0.5610 1.0257 264
## total_shrub_cover-Sus_scrofa 0.7921 1.0158 253
## avg_veg_height-Canis_latrans 1.0556 1.0577 592
## avg_veg_height-Sciurus_niger 0.9219 1.0497 498
## avg_veg_height-Procyon_lotor 1.1398 1.0465 835
## avg_veg_height-Dasypus_novemcinctus 1.2911 1.0341 624
## avg_veg_height-Lynx_rufus 1.1488 1.0345 592
## avg_veg_height-Didelphis_virginiana 0.9284 1.0175 690
## avg_veg_height-Sylvilagus_floridanus 1.0558 1.0995 418
## avg_veg_height-Meleagris_gallopavo 1.0665 1.0412 393
## avg_veg_height-Sciurus_carolinensis 1.5854 1.0149 712
## avg_veg_height-Vulpes_vulpes 1.1125 1.0381 689
## avg_veg_height-Sus_scrofa 1.2329 1.0421 654
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7746 0.1893 -3.1762 -2.7647 -2.4267 1.0015
## (Intercept)-Sciurus_niger -3.7655 0.5582 -4.8964 -3.7648 -2.7138 1.1254
## (Intercept)-Procyon_lotor -2.3147 0.1381 -2.6009 -2.3110 -2.0602 1.0018
## (Intercept)-Dasypus_novemcinctus -1.8577 0.1839 -2.2165 -1.8499 -1.5064 1.0201
## (Intercept)-Lynx_rufus -3.5046 0.3238 -4.1466 -3.4856 -2.9144 1.0246
## (Intercept)-Didelphis_virginiana -2.7991 0.3193 -3.4459 -2.7877 -2.2068 1.0108
## (Intercept)-Sylvilagus_floridanus -3.2593 0.2685 -3.8268 -3.2456 -2.7575 1.0152
## (Intercept)-Meleagris_gallopavo -3.5900 0.4860 -4.5652 -3.5757 -2.6660 1.1785
## (Intercept)-Sciurus_carolinensis -2.8735 0.3195 -3.5168 -2.8693 -2.2632 1.0112
## (Intercept)-Vulpes_vulpes -4.0055 0.5948 -5.1690 -3.9963 -2.8781 1.1372
## (Intercept)-Sus_scrofa -3.5985 0.5366 -4.7198 -3.5819 -2.5818 1.1441
## shrub_cover-Canis_latrans -0.2567 0.2460 -0.7286 -0.2555 0.2295 1.0171
## shrub_cover-Sciurus_niger 0.0439 0.5647 -1.0551 0.0525 1.1705 1.0828
## shrub_cover-Procyon_lotor 0.3163 0.1621 -0.0127 0.3180 0.6256 1.0226
## shrub_cover-Dasypus_novemcinctus 1.1132 0.3670 0.4103 1.1060 1.8289 1.0199
## shrub_cover-Lynx_rufus 0.1450 0.3833 -0.6646 0.1632 0.8251 1.0748
## shrub_cover-Didelphis_virginiana 1.2867 0.4276 0.5195 1.2589 2.1864 1.0051
## shrub_cover-Sylvilagus_floridanus 0.7743 0.4092 -0.0680 0.7832 1.5567 1.0030
## shrub_cover-Meleagris_gallopavo -0.3328 0.4652 -1.2729 -0.3377 0.5497 1.1026
## shrub_cover-Sciurus_carolinensis 1.2367 0.4246 0.4398 1.2219 2.0920 1.0118
## shrub_cover-Vulpes_vulpes 0.4239 0.6678 -0.9683 0.4422 1.7266 1.0863
## shrub_cover-Sus_scrofa 1.1423 0.8196 -0.4442 1.1281 2.8567 1.0507
## veg_height-Canis_latrans -0.5806 0.1865 -0.9632 -0.5740 -0.2235 1.0122
## veg_height-Sciurus_niger 0.1349 0.4579 -0.7530 0.1184 1.0801 1.0350
## veg_height-Procyon_lotor 0.3426 0.1268 0.1078 0.3408 0.5919 1.0101
## veg_height-Dasypus_novemcinctus 0.2795 0.1440 0.0025 0.2784 0.5627 1.0003
## veg_height-Lynx_rufus 0.0436 0.2476 -0.4649 0.0483 0.5054 1.0253
## veg_height-Didelphis_virginiana 0.4418 0.2594 -0.0507 0.4376 0.9702 1.0009
## veg_height-Sylvilagus_floridanus 0.0847 0.2548 -0.4202 0.0938 0.5785 1.0086
## veg_height-Meleagris_gallopavo -0.1081 0.4553 -1.0570 -0.0953 0.7972 1.0469
## veg_height-Sciurus_carolinensis 0.1202 0.2290 -0.3120 0.1144 0.5832 1.0127
## veg_height-Vulpes_vulpes -0.0979 0.3237 -0.7418 -0.0929 0.5308 1.0074
## veg_height-Sus_scrofa -0.1343 0.3430 -0.8701 -0.1159 0.5250 1.0093
## ESS
## (Intercept)-Canis_latrans 728
## (Intercept)-Sciurus_niger 262
## (Intercept)-Procyon_lotor 1245
## (Intercept)-Dasypus_novemcinctus 497
## (Intercept)-Lynx_rufus 425
## (Intercept)-Didelphis_virginiana 334
## (Intercept)-Sylvilagus_floridanus 357
## (Intercept)-Meleagris_gallopavo 300
## (Intercept)-Sciurus_carolinensis 394
## (Intercept)-Vulpes_vulpes 237
## (Intercept)-Sus_scrofa 130
## shrub_cover-Canis_latrans 407
## shrub_cover-Sciurus_niger 341
## shrub_cover-Procyon_lotor 1460
## shrub_cover-Dasypus_novemcinctus 274
## shrub_cover-Lynx_rufus 364
## shrub_cover-Didelphis_virginiana 235
## shrub_cover-Sylvilagus_floridanus 383
## shrub_cover-Meleagris_gallopavo 423
## shrub_cover-Sciurus_carolinensis 212
## shrub_cover-Vulpes_vulpes 260
## shrub_cover-Sus_scrofa 146
## veg_height-Canis_latrans 774
## veg_height-Sciurus_niger 428
## veg_height-Procyon_lotor 1518
## veg_height-Dasypus_novemcinctus 1643
## veg_height-Lynx_rufus 618
## veg_height-Didelphis_virginiana 755
## veg_height-Sylvilagus_floridanus 650
## veg_height-Meleagris_gallopavo 371
## veg_height-Sciurus_carolinensis 828
## veg_height-Vulpes_vulpes 620
## veg_height-Sus_scrofa 721
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6615
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4624 0.3246 -1.1216 -0.4609 0.1871 1.0001 827
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7903 0.7218 0.1454 0.598 2.5851 1.0159 479
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0163 0.3067 -3.6412 -3.0119 -2.4194 1.0011 969
## shrub_cover 0.1771 0.2905 -0.3870 0.1763 0.7632 1.0010 877
## veg_height 0.0156 0.1684 -0.3179 0.0168 0.3599 1.0027 1136
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9032 0.6042 0.2511 0.7580 2.3906 1.0164 874
## shrub_cover 0.6887 0.5326 0.1286 0.5469 2.0905 1.0033 761
## veg_height 0.2058 0.1473 0.0537 0.1657 0.5911 1.0034 1207
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2235 0.3844 -0.4844 0.2047 1.0325 1.0003
## (Intercept)-Sciurus_niger -0.6880 0.6127 -1.8309 -0.7170 0.6398 1.0005
## (Intercept)-Procyon_lotor 0.5119 0.3902 -0.2091 0.4990 1.3133 1.0061
## (Intercept)-Dasypus_novemcinctus -0.5546 0.3474 -1.2492 -0.5501 0.1020 1.0004
## (Intercept)-Lynx_rufus 0.0605 0.5600 -0.8826 0.0004 1.3555 1.0534
## (Intercept)-Didelphis_virginiana -1.0328 0.4281 -1.9187 -1.0199 -0.2399 1.0024
## (Intercept)-Sylvilagus_floridanus -0.4020 0.4112 -1.1814 -0.4053 0.4254 1.0036
## (Intercept)-Meleagris_gallopavo 0.1097 0.7392 -0.9891 0.0173 1.7205 1.0119
## (Intercept)-Sciurus_carolinensis -1.0385 0.4343 -1.9317 -1.0239 -0.2164 1.0000
## (Intercept)-Vulpes_vulpes -1.0470 0.6663 -2.3467 -1.0550 0.2983 1.0101
## (Intercept)-Sus_scrofa -1.3085 0.5775 -2.5289 -1.2880 -0.2743 1.0034
## ESS
## (Intercept)-Canis_latrans 1711
## (Intercept)-Sciurus_niger 493
## (Intercept)-Procyon_lotor 1937
## (Intercept)-Dasypus_novemcinctus 2754
## (Intercept)-Lynx_rufus 600
## (Intercept)-Didelphis_virginiana 2069
## (Intercept)-Sylvilagus_floridanus 1773
## (Intercept)-Meleagris_gallopavo 252
## (Intercept)-Sciurus_carolinensis 1499
## (Intercept)-Vulpes_vulpes 360
## (Intercept)-Sus_scrofa 822
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7384 0.1895 -3.1196 -2.7291 -2.3863 1.0151
## (Intercept)-Sciurus_niger -3.9243 0.5321 -4.9421 -3.9341 -2.8899 1.0039
## (Intercept)-Procyon_lotor -2.2954 0.1391 -2.5869 -2.2913 -2.0409 1.0081
## (Intercept)-Dasypus_novemcinctus -1.7522 0.1579 -2.0667 -1.7456 -1.4571 1.0083
## (Intercept)-Lynx_rufus -3.6194 0.3373 -4.2630 -3.6189 -2.9687 1.0327
## (Intercept)-Didelphis_virginiana -2.5985 0.2783 -3.1737 -2.5875 -2.0779 1.0012
## (Intercept)-Sylvilagus_floridanus -3.1328 0.2665 -3.6866 -3.1216 -2.6490 1.0044
## (Intercept)-Meleagris_gallopavo -3.9889 0.4729 -4.8613 -3.9960 -3.0369 1.0282
## (Intercept)-Sciurus_carolinensis -2.6575 0.3061 -3.2938 -2.6439 -2.0889 1.0010
## (Intercept)-Vulpes_vulpes -3.9956 0.6081 -5.2125 -3.9789 -2.8832 1.0175
## (Intercept)-Sus_scrofa -3.3964 0.5528 -4.4802 -3.3970 -2.2887 1.0148
## shrub_cover-Canis_latrans -0.3040 0.2217 -0.7441 -0.3074 0.1350 1.0180
## shrub_cover-Sciurus_niger -0.3697 0.4862 -1.3232 -0.3717 0.6230 1.0030
## shrub_cover-Procyon_lotor 0.2520 0.1622 -0.0749 0.2496 0.5619 1.0032
## shrub_cover-Dasypus_novemcinctus 0.8437 0.2953 0.2857 0.8351 1.4410 1.0309
## shrub_cover-Lynx_rufus -0.3306 0.3672 -1.0414 -0.3291 0.3972 1.0034
## shrub_cover-Didelphis_virginiana 0.9634 0.3740 0.2842 0.9542 1.7396 1.0064
## shrub_cover-Sylvilagus_floridanus 0.2659 0.4199 -0.4832 0.2445 1.1242 1.0105
## shrub_cover-Meleagris_gallopavo -0.7539 0.4152 -1.5370 -0.7458 0.0836 1.0350
## shrub_cover-Sciurus_carolinensis 0.8376 0.4150 0.0326 0.8280 1.6510 1.0042
## shrub_cover-Vulpes_vulpes -0.1375 0.5918 -1.3812 -0.1248 1.0275 1.0085
## shrub_cover-Sus_scrofa 0.6871 0.7870 -0.8662 0.6610 2.2733 1.0064
## veg_height-Canis_latrans -0.5790 0.1845 -0.9585 -0.5749 -0.2295 1.0311
## veg_height-Sciurus_niger -0.0560 0.4109 -0.8663 -0.0608 0.7739 1.0013
## veg_height-Procyon_lotor 0.3413 0.1220 0.1029 0.3406 0.5831 1.0044
## veg_height-Dasypus_novemcinctus 0.2463 0.1324 -0.0095 0.2460 0.5140 1.0107
## veg_height-Lynx_rufus 0.0266 0.2424 -0.4523 0.0326 0.4803 1.0125
## veg_height-Didelphis_virginiana 0.4397 0.2434 -0.0230 0.4276 0.9508 1.0043
## veg_height-Sylvilagus_floridanus 0.1226 0.2380 -0.3403 0.1275 0.5912 1.0029
## veg_height-Meleagris_gallopavo -0.2464 0.3515 -0.9628 -0.2383 0.4545 1.0345
## veg_height-Sciurus_carolinensis 0.0785 0.2109 -0.3349 0.0750 0.4939 1.0028
## veg_height-Vulpes_vulpes -0.1169 0.3086 -0.7686 -0.1024 0.4494 1.0056
## veg_height-Sus_scrofa -0.1331 0.3307 -0.8105 -0.1205 0.4841 1.0012
## ESS
## (Intercept)-Canis_latrans 776
## (Intercept)-Sciurus_niger 264
## (Intercept)-Procyon_lotor 1328
## (Intercept)-Dasypus_novemcinctus 1220
## (Intercept)-Lynx_rufus 298
## (Intercept)-Didelphis_virginiana 895
## (Intercept)-Sylvilagus_floridanus 655
## (Intercept)-Meleagris_gallopavo 208
## (Intercept)-Sciurus_carolinensis 866
## (Intercept)-Vulpes_vulpes 252
## (Intercept)-Sus_scrofa 539
## shrub_cover-Canis_latrans 861
## shrub_cover-Sciurus_niger 423
## shrub_cover-Procyon_lotor 1415
## shrub_cover-Dasypus_novemcinctus 1035
## shrub_cover-Lynx_rufus 363
## shrub_cover-Didelphis_virginiana 752
## shrub_cover-Sylvilagus_floridanus 510
## shrub_cover-Meleagris_gallopavo 174
## shrub_cover-Sciurus_carolinensis 756
## shrub_cover-Vulpes_vulpes 608
## shrub_cover-Sus_scrofa 464
## veg_height-Canis_latrans 757
## veg_height-Sciurus_niger 749
## veg_height-Procyon_lotor 1719
## veg_height-Dasypus_novemcinctus 1732
## veg_height-Lynx_rufus 758
## veg_height-Didelphis_virginiana 1151
## veg_height-Sylvilagus_floridanus 934
## veg_height-Meleagris_gallopavo 555
## veg_height-Sciurus_carolinensis 1142
## veg_height-Vulpes_vulpes 747
## veg_height-Sus_scrofa 1042
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6425
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6598 0.3733 -1.4132 -0.6602 0.0592 1.0331 430
## Veg_shannon_index 0.3563 0.2538 -0.1322 0.3504 0.8651 1.0093 735
## Avg_Cogongrass_Cover 0.2330 0.2712 -0.3303 0.2402 0.7299 1.0074 614
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6472 0.7258 0.0500 0.4379 2.4940 1.0099 538
## Veg_shannon_index 0.2713 0.2790 0.0361 0.1825 1.0624 1.0074 839
## Avg_Cogongrass_Cover 0.3278 0.3908 0.0403 0.2070 1.3541 1.0070 705
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0226 0.8001 0.1036 0.8298 3.0115 1.045 165
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0129 0.3079 -3.6467 -3.0055 -2.4171 1.0024 1057
## shrub_cover 0.1611 0.2781 -0.3940 0.1632 0.7180 1.0065 1229
## veg_height 0.0119 0.1644 -0.3221 0.0142 0.3182 1.0040 1081
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8986 0.6357 0.2636 0.7355 2.5385 1.0559 446
## shrub_cover 0.6630 0.5079 0.1615 0.5256 2.0354 1.0344 398
## veg_height 0.1998 0.1425 0.0524 0.1663 0.5661 1.0076 1363
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1441 0.6119 -1.3062 -0.1708
## (Intercept)-Sciurus_niger -0.7143 0.7069 -2.1208 -0.7266
## (Intercept)-Procyon_lotor -0.0557 0.6313 -1.2558 -0.0571
## (Intercept)-Dasypus_novemcinctus -0.6878 0.5164 -1.7741 -0.6778
## (Intercept)-Lynx_rufus -0.3917 0.6777 -1.6157 -0.4448
## (Intercept)-Didelphis_virginiana -1.0540 0.5340 -2.1694 -1.0324
## (Intercept)-Sylvilagus_floridanus -0.5804 0.5596 -1.6623 -0.5867
## (Intercept)-Meleagris_gallopavo -0.3325 0.6756 -1.6104 -0.3653
## (Intercept)-Sciurus_carolinensis -1.0819 0.5615 -2.2896 -1.0490
## (Intercept)-Vulpes_vulpes -1.0197 0.7266 -2.5336 -0.9769
## (Intercept)-Sus_scrofa -1.3435 0.7020 -2.9105 -1.2629
## Veg_shannon_index-Canis_latrans 0.6277 0.3956 -0.0666 0.5963
## Veg_shannon_index-Sciurus_niger 0.3625 0.4820 -0.5958 0.3600
## Veg_shannon_index-Procyon_lotor 0.4644 0.3730 -0.2110 0.4459
## Veg_shannon_index-Dasypus_novemcinctus 0.1926 0.3498 -0.5230 0.2019
## Veg_shannon_index-Lynx_rufus 0.2182 0.4825 -0.8058 0.2371
## Veg_shannon_index-Didelphis_virginiana 0.4756 0.3754 -0.2090 0.4558
## Veg_shannon_index-Sylvilagus_floridanus 0.4339 0.4076 -0.3363 0.4266
## Veg_shannon_index-Meleagris_gallopavo 0.5201 0.4796 -0.3254 0.4893
## Veg_shannon_index-Sciurus_carolinensis -0.0198 0.4149 -0.9168 0.0165
## Veg_shannon_index-Vulpes_vulpes 0.0939 0.4563 -0.8634 0.1132
## Veg_shannon_index-Sus_scrofa 0.6173 0.4922 -0.2117 0.5684
## Avg_Cogongrass_Cover-Canis_latrans 0.5470 0.3974 -0.1428 0.5213
## Avg_Cogongrass_Cover-Sciurus_niger -0.1463 0.6075 -1.4644 -0.0866
## Avg_Cogongrass_Cover-Procyon_lotor 0.3405 0.3659 -0.3668 0.3312
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4002 0.3370 -0.2662 0.3876
## Avg_Cogongrass_Cover-Lynx_rufus 0.5434 0.4136 -0.1827 0.5132
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4064 0.3854 -0.3225 0.4040
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1383 0.4690 -1.1862 -0.1009
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1145 0.5887 -1.4073 -0.0478
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3850 0.3682 -0.3080 0.3685
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3537 0.4615 -0.5477 0.3419
## Avg_Cogongrass_Cover-Sus_scrofa 0.0490 0.5225 -1.1191 0.0842
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1662 1.0067 421
## (Intercept)-Sciurus_niger 0.7345 1.0212 389
## (Intercept)-Procyon_lotor 1.1679 1.0227 283
## (Intercept)-Dasypus_novemcinctus 0.3042 1.0183 811
## (Intercept)-Lynx_rufus 1.0890 1.0004 318
## (Intercept)-Didelphis_virginiana -0.0621 1.0157 767
## (Intercept)-Sylvilagus_floridanus 0.5609 1.0103 741
## (Intercept)-Meleagris_gallopavo 1.0818 1.0164 328
## (Intercept)-Sciurus_carolinensis -0.0791 1.0161 933
## (Intercept)-Vulpes_vulpes 0.3628 1.0282 417
## (Intercept)-Sus_scrofa -0.1836 1.0450 519
## Veg_shannon_index-Canis_latrans 1.4937 1.0004 1043
## Veg_shannon_index-Sciurus_niger 1.3804 1.0042 1000
## Veg_shannon_index-Procyon_lotor 1.2607 1.0138 1256
## Veg_shannon_index-Dasypus_novemcinctus 0.8627 1.0061 1513
## Veg_shannon_index-Lynx_rufus 1.1337 1.0086 995
## Veg_shannon_index-Didelphis_virginiana 1.2845 1.0070 1602
## Veg_shannon_index-Sylvilagus_floridanus 1.2366 1.0086 1295
## Veg_shannon_index-Meleagris_gallopavo 1.5831 1.0023 912
## Veg_shannon_index-Sciurus_carolinensis 0.7288 1.0037 1198
## Veg_shannon_index-Vulpes_vulpes 0.9462 1.0014 938
## Veg_shannon_index-Sus_scrofa 1.7472 1.0237 932
## Avg_Cogongrass_Cover-Canis_latrans 1.4396 1.0008 1507
## Avg_Cogongrass_Cover-Sciurus_niger 0.8483 1.0036 736
## Avg_Cogongrass_Cover-Procyon_lotor 1.0935 1.0076 1349
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0965 1.0024 2117
## Avg_Cogongrass_Cover-Lynx_rufus 1.4545 1.0018 1336
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1719 1.0016 1701
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6806 1.0069 799
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8792 1.0024 598
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1491 1.0002 1681
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3318 1.0049 1364
## Avg_Cogongrass_Cover-Sus_scrofa 0.9742 1.0026 985
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7269 0.1786 -3.0866 -2.7236 -2.3929 1.0148
## (Intercept)-Sciurus_niger -3.9993 0.5607 -5.1669 -3.9797 -2.9477 1.0352
## (Intercept)-Procyon_lotor -2.3034 0.1434 -2.5992 -2.2972 -2.0425 1.0364
## (Intercept)-Dasypus_novemcinctus -1.7605 0.1607 -2.0850 -1.7570 -1.4512 1.0026
## (Intercept)-Lynx_rufus -3.5927 0.3302 -4.2381 -3.5874 -2.9621 1.0130
## (Intercept)-Didelphis_virginiana -2.6014 0.2861 -3.2001 -2.5883 -2.0767 1.0076
## (Intercept)-Sylvilagus_floridanus -3.1698 0.2782 -3.7412 -3.1600 -2.6450 1.0408
## (Intercept)-Meleagris_gallopavo -3.9429 0.4228 -4.7756 -3.9338 -3.1109 1.0169
## (Intercept)-Sciurus_carolinensis -2.6695 0.3148 -3.3251 -2.6559 -2.0863 1.0164
## (Intercept)-Vulpes_vulpes -4.0488 0.6292 -5.3586 -4.0288 -2.8945 1.0186
## (Intercept)-Sus_scrofa -3.3285 0.5434 -4.4150 -3.3344 -2.2385 1.0089
## shrub_cover-Canis_latrans -0.2931 0.2151 -0.7136 -0.2935 0.1216 1.0085
## shrub_cover-Sciurus_niger -0.4434 0.4746 -1.3678 -0.4371 0.4864 1.0195
## shrub_cover-Procyon_lotor 0.2367 0.1691 -0.1049 0.2400 0.5625 1.0050
## shrub_cover-Dasypus_novemcinctus 0.8521 0.3004 0.2957 0.8422 1.4691 1.0216
## shrub_cover-Lynx_rufus -0.2590 0.3549 -0.9821 -0.2541 0.4319 1.0388
## shrub_cover-Didelphis_virginiana 0.9925 0.3698 0.3088 0.9849 1.7374 1.0275
## shrub_cover-Sylvilagus_floridanus 0.1945 0.4141 -0.5817 0.1883 1.0353 1.0427
## shrub_cover-Meleagris_gallopavo -0.7356 0.3691 -1.4883 -0.7244 -0.0483 1.0046
## shrub_cover-Sciurus_carolinensis 0.8637 0.4053 0.0886 0.8574 1.6610 1.0072
## shrub_cover-Vulpes_vulpes -0.1728 0.5784 -1.3743 -0.1535 0.9178 1.0018
## shrub_cover-Sus_scrofa 0.5794 0.7660 -0.9014 0.5674 2.1264 1.0206
## veg_height-Canis_latrans -0.5761 0.1837 -0.9457 -0.5728 -0.2360 1.0288
## veg_height-Sciurus_niger -0.0303 0.4097 -0.8437 -0.0321 0.8097 1.0034
## veg_height-Procyon_lotor 0.3312 0.1220 0.0983 0.3286 0.5706 1.0054
## veg_height-Dasypus_novemcinctus 0.2447 0.1337 -0.0027 0.2436 0.5224 1.0019
## veg_height-Lynx_rufus -0.0102 0.2499 -0.5072 -0.0070 0.4675 1.0076
## veg_height-Didelphis_virginiana 0.4269 0.2417 -0.0115 0.4192 0.9130 1.0000
## veg_height-Sylvilagus_floridanus 0.1412 0.2349 -0.3149 0.1434 0.5968 1.0128
## veg_height-Meleagris_gallopavo -0.2030 0.3621 -0.9412 -0.1961 0.4988 1.0048
## veg_height-Sciurus_carolinensis 0.0810 0.2133 -0.3235 0.0775 0.5140 1.0040
## veg_height-Vulpes_vulpes -0.1262 0.3073 -0.7526 -0.1233 0.4715 1.0033
## veg_height-Sus_scrofa -0.1439 0.3296 -0.8465 -0.1257 0.4660 1.0025
## ESS
## (Intercept)-Canis_latrans 867
## (Intercept)-Sciurus_niger 187
## (Intercept)-Procyon_lotor 1051
## (Intercept)-Dasypus_novemcinctus 1499
## (Intercept)-Lynx_rufus 354
## (Intercept)-Didelphis_virginiana 650
## (Intercept)-Sylvilagus_floridanus 551
## (Intercept)-Meleagris_gallopavo 270
## (Intercept)-Sciurus_carolinensis 789
## (Intercept)-Vulpes_vulpes 216
## (Intercept)-Sus_scrofa 542
## shrub_cover-Canis_latrans 979
## shrub_cover-Sciurus_niger 348
## shrub_cover-Procyon_lotor 1217
## shrub_cover-Dasypus_novemcinctus 1281
## shrub_cover-Lynx_rufus 491
## shrub_cover-Didelphis_virginiana 676
## shrub_cover-Sylvilagus_floridanus 537
## shrub_cover-Meleagris_gallopavo 305
## shrub_cover-Sciurus_carolinensis 828
## shrub_cover-Vulpes_vulpes 443
## shrub_cover-Sus_scrofa 651
## veg_height-Canis_latrans 778
## veg_height-Sciurus_niger 694
## veg_height-Procyon_lotor 1715
## veg_height-Dasypus_novemcinctus 1656
## veg_height-Lynx_rufus 730
## veg_height-Didelphis_virginiana 976
## veg_height-Sylvilagus_floridanus 838
## veg_height-Meleagris_gallopavo 499
## veg_height-Sciurus_carolinensis 1208
## veg_height-Vulpes_vulpes 693
## veg_height-Sus_scrofa 1184
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6723
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5678 0.4194 -1.3968 -0.5631 0.2380 1.0660 221
## Cogon_Patch_Size -0.1261 0.3708 -0.9441 -0.1095 0.5475 1.0160 605
## Avg_Cogongrass_Cover 0.0300 0.3265 -0.6024 0.0221 0.6867 1.0240 502
## total_shrub_cover -0.8230 0.4246 -1.8179 -0.7782 -0.1126 1.0295 191
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6084 0.6788 0.0501 0.3889 2.5939 1.0588 429
## Cogon_Patch_Size 0.6569 1.0883 0.0510 0.3656 2.8391 1.1178 375
## Avg_Cogongrass_Cover 0.4201 0.4650 0.0475 0.2654 1.6746 1.0134 603
## total_shrub_cover 0.5767 0.6656 0.0508 0.3489 2.4183 1.1104 279
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6046 1.462 0.1819 1.2099 5.2432 1.1388 127
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0221 0.2874 -3.5900 -3.0182 -2.4599 1.0342 741
## shrub_cover 0.4745 0.3092 -0.1321 0.4718 1.0921 1.0163 407
## veg_height 0.0440 0.1696 -0.3050 0.0500 0.3701 1.0002 953
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7626 0.5806 0.1940 0.6223 2.1489 1.0440 463
## shrub_cover 0.6828 0.5244 0.1355 0.5476 2.0036 1.0484 459
## veg_height 0.1994 0.1443 0.0506 0.1633 0.5646 1.0006 1355
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0639 0.6432 -1.2379 -0.0963
## (Intercept)-Sciurus_niger -0.7839 0.6755 -2.2321 -0.7570
## (Intercept)-Procyon_lotor 0.0267 0.7020 -1.2788 -0.0149
## (Intercept)-Dasypus_novemcinctus -0.5727 0.5988 -1.7936 -0.5675
## (Intercept)-Lynx_rufus -0.4390 0.6707 -1.7321 -0.4550
## (Intercept)-Didelphis_virginiana -0.8457 0.6092 -2.1147 -0.8201
## (Intercept)-Sylvilagus_floridanus -0.3966 0.6406 -1.6166 -0.4182
## (Intercept)-Meleagris_gallopavo -0.5439 0.6657 -1.8292 -0.5623
## (Intercept)-Sciurus_carolinensis -0.8625 0.6420 -2.1701 -0.8524
## (Intercept)-Vulpes_vulpes -0.8865 0.7740 -2.5154 -0.8507
## (Intercept)-Sus_scrofa -0.9951 0.7421 -2.5549 -0.9343
## Cogon_Patch_Size-Canis_latrans 0.5738 0.6065 -0.3459 0.4691
## Cogon_Patch_Size-Sciurus_niger -0.4411 0.8043 -2.2189 -0.3254
## Cogon_Patch_Size-Procyon_lotor -0.1998 0.4407 -1.1396 -0.1805
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0234 0.4291 -0.8844 -0.0115
## Cogon_Patch_Size-Lynx_rufus -0.1385 0.6839 -1.4280 -0.1542
## Cogon_Patch_Size-Didelphis_virginiana 0.5274 0.4931 -0.3308 0.4918
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6723 0.8390 -2.8205 -0.5128
## Cogon_Patch_Size-Meleagris_gallopavo -0.0272 0.6360 -1.2271 -0.0357
## Cogon_Patch_Size-Sciurus_carolinensis -0.5195 0.6774 -2.2418 -0.4179
## Cogon_Patch_Size-Vulpes_vulpes -0.3476 0.7644 -2.1527 -0.2707
## Cogon_Patch_Size-Sus_scrofa -0.2466 0.6748 -1.7843 -0.1793
## Avg_Cogongrass_Cover-Canis_latrans 0.2696 0.4440 -0.5318 0.2434
## Avg_Cogongrass_Cover-Sciurus_niger -0.3891 0.6474 -1.8273 -0.3318
## Avg_Cogongrass_Cover-Procyon_lotor 0.0381 0.4377 -0.7963 0.0321
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2932 0.4121 -0.4522 0.2684
## Avg_Cogongrass_Cover-Lynx_rufus 0.4322 0.5229 -0.4640 0.3838
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0725 0.4733 -0.8674 0.0751
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2875 0.5304 -1.3769 -0.2714
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4405 0.6931 -1.9663 -0.3977
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2931 0.4652 -0.5924 0.2858
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2386 0.5342 -0.7263 0.2094
## Avg_Cogongrass_Cover-Sus_scrofa -0.1530 0.6222 -1.4449 -0.1167
## total_shrub_cover-Canis_latrans -0.0741 0.5408 -1.0721 -0.1072
## total_shrub_cover-Sciurus_niger -0.9014 0.7083 -2.5120 -0.8467
## total_shrub_cover-Procyon_lotor -1.0957 0.5486 -2.3607 -1.0070
## total_shrub_cover-Dasypus_novemcinctus -0.4684 0.5391 -1.6915 -0.4237
## total_shrub_cover-Lynx_rufus -1.1382 0.7705 -3.0143 -1.0197
## total_shrub_cover-Didelphis_virginiana -0.8738 0.5872 -2.2914 -0.7984
## total_shrub_cover-Sylvilagus_floridanus -0.9992 0.7316 -2.7406 -0.9035
## total_shrub_cover-Meleagris_gallopavo -1.3308 0.7866 -3.2705 -1.1983
## total_shrub_cover-Sciurus_carolinensis -0.7918 0.6388 -2.2813 -0.7418
## total_shrub_cover-Vulpes_vulpes -0.9139 0.8208 -2.8656 -0.8212
## total_shrub_cover-Sus_scrofa -0.6867 0.7618 -2.4087 -0.6535
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2728 1.0171 445
## (Intercept)-Sciurus_niger 0.4779 1.0632 448
## (Intercept)-Procyon_lotor 1.4693 1.0318 290
## (Intercept)-Dasypus_novemcinctus 0.5707 1.0216 369
## (Intercept)-Lynx_rufus 1.0083 1.0465 511
## (Intercept)-Didelphis_virginiana 0.2943 1.0385 422
## (Intercept)-Sylvilagus_floridanus 0.8974 1.0122 419
## (Intercept)-Meleagris_gallopavo 0.8402 1.0377 493
## (Intercept)-Sciurus_carolinensis 0.3838 1.0402 453
## (Intercept)-Vulpes_vulpes 0.5822 1.0448 287
## (Intercept)-Sus_scrofa 0.3737 1.0439 299
## Cogon_Patch_Size-Canis_latrans 2.0226 1.0132 761
## Cogon_Patch_Size-Sciurus_niger 0.7421 1.0306 453
## Cogon_Patch_Size-Procyon_lotor 0.6028 1.0124 1114
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8165 1.0175 1489
## Cogon_Patch_Size-Lynx_rufus 1.2805 1.0307 581
## Cogon_Patch_Size-Didelphis_virginiana 1.6133 1.0251 917
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4870 1.0056 445
## Cogon_Patch_Size-Meleagris_gallopavo 1.2881 1.0217 837
## Cogon_Patch_Size-Sciurus_carolinensis 0.4835 1.0060 453
## Cogon_Patch_Size-Vulpes_vulpes 0.9700 1.0125 511
## Cogon_Patch_Size-Sus_scrofa 0.9086 1.0048 927
## Avg_Cogongrass_Cover-Canis_latrans 1.2490 1.0128 1219
## Avg_Cogongrass_Cover-Sciurus_niger 0.7190 1.0048 546
## Avg_Cogongrass_Cover-Procyon_lotor 0.9143 1.0084 1138
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1629 1.0286 1126
## Avg_Cogongrass_Cover-Lynx_rufus 1.5979 1.0161 1037
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0041 1.0131 1024
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7041 1.0110 566
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7909 1.0021 429
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2695 1.0116 996
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3872 1.0446 536
## Avg_Cogongrass_Cover-Sus_scrofa 1.0303 1.0053 806
## total_shrub_cover-Canis_latrans 1.1263 1.0164 505
## total_shrub_cover-Sciurus_niger 0.3112 1.0451 402
## total_shrub_cover-Procyon_lotor -0.2045 1.0276 486
## total_shrub_cover-Dasypus_novemcinctus 0.4810 1.0068 476
## total_shrub_cover-Lynx_rufus 0.0870 1.0472 265
## total_shrub_cover-Didelphis_virginiana 0.0916 1.0010 474
## total_shrub_cover-Sylvilagus_floridanus 0.1743 1.0671 240
## total_shrub_cover-Meleagris_gallopavo -0.1596 1.1025 270
## total_shrub_cover-Sciurus_carolinensis 0.3254 1.0142 403
## total_shrub_cover-Vulpes_vulpes 0.5178 1.0611 252
## total_shrub_cover-Sus_scrofa 0.7405 1.0273 304
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7478 0.1783 -3.1051 -2.7452 -2.4060 1.0083
## (Intercept)-Sciurus_niger -3.7498 0.5698 -4.9500 -3.7207 -2.7280 1.0712
## (Intercept)-Procyon_lotor -2.3041 0.1413 -2.6001 -2.3000 -2.0401 1.0005
## (Intercept)-Dasypus_novemcinctus -1.8231 0.1769 -2.1869 -1.8126 -1.5033 1.0146
## (Intercept)-Lynx_rufus -3.4802 0.3173 -4.1345 -3.4711 -2.8970 1.0176
## (Intercept)-Didelphis_virginiana -2.6970 0.3004 -3.2956 -2.6881 -2.1322 1.0202
## (Intercept)-Sylvilagus_floridanus -3.2276 0.2745 -3.7794 -3.2275 -2.6931 1.0081
## (Intercept)-Meleagris_gallopavo -3.6230 0.5024 -4.6735 -3.6063 -2.6713 1.1036
## (Intercept)-Sciurus_carolinensis -2.8692 0.3429 -3.5798 -2.8519 -2.2280 1.0200
## (Intercept)-Vulpes_vulpes -4.0058 0.6464 -5.3856 -3.9449 -2.8832 1.0910
## (Intercept)-Sus_scrofa -3.5778 0.5451 -4.5954 -3.5927 -2.4357 1.0209
## shrub_cover-Canis_latrans -0.2452 0.2278 -0.6919 -0.2429 0.1781 1.0004
## shrub_cover-Sciurus_niger -0.0068 0.5512 -1.0870 -0.0163 1.0538 1.0837
## shrub_cover-Procyon_lotor 0.3079 0.1574 -0.0082 0.3109 0.6105 1.0004
## shrub_cover-Dasypus_novemcinctus 1.0289 0.3416 0.3910 1.0228 1.7122 1.0422
## shrub_cover-Lynx_rufus 0.1221 0.3767 -0.6693 0.1422 0.7810 1.0232
## shrub_cover-Didelphis_virginiana 1.1784 0.4056 0.4502 1.1630 2.0165 1.0253
## shrub_cover-Sylvilagus_floridanus 0.6827 0.4250 -0.1977 0.7088 1.4490 1.0193
## shrub_cover-Meleagris_gallopavo -0.3796 0.4643 -1.2932 -0.3822 0.4978 1.0673
## shrub_cover-Sciurus_carolinensis 1.1589 0.4361 0.2997 1.1498 2.0341 1.0470
## shrub_cover-Vulpes_vulpes 0.3766 0.6382 -0.8304 0.3694 1.6705 1.0586
## shrub_cover-Sus_scrofa 1.0964 0.8042 -0.5484 1.0790 2.6544 1.0277
## veg_height-Canis_latrans -0.5637 0.1852 -0.9341 -0.5572 -0.2125 1.0020
## veg_height-Sciurus_niger 0.1201 0.4019 -0.6144 0.1035 0.9924 1.0068
## veg_height-Procyon_lotor 0.3439 0.1231 0.1026 0.3440 0.5840 1.0025
## veg_height-Dasypus_novemcinctus 0.2640 0.1407 -0.0046 0.2634 0.5499 1.0009
## veg_height-Lynx_rufus 0.0490 0.2268 -0.4066 0.0511 0.4941 1.0038
## veg_height-Didelphis_virginiana 0.4363 0.2449 -0.0236 0.4251 0.9482 1.0037
## veg_height-Sylvilagus_floridanus 0.0932 0.2418 -0.3760 0.0950 0.5794 1.0111
## veg_height-Meleagris_gallopavo -0.1334 0.3838 -0.8579 -0.1423 0.6423 1.0105
## veg_height-Sciurus_carolinensis 0.1435 0.2305 -0.2986 0.1389 0.5952 1.0002
## veg_height-Vulpes_vulpes -0.1077 0.3194 -0.7707 -0.0903 0.4723 1.0167
## veg_height-Sus_scrofa -0.1336 0.3187 -0.7690 -0.1241 0.4854 1.0043
## ESS
## (Intercept)-Canis_latrans 885
## (Intercept)-Sciurus_niger 305
## (Intercept)-Procyon_lotor 1350
## (Intercept)-Dasypus_novemcinctus 726
## (Intercept)-Lynx_rufus 439
## (Intercept)-Didelphis_virginiana 412
## (Intercept)-Sylvilagus_floridanus 489
## (Intercept)-Meleagris_gallopavo 298
## (Intercept)-Sciurus_carolinensis 456
## (Intercept)-Vulpes_vulpes 187
## (Intercept)-Sus_scrofa 222
## shrub_cover-Canis_latrans 758
## shrub_cover-Sciurus_niger 352
## shrub_cover-Procyon_lotor 1634
## shrub_cover-Dasypus_novemcinctus 537
## shrub_cover-Lynx_rufus 424
## shrub_cover-Didelphis_virginiana 439
## shrub_cover-Sylvilagus_floridanus 272
## shrub_cover-Meleagris_gallopavo 330
## shrub_cover-Sciurus_carolinensis 378
## shrub_cover-Vulpes_vulpes 362
## shrub_cover-Sus_scrofa 171
## veg_height-Canis_latrans 723
## veg_height-Sciurus_niger 755
## veg_height-Procyon_lotor 1535
## veg_height-Dasypus_novemcinctus 1585
## veg_height-Lynx_rufus 848
## veg_height-Didelphis_virginiana 884
## veg_height-Sylvilagus_floridanus 607
## veg_height-Meleagris_gallopavo 572
## veg_height-Sciurus_carolinensis 727
## veg_height-Vulpes_vulpes 618
## veg_height-Sus_scrofa 949
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6537
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6794 0.4459 -1.5608 -0.6867 0.2462 1.0147 519
## Tree_Density -0.8006 0.4230 -1.7125 -0.7676 -0.0829 1.0417 394
## Avg_Canopy_Cover 1.1169 0.3494 0.4994 1.0960 1.8622 1.0392 723
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3985 1.5293 0.0936 0.9451 5.3265 1.1658 256
## Tree_Density 0.6692 0.9626 0.0503 0.3791 3.0718 1.0241 451
## Avg_Canopy_Cover 0.7370 0.7640 0.0778 0.5148 2.6866 1.0470 557
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6802 0.6754 0.0515 0.4613 2.4503 1.0819 135
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.0460 0.3104 -3.6820 -3.0467 -2.4428 1.0063 806
## shrub_cover 0.2142 0.2895 -0.3589 0.2168 0.7960 1.0039 1096
## veg_height 0.0356 0.1659 -0.2843 0.0394 0.3598 1.0079 1163
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9341 0.6356 0.2701 0.7662 2.5902 1.0184 596
## shrub_cover 0.6914 0.5386 0.1477 0.5492 2.0403 1.0111 565
## veg_height 0.2028 0.1398 0.0527 0.1657 0.5846 1.0340 1146
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0461 0.5944 -1.0732 0.0301 1.2420
## (Intercept)-Sciurus_niger -0.5899 0.9146 -2.2371 -0.6596 1.4714
## (Intercept)-Procyon_lotor 0.3357 0.6898 -1.0258 0.3293 1.6821
## (Intercept)-Dasypus_novemcinctus -0.9135 0.5595 -2.0744 -0.8992 0.1435
## (Intercept)-Lynx_rufus 0.1231 1.0503 -1.4183 -0.0467 2.7639
## (Intercept)-Didelphis_virginiana -1.4217 0.6664 -2.8012 -1.3927 -0.2228
## (Intercept)-Sylvilagus_floridanus -0.6892 0.6260 -1.9303 -0.6878 0.5514
## (Intercept)-Meleagris_gallopavo -0.2069 0.8778 -1.6732 -0.2934 1.8264
## (Intercept)-Sciurus_carolinensis -1.4374 0.6929 -2.9549 -1.3956 -0.2441
## (Intercept)-Vulpes_vulpes -1.3160 0.9887 -3.1294 -1.3028 0.6129
## (Intercept)-Sus_scrofa -1.7997 0.9064 -3.6999 -1.7189 -0.2536
## Tree_Density-Canis_latrans -0.9657 0.5851 -2.3379 -0.9007 -0.0228
## Tree_Density-Sciurus_niger -0.8236 0.6969 -2.3967 -0.7667 0.3973
## Tree_Density-Procyon_lotor -0.5058 0.4191 -1.3636 -0.4918 0.2730
## Tree_Density-Dasypus_novemcinctus -1.3421 0.8338 -3.3889 -1.1708 -0.1949
## Tree_Density-Lynx_rufus 0.0824 0.6758 -1.0491 0.0024 1.6461
## Tree_Density-Didelphis_virginiana -0.9509 0.7237 -2.6470 -0.8513 0.1726
## Tree_Density-Sylvilagus_floridanus -1.0161 0.7047 -2.6325 -0.9284 0.1076
## Tree_Density-Meleagris_gallopavo -0.9927 0.7690 -2.7862 -0.9024 0.2777
## Tree_Density-Sciurus_carolinensis -0.8607 0.7035 -2.4239 -0.7780 0.3249
## Tree_Density-Vulpes_vulpes -0.6861 0.7287 -2.3294 -0.6451 0.6834
## Tree_Density-Sus_scrofa -0.8640 0.7234 -2.5176 -0.7770 0.3527
## Avg_Canopy_Cover-Canis_latrans 0.0233 0.4664 -0.8815 0.0284 0.9185
## Avg_Canopy_Cover-Sciurus_niger 1.0132 0.7980 -0.3893 0.9492 2.9036
## Avg_Canopy_Cover-Procyon_lotor 1.0580 0.4744 0.2015 1.0271 2.0883
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1144 0.4647 0.2742 1.0974 2.1076
## Avg_Canopy_Cover-Lynx_rufus 0.8083 0.7101 -0.4831 0.7655 2.3328
## Avg_Canopy_Cover-Didelphis_virginiana 1.4827 0.6281 0.5013 1.3832 2.9718
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.9065 0.8303 0.6673 1.7681 3.8240
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4715 0.7094 0.3131 1.3838 3.1520
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4264 0.5857 0.4658 1.3758 2.7708
## Avg_Canopy_Cover-Vulpes_vulpes 1.1178 0.6435 -0.0139 1.0726 2.5519
## Avg_Canopy_Cover-Sus_scrofa 1.2459 0.5916 0.2321 1.2015 2.5721
## Rhat ESS
## (Intercept)-Canis_latrans 1.0202 680
## (Intercept)-Sciurus_niger 1.0636 324
## (Intercept)-Procyon_lotor 1.0436 325
## (Intercept)-Dasypus_novemcinctus 1.0015 987
## (Intercept)-Lynx_rufus 1.1849 155
## (Intercept)-Didelphis_virginiana 1.0585 609
## (Intercept)-Sylvilagus_floridanus 1.0025 1058
## (Intercept)-Meleagris_gallopavo 1.0333 327
## (Intercept)-Sciurus_carolinensis 1.0544 619
## (Intercept)-Vulpes_vulpes 1.1117 243
## (Intercept)-Sus_scrofa 1.0390 352
## Tree_Density-Canis_latrans 1.0133 697
## Tree_Density-Sciurus_niger 1.0098 618
## Tree_Density-Procyon_lotor 1.0156 1516
## Tree_Density-Dasypus_novemcinctus 1.0303 411
## Tree_Density-Lynx_rufus 1.0054 433
## Tree_Density-Didelphis_virginiana 1.0612 546
## Tree_Density-Sylvilagus_floridanus 1.0031 637
## Tree_Density-Meleagris_gallopavo 1.0118 553
## Tree_Density-Sciurus_carolinensis 1.0145 551
## Tree_Density-Vulpes_vulpes 1.0214 508
## Tree_Density-Sus_scrofa 1.0185 736
## Avg_Canopy_Cover-Canis_latrans 1.0060 950
## Avg_Canopy_Cover-Sciurus_niger 1.0358 547
## Avg_Canopy_Cover-Procyon_lotor 1.0188 1188
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0122 1569
## Avg_Canopy_Cover-Lynx_rufus 1.0348 467
## Avg_Canopy_Cover-Didelphis_virginiana 1.0135 816
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0208 427
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0297 689
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0341 729
## Avg_Canopy_Cover-Vulpes_vulpes 1.0235 874
## Avg_Canopy_Cover-Sus_scrofa 1.0138 933
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7484 0.1862 -3.1232 -2.7407 -2.4146 1.0130
## (Intercept)-Sciurus_niger -4.1291 0.5667 -5.2645 -4.1353 -3.0209 1.0607
## (Intercept)-Procyon_lotor -2.3048 0.1401 -2.5921 -2.3027 -2.0458 1.0000
## (Intercept)-Dasypus_novemcinctus -1.7694 0.1610 -2.0948 -1.7684 -1.4730 0.9997
## (Intercept)-Lynx_rufus -3.7131 0.3332 -4.3983 -3.7022 -3.0846 1.1069
## (Intercept)-Didelphis_virginiana -2.6399 0.2958 -3.2336 -2.6284 -2.0732 1.0005
## (Intercept)-Sylvilagus_floridanus -3.1319 0.2682 -3.7065 -3.1201 -2.6238 1.0104
## (Intercept)-Meleagris_gallopavo -3.9520 0.4259 -4.8167 -3.9551 -3.1284 1.0319
## (Intercept)-Sciurus_carolinensis -2.7316 0.3134 -3.3496 -2.7266 -2.1330 1.0025
## (Intercept)-Vulpes_vulpes -4.0550 0.6293 -5.3902 -4.0163 -2.9350 1.0445
## (Intercept)-Sus_scrofa -3.3443 0.5508 -4.4094 -3.3451 -2.2562 1.0121
## shrub_cover-Canis_latrans -0.2953 0.2276 -0.7397 -0.2991 0.1513 1.0146
## shrub_cover-Sciurus_niger -0.4240 0.4734 -1.3572 -0.4257 0.5028 1.0076
## shrub_cover-Procyon_lotor 0.2499 0.1623 -0.0817 0.2513 0.5601 1.0001
## shrub_cover-Dasypus_novemcinctus 0.8847 0.2987 0.3211 0.8819 1.4876 1.0023
## shrub_cover-Lynx_rufus -0.2939 0.3310 -0.9258 -0.2987 0.3751 1.0718
## shrub_cover-Didelphis_virginiana 1.0153 0.3626 0.3473 0.9979 1.8026 1.0261
## shrub_cover-Sylvilagus_floridanus 0.4064 0.4061 -0.3881 0.3997 1.2277 1.0014
## shrub_cover-Meleagris_gallopavo -0.7101 0.3942 -1.5026 -0.7003 0.0337 1.0077
## shrub_cover-Sciurus_carolinensis 0.9378 0.4128 0.1340 0.9402 1.7384 1.0057
## shrub_cover-Vulpes_vulpes -0.0682 0.5807 -1.2462 -0.0602 1.0685 1.0060
## shrub_cover-Sus_scrofa 0.7075 0.7861 -0.8525 0.6864 2.3017 1.0253
## veg_height-Canis_latrans -0.5678 0.1858 -0.9569 -0.5657 -0.2158 1.0093
## veg_height-Sciurus_niger -0.0226 0.3849 -0.7713 -0.0200 0.7606 1.0093
## veg_height-Procyon_lotor 0.3430 0.1222 0.1083 0.3396 0.5917 1.0050
## veg_height-Dasypus_novemcinctus 0.2570 0.1348 -0.0024 0.2546 0.5212 1.0032
## veg_height-Lynx_rufus 0.0767 0.2431 -0.4198 0.0816 0.5431 1.0277
## veg_height-Didelphis_virginiana 0.4655 0.2428 0.0206 0.4579 0.9590 1.0010
## veg_height-Sylvilagus_floridanus 0.1579 0.2371 -0.3150 0.1575 0.6204 1.0001
## veg_height-Meleagris_gallopavo -0.2028 0.3339 -0.8732 -0.1867 0.4208 1.0128
## veg_height-Sciurus_carolinensis 0.1200 0.2157 -0.2973 0.1175 0.5539 1.0036
## veg_height-Vulpes_vulpes -0.1109 0.3043 -0.7478 -0.0981 0.4573 1.0127
## veg_height-Sus_scrofa -0.1093 0.3207 -0.7551 -0.1065 0.5073 1.0034
## ESS
## (Intercept)-Canis_latrans 875
## (Intercept)-Sciurus_niger 200
## (Intercept)-Procyon_lotor 1390
## (Intercept)-Dasypus_novemcinctus 1423
## (Intercept)-Lynx_rufus 235
## (Intercept)-Didelphis_virginiana 733
## (Intercept)-Sylvilagus_floridanus 634
## (Intercept)-Meleagris_gallopavo 231
## (Intercept)-Sciurus_carolinensis 690
## (Intercept)-Vulpes_vulpes 199
## (Intercept)-Sus_scrofa 549
## shrub_cover-Canis_latrans 773
## shrub_cover-Sciurus_niger 442
## shrub_cover-Procyon_lotor 1470
## shrub_cover-Dasypus_novemcinctus 1061
## shrub_cover-Lynx_rufus 500
## shrub_cover-Didelphis_virginiana 633
## shrub_cover-Sylvilagus_floridanus 631
## shrub_cover-Meleagris_gallopavo 353
## shrub_cover-Sciurus_carolinensis 609
## shrub_cover-Vulpes_vulpes 520
## shrub_cover-Sus_scrofa 489
## veg_height-Canis_latrans 846
## veg_height-Sciurus_niger 659
## veg_height-Procyon_lotor 1603
## veg_height-Dasypus_novemcinctus 1832
## veg_height-Lynx_rufus 688
## veg_height-Didelphis_virginiana 1013
## veg_height-Sylvilagus_floridanus 1054
## veg_height-Meleagris_gallopavo 571
## veg_height-Sciurus_carolinensis 898
## veg_height-Vulpes_vulpes 745
## veg_height-Sus_scrofa 1373
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6337
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2523 0.3775 -2.0032 -1.2432 -0.5166 1.0174 527
## Avg_Cogongrass_Cover -0.7548 0.3553 -1.4799 -0.7486 -0.0664 1.0115 410
## I(Avg_Cogongrass_Cover^2) 0.7118 0.3078 0.1357 0.7032 1.3527 1.0086 407
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6839 0.6889 0.0675 0.4846 2.5019 1.0124 524
## Avg_Cogongrass_Cover 0.4124 0.5087 0.0423 0.2634 1.7411 1.0279 603
## I(Avg_Cogongrass_Cover^2) 0.5048 0.7555 0.0449 0.2897 2.3063 1.0289 369
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5929 0.5443 0.0602 0.4171 2.0499 1.1939 146
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9726 0.2943 -3.5697 -2.9736 -2.3909 1.0177 1030
## shrub_cover 0.2010 0.2725 -0.3601 0.2060 0.7362 1.0084 1138
## veg_height 0.0618 0.1635 -0.2699 0.0613 0.3809 1.0018 1014
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7959 0.5814 0.2150 0.6416 2.2092 1.0042 703
## shrub_cover 0.5963 0.4906 0.1273 0.4714 1.8133 1.0118 628
## veg_height 0.1931 0.1459 0.0513 0.1561 0.5569 1.0297 1268
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7743 0.5611 -1.8248 -0.7980
## (Intercept)-Sciurus_niger -1.2305 0.6706 -2.5411 -1.2432
## (Intercept)-Procyon_lotor -0.6031 0.5748 -1.6728 -0.6258
## (Intercept)-Dasypus_novemcinctus -1.2969 0.5250 -2.3896 -1.2733
## (Intercept)-Lynx_rufus -1.2226 0.6370 -2.4958 -1.2226
## (Intercept)-Didelphis_virginiana -1.5823 0.5868 -2.8357 -1.5393
## (Intercept)-Sylvilagus_floridanus -1.1807 0.5598 -2.2659 -1.1751
## (Intercept)-Meleagris_gallopavo -0.7263 0.6914 -1.9618 -0.7923
## (Intercept)-Sciurus_carolinensis -1.8366 0.6244 -3.2279 -1.7767
## (Intercept)-Vulpes_vulpes -1.8057 0.8134 -3.5789 -1.7326
## (Intercept)-Sus_scrofa -1.8518 0.6927 -3.3599 -1.7890
## Avg_Cogongrass_Cover-Canis_latrans -0.4255 0.5093 -1.3924 -0.4564
## Avg_Cogongrass_Cover-Sciurus_niger -1.0417 0.6700 -2.6018 -0.9719
## Avg_Cogongrass_Cover-Procyon_lotor -0.7197 0.4901 -1.7063 -0.7090
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5179 0.4611 -1.4227 -0.5290
## Avg_Cogongrass_Cover-Lynx_rufus -0.6518 0.5440 -1.7821 -0.6393
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4441 0.5153 -1.4163 -0.4579
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1645 0.6040 -2.4921 -1.1222
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.1038 0.6687 -2.6246 -1.0301
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7106 0.5263 -1.7776 -0.6973
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7134 0.5830 -1.8837 -0.7102
## Avg_Cogongrass_Cover-Sus_scrofa -0.9318 0.6398 -2.4058 -0.8748
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2912 0.7288 0.2960 1.1444
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.1743 0.6383 -1.3065 0.2352
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.0972 0.6132 0.2244 0.9969
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7073 0.3621 0.0503 0.6861
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1597 0.5130 0.3401 1.0928
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5394 0.4056 -0.1955 0.5217
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7020 0.4367 -0.0731 0.6679
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3208 0.6222 -0.9892 0.3380
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8755 0.3971 0.1570 0.8507
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8043 0.4504 0.0074 0.7618
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2562 0.6191 -1.1814 0.3300
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3916 1.0125 851
## (Intercept)-Sciurus_niger 0.1234 1.0178 422
## (Intercept)-Procyon_lotor 0.5716 1.0018 587
## (Intercept)-Dasypus_novemcinctus -0.3071 1.0176 1241
## (Intercept)-Lynx_rufus 0.0347 1.0345 667
## (Intercept)-Didelphis_virginiana -0.5305 1.0069 916
## (Intercept)-Sylvilagus_floridanus -0.0642 1.0058 951
## (Intercept)-Meleagris_gallopavo 0.7752 1.0060 465
## (Intercept)-Sciurus_carolinensis -0.7830 1.0114 605
## (Intercept)-Vulpes_vulpes -0.3897 1.0179 385
## (Intercept)-Sus_scrofa -0.6993 1.0665 583
## Avg_Cogongrass_Cover-Canis_latrans 0.6426 1.0047 1030
## Avg_Cogongrass_Cover-Sciurus_niger 0.0697 1.0140 478
## Avg_Cogongrass_Cover-Procyon_lotor 0.2442 1.0022 1189
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4448 1.0066 917
## Avg_Cogongrass_Cover-Lynx_rufus 0.4060 1.0082 765
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6340 1.0112 947
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1337 1.0078 610
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0145 1.0018 455
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3192 1.0103 732
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4354 1.0088 744
## Avg_Cogongrass_Cover-Sus_scrofa 0.1669 1.0201 656
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0963 1.0055 368
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.2657 1.0124 299
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6961 1.0153 394
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4712 1.0062 879
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3498 1.0106 561
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4300 1.0328 721
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6550 1.0209 618
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.5961 1.0126 339
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7696 1.0236 668
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.8205 1.0018 551
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2876 1.0236 389
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7407 0.1796 -3.1017 -2.7371 -2.3969 0.9999
## (Intercept)-Sciurus_niger -3.7914 0.5680 -4.9768 -3.7545 -2.7836 1.0392
## (Intercept)-Procyon_lotor -2.3143 0.1473 -2.6164 -2.3100 -2.0391 1.0097
## (Intercept)-Dasypus_novemcinctus -1.7662 0.1632 -2.0999 -1.7610 -1.4623 1.0038
## (Intercept)-Lynx_rufus -3.5073 0.3285 -4.1960 -3.4964 -2.9070 1.0766
## (Intercept)-Didelphis_virginiana -2.6037 0.2897 -3.1982 -2.5879 -2.0621 1.0019
## (Intercept)-Sylvilagus_floridanus -3.1392 0.2763 -3.7090 -3.1274 -2.6193 1.0082
## (Intercept)-Meleagris_gallopavo -3.8578 0.4758 -4.8210 -3.8537 -2.9128 1.0070
## (Intercept)-Sciurus_carolinensis -2.6825 0.3041 -3.3073 -2.6709 -2.1160 1.0063
## (Intercept)-Vulpes_vulpes -3.9068 0.6262 -5.2480 -3.8584 -2.7996 1.0095
## (Intercept)-Sus_scrofa -3.2678 0.5351 -4.3589 -3.2676 -2.2243 1.0160
## shrub_cover-Canis_latrans -0.2534 0.2210 -0.6884 -0.2525 0.1823 1.0152
## shrub_cover-Sciurus_niger -0.2872 0.4869 -1.3450 -0.2760 0.6644 1.0088
## shrub_cover-Procyon_lotor 0.2372 0.1717 -0.1098 0.2393 0.5660 1.0002
## shrub_cover-Dasypus_novemcinctus 0.8541 0.2949 0.3021 0.8499 1.4455 1.0042
## shrub_cover-Lynx_rufus -0.1876 0.3619 -0.9013 -0.1898 0.5281 1.0167
## shrub_cover-Didelphis_virginiana 0.9687 0.3706 0.3112 0.9513 1.7579 1.0105
## shrub_cover-Sylvilagus_floridanus 0.2811 0.4083 -0.4958 0.2641 1.1222 1.0539
## shrub_cover-Meleagris_gallopavo -0.6587 0.4052 -1.4894 -0.6567 0.1542 1.0101
## shrub_cover-Sciurus_carolinensis 0.8488 0.4053 0.0653 0.8444 1.6368 1.0215
## shrub_cover-Vulpes_vulpes -0.0510 0.5667 -1.1620 -0.0474 1.0879 1.0043
## shrub_cover-Sus_scrofa 0.5477 0.7371 -0.9767 0.5480 1.9446 1.0276
## veg_height-Canis_latrans -0.5574 0.1858 -0.9468 -0.5508 -0.2163 1.0146
## veg_height-Sciurus_niger 0.1144 0.4157 -0.6555 0.0988 0.9683 1.0047
## veg_height-Procyon_lotor 0.3422 0.1227 0.1021 0.3438 0.5848 1.0121
## veg_height-Dasypus_novemcinctus 0.2553 0.1354 -0.0066 0.2558 0.5247 1.0023
## veg_height-Lynx_rufus 0.0839 0.2390 -0.3879 0.0820 0.5405 1.0117
## veg_height-Didelphis_virginiana 0.4150 0.2477 -0.0387 0.4058 0.9406 1.0046
## veg_height-Sylvilagus_floridanus 0.1644 0.2355 -0.2919 0.1652 0.6315 1.0126
## veg_height-Meleagris_gallopavo -0.0662 0.3701 -0.8248 -0.0637 0.6692 1.0060
## veg_height-Sciurus_carolinensis 0.1080 0.2095 -0.2971 0.1107 0.5120 1.0031
## veg_height-Vulpes_vulpes -0.0715 0.3056 -0.6869 -0.0550 0.4991 1.0020
## veg_height-Sus_scrofa -0.0819 0.3223 -0.7471 -0.0688 0.5230 1.0015
## ESS
## (Intercept)-Canis_latrans 907
## (Intercept)-Sciurus_niger 231
## (Intercept)-Procyon_lotor 1138
## (Intercept)-Dasypus_novemcinctus 1466
## (Intercept)-Lynx_rufus 386
## (Intercept)-Didelphis_virginiana 711
## (Intercept)-Sylvilagus_floridanus 610
## (Intercept)-Meleagris_gallopavo 194
## (Intercept)-Sciurus_carolinensis 751
## (Intercept)-Vulpes_vulpes 216
## (Intercept)-Sus_scrofa 549
## shrub_cover-Canis_latrans 727
## shrub_cover-Sciurus_niger 492
## shrub_cover-Procyon_lotor 1193
## shrub_cover-Dasypus_novemcinctus 1292
## shrub_cover-Lynx_rufus 531
## shrub_cover-Didelphis_virginiana 655
## shrub_cover-Sylvilagus_floridanus 593
## shrub_cover-Meleagris_gallopavo 226
## shrub_cover-Sciurus_carolinensis 711
## shrub_cover-Vulpes_vulpes 584
## shrub_cover-Sus_scrofa 782
## veg_height-Canis_latrans 763
## veg_height-Sciurus_niger 651
## veg_height-Procyon_lotor 1357
## veg_height-Dasypus_novemcinctus 1925
## veg_height-Lynx_rufus 891
## veg_height-Didelphis_virginiana 934
## veg_height-Sylvilagus_floridanus 890
## veg_height-Meleagris_gallopavo 463
## veg_height-Sciurus_carolinensis 1220
## veg_height-Vulpes_vulpes 606
## veg_height-Sus_scrofa 1229
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7218
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0159 0.6998 -3.4705 -1.9951 -0.7198 1.0537 234
## Cogon_Patch_Size -0.0788 0.6702 -1.4201 -0.0767 1.2655 1.0392 247
## Veg_shannon_index 1.0346 0.4726 0.1518 1.0205 1.9847 1.0445 278
## total_shrub_cover -1.2953 0.6532 -2.7140 -1.2608 -0.0945 1.0647 220
## Avg_Cogongrass_Cover -0.0632 0.9241 -1.7009 -0.0988 1.8854 1.0590 123
## Tree_Density -2.2323 0.8024 -3.9043 -2.1906 -0.7390 1.1593 181
## Avg_Canopy_Cover 2.0419 0.7048 0.7837 1.9851 3.5243 1.0338 212
## I(Avg_Cogongrass_Cover^2) 1.3107 0.6234 0.0706 1.3075 2.5446 1.0177 184
## avg_veg_height -0.1886 0.5534 -1.3822 -0.1659 0.8401 1.0803 171
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3920 3.5418 0.0701 1.2808 11.6968 1.0822 148
## Cogon_Patch_Size 2.2046 2.8593 0.0728 1.2202 10.2399 1.0115 263
## Veg_shannon_index 0.6947 0.9079 0.0460 0.3715 3.2697 1.0145 375
## total_shrub_cover 2.0551 2.4732 0.0909 1.2988 8.8111 1.1880 168
## Avg_Cogongrass_Cover 1.5201 2.6883 0.0513 0.5572 8.8239 1.0927 236
## Tree_Density 2.4179 3.5131 0.0746 1.2458 12.1464 1.0446 204
## Avg_Canopy_Cover 3.2630 3.9297 0.1575 2.0511 12.9935 1.0691 253
## I(Avg_Cogongrass_Cover^2) 1.8426 2.4590 0.0832 1.0250 8.6045 1.3457 186
## avg_veg_height 0.8636 1.5738 0.0443 0.3677 4.9672 1.0215 216
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5663 1.7372 0.0636 0.9573 6.4189 1.0259 77
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -3.1021 0.3264 -3.7685 -3.0984 -2.4711 1.0181 569
## shrub_cover 0.4302 0.3090 -0.1892 0.4279 1.0605 1.0038 579
## veg_height 0.0694 0.1691 -0.2789 0.0702 0.3968 1.0466 647
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9787 0.6839 0.2663 0.8053 2.7407 1.0216 577
## shrub_cover 0.7431 0.5648 0.1579 0.5972 2.1845 1.0370 481
## veg_height 0.2076 0.1621 0.0520 0.1702 0.5743 1.0087 977
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4451 1.0170 -3.3368 -1.5099
## (Intercept)-Sciurus_niger -1.4768 1.2638 -3.6141 -1.6267
## (Intercept)-Procyon_lotor -1.1108 0.9952 -2.9463 -1.1240
## (Intercept)-Dasypus_novemcinctus -2.2467 0.9030 -4.1878 -2.1807
## (Intercept)-Lynx_rufus -1.5153 1.3639 -3.9030 -1.6294
## (Intercept)-Didelphis_virginiana -2.8951 1.1930 -5.6535 -2.7300
## (Intercept)-Sylvilagus_floridanus -2.0004 1.0763 -4.2647 -1.9813
## (Intercept)-Meleagris_gallopavo -1.8782 1.1900 -4.3333 -1.8716
## (Intercept)-Sciurus_carolinensis -3.0931 1.2875 -6.1208 -2.9311
## (Intercept)-Vulpes_vulpes -2.9809 1.5462 -6.7464 -2.7096
## (Intercept)-Sus_scrofa -3.2498 1.6296 -7.2769 -2.9151
## Cogon_Patch_Size-Canis_latrans 1.2129 1.1649 -0.6077 1.0716
## Cogon_Patch_Size-Sciurus_niger -0.9075 1.5594 -4.7569 -0.6268
## Cogon_Patch_Size-Procyon_lotor -0.4827 0.7547 -2.1022 -0.4569
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0335 0.7981 -1.5438 0.0258
## Cogon_Patch_Size-Lynx_rufus -0.2156 1.2463 -2.7865 -0.2013
## Cogon_Patch_Size-Didelphis_virginiana 1.1934 1.0350 -0.5347 1.1032
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0609 1.4033 -4.5068 -0.8420
## Cogon_Patch_Size-Meleagris_gallopavo 0.3614 1.3034 -1.7192 0.2059
## Cogon_Patch_Size-Sciurus_carolinensis -0.6768 1.0897 -3.1117 -0.5658
## Cogon_Patch_Size-Vulpes_vulpes -0.2454 1.4592 -3.4252 -0.2402
## Cogon_Patch_Size-Sus_scrofa -0.3931 1.2836 -3.2422 -0.3059
## Veg_shannon_index-Canis_latrans 1.3250 0.6947 0.1711 1.2578
## Veg_shannon_index-Sciurus_niger 1.0998 0.9022 -0.5656 1.0422
## Veg_shannon_index-Procyon_lotor 1.2257 0.6212 0.1187 1.1875
## Veg_shannon_index-Dasypus_novemcinctus 0.6590 0.6114 -0.6340 0.6773
## Veg_shannon_index-Lynx_rufus 1.0839 0.8358 -0.5064 1.0317
## Veg_shannon_index-Didelphis_virginiana 1.2218 0.7113 -0.0444 1.1581
## Veg_shannon_index-Sylvilagus_floridanus 1.0768 0.7110 -0.2141 1.0153
## Veg_shannon_index-Meleagris_gallopavo 1.2955 0.7861 -0.0425 1.2169
## Veg_shannon_index-Sciurus_carolinensis 0.4763 0.8018 -1.3705 0.5669
## Veg_shannon_index-Vulpes_vulpes 0.7624 0.8461 -1.0453 0.7968
## Veg_shannon_index-Sus_scrofa 1.3603 0.8458 -0.0843 1.2754
## total_shrub_cover-Canis_latrans 0.0740 0.8084 -1.3436 0.0102
## total_shrub_cover-Sciurus_niger -1.6577 1.2501 -4.4964 -1.4954
## total_shrub_cover-Procyon_lotor -1.5734 0.7379 -3.2108 -1.5145
## total_shrub_cover-Dasypus_novemcinctus -0.5867 0.8609 -2.4625 -0.5294
## total_shrub_cover-Lynx_rufus -1.8617 1.2544 -4.8263 -1.6950
## total_shrub_cover-Didelphis_virginiana -1.5800 1.1010 -4.1605 -1.4120
## total_shrub_cover-Sylvilagus_floridanus -1.2688 1.2156 -3.8558 -1.1787
## total_shrub_cover-Meleagris_gallopavo -2.6310 1.4757 -6.1352 -2.3878
## total_shrub_cover-Sciurus_carolinensis -1.3103 1.3104 -4.4778 -1.1368
## total_shrub_cover-Vulpes_vulpes -1.6438 1.4222 -5.1227 -1.4458
## total_shrub_cover-Sus_scrofa -1.2312 1.3656 -4.4406 -1.1217
## Avg_Cogongrass_Cover-Canis_latrans 0.1474 1.2132 -2.1066 0.0716
## Avg_Cogongrass_Cover-Sciurus_niger -0.8018 1.6498 -5.0053 -0.6026
## Avg_Cogongrass_Cover-Procyon_lotor -0.1107 1.1656 -2.3769 -0.1028
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5892 1.3083 -1.6368 0.4322
## Avg_Cogongrass_Cover-Lynx_rufus 0.0897 1.3078 -2.3429 0.0233
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2179 1.3351 -2.1198 0.1232
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6889 1.3200 -3.6982 -0.5853
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3430 1.3878 -3.3253 -0.3126
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0193 1.2379 -2.3063 -0.0284
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2819 1.4069 -2.1354 0.1467
## Avg_Cogongrass_Cover-Sus_scrofa -0.1574 1.3411 -2.8092 -0.1452
## Tree_Density-Canis_latrans -2.9750 1.3293 -6.1079 -2.7915
## Tree_Density-Sciurus_niger -2.0917 1.4040 -4.8135 -2.1017
## Tree_Density-Procyon_lotor -2.2562 1.0256 -4.5205 -2.1894
## Tree_Density-Dasypus_novemcinctus -3.7843 1.7621 -8.1902 -3.4139
## Tree_Density-Lynx_rufus -1.1188 1.4129 -3.6521 -1.1883
## Tree_Density-Didelphis_virginiana -2.2589 1.2121 -4.8653 -2.1887
## Tree_Density-Sylvilagus_floridanus -2.6407 1.3695 -5.9549 -2.5014
## Tree_Density-Meleagris_gallopavo -2.3808 1.4517 -5.6391 -2.3273
## Tree_Density-Sciurus_carolinensis -2.3485 1.3760 -5.2877 -2.2776
## Tree_Density-Vulpes_vulpes -2.0539 1.5989 -5.1623 -2.0778
## Tree_Density-Sus_scrofa -2.3896 1.4376 -5.6826 -2.2750
## Avg_Canopy_Cover-Canis_latrans 0.1543 0.6712 -1.1692 0.1612
## Avg_Canopy_Cover-Sciurus_niger 1.9593 1.7908 -1.1366 1.7678
## Avg_Canopy_Cover-Procyon_lotor 1.6430 0.7848 0.2301 1.5864
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2508 0.9088 0.7881 2.1457
## Avg_Canopy_Cover-Lynx_rufus 1.2143 1.3353 -1.1894 1.1500
## Avg_Canopy_Cover-Didelphis_virginiana 3.2449 1.4968 1.1347 2.9753
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.8286 1.7605 1.1408 3.5629
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5443 1.3955 0.5392 2.2739
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1362 1.5250 1.0405 2.8499
## Avg_Canopy_Cover-Vulpes_vulpes 2.6090 1.5910 0.3502 2.3032
## Avg_Canopy_Cover-Sus_scrofa 2.2814 1.1886 0.4572 2.1031
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1582 1.0498 0.6503 1.9803
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.6492 1.2945 -2.1411 0.7396
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1753 0.9349 0.6885 2.0314
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3623 0.7406 0.0288 1.3259
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5351 1.2490 0.7257 2.3364
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8935 0.7674 -0.6171 0.8779
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0716 0.8359 -0.4668 1.0145
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4311 1.4637 -2.7364 0.4859
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5300 0.8046 0.1367 1.4659
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.7707 0.9609 0.2411 1.6779
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.7297 1.3664 -2.7095 0.8384
## avg_veg_height-Canis_latrans -0.2886 0.6712 -1.6916 -0.2540
## avg_veg_height-Sciurus_niger -0.6769 1.2118 -3.6850 -0.4565
## avg_veg_height-Procyon_lotor 0.0091 0.6590 -1.3153 0.0150
## avg_veg_height-Dasypus_novemcinctus 0.2604 0.7060 -1.0516 0.2508
## avg_veg_height-Lynx_rufus -0.6046 1.0574 -3.2800 -0.4519
## avg_veg_height-Didelphis_virginiana -0.4007 0.8615 -2.4097 -0.3073
## avg_veg_height-Sylvilagus_floridanus -0.3255 0.8631 -2.1657 -0.2586
## avg_veg_height-Meleagris_gallopavo -0.2454 0.9623 -2.3987 -0.2036
## avg_veg_height-Sciurus_carolinensis 0.3425 0.8586 -1.0863 0.2621
## avg_veg_height-Vulpes_vulpes -0.1759 0.9337 -2.1289 -0.1253
## avg_veg_height-Sus_scrofa -0.0728 0.8686 -1.7925 -0.0898
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7012 1.0099 364
## (Intercept)-Sciurus_niger 1.4543 1.0125 200
## (Intercept)-Procyon_lotor 0.9432 1.0039 190
## (Intercept)-Dasypus_novemcinctus -0.6281 1.0752 337
## (Intercept)-Lynx_rufus 1.4762 1.0139 211
## (Intercept)-Didelphis_virginiana -0.9961 1.0774 250
## (Intercept)-Sylvilagus_floridanus 0.1022 1.0115 385
## (Intercept)-Meleagris_gallopavo 0.5753 1.0682 258
## (Intercept)-Sciurus_carolinensis -0.9976 1.0596 194
## (Intercept)-Vulpes_vulpes -0.5722 1.1781 173
## (Intercept)-Sus_scrofa -0.9209 1.0906 130
## Cogon_Patch_Size-Canis_latrans 3.9623 1.0186 385
## Cogon_Patch_Size-Sciurus_niger 1.5538 1.0025 190
## Cogon_Patch_Size-Procyon_lotor 0.9765 1.0366 314
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6893 1.0768 363
## Cogon_Patch_Size-Lynx_rufus 2.2988 1.0082 340
## Cogon_Patch_Size-Didelphis_virginiana 3.5202 1.0187 315
## Cogon_Patch_Size-Sylvilagus_floridanus 1.0643 1.0111 262
## Cogon_Patch_Size-Meleagris_gallopavo 3.5145 1.0450 301
## Cogon_Patch_Size-Sciurus_carolinensis 1.1923 1.0188 426
## Cogon_Patch_Size-Vulpes_vulpes 2.7439 1.0216 282
## Cogon_Patch_Size-Sus_scrofa 1.9995 1.0111 452
## Veg_shannon_index-Canis_latrans 2.8839 1.0271 352
## Veg_shannon_index-Sciurus_niger 3.0823 1.0467 467
## Veg_shannon_index-Procyon_lotor 2.5341 1.0082 271
## Veg_shannon_index-Dasypus_novemcinctus 1.8195 1.0162 477
## Veg_shannon_index-Lynx_rufus 2.9208 1.0948 442
## Veg_shannon_index-Didelphis_virginiana 2.7991 1.0392 350
## Veg_shannon_index-Sylvilagus_floridanus 2.6126 1.0175 515
## Veg_shannon_index-Meleagris_gallopavo 3.1170 1.0174 507
## Veg_shannon_index-Sciurus_carolinensis 1.8719 1.0039 415
## Veg_shannon_index-Vulpes_vulpes 2.3080 1.0052 430
## Veg_shannon_index-Sus_scrofa 3.2869 1.0113 368
## total_shrub_cover-Canis_latrans 1.9061 1.0347 447
## total_shrub_cover-Sciurus_niger 0.4786 1.0296 216
## total_shrub_cover-Procyon_lotor -0.2996 1.0188 528
## total_shrub_cover-Dasypus_novemcinctus 0.9443 1.0561 451
## total_shrub_cover-Lynx_rufus 0.2145 1.0394 194
## total_shrub_cover-Didelphis_virginiana 0.1612 1.0442 263
## total_shrub_cover-Sylvilagus_floridanus 0.9110 1.1088 247
## total_shrub_cover-Meleagris_gallopavo -0.4154 1.2391 172
## total_shrub_cover-Sciurus_carolinensis 0.7642 1.0387 206
## total_shrub_cover-Vulpes_vulpes 0.6905 1.0330 214
## total_shrub_cover-Sus_scrofa 1.1312 1.1336 232
## Avg_Cogongrass_Cover-Canis_latrans 2.6754 1.0525 194
## Avg_Cogongrass_Cover-Sciurus_niger 1.8339 1.0519 176
## Avg_Cogongrass_Cover-Procyon_lotor 2.2806 1.0365 204
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5064 1.1038 182
## Avg_Cogongrass_Cover-Lynx_rufus 2.7725 1.0814 207
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.1680 1.0768 209
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6737 1.0156 239
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2890 1.0089 250
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6490 1.0499 250
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3817 1.0578 237
## Avg_Cogongrass_Cover-Sus_scrofa 2.5381 1.0271 238
## Tree_Density-Canis_latrans -0.9126 1.0469 257
## Tree_Density-Sciurus_niger 0.6907 1.0962 345
## Tree_Density-Procyon_lotor -0.4256 1.0444 273
## Tree_Density-Dasypus_novemcinctus -1.4656 1.0303 159
## Tree_Density-Lynx_rufus 1.7700 1.1831 254
## Tree_Density-Didelphis_virginiana -0.0015 1.0342 314
## Tree_Density-Sylvilagus_floridanus -0.2631 1.0244 372
## Tree_Density-Meleagris_gallopavo 0.4186 1.0805 195
## Tree_Density-Sciurus_carolinensis 0.2603 1.0269 287
## Tree_Density-Vulpes_vulpes 1.3942 1.1155 298
## Tree_Density-Sus_scrofa 0.1194 1.0525 305
## Avg_Canopy_Cover-Canis_latrans 1.4238 1.0023 630
## Avg_Canopy_Cover-Sciurus_niger 6.0285 1.0965 192
## Avg_Canopy_Cover-Procyon_lotor 3.2983 1.0070 436
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.3598 1.0058 414
## Avg_Canopy_Cover-Lynx_rufus 4.1211 1.0525 234
## Avg_Canopy_Cover-Didelphis_virginiana 6.7784 1.0121 165
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.7337 1.0325 185
## Avg_Canopy_Cover-Meleagris_gallopavo 5.9138 1.0802 196
## Avg_Canopy_Cover-Sciurus_carolinensis 7.0602 1.0171 177
## Avg_Canopy_Cover-Vulpes_vulpes 6.7000 1.0283 228
## Avg_Canopy_Cover-Sus_scrofa 5.0599 1.0774 444
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7677 1.1117 238
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 3.0416 1.0992 190
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.2819 1.0355 284
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8942 1.0251 552
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6173 1.2309 167
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.4288 1.0640 255
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.8618 1.0320 296
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 3.2267 1.1659 109
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.2817 1.0240 479
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.9401 1.0467 352
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 3.2267 1.1417 176
## avg_veg_height-Canis_latrans 0.9569 1.0263 318
## avg_veg_height-Sciurus_niger 1.0356 1.0920 138
## avg_veg_height-Procyon_lotor 1.2625 1.0412 419
## avg_veg_height-Dasypus_novemcinctus 1.7512 1.0294 468
## avg_veg_height-Lynx_rufus 1.0474 1.0341 257
## avg_veg_height-Didelphis_virginiana 1.0467 1.0237 260
## avg_veg_height-Sylvilagus_floridanus 1.1754 1.0135 298
## avg_veg_height-Meleagris_gallopavo 1.5751 1.1188 222
## avg_veg_height-Sciurus_carolinensis 2.2684 1.0186 466
## avg_veg_height-Vulpes_vulpes 1.5334 1.0476 352
## avg_veg_height-Sus_scrofa 1.8476 1.0383 333
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7276 0.1776 -3.0962 -2.7267 -2.3849 1.0065
## (Intercept)-Sciurus_niger -4.2168 0.5241 -5.2460 -4.2330 -3.1536 1.0136
## (Intercept)-Procyon_lotor -2.3029 0.1360 -2.5742 -2.3010 -2.0422 1.0003
## (Intercept)-Dasypus_novemcinctus -1.8191 0.1679 -2.1563 -1.8160 -1.5024 1.0099
## (Intercept)-Lynx_rufus -3.6817 0.3388 -4.3428 -3.6757 -3.0469 1.0188
## (Intercept)-Didelphis_virginiana -2.7210 0.3099 -3.3652 -2.7039 -2.1553 1.0154
## (Intercept)-Sylvilagus_floridanus -3.2108 0.2547 -3.7423 -3.1973 -2.7358 1.0046
## (Intercept)-Meleagris_gallopavo -3.7345 0.4729 -4.6725 -3.7369 -2.7610 1.1417
## (Intercept)-Sciurus_carolinensis -2.8607 0.3226 -3.4903 -2.8651 -2.2212 1.0065
## (Intercept)-Vulpes_vulpes -4.2856 0.6015 -5.5581 -4.2742 -3.1677 1.0282
## (Intercept)-Sus_scrofa -3.6930 0.5676 -4.7805 -3.7069 -2.5224 1.1917
## shrub_cover-Canis_latrans -0.2999 0.2390 -0.7663 -0.3068 0.1705 1.0071
## shrub_cover-Sciurus_niger -0.1685 0.4950 -1.1221 -0.1749 0.8106 1.0224
## shrub_cover-Procyon_lotor 0.2774 0.1574 -0.0404 0.2798 0.5831 1.0007
## shrub_cover-Dasypus_novemcinctus 1.0295 0.3197 0.3974 1.0364 1.6494 1.0372
## shrub_cover-Lynx_rufus -0.0202 0.3829 -0.7965 -0.0073 0.7166 1.0133
## shrub_cover-Didelphis_virginiana 1.1754 0.4195 0.4404 1.1409 2.0689 1.0207
## shrub_cover-Sylvilagus_floridanus 0.6130 0.4067 -0.1886 0.6034 1.4084 1.0349
## shrub_cover-Meleagris_gallopavo -0.4831 0.4396 -1.3218 -0.4874 0.3855 1.0787
## shrub_cover-Sciurus_carolinensis 1.1529 0.4034 0.3288 1.1603 1.9206 1.0000
## shrub_cover-Vulpes_vulpes 0.2987 0.5963 -0.8479 0.2963 1.4888 1.0198
## shrub_cover-Sus_scrofa 1.2006 0.7903 -0.3592 1.1800 2.9390 1.1028
## veg_height-Canis_latrans -0.5430 0.1863 -0.9149 -0.5441 -0.1850 1.0067
## veg_height-Sciurus_niger 0.1217 0.3917 -0.6146 0.1129 0.9661 1.0694
## veg_height-Procyon_lotor 0.3572 0.1215 0.1119 0.3586 0.5973 1.0021
## veg_height-Dasypus_novemcinctus 0.2783 0.1378 0.0088 0.2775 0.5498 1.0084
## veg_height-Lynx_rufus 0.1603 0.2307 -0.3186 0.1659 0.5993 1.0129
## veg_height-Didelphis_virginiana 0.4856 0.2510 0.0069 0.4785 0.9919 1.0022
## veg_height-Sylvilagus_floridanus 0.1538 0.2508 -0.3398 0.1549 0.6412 1.0106
## veg_height-Meleagris_gallopavo -0.1304 0.3831 -0.8900 -0.1319 0.6454 1.1415
## veg_height-Sciurus_carolinensis 0.1756 0.2221 -0.2504 0.1709 0.6354 1.0205
## veg_height-Vulpes_vulpes -0.1491 0.3428 -0.8540 -0.1267 0.4688 1.0018
## veg_height-Sus_scrofa -0.1589 0.3300 -0.8552 -0.1400 0.4434 1.0305
## ESS
## (Intercept)-Canis_latrans 973
## (Intercept)-Sciurus_niger 182
## (Intercept)-Procyon_lotor 1339
## (Intercept)-Dasypus_novemcinctus 975
## (Intercept)-Lynx_rufus 280
## (Intercept)-Didelphis_virginiana 323
## (Intercept)-Sylvilagus_floridanus 628
## (Intercept)-Meleagris_gallopavo 190
## (Intercept)-Sciurus_carolinensis 416
## (Intercept)-Vulpes_vulpes 184
## (Intercept)-Sus_scrofa 117
## shrub_cover-Canis_latrans 630
## shrub_cover-Sciurus_niger 287
## shrub_cover-Procyon_lotor 1367
## shrub_cover-Dasypus_novemcinctus 602
## shrub_cover-Lynx_rufus 382
## shrub_cover-Didelphis_virginiana 328
## shrub_cover-Sylvilagus_floridanus 393
## shrub_cover-Meleagris_gallopavo 321
## shrub_cover-Sciurus_carolinensis 438
## shrub_cover-Vulpes_vulpes 334
## shrub_cover-Sus_scrofa 174
## veg_height-Canis_latrans 543
## veg_height-Sciurus_niger 315
## veg_height-Procyon_lotor 1225
## veg_height-Dasypus_novemcinctus 1596
## veg_height-Lynx_rufus 601
## veg_height-Didelphis_virginiana 735
## veg_height-Sylvilagus_floridanus 693
## veg_height-Meleagris_gallopavo 335
## veg_height-Sciurus_carolinensis 962
## veg_height-Vulpes_vulpes 514
## veg_height-Sus_scrofa 627
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.792
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6304 0.3242 -1.2606 -0.6346 0.032 1.0028 1099
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8353 0.6395 0.1787 0.6608 2.4638 1.0104 1426
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6993 0.2751 -3.2558 -2.7032 -2.1584 1.0021 1627
## week 0.1404 0.1956 -0.2424 0.1444 0.5131 1.0135 854
## I(week^2) -0.2184 0.1057 -0.4383 -0.2149 -0.0298 1.0105 786
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7224 0.5614 0.2037 0.5832 2.0348 1.0163 1187
## week 0.2082 0.2042 0.0363 0.1536 0.7316 1.0439 656
## I(week^2) 0.0644 0.0544 0.0177 0.0488 0.2136 1.0137 327
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1339 0.3798 -0.5755 0.1259 0.9109 1.0010
## (Intercept)-Sciurus_niger -0.8662 0.6021 -1.9520 -0.8862 0.4266 1.0178
## (Intercept)-Procyon_lotor 0.4920 0.3786 -0.2252 0.4863 1.2641 1.0003
## (Intercept)-Dasypus_novemcinctus -0.6547 0.3380 -1.3175 -0.6506 -0.0140 1.0072
## (Intercept)-Lynx_rufus -0.0832 0.5599 -0.9961 -0.1481 1.1991 1.0438
## (Intercept)-Didelphis_virginiana -1.2513 0.4065 -2.0967 -1.2240 -0.5043 1.0021
## (Intercept)-Sylvilagus_floridanus -0.4408 0.4357 -1.2738 -0.4455 0.4566 1.0049
## (Intercept)-Meleagris_gallopavo -0.4588 0.5005 -1.3652 -0.4790 0.6185 1.0104
## (Intercept)-Sciurus_carolinensis -1.2201 0.4167 -2.0795 -1.2123 -0.4419 0.9997
## (Intercept)-Vulpes_vulpes -1.2171 0.7173 -2.5476 -1.2579 0.3321 1.0006
## (Intercept)-Sus_scrofa -1.5948 0.5494 -2.6764 -1.5803 -0.5278 1.0022
## ESS
## (Intercept)-Canis_latrans 2144
## (Intercept)-Sciurus_niger 511
## (Intercept)-Procyon_lotor 2269
## (Intercept)-Dasypus_novemcinctus 3116
## (Intercept)-Lynx_rufus 489
## (Intercept)-Didelphis_virginiana 2138
## (Intercept)-Sylvilagus_floridanus 1281
## (Intercept)-Meleagris_gallopavo 761
## (Intercept)-Sciurus_carolinensis 2114
## (Intercept)-Vulpes_vulpes 290
## (Intercept)-Sus_scrofa 732
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4624 0.1835 -2.8356 -2.4613 -2.1217 1.0027
## (Intercept)-Sciurus_niger -3.5915 0.4987 -4.6054 -3.5594 -2.6990 1.0145
## (Intercept)-Procyon_lotor -2.1761 0.1464 -2.4602 -2.1739 -1.8901 1.0028
## (Intercept)-Dasypus_novemcinctus -1.4995 0.1567 -1.8191 -1.4969 -1.2016 1.0066
## (Intercept)-Lynx_rufus -3.3062 0.3224 -3.9721 -3.2980 -2.7242 1.0304
## (Intercept)-Didelphis_virginiana -2.2092 0.2637 -2.7461 -2.2044 -1.7049 1.0102
## (Intercept)-Sylvilagus_floridanus -3.0559 0.2972 -3.6825 -3.0489 -2.4826 1.0067
## (Intercept)-Meleagris_gallopavo -3.2141 0.3424 -3.9347 -3.1989 -2.6196 1.0147
## (Intercept)-Sciurus_carolinensis -2.3652 0.2791 -2.9334 -2.3547 -1.8386 1.0023
## (Intercept)-Vulpes_vulpes -3.5960 0.6134 -4.9380 -3.5509 -2.5469 1.0087
## (Intercept)-Sus_scrofa -2.9341 0.4811 -4.0126 -2.9055 -2.0963 1.0014
## week-Canis_latrans 0.4412 0.2485 -0.0210 0.4392 0.9506 1.0003
## week-Sciurus_niger -0.2384 0.4336 -1.2091 -0.1923 0.4831 1.0167
## week-Procyon_lotor 0.1475 0.1938 -0.2236 0.1483 0.5243 1.0045
## week-Dasypus_novemcinctus 0.0473 0.2098 -0.3713 0.0502 0.4549 1.0057
## week-Lynx_rufus 0.2456 0.3073 -0.3495 0.2472 0.8691 1.0036
## week-Didelphis_virginiana -0.0031 0.3177 -0.6541 0.0119 0.5821 1.0057
## week-Sylvilagus_floridanus 0.0276 0.2961 -0.5518 0.0300 0.6068 1.0038
## week-Meleagris_gallopavo -0.1424 0.3545 -0.8873 -0.1191 0.4823 1.0032
## week-Sciurus_carolinensis 0.5233 0.3330 -0.0543 0.5000 1.2432 1.0000
## week-Vulpes_vulpes 0.0959 0.3883 -0.7337 0.1151 0.8192 1.0036
## week-Sus_scrofa 0.4115 0.3836 -0.2628 0.3844 1.2741 1.0117
## I(week^2)-Canis_latrans -0.1921 0.1062 -0.4096 -0.1915 0.0061 1.0066
## I(week^2)-Sciurus_niger -0.2428 0.2093 -0.6913 -0.2345 0.1314 1.0039
## I(week^2)-Procyon_lotor -0.1087 0.0861 -0.2777 -0.1084 0.0592 1.0107
## I(week^2)-Dasypus_novemcinctus -0.1487 0.1003 -0.3510 -0.1460 0.0429 1.0140
## I(week^2)-Lynx_rufus -0.1970 0.1449 -0.4899 -0.1903 0.0677 1.0279
## I(week^2)-Didelphis_virginiana -0.3434 0.1984 -0.7908 -0.3268 -0.0043 1.0065
## I(week^2)-Sylvilagus_floridanus -0.1524 0.1505 -0.4667 -0.1416 0.1273 1.0244
## I(week^2)-Meleagris_gallopavo -0.3289 0.2115 -0.8230 -0.3074 0.0256 1.0036
## I(week^2)-Sciurus_carolinensis -0.1868 0.1356 -0.4602 -0.1847 0.0706 1.0003
## I(week^2)-Vulpes_vulpes -0.3465 0.2430 -0.9230 -0.3122 0.0324 1.0422
## I(week^2)-Sus_scrofa -0.1542 0.1647 -0.5009 -0.1473 0.1601 1.0033
## ESS
## (Intercept)-Canis_latrans 1033
## (Intercept)-Sciurus_niger 305
## (Intercept)-Procyon_lotor 1740
## (Intercept)-Dasypus_novemcinctus 1944
## (Intercept)-Lynx_rufus 470
## (Intercept)-Didelphis_virginiana 1513
## (Intercept)-Sylvilagus_floridanus 638
## (Intercept)-Meleagris_gallopavo 437
## (Intercept)-Sciurus_carolinensis 1469
## (Intercept)-Vulpes_vulpes 212
## (Intercept)-Sus_scrofa 497
## week-Canis_latrans 1137
## week-Sciurus_niger 580
## week-Procyon_lotor 1681
## week-Dasypus_novemcinctus 1650
## week-Lynx_rufus 849
## week-Didelphis_virginiana 1021
## week-Sylvilagus_floridanus 1104
## week-Meleagris_gallopavo 453
## week-Sciurus_carolinensis 1224
## week-Vulpes_vulpes 901
## week-Sus_scrofa 1176
## I(week^2)-Canis_latrans 1177
## I(week^2)-Sciurus_niger 501
## I(week^2)-Procyon_lotor 1457
## I(week^2)-Dasypus_novemcinctus 1284
## I(week^2)-Lynx_rufus 799
## I(week^2)-Didelphis_virginiana 457
## I(week^2)-Sylvilagus_floridanus 849
## I(week^2)-Meleagris_gallopavo 323
## I(week^2)-Sciurus_carolinensis 1475
## I(week^2)-Vulpes_vulpes 277
## I(week^2)-Sus_scrofa 1419
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8898
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2931 0.6488 -2.5714 -1.3050 0.0138 1.0074 417
## Cogon_Patch_Size -0.7646 0.5480 -1.8720 -0.7570 0.3015 1.0311 535
## Veg_shannon_index 0.9317 0.3992 0.1154 0.9289 1.7377 1.0164 249
## total_shrub_cover -0.5790 0.4582 -1.5219 -0.5561 0.2851 1.0034 645
## Avg_Cogongrass_Cover 2.0214 0.6736 0.7851 1.9997 3.3911 1.0306 173
## Tree_Density -1.9954 0.6285 -3.2814 -1.9598 -0.7994 1.0040 220
## Avg_Canopy_Cover 1.7373 0.4809 0.8986 1.7010 2.8315 1.0103 255
## avg_veg_height -0.6069 0.4227 -1.4502 -0.5987 0.1865 1.0041 256
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.3385 3.5759 0.1599 2.3897 12.6375 1.0643 264
## Cogon_Patch_Size 1.9282 2.5930 0.1046 1.1659 8.2264 1.0052 250
## Veg_shannon_index 0.5910 0.8427 0.0491 0.3329 2.7348 1.0416 434
## total_shrub_cover 1.3409 1.5514 0.0784 0.8445 5.6722 1.0078 274
## Avg_Cogongrass_Cover 1.0234 1.6698 0.0512 0.4788 5.1592 1.0181 271
## Tree_Density 1.5041 2.0630 0.0647 0.7491 7.2224 1.0054 209
## Avg_Canopy_Cover 1.0764 1.2000 0.0853 0.7324 4.1485 1.0239 462
## avg_veg_height 0.3515 0.4361 0.0391 0.2124 1.4603 1.0024 589
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8671 1.8094 0.1086 1.2996 6.6048 1.0235 82
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7621 0.3125 -3.3693 -2.7650 -2.1200 1.0018 2038
## week 0.1366 0.1846 -0.2300 0.1392 0.4928 1.0166 1031
## I(week^2) -0.2186 0.1062 -0.4365 -0.2139 -0.0234 1.0827 438
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9417 0.6564 0.2839 0.7753 2.5752 1.0237 841
## week 0.2123 0.2196 0.0351 0.1501 0.7628 1.1396 220
## I(week^2) 0.0651 0.0526 0.0180 0.0499 0.1978 1.0721 419
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0181 0.9636 -1.8739 0.0383
## (Intercept)-Sciurus_niger -0.6310 1.4215 -2.9923 -0.7931
## (Intercept)-Procyon_lotor 0.1185 1.0103 -1.9442 0.1490
## (Intercept)-Dasypus_novemcinctus -1.4954 0.8343 -3.2521 -1.4524
## (Intercept)-Lynx_rufus -0.0032 1.5258 -2.4972 -0.1826
## (Intercept)-Didelphis_virginiana -2.5560 0.9983 -4.8462 -2.4684
## (Intercept)-Sylvilagus_floridanus -1.3166 1.0100 -3.3297 -1.3142
## (Intercept)-Meleagris_gallopavo -1.4841 1.0970 -3.7741 -1.4418
## (Intercept)-Sciurus_carolinensis -2.6312 1.0614 -4.8731 -2.5510
## (Intercept)-Vulpes_vulpes -2.2555 1.2486 -4.9541 -2.1931
## (Intercept)-Sus_scrofa -3.4503 1.3873 -6.5262 -3.3419
## Cogon_Patch_Size-Canis_latrans 0.4065 0.9637 -1.0680 0.2682
## Cogon_Patch_Size-Sciurus_niger -1.3494 1.2951 -4.3309 -1.2495
## Cogon_Patch_Size-Procyon_lotor -1.0010 0.6642 -2.3964 -0.9825
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8061 0.6028 -2.0389 -0.7770
## Cogon_Patch_Size-Lynx_rufus -0.7634 1.1349 -2.7969 -0.8166
## Cogon_Patch_Size-Didelphis_virginiana 0.6893 0.8650 -0.7396 0.6000
## Cogon_Patch_Size-Sylvilagus_floridanus -1.7581 1.1303 -4.4181 -1.6004
## Cogon_Patch_Size-Meleagris_gallopavo -0.5063 0.9265 -2.1627 -0.5737
## Cogon_Patch_Size-Sciurus_carolinensis -1.5349 0.9919 -3.8949 -1.3938
## Cogon_Patch_Size-Vulpes_vulpes -1.2051 1.3639 -4.1438 -1.1422
## Cogon_Patch_Size-Sus_scrofa -1.0719 1.0752 -3.5128 -0.9360
## Veg_shannon_index-Canis_latrans 1.2421 0.5771 0.2506 1.1890
## Veg_shannon_index-Sciurus_niger 1.0309 0.7924 -0.4898 0.9994
## Veg_shannon_index-Procyon_lotor 1.1999 0.5306 0.2616 1.1670
## Veg_shannon_index-Dasypus_novemcinctus 0.6992 0.4988 -0.3251 0.7042
## Veg_shannon_index-Lynx_rufus 0.8554 0.7514 -0.6722 0.8674
## Veg_shannon_index-Didelphis_virginiana 1.0436 0.5924 -0.0510 1.0173
## Veg_shannon_index-Sylvilagus_floridanus 1.0082 0.6179 -0.1375 0.9773
## Veg_shannon_index-Meleagris_gallopavo 1.2096 0.6551 0.0587 1.1511
## Veg_shannon_index-Sciurus_carolinensis 0.3696 0.6433 -1.1003 0.4229
## Veg_shannon_index-Vulpes_vulpes 0.4761 0.7372 -1.2905 0.5746
## Veg_shannon_index-Sus_scrofa 1.3314 0.7230 0.0800 1.2646
## total_shrub_cover-Canis_latrans 0.1577 0.6177 -0.9185 0.1176
## total_shrub_cover-Sciurus_niger -1.0651 0.9564 -3.2392 -0.9566
## total_shrub_cover-Procyon_lotor -0.9671 0.6163 -2.3153 -0.9260
## total_shrub_cover-Dasypus_novemcinctus 0.0485 0.5512 -0.9978 0.0279
## total_shrub_cover-Lynx_rufus -1.1636 1.0402 -3.6953 -0.9975
## total_shrub_cover-Didelphis_virginiana -0.5903 0.6735 -2.0088 -0.5515
## total_shrub_cover-Sylvilagus_floridanus -0.3197 0.7637 -1.8656 -0.3340
## total_shrub_cover-Meleagris_gallopavo -2.0115 1.2329 -5.0333 -1.8169
## total_shrub_cover-Sciurus_carolinensis -0.0961 0.6444 -1.3798 -0.1212
## total_shrub_cover-Vulpes_vulpes -0.7297 0.9064 -2.7988 -0.6468
## total_shrub_cover-Sus_scrofa 0.0231 0.8246 -1.5249 -0.0254
## Avg_Cogongrass_Cover-Canis_latrans 2.3697 0.8739 0.8265 2.3044
## Avg_Cogongrass_Cover-Sciurus_niger 1.4930 1.2312 -1.2520 1.6240
## Avg_Cogongrass_Cover-Procyon_lotor 2.2624 0.8450 0.7226 2.2089
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5700 0.9071 1.0521 2.4990
## Avg_Cogongrass_Cover-Lynx_rufus 2.5152 0.9858 0.8613 2.4214
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1815 0.8169 0.6525 2.1478
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5021 0.9211 -0.3002 1.5181
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5883 1.1555 -1.0317 1.7068
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3553 0.8554 0.8103 2.3121
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.5092 1.0064 0.6850 2.4497
## Avg_Cogongrass_Cover-Sus_scrofa 1.6325 1.0319 -0.5694 1.6815
## Tree_Density-Canis_latrans -2.4155 1.0444 -5.0162 -2.2492
## Tree_Density-Sciurus_niger -2.0432 1.0824 -4.3453 -1.9969
## Tree_Density-Procyon_lotor -1.6010 0.7271 -3.0382 -1.6054
## Tree_Density-Dasypus_novemcinctus -3.0783 1.3626 -6.5262 -2.7885
## Tree_Density-Lynx_rufus -0.8756 1.0898 -2.7240 -0.9571
## Tree_Density-Didelphis_virginiana -2.2021 0.9191 -4.2508 -2.1071
## Tree_Density-Sylvilagus_floridanus -2.3880 1.1315 -4.9425 -2.2455
## Tree_Density-Meleagris_gallopavo -2.1817 1.0649 -4.5760 -2.0859
## Tree_Density-Sciurus_carolinensis -2.2982 1.0185 -4.6009 -2.1759
## Tree_Density-Vulpes_vulpes -1.9137 1.2020 -4.5371 -1.8736
## Tree_Density-Sus_scrofa -2.0678 1.0743 -4.3919 -1.9656
## Avg_Canopy_Cover-Canis_latrans 0.5501 0.6523 -0.7058 0.5408
## Avg_Canopy_Cover-Sciurus_niger 1.6728 1.0903 -0.4009 1.6389
## Avg_Canopy_Cover-Procyon_lotor 1.6909 0.6210 0.5879 1.6481
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8882 0.6183 0.8228 1.8406
## Avg_Canopy_Cover-Lynx_rufus 1.2395 0.9541 -0.5176 1.1790
## Avg_Canopy_Cover-Didelphis_virginiana 2.3263 0.7578 1.0745 2.2545
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.6328 1.0639 1.0339 2.4538
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0762 0.9245 0.7399 1.9575
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0379 0.6900 0.8985 1.9718
## Avg_Canopy_Cover-Vulpes_vulpes 1.9198 0.8266 0.5213 1.8352
## Avg_Canopy_Cover-Sus_scrofa 1.8556 0.7391 0.5795 1.7861
## avg_veg_height-Canis_latrans -0.7401 0.5449 -1.8962 -0.7206
## avg_veg_height-Sciurus_niger -0.8354 0.7305 -2.5429 -0.7654
## avg_veg_height-Procyon_lotor -0.4683 0.5295 -1.5373 -0.4588
## avg_veg_height-Dasypus_novemcinctus -0.3701 0.5364 -1.3790 -0.3775
## avg_veg_height-Lynx_rufus -0.7023 0.6628 -2.1210 -0.6742
## avg_veg_height-Didelphis_virginiana -0.6818 0.5745 -1.8657 -0.6682
## avg_veg_height-Sylvilagus_floridanus -0.7632 0.6090 -2.1265 -0.7235
## avg_veg_height-Meleagris_gallopavo -0.6998 0.6647 -2.1192 -0.6653
## avg_veg_height-Sciurus_carolinensis -0.2902 0.5875 -1.3673 -0.3187
## avg_veg_height-Vulpes_vulpes -0.6239 0.6513 -2.0137 -0.6062
## avg_veg_height-Sus_scrofa -0.6478 0.6074 -1.9503 -0.6325
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9129 1.0230 160
## (Intercept)-Sciurus_niger 2.6576 1.0142 252
## (Intercept)-Procyon_lotor 1.9869 1.0574 170
## (Intercept)-Dasypus_novemcinctus 0.1032 1.0256 662
## (Intercept)-Lynx_rufus 3.5460 1.0792 128
## (Intercept)-Didelphis_virginiana -0.9311 1.0361 331
## (Intercept)-Sylvilagus_floridanus 0.8402 1.0069 494
## (Intercept)-Meleagris_gallopavo 0.6835 1.0246 380
## (Intercept)-Sciurus_carolinensis -0.7800 1.0406 375
## (Intercept)-Vulpes_vulpes 0.1142 1.0160 309
## (Intercept)-Sus_scrofa -1.1400 1.0551 177
## Cogon_Patch_Size-Canis_latrans 2.7225 1.0194 685
## Cogon_Patch_Size-Sciurus_niger 1.0050 1.0127 415
## Cogon_Patch_Size-Procyon_lotor 0.2921 1.0101 273
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3341 1.0165 548
## Cogon_Patch_Size-Lynx_rufus 1.7026 1.0056 368
## Cogon_Patch_Size-Didelphis_virginiana 2.6457 1.0016 629
## Cogon_Patch_Size-Sylvilagus_floridanus -0.0581 1.0170 509
## Cogon_Patch_Size-Meleagris_gallopavo 1.5738 1.0270 518
## Cogon_Patch_Size-Sciurus_carolinensis -0.0579 1.0136 319
## Cogon_Patch_Size-Vulpes_vulpes 1.4800 1.0474 351
## Cogon_Patch_Size-Sus_scrofa 0.7387 1.0234 737
## Veg_shannon_index-Canis_latrans 2.5460 1.0148 399
## Veg_shannon_index-Sciurus_niger 2.7256 1.0131 435
## Veg_shannon_index-Procyon_lotor 2.3351 1.0057 378
## Veg_shannon_index-Dasypus_novemcinctus 1.6646 1.0244 593
## Veg_shannon_index-Lynx_rufus 2.3684 1.0263 396
## Veg_shannon_index-Didelphis_virginiana 2.2932 1.0067 498
## Veg_shannon_index-Sylvilagus_floridanus 2.3008 1.0328 496
## Veg_shannon_index-Meleagris_gallopavo 2.7100 1.0328 369
## Veg_shannon_index-Sciurus_carolinensis 1.4921 1.0151 551
## Veg_shannon_index-Vulpes_vulpes 1.6658 1.0170 543
## Veg_shannon_index-Sus_scrofa 3.0340 1.0211 605
## total_shrub_cover-Canis_latrans 1.5319 1.0018 1002
## total_shrub_cover-Sciurus_niger 0.5363 1.0081 327
## total_shrub_cover-Procyon_lotor 0.1034 1.0371 828
## total_shrub_cover-Dasypus_novemcinctus 1.1762 1.0123 1182
## total_shrub_cover-Lynx_rufus 0.4455 1.0017 271
## total_shrub_cover-Didelphis_virginiana 0.6410 1.0017 1182
## total_shrub_cover-Sylvilagus_floridanus 1.2679 1.0120 789
## total_shrub_cover-Meleagris_gallopavo -0.2100 1.0159 263
## total_shrub_cover-Sciurus_carolinensis 1.2361 1.0062 1482
## total_shrub_cover-Vulpes_vulpes 0.8262 1.0055 686
## total_shrub_cover-Sus_scrofa 1.8519 1.0095 1075
## Avg_Cogongrass_Cover-Canis_latrans 4.2379 1.0069 237
## Avg_Cogongrass_Cover-Sciurus_niger 3.5458 1.0693 272
## Avg_Cogongrass_Cover-Procyon_lotor 4.1217 1.0046 192
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6065 1.0089 173
## Avg_Cogongrass_Cover-Lynx_rufus 4.7260 1.0113 266
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8760 1.0076 260
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3204 1.0159 313
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.5722 1.0117 302
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1467 1.0174 234
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.7304 1.0118 164
## Avg_Cogongrass_Cover-Sus_scrofa 3.5329 1.0084 309
## Tree_Density-Canis_latrans -0.7442 1.0034 239
## Tree_Density-Sciurus_niger 0.0083 1.0116 399
## Tree_Density-Procyon_lotor -0.1606 1.0031 324
## Tree_Density-Dasypus_novemcinctus -1.2154 1.0038 190
## Tree_Density-Lynx_rufus 1.4886 1.0032 204
## Tree_Density-Didelphis_virginiana -0.6465 1.0032 481
## Tree_Density-Sylvilagus_floridanus -0.5979 1.0029 292
## Tree_Density-Meleagris_gallopavo -0.2636 1.0039 496
## Tree_Density-Sciurus_carolinensis -0.6253 1.0052 356
## Tree_Density-Vulpes_vulpes 0.5037 1.0154 397
## Tree_Density-Sus_scrofa -0.1620 1.0062 512
## Avg_Canopy_Cover-Canis_latrans 1.8423 1.0023 499
## Avg_Canopy_Cover-Sciurus_niger 4.0144 1.0024 348
## Avg_Canopy_Cover-Procyon_lotor 2.9982 1.0111 422
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3101 1.0035 317
## Avg_Canopy_Cover-Lynx_rufus 3.2723 1.0019 342
## Avg_Canopy_Cover-Didelphis_virginiana 4.0088 1.0092 234
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.2981 1.0509 319
## Avg_Canopy_Cover-Meleagris_gallopavo 4.2867 1.0466 490
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7002 1.0129 343
## Avg_Canopy_Cover-Vulpes_vulpes 3.8999 1.0022 455
## Avg_Canopy_Cover-Sus_scrofa 3.5220 1.0019 551
## avg_veg_height-Canis_latrans 0.2730 1.0020 424
## avg_veg_height-Sciurus_niger 0.3989 1.0157 414
## avg_veg_height-Procyon_lotor 0.5779 1.0055 474
## avg_veg_height-Dasypus_novemcinctus 0.7379 1.0002 526
## avg_veg_height-Lynx_rufus 0.5123 1.0053 432
## avg_veg_height-Didelphis_virginiana 0.4101 1.0029 506
## avg_veg_height-Sylvilagus_floridanus 0.3108 1.0038 554
## avg_veg_height-Meleagris_gallopavo 0.4902 1.0024 526
## avg_veg_height-Sciurus_carolinensis 0.9403 1.0008 579
## avg_veg_height-Vulpes_vulpes 0.6166 1.0017 569
## avg_veg_height-Sus_scrofa 0.4940 1.0061 587
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4926 0.1864 -2.8683 -2.4883 -2.1458 1.0072
## (Intercept)-Sciurus_niger -4.1168 0.4772 -5.0829 -4.1086 -3.1666 1.0234
## (Intercept)-Procyon_lotor -2.1791 0.1459 -2.4670 -2.1758 -1.9011 1.0024
## (Intercept)-Dasypus_novemcinctus -1.4969 0.1554 -1.8071 -1.4934 -1.1977 1.0015
## (Intercept)-Lynx_rufus -3.4634 0.3158 -4.0841 -3.4518 -2.8601 1.0286
## (Intercept)-Didelphis_virginiana -2.1640 0.2600 -2.6909 -2.1553 -1.6809 1.0025
## (Intercept)-Sylvilagus_floridanus -3.1020 0.2918 -3.6737 -3.0924 -2.5628 1.0047
## (Intercept)-Meleagris_gallopavo -3.2802 0.3209 -3.9257 -3.2720 -2.6833 1.0177
## (Intercept)-Sciurus_carolinensis -2.3414 0.2843 -2.9323 -2.3309 -1.8124 1.0046
## (Intercept)-Vulpes_vulpes -3.8242 0.5721 -5.0404 -3.8055 -2.7833 1.0046
## (Intercept)-Sus_scrofa -2.8909 0.4534 -3.8193 -2.8741 -2.0538 1.0262
## week-Canis_latrans 0.4325 0.2445 -0.0182 0.4264 0.9340 1.0106
## week-Sciurus_niger -0.2480 0.4228 -1.2506 -0.2013 0.4575 1.0738
## week-Procyon_lotor 0.1457 0.1944 -0.2295 0.1412 0.5410 1.0052
## week-Dasypus_novemcinctus 0.0460 0.2044 -0.3634 0.0445 0.4409 1.0048
## week-Lynx_rufus 0.2366 0.3022 -0.3423 0.2295 0.8453 1.0103
## week-Didelphis_virginiana -0.0054 0.3119 -0.6280 0.0030 0.5939 1.0055
## week-Sylvilagus_floridanus 0.0061 0.2928 -0.5737 0.0073 0.5476 1.0036
## week-Meleagris_gallopavo -0.1825 0.3560 -0.9440 -0.1607 0.4405 1.0205
## week-Sciurus_carolinensis 0.5183 0.3435 -0.0878 0.4894 1.2619 1.0415
## week-Vulpes_vulpes 0.1039 0.3920 -0.6859 0.1035 0.8841 1.0097
## week-Sus_scrofa 0.4037 0.3867 -0.3085 0.3758 1.2418 1.0319
## I(week^2)-Canis_latrans -0.1913 0.1028 -0.3990 -0.1873 -0.0017 1.0224
## I(week^2)-Sciurus_niger -0.2386 0.2104 -0.6942 -0.2242 0.1398 1.0583
## I(week^2)-Procyon_lotor -0.1089 0.0849 -0.2760 -0.1103 0.0563 1.0122
## I(week^2)-Dasypus_novemcinctus -0.1446 0.0966 -0.3433 -0.1444 0.0422 1.0147
## I(week^2)-Lynx_rufus -0.1932 0.1444 -0.5023 -0.1879 0.0651 1.0174
## I(week^2)-Didelphis_virginiana -0.3361 0.1970 -0.7731 -0.3149 -0.0100 1.0120
## I(week^2)-Sylvilagus_floridanus -0.1382 0.1415 -0.4337 -0.1351 0.1252 1.0135
## I(week^2)-Meleagris_gallopavo -0.3655 0.2318 -0.9196 -0.3345 0.0043 1.0959
## I(week^2)-Sciurus_carolinensis -0.1865 0.1414 -0.4626 -0.1803 0.0704 1.0448
## I(week^2)-Vulpes_vulpes -0.3326 0.2320 -0.8427 -0.3087 0.0578 1.0640
## I(week^2)-Sus_scrofa -0.1517 0.1686 -0.4980 -0.1453 0.1579 1.0167
## ESS
## (Intercept)-Canis_latrans 1188
## (Intercept)-Sciurus_niger 202
## (Intercept)-Procyon_lotor 1818
## (Intercept)-Dasypus_novemcinctus 2521
## (Intercept)-Lynx_rufus 247
## (Intercept)-Didelphis_virginiana 1503
## (Intercept)-Sylvilagus_floridanus 602
## (Intercept)-Meleagris_gallopavo 647
## (Intercept)-Sciurus_carolinensis 1271
## (Intercept)-Vulpes_vulpes 249
## (Intercept)-Sus_scrofa 622
## week-Canis_latrans 1198
## week-Sciurus_niger 251
## week-Procyon_lotor 1867
## week-Dasypus_novemcinctus 2046
## week-Lynx_rufus 1006
## week-Didelphis_virginiana 1077
## week-Sylvilagus_floridanus 1207
## week-Meleagris_gallopavo 366
## week-Sciurus_carolinensis 1054
## week-Vulpes_vulpes 1005
## week-Sus_scrofa 1194
## I(week^2)-Canis_latrans 1089
## I(week^2)-Sciurus_niger 346
## I(week^2)-Procyon_lotor 1766
## I(week^2)-Dasypus_novemcinctus 1868
## I(week^2)-Lynx_rufus 730
## I(week^2)-Didelphis_virginiana 615
## I(week^2)-Sylvilagus_floridanus 935
## I(week^2)-Meleagris_gallopavo 189
## I(week^2)-Sciurus_carolinensis 1252
## I(week^2)-Vulpes_vulpes 367
## I(week^2)-Sus_scrofa 1394
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8667
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8041 0.3619 -1.5511 -0.7906 -0.0849 1.0008 532
## Avg_Cogongrass_Cover 0.0561 0.3145 -0.6333 0.0657 0.6263 1.0006 483
## total_shrub_cover -0.4533 0.2820 -1.0435 -0.4413 0.0783 1.0055 1161
## avg_veg_height -0.0393 0.2762 -0.5765 -0.0405 0.4939 1.0012 444
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7144 0.7575 0.0644 0.5104 2.6992 1.0089 506
## Avg_Cogongrass_Cover 0.3743 0.4566 0.0412 0.2476 1.4182 1.0248 543
## total_shrub_cover 0.4937 0.5057 0.0557 0.3393 1.9502 0.9998 752
## avg_veg_height 0.1960 0.2086 0.0327 0.1327 0.7336 1.0009 1193
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1015 0.9305 0.1012 0.8398 3.669 1.0211 194
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7098 0.2865 -3.2921 -2.6997 -2.1466 1.0012 1268
## week 0.1402 0.1900 -0.2574 0.1391 0.5128 1.0113 1040
## I(week^2) -0.2151 0.1035 -0.4319 -0.2139 -0.0223 1.0474 695
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7400 0.5233 0.2031 0.6048 2.0355 1.0049 647
## week 0.2084 0.1869 0.0348 0.1553 0.7264 1.0299 647
## I(week^2) 0.0641 0.0593 0.0173 0.0490 0.1986 1.2094 397
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1826 0.6070 -1.3032 -0.1998
## (Intercept)-Sciurus_niger -0.9363 0.7091 -2.3263 -0.9395
## (Intercept)-Procyon_lotor 0.0091 0.6389 -1.1931 -0.0115
## (Intercept)-Dasypus_novemcinctus -0.7914 0.5024 -1.8081 -0.7908
## (Intercept)-Lynx_rufus -0.5587 0.6261 -1.7832 -0.5707
## (Intercept)-Didelphis_virginiana -1.1985 0.5594 -2.3429 -1.1761
## (Intercept)-Sylvilagus_floridanus -0.5892 0.5876 -1.7107 -0.6095
## (Intercept)-Meleagris_gallopavo -0.8653 0.5826 -2.0639 -0.8386
## (Intercept)-Sciurus_carolinensis -1.2543 0.5937 -2.4527 -1.2217
## (Intercept)-Vulpes_vulpes -1.1996 0.7624 -2.7520 -1.1569
## (Intercept)-Sus_scrofa -1.4832 0.6885 -2.9722 -1.4408
## Avg_Cogongrass_Cover-Canis_latrans 0.3744 0.4238 -0.3921 0.3496
## Avg_Cogongrass_Cover-Sciurus_niger -0.3070 0.6625 -1.8291 -0.2327
## Avg_Cogongrass_Cover-Procyon_lotor 0.0348 0.4334 -0.8426 0.0371
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2524 0.3862 -0.5320 0.2641
## Avg_Cogongrass_Cover-Lynx_rufus 0.4241 0.4769 -0.4292 0.3833
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3191 0.4302 -0.5151 0.3099
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2849 0.5070 -1.3682 -0.2422
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3875 0.5789 -1.6922 -0.3297
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2325 0.4286 -0.6018 0.2278
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2196 0.4948 -0.7866 0.2256
## Avg_Cogongrass_Cover-Sus_scrofa -0.1901 0.5839 -1.5282 -0.1163
## total_shrub_cover-Canis_latrans 0.0726 0.4317 -0.7015 0.0422
## total_shrub_cover-Sciurus_niger -0.6869 0.5563 -1.9434 -0.6389
## total_shrub_cover-Procyon_lotor -0.8632 0.4652 -1.8911 -0.8223
## total_shrub_cover-Dasypus_novemcinctus -0.0948 0.3697 -0.8048 -0.1006
## total_shrub_cover-Lynx_rufus -0.8545 0.5946 -2.2017 -0.7901
## total_shrub_cover-Didelphis_virginiana -0.2999 0.4018 -1.1054 -0.3003
## total_shrub_cover-Sylvilagus_floridanus -0.4317 0.4866 -1.4866 -0.4013
## total_shrub_cover-Meleagris_gallopavo -1.1762 0.6055 -2.5816 -1.1049
## total_shrub_cover-Sciurus_carolinensis -0.1650 0.4202 -0.9436 -0.1716
## total_shrub_cover-Vulpes_vulpes -0.4816 0.5666 -1.6367 -0.4439
## total_shrub_cover-Sus_scrofa -0.0387 0.5232 -1.0207 -0.0581
## avg_veg_height-Canis_latrans -0.0833 0.3701 -0.8129 -0.0794
## avg_veg_height-Sciurus_niger -0.1824 0.4511 -1.1300 -0.1573
## avg_veg_height-Procyon_lotor 0.0656 0.3822 -0.6449 0.0643
## avg_veg_height-Dasypus_novemcinctus 0.1490 0.3708 -0.5346 0.1449
## avg_veg_height-Lynx_rufus -0.0334 0.4264 -0.8772 -0.0339
## avg_veg_height-Didelphis_virginiana -0.0535 0.3943 -0.8389 -0.0455
## avg_veg_height-Sylvilagus_floridanus -0.1459 0.4092 -1.0147 -0.1295
## avg_veg_height-Meleagris_gallopavo -0.2302 0.4543 -1.2126 -0.1944
## avg_veg_height-Sciurus_carolinensis 0.2250 0.4065 -0.5284 0.2026
## avg_veg_height-Vulpes_vulpes -0.0863 0.4393 -0.9592 -0.0800
## avg_veg_height-Sus_scrofa -0.0378 0.4203 -0.8848 -0.0271
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0695 1.0142 465
## (Intercept)-Sciurus_niger 0.5285 1.0118 477
## (Intercept)-Procyon_lotor 1.3069 1.0019 419
## (Intercept)-Dasypus_novemcinctus 0.1903 1.0012 1363
## (Intercept)-Lynx_rufus 0.7899 1.0097 722
## (Intercept)-Didelphis_virginiana -0.1609 1.0049 850
## (Intercept)-Sylvilagus_floridanus 0.6738 1.0032 785
## (Intercept)-Meleagris_gallopavo 0.2615 1.0002 741
## (Intercept)-Sciurus_carolinensis -0.2010 1.0024 685
## (Intercept)-Vulpes_vulpes 0.3030 1.0072 336
## (Intercept)-Sus_scrofa -0.2709 1.0006 476
## Avg_Cogongrass_Cover-Canis_latrans 1.2808 1.0038 1192
## Avg_Cogongrass_Cover-Sciurus_niger 0.7254 1.0104 494
## Avg_Cogongrass_Cover-Procyon_lotor 0.8864 1.0008 1301
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9952 1.0022 1313
## Avg_Cogongrass_Cover-Lynx_rufus 1.4890 1.0040 1126
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2096 1.0052 1166
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6049 1.0029 873
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5455 1.0058 747
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0916 1.0001 1446
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.2117 1.0011 1022
## Avg_Cogongrass_Cover-Sus_scrofa 0.7893 1.0179 655
## total_shrub_cover-Canis_latrans 0.9953 1.0038 1533
## total_shrub_cover-Sciurus_niger 0.2995 1.0161 930
## total_shrub_cover-Procyon_lotor -0.0794 1.0083 1124
## total_shrub_cover-Dasypus_novemcinctus 0.6614 1.0025 2070
## total_shrub_cover-Lynx_rufus 0.1304 1.0002 754
## total_shrub_cover-Didelphis_virginiana 0.4919 1.0002 2123
## total_shrub_cover-Sylvilagus_floridanus 0.4696 1.0002 1291
## total_shrub_cover-Meleagris_gallopavo -0.1781 1.0024 744
## total_shrub_cover-Sciurus_carolinensis 0.6935 1.0036 1760
## total_shrub_cover-Vulpes_vulpes 0.5394 1.0095 1080
## total_shrub_cover-Sus_scrofa 1.1345 1.0037 1346
## avg_veg_height-Canis_latrans 0.6420 0.9999 1029
## avg_veg_height-Sciurus_niger 0.6542 1.0005 873
## avg_veg_height-Procyon_lotor 0.8411 1.0004 952
## avg_veg_height-Dasypus_novemcinctus 0.9206 1.0007 1027
## avg_veg_height-Lynx_rufus 0.7806 1.0003 864
## avg_veg_height-Didelphis_virginiana 0.7081 1.0020 809
## avg_veg_height-Sylvilagus_floridanus 0.6116 1.0039 921
## avg_veg_height-Meleagris_gallopavo 0.5885 1.0035 857
## avg_veg_height-Sciurus_carolinensis 1.0882 1.0011 1005
## avg_veg_height-Vulpes_vulpes 0.7627 1.0007 768
## avg_veg_height-Sus_scrofa 0.7931 1.0011 733
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4806 0.1886 -2.8671 -2.4748 -2.1359 1.0008
## (Intercept)-Sciurus_niger -3.6454 0.4922 -4.6453 -3.6210 -2.7309 1.0121
## (Intercept)-Procyon_lotor -2.1788 0.1451 -2.4786 -2.1753 -1.9024 1.0037
## (Intercept)-Dasypus_novemcinctus -1.4953 0.1538 -1.8094 -1.4957 -1.2045 1.0005
## (Intercept)-Lynx_rufus -3.3164 0.2951 -3.9217 -3.3011 -2.7644 1.0215
## (Intercept)-Didelphis_virginiana -2.2037 0.2829 -2.7921 -2.1942 -1.6660 1.0076
## (Intercept)-Sylvilagus_floridanus -3.0827 0.3097 -3.7189 -3.0621 -2.5070 1.0070
## (Intercept)-Meleagris_gallopavo -3.1527 0.3204 -3.8052 -3.1480 -2.5591 1.0023
## (Intercept)-Sciurus_carolinensis -2.3853 0.2906 -2.9670 -2.3829 -1.8332 1.0009
## (Intercept)-Vulpes_vulpes -3.6865 0.6093 -4.9761 -3.6686 -2.5756 1.0077
## (Intercept)-Sus_scrofa -2.8891 0.4575 -3.8612 -2.8597 -2.0564 1.0108
## week-Canis_latrans 0.4449 0.2453 -0.0283 0.4384 0.9326 0.9998
## week-Sciurus_niger -0.2558 0.4133 -1.1799 -0.2122 0.4648 1.0433
## week-Procyon_lotor 0.1526 0.1942 -0.2295 0.1503 0.5342 1.0017
## week-Dasypus_novemcinctus 0.0559 0.2147 -0.3758 0.0575 0.4632 1.0034
## week-Lynx_rufus 0.2594 0.2918 -0.2962 0.2517 0.8792 1.0052
## week-Didelphis_virginiana -0.0079 0.3101 -0.6397 -0.0044 0.5912 1.0100
## week-Sylvilagus_floridanus 0.0150 0.2959 -0.5532 0.0172 0.5914 1.0046
## week-Meleagris_gallopavo -0.1471 0.3592 -0.9021 -0.1245 0.4952 1.0030
## week-Sciurus_carolinensis 0.5256 0.3386 -0.0759 0.4954 1.2686 1.0236
## week-Vulpes_vulpes 0.0787 0.4061 -0.7757 0.0933 0.8767 1.0029
## week-Sus_scrofa 0.3885 0.3719 -0.2725 0.3580 1.1852 1.0059
## I(week^2)-Canis_latrans -0.1920 0.1006 -0.3946 -0.1890 0.0016 1.0014
## I(week^2)-Sciurus_niger -0.2381 0.2089 -0.6628 -0.2283 0.1432 1.0361
## I(week^2)-Procyon_lotor -0.1128 0.0860 -0.2811 -0.1133 0.0537 0.9999
## I(week^2)-Dasypus_novemcinctus -0.1520 0.0986 -0.3557 -0.1494 0.0390 1.0075
## I(week^2)-Lynx_rufus -0.1956 0.1391 -0.4784 -0.1923 0.0632 1.0044
## I(week^2)-Didelphis_virginiana -0.3450 0.2058 -0.8127 -0.3220 -0.0001 1.0277
## I(week^2)-Sylvilagus_floridanus -0.1524 0.1498 -0.4513 -0.1457 0.1299 1.0022
## I(week^2)-Meleagris_gallopavo -0.3215 0.2078 -0.7701 -0.2994 0.0362 1.0450
## I(week^2)-Sciurus_carolinensis -0.1866 0.1371 -0.4565 -0.1830 0.0771 1.0118
## I(week^2)-Vulpes_vulpes -0.3477 0.2691 -0.9256 -0.3144 0.0490 1.0867
## I(week^2)-Sus_scrofa -0.1506 0.1641 -0.4922 -0.1456 0.1486 1.0105
## ESS
## (Intercept)-Canis_latrans 1157
## (Intercept)-Sciurus_niger 234
## (Intercept)-Procyon_lotor 1646
## (Intercept)-Dasypus_novemcinctus 2496
## (Intercept)-Lynx_rufus 620
## (Intercept)-Didelphis_virginiana 1254
## (Intercept)-Sylvilagus_floridanus 525
## (Intercept)-Meleagris_gallopavo 630
## (Intercept)-Sciurus_carolinensis 1068
## (Intercept)-Vulpes_vulpes 224
## (Intercept)-Sus_scrofa 624
## week-Canis_latrans 1221
## week-Sciurus_niger 612
## week-Procyon_lotor 1686
## week-Dasypus_novemcinctus 1827
## week-Lynx_rufus 1204
## week-Didelphis_virginiana 1266
## week-Sylvilagus_floridanus 864
## week-Meleagris_gallopavo 709
## week-Sciurus_carolinensis 1148
## week-Vulpes_vulpes 913
## week-Sus_scrofa 1388
## I(week^2)-Canis_latrans 1238
## I(week^2)-Sciurus_niger 500
## I(week^2)-Procyon_lotor 1666
## I(week^2)-Dasypus_novemcinctus 1730
## I(week^2)-Lynx_rufus 780
## I(week^2)-Didelphis_virginiana 515
## I(week^2)-Sylvilagus_floridanus 549
## I(week^2)-Meleagris_gallopavo 356
## I(week^2)-Sciurus_carolinensis 1000
## I(week^2)-Vulpes_vulpes 347
## I(week^2)-Sus_scrofa 1277
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8165
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9364 0.4198 -1.7731 -0.9260 -0.1309 1.0020 571
## Tree_Density -0.7658 0.3744 -1.5986 -0.7427 -0.0901 1.0024 543
## Avg_Canopy_Cover 0.9817 0.3138 0.4073 0.9694 1.6614 1.0122 626
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2639 1.2310 0.1043 0.9309 4.4570 1.0076 594
## Tree_Density 0.6389 0.8376 0.0484 0.3694 2.9399 1.0072 397
## Avg_Canopy_Cover 0.4932 0.5098 0.0595 0.3424 1.9083 1.0093 694
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6755 0.7473 0.05 0.4189 2.8062 1.0814 134
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7022 0.2789 -3.2634 -2.7021 -2.1476 1.0041 1594
## week 0.1385 0.1960 -0.2547 0.1428 0.5133 1.0082 1018
## I(week^2) -0.2165 0.1016 -0.4194 -0.2149 -0.0166 1.0157 736
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7688 0.5467 0.2208 0.6349 2.0745 1.0026 686
## week 0.2129 0.2094 0.0358 0.1543 0.7474 1.0059 938
## I(week^2) 0.0652 0.0583 0.0184 0.0509 0.1967 1.0573 506
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.1176 0.5953 -1.3075 -0.1204 1.0121
## (Intercept)-Sciurus_niger -0.9291 0.7549 -2.3838 -0.9447 0.6657
## (Intercept)-Procyon_lotor 0.2139 0.6727 -1.1635 0.2388 1.5243
## (Intercept)-Dasypus_novemcinctus -1.0496 0.5445 -2.1769 -1.0221 -0.0285
## (Intercept)-Lynx_rufus -0.2550 0.8815 -1.6580 -0.3495 1.7705
## (Intercept)-Didelphis_virginiana -1.6721 0.6441 -3.0618 -1.6317 -0.4937
## (Intercept)-Sylvilagus_floridanus -0.8601 0.6024 -2.1056 -0.8514 0.2884
## (Intercept)-Meleagris_gallopavo -0.7164 0.6450 -1.9426 -0.7476 0.6220
## (Intercept)-Sciurus_carolinensis -1.6997 0.6406 -3.0352 -1.6548 -0.5463
## (Intercept)-Vulpes_vulpes -1.5496 0.8402 -3.1884 -1.5393 0.0486
## (Intercept)-Sus_scrofa -2.0793 0.8203 -3.8573 -2.0252 -0.6648
## Tree_Density-Canis_latrans -0.9179 0.5623 -2.2698 -0.8606 0.0189
## Tree_Density-Sciurus_niger -0.7526 0.6655 -2.1916 -0.7177 0.5254
## Tree_Density-Procyon_lotor -0.5040 0.3947 -1.3115 -0.4908 0.2564
## Tree_Density-Dasypus_novemcinctus -1.3067 0.7866 -3.1932 -1.1313 -0.2074
## Tree_Density-Lynx_rufus 0.0778 0.6224 -0.9137 0.0195 1.5535
## Tree_Density-Didelphis_virginiana -0.9615 0.6716 -2.5868 -0.8657 0.0755
## Tree_Density-Sylvilagus_floridanus -1.0173 0.6736 -2.5909 -0.9149 0.0528
## Tree_Density-Meleagris_gallopavo -0.8751 0.6377 -2.3107 -0.8224 0.2411
## Tree_Density-Sciurus_carolinensis -0.8868 0.6668 -2.3927 -0.8238 0.2568
## Tree_Density-Vulpes_vulpes -0.6522 0.7043 -2.1343 -0.6372 0.6759
## Tree_Density-Sus_scrofa -0.8740 0.7086 -2.5401 -0.7883 0.2812
## Avg_Canopy_Cover-Canis_latrans 0.1520 0.4502 -0.7646 0.1636 1.0238
## Avg_Canopy_Cover-Sciurus_niger 0.8390 0.6859 -0.3961 0.8142 2.3149
## Avg_Canopy_Cover-Procyon_lotor 0.9824 0.4252 0.1725 0.9625 1.8767
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9976 0.4012 0.2812 0.9707 1.8440
## Avg_Canopy_Cover-Lynx_rufus 0.7080 0.5685 -0.3948 0.7061 1.9252
## Avg_Canopy_Cover-Didelphis_virginiana 1.2181 0.4631 0.4062 1.1788 2.2403
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.5663 0.6626 0.5701 1.4671 3.1631
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3220 0.6233 0.3189 1.2421 2.7606
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1574 0.4579 0.3329 1.1358 2.1494
## Avg_Canopy_Cover-Vulpes_vulpes 1.0050 0.5738 -0.0601 0.9583 2.2688
## Avg_Canopy_Cover-Sus_scrofa 1.1044 0.4836 0.2219 1.0839 2.1434
## Rhat ESS
## (Intercept)-Canis_latrans 1.0439 401
## (Intercept)-Sciurus_niger 1.0059 428
## (Intercept)-Procyon_lotor 1.0348 331
## (Intercept)-Dasypus_novemcinctus 1.0025 813
## (Intercept)-Lynx_rufus 1.0051 261
## (Intercept)-Didelphis_virginiana 1.0079 749
## (Intercept)-Sylvilagus_floridanus 1.0007 867
## (Intercept)-Meleagris_gallopavo 1.0155 856
## (Intercept)-Sciurus_carolinensis 1.0023 512
## (Intercept)-Vulpes_vulpes 1.0124 414
## (Intercept)-Sus_scrofa 1.0087 588
## Tree_Density-Canis_latrans 1.0025 769
## Tree_Density-Sciurus_niger 1.0038 877
## Tree_Density-Procyon_lotor 1.0006 1750
## Tree_Density-Dasypus_novemcinctus 1.0088 346
## Tree_Density-Lynx_rufus 1.0118 628
## Tree_Density-Didelphis_virginiana 1.0017 626
## Tree_Density-Sylvilagus_floridanus 1.0021 740
## Tree_Density-Meleagris_gallopavo 1.0020 810
## Tree_Density-Sciurus_carolinensis 1.0100 812
## Tree_Density-Vulpes_vulpes 1.0026 830
## Tree_Density-Sus_scrofa 1.0020 955
## Avg_Canopy_Cover-Canis_latrans 1.0015 843
## Avg_Canopy_Cover-Sciurus_niger 1.0059 490
## Avg_Canopy_Cover-Procyon_lotor 1.0007 1721
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0032 1935
## Avg_Canopy_Cover-Lynx_rufus 1.0030 780
## Avg_Canopy_Cover-Didelphis_virginiana 1.0105 1245
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0020 640
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0014 531
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0013 1233
## Avg_Canopy_Cover-Vulpes_vulpes 1.0102 732
## Avg_Canopy_Cover-Sus_scrofa 1.0019 1118
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4798 0.1885 -2.8500 -2.4747 -2.1165 0.9999
## (Intercept)-Sciurus_niger -3.7257 0.4977 -4.7857 -3.7096 -2.7947 1.0043
## (Intercept)-Procyon_lotor -2.1783 0.1462 -2.4873 -2.1755 -1.8888 1.0001
## (Intercept)-Dasypus_novemcinctus -1.4913 0.1572 -1.8106 -1.4923 -1.1937 1.0018
## (Intercept)-Lynx_rufus -3.3695 0.3248 -4.0433 -3.3653 -2.7523 1.0093
## (Intercept)-Didelphis_virginiana -2.1940 0.2656 -2.7483 -2.1886 -1.6922 1.0003
## (Intercept)-Sylvilagus_floridanus -3.0039 0.2770 -3.5841 -2.9909 -2.4994 1.0030
## (Intercept)-Meleagris_gallopavo -3.2170 0.3265 -3.8860 -3.2124 -2.6054 1.0066
## (Intercept)-Sciurus_carolinensis -2.3558 0.2802 -2.9306 -2.3534 -1.8280 1.0008
## (Intercept)-Vulpes_vulpes -3.6487 0.6182 -4.9293 -3.6153 -2.5364 1.0166
## (Intercept)-Sus_scrofa -2.8281 0.4327 -3.7633 -2.8066 -2.0510 1.0047
## week-Canis_latrans 0.4417 0.2469 -0.0208 0.4323 0.9538 1.0089
## week-Sciurus_niger -0.2504 0.4386 -1.2500 -0.2033 0.4864 1.0027
## week-Procyon_lotor 0.1517 0.1965 -0.2376 0.1584 0.5390 1.0019
## week-Dasypus_novemcinctus 0.0533 0.2129 -0.3693 0.0535 0.4730 1.0028
## week-Lynx_rufus 0.2566 0.3017 -0.3179 0.2519 0.8661 1.0061
## week-Didelphis_virginiana 0.0067 0.3187 -0.6440 0.0183 0.6050 1.0009
## week-Sylvilagus_floridanus 0.0121 0.3003 -0.5659 0.0131 0.5990 1.0089
## week-Meleagris_gallopavo -0.1499 0.3525 -0.8893 -0.1353 0.4780 1.0009
## week-Sciurus_carolinensis 0.5244 0.3270 -0.0822 0.4973 1.2288 1.0020
## week-Vulpes_vulpes 0.0944 0.3845 -0.6972 0.0994 0.8560 1.0053
## week-Sus_scrofa 0.3942 0.3790 -0.2990 0.3799 1.1738 1.0007
## I(week^2)-Canis_latrans -0.1877 0.1008 -0.3873 -0.1881 0.0088 1.0046
## I(week^2)-Sciurus_niger -0.2520 0.2204 -0.7412 -0.2321 0.1324 1.0616
## I(week^2)-Procyon_lotor -0.1122 0.0864 -0.2860 -0.1129 0.0582 1.0059
## I(week^2)-Dasypus_novemcinctus -0.1526 0.1006 -0.3552 -0.1491 0.0399 1.0067
## I(week^2)-Lynx_rufus -0.1942 0.1415 -0.4877 -0.1897 0.0685 1.0241
## I(week^2)-Didelphis_virginiana -0.3396 0.2028 -0.8120 -0.3146 -0.0127 1.0210
## I(week^2)-Sylvilagus_floridanus -0.1477 0.1395 -0.4319 -0.1456 0.1178 1.0235
## I(week^2)-Meleagris_gallopavo -0.3438 0.2202 -0.8677 -0.3241 0.0337 1.0036
## I(week^2)-Sciurus_carolinensis -0.1862 0.1331 -0.4687 -0.1800 0.0617 1.0025
## I(week^2)-Vulpes_vulpes -0.3424 0.2334 -0.8738 -0.3214 0.0589 1.0048
## I(week^2)-Sus_scrofa -0.1504 0.1687 -0.5048 -0.1450 0.1784 1.0063
## ESS
## (Intercept)-Canis_latrans 1192
## (Intercept)-Sciurus_niger 214
## (Intercept)-Procyon_lotor 1633
## (Intercept)-Dasypus_novemcinctus 1877
## (Intercept)-Lynx_rufus 408
## (Intercept)-Didelphis_virginiana 1549
## (Intercept)-Sylvilagus_floridanus 644
## (Intercept)-Meleagris_gallopavo 561
## (Intercept)-Sciurus_carolinensis 1459
## (Intercept)-Vulpes_vulpes 229
## (Intercept)-Sus_scrofa 807
## week-Canis_latrans 1281
## week-Sciurus_niger 541
## week-Procyon_lotor 1598
## week-Dasypus_novemcinctus 1833
## week-Lynx_rufus 1133
## week-Didelphis_virginiana 1326
## week-Sylvilagus_floridanus 1155
## week-Meleagris_gallopavo 657
## week-Sciurus_carolinensis 1166
## week-Vulpes_vulpes 985
## week-Sus_scrofa 1140
## I(week^2)-Canis_latrans 1575
## I(week^2)-Sciurus_niger 377
## I(week^2)-Procyon_lotor 1388
## I(week^2)-Dasypus_novemcinctus 1726
## I(week^2)-Lynx_rufus 795
## I(week^2)-Didelphis_virginiana 544
## I(week^2)-Sylvilagus_floridanus 996
## I(week^2)-Meleagris_gallopavo 286
## I(week^2)-Sciurus_carolinensis 1665
## I(week^2)-Vulpes_vulpes 453
## I(week^2)-Sus_scrofa 1437
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8097
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8718 0.3888 -1.6566 -0.8637 -0.1002 1.0073 261
## Cogon_Patch_Size -0.1995 0.3424 -0.9445 -0.1785 0.4027 1.0053 739
## Avg_Cogongrass_Cover 0.0943 0.2751 -0.4859 0.1107 0.5921 1.0134 799
## total_shrub_cover -0.4024 0.2823 -1.0185 -0.3787 0.1100 1.0059 978
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6918 0.7670 0.0547 0.4758 2.5101 1.0166 638
## Cogon_Patch_Size 0.6178 0.7218 0.0543 0.4078 2.4042 1.0198 626
## Avg_Cogongrass_Cover 0.3603 0.4398 0.0443 0.2275 1.4100 1.0127 764
## total_shrub_cover 0.4429 0.4754 0.0481 0.2926 1.7529 1.0080 686
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2625 1.0062 0.1062 1.0406 3.9913 1.0334 152
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6954 0.2828 -3.2674 -2.6887 -2.1652 1.0043 1376
## week 0.1351 0.1929 -0.2457 0.1293 0.5282 1.0140 761
## I(week^2) -0.2181 0.1037 -0.4350 -0.2129 -0.0336 1.0415 645
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7445 0.5309 0.2065 0.6075 2.1930 1.0019 595
## week 0.2152 0.2164 0.0334 0.1511 0.7628 1.1260 548
## I(week^2) 0.0633 0.0503 0.0178 0.0503 0.1926 1.0335 568
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2216 0.6463 -1.5002 -0.2389
## (Intercept)-Sciurus_niger -1.0202 0.6854 -2.3307 -1.0014
## (Intercept)-Procyon_lotor -0.1004 0.6965 -1.4644 -0.1059
## (Intercept)-Dasypus_novemcinctus -0.8696 0.5385 -2.0553 -0.8380
## (Intercept)-Lynx_rufus -0.6380 0.6560 -1.9489 -0.6560
## (Intercept)-Didelphis_virginiana -1.2601 0.5855 -2.5072 -1.2361
## (Intercept)-Sylvilagus_floridanus -0.7315 0.6325 -1.9693 -0.7285
## (Intercept)-Meleagris_gallopavo -0.8713 0.6103 -2.0940 -0.8587
## (Intercept)-Sciurus_carolinensis -1.3280 0.6012 -2.5845 -1.2799
## (Intercept)-Vulpes_vulpes -1.2479 0.7422 -2.7873 -1.2094
## (Intercept)-Sus_scrofa -1.5286 0.7246 -3.0988 -1.4731
## Cogon_Patch_Size-Canis_latrans 0.5465 0.5749 -0.3626 0.4664
## Cogon_Patch_Size-Sciurus_niger -0.4871 0.7333 -2.2085 -0.4272
## Cogon_Patch_Size-Procyon_lotor -0.2300 0.4301 -1.0636 -0.2284
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1274 0.4011 -0.9336 -0.1218
## Cogon_Patch_Size-Lynx_rufus -0.2752 0.6372 -1.5177 -0.2802
## Cogon_Patch_Size-Didelphis_virginiana 0.5577 0.4821 -0.3126 0.5331
## Cogon_Patch_Size-Sylvilagus_floridanus -0.7685 0.7495 -2.6929 -0.6578
## Cogon_Patch_Size-Meleagris_gallopavo -0.1238 0.5513 -1.2949 -0.1104
## Cogon_Patch_Size-Sciurus_carolinensis -0.5776 0.5888 -1.9307 -0.5091
## Cogon_Patch_Size-Vulpes_vulpes -0.4363 0.6819 -1.9332 -0.3683
## Cogon_Patch_Size-Sus_scrofa -0.3096 0.6595 -1.8028 -0.2530
## Avg_Cogongrass_Cover-Canis_latrans 0.2415 0.3876 -0.4934 0.2273
## Avg_Cogongrass_Cover-Sciurus_niger -0.2740 0.5880 -1.6416 -0.2093
## Avg_Cogongrass_Cover-Procyon_lotor 0.1590 0.3975 -0.6321 0.1601
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3341 0.3623 -0.3650 0.3363
## Avg_Cogongrass_Cover-Lynx_rufus 0.4754 0.4644 -0.2779 0.4298
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1653 0.3938 -0.6292 0.1748
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1918 0.4717 -1.2102 -0.1579
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3693 0.5614 -1.6609 -0.3027
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4132 0.3920 -0.3278 0.3961
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2591 0.4529 -0.6278 0.2429
## Avg_Cogongrass_Cover-Sus_scrofa -0.1524 0.5432 -1.4141 -0.0874
## total_shrub_cover-Canis_latrans 0.0172 0.4167 -0.7577 -0.0065
## total_shrub_cover-Sciurus_niger -0.6229 0.5133 -1.7583 -0.5736
## total_shrub_cover-Procyon_lotor -0.7831 0.4576 -1.8378 -0.7450
## total_shrub_cover-Dasypus_novemcinctus -0.1037 0.3690 -0.8505 -0.1030
## total_shrub_cover-Lynx_rufus -0.7474 0.5937 -2.2012 -0.6718
## total_shrub_cover-Didelphis_virginiana -0.3470 0.4200 -1.2027 -0.3392
## total_shrub_cover-Sylvilagus_floridanus -0.3242 0.4819 -1.3247 -0.3126
## total_shrub_cover-Meleagris_gallopavo -1.0920 0.6168 -2.5481 -0.9889
## total_shrub_cover-Sciurus_carolinensis -0.1491 0.4275 -0.9508 -0.1652
## total_shrub_cover-Vulpes_vulpes -0.3728 0.5490 -1.4952 -0.3648
## total_shrub_cover-Sus_scrofa -0.0459 0.5160 -0.9721 -0.0731
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0931 1.0017 422
## (Intercept)-Sciurus_niger 0.3479 1.0044 364
## (Intercept)-Procyon_lotor 1.2285 1.0192 314
## (Intercept)-Dasypus_novemcinctus 0.1355 1.0041 614
## (Intercept)-Lynx_rufus 0.7229 1.0190 336
## (Intercept)-Didelphis_virginiana -0.1803 1.0020 639
## (Intercept)-Sylvilagus_floridanus 0.5210 1.0116 541
## (Intercept)-Meleagris_gallopavo 0.3494 0.9996 600
## (Intercept)-Sciurus_carolinensis -0.2498 1.0040 761
## (Intercept)-Vulpes_vulpes 0.1636 1.0079 488
## (Intercept)-Sus_scrofa -0.2423 1.0020 538
## Cogon_Patch_Size-Canis_latrans 1.8889 1.0255 1006
## Cogon_Patch_Size-Sciurus_niger 0.7408 1.0027 834
## Cogon_Patch_Size-Procyon_lotor 0.6056 1.0044 1607
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6622 1.0054 1963
## Cogon_Patch_Size-Lynx_rufus 1.0683 1.0181 892
## Cogon_Patch_Size-Didelphis_virginiana 1.5913 1.0184 1223
## Cogon_Patch_Size-Sylvilagus_floridanus 0.3510 1.0143 555
## Cogon_Patch_Size-Meleagris_gallopavo 0.9446 1.0088 1261
## Cogon_Patch_Size-Sciurus_carolinensis 0.3542 1.0045 697
## Cogon_Patch_Size-Vulpes_vulpes 0.7768 1.0173 866
## Cogon_Patch_Size-Sus_scrofa 0.8530 1.0008 959
## Avg_Cogongrass_Cover-Canis_latrans 1.0379 1.0085 1784
## Avg_Cogongrass_Cover-Sciurus_niger 0.6674 1.0141 780
## Avg_Cogongrass_Cover-Procyon_lotor 0.9646 1.0055 1616
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0530 1.0052 1869
## Avg_Cogongrass_Cover-Lynx_rufus 1.5916 1.0128 1267
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9237 1.0044 1948
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6401 1.0090 1040
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5247 1.0048 765
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2114 1.0052 1298
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1913 1.0077 1347
## Avg_Cogongrass_Cover-Sus_scrofa 0.7403 1.0142 927
## total_shrub_cover-Canis_latrans 0.9011 1.0133 1392
## total_shrub_cover-Sciurus_niger 0.2886 1.0050 1080
## total_shrub_cover-Procyon_lotor -0.0208 1.0014 949
## total_shrub_cover-Dasypus_novemcinctus 0.6216 1.0068 2069
## total_shrub_cover-Lynx_rufus 0.2340 1.0030 604
## total_shrub_cover-Didelphis_virginiana 0.4617 1.0009 1956
## total_shrub_cover-Sylvilagus_floridanus 0.5809 1.0132 1360
## total_shrub_cover-Meleagris_gallopavo -0.1646 1.0098 751
## total_shrub_cover-Sciurus_carolinensis 0.7346 1.0131 1851
## total_shrub_cover-Vulpes_vulpes 0.7053 1.0121 932
## total_shrub_cover-Sus_scrofa 1.0398 1.0032 955
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4701 0.1913 -2.8581 -2.4653 -2.1095 1.0087
## (Intercept)-Sciurus_niger -3.6199 0.5204 -4.7316 -3.5789 -2.6738 1.0480
## (Intercept)-Procyon_lotor -2.1958 0.1491 -2.4953 -2.1915 -1.9129 1.0006
## (Intercept)-Dasypus_novemcinctus -1.4934 0.1579 -1.8069 -1.4928 -1.1898 1.0001
## (Intercept)-Lynx_rufus -3.3110 0.3008 -3.9374 -3.2969 -2.7567 1.0078
## (Intercept)-Didelphis_virginiana -2.1919 0.2707 -2.7579 -2.1876 -1.6793 1.0083
## (Intercept)-Sylvilagus_floridanus -3.0940 0.2931 -3.7055 -3.0847 -2.5525 1.0225
## (Intercept)-Meleagris_gallopavo -3.1659 0.3081 -3.8048 -3.1533 -2.5921 1.0017
## (Intercept)-Sciurus_carolinensis -2.3435 0.2874 -2.9431 -2.3324 -1.8125 0.9997
## (Intercept)-Vulpes_vulpes -3.6344 0.6293 -4.9418 -3.5990 -2.5026 1.0047
## (Intercept)-Sus_scrofa -2.9218 0.4848 -3.9566 -2.8839 -2.0968 1.0004
## week-Canis_latrans 0.4327 0.2488 -0.0095 0.4158 0.9578 1.0224
## week-Sciurus_niger -0.2647 0.4380 -1.2806 -0.2190 0.4479 1.0252
## week-Procyon_lotor 0.1491 0.1938 -0.2263 0.1517 0.5322 1.0007
## week-Dasypus_novemcinctus 0.0500 0.2144 -0.3661 0.0488 0.4756 1.0032
## week-Lynx_rufus 0.2508 0.3090 -0.3403 0.2449 0.8727 1.0218
## week-Didelphis_virginiana 0.0029 0.3274 -0.6864 0.0149 0.6237 1.0041
## week-Sylvilagus_floridanus 0.0222 0.2971 -0.5877 0.0324 0.6058 1.0115
## week-Meleagris_gallopavo -0.1637 0.3667 -0.9480 -0.1460 0.4825 1.0249
## week-Sciurus_carolinensis 0.5062 0.3285 -0.0807 0.4874 1.1997 1.0107
## week-Vulpes_vulpes 0.0989 0.3965 -0.7261 0.1110 0.8334 1.0043
## week-Sus_scrofa 0.3817 0.3737 -0.2941 0.3544 1.1867 1.0105
## I(week^2)-Canis_latrans -0.1900 0.1045 -0.4064 -0.1891 0.0007 1.0116
## I(week^2)-Sciurus_niger -0.2404 0.2065 -0.6887 -0.2230 0.1248 1.0312
## I(week^2)-Procyon_lotor -0.1075 0.0847 -0.2772 -0.1062 0.0546 1.0005
## I(week^2)-Dasypus_novemcinctus -0.1478 0.1011 -0.3506 -0.1453 0.0506 0.9997
## I(week^2)-Lynx_rufus -0.1988 0.1440 -0.4932 -0.1952 0.0729 1.0060
## I(week^2)-Didelphis_virginiana -0.3423 0.2001 -0.7921 -0.3212 -0.0170 1.0006
## I(week^2)-Sylvilagus_floridanus -0.1526 0.1459 -0.4563 -0.1502 0.1208 1.0129
## I(week^2)-Meleagris_gallopavo -0.3446 0.2236 -0.8679 -0.3239 0.0274 1.0481
## I(week^2)-Sciurus_carolinensis -0.1842 0.1350 -0.4592 -0.1804 0.0682 1.0076
## I(week^2)-Vulpes_vulpes -0.3352 0.2265 -0.8438 -0.3128 0.0505 1.0205
## I(week^2)-Sus_scrofa -0.1468 0.1629 -0.4828 -0.1439 0.1667 1.0011
## ESS
## (Intercept)-Canis_latrans 1024
## (Intercept)-Sciurus_niger 216
## (Intercept)-Procyon_lotor 1425
## (Intercept)-Dasypus_novemcinctus 2174
## (Intercept)-Lynx_rufus 436
## (Intercept)-Didelphis_virginiana 1378
## (Intercept)-Sylvilagus_floridanus 612
## (Intercept)-Meleagris_gallopavo 672
## (Intercept)-Sciurus_carolinensis 1247
## (Intercept)-Vulpes_vulpes 274
## (Intercept)-Sus_scrofa 526
## week-Canis_latrans 1234
## week-Sciurus_niger 433
## week-Procyon_lotor 1776
## week-Dasypus_novemcinctus 1996
## week-Lynx_rufus 985
## week-Didelphis_virginiana 1081
## week-Sylvilagus_floridanus 1040
## week-Meleagris_gallopavo 515
## week-Sciurus_carolinensis 1266
## week-Vulpes_vulpes 877
## week-Sus_scrofa 1109
## I(week^2)-Canis_latrans 1269
## I(week^2)-Sciurus_niger 589
## I(week^2)-Procyon_lotor 1711
## I(week^2)-Dasypus_novemcinctus 1663
## I(week^2)-Lynx_rufus 812
## I(week^2)-Didelphis_virginiana 524
## I(week^2)-Sylvilagus_floridanus 793
## I(week^2)-Meleagris_gallopavo 285
## I(week^2)-Sciurus_carolinensis 1394
## I(week^2)-Vulpes_vulpes 461
## I(week^2)-Sus_scrofa 1544
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9698
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.8090 0.3468 -1.5009 -0.8130 -0.1151 1.0102 448
## Veg_shannon_index 0.3520 0.2369 -0.1008 0.3434 0.8468 1.0299 863
## Avg_Cogongrass_Cover 0.1987 0.2558 -0.3243 0.2015 0.7152 1.0106 864
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6298 0.5968 0.0611 0.4580 2.1253 1.0023 699
## Veg_shannon_index 0.2394 0.2366 0.0335 0.1671 0.8786 1.0100 830
## Avg_Cogongrass_Cover 0.3342 0.3628 0.0429 0.2179 1.2935 1.0225 808
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9857 0.7912 0.1032 0.7914 3.0815 1.0778 179
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7059 0.2799 -3.2661 -2.6978 -2.1457 1.0046 1556
## week 0.1385 0.1935 -0.2593 0.1427 0.4990 1.0039 928
## I(week^2) -0.2260 0.1109 -0.4644 -0.2220 -0.0336 1.0047 431
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7627 0.6128 0.2013 0.6073 2.2182 1.0171 475
## week 0.2149 0.1907 0.0359 0.1599 0.7445 1.0509 657
## I(week^2) 0.0728 0.0741 0.0184 0.0526 0.2504 1.1814 193
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2637 0.5705 -1.3378 -0.2869
## (Intercept)-Sciurus_niger -0.9131 0.6620 -2.2081 -0.9258
## (Intercept)-Procyon_lotor -0.0996 0.6061 -1.2445 -0.1147
## (Intercept)-Dasypus_novemcinctus -0.8152 0.4774 -1.7791 -0.8178
## (Intercept)-Lynx_rufus -0.5592 0.6490 -1.7296 -0.5993
## (Intercept)-Didelphis_virginiana -1.2207 0.5389 -2.3579 -1.2091
## (Intercept)-Sylvilagus_floridanus -0.6716 0.5507 -1.7369 -0.6913
## (Intercept)-Meleagris_gallopavo -0.7087 0.5949 -1.8876 -0.7175
## (Intercept)-Sciurus_carolinensis -1.2438 0.5500 -2.3905 -1.2138
## (Intercept)-Vulpes_vulpes -1.1712 0.7124 -2.5796 -1.1637
## (Intercept)-Sus_scrofa -1.5262 0.6735 -2.9769 -1.4800
## Veg_shannon_index-Canis_latrans 0.6171 0.3748 -0.0649 0.6006
## Veg_shannon_index-Sciurus_niger 0.3091 0.4498 -0.5742 0.2981
## Veg_shannon_index-Procyon_lotor 0.4692 0.3740 -0.2202 0.4471
## Veg_shannon_index-Dasypus_novemcinctus 0.1981 0.3300 -0.4786 0.1997
## Veg_shannon_index-Lynx_rufus 0.2034 0.4383 -0.7010 0.2093
## Veg_shannon_index-Didelphis_virginiana 0.4599 0.3721 -0.2104 0.4417
## Veg_shannon_index-Sylvilagus_floridanus 0.4282 0.3881 -0.2822 0.4083
## Veg_shannon_index-Meleagris_gallopavo 0.4533 0.4283 -0.3258 0.4316
## Veg_shannon_index-Sciurus_carolinensis 0.0274 0.3798 -0.7552 0.0439
## Veg_shannon_index-Vulpes_vulpes 0.0894 0.4329 -0.8444 0.1067
## Veg_shannon_index-Sus_scrofa 0.6018 0.4473 -0.1817 0.5600
## Avg_Cogongrass_Cover-Canis_latrans 0.4714 0.3798 -0.1905 0.4485
## Avg_Cogongrass_Cover-Sciurus_niger -0.1907 0.5518 -1.4470 -0.1311
## Avg_Cogongrass_Cover-Procyon_lotor 0.3530 0.3920 -0.3377 0.3346
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3896 0.3393 -0.2732 0.3801
## Avg_Cogongrass_Cover-Lynx_rufus 0.5500 0.4388 -0.1962 0.5119
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4130 0.3878 -0.3180 0.3962
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1740 0.4372 -1.1061 -0.1469
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2100 0.5198 -1.3436 -0.1449
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3717 0.3688 -0.3154 0.3621
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3217 0.4656 -0.5418 0.2963
## Avg_Cogongrass_Cover-Sus_scrofa -0.0542 0.4982 -1.1335 -0.0100
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9204 1.0006 599
## (Intercept)-Sciurus_niger 0.4456 1.0028 442
## (Intercept)-Procyon_lotor 1.1203 1.0076 422
## (Intercept)-Dasypus_novemcinctus 0.1288 1.0033 1146
## (Intercept)-Lynx_rufus 0.8537 1.0419 591
## (Intercept)-Didelphis_virginiana -0.2050 1.0030 932
## (Intercept)-Sylvilagus_floridanus 0.4758 1.0036 718
## (Intercept)-Meleagris_gallopavo 0.4998 1.0126 725
## (Intercept)-Sciurus_carolinensis -0.2254 1.0093 1100
## (Intercept)-Vulpes_vulpes 0.2507 1.0037 357
## (Intercept)-Sus_scrofa -0.3495 1.0018 684
## Veg_shannon_index-Canis_latrans 1.4189 1.0129 1485
## Veg_shannon_index-Sciurus_niger 1.2522 1.0136 1167
## Veg_shannon_index-Procyon_lotor 1.2859 1.0042 1167
## Veg_shannon_index-Dasypus_novemcinctus 0.8216 1.0004 1779
## Veg_shannon_index-Lynx_rufus 1.0295 1.0125 1179
## Veg_shannon_index-Didelphis_virginiana 1.2604 1.0198 1864
## Veg_shannon_index-Sylvilagus_floridanus 1.2760 1.0032 1632
## Veg_shannon_index-Meleagris_gallopavo 1.3487 1.0089 1095
## Veg_shannon_index-Sciurus_carolinensis 0.7312 1.0256 1420
## Veg_shannon_index-Vulpes_vulpes 0.9190 1.0065 1097
## Veg_shannon_index-Sus_scrofa 1.5926 1.0129 1037
## Avg_Cogongrass_Cover-Canis_latrans 1.2932 1.0061 1450
## Avg_Cogongrass_Cover-Sciurus_niger 0.7425 1.0170 721
## Avg_Cogongrass_Cover-Procyon_lotor 1.2205 1.0005 1342
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0668 1.0021 2427
## Avg_Cogongrass_Cover-Lynx_rufus 1.5534 1.0113 1435
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2064 1.0044 1879
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5931 1.0179 945
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6878 1.0052 1010
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1249 1.0010 1876
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3525 1.0006 1155
## Avg_Cogongrass_Cover-Sus_scrofa 0.8166 1.0184 1111
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4530 0.1858 -2.8227 -2.4519 -2.1083 1.0000
## (Intercept)-Sciurus_niger -3.6237 0.5370 -4.7548 -3.5769 -2.6821 1.0195
## (Intercept)-Procyon_lotor -2.1916 0.1520 -2.4947 -2.1860 -1.9043 1.0019
## (Intercept)-Dasypus_novemcinctus -1.4961 0.1605 -1.8167 -1.4901 -1.2007 1.0049
## (Intercept)-Lynx_rufus -3.3125 0.3111 -3.9622 -3.2916 -2.7353 1.0543
## (Intercept)-Didelphis_virginiana -2.1898 0.2685 -2.7478 -2.1792 -1.6889 1.0069
## (Intercept)-Sylvilagus_floridanus -3.0651 0.3042 -3.7001 -3.0606 -2.5029 1.0028
## (Intercept)-Meleagris_gallopavo -3.2487 0.3400 -3.9546 -3.2332 -2.6106 1.0096
## (Intercept)-Sciurus_carolinensis -2.3633 0.2872 -2.9621 -2.3503 -1.8312 1.0017
## (Intercept)-Vulpes_vulpes -3.6317 0.6030 -4.8748 -3.5860 -2.5575 1.0081
## (Intercept)-Sus_scrofa -2.9106 0.4704 -3.9277 -2.8828 -2.0849 1.0131
## week-Canis_latrans 0.4375 0.2455 -0.0163 0.4276 0.9520 1.0130
## week-Sciurus_niger -0.2731 0.4324 -1.2381 -0.2234 0.4561 1.0354
## week-Procyon_lotor 0.1460 0.1993 -0.2505 0.1455 0.5492 1.0025
## week-Dasypus_novemcinctus 0.0556 0.2104 -0.3550 0.0599 0.4596 1.0029
## week-Lynx_rufus 0.2426 0.3055 -0.3495 0.2353 0.8657 1.0105
## week-Didelphis_virginiana 0.0054 0.3187 -0.6438 0.0113 0.5904 1.0011
## week-Sylvilagus_floridanus 0.0163 0.2976 -0.6028 0.0244 0.5838 1.0018
## week-Meleagris_gallopavo -0.1713 0.3646 -1.0160 -0.1404 0.4533 1.0109
## week-Sciurus_carolinensis 0.5286 0.3350 -0.0498 0.4999 1.2509 1.0250
## week-Vulpes_vulpes 0.0776 0.4035 -0.7736 0.0974 0.8266 1.0070
## week-Sus_scrofa 0.4030 0.3763 -0.3010 0.3867 1.2020 1.0336
## I(week^2)-Canis_latrans -0.1915 0.1062 -0.4116 -0.1881 0.0034 1.0010
## I(week^2)-Sciurus_niger -0.2558 0.2255 -0.7767 -0.2375 0.1288 1.0344
## I(week^2)-Procyon_lotor -0.1071 0.0877 -0.2749 -0.1055 0.0686 1.0028
## I(week^2)-Dasypus_novemcinctus -0.1477 0.0987 -0.3422 -0.1460 0.0442 1.0069
## I(week^2)-Lynx_rufus -0.1996 0.1496 -0.5064 -0.1915 0.0686 1.0148
## I(week^2)-Didelphis_virginiana -0.3553 0.2210 -0.8362 -0.3323 -0.0018 1.0173
## I(week^2)-Sylvilagus_floridanus -0.1534 0.1517 -0.4604 -0.1495 0.1402 1.0121
## I(week^2)-Meleagris_gallopavo -0.3856 0.2768 -1.0022 -0.3469 -0.0030 1.0404
## I(week^2)-Sciurus_carolinensis -0.1875 0.1373 -0.4641 -0.1801 0.0693 1.0170
## I(week^2)-Vulpes_vulpes -0.3620 0.2652 -0.9985 -0.3243 0.0483 1.0274
## I(week^2)-Sus_scrofa -0.1522 0.1648 -0.5013 -0.1437 0.1507 1.0126
## ESS
## (Intercept)-Canis_latrans 1523
## (Intercept)-Sciurus_niger 247
## (Intercept)-Procyon_lotor 1775
## (Intercept)-Dasypus_novemcinctus 1933
## (Intercept)-Lynx_rufus 434
## (Intercept)-Didelphis_virginiana 1477
## (Intercept)-Sylvilagus_floridanus 621
## (Intercept)-Meleagris_gallopavo 475
## (Intercept)-Sciurus_carolinensis 1354
## (Intercept)-Vulpes_vulpes 281
## (Intercept)-Sus_scrofa 582
## week-Canis_latrans 1301
## week-Sciurus_niger 532
## week-Procyon_lotor 1667
## week-Dasypus_novemcinctus 2241
## week-Lynx_rufus 951
## week-Didelphis_virginiana 1350
## week-Sylvilagus_floridanus 994
## week-Meleagris_gallopavo 451
## week-Sciurus_carolinensis 1174
## week-Vulpes_vulpes 614
## week-Sus_scrofa 1297
## I(week^2)-Canis_latrans 1346
## I(week^2)-Sciurus_niger 477
## I(week^2)-Procyon_lotor 1568
## I(week^2)-Dasypus_novemcinctus 1766
## I(week^2)-Lynx_rufus 699
## I(week^2)-Didelphis_virginiana 494
## I(week^2)-Sylvilagus_floridanus 778
## I(week^2)-Meleagris_gallopavo 171
## I(week^2)-Sciurus_carolinensis 1288
## I(week^2)-Vulpes_vulpes 363
## I(week^2)-Sus_scrofa 1397
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9648
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7696 0.3333 -1.4324 -0.7601 -0.1249 1.0050 582
## Avg_Cogongrass_Cover 0.0973 0.2297 -0.3880 0.1094 0.5294 1.0014 1203
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6081 0.6856 0.0540 0.4227 2.2949 1.0193 678
## Avg_Cogongrass_Cover 0.3080 0.3415 0.0397 0.2062 1.3042 1.0433 702
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8774 0.6563 0.0864 0.7271 2.5579 1.0158 202
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6977 0.2851 -3.2681 -2.6880 -2.1364 1.0033 1767
## week 0.1399 0.1873 -0.2358 0.1411 0.5032 1.0008 763
## I(week^2) -0.2252 0.1083 -0.4586 -0.2173 -0.0313 1.0388 526
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7533 0.5606 0.2175 0.6063 2.1902 1.0077 863
## week 0.2184 0.2108 0.0348 0.1590 0.7545 1.0088 613
## I(week^2) 0.0664 0.0582 0.0179 0.0497 0.2155 1.0502 505
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.2184 0.5289 -1.1958 -0.2469
## (Intercept)-Sciurus_niger -0.8935 0.6342 -2.1655 -0.8986
## (Intercept)-Procyon_lotor -0.0515 0.5938 -1.1508 -0.0748
## (Intercept)-Dasypus_novemcinctus -0.7747 0.4777 -1.7557 -0.7632
## (Intercept)-Lynx_rufus -0.5555 0.5712 -1.6364 -0.5631
## (Intercept)-Didelphis_virginiana -1.1476 0.5300 -2.2869 -1.1154
## (Intercept)-Sylvilagus_floridanus -0.6121 0.5369 -1.6705 -0.6144
## (Intercept)-Meleagris_gallopavo -0.6745 0.5687 -1.7214 -0.6811
## (Intercept)-Sciurus_carolinensis -1.1914 0.5400 -2.3079 -1.1586
## (Intercept)-Vulpes_vulpes -1.1570 0.7121 -2.7125 -1.1260
## (Intercept)-Sus_scrofa -1.3741 0.6468 -2.7549 -1.3293
## Avg_Cogongrass_Cover-Canis_latrans 0.3100 0.3493 -0.3198 0.2831
## Avg_Cogongrass_Cover-Sciurus_niger -0.2482 0.5036 -1.3916 -0.1967
## Avg_Cogongrass_Cover-Procyon_lotor 0.1885 0.3452 -0.4584 0.1741
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3156 0.3230 -0.2865 0.3075
## Avg_Cogongrass_Cover-Lynx_rufus 0.4252 0.3926 -0.2422 0.3822
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2995 0.3544 -0.4054 0.2962
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2464 0.4185 -1.1928 -0.2094
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3033 0.4725 -1.3434 -0.2533
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3086 0.3548 -0.3616 0.2920
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2327 0.4256 -0.5727 0.2155
## Avg_Cogongrass_Cover-Sus_scrofa -0.1579 0.4858 -1.3067 -0.1040
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8987 1.0074 715
## (Intercept)-Sciurus_niger 0.4140 1.0006 493
## (Intercept)-Procyon_lotor 1.1620 1.0118 561
## (Intercept)-Dasypus_novemcinctus 0.1223 1.0145 1082
## (Intercept)-Lynx_rufus 0.6353 1.0033 753
## (Intercept)-Didelphis_virginiana -0.1821 1.0244 923
## (Intercept)-Sylvilagus_floridanus 0.4908 1.0023 835
## (Intercept)-Meleagris_gallopavo 0.4463 1.0082 686
## (Intercept)-Sciurus_carolinensis -0.1856 1.0048 737
## (Intercept)-Vulpes_vulpes 0.1604 1.0089 298
## (Intercept)-Sus_scrofa -0.2467 1.0028 661
## Avg_Cogongrass_Cover-Canis_latrans 1.0527 1.0064 1705
## Avg_Cogongrass_Cover-Sciurus_niger 0.5833 1.0167 802
## Avg_Cogongrass_Cover-Procyon_lotor 0.9004 1.0012 2229
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9795 1.0110 2543
## Avg_Cogongrass_Cover-Lynx_rufus 1.3048 1.0314 1460
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0155 1.0026 2150
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.4631 1.0013 1453
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.4900 1.0024 1037
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0519 1.0031 2198
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.0999 1.0034 1525
## Avg_Cogongrass_Cover-Sus_scrofa 0.6766 1.0067 1083
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4611 0.1881 -2.8463 -2.4543 -2.1057 1.0068
## (Intercept)-Sciurus_niger -3.6036 0.5256 -4.7326 -3.5596 -2.7134 1.0179
## (Intercept)-Procyon_lotor -2.1826 0.1530 -2.4999 -2.1793 -1.8891 1.0070
## (Intercept)-Dasypus_novemcinctus -1.4949 0.1553 -1.7987 -1.4942 -1.1893 1.0009
## (Intercept)-Lynx_rufus -3.2768 0.3047 -3.8766 -3.2717 -2.7005 1.0035
## (Intercept)-Didelphis_virginiana -2.2054 0.2723 -2.7614 -2.1952 -1.6934 1.0123
## (Intercept)-Sylvilagus_floridanus -3.0463 0.3024 -3.6950 -3.0318 -2.4872 1.0064
## (Intercept)-Meleagris_gallopavo -3.2035 0.3322 -3.9130 -3.1902 -2.6015 1.0057
## (Intercept)-Sciurus_carolinensis -2.3589 0.2879 -2.9435 -2.3513 -1.8247 1.0018
## (Intercept)-Vulpes_vulpes -3.6693 0.6174 -4.9435 -3.6309 -2.5571 1.0100
## (Intercept)-Sus_scrofa -2.9347 0.4889 -3.9648 -2.9079 -2.0689 1.0116
## week-Canis_latrans 0.4402 0.2447 -0.0161 0.4310 0.9534 1.0033
## week-Sciurus_niger -0.2860 0.4388 -1.2935 -0.2316 0.4469 1.0012
## week-Procyon_lotor 0.1567 0.1995 -0.2261 0.1565 0.5473 1.0243
## week-Dasypus_novemcinctus 0.0534 0.2131 -0.3738 0.0547 0.4722 1.0003
## week-Lynx_rufus 0.2606 0.2958 -0.3204 0.2542 0.8670 1.0017
## week-Didelphis_virginiana -0.0048 0.3184 -0.6799 0.0006 0.6000 1.0018
## week-Sylvilagus_floridanus 0.0186 0.2972 -0.5700 0.0235 0.5828 1.0048
## week-Meleagris_gallopavo -0.1416 0.3533 -0.9238 -0.1061 0.4815 1.0102
## week-Sciurus_carolinensis 0.5335 0.3378 -0.0576 0.5075 1.2736 1.0030
## week-Vulpes_vulpes 0.0980 0.4077 -0.7390 0.1118 0.8628 1.0027
## week-Sus_scrofa 0.4073 0.3799 -0.2755 0.3828 1.2266 1.0066
## I(week^2)-Canis_latrans -0.1890 0.1012 -0.3906 -0.1873 -0.0005 1.0046
## I(week^2)-Sciurus_niger -0.2652 0.2188 -0.7564 -0.2447 0.1175 1.0135
## I(week^2)-Procyon_lotor -0.1096 0.0886 -0.2764 -0.1090 0.0635 1.0217
## I(week^2)-Dasypus_novemcinctus -0.1484 0.0992 -0.3475 -0.1509 0.0469 1.0007
## I(week^2)-Lynx_rufus -0.2014 0.1427 -0.4958 -0.1936 0.0641 1.0120
## I(week^2)-Didelphis_virginiana -0.3447 0.2158 -0.8603 -0.3171 0.0040 1.0264
## I(week^2)-Sylvilagus_floridanus -0.1597 0.1445 -0.4442 -0.1588 0.1091 0.9997
## I(week^2)-Meleagris_gallopavo -0.3391 0.2259 -0.8579 -0.3177 0.0277 1.0641
## I(week^2)-Sciurus_carolinensis -0.1892 0.1399 -0.4857 -0.1829 0.0725 1.0107
## I(week^2)-Vulpes_vulpes -0.3616 0.2593 -0.9929 -0.3219 0.0559 1.0464
## I(week^2)-Sus_scrofa -0.1628 0.1642 -0.5017 -0.1574 0.1462 1.0083
## ESS
## (Intercept)-Canis_latrans 1068
## (Intercept)-Sciurus_niger 254
## (Intercept)-Procyon_lotor 1593
## (Intercept)-Dasypus_novemcinctus 2404
## (Intercept)-Lynx_rufus 531
## (Intercept)-Didelphis_virginiana 1471
## (Intercept)-Sylvilagus_floridanus 581
## (Intercept)-Meleagris_gallopavo 527
## (Intercept)-Sciurus_carolinensis 1319
## (Intercept)-Vulpes_vulpes 202
## (Intercept)-Sus_scrofa 617
## week-Canis_latrans 1247
## week-Sciurus_niger 493
## week-Procyon_lotor 1676
## week-Dasypus_novemcinctus 1715
## week-Lynx_rufus 922
## week-Didelphis_virginiana 1064
## week-Sylvilagus_floridanus 1248
## week-Meleagris_gallopavo 617
## week-Sciurus_carolinensis 1046
## week-Vulpes_vulpes 776
## week-Sus_scrofa 1106
## I(week^2)-Canis_latrans 1437
## I(week^2)-Sciurus_niger 394
## I(week^2)-Procyon_lotor 1546
## I(week^2)-Dasypus_novemcinctus 1692
## I(week^2)-Lynx_rufus 804
## I(week^2)-Didelphis_virginiana 551
## I(week^2)-Sylvilagus_floridanus 808
## I(week^2)-Meleagris_gallopavo 295
## I(week^2)-Sciurus_carolinensis 963
## I(week^2)-Vulpes_vulpes 370
## I(week^2)-Sus_scrofa 1420
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9985
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4304 0.3802 -2.2017 -1.4269 -0.6846 1.0469 496
## Avg_Cogongrass_Cover -0.7296 0.3337 -1.3836 -0.7232 -0.0777 1.0390 461
## I(Avg_Cogongrass_Cover^2) 0.6757 0.3177 0.0566 0.6668 1.3364 1.0171 628
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6425 0.6542 0.0612 0.4538 2.2819 1.0037 675
## Avg_Cogongrass_Cover 0.3272 0.4241 0.0387 0.2000 1.4371 1.0672 653
## I(Avg_Cogongrass_Cover^2) 0.5949 0.9793 0.0399 0.3005 2.9312 1.0989 216
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6206 0.6067 0.0683 0.4563 2.0319 1.1464 159
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6729 0.2653 -3.2139 -2.6684 -2.1724 1.0237 1513
## week 0.1461 0.1851 -0.2283 0.1450 0.5164 1.0339 952
## I(week^2) -0.2184 0.1038 -0.4375 -0.2110 -0.0350 1.0085 604
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6628 0.4752 0.1810 0.5352 1.8122 1.0448 917
## week 0.1970 0.1857 0.0341 0.1412 0.7122 1.0024 615
## I(week^2) 0.0624 0.0480 0.0178 0.0494 0.1899 1.0715 635
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.9122 0.5906 -1.9920 -0.9438
## (Intercept)-Sciurus_niger -1.4257 0.6444 -2.7318 -1.4191
## (Intercept)-Procyon_lotor -0.7262 0.6137 -1.8657 -0.7430
## (Intercept)-Dasypus_novemcinctus -1.4256 0.5184 -2.4924 -1.4150
## (Intercept)-Lynx_rufus -1.4039 0.6112 -2.6037 -1.4003
## (Intercept)-Didelphis_virginiana -1.7556 0.5707 -2.9762 -1.7198
## (Intercept)-Sylvilagus_floridanus -1.2533 0.6051 -2.3950 -1.2657
## (Intercept)-Meleagris_gallopavo -1.1620 0.6020 -2.2814 -1.1861
## (Intercept)-Sciurus_carolinensis -2.0112 0.6284 -3.3773 -1.9688
## (Intercept)-Vulpes_vulpes -1.9766 0.7430 -3.5828 -1.9186
## (Intercept)-Sus_scrofa -2.0021 0.6702 -3.4759 -1.9244
## Avg_Cogongrass_Cover-Canis_latrans -0.5127 0.4808 -1.3937 -0.5192
## Avg_Cogongrass_Cover-Sciurus_niger -0.9501 0.5835 -2.2168 -0.9070
## Avg_Cogongrass_Cover-Procyon_lotor -0.6778 0.4765 -1.6005 -0.6848
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5505 0.4481 -1.4353 -0.5590
## Avg_Cogongrass_Cover-Lynx_rufus -0.6227 0.5025 -1.5776 -0.6213
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4139 0.4945 -1.3036 -0.4359
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0903 0.5411 -2.2807 -1.0431
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9611 0.5431 -2.1798 -0.9346
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7026 0.4790 -1.6779 -0.6970
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7098 0.5452 -1.7842 -0.7043
## Avg_Cogongrass_Cover-Sus_scrofa -0.9058 0.5578 -2.1078 -0.8593
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2994 0.7816 0.2858 1.1035
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.1239 0.6554 -1.4789 0.2250
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1896 0.7249 0.2743 1.0318
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7009 0.3360 0.0609 0.6950
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1561 0.5249 0.3363 1.0725
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4919 0.3803 -0.2231 0.4846
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6860 0.4386 -0.0878 0.6551
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1663 0.5464 -1.0637 0.2162
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8434 0.3687 0.1614 0.8206
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.7994 0.4906 0.0384 0.7404
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.1294 0.6723 -1.6863 0.2457
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2587 1.0193 809
## (Intercept)-Sciurus_niger -0.0739 1.0386 540
## (Intercept)-Procyon_lotor 0.4851 1.0245 587
## (Intercept)-Dasypus_novemcinctus -0.4360 1.0061 891
## (Intercept)-Lynx_rufus -0.1951 1.0132 820
## (Intercept)-Didelphis_virginiana -0.6991 1.0125 735
## (Intercept)-Sylvilagus_floridanus -0.0628 1.0214 850
## (Intercept)-Meleagris_gallopavo 0.0928 1.0193 748
## (Intercept)-Sciurus_carolinensis -0.9186 1.0041 655
## (Intercept)-Vulpes_vulpes -0.6449 1.0099 486
## (Intercept)-Sus_scrofa -0.8136 1.0015 768
## Avg_Cogongrass_Cover-Canis_latrans 0.4864 1.0209 1047
## Avg_Cogongrass_Cover-Sciurus_niger 0.0598 1.0213 616
## Avg_Cogongrass_Cover-Procyon_lotor 0.3114 1.0116 906
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3368 1.0240 774
## Avg_Cogongrass_Cover-Lynx_rufus 0.3808 1.0071 990
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6343 1.0040 950
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1504 1.0318 681
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0094 1.0300 697
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2369 1.0243 869
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3790 1.0042 856
## Avg_Cogongrass_Cover-Sus_scrofa 0.1135 1.0268 771
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3596 1.0160 258
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.1739 1.0061 353
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0910 1.0362 330
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4180 1.0077 1040
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4396 1.0282 521
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2633 1.0032 643
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6903 1.0188 706
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.0909 1.0120 518
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6575 1.0107 904
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.9973 1.0313 328
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.1109 1.0169 371
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4866 0.1836 -2.8412 -2.4856 -2.1203 1.0079
## (Intercept)-Sciurus_niger -3.5202 0.5019 -4.5472 -3.4955 -2.6018 1.1092
## (Intercept)-Procyon_lotor -2.1912 0.1466 -2.4911 -2.1882 -1.9146 1.0003
## (Intercept)-Dasypus_novemcinctus -1.4961 0.1564 -1.8192 -1.4934 -1.2000 1.0010
## (Intercept)-Lynx_rufus -3.1952 0.2992 -3.8093 -3.1819 -2.6345 1.0032
## (Intercept)-Didelphis_virginiana -2.2191 0.2687 -2.7941 -2.1988 -1.7309 1.0041
## (Intercept)-Sylvilagus_floridanus -3.0599 0.3050 -3.7062 -3.0505 -2.5014 1.0105
## (Intercept)-Meleagris_gallopavo -3.1637 0.3413 -3.8762 -3.1469 -2.5450 1.0161
## (Intercept)-Sciurus_carolinensis -2.3575 0.2865 -2.9658 -2.3376 -1.8147 1.0051
## (Intercept)-Vulpes_vulpes -3.5202 0.5969 -4.8000 -3.4797 -2.4759 1.0253
## (Intercept)-Sus_scrofa -2.8439 0.4503 -3.8066 -2.8276 -2.0294 1.0323
## week-Canis_latrans 0.4226 0.2380 -0.0223 0.4135 0.9100 1.0050
## week-Sciurus_niger -0.2294 0.4160 -1.1803 -0.1746 0.4515 1.0022
## week-Procyon_lotor 0.1492 0.1932 -0.2299 0.1455 0.5389 1.0052
## week-Dasypus_novemcinctus 0.0541 0.2108 -0.3633 0.0566 0.4692 1.0075
## week-Lynx_rufus 0.2398 0.2992 -0.3202 0.2261 0.8332 1.0281
## week-Didelphis_virginiana -0.0011 0.3114 -0.6472 0.0058 0.5799 1.0326
## week-Sylvilagus_floridanus 0.0419 0.2913 -0.5649 0.0508 0.6088 1.0152
## week-Meleagris_gallopavo -0.1159 0.3516 -0.8785 -0.0922 0.5086 1.0258
## week-Sciurus_carolinensis 0.5252 0.3381 -0.0782 0.4996 1.2574 1.0168
## week-Vulpes_vulpes 0.1187 0.3878 -0.6567 0.1218 0.8961 1.0114
## week-Sus_scrofa 0.3962 0.3659 -0.2705 0.3742 1.1522 1.0138
## I(week^2)-Canis_latrans -0.1902 0.1024 -0.3932 -0.1892 0.0003 1.0069
## I(week^2)-Sciurus_niger -0.2482 0.2099 -0.7496 -0.2268 0.1000 1.0078
## I(week^2)-Procyon_lotor -0.1088 0.0859 -0.2847 -0.1080 0.0547 1.0017
## I(week^2)-Dasypus_novemcinctus -0.1509 0.0979 -0.3433 -0.1480 0.0321 1.0022
## I(week^2)-Lynx_rufus -0.2064 0.1515 -0.5319 -0.2002 0.0722 1.0278
## I(week^2)-Didelphis_virginiana -0.3447 0.1980 -0.7870 -0.3230 -0.0180 1.0355
## I(week^2)-Sylvilagus_floridanus -0.1601 0.1480 -0.4571 -0.1543 0.1156 1.0239
## I(week^2)-Meleagris_gallopavo -0.3294 0.2174 -0.8474 -0.3057 0.0304 1.0251
## I(week^2)-Sciurus_carolinensis -0.1893 0.1363 -0.4850 -0.1841 0.0546 1.0132
## I(week^2)-Vulpes_vulpes -0.3287 0.2293 -0.8579 -0.3006 0.0351 1.0535
## I(week^2)-Sus_scrofa -0.1586 0.1637 -0.4950 -0.1525 0.1542 1.0052
## ESS
## (Intercept)-Canis_latrans 1290
## (Intercept)-Sciurus_niger 330
## (Intercept)-Procyon_lotor 1595
## (Intercept)-Dasypus_novemcinctus 2459
## (Intercept)-Lynx_rufus 493
## (Intercept)-Didelphis_virginiana 1432
## (Intercept)-Sylvilagus_floridanus 649
## (Intercept)-Meleagris_gallopavo 472
## (Intercept)-Sciurus_carolinensis 1139
## (Intercept)-Vulpes_vulpes 258
## (Intercept)-Sus_scrofa 748
## week-Canis_latrans 1080
## week-Sciurus_niger 438
## week-Procyon_lotor 1705
## week-Dasypus_novemcinctus 2095
## week-Lynx_rufus 1084
## week-Didelphis_virginiana 1198
## week-Sylvilagus_floridanus 1092
## week-Meleagris_gallopavo 571
## week-Sciurus_carolinensis 1000
## week-Vulpes_vulpes 1045
## week-Sus_scrofa 1118
## I(week^2)-Canis_latrans 1216
## I(week^2)-Sciurus_niger 481
## I(week^2)-Procyon_lotor 1690
## I(week^2)-Dasypus_novemcinctus 1606
## I(week^2)-Lynx_rufus 684
## I(week^2)-Didelphis_virginiana 468
## I(week^2)-Sylvilagus_floridanus 727
## I(week^2)-Meleagris_gallopavo 267
## I(week^2)-Sciurus_carolinensis 1242
## I(week^2)-Vulpes_vulpes 499
## I(week^2)-Sus_scrofa 1461
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.1023
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2153 0.6831 -3.5441 -2.2492 -0.8494 1.0200 420
## Cogon_Patch_Size -0.2760 0.6103 -1.5483 -0.2605 0.9214 1.0152 519
## Veg_shannon_index 0.9529 0.4318 0.1177 0.9307 1.8340 1.0284 333
## total_shrub_cover -0.7532 0.4972 -1.8110 -0.7328 0.1750 1.0076 484
## Avg_Cogongrass_Cover 0.4738 0.8534 -1.1603 0.4511 2.1831 1.0667 140
## Tree_Density -2.3027 0.7142 -3.7107 -2.2717 -0.9699 1.0237 218
## Avg_Canopy_Cover 1.8012 0.5362 0.8624 1.7483 3.0130 1.0263 160
## I(Avg_Cogongrass_Cover^2) 1.0919 0.5864 -0.1581 1.0930 2.2152 1.0174 410
## avg_veg_height -0.3759 0.4517 -1.2764 -0.3651 0.4982 1.0324 211
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8916 3.4758 0.1062 1.8633 11.4444 1.0110 240
## Cogon_Patch_Size 2.3408 2.8860 0.1679 1.4507 10.0617 1.1048 318
## Veg_shannon_index 0.6985 1.1817 0.0486 0.3653 3.2754 1.1893 257
## total_shrub_cover 1.8586 2.2230 0.1234 1.2149 7.7275 1.0325 277
## Avg_Cogongrass_Cover 1.0840 1.7830 0.0487 0.4982 5.3869 1.0397 328
## Tree_Density 1.5554 2.6951 0.0562 0.6501 8.8737 1.0456 157
## Avg_Canopy_Cover 1.3620 1.8786 0.0719 0.7557 6.5134 1.1105 228
## I(Avg_Cogongrass_Cover^2) 2.3443 3.1158 0.0844 1.3524 10.3916 1.0868 182
## avg_veg_height 0.4422 0.6480 0.0416 0.2483 1.9943 1.1160 343
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8004 2.2764 0.0804 1.1341 8.1102 2.0425 62
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7610 0.3096 -3.3850 -2.7643 -2.1448 1.0021 1534
## week 0.1386 0.1945 -0.2327 0.1370 0.5100 1.0075 885
## I(week^2) -0.2166 0.1038 -0.4427 -0.2107 -0.0283 1.0343 565
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9875 0.6962 0.3027 0.8161 2.6990 1.0303 802
## week 0.2148 0.2043 0.0368 0.1576 0.7545 1.0921 714
## I(week^2) 0.0648 0.0513 0.0179 0.0503 0.2042 1.0343 481
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5562 1.0053 -3.4357 -1.5921
## (Intercept)-Sciurus_niger -1.2852 1.4069 -3.5824 -1.4691
## (Intercept)-Procyon_lotor -1.1476 1.0421 -3.1693 -1.1482
## (Intercept)-Dasypus_novemcinctus -2.4248 0.8874 -4.3648 -2.3798
## (Intercept)-Lynx_rufus -1.6009 1.2669 -3.9003 -1.7064
## (Intercept)-Didelphis_virginiana -3.2649 1.1392 -5.8094 -3.1404
## (Intercept)-Sylvilagus_floridanus -2.1851 1.0095 -4.1760 -2.1731
## (Intercept)-Meleagris_gallopavo -2.1227 1.1412 -4.4942 -2.1210
## (Intercept)-Sciurus_carolinensis -3.7040 1.1992 -6.3855 -3.5946
## (Intercept)-Vulpes_vulpes -3.4048 1.4265 -6.5299 -3.2599
## (Intercept)-Sus_scrofa -3.8795 1.4576 -7.1025 -3.6778
## Cogon_Patch_Size-Canis_latrans 1.2068 1.0788 -0.4514 1.0394
## Cogon_Patch_Size-Sciurus_niger -1.0052 1.5547 -4.4474 -0.8605
## Cogon_Patch_Size-Procyon_lotor -0.6163 0.7724 -2.1040 -0.6105
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3185 0.6517 -1.6848 -0.2887
## Cogon_Patch_Size-Lynx_rufus -0.3558 1.3034 -2.8535 -0.4022
## Cogon_Patch_Size-Didelphis_virginiana 1.3145 0.9613 -0.2023 1.2023
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3510 1.2745 -4.3691 -1.1689
## Cogon_Patch_Size-Meleagris_gallopavo -0.0407 1.0200 -1.8925 -0.0941
## Cogon_Patch_Size-Sciurus_carolinensis -1.0515 1.0092 -3.4507 -0.8981
## Cogon_Patch_Size-Vulpes_vulpes -0.4902 1.4635 -3.4831 -0.4980
## Cogon_Patch_Size-Sus_scrofa -0.5594 1.1358 -3.2004 -0.4747
## Veg_shannon_index-Canis_latrans 1.3259 0.6621 0.2425 1.2516
## Veg_shannon_index-Sciurus_niger 1.0275 0.8933 -0.5725 0.9748
## Veg_shannon_index-Procyon_lotor 1.1482 0.5984 0.0905 1.0966
## Veg_shannon_index-Dasypus_novemcinctus 0.6549 0.5209 -0.3665 0.6668
## Veg_shannon_index-Lynx_rufus 0.9525 0.8494 -0.6943 0.9446
## Veg_shannon_index-Didelphis_virginiana 1.0313 0.6538 -0.1805 0.9894
## Veg_shannon_index-Sylvilagus_floridanus 1.0061 0.6788 -0.2053 0.9622
## Veg_shannon_index-Meleagris_gallopavo 1.1990 0.7529 -0.0067 1.1094
## Veg_shannon_index-Sciurus_carolinensis 0.3575 0.7183 -1.2133 0.4288
## Veg_shannon_index-Vulpes_vulpes 0.6624 0.8759 -1.1069 0.7149
## Veg_shannon_index-Sus_scrofa 1.4019 0.8417 0.0905 1.2827
## total_shrub_cover-Canis_latrans -0.0277 0.6766 -1.2523 -0.0745
## total_shrub_cover-Sciurus_niger -1.3804 1.1414 -4.0906 -1.2447
## total_shrub_cover-Procyon_lotor -1.2131 0.6595 -2.6615 -1.1530
## total_shrub_cover-Dasypus_novemcinctus 0.0509 0.5780 -1.0697 0.0367
## total_shrub_cover-Lynx_rufus -1.5603 1.1067 -4.1843 -1.3865
## total_shrub_cover-Didelphis_virginiana -0.7685 0.7788 -2.3832 -0.7387
## total_shrub_cover-Sylvilagus_floridanus -0.4506 0.8801 -2.3090 -0.4387
## total_shrub_cover-Meleagris_gallopavo -2.5718 1.3840 -5.7986 -2.3868
## total_shrub_cover-Sciurus_carolinensis -0.0558 0.7386 -1.4726 -0.0812
## total_shrub_cover-Vulpes_vulpes -0.9436 1.1717 -3.4912 -0.8367
## total_shrub_cover-Sus_scrofa -0.0133 0.9527 -1.7770 -0.1001
## Avg_Cogongrass_Cover-Canis_latrans 0.4510 1.0708 -1.7306 0.4605
## Avg_Cogongrass_Cover-Sciurus_niger 0.0265 1.3641 -3.1115 0.1611
## Avg_Cogongrass_Cover-Procyon_lotor 0.5769 1.0428 -1.4320 0.5541
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0296 1.1345 -1.0438 0.9474
## Avg_Cogongrass_Cover-Lynx_rufus 0.6447 1.1711 -1.5301 0.5953
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7879 1.1417 -1.3484 0.7499
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0307 1.1800 -2.6509 0.0552
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2073 1.1886 -2.2062 0.2373
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.6169 1.1130 -1.4404 0.5796
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.7172 1.2179 -1.5843 0.6679
## Avg_Cogongrass_Cover-Sus_scrofa 0.2649 1.1915 -2.3348 0.2775
## Tree_Density-Canis_latrans -2.9487 1.2350 -5.9560 -2.7879
## Tree_Density-Sciurus_niger -2.2293 1.1860 -4.6496 -2.2120
## Tree_Density-Procyon_lotor -2.2400 0.9191 -4.1108 -2.2040
## Tree_Density-Dasypus_novemcinctus -3.3130 1.3955 -6.9217 -3.0592
## Tree_Density-Lynx_rufus -1.4789 1.2583 -3.6261 -1.5909
## Tree_Density-Didelphis_virginiana -2.3829 0.9906 -4.5844 -2.3174
## Tree_Density-Sylvilagus_floridanus -2.5631 1.1645 -5.1745 -2.4759
## Tree_Density-Meleagris_gallopavo -2.3708 1.0827 -4.6796 -2.3460
## Tree_Density-Sciurus_carolinensis -2.5878 1.0783 -5.0014 -2.4868
## Tree_Density-Vulpes_vulpes -2.2422 1.3075 -4.8671 -2.2202
## Tree_Density-Sus_scrofa -2.3383 1.2310 -4.9864 -2.2502
## Avg_Canopy_Cover-Canis_latrans 0.4963 0.7154 -0.9979 0.5261
## Avg_Canopy_Cover-Sciurus_niger 1.9397 1.2986 -0.2295 1.8065
## Avg_Canopy_Cover-Procyon_lotor 1.6738 0.6756 0.4411 1.6320
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8792 0.6579 0.7724 1.8027
## Avg_Canopy_Cover-Lynx_rufus 1.3073 1.0046 -0.7885 1.3436
## Avg_Canopy_Cover-Didelphis_virginiana 2.3826 0.8447 1.0286 2.2434
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.8131 1.3472 0.9923 2.5051
## Avg_Canopy_Cover-Meleagris_gallopavo 2.0678 0.9462 0.6794 1.9230
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0684 0.7765 0.8529 1.9577
## Avg_Canopy_Cover-Vulpes_vulpes 2.1152 1.0909 0.5142 1.9179
## Avg_Canopy_Cover-Sus_scrofa 1.8807 0.7763 0.6042 1.7972
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3069 1.2531 0.6667 2.0553
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.8655 1.6554 -2.1583 0.8614
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0846 1.1553 0.4506 1.8810
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.1859 0.6635 0.0145 1.1469
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4337 1.2434 0.6416 2.2195
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6424 0.6592 -0.6850 0.6601
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8583 0.7911 -0.5391 0.7980
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.1287 1.3692 -3.3508 0.0452
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3158 0.7207 0.0411 1.2512
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6283 0.9404 0.1978 1.4807
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa -0.2462 1.4215 -3.7877 0.0244
## avg_veg_height-Canis_latrans -0.5688 0.6056 -1.8757 -0.5493
## avg_veg_height-Sciurus_niger -0.5893 0.8062 -2.4057 -0.5143
## avg_veg_height-Procyon_lotor -0.1228 0.5959 -1.2388 -0.1504
## avg_veg_height-Dasypus_novemcinctus -0.0674 0.6036 -1.1524 -0.1059
## avg_veg_height-Lynx_rufus -0.5314 0.7326 -2.1176 -0.4989
## avg_veg_height-Didelphis_virginiana -0.4375 0.6259 -1.7611 -0.4184
## avg_veg_height-Sylvilagus_floridanus -0.4738 0.6578 -1.8399 -0.4561
## avg_veg_height-Meleagris_gallopavo -0.4401 0.7402 -2.0056 -0.3979
## avg_veg_height-Sciurus_carolinensis -0.0623 0.6337 -1.2373 -0.0879
## avg_veg_height-Vulpes_vulpes -0.4635 0.7055 -1.9421 -0.4382
## avg_veg_height-Sus_scrofa -0.4026 0.6726 -1.7977 -0.3782
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4499 1.0187 424
## (Intercept)-Sciurus_niger 1.9928 1.0198 205
## (Intercept)-Procyon_lotor 0.8128 1.0748 334
## (Intercept)-Dasypus_novemcinctus -0.8195 1.0080 323
## (Intercept)-Lynx_rufus 1.1169 1.0742 270
## (Intercept)-Didelphis_virginiana -1.3405 1.0113 355
## (Intercept)-Sylvilagus_floridanus -0.0509 1.0105 628
## (Intercept)-Meleagris_gallopavo 0.1124 1.0132 517
## (Intercept)-Sciurus_carolinensis -1.6567 1.0187 265
## (Intercept)-Vulpes_vulpes -0.8612 1.0084 273
## (Intercept)-Sus_scrofa -1.5945 1.0103 237
## Cogon_Patch_Size-Canis_latrans 3.7965 1.0316 440
## Cogon_Patch_Size-Sciurus_niger 1.7197 1.0350 354
## Cogon_Patch_Size-Procyon_lotor 0.9064 1.0026 444
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9081 1.0511 577
## Cogon_Patch_Size-Lynx_rufus 2.3151 1.0183 381
## Cogon_Patch_Size-Didelphis_virginiana 3.5367 1.1326 229
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6979 1.0488 304
## Cogon_Patch_Size-Meleagris_gallopavo 2.1424 1.0106 525
## Cogon_Patch_Size-Sciurus_carolinensis 0.5569 1.0088 592
## Cogon_Patch_Size-Vulpes_vulpes 2.5207 1.0591 317
## Cogon_Patch_Size-Sus_scrofa 1.4909 1.0101 859
## Veg_shannon_index-Canis_latrans 2.8253 1.0279 535
## Veg_shannon_index-Sciurus_niger 3.1244 1.0388 369
## Veg_shannon_index-Procyon_lotor 2.4383 1.0913 264
## Veg_shannon_index-Dasypus_novemcinctus 1.6479 1.0074 810
## Veg_shannon_index-Lynx_rufus 2.7125 1.0054 581
## Veg_shannon_index-Didelphis_virginiana 2.4434 1.0028 779
## Veg_shannon_index-Sylvilagus_floridanus 2.5160 1.0250 432
## Veg_shannon_index-Meleagris_gallopavo 2.8943 1.0175 565
## Veg_shannon_index-Sciurus_carolinensis 1.5925 1.0061 657
## Veg_shannon_index-Vulpes_vulpes 2.2168 1.0360 376
## Veg_shannon_index-Sus_scrofa 3.4517 1.0289 460
## total_shrub_cover-Canis_latrans 1.4494 1.0017 744
## total_shrub_cover-Sciurus_niger 0.4977 1.0114 373
## total_shrub_cover-Procyon_lotor -0.0657 1.0070 911
## total_shrub_cover-Dasypus_novemcinctus 1.1843 1.0045 1068
## total_shrub_cover-Lynx_rufus 0.1500 1.0453 338
## total_shrub_cover-Didelphis_virginiana 0.7084 1.0230 795
## total_shrub_cover-Sylvilagus_floridanus 1.2452 1.0113 678
## total_shrub_cover-Meleagris_gallopavo -0.4458 1.0207 296
## total_shrub_cover-Sciurus_carolinensis 1.4741 1.0132 1110
## total_shrub_cover-Vulpes_vulpes 1.0352 1.0124 382
## total_shrub_cover-Sus_scrofa 2.0938 1.0046 847
## Avg_Cogongrass_Cover-Canis_latrans 2.5350 1.0539 272
## Avg_Cogongrass_Cover-Sciurus_niger 2.4739 1.0309 238
## Avg_Cogongrass_Cover-Procyon_lotor 2.6976 1.0632 265
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.4812 1.0906 241
## Avg_Cogongrass_Cover-Lynx_rufus 3.1747 1.0397 278
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.1790 1.0533 306
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.1835 1.0278 227
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.5128 1.0433 257
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9231 1.0250 243
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.2952 1.0189 271
## Avg_Cogongrass_Cover-Sus_scrofa 2.5444 1.0341 279
## Tree_Density-Canis_latrans -1.0087 1.0105 307
## Tree_Density-Sciurus_niger 0.2576 1.0013 462
## Tree_Density-Procyon_lotor -0.5664 1.0157 298
## Tree_Density-Dasypus_novemcinctus -1.4006 1.0283 164
## Tree_Density-Lynx_rufus 1.2606 1.0194 287
## Tree_Density-Didelphis_virginiana -0.6382 1.0076 383
## Tree_Density-Sylvilagus_floridanus -0.5273 1.0163 314
## Tree_Density-Meleagris_gallopavo -0.3258 1.0122 351
## Tree_Density-Sciurus_carolinensis -0.8651 1.0364 229
## Tree_Density-Vulpes_vulpes 0.2780 1.0100 308
## Tree_Density-Sus_scrofa -0.1007 1.0355 330
## Avg_Canopy_Cover-Canis_latrans 1.8064 1.0321 480
## Avg_Canopy_Cover-Sciurus_niger 4.9726 1.0132 143
## Avg_Canopy_Cover-Procyon_lotor 3.2290 1.0259 449
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3694 1.0352 204
## Avg_Canopy_Cover-Lynx_rufus 3.2931 1.0034 429
## Avg_Canopy_Cover-Didelphis_virginiana 4.3704 1.0394 171
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.2447 1.0644 129
## Avg_Canopy_Cover-Meleagris_gallopavo 4.5184 1.0441 365
## Avg_Canopy_Cover-Sciurus_carolinensis 3.9028 1.0107 329
## Avg_Canopy_Cover-Vulpes_vulpes 4.8555 1.0691 205
## Avg_Canopy_Cover-Sus_scrofa 3.6427 1.0104 310
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4745 1.0836 198
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 4.3059 1.0519 119
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.9607 1.0450 210
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.6506 1.0217 573
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.3807 1.0397 250
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.9234 1.0402 518
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.6201 1.0233 515
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.1120 1.0952 173
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.9155 1.0056 733
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.8254 1.0106 403
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.8162 1.0660 205
## avg_veg_height-Canis_latrans 0.5726 1.0191 381
## avg_veg_height-Sciurus_niger 0.7415 1.0087 324
## avg_veg_height-Procyon_lotor 1.0867 1.0332 579
## avg_veg_height-Dasypus_novemcinctus 1.2115 1.0483 555
## avg_veg_height-Lynx_rufus 0.7782 1.0195 381
## avg_veg_height-Didelphis_virginiana 0.7162 1.0103 453
## avg_veg_height-Sylvilagus_floridanus 0.7889 1.0262 499
## avg_veg_height-Meleagris_gallopavo 0.8533 1.0220 290
## avg_veg_height-Sciurus_carolinensis 1.3067 1.0165 599
## avg_veg_height-Vulpes_vulpes 0.8712 1.0232 425
## avg_veg_height-Sus_scrofa 0.8863 1.0168 464
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4773 0.1908 -2.8710 -2.4737 -2.1193 1.0012
## (Intercept)-Sciurus_niger -4.1694 0.4840 -5.1505 -4.1641 -3.2194 1.0535
## (Intercept)-Procyon_lotor -2.1819 0.1486 -2.4739 -2.1802 -1.9014 1.0019
## (Intercept)-Dasypus_novemcinctus -1.4872 0.1582 -1.8094 -1.4845 -1.1853 1.0016
## (Intercept)-Lynx_rufus -3.4711 0.2957 -4.0645 -3.4655 -2.9016 1.0066
## (Intercept)-Didelphis_virginiana -2.1631 0.2588 -2.6877 -2.1578 -1.6824 1.0017
## (Intercept)-Sylvilagus_floridanus -3.0968 0.2977 -3.6994 -3.0858 -2.5534 1.0274
## (Intercept)-Meleagris_gallopavo -3.2077 0.3294 -3.8718 -3.1991 -2.5863 1.0145
## (Intercept)-Sciurus_carolinensis -2.3452 0.2774 -2.9015 -2.3408 -1.8249 1.0035
## (Intercept)-Vulpes_vulpes -3.9370 0.5743 -5.1081 -3.9263 -2.8725 1.0394
## (Intercept)-Sus_scrofa -2.8646 0.4489 -3.7705 -2.8396 -2.0484 1.0018
## week-Canis_latrans 0.4431 0.2471 -0.0227 0.4471 0.9559 1.0209
## week-Sciurus_niger -0.2561 0.4225 -1.2692 -0.2045 0.4373 1.0606
## week-Procyon_lotor 0.1525 0.1936 -0.2164 0.1498 0.5330 1.0118
## week-Dasypus_novemcinctus 0.0580 0.2106 -0.3525 0.0499 0.5008 1.0005
## week-Lynx_rufus 0.2469 0.3027 -0.3142 0.2358 0.8738 1.0011
## week-Didelphis_virginiana 0.0158 0.3217 -0.6531 0.0297 0.6277 1.0042
## week-Sylvilagus_floridanus 0.0133 0.2947 -0.5853 0.0375 0.5509 1.0037
## week-Meleagris_gallopavo -0.1583 0.3449 -0.9092 -0.1423 0.4793 1.0336
## week-Sciurus_carolinensis 0.5231 0.3262 -0.0666 0.5128 1.2271 1.0159
## week-Vulpes_vulpes 0.0986 0.4061 -0.7127 0.1079 0.9178 1.0220
## week-Sus_scrofa 0.3897 0.3854 -0.2925 0.3575 1.2358 1.0033
## I(week^2)-Canis_latrans -0.1902 0.1013 -0.3938 -0.1894 0.0018 1.0304
## I(week^2)-Sciurus_niger -0.2449 0.2189 -0.7327 -0.2240 0.1368 1.0178
## I(week^2)-Procyon_lotor -0.1107 0.0875 -0.2849 -0.1099 0.0598 1.0014
## I(week^2)-Dasypus_novemcinctus -0.1532 0.0996 -0.3605 -0.1499 0.0363 1.0058
## I(week^2)-Lynx_rufus -0.1884 0.1387 -0.4719 -0.1826 0.0730 1.0302
## I(week^2)-Didelphis_virginiana -0.3446 0.2078 -0.8576 -0.3151 -0.0248 1.0059
## I(week^2)-Sylvilagus_floridanus -0.1496 0.1472 -0.4560 -0.1445 0.1181 1.0460
## I(week^2)-Meleagris_gallopavo -0.3166 0.2067 -0.7955 -0.2980 0.0249 1.0811
## I(week^2)-Sciurus_carolinensis -0.1876 0.1364 -0.4624 -0.1841 0.0787 1.0160
## I(week^2)-Vulpes_vulpes -0.3569 0.2620 -0.9842 -0.3197 0.0475 1.0465
## I(week^2)-Sus_scrofa -0.1481 0.1705 -0.5025 -0.1455 0.1743 1.0079
## ESS
## (Intercept)-Canis_latrans 936
## (Intercept)-Sciurus_niger 184
## (Intercept)-Procyon_lotor 1528
## (Intercept)-Dasypus_novemcinctus 2491
## (Intercept)-Lynx_rufus 387
## (Intercept)-Didelphis_virginiana 1689
## (Intercept)-Sylvilagus_floridanus 564
## (Intercept)-Meleagris_gallopavo 466
## (Intercept)-Sciurus_carolinensis 1078
## (Intercept)-Vulpes_vulpes 180
## (Intercept)-Sus_scrofa 758
## week-Canis_latrans 1174
## week-Sciurus_niger 403
## week-Procyon_lotor 1631
## week-Dasypus_novemcinctus 1975
## week-Lynx_rufus 954
## week-Didelphis_virginiana 1096
## week-Sylvilagus_floridanus 1138
## week-Meleagris_gallopavo 511
## week-Sciurus_carolinensis 1261
## week-Vulpes_vulpes 766
## week-Sus_scrofa 1260
## I(week^2)-Canis_latrans 1388
## I(week^2)-Sciurus_niger 303
## I(week^2)-Procyon_lotor 1591
## I(week^2)-Dasypus_novemcinctus 1957
## I(week^2)-Lynx_rufus 781
## I(week^2)-Didelphis_virginiana 486
## I(week^2)-Sylvilagus_floridanus 845
## I(week^2)-Meleagris_gallopavo 311
## I(week^2)-Sciurus_carolinensis 1384
## I(week^2)-Vulpes_vulpes 267
## I(week^2)-Sus_scrofa 1327
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_null_T)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.135
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4715 0.3247 -1.1195 -0.4716 0.188 1.0026 940
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8031 0.6186 0.1584 0.6356 2.3935 1.0085 1322
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8939 0.3089 -3.5183 -2.8941 -2.2662 1.0000 1473
## shrub_cover 0.1674 0.2886 -0.3919 0.1645 0.7379 1.0003 1204
## veg_height 0.0137 0.1669 -0.3183 0.0162 0.3344 1.0000 910
## week 0.1267 0.1947 -0.2687 0.1286 0.5096 1.0068 900
## I(week^2) -0.2121 0.1032 -0.4190 -0.2104 -0.0210 1.0184 773
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8871 0.5862 0.2432 0.7332 2.3762 1.0049 808
## shrub_cover 0.6697 0.5631 0.1470 0.5302 2.0813 1.0112 609
## veg_height 0.2050 0.1523 0.0558 0.1641 0.5730 1.0116 1067
## week 0.2081 0.1870 0.0372 0.1533 0.7263 1.0020 742
## I(week^2) 0.0643 0.0519 0.0179 0.0503 0.2003 1.0132 699
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2259 0.3880 -0.4791 0.2100 1.0305 1.0035
## (Intercept)-Sciurus_niger -0.7395 0.6163 -1.8558 -0.7703 0.5983 1.0035
## (Intercept)-Procyon_lotor 0.5276 0.3804 -0.1925 0.5115 1.2925 1.0008
## (Intercept)-Dasypus_novemcinctus -0.5606 0.3534 -1.2573 -0.5573 0.1310 1.0003
## (Intercept)-Lynx_rufus 0.0901 0.5684 -0.8438 0.0303 1.4038 1.0140
## (Intercept)-Didelphis_virginiana -1.0707 0.4307 -1.9448 -1.0559 -0.2659 1.0050
## (Intercept)-Sylvilagus_floridanus -0.3854 0.4254 -1.1790 -0.4007 0.4877 1.0048
## (Intercept)-Meleagris_gallopavo 0.0779 0.6373 -0.9688 0.0092 1.5109 1.0074
## (Intercept)-Sciurus_carolinensis -1.0554 0.4258 -1.9105 -1.0461 -0.2626 1.0057
## (Intercept)-Vulpes_vulpes -1.0964 0.6545 -2.3694 -1.0935 0.2271 1.0170
## (Intercept)-Sus_scrofa -1.3446 0.5760 -2.5633 -1.3300 -0.2687 1.0100
## ESS
## (Intercept)-Canis_latrans 1854
## (Intercept)-Sciurus_niger 374
## (Intercept)-Procyon_lotor 1721
## (Intercept)-Dasypus_novemcinctus 2289
## (Intercept)-Lynx_rufus 574
## (Intercept)-Didelphis_virginiana 1600
## (Intercept)-Sylvilagus_floridanus 1392
## (Intercept)-Meleagris_gallopavo 627
## (Intercept)-Sciurus_carolinensis 1682
## (Intercept)-Vulpes_vulpes 492
## (Intercept)-Sus_scrofa 775
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5930 0.2005 -2.9935 -2.5876 -2.2224 1.0142
## (Intercept)-Sciurus_niger -3.8124 0.5724 -4.9486 -3.7971 -2.7472 1.0035
## (Intercept)-Procyon_lotor -2.2080 0.1630 -2.5426 -2.2040 -1.9070 1.0009
## (Intercept)-Dasypus_novemcinctus -1.6431 0.1745 -1.9915 -1.6397 -1.3068 1.0040
## (Intercept)-Lynx_rufus -3.5168 0.3386 -4.1828 -3.5107 -2.8664 1.0102
## (Intercept)-Didelphis_virginiana -2.4395 0.3060 -3.0562 -2.4294 -1.8613 1.0078
## (Intercept)-Sylvilagus_floridanus -3.0713 0.2911 -3.6579 -3.0623 -2.5112 1.0121
## (Intercept)-Meleagris_gallopavo -3.8653 0.4323 -4.7463 -3.8756 -3.0089 1.0010
## (Intercept)-Sciurus_carolinensis -2.5231 0.3278 -3.1929 -2.5065 -1.9356 1.0166
## (Intercept)-Vulpes_vulpes -3.8014 0.6131 -5.0115 -3.7758 -2.6799 1.0189
## (Intercept)-Sus_scrofa -3.2860 0.5811 -4.4523 -3.2763 -2.1558 1.0111
## shrub_cover-Canis_latrans -0.3076 0.2197 -0.7362 -0.3014 0.1318 1.0040
## shrub_cover-Sciurus_niger -0.3444 0.4762 -1.3331 -0.3488 0.5973 1.0042
## shrub_cover-Procyon_lotor 0.2437 0.1574 -0.0673 0.2439 0.5449 1.0117
## shrub_cover-Dasypus_novemcinctus 0.8312 0.2929 0.2764 0.8238 1.3876 1.0050
## shrub_cover-Lynx_rufus -0.3296 0.3623 -1.0540 -0.3237 0.3778 1.0073
## shrub_cover-Didelphis_virginiana 0.9611 0.3702 0.2789 0.9382 1.7397 1.0016
## shrub_cover-Sylvilagus_floridanus 0.2423 0.4248 -0.5391 0.2192 1.1087 1.0222
## shrub_cover-Meleagris_gallopavo -0.7443 0.3814 -1.5151 -0.7518 0.0078 1.0024
## shrub_cover-Sciurus_carolinensis 0.8289 0.4115 0.0444 0.8200 1.6686 1.0151
## shrub_cover-Vulpes_vulpes -0.1271 0.5760 -1.3539 -0.1257 0.9597 1.0309
## shrub_cover-Sus_scrofa 0.6352 0.7847 -0.8936 0.6184 2.1853 1.0289
## veg_height-Canis_latrans -0.5807 0.1846 -0.9489 -0.5805 -0.2256 1.0087
## veg_height-Sciurus_niger -0.0439 0.4118 -0.8364 -0.0418 0.8152 1.0050
## veg_height-Procyon_lotor 0.3389 0.1218 0.1006 0.3377 0.5807 1.0034
## veg_height-Dasypus_novemcinctus 0.2434 0.1342 -0.0191 0.2433 0.5009 1.0008
## veg_height-Lynx_rufus 0.0394 0.2387 -0.4506 0.0425 0.4859 1.0057
## veg_height-Didelphis_virginiana 0.4353 0.2407 -0.0147 0.4303 0.9203 1.0012
## veg_height-Sylvilagus_floridanus 0.1318 0.2341 -0.3306 0.1288 0.5913 1.0299
## veg_height-Meleagris_gallopavo -0.2603 0.3483 -0.9826 -0.2556 0.3843 1.0038
## veg_height-Sciurus_carolinensis 0.0765 0.2144 -0.3531 0.0803 0.4973 1.0046
## veg_height-Vulpes_vulpes -0.1211 0.3064 -0.7559 -0.1064 0.4345 1.0095
## veg_height-Sus_scrofa -0.1338 0.3272 -0.8179 -0.1248 0.4789 1.0012
## week-Canis_latrans 0.4363 0.2495 -0.0196 0.4254 0.9531 1.0092
## week-Sciurus_niger -0.2789 0.4275 -1.2212 -0.2339 0.4435 1.0063
## week-Procyon_lotor 0.1497 0.1965 -0.2278 0.1488 0.5458 1.0004
## week-Dasypus_novemcinctus 0.0414 0.2089 -0.3579 0.0390 0.4437 1.0026
## week-Lynx_rufus 0.2321 0.3028 -0.3438 0.2236 0.8762 1.0062
## week-Didelphis_virginiana -0.0115 0.3172 -0.6715 -0.0011 0.5775 1.0031
## week-Sylvilagus_floridanus -0.0124 0.2872 -0.6026 -0.0109 0.5453 1.0048
## week-Meleagris_gallopavo -0.1337 0.3475 -0.8640 -0.1087 0.4968 1.0070
## week-Sciurus_carolinensis 0.5153 0.3365 -0.0792 0.4944 1.2546 1.0039
## week-Vulpes_vulpes 0.0700 0.3920 -0.7106 0.0654 0.8186 1.0167
## week-Sus_scrofa 0.3885 0.3689 -0.2724 0.3649 1.1725 1.0081
## I(week^2)-Canis_latrans -0.1879 0.1037 -0.4005 -0.1849 0.0086 1.0112
## I(week^2)-Sciurus_niger -0.2301 0.2020 -0.6549 -0.2206 0.1402 1.0312
## I(week^2)-Procyon_lotor -0.1097 0.0861 -0.2815 -0.1084 0.0585 1.0037
## I(week^2)-Dasypus_novemcinctus -0.1501 0.0981 -0.3431 -0.1485 0.0400 1.0035
## I(week^2)-Lynx_rufus -0.1874 0.1452 -0.4921 -0.1832 0.0834 1.0023
## I(week^2)-Didelphis_virginiana -0.3409 0.2090 -0.8421 -0.3135 -0.0069 1.0137
## I(week^2)-Sylvilagus_floridanus -0.1417 0.1479 -0.4479 -0.1350 0.1317 1.0070
## I(week^2)-Meleagris_gallopavo -0.3411 0.2116 -0.8283 -0.3175 0.0151 1.0252
## I(week^2)-Sciurus_carolinensis -0.1840 0.1372 -0.4729 -0.1799 0.0756 1.0002
## I(week^2)-Vulpes_vulpes -0.3291 0.2350 -0.8883 -0.3025 0.0511 1.0444
## I(week^2)-Sus_scrofa -0.1531 0.1638 -0.4804 -0.1487 0.1526 1.0030
## ESS
## (Intercept)-Canis_latrans 943
## (Intercept)-Sciurus_niger 277
## (Intercept)-Procyon_lotor 1268
## (Intercept)-Dasypus_novemcinctus 1811
## (Intercept)-Lynx_rufus 351
## (Intercept)-Didelphis_virginiana 853
## (Intercept)-Sylvilagus_floridanus 605
## (Intercept)-Meleagris_gallopavo 277
## (Intercept)-Sciurus_carolinensis 907
## (Intercept)-Vulpes_vulpes 285
## (Intercept)-Sus_scrofa 517
## shrub_cover-Canis_latrans 795
## shrub_cover-Sciurus_niger 411
## shrub_cover-Procyon_lotor 1603
## shrub_cover-Dasypus_novemcinctus 1277
## shrub_cover-Lynx_rufus 389
## shrub_cover-Didelphis_virginiana 628
## shrub_cover-Sylvilagus_floridanus 543
## shrub_cover-Meleagris_gallopavo 305
## shrub_cover-Sciurus_carolinensis 667
## shrub_cover-Vulpes_vulpes 597
## shrub_cover-Sus_scrofa 483
## veg_height-Canis_latrans 632
## veg_height-Sciurus_niger 780
## veg_height-Procyon_lotor 1445
## veg_height-Dasypus_novemcinctus 1975
## veg_height-Lynx_rufus 762
## veg_height-Didelphis_virginiana 1096
## veg_height-Sylvilagus_floridanus 906
## veg_height-Meleagris_gallopavo 584
## veg_height-Sciurus_carolinensis 1144
## veg_height-Vulpes_vulpes 788
## veg_height-Sus_scrofa 1174
## week-Canis_latrans 1125
## week-Sciurus_niger 552
## week-Procyon_lotor 1705
## week-Dasypus_novemcinctus 2219
## week-Lynx_rufus 968
## week-Didelphis_virginiana 1014
## week-Sylvilagus_floridanus 991
## week-Meleagris_gallopavo 512
## week-Sciurus_carolinensis 842
## week-Vulpes_vulpes 880
## week-Sus_scrofa 1343
## I(week^2)-Canis_latrans 1303
## I(week^2)-Sciurus_niger 466
## I(week^2)-Procyon_lotor 1566
## I(week^2)-Dasypus_novemcinctus 1699
## I(week^2)-Lynx_rufus 630
## I(week^2)-Didelphis_virginiana 429
## I(week^2)-Sylvilagus_floridanus 853
## I(week^2)-Meleagris_gallopavo 309
## I(week^2)-Sciurus_carolinensis 1300
## I(week^2)-Vulpes_vulpes 487
## I(week^2)-Sus_scrofa 1318
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_full_T)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.227
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9624 0.6566 -2.2694 -0.9691 0.3731 1.0470 259
## Cogon_Patch_Size -0.6718 0.6025 -1.8764 -0.6546 0.5018 1.0047 352
## Veg_shannon_index 1.0260 0.4481 0.1936 0.9996 1.9512 1.0209 250
## total_shrub_cover -0.8223 0.5764 -2.0346 -0.7861 0.1942 1.0265 162
## Avg_Cogongrass_Cover 1.9264 0.7429 0.4254 1.9571 3.3819 1.0694 167
## Tree_Density -1.9817 0.7144 -3.5251 -1.9374 -0.6755 1.0114 292
## Avg_Canopy_Cover 1.9715 0.6103 0.8936 1.9302 3.3042 1.0040 286
## avg_veg_height -0.4831 0.4736 -1.3939 -0.4884 0.4858 1.0005 231
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1048 3.3572 0.1244 2.0720 12.3025 1.0295 135
## Cogon_Patch_Size 1.9409 2.9277 0.0839 1.0028 10.0027 1.0429 288
## Veg_shannon_index 0.6640 0.9513 0.0481 0.3777 2.9668 1.0080 502
## total_shrub_cover 1.5197 2.0106 0.0784 0.8704 6.6358 1.0327 176
## Avg_Cogongrass_Cover 1.2519 2.1539 0.0563 0.5871 6.8425 1.3222 165
## Tree_Density 2.1972 3.4498 0.0683 1.0110 11.4197 1.0340 218
## Avg_Canopy_Cover 2.1506 2.7970 0.1541 1.3382 9.5064 1.0405 205
## avg_veg_height 0.4382 0.6960 0.0417 0.2338 2.0530 1.0227 533
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.2032 2.4728 0.0665 1.4304 8.3117 1.0445 63
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9611 0.3146 -3.5790 -2.9655 -2.3353 1.0036 1810
## shrub_cover 0.3404 0.2918 -0.2037 0.3389 0.9202 1.0199 395
## veg_height 0.0261 0.1755 -0.3192 0.0249 0.3884 1.0332 790
## week 0.1322 0.1987 -0.2642 0.1356 0.5193 1.0011 833
## I(week^2) -0.2196 0.1031 -0.4462 -0.2154 -0.0213 1.0283 516
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9891 0.7066 0.2831 0.8184 2.5708 1.0158 862
## shrub_cover 0.6626 0.5129 0.1466 0.5311 2.0041 1.0018 528
## veg_height 0.2185 0.1532 0.0573 0.1773 0.6354 1.0180 1033
## week 0.2323 0.2364 0.0378 0.1642 0.8157 1.0458 416
## I(week^2) 0.0653 0.0540 0.0185 0.0493 0.2088 1.0158 502
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2371 1.0122 -1.5753 0.2171
## (Intercept)-Sciurus_niger -0.4140 1.3309 -2.8135 -0.5474
## (Intercept)-Procyon_lotor 0.3083 0.9986 -1.6441 0.3679
## (Intercept)-Dasypus_novemcinctus -1.2892 0.8562 -3.1937 -1.2178
## (Intercept)-Lynx_rufus 0.3292 1.6879 -2.3536 0.0366
## (Intercept)-Didelphis_virginiana -2.1509 1.1200 -4.6309 -2.0827
## (Intercept)-Sylvilagus_floridanus -1.1248 1.0411 -3.3510 -1.0771
## (Intercept)-Meleagris_gallopavo -0.8265 1.2721 -3.2193 -0.9122
## (Intercept)-Sciurus_carolinensis -2.1116 1.0921 -4.4316 -2.0196
## (Intercept)-Vulpes_vulpes -1.8314 1.3674 -4.6357 -1.7715
## (Intercept)-Sus_scrofa -2.7384 1.6154 -6.4382 -2.5341
## Cogon_Patch_Size-Canis_latrans 0.3800 0.9976 -1.1215 0.2325
## Cogon_Patch_Size-Sciurus_niger -1.3479 1.4228 -4.6468 -1.1442
## Cogon_Patch_Size-Procyon_lotor -0.9933 0.6952 -2.4586 -0.9488
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5487 0.7431 -1.9728 -0.5697
## Cogon_Patch_Size-Lynx_rufus -0.6722 1.1771 -2.8483 -0.7093
## Cogon_Patch_Size-Didelphis_virginiana 0.5454 0.8759 -0.9327 0.4360
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6628 1.4141 -5.2339 -1.4349
## Cogon_Patch_Size-Meleagris_gallopavo -0.3461 1.1899 -2.3549 -0.4336
## Cogon_Patch_Size-Sciurus_carolinensis -1.3076 1.0250 -3.6854 -1.1787
## Cogon_Patch_Size-Vulpes_vulpes -1.1184 1.2940 -4.2741 -0.9710
## Cogon_Patch_Size-Sus_scrofa -0.9975 1.1356 -3.6511 -0.8588
## Veg_shannon_index-Canis_latrans 1.3215 0.6254 0.2127 1.2646
## Veg_shannon_index-Sciurus_niger 1.1939 0.8370 -0.3448 1.1230
## Veg_shannon_index-Procyon_lotor 1.2854 0.5978 0.2455 1.2476
## Veg_shannon_index-Dasypus_novemcinctus 0.7072 0.5565 -0.4202 0.7252
## Veg_shannon_index-Lynx_rufus 0.9940 0.7900 -0.5215 0.9517
## Veg_shannon_index-Didelphis_virginiana 1.2085 0.6560 0.0615 1.1546
## Veg_shannon_index-Sylvilagus_floridanus 1.1021 0.6751 -0.2053 1.0711
## Veg_shannon_index-Meleagris_gallopavo 1.3112 0.7615 -0.0094 1.2218
## Veg_shannon_index-Sciurus_carolinensis 0.4102 0.7255 -1.1437 0.4635
## Veg_shannon_index-Vulpes_vulpes 0.5477 0.7870 -1.1785 0.6021
## Veg_shannon_index-Sus_scrofa 1.4397 0.8217 0.1510 1.3463
## total_shrub_cover-Canis_latrans 0.3384 0.8572 -0.9961 0.2145
## total_shrub_cover-Sciurus_niger -0.9944 1.1194 -3.5268 -0.8881
## total_shrub_cover-Procyon_lotor -1.1443 0.6500 -2.5926 -1.0910
## total_shrub_cover-Dasypus_novemcinctus -0.3101 0.7535 -1.9525 -0.2687
## total_shrub_cover-Lynx_rufus -1.2585 1.2387 -4.2194 -1.0502
## total_shrub_cover-Didelphis_virginiana -1.0737 0.9131 -3.3199 -0.9601
## total_shrub_cover-Sylvilagus_floridanus -0.7755 0.9997 -2.9325 -0.7075
## total_shrub_cover-Meleagris_gallopavo -1.9273 1.3609 -5.0763 -1.7114
## total_shrub_cover-Sciurus_carolinensis -0.7484 0.9501 -2.9787 -0.6608
## total_shrub_cover-Vulpes_vulpes -1.1419 1.2527 -4.0783 -0.9923
## total_shrub_cover-Sus_scrofa -0.6013 1.1449 -3.1011 -0.5492
## Avg_Cogongrass_Cover-Canis_latrans 2.3745 0.9424 0.7773 2.2814
## Avg_Cogongrass_Cover-Sciurus_niger 1.2492 1.4145 -2.1235 1.5005
## Avg_Cogongrass_Cover-Procyon_lotor 2.1585 0.8800 0.4469 2.1514
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5243 0.9595 0.8109 2.4402
## Avg_Cogongrass_Cover-Lynx_rufus 2.3770 0.9773 0.6656 2.3137
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0790 0.9521 0.3144 2.0513
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2952 1.0828 -0.9489 1.3765
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5695 1.3162 -1.4761 1.7097
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2492 0.9480 0.4703 2.2210
## Avg_Cogongrass_Cover-Vulpes_vulpes 2.4951 1.1064 0.5530 2.4092
## Avg_Cogongrass_Cover-Sus_scrofa 1.6139 1.2054 -1.1146 1.7362
## Tree_Density-Canis_latrans -2.6923 1.2369 -5.7307 -2.4578
## Tree_Density-Sciurus_niger -2.0179 1.2991 -4.8187 -1.9622
## Tree_Density-Procyon_lotor -1.5972 0.7673 -3.0902 -1.6111
## Tree_Density-Dasypus_novemcinctus -3.3463 1.6116 -7.3720 -2.9858
## Tree_Density-Lynx_rufus -0.7278 1.2544 -2.8179 -0.8880
## Tree_Density-Didelphis_virginiana -2.1212 1.1317 -4.5038 -2.0753
## Tree_Density-Sylvilagus_floridanus -2.3809 1.3553 -5.6812 -2.2003
## Tree_Density-Meleagris_gallopavo -2.2065 1.3623 -5.2150 -2.1286
## Tree_Density-Sciurus_carolinensis -2.1905 1.2967 -5.1684 -2.0773
## Tree_Density-Vulpes_vulpes -1.9126 1.3246 -4.6769 -1.8802
## Tree_Density-Sus_scrofa -2.1189 1.3613 -5.2612 -1.9925
## Avg_Canopy_Cover-Canis_latrans 0.3324 0.6775 -1.0048 0.3289
## Avg_Canopy_Cover-Sciurus_niger 1.9917 1.5719 -0.7963 1.8912
## Avg_Canopy_Cover-Procyon_lotor 1.7445 0.7195 0.4823 1.6951
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1478 0.7482 0.9210 2.0634
## Avg_Canopy_Cover-Lynx_rufus 1.3237 1.1956 -0.9645 1.2573
## Avg_Canopy_Cover-Didelphis_virginiana 2.8756 1.1219 1.2159 2.7198
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3499 1.5036 1.1541 3.1365
## Avg_Canopy_Cover-Meleagris_gallopavo 2.4386 1.1586 0.5777 2.2901
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6506 1.0887 1.0448 2.4976
## Avg_Canopy_Cover-Vulpes_vulpes 2.3308 1.2566 0.4225 2.0883
## Avg_Canopy_Cover-Sus_scrofa 2.1461 1.0566 0.4925 2.0253
## avg_veg_height-Canis_latrans -0.5346 0.5759 -1.6462 -0.5431
## avg_veg_height-Sciurus_niger -0.7527 0.7828 -2.5203 -0.6680
## avg_veg_height-Procyon_lotor -0.4377 0.5517 -1.5171 -0.4291
## avg_veg_height-Dasypus_novemcinctus -0.2380 0.5957 -1.3321 -0.2647
## avg_veg_height-Lynx_rufus -0.6343 0.7196 -2.2226 -0.6197
## avg_veg_height-Didelphis_virginiana -0.6244 0.6443 -1.9727 -0.5984
## avg_veg_height-Sylvilagus_floridanus -0.6451 0.6690 -2.0406 -0.6246
## avg_veg_height-Meleagris_gallopavo -0.4620 0.7924 -2.0146 -0.4717
## avg_veg_height-Sciurus_carolinensis -0.1282 0.6842 -1.2951 -0.1921
## avg_veg_height-Vulpes_vulpes -0.4582 0.7068 -1.8294 -0.4512
## avg_veg_height-Sus_scrofa -0.4959 0.6910 -1.8345 -0.4964
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4211 1.0601 311
## (Intercept)-Sciurus_niger 2.7025 1.0426 265
## (Intercept)-Procyon_lotor 2.1541 1.0666 134
## (Intercept)-Dasypus_novemcinctus 0.2112 1.0497 394
## (Intercept)-Lynx_rufus 4.3530 1.0636 106
## (Intercept)-Didelphis_virginiana -0.2047 1.0628 346
## (Intercept)-Sylvilagus_floridanus 0.8579 1.0203 509
## (Intercept)-Meleagris_gallopavo 2.0909 1.0591 277
## (Intercept)-Sciurus_carolinensis -0.2123 1.0691 305
## (Intercept)-Vulpes_vulpes 0.7328 1.0067 233
## (Intercept)-Sus_scrofa -0.2368 1.0515 160
## Cogon_Patch_Size-Canis_latrans 2.8367 1.0145 547
## Cogon_Patch_Size-Sciurus_niger 0.9990 1.0044 296
## Cogon_Patch_Size-Procyon_lotor 0.2621 1.0142 272
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0576 1.0021 540
## Cogon_Patch_Size-Lynx_rufus 1.8752 1.0065 264
## Cogon_Patch_Size-Didelphis_virginiana 2.5381 1.0149 348
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2314 1.0064 366
## Cogon_Patch_Size-Meleagris_gallopavo 2.4878 1.0279 397
## Cogon_Patch_Size-Sciurus_carolinensis 0.2764 1.0043 519
## Cogon_Patch_Size-Vulpes_vulpes 1.0493 1.0150 299
## Cogon_Patch_Size-Sus_scrofa 0.8508 1.0090 411
## Veg_shannon_index-Canis_latrans 2.6932 1.0042 318
## Veg_shannon_index-Sciurus_niger 3.0445 1.0051 607
## Veg_shannon_index-Procyon_lotor 2.5735 1.0344 210
## Veg_shannon_index-Dasypus_novemcinctus 1.7886 1.0029 483
## Veg_shannon_index-Lynx_rufus 2.6185 1.0113 399
## Veg_shannon_index-Didelphis_virginiana 2.6841 1.0000 475
## Veg_shannon_index-Sylvilagus_floridanus 2.4680 1.0013 450
## Veg_shannon_index-Meleagris_gallopavo 3.0930 1.0026 581
## Veg_shannon_index-Sciurus_carolinensis 1.7236 1.0228 426
## Veg_shannon_index-Vulpes_vulpes 2.0086 1.0150 452
## Veg_shannon_index-Sus_scrofa 3.2673 1.0076 388
## total_shrub_cover-Canis_latrans 2.4223 1.0085 307
## total_shrub_cover-Sciurus_niger 0.9980 1.0134 234
## total_shrub_cover-Procyon_lotor -0.0295 1.0049 451
## total_shrub_cover-Dasypus_novemcinctus 1.0181 1.0075 427
## total_shrub_cover-Lynx_rufus 0.6853 1.0244 213
## total_shrub_cover-Didelphis_virginiana 0.3718 1.0139 358
## total_shrub_cover-Sylvilagus_floridanus 1.1952 1.0140 344
## total_shrub_cover-Meleagris_gallopavo 0.1204 1.0285 172
## total_shrub_cover-Sciurus_carolinensis 0.8851 1.0194 232
## total_shrub_cover-Vulpes_vulpes 0.8386 1.0394 254
## total_shrub_cover-Sus_scrofa 1.5980 1.0153 201
## Avg_Cogongrass_Cover-Canis_latrans 4.4927 1.0104 304
## Avg_Cogongrass_Cover-Sciurus_niger 3.4209 1.2298 174
## Avg_Cogongrass_Cover-Procyon_lotor 3.9513 1.0155 251
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6803 1.0303 217
## Avg_Cogongrass_Cover-Lynx_rufus 4.5380 1.0082 321
## Avg_Cogongrass_Cover-Didelphis_virginiana 4.0878 1.0167 269
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2339 1.1186 267
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.8125 1.1593 232
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2768 1.0103 308
## Avg_Cogongrass_Cover-Vulpes_vulpes 4.9516 1.0347 260
## Avg_Cogongrass_Cover-Sus_scrofa 3.6978 1.1040 251
## Tree_Density-Canis_latrans -0.8767 1.0183 247
## Tree_Density-Sciurus_niger 0.5040 1.0288 316
## Tree_Density-Procyon_lotor 0.0407 1.0056 494
## Tree_Density-Dasypus_novemcinctus -1.2010 1.0471 165
## Tree_Density-Lynx_rufus 2.0353 1.0141 221
## Tree_Density-Didelphis_virginiana 0.0258 1.0145 411
## Tree_Density-Sylvilagus_floridanus -0.0355 1.0173 317
## Tree_Density-Meleagris_gallopavo 0.4292 1.0167 430
## Tree_Density-Sciurus_carolinensis 0.1465 1.0705 354
## Tree_Density-Vulpes_vulpes 0.8313 1.0249 404
## Tree_Density-Sus_scrofa 0.2987 1.0164 479
## Avg_Canopy_Cover-Canis_latrans 1.6472 1.0140 540
## Avg_Canopy_Cover-Sciurus_niger 5.3842 1.0499 210
## Avg_Canopy_Cover-Procyon_lotor 3.3979 1.0002 458
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7493 1.0038 262
## Avg_Canopy_Cover-Lynx_rufus 3.9660 1.0477 327
## Avg_Canopy_Cover-Didelphis_virginiana 5.5795 1.0027 186
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.0210 1.0183 212
## Avg_Canopy_Cover-Meleagris_gallopavo 5.0723 1.0068 207
## Avg_Canopy_Cover-Sciurus_carolinensis 5.2252 1.0027 357
## Avg_Canopy_Cover-Vulpes_vulpes 5.3702 1.0562 320
## Avg_Canopy_Cover-Sus_scrofa 4.6453 1.0078 382
## avg_veg_height-Canis_latrans 0.6321 1.0020 354
## avg_veg_height-Sciurus_niger 0.5972 1.0058 361
## avg_veg_height-Procyon_lotor 0.7095 1.0019 339
## avg_veg_height-Dasypus_novemcinctus 1.0955 1.0012 284
## avg_veg_height-Lynx_rufus 0.7870 1.0034 302
## avg_veg_height-Didelphis_virginiana 0.5600 1.0035 488
## avg_veg_height-Sylvilagus_floridanus 0.6121 1.0173 458
## avg_veg_height-Meleagris_gallopavo 1.1669 1.0047 386
## avg_veg_height-Sciurus_carolinensis 1.4247 1.0137 486
## avg_veg_height-Vulpes_vulpes 1.0345 1.0018 365
## avg_veg_height-Sus_scrofa 0.9526 1.0129 451
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6029 0.2011 -3.0209 -2.5959 -2.2301 1.0222
## (Intercept)-Sciurus_niger -4.2811 0.5143 -5.2442 -4.3004 -3.2271 1.0190
## (Intercept)-Procyon_lotor -2.2094 0.1589 -2.5239 -2.2065 -1.9047 0.9998
## (Intercept)-Dasypus_novemcinctus -1.6719 0.1878 -2.0453 -1.6683 -1.3168 1.0002
## (Intercept)-Lynx_rufus -3.5754 0.3749 -4.3307 -3.5692 -2.8922 1.0382
## (Intercept)-Didelphis_virginiana -2.4827 0.3196 -3.1448 -2.4782 -1.8855 1.0013
## (Intercept)-Sylvilagus_floridanus -3.1018 0.2821 -3.6637 -3.0921 -2.5844 1.0156
## (Intercept)-Meleagris_gallopavo -3.7011 0.4563 -4.6163 -3.6838 -2.8371 1.0849
## (Intercept)-Sciurus_carolinensis -2.6737 0.3489 -3.3578 -2.6649 -2.0247 1.0562
## (Intercept)-Vulpes_vulpes -3.9705 0.5837 -5.1359 -3.9746 -2.8809 1.0137
## (Intercept)-Sus_scrofa -3.3939 0.5563 -4.4887 -3.4069 -2.3208 1.0062
## shrub_cover-Canis_latrans -0.3373 0.2246 -0.7728 -0.3312 0.0886 1.0145
## shrub_cover-Sciurus_niger -0.2173 0.4939 -1.1636 -0.2201 0.8254 1.0662
## shrub_cover-Procyon_lotor 0.2760 0.1606 -0.0515 0.2836 0.5757 1.0092
## shrub_cover-Dasypus_novemcinctus 0.9499 0.3249 0.3402 0.9453 1.6017 1.0059
## shrub_cover-Lynx_rufus -0.0599 0.3923 -0.8043 -0.0553 0.7051 1.0322
## shrub_cover-Didelphis_virginiana 1.0480 0.3913 0.3347 1.0295 1.8744 1.0036
## shrub_cover-Sylvilagus_floridanus 0.5082 0.4064 -0.3088 0.5180 1.2948 1.0116
## shrub_cover-Meleagris_gallopavo -0.5249 0.4334 -1.3708 -0.5203 0.3357 1.0319
## shrub_cover-Sciurus_carolinensis 1.0522 0.4356 0.2272 1.0450 1.9251 1.0974
## shrub_cover-Vulpes_vulpes 0.2255 0.5674 -0.9278 0.2272 1.3693 1.0185
## shrub_cover-Sus_scrofa 0.8900 0.7701 -0.6506 0.9070 2.3710 1.0222
## veg_height-Canis_latrans -0.5829 0.1790 -0.9401 -0.5815 -0.2475 1.0422
## veg_height-Sciurus_niger 0.0050 0.3865 -0.7329 -0.0064 0.8103 1.0997
## veg_height-Procyon_lotor 0.3471 0.1233 0.1136 0.3462 0.5937 0.9999
## veg_height-Dasypus_novemcinctus 0.2610 0.1375 -0.0021 0.2576 0.5401 1.0008
## veg_height-Lynx_rufus 0.1038 0.2480 -0.3891 0.1118 0.5818 1.0185
## veg_height-Didelphis_virginiana 0.4757 0.2503 0.0206 0.4617 0.9881 1.0039
## veg_height-Sylvilagus_floridanus 0.1401 0.2387 -0.3344 0.1399 0.5841 1.0052
## veg_height-Meleagris_gallopavo -0.2280 0.3652 -0.9499 -0.2205 0.4784 1.0717
## veg_height-Sciurus_carolinensis 0.1423 0.2241 -0.2772 0.1358 0.5963 1.0055
## veg_height-Vulpes_vulpes -0.1945 0.3313 -0.8861 -0.1792 0.4008 1.0107
## veg_height-Sus_scrofa -0.1630 0.3315 -0.8414 -0.1568 0.4524 1.0176
## week-Canis_latrans 0.4500 0.2515 -0.0245 0.4421 0.9603 1.0182
## week-Sciurus_niger -0.2983 0.4739 -1.4458 -0.2344 0.4485 1.0507
## week-Procyon_lotor 0.1520 0.1969 -0.2220 0.1529 0.5435 1.0024
## week-Dasypus_novemcinctus 0.0539 0.2124 -0.3661 0.0544 0.4654 1.0008
## week-Lynx_rufus 0.2367 0.2990 -0.3423 0.2318 0.8309 1.0027
## week-Didelphis_virginiana -0.0064 0.3143 -0.6579 0.0079 0.5565 1.0108
## week-Sylvilagus_floridanus 0.0093 0.3041 -0.6041 0.0114 0.5832 1.0002
## week-Meleagris_gallopavo -0.1739 0.3643 -0.9372 -0.1573 0.4699 1.0320
## week-Sciurus_carolinensis 0.5312 0.3357 -0.0734 0.5160 1.2522 1.0075
## week-Vulpes_vulpes 0.0848 0.4061 -0.8135 0.1109 0.8496 1.0507
## week-Sus_scrofa 0.4055 0.3810 -0.2947 0.3872 1.2174 1.0045
## I(week^2)-Canis_latrans -0.1969 0.1044 -0.4101 -0.1929 0.0036 1.0120
## I(week^2)-Sciurus_niger -0.2501 0.2155 -0.7146 -0.2337 0.1263 1.0481
## I(week^2)-Procyon_lotor -0.1077 0.0888 -0.2905 -0.1051 0.0635 1.0011
## I(week^2)-Dasypus_novemcinctus -0.1520 0.1008 -0.3516 -0.1492 0.0409 1.0013
## I(week^2)-Lynx_rufus -0.1920 0.1414 -0.4835 -0.1865 0.0648 1.0070
## I(week^2)-Didelphis_virginiana -0.3510 0.2041 -0.8174 -0.3279 -0.0132 1.0069
## I(week^2)-Sylvilagus_floridanus -0.1402 0.1524 -0.4526 -0.1346 0.1486 1.0072
## I(week^2)-Meleagris_gallopavo -0.3486 0.2336 -0.9124 -0.3198 0.0330 1.0510
## I(week^2)-Sciurus_carolinensis -0.1901 0.1382 -0.4639 -0.1831 0.0684 1.0076
## I(week^2)-Vulpes_vulpes -0.3383 0.2418 -0.9298 -0.3149 0.0429 1.0957
## I(week^2)-Sus_scrofa -0.1572 0.1674 -0.5097 -0.1571 0.1656 1.0070
## ESS
## (Intercept)-Canis_latrans 761
## (Intercept)-Sciurus_niger 177
## (Intercept)-Procyon_lotor 1620
## (Intercept)-Dasypus_novemcinctus 1114
## (Intercept)-Lynx_rufus 284
## (Intercept)-Didelphis_virginiana 478
## (Intercept)-Sylvilagus_floridanus 689
## (Intercept)-Meleagris_gallopavo 314
## (Intercept)-Sciurus_carolinensis 515
## (Intercept)-Vulpes_vulpes 272
## (Intercept)-Sus_scrofa 250
## shrub_cover-Canis_latrans 670
## shrub_cover-Sciurus_niger 312
## shrub_cover-Procyon_lotor 1394
## shrub_cover-Dasypus_novemcinctus 542
## shrub_cover-Lynx_rufus 251
## shrub_cover-Didelphis_virginiana 349
## shrub_cover-Sylvilagus_floridanus 370
## shrub_cover-Meleagris_gallopavo 341
## shrub_cover-Sciurus_carolinensis 221
## shrub_cover-Vulpes_vulpes 377
## shrub_cover-Sus_scrofa 248
## veg_height-Canis_latrans 691
## veg_height-Sciurus_niger 346
## veg_height-Procyon_lotor 1678
## veg_height-Dasypus_novemcinctus 1807
## veg_height-Lynx_rufus 602
## veg_height-Didelphis_virginiana 1082
## veg_height-Sylvilagus_floridanus 828
## veg_height-Meleagris_gallopavo 389
## veg_height-Sciurus_carolinensis 857
## veg_height-Vulpes_vulpes 536
## veg_height-Sus_scrofa 880
## week-Canis_latrans 1058
## week-Sciurus_niger 276
## week-Procyon_lotor 1713
## week-Dasypus_novemcinctus 1785
## week-Lynx_rufus 797
## week-Didelphis_virginiana 1020
## week-Sylvilagus_floridanus 892
## week-Meleagris_gallopavo 528
## week-Sciurus_carolinensis 951
## week-Vulpes_vulpes 667
## week-Sus_scrofa 1076
## I(week^2)-Canis_latrans 1412
## I(week^2)-Sciurus_niger 382
## I(week^2)-Procyon_lotor 1849
## I(week^2)-Dasypus_novemcinctus 1712
## I(week^2)-Lynx_rufus 602
## I(week^2)-Didelphis_virginiana 456
## I(week^2)-Sylvilagus_floridanus 463
## I(week^2)-Meleagris_gallopavo 219
## I(week^2)-Sciurus_carolinensis 1174
## I(week^2)-Vulpes_vulpes 379
## I(week^2)-Sus_scrofa 1286
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cover_T)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.2667
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3853 0.4334 -1.2113 -0.4007 0.5209 1.0016 176
## Avg_Cogongrass_Cover -0.0907 0.3677 -0.8491 -0.0874 0.5901 1.0106 431
## total_shrub_cover -0.9864 0.4650 -2.0272 -0.9378 -0.2386 1.0441 186
## avg_veg_height 0.1060 0.3444 -0.5622 0.1024 0.8047 1.0193 333
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7334 0.8583 0.0571 0.4639 3.1155 1.0684 422
## Avg_Cogongrass_Cover 0.5314 0.6181 0.0498 0.3295 2.1268 1.0218 375
## total_shrub_cover 0.9171 1.2892 0.0686 0.5422 4.1692 1.0333 204
## avg_veg_height 0.3241 0.3953 0.0360 0.1961 1.3335 1.0512 582
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5637 1.5164 0.0958 1.1197 5.6756 1.0994 141
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9465 0.3042 -3.5725 -2.9400 -2.3560 1.0095 550
## shrub_cover 0.5173 0.3034 -0.0957 0.5188 1.1220 1.0126 816
## veg_height 0.0331 0.1786 -0.3323 0.0371 0.3775 1.0255 691
## week 0.1414 0.1895 -0.2300 0.1426 0.5028 1.0101 943
## I(week^2) -0.2220 0.1056 -0.4484 -0.2152 -0.0252 1.0146 656
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8291 0.6069 0.1936 0.6640 2.4590 1.0068 400
## shrub_cover 0.7725 0.6126 0.1518 0.6081 2.3726 1.0479 223
## veg_height 0.2302 0.1824 0.0543 0.1834 0.6787 1.0061 1190
## week 0.2118 0.2073 0.0376 0.1518 0.7215 1.0107 828
## I(week^2) 0.0648 0.0540 0.0184 0.0500 0.1977 1.0561 448
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1366 0.6576 -1.0279 0.0982
## (Intercept)-Sciurus_niger -0.5929 0.7705 -2.1403 -0.5920
## (Intercept)-Procyon_lotor 0.2566 0.6877 -1.0233 0.2325
## (Intercept)-Dasypus_novemcinctus -0.4217 0.6272 -1.6281 -0.4445
## (Intercept)-Lynx_rufus -0.2553 0.6942 -1.6327 -0.2609
## (Intercept)-Didelphis_virginiana -0.6738 0.6793 -2.1041 -0.6555
## (Intercept)-Sylvilagus_floridanus -0.0590 0.7115 -1.3050 -0.1408
## (Intercept)-Meleagris_gallopavo -0.4127 0.7474 -1.8445 -0.4293
## (Intercept)-Sciurus_carolinensis -0.6882 0.6815 -2.1042 -0.6691
## (Intercept)-Vulpes_vulpes -0.6793 0.8758 -2.4953 -0.6242
## (Intercept)-Sus_scrofa -0.9076 0.8282 -2.6977 -0.8388
## Avg_Cogongrass_Cover-Canis_latrans 0.3328 0.5348 -0.6248 0.2969
## Avg_Cogongrass_Cover-Sciurus_niger -0.5529 0.7803 -2.2530 -0.4649
## Avg_Cogongrass_Cover-Procyon_lotor -0.1389 0.5012 -1.1951 -0.1224
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1193 0.4604 -0.7560 0.1139
## Avg_Cogongrass_Cover-Lynx_rufus 0.3610 0.5656 -0.6227 0.3112
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1188 0.5304 -0.9059 0.1024
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5169 0.6237 -1.9633 -0.4541
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5614 0.7209 -2.2268 -0.4784
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0124 0.5159 -1.0408 0.0250
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.1096 0.5956 -1.0317 0.1033
## Avg_Cogongrass_Cover-Sus_scrofa -0.3370 0.7032 -1.9248 -0.2608
## total_shrub_cover-Canis_latrans 0.0619 0.6241 -1.0388 0.0065
## total_shrub_cover-Sciurus_niger -1.0733 0.8764 -2.9801 -0.9963
## total_shrub_cover-Procyon_lotor -1.2586 0.5922 -2.6186 -1.1903
## total_shrub_cover-Dasypus_novemcinctus -0.5808 0.6418 -2.1323 -0.5047
## total_shrub_cover-Lynx_rufus -1.3390 0.8217 -3.2489 -1.2110
## total_shrub_cover-Didelphis_virginiana -0.9947 0.6745 -2.6054 -0.9251
## total_shrub_cover-Sylvilagus_floridanus -1.4221 0.8668 -3.4612 -1.2721
## total_shrub_cover-Meleagris_gallopavo -1.5086 0.8247 -3.4347 -1.3920
## total_shrub_cover-Sciurus_carolinensis -1.0782 0.8401 -3.1605 -0.9548
## total_shrub_cover-Vulpes_vulpes -1.1803 1.0938 -3.8189 -1.0223
## total_shrub_cover-Sus_scrofa -0.7953 0.8168 -2.5492 -0.7656
## avg_veg_height-Canis_latrans 0.1157 0.4612 -0.7667 0.1050
## avg_veg_height-Sciurus_niger -0.1858 0.6387 -1.6424 -0.1312
## avg_veg_height-Procyon_lotor 0.1649 0.4851 -0.7404 0.1477
## avg_veg_height-Dasypus_novemcinctus 0.3446 0.4745 -0.5101 0.3146
## avg_veg_height-Lynx_rufus 0.0709 0.5686 -1.0856 0.0650
## avg_veg_height-Didelphis_virginiana 0.0100 0.5072 -1.0644 0.0252
## avg_veg_height-Sylvilagus_floridanus 0.0725 0.5144 -0.9088 0.0538
## avg_veg_height-Meleagris_gallopavo -0.1256 0.6579 -1.6028 -0.0907
## avg_veg_height-Sciurus_carolinensis 0.4757 0.5339 -0.4395 0.4300
## avg_veg_height-Vulpes_vulpes 0.0724 0.5477 -1.0426 0.0632
## avg_veg_height-Sus_scrofa 0.1575 0.5326 -0.8490 0.1477
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5393 1.0404 451
## (Intercept)-Sciurus_niger 0.9494 1.0143 349
## (Intercept)-Procyon_lotor 1.6860 1.0269 478
## (Intercept)-Dasypus_novemcinctus 0.9041 1.0119 504
## (Intercept)-Lynx_rufus 1.2122 1.0032 522
## (Intercept)-Didelphis_virginiana 0.6470 1.0135 431
## (Intercept)-Sylvilagus_floridanus 1.5427 1.0057 348
## (Intercept)-Meleagris_gallopavo 1.0883 1.0217 417
## (Intercept)-Sciurus_carolinensis 0.6478 1.0047 293
## (Intercept)-Vulpes_vulpes 1.0239 1.0751 218
## (Intercept)-Sus_scrofa 0.5626 1.0582 306
## Avg_Cogongrass_Cover-Canis_latrans 1.5265 1.0050 747
## Avg_Cogongrass_Cover-Sciurus_niger 0.6731 1.0275 386
## Avg_Cogongrass_Cover-Procyon_lotor 0.7941 1.0060 774
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0376 1.0088 938
## Avg_Cogongrass_Cover-Lynx_rufus 1.6933 1.0003 847
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2335 1.0016 887
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5316 1.0049 553
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6391 1.0078 380
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9877 1.0060 781
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3466 1.0053 653
## Avg_Cogongrass_Cover-Sus_scrofa 0.8686 1.0174 601
## total_shrub_cover-Canis_latrans 1.4367 1.0108 408
## total_shrub_cover-Sciurus_niger 0.4507 1.1490 322
## total_shrub_cover-Procyon_lotor -0.2688 1.0117 357
## total_shrub_cover-Dasypus_novemcinctus 0.4877 1.0016 317
## total_shrub_cover-Lynx_rufus 0.0160 1.0220 240
## total_shrub_cover-Didelphis_virginiana 0.1083 1.0379 360
## total_shrub_cover-Sylvilagus_floridanus -0.1483 1.0159 164
## total_shrub_cover-Meleagris_gallopavo -0.2048 1.0258 295
## total_shrub_cover-Sciurus_carolinensis 0.1966 1.0184 142
## total_shrub_cover-Vulpes_vulpes 0.5368 1.0039 208
## total_shrub_cover-Sus_scrofa 0.8141 1.0144 329
## avg_veg_height-Canis_latrans 1.0462 1.0034 709
## avg_veg_height-Sciurus_niger 0.9717 1.0418 450
## avg_veg_height-Procyon_lotor 1.1671 1.0193 745
## avg_veg_height-Dasypus_novemcinctus 1.3704 1.0035 563
## avg_veg_height-Lynx_rufus 1.2048 1.0042 696
## avg_veg_height-Didelphis_virginiana 0.9861 1.0047 729
## avg_veg_height-Sylvilagus_floridanus 1.1533 1.0203 657
## avg_veg_height-Meleagris_gallopavo 1.0897 1.0372 398
## avg_veg_height-Sciurus_carolinensis 1.6517 1.0141 662
## avg_veg_height-Vulpes_vulpes 1.1516 1.0154 716
## avg_veg_height-Sus_scrofa 1.2349 1.0057 776
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6332 0.2083 -3.0665 -2.6286 -2.2368 1.0030
## (Intercept)-Sciurus_niger -3.7616 0.5947 -4.8848 -3.7392 -2.6457 1.0765
## (Intercept)-Procyon_lotor -2.2203 0.1588 -2.5405 -2.2152 -1.9254 1.0100
## (Intercept)-Dasypus_novemcinctus -1.7397 0.1986 -2.1340 -1.7334 -1.3633 1.0038
## (Intercept)-Lynx_rufus -3.3859 0.3444 -4.1070 -3.3653 -2.7781 1.0027
## (Intercept)-Didelphis_virginiana -2.6357 0.3287 -3.2825 -2.6261 -1.9946 1.0288
## (Intercept)-Sylvilagus_floridanus -3.2194 0.2841 -3.7937 -3.2148 -2.6787 1.0066
## (Intercept)-Meleagris_gallopavo -3.5140 0.5266 -4.5578 -3.4937 -2.5242 1.0368
## (Intercept)-Sciurus_carolinensis -2.7633 0.3549 -3.4915 -2.7567 -2.0891 1.0137
## (Intercept)-Vulpes_vulpes -3.9810 0.6823 -5.4272 -3.9503 -2.7526 1.0265
## (Intercept)-Sus_scrofa -3.5039 0.5249 -4.5498 -3.5106 -2.4753 1.0153
## shrub_cover-Canis_latrans -0.2625 0.2446 -0.7326 -0.2630 0.2309 1.0237
## shrub_cover-Sciurus_niger 0.0087 0.5694 -1.1020 0.0155 1.1083 1.0758
## shrub_cover-Procyon_lotor 0.3227 0.1614 0.0038 0.3235 0.6281 1.0016
## shrub_cover-Dasypus_novemcinctus 1.1282 0.3660 0.4486 1.1182 1.8527 1.0000
## shrub_cover-Lynx_rufus 0.1116 0.3709 -0.6537 0.1301 0.7820 1.0128
## shrub_cover-Didelphis_virginiana 1.3401 0.4409 0.5300 1.3227 2.2433 1.0339
## shrub_cover-Sylvilagus_floridanus 0.8231 0.3975 0.0492 0.8243 1.6163 1.0680
## shrub_cover-Meleagris_gallopavo -0.3802 0.4809 -1.3513 -0.3722 0.5290 1.0422
## shrub_cover-Sciurus_carolinensis 1.2677 0.4397 0.4000 1.2601 2.1334 1.0474
## shrub_cover-Vulpes_vulpes 0.3700 0.6611 -1.1296 0.4089 1.5772 1.0074
## shrub_cover-Sus_scrofa 1.1824 0.7605 -0.3456 1.1898 2.6973 1.0070
## veg_height-Canis_latrans -0.6006 0.1886 -0.9835 -0.5967 -0.2312 1.0116
## veg_height-Sciurus_niger 0.1473 0.4687 -0.7449 0.1290 1.1646 1.0240
## veg_height-Procyon_lotor 0.3457 0.1255 0.1006 0.3456 0.5907 1.0075
## veg_height-Dasypus_novemcinctus 0.2760 0.1391 -0.0015 0.2748 0.5519 1.0013
## veg_height-Lynx_rufus 0.0452 0.2511 -0.4597 0.0574 0.5292 1.0109
## veg_height-Didelphis_virginiana 0.4455 0.2582 -0.0291 0.4360 0.9865 1.0121
## veg_height-Sylvilagus_floridanus 0.0486 0.2488 -0.4500 0.0519 0.5317 1.0331
## veg_height-Meleagris_gallopavo -0.1483 0.4216 -0.9672 -0.1443 0.6859 1.0153
## veg_height-Sciurus_carolinensis 0.1269 0.2408 -0.3289 0.1258 0.6090 1.0097
## veg_height-Vulpes_vulpes -0.1391 0.3534 -0.9141 -0.1064 0.4976 1.0307
## veg_height-Sus_scrofa -0.1523 0.3207 -0.7963 -0.1503 0.4677 1.0029
## week-Canis_latrans 0.4476 0.2469 0.0012 0.4368 0.9389 1.0028
## week-Sciurus_niger -0.2604 0.4232 -1.2318 -0.2102 0.4474 1.0054
## week-Procyon_lotor 0.1460 0.1998 -0.2444 0.1478 0.5456 1.0071
## week-Dasypus_novemcinctus 0.0522 0.2134 -0.3635 0.0462 0.4696 1.0033
## week-Lynx_rufus 0.2466 0.3041 -0.3232 0.2442 0.8681 1.0111
## week-Didelphis_virginiana 0.0232 0.3149 -0.6175 0.0320 0.6064 1.0116
## week-Sylvilagus_floridanus -0.0028 0.2992 -0.6137 -0.0014 0.5807 1.0074
## week-Meleagris_gallopavo -0.1450 0.3511 -0.8797 -0.1215 0.4844 1.0102
## week-Sciurus_carolinensis 0.5280 0.3354 -0.0718 0.5057 1.2616 1.0034
## week-Vulpes_vulpes 0.1134 0.4017 -0.7126 0.1189 0.9349 1.0038
## week-Sus_scrofa 0.3977 0.3891 -0.2913 0.3828 1.2643 1.0130
## I(week^2)-Canis_latrans -0.1932 0.1045 -0.4056 -0.1929 0.0013 1.0011
## I(week^2)-Sciurus_niger -0.2350 0.2119 -0.6876 -0.2168 0.1397 1.0033
## I(week^2)-Procyon_lotor -0.1089 0.0877 -0.2827 -0.1087 0.0609 1.0065
## I(week^2)-Dasypus_novemcinctus -0.1546 0.1012 -0.3590 -0.1503 0.0333 1.0012
## I(week^2)-Lynx_rufus -0.1950 0.1467 -0.4935 -0.1884 0.0714 1.0329
## I(week^2)-Didelphis_virginiana -0.3514 0.1926 -0.7772 -0.3323 -0.0330 1.0696
## I(week^2)-Sylvilagus_floridanus -0.1522 0.1528 -0.4664 -0.1430 0.1201 1.0376
## I(week^2)-Meleagris_gallopavo -0.3554 0.2439 -0.8856 -0.3186 0.0132 1.0512
## I(week^2)-Sciurus_carolinensis -0.1918 0.1394 -0.4764 -0.1883 0.0681 1.0004
## I(week^2)-Vulpes_vulpes -0.3396 0.2309 -0.8271 -0.3164 0.0741 1.0144
## I(week^2)-Sus_scrofa -0.1693 0.1668 -0.5184 -0.1608 0.1406 1.0025
## ESS
## (Intercept)-Canis_latrans 719
## (Intercept)-Sciurus_niger 265
## (Intercept)-Procyon_lotor 1584
## (Intercept)-Dasypus_novemcinctus 596
## (Intercept)-Lynx_rufus 399
## (Intercept)-Didelphis_virginiana 405
## (Intercept)-Sylvilagus_floridanus 683
## (Intercept)-Meleagris_gallopavo 302
## (Intercept)-Sciurus_carolinensis 325
## (Intercept)-Vulpes_vulpes 134
## (Intercept)-Sus_scrofa 229
## shrub_cover-Canis_latrans 370
## shrub_cover-Sciurus_niger 374
## shrub_cover-Procyon_lotor 1320
## shrub_cover-Dasypus_novemcinctus 320
## shrub_cover-Lynx_rufus 441
## shrub_cover-Didelphis_virginiana 285
## shrub_cover-Sylvilagus_floridanus 424
## shrub_cover-Meleagris_gallopavo 255
## shrub_cover-Sciurus_carolinensis 260
## shrub_cover-Vulpes_vulpes 243
## shrub_cover-Sus_scrofa 212
## veg_height-Canis_latrans 723
## veg_height-Sciurus_niger 499
## veg_height-Procyon_lotor 1592
## veg_height-Dasypus_novemcinctus 1752
## veg_height-Lynx_rufus 652
## veg_height-Didelphis_virginiana 804
## veg_height-Sylvilagus_floridanus 612
## veg_height-Meleagris_gallopavo 400
## veg_height-Sciurus_carolinensis 720
## veg_height-Vulpes_vulpes 454
## veg_height-Sus_scrofa 718
## week-Canis_latrans 1339
## week-Sciurus_niger 564
## week-Procyon_lotor 1762
## week-Dasypus_novemcinctus 1858
## week-Lynx_rufus 855
## week-Didelphis_virginiana 919
## week-Sylvilagus_floridanus 784
## week-Meleagris_gallopavo 604
## week-Sciurus_carolinensis 992
## week-Vulpes_vulpes 675
## week-Sus_scrofa 1072
## I(week^2)-Canis_latrans 1201
## I(week^2)-Sciurus_niger 483
## I(week^2)-Procyon_lotor 1707
## I(week^2)-Dasypus_novemcinctus 1655
## I(week^2)-Lynx_rufus 552
## I(week^2)-Didelphis_virginiana 463
## I(week^2)-Sylvilagus_floridanus 563
## I(week^2)-Meleagris_gallopavo 252
## I(week^2)-Sciurus_carolinensis 1046
## I(week^2)-Vulpes_vulpes 465
## I(week^2)-Sus_scrofa 922
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T<- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_canopy_T)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.1425
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6857 0.4304 -1.5393 -0.6799 0.1924 1.0178 575
## Tree_Density -0.8019 0.4070 -1.7188 -0.7751 -0.0801 1.0014 344
## Avg_Canopy_Cover 1.1018 0.3580 0.4654 1.0779 1.8799 1.0052 432
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2956 1.3699 0.1151 0.9307 4.8235 1.1599 228
## Tree_Density 0.6845 1.0633 0.0472 0.3651 3.2911 1.0270 389
## Avg_Canopy_Cover 0.7036 0.7838 0.0686 0.4754 2.7461 1.0261 624
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5685 0.6146 0.0422 0.3454 2.2813 1.1554 109
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9376 0.3233 -3.5669 -2.9422 -2.2918 1.0073 1182
## shrub_cover 0.2097 0.2963 -0.3834 0.2078 0.8049 1.0003 1264
## veg_height 0.0393 0.1649 -0.2948 0.0452 0.3588 1.0001 1080
## week 0.1450 0.1900 -0.2303 0.1456 0.5163 1.0057 841
## I(week^2) -0.2179 0.1046 -0.4391 -0.2113 -0.0307 1.0638 579
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9575 0.6332 0.2714 0.7967 2.6230 1.0241 724
## shrub_cover 0.7133 0.5145 0.1627 0.5862 1.9863 1.0026 687
## veg_height 0.2071 0.1506 0.0535 0.1649 0.6175 1.0090 1167
## week 0.2108 0.1949 0.0361 0.1520 0.7402 1.1131 455
## I(week^2) 0.0641 0.0633 0.0180 0.0487 0.1986 1.1112 349
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.0744 0.6039 -1.0524 0.0516 1.3010
## (Intercept)-Sciurus_niger -0.6363 0.8227 -2.1007 -0.6863 1.1246
## (Intercept)-Procyon_lotor 0.3492 0.6380 -0.8897 0.3531 1.5800
## (Intercept)-Dasypus_novemcinctus -0.9096 0.5618 -2.0354 -0.8977 0.1214
## (Intercept)-Lynx_rufus 0.1375 0.9201 -1.4396 0.0230 2.2523
## (Intercept)-Didelphis_virginiana -1.3967 0.6609 -2.8453 -1.3664 -0.2058
## (Intercept)-Sylvilagus_floridanus -0.6899 0.5928 -1.8898 -0.6913 0.4822
## (Intercept)-Meleagris_gallopavo -0.1714 0.8641 -1.6054 -0.2426 1.7856
## (Intercept)-Sciurus_carolinensis -1.4555 0.6957 -2.9771 -1.3985 -0.2386
## (Intercept)-Vulpes_vulpes -1.3384 0.8698 -3.0782 -1.3246 0.3494
## (Intercept)-Sus_scrofa -1.7878 0.8837 -3.7465 -1.7015 -0.2743
## Tree_Density-Canis_latrans -0.9668 0.5769 -2.2827 -0.8943 -0.0391
## Tree_Density-Sciurus_niger -0.8251 0.6925 -2.4049 -0.7628 0.3672
## Tree_Density-Procyon_lotor -0.5193 0.4228 -1.3931 -0.5148 0.2800
## Tree_Density-Dasypus_novemcinctus -1.3374 0.8698 -3.4948 -1.1703 -0.1825
## Tree_Density-Lynx_rufus 0.0788 0.6594 -0.9645 -0.0171 1.6028
## Tree_Density-Didelphis_virginiana -0.9343 0.6738 -2.4691 -0.8488 0.1455
## Tree_Density-Sylvilagus_floridanus -1.0656 0.7481 -2.9598 -0.9575 0.0833
## Tree_Density-Meleagris_gallopavo -1.0190 0.7490 -2.7574 -0.9236 0.2071
## Tree_Density-Sciurus_carolinensis -0.8749 0.7076 -2.6451 -0.7926 0.3069
## Tree_Density-Vulpes_vulpes -0.6523 0.7215 -2.1686 -0.6360 0.7030
## Tree_Density-Sus_scrofa -0.9013 0.7558 -2.7543 -0.8121 0.2946
## Avg_Canopy_Cover-Canis_latrans 0.0555 0.4808 -0.9039 0.0593 0.9633
## Avg_Canopy_Cover-Sciurus_niger 0.9467 0.7540 -0.4306 0.8997 2.6038
## Avg_Canopy_Cover-Procyon_lotor 1.0245 0.4578 0.1920 1.0016 1.9853
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0891 0.4711 0.2742 1.0638 2.1448
## Avg_Canopy_Cover-Lynx_rufus 0.8087 0.6594 -0.4303 0.7833 2.1724
## Avg_Canopy_Cover-Didelphis_virginiana 1.4614 0.6274 0.4916 1.3876 2.8980
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.8647 0.8330 0.6485 1.7227 3.9003
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4053 0.6730 0.3154 1.3235 2.9149
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4074 0.5906 0.4687 1.3305 2.7768
## Avg_Canopy_Cover-Vulpes_vulpes 1.1190 0.6460 0.0504 1.0454 2.6290
## Avg_Canopy_Cover-Sus_scrofa 1.2733 0.6346 0.2069 1.2038 2.7915
## Rhat ESS
## (Intercept)-Canis_latrans 1.0545 561
## (Intercept)-Sciurus_niger 1.0177 386
## (Intercept)-Procyon_lotor 1.0313 331
## (Intercept)-Dasypus_novemcinctus 1.0089 788
## (Intercept)-Lynx_rufus 1.0367 249
## (Intercept)-Didelphis_virginiana 1.0242 677
## (Intercept)-Sylvilagus_floridanus 1.0102 1089
## (Intercept)-Meleagris_gallopavo 1.0244 327
## (Intercept)-Sciurus_carolinensis 1.0470 459
## (Intercept)-Vulpes_vulpes 1.0080 365
## (Intercept)-Sus_scrofa 1.0735 401
## Tree_Density-Canis_latrans 1.0059 730
## Tree_Density-Sciurus_niger 1.0031 596
## Tree_Density-Procyon_lotor 1.0011 1710
## Tree_Density-Dasypus_novemcinctus 1.0042 415
## Tree_Density-Lynx_rufus 1.0227 634
## Tree_Density-Didelphis_virginiana 1.0052 806
## Tree_Density-Sylvilagus_floridanus 1.0061 466
## Tree_Density-Meleagris_gallopavo 1.0051 595
## Tree_Density-Sciurus_carolinensis 1.0128 576
## Tree_Density-Vulpes_vulpes 1.0196 674
## Tree_Density-Sus_scrofa 1.0064 484
## Avg_Canopy_Cover-Canis_latrans 1.0046 992
## Avg_Canopy_Cover-Sciurus_niger 1.0308 480
## Avg_Canopy_Cover-Procyon_lotor 1.0062 1058
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0116 1372
## Avg_Canopy_Cover-Lynx_rufus 1.0214 671
## Avg_Canopy_Cover-Didelphis_virginiana 1.0102 581
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0202 375
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0157 464
## Avg_Canopy_Cover-Sciurus_carolinensis 0.9999 603
## Avg_Canopy_Cover-Vulpes_vulpes 1.0064 868
## Avg_Canopy_Cover-Sus_scrofa 1.0037 685
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6040 0.2049 -3.0267 -2.5958 -2.2297 1.0024
## (Intercept)-Sciurus_niger -4.0391 0.5863 -5.1702 -4.0481 -2.8472 1.0338
## (Intercept)-Procyon_lotor -2.2119 0.1581 -2.5291 -2.2088 -1.9163 1.0039
## (Intercept)-Dasypus_novemcinctus -1.6501 0.1792 -2.0127 -1.6490 -1.2983 1.0009
## (Intercept)-Lynx_rufus -3.6004 0.3477 -4.3239 -3.5919 -2.9309 1.0013
## (Intercept)-Didelphis_virginiana -2.4664 0.3082 -3.1163 -2.4503 -1.9016 1.0034
## (Intercept)-Sylvilagus_floridanus -3.0587 0.2872 -3.6658 -3.0535 -2.5255 1.0015
## (Intercept)-Meleagris_gallopavo -3.8440 0.4401 -4.7004 -3.8257 -3.0217 1.0196
## (Intercept)-Sciurus_carolinensis -2.6012 0.3323 -3.2823 -2.6026 -1.9553 1.0252
## (Intercept)-Vulpes_vulpes -3.8860 0.6229 -5.1814 -3.8553 -2.7365 1.0050
## (Intercept)-Sus_scrofa -3.3101 0.5953 -4.4799 -3.3129 -2.1592 1.0361
## shrub_cover-Canis_latrans -0.3018 0.2205 -0.7456 -0.3023 0.1238 1.0100
## shrub_cover-Sciurus_niger -0.4053 0.4891 -1.4153 -0.3987 0.5851 1.0043
## shrub_cover-Procyon_lotor 0.2530 0.1620 -0.0657 0.2528 0.5577 1.0144
## shrub_cover-Dasypus_novemcinctus 0.8849 0.3024 0.2971 0.8753 1.4672 1.0056
## shrub_cover-Lynx_rufus -0.3185 0.3496 -1.0134 -0.3177 0.3637 1.0164
## shrub_cover-Didelphis_virginiana 1.0286 0.3624 0.3491 1.0182 1.7355 1.0115
## shrub_cover-Sylvilagus_floridanus 0.3884 0.4054 -0.3876 0.3752 1.2191 1.0068
## shrub_cover-Meleagris_gallopavo -0.7339 0.3876 -1.5135 -0.7188 -0.0130 1.0237
## shrub_cover-Sciurus_carolinensis 0.9406 0.4323 0.0981 0.9447 1.7755 1.0075
## shrub_cover-Vulpes_vulpes -0.0743 0.5891 -1.2802 -0.0753 1.0400 1.0169
## shrub_cover-Sus_scrofa 0.7765 0.8195 -0.8189 0.7623 2.4180 1.0067
## veg_height-Canis_latrans -0.5816 0.1882 -0.9550 -0.5770 -0.2117 1.0012
## veg_height-Sciurus_niger -0.0018 0.3960 -0.7679 -0.0058 0.7929 1.0110
## veg_height-Procyon_lotor 0.3447 0.1235 0.1104 0.3407 0.5897 1.0106
## veg_height-Dasypus_novemcinctus 0.2633 0.1340 0.0082 0.2632 0.5280 0.9998
## veg_height-Lynx_rufus 0.0636 0.2343 -0.4206 0.0666 0.5176 1.0027
## veg_height-Didelphis_virginiana 0.4782 0.2388 0.0312 0.4686 0.9682 0.9999
## veg_height-Sylvilagus_floridanus 0.1547 0.2343 -0.2959 0.1572 0.6281 1.0117
## veg_height-Meleagris_gallopavo -0.2006 0.3399 -0.8983 -0.1878 0.4304 1.0019
## veg_height-Sciurus_carolinensis 0.1295 0.2143 -0.2914 0.1277 0.5585 1.0022
## veg_height-Vulpes_vulpes -0.1122 0.3114 -0.7744 -0.0955 0.4547 1.0073
## veg_height-Sus_scrofa -0.1123 0.3272 -0.7984 -0.1109 0.4824 1.0012
## week-Canis_latrans 0.4483 0.2508 0.0003 0.4417 0.9595 1.0322
## week-Sciurus_niger -0.2470 0.4364 -1.2762 -0.1966 0.4798 1.1131
## week-Procyon_lotor 0.1454 0.1981 -0.2370 0.1432 0.5383 1.0053
## week-Dasypus_novemcinctus 0.0583 0.2115 -0.3533 0.0598 0.4702 1.0025
## week-Lynx_rufus 0.2352 0.3011 -0.3401 0.2286 0.8479 1.0152
## week-Didelphis_virginiana 0.0257 0.3245 -0.6612 0.0427 0.6191 1.0101
## week-Sylvilagus_floridanus 0.0017 0.3023 -0.6000 0.0034 0.6012 1.0022
## week-Meleagris_gallopavo -0.1342 0.3549 -0.9158 -0.1096 0.5039 1.0721
## week-Sciurus_carolinensis 0.5429 0.3227 -0.0430 0.5257 1.2536 1.0278
## week-Vulpes_vulpes 0.0998 0.4031 -0.7432 0.1088 0.8926 1.0125
## week-Sus_scrofa 0.3911 0.3739 -0.2807 0.3721 1.1847 1.0288
## I(week^2)-Canis_latrans -0.1925 0.1082 -0.4094 -0.1902 0.0077 1.0376
## I(week^2)-Sciurus_niger -0.2282 0.2057 -0.6776 -0.2169 0.1370 1.0695
## I(week^2)-Procyon_lotor -0.1072 0.0869 -0.2839 -0.1055 0.0565 1.0102
## I(week^2)-Dasypus_novemcinctus -0.1567 0.1002 -0.3580 -0.1550 0.0364 1.0020
## I(week^2)-Lynx_rufus -0.1880 0.1429 -0.4838 -0.1851 0.0820 1.0159
## I(week^2)-Didelphis_virginiana -0.3422 0.2149 -0.8067 -0.3165 -0.0175 1.0388
## I(week^2)-Sylvilagus_floridanus -0.1427 0.1476 -0.4548 -0.1389 0.1309 1.0528
## I(week^2)-Meleagris_gallopavo -0.3366 0.2269 -0.8290 -0.3094 0.0209 1.1055
## I(week^2)-Sciurus_carolinensis -0.1909 0.1365 -0.4698 -0.1852 0.0652 1.0227
## I(week^2)-Vulpes_vulpes -0.3325 0.2415 -0.9081 -0.3080 0.0599 1.0207
## I(week^2)-Sus_scrofa -0.1537 0.1623 -0.4790 -0.1491 0.1488 1.0120
## ESS
## (Intercept)-Canis_latrans 892
## (Intercept)-Sciurus_niger 208
## (Intercept)-Procyon_lotor 1517
## (Intercept)-Dasypus_novemcinctus 1568
## (Intercept)-Lynx_rufus 284
## (Intercept)-Didelphis_virginiana 852
## (Intercept)-Sylvilagus_floridanus 764
## (Intercept)-Meleagris_gallopavo 308
## (Intercept)-Sciurus_carolinensis 612
## (Intercept)-Vulpes_vulpes 262
## (Intercept)-Sus_scrofa 479
## shrub_cover-Canis_latrans 872
## shrub_cover-Sciurus_niger 404
## shrub_cover-Procyon_lotor 1462
## shrub_cover-Dasypus_novemcinctus 926
## shrub_cover-Lynx_rufus 455
## shrub_cover-Didelphis_virginiana 768
## shrub_cover-Sylvilagus_floridanus 645
## shrub_cover-Meleagris_gallopavo 333
## shrub_cover-Sciurus_carolinensis 686
## shrub_cover-Vulpes_vulpes 577
## shrub_cover-Sus_scrofa 521
## veg_height-Canis_latrans 706
## veg_height-Sciurus_niger 613
## veg_height-Procyon_lotor 1650
## veg_height-Dasypus_novemcinctus 2026
## veg_height-Lynx_rufus 788
## veg_height-Didelphis_virginiana 1173
## veg_height-Sylvilagus_floridanus 1128
## veg_height-Meleagris_gallopavo 666
## veg_height-Sciurus_carolinensis 1051
## veg_height-Vulpes_vulpes 629
## veg_height-Sus_scrofa 1185
## week-Canis_latrans 1234
## week-Sciurus_niger 474
## week-Procyon_lotor 1623
## week-Dasypus_novemcinctus 1610
## week-Lynx_rufus 860
## week-Didelphis_virginiana 900
## week-Sylvilagus_floridanus 924
## week-Meleagris_gallopavo 467
## week-Sciurus_carolinensis 1051
## week-Vulpes_vulpes 933
## week-Sus_scrofa 1184
## I(week^2)-Canis_latrans 1194
## I(week^2)-Sciurus_niger 528
## I(week^2)-Procyon_lotor 1581
## I(week^2)-Dasypus_novemcinctus 1953
## I(week^2)-Lynx_rufus 711
## I(week^2)-Didelphis_virginiana 459
## I(week^2)-Sylvilagus_floridanus 767
## I(week^2)-Meleagris_gallopavo 228
## I(week^2)-Sciurus_carolinensis 1103
## I(week^2)-Vulpes_vulpes 389
## I(week^2)-Sus_scrofa 1467
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_move_T)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.2148
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5636 0.4123 -1.3974 -0.5626 0.2825 1.0458 244
## Cogon_Patch_Size -0.1336 0.3577 -0.8964 -0.1214 0.5395 1.0091 659
## Avg_Cogongrass_Cover 0.0404 0.3247 -0.6066 0.0399 0.6730 1.0222 481
## total_shrub_cover -0.8295 0.4051 -1.7540 -0.8017 -0.1083 1.0154 230
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6390 0.7319 0.0571 0.4025 2.5226 1.0355 480
## Cogon_Patch_Size 0.6595 0.8172 0.0509 0.4007 2.9718 1.0750 471
## Avg_Cogongrass_Cover 0.4314 0.5211 0.0431 0.2617 1.8965 1.0089 452
## total_shrub_cover 0.6126 0.7193 0.0542 0.3875 2.5651 1.0202 374
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6458 1.3347 0.1514 1.2652 5.2761 1.0137 143
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9131 0.3023 -3.5241 -2.9041 -2.3362 1.0197 1017
## shrub_cover 0.4867 0.2996 -0.0961 0.4815 1.0931 1.0111 371
## veg_height 0.0536 0.1759 -0.2963 0.0536 0.4122 1.0110 705
## week 0.1315 0.2010 -0.2746 0.1354 0.5354 1.0056 805
## I(week^2) -0.2177 0.1003 -0.4260 -0.2137 -0.0311 1.0176 727
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7984 0.5871 0.1911 0.6505 2.2937 1.0055 609
## shrub_cover 0.6597 0.5119 0.1379 0.5198 2.0534 1.0152 532
## veg_height 0.2144 0.1647 0.0551 0.1691 0.6776 1.0131 1043
## week 0.2265 0.2061 0.0387 0.1656 0.8003 1.0221 600
## I(week^2) 0.0641 0.0537 0.0182 0.0494 0.1839 1.0157 982
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0456 0.6528 -1.2493 -0.0780
## (Intercept)-Sciurus_niger -0.7815 0.7108 -2.2388 -0.7482
## (Intercept)-Procyon_lotor 0.0709 0.6979 -1.1911 0.0315
## (Intercept)-Dasypus_novemcinctus -0.5870 0.5663 -1.7919 -0.5738
## (Intercept)-Lynx_rufus -0.4369 0.6697 -1.6937 -0.4551
## (Intercept)-Didelphis_virginiana -0.8596 0.6072 -2.0895 -0.8434
## (Intercept)-Sylvilagus_floridanus -0.3611 0.6619 -1.5939 -0.3981
## (Intercept)-Meleagris_gallopavo -0.5766 0.6804 -1.9062 -0.5933
## (Intercept)-Sciurus_carolinensis -0.8425 0.6576 -2.2138 -0.8064
## (Intercept)-Vulpes_vulpes -0.8629 0.7830 -2.5078 -0.8387
## (Intercept)-Sus_scrofa -1.0119 0.7716 -2.6785 -0.9411
## Cogon_Patch_Size-Canis_latrans 0.5772 0.6082 -0.3543 0.4885
## Cogon_Patch_Size-Sciurus_niger -0.4225 0.7428 -2.1645 -0.3222
## Cogon_Patch_Size-Procyon_lotor -0.2009 0.4583 -1.1575 -0.1832
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0373 0.4334 -0.8886 -0.0416
## Cogon_Patch_Size-Lynx_rufus -0.1068 0.6838 -1.3854 -0.1450
## Cogon_Patch_Size-Didelphis_virginiana 0.5339 0.4988 -0.3453 0.4961
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6891 0.7604 -2.5284 -0.5685
## Cogon_Patch_Size-Meleagris_gallopavo -0.0271 0.6231 -1.2388 -0.0431
## Cogon_Patch_Size-Sciurus_carolinensis -0.5695 0.6374 -2.0544 -0.4810
## Cogon_Patch_Size-Vulpes_vulpes -0.3723 0.7676 -2.1027 -0.3066
## Cogon_Patch_Size-Sus_scrofa -0.2544 0.6782 -1.7923 -0.1924
## Avg_Cogongrass_Cover-Canis_latrans 0.2678 0.4361 -0.5034 0.2416
## Avg_Cogongrass_Cover-Sciurus_niger -0.3967 0.7445 -2.0709 -0.3078
## Avg_Cogongrass_Cover-Procyon_lotor 0.0372 0.4511 -0.9033 0.0293
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3035 0.4238 -0.4846 0.2807
## Avg_Cogongrass_Cover-Lynx_rufus 0.4292 0.5136 -0.4500 0.3763
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0823 0.4548 -0.8123 0.0914
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2612 0.5540 -1.4334 -0.2227
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4207 0.7100 -2.0579 -0.3429
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2887 0.4687 -0.5981 0.2737
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2472 0.5205 -0.7180 0.2158
## Avg_Cogongrass_Cover-Sus_scrofa -0.1413 0.6235 -1.5095 -0.1086
## total_shrub_cover-Canis_latrans -0.0341 0.5564 -1.0643 -0.0643
## total_shrub_cover-Sciurus_niger -0.9339 0.6943 -2.4359 -0.8842
## total_shrub_cover-Procyon_lotor -1.1185 0.5434 -2.3947 -1.0632
## total_shrub_cover-Dasypus_novemcinctus -0.4450 0.5057 -1.5038 -0.4146
## total_shrub_cover-Lynx_rufus -1.1572 0.7349 -2.8770 -1.0876
## total_shrub_cover-Didelphis_virginiana -0.8527 0.5566 -2.1372 -0.7984
## total_shrub_cover-Sylvilagus_floridanus -1.0566 0.7298 -2.7703 -0.9784
## total_shrub_cover-Meleagris_gallopavo -1.3363 0.7333 -3.0443 -1.2490
## total_shrub_cover-Sciurus_carolinensis -0.8387 0.6513 -2.3218 -0.7838
## total_shrub_cover-Vulpes_vulpes -0.9147 0.8298 -2.8591 -0.8450
## total_shrub_cover-Sus_scrofa -0.6701 0.7072 -2.2177 -0.6510
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2777 1.0341 540
## (Intercept)-Sciurus_niger 0.6095 1.0207 477
## (Intercept)-Procyon_lotor 1.5500 1.0357 430
## (Intercept)-Dasypus_novemcinctus 0.5435 1.0324 573
## (Intercept)-Lynx_rufus 0.9263 1.0212 525
## (Intercept)-Didelphis_virginiana 0.3332 1.0067 577
## (Intercept)-Sylvilagus_floridanus 1.0419 1.0641 466
## (Intercept)-Meleagris_gallopavo 0.8480 1.0355 480
## (Intercept)-Sciurus_carolinensis 0.4082 1.0113 461
## (Intercept)-Vulpes_vulpes 0.6657 1.0262 384
## (Intercept)-Sus_scrofa 0.3427 1.0060 392
## Cogon_Patch_Size-Canis_latrans 2.0277 1.0090 881
## Cogon_Patch_Size-Sciurus_niger 0.7754 1.0266 624
## Cogon_Patch_Size-Procyon_lotor 0.6600 1.0061 1311
## Cogon_Patch_Size-Dasypus_novemcinctus 0.8534 1.0026 1363
## Cogon_Patch_Size-Lynx_rufus 1.3795 1.0088 701
## Cogon_Patch_Size-Didelphis_virginiana 1.6007 1.0015 1010
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4514 1.0302 523
## Cogon_Patch_Size-Meleagris_gallopavo 1.2851 1.0044 1032
## Cogon_Patch_Size-Sciurus_carolinensis 0.4707 1.0288 690
## Cogon_Patch_Size-Vulpes_vulpes 1.0780 1.0183 577
## Cogon_Patch_Size-Sus_scrofa 0.9225 1.0063 930
## Avg_Cogongrass_Cover-Canis_latrans 1.1945 1.0107 1122
## Avg_Cogongrass_Cover-Sciurus_niger 0.7918 1.0095 386
## Avg_Cogongrass_Cover-Procyon_lotor 0.9520 1.0066 1210
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1898 1.0017 1371
## Avg_Cogongrass_Cover-Lynx_rufus 1.6074 1.0139 982
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9678 1.0095 1014
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7272 1.0197 746
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7913 1.0096 417
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2499 1.0035 878
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3617 1.0120 884
## Avg_Cogongrass_Cover-Sus_scrofa 0.9626 1.0080 738
## total_shrub_cover-Canis_latrans 1.1333 1.0086 436
## total_shrub_cover-Sciurus_niger 0.3387 1.0072 438
## total_shrub_cover-Procyon_lotor -0.1970 1.0187 501
## total_shrub_cover-Dasypus_novemcinctus 0.5224 1.0006 641
## total_shrub_cover-Lynx_rufus 0.1008 1.0231 317
## total_shrub_cover-Didelphis_virginiana 0.0951 1.0023 503
## total_shrub_cover-Sylvilagus_floridanus 0.1839 1.0014 275
## total_shrub_cover-Meleagris_gallopavo -0.1092 1.0193 440
## total_shrub_cover-Sciurus_carolinensis 0.2797 1.0026 301
## total_shrub_cover-Vulpes_vulpes 0.5485 1.0108 334
## total_shrub_cover-Sus_scrofa 0.6708 1.0070 295
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5907 0.2008 -3.0090 -2.5842 -2.2128 1.0011
## (Intercept)-Sciurus_niger -3.7331 0.5728 -4.8279 -3.7232 -2.6409 1.0320
## (Intercept)-Procyon_lotor -2.2190 0.1519 -2.5207 -2.2156 -1.9413 1.0010
## (Intercept)-Dasypus_novemcinctus -1.7104 0.1965 -2.1264 -1.7028 -1.3489 1.0000
## (Intercept)-Lynx_rufus -3.3595 0.3319 -4.0392 -3.3515 -2.7321 1.0058
## (Intercept)-Didelphis_virginiana -2.5380 0.3139 -3.1616 -2.5283 -1.9394 1.0088
## (Intercept)-Sylvilagus_floridanus -3.1610 0.2865 -3.7371 -3.1574 -2.6251 1.0208
## (Intercept)-Meleagris_gallopavo -3.5027 0.5097 -4.4950 -3.4901 -2.5163 1.0112
## (Intercept)-Sciurus_carolinensis -2.7440 0.3532 -3.4344 -2.7383 -2.0658 1.0313
## (Intercept)-Vulpes_vulpes -3.8963 0.6393 -5.1997 -3.8586 -2.7628 1.0582
## (Intercept)-Sus_scrofa -3.5100 0.5342 -4.5964 -3.5077 -2.4795 1.0035
## shrub_cover-Canis_latrans -0.2578 0.2344 -0.7238 -0.2507 0.1853 1.0384
## shrub_cover-Sciurus_niger 0.0653 0.5641 -1.0322 0.0694 1.1584 1.0229
## shrub_cover-Procyon_lotor 0.3210 0.1619 -0.0160 0.3270 0.6295 1.0016
## shrub_cover-Dasypus_novemcinctus 1.0354 0.3370 0.3891 1.0240 1.6979 1.0040
## shrub_cover-Lynx_rufus 0.1361 0.3724 -0.6433 0.1500 0.8123 1.0497
## shrub_cover-Didelphis_virginiana 1.1675 0.3983 0.4143 1.1570 1.9836 1.0111
## shrub_cover-Sylvilagus_floridanus 0.7308 0.4166 -0.1459 0.7505 1.5029 1.0089
## shrub_cover-Meleagris_gallopavo -0.3605 0.4673 -1.2682 -0.3720 0.5205 1.0164
## shrub_cover-Sciurus_carolinensis 1.1826 0.4325 0.3413 1.1803 2.0259 1.0248
## shrub_cover-Vulpes_vulpes 0.3407 0.6897 -1.0327 0.3669 1.6233 1.0150
## shrub_cover-Sus_scrofa 1.1177 0.7385 -0.3600 1.1227 2.6327 1.0026
## veg_height-Canis_latrans -0.5668 0.1844 -0.9471 -0.5634 -0.2206 1.0161
## veg_height-Sciurus_niger 0.1234 0.4533 -0.7075 0.0990 1.0770 1.0168
## veg_height-Procyon_lotor 0.3437 0.1232 0.1039 0.3440 0.5847 1.0014
## veg_height-Dasypus_novemcinctus 0.2732 0.1391 -0.0014 0.2735 0.5495 1.0017
## veg_height-Lynx_rufus 0.0604 0.2422 -0.4337 0.0630 0.5167 1.0035
## veg_height-Didelphis_virginiana 0.4640 0.2471 -0.0039 0.4591 0.9696 1.0227
## veg_height-Sylvilagus_floridanus 0.0768 0.2391 -0.3857 0.0773 0.5518 1.0067
## veg_height-Meleagris_gallopavo -0.1544 0.4158 -0.9704 -0.1479 0.6795 1.0105
## veg_height-Sciurus_carolinensis 0.1511 0.2308 -0.2884 0.1424 0.6180 1.0241
## veg_height-Vulpes_vulpes -0.0946 0.3259 -0.7608 -0.0909 0.5270 1.0279
## veg_height-Sus_scrofa -0.1430 0.3321 -0.8078 -0.1338 0.4768 1.0084
## week-Canis_latrans 0.4420 0.2487 -0.0315 0.4274 0.9415 1.0014
## week-Sciurus_niger -0.3296 0.4621 -1.3531 -0.2780 0.4201 1.0380
## week-Procyon_lotor 0.1442 0.1969 -0.2437 0.1432 0.5424 1.0018
## week-Dasypus_novemcinctus 0.0487 0.2166 -0.3871 0.0468 0.4686 1.0005
## week-Lynx_rufus 0.2452 0.3126 -0.3579 0.2393 0.9030 1.0018
## week-Didelphis_virginiana 0.0093 0.3228 -0.6776 0.0241 0.6039 1.0018
## week-Sylvilagus_floridanus 0.0289 0.3055 -0.5745 0.0350 0.6215 1.0175
## week-Meleagris_gallopavo -0.1416 0.3566 -0.9235 -0.1150 0.5062 1.0158
## week-Sciurus_carolinensis 0.5468 0.3408 -0.0580 0.5262 1.2570 1.0088
## week-Vulpes_vulpes 0.0981 0.4080 -0.7823 0.1070 0.8826 1.0364
## week-Sus_scrofa 0.4214 0.3763 -0.2701 0.3958 1.2239 1.0141
## I(week^2)-Canis_latrans -0.1942 0.1065 -0.4112 -0.1904 0.0065 1.0109
## I(week^2)-Sciurus_niger -0.2599 0.2141 -0.7219 -0.2381 0.1289 1.0143
## I(week^2)-Procyon_lotor -0.1078 0.0870 -0.2774 -0.1077 0.0633 1.0138
## I(week^2)-Dasypus_novemcinctus -0.1509 0.1004 -0.3487 -0.1501 0.0444 1.0012
## I(week^2)-Lynx_rufus -0.1805 0.1386 -0.4632 -0.1769 0.0778 1.0013
## I(week^2)-Didelphis_virginiana -0.3372 0.1870 -0.7590 -0.3216 -0.0144 1.0195
## I(week^2)-Sylvilagus_floridanus -0.1574 0.1444 -0.4522 -0.1544 0.1210 1.0060
## I(week^2)-Meleagris_gallopavo -0.3263 0.1969 -0.7590 -0.3090 0.0128 1.0342
## I(week^2)-Sciurus_carolinensis -0.1984 0.1417 -0.4905 -0.1963 0.0627 1.0121
## I(week^2)-Vulpes_vulpes -0.3394 0.2432 -0.9107 -0.3080 0.0445 1.0665
## I(week^2)-Sus_scrofa -0.1691 0.1633 -0.5011 -0.1620 0.1388 1.0041
## ESS
## (Intercept)-Canis_latrans 883
## (Intercept)-Sciurus_niger 318
## (Intercept)-Procyon_lotor 1424
## (Intercept)-Dasypus_novemcinctus 967
## (Intercept)-Lynx_rufus 458
## (Intercept)-Didelphis_virginiana 647
## (Intercept)-Sylvilagus_floridanus 607
## (Intercept)-Meleagris_gallopavo 257
## (Intercept)-Sciurus_carolinensis 376
## (Intercept)-Vulpes_vulpes 254
## (Intercept)-Sus_scrofa 269
## shrub_cover-Canis_latrans 468
## shrub_cover-Sciurus_niger 304
## shrub_cover-Procyon_lotor 1418
## shrub_cover-Dasypus_novemcinctus 667
## shrub_cover-Lynx_rufus 428
## shrub_cover-Didelphis_virginiana 367
## shrub_cover-Sylvilagus_floridanus 426
## shrub_cover-Meleagris_gallopavo 332
## shrub_cover-Sciurus_carolinensis 295
## shrub_cover-Vulpes_vulpes 295
## shrub_cover-Sus_scrofa 252
## veg_height-Canis_latrans 629
## veg_height-Sciurus_niger 444
## veg_height-Procyon_lotor 2221
## veg_height-Dasypus_novemcinctus 1923
## veg_height-Lynx_rufus 727
## veg_height-Didelphis_virginiana 1050
## veg_height-Sylvilagus_floridanus 710
## veg_height-Meleagris_gallopavo 437
## veg_height-Sciurus_carolinensis 794
## veg_height-Vulpes_vulpes 618
## veg_height-Sus_scrofa 898
## week-Canis_latrans 1104
## week-Sciurus_niger 472
## week-Procyon_lotor 1703
## week-Dasypus_novemcinctus 1775
## week-Lynx_rufus 827
## week-Didelphis_virginiana 932
## week-Sylvilagus_floridanus 1104
## week-Meleagris_gallopavo 609
## week-Sciurus_carolinensis 684
## week-Vulpes_vulpes 554
## week-Sus_scrofa 952
## I(week^2)-Canis_latrans 1140
## I(week^2)-Sciurus_niger 414
## I(week^2)-Procyon_lotor 1441
## I(week^2)-Dasypus_novemcinctus 1639
## I(week^2)-Lynx_rufus 837
## I(week^2)-Didelphis_virginiana 510
## I(week^2)-Sylvilagus_floridanus 830
## I(week^2)-Meleagris_gallopavo 374
## I(week^2)-Sciurus_carolinensis 1034
## I(week^2)-Vulpes_vulpes 346
## I(week^2)-Sus_scrofa 813
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_forage_T)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.1617
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6224 0.3712 -1.3530 -0.6189 0.1076 1.0103 468
## Veg_shannon_index 0.3496 0.2568 -0.1459 0.3464 0.8376 1.0081 642
## Avg_Cogongrass_Cover 0.2132 0.2830 -0.3494 0.2235 0.7625 1.0115 655
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6872 0.9485 0.0571 0.4552 2.7225 1.1070 662
## Veg_shannon_index 0.2755 0.2976 0.0366 0.1860 1.0091 1.0258 956
## Avg_Cogongrass_Cover 0.3839 0.4876 0.0432 0.2299 1.5883 1.0422 572
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0491 0.8601 0.0967 0.8408 3.1307 1.0561 156
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9120 0.3234 -3.5557 -2.9092 -2.2647 1.0077 1513
## shrub_cover 0.1737 0.2884 -0.4161 0.1728 0.7484 1.0056 1308
## veg_height 0.0192 0.1779 -0.3415 0.0276 0.3588 1.0145 1122
## week 0.1459 0.1951 -0.2403 0.1488 0.5253 1.0093 864
## I(week^2) -0.2228 0.1084 -0.4568 -0.2173 -0.0287 1.0038 542
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9526 0.6859 0.2523 0.7868 2.6032 1.0333 636
## shrub_cover 0.7133 0.5656 0.1646 0.5587 2.1495 1.0150 726
## veg_height 0.2150 0.1588 0.0540 0.1740 0.6391 1.0120 1143
## week 0.2161 0.2136 0.0337 0.1532 0.7882 1.0157 452
## I(week^2) 0.0664 0.0594 0.0184 0.0503 0.2064 1.0154 368
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1226 0.5907 -1.2501 -0.1481
## (Intercept)-Sciurus_niger -0.6590 0.6998 -2.0084 -0.6832
## (Intercept)-Procyon_lotor -0.0106 0.6263 -1.2042 -0.0362
## (Intercept)-Dasypus_novemcinctus -0.6684 0.5010 -1.7081 -0.6559
## (Intercept)-Lynx_rufus -0.3420 0.6659 -1.5485 -0.3748
## (Intercept)-Didelphis_virginiana -1.0328 0.5685 -2.2351 -1.0043
## (Intercept)-Sylvilagus_floridanus -0.5522 0.5489 -1.6082 -0.5479
## (Intercept)-Meleagris_gallopavo -0.2810 0.7157 -1.5486 -0.3467
## (Intercept)-Sciurus_carolinensis -1.0366 0.5661 -2.2367 -1.0028
## (Intercept)-Vulpes_vulpes -0.9869 0.7312 -2.5544 -0.9515
## (Intercept)-Sus_scrofa -1.3116 0.7186 -2.8132 -1.2377
## Veg_shannon_index-Canis_latrans 0.6284 0.3939 -0.0761 0.6067
## Veg_shannon_index-Sciurus_niger 0.3717 0.4922 -0.6027 0.3606
## Veg_shannon_index-Procyon_lotor 0.4458 0.3725 -0.2632 0.4342
## Veg_shannon_index-Dasypus_novemcinctus 0.1774 0.3385 -0.5056 0.1834
## Veg_shannon_index-Lynx_rufus 0.2399 0.4820 -0.7719 0.2484
## Veg_shannon_index-Didelphis_virginiana 0.4797 0.4005 -0.2587 0.4643
## Veg_shannon_index-Sylvilagus_floridanus 0.4022 0.4126 -0.3923 0.3994
## Veg_shannon_index-Meleagris_gallopavo 0.4934 0.4846 -0.3781 0.4633
## Veg_shannon_index-Sciurus_carolinensis -0.0221 0.4118 -0.9055 0.0057
## Veg_shannon_index-Vulpes_vulpes 0.0941 0.4601 -0.8940 0.1146
## Veg_shannon_index-Sus_scrofa 0.6063 0.4923 -0.2505 0.5737
## Avg_Cogongrass_Cover-Canis_latrans 0.5502 0.4231 -0.1964 0.5170
## Avg_Cogongrass_Cover-Sciurus_niger -0.1874 0.6134 -1.6002 -0.1243
## Avg_Cogongrass_Cover-Procyon_lotor 0.3121 0.3732 -0.4034 0.3019
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4095 0.3502 -0.2345 0.3996
## Avg_Cogongrass_Cover-Lynx_rufus 0.5514 0.4608 -0.2411 0.5092
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3998 0.3942 -0.3332 0.3872
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1687 0.4669 -1.1893 -0.1239
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1844 0.6365 -1.6284 -0.1341
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3618 0.3729 -0.3578 0.3513
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.3486 0.5009 -0.5932 0.3221
## Avg_Cogongrass_Cover-Sus_scrofa -0.0044 0.5420 -1.2129 0.0419
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0687 1.0298 469
## (Intercept)-Sciurus_niger 0.8820 1.0514 514
## (Intercept)-Procyon_lotor 1.2790 1.0750 381
## (Intercept)-Dasypus_novemcinctus 0.3108 1.0016 963
## (Intercept)-Lynx_rufus 1.0827 1.0242 422
## (Intercept)-Didelphis_virginiana 0.0355 1.0043 1042
## (Intercept)-Sylvilagus_floridanus 0.5562 1.0124 875
## (Intercept)-Meleagris_gallopavo 1.4111 1.0338 342
## (Intercept)-Sciurus_carolinensis -0.0279 1.0111 1058
## (Intercept)-Vulpes_vulpes 0.3525 1.0034 358
## (Intercept)-Sus_scrofa -0.0444 1.0315 498
## Veg_shannon_index-Canis_latrans 1.4736 1.0104 1256
## Veg_shannon_index-Sciurus_niger 1.3894 1.0130 817
## Veg_shannon_index-Procyon_lotor 1.2490 1.0143 1041
## Veg_shannon_index-Dasypus_novemcinctus 0.8279 0.9999 2040
## Veg_shannon_index-Lynx_rufus 1.1487 1.0190 979
## Veg_shannon_index-Didelphis_virginiana 1.3329 1.0094 1095
## Veg_shannon_index-Sylvilagus_floridanus 1.2335 1.0052 1414
## Veg_shannon_index-Meleagris_gallopavo 1.5613 1.0312 976
## Veg_shannon_index-Sciurus_carolinensis 0.7124 1.0093 1130
## Veg_shannon_index-Vulpes_vulpes 0.9486 1.0087 957
## Veg_shannon_index-Sus_scrofa 1.6787 1.0050 987
## Avg_Cogongrass_Cover-Canis_latrans 1.4542 1.0186 1398
## Avg_Cogongrass_Cover-Sciurus_niger 0.8186 1.0034 547
## Avg_Cogongrass_Cover-Procyon_lotor 1.1021 1.0013 1341
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1568 1.0148 1424
## Avg_Cogongrass_Cover-Lynx_rufus 1.5957 1.0180 1120
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2417 1.0063 1630
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6661 1.0067 750
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9193 1.0318 469
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1089 1.0091 1611
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.3752 1.0295 1035
## Avg_Cogongrass_Cover-Sus_scrofa 0.9585 1.0068 709
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5821 0.1959 -2.9744 -2.5752 -2.2225 1.0047
## (Intercept)-Sciurus_niger -3.9454 0.5908 -5.1183 -3.9324 -2.8331 1.1741
## (Intercept)-Procyon_lotor -2.2076 0.1601 -2.5272 -2.2055 -1.9047 1.0020
## (Intercept)-Dasypus_novemcinctus -1.6449 0.1803 -2.0023 -1.6411 -1.2934 1.0115
## (Intercept)-Lynx_rufus -3.5050 0.3516 -4.1759 -3.4957 -2.8397 1.0202
## (Intercept)-Didelphis_virginiana -2.4496 0.3032 -3.0579 -2.4379 -1.8950 1.0096
## (Intercept)-Sylvilagus_floridanus -3.0957 0.2942 -3.7023 -3.0914 -2.5370 1.0077
## (Intercept)-Meleagris_gallopavo -3.8484 0.4581 -4.7500 -3.8465 -2.9345 1.0156
## (Intercept)-Sciurus_carolinensis -2.5444 0.3351 -3.2448 -2.5293 -1.9477 1.0017
## (Intercept)-Vulpes_vulpes -3.8889 0.6450 -5.2826 -3.8683 -2.7191 1.0062
## (Intercept)-Sus_scrofa -3.2632 0.5686 -4.4016 -3.2612 -2.1379 1.0240
## shrub_cover-Canis_latrans -0.2965 0.2158 -0.7153 -0.2941 0.1228 1.0132
## shrub_cover-Sciurus_niger -0.4581 0.4923 -1.4581 -0.4685 0.4902 1.0986
## shrub_cover-Procyon_lotor 0.2373 0.1658 -0.0981 0.2441 0.5416 1.0049
## shrub_cover-Dasypus_novemcinctus 0.8684 0.3047 0.2906 0.8626 1.4817 1.0021
## shrub_cover-Lynx_rufus -0.2751 0.3790 -1.0456 -0.2614 0.4592 1.0066
## shrub_cover-Didelphis_virginiana 1.0061 0.3857 0.3078 0.9793 1.8365 1.0019
## shrub_cover-Sylvilagus_floridanus 0.2647 0.4149 -0.4946 0.2500 1.1201 1.0023
## shrub_cover-Meleagris_gallopavo -0.7603 0.3936 -1.5242 -0.7556 -0.0060 1.0128
## shrub_cover-Sciurus_carolinensis 0.8773 0.4137 0.0661 0.8773 1.7002 1.0062
## shrub_cover-Vulpes_vulpes -0.1181 0.5754 -1.2917 -0.1017 0.9814 1.0010
## shrub_cover-Sus_scrofa 0.6010 0.7961 -0.9706 0.6055 2.1977 1.0001
## veg_height-Canis_latrans -0.5890 0.1839 -0.9702 -0.5829 -0.2469 1.0025
## veg_height-Sciurus_niger -0.0158 0.4236 -0.8775 -0.0133 0.8374 1.0100
## veg_height-Procyon_lotor 0.3355 0.1232 0.0999 0.3337 0.5760 1.0058
## veg_height-Dasypus_novemcinctus 0.2546 0.1368 -0.0113 0.2502 0.5279 1.0047
## veg_height-Lynx_rufus -0.0079 0.2485 -0.5129 0.0016 0.4508 1.0014
## veg_height-Didelphis_virginiana 0.4513 0.2535 -0.0161 0.4386 0.9648 1.0030
## veg_height-Sylvilagus_floridanus 0.1465 0.2473 -0.3334 0.1438 0.6323 1.0096
## veg_height-Meleagris_gallopavo -0.1955 0.3687 -0.9305 -0.1968 0.5162 1.0157
## veg_height-Sciurus_carolinensis 0.0842 0.2168 -0.3187 0.0770 0.5204 1.0045
## veg_height-Vulpes_vulpes -0.1309 0.3114 -0.7802 -0.1170 0.4552 1.0049
## veg_height-Sus_scrofa -0.1271 0.3376 -0.8584 -0.1133 0.4796 1.0081
## week-Canis_latrans 0.4563 0.2511 -0.0096 0.4418 0.9683 1.0070
## week-Sciurus_niger -0.2616 0.4323 -1.2757 -0.2159 0.4450 1.0308
## week-Procyon_lotor 0.1597 0.1987 -0.2278 0.1586 0.5517 1.0015
## week-Dasypus_novemcinctus 0.0618 0.2124 -0.3659 0.0632 0.4711 1.0086
## week-Lynx_rufus 0.2555 0.3134 -0.3329 0.2423 0.9036 1.0224
## week-Didelphis_virginiana 0.0000 0.3233 -0.6695 0.0115 0.6116 1.0103
## week-Sylvilagus_floridanus 0.0228 0.2897 -0.5522 0.0277 0.5855 1.0119
## week-Meleagris_gallopavo -0.1343 0.3466 -0.8655 -0.1193 0.4956 1.0108
## week-Sciurus_carolinensis 0.5391 0.3430 -0.0619 0.5154 1.2970 1.0097
## week-Vulpes_vulpes 0.0920 0.3959 -0.7388 0.1042 0.8703 1.0103
## week-Sus_scrofa 0.3959 0.3787 -0.2819 0.3684 1.1972 1.0041
## I(week^2)-Canis_latrans -0.1962 0.1049 -0.4074 -0.1941 0.0042 1.0040
## I(week^2)-Sciurus_niger -0.2552 0.2229 -0.7557 -0.2410 0.1339 1.0082
## I(week^2)-Procyon_lotor -0.1146 0.0875 -0.2839 -0.1148 0.0568 1.0007
## I(week^2)-Dasypus_novemcinctus -0.1568 0.0991 -0.3512 -0.1532 0.0287 1.0013
## I(week^2)-Lynx_rufus -0.1935 0.1450 -0.4890 -0.1878 0.0787 1.0003
## I(week^2)-Didelphis_virginiana -0.3623 0.2085 -0.8332 -0.3391 -0.0163 1.0292
## I(week^2)-Sylvilagus_floridanus -0.1452 0.1469 -0.4500 -0.1434 0.1397 1.0048
## I(week^2)-Meleagris_gallopavo -0.3530 0.2153 -0.8375 -0.3364 0.0156 1.0186
## I(week^2)-Sciurus_carolinensis -0.1949 0.1368 -0.4806 -0.1906 0.0674 1.0005
## I(week^2)-Vulpes_vulpes -0.3448 0.2591 -0.9741 -0.3051 0.0640 1.0230
## I(week^2)-Sus_scrofa -0.1591 0.1651 -0.5081 -0.1572 0.1559 1.0010
## ESS
## (Intercept)-Canis_latrans 793
## (Intercept)-Sciurus_niger 191
## (Intercept)-Procyon_lotor 1571
## (Intercept)-Dasypus_novemcinctus 1644
## (Intercept)-Lynx_rufus 392
## (Intercept)-Didelphis_virginiana 758
## (Intercept)-Sylvilagus_floridanus 595
## (Intercept)-Meleagris_gallopavo 197
## (Intercept)-Sciurus_carolinensis 678
## (Intercept)-Vulpes_vulpes 245
## (Intercept)-Sus_scrofa 582
## shrub_cover-Canis_latrans 820
## shrub_cover-Sciurus_niger 421
## shrub_cover-Procyon_lotor 1261
## shrub_cover-Dasypus_novemcinctus 1223
## shrub_cover-Lynx_rufus 425
## shrub_cover-Didelphis_virginiana 712
## shrub_cover-Sylvilagus_floridanus 540
## shrub_cover-Meleagris_gallopavo 265
## shrub_cover-Sciurus_carolinensis 762
## shrub_cover-Vulpes_vulpes 591
## shrub_cover-Sus_scrofa 660
## veg_height-Canis_latrans 700
## veg_height-Sciurus_niger 570
## veg_height-Procyon_lotor 1611
## veg_height-Dasypus_novemcinctus 1966
## veg_height-Lynx_rufus 591
## veg_height-Didelphis_virginiana 985
## veg_height-Sylvilagus_floridanus 800
## veg_height-Meleagris_gallopavo 552
## veg_height-Sciurus_carolinensis 1001
## veg_height-Vulpes_vulpes 673
## veg_height-Sus_scrofa 1159
## week-Canis_latrans 828
## week-Sciurus_niger 349
## week-Procyon_lotor 1821
## week-Dasypus_novemcinctus 2287
## week-Lynx_rufus 834
## week-Didelphis_virginiana 1116
## week-Sylvilagus_floridanus 930
## week-Meleagris_gallopavo 632
## week-Sciurus_carolinensis 871
## week-Vulpes_vulpes 787
## week-Sus_scrofa 1048
## I(week^2)-Canis_latrans 1038
## I(week^2)-Sciurus_niger 475
## I(week^2)-Procyon_lotor 1775
## I(week^2)-Dasypus_novemcinctus 2113
## I(week^2)-Lynx_rufus 634
## I(week^2)-Didelphis_virginiana 473
## I(week^2)-Sylvilagus_floridanus 792
## I(week^2)-Meleagris_gallopavo 239
## I(week^2)-Sciurus_carolinensis 1091
## I(week^2)-Vulpes_vulpes 330
## I(week^2)-Sus_scrofa 1019
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogon_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.1023
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6272 0.3683 -1.3702 -0.6123 0.0852 1.0020 460
## Avg_Cogongrass_Cover 0.1124 0.2534 -0.3962 0.1137 0.5860 1.0247 744
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6069 0.8271 0.0517 0.3799 2.3453 1.0573 375
## Avg_Cogongrass_Cover 0.3272 0.3722 0.0374 0.2133 1.2807 1.0169 743
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0034 0.8271 0.1003 0.7909 3.1385 1.0716 194
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8997 0.3043 -3.5052 -2.9005 -2.3021 1.0055 1386
## shrub_cover 0.1973 0.2804 -0.3502 0.1911 0.7887 1.0016 922
## veg_height 0.0174 0.1719 -0.3421 0.0199 0.3495 1.0120 946
## week 0.1343 0.1927 -0.2337 0.1352 0.5041 1.0083 1000
## I(week^2) -0.2251 0.1068 -0.4559 -0.2199 -0.0286 1.0072 466
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8706 0.6089 0.2331 0.7180 2.3329 1.0043 565
## shrub_cover 0.6347 0.4783 0.1424 0.5129 1.9202 1.0185 607
## veg_height 0.2071 0.1582 0.0525 0.1644 0.5972 1.0144 1128
## week 0.2263 0.2416 0.0353 0.1573 0.8592 1.0850 158
## I(week^2) 0.0682 0.0645 0.0186 0.0498 0.2120 1.1077 491
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1110 0.5924 -1.2199 -0.1345
## (Intercept)-Sciurus_niger -0.7838 0.6110 -1.9839 -0.7896
## (Intercept)-Procyon_lotor -0.0481 0.6063 -1.1798 -0.0712
## (Intercept)-Dasypus_novemcinctus -0.6601 0.4833 -1.6232 -0.6564
## (Intercept)-Lynx_rufus -0.4276 0.6195 -1.6131 -0.4532
## (Intercept)-Didelphis_virginiana -0.9604 0.5298 -2.0926 -0.9380
## (Intercept)-Sylvilagus_floridanus -0.5702 0.5311 -1.6088 -0.5756
## (Intercept)-Meleagris_gallopavo -0.2955 0.8016 -1.5034 -0.3741
## (Intercept)-Sciurus_carolinensis -1.0345 0.5732 -2.2803 -0.9976
## (Intercept)-Vulpes_vulpes -0.9595 0.7143 -2.4097 -0.9269
## (Intercept)-Sus_scrofa -1.2021 0.6521 -2.6555 -1.1556
## Avg_Cogongrass_Cover-Canis_latrans 0.3742 0.3697 -0.3012 0.3468
## Avg_Cogongrass_Cover-Sciurus_niger -0.2805 0.5697 -1.6252 -0.2203
## Avg_Cogongrass_Cover-Procyon_lotor 0.1728 0.3382 -0.4782 0.1655
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3199 0.3314 -0.3095 0.3125
## Avg_Cogongrass_Cover-Lynx_rufus 0.4174 0.4162 -0.3041 0.3784
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.2885 0.3776 -0.4278 0.2736
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2406 0.4382 -1.2045 -0.2063
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2916 0.5773 -1.5801 -0.2394
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3198 0.3576 -0.3385 0.3068
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.2496 0.4353 -0.5947 0.2473
## Avg_Cogongrass_Cover-Sus_scrofa -0.0698 0.5058 -1.1803 -0.0385
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0941 1.0168 372
## (Intercept)-Sciurus_niger 0.3794 1.0108 532
## (Intercept)-Procyon_lotor 1.1677 1.0355 298
## (Intercept)-Dasypus_novemcinctus 0.2521 1.0171 1137
## (Intercept)-Lynx_rufus 0.8831 1.0374 445
## (Intercept)-Didelphis_virginiana 0.0110 1.0095 1031
## (Intercept)-Sylvilagus_floridanus 0.4572 1.0035 736
## (Intercept)-Meleagris_gallopavo 1.2651 1.0310 218
## (Intercept)-Sciurus_carolinensis 0.0011 1.0028 724
## (Intercept)-Vulpes_vulpes 0.3716 1.0028 442
## (Intercept)-Sus_scrofa -0.0844 1.0122 543
## Avg_Cogongrass_Cover-Canis_latrans 1.1557 1.0006 1606
## Avg_Cogongrass_Cover-Sciurus_niger 0.6676 1.0087 640
## Avg_Cogongrass_Cover-Procyon_lotor 0.8721 1.0020 2246
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9999 1.0053 2070
## Avg_Cogongrass_Cover-Lynx_rufus 1.3136 1.0009 1303
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0760 1.0180 1482
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5361 1.0163 953
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7441 1.0331 550
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0628 1.0079 1809
## Avg_Cogongrass_Cover-Vulpes_vulpes 1.1591 1.0038 1469
## Avg_Cogongrass_Cover-Sus_scrofa 0.8384 1.0157 942
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5944 0.2004 -2.9957 -2.5874 -2.2153 1.0001
## (Intercept)-Sciurus_niger -3.8069 0.5467 -4.8790 -3.8014 -2.7737 1.0541
## (Intercept)-Procyon_lotor -2.2108 0.1626 -2.5363 -2.2068 -1.8981 1.0162
## (Intercept)-Dasypus_novemcinctus -1.6471 0.1795 -2.0125 -1.6421 -1.2928 1.0022
## (Intercept)-Lynx_rufus -3.4428 0.3424 -4.1289 -3.4355 -2.8071 1.0046
## (Intercept)-Didelphis_virginiana -2.4538 0.3073 -3.0782 -2.4434 -1.8967 1.0115
## (Intercept)-Sylvilagus_floridanus -3.0661 0.2903 -3.6637 -3.0474 -2.5448 1.0031
## (Intercept)-Meleagris_gallopavo -3.8102 0.4465 -4.6837 -3.7973 -2.9492 1.0251
## (Intercept)-Sciurus_carolinensis -2.5389 0.3272 -3.1887 -2.5304 -1.9281 1.0044
## (Intercept)-Vulpes_vulpes -3.9012 0.6412 -5.2001 -3.8921 -2.7423 1.0147
## (Intercept)-Sus_scrofa -3.2695 0.5601 -4.3924 -3.2618 -2.1879 1.0266
## shrub_cover-Canis_latrans -0.2874 0.2154 -0.6954 -0.2868 0.1429 1.0328
## shrub_cover-Sciurus_niger -0.3471 0.4809 -1.3055 -0.3525 0.5740 1.0194
## shrub_cover-Procyon_lotor 0.2471 0.1691 -0.0938 0.2471 0.5717 1.0065
## shrub_cover-Dasypus_novemcinctus 0.8505 0.2917 0.3026 0.8539 1.4489 1.0038
## shrub_cover-Lynx_rufus -0.2362 0.3604 -0.9721 -0.2302 0.4700 1.0078
## shrub_cover-Didelphis_virginiana 1.0075 0.3790 0.2920 0.9923 1.7607 1.0221
## shrub_cover-Sylvilagus_floridanus 0.3057 0.4109 -0.4452 0.2933 1.1514 1.0146
## shrub_cover-Meleagris_gallopavo -0.6887 0.3956 -1.4841 -0.6890 0.0815 1.0048
## shrub_cover-Sciurus_carolinensis 0.8281 0.4104 0.0468 0.8252 1.6279 1.0070
## shrub_cover-Vulpes_vulpes -0.0924 0.5565 -1.2227 -0.0834 0.9906 1.0264
## shrub_cover-Sus_scrofa 0.6407 0.7572 -0.8852 0.6260 2.2030 1.0099
## veg_height-Canis_latrans -0.5781 0.1876 -0.9609 -0.5753 -0.2176 1.0006
## veg_height-Sciurus_niger 0.0262 0.4243 -0.7710 0.0131 0.9350 1.0104
## veg_height-Procyon_lotor 0.3356 0.1231 0.0968 0.3315 0.5800 1.0008
## veg_height-Dasypus_novemcinctus 0.2487 0.1365 -0.0219 0.2466 0.5207 1.0017
## veg_height-Lynx_rufus -0.0108 0.2429 -0.5075 -0.0081 0.4598 1.0104
## veg_height-Didelphis_virginiana 0.4228 0.2499 -0.0378 0.4125 0.9415 1.0012
## veg_height-Sylvilagus_floridanus 0.1412 0.2390 -0.3230 0.1379 0.6193 1.0007
## veg_height-Meleagris_gallopavo -0.1818 0.3767 -0.9713 -0.1891 0.5300 1.0066
## veg_height-Sciurus_carolinensis 0.0837 0.2202 -0.3396 0.0810 0.5314 1.0007
## veg_height-Vulpes_vulpes -0.1448 0.3254 -0.8724 -0.1203 0.4272 1.0016
## veg_height-Sus_scrofa -0.1294 0.3334 -0.8130 -0.1174 0.5005 1.0210
## week-Canis_latrans 0.4543 0.2591 -0.0294 0.4450 1.0000 1.0081
## week-Sciurus_niger -0.2850 0.4612 -1.3473 -0.2182 0.4222 1.1076
## week-Procyon_lotor 0.1501 0.1948 -0.2279 0.1487 0.5360 1.0120
## week-Dasypus_novemcinctus 0.0467 0.2129 -0.3679 0.0508 0.4680 1.0065
## week-Lynx_rufus 0.2542 0.3046 -0.3145 0.2465 0.8823 1.0117
## week-Didelphis_virginiana -0.0012 0.3311 -0.7193 0.0105 0.6142 1.0016
## week-Sylvilagus_floridanus 0.0117 0.2984 -0.5960 0.0150 0.5998 0.9999
## week-Meleagris_gallopavo -0.1599 0.3655 -0.9578 -0.1302 0.4674 1.0216
## week-Sciurus_carolinensis 0.5387 0.3449 -0.0607 0.5089 1.2976 1.0031
## week-Vulpes_vulpes 0.0942 0.4018 -0.7453 0.0993 0.8773 1.0024
## week-Sus_scrofa 0.4078 0.3868 -0.2829 0.3747 1.2542 1.0012
## I(week^2)-Canis_latrans -0.1960 0.1068 -0.4104 -0.1929 0.0022 1.0017
## I(week^2)-Sciurus_niger -0.2484 0.2300 -0.7524 -0.2354 0.1500 1.0384
## I(week^2)-Procyon_lotor -0.1102 0.0880 -0.2916 -0.1089 0.0534 1.0064
## I(week^2)-Dasypus_novemcinctus -0.1489 0.0989 -0.3530 -0.1460 0.0351 1.0072
## I(week^2)-Lynx_rufus -0.1905 0.1418 -0.4797 -0.1898 0.0857 1.0139
## I(week^2)-Didelphis_virginiana -0.3639 0.2125 -0.8593 -0.3349 -0.0260 1.0034
## I(week^2)-Sylvilagus_floridanus -0.1537 0.1490 -0.4542 -0.1486 0.1282 1.0072
## I(week^2)-Meleagris_gallopavo -0.3630 0.2344 -0.9355 -0.3318 -0.0017 1.0446
## I(week^2)-Sciurus_carolinensis -0.1920 0.1392 -0.4702 -0.1851 0.0670 1.0005
## I(week^2)-Vulpes_vulpes -0.3470 0.2406 -0.9037 -0.3176 0.0438 1.0061
## I(week^2)-Sus_scrofa -0.1646 0.1714 -0.5163 -0.1614 0.1479 1.0086
## ESS
## (Intercept)-Canis_latrans 984
## (Intercept)-Sciurus_niger 316
## (Intercept)-Procyon_lotor 1487
## (Intercept)-Dasypus_novemcinctus 1580
## (Intercept)-Lynx_rufus 439
## (Intercept)-Didelphis_virginiana 865
## (Intercept)-Sylvilagus_floridanus 611
## (Intercept)-Meleagris_gallopavo 204
## (Intercept)-Sciurus_carolinensis 971
## (Intercept)-Vulpes_vulpes 231
## (Intercept)-Sus_scrofa 457
## shrub_cover-Canis_latrans 918
## shrub_cover-Sciurus_niger 449
## shrub_cover-Procyon_lotor 1383
## shrub_cover-Dasypus_novemcinctus 1548
## shrub_cover-Lynx_rufus 435
## shrub_cover-Didelphis_virginiana 703
## shrub_cover-Sylvilagus_floridanus 464
## shrub_cover-Meleagris_gallopavo 268
## shrub_cover-Sciurus_carolinensis 784
## shrub_cover-Vulpes_vulpes 606
## shrub_cover-Sus_scrofa 597
## veg_height-Canis_latrans 687
## veg_height-Sciurus_niger 616
## veg_height-Procyon_lotor 1965
## veg_height-Dasypus_novemcinctus 1760
## veg_height-Lynx_rufus 744
## veg_height-Didelphis_virginiana 1060
## veg_height-Sylvilagus_floridanus 899
## veg_height-Meleagris_gallopavo 427
## veg_height-Sciurus_carolinensis 1056
## veg_height-Vulpes_vulpes 526
## veg_height-Sus_scrofa 856
## week-Canis_latrans 833
## week-Sciurus_niger 251
## week-Procyon_lotor 1657
## week-Dasypus_novemcinctus 1937
## week-Lynx_rufus 751
## week-Didelphis_virginiana 1086
## week-Sylvilagus_floridanus 1161
## week-Meleagris_gallopavo 326
## week-Sciurus_carolinensis 869
## week-Vulpes_vulpes 853
## week-Sus_scrofa 969
## I(week^2)-Canis_latrans 1059
## I(week^2)-Sciurus_niger 259
## I(week^2)-Procyon_lotor 1413
## I(week^2)-Dasypus_novemcinctus 1789
## I(week^2)-Lynx_rufus 716
## I(week^2)-Didelphis_virginiana 404
## I(week^2)-Sylvilagus_floridanus 756
## I(week^2)-Meleagris_gallopavo 200
## I(week^2)-Sciurus_carolinensis 1043
## I(week^2)-Vulpes_vulpes 366
## I(week^2)-Sus_scrofa 981
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogonQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.1305
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2561 0.3922 -2.0451 -1.2491 -0.5089 1.0432 516
## Avg_Cogongrass_Cover -0.7478 0.3715 -1.5002 -0.7470 -0.0121 1.0197 343
## I(Avg_Cogongrass_Cover^2) 0.7217 0.3319 0.1197 0.7033 1.4552 1.0049 326
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7040 0.7701 0.0628 0.4810 2.6973 1.0369 503
## Avg_Cogongrass_Cover 0.3882 0.4251 0.0400 0.2475 1.5508 1.0202 732
## I(Avg_Cogongrass_Cover^2) 0.5563 0.9793 0.0419 0.2618 3.1684 1.0863 135
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7125 0.6708 0.0644 0.5122 2.6036 1.0267 190
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8735 0.3108 -3.4939 -2.8717 -2.2808 1.0034 1347
## shrub_cover 0.1859 0.2793 -0.3713 0.1842 0.7332 1.0007 1269
## veg_height 0.0519 0.1716 -0.3043 0.0563 0.3775 1.0031 864
## week 0.1456 0.1959 -0.2540 0.1435 0.5239 1.0057 839
## I(week^2) -0.2247 0.1055 -0.4440 -0.2226 -0.0291 1.0386 659
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8669 0.6287 0.2305 0.7073 2.4541 1.0139 575
## shrub_cover 0.6573 0.5283 0.1428 0.5165 1.9619 1.0079 711
## veg_height 0.2049 0.1732 0.0521 0.1630 0.6077 1.0313 1069
## week 0.2110 0.1960 0.0342 0.1523 0.7181 1.0519 771
## I(week^2) 0.0667 0.0797 0.0182 0.0511 0.2068 1.1367 676
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8083 0.6064 -1.9865 -0.8276
## (Intercept)-Sciurus_niger -1.2463 0.6997 -2.5488 -1.2651
## (Intercept)-Procyon_lotor -0.6199 0.6283 -1.8476 -0.6467
## (Intercept)-Dasypus_novemcinctus -1.3259 0.5328 -2.4062 -1.3146
## (Intercept)-Lynx_rufus -1.2215 0.6393 -2.5223 -1.2158
## (Intercept)-Didelphis_virginiana -1.5819 0.5874 -2.8088 -1.5558
## (Intercept)-Sylvilagus_floridanus -1.1943 0.5960 -2.3759 -1.1985
## (Intercept)-Meleagris_gallopavo -0.7412 0.7586 -2.0934 -0.8161
## (Intercept)-Sciurus_carolinensis -1.8626 0.6655 -3.3181 -1.7999
## (Intercept)-Vulpes_vulpes -1.7936 0.7863 -3.3915 -1.7516
## (Intercept)-Sus_scrofa -1.8373 0.6948 -3.3635 -1.7907
## Avg_Cogongrass_Cover-Canis_latrans -0.4385 0.5098 -1.3617 -0.4760
## Avg_Cogongrass_Cover-Sciurus_niger -1.0282 0.6525 -2.3840 -0.9888
## Avg_Cogongrass_Cover-Procyon_lotor -0.7051 0.4999 -1.6906 -0.7158
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.5248 0.4812 -1.4627 -0.5316
## Avg_Cogongrass_Cover-Lynx_rufus -0.6531 0.5732 -1.7639 -0.6653
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.4415 0.5302 -1.4099 -0.4706
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1620 0.5860 -2.4380 -1.1254
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0615 0.6522 -2.5000 -1.0177
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.7172 0.5236 -1.7839 -0.7172
## Avg_Cogongrass_Cover-Vulpes_vulpes -0.7148 0.5839 -1.8713 -0.7145
## Avg_Cogongrass_Cover-Sus_scrofa -0.9038 0.6201 -2.2438 -0.8807
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2494 0.7549 0.2602 1.0840
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.2142 0.6916 -1.3764 0.2706
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1355 0.7403 0.2564 1.0059
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.7081 0.3578 0.0311 0.6974
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1900 0.6082 0.2969 1.1009
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.5328 0.4143 -0.2443 0.5189
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7016 0.4447 -0.0955 0.6730
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4291 0.7665 -0.8191 0.3859
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8762 0.3931 0.1444 0.8525
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 0.8510 0.6461 0.0422 0.7572
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2716 0.6268 -1.2743 0.3451
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4242 1.0270 516
## (Intercept)-Sciurus_niger 0.1628 1.0430 372
## (Intercept)-Procyon_lotor 0.6247 1.0281 495
## (Intercept)-Dasypus_novemcinctus -0.2781 1.0259 731
## (Intercept)-Lynx_rufus 0.0323 1.0122 526
## (Intercept)-Didelphis_virginiana -0.4717 1.0107 904
## (Intercept)-Sylvilagus_floridanus -0.0398 1.0139 839
## (Intercept)-Meleagris_gallopavo 0.9257 1.0101 424
## (Intercept)-Sciurus_carolinensis -0.6936 1.0191 628
## (Intercept)-Vulpes_vulpes -0.3067 1.0495 353
## (Intercept)-Sus_scrofa -0.6176 1.0228 660
## Avg_Cogongrass_Cover-Canis_latrans 0.6063 1.0066 730
## Avg_Cogongrass_Cover-Sciurus_niger 0.2145 1.0116 558
## Avg_Cogongrass_Cover-Procyon_lotor 0.3059 1.0058 600
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4239 1.0070 722
## Avg_Cogongrass_Cover-Lynx_rufus 0.5518 1.0136 582
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6677 1.0039 627
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1115 1.0315 599
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1138 1.0211 502
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3228 1.0093 647
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4426 1.0099 607
## Avg_Cogongrass_Cover-Sus_scrofa 0.2123 1.0131 760
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.2515 1.0196 284
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 1.3968 1.0200 238
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.1742 1.0465 257
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4407 1.0077 868
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5686 1.0289 266
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.4327 1.0335 482
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.6303 1.0104 658
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.8643 1.1911 144
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7226 1.0095 691
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 2.0943 1.1002 161
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 1.2819 1.0231 322
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5960 0.1980 -3.0132 -2.5870 -2.2286 1.0114
## (Intercept)-Sciurus_niger -3.7838 0.5733 -4.9816 -3.7702 -2.6895 1.0266
## (Intercept)-Procyon_lotor -2.2209 0.1643 -2.5406 -2.2197 -1.9049 1.0049
## (Intercept)-Dasypus_novemcinctus -1.6509 0.1764 -1.9945 -1.6486 -1.3090 1.0050
## (Intercept)-Lynx_rufus -3.4060 0.3405 -4.0859 -3.3957 -2.7929 1.0174
## (Intercept)-Didelphis_virginiana -2.4626 0.3051 -3.1087 -2.4542 -1.8963 1.0189
## (Intercept)-Sylvilagus_floridanus -3.0905 0.3013 -3.6755 -3.0788 -2.5229 1.0140
## (Intercept)-Meleagris_gallopavo -3.8031 0.4786 -4.7760 -3.7993 -2.8330 1.0095
## (Intercept)-Sciurus_carolinensis -2.5381 0.3297 -3.2259 -2.5274 -1.9237 1.0044
## (Intercept)-Vulpes_vulpes -3.8380 0.6584 -5.3196 -3.7859 -2.7008 1.0088
## (Intercept)-Sus_scrofa -3.2246 0.5698 -4.3180 -3.2279 -2.0814 1.0034
## shrub_cover-Canis_latrans -0.2785 0.2243 -0.7377 -0.2760 0.1506 1.0036
## shrub_cover-Sciurus_niger -0.3141 0.4602 -1.2343 -0.3118 0.6143 1.0026
## shrub_cover-Procyon_lotor 0.2377 0.1670 -0.0920 0.2388 0.5549 1.0034
## shrub_cover-Dasypus_novemcinctus 0.8511 0.2952 0.2611 0.8476 1.4331 1.0001
## shrub_cover-Lynx_rufus -0.2408 0.3553 -0.9433 -0.2459 0.4666 1.0082
## shrub_cover-Didelphis_virginiana 0.9991 0.3914 0.2737 0.9785 1.8273 1.0266
## shrub_cover-Sylvilagus_floridanus 0.2087 0.4099 -0.5654 0.2008 1.0103 1.0177
## shrub_cover-Meleagris_gallopavo -0.7178 0.4002 -1.5267 -0.7070 0.0626 1.0022
## shrub_cover-Sciurus_carolinensis 0.8244 0.4064 0.0411 0.8205 1.6482 1.0170
## shrub_cover-Vulpes_vulpes -0.1427 0.6010 -1.3837 -0.1275 1.0145 1.0243
## shrub_cover-Sus_scrofa 0.5779 0.8054 -1.0096 0.5573 2.2145 1.0213
## veg_height-Canis_latrans -0.5732 0.1930 -0.9628 -0.5708 -0.2172 1.0055
## veg_height-Sciurus_niger 0.0964 0.4173 -0.7107 0.0881 0.9581 1.0115
## veg_height-Procyon_lotor 0.3491 0.1199 0.1171 0.3496 0.5822 1.0063
## veg_height-Dasypus_novemcinctus 0.2526 0.1368 -0.0107 0.2478 0.5341 1.0023
## veg_height-Lynx_rufus 0.0593 0.2379 -0.4351 0.0709 0.5061 1.0308
## veg_height-Didelphis_virginiana 0.4235 0.2512 -0.0438 0.4137 0.9506 1.0171
## veg_height-Sylvilagus_floridanus 0.1664 0.2520 -0.3175 0.1631 0.6639 1.0034
## veg_height-Meleagris_gallopavo -0.1320 0.3700 -0.8729 -0.1258 0.5837 1.0239
## veg_height-Sciurus_carolinensis 0.1035 0.2168 -0.3114 0.1004 0.5426 1.0039
## veg_height-Vulpes_vulpes -0.0838 0.3109 -0.7411 -0.0624 0.4755 1.0029
## veg_height-Sus_scrofa -0.0931 0.3348 -0.8145 -0.0766 0.5055 0.9997
## week-Canis_latrans 0.4498 0.2443 -0.0014 0.4441 0.9471 1.0157
## week-Sciurus_niger -0.2600 0.4227 -1.1995 -0.2094 0.4715 1.0033
## week-Procyon_lotor 0.1606 0.2013 -0.2266 0.1598 0.5559 1.0026
## week-Dasypus_novemcinctus 0.0541 0.2142 -0.3796 0.0544 0.4644 1.0049
## week-Lynx_rufus 0.2523 0.3029 -0.3294 0.2407 0.8787 1.0427
## week-Didelphis_virginiana 0.0154 0.3267 -0.6510 0.0273 0.6445 1.0136
## week-Sylvilagus_floridanus 0.0267 0.3033 -0.5879 0.0322 0.5986 1.0037
## week-Meleagris_gallopavo -0.1337 0.3638 -0.9316 -0.1087 0.5025 1.0294
## week-Sciurus_carolinensis 0.5373 0.3319 -0.0477 0.5195 1.2716 1.0287
## week-Vulpes_vulpes 0.1027 0.3949 -0.6921 0.1023 0.8629 1.0047
## week-Sus_scrofa 0.4172 0.3771 -0.2692 0.3997 1.2391 1.0195
## I(week^2)-Canis_latrans -0.1920 0.1038 -0.3969 -0.1900 0.0017 1.0230
## I(week^2)-Sciurus_niger -0.2465 0.2148 -0.7167 -0.2301 0.1353 1.0025
## I(week^2)-Procyon_lotor -0.1140 0.0869 -0.2909 -0.1135 0.0538 1.0024
## I(week^2)-Dasypus_novemcinctus -0.1530 0.1026 -0.3616 -0.1481 0.0447 1.0077
## I(week^2)-Lynx_rufus -0.1955 0.1405 -0.4789 -0.1911 0.0726 1.0067
## I(week^2)-Didelphis_virginiana -0.3556 0.2144 -0.8421 -0.3319 -0.0146 1.0254
## I(week^2)-Sylvilagus_floridanus -0.1572 0.1496 -0.4711 -0.1521 0.1238 1.0003
## I(week^2)-Meleagris_gallopavo -0.3573 0.2195 -0.8765 -0.3331 0.0057 1.0918
## I(week^2)-Sciurus_carolinensis -0.1920 0.1380 -0.4827 -0.1836 0.0620 1.0074
## I(week^2)-Vulpes_vulpes -0.3417 0.2353 -0.8749 -0.3193 0.0510 1.0481
## I(week^2)-Sus_scrofa -0.1620 0.1637 -0.4870 -0.1596 0.1465 1.0164
## ESS
## (Intercept)-Canis_latrans 972
## (Intercept)-Sciurus_niger 277
## (Intercept)-Procyon_lotor 1358
## (Intercept)-Dasypus_novemcinctus 1561
## (Intercept)-Lynx_rufus 419
## (Intercept)-Didelphis_virginiana 859
## (Intercept)-Sylvilagus_floridanus 541
## (Intercept)-Meleagris_gallopavo 271
## (Intercept)-Sciurus_carolinensis 870
## (Intercept)-Vulpes_vulpes 257
## (Intercept)-Sus_scrofa 592
## shrub_cover-Canis_latrans 893
## shrub_cover-Sciurus_niger 614
## shrub_cover-Procyon_lotor 1220
## shrub_cover-Dasypus_novemcinctus 1391
## shrub_cover-Lynx_rufus 527
## shrub_cover-Didelphis_virginiana 512
## shrub_cover-Sylvilagus_floridanus 520
## shrub_cover-Meleagris_gallopavo 290
## shrub_cover-Sciurus_carolinensis 885
## shrub_cover-Vulpes_vulpes 626
## shrub_cover-Sus_scrofa 658
## veg_height-Canis_latrans 794
## veg_height-Sciurus_niger 705
## veg_height-Procyon_lotor 1517
## veg_height-Dasypus_novemcinctus 2041
## veg_height-Lynx_rufus 726
## veg_height-Didelphis_virginiana 849
## veg_height-Sylvilagus_floridanus 708
## veg_height-Meleagris_gallopavo 368
## veg_height-Sciurus_carolinensis 1050
## veg_height-Vulpes_vulpes 683
## veg_height-Sus_scrofa 947
## week-Canis_latrans 1059
## week-Sciurus_niger 583
## week-Procyon_lotor 1583
## week-Dasypus_novemcinctus 1982
## week-Lynx_rufus 990
## week-Didelphis_virginiana 864
## week-Sylvilagus_floridanus 998
## week-Meleagris_gallopavo 482
## week-Sciurus_carolinensis 952
## week-Vulpes_vulpes 790
## week-Sus_scrofa 1067
## I(week^2)-Canis_latrans 1402
## I(week^2)-Sciurus_niger 504
## I(week^2)-Procyon_lotor 1568
## I(week^2)-Dasypus_novemcinctus 1824
## I(week^2)-Lynx_rufus 949
## I(week^2)-Didelphis_virginiana 379
## I(week^2)-Sylvilagus_floridanus 784
## I(week^2)-Meleagris_gallopavo 266
## I(week^2)-Sciurus_carolinensis 964
## I(week^2)-Vulpes_vulpes 504
## I(week^2)-Sus_scrofa 1301
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_fullQ_T)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.2042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9718 0.7037 -3.3715 -1.9522 -0.5642 1.0256 229
## Cogon_Patch_Size -0.1939 0.6612 -1.5094 -0.1899 1.1101 1.0142 392
## Veg_shannon_index 0.9937 0.4646 0.1319 0.9629 1.9998 1.0979 238
## total_shrub_cover -1.1267 0.6034 -2.4900 -1.0763 -0.0937 1.0191 244
## Avg_Cogongrass_Cover 0.1538 0.9460 -1.7138 0.2032 1.9526 1.0677 134
## Tree_Density -2.1814 0.8310 -3.9603 -2.1269 -0.6400 1.0430 195
## Avg_Canopy_Cover 1.9537 0.6778 0.8032 1.8779 3.5246 1.0829 179
## I(Avg_Cogongrass_Cover^2) 1.1689 0.6274 -0.0434 1.1473 2.4844 1.0405 298
## avg_veg_height -0.2514 0.5213 -1.2780 -0.2524 0.7650 1.0132 206
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4006 3.0912 0.0985 1.4038 10.7740 1.2588 226
## Cogon_Patch_Size 2.3820 3.1511 0.0854 1.3880 10.4786 1.0706 207
## Veg_shannon_index 0.6921 1.0051 0.0441 0.3645 3.2541 1.0107 345
## total_shrub_cover 1.7693 2.4145 0.0863 1.0093 8.6103 1.0778 83
## Avg_Cogongrass_Cover 1.3413 2.2681 0.0546 0.5712 7.5183 1.1902 213
## Tree_Density 2.5004 4.7402 0.0640 1.0568 13.3160 1.1082 242
## Avg_Canopy_Cover 2.3352 3.0348 0.1044 1.4334 9.7069 1.1204 187
## I(Avg_Cogongrass_Cover^2) 2.3982 3.6761 0.0736 1.1770 12.5361 1.1088 100
## avg_veg_height 0.6098 0.9112 0.0467 0.3070 3.1156 1.0152 333
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.643 2.2479 0.0517 0.8592 8.4321 1.1847 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9666 0.3161 -3.6045 -2.9608 -2.3566 1.0085 1049
## shrub_cover 0.3996 0.2851 -0.1757 0.4028 0.9528 1.0078 624
## veg_height 0.0750 0.1686 -0.2558 0.0726 0.3988 1.0153 856
## week 0.1357 0.2015 -0.2856 0.1436 0.5292 1.0172 736
## I(week^2) -0.2373 0.1158 -0.4865 -0.2298 -0.0369 1.0107 348
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9737 0.6924 0.2682 0.7992 2.6740 1.0316 501
## shrub_cover 0.6893 0.5073 0.1514 0.5536 2.0416 1.0088 627
## veg_height 0.2105 0.1676 0.0554 0.1679 0.6208 1.0031 1130
## week 0.2369 0.2590 0.0359 0.1582 0.8873 1.0194 323
## I(week^2) 0.0756 0.0882 0.0181 0.0535 0.2552 1.1991 109
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3891 1.0215 -3.3570 -1.3739
## (Intercept)-Sciurus_niger -1.3381 1.3669 -3.6434 -1.4786
## (Intercept)-Procyon_lotor -1.0181 1.0442 -3.1528 -0.9996
## (Intercept)-Dasypus_novemcinctus -2.2061 0.9368 -4.2518 -2.1407
## (Intercept)-Lynx_rufus -1.3552 1.4148 -3.7280 -1.4875
## (Intercept)-Didelphis_virginiana -2.9019 1.1325 -5.4272 -2.8096
## (Intercept)-Sylvilagus_floridanus -1.9472 1.0267 -3.9571 -1.9487
## (Intercept)-Meleagris_gallopavo -1.6765 1.2340 -4.0127 -1.7259
## (Intercept)-Sciurus_carolinensis -3.2083 1.2259 -5.9663 -3.1042
## (Intercept)-Vulpes_vulpes -3.0847 1.5105 -6.5298 -2.8947
## (Intercept)-Sus_scrofa -3.1915 1.4099 -6.5941 -2.9946
## Cogon_Patch_Size-Canis_latrans 1.2157 1.2203 -0.5629 1.0212
## Cogon_Patch_Size-Sciurus_niger -0.8937 1.4908 -4.4783 -0.7214
## Cogon_Patch_Size-Procyon_lotor -0.6341 0.7941 -2.3070 -0.5898
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0635 0.8011 -1.5755 -0.0973
## Cogon_Patch_Size-Lynx_rufus -0.3823 1.3567 -2.9554 -0.4132
## Cogon_Patch_Size-Didelphis_virginiana 1.1593 1.0335 -0.5599 1.0385
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2582 1.4250 -4.8262 -1.0146
## Cogon_Patch_Size-Meleagris_gallopavo 0.2290 1.2732 -1.7786 0.0639
## Cogon_Patch_Size-Sciurus_carolinensis -0.9388 1.2304 -3.9010 -0.7464
## Cogon_Patch_Size-Vulpes_vulpes -0.4743 1.4021 -3.3489 -0.4176
## Cogon_Patch_Size-Sus_scrofa -0.5496 1.2720 -3.5041 -0.4265
## Veg_shannon_index-Canis_latrans 1.3399 0.7258 0.1870 1.2401
## Veg_shannon_index-Sciurus_niger 1.1049 0.9002 -0.4296 1.0227
## Veg_shannon_index-Procyon_lotor 1.2106 0.6307 0.1410 1.1583
## Veg_shannon_index-Dasypus_novemcinctus 0.6481 0.5772 -0.5493 0.6640
## Veg_shannon_index-Lynx_rufus 1.0289 0.8715 -0.6026 0.9756
## Veg_shannon_index-Didelphis_virginiana 1.1330 0.7065 -0.0790 1.0604
## Veg_shannon_index-Sylvilagus_floridanus 1.0282 0.7102 -0.2380 0.9894
## Veg_shannon_index-Meleagris_gallopavo 1.2207 0.7570 -0.0858 1.1402
## Veg_shannon_index-Sciurus_carolinensis 0.4167 0.7517 -1.2375 0.4943
## Veg_shannon_index-Vulpes_vulpes 0.6711 0.8120 -1.1465 0.7222
## Veg_shannon_index-Sus_scrofa 1.3897 0.8682 0.0803 1.2589
## total_shrub_cover-Canis_latrans 0.1245 0.8820 -1.2717 0.0199
## total_shrub_cover-Sciurus_niger -1.4970 1.1094 -4.0318 -1.3596
## total_shrub_cover-Procyon_lotor -1.4569 0.7057 -3.0507 -1.3967
## total_shrub_cover-Dasypus_novemcinctus -0.4642 0.7744 -2.0877 -0.4232
## total_shrub_cover-Lynx_rufus -1.6306 1.1924 -4.4147 -1.4760
## total_shrub_cover-Didelphis_virginiana -1.3222 1.0227 -4.0646 -1.1818
## total_shrub_cover-Sylvilagus_floridanus -1.1437 1.0668 -3.8349 -1.0216
## total_shrub_cover-Meleagris_gallopavo -2.3617 1.3509 -5.5802 -2.1297
## total_shrub_cover-Sciurus_carolinensis -1.0024 1.0988 -3.7078 -0.8868
## total_shrub_cover-Vulpes_vulpes -1.4124 1.3440 -4.6216 -1.2342
## total_shrub_cover-Sus_scrofa -1.0243 1.3148 -4.0287 -0.8792
## Avg_Cogongrass_Cover-Canis_latrans 0.3558 1.2193 -2.1008 0.3962
## Avg_Cogongrass_Cover-Sciurus_niger -0.4659 1.5667 -4.3455 -0.2618
## Avg_Cogongrass_Cover-Procyon_lotor 0.1840 1.1981 -2.2579 0.2004
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7830 1.2608 -1.5262 0.7480
## Avg_Cogongrass_Cover-Lynx_rufus 0.2703 1.3119 -2.4630 0.3125
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3707 1.2271 -2.0079 0.3827
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4289 1.3737 -3.4776 -0.3008
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1273 1.3457 -3.0299 -0.0367
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2404 1.2119 -2.1364 0.2964
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4857 1.3468 -1.9789 0.4803
## Avg_Cogongrass_Cover-Sus_scrofa 0.0191 1.3412 -2.8328 0.0812
## Tree_Density-Canis_latrans -2.9996 1.4156 -6.3875 -2.7841
## Tree_Density-Sciurus_niger -2.1975 1.5547 -5.5205 -2.0969
## Tree_Density-Procyon_lotor -2.2620 1.0354 -4.5360 -2.1824
## Tree_Density-Dasypus_novemcinctus -3.6706 1.7456 -8.1230 -3.2906
## Tree_Density-Lynx_rufus -1.0959 1.5041 -3.6610 -1.2843
## Tree_Density-Didelphis_virginiana -2.2356 1.1838 -4.8190 -2.1521
## Tree_Density-Sylvilagus_floridanus -2.6354 1.4156 -5.9100 -2.4610
## Tree_Density-Meleagris_gallopavo -2.3364 1.3966 -5.4982 -2.2335
## Tree_Density-Sciurus_carolinensis -2.4146 1.2909 -5.2437 -2.3377
## Tree_Density-Vulpes_vulpes -1.9640 1.4578 -4.8618 -2.0037
## Tree_Density-Sus_scrofa -2.2264 1.4425 -5.3683 -2.1118
## Avg_Canopy_Cover-Canis_latrans 0.2975 0.6952 -1.0695 0.2975
## Avg_Canopy_Cover-Sciurus_niger 1.9801 1.6567 -0.6093 1.7812
## Avg_Canopy_Cover-Procyon_lotor 1.6368 0.7647 0.3002 1.5983
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1706 0.8592 0.7691 2.0783
## Avg_Canopy_Cover-Lynx_rufus 1.3622 1.2360 -0.9365 1.2949
## Avg_Canopy_Cover-Didelphis_virginiana 2.8700 1.2256 1.1144 2.6383
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3867 1.5799 1.1137 3.0812
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3385 1.2135 0.6227 2.1151
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6816 1.2037 0.9389 2.4797
## Avg_Canopy_Cover-Vulpes_vulpes 2.3702 1.3593 0.4472 2.1188
## Avg_Canopy_Cover-Sus_scrofa 2.1170 1.1321 0.4298 1.9235
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2351 1.2307 0.5476 1.9722
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3306 1.4795 -3.1176 0.4697
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1392 1.0959 0.5182 1.9929
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2739 0.7428 -0.0064 1.2176
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5357 1.4117 0.5095 2.2634
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8020 0.9166 -0.6352 0.7298
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0770 1.0535 -0.5495 0.9728
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0226 1.4965 -3.2993 0.1322
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3870 0.8101 0.0280 1.3178
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6201 0.8901 0.1655 1.4995
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2631 1.5629 -3.5157 0.4546
## avg_veg_height-Canis_latrans -0.3559 0.6476 -1.6639 -0.3460
## avg_veg_height-Sciurus_niger -0.6205 0.9333 -2.8073 -0.5250
## avg_veg_height-Procyon_lotor -0.0797 0.6538 -1.3773 -0.0894
## avg_veg_height-Dasypus_novemcinctus 0.1409 0.6655 -1.0489 0.1057
## avg_veg_height-Lynx_rufus -0.5922 0.9538 -2.9653 -0.4757
## avg_veg_height-Didelphis_virginiana -0.3807 0.7462 -1.9342 -0.3505
## avg_veg_height-Sylvilagus_floridanus -0.3279 0.7420 -1.8857 -0.3100
## avg_veg_height-Meleagris_gallopavo -0.3055 0.9122 -2.3755 -0.2687
## avg_veg_height-Sciurus_carolinensis 0.1558 0.7452 -1.1651 0.1076
## avg_veg_height-Vulpes_vulpes -0.2614 0.8135 -1.9173 -0.2528
## avg_veg_height-Sus_scrofa -0.2209 0.7933 -1.8577 -0.2295
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6138 1.0208 206
## (Intercept)-Sciurus_niger 1.7573 1.2030 195
## (Intercept)-Procyon_lotor 1.0968 1.0336 171
## (Intercept)-Dasypus_novemcinctus -0.4677 1.0472 424
## (Intercept)-Lynx_rufus 1.8363 1.0802 205
## (Intercept)-Didelphis_virginiana -1.0304 1.0510 342
## (Intercept)-Sylvilagus_floridanus 0.1351 1.0143 409
## (Intercept)-Meleagris_gallopavo 0.9879 1.0330 293
## (Intercept)-Sciurus_carolinensis -1.2014 1.0432 267
## (Intercept)-Vulpes_vulpes -0.5202 1.0884 242
## (Intercept)-Sus_scrofa -0.9082 1.1379 228
## Cogon_Patch_Size-Canis_latrans 4.2612 1.0040 312
## Cogon_Patch_Size-Sciurus_niger 1.7291 1.0752 341
## Cogon_Patch_Size-Procyon_lotor 0.8079 1.0551 316
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6510 1.0296 496
## Cogon_Patch_Size-Lynx_rufus 2.3539 1.0392 276
## Cogon_Patch_Size-Didelphis_virginiana 3.5863 1.0033 256
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9354 1.0316 326
## Cogon_Patch_Size-Meleagris_gallopavo 3.2016 1.0157 269
## Cogon_Patch_Size-Sciurus_carolinensis 0.9631 1.0320 258
## Cogon_Patch_Size-Vulpes_vulpes 2.2112 1.0216 304
## Cogon_Patch_Size-Sus_scrofa 1.6585 1.0089 372
## Veg_shannon_index-Canis_latrans 3.0027 1.0312 432
## Veg_shannon_index-Sciurus_niger 3.2443 1.0713 340
## Veg_shannon_index-Procyon_lotor 2.6521 1.0855 284
## Veg_shannon_index-Dasypus_novemcinctus 1.7486 1.0329 536
## Veg_shannon_index-Lynx_rufus 2.8323 1.0227 324
## Veg_shannon_index-Didelphis_virginiana 2.7827 1.0315 559
## Veg_shannon_index-Sylvilagus_floridanus 2.5635 1.0244 651
## Veg_shannon_index-Meleagris_gallopavo 2.9404 1.0248 519
## Veg_shannon_index-Sciurus_carolinensis 1.6940 1.0499 713
## Veg_shannon_index-Vulpes_vulpes 2.1591 1.0511 400
## Veg_shannon_index-Sus_scrofa 3.3771 1.0352 390
## total_shrub_cover-Canis_latrans 2.1627 1.0271 269
## total_shrub_cover-Sciurus_niger 0.3813 1.0247 302
## total_shrub_cover-Procyon_lotor -0.2769 1.0100 574
## total_shrub_cover-Dasypus_novemcinctus 0.9891 1.0133 488
## total_shrub_cover-Lynx_rufus 0.3197 1.0109 202
## total_shrub_cover-Didelphis_virginiana 0.2666 1.0118 233
## total_shrub_cover-Sylvilagus_floridanus 0.6677 1.0116 398
## total_shrub_cover-Meleagris_gallopavo -0.3628 1.0391 146
## total_shrub_cover-Sciurus_carolinensis 0.8465 1.0209 275
## total_shrub_cover-Vulpes_vulpes 0.7558 1.0777 216
## total_shrub_cover-Sus_scrofa 1.0315 1.0876 142
## Avg_Cogongrass_Cover-Canis_latrans 2.8639 1.0319 203
## Avg_Cogongrass_Cover-Sciurus_niger 2.0274 1.1105 167
## Avg_Cogongrass_Cover-Procyon_lotor 2.5623 1.0299 195
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.4503 1.0424 228
## Avg_Cogongrass_Cover-Lynx_rufus 2.7532 1.0439 198
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8272 1.0282 216
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8623 1.0937 150
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2348 1.0590 220
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5334 1.0589 217
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3708 1.0199 270
## Avg_Cogongrass_Cover-Sus_scrofa 2.4807 1.0594 201
## Tree_Density-Canis_latrans -0.8526 1.0166 208
## Tree_Density-Sciurus_niger 0.6045 1.0470 172
## Tree_Density-Procyon_lotor -0.4280 1.0181 210
## Tree_Density-Dasypus_novemcinctus -1.3825 1.0971 186
## Tree_Density-Lynx_rufus 2.4695 1.0197 206
## Tree_Density-Didelphis_virginiana -0.0105 1.0219 391
## Tree_Density-Sylvilagus_floridanus -0.2460 1.0253 198
## Tree_Density-Meleagris_gallopavo 0.2205 1.0496 248
## Tree_Density-Sciurus_carolinensis 0.0651 1.0934 213
## Tree_Density-Vulpes_vulpes 1.1047 1.0136 325
## Tree_Density-Sus_scrofa 0.4424 1.0514 300
## Avg_Canopy_Cover-Canis_latrans 1.7023 1.0243 406
## Avg_Canopy_Cover-Sciurus_niger 6.1478 1.2150 162
## Avg_Canopy_Cover-Procyon_lotor 3.3143 1.0680 323
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2447 1.0379 198
## Avg_Canopy_Cover-Lynx_rufus 3.9511 1.0870 243
## Avg_Canopy_Cover-Didelphis_virginiana 5.8447 1.0544 205
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1601 1.0605 134
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3415 1.0559 248
## Avg_Canopy_Cover-Sciurus_carolinensis 5.6704 1.0283 221
## Avg_Canopy_Cover-Vulpes_vulpes 6.0067 1.1069 134
## Avg_Canopy_Cover-Sus_scrofa 4.8838 1.0984 357
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4277 1.0443 101
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 2.9076 1.1293 134
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6086 1.0500 192
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9910 1.0038 365
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1515 1.0573 156
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6967 1.0718 116
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.5159 1.0324 142
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5907 1.0419 121
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3035 1.0202 328
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.7094 1.0269 399
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.9927 1.2260 88
## avg_veg_height-Canis_latrans 0.8858 1.0170 337
## avg_veg_height-Sciurus_niger 0.9061 1.0352 281
## avg_veg_height-Procyon_lotor 1.2325 1.0084 381
## avg_veg_height-Dasypus_novemcinctus 1.5695 1.0004 341
## avg_veg_height-Lynx_rufus 0.9458 1.0161 256
## avg_veg_height-Didelphis_virginiana 1.0188 1.0023 400
## avg_veg_height-Sylvilagus_floridanus 1.1446 1.0114 413
## avg_veg_height-Meleagris_gallopavo 1.3752 1.0056 246
## avg_veg_height-Sciurus_carolinensis 1.7900 1.0139 345
## avg_veg_height-Vulpes_vulpes 1.3401 1.0163 280
## avg_veg_height-Sus_scrofa 1.3704 1.0015 378
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5677 0.1927 -2.9615 -2.5619 -2.2057 1.0032
## (Intercept)-Sciurus_niger -4.1456 0.5693 -5.2302 -4.1706 -3.0028 1.2090
## (Intercept)-Procyon_lotor -2.2182 0.1594 -2.5441 -2.2128 -1.9256 1.0048
## (Intercept)-Dasypus_novemcinctus -1.6918 0.1847 -2.0784 -1.6860 -1.3275 1.0019
## (Intercept)-Lynx_rufus -3.5762 0.3400 -4.2624 -3.5679 -2.9380 1.0256
## (Intercept)-Didelphis_virginiana -2.5022 0.2923 -3.0864 -2.5111 -1.9037 1.0072
## (Intercept)-Sylvilagus_floridanus -3.1496 0.2817 -3.7110 -3.1453 -2.6168 1.0047
## (Intercept)-Meleagris_gallopavo -3.5653 0.4990 -4.5493 -3.5580 -2.5987 1.0954
## (Intercept)-Sciurus_carolinensis -2.6777 0.3347 -3.3366 -2.6731 -2.0548 1.0175
## (Intercept)-Vulpes_vulpes -4.0416 0.6081 -5.2943 -4.0110 -2.9373 1.0145
## (Intercept)-Sus_scrofa -3.4862 0.5654 -4.5659 -3.5051 -2.3096 1.0089
## shrub_cover-Canis_latrans -0.2925 0.2308 -0.7310 -0.2891 0.1555 1.0056
## shrub_cover-Sciurus_niger -0.1411 0.5011 -1.1507 -0.1485 0.8859 1.0159
## shrub_cover-Procyon_lotor 0.2926 0.1628 -0.0148 0.2905 0.6183 1.0070
## shrub_cover-Dasypus_novemcinctus 1.0065 0.3182 0.3945 1.0071 1.6201 1.0134
## shrub_cover-Lynx_rufus -0.0286 0.3753 -0.7858 -0.0217 0.6782 1.0067
## shrub_cover-Didelphis_virginiana 1.1199 0.3717 0.4037 1.1136 1.8406 1.0110
## shrub_cover-Sylvilagus_floridanus 0.5832 0.4014 -0.2385 0.5820 1.3496 1.0275
## shrub_cover-Meleagris_gallopavo -0.4638 0.4432 -1.3505 -0.4597 0.3638 1.0832
## shrub_cover-Sciurus_carolinensis 1.0797 0.4119 0.2929 1.0768 1.8593 1.0303
## shrub_cover-Vulpes_vulpes 0.2955 0.5869 -0.9409 0.3257 1.3598 1.0512
## shrub_cover-Sus_scrofa 1.1026 0.7649 -0.4513 1.1231 2.5272 1.0008
## veg_height-Canis_latrans -0.5453 0.1813 -0.8996 -0.5456 -0.1974 1.0026
## veg_height-Sciurus_niger 0.1395 0.4097 -0.5929 0.1164 1.0383 1.0628
## veg_height-Procyon_lotor 0.3659 0.1218 0.1240 0.3667 0.6011 1.0044
## veg_height-Dasypus_novemcinctus 0.2820 0.1406 0.0076 0.2820 0.5664 1.0013
## veg_height-Lynx_rufus 0.1691 0.2303 -0.2913 0.1719 0.6335 1.0066
## veg_height-Didelphis_virginiana 0.4822 0.2453 0.0177 0.4713 0.9811 1.0038
## veg_height-Sylvilagus_floridanus 0.1520 0.2488 -0.3119 0.1468 0.6515 1.0117
## veg_height-Meleagris_gallopavo -0.1123 0.3851 -0.8690 -0.1142 0.6764 1.0251
## veg_height-Sciurus_carolinensis 0.1605 0.2258 -0.2649 0.1558 0.6180 1.0129
## veg_height-Vulpes_vulpes -0.1437 0.3114 -0.7879 -0.1252 0.4172 1.0029
## veg_height-Sus_scrofa -0.1357 0.3233 -0.7886 -0.1237 0.4550 1.0212
## week-Canis_latrans 0.4458 0.2533 -0.0203 0.4374 0.9765 1.0079
## week-Sciurus_niger -0.3096 0.4868 -1.4378 -0.2451 0.4482 1.0126
## week-Procyon_lotor 0.1408 0.1966 -0.2414 0.1422 0.5244 1.0133
## week-Dasypus_novemcinctus 0.0534 0.2078 -0.3660 0.0557 0.4533 1.0008
## week-Lynx_rufus 0.2436 0.3187 -0.3555 0.2289 0.9014 1.0166
## week-Didelphis_virginiana -0.0082 0.3274 -0.6740 0.0044 0.6034 1.0026
## week-Sylvilagus_floridanus 0.0148 0.3110 -0.6549 0.0239 0.5977 1.0063
## week-Meleagris_gallopavo -0.1781 0.3839 -1.0663 -0.1344 0.4753 1.0421
## week-Sciurus_carolinensis 0.5504 0.3486 -0.0633 0.5265 1.3080 1.0013
## week-Vulpes_vulpes 0.0938 0.4129 -0.7947 0.1091 0.8936 1.0156
## week-Sus_scrofa 0.4089 0.3973 -0.2698 0.3723 1.2704 1.0056
## I(week^2)-Canis_latrans -0.1939 0.1048 -0.4032 -0.1907 0.0085 1.0064
## I(week^2)-Sciurus_niger -0.2935 0.2647 -0.8909 -0.2618 0.0982 1.0458
## I(week^2)-Procyon_lotor -0.1042 0.0861 -0.2780 -0.1031 0.0596 1.0102
## I(week^2)-Dasypus_novemcinctus -0.1538 0.1003 -0.3560 -0.1518 0.0372 0.9998
## I(week^2)-Lynx_rufus -0.2013 0.1514 -0.5262 -0.1911 0.0735 1.0377
## I(week^2)-Didelphis_virginiana -0.3810 0.2167 -0.8850 -0.3586 -0.0225 1.0194
## I(week^2)-Sylvilagus_floridanus -0.1446 0.1506 -0.4366 -0.1365 0.1399 1.0011
## I(week^2)-Meleagris_gallopavo -0.3807 0.2525 -0.9536 -0.3425 0.0220 1.0279
## I(week^2)-Sciurus_carolinensis -0.1994 0.1421 -0.4849 -0.1942 0.0646 1.0013
## I(week^2)-Vulpes_vulpes -0.3810 0.3117 -1.1800 -0.3306 0.0378 1.1159
## I(week^2)-Sus_scrofa -0.1645 0.1731 -0.5229 -0.1567 0.1499 1.0040
## ESS
## (Intercept)-Canis_latrans 1006
## (Intercept)-Sciurus_niger 147
## (Intercept)-Procyon_lotor 1210
## (Intercept)-Dasypus_novemcinctus 1089
## (Intercept)-Lynx_rufus 361
## (Intercept)-Didelphis_virginiana 459
## (Intercept)-Sylvilagus_floridanus 803
## (Intercept)-Meleagris_gallopavo 190
## (Intercept)-Sciurus_carolinensis 378
## (Intercept)-Vulpes_vulpes 217
## (Intercept)-Sus_scrofa 217
## shrub_cover-Canis_latrans 617
## shrub_cover-Sciurus_niger 301
## shrub_cover-Procyon_lotor 1465
## shrub_cover-Dasypus_novemcinctus 605
## shrub_cover-Lynx_rufus 349
## shrub_cover-Didelphis_virginiana 458
## shrub_cover-Sylvilagus_floridanus 517
## shrub_cover-Meleagris_gallopavo 319
## shrub_cover-Sciurus_carolinensis 284
## shrub_cover-Vulpes_vulpes 350
## shrub_cover-Sus_scrofa 211
## veg_height-Canis_latrans 902
## veg_height-Sciurus_niger 293
## veg_height-Procyon_lotor 1345
## veg_height-Dasypus_novemcinctus 1471
## veg_height-Lynx_rufus 711
## veg_height-Didelphis_virginiana 945
## veg_height-Sylvilagus_floridanus 646
## veg_height-Meleagris_gallopavo 364
## veg_height-Sciurus_carolinensis 891
## veg_height-Vulpes_vulpes 559
## veg_height-Sus_scrofa 783
## week-Canis_latrans 1158
## week-Sciurus_niger 258
## week-Procyon_lotor 1754
## week-Dasypus_novemcinctus 1810
## week-Lynx_rufus 688
## week-Didelphis_virginiana 801
## week-Sylvilagus_floridanus 1107
## week-Meleagris_gallopavo 321
## week-Sciurus_carolinensis 1046
## week-Vulpes_vulpes 676
## week-Sus_scrofa 898
## I(week^2)-Canis_latrans 1158
## I(week^2)-Sciurus_niger 96
## I(week^2)-Procyon_lotor 1595
## I(week^2)-Dasypus_novemcinctus 1609
## I(week^2)-Lynx_rufus 560
## I(week^2)-Didelphis_virginiana 367
## I(week^2)-Sylvilagus_floridanus 745
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1330
## I(week^2)-Vulpes_vulpes 97
## I(week^2)-Sus_scrofa 1133
waicOcc(ms_full_full_T, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -1057.3289 108.9152 2332.4882
waicOcc(ms_full_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1098.6211 105.1342 2407.5105
waicOcc(ms_full_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1093.28422 87.40184 2361.37212
waicOcc(ms_full_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1096.8047 101.8984 2397.4063
waicOcc(ms_full_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1106.99036 92.49602 2398.97276
waicOcc(ms_full_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1112.07900 86.47883 2397.11567
waicOcc(ms_full_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1127.60275 71.88676 2398.97902
waicOcc(ms_full_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1106.0151 89.3718 2390.7738
waicOcc(ms_full_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1053.3431 109.0889 2324.8640
waicOcc(ms_null_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1170.23631 31.05659 2402.58579
waicOcc(ms_null_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1096.85066 70.41303 2334.52738
waicOcc(ms_null_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1141.78833 58.75263 2401.08193
waicOcc(ms_null_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1135.61661 48.85778 2368.94878
waicOcc(ms_null_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1139.02915 58.04877 2394.15584
waicOcc(ms_null_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1147.91649 52.07087 2399.97473
waicOcc(ms_null_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1152.44551 47.82331 2400.53763
waicOcc(ms_null_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1146.31528 49.98277 2392.59610
waicOcc(ms_null_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1092.91697 68.73386 2323.30166
waicOcc(ms_week_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1092.90697 76.74598 2339.30591
waicOcc(ms_week_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1137.99268 64.80045 2405.58626
waicOcc(ms_week_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1165.10918 38.55314 2407.32463
waicOcc(ms_week_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1142.63303 59.94794 2405.16194
waicOcc(ms_week_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1133.43597 65.66441 2398.20076
waicOcc(ms_week_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1130.98868 55.55095 2373.07925
waicOcc(ms_week_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1147.20673 55.24861 2404.91068
waicOcc(ms_week_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1140.80276 58.02758 2397.66067
waicOcc(ms_week_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1087.59476 76.42839 2328.04631
waicOcc(ms_cover_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1063.12819 99.64288 2325.54213
waicOcc(ms_cover_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1102.91769 98.23963 2402.31464
waicOcc(ms_cover_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1133.01128 63.49379 2393.01013
waicOcc(ms_cover_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1111.73058 84.94023 2393.34163
waicOcc(ms_cover_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1102.34025 94.76341 2394.20731
waicOcc(ms_cover_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1097.76056 79.97539 2355.47189
waicOcc(ms_cover_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1116.83535 79.32206 2392.31481
waicOcc(ms_cover_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1111.43061 82.56021 2387.98163
waicOcc(ms_cover_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1058.8518 101.1432 2319.9900
waicOcc(ms_weekQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1085.71211 85.06135 2341.54693
waicOcc(ms_weekQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1130.38613 73.75219 2408.27664
waicOcc(ms_weekQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1158.11985 47.90255 2412.04481
waicOcc(ms_weekQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1135.46329 70.03104 2410.98867
waicOcc(ms_weekQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1126.52318 74.86306 2402.77249
waicOcc(ms_weekQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1123.41475 64.94457 2376.71864
waicOcc(ms_weekQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1140.16251 63.97019 2408.26541
waicOcc(ms_weekQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1134.16530 66.41065 2401.15190
waicOcc(ms_weekQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1079.05336 85.76346 2329.63364
waicOcc(ms_fullQ_full_T, by.sp = FALSE)
## elpd pD WAIC
## -1050.5574 119.3166 2339.7480
waicOcc(ms_fullQ_cover_T, by.sp = FALSE)
## elpd pD WAIC
## -1090.5543 114.5407 2410.1902
waicOcc(ms_fullQ_null_T, by.sp = FALSE)
## elpd pD WAIC
## -1120.82944 79.86222 2401.38333
waicOcc(ms_fullQ_forage_T, by.sp = FALSE)
## elpd pD WAIC
## -1099.2554 103.0605 2404.6317
waicOcc(ms_fullQ_move_T, by.sp = FALSE)
## elpd pD WAIC
## -1089.1259 110.8907 2400.0332
waicOcc(ms_fullQ_canopy_T, by.sp = FALSE)
## elpd pD WAIC
## -1086.16759 95.80242 2363.94003
waicOcc(ms_fullQ_cogon_T, by.sp = FALSE)
## elpd pD WAIC
## -1103.49118 98.20959 2403.40154
waicOcc(ms_fullQ_cogonQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1097.9975 100.7107 2397.4164
waicOcc(ms_fullQ_fullQ_T, by.sp = FALSE)
## elpd pD WAIC
## -1045.7827 117.4097 2326.3847
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T <- ppcOcc(ms_fullQ_fullQ_T, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 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_T)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.348
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.661
## Sciurus_niger Bayesian p-value: 0.2023
## Procyon_lotor Bayesian p-value: 0.0847
## Dasypus_novemcinctus Bayesian p-value: 0
## Lynx_rufus Bayesian p-value: 0.3447
## Didelphis_virginiana Bayesian p-value: 0.3833
## Sylvilagus_floridanus Bayesian p-value: 0.4093
## Meleagris_gallopavo Bayesian p-value: 0.626
## Sciurus_carolinensis Bayesian p-value: 0.3427
## Vulpes_vulpes Bayesian p-value: 0.2403
## Sus_scrofa Bayesian p-value: 0.5333
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.2042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9718 0.7037 -3.3715 -1.9522 -0.5642 1.0256 229
## Cogon_Patch_Size -0.1939 0.6612 -1.5094 -0.1899 1.1101 1.0142 392
## Veg_shannon_index 0.9937 0.4646 0.1319 0.9629 1.9998 1.0979 238
## total_shrub_cover -1.1267 0.6034 -2.4900 -1.0763 -0.0937 1.0191 244
## Avg_Cogongrass_Cover 0.1538 0.9460 -1.7138 0.2032 1.9526 1.0677 134
## Tree_Density -2.1814 0.8310 -3.9603 -2.1269 -0.6400 1.0430 195
## Avg_Canopy_Cover 1.9537 0.6778 0.8032 1.8779 3.5246 1.0829 179
## I(Avg_Cogongrass_Cover^2) 1.1689 0.6274 -0.0434 1.1473 2.4844 1.0405 298
## avg_veg_height -0.2514 0.5213 -1.2780 -0.2524 0.7650 1.0132 206
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4006 3.0912 0.0985 1.4038 10.7740 1.2588 226
## Cogon_Patch_Size 2.3820 3.1511 0.0854 1.3880 10.4786 1.0706 207
## Veg_shannon_index 0.6921 1.0051 0.0441 0.3645 3.2541 1.0107 345
## total_shrub_cover 1.7693 2.4145 0.0863 1.0093 8.6103 1.0778 83
## Avg_Cogongrass_Cover 1.3413 2.2681 0.0546 0.5712 7.5183 1.1902 213
## Tree_Density 2.5004 4.7402 0.0640 1.0568 13.3160 1.1082 242
## Avg_Canopy_Cover 2.3352 3.0348 0.1044 1.4334 9.7069 1.1204 187
## I(Avg_Cogongrass_Cover^2) 2.3982 3.6761 0.0736 1.1770 12.5361 1.1088 100
## avg_veg_height 0.6098 0.9112 0.0467 0.3070 3.1156 1.0152 333
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.643 2.2479 0.0517 0.8592 8.4321 1.1847 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9666 0.3161 -3.6045 -2.9608 -2.3566 1.0085 1049
## shrub_cover 0.3996 0.2851 -0.1757 0.4028 0.9528 1.0078 624
## veg_height 0.0750 0.1686 -0.2558 0.0726 0.3988 1.0153 856
## week 0.1357 0.2015 -0.2856 0.1436 0.5292 1.0172 736
## I(week^2) -0.2373 0.1158 -0.4865 -0.2298 -0.0369 1.0107 348
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9737 0.6924 0.2682 0.7992 2.6740 1.0316 501
## shrub_cover 0.6893 0.5073 0.1514 0.5536 2.0416 1.0088 627
## veg_height 0.2105 0.1676 0.0554 0.1679 0.6208 1.0031 1130
## week 0.2369 0.2590 0.0359 0.1582 0.8873 1.0194 323
## I(week^2) 0.0756 0.0882 0.0181 0.0535 0.2552 1.1991 109
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3891 1.0215 -3.3570 -1.3739
## (Intercept)-Sciurus_niger -1.3381 1.3669 -3.6434 -1.4786
## (Intercept)-Procyon_lotor -1.0181 1.0442 -3.1528 -0.9996
## (Intercept)-Dasypus_novemcinctus -2.2061 0.9368 -4.2518 -2.1407
## (Intercept)-Lynx_rufus -1.3552 1.4148 -3.7280 -1.4875
## (Intercept)-Didelphis_virginiana -2.9019 1.1325 -5.4272 -2.8096
## (Intercept)-Sylvilagus_floridanus -1.9472 1.0267 -3.9571 -1.9487
## (Intercept)-Meleagris_gallopavo -1.6765 1.2340 -4.0127 -1.7259
## (Intercept)-Sciurus_carolinensis -3.2083 1.2259 -5.9663 -3.1042
## (Intercept)-Vulpes_vulpes -3.0847 1.5105 -6.5298 -2.8947
## (Intercept)-Sus_scrofa -3.1915 1.4099 -6.5941 -2.9946
## Cogon_Patch_Size-Canis_latrans 1.2157 1.2203 -0.5629 1.0212
## Cogon_Patch_Size-Sciurus_niger -0.8937 1.4908 -4.4783 -0.7214
## Cogon_Patch_Size-Procyon_lotor -0.6341 0.7941 -2.3070 -0.5898
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0635 0.8011 -1.5755 -0.0973
## Cogon_Patch_Size-Lynx_rufus -0.3823 1.3567 -2.9554 -0.4132
## Cogon_Patch_Size-Didelphis_virginiana 1.1593 1.0335 -0.5599 1.0385
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2582 1.4250 -4.8262 -1.0146
## Cogon_Patch_Size-Meleagris_gallopavo 0.2290 1.2732 -1.7786 0.0639
## Cogon_Patch_Size-Sciurus_carolinensis -0.9388 1.2304 -3.9010 -0.7464
## Cogon_Patch_Size-Vulpes_vulpes -0.4743 1.4021 -3.3489 -0.4176
## Cogon_Patch_Size-Sus_scrofa -0.5496 1.2720 -3.5041 -0.4265
## Veg_shannon_index-Canis_latrans 1.3399 0.7258 0.1870 1.2401
## Veg_shannon_index-Sciurus_niger 1.1049 0.9002 -0.4296 1.0227
## Veg_shannon_index-Procyon_lotor 1.2106 0.6307 0.1410 1.1583
## Veg_shannon_index-Dasypus_novemcinctus 0.6481 0.5772 -0.5493 0.6640
## Veg_shannon_index-Lynx_rufus 1.0289 0.8715 -0.6026 0.9756
## Veg_shannon_index-Didelphis_virginiana 1.1330 0.7065 -0.0790 1.0604
## Veg_shannon_index-Sylvilagus_floridanus 1.0282 0.7102 -0.2380 0.9894
## Veg_shannon_index-Meleagris_gallopavo 1.2207 0.7570 -0.0858 1.1402
## Veg_shannon_index-Sciurus_carolinensis 0.4167 0.7517 -1.2375 0.4943
## Veg_shannon_index-Vulpes_vulpes 0.6711 0.8120 -1.1465 0.7222
## Veg_shannon_index-Sus_scrofa 1.3897 0.8682 0.0803 1.2589
## total_shrub_cover-Canis_latrans 0.1245 0.8820 -1.2717 0.0199
## total_shrub_cover-Sciurus_niger -1.4970 1.1094 -4.0318 -1.3596
## total_shrub_cover-Procyon_lotor -1.4569 0.7057 -3.0507 -1.3967
## total_shrub_cover-Dasypus_novemcinctus -0.4642 0.7744 -2.0877 -0.4232
## total_shrub_cover-Lynx_rufus -1.6306 1.1924 -4.4147 -1.4760
## total_shrub_cover-Didelphis_virginiana -1.3222 1.0227 -4.0646 -1.1818
## total_shrub_cover-Sylvilagus_floridanus -1.1437 1.0668 -3.8349 -1.0216
## total_shrub_cover-Meleagris_gallopavo -2.3617 1.3509 -5.5802 -2.1297
## total_shrub_cover-Sciurus_carolinensis -1.0024 1.0988 -3.7078 -0.8868
## total_shrub_cover-Vulpes_vulpes -1.4124 1.3440 -4.6216 -1.2342
## total_shrub_cover-Sus_scrofa -1.0243 1.3148 -4.0287 -0.8792
## Avg_Cogongrass_Cover-Canis_latrans 0.3558 1.2193 -2.1008 0.3962
## Avg_Cogongrass_Cover-Sciurus_niger -0.4659 1.5667 -4.3455 -0.2618
## Avg_Cogongrass_Cover-Procyon_lotor 0.1840 1.1981 -2.2579 0.2004
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7830 1.2608 -1.5262 0.7480
## Avg_Cogongrass_Cover-Lynx_rufus 0.2703 1.3119 -2.4630 0.3125
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3707 1.2271 -2.0079 0.3827
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4289 1.3737 -3.4776 -0.3008
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1273 1.3457 -3.0299 -0.0367
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2404 1.2119 -2.1364 0.2964
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4857 1.3468 -1.9789 0.4803
## Avg_Cogongrass_Cover-Sus_scrofa 0.0191 1.3412 -2.8328 0.0812
## Tree_Density-Canis_latrans -2.9996 1.4156 -6.3875 -2.7841
## Tree_Density-Sciurus_niger -2.1975 1.5547 -5.5205 -2.0969
## Tree_Density-Procyon_lotor -2.2620 1.0354 -4.5360 -2.1824
## Tree_Density-Dasypus_novemcinctus -3.6706 1.7456 -8.1230 -3.2906
## Tree_Density-Lynx_rufus -1.0959 1.5041 -3.6610 -1.2843
## Tree_Density-Didelphis_virginiana -2.2356 1.1838 -4.8190 -2.1521
## Tree_Density-Sylvilagus_floridanus -2.6354 1.4156 -5.9100 -2.4610
## Tree_Density-Meleagris_gallopavo -2.3364 1.3966 -5.4982 -2.2335
## Tree_Density-Sciurus_carolinensis -2.4146 1.2909 -5.2437 -2.3377
## Tree_Density-Vulpes_vulpes -1.9640 1.4578 -4.8618 -2.0037
## Tree_Density-Sus_scrofa -2.2264 1.4425 -5.3683 -2.1118
## Avg_Canopy_Cover-Canis_latrans 0.2975 0.6952 -1.0695 0.2975
## Avg_Canopy_Cover-Sciurus_niger 1.9801 1.6567 -0.6093 1.7812
## Avg_Canopy_Cover-Procyon_lotor 1.6368 0.7647 0.3002 1.5983
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1706 0.8592 0.7691 2.0783
## Avg_Canopy_Cover-Lynx_rufus 1.3622 1.2360 -0.9365 1.2949
## Avg_Canopy_Cover-Didelphis_virginiana 2.8700 1.2256 1.1144 2.6383
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3867 1.5799 1.1137 3.0812
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3385 1.2135 0.6227 2.1151
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6816 1.2037 0.9389 2.4797
## Avg_Canopy_Cover-Vulpes_vulpes 2.3702 1.3593 0.4472 2.1188
## Avg_Canopy_Cover-Sus_scrofa 2.1170 1.1321 0.4298 1.9235
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2351 1.2307 0.5476 1.9722
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3306 1.4795 -3.1176 0.4697
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1392 1.0959 0.5182 1.9929
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2739 0.7428 -0.0064 1.2176
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5357 1.4117 0.5095 2.2634
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8020 0.9166 -0.6352 0.7298
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0770 1.0535 -0.5495 0.9728
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0226 1.4965 -3.2993 0.1322
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3870 0.8101 0.0280 1.3178
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6201 0.8901 0.1655 1.4995
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2631 1.5629 -3.5157 0.4546
## avg_veg_height-Canis_latrans -0.3559 0.6476 -1.6639 -0.3460
## avg_veg_height-Sciurus_niger -0.6205 0.9333 -2.8073 -0.5250
## avg_veg_height-Procyon_lotor -0.0797 0.6538 -1.3773 -0.0894
## avg_veg_height-Dasypus_novemcinctus 0.1409 0.6655 -1.0489 0.1057
## avg_veg_height-Lynx_rufus -0.5922 0.9538 -2.9653 -0.4757
## avg_veg_height-Didelphis_virginiana -0.3807 0.7462 -1.9342 -0.3505
## avg_veg_height-Sylvilagus_floridanus -0.3279 0.7420 -1.8857 -0.3100
## avg_veg_height-Meleagris_gallopavo -0.3055 0.9122 -2.3755 -0.2687
## avg_veg_height-Sciurus_carolinensis 0.1558 0.7452 -1.1651 0.1076
## avg_veg_height-Vulpes_vulpes -0.2614 0.8135 -1.9173 -0.2528
## avg_veg_height-Sus_scrofa -0.2209 0.7933 -1.8577 -0.2295
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6138 1.0208 206
## (Intercept)-Sciurus_niger 1.7573 1.2030 195
## (Intercept)-Procyon_lotor 1.0968 1.0336 171
## (Intercept)-Dasypus_novemcinctus -0.4677 1.0472 424
## (Intercept)-Lynx_rufus 1.8363 1.0802 205
## (Intercept)-Didelphis_virginiana -1.0304 1.0510 342
## (Intercept)-Sylvilagus_floridanus 0.1351 1.0143 409
## (Intercept)-Meleagris_gallopavo 0.9879 1.0330 293
## (Intercept)-Sciurus_carolinensis -1.2014 1.0432 267
## (Intercept)-Vulpes_vulpes -0.5202 1.0884 242
## (Intercept)-Sus_scrofa -0.9082 1.1379 228
## Cogon_Patch_Size-Canis_latrans 4.2612 1.0040 312
## Cogon_Patch_Size-Sciurus_niger 1.7291 1.0752 341
## Cogon_Patch_Size-Procyon_lotor 0.8079 1.0551 316
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6510 1.0296 496
## Cogon_Patch_Size-Lynx_rufus 2.3539 1.0392 276
## Cogon_Patch_Size-Didelphis_virginiana 3.5863 1.0033 256
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9354 1.0316 326
## Cogon_Patch_Size-Meleagris_gallopavo 3.2016 1.0157 269
## Cogon_Patch_Size-Sciurus_carolinensis 0.9631 1.0320 258
## Cogon_Patch_Size-Vulpes_vulpes 2.2112 1.0216 304
## Cogon_Patch_Size-Sus_scrofa 1.6585 1.0089 372
## Veg_shannon_index-Canis_latrans 3.0027 1.0312 432
## Veg_shannon_index-Sciurus_niger 3.2443 1.0713 340
## Veg_shannon_index-Procyon_lotor 2.6521 1.0855 284
## Veg_shannon_index-Dasypus_novemcinctus 1.7486 1.0329 536
## Veg_shannon_index-Lynx_rufus 2.8323 1.0227 324
## Veg_shannon_index-Didelphis_virginiana 2.7827 1.0315 559
## Veg_shannon_index-Sylvilagus_floridanus 2.5635 1.0244 651
## Veg_shannon_index-Meleagris_gallopavo 2.9404 1.0248 519
## Veg_shannon_index-Sciurus_carolinensis 1.6940 1.0499 713
## Veg_shannon_index-Vulpes_vulpes 2.1591 1.0511 400
## Veg_shannon_index-Sus_scrofa 3.3771 1.0352 390
## total_shrub_cover-Canis_latrans 2.1627 1.0271 269
## total_shrub_cover-Sciurus_niger 0.3813 1.0247 302
## total_shrub_cover-Procyon_lotor -0.2769 1.0100 574
## total_shrub_cover-Dasypus_novemcinctus 0.9891 1.0133 488
## total_shrub_cover-Lynx_rufus 0.3197 1.0109 202
## total_shrub_cover-Didelphis_virginiana 0.2666 1.0118 233
## total_shrub_cover-Sylvilagus_floridanus 0.6677 1.0116 398
## total_shrub_cover-Meleagris_gallopavo -0.3628 1.0391 146
## total_shrub_cover-Sciurus_carolinensis 0.8465 1.0209 275
## total_shrub_cover-Vulpes_vulpes 0.7558 1.0777 216
## total_shrub_cover-Sus_scrofa 1.0315 1.0876 142
## Avg_Cogongrass_Cover-Canis_latrans 2.8639 1.0319 203
## Avg_Cogongrass_Cover-Sciurus_niger 2.0274 1.1105 167
## Avg_Cogongrass_Cover-Procyon_lotor 2.5623 1.0299 195
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.4503 1.0424 228
## Avg_Cogongrass_Cover-Lynx_rufus 2.7532 1.0439 198
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8272 1.0282 216
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8623 1.0937 150
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2348 1.0590 220
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5334 1.0589 217
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3708 1.0199 270
## Avg_Cogongrass_Cover-Sus_scrofa 2.4807 1.0594 201
## Tree_Density-Canis_latrans -0.8526 1.0166 208
## Tree_Density-Sciurus_niger 0.6045 1.0470 172
## Tree_Density-Procyon_lotor -0.4280 1.0181 210
## Tree_Density-Dasypus_novemcinctus -1.3825 1.0971 186
## Tree_Density-Lynx_rufus 2.4695 1.0197 206
## Tree_Density-Didelphis_virginiana -0.0105 1.0219 391
## Tree_Density-Sylvilagus_floridanus -0.2460 1.0253 198
## Tree_Density-Meleagris_gallopavo 0.2205 1.0496 248
## Tree_Density-Sciurus_carolinensis 0.0651 1.0934 213
## Tree_Density-Vulpes_vulpes 1.1047 1.0136 325
## Tree_Density-Sus_scrofa 0.4424 1.0514 300
## Avg_Canopy_Cover-Canis_latrans 1.7023 1.0243 406
## Avg_Canopy_Cover-Sciurus_niger 6.1478 1.2150 162
## Avg_Canopy_Cover-Procyon_lotor 3.3143 1.0680 323
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2447 1.0379 198
## Avg_Canopy_Cover-Lynx_rufus 3.9511 1.0870 243
## Avg_Canopy_Cover-Didelphis_virginiana 5.8447 1.0544 205
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1601 1.0605 134
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3415 1.0559 248
## Avg_Canopy_Cover-Sciurus_carolinensis 5.6704 1.0283 221
## Avg_Canopy_Cover-Vulpes_vulpes 6.0067 1.1069 134
## Avg_Canopy_Cover-Sus_scrofa 4.8838 1.0984 357
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4277 1.0443 101
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 2.9076 1.1293 134
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6086 1.0500 192
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9910 1.0038 365
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1515 1.0573 156
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6967 1.0718 116
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.5159 1.0324 142
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5907 1.0419 121
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3035 1.0202 328
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.7094 1.0269 399
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.9927 1.2260 88
## avg_veg_height-Canis_latrans 0.8858 1.0170 337
## avg_veg_height-Sciurus_niger 0.9061 1.0352 281
## avg_veg_height-Procyon_lotor 1.2325 1.0084 381
## avg_veg_height-Dasypus_novemcinctus 1.5695 1.0004 341
## avg_veg_height-Lynx_rufus 0.9458 1.0161 256
## avg_veg_height-Didelphis_virginiana 1.0188 1.0023 400
## avg_veg_height-Sylvilagus_floridanus 1.1446 1.0114 413
## avg_veg_height-Meleagris_gallopavo 1.3752 1.0056 246
## avg_veg_height-Sciurus_carolinensis 1.7900 1.0139 345
## avg_veg_height-Vulpes_vulpes 1.3401 1.0163 280
## avg_veg_height-Sus_scrofa 1.3704 1.0015 378
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5677 0.1927 -2.9615 -2.5619 -2.2057 1.0032
## (Intercept)-Sciurus_niger -4.1456 0.5693 -5.2302 -4.1706 -3.0028 1.2090
## (Intercept)-Procyon_lotor -2.2182 0.1594 -2.5441 -2.2128 -1.9256 1.0048
## (Intercept)-Dasypus_novemcinctus -1.6918 0.1847 -2.0784 -1.6860 -1.3275 1.0019
## (Intercept)-Lynx_rufus -3.5762 0.3400 -4.2624 -3.5679 -2.9380 1.0256
## (Intercept)-Didelphis_virginiana -2.5022 0.2923 -3.0864 -2.5111 -1.9037 1.0072
## (Intercept)-Sylvilagus_floridanus -3.1496 0.2817 -3.7110 -3.1453 -2.6168 1.0047
## (Intercept)-Meleagris_gallopavo -3.5653 0.4990 -4.5493 -3.5580 -2.5987 1.0954
## (Intercept)-Sciurus_carolinensis -2.6777 0.3347 -3.3366 -2.6731 -2.0548 1.0175
## (Intercept)-Vulpes_vulpes -4.0416 0.6081 -5.2943 -4.0110 -2.9373 1.0145
## (Intercept)-Sus_scrofa -3.4862 0.5654 -4.5659 -3.5051 -2.3096 1.0089
## shrub_cover-Canis_latrans -0.2925 0.2308 -0.7310 -0.2891 0.1555 1.0056
## shrub_cover-Sciurus_niger -0.1411 0.5011 -1.1507 -0.1485 0.8859 1.0159
## shrub_cover-Procyon_lotor 0.2926 0.1628 -0.0148 0.2905 0.6183 1.0070
## shrub_cover-Dasypus_novemcinctus 1.0065 0.3182 0.3945 1.0071 1.6201 1.0134
## shrub_cover-Lynx_rufus -0.0286 0.3753 -0.7858 -0.0217 0.6782 1.0067
## shrub_cover-Didelphis_virginiana 1.1199 0.3717 0.4037 1.1136 1.8406 1.0110
## shrub_cover-Sylvilagus_floridanus 0.5832 0.4014 -0.2385 0.5820 1.3496 1.0275
## shrub_cover-Meleagris_gallopavo -0.4638 0.4432 -1.3505 -0.4597 0.3638 1.0832
## shrub_cover-Sciurus_carolinensis 1.0797 0.4119 0.2929 1.0768 1.8593 1.0303
## shrub_cover-Vulpes_vulpes 0.2955 0.5869 -0.9409 0.3257 1.3598 1.0512
## shrub_cover-Sus_scrofa 1.1026 0.7649 -0.4513 1.1231 2.5272 1.0008
## veg_height-Canis_latrans -0.5453 0.1813 -0.8996 -0.5456 -0.1974 1.0026
## veg_height-Sciurus_niger 0.1395 0.4097 -0.5929 0.1164 1.0383 1.0628
## veg_height-Procyon_lotor 0.3659 0.1218 0.1240 0.3667 0.6011 1.0044
## veg_height-Dasypus_novemcinctus 0.2820 0.1406 0.0076 0.2820 0.5664 1.0013
## veg_height-Lynx_rufus 0.1691 0.2303 -0.2913 0.1719 0.6335 1.0066
## veg_height-Didelphis_virginiana 0.4822 0.2453 0.0177 0.4713 0.9811 1.0038
## veg_height-Sylvilagus_floridanus 0.1520 0.2488 -0.3119 0.1468 0.6515 1.0117
## veg_height-Meleagris_gallopavo -0.1123 0.3851 -0.8690 -0.1142 0.6764 1.0251
## veg_height-Sciurus_carolinensis 0.1605 0.2258 -0.2649 0.1558 0.6180 1.0129
## veg_height-Vulpes_vulpes -0.1437 0.3114 -0.7879 -0.1252 0.4172 1.0029
## veg_height-Sus_scrofa -0.1357 0.3233 -0.7886 -0.1237 0.4550 1.0212
## week-Canis_latrans 0.4458 0.2533 -0.0203 0.4374 0.9765 1.0079
## week-Sciurus_niger -0.3096 0.4868 -1.4378 -0.2451 0.4482 1.0126
## week-Procyon_lotor 0.1408 0.1966 -0.2414 0.1422 0.5244 1.0133
## week-Dasypus_novemcinctus 0.0534 0.2078 -0.3660 0.0557 0.4533 1.0008
## week-Lynx_rufus 0.2436 0.3187 -0.3555 0.2289 0.9014 1.0166
## week-Didelphis_virginiana -0.0082 0.3274 -0.6740 0.0044 0.6034 1.0026
## week-Sylvilagus_floridanus 0.0148 0.3110 -0.6549 0.0239 0.5977 1.0063
## week-Meleagris_gallopavo -0.1781 0.3839 -1.0663 -0.1344 0.4753 1.0421
## week-Sciurus_carolinensis 0.5504 0.3486 -0.0633 0.5265 1.3080 1.0013
## week-Vulpes_vulpes 0.0938 0.4129 -0.7947 0.1091 0.8936 1.0156
## week-Sus_scrofa 0.4089 0.3973 -0.2698 0.3723 1.2704 1.0056
## I(week^2)-Canis_latrans -0.1939 0.1048 -0.4032 -0.1907 0.0085 1.0064
## I(week^2)-Sciurus_niger -0.2935 0.2647 -0.8909 -0.2618 0.0982 1.0458
## I(week^2)-Procyon_lotor -0.1042 0.0861 -0.2780 -0.1031 0.0596 1.0102
## I(week^2)-Dasypus_novemcinctus -0.1538 0.1003 -0.3560 -0.1518 0.0372 0.9998
## I(week^2)-Lynx_rufus -0.2013 0.1514 -0.5262 -0.1911 0.0735 1.0377
## I(week^2)-Didelphis_virginiana -0.3810 0.2167 -0.8850 -0.3586 -0.0225 1.0194
## I(week^2)-Sylvilagus_floridanus -0.1446 0.1506 -0.4366 -0.1365 0.1399 1.0011
## I(week^2)-Meleagris_gallopavo -0.3807 0.2525 -0.9536 -0.3425 0.0220 1.0279
## I(week^2)-Sciurus_carolinensis -0.1994 0.1421 -0.4849 -0.1942 0.0646 1.0013
## I(week^2)-Vulpes_vulpes -0.3810 0.3117 -1.1800 -0.3306 0.0378 1.1159
## I(week^2)-Sus_scrofa -0.1645 0.1731 -0.5229 -0.1567 0.1499 1.0040
## ESS
## (Intercept)-Canis_latrans 1006
## (Intercept)-Sciurus_niger 147
## (Intercept)-Procyon_lotor 1210
## (Intercept)-Dasypus_novemcinctus 1089
## (Intercept)-Lynx_rufus 361
## (Intercept)-Didelphis_virginiana 459
## (Intercept)-Sylvilagus_floridanus 803
## (Intercept)-Meleagris_gallopavo 190
## (Intercept)-Sciurus_carolinensis 378
## (Intercept)-Vulpes_vulpes 217
## (Intercept)-Sus_scrofa 217
## shrub_cover-Canis_latrans 617
## shrub_cover-Sciurus_niger 301
## shrub_cover-Procyon_lotor 1465
## shrub_cover-Dasypus_novemcinctus 605
## shrub_cover-Lynx_rufus 349
## shrub_cover-Didelphis_virginiana 458
## shrub_cover-Sylvilagus_floridanus 517
## shrub_cover-Meleagris_gallopavo 319
## shrub_cover-Sciurus_carolinensis 284
## shrub_cover-Vulpes_vulpes 350
## shrub_cover-Sus_scrofa 211
## veg_height-Canis_latrans 902
## veg_height-Sciurus_niger 293
## veg_height-Procyon_lotor 1345
## veg_height-Dasypus_novemcinctus 1471
## veg_height-Lynx_rufus 711
## veg_height-Didelphis_virginiana 945
## veg_height-Sylvilagus_floridanus 646
## veg_height-Meleagris_gallopavo 364
## veg_height-Sciurus_carolinensis 891
## veg_height-Vulpes_vulpes 559
## veg_height-Sus_scrofa 783
## week-Canis_latrans 1158
## week-Sciurus_niger 258
## week-Procyon_lotor 1754
## week-Dasypus_novemcinctus 1810
## week-Lynx_rufus 688
## week-Didelphis_virginiana 801
## week-Sylvilagus_floridanus 1107
## week-Meleagris_gallopavo 321
## week-Sciurus_carolinensis 1046
## week-Vulpes_vulpes 676
## week-Sus_scrofa 898
## I(week^2)-Canis_latrans 1158
## I(week^2)-Sciurus_niger 96
## I(week^2)-Procyon_lotor 1595
## I(week^2)-Dasypus_novemcinctus 1609
## I(week^2)-Lynx_rufus 560
## I(week^2)-Didelphis_virginiana 367
## I(week^2)-Sylvilagus_floridanus 745
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1330
## I(week^2)-Vulpes_vulpes 97
## I(week^2)-Sus_scrofa 1133
names(ms_fullQ_fullQ_T)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T$beta.samples)
## 'mcmc' num [1:3000, 1:99] 0.3 0.741 1.842 0.896 0.461 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:99] "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1353333
MCMCplot(ms_fullQ_fullQ_T$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:3000, 1:11, 1:100] 0.301 0.409 0.861 0.167 0.369 ...
## $ z.0.samples : int [1:3000, 1:11, 1:100] 0 1 1 0 0 0 1 0 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:11, 1:100] 0.301 0.409 0.861 0.167 0.369 ...
psi.0.quants <- apply(out.pred$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] 0.00153 0.13188 0.89743 0.00167 0.1331 ...
## - 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 0.132 0.133 0.129 0.13 0.129 ...
## $ psi.low : num 0.00153 0.00167 0.00158 0.0017 0.00172 ...
## $ psi.high : num 0.897 0.892 0.893 0.883 0.883 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 1.2042
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9718 0.7037 -3.3715 -1.9522 -0.5642 1.0256 229
## Cogon_Patch_Size -0.1939 0.6612 -1.5094 -0.1899 1.1101 1.0142 392
## Veg_shannon_index 0.9937 0.4646 0.1319 0.9629 1.9998 1.0979 238
## total_shrub_cover -1.1267 0.6034 -2.4900 -1.0763 -0.0937 1.0191 244
## Avg_Cogongrass_Cover 0.1538 0.9460 -1.7138 0.2032 1.9526 1.0677 134
## Tree_Density -2.1814 0.8310 -3.9603 -2.1269 -0.6400 1.0430 195
## Avg_Canopy_Cover 1.9537 0.6778 0.8032 1.8779 3.5246 1.0829 179
## I(Avg_Cogongrass_Cover^2) 1.1689 0.6274 -0.0434 1.1473 2.4844 1.0405 298
## avg_veg_height -0.2514 0.5213 -1.2780 -0.2524 0.7650 1.0132 206
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4006 3.0912 0.0985 1.4038 10.7740 1.2588 226
## Cogon_Patch_Size 2.3820 3.1511 0.0854 1.3880 10.4786 1.0706 207
## Veg_shannon_index 0.6921 1.0051 0.0441 0.3645 3.2541 1.0107 345
## total_shrub_cover 1.7693 2.4145 0.0863 1.0093 8.6103 1.0778 83
## Avg_Cogongrass_Cover 1.3413 2.2681 0.0546 0.5712 7.5183 1.1902 213
## Tree_Density 2.5004 4.7402 0.0640 1.0568 13.3160 1.1082 242
## Avg_Canopy_Cover 2.3352 3.0348 0.1044 1.4334 9.7069 1.1204 187
## I(Avg_Cogongrass_Cover^2) 2.3982 3.6761 0.0736 1.1770 12.5361 1.1088 100
## avg_veg_height 0.6098 0.9112 0.0467 0.3070 3.1156 1.0152 333
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.643 2.2479 0.0517 0.8592 8.4321 1.1847 52
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.9666 0.3161 -3.6045 -2.9608 -2.3566 1.0085 1049
## shrub_cover 0.3996 0.2851 -0.1757 0.4028 0.9528 1.0078 624
## veg_height 0.0750 0.1686 -0.2558 0.0726 0.3988 1.0153 856
## week 0.1357 0.2015 -0.2856 0.1436 0.5292 1.0172 736
## I(week^2) -0.2373 0.1158 -0.4865 -0.2298 -0.0369 1.0107 348
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9737 0.6924 0.2682 0.7992 2.6740 1.0316 501
## shrub_cover 0.6893 0.5073 0.1514 0.5536 2.0416 1.0088 627
## veg_height 0.2105 0.1676 0.0554 0.1679 0.6208 1.0031 1130
## week 0.2369 0.2590 0.0359 0.1582 0.8873 1.0194 323
## I(week^2) 0.0756 0.0882 0.0181 0.0535 0.2552 1.1991 109
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3891 1.0215 -3.3570 -1.3739
## (Intercept)-Sciurus_niger -1.3381 1.3669 -3.6434 -1.4786
## (Intercept)-Procyon_lotor -1.0181 1.0442 -3.1528 -0.9996
## (Intercept)-Dasypus_novemcinctus -2.2061 0.9368 -4.2518 -2.1407
## (Intercept)-Lynx_rufus -1.3552 1.4148 -3.7280 -1.4875
## (Intercept)-Didelphis_virginiana -2.9019 1.1325 -5.4272 -2.8096
## (Intercept)-Sylvilagus_floridanus -1.9472 1.0267 -3.9571 -1.9487
## (Intercept)-Meleagris_gallopavo -1.6765 1.2340 -4.0127 -1.7259
## (Intercept)-Sciurus_carolinensis -3.2083 1.2259 -5.9663 -3.1042
## (Intercept)-Vulpes_vulpes -3.0847 1.5105 -6.5298 -2.8947
## (Intercept)-Sus_scrofa -3.1915 1.4099 -6.5941 -2.9946
## Cogon_Patch_Size-Canis_latrans 1.2157 1.2203 -0.5629 1.0212
## Cogon_Patch_Size-Sciurus_niger -0.8937 1.4908 -4.4783 -0.7214
## Cogon_Patch_Size-Procyon_lotor -0.6341 0.7941 -2.3070 -0.5898
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0635 0.8011 -1.5755 -0.0973
## Cogon_Patch_Size-Lynx_rufus -0.3823 1.3567 -2.9554 -0.4132
## Cogon_Patch_Size-Didelphis_virginiana 1.1593 1.0335 -0.5599 1.0385
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2582 1.4250 -4.8262 -1.0146
## Cogon_Patch_Size-Meleagris_gallopavo 0.2290 1.2732 -1.7786 0.0639
## Cogon_Patch_Size-Sciurus_carolinensis -0.9388 1.2304 -3.9010 -0.7464
## Cogon_Patch_Size-Vulpes_vulpes -0.4743 1.4021 -3.3489 -0.4176
## Cogon_Patch_Size-Sus_scrofa -0.5496 1.2720 -3.5041 -0.4265
## Veg_shannon_index-Canis_latrans 1.3399 0.7258 0.1870 1.2401
## Veg_shannon_index-Sciurus_niger 1.1049 0.9002 -0.4296 1.0227
## Veg_shannon_index-Procyon_lotor 1.2106 0.6307 0.1410 1.1583
## Veg_shannon_index-Dasypus_novemcinctus 0.6481 0.5772 -0.5493 0.6640
## Veg_shannon_index-Lynx_rufus 1.0289 0.8715 -0.6026 0.9756
## Veg_shannon_index-Didelphis_virginiana 1.1330 0.7065 -0.0790 1.0604
## Veg_shannon_index-Sylvilagus_floridanus 1.0282 0.7102 -0.2380 0.9894
## Veg_shannon_index-Meleagris_gallopavo 1.2207 0.7570 -0.0858 1.1402
## Veg_shannon_index-Sciurus_carolinensis 0.4167 0.7517 -1.2375 0.4943
## Veg_shannon_index-Vulpes_vulpes 0.6711 0.8120 -1.1465 0.7222
## Veg_shannon_index-Sus_scrofa 1.3897 0.8682 0.0803 1.2589
## total_shrub_cover-Canis_latrans 0.1245 0.8820 -1.2717 0.0199
## total_shrub_cover-Sciurus_niger -1.4970 1.1094 -4.0318 -1.3596
## total_shrub_cover-Procyon_lotor -1.4569 0.7057 -3.0507 -1.3967
## total_shrub_cover-Dasypus_novemcinctus -0.4642 0.7744 -2.0877 -0.4232
## total_shrub_cover-Lynx_rufus -1.6306 1.1924 -4.4147 -1.4760
## total_shrub_cover-Didelphis_virginiana -1.3222 1.0227 -4.0646 -1.1818
## total_shrub_cover-Sylvilagus_floridanus -1.1437 1.0668 -3.8349 -1.0216
## total_shrub_cover-Meleagris_gallopavo -2.3617 1.3509 -5.5802 -2.1297
## total_shrub_cover-Sciurus_carolinensis -1.0024 1.0988 -3.7078 -0.8868
## total_shrub_cover-Vulpes_vulpes -1.4124 1.3440 -4.6216 -1.2342
## total_shrub_cover-Sus_scrofa -1.0243 1.3148 -4.0287 -0.8792
## Avg_Cogongrass_Cover-Canis_latrans 0.3558 1.2193 -2.1008 0.3962
## Avg_Cogongrass_Cover-Sciurus_niger -0.4659 1.5667 -4.3455 -0.2618
## Avg_Cogongrass_Cover-Procyon_lotor 0.1840 1.1981 -2.2579 0.2004
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7830 1.2608 -1.5262 0.7480
## Avg_Cogongrass_Cover-Lynx_rufus 0.2703 1.3119 -2.4630 0.3125
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3707 1.2271 -2.0079 0.3827
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4289 1.3737 -3.4776 -0.3008
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1273 1.3457 -3.0299 -0.0367
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2404 1.2119 -2.1364 0.2964
## Avg_Cogongrass_Cover-Vulpes_vulpes 0.4857 1.3468 -1.9789 0.4803
## Avg_Cogongrass_Cover-Sus_scrofa 0.0191 1.3412 -2.8328 0.0812
## Tree_Density-Canis_latrans -2.9996 1.4156 -6.3875 -2.7841
## Tree_Density-Sciurus_niger -2.1975 1.5547 -5.5205 -2.0969
## Tree_Density-Procyon_lotor -2.2620 1.0354 -4.5360 -2.1824
## Tree_Density-Dasypus_novemcinctus -3.6706 1.7456 -8.1230 -3.2906
## Tree_Density-Lynx_rufus -1.0959 1.5041 -3.6610 -1.2843
## Tree_Density-Didelphis_virginiana -2.2356 1.1838 -4.8190 -2.1521
## Tree_Density-Sylvilagus_floridanus -2.6354 1.4156 -5.9100 -2.4610
## Tree_Density-Meleagris_gallopavo -2.3364 1.3966 -5.4982 -2.2335
## Tree_Density-Sciurus_carolinensis -2.4146 1.2909 -5.2437 -2.3377
## Tree_Density-Vulpes_vulpes -1.9640 1.4578 -4.8618 -2.0037
## Tree_Density-Sus_scrofa -2.2264 1.4425 -5.3683 -2.1118
## Avg_Canopy_Cover-Canis_latrans 0.2975 0.6952 -1.0695 0.2975
## Avg_Canopy_Cover-Sciurus_niger 1.9801 1.6567 -0.6093 1.7812
## Avg_Canopy_Cover-Procyon_lotor 1.6368 0.7647 0.3002 1.5983
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1706 0.8592 0.7691 2.0783
## Avg_Canopy_Cover-Lynx_rufus 1.3622 1.2360 -0.9365 1.2949
## Avg_Canopy_Cover-Didelphis_virginiana 2.8700 1.2256 1.1144 2.6383
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.3867 1.5799 1.1137 3.0812
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3385 1.2135 0.6227 2.1151
## Avg_Canopy_Cover-Sciurus_carolinensis 2.6816 1.2037 0.9389 2.4797
## Avg_Canopy_Cover-Vulpes_vulpes 2.3702 1.3593 0.4472 2.1188
## Avg_Canopy_Cover-Sus_scrofa 2.1170 1.1321 0.4298 1.9235
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2351 1.2307 0.5476 1.9722
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 0.3306 1.4795 -3.1176 0.4697
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1392 1.0959 0.5182 1.9929
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2739 0.7428 -0.0064 1.2176
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5357 1.4117 0.5095 2.2634
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8020 0.9166 -0.6352 0.7298
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0770 1.0535 -0.5495 0.9728
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0226 1.4965 -3.2993 0.1322
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.3870 0.8101 0.0280 1.3178
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 1.6201 0.8901 0.1655 1.4995
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 0.2631 1.5629 -3.5157 0.4546
## avg_veg_height-Canis_latrans -0.3559 0.6476 -1.6639 -0.3460
## avg_veg_height-Sciurus_niger -0.6205 0.9333 -2.8073 -0.5250
## avg_veg_height-Procyon_lotor -0.0797 0.6538 -1.3773 -0.0894
## avg_veg_height-Dasypus_novemcinctus 0.1409 0.6655 -1.0489 0.1057
## avg_veg_height-Lynx_rufus -0.5922 0.9538 -2.9653 -0.4757
## avg_veg_height-Didelphis_virginiana -0.3807 0.7462 -1.9342 -0.3505
## avg_veg_height-Sylvilagus_floridanus -0.3279 0.7420 -1.8857 -0.3100
## avg_veg_height-Meleagris_gallopavo -0.3055 0.9122 -2.3755 -0.2687
## avg_veg_height-Sciurus_carolinensis 0.1558 0.7452 -1.1651 0.1076
## avg_veg_height-Vulpes_vulpes -0.2614 0.8135 -1.9173 -0.2528
## avg_veg_height-Sus_scrofa -0.2209 0.7933 -1.8577 -0.2295
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6138 1.0208 206
## (Intercept)-Sciurus_niger 1.7573 1.2030 195
## (Intercept)-Procyon_lotor 1.0968 1.0336 171
## (Intercept)-Dasypus_novemcinctus -0.4677 1.0472 424
## (Intercept)-Lynx_rufus 1.8363 1.0802 205
## (Intercept)-Didelphis_virginiana -1.0304 1.0510 342
## (Intercept)-Sylvilagus_floridanus 0.1351 1.0143 409
## (Intercept)-Meleagris_gallopavo 0.9879 1.0330 293
## (Intercept)-Sciurus_carolinensis -1.2014 1.0432 267
## (Intercept)-Vulpes_vulpes -0.5202 1.0884 242
## (Intercept)-Sus_scrofa -0.9082 1.1379 228
## Cogon_Patch_Size-Canis_latrans 4.2612 1.0040 312
## Cogon_Patch_Size-Sciurus_niger 1.7291 1.0752 341
## Cogon_Patch_Size-Procyon_lotor 0.8079 1.0551 316
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6510 1.0296 496
## Cogon_Patch_Size-Lynx_rufus 2.3539 1.0392 276
## Cogon_Patch_Size-Didelphis_virginiana 3.5863 1.0033 256
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9354 1.0316 326
## Cogon_Patch_Size-Meleagris_gallopavo 3.2016 1.0157 269
## Cogon_Patch_Size-Sciurus_carolinensis 0.9631 1.0320 258
## Cogon_Patch_Size-Vulpes_vulpes 2.2112 1.0216 304
## Cogon_Patch_Size-Sus_scrofa 1.6585 1.0089 372
## Veg_shannon_index-Canis_latrans 3.0027 1.0312 432
## Veg_shannon_index-Sciurus_niger 3.2443 1.0713 340
## Veg_shannon_index-Procyon_lotor 2.6521 1.0855 284
## Veg_shannon_index-Dasypus_novemcinctus 1.7486 1.0329 536
## Veg_shannon_index-Lynx_rufus 2.8323 1.0227 324
## Veg_shannon_index-Didelphis_virginiana 2.7827 1.0315 559
## Veg_shannon_index-Sylvilagus_floridanus 2.5635 1.0244 651
## Veg_shannon_index-Meleagris_gallopavo 2.9404 1.0248 519
## Veg_shannon_index-Sciurus_carolinensis 1.6940 1.0499 713
## Veg_shannon_index-Vulpes_vulpes 2.1591 1.0511 400
## Veg_shannon_index-Sus_scrofa 3.3771 1.0352 390
## total_shrub_cover-Canis_latrans 2.1627 1.0271 269
## total_shrub_cover-Sciurus_niger 0.3813 1.0247 302
## total_shrub_cover-Procyon_lotor -0.2769 1.0100 574
## total_shrub_cover-Dasypus_novemcinctus 0.9891 1.0133 488
## total_shrub_cover-Lynx_rufus 0.3197 1.0109 202
## total_shrub_cover-Didelphis_virginiana 0.2666 1.0118 233
## total_shrub_cover-Sylvilagus_floridanus 0.6677 1.0116 398
## total_shrub_cover-Meleagris_gallopavo -0.3628 1.0391 146
## total_shrub_cover-Sciurus_carolinensis 0.8465 1.0209 275
## total_shrub_cover-Vulpes_vulpes 0.7558 1.0777 216
## total_shrub_cover-Sus_scrofa 1.0315 1.0876 142
## Avg_Cogongrass_Cover-Canis_latrans 2.8639 1.0319 203
## Avg_Cogongrass_Cover-Sciurus_niger 2.0274 1.1105 167
## Avg_Cogongrass_Cover-Procyon_lotor 2.5623 1.0299 195
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.4503 1.0424 228
## Avg_Cogongrass_Cover-Lynx_rufus 2.7532 1.0439 198
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.8272 1.0282 216
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.8623 1.0937 150
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2348 1.0590 220
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.5334 1.0589 217
## Avg_Cogongrass_Cover-Vulpes_vulpes 3.3708 1.0199 270
## Avg_Cogongrass_Cover-Sus_scrofa 2.4807 1.0594 201
## Tree_Density-Canis_latrans -0.8526 1.0166 208
## Tree_Density-Sciurus_niger 0.6045 1.0470 172
## Tree_Density-Procyon_lotor -0.4280 1.0181 210
## Tree_Density-Dasypus_novemcinctus -1.3825 1.0971 186
## Tree_Density-Lynx_rufus 2.4695 1.0197 206
## Tree_Density-Didelphis_virginiana -0.0105 1.0219 391
## Tree_Density-Sylvilagus_floridanus -0.2460 1.0253 198
## Tree_Density-Meleagris_gallopavo 0.2205 1.0496 248
## Tree_Density-Sciurus_carolinensis 0.0651 1.0934 213
## Tree_Density-Vulpes_vulpes 1.1047 1.0136 325
## Tree_Density-Sus_scrofa 0.4424 1.0514 300
## Avg_Canopy_Cover-Canis_latrans 1.7023 1.0243 406
## Avg_Canopy_Cover-Sciurus_niger 6.1478 1.2150 162
## Avg_Canopy_Cover-Procyon_lotor 3.3143 1.0680 323
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.2447 1.0379 198
## Avg_Canopy_Cover-Lynx_rufus 3.9511 1.0870 243
## Avg_Canopy_Cover-Didelphis_virginiana 5.8447 1.0544 205
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1601 1.0605 134
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3415 1.0559 248
## Avg_Canopy_Cover-Sciurus_carolinensis 5.6704 1.0283 221
## Avg_Canopy_Cover-Vulpes_vulpes 6.0067 1.1069 134
## Avg_Canopy_Cover-Sus_scrofa 4.8838 1.0984 357
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4277 1.0443 101
## I(Avg_Cogongrass_Cover^2)-Sciurus_niger 2.9076 1.1293 134
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6086 1.0500 192
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9910 1.0038 365
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1515 1.0573 156
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6967 1.0718 116
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.5159 1.0324 142
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5907 1.0419 121
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3035 1.0202 328
## I(Avg_Cogongrass_Cover^2)-Vulpes_vulpes 3.7094 1.0269 399
## I(Avg_Cogongrass_Cover^2)-Sus_scrofa 2.9927 1.2260 88
## avg_veg_height-Canis_latrans 0.8858 1.0170 337
## avg_veg_height-Sciurus_niger 0.9061 1.0352 281
## avg_veg_height-Procyon_lotor 1.2325 1.0084 381
## avg_veg_height-Dasypus_novemcinctus 1.5695 1.0004 341
## avg_veg_height-Lynx_rufus 0.9458 1.0161 256
## avg_veg_height-Didelphis_virginiana 1.0188 1.0023 400
## avg_veg_height-Sylvilagus_floridanus 1.1446 1.0114 413
## avg_veg_height-Meleagris_gallopavo 1.3752 1.0056 246
## avg_veg_height-Sciurus_carolinensis 1.7900 1.0139 345
## avg_veg_height-Vulpes_vulpes 1.3401 1.0163 280
## avg_veg_height-Sus_scrofa 1.3704 1.0015 378
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5677 0.1927 -2.9615 -2.5619 -2.2057 1.0032
## (Intercept)-Sciurus_niger -4.1456 0.5693 -5.2302 -4.1706 -3.0028 1.2090
## (Intercept)-Procyon_lotor -2.2182 0.1594 -2.5441 -2.2128 -1.9256 1.0048
## (Intercept)-Dasypus_novemcinctus -1.6918 0.1847 -2.0784 -1.6860 -1.3275 1.0019
## (Intercept)-Lynx_rufus -3.5762 0.3400 -4.2624 -3.5679 -2.9380 1.0256
## (Intercept)-Didelphis_virginiana -2.5022 0.2923 -3.0864 -2.5111 -1.9037 1.0072
## (Intercept)-Sylvilagus_floridanus -3.1496 0.2817 -3.7110 -3.1453 -2.6168 1.0047
## (Intercept)-Meleagris_gallopavo -3.5653 0.4990 -4.5493 -3.5580 -2.5987 1.0954
## (Intercept)-Sciurus_carolinensis -2.6777 0.3347 -3.3366 -2.6731 -2.0548 1.0175
## (Intercept)-Vulpes_vulpes -4.0416 0.6081 -5.2943 -4.0110 -2.9373 1.0145
## (Intercept)-Sus_scrofa -3.4862 0.5654 -4.5659 -3.5051 -2.3096 1.0089
## shrub_cover-Canis_latrans -0.2925 0.2308 -0.7310 -0.2891 0.1555 1.0056
## shrub_cover-Sciurus_niger -0.1411 0.5011 -1.1507 -0.1485 0.8859 1.0159
## shrub_cover-Procyon_lotor 0.2926 0.1628 -0.0148 0.2905 0.6183 1.0070
## shrub_cover-Dasypus_novemcinctus 1.0065 0.3182 0.3945 1.0071 1.6201 1.0134
## shrub_cover-Lynx_rufus -0.0286 0.3753 -0.7858 -0.0217 0.6782 1.0067
## shrub_cover-Didelphis_virginiana 1.1199 0.3717 0.4037 1.1136 1.8406 1.0110
## shrub_cover-Sylvilagus_floridanus 0.5832 0.4014 -0.2385 0.5820 1.3496 1.0275
## shrub_cover-Meleagris_gallopavo -0.4638 0.4432 -1.3505 -0.4597 0.3638 1.0832
## shrub_cover-Sciurus_carolinensis 1.0797 0.4119 0.2929 1.0768 1.8593 1.0303
## shrub_cover-Vulpes_vulpes 0.2955 0.5869 -0.9409 0.3257 1.3598 1.0512
## shrub_cover-Sus_scrofa 1.1026 0.7649 -0.4513 1.1231 2.5272 1.0008
## veg_height-Canis_latrans -0.5453 0.1813 -0.8996 -0.5456 -0.1974 1.0026
## veg_height-Sciurus_niger 0.1395 0.4097 -0.5929 0.1164 1.0383 1.0628
## veg_height-Procyon_lotor 0.3659 0.1218 0.1240 0.3667 0.6011 1.0044
## veg_height-Dasypus_novemcinctus 0.2820 0.1406 0.0076 0.2820 0.5664 1.0013
## veg_height-Lynx_rufus 0.1691 0.2303 -0.2913 0.1719 0.6335 1.0066
## veg_height-Didelphis_virginiana 0.4822 0.2453 0.0177 0.4713 0.9811 1.0038
## veg_height-Sylvilagus_floridanus 0.1520 0.2488 -0.3119 0.1468 0.6515 1.0117
## veg_height-Meleagris_gallopavo -0.1123 0.3851 -0.8690 -0.1142 0.6764 1.0251
## veg_height-Sciurus_carolinensis 0.1605 0.2258 -0.2649 0.1558 0.6180 1.0129
## veg_height-Vulpes_vulpes -0.1437 0.3114 -0.7879 -0.1252 0.4172 1.0029
## veg_height-Sus_scrofa -0.1357 0.3233 -0.7886 -0.1237 0.4550 1.0212
## week-Canis_latrans 0.4458 0.2533 -0.0203 0.4374 0.9765 1.0079
## week-Sciurus_niger -0.3096 0.4868 -1.4378 -0.2451 0.4482 1.0126
## week-Procyon_lotor 0.1408 0.1966 -0.2414 0.1422 0.5244 1.0133
## week-Dasypus_novemcinctus 0.0534 0.2078 -0.3660 0.0557 0.4533 1.0008
## week-Lynx_rufus 0.2436 0.3187 -0.3555 0.2289 0.9014 1.0166
## week-Didelphis_virginiana -0.0082 0.3274 -0.6740 0.0044 0.6034 1.0026
## week-Sylvilagus_floridanus 0.0148 0.3110 -0.6549 0.0239 0.5977 1.0063
## week-Meleagris_gallopavo -0.1781 0.3839 -1.0663 -0.1344 0.4753 1.0421
## week-Sciurus_carolinensis 0.5504 0.3486 -0.0633 0.5265 1.3080 1.0013
## week-Vulpes_vulpes 0.0938 0.4129 -0.7947 0.1091 0.8936 1.0156
## week-Sus_scrofa 0.4089 0.3973 -0.2698 0.3723 1.2704 1.0056
## I(week^2)-Canis_latrans -0.1939 0.1048 -0.4032 -0.1907 0.0085 1.0064
## I(week^2)-Sciurus_niger -0.2935 0.2647 -0.8909 -0.2618 0.0982 1.0458
## I(week^2)-Procyon_lotor -0.1042 0.0861 -0.2780 -0.1031 0.0596 1.0102
## I(week^2)-Dasypus_novemcinctus -0.1538 0.1003 -0.3560 -0.1518 0.0372 0.9998
## I(week^2)-Lynx_rufus -0.2013 0.1514 -0.5262 -0.1911 0.0735 1.0377
## I(week^2)-Didelphis_virginiana -0.3810 0.2167 -0.8850 -0.3586 -0.0225 1.0194
## I(week^2)-Sylvilagus_floridanus -0.1446 0.1506 -0.4366 -0.1365 0.1399 1.0011
## I(week^2)-Meleagris_gallopavo -0.3807 0.2525 -0.9536 -0.3425 0.0220 1.0279
## I(week^2)-Sciurus_carolinensis -0.1994 0.1421 -0.4849 -0.1942 0.0646 1.0013
## I(week^2)-Vulpes_vulpes -0.3810 0.3117 -1.1800 -0.3306 0.0378 1.1159
## I(week^2)-Sus_scrofa -0.1645 0.1731 -0.5229 -0.1567 0.1499 1.0040
## ESS
## (Intercept)-Canis_latrans 1006
## (Intercept)-Sciurus_niger 147
## (Intercept)-Procyon_lotor 1210
## (Intercept)-Dasypus_novemcinctus 1089
## (Intercept)-Lynx_rufus 361
## (Intercept)-Didelphis_virginiana 459
## (Intercept)-Sylvilagus_floridanus 803
## (Intercept)-Meleagris_gallopavo 190
## (Intercept)-Sciurus_carolinensis 378
## (Intercept)-Vulpes_vulpes 217
## (Intercept)-Sus_scrofa 217
## shrub_cover-Canis_latrans 617
## shrub_cover-Sciurus_niger 301
## shrub_cover-Procyon_lotor 1465
## shrub_cover-Dasypus_novemcinctus 605
## shrub_cover-Lynx_rufus 349
## shrub_cover-Didelphis_virginiana 458
## shrub_cover-Sylvilagus_floridanus 517
## shrub_cover-Meleagris_gallopavo 319
## shrub_cover-Sciurus_carolinensis 284
## shrub_cover-Vulpes_vulpes 350
## shrub_cover-Sus_scrofa 211
## veg_height-Canis_latrans 902
## veg_height-Sciurus_niger 293
## veg_height-Procyon_lotor 1345
## veg_height-Dasypus_novemcinctus 1471
## veg_height-Lynx_rufus 711
## veg_height-Didelphis_virginiana 945
## veg_height-Sylvilagus_floridanus 646
## veg_height-Meleagris_gallopavo 364
## veg_height-Sciurus_carolinensis 891
## veg_height-Vulpes_vulpes 559
## veg_height-Sus_scrofa 783
## week-Canis_latrans 1158
## week-Sciurus_niger 258
## week-Procyon_lotor 1754
## week-Dasypus_novemcinctus 1810
## week-Lynx_rufus 688
## week-Didelphis_virginiana 801
## week-Sylvilagus_floridanus 1107
## week-Meleagris_gallopavo 321
## week-Sciurus_carolinensis 1046
## week-Vulpes_vulpes 676
## week-Sus_scrofa 898
## I(week^2)-Canis_latrans 1158
## I(week^2)-Sciurus_niger 96
## I(week^2)-Procyon_lotor 1595
## I(week^2)-Dasypus_novemcinctus 1609
## I(week^2)-Lynx_rufus 560
## I(week^2)-Didelphis_virginiana 367
## I(week^2)-Sylvilagus_floridanus 745
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1330
## I(week^2)-Vulpes_vulpes 97
## I(week^2)-Sus_scrofa 1133
names(ms_fullQ_fullQ_T)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T$beta.samples)
## 'mcmc' num [1:3000, 1:99] 0.3 0.741 1.842 0.896 0.461 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:99] "(Intercept)-Canis_latrans" "(Intercept)-Sciurus_niger" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" ...
mean(ms_fullQ_fullQ_T$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.1353333
MCMCplot(ms_fullQ_fullQ_T$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 206 × 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 196 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] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter((Class == "Mammalia" | Name == "Meleagris_gallopavo") & Name != "Odocoileus_virginianus") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 50
species_subset <- species_counts %>%
filter(count > 10) %>%
pull(Name)
# Filter CameraData to only include species with count > 50
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 8
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$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, 1:8] 0 0 0 0 0 0 0 0 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 [1:8] "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" "Lynx_rufus" ...
## $ 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 [1:8, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:8] "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" "Lynx_rufus" ...
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5738
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3814 0.3303 -1.0201 -0.3833 0.2939 1.0033 2143
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7042 0.6787 0.118 0.5211 2.4814 1.0153 1415
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5656 0.2813 -3.1101 -2.5718 -1.9676 1.0016 2254
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5921 0.5275 0.1475 0.4605 1.8965 1.0378 1639
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1647 0.3759 -0.5416 0.1499 0.9519 1.0048
## (Intercept)-Procyon_lotor 0.4978 0.3859 -0.2015 0.4829 1.2725 0.9998
## (Intercept)-Dasypus_novemcinctus -0.5925 0.3328 -1.2723 -0.5778 0.0205 1.0052
## (Intercept)-Lynx_rufus -0.1160 0.4841 -0.9650 -0.1501 0.9171 1.0089
## (Intercept)-Didelphis_virginiana -1.1589 0.4115 -1.9930 -1.1416 -0.3874 1.0014
## (Intercept)-Sylvilagus_floridanus -0.4151 0.4607 -1.1973 -0.4327 0.4368 1.0194
## (Intercept)-Meleagris_gallopavo -0.4106 0.4809 -1.2870 -0.4283 0.6255 1.0248
## (Intercept)-Sciurus_carolinensis -1.1098 0.4033 -1.9332 -1.0999 -0.3540 1.0065
## ESS
## (Intercept)-Canis_latrans 2022
## (Intercept)-Procyon_lotor 2282
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 768
## (Intercept)-Didelphis_virginiana 1960
## (Intercept)-Sylvilagus_floridanus 889
## (Intercept)-Meleagris_gallopavo 873
## (Intercept)-Sciurus_carolinensis 1794
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5999 0.1711 -2.9544 -2.5927 -2.2857 1.0055
## (Intercept)-Procyon_lotor -2.2665 0.1268 -2.5220 -2.2670 -2.0198 1.0007
## (Intercept)-Dasypus_novemcinctus -1.6031 0.1376 -1.8806 -1.5989 -1.3434 1.0060
## (Intercept)-Lynx_rufus -3.3656 0.3051 -3.9799 -3.3539 -2.8167 1.0675
## (Intercept)-Didelphis_virginiana -2.3387 0.2452 -2.8585 -2.3245 -1.9055 1.0196
## (Intercept)-Sylvilagus_floridanus -3.0890 0.2785 -3.6688 -3.0803 -2.5904 1.0020
## (Intercept)-Meleagris_gallopavo -3.2837 0.3226 -3.9389 -3.2659 -2.7039 1.0248
## (Intercept)-Sciurus_carolinensis -2.4669 0.2575 -3.0059 -2.4575 -2.0016 1.0040
## ESS
## (Intercept)-Canis_latrans 1058
## (Intercept)-Procyon_lotor 1677
## (Intercept)-Dasypus_novemcinctus 2245
## (Intercept)-Lynx_rufus 413
## (Intercept)-Didelphis_virginiana 1384
## (Intercept)-Sylvilagus_floridanus 572
## (Intercept)-Meleagris_gallopavo 439
## (Intercept)-Sciurus_carolinensis 1177
# Includes all covariates of detection and occupancy
ms_full_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.874
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7934 0.6890 -2.2557 -0.7771 0.5212 1.0070 199
## Cogon_Patch_Size -0.4051 0.7143 -1.8622 -0.3846 0.9703 1.0032 428
## Veg_shannon_index 1.0437 0.5125 0.0783 1.0347 2.1172 1.0066 239
## total_shrub_cover -0.9264 0.6653 -2.3239 -0.9018 0.3454 1.0165 277
## Avg_Cogongrass_Cover 1.9112 0.7675 0.3768 1.9167 3.3306 1.0012 210
## Tree_Density -1.8544 0.8177 -3.4747 -1.8601 -0.2119 1.0136 423
## Avg_Canopy_Cover 1.9177 0.7956 0.4364 1.8787 3.4844 1.0216 503
## avg_veg_height -0.3502 0.5142 -1.4030 -0.3431 0.6366 1.0132 298
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2824 3.0701 0.0928 1.3630 10.2458 1.0152 270
## Cogon_Patch_Size 2.9338 5.5329 0.0953 1.4400 14.8276 1.1498 355
## Veg_shannon_index 0.6799 1.1837 0.0473 0.3361 3.4721 1.0454 615
## total_shrub_cover 2.7202 4.5180 0.1008 1.4066 12.6921 1.0423 236
## Avg_Cogongrass_Cover 1.2713 2.3849 0.0514 0.5228 7.0200 1.0024 319
## Tree_Density 4.2621 8.0137 0.0996 1.9069 22.9228 1.0557 283
## Avg_Canopy_Cover 4.8764 6.5756 0.1845 2.7930 21.7599 1.0181 301
## avg_veg_height 0.5823 0.9702 0.0433 0.2774 2.9824 1.0703 420
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.3877 3.8166 0.1099 2.1468 14.1758 1.0104 97
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7662 0.3127 -3.3753 -2.7718 -2.1568 1.0047 1804
## shrub_cover 0.4034 0.3195 -0.2257 0.3948 1.0235 1.0017 939
## veg_height 0.0878 0.2045 -0.3190 0.0915 0.4953 1.0086 1574
## week -0.0994 0.1412 -0.3956 -0.0933 0.1778 1.0012 1282
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6766 0.6581 0.1568 0.5127 2.1844 1.0720 804
## shrub_cover 0.7104 0.6598 0.1360 0.5391 2.4224 1.0901 964
## veg_height 0.2728 0.2583 0.0589 0.2012 0.8796 1.0071 1597
## week 0.1110 0.1097 0.0256 0.0805 0.3714 1.0170 1606
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1403 1.1529 -2.0557 0.1063
## (Intercept)-Procyon_lotor 0.0477 1.0548 -2.1201 0.0764
## (Intercept)-Dasypus_novemcinctus -1.2464 0.9109 -3.1984 -1.1864
## (Intercept)-Lynx_rufus -0.2496 1.3385 -2.6806 -0.3102
## (Intercept)-Didelphis_virginiana -1.8607 1.0975 -4.1914 -1.7876
## (Intercept)-Sylvilagus_floridanus -0.9832 1.0526 -3.0503 -0.9603
## (Intercept)-Meleagris_gallopavo -0.9718 1.1635 -3.3159 -0.9504
## (Intercept)-Sciurus_carolinensis -1.8570 1.1876 -4.4881 -1.7736
## Cogon_Patch_Size-Canis_latrans 0.6695 1.2115 -1.0700 0.4698
## Cogon_Patch_Size-Procyon_lotor -0.9925 0.7813 -2.5938 -0.9636
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4548 0.8376 -2.0798 -0.4669
## Cogon_Patch_Size-Lynx_rufus -0.7003 1.3539 -3.3200 -0.7131
## Cogon_Patch_Size-Didelphis_virginiana 0.9290 1.0467 -0.7155 0.7864
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6554 1.5829 -5.5256 -1.3970
## Cogon_Patch_Size-Meleagris_gallopavo -0.0475 1.3744 -2.1985 -0.2102
## Cogon_Patch_Size-Sciurus_carolinensis -1.3426 1.2891 -4.3480 -1.1677
## Veg_shannon_index-Canis_latrans 1.3417 0.6890 0.1294 1.2977
## Veg_shannon_index-Procyon_lotor 1.3463 0.6538 0.1936 1.2834
## Veg_shannon_index-Dasypus_novemcinctus 0.7312 0.5965 -0.4429 0.7372
## Veg_shannon_index-Lynx_rufus 0.9379 0.8046 -0.7087 0.9367
## Veg_shannon_index-Didelphis_virginiana 1.2212 0.6975 0.0070 1.1577
## Veg_shannon_index-Sylvilagus_floridanus 1.1507 0.7015 -0.1656 1.1223
## Veg_shannon_index-Meleagris_gallopavo 1.3291 0.8117 -0.0621 1.2491
## Veg_shannon_index-Sciurus_carolinensis 0.4600 0.8182 -1.3216 0.5368
## total_shrub_cover-Canis_latrans 0.5750 1.0310 -0.9604 0.3892
## total_shrub_cover-Procyon_lotor -1.2168 0.7076 -2.8142 -1.1449
## total_shrub_cover-Dasypus_novemcinctus -0.3681 0.8801 -2.3878 -0.2900
## total_shrub_cover-Lynx_rufus -1.6727 1.4196 -4.9535 -1.4727
## total_shrub_cover-Didelphis_virginiana -1.2974 1.0577 -3.8420 -1.1453
## total_shrub_cover-Sylvilagus_floridanus -1.0410 1.3669 -4.6610 -0.8463
## total_shrub_cover-Meleagris_gallopavo -2.2871 1.4652 -5.7406 -2.0480
## total_shrub_cover-Sciurus_carolinensis -1.0595 1.1845 -3.8763 -0.8994
## Avg_Cogongrass_Cover-Canis_latrans 2.3023 0.9505 0.6191 2.2482
## Avg_Cogongrass_Cover-Procyon_lotor 2.0966 0.8917 0.4227 2.0432
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5296 1.0296 0.6863 2.4344
## Avg_Cogongrass_Cover-Lynx_rufus 2.3015 1.0370 0.4976 2.2280
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0044 0.9876 0.1956 1.9638
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2879 1.1630 -1.3120 1.3888
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4440 1.3226 -1.6508 1.6217
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2107 0.9751 0.3846 2.1831
## Tree_Density-Canis_latrans -2.8323 1.4220 -6.2089 -2.6013
## Tree_Density-Procyon_lotor -1.5135 0.8264 -3.1296 -1.5393
## Tree_Density-Dasypus_novemcinctus -3.8789 2.0948 -9.0564 -3.3876
## Tree_Density-Lynx_rufus -0.2386 1.3894 -2.4702 -0.3946
## Tree_Density-Didelphis_virginiana -2.1552 1.3638 -5.1204 -2.0488
## Tree_Density-Sylvilagus_floridanus -2.5413 1.5470 -6.1650 -2.3464
## Tree_Density-Meleagris_gallopavo -2.1945 1.3978 -5.2395 -2.0875
## Tree_Density-Sciurus_carolinensis -2.1652 1.4945 -5.3928 -2.0600
## Avg_Canopy_Cover-Canis_latrans 0.1277 0.6724 -1.2250 0.1392
## Avg_Canopy_Cover-Procyon_lotor 1.7723 0.8152 0.3583 1.7128
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3677 0.9064 0.8668 2.2633
## Avg_Canopy_Cover-Lynx_rufus 0.7693 1.3085 -1.6111 0.7017
## Avg_Canopy_Cover-Didelphis_virginiana 3.3664 1.4587 1.2584 3.1062
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.2291 2.0489 1.2822 3.9145
## Avg_Canopy_Cover-Meleagris_gallopavo 2.7089 1.4991 0.6047 2.4202
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1788 1.5310 1.0999 2.8731
## avg_veg_height-Canis_latrans -0.4677 0.6181 -1.7194 -0.4504
## avg_veg_height-Procyon_lotor -0.3428 0.5840 -1.5090 -0.3276
## avg_veg_height-Dasypus_novemcinctus -0.1378 0.6229 -1.2884 -0.1614
## avg_veg_height-Lynx_rufus -0.5152 0.7755 -2.0754 -0.5162
## avg_veg_height-Didelphis_virginiana -0.5781 0.7058 -2.1518 -0.5326
## avg_veg_height-Sylvilagus_floridanus -0.5688 0.7510 -2.1758 -0.5128
## avg_veg_height-Meleagris_gallopavo -0.3659 0.8356 -2.0352 -0.3551
## avg_veg_height-Sciurus_carolinensis 0.0436 0.7731 -1.2634 -0.0358
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5547 1.0434 193
## (Intercept)-Procyon_lotor 2.0107 1.0454 117
## (Intercept)-Dasypus_novemcinctus 0.3864 1.0147 325
## (Intercept)-Lynx_rufus 2.7016 1.0190 113
## (Intercept)-Didelphis_virginiana 0.0662 1.0383 363
## (Intercept)-Sylvilagus_floridanus 1.2050 1.0139 345
## (Intercept)-Meleagris_gallopavo 1.3745 1.0270 295
## (Intercept)-Sciurus_carolinensis 0.2106 1.0109 260
## Cogon_Patch_Size-Canis_latrans 3.5669 1.0548 493
## Cogon_Patch_Size-Procyon_lotor 0.4821 1.0256 265
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2743 1.0002 505
## Cogon_Patch_Size-Lynx_rufus 2.0153 1.0088 317
## Cogon_Patch_Size-Didelphis_virginiana 3.3306 1.0650 289
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7507 1.0510 281
## Cogon_Patch_Size-Meleagris_gallopavo 3.5376 1.0338 348
## Cogon_Patch_Size-Sciurus_carolinensis 0.6967 1.0633 318
## Veg_shannon_index-Canis_latrans 2.8335 1.0031 386
## Veg_shannon_index-Procyon_lotor 2.8238 1.0083 222
## Veg_shannon_index-Dasypus_novemcinctus 1.9066 1.0121 461
## Veg_shannon_index-Lynx_rufus 2.5727 1.0002 401
## Veg_shannon_index-Didelphis_virginiana 2.7564 1.0027 467
## Veg_shannon_index-Sylvilagus_floridanus 2.6707 1.0087 357
## Veg_shannon_index-Meleagris_gallopavo 3.1124 1.0048 351
## Veg_shannon_index-Sciurus_carolinensis 1.8056 1.0166 393
## total_shrub_cover-Canis_latrans 3.0896 1.0056 194
## total_shrub_cover-Procyon_lotor 0.0126 1.0146 687
## total_shrub_cover-Dasypus_novemcinctus 1.1703 1.0116 262
## total_shrub_cover-Lynx_rufus 0.6583 1.0207 136
## total_shrub_cover-Didelphis_virginiana 0.4364 1.0350 415
## total_shrub_cover-Sylvilagus_floridanus 1.1680 1.0070 275
## total_shrub_cover-Meleagris_gallopavo -0.0797 1.0894 171
## total_shrub_cover-Sciurus_carolinensis 0.8806 1.0061 279
## Avg_Cogongrass_Cover-Canis_latrans 4.3177 1.0072 224
## Avg_Cogongrass_Cover-Procyon_lotor 3.9212 1.0110 241
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.8184 1.0107 204
## Avg_Cogongrass_Cover-Lynx_rufus 4.5443 1.0036 254
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.9702 1.0046 253
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3077 1.0009 265
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6517 1.0104 240
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2886 1.0136 266
## Tree_Density-Canis_latrans -0.6363 1.0011 349
## Tree_Density-Procyon_lotor 0.2211 1.0028 720
## Tree_Density-Dasypus_novemcinctus -1.2501 1.0341 158
## Tree_Density-Lynx_rufus 2.9242 1.0149 221
## Tree_Density-Didelphis_virginiana 0.4586 1.0201 385
## Tree_Density-Sylvilagus_floridanus 0.0953 1.0276 286
## Tree_Density-Meleagris_gallopavo 0.4328 1.0107 424
## Tree_Density-Sciurus_carolinensis 0.7780 1.0262 438
## Avg_Canopy_Cover-Canis_latrans 1.4348 1.0111 888
## Avg_Canopy_Cover-Procyon_lotor 3.5151 1.0008 389
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.3488 1.0031 244
## Avg_Canopy_Cover-Lynx_rufus 3.7317 1.1210 166
## Avg_Canopy_Cover-Didelphis_virginiana 6.9707 1.0227 177
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.1313 1.0160 163
## Avg_Canopy_Cover-Meleagris_gallopavo 6.4154 1.0152 300
## Avg_Canopy_Cover-Sciurus_carolinensis 6.9214 1.0056 174
## avg_veg_height-Canis_latrans 0.7323 1.0051 433
## avg_veg_height-Procyon_lotor 0.8226 1.0044 521
## avg_veg_height-Dasypus_novemcinctus 1.1656 1.0034 470
## avg_veg_height-Lynx_rufus 1.0038 1.0063 346
## avg_veg_height-Didelphis_virginiana 0.6864 1.0101 476
## avg_veg_height-Sylvilagus_floridanus 0.8187 1.0343 557
## avg_veg_height-Meleagris_gallopavo 1.2690 1.0136 383
## avg_veg_height-Sciurus_carolinensis 1.8512 1.0159 500
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7568 0.1817 -3.1280 -2.7496 -2.4172 1.0205
## (Intercept)-Procyon_lotor -2.3041 0.1393 -2.5857 -2.3009 -2.0451 1.0154
## (Intercept)-Dasypus_novemcinctus -1.8258 0.1666 -2.1625 -1.8216 -1.5094 1.0021
## (Intercept)-Lynx_rufus -3.5455 0.3450 -4.2930 -3.5210 -2.9308 1.0245
## (Intercept)-Didelphis_virginiana -2.6704 0.2928 -3.2673 -2.6714 -2.1117 1.0073
## (Intercept)-Sylvilagus_floridanus -3.1843 0.2498 -3.7031 -3.1729 -2.7076 1.0100
## (Intercept)-Meleagris_gallopavo -3.7020 0.4523 -4.6413 -3.6885 -2.8604 1.0864
## (Intercept)-Sciurus_carolinensis -2.8163 0.3119 -3.4214 -2.8126 -2.2094 1.0031
## shrub_cover-Canis_latrans -0.3544 0.2262 -0.7730 -0.3549 0.1101 1.0051
## shrub_cover-Procyon_lotor 0.2886 0.1608 -0.0308 0.2921 0.6043 1.0009
## shrub_cover-Dasypus_novemcinctus 1.0030 0.3128 0.3941 1.0101 1.6107 1.0094
## shrub_cover-Lynx_rufus 0.0864 0.3926 -0.7543 0.1033 0.7715 1.0320
## shrub_cover-Didelphis_virginiana 1.0983 0.3770 0.3968 1.0873 1.8754 1.0328
## shrub_cover-Sylvilagus_floridanus 0.5631 0.4123 -0.2614 0.5654 1.3722 1.0058
## shrub_cover-Meleagris_gallopavo -0.4139 0.4545 -1.3298 -0.4054 0.4452 1.0578
## shrub_cover-Sciurus_carolinensis 1.0980 0.4056 0.2781 1.1035 1.8811 1.0047
## veg_height-Canis_latrans -0.5893 0.1825 -0.9509 -0.5859 -0.2437 1.0144
## veg_height-Procyon_lotor 0.3550 0.1213 0.1228 0.3547 0.5945 0.9996
## veg_height-Dasypus_novemcinctus 0.2772 0.1403 0.0019 0.2758 0.5467 1.0029
## veg_height-Lynx_rufus 0.0712 0.2387 -0.4422 0.0736 0.5196 1.0075
## veg_height-Didelphis_virginiana 0.5019 0.2465 0.0434 0.4956 1.0040 1.0030
## veg_height-Sylvilagus_floridanus 0.1658 0.2471 -0.3133 0.1690 0.6446 1.0056
## veg_height-Meleagris_gallopavo -0.2103 0.3839 -1.0072 -0.1998 0.5426 1.0213
## veg_height-Sciurus_carolinensis 0.1481 0.2212 -0.2691 0.1372 0.5945 1.0021
## week-Canis_latrans 0.0646 0.1317 -0.2040 0.0695 0.3154 1.0032
## week-Procyon_lotor -0.0600 0.1210 -0.3059 -0.0568 0.1656 1.0036
## week-Dasypus_novemcinctus -0.1790 0.1426 -0.4718 -0.1739 0.0870 1.0015
## week-Lynx_rufus -0.0563 0.1960 -0.4473 -0.0493 0.3121 1.0035
## week-Didelphis_virginiana -0.2294 0.2155 -0.6944 -0.2189 0.1572 1.0140
## week-Sylvilagus_floridanus -0.1692 0.2096 -0.6184 -0.1542 0.2082 1.0006
## week-Meleagris_gallopavo -0.2973 0.2598 -0.8796 -0.2681 0.1491 1.0042
## week-Sciurus_carolinensis 0.1182 0.1845 -0.2630 0.1208 0.4774 1.0004
## ESS
## (Intercept)-Canis_latrans 704
## (Intercept)-Procyon_lotor 1449
## (Intercept)-Dasypus_novemcinctus 964
## (Intercept)-Lynx_rufus 212
## (Intercept)-Didelphis_virginiana 553
## (Intercept)-Sylvilagus_floridanus 706
## (Intercept)-Meleagris_gallopavo 236
## (Intercept)-Sciurus_carolinensis 425
## shrub_cover-Canis_latrans 563
## shrub_cover-Procyon_lotor 1544
## shrub_cover-Dasypus_novemcinctus 613
## shrub_cover-Lynx_rufus 220
## shrub_cover-Didelphis_virginiana 502
## shrub_cover-Sylvilagus_floridanus 323
## shrub_cover-Meleagris_gallopavo 285
## shrub_cover-Sciurus_carolinensis 386
## veg_height-Canis_latrans 721
## veg_height-Procyon_lotor 1611
## veg_height-Dasypus_novemcinctus 1495
## veg_height-Lynx_rufus 674
## veg_height-Didelphis_virginiana 752
## veg_height-Sylvilagus_floridanus 871
## veg_height-Meleagris_gallopavo 348
## veg_height-Sciurus_carolinensis 736
## week-Canis_latrans 1618
## week-Procyon_lotor 1662
## week-Dasypus_novemcinctus 2020
## week-Lynx_rufus 958
## week-Didelphis_virginiana 1246
## week-Sylvilagus_floridanus 911
## week-Meleagris_gallopavo 655
## week-Sciurus_carolinensis 1560
#Includes all covariates of detection and only null for occupancy
ms_full_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8362
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.206 0.3421 -0.8521 -0.215 0.4846 1.0059 1295
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6971 0.6797 0.0947 0.5021 2.5682 1.0198 1538
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7633 0.3256 -3.4079 -2.7679 -2.0910 1.0047 2464
## shrub_cover 0.2468 0.3196 -0.3892 0.2409 0.8840 1.0044 1775
## veg_height 0.0593 0.1996 -0.3393 0.0610 0.4490 1.0042 1382
## week -0.0977 0.1458 -0.3870 -0.0923 0.1765 1.0036 1627
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8127 0.7195 0.1723 0.6135 2.5688 1.0033 1220
## shrub_cover 0.7263 0.6951 0.1376 0.5299 2.4970 1.0211 1177
## veg_height 0.2653 0.2382 0.0599 0.2022 0.8290 1.0024 1219
## week 0.1110 0.1058 0.0242 0.0816 0.3615 1.0016 1703
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2642 0.3876 -0.4225 0.2442 1.0935 0.9996
## (Intercept)-Procyon_lotor 0.5297 0.3764 -0.1699 0.5195 1.3105 1.0111
## (Intercept)-Dasypus_novemcinctus -0.4947 0.3428 -1.1786 -0.4898 0.1445 1.0010
## (Intercept)-Lynx_rufus 0.0804 0.5312 -0.8143 0.0182 1.3149 1.0056
## (Intercept)-Didelphis_virginiana -0.9485 0.4328 -1.8407 -0.9280 -0.1481 1.0052
## (Intercept)-Sylvilagus_floridanus -0.3173 0.4437 -1.1390 -0.3405 0.6423 1.0075
## (Intercept)-Meleagris_gallopavo 0.0978 0.6364 -0.9851 0.0291 1.5494 1.0109
## (Intercept)-Sciurus_carolinensis -0.9315 0.4372 -1.8104 -0.9205 -0.1274 1.0011
## ESS
## (Intercept)-Canis_latrans 1982
## (Intercept)-Procyon_lotor 1880
## (Intercept)-Dasypus_novemcinctus 2453
## (Intercept)-Lynx_rufus 485
## (Intercept)-Didelphis_virginiana 1453
## (Intercept)-Sylvilagus_floridanus 1114
## (Intercept)-Meleagris_gallopavo 355
## (Intercept)-Sciurus_carolinensis 1454
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7479 0.1859 -3.1285 -2.7445 -2.4081 1.0137
## (Intercept)-Procyon_lotor -2.2988 0.1419 -2.5791 -2.2928 -2.0412 1.0142
## (Intercept)-Dasypus_novemcinctus -1.7718 0.1616 -2.0921 -1.7701 -1.4554 1.0065
## (Intercept)-Lynx_rufus -3.5889 0.3411 -4.2606 -3.5823 -2.9538 1.0116
## (Intercept)-Didelphis_virginiana -2.6341 0.2844 -3.2036 -2.6278 -2.0839 1.0046
## (Intercept)-Sylvilagus_floridanus -3.1634 0.2876 -3.8025 -3.1445 -2.6410 1.0138
## (Intercept)-Meleagris_gallopavo -3.9429 0.4864 -4.8892 -3.9384 -2.9796 1.0122
## (Intercept)-Sciurus_carolinensis -2.6828 0.3093 -3.2971 -2.6769 -2.1032 1.0011
## shrub_cover-Canis_latrans -0.3014 0.2186 -0.7247 -0.3001 0.1345 1.0063
## shrub_cover-Procyon_lotor 0.2584 0.1678 -0.0796 0.2650 0.5859 1.0143
## shrub_cover-Dasypus_novemcinctus 0.8674 0.3029 0.3110 0.8553 1.4861 1.0085
## shrub_cover-Lynx_rufus -0.2855 0.3754 -0.9992 -0.2953 0.4316 0.9999
## shrub_cover-Didelphis_virginiana 0.9965 0.3732 0.3087 0.9703 1.7559 1.0018
## shrub_cover-Sylvilagus_floridanus 0.3026 0.4262 -0.5001 0.2919 1.1613 1.0169
## shrub_cover-Meleagris_gallopavo -0.6757 0.4224 -1.4769 -0.6836 0.2299 1.0070
## shrub_cover-Sciurus_carolinensis 0.8943 0.4161 0.1049 0.8820 1.7154 1.0067
## veg_height-Canis_latrans -0.5917 0.1875 -0.9665 -0.5873 -0.2298 1.0063
## veg_height-Procyon_lotor 0.3447 0.1226 0.1133 0.3437 0.5888 1.0057
## veg_height-Dasypus_novemcinctus 0.2545 0.1390 -0.0111 0.2552 0.5261 1.0006
## veg_height-Lynx_rufus 0.0345 0.2518 -0.4733 0.0444 0.5153 1.0036
## veg_height-Didelphis_virginiana 0.4703 0.2509 -0.0055 0.4610 0.9765 1.0089
## veg_height-Sylvilagus_floridanus 0.1272 0.2533 -0.3707 0.1269 0.6239 1.0049
## veg_height-Meleagris_gallopavo -0.2349 0.3914 -1.0086 -0.2275 0.5275 1.0269
## veg_height-Sciurus_carolinensis 0.0815 0.2255 -0.3493 0.0745 0.5370 1.0027
## week-Canis_latrans 0.0581 0.1339 -0.2180 0.0615 0.3107 1.0133
## week-Procyon_lotor -0.0611 0.1184 -0.3060 -0.0545 0.1594 1.0089
## week-Dasypus_novemcinctus -0.1750 0.1383 -0.4588 -0.1724 0.0842 1.0009
## week-Lynx_rufus -0.0431 0.1981 -0.4516 -0.0308 0.3291 1.0275
## week-Didelphis_virginiana -0.2442 0.2278 -0.7411 -0.2273 0.1606 1.0031
## week-Sylvilagus_floridanus -0.1735 0.2093 -0.6060 -0.1653 0.2228 1.0142
## week-Meleagris_gallopavo -0.2799 0.2400 -0.7988 -0.2596 0.1324 1.0044
## week-Sciurus_carolinensis 0.1284 0.1841 -0.2369 0.1285 0.4798 1.0010
## ESS
## (Intercept)-Canis_latrans 821
## (Intercept)-Procyon_lotor 1320
## (Intercept)-Dasypus_novemcinctus 1591
## (Intercept)-Lynx_rufus 259
## (Intercept)-Didelphis_virginiana 710
## (Intercept)-Sylvilagus_floridanus 609
## (Intercept)-Meleagris_gallopavo 164
## (Intercept)-Sciurus_carolinensis 816
## shrub_cover-Canis_latrans 759
## shrub_cover-Procyon_lotor 1367
## shrub_cover-Dasypus_novemcinctus 1579
## shrub_cover-Lynx_rufus 489
## shrub_cover-Didelphis_virginiana 706
## shrub_cover-Sylvilagus_floridanus 446
## shrub_cover-Meleagris_gallopavo 218
## shrub_cover-Sciurus_carolinensis 722
## veg_height-Canis_latrans 592
## veg_height-Procyon_lotor 1464
## veg_height-Dasypus_novemcinctus 1953
## veg_height-Lynx_rufus 746
## veg_height-Didelphis_virginiana 1114
## veg_height-Sylvilagus_floridanus 794
## veg_height-Meleagris_gallopavo 448
## veg_height-Sciurus_carolinensis 1098
## week-Canis_latrans 1642
## week-Procyon_lotor 1694
## week-Dasypus_novemcinctus 1910
## week-Lynx_rufus 873
## week-Didelphis_virginiana 1385
## week-Sylvilagus_floridanus 981
## week-Meleagris_gallopavo 692
## week-Sciurus_carolinensis 1812
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.886
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1574 0.4352 -0.9929 -0.1546 0.6992 1.0043 329
## Avg_Cogongrass_Cover -0.0046 0.3735 -0.7728 0.0089 0.6811 1.0076 657
## total_shrub_cover -0.9368 0.4920 -2.0585 -0.8834 -0.0715 1.0181 251
## avg_veg_height 0.1188 0.4028 -0.6482 0.1051 0.9318 1.0026 391
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6649 0.8819 0.0522 0.4045 2.8541 1.0161 627
## Avg_Cogongrass_Cover 0.5058 0.6564 0.0466 0.2993 2.1759 1.0574 693
## total_shrub_cover 0.9774 1.2636 0.0649 0.5660 4.5772 1.0456 390
## avg_veg_height 0.4234 0.9649 0.0376 0.2237 1.9389 1.1535 653
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0611 1.0259 0.0761 0.7225 3.7322 1.0065 212
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7761 0.2867 -3.3399 -2.7788 -2.1858 1.0022 859
## shrub_cover 0.5637 0.3345 -0.0751 0.5635 1.2285 1.0169 898
## veg_height 0.0984 0.2021 -0.3040 0.1019 0.4992 1.0035 929
## week -0.0980 0.1405 -0.3946 -0.0937 0.1785 1.0069 1480
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5485 0.4623 0.1293 0.4112 1.7855 1.0059 1234
## shrub_cover 0.7383 0.6421 0.1379 0.5533 2.3684 1.0015 681
## veg_height 0.2555 0.2184 0.0537 0.2005 0.7968 1.0048 1506
## week 0.1119 0.0993 0.0250 0.0816 0.3765 1.0048 1412
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2559 0.6086 -0.8318 0.2115
## (Intercept)-Procyon_lotor 0.4110 0.6314 -0.7580 0.3920
## (Intercept)-Dasypus_novemcinctus -0.2830 0.5995 -1.4343 -0.2901
## (Intercept)-Lynx_rufus -0.1177 0.6301 -1.3115 -0.1489
## (Intercept)-Didelphis_virginiana -0.5572 0.6776 -1.8732 -0.5373
## (Intercept)-Sylvilagus_floridanus -0.0245 0.6428 -1.1859 -0.0603
## (Intercept)-Meleagris_gallopavo -0.4075 0.7179 -1.8271 -0.3992
## (Intercept)-Sciurus_carolinensis -0.5764 0.6656 -2.0410 -0.5353
## Avg_Cogongrass_Cover-Canis_latrans 0.3458 0.5187 -0.5893 0.3236
## Avg_Cogongrass_Cover-Procyon_lotor -0.0966 0.4779 -1.0501 -0.0909
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1590 0.4725 -0.7517 0.1479
## Avg_Cogongrass_Cover-Lynx_rufus 0.3888 0.5477 -0.5827 0.3574
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1355 0.5240 -0.9015 0.1250
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4484 0.5902 -1.7423 -0.4088
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5052 0.7034 -2.1011 -0.4408
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0132 0.5095 -1.0359 0.0331
## total_shrub_cover-Canis_latrans 0.0809 0.6525 -1.0789 0.0383
## total_shrub_cover-Procyon_lotor -1.2422 0.6087 -2.6690 -1.1547
## total_shrub_cover-Dasypus_novemcinctus -0.5689 0.6593 -2.1401 -0.4859
## total_shrub_cover-Lynx_rufus -1.3680 0.8228 -3.2706 -1.2473
## total_shrub_cover-Didelphis_virginiana -0.9817 0.7363 -2.6855 -0.8738
## total_shrub_cover-Sylvilagus_floridanus -1.2588 0.8371 -3.2974 -1.1354
## total_shrub_cover-Meleagris_gallopavo -1.5159 0.7980 -3.3084 -1.4208
## total_shrub_cover-Sciurus_carolinensis -1.0128 0.7642 -2.8014 -0.9107
## avg_veg_height-Canis_latrans 0.1072 0.4917 -0.8218 0.0982
## avg_veg_height-Procyon_lotor 0.1419 0.4845 -0.7779 0.1395
## avg_veg_height-Dasypus_novemcinctus 0.3534 0.5060 -0.5555 0.3207
## avg_veg_height-Lynx_rufus 0.0346 0.5868 -1.1527 0.0471
## avg_veg_height-Didelphis_virginiana 0.0105 0.5324 -1.0699 0.0161
## avg_veg_height-Sylvilagus_floridanus 0.0366 0.5532 -1.0722 0.0240
## avg_veg_height-Meleagris_gallopavo -0.2473 0.7472 -1.9052 -0.1713
## avg_veg_height-Sciurus_carolinensis 0.4842 0.5414 -0.4605 0.4367
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6276 1.0073 551
## (Intercept)-Procyon_lotor 1.7532 1.0013 572
## (Intercept)-Dasypus_novemcinctus 0.9564 1.0200 434
## (Intercept)-Lynx_rufus 1.2344 1.0026 705
## (Intercept)-Didelphis_virginiana 0.7670 1.0055 415
## (Intercept)-Sylvilagus_floridanus 1.3499 1.0117 382
## (Intercept)-Meleagris_gallopavo 1.0320 1.0090 354
## (Intercept)-Sciurus_carolinensis 0.6384 1.0147 282
## Avg_Cogongrass_Cover-Canis_latrans 1.4548 1.0121 800
## Avg_Cogongrass_Cover-Procyon_lotor 0.8077 1.0037 1172
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1039 1.0040 1117
## Avg_Cogongrass_Cover-Lynx_rufus 1.6028 1.0047 1034
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2059 1.0023 837
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5719 1.0259 860
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7395 1.0130 641
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0141 1.0031 789
## total_shrub_cover-Canis_latrans 1.5537 1.0144 528
## total_shrub_cover-Procyon_lotor -0.2469 1.0093 434
## total_shrub_cover-Dasypus_novemcinctus 0.5031 1.0372 319
## total_shrub_cover-Lynx_rufus -0.0773 1.0269 257
## total_shrub_cover-Didelphis_virginiana 0.2136 1.0112 196
## total_shrub_cover-Sylvilagus_floridanus 0.0464 1.0427 181
## total_shrub_cover-Meleagris_gallopavo -0.2333 1.0106 308
## total_shrub_cover-Sciurus_carolinensis 0.2459 1.0113 273
## avg_veg_height-Canis_latrans 1.1191 1.0012 682
## avg_veg_height-Procyon_lotor 1.1312 1.0022 787
## avg_veg_height-Dasypus_novemcinctus 1.4766 1.0132 618
## avg_veg_height-Lynx_rufus 1.1470 1.0003 637
## avg_veg_height-Didelphis_virginiana 1.0286 1.0020 574
## avg_veg_height-Sylvilagus_floridanus 1.1583 1.0122 672
## avg_veg_height-Meleagris_gallopavo 1.0498 1.0086 370
## avg_veg_height-Sciurus_carolinensis 1.6931 1.0015 581
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7886 0.1878 -3.1632 -2.7835 -2.4352 1.0070
## (Intercept)-Procyon_lotor -2.3142 0.1400 -2.5980 -2.3140 -2.0534 1.0034
## (Intercept)-Dasypus_novemcinctus -1.8744 0.1830 -2.2513 -1.8710 -1.5243 1.0186
## (Intercept)-Lynx_rufus -3.4249 0.3165 -4.0939 -3.4161 -2.8434 1.0052
## (Intercept)-Didelphis_virginiana -2.8072 0.3156 -3.4496 -2.8037 -2.2104 1.0027
## (Intercept)-Sylvilagus_floridanus -3.2434 0.2613 -3.7786 -3.2346 -2.7415 1.0440
## (Intercept)-Meleagris_gallopavo -3.4684 0.5059 -4.4924 -3.4500 -2.5467 1.0108
## (Intercept)-Sciurus_carolinensis -2.8784 0.3341 -3.5792 -2.8596 -2.2614 1.0119
## shrub_cover-Canis_latrans -0.2452 0.2506 -0.7249 -0.2447 0.2539 1.0188
## shrub_cover-Procyon_lotor 0.3340 0.1611 0.0104 0.3369 0.6368 1.0070
## shrub_cover-Dasypus_novemcinctus 1.1268 0.3558 0.4691 1.1160 1.8511 1.0576
## shrub_cover-Lynx_rufus 0.2230 0.3489 -0.4924 0.2411 0.8531 1.0178
## shrub_cover-Didelphis_virginiana 1.3039 0.4327 0.5024 1.2820 2.1696 1.0040
## shrub_cover-Sylvilagus_floridanus 0.8070 0.4128 -0.0632 0.8271 1.5666 1.0077
## shrub_cover-Meleagris_gallopavo -0.2215 0.4708 -1.1705 -0.2044 0.6415 1.0071
## shrub_cover-Sciurus_carolinensis 1.2434 0.4293 0.3844 1.2465 2.0658 1.0096
## veg_height-Canis_latrans -0.5888 0.1968 -0.9890 -0.5820 -0.2258 1.0034
## veg_height-Procyon_lotor 0.3548 0.1208 0.1116 0.3528 0.5894 1.0027
## veg_height-Dasypus_novemcinctus 0.2861 0.1424 0.0102 0.2842 0.5666 1.0045
## veg_height-Lynx_rufus 0.0592 0.2459 -0.4407 0.0674 0.5145 1.0277
## veg_height-Didelphis_virginiana 0.4539 0.2613 -0.0204 0.4423 1.0017 1.0034
## veg_height-Sylvilagus_floridanus 0.0987 0.2480 -0.3759 0.0949 0.5815 1.0199
## veg_height-Meleagris_gallopavo 0.0156 0.4582 -0.8609 0.0030 0.9381 1.0035
## veg_height-Sciurus_carolinensis 0.1361 0.2368 -0.3119 0.1282 0.6237 1.0134
## week-Canis_latrans 0.0580 0.1342 -0.2073 0.0609 0.3184 1.0021
## week-Procyon_lotor -0.0608 0.1206 -0.3074 -0.0585 0.1702 1.0143
## week-Dasypus_novemcinctus -0.1772 0.1410 -0.4716 -0.1716 0.0829 1.0059
## week-Lynx_rufus -0.0533 0.1976 -0.4646 -0.0461 0.3168 1.0096
## week-Didelphis_virginiana -0.2331 0.2178 -0.7087 -0.2143 0.1510 1.0019
## week-Sylvilagus_floridanus -0.1774 0.2126 -0.6228 -0.1622 0.2044 1.0245
## week-Meleagris_gallopavo -0.2791 0.2421 -0.8269 -0.2570 0.1400 1.0136
## week-Sciurus_carolinensis 0.1239 0.1836 -0.2417 0.1273 0.4767 0.9996
## ESS
## (Intercept)-Canis_latrans 522
## (Intercept)-Procyon_lotor 1298
## (Intercept)-Dasypus_novemcinctus 599
## (Intercept)-Lynx_rufus 528
## (Intercept)-Didelphis_virginiana 327
## (Intercept)-Sylvilagus_floridanus 562
## (Intercept)-Meleagris_gallopavo 274
## (Intercept)-Sciurus_carolinensis 289
## shrub_cover-Canis_latrans 473
## shrub_cover-Procyon_lotor 1368
## shrub_cover-Dasypus_novemcinctus 372
## shrub_cover-Lynx_rufus 491
## shrub_cover-Didelphis_virginiana 278
## shrub_cover-Sylvilagus_floridanus 228
## shrub_cover-Meleagris_gallopavo 344
## shrub_cover-Sciurus_carolinensis 265
## veg_height-Canis_latrans 535
## veg_height-Procyon_lotor 1558
## veg_height-Dasypus_novemcinctus 1538
## veg_height-Lynx_rufus 618
## veg_height-Didelphis_virginiana 606
## veg_height-Sylvilagus_floridanus 623
## veg_height-Meleagris_gallopavo 310
## veg_height-Sciurus_carolinensis 630
## week-Canis_latrans 1522
## week-Procyon_lotor 1540
## week-Dasypus_novemcinctus 1719
## week-Lynx_rufus 930
## week-Didelphis_virginiana 1191
## week-Sylvilagus_floridanus 837
## week-Meleagris_gallopavo 778
## week-Sciurus_carolinensis 1424
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8493
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4309 0.4589 -1.3208 -0.4396 0.5341 1.0641 471
## Tree_Density -0.8113 0.4953 -1.9025 -0.7825 0.0761 1.0022 774
## Avg_Canopy_Cover 1.1501 0.4621 0.2887 1.1337 2.0903 1.0011 862
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0838 1.2749 0.0800 0.7122 4.4765 1.0077 505
## Tree_Density 1.1387 2.3257 0.0631 0.5843 5.2034 1.1067 838
## Avg_Canopy_Cover 1.2454 1.4470 0.0990 0.8216 4.8231 1.0254 833
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6491 0.7096 0.0556 0.4049 2.7557 1.0367 153
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7714 0.3295 -3.3930 -2.7828 -2.0889 1.0019 2306
## shrub_cover 0.2885 0.3192 -0.3666 0.2860 0.9389 1.0062 1725
## veg_height 0.1073 0.2010 -0.2888 0.1038 0.5064 1.0041 1617
## week -0.0946 0.1404 -0.3715 -0.0900 0.1694 1.0100 1435
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7911 0.8065 0.1768 0.5953 2.4986 1.0231 1308
## shrub_cover 0.7498 0.6696 0.1486 0.5702 2.5814 1.0131 1256
## veg_height 0.2549 0.2012 0.0613 0.1971 0.7883 1.0099 1896
## week 0.1081 0.0993 0.0245 0.0798 0.3525 1.0141 1473
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1019 0.5727 -0.9803 0.0782 1.3018
## (Intercept)-Procyon_lotor 0.3359 0.6258 -0.8567 0.3219 1.6052
## (Intercept)-Dasypus_novemcinctus -0.8421 0.5752 -2.0486 -0.8028 0.2537
## (Intercept)-Lynx_rufus 0.0624 0.8490 -1.3216 -0.0350 2.1331
## (Intercept)-Didelphis_virginiana -1.2525 0.6732 -2.6393 -1.2268 -0.0231
## (Intercept)-Sylvilagus_floridanus -0.5924 0.5872 -1.8017 -0.5882 0.5484
## (Intercept)-Meleagris_gallopavo -0.1879 0.7658 -1.5048 -0.2502 1.4940
## (Intercept)-Sciurus_carolinensis -1.2798 0.7081 -2.8228 -1.2243 -0.0751
## Tree_Density-Canis_latrans -1.0139 0.6419 -2.4632 -0.9435 0.0333
## Tree_Density-Procyon_lotor -0.4843 0.4530 -1.3640 -0.4852 0.3930
## Tree_Density-Dasypus_novemcinctus -1.5122 0.9745 -3.9448 -1.3080 -0.2242
## Tree_Density-Lynx_rufus 0.2792 0.7924 -0.8952 0.1461 2.1541
## Tree_Density-Didelphis_virginiana -1.0146 0.7802 -2.8531 -0.9043 0.2304
## Tree_Density-Sylvilagus_floridanus -1.1401 0.7854 -3.0869 -1.0260 0.1178
## Tree_Density-Meleagris_gallopavo -1.0920 0.8665 -3.1239 -0.9754 0.3156
## Tree_Density-Sciurus_carolinensis -0.8520 0.7709 -2.6872 -0.7613 0.4446
## Avg_Canopy_Cover-Canis_latrans -0.0982 0.4831 -1.0357 -0.1091 0.8729
## Avg_Canopy_Cover-Procyon_lotor 1.0257 0.4798 0.1421 0.9964 2.0517
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1392 0.5109 0.2294 1.1011 2.2385
## Avg_Canopy_Cover-Lynx_rufus 0.7375 0.7350 -0.5533 0.7093 2.3231
## Avg_Canopy_Cover-Didelphis_virginiana 1.5994 0.7085 0.4937 1.5099 3.2558
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.1804 0.9751 0.7091 2.0414 4.4476
## Avg_Canopy_Cover-Meleagris_gallopavo 1.6076 0.8164 0.2639 1.5127 3.5656
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5233 0.7050 0.4326 1.4248 3.1755
## Rhat ESS
## (Intercept)-Canis_latrans 1.0249 596
## (Intercept)-Procyon_lotor 1.0321 497
## (Intercept)-Dasypus_novemcinctus 1.0252 751
## (Intercept)-Lynx_rufus 1.0363 345
## (Intercept)-Didelphis_virginiana 1.0224 517
## (Intercept)-Sylvilagus_floridanus 1.0688 778
## (Intercept)-Meleagris_gallopavo 1.0321 430
## (Intercept)-Sciurus_carolinensis 1.0228 503
## Tree_Density-Canis_latrans 1.0096 931
## Tree_Density-Procyon_lotor 1.0071 2191
## Tree_Density-Dasypus_novemcinctus 1.0234 486
## Tree_Density-Lynx_rufus 1.0083 587
## Tree_Density-Didelphis_virginiana 1.0011 675
## Tree_Density-Sylvilagus_floridanus 1.0069 708
## Tree_Density-Meleagris_gallopavo 1.0229 655
## Tree_Density-Sciurus_carolinensis 1.0027 786
## Avg_Canopy_Cover-Canis_latrans 1.0030 1187
## Avg_Canopy_Cover-Procyon_lotor 1.0024 1551
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0080 1056
## Avg_Canopy_Cover-Lynx_rufus 1.0033 428
## Avg_Canopy_Cover-Didelphis_virginiana 1.0029 855
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0012 530
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0063 625
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0110 786
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7596 0.1920 -3.1618 -2.7537 -2.4030 1.0004
## (Intercept)-Procyon_lotor -2.3041 0.1425 -2.5879 -2.2993 -2.0316 1.0025
## (Intercept)-Dasypus_novemcinctus -1.7953 0.1656 -2.1342 -1.7915 -1.4767 1.0261
## (Intercept)-Lynx_rufus -3.6583 0.3367 -4.3443 -3.6416 -3.0387 1.0504
## (Intercept)-Didelphis_virginiana -2.6825 0.2934 -3.2878 -2.6785 -2.1360 1.0209
## (Intercept)-Sylvilagus_floridanus -3.1340 0.2502 -3.6564 -3.1225 -2.6668 1.0165
## (Intercept)-Meleagris_gallopavo -3.8677 0.4388 -4.7832 -3.8520 -3.0777 1.0356
## (Intercept)-Sciurus_carolinensis -2.7646 0.3236 -3.4359 -2.7525 -2.1716 1.0388
## shrub_cover-Canis_latrans -0.2916 0.2290 -0.7366 -0.2914 0.1347 1.0102
## shrub_cover-Procyon_lotor 0.2602 0.1612 -0.0571 0.2608 0.5755 1.0045
## shrub_cover-Dasypus_novemcinctus 0.9138 0.3033 0.3311 0.9176 1.5069 1.0170
## shrub_cover-Lynx_rufus -0.2549 0.3542 -0.9656 -0.2530 0.4391 1.0368
## shrub_cover-Didelphis_virginiana 1.0388 0.3747 0.3311 1.0287 1.7721 1.0494
## shrub_cover-Sylvilagus_floridanus 0.4591 0.3967 -0.3177 0.4710 1.2291 1.0396
## shrub_cover-Meleagris_gallopavo -0.6398 0.4080 -1.4633 -0.6304 0.1284 1.0296
## shrub_cover-Sciurus_carolinensis 0.9644 0.4243 0.1525 0.9568 1.8198 1.0234
## veg_height-Canis_latrans -0.5907 0.1914 -0.9750 -0.5882 -0.2301 1.0021
## veg_height-Procyon_lotor 0.3555 0.1237 0.1226 0.3529 0.5992 1.0063
## veg_height-Dasypus_novemcinctus 0.2759 0.1391 0.0118 0.2769 0.5598 1.0004
## veg_height-Lynx_rufus 0.1024 0.2400 -0.3746 0.1094 0.5512 1.0122
## veg_height-Didelphis_virginiana 0.5162 0.2448 0.0594 0.5105 0.9968 1.0020
## veg_height-Sylvilagus_floridanus 0.1681 0.2423 -0.3055 0.1726 0.6419 1.0119
## veg_height-Meleagris_gallopavo -0.1551 0.3519 -0.8699 -0.1454 0.5256 1.0052
## veg_height-Sciurus_carolinensis 0.1493 0.2283 -0.2902 0.1420 0.6176 1.0383
## week-Canis_latrans 0.0616 0.1350 -0.2149 0.0672 0.3151 1.0204
## week-Procyon_lotor -0.0551 0.1192 -0.2977 -0.0536 0.1732 1.0000
## week-Dasypus_novemcinctus -0.1753 0.1373 -0.4571 -0.1728 0.0853 1.0008
## week-Lynx_rufus -0.0574 0.1943 -0.4528 -0.0524 0.2999 1.0030
## week-Didelphis_virginiana -0.2308 0.2116 -0.6709 -0.2158 0.1537 1.0070
## week-Sylvilagus_floridanus -0.1721 0.2098 -0.6127 -0.1664 0.2106 1.0119
## week-Meleagris_gallopavo -0.2835 0.2394 -0.8136 -0.2682 0.1243 1.0072
## week-Sciurus_carolinensis 0.1189 0.1818 -0.2496 0.1208 0.4735 1.0040
## ESS
## (Intercept)-Canis_latrans 606
## (Intercept)-Procyon_lotor 1395
## (Intercept)-Dasypus_novemcinctus 1366
## (Intercept)-Lynx_rufus 241
## (Intercept)-Didelphis_virginiana 715
## (Intercept)-Sylvilagus_floridanus 540
## (Intercept)-Meleagris_gallopavo 222
## (Intercept)-Sciurus_carolinensis 487
## shrub_cover-Canis_latrans 824
## shrub_cover-Procyon_lotor 1511
## shrub_cover-Dasypus_novemcinctus 1150
## shrub_cover-Lynx_rufus 343
## shrub_cover-Didelphis_virginiana 673
## shrub_cover-Sylvilagus_floridanus 612
## shrub_cover-Meleagris_gallopavo 247
## shrub_cover-Sciurus_carolinensis 584
## veg_height-Canis_latrans 804
## veg_height-Procyon_lotor 1587
## veg_height-Dasypus_novemcinctus 1550
## veg_height-Lynx_rufus 736
## veg_height-Didelphis_virginiana 1240
## veg_height-Sylvilagus_floridanus 1046
## veg_height-Meleagris_gallopavo 612
## veg_height-Sciurus_carolinensis 898
## week-Canis_latrans 1531
## week-Procyon_lotor 1709
## week-Dasypus_novemcinctus 1922
## week-Lynx_rufus 935
## week-Didelphis_virginiana 1375
## week-Sylvilagus_floridanus 1055
## week-Meleagris_gallopavo 683
## week-Sciurus_carolinensis 1646
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8692
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2585 0.4119 -1.0740 -0.2670 0.5556 1.0130 492
## Cogon_Patch_Size 0.0048 0.4200 -0.8574 0.0110 0.8078 1.0034 931
## Avg_Cogongrass_Cover 0.0927 0.3498 -0.5831 0.0906 0.8310 1.0151 743
## total_shrub_cover -0.8830 0.4448 -1.8254 -0.8534 -0.0784 1.0173 444
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6155 0.7644 0.0536 0.3840 2.5209 1.0013 977
## Cogon_Patch_Size 0.8188 1.3936 0.0572 0.4207 4.2932 1.0888 543
## Avg_Cogongrass_Cover 0.4788 0.6497 0.0402 0.2742 2.1913 1.0036 753
## total_shrub_cover 0.8647 1.3105 0.0612 0.4940 3.8918 1.0225 393
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2613 1.3583 0.0827 0.8581 4.6883 1.0151 135
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7496 0.2898 -3.3189 -2.7521 -2.1605 1.0025 1504
## shrub_cover 0.5102 0.3215 -0.1296 0.5056 1.1741 1.0069 1355
## veg_height 0.0944 0.2014 -0.3265 0.0958 0.4812 1.0029 1427
## week -0.1028 0.1385 -0.3916 -0.0966 0.1690 1.0008 1243
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5776 0.5501 0.1233 0.4380 1.8534 1.0258 1171
## shrub_cover 0.6774 0.7189 0.1193 0.4977 2.2433 1.0419 929
## veg_height 0.2557 0.2699 0.0564 0.1882 0.8045 1.0456 1272
## week 0.1092 0.1023 0.0232 0.0798 0.3735 1.0025 1730
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1692 0.5947 -0.9308 0.1553
## (Intercept)-Procyon_lotor 0.2810 0.6250 -0.8842 0.2610
## (Intercept)-Dasypus_novemcinctus -0.4231 0.5536 -1.5448 -0.4213
## (Intercept)-Lynx_rufus -0.2024 0.6223 -1.3883 -0.2327
## (Intercept)-Didelphis_virginiana -0.6994 0.6335 -2.0186 -0.6730
## (Intercept)-Sylvilagus_floridanus -0.1805 0.6174 -1.3493 -0.1884
## (Intercept)-Meleagris_gallopavo -0.4423 0.6425 -1.7175 -0.4386
## (Intercept)-Sciurus_carolinensis -0.6749 0.6550 -2.0776 -0.6514
## Cogon_Patch_Size-Canis_latrans 0.6916 0.6804 -0.3311 0.5729
## Cogon_Patch_Size-Procyon_lotor -0.1349 0.4577 -1.0999 -0.1149
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0257 0.4522 -0.8953 0.0264
## Cogon_Patch_Size-Lynx_rufus -0.0308 0.7269 -1.3689 -0.0696
## Cogon_Patch_Size-Didelphis_virginiana 0.5798 0.5160 -0.3406 0.5335
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6416 0.8615 -2.7817 -0.4810
## Cogon_Patch_Size-Meleagris_gallopavo 0.0531 0.6213 -1.1043 0.0402
## Cogon_Patch_Size-Sciurus_carolinensis -0.5106 0.6592 -2.0776 -0.4192
## Avg_Cogongrass_Cover-Canis_latrans 0.2729 0.4554 -0.5383 0.2424
## Avg_Cogongrass_Cover-Procyon_lotor 0.0606 0.4481 -0.8470 0.0486
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2934 0.4399 -0.5229 0.2690
## Avg_Cogongrass_Cover-Lynx_rufus 0.4567 0.5500 -0.4642 0.4023
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0611 0.4615 -0.8660 0.0531
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2584 0.5448 -1.4026 -0.2258
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4593 0.7166 -2.0827 -0.3955
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3239 0.4864 -0.5728 0.2917
## total_shrub_cover-Canis_latrans -0.0180 0.6331 -1.1035 -0.0679
## total_shrub_cover-Procyon_lotor -1.1862 0.5634 -2.5360 -1.1198
## total_shrub_cover-Dasypus_novemcinctus -0.5338 0.5834 -1.8976 -0.4899
## total_shrub_cover-Lynx_rufus -1.3154 0.8121 -3.2177 -1.1921
## total_shrub_cover-Didelphis_virginiana -0.9108 0.6018 -2.2402 -0.8528
## total_shrub_cover-Sylvilagus_floridanus -1.1053 0.7526 -2.8788 -1.0064
## total_shrub_cover-Meleagris_gallopavo -1.4691 0.7624 -3.2779 -1.3580
## total_shrub_cover-Sciurus_carolinensis -0.8789 0.7214 -2.5597 -0.8055
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4309 1.0025 625
## (Intercept)-Procyon_lotor 1.5561 1.0051 502
## (Intercept)-Dasypus_novemcinctus 0.6682 1.0028 650
## (Intercept)-Lynx_rufus 1.1232 1.0357 618
## (Intercept)-Didelphis_virginiana 0.4855 1.0163 608
## (Intercept)-Sylvilagus_floridanus 1.0308 1.0047 594
## (Intercept)-Meleagris_gallopavo 0.7971 1.0164 587
## (Intercept)-Sciurus_carolinensis 0.5380 1.0144 585
## Cogon_Patch_Size-Canis_latrans 2.2441 1.0057 1055
## Cogon_Patch_Size-Procyon_lotor 0.7677 1.0031 1166
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9233 1.0185 1686
## Cogon_Patch_Size-Lynx_rufus 1.5808 1.0336 693
## Cogon_Patch_Size-Didelphis_virginiana 1.6975 1.0036 1045
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5059 1.0155 507
## Cogon_Patch_Size-Meleagris_gallopavo 1.3140 1.0074 912
## Cogon_Patch_Size-Sciurus_carolinensis 0.5570 1.0136 792
## Avg_Cogongrass_Cover-Canis_latrans 1.2565 1.0148 1022
## Avg_Cogongrass_Cover-Procyon_lotor 0.9738 1.0093 1605
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2022 1.0120 1415
## Avg_Cogongrass_Cover-Lynx_rufus 1.7613 1.0141 837
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0025 1.0088 1033
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7575 1.0162 973
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7718 1.0047 454
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3628 1.0200 971
## total_shrub_cover-Canis_latrans 1.4406 1.0140 494
## total_shrub_cover-Procyon_lotor -0.2692 1.0021 491
## total_shrub_cover-Dasypus_novemcinctus 0.4704 1.0372 488
## total_shrub_cover-Lynx_rufus -0.0832 1.0047 338
## total_shrub_cover-Didelphis_virginiana 0.1250 1.0219 596
## total_shrub_cover-Sylvilagus_floridanus 0.1191 1.0158 321
## total_shrub_cover-Meleagris_gallopavo -0.3019 1.0138 401
## total_shrub_cover-Sciurus_carolinensis 0.3470 1.0182 464
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7458 0.1858 -3.1339 -2.7382 -2.4018 1.0304
## (Intercept)-Procyon_lotor -2.3101 0.1406 -2.5909 -2.3095 -2.0390 1.0028
## (Intercept)-Dasypus_novemcinctus -1.8595 0.1794 -2.2407 -1.8503 -1.5362 1.0029
## (Intercept)-Lynx_rufus -3.3900 0.2987 -3.9952 -3.3804 -2.8459 1.0174
## (Intercept)-Didelphis_virginiana -2.7157 0.2935 -3.2995 -2.7069 -2.1529 1.0168
## (Intercept)-Sylvilagus_floridanus -3.2507 0.2593 -3.7956 -3.2440 -2.7544 1.0076
## (Intercept)-Meleagris_gallopavo -3.4862 0.4845 -4.4595 -3.4617 -2.5709 1.0090
## (Intercept)-Sciurus_carolinensis -2.8472 0.3262 -3.4853 -2.8475 -2.2150 1.0154
## shrub_cover-Canis_latrans -0.2614 0.2376 -0.7350 -0.2604 0.1889 1.0145
## shrub_cover-Procyon_lotor 0.3253 0.1595 0.0151 0.3244 0.6401 1.0037
## shrub_cover-Dasypus_novemcinctus 1.0560 0.3430 0.4084 1.0478 1.7296 1.0261
## shrub_cover-Lynx_rufus 0.2053 0.3422 -0.5012 0.2205 0.8292 1.0059
## shrub_cover-Didelphis_virginiana 1.1730 0.3937 0.4313 1.1584 1.9701 1.0025
## shrub_cover-Sylvilagus_floridanus 0.7590 0.4204 -0.0339 0.7506 1.5957 1.0251
## shrub_cover-Meleagris_gallopavo -0.2181 0.4605 -1.1248 -0.2103 0.6674 1.0084
## shrub_cover-Sciurus_carolinensis 1.1748 0.4147 0.3560 1.1679 1.9941 1.0066
## veg_height-Canis_latrans -0.5645 0.1923 -0.9593 -0.5585 -0.2043 1.0108
## veg_height-Procyon_lotor 0.3520 0.1193 0.1113 0.3525 0.5835 1.0023
## veg_height-Dasypus_novemcinctus 0.2854 0.1416 0.0187 0.2814 0.5601 1.0015
## veg_height-Lynx_rufus 0.0564 0.2301 -0.4076 0.0620 0.4810 1.0175
## veg_height-Didelphis_virginiana 0.4594 0.2430 -0.0003 0.4491 0.9586 1.0068
## veg_height-Sylvilagus_floridanus 0.0981 0.2521 -0.4028 0.0977 0.5896 1.0147
## veg_height-Meleagris_gallopavo -0.0760 0.4439 -0.9761 -0.0659 0.8379 1.0067
## veg_height-Sciurus_carolinensis 0.1409 0.2313 -0.3131 0.1365 0.6060 1.0089
## week-Canis_latrans 0.0584 0.1347 -0.2087 0.0644 0.3135 1.0015
## week-Procyon_lotor -0.0587 0.1199 -0.3072 -0.0529 0.1655 1.0022
## week-Dasypus_novemcinctus -0.1770 0.1425 -0.4799 -0.1748 0.0953 1.0036
## week-Lynx_rufus -0.0556 0.1925 -0.4490 -0.0482 0.3056 1.0058
## week-Didelphis_virginiana -0.2302 0.2181 -0.6984 -0.2177 0.1675 1.0004
## week-Sylvilagus_floridanus -0.1816 0.2078 -0.6303 -0.1721 0.1841 1.0028
## week-Meleagris_gallopavo -0.2934 0.2422 -0.8310 -0.2717 0.1321 1.0027
## week-Sciurus_carolinensis 0.1189 0.1809 -0.2304 0.1184 0.4723 1.0035
## ESS
## (Intercept)-Canis_latrans 518
## (Intercept)-Procyon_lotor 1296
## (Intercept)-Dasypus_novemcinctus 668
## (Intercept)-Lynx_rufus 565
## (Intercept)-Didelphis_virginiana 506
## (Intercept)-Sylvilagus_floridanus 521
## (Intercept)-Meleagris_gallopavo 369
## (Intercept)-Sciurus_carolinensis 433
## shrub_cover-Canis_latrans 592
## shrub_cover-Procyon_lotor 1533
## shrub_cover-Dasypus_novemcinctus 449
## shrub_cover-Lynx_rufus 563
## shrub_cover-Didelphis_virginiana 432
## shrub_cover-Sylvilagus_floridanus 293
## shrub_cover-Meleagris_gallopavo 356
## shrub_cover-Sciurus_carolinensis 396
## veg_height-Canis_latrans 699
## veg_height-Procyon_lotor 1466
## veg_height-Dasypus_novemcinctus 1887
## veg_height-Lynx_rufus 852
## veg_height-Didelphis_virginiana 978
## veg_height-Sylvilagus_floridanus 563
## veg_height-Meleagris_gallopavo 422
## veg_height-Sciurus_carolinensis 825
## week-Canis_latrans 1504
## week-Procyon_lotor 1732
## week-Dasypus_novemcinctus 1984
## week-Lynx_rufus 1008
## week-Didelphis_virginiana 1071
## week-Sylvilagus_floridanus 837
## week-Meleagris_gallopavo 836
## week-Sciurus_carolinensis 1539
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8332
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3536 0.4023 -1.1640 -0.3584 0.4715 1.0324 534
## Veg_shannon_index 0.3679 0.2967 -0.2184 0.3649 0.9610 1.0123 927
## Avg_Cogongrass_Cover 0.3515 0.2961 -0.2325 0.3542 0.9157 1.0072 853
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6524 0.8053 0.0526 0.4095 2.6790 1.0134 716
## Veg_shannon_index 0.3118 0.4481 0.0392 0.1913 1.3724 1.0312 1128
## Avg_Cogongrass_Cover 0.3238 0.4381 0.0376 0.1991 1.3402 1.0109 1282
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8153 0.8653 0.0621 0.5853 3.0432 1.1024 212
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7573 0.3267 -3.3902 -2.7629 -2.0517 1.0008 2140
## shrub_cover 0.2678 0.3091 -0.3580 0.2676 0.8893 1.0188 1404
## veg_height 0.0711 0.2005 -0.3336 0.0716 0.4830 1.0105 1595
## week -0.0944 0.1403 -0.3921 -0.0908 0.1697 1.0072 1290
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7946 0.6437 0.1836 0.6008 2.4963 1.0049 636
## shrub_cover 0.6897 0.5691 0.1456 0.5261 2.2388 1.0046 756
## veg_height 0.2670 0.2294 0.0616 0.2034 0.8230 1.0200 1419
## week 0.1071 0.0955 0.0249 0.0795 0.3601 1.0008 1524
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0490 0.5495 -1.0028 0.0406
## (Intercept)-Procyon_lotor 0.1528 0.5601 -0.9364 0.1515
## (Intercept)-Dasypus_novemcinctus -0.5513 0.4832 -1.5570 -0.5261
## (Intercept)-Lynx_rufus -0.2226 0.6574 -1.4874 -0.2555
## (Intercept)-Didelphis_virginiana -0.9138 0.5899 -2.1181 -0.8766
## (Intercept)-Sylvilagus_floridanus -0.3955 0.5630 -1.4865 -0.4033
## (Intercept)-Meleagris_gallopavo -0.1116 0.6961 -1.3455 -0.1595
## (Intercept)-Sciurus_carolinensis -0.8984 0.5705 -2.0847 -0.8745
## Veg_shannon_index-Canis_latrans 0.6494 0.3918 -0.0558 0.6269
## Veg_shannon_index-Procyon_lotor 0.4682 0.3776 -0.2354 0.4401
## Veg_shannon_index-Dasypus_novemcinctus 0.1871 0.3519 -0.5499 0.1962
## Veg_shannon_index-Lynx_rufus 0.2164 0.5051 -0.8960 0.2523
## Veg_shannon_index-Didelphis_virginiana 0.4938 0.3825 -0.1962 0.4752
## Veg_shannon_index-Sylvilagus_floridanus 0.4500 0.4290 -0.3448 0.4370
## Veg_shannon_index-Meleagris_gallopavo 0.5548 0.5034 -0.3243 0.5107
## Veg_shannon_index-Sciurus_carolinensis -0.0118 0.4275 -0.9416 0.0212
## Avg_Cogongrass_Cover-Canis_latrans 0.6112 0.4082 -0.1005 0.5791
## Avg_Cogongrass_Cover-Procyon_lotor 0.3866 0.3720 -0.3307 0.3696
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4575 0.3374 -0.1759 0.4423
## Avg_Cogongrass_Cover-Lynx_rufus 0.5794 0.4232 -0.1959 0.5507
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4507 0.3783 -0.2779 0.4461
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0625 0.4669 -1.1099 -0.0219
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0025 0.6187 -1.4128 0.0500
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4270 0.3640 -0.3012 0.4206
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1501 1.0186 557
## (Intercept)-Procyon_lotor 1.2777 1.0173 389
## (Intercept)-Dasypus_novemcinctus 0.3795 1.0159 1089
## (Intercept)-Lynx_rufus 1.2500 1.0139 437
## (Intercept)-Didelphis_virginiana 0.1703 1.0086 735
## (Intercept)-Sylvilagus_floridanus 0.7845 1.0256 658
## (Intercept)-Meleagris_gallopavo 1.4591 1.0764 316
## (Intercept)-Sciurus_carolinensis 0.1667 1.0087 847
## Veg_shannon_index-Canis_latrans 1.4754 1.0002 1682
## Veg_shannon_index-Procyon_lotor 1.2897 1.0043 1458
## Veg_shannon_index-Dasypus_novemcinctus 0.8612 1.0006 1847
## Veg_shannon_index-Lynx_rufus 1.1343 1.0078 997
## Veg_shannon_index-Didelphis_virginiana 1.3104 1.0180 1593
## Veg_shannon_index-Sylvilagus_floridanus 1.3246 1.0095 1396
## Veg_shannon_index-Meleagris_gallopavo 1.6349 1.0293 1102
## Veg_shannon_index-Sciurus_carolinensis 0.7506 1.0028 1325
## Avg_Cogongrass_Cover-Canis_latrans 1.4961 1.0061 1541
## Avg_Cogongrass_Cover-Procyon_lotor 1.1737 1.0040 1378
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1433 1.0161 1568
## Avg_Cogongrass_Cover-Lynx_rufus 1.4977 1.0012 1388
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1979 1.0057 1824
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7672 1.0086 1065
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.0622 1.0097 588
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1717 1.0003 1962
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7482 0.1796 -3.1087 -2.7439 -2.4077 1.0062
## (Intercept)-Procyon_lotor -2.3032 0.1447 -2.6016 -2.2978 -2.0301 1.0072
## (Intercept)-Dasypus_novemcinctus -1.7850 0.1599 -2.1042 -1.7847 -1.4836 0.9997
## (Intercept)-Lynx_rufus -3.5789 0.3414 -4.2645 -3.5701 -2.9335 1.0085
## (Intercept)-Didelphis_virginiana -2.6429 0.2985 -3.2835 -2.6244 -2.0963 1.0008
## (Intercept)-Sylvilagus_floridanus -3.1902 0.2890 -3.7871 -3.1802 -2.6699 1.0098
## (Intercept)-Meleagris_gallopavo -3.9209 0.4886 -4.9168 -3.8924 -2.9969 1.0327
## (Intercept)-Sciurus_carolinensis -2.6962 0.3138 -3.3363 -2.6813 -2.1089 1.0041
## shrub_cover-Canis_latrans -0.2752 0.2176 -0.7032 -0.2774 0.1593 1.0106
## shrub_cover-Procyon_lotor 0.2434 0.1710 -0.1089 0.2476 0.5716 1.0003
## shrub_cover-Dasypus_novemcinctus 0.8750 0.3006 0.3110 0.8724 1.4801 1.0091
## shrub_cover-Lynx_rufus -0.2221 0.3602 -0.9215 -0.2241 0.4865 1.0248
## shrub_cover-Didelphis_virginiana 1.0065 0.3677 0.3249 0.9881 1.7612 1.0011
## shrub_cover-Sylvilagus_floridanus 0.2729 0.4375 -0.5603 0.2596 1.1859 1.0029
## shrub_cover-Meleagris_gallopavo -0.6796 0.4208 -1.5411 -0.6741 0.1170 1.0347
## shrub_cover-Sciurus_carolinensis 0.8973 0.4153 0.1028 0.8939 1.7305 1.0069
## veg_height-Canis_latrans -0.5938 0.1893 -0.9729 -0.5930 -0.2300 1.0316
## veg_height-Procyon_lotor 0.3385 0.1178 0.1096 0.3368 0.5760 1.0090
## veg_height-Dasypus_novemcinctus 0.2628 0.1359 0.0002 0.2611 0.5322 1.0086
## veg_height-Lynx_rufus 0.0417 0.2488 -0.4605 0.0501 0.5184 1.0047
## veg_height-Didelphis_virginiana 0.4610 0.2493 0.0056 0.4553 0.9746 1.0052
## veg_height-Sylvilagus_floridanus 0.1495 0.2491 -0.3335 0.1508 0.6213 1.0044
## veg_height-Meleagris_gallopavo -0.2032 0.3856 -0.9438 -0.2149 0.5875 1.0232
## veg_height-Sciurus_carolinensis 0.0921 0.2221 -0.3219 0.0842 0.5483 1.0004
## week-Canis_latrans 0.0629 0.1371 -0.2204 0.0683 0.3187 1.0013
## week-Procyon_lotor -0.0551 0.1195 -0.3173 -0.0509 0.1638 1.0058
## week-Dasypus_novemcinctus -0.1699 0.1398 -0.4596 -0.1647 0.0972 1.0000
## week-Lynx_rufus -0.0458 0.1932 -0.4402 -0.0379 0.3218 1.0058
## week-Didelphis_virginiana -0.2324 0.2200 -0.7178 -0.2193 0.1616 1.0079
## week-Sylvilagus_floridanus -0.1664 0.2100 -0.6203 -0.1530 0.2172 1.0057
## week-Meleagris_gallopavo -0.2807 0.2355 -0.7941 -0.2623 0.1235 1.0025
## week-Sciurus_carolinensis 0.1228 0.1846 -0.2299 0.1274 0.4715 1.0028
## ESS
## (Intercept)-Canis_latrans 881
## (Intercept)-Procyon_lotor 1282
## (Intercept)-Dasypus_novemcinctus 1723
## (Intercept)-Lynx_rufus 347
## (Intercept)-Didelphis_virginiana 790
## (Intercept)-Sylvilagus_floridanus 525
## (Intercept)-Meleagris_gallopavo 160
## (Intercept)-Sciurus_carolinensis 697
## shrub_cover-Canis_latrans 960
## shrub_cover-Procyon_lotor 1408
## shrub_cover-Dasypus_novemcinctus 1260
## shrub_cover-Lynx_rufus 423
## shrub_cover-Didelphis_virginiana 725
## shrub_cover-Sylvilagus_floridanus 443
## shrub_cover-Meleagris_gallopavo 199
## shrub_cover-Sciurus_carolinensis 761
## veg_height-Canis_latrans 787
## veg_height-Procyon_lotor 1758
## veg_height-Dasypus_novemcinctus 2113
## veg_height-Lynx_rufus 783
## veg_height-Didelphis_virginiana 1021
## veg_height-Sylvilagus_floridanus 844
## veg_height-Meleagris_gallopavo 454
## veg_height-Sciurus_carolinensis 1129
## week-Canis_latrans 1497
## week-Procyon_lotor 1695
## week-Dasypus_novemcinctus 2025
## week-Lynx_rufus 928
## week-Didelphis_virginiana 1324
## week-Sylvilagus_floridanus 901
## week-Meleagris_gallopavo 731
## week-Sciurus_carolinensis 1580
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.877
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9990 0.4220 -1.7816 -1.0122 -0.1216 1.0007 642
## Avg_Cogongrass_Cover -0.6240 0.4108 -1.4596 -0.6208 0.1446 1.0130 607
## I(Avg_Cogongrass_Cover^2) 0.8319 0.4092 0.1150 0.8078 1.6927 1.0158 407
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6760 0.8099 0.0568 0.4241 2.9021 1.0079 650
## Avg_Cogongrass_Cover 0.5379 0.9508 0.0439 0.2835 2.4751 1.0239 674
## I(Avg_Cogongrass_Cover^2) 0.7596 2.3633 0.0442 0.2956 3.6560 1.6368 126
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4898 0.5417 0.0465 0.3169 2.0094 1.0378 236
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7382 0.3165 -3.3685 -2.7359 -2.0986 0.9999 2061
## shrub_cover 0.2608 0.3104 -0.3513 0.2618 0.8783 1.0068 1722
## veg_height 0.0986 0.2042 -0.3200 0.0978 0.4918 1.0003 1244
## week -0.0978 0.1388 -0.3928 -0.0941 0.1732 1.0016 1472
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7269 0.6945 0.1596 0.5584 2.4156 1.0205 879
## shrub_cover 0.6742 0.5965 0.1229 0.5101 2.3108 1.0035 1153
## veg_height 0.2455 0.2152 0.0558 0.1872 0.8135 1.0018 1643
## week 0.1103 0.1116 0.0243 0.0802 0.3645 1.0061 1589
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6855 0.5799 -1.7555 -0.7002
## (Intercept)-Procyon_lotor -0.4875 0.5884 -1.5916 -0.5056
## (Intercept)-Dasypus_novemcinctus -1.1754 0.5130 -2.2160 -1.1631
## (Intercept)-Lynx_rufus -1.0862 0.6331 -2.3477 -1.0905
## (Intercept)-Didelphis_virginiana -1.4359 0.5751 -2.7136 -1.3949
## (Intercept)-Sylvilagus_floridanus -1.0597 0.5604 -2.1776 -1.0540
## (Intercept)-Meleagris_gallopavo -0.5566 0.8015 -1.8821 -0.6652
## (Intercept)-Sciurus_carolinensis -1.6919 0.6416 -3.0974 -1.6390
## Avg_Cogongrass_Cover-Canis_latrans -0.3055 0.5461 -1.2695 -0.3348
## Avg_Cogongrass_Cover-Procyon_lotor -0.6430 0.5238 -1.7320 -0.6359
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4359 0.4931 -1.3382 -0.4460
## Avg_Cogongrass_Cover-Lynx_rufus -0.5833 0.5914 -1.7820 -0.5623
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3292 0.5480 -1.3770 -0.3518
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1483 0.6811 -2.7328 -1.0632
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0406 0.7553 -2.7673 -0.9512
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6238 0.5461 -1.7774 -0.6125
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3518 0.9084 0.2287 1.1532
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.3055 1.3265 0.2159 1.0633
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6818 0.3755 -0.0224 0.6610
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2054 0.5598 0.3197 1.1422
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4725 0.4286 -0.3376 0.4623
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6824 0.4620 -0.1291 0.6496
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3822 0.7784 -1.0806 0.3355
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8525 0.4225 0.0969 0.8141
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4779 1.0016 828
## (Intercept)-Procyon_lotor 0.6778 1.0277 592
## (Intercept)-Dasypus_novemcinctus -0.1708 1.0018 1047
## (Intercept)-Lynx_rufus 0.2366 1.0167 527
## (Intercept)-Didelphis_virginiana -0.4296 1.0057 1033
## (Intercept)-Sylvilagus_floridanus 0.0272 1.0106 952
## (Intercept)-Meleagris_gallopavo 1.3745 1.0042 363
## (Intercept)-Sciurus_carolinensis -0.5852 1.0161 877
## Avg_Cogongrass_Cover-Canis_latrans 0.8241 1.0218 948
## Avg_Cogongrass_Cover-Procyon_lotor 0.3520 1.0024 1091
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5919 1.0012 1070
## Avg_Cogongrass_Cover-Lynx_rufus 0.5692 1.0006 807
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8291 1.0105 821
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0635 1.0119 611
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2110 1.0068 453
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4299 1.0025 977
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5676 1.1310 183
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.5964 1.3456 88
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4892 1.0106 994
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4884 1.0037 654
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3918 1.0231 772
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7088 1.0171 657
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.2404 1.0979 208
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7842 1.0100 914
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7501 0.1833 -3.1483 -2.7506 -2.3953 1.0041
## (Intercept)-Procyon_lotor -2.3229 0.1484 -2.6185 -2.3162 -2.0454 1.0090
## (Intercept)-Dasypus_novemcinctus -1.7820 0.1636 -2.1194 -1.7796 -1.4660 1.0036
## (Intercept)-Lynx_rufus -3.4977 0.3346 -4.1769 -3.4846 -2.8522 1.0141
## (Intercept)-Didelphis_virginiana -2.6434 0.2849 -3.2152 -2.6397 -2.1012 1.0032
## (Intercept)-Sylvilagus_floridanus -3.1622 0.2801 -3.7444 -3.1472 -2.6497 1.0222
## (Intercept)-Meleagris_gallopavo -3.8336 0.5127 -4.7908 -3.8591 -2.7711 1.0076
## (Intercept)-Sciurus_carolinensis -2.6921 0.3047 -3.3046 -2.6918 -2.1160 1.0051
## shrub_cover-Canis_latrans -0.2581 0.2169 -0.6941 -0.2570 0.1587 1.0014
## shrub_cover-Procyon_lotor 0.2264 0.1703 -0.1134 0.2341 0.5501 1.0078
## shrub_cover-Dasypus_novemcinctus 0.8661 0.2929 0.3183 0.8588 1.4610 1.0009
## shrub_cover-Lynx_rufus -0.1892 0.3693 -0.9255 -0.1835 0.5230 1.0118
## shrub_cover-Didelphis_virginiana 0.9884 0.3829 0.3034 0.9718 1.8165 1.0179
## shrub_cover-Sylvilagus_floridanus 0.2503 0.4206 -0.5227 0.2362 1.1364 1.0039
## shrub_cover-Meleagris_gallopavo -0.5939 0.4311 -1.4029 -0.6161 0.3275 1.0224
## shrub_cover-Sciurus_carolinensis 0.8630 0.4005 0.0828 0.8584 1.6556 1.0115
## veg_height-Canis_latrans -0.5827 0.1881 -0.9562 -0.5769 -0.2270 1.0008
## veg_height-Procyon_lotor 0.3494 0.1260 0.1071 0.3460 0.6074 1.0103
## veg_height-Dasypus_novemcinctus 0.2608 0.1337 0.0032 0.2564 0.5278 1.0041
## veg_height-Lynx_rufus 0.0863 0.2415 -0.3950 0.0875 0.5512 1.0001
## veg_height-Didelphis_virginiana 0.4439 0.2553 -0.0351 0.4376 0.9719 1.0085
## veg_height-Sylvilagus_floridanus 0.1823 0.2440 -0.2962 0.1793 0.6676 1.0048
## veg_height-Meleagris_gallopavo -0.0566 0.3998 -0.8461 -0.0683 0.7466 1.0061
## veg_height-Sciurus_carolinensis 0.1066 0.2200 -0.3152 0.1037 0.5494 1.0151
## week-Canis_latrans 0.0586 0.1359 -0.2128 0.0616 0.3138 1.0078
## week-Procyon_lotor -0.0622 0.1179 -0.2996 -0.0626 0.1623 1.0061
## week-Dasypus_novemcinctus -0.1749 0.1386 -0.4559 -0.1699 0.0802 1.0002
## week-Lynx_rufus -0.0565 0.1989 -0.4629 -0.0490 0.3158 1.0021
## week-Didelphis_virginiana -0.2284 0.2223 -0.7065 -0.2163 0.1639 1.0021
## week-Sylvilagus_floridanus -0.1683 0.1981 -0.5819 -0.1599 0.1889 1.0063
## week-Meleagris_gallopavo -0.2764 0.2407 -0.8170 -0.2589 0.1320 1.0030
## week-Sciurus_carolinensis 0.1297 0.1854 -0.2490 0.1313 0.4814 1.0044
## ESS
## (Intercept)-Canis_latrans 676
## (Intercept)-Procyon_lotor 971
## (Intercept)-Dasypus_novemcinctus 1563
## (Intercept)-Lynx_rufus 374
## (Intercept)-Didelphis_virginiana 714
## (Intercept)-Sylvilagus_floridanus 449
## (Intercept)-Meleagris_gallopavo 200
## (Intercept)-Sciurus_carolinensis 848
## shrub_cover-Canis_latrans 808
## shrub_cover-Procyon_lotor 926
## shrub_cover-Dasypus_novemcinctus 1245
## shrub_cover-Lynx_rufus 431
## shrub_cover-Didelphis_virginiana 543
## shrub_cover-Sylvilagus_floridanus 464
## shrub_cover-Meleagris_gallopavo 213
## shrub_cover-Sciurus_carolinensis 918
## veg_height-Canis_latrans 640
## veg_height-Procyon_lotor 1677
## veg_height-Dasypus_novemcinctus 2153
## veg_height-Lynx_rufus 856
## veg_height-Didelphis_virginiana 912
## veg_height-Sylvilagus_floridanus 698
## veg_height-Meleagris_gallopavo 417
## veg_height-Sciurus_carolinensis 1095
## week-Canis_latrans 1512
## week-Procyon_lotor 1714
## week-Dasypus_novemcinctus 1996
## week-Lynx_rufus 1108
## week-Didelphis_virginiana 1349
## week-Sylvilagus_floridanus 994
## week-Meleagris_gallopavo 725
## week-Sciurus_carolinensis 1739
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9047
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8073 0.6935 -3.1314 -1.8188 -0.3929 1.0428 297
## Cogon_Patch_Size 0.1240 0.7428 -1.5081 0.1489 1.5152 1.0085 435
## Veg_shannon_index 0.9151 0.4844 -0.0292 0.9035 1.8862 1.0321 358
## total_shrub_cover -1.1863 0.6579 -2.5768 -1.1449 0.0185 1.0079 448
## Avg_Cogongrass_Cover -0.1078 1.0501 -2.0691 -0.1439 2.0641 1.0268 142
## Tree_Density -2.1427 0.8804 -3.8667 -2.1417 -0.4005 1.0589 268
## Avg_Canopy_Cover 1.7801 0.7499 0.3050 1.7648 3.2645 1.0175 770
## I(Avg_Cogongrass_Cover^2) 1.3803 0.6744 0.0967 1.3716 2.7275 1.0089 435
## avg_veg_height -0.0805 0.5893 -1.2310 -0.0691 1.0564 1.0267 220
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8181 2.8820 0.0634 0.8588 9.0906 1.0896 243
## Cogon_Patch_Size 2.6776 4.2382 0.0884 1.4028 13.2636 1.0929 362
## Veg_shannon_index 0.6977 1.2459 0.0461 0.3509 3.4421 1.0425 393
## total_shrub_cover 2.3970 3.3625 0.1013 1.3889 11.1237 1.0480 512
## Avg_Cogongrass_Cover 1.5009 2.6191 0.0533 0.6278 7.8627 1.0112 467
## Tree_Density 3.6413 6.4441 0.0635 1.5679 19.3434 1.0202 202
## Avg_Canopy_Cover 4.4545 7.0003 0.2325 2.3962 21.7991 1.0224 297
## I(Avg_Cogongrass_Cover^2) 2.3988 3.5571 0.0741 1.2315 12.3712 1.0148 218
## avg_veg_height 0.7133 1.0452 0.0467 0.3560 3.6050 1.0417 672
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3291 1.6693 0.0647 0.7589 5.6781 1.2196 86
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7703 0.2960 -3.2916 -2.7780 -2.1626 1.0032 1454
## shrub_cover 0.4624 0.3093 -0.1287 0.4650 1.0643 1.0035 1294
## veg_height 0.1442 0.1898 -0.2479 0.1460 0.4995 1.0019 1098
## week -0.1013 0.1372 -0.3873 -0.0973 0.1476 1.0061 1424
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6249 0.5404 0.1467 0.4688 2.0455 1.0043 1175
## shrub_cover 0.6817 0.6360 0.1206 0.4981 2.3183 1.0041 790
## veg_height 0.2414 0.2180 0.0562 0.1795 0.8115 1.0038 1687
## week 0.1086 0.1000 0.0253 0.0796 0.3532 1.0062 1559
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4621 0.9819 -3.2325 -1.5030
## (Intercept)-Procyon_lotor -1.0541 0.9762 -2.8499 -1.0833
## (Intercept)-Dasypus_novemcinctus -2.1794 0.8900 -4.0825 -2.1310
## (Intercept)-Lynx_rufus -1.5136 1.2260 -3.6036 -1.6405
## (Intercept)-Didelphis_virginiana -2.6966 1.1038 -5.2775 -2.5588
## (Intercept)-Sylvilagus_floridanus -1.9298 0.9967 -4.0465 -1.9156
## (Intercept)-Meleagris_gallopavo -1.8615 1.0960 -4.0300 -1.8620
## (Intercept)-Sciurus_carolinensis -2.9074 1.2590 -5.9132 -2.7279
## Cogon_Patch_Size-Canis_latrans 1.4437 1.2357 -0.4444 1.2423
## Cogon_Patch_Size-Procyon_lotor -0.4194 0.8396 -2.2062 -0.3849
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1126 0.8745 -1.6276 0.1084
## Cogon_Patch_Size-Lynx_rufus -0.1690 1.3729 -3.0461 -0.1233
## Cogon_Patch_Size-Didelphis_virginiana 1.3502 0.9872 -0.3270 1.2445
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0072 1.5192 -4.7026 -0.7587
## Cogon_Patch_Size-Meleagris_gallopavo 0.3923 1.1695 -1.6697 0.3000
## Cogon_Patch_Size-Sciurus_carolinensis -0.6505 1.2283 -3.5802 -0.5140
## Veg_shannon_index-Canis_latrans 1.2581 0.6653 0.1158 1.2011
## Veg_shannon_index-Procyon_lotor 1.1386 0.6076 0.0769 1.0925
## Veg_shannon_index-Dasypus_novemcinctus 0.5776 0.5947 -0.6572 0.5853
## Veg_shannon_index-Lynx_rufus 0.8905 0.8202 -0.7500 0.8658
## Veg_shannon_index-Didelphis_virginiana 1.0930 0.7019 -0.1152 1.0297
## Veg_shannon_index-Sylvilagus_floridanus 1.0133 0.7109 -0.3010 0.9750
## Veg_shannon_index-Meleagris_gallopavo 1.2307 0.7793 -0.0756 1.1292
## Veg_shannon_index-Sciurus_carolinensis 0.3820 0.7957 -1.3761 0.4533
## total_shrub_cover-Canis_latrans 0.1222 0.8651 -1.4149 0.0525
## total_shrub_cover-Procyon_lotor -1.5764 0.7509 -3.2534 -1.5176
## total_shrub_cover-Dasypus_novemcinctus -0.5500 0.8781 -2.4284 -0.5111
## total_shrub_cover-Lynx_rufus -1.8721 1.3140 -4.7962 -1.7244
## total_shrub_cover-Didelphis_virginiana -1.4629 1.0951 -4.0454 -1.3324
## total_shrub_cover-Sylvilagus_floridanus -1.2873 1.1735 -4.0007 -1.1406
## total_shrub_cover-Meleagris_gallopavo -2.6844 1.4253 -6.0232 -2.4963
## total_shrub_cover-Sciurus_carolinensis -1.1971 1.2463 -4.0807 -1.0793
## Avg_Cogongrass_Cover-Canis_latrans -0.0148 1.2556 -2.4148 -0.0442
## Avg_Cogongrass_Cover-Procyon_lotor -0.2348 1.3096 -2.8492 -0.2497
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5754 1.4231 -1.8586 0.4422
## Avg_Cogongrass_Cover-Lynx_rufus -0.0928 1.3977 -2.7835 -0.1491
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0914 1.3567 -2.5215 0.0714
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7573 1.3767 -3.7620 -0.7019
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4630 1.4348 -3.5169 -0.4299
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0259 1.3339 -2.4986 -0.0237
## Tree_Density-Canis_latrans -3.0866 1.4006 -6.5031 -2.9245
## Tree_Density-Procyon_lotor -2.2617 1.0746 -4.5049 -2.2089
## Tree_Density-Dasypus_novemcinctus -3.9144 1.8337 -8.4333 -3.4973
## Tree_Density-Lynx_rufus -0.8520 1.6319 -3.6260 -0.9509
## Tree_Density-Didelphis_virginiana -2.1193 1.2802 -4.6108 -2.1265
## Tree_Density-Sylvilagus_floridanus -2.7130 1.5285 -6.3408 -2.5589
## Tree_Density-Meleagris_gallopavo -2.3300 1.4424 -5.5511 -2.2433
## Tree_Density-Sciurus_carolinensis -2.2993 1.5578 -5.7831 -2.2476
## Avg_Canopy_Cover-Canis_latrans 0.0849 0.6787 -1.3361 0.0913
## Avg_Canopy_Cover-Procyon_lotor 1.5622 0.8021 0.1220 1.5304
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2266 0.9146 0.7346 2.1095
## Avg_Canopy_Cover-Lynx_rufus 0.9728 1.3809 -1.5140 0.8741
## Avg_Canopy_Cover-Didelphis_virginiana 3.1526 1.4890 1.0792 2.8487
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9374 2.0884 1.1380 3.5517
## Avg_Canopy_Cover-Meleagris_gallopavo 2.3088 1.2670 0.4625 2.1160
## Avg_Canopy_Cover-Sciurus_carolinensis 3.0046 1.5827 0.9777 2.6804
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4069 1.2043 0.6555 2.1902
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4075 1.1623 0.7093 2.2098
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3744 0.7663 -0.0096 1.3212
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.6107 1.3682 0.6453 2.3227
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.7792 0.7545 -0.6970 0.7625
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0744 0.9259 -0.5222 1.0161
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0892 1.3361 -2.8271 0.1790
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.4800 0.8113 0.0283 1.4136
## avg_veg_height-Canis_latrans -0.2272 0.6673 -1.6394 -0.2006
## avg_veg_height-Procyon_lotor 0.0551 0.7118 -1.3595 0.0438
## avg_veg_height-Dasypus_novemcinctus 0.3010 0.7349 -1.0671 0.2758
## avg_veg_height-Lynx_rufus -0.4408 0.9249 -2.4892 -0.3521
## avg_veg_height-Didelphis_virginiana -0.2871 0.7986 -1.9649 -0.2552
## avg_veg_height-Sylvilagus_floridanus -0.2155 0.8096 -1.9076 -0.1939
## avg_veg_height-Meleagris_gallopavo -0.2142 0.9808 -2.3588 -0.1670
## avg_veg_height-Sciurus_carolinensis 0.3478 0.8172 -1.0785 0.2849
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6889 1.0092 338
## (Intercept)-Procyon_lotor 0.9463 1.0093 293
## (Intercept)-Dasypus_novemcinctus -0.5936 1.0536 501
## (Intercept)-Lynx_rufus 1.3326 1.0187 256
## (Intercept)-Didelphis_virginiana -0.8745 1.0449 324
## (Intercept)-Sylvilagus_floridanus 0.0132 1.0305 653
## (Intercept)-Meleagris_gallopavo 0.2810 1.0620 260
## (Intercept)-Sciurus_carolinensis -0.9067 1.0272 229
## Cogon_Patch_Size-Canis_latrans 4.4722 1.0714 308
## Cogon_Patch_Size-Procyon_lotor 1.0964 1.0052 305
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8912 1.0020 519
## Cogon_Patch_Size-Lynx_rufus 2.4989 1.0114 255
## Cogon_Patch_Size-Didelphis_virginiana 3.5753 1.0601 268
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2360 1.0792 289
## Cogon_Patch_Size-Meleagris_gallopavo 2.9828 1.0169 392
## Cogon_Patch_Size-Sciurus_carolinensis 1.3609 1.0250 348
## Veg_shannon_index-Canis_latrans 2.7445 1.0388 491
## Veg_shannon_index-Procyon_lotor 2.4774 1.0442 283
## Veg_shannon_index-Dasypus_novemcinctus 1.7497 1.0036 571
## Veg_shannon_index-Lynx_rufus 2.5170 1.0200 545
## Veg_shannon_index-Didelphis_virginiana 2.5832 1.0080 541
## Veg_shannon_index-Sylvilagus_floridanus 2.5317 1.0374 536
## Veg_shannon_index-Meleagris_gallopavo 3.0625 1.0258 615
## Veg_shannon_index-Sciurus_carolinensis 1.7975 1.0036 475
## total_shrub_cover-Canis_latrans 2.1693 1.0099 444
## total_shrub_cover-Procyon_lotor -0.2919 1.0194 431
## total_shrub_cover-Dasypus_novemcinctus 1.0659 1.0006 526
## total_shrub_cover-Lynx_rufus 0.2448 1.0059 215
## total_shrub_cover-Didelphis_virginiana 0.2833 1.0243 410
## total_shrub_cover-Sylvilagus_floridanus 0.7088 1.0230 384
## total_shrub_cover-Meleagris_gallopavo -0.4505 1.0203 243
## total_shrub_cover-Sciurus_carolinensis 0.8613 1.0734 324
## Avg_Cogongrass_Cover-Canis_latrans 2.5733 1.0143 234
## Avg_Cogongrass_Cover-Procyon_lotor 2.3714 1.0168 203
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.6888 1.0209 238
## Avg_Cogongrass_Cover-Lynx_rufus 2.8708 1.0143 255
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.9686 1.0297 254
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7815 1.0116 238
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2864 1.0370 180
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7686 1.0166 238
## Tree_Density-Canis_latrans -0.8269 1.0042 234
## Tree_Density-Procyon_lotor -0.2744 1.0200 328
## Tree_Density-Dasypus_novemcinctus -1.3458 1.0025 232
## Tree_Density-Lynx_rufus 2.6980 1.0889 219
## Tree_Density-Didelphis_virginiana 0.6360 1.0217 473
## Tree_Density-Sylvilagus_floridanus -0.1199 1.0058 364
## Tree_Density-Meleagris_gallopavo 0.3425 1.0162 319
## Tree_Density-Sciurus_carolinensis 0.7533 1.0677 285
## Avg_Canopy_Cover-Canis_latrans 1.3807 1.0193 584
## Avg_Canopy_Cover-Procyon_lotor 3.2486 1.0218 494
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.3795 1.0172 289
## Avg_Canopy_Cover-Lynx_rufus 3.9747 1.0153 303
## Avg_Canopy_Cover-Didelphis_virginiana 6.7486 1.0597 308
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.4122 1.0326 208
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3076 1.0070 312
## Avg_Canopy_Cover-Sciurus_carolinensis 6.7955 1.0266 304
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.3884 1.0326 256
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.1258 1.0427 224
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0406 1.0322 454
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.1144 1.0385 199
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.2521 1.0542 315
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2376 1.0210 293
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.3897 1.0348 161
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.2244 1.0091 551
## avg_veg_height-Canis_latrans 1.0367 1.0024 330
## avg_veg_height-Procyon_lotor 1.4538 1.0279 271
## avg_veg_height-Dasypus_novemcinctus 1.8759 1.0194 462
## avg_veg_height-Lynx_rufus 1.1859 1.0114 326
## avg_veg_height-Didelphis_virginiana 1.1770 1.0339 295
## avg_veg_height-Sylvilagus_floridanus 1.3440 1.0225 339
## avg_veg_height-Meleagris_gallopavo 1.6095 1.0078 239
## avg_veg_height-Sciurus_carolinensis 2.1220 1.0111 501
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7128 0.1777 -3.0700 -2.7068 -2.3778 1.0099
## (Intercept)-Procyon_lotor -2.3161 0.1423 -2.6098 -2.3146 -2.0547 1.0154
## (Intercept)-Dasypus_novemcinctus -1.8386 0.1690 -2.1743 -1.8336 -1.5086 1.0053
## (Intercept)-Lynx_rufus -3.5721 0.3159 -4.1900 -3.5682 -2.9725 1.0150
## (Intercept)-Didelphis_virginiana -2.7251 0.2946 -3.3357 -2.7170 -2.1650 1.0343
## (Intercept)-Sylvilagus_floridanus -3.1762 0.2554 -3.6929 -3.1707 -2.6960 1.0065
## (Intercept)-Meleagris_gallopavo -3.5401 0.4726 -4.4797 -3.5501 -2.6024 1.0029
## (Intercept)-Sciurus_carolinensis -2.8708 0.3191 -3.5114 -2.8697 -2.2692 1.0208
## shrub_cover-Canis_latrans -0.2512 0.2350 -0.7063 -0.2542 0.2178 1.0130
## shrub_cover-Procyon_lotor 0.2879 0.1638 -0.0288 0.2926 0.6012 1.0017
## shrub_cover-Dasypus_novemcinctus 1.0298 0.3228 0.3901 1.0343 1.6693 1.0141
## shrub_cover-Lynx_rufus 0.0641 0.3588 -0.6406 0.0750 0.7464 1.0039
## shrub_cover-Didelphis_virginiana 1.1524 0.3805 0.4501 1.1339 1.9252 1.0126
## shrub_cover-Sylvilagus_floridanus 0.5984 0.3913 -0.1582 0.5975 1.3838 1.0165
## shrub_cover-Meleagris_gallopavo -0.2891 0.4400 -1.1574 -0.2814 0.5749 1.0029
## shrub_cover-Sciurus_carolinensis 1.1401 0.4047 0.3825 1.1366 1.9492 1.0004
## veg_height-Canis_latrans -0.5235 0.1850 -0.8932 -0.5192 -0.1718 1.0032
## veg_height-Procyon_lotor 0.3702 0.1241 0.1271 0.3683 0.6136 1.0003
## veg_height-Dasypus_novemcinctus 0.2897 0.1402 0.0233 0.2915 0.5687 1.0015
## veg_height-Lynx_rufus 0.1656 0.2321 -0.3101 0.1680 0.6250 1.0074
## veg_height-Didelphis_virginiana 0.5089 0.2423 0.0538 0.5000 1.0087 1.0042
## veg_height-Sylvilagus_floridanus 0.1725 0.2413 -0.3107 0.1691 0.6483 1.0101
## veg_height-Meleagris_gallopavo -0.0231 0.4269 -0.8591 -0.0301 0.8168 1.0101
## veg_height-Sciurus_carolinensis 0.1779 0.2189 -0.2558 0.1762 0.6251 1.0222
## week-Canis_latrans 0.0565 0.1345 -0.2163 0.0596 0.3137 1.0078
## week-Procyon_lotor -0.0576 0.1209 -0.3081 -0.0534 0.1785 1.0117
## week-Dasypus_novemcinctus -0.1786 0.1389 -0.4690 -0.1736 0.0771 1.0014
## week-Lynx_rufus -0.0481 0.1929 -0.4454 -0.0432 0.2989 1.0031
## week-Didelphis_virginiana -0.2338 0.2216 -0.7076 -0.2194 0.1500 1.0045
## week-Sylvilagus_floridanus -0.1697 0.2022 -0.6020 -0.1617 0.2008 1.0053
## week-Meleagris_gallopavo -0.3035 0.2462 -0.8366 -0.2849 0.1448 1.0258
## week-Sciurus_carolinensis 0.1120 0.1844 -0.2550 0.1168 0.4569 1.0090
## ESS
## (Intercept)-Canis_latrans 673
## (Intercept)-Procyon_lotor 1044
## (Intercept)-Dasypus_novemcinctus 851
## (Intercept)-Lynx_rufus 278
## (Intercept)-Didelphis_virginiana 413
## (Intercept)-Sylvilagus_floridanus 573
## (Intercept)-Meleagris_gallopavo 257
## (Intercept)-Sciurus_carolinensis 526
## shrub_cover-Canis_latrans 625
## shrub_cover-Procyon_lotor 1439
## shrub_cover-Dasypus_novemcinctus 477
## shrub_cover-Lynx_rufus 335
## shrub_cover-Didelphis_virginiana 417
## shrub_cover-Sylvilagus_floridanus 467
## shrub_cover-Meleagris_gallopavo 349
## shrub_cover-Sciurus_carolinensis 438
## veg_height-Canis_latrans 821
## veg_height-Procyon_lotor 1302
## veg_height-Dasypus_novemcinctus 1739
## veg_height-Lynx_rufus 587
## veg_height-Didelphis_virginiana 1052
## veg_height-Sylvilagus_floridanus 862
## veg_height-Meleagris_gallopavo 178
## veg_height-Sciurus_carolinensis 941
## week-Canis_latrans 1646
## week-Procyon_lotor 1687
## week-Dasypus_novemcinctus 1751
## week-Lynx_rufus 938
## week-Didelphis_virginiana 1101
## week-Sylvilagus_floridanus 1049
## week-Meleagris_gallopavo 827
## week-Sciurus_carolinensis 1460
# Includes all covariates of occupancy and null for detection
ms_null_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.576
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9720 0.6760 -2.2650 -0.9786 0.3849 1.0020 538
## Cogon_Patch_Size -0.5193 0.6057 -1.7106 -0.5291 0.7560 1.0029 896
## Veg_shannon_index 0.9479 0.4105 0.1492 0.9424 1.7667 1.0045 464
## total_shrub_cover -0.5838 0.5306 -1.6890 -0.5647 0.4564 1.0138 1167
## Avg_Cogongrass_Cover 2.0893 0.6881 0.7099 2.0989 3.4997 1.0080 266
## Tree_Density -1.9045 0.6970 -3.3126 -1.8685 -0.5706 1.0148 485
## Avg_Canopy_Cover 1.6829 0.5752 0.5829 1.6749 2.8355 1.0048 650
## avg_veg_height -0.5447 0.4628 -1.4803 -0.5432 0.3629 1.0461 304
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6513 3.5332 0.1067 1.6028 11.9271 1.0364 489
## Cogon_Patch_Size 2.1840 2.7622 0.1139 1.3132 9.5849 1.0176 509
## Veg_shannon_index 0.4721 0.6458 0.0400 0.2671 2.0864 1.0062 786
## total_shrub_cover 1.7403 2.2098 0.0951 1.0919 7.2982 1.0292 399
## Avg_Cogongrass_Cover 0.9057 1.4697 0.0525 0.4302 4.5339 1.0652 457
## Tree_Density 2.5560 5.1472 0.0711 1.1929 12.4269 1.2318 290
## Avg_Canopy_Cover 2.0119 3.1786 0.1109 1.1193 9.2017 1.1411 457
## avg_veg_height 0.4278 0.5919 0.0368 0.2513 1.8244 1.0160 995
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0233 2.0845 0.0878 1.4093 7.7451 1.1436 103
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5878 0.2913 -3.1546 -2.5908 -2.0161 1.0069 2369
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6465 0.5469 0.1661 0.4936 2.0256 1.0059 1619
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0149 0.9252 -1.7916 -0.0161
## (Intercept)-Procyon_lotor 0.1202 0.9597 -1.8559 0.1688
## (Intercept)-Dasypus_novemcinctus -1.4305 0.8269 -3.1704 -1.3635
## (Intercept)-Lynx_rufus -0.1610 1.4028 -2.2864 -0.3771
## (Intercept)-Didelphis_virginiana -2.2581 0.9875 -4.3480 -2.2141
## (Intercept)-Sylvilagus_floridanus -1.2321 0.9733 -3.2031 -1.2251
## (Intercept)-Meleagris_gallopavo -1.3141 1.0574 -3.5477 -1.2687
## (Intercept)-Sciurus_carolinensis -2.3797 1.0337 -4.6331 -2.2952
## Cogon_Patch_Size-Canis_latrans 0.5770 1.0221 -0.9262 0.4377
## Cogon_Patch_Size-Procyon_lotor -0.9631 0.7012 -2.3814 -0.9422
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7779 0.5993 -1.9844 -0.7615
## Cogon_Patch_Size-Lynx_rufus -0.7316 1.1912 -2.9434 -0.8092
## Cogon_Patch_Size-Didelphis_virginiana 0.8412 0.9021 -0.7044 0.7659
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6783 1.2381 -4.7032 -1.4882
## Cogon_Patch_Size-Meleagris_gallopavo -0.4281 1.0029 -2.2547 -0.5067
## Cogon_Patch_Size-Sciurus_carolinensis -1.4398 0.9694 -3.8023 -1.2991
## Veg_shannon_index-Canis_latrans 1.1996 0.5442 0.2456 1.1642
## Veg_shannon_index-Procyon_lotor 1.1931 0.5433 0.1997 1.1560
## Veg_shannon_index-Dasypus_novemcinctus 0.7387 0.4757 -0.2502 0.7463
## Veg_shannon_index-Lynx_rufus 0.8745 0.7031 -0.5511 0.8719
## Veg_shannon_index-Didelphis_virginiana 1.0357 0.5685 -0.0009 0.9998
## Veg_shannon_index-Sylvilagus_floridanus 1.0300 0.6074 -0.0871 1.0010
## Veg_shannon_index-Meleagris_gallopavo 1.1811 0.6326 0.0609 1.1412
## Veg_shannon_index-Sciurus_carolinensis 0.4371 0.6005 -0.8986 0.4805
## total_shrub_cover-Canis_latrans 0.2147 0.7029 -0.9467 0.1481
## total_shrub_cover-Procyon_lotor -0.9953 0.6138 -2.3358 -0.9570
## total_shrub_cover-Dasypus_novemcinctus 0.0918 0.5826 -1.0096 0.0812
## total_shrub_cover-Lynx_rufus -1.2944 1.0705 -3.9525 -1.1369
## total_shrub_cover-Didelphis_virginiana -0.6302 0.7313 -2.1611 -0.5947
## total_shrub_cover-Sylvilagus_floridanus -0.2894 0.8610 -2.1247 -0.2852
## total_shrub_cover-Meleagris_gallopavo -2.1642 1.2294 -4.9435 -2.0235
## total_shrub_cover-Sciurus_carolinensis -0.0498 0.7147 -1.4018 -0.0754
## Avg_Cogongrass_Cover-Canis_latrans 2.3088 0.8207 0.7761 2.2815
## Avg_Cogongrass_Cover-Procyon_lotor 2.2308 0.8555 0.6022 2.2330
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5731 0.8667 1.0663 2.5137
## Avg_Cogongrass_Cover-Lynx_rufus 2.4273 0.9258 0.7990 2.3651
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1955 0.8212 0.6503 2.1937
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.5768 0.9519 -0.4985 1.6243
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.6299 1.1460 -1.0547 1.7557
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.4022 0.8476 0.9055 2.3517
## Tree_Density-Canis_latrans -2.4928 1.1461 -5.2053 -2.3317
## Tree_Density-Procyon_lotor -1.5221 0.7519 -2.9519 -1.5414
## Tree_Density-Dasypus_novemcinctus -3.2577 1.5342 -7.1231 -2.9237
## Tree_Density-Lynx_rufus -0.5437 1.2106 -2.5565 -0.6684
## Tree_Density-Didelphis_virginiana -2.1827 1.0312 -4.4998 -2.0762
## Tree_Density-Sylvilagus_floridanus -2.4872 1.4215 -5.8246 -2.2873
## Tree_Density-Meleagris_gallopavo -2.1056 1.2082 -4.8005 -2.0234
## Tree_Density-Sciurus_carolinensis -2.3520 1.1929 -5.1348 -2.1784
## Avg_Canopy_Cover-Canis_latrans 0.4092 0.6521 -0.8631 0.3988
## Avg_Canopy_Cover-Procyon_lotor 1.6657 0.6487 0.5264 1.6228
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9553 0.6417 0.8494 1.8944
## Avg_Canopy_Cover-Lynx_rufus 0.9381 1.0547 -1.0615 0.8994
## Avg_Canopy_Cover-Didelphis_virginiana 2.4895 0.8597 1.1531 2.3534
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9864 1.3934 1.0292 2.7325
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2260 1.0591 0.6430 2.0402
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0684 0.7271 0.8523 1.9899
## avg_veg_height-Canis_latrans -0.7700 0.5693 -1.9058 -0.7661
## avg_veg_height-Procyon_lotor -0.4235 0.5542 -1.5274 -0.4343
## avg_veg_height-Dasypus_novemcinctus -0.3174 0.5652 -1.3951 -0.3470
## avg_veg_height-Lynx_rufus -0.6601 0.6674 -2.0372 -0.6464
## avg_veg_height-Didelphis_virginiana -0.6550 0.6310 -1.9601 -0.6397
## avg_veg_height-Sylvilagus_floridanus -0.7468 0.6644 -2.1928 -0.7226
## avg_veg_height-Meleagris_gallopavo -0.6888 0.7282 -2.3081 -0.6530
## avg_veg_height-Sciurus_carolinensis -0.2125 0.6265 -1.3275 -0.2446
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7740 1.0714 406
## (Intercept)-Procyon_lotor 1.9126 1.0519 246
## (Intercept)-Dasypus_novemcinctus -0.0199 1.0067 478
## (Intercept)-Lynx_rufus 3.4899 1.0753 168
## (Intercept)-Didelphis_virginiana -0.4949 1.0896 492
## (Intercept)-Sylvilagus_floridanus 0.6869 1.0106 533
## (Intercept)-Meleagris_gallopavo 0.7520 1.0272 475
## (Intercept)-Sciurus_carolinensis -0.6383 1.0199 434
## Cogon_Patch_Size-Canis_latrans 3.0603 1.0119 679
## Cogon_Patch_Size-Procyon_lotor 0.3985 1.0099 438
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3612 1.0106 640
## Cogon_Patch_Size-Lynx_rufus 1.9797 1.0203 323
## Cogon_Patch_Size-Didelphis_virginiana 2.8635 1.0231 570
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1923 1.0199 473
## Cogon_Patch_Size-Meleagris_gallopavo 1.8008 1.0158 553
## Cogon_Patch_Size-Sciurus_carolinensis 0.0947 1.0013 588
## Veg_shannon_index-Canis_latrans 2.3973 1.0049 477
## Veg_shannon_index-Procyon_lotor 2.3889 1.0083 402
## Veg_shannon_index-Dasypus_novemcinctus 1.6745 1.0103 457
## Veg_shannon_index-Lynx_rufus 2.3191 1.0029 610
## Veg_shannon_index-Didelphis_virginiana 2.2692 1.0098 725
## Veg_shannon_index-Sylvilagus_floridanus 2.3445 1.0058 620
## Veg_shannon_index-Meleagris_gallopavo 2.6008 1.0009 755
## Veg_shannon_index-Sciurus_carolinensis 1.5112 1.0050 733
## total_shrub_cover-Canis_latrans 1.7748 1.0221 741
## total_shrub_cover-Procyon_lotor 0.1317 1.0073 1155
## total_shrub_cover-Dasypus_novemcinctus 1.2518 1.0005 1071
## total_shrub_cover-Lynx_rufus 0.3893 1.0684 345
## total_shrub_cover-Didelphis_virginiana 0.7412 1.0046 1158
## total_shrub_cover-Sylvilagus_floridanus 1.4093 1.0101 573
## total_shrub_cover-Meleagris_gallopavo -0.2313 1.0802 314
## total_shrub_cover-Sciurus_carolinensis 1.4020 1.0053 1088
## Avg_Cogongrass_Cover-Canis_latrans 4.0144 1.0121 342
## Avg_Cogongrass_Cover-Procyon_lotor 3.9965 1.0099 375
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.4524 1.0277 293
## Avg_Cogongrass_Cover-Lynx_rufus 4.4033 1.0073 400
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8565 1.0256 365
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2803 1.0060 490
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.5395 1.0271 315
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.2648 1.0064 369
## Tree_Density-Canis_latrans -0.7697 1.0137 378
## Tree_Density-Procyon_lotor 0.0191 1.0081 686
## Tree_Density-Dasypus_novemcinctus -1.2915 1.0256 197
## Tree_Density-Lynx_rufus 1.9993 1.0172 290
## Tree_Density-Didelphis_virginiana -0.4865 1.0085 625
## Tree_Density-Sylvilagus_floridanus -0.4096 1.0172 274
## Tree_Density-Meleagris_gallopavo 0.1590 1.0019 547
## Tree_Density-Sciurus_carolinensis -0.5870 1.0394 336
## Avg_Canopy_Cover-Canis_latrans 1.7431 1.0072 939
## Avg_Canopy_Cover-Procyon_lotor 3.0236 1.0051 716
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3875 1.0081 435
## Avg_Canopy_Cover-Lynx_rufus 3.0905 1.0017 331
## Avg_Canopy_Cover-Didelphis_virginiana 4.5847 1.0244 319
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.3083 1.0676 265
## Avg_Canopy_Cover-Meleagris_gallopavo 4.9911 1.0328 325
## Avg_Canopy_Cover-Sciurus_carolinensis 3.7674 1.0023 559
## avg_veg_height-Canis_latrans 0.3648 1.0304 546
## avg_veg_height-Procyon_lotor 0.7306 1.0160 552
## avg_veg_height-Dasypus_novemcinctus 0.8529 1.0433 478
## avg_veg_height-Lynx_rufus 0.6861 1.0190 589
## avg_veg_height-Didelphis_virginiana 0.6117 1.0277 508
## avg_veg_height-Sylvilagus_floridanus 0.4963 1.0274 545
## avg_veg_height-Meleagris_gallopavo 0.7028 1.0433 463
## avg_veg_height-Sciurus_carolinensis 1.1610 1.0184 500
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6255 0.1731 -2.9711 -2.6219 -2.2969 1.0034
## (Intercept)-Procyon_lotor -2.2643 0.1273 -2.5213 -2.2636 -2.0155 1.0087
## (Intercept)-Dasypus_novemcinctus -1.6038 0.1385 -1.8779 -1.6031 -1.3468 1.0034
## (Intercept)-Lynx_rufus -3.5028 0.3123 -4.1777 -3.4913 -2.9305 1.0427
## (Intercept)-Didelphis_virginiana -2.3203 0.2405 -2.8337 -2.3139 -1.8699 1.0023
## (Intercept)-Sylvilagus_floridanus -3.1366 0.2669 -3.6982 -3.1233 -2.6545 1.0189
## (Intercept)-Meleagris_gallopavo -3.3241 0.2828 -3.8868 -3.3174 -2.7969 1.0068
## (Intercept)-Sciurus_carolinensis -2.4672 0.2652 -3.0238 -2.4582 -1.9888 1.0132
## ESS
## (Intercept)-Canis_latrans 992
## (Intercept)-Procyon_lotor 1338
## (Intercept)-Dasypus_novemcinctus 2050
## (Intercept)-Lynx_rufus 307
## (Intercept)-Didelphis_virginiana 1445
## (Intercept)-Sylvilagus_floridanus 510
## (Intercept)-Meleagris_gallopavo 483
## (Intercept)-Sciurus_carolinensis 1010
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5787
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5327 0.3929 -1.3456 -0.5201 0.2226 1.0025 642
## Avg_Cogongrass_Cover 0.1504 0.3364 -0.5359 0.1569 0.7868 1.0037 818
## total_shrub_cover -0.4817 0.3479 -1.2425 -0.4694 0.1521 1.0018 1269
## avg_veg_height -0.0181 0.3228 -0.6396 -0.0192 0.6112 1.0231 607
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7559 0.8792 0.0642 0.5069 3.1053 1.0159 818
## Avg_Cogongrass_Cover 0.4147 0.5506 0.0413 0.2492 1.8619 1.0037 950
## total_shrub_cover 0.6635 0.8379 0.0610 0.4068 3.0707 1.0245 909
## avg_veg_height 0.2706 0.3377 0.0374 0.1700 1.1320 1.0082 1303
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7053 0.7178 0.0588 0.4784 2.6753 1.0824 182
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5705 0.2838 -3.1203 -2.5714 -1.9755 1.0013 2484
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5899 0.5075 0.1484 0.4601 1.8903 1.0165 1948
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0128 0.5607 -1.0234 -0.0301
## (Intercept)-Procyon_lotor 0.2081 0.6130 -0.9766 0.1935
## (Intercept)-Dasypus_novemcinctus -0.6850 0.4678 -1.6068 -0.6841
## (Intercept)-Lynx_rufus -0.3910 0.6255 -1.5653 -0.4106
## (Intercept)-Didelphis_virginiana -1.1290 0.5380 -2.2353 -1.1193
## (Intercept)-Sylvilagus_floridanus -0.4490 0.5855 -1.5269 -0.4774
## (Intercept)-Meleagris_gallopavo -0.7352 0.6018 -1.9687 -0.7219
## (Intercept)-Sciurus_carolinensis -1.1832 0.5656 -2.4483 -1.1442
## Avg_Cogongrass_Cover-Canis_latrans 0.4036 0.4567 -0.4472 0.3783
## Avg_Cogongrass_Cover-Procyon_lotor 0.0764 0.4306 -0.7624 0.0711
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2701 0.3934 -0.5062 0.2623
## Avg_Cogongrass_Cover-Lynx_rufus 0.4787 0.4923 -0.4023 0.4542
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3456 0.4345 -0.4609 0.3316
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2620 0.5372 -1.4422 -0.2079
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3827 0.6194 -1.8666 -0.3081
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2441 0.4167 -0.5764 0.2458
## total_shrub_cover-Canis_latrans 0.0857 0.4453 -0.7224 0.0622
## total_shrub_cover-Procyon_lotor -0.9041 0.4862 -2.0181 -0.8521
## total_shrub_cover-Dasypus_novemcinctus -0.0815 0.3774 -0.7865 -0.0944
## total_shrub_cover-Lynx_rufus -0.9085 0.6180 -2.3512 -0.8268
## total_shrub_cover-Didelphis_virginiana -0.2986 0.4189 -1.1101 -0.3015
## total_shrub_cover-Sylvilagus_floridanus -0.4312 0.5263 -1.5610 -0.4140
## total_shrub_cover-Meleagris_gallopavo -1.2766 0.7027 -2.9494 -1.1520
## total_shrub_cover-Sciurus_carolinensis -0.1440 0.4367 -1.0119 -0.1470
## avg_veg_height-Canis_latrans -0.0949 0.4006 -0.8706 -0.0981
## avg_veg_height-Procyon_lotor 0.0725 0.4076 -0.7073 0.0549
## avg_veg_height-Dasypus_novemcinctus 0.1748 0.4043 -0.6040 0.1676
## avg_veg_height-Lynx_rufus -0.0562 0.4773 -1.0047 -0.0556
## avg_veg_height-Didelphis_virginiana -0.0594 0.4298 -0.9084 -0.0499
## avg_veg_height-Sylvilagus_floridanus -0.1590 0.4402 -1.0831 -0.1452
## avg_veg_height-Meleagris_gallopavo -0.2530 0.5029 -1.3362 -0.2168
## avg_veg_height-Sciurus_carolinensis 0.2427 0.4344 -0.5693 0.2152
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1477 1.0109 564
## (Intercept)-Procyon_lotor 1.4385 1.0061 411
## (Intercept)-Dasypus_novemcinctus 0.2229 1.0021 1178
## (Intercept)-Lynx_rufus 0.9515 1.0106 500
## (Intercept)-Didelphis_virginiana -0.1580 1.0143 912
## (Intercept)-Sylvilagus_floridanus 0.7937 1.0073 762
## (Intercept)-Meleagris_gallopavo 0.4597 1.0017 696
## (Intercept)-Sciurus_carolinensis -0.1868 1.0270 782
## Avg_Cogongrass_Cover-Canis_latrans 1.3515 1.0022 1406
## Avg_Cogongrass_Cover-Procyon_lotor 0.9377 1.0053 1332
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0587 1.0056 1258
## Avg_Cogongrass_Cover-Lynx_rufus 1.5138 1.0153 1173
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2375 1.0076 1431
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6523 1.0070 896
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6138 1.0202 830
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0604 1.0006 1580
## total_shrub_cover-Canis_latrans 1.0480 1.0116 1609
## total_shrub_cover-Procyon_lotor -0.0910 1.0123 1298
## total_shrub_cover-Dasypus_novemcinctus 0.6813 1.0039 2212
## total_shrub_cover-Lynx_rufus 0.0754 1.0106 662
## total_shrub_cover-Didelphis_virginiana 0.5502 1.0043 2235
## total_shrub_cover-Sylvilagus_floridanus 0.5617 1.0105 1047
## total_shrub_cover-Meleagris_gallopavo -0.2658 1.0128 729
## total_shrub_cover-Sciurus_carolinensis 0.7179 1.0023 2015
## avg_veg_height-Canis_latrans 0.7019 1.0043 1253
## avg_veg_height-Procyon_lotor 0.9244 1.0113 1039
## avg_veg_height-Dasypus_novemcinctus 1.0211 1.0114 1082
## avg_veg_height-Lynx_rufus 0.9244 1.0158 908
## avg_veg_height-Didelphis_virginiana 0.7888 1.0040 1162
## avg_veg_height-Sylvilagus_floridanus 0.6725 1.0055 941
## avg_veg_height-Meleagris_gallopavo 0.6720 1.0054 768
## avg_veg_height-Sciurus_carolinensis 1.1799 1.0211 1165
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6154 0.1705 -2.9733 -2.6093 -2.3008 1.0235
## (Intercept)-Procyon_lotor -2.2791 0.1265 -2.5370 -2.2788 -2.0390 1.0070
## (Intercept)-Dasypus_novemcinctus -1.6087 0.1357 -1.8747 -1.6068 -1.3565 1.0014
## (Intercept)-Lynx_rufus -3.3804 0.2894 -3.9859 -3.3711 -2.8398 1.0020
## (Intercept)-Didelphis_virginiana -2.3512 0.2519 -2.8610 -2.3473 -1.8862 1.0051
## (Intercept)-Sylvilagus_floridanus -3.1328 0.2806 -3.7111 -3.1307 -2.6054 1.0150
## (Intercept)-Meleagris_gallopavo -3.2165 0.3007 -3.8437 -3.1988 -2.6755 1.0332
## (Intercept)-Sciurus_carolinensis -2.4818 0.2594 -3.0309 -2.4721 -2.0105 1.0061
## ESS
## (Intercept)-Canis_latrans 1016
## (Intercept)-Procyon_lotor 1474
## (Intercept)-Dasypus_novemcinctus 2361
## (Intercept)-Lynx_rufus 403
## (Intercept)-Didelphis_virginiana 1342
## (Intercept)-Sylvilagus_floridanus 469
## (Intercept)-Meleagris_gallopavo 443
## (Intercept)-Sciurus_carolinensis 1147
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5625
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6615 0.4427 -1.5382 -0.6634 0.2183 1.0110 530
## Tree_Density -0.7843 0.4621 -1.8447 -0.7384 -0.0006 1.0275 867
## Avg_Canopy_Cover 0.9921 0.3812 0.2778 0.9790 1.7872 1.0107 1176
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0577 1.1732 0.0771 0.7273 3.8917 1.0101 717
## Tree_Density 1.0401 1.5930 0.0549 0.5133 5.0965 1.0149 502
## Avg_Canopy_Cover 0.7828 1.0713 0.0700 0.4780 3.2205 1.0068 838
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6147 0.6888 0.0523 0.3881 2.553 1.0084 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5655 0.2882 -3.0959 -2.5793 -1.9535 1.0058 2087
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6157 0.523 0.1631 0.4691 1.8743 1.0403 1507
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0755 0.5966 -1.2205 -0.0947 1.1589
## (Intercept)-Procyon_lotor 0.2303 0.6428 -1.0581 0.2551 1.4643
## (Intercept)-Dasypus_novemcinctus -0.9890 0.5501 -2.1610 -0.9496 -0.0213
## (Intercept)-Lynx_rufus -0.2368 0.7481 -1.5375 -0.2887 1.3859
## (Intercept)-Didelphis_virginiana -1.5158 0.6410 -2.9258 -1.4790 -0.4042
## (Intercept)-Sylvilagus_floridanus -0.7423 0.5903 -1.9546 -0.7413 0.3931
## (Intercept)-Meleagris_gallopavo -0.6597 0.6525 -1.9699 -0.6784 0.7228
## (Intercept)-Sciurus_carolinensis -1.5379 0.6426 -2.9039 -1.4987 -0.3865
## Tree_Density-Canis_latrans -0.9326 0.5935 -2.2600 -0.8618 0.0238
## Tree_Density-Procyon_lotor -0.4632 0.4255 -1.3044 -0.4574 0.3697
## Tree_Density-Dasypus_novemcinctus -1.4417 0.9200 -3.7854 -1.2572 -0.1768
## Tree_Density-Lynx_rufus 0.2794 0.7834 -0.8167 0.1388 2.2394
## Tree_Density-Didelphis_virginiana -1.0349 0.7830 -2.9990 -0.8968 0.1110
## Tree_Density-Sylvilagus_floridanus -1.1324 0.8016 -3.1921 -0.9921 0.1006
## Tree_Density-Meleagris_gallopavo -0.9190 0.7182 -2.5950 -0.8301 0.2543
## Tree_Density-Sciurus_carolinensis -0.9064 0.7287 -2.6142 -0.8130 0.2528
## Avg_Canopy_Cover-Canis_latrans 0.0684 0.4737 -0.9001 0.0831 0.9742
## Avg_Canopy_Cover-Procyon_lotor 0.9789 0.4445 0.1688 0.9595 1.9288
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0206 0.4285 0.2414 0.9983 1.9214
## Avg_Canopy_Cover-Lynx_rufus 0.6420 0.6110 -0.5088 0.6130 1.9981
## Avg_Canopy_Cover-Didelphis_virginiana 1.2222 0.5089 0.3576 1.1698 2.3924
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7401 0.8055 0.5764 1.5931 3.7772
## Avg_Canopy_Cover-Meleagris_gallopavo 1.4054 0.6948 0.2845 1.3101 3.0435
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1846 0.4918 0.3183 1.1422 2.2717
## Rhat ESS
## (Intercept)-Canis_latrans 1.0073 476
## (Intercept)-Procyon_lotor 1.0029 349
## (Intercept)-Dasypus_novemcinctus 1.0029 878
## (Intercept)-Lynx_rufus 1.0130 305
## (Intercept)-Didelphis_virginiana 1.0084 674
## (Intercept)-Sylvilagus_floridanus 1.0171 973
## (Intercept)-Meleagris_gallopavo 1.0138 624
## (Intercept)-Sciurus_carolinensis 1.0152 821
## Tree_Density-Canis_latrans 1.0242 1111
## Tree_Density-Procyon_lotor 1.0001 1951
## Tree_Density-Dasypus_novemcinctus 1.0283 355
## Tree_Density-Lynx_rufus 1.0056 458
## Tree_Density-Didelphis_virginiana 1.0438 556
## Tree_Density-Sylvilagus_floridanus 1.0432 558
## Tree_Density-Meleagris_gallopavo 1.0257 977
## Tree_Density-Sciurus_carolinensis 1.0106 770
## Avg_Canopy_Cover-Canis_latrans 1.0104 1034
## Avg_Canopy_Cover-Procyon_lotor 1.0092 1375
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0139 1811
## Avg_Canopy_Cover-Lynx_rufus 1.0143 882
## Avg_Canopy_Cover-Didelphis_virginiana 1.0031 1173
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0033 564
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0090 729
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0045 1286
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6130 0.1722 -2.9503 -2.6101 -2.2847 1.0082
## (Intercept)-Procyon_lotor -2.2629 0.1277 -2.5224 -2.2586 -2.0251 1.0129
## (Intercept)-Dasypus_novemcinctus -1.6041 0.1382 -1.8764 -1.6019 -1.3427 1.0000
## (Intercept)-Lynx_rufus -3.4350 0.2822 -3.9946 -3.4275 -2.9064 1.0184
## (Intercept)-Didelphis_virginiana -2.3385 0.2422 -2.8247 -2.3338 -1.8874 1.0144
## (Intercept)-Sylvilagus_floridanus -3.0604 0.2558 -3.5914 -3.0559 -2.5805 1.0129
## (Intercept)-Meleagris_gallopavo -3.2912 0.2926 -3.9094 -3.2759 -2.7576 1.0124
## (Intercept)-Sciurus_carolinensis -2.4627 0.2611 -2.9994 -2.4500 -1.9886 1.0132
## ESS
## (Intercept)-Canis_latrans 831
## (Intercept)-Procyon_lotor 1539
## (Intercept)-Dasypus_novemcinctus 2436
## (Intercept)-Lynx_rufus 356
## (Intercept)-Didelphis_virginiana 1425
## (Intercept)-Sylvilagus_floridanus 720
## (Intercept)-Meleagris_gallopavo 507
## (Intercept)-Sciurus_carolinensis 1208
# Includes movement covariates of occupancy and null for detection
ms_null_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5425
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5181 0.4170 -1.3317 -0.5234 0.3825 1.0036 589
## Cogon_Patch_Size -0.0474 0.3899 -0.8818 -0.0430 0.7067 1.0007 1274
## Avg_Cogongrass_Cover 0.1529 0.3221 -0.4864 0.1536 0.7869 1.0016 1192
## total_shrub_cover -0.4716 0.3392 -1.1601 -0.4626 0.2222 1.0020 1426
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7941 1.0305 0.0622 0.4959 3.3816 1.0495 641
## Cogon_Patch_Size 0.7911 1.1332 0.0564 0.4536 3.7152 1.0174 831
## Avg_Cogongrass_Cover 0.4163 0.5717 0.0435 0.2478 1.7491 1.0349 957
## total_shrub_cover 0.6551 0.9110 0.0595 0.3987 2.8253 1.0298 825
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9165 0.8639 0.0768 0.6797 3.1121 1.0378 192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5577 0.2849 -3.1031 -2.5671 -1.9239 1.0017 2499
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5933 0.4871 0.1474 0.458 1.9109 1.0206 2364
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0410 0.5991 -1.0150 0.0082
## (Intercept)-Procyon_lotor 0.1790 0.6400 -1.0125 0.1788
## (Intercept)-Dasypus_novemcinctus -0.6803 0.4970 -1.6867 -0.6716
## (Intercept)-Lynx_rufus -0.4133 0.6632 -1.6648 -0.4367
## (Intercept)-Didelphis_virginiana -1.1114 0.6068 -2.3868 -1.0688
## (Intercept)-Sylvilagus_floridanus -0.5000 0.6283 -1.6542 -0.5506
## (Intercept)-Meleagris_gallopavo -0.7049 0.6142 -1.9508 -0.6809
## (Intercept)-Sciurus_carolinensis -1.1656 0.6278 -2.4538 -1.1216
## Cogon_Patch_Size-Canis_latrans 0.6608 0.6464 -0.2961 0.5583
## Cogon_Patch_Size-Procyon_lotor -0.1622 0.4469 -1.0654 -0.1546
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0809 0.4168 -0.9428 -0.0719
## Cogon_Patch_Size-Lynx_rufus -0.1571 0.6875 -1.4454 -0.1913
## Cogon_Patch_Size-Didelphis_virginiana 0.6266 0.5098 -0.2147 0.5688
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6964 0.7915 -2.6830 -0.5641
## Cogon_Patch_Size-Meleagris_gallopavo -0.0310 0.5734 -1.2125 -0.0204
## Cogon_Patch_Size-Sciurus_carolinensis -0.5518 0.6298 -2.0871 -0.4701
## Avg_Cogongrass_Cover-Canis_latrans 0.2572 0.3928 -0.4718 0.2437
## Avg_Cogongrass_Cover-Procyon_lotor 0.1652 0.4331 -0.6461 0.1463
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3475 0.3769 -0.3440 0.3306
## Avg_Cogongrass_Cover-Lynx_rufus 0.5004 0.4990 -0.3630 0.4610
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1721 0.4134 -0.6769 0.1749
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2016 0.4969 -1.3136 -0.1629
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4170 0.5960 -1.7483 -0.3596
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4166 0.4046 -0.3309 0.3996
## total_shrub_cover-Canis_latrans 0.0468 0.4556 -0.7779 0.0161
## total_shrub_cover-Procyon_lotor -0.8747 0.4838 -1.9705 -0.8297
## total_shrub_cover-Dasypus_novemcinctus -0.1025 0.3888 -0.8333 -0.1223
## total_shrub_cover-Lynx_rufus -0.8586 0.6347 -2.3515 -0.7745
## total_shrub_cover-Didelphis_virginiana -0.3699 0.4339 -1.2635 -0.3711
## total_shrub_cover-Sylvilagus_floridanus -0.3890 0.5531 -1.5755 -0.3642
## total_shrub_cover-Meleagris_gallopavo -1.2233 0.6725 -2.8206 -1.1263
## total_shrub_cover-Sciurus_carolinensis -0.1344 0.4310 -0.9732 -0.1459
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3092 1.0108 583
## (Intercept)-Procyon_lotor 1.4522 1.0086 353
## (Intercept)-Dasypus_novemcinctus 0.2708 1.0171 1096
## (Intercept)-Lynx_rufus 0.9566 1.0183 488
## (Intercept)-Didelphis_virginiana 0.0133 1.0132 744
## (Intercept)-Sylvilagus_floridanus 0.8092 1.0084 546
## (Intercept)-Meleagris_gallopavo 0.4794 1.0125 645
## (Intercept)-Sciurus_carolinensis -0.0710 1.0046 569
## Cogon_Patch_Size-Canis_latrans 2.2089 1.0080 966
## Cogon_Patch_Size-Procyon_lotor 0.6875 1.0023 1638
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7134 1.0031 2110
## Cogon_Patch_Size-Lynx_rufus 1.3311 1.0099 892
## Cogon_Patch_Size-Didelphis_virginiana 1.7252 1.0074 1037
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4871 1.0019 774
## Cogon_Patch_Size-Meleagris_gallopavo 1.0665 1.0033 1292
## Cogon_Patch_Size-Sciurus_carolinensis 0.4263 1.0009 885
## Avg_Cogongrass_Cover-Canis_latrans 1.0552 1.0021 1595
## Avg_Cogongrass_Cover-Procyon_lotor 1.0498 1.0051 1775
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1510 1.0006 1824
## Avg_Cogongrass_Cover-Lynx_rufus 1.6131 1.0047 1123
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9684 1.0102 1760
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6686 1.0037 1120
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5747 1.0015 848
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2782 1.0030 1917
## total_shrub_cover-Canis_latrans 1.0152 1.0013 1211
## total_shrub_cover-Procyon_lotor -0.0619 1.0016 1210
## total_shrub_cover-Dasypus_novemcinctus 0.6925 1.0022 2337
## total_shrub_cover-Lynx_rufus 0.1528 1.0016 505
## total_shrub_cover-Didelphis_virginiana 0.4636 1.0030 2136
## total_shrub_cover-Sylvilagus_floridanus 0.6013 1.0078 707
## total_shrub_cover-Meleagris_gallopavo -0.1900 1.0128 633
## total_shrub_cover-Sciurus_carolinensis 0.7482 1.0052 1915
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5916 0.1663 -2.9366 -2.5845 -2.2826 1.0078
## (Intercept)-Procyon_lotor -2.2785 0.1287 -2.5427 -2.2737 -2.0428 1.0017
## (Intercept)-Dasypus_novemcinctus -1.6028 0.1319 -1.8644 -1.5990 -1.3587 1.0000
## (Intercept)-Lynx_rufus -3.3794 0.2936 -3.9797 -3.3689 -2.8231 1.0273
## (Intercept)-Didelphis_virginiana -2.3304 0.2422 -2.8145 -2.3195 -1.8876 1.0135
## (Intercept)-Sylvilagus_floridanus -3.1461 0.2926 -3.7586 -3.1306 -2.6171 1.0139
## (Intercept)-Meleagris_gallopavo -3.2270 0.2917 -3.8131 -3.2207 -2.6750 1.0096
## (Intercept)-Sciurus_carolinensis -2.4715 0.2636 -3.0343 -2.4591 -1.9963 1.0010
## ESS
## (Intercept)-Canis_latrans 1071
## (Intercept)-Procyon_lotor 1523
## (Intercept)-Dasypus_novemcinctus 2650
## (Intercept)-Lynx_rufus 447
## (Intercept)-Didelphis_virginiana 1332
## (Intercept)-Sylvilagus_floridanus 432
## (Intercept)-Meleagris_gallopavo 556
## (Intercept)-Sciurus_carolinensis 910
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.543
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5664 0.3735 -1.3105 -0.5631 0.1487 1.0085 624
## Veg_shannon_index 0.3690 0.2676 -0.1504 0.3674 0.9075 1.0084 968
## Avg_Cogongrass_Cover 0.3543 0.2809 -0.2142 0.3583 0.9098 1.0072 806
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5858 0.6723 0.0515 0.3846 2.4613 1.0036 807
## Veg_shannon_index 0.2716 0.3337 0.0354 0.1683 1.1331 1.0047 1403
## Avg_Cogongrass_Cover 0.3177 0.4163 0.0401 0.1942 1.3285 1.0094 1260
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8669 0.8275 0.0863 0.6379 2.7639 1.0584 185
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.566 0.2871 -3.1173 -2.5737 -1.971 1.0006 2293
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6083 0.5294 0.1578 0.4629 1.8652 1.0094 1924
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1621 0.5516 -1.2002 -0.1855
## (Intercept)-Procyon_lotor 0.0105 0.5883 -1.1016 0.0038
## (Intercept)-Dasypus_novemcinctus -0.6796 0.4712 -1.6494 -0.6712
## (Intercept)-Lynx_rufus -0.4893 0.5835 -1.6147 -0.5091
## (Intercept)-Didelphis_virginiana -1.0850 0.5415 -2.2596 -1.0462
## (Intercept)-Sylvilagus_floridanus -0.5366 0.5412 -1.5709 -0.5509
## (Intercept)-Meleagris_gallopavo -0.6084 0.5539 -1.6213 -0.6192
## (Intercept)-Sciurus_carolinensis -1.0819 0.5351 -2.2485 -1.0431
## Veg_shannon_index-Canis_latrans 0.6413 0.3972 -0.0664 0.6072
## Veg_shannon_index-Procyon_lotor 0.4831 0.3664 -0.1861 0.4611
## Veg_shannon_index-Dasypus_novemcinctus 0.2246 0.3372 -0.4756 0.2368
## Veg_shannon_index-Lynx_rufus 0.1940 0.4590 -0.8186 0.2257
## Veg_shannon_index-Didelphis_virginiana 0.4735 0.3858 -0.2352 0.4579
## Veg_shannon_index-Sylvilagus_floridanus 0.4688 0.4329 -0.3365 0.4523
## Veg_shannon_index-Meleagris_gallopavo 0.4865 0.4326 -0.3228 0.4604
## Veg_shannon_index-Sciurus_carolinensis 0.0539 0.3829 -0.7830 0.0805
## Avg_Cogongrass_Cover-Canis_latrans 0.5438 0.3810 -0.1539 0.5249
## Avg_Cogongrass_Cover-Procyon_lotor 0.4134 0.3824 -0.2669 0.3926
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4518 0.3320 -0.1821 0.4423
## Avg_Cogongrass_Cover-Lynx_rufus 0.6114 0.4248 -0.1212 0.5808
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4916 0.3629 -0.1762 0.4749
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0496 0.4424 -1.0080 -0.0030
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0609 0.5201 -1.2197 -0.0197
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4419 0.3631 -0.2707 0.4322
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9555 1.0049 677
## (Intercept)-Procyon_lotor 1.1971 1.0115 499
## (Intercept)-Dasypus_novemcinctus 0.2014 1.0124 1029
## (Intercept)-Lynx_rufus 0.7258 1.0043 627
## (Intercept)-Didelphis_virginiana -0.1355 1.0041 774
## (Intercept)-Sylvilagus_floridanus 0.6359 1.0032 741
## (Intercept)-Meleagris_gallopavo 0.5515 1.0126 769
## (Intercept)-Sciurus_carolinensis -0.1205 1.0125 897
## Veg_shannon_index-Canis_latrans 1.5153 1.0038 1216
## Veg_shannon_index-Procyon_lotor 1.2749 1.0076 1439
## Veg_shannon_index-Dasypus_novemcinctus 0.8570 1.0083 1782
## Veg_shannon_index-Lynx_rufus 1.0248 1.0035 1150
## Veg_shannon_index-Didelphis_virginiana 1.2626 1.0049 1521
## Veg_shannon_index-Sylvilagus_floridanus 1.4081 1.0013 1146
## Veg_shannon_index-Meleagris_gallopavo 1.4294 1.0130 1132
## Veg_shannon_index-Sciurus_carolinensis 0.7352 1.0065 1542
## Avg_Cogongrass_Cover-Canis_latrans 1.3721 1.0048 1716
## Avg_Cogongrass_Cover-Procyon_lotor 1.2052 1.0032 1770
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1456 1.0005 2297
## Avg_Cogongrass_Cover-Lynx_rufus 1.5523 1.0066 1427
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2635 1.0027 1881
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7512 1.0066 1250
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8516 1.0112 847
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1882 1.0054 1809
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5912 0.1638 -2.9276 -2.5864 -2.2785 1.0028
## (Intercept)-Procyon_lotor -2.2741 0.1276 -2.5226 -2.2713 -2.0266 1.0054
## (Intercept)-Dasypus_novemcinctus -1.6134 0.1362 -1.8872 -1.6090 -1.3530 1.0018
## (Intercept)-Lynx_rufus -3.3693 0.2980 -3.9911 -3.3587 -2.8151 1.0330
## (Intercept)-Didelphis_virginiana -2.3400 0.2471 -2.8490 -2.3304 -1.8764 1.0127
## (Intercept)-Sylvilagus_floridanus -3.1215 0.2854 -3.7146 -3.1038 -2.6070 1.0037
## (Intercept)-Meleagris_gallopavo -3.3001 0.3117 -3.9197 -3.2830 -2.7169 1.0002
## (Intercept)-Sciurus_carolinensis -2.4773 0.2669 -3.0408 -2.4646 -1.9876 1.0136
## ESS
## (Intercept)-Canis_latrans 1058
## (Intercept)-Procyon_lotor 1568
## (Intercept)-Dasypus_novemcinctus 2096
## (Intercept)-Lynx_rufus 455
## (Intercept)-Didelphis_virginiana 1240
## (Intercept)-Sylvilagus_floridanus 501
## (Intercept)-Meleagris_gallopavo 378
## (Intercept)-Sciurus_carolinensis 1153
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5387
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1341 0.4064 -1.9486 -1.1362 -0.3460 1.0046 601
## Avg_Cogongrass_Cover -0.5649 0.3653 -1.2563 -0.5765 0.1592 1.0094 664
## I(Avg_Cogongrass_Cover^2) 0.7640 0.3725 0.1044 0.7385 1.5975 1.0199 473
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5921 0.6985 0.0559 0.3786 2.3181 1.0074 1214
## Avg_Cogongrass_Cover 0.3799 0.4860 0.0410 0.2284 1.5449 1.0017 788
## I(Avg_Cogongrass_Cover^2) 0.5806 1.1090 0.0450 0.2765 2.8201 1.1798 369
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5135 0.5263 0.0479 0.3505 1.895 1.0152 193
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5651 0.2778 -3.0994 -2.5725 -2.0158 1.0029 1825
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5624 0.4721 0.149 0.4267 1.852 1.0079 1990
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7757 0.5471 -1.8428 -0.7760
## (Intercept)-Procyon_lotor -0.5844 0.5759 -1.6925 -0.5956
## (Intercept)-Dasypus_novemcinctus -1.2407 0.5165 -2.2861 -1.2155
## (Intercept)-Lynx_rufus -1.2665 0.5823 -2.4673 -1.2533
## (Intercept)-Didelphis_virginiana -1.5657 0.5636 -2.7551 -1.5270
## (Intercept)-Sylvilagus_floridanus -1.0978 0.5700 -2.1881 -1.1162
## (Intercept)-Meleagris_gallopavo -0.9748 0.5874 -2.0836 -0.9942
## (Intercept)-Sciurus_carolinensis -1.7959 0.6242 -3.1469 -1.7532
## Avg_Cogongrass_Cover-Canis_latrans -0.3838 0.4939 -1.3060 -0.4069
## Avg_Cogongrass_Cover-Procyon_lotor -0.5547 0.4956 -1.5123 -0.5679
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4178 0.4625 -1.3066 -0.4335
## Avg_Cogongrass_Cover-Lynx_rufus -0.4775 0.5264 -1.4533 -0.4925
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2734 0.5140 -1.2241 -0.3079
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0064 0.5823 -2.3072 -0.9450
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8427 0.5389 -1.9816 -0.8199
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5742 0.5016 -1.5931 -0.5721
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.2544 0.7945 0.2264 1.0694
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1977 0.7863 0.2398 1.0079
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6477 0.3438 -0.0031 0.6356
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1156 0.5109 0.2907 1.0512
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4366 0.4062 -0.3493 0.4320
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6818 0.4673 -0.1213 0.6485
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2204 0.6011 -1.0335 0.2224
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7812 0.3781 0.0859 0.7570
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.2931 1.0017 633
## (Intercept)-Procyon_lotor 0.5835 1.0007 689
## (Intercept)-Dasypus_novemcinctus -0.2364 1.0044 1058
## (Intercept)-Lynx_rufus -0.1832 1.0108 848
## (Intercept)-Didelphis_virginiana -0.4919 1.0006 934
## (Intercept)-Sylvilagus_floridanus 0.0859 1.0096 973
## (Intercept)-Meleagris_gallopavo 0.2224 1.0017 894
## (Intercept)-Sciurus_carolinensis -0.7102 1.0107 875
## Avg_Cogongrass_Cover-Canis_latrans 0.6254 1.0112 782
## Avg_Cogongrass_Cover-Procyon_lotor 0.4389 1.0030 1098
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5277 1.0080 1019
## Avg_Cogongrass_Cover-Lynx_rufus 0.5858 1.0164 1015
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7990 1.0139 980
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0216 1.0199 662
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1411 1.0086 1041
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4163 1.0025 935
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.3515 1.0685 282
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.2505 1.0702 344
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3645 1.0106 1041
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3102 1.0012 642
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.2285 1.0167 803
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7045 1.0176 652
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3854 1.0684 416
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6018 1.0041 1082
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6243 0.1637 -2.9421 -2.6207 -2.3107 1.0011
## (Intercept)-Procyon_lotor -2.2794 0.1296 -2.5435 -2.2778 -2.0310 1.0062
## (Intercept)-Dasypus_novemcinctus -1.6076 0.1375 -1.8900 -1.6057 -1.3380 1.0009
## (Intercept)-Lynx_rufus -3.2807 0.2756 -3.8345 -3.2674 -2.7721 1.0172
## (Intercept)-Didelphis_virginiana -2.3590 0.2556 -2.9172 -2.3413 -1.9053 1.0035
## (Intercept)-Sylvilagus_floridanus -3.1160 0.2808 -3.7047 -3.0999 -2.6152 1.0183
## (Intercept)-Meleagris_gallopavo -3.2790 0.3274 -4.0006 -3.2522 -2.7047 1.0578
## (Intercept)-Sciurus_carolinensis -2.4779 0.2673 -3.0386 -2.4651 -1.9938 1.0035
## ESS
## (Intercept)-Canis_latrans 1132
## (Intercept)-Procyon_lotor 1458
## (Intercept)-Dasypus_novemcinctus 2407
## (Intercept)-Lynx_rufus 569
## (Intercept)-Didelphis_virginiana 1156
## (Intercept)-Sylvilagus_floridanus 491
## (Intercept)-Meleagris_gallopavo 353
## (Intercept)-Sciurus_carolinensis 919
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5632
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9884 0.7001 -3.3849 -1.9836 -0.5491 1.0361 343
## Cogon_Patch_Size -0.0102 0.6767 -1.3803 -0.0047 1.3025 1.0219 422
## Veg_shannon_index 0.8892 0.4481 0.0378 0.8685 1.8396 1.0059 493
## total_shrub_cover -0.7355 0.5769 -1.9584 -0.7128 0.3068 1.0105 1319
## Avg_Cogongrass_Cover 0.3336 0.9041 -1.3456 0.3462 2.0962 1.0697 181
## Tree_Density -2.2442 0.8089 -3.9542 -2.2251 -0.7000 1.0135 229
## Avg_Canopy_Cover 1.6528 0.6295 0.4528 1.6246 2.9830 1.0028 473
## I(Avg_Cogongrass_Cover^2) 1.2575 0.6291 0.0024 1.2522 2.5446 1.0603 321
## avg_veg_height -0.1795 0.4975 -1.1210 -0.2006 0.8209 1.0519 194
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9696 3.5542 0.0643 1.0495 9.3524 1.0461 459
## Cogon_Patch_Size 2.5751 3.2657 0.1268 1.5606 11.4568 1.0697 396
## Veg_shannon_index 0.6018 0.8707 0.0451 0.3049 2.8287 1.0610 707
## total_shrub_cover 2.1929 3.0170 0.1308 1.3165 9.5246 1.0059 630
## Avg_Cogongrass_Cover 1.1487 1.8976 0.0513 0.5035 6.0759 1.0086 700
## Tree_Density 2.4261 4.9002 0.0684 0.9570 13.3553 1.0714 345
## Avg_Canopy_Cover 2.5236 4.3199 0.1030 1.3733 11.6740 1.0810 212
## I(Avg_Cogongrass_Cover^2) 2.1027 3.2960 0.0897 1.1177 9.9260 1.0628 278
## avg_veg_height 0.5170 0.7828 0.0432 0.2723 2.4081 1.0332 553
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.8595 2.5082 0.0554 1.0179 8.9763 1.2983 77
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5751 0.2932 -3.1368 -2.5796 -1.9722 1.008 2482
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6578 0.672 0.1698 0.5013 2.0552 1.0799 2248
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5440 0.9962 -3.4765 -1.5528
## (Intercept)-Procyon_lotor -1.2135 1.0054 -3.1398 -1.2376
## (Intercept)-Dasypus_novemcinctus -2.3750 0.8807 -4.2718 -2.3068
## (Intercept)-Lynx_rufus -1.6002 1.1531 -3.6983 -1.6903
## (Intercept)-Didelphis_virginiana -2.9478 1.0498 -5.2610 -2.8111
## (Intercept)-Sylvilagus_floridanus -2.0981 0.9650 -4.0446 -2.1121
## (Intercept)-Meleagris_gallopavo -2.0315 1.0471 -4.1485 -2.0300
## (Intercept)-Sciurus_carolinensis -3.2976 1.1726 -5.9832 -3.1367
## Cogon_Patch_Size-Canis_latrans 1.3287 1.1037 -0.3894 1.1632
## Cogon_Patch_Size-Procyon_lotor -0.5033 0.8350 -2.1506 -0.5036
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2440 0.7041 -1.6998 -0.2240
## Cogon_Patch_Size-Lynx_rufus -0.2481 1.3833 -2.9937 -0.2393
## Cogon_Patch_Size-Didelphis_virginiana 1.4637 1.0248 -0.1881 1.3357
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1902 1.3170 -4.1733 -0.9876
## Cogon_Patch_Size-Meleagris_gallopavo 0.1746 1.1077 -1.7667 0.0972
## Cogon_Patch_Size-Sciurus_carolinensis -0.8686 1.0541 -3.3169 -0.7403
## Veg_shannon_index-Canis_latrans 1.2380 0.6646 0.1202 1.1586
## Veg_shannon_index-Procyon_lotor 1.0941 0.5836 0.0909 1.0429
## Veg_shannon_index-Dasypus_novemcinctus 0.6286 0.5386 -0.4215 0.6359
## Veg_shannon_index-Lynx_rufus 0.8697 0.7934 -0.6406 0.8503
## Veg_shannon_index-Didelphis_virginiana 0.9681 0.6229 -0.2029 0.9407
## Veg_shannon_index-Sylvilagus_floridanus 0.9621 0.6371 -0.1959 0.9476
## Veg_shannon_index-Meleagris_gallopavo 1.1674 0.7046 -0.0683 1.1032
## Veg_shannon_index-Sciurus_carolinensis 0.3637 0.6886 -1.2092 0.4116
## total_shrub_cover-Canis_latrans 0.0508 0.7052 -1.2148 -0.0080
## total_shrub_cover-Procyon_lotor -1.1950 0.6713 -2.6822 -1.1428
## total_shrub_cover-Dasypus_novemcinctus 0.1048 0.5961 -1.0611 0.0818
## total_shrub_cover-Lynx_rufus -1.6845 1.2488 -4.9064 -1.4711
## total_shrub_cover-Didelphis_virginiana -0.7153 0.7360 -2.2821 -0.6804
## total_shrub_cover-Sylvilagus_floridanus -0.3774 0.9714 -2.3077 -0.3669
## total_shrub_cover-Meleagris_gallopavo -2.5333 1.3584 -5.5633 -2.3560
## total_shrub_cover-Sciurus_carolinensis -0.0489 0.7494 -1.5025 -0.0722
## Avg_Cogongrass_Cover-Canis_latrans 0.2417 1.1035 -1.9223 0.2527
## Avg_Cogongrass_Cover-Procyon_lotor 0.3689 1.1351 -1.7795 0.3400
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8645 1.2319 -1.2460 0.7782
## Avg_Cogongrass_Cover-Lynx_rufus 0.4677 1.2155 -1.8143 0.4209
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6051 1.1726 -1.6628 0.5615
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2349 1.2374 -2.9558 -0.1494
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0036 1.2594 -2.6676 0.0662
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4962 1.1967 -1.7661 0.4657
## Tree_Density-Canis_latrans -2.9783 1.3502 -6.3716 -2.7859
## Tree_Density-Procyon_lotor -2.2015 0.9907 -4.2848 -2.1627
## Tree_Density-Dasypus_novemcinctus -3.4954 1.5446 -7.5213 -3.1695
## Tree_Density-Lynx_rufus -1.2382 1.4475 -3.7760 -1.3956
## Tree_Density-Didelphis_virginiana -2.2800 1.0283 -4.5755 -2.2151
## Tree_Density-Sylvilagus_floridanus -2.6649 1.2508 -5.6510 -2.5296
## Tree_Density-Meleagris_gallopavo -2.3244 1.3095 -5.0936 -2.2897
## Tree_Density-Sciurus_carolinensis -2.5062 1.1857 -5.3423 -2.3580
## Avg_Canopy_Cover-Canis_latrans 0.2579 0.7184 -1.2344 0.2695
## Avg_Canopy_Cover-Procyon_lotor 1.6183 0.7025 0.3534 1.5766
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9073 0.7166 0.7038 1.8343
## Avg_Canopy_Cover-Lynx_rufus 0.9216 1.1013 -1.1823 0.9324
## Avg_Canopy_Cover-Didelphis_virginiana 2.4265 0.9115 1.0364 2.2899
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2012 1.6200 0.9658 2.8653
## Avg_Canopy_Cover-Meleagris_gallopavo 2.2481 1.2885 0.4646 1.9905
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0987 0.9395 0.7355 1.9500
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2203 1.1041 0.6115 2.0458
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1854 1.1605 0.5305 2.0081
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2575 0.7294 0.0023 1.2068
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4888 1.3445 0.5960 2.2659
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6625 0.6949 -0.7192 0.6671
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9597 0.8503 -0.5261 0.9008
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.0367 1.2065 -2.5191 0.1285
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2709 0.7329 -0.0187 1.2179
## avg_veg_height-Canis_latrans -0.4485 0.6168 -1.7308 -0.4378
## avg_veg_height-Procyon_lotor 0.0276 0.6060 -1.0630 -0.0106
## avg_veg_height-Dasypus_novemcinctus 0.0875 0.6007 -1.0621 0.0775
## avg_veg_height-Lynx_rufus -0.3503 0.8221 -2.0122 -0.3284
## avg_veg_height-Didelphis_virginiana -0.2971 0.6845 -1.7151 -0.2820
## avg_veg_height-Sylvilagus_floridanus -0.3492 0.6959 -1.7827 -0.3188
## avg_veg_height-Meleagris_gallopavo -0.2647 0.7621 -1.7676 -0.2656
## avg_veg_height-Sciurus_carolinensis 0.1029 0.6838 -1.1352 0.0520
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4053 1.0495 447
## (Intercept)-Procyon_lotor 0.8241 1.0545 269
## (Intercept)-Dasypus_novemcinctus -0.8434 1.0588 236
## (Intercept)-Lynx_rufus 0.8824 1.0551 285
## (Intercept)-Didelphis_virginiana -1.1869 1.0001 433
## (Intercept)-Sylvilagus_floridanus -0.2654 1.0079 373
## (Intercept)-Meleagris_gallopavo -0.0561 1.0576 361
## (Intercept)-Sciurus_carolinensis -1.4367 1.0052 367
## Cogon_Patch_Size-Canis_latrans 3.9106 1.0375 657
## Cogon_Patch_Size-Procyon_lotor 1.0508 1.0154 369
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0882 1.0339 509
## Cogon_Patch_Size-Lynx_rufus 2.5446 1.0079 305
## Cogon_Patch_Size-Didelphis_virginiana 3.9104 1.0996 204
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8642 1.0251 484
## Cogon_Patch_Size-Meleagris_gallopavo 2.6926 1.0862 523
## Cogon_Patch_Size-Sciurus_carolinensis 0.8296 1.0073 375
## Veg_shannon_index-Canis_latrans 2.7441 1.0275 632
## Veg_shannon_index-Procyon_lotor 2.3319 1.0555 551
## Veg_shannon_index-Dasypus_novemcinctus 1.6196 1.0096 845
## Veg_shannon_index-Lynx_rufus 2.4825 1.0062 505
## Veg_shannon_index-Didelphis_virginiana 2.3273 1.0060 857
## Veg_shannon_index-Sylvilagus_floridanus 2.3251 1.0085 844
## Veg_shannon_index-Meleagris_gallopavo 2.8208 1.0380 683
## Veg_shannon_index-Sciurus_carolinensis 1.5787 1.0288 718
## total_shrub_cover-Canis_latrans 1.5687 1.0070 978
## total_shrub_cover-Procyon_lotor 0.0212 1.0071 790
## total_shrub_cover-Dasypus_novemcinctus 1.3351 0.9997 980
## total_shrub_cover-Lynx_rufus 0.1322 1.0114 265
## total_shrub_cover-Didelphis_virginiana 0.6666 1.0086 1144
## total_shrub_cover-Sylvilagus_floridanus 1.7206 1.0083 652
## total_shrub_cover-Meleagris_gallopavo -0.4797 1.0175 332
## total_shrub_cover-Sciurus_carolinensis 1.5086 1.0003 1203
## Avg_Cogongrass_Cover-Canis_latrans 2.4061 1.0686 262
## Avg_Cogongrass_Cover-Procyon_lotor 2.6905 1.0665 218
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5006 1.0610 185
## Avg_Cogongrass_Cover-Lynx_rufus 2.9243 1.0344 238
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0057 1.0620 268
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.9550 1.0221 254
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.3117 1.0119 323
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8749 1.0578 253
## Tree_Density-Canis_latrans -0.9308 1.0116 344
## Tree_Density-Procyon_lotor -0.3248 1.0285 392
## Tree_Density-Dasypus_novemcinctus -1.3894 1.0538 137
## Tree_Density-Lynx_rufus 2.0692 1.0272 183
## Tree_Density-Didelphis_virginiana -0.5133 1.0033 242
## Tree_Density-Sylvilagus_floridanus -0.5660 1.0036 322
## Tree_Density-Meleagris_gallopavo 0.1689 1.0170 455
## Tree_Density-Sciurus_carolinensis -0.6109 1.0183 223
## Avg_Canopy_Cover-Canis_latrans 1.6366 1.0894 422
## Avg_Canopy_Cover-Procyon_lotor 3.1169 1.0150 485
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.5063 1.0265 325
## Avg_Canopy_Cover-Lynx_rufus 3.1508 1.0455 385
## Avg_Canopy_Cover-Didelphis_virginiana 4.6379 1.0302 234
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.1696 1.0510 204
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3557 1.0239 142
## Avg_Canopy_Cover-Sciurus_carolinensis 4.2236 1.0574 240
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7493 1.0142 305
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.0740 1.0279 268
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8730 1.0403 540
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6903 1.0602 189
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0056 1.0574 433
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9117 1.0414 316
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.2369 1.1274 261
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.8364 1.0415 432
## avg_veg_height-Canis_latrans 0.6914 1.0114 310
## avg_veg_height-Procyon_lotor 1.2821 1.0403 440
## avg_veg_height-Dasypus_novemcinctus 1.3149 1.0354 527
## avg_veg_height-Lynx_rufus 1.1736 1.0522 558
## avg_veg_height-Didelphis_virginiana 1.0479 1.0373 281
## avg_veg_height-Sylvilagus_floridanus 0.9457 1.0047 289
## avg_veg_height-Meleagris_gallopavo 1.2698 1.0410 469
## avg_veg_height-Sciurus_carolinensis 1.6004 1.0122 570
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6036 0.1642 -2.9401 -2.6014 -2.2854 1.0036
## (Intercept)-Procyon_lotor -2.2637 0.1251 -2.5116 -2.2619 -2.0232 1.0057
## (Intercept)-Dasypus_novemcinctus -1.6026 0.1326 -1.8767 -1.6018 -1.3500 1.0006
## (Intercept)-Lynx_rufus -3.5362 0.2922 -4.1145 -3.5355 -2.9696 1.0490
## (Intercept)-Didelphis_virginiana -2.3273 0.2401 -2.8225 -2.3106 -1.8828 1.0060
## (Intercept)-Sylvilagus_floridanus -3.1479 0.2619 -3.6982 -3.1321 -2.6705 1.0238
## (Intercept)-Meleagris_gallopavo -3.2727 0.2936 -3.8885 -3.2660 -2.7181 1.0108
## (Intercept)-Sciurus_carolinensis -2.4757 0.2674 -3.0224 -2.4662 -1.9870 1.0001
## ESS
## (Intercept)-Canis_latrans 834
## (Intercept)-Procyon_lotor 1456
## (Intercept)-Dasypus_novemcinctus 2448
## (Intercept)-Lynx_rufus 318
## (Intercept)-Didelphis_virginiana 1541
## (Intercept)-Sylvilagus_floridanus 597
## (Intercept)-Meleagris_gallopavo 378
## (Intercept)-Sciurus_carolinensis 856
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7977
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3340 0.3688 -1.0544 -0.3459 0.3991 1.0086 705
## Avg_Cogongrass_Cover 0.2101 0.2767 -0.3308 0.2064 0.7638 1.0032 988
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5814 0.6969 0.0583 0.3646 2.3803 1.0139 682
## Avg_Cogongrass_Cover 0.3411 0.4513 0.0388 0.2090 1.5420 1.0019 1123
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7009 0.6731 0.0647 0.4858 2.5282 1.0426 224
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7395 0.3211 -3.3615 -2.7451 -2.0923 1.0045 1581
## shrub_cover 0.2729 0.3052 -0.3596 0.2737 0.8609 1.0215 1844
## veg_height 0.0749 0.2000 -0.3443 0.0780 0.4575 1.0118 1169
## week -0.0949 0.1442 -0.3960 -0.0863 0.1717 1.0007 1435
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7394 0.6452 0.1650 0.5714 2.4239 1.0164 805
## shrub_cover 0.6636 0.5701 0.1332 0.5139 2.0660 1.0003 1325
## veg_height 0.2550 0.2487 0.0548 0.1897 0.8224 1.0285 1375
## week 0.1113 0.0990 0.0246 0.0840 0.3776 1.0048 1530
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0778 0.5128 -0.8914 0.0640
## (Intercept)-Procyon_lotor 0.1847 0.5371 -0.8645 0.1689
## (Intercept)-Dasypus_novemcinctus -0.5266 0.4630 -1.5028 -0.5093
## (Intercept)-Lynx_rufus -0.2312 0.5803 -1.2957 -0.2590
## (Intercept)-Didelphis_virginiana -0.8452 0.5350 -1.9778 -0.8308
## (Intercept)-Sylvilagus_floridanus -0.3993 0.4956 -1.3808 -0.4018
## (Intercept)-Meleagris_gallopavo -0.1362 0.6272 -1.2232 -0.1721
## (Intercept)-Sciurus_carolinensis -0.8611 0.5396 -2.0523 -0.8303
## Avg_Cogongrass_Cover-Canis_latrans 0.4142 0.3793 -0.2502 0.3876
## Avg_Cogongrass_Cover-Procyon_lotor 0.2325 0.3557 -0.4343 0.2223
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3566 0.3261 -0.2516 0.3474
## Avg_Cogongrass_Cover-Lynx_rufus 0.4676 0.4217 -0.2468 0.4360
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3300 0.3718 -0.3626 0.3172
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2062 0.4526 -1.2052 -0.1686
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2265 0.5843 -1.5394 -0.1788
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3509 0.3617 -0.3157 0.3446
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1683 1.0046 820
## (Intercept)-Procyon_lotor 1.2841 1.0151 501
## (Intercept)-Dasypus_novemcinctus 0.3330 1.0001 1136
## (Intercept)-Lynx_rufus 0.9889 1.0100 696
## (Intercept)-Didelphis_virginiana 0.1350 1.0264 969
## (Intercept)-Sylvilagus_floridanus 0.6035 1.0126 914
## (Intercept)-Meleagris_gallopavo 1.2984 1.0145 493
## (Intercept)-Sciurus_carolinensis 0.0904 1.0176 775
## Avg_Cogongrass_Cover-Canis_latrans 1.2357 1.0017 1597
## Avg_Cogongrass_Cover-Procyon_lotor 0.9651 1.0028 1718
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0363 1.0022 2210
## Avg_Cogongrass_Cover-Lynx_rufus 1.4360 1.0011 1249
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1127 1.0015 1613
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5917 1.0049 971
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8368 1.0012 505
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0964 1.0012 1518
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7437 0.1899 -3.1348 -2.7364 -2.3971 1.0108
## (Intercept)-Procyon_lotor -2.3063 0.1431 -2.5966 -2.3036 -2.0344 1.0005
## (Intercept)-Dasypus_novemcinctus -1.7796 0.1623 -2.1202 -1.7746 -1.4773 1.0067
## (Intercept)-Lynx_rufus -3.5198 0.3196 -4.1532 -3.5127 -2.8874 1.0161
## (Intercept)-Didelphis_virginiana -2.6271 0.2871 -3.2074 -2.6224 -2.0868 1.0088
## (Intercept)-Sylvilagus_floridanus -3.1470 0.2762 -3.7164 -3.1326 -2.6343 1.0108
## (Intercept)-Meleagris_gallopavo -3.8446 0.4836 -4.7644 -3.8690 -2.8171 1.0440
## (Intercept)-Sciurus_carolinensis -2.6754 0.3027 -3.2882 -2.6677 -2.1073 1.0058
## shrub_cover-Canis_latrans -0.2655 0.2208 -0.7068 -0.2600 0.1507 1.0073
## shrub_cover-Procyon_lotor 0.2537 0.1635 -0.0702 0.2563 0.5649 1.0060
## shrub_cover-Dasypus_novemcinctus 0.8738 0.2914 0.3421 0.8641 1.4441 1.0076
## shrub_cover-Lynx_rufus -0.1842 0.3642 -0.8876 -0.1909 0.5435 1.0482
## shrub_cover-Didelphis_virginiana 0.9927 0.3819 0.3188 0.9725 1.7911 1.0175
## shrub_cover-Sylvilagus_floridanus 0.2970 0.4259 -0.4989 0.2763 1.1809 1.0036
## shrub_cover-Meleagris_gallopavo -0.6083 0.4206 -1.4070 -0.6109 0.2691 1.0367
## shrub_cover-Sciurus_carolinensis 0.9011 0.4010 0.1406 0.8976 1.6931 1.0023
## veg_height-Canis_latrans -0.5736 0.1953 -0.9626 -0.5618 -0.2114 1.0099
## veg_height-Procyon_lotor 0.3392 0.1243 0.0950 0.3403 0.5743 1.0127
## veg_height-Dasypus_novemcinctus 0.2508 0.1371 -0.0095 0.2445 0.5295 1.0059
## veg_height-Lynx_rufus 0.0127 0.2455 -0.4759 0.0244 0.4807 1.0058
## veg_height-Didelphis_virginiana 0.4514 0.2405 -0.0062 0.4490 0.9522 1.0067
## veg_height-Sylvilagus_floridanus 0.1533 0.2451 -0.3421 0.1580 0.6236 1.0136
## veg_height-Meleagris_gallopavo -0.1471 0.4162 -0.9743 -0.1455 0.6880 1.0264
## veg_height-Sciurus_carolinensis 0.0952 0.2171 -0.3268 0.0915 0.5216 1.0203
## week-Canis_latrans 0.0672 0.1324 -0.1909 0.0686 0.3216 1.0022
## week-Procyon_lotor -0.0547 0.1213 -0.3098 -0.0491 0.1776 1.0010
## week-Dasypus_novemcinctus -0.1788 0.1416 -0.4735 -0.1712 0.0826 1.0017
## week-Lynx_rufus -0.0590 0.1992 -0.4811 -0.0493 0.3041 1.0080
## week-Didelphis_virginiana -0.2356 0.2259 -0.7167 -0.2264 0.1803 1.0096
## week-Sylvilagus_floridanus -0.1716 0.2093 -0.6152 -0.1567 0.1951 1.0051
## week-Meleagris_gallopavo -0.2978 0.2437 -0.8374 -0.2860 0.1297 1.0143
## week-Sciurus_carolinensis 0.1262 0.1847 -0.2460 0.1272 0.4823 1.0008
## ESS
## (Intercept)-Canis_latrans 677
## (Intercept)-Procyon_lotor 1316
## (Intercept)-Dasypus_novemcinctus 1459
## (Intercept)-Lynx_rufus 387
## (Intercept)-Didelphis_virginiana 739
## (Intercept)-Sylvilagus_floridanus 683
## (Intercept)-Meleagris_gallopavo 235
## (Intercept)-Sciurus_carolinensis 808
## shrub_cover-Canis_latrans 817
## shrub_cover-Procyon_lotor 1472
## shrub_cover-Dasypus_novemcinctus 1229
## shrub_cover-Lynx_rufus 473
## shrub_cover-Didelphis_virginiana 632
## shrub_cover-Sylvilagus_floridanus 545
## shrub_cover-Meleagris_gallopavo 273
## shrub_cover-Sciurus_carolinensis 776
## veg_height-Canis_latrans 679
## veg_height-Procyon_lotor 1604
## veg_height-Dasypus_novemcinctus 1868
## veg_height-Lynx_rufus 805
## veg_height-Didelphis_virginiana 1133
## veg_height-Sylvilagus_floridanus 827
## veg_height-Meleagris_gallopavo 280
## veg_height-Sciurus_carolinensis 1052
## week-Canis_latrans 1647
## week-Procyon_lotor 1597
## week-Dasypus_novemcinctus 2013
## week-Lynx_rufus 1000
## week-Didelphis_virginiana 1396
## week-Sylvilagus_floridanus 1056
## week-Meleagris_gallopavo 715
## week-Sciurus_carolinensis 1794
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5502
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4776 0.3433 -1.1469 -0.4762 0.2138 1.0015 1061
## Avg_Cogongrass_Cover 0.2060 0.2628 -0.2989 0.2018 0.7271 1.0137 1291
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.565 0.6288 0.0527 0.3875 2.1412 1.0165 1140
## Avg_Cogongrass_Cover 0.288 0.3250 0.0380 0.1884 1.1659 1.0214 1202
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5951 0.5187 0.0601 0.4384 1.9647 1.0348 220
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5512 0.2853 -3.1023 -2.5549 -1.951 1.0032 2258
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5942 0.4911 0.1446 0.4556 1.8194 1.0181 1798
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0299 0.4925 -0.9571 -0.0516
## (Intercept)-Procyon_lotor 0.1171 0.5296 -0.8814 0.1061
## (Intercept)-Dasypus_novemcinctus -0.6169 0.4405 -1.5085 -0.6214
## (Intercept)-Lynx_rufus -0.3773 0.5495 -1.3923 -0.4026
## (Intercept)-Didelphis_virginiana -1.0030 0.5039 -2.0527 -0.9899
## (Intercept)-Sylvilagus_floridanus -0.4947 0.4950 -1.5012 -0.4978
## (Intercept)-Meleagris_gallopavo -0.5340 0.5123 -1.5266 -0.5432
## (Intercept)-Sciurus_carolinensis -1.0497 0.5142 -2.1266 -1.0423
## Avg_Cogongrass_Cover-Canis_latrans 0.3533 0.3459 -0.2864 0.3430
## Avg_Cogongrass_Cover-Procyon_lotor 0.2436 0.3447 -0.4414 0.2418
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3486 0.3202 -0.2657 0.3403
## Avg_Cogongrass_Cover-Lynx_rufus 0.4675 0.3772 -0.1995 0.4443
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3355 0.3496 -0.3218 0.3309
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1722 0.4086 -1.0327 -0.1318
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2308 0.4823 -1.2867 -0.1820
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3317 0.3357 -0.3249 0.3248
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9670 1.0111 943
## (Intercept)-Procyon_lotor 1.1922 1.0035 715
## (Intercept)-Dasypus_novemcinctus 0.2187 1.0022 1351
## (Intercept)-Lynx_rufus 0.7537 1.0041 675
## (Intercept)-Didelphis_virginiana -0.0538 1.0046 826
## (Intercept)-Sylvilagus_floridanus 0.5097 1.0144 1199
## (Intercept)-Meleagris_gallopavo 0.5062 1.0015 1012
## (Intercept)-Sciurus_carolinensis -0.0840 1.0093 1054
## Avg_Cogongrass_Cover-Canis_latrans 1.0833 1.0008 2102
## Avg_Cogongrass_Cover-Procyon_lotor 0.9338 1.0102 2153
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0064 1.0081 2234
## Avg_Cogongrass_Cover-Lynx_rufus 1.3170 1.0056 1503
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0530 1.0145 1952
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5229 1.0040 1474
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5973 1.0100 1058
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0321 1.0016 2060
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5990 0.1622 -2.9285 -2.5948 -2.2980 0.9999
## (Intercept)-Procyon_lotor -2.2736 0.1287 -2.5450 -2.2699 -2.0291 1.0043
## (Intercept)-Dasypus_novemcinctus -1.6037 0.1351 -1.8834 -1.6029 -1.3410 0.9998
## (Intercept)-Lynx_rufus -3.3401 0.3062 -3.9685 -3.3223 -2.7883 1.0059
## (Intercept)-Didelphis_virginiana -2.3483 0.2412 -2.8305 -2.3413 -1.8875 1.0105
## (Intercept)-Sylvilagus_floridanus -3.0879 0.2642 -3.6325 -3.0822 -2.6004 1.0010
## (Intercept)-Meleagris_gallopavo -3.2771 0.3219 -3.9273 -3.2586 -2.6900 1.0033
## (Intercept)-Sciurus_carolinensis -2.4580 0.2545 -2.9870 -2.4547 -1.9715 1.0083
## ESS
## (Intercept)-Canis_latrans 1156
## (Intercept)-Procyon_lotor 1579
## (Intercept)-Dasypus_novemcinctus 2344
## (Intercept)-Lynx_rufus 391
## (Intercept)-Didelphis_virginiana 1385
## (Intercept)-Sylvilagus_floridanus 611
## (Intercept)-Meleagris_gallopavo 454
## (Intercept)-Sciurus_carolinensis 1234
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.768
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5293 0.3474 -1.2039 -0.5276 0.1630 1.0084 806
## Avg_Cogongrass_Cover 0.2114 0.2564 -0.3197 0.2151 0.7178 1.0013 1239
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5376 0.6925 0.0512 0.3491 2.0119 1.0193 1624
## Avg_Cogongrass_Cover 0.2947 0.3801 0.0366 0.1801 1.2281 1.0366 1222
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6702 0.5688 0.0693 0.5223 2.1519 1.0531 261
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5714 0.2757 -3.1064 -2.5721 -2.0015 1.0044 2625
## week -0.1012 0.1413 -0.4008 -0.0962 0.1733 1.0011 1338
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5757 0.5150 0.1446 0.4403 1.8007 1.0186 1608
## week 0.1090 0.1037 0.0236 0.0806 0.3670 1.0320 1629
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0975 0.4987 -1.0189 -0.1252
## (Intercept)-Procyon_lotor 0.0461 0.5199 -0.9537 0.0348
## (Intercept)-Dasypus_novemcinctus -0.6541 0.4653 -1.6636 -0.6492
## (Intercept)-Lynx_rufus -0.4106 0.5312 -1.4320 -0.4163
## (Intercept)-Didelphis_virginiana -1.0178 0.5158 -2.1648 -0.9840
## (Intercept)-Sylvilagus_floridanus -0.5304 0.4858 -1.4493 -0.5459
## (Intercept)-Meleagris_gallopavo -0.5999 0.5150 -1.6764 -0.5894
## (Intercept)-Sciurus_carolinensis -1.0570 0.5195 -2.1886 -1.0141
## Avg_Cogongrass_Cover-Canis_latrans 0.3567 0.3428 -0.2534 0.3339
## Avg_Cogongrass_Cover-Procyon_lotor 0.2507 0.3424 -0.3999 0.2385
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3506 0.3154 -0.2351 0.3367
## Avg_Cogongrass_Cover-Lynx_rufus 0.4754 0.3935 -0.2232 0.4471
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3385 0.3411 -0.3277 0.3286
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1684 0.4194 -1.1131 -0.1328
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2120 0.4702 -1.2695 -0.1537
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3508 0.3398 -0.3050 0.3435
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9683 0.9999 767
## (Intercept)-Procyon_lotor 1.0971 1.0097 727
## (Intercept)-Dasypus_novemcinctus 0.2272 1.0147 1173
## (Intercept)-Lynx_rufus 0.6504 1.0047 839
## (Intercept)-Didelphis_virginiana -0.0729 1.0041 999
## (Intercept)-Sylvilagus_floridanus 0.4572 1.0030 920
## (Intercept)-Meleagris_gallopavo 0.3855 1.0139 831
## (Intercept)-Sciurus_carolinensis -0.1187 1.0110 620
## Avg_Cogongrass_Cover-Canis_latrans 1.0961 1.0022 2243
## Avg_Cogongrass_Cover-Procyon_lotor 0.9700 1.0028 2005
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.9952 1.0011 2087
## Avg_Cogongrass_Cover-Lynx_rufus 1.3765 1.0001 1460
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0301 1.0013 2367
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5498 1.0027 1096
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5620 1.0048 1063
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0568 1.0029 1766
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6110 0.1646 -2.9467 -2.6061 -2.3025 1.0060
## (Intercept)-Procyon_lotor -2.2796 0.1300 -2.5411 -2.2785 -2.0209 1.0037
## (Intercept)-Dasypus_novemcinctus -1.6242 0.1386 -1.8961 -1.6234 -1.3623 0.9998
## (Intercept)-Lynx_rufus -3.3678 0.2800 -3.9399 -3.3612 -2.8367 1.0017
## (Intercept)-Didelphis_virginiana -2.3793 0.2475 -2.8751 -2.3687 -1.9287 1.0010
## (Intercept)-Sylvilagus_floridanus -3.0984 0.2753 -3.6751 -3.0773 -2.6101 1.0116
## (Intercept)-Meleagris_gallopavo -3.2842 0.3160 -3.9855 -3.2693 -2.7220 1.0103
## (Intercept)-Sciurus_carolinensis -2.4865 0.2570 -3.0259 -2.4730 -2.0172 1.0278
## week-Canis_latrans 0.0605 0.1323 -0.2102 0.0636 0.3089 1.0075
## week-Procyon_lotor -0.0552 0.1165 -0.2890 -0.0541 0.1666 1.0021
## week-Dasypus_novemcinctus -0.1753 0.1380 -0.4550 -0.1709 0.0870 1.0024
## week-Lynx_rufus -0.0411 0.1929 -0.4310 -0.0365 0.3202 1.0013
## week-Didelphis_virginiana -0.2363 0.2238 -0.7233 -0.2223 0.1624 1.0062
## week-Sylvilagus_floridanus -0.1726 0.2105 -0.6172 -0.1560 0.2164 1.0113
## week-Meleagris_gallopavo -0.2936 0.2447 -0.8386 -0.2769 0.1280 1.0029
## week-Sciurus_carolinensis 0.1200 0.1825 -0.2365 0.1226 0.4719 1.0002
## ESS
## (Intercept)-Canis_latrans 1054
## (Intercept)-Procyon_lotor 1642
## (Intercept)-Dasypus_novemcinctus 2197
## (Intercept)-Lynx_rufus 433
## (Intercept)-Didelphis_virginiana 1297
## (Intercept)-Sylvilagus_floridanus 537
## (Intercept)-Meleagris_gallopavo 416
## (Intercept)-Sciurus_carolinensis 1032
## week-Canis_latrans 1630
## week-Procyon_lotor 1700
## week-Dasypus_novemcinctus 2072
## week-Lynx_rufus 913
## week-Didelphis_virginiana 1441
## week-Sylvilagus_floridanus 954
## week-Meleagris_gallopavo 809
## week-Sciurus_carolinensis 1965
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7862
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9357 0.6269 -2.1666 -0.9457 0.3282 1.0272 727
## Cogon_Patch_Size -0.4753 0.5755 -1.6484 -0.4677 0.6638 1.0039 627
## Veg_shannon_index 0.8895 0.4125 0.1084 0.8803 1.7417 1.0183 313
## total_shrub_cover -0.5525 0.4939 -1.5482 -0.5430 0.3987 1.0088 1154
## Avg_Cogongrass_Cover 1.9705 0.6841 0.6306 1.9752 3.3591 1.0350 228
## Tree_Density -1.8443 0.6452 -3.1630 -1.8145 -0.6344 1.0040 420
## Avg_Canopy_Cover 1.6352 0.5750 0.5652 1.6052 2.8443 1.0017 822
## avg_veg_height -0.4999 0.4362 -1.3604 -0.5020 0.3407 1.0670 358
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3031 2.8975 0.0998 1.5042 9.3134 1.0752 380
## Cogon_Patch_Size 1.8042 2.8485 0.0893 1.0223 7.7509 1.0887 477
## Veg_shannon_index 0.4970 0.6193 0.0443 0.2837 2.2022 1.0065 862
## total_shrub_cover 1.5320 1.9611 0.0801 0.8832 7.0015 1.0918 315
## Avg_Cogongrass_Cover 0.7813 1.2234 0.0477 0.3968 4.0858 1.0386 703
## Tree_Density 1.9762 2.9802 0.0678 0.9345 9.5741 1.0899 292
## Avg_Canopy_Cover 1.8596 2.6111 0.1141 1.0622 9.0106 1.0096 337
## avg_veg_height 0.3904 0.5056 0.0398 0.2339 1.8044 1.0067 1003
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6952 1.7283 0.1102 1.1531 6.2538 1.0612 120
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5862 0.2900 -3.1433 -2.5987 -1.9842 1.0056 2490
## week -0.0960 0.1391 -0.3808 -0.0931 0.1642 1.0018 1717
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6594 0.5508 0.1795 0.5070 2.1347 1.0035 1764
## week 0.1070 0.0991 0.0248 0.0796 0.3549 1.0098 1818
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0316 0.8757 -1.7005 -0.0517
## (Intercept)-Procyon_lotor 0.1694 0.8729 -1.5376 0.1684
## (Intercept)-Dasypus_novemcinctus -1.3112 0.7585 -2.9255 -1.2614
## (Intercept)-Lynx_rufus -0.2353 1.2082 -2.2498 -0.3718
## (Intercept)-Didelphis_virginiana -2.1592 0.9234 -4.2024 -2.0802
## (Intercept)-Sylvilagus_floridanus -1.1691 0.8737 -2.9298 -1.1506
## (Intercept)-Meleagris_gallopavo -1.2275 0.9749 -3.2537 -1.2156
## (Intercept)-Sciurus_carolinensis -2.2843 0.9777 -4.3975 -2.2204
## Cogon_Patch_Size-Canis_latrans 0.5248 1.0460 -0.9097 0.3497
## Cogon_Patch_Size-Procyon_lotor -0.8733 0.6572 -2.2244 -0.8598
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6849 0.5913 -1.8480 -0.6755
## Cogon_Patch_Size-Lynx_rufus -0.6903 1.0924 -2.8256 -0.7012
## Cogon_Patch_Size-Didelphis_virginiana 0.7113 0.8607 -0.6504 0.5987
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4125 1.1330 -4.0901 -1.2678
## Cogon_Patch_Size-Meleagris_gallopavo -0.3577 0.9168 -2.0716 -0.4042
## Cogon_Patch_Size-Sciurus_carolinensis -1.2889 0.9154 -3.4341 -1.1572
## Veg_shannon_index-Canis_latrans 1.1510 0.5501 0.1733 1.1075
## Veg_shannon_index-Procyon_lotor 1.1481 0.5419 0.1973 1.1161
## Veg_shannon_index-Dasypus_novemcinctus 0.6689 0.4874 -0.2977 0.6757
## Veg_shannon_index-Lynx_rufus 0.8201 0.6771 -0.5819 0.8038
## Veg_shannon_index-Didelphis_virginiana 0.9842 0.5746 -0.1014 0.9477
## Veg_shannon_index-Sylvilagus_floridanus 0.9834 0.5879 -0.1068 0.9536
## Veg_shannon_index-Meleagris_gallopavo 1.1728 0.6802 0.0386 1.1013
## Veg_shannon_index-Sciurus_carolinensis 0.3581 0.6215 -0.9873 0.4038
## total_shrub_cover-Canis_latrans 0.1812 0.6654 -0.9246 0.1245
## total_shrub_cover-Procyon_lotor -0.9640 0.5899 -2.2083 -0.9281
## total_shrub_cover-Dasypus_novemcinctus 0.0508 0.5414 -0.9859 0.0398
## total_shrub_cover-Lynx_rufus -1.1580 0.9779 -3.5493 -0.9969
## total_shrub_cover-Didelphis_virginiana -0.5907 0.6908 -2.0359 -0.5558
## total_shrub_cover-Sylvilagus_floridanus -0.2893 0.7680 -1.8514 -0.2940
## total_shrub_cover-Meleagris_gallopavo -1.9772 1.1807 -4.8056 -1.7954
## total_shrub_cover-Sciurus_carolinensis -0.0752 0.6823 -1.4199 -0.1022
## Avg_Cogongrass_Cover-Canis_latrans 2.1656 0.7826 0.7093 2.1546
## Avg_Cogongrass_Cover-Procyon_lotor 2.1030 0.8088 0.6128 2.0719
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4061 0.8579 0.8520 2.3579
## Avg_Cogongrass_Cover-Lynx_rufus 2.3296 0.8788 0.7271 2.2686
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.0611 0.8026 0.5889 2.0404
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.4745 0.9358 -0.4704 1.5034
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.5173 1.1095 -0.9963 1.6017
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.2204 0.8340 0.6941 2.1722
## Tree_Density-Canis_latrans -2.3513 1.0437 -4.7350 -2.2022
## Tree_Density-Procyon_lotor -1.4697 0.7155 -2.8863 -1.4603
## Tree_Density-Dasypus_novemcinctus -2.9986 1.3324 -6.3446 -2.7515
## Tree_Density-Lynx_rufus -0.5760 1.1249 -2.4134 -0.6994
## Tree_Density-Didelphis_virginiana -2.0925 0.9566 -4.2693 -1.9863
## Tree_Density-Sylvilagus_floridanus -2.3156 1.1331 -4.9065 -2.1838
## Tree_Density-Meleagris_gallopavo -2.0520 1.1040 -4.4325 -1.9698
## Tree_Density-Sciurus_carolinensis -2.1685 1.0409 -4.6501 -2.0330
## Avg_Canopy_Cover-Canis_latrans 0.3735 0.6147 -0.8919 0.4057
## Avg_Canopy_Cover-Procyon_lotor 1.6048 0.6276 0.4839 1.5603
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8501 0.6094 0.8038 1.7962
## Avg_Canopy_Cover-Lynx_rufus 0.8843 0.9726 -1.1288 0.8962
## Avg_Canopy_Cover-Didelphis_virginiana 2.3121 0.7899 1.0429 2.2222
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.9221 1.4139 1.0075 2.6220
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1396 1.0265 0.5827 1.9729
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0211 0.7288 0.8076 1.9329
## avg_veg_height-Canis_latrans -0.6841 0.5503 -1.8380 -0.6663
## avg_veg_height-Procyon_lotor -0.3807 0.5283 -1.3806 -0.3801
## avg_veg_height-Dasypus_novemcinctus -0.2727 0.5532 -1.3753 -0.2846
## avg_veg_height-Lynx_rufus -0.6012 0.6368 -1.9641 -0.5842
## avg_veg_height-Didelphis_virginiana -0.5780 0.5818 -1.7619 -0.5466
## avg_veg_height-Sylvilagus_floridanus -0.6934 0.6128 -1.9856 -0.6747
## avg_veg_height-Meleagris_gallopavo -0.6412 0.6836 -2.1165 -0.5967
## avg_veg_height-Sciurus_carolinensis -0.1982 0.5860 -1.2667 -0.2005
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.7166 1.0962 286
## (Intercept)-Procyon_lotor 1.8326 1.0408 288
## (Intercept)-Dasypus_novemcinctus 0.0820 1.0249 710
## (Intercept)-Lynx_rufus 2.8102 1.1602 272
## (Intercept)-Didelphis_virginiana -0.5845 1.0252 498
## (Intercept)-Sylvilagus_floridanus 0.5168 1.0089 792
## (Intercept)-Meleagris_gallopavo 0.7188 1.0039 426
## (Intercept)-Sciurus_carolinensis -0.5724 1.0251 464
## Cogon_Patch_Size-Canis_latrans 2.8417 1.0294 452
## Cogon_Patch_Size-Procyon_lotor 0.4119 1.0110 376
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4330 1.0053 592
## Cogon_Patch_Size-Lynx_rufus 1.5229 1.0288 409
## Cogon_Patch_Size-Didelphis_virginiana 2.6678 1.0070 590
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4380 1.0049 551
## Cogon_Patch_Size-Meleagris_gallopavo 1.6429 1.0112 697
## Cogon_Patch_Size-Sciurus_carolinensis 0.1029 1.0108 505
## Veg_shannon_index-Canis_latrans 2.3714 1.0080 698
## Veg_shannon_index-Procyon_lotor 2.3548 1.0000 457
## Veg_shannon_index-Dasypus_novemcinctus 1.6054 1.0014 663
## Veg_shannon_index-Lynx_rufus 2.1587 1.0132 421
## Veg_shannon_index-Didelphis_virginiana 2.1968 1.0140 615
## Veg_shannon_index-Sylvilagus_floridanus 2.2427 1.0062 798
## Veg_shannon_index-Meleagris_gallopavo 2.8246 1.0079 553
## Veg_shannon_index-Sciurus_carolinensis 1.4714 1.0221 446
## total_shrub_cover-Canis_latrans 1.7096 1.0547 748
## total_shrub_cover-Procyon_lotor 0.1167 1.0019 1136
## total_shrub_cover-Dasypus_novemcinctus 1.1409 1.0155 1225
## total_shrub_cover-Lynx_rufus 0.3092 1.0481 354
## total_shrub_cover-Didelphis_virginiana 0.7349 1.0045 1193
## total_shrub_cover-Sylvilagus_floridanus 1.2764 1.0129 828
## total_shrub_cover-Meleagris_gallopavo -0.2199 1.0644 216
## total_shrub_cover-Sciurus_carolinensis 1.2971 1.0096 976
## Avg_Cogongrass_Cover-Canis_latrans 3.8015 1.0121 334
## Avg_Cogongrass_Cover-Procyon_lotor 3.7538 1.0178 250
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.2343 1.0161 315
## Avg_Cogongrass_Cover-Lynx_rufus 4.1961 1.0051 343
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.6923 1.0122 335
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2667 1.0522 364
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.5208 1.0672 332
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0129 1.0126 253
## Tree_Density-Canis_latrans -0.7268 1.0044 573
## Tree_Density-Procyon_lotor -0.0022 1.0006 620
## Tree_Density-Dasypus_novemcinctus -1.1019 1.0763 280
## Tree_Density-Lynx_rufus 1.9872 1.0342 321
## Tree_Density-Didelphis_virginiana -0.5119 1.0104 637
## Tree_Density-Sylvilagus_floridanus -0.4482 1.0280 550
## Tree_Density-Meleagris_gallopavo 0.0120 1.0227 662
## Tree_Density-Sciurus_carolinensis -0.4888 1.0450 383
## Avg_Canopy_Cover-Canis_latrans 1.5002 1.0034 962
## Avg_Canopy_Cover-Procyon_lotor 2.9826 1.0110 825
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.2146 1.0013 770
## Avg_Canopy_Cover-Lynx_rufus 2.7494 1.0176 469
## Avg_Canopy_Cover-Didelphis_virginiana 4.1374 1.0007 461
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.2569 1.0005 288
## Avg_Canopy_Cover-Meleagris_gallopavo 4.6299 1.0064 489
## Avg_Canopy_Cover-Sciurus_carolinensis 3.6842 1.0100 519
## avg_veg_height-Canis_latrans 0.3611 1.0244 534
## avg_veg_height-Procyon_lotor 0.6325 1.0262 635
## avg_veg_height-Dasypus_novemcinctus 0.8654 1.0452 626
## avg_veg_height-Lynx_rufus 0.6183 1.0369 526
## avg_veg_height-Didelphis_virginiana 0.5046 1.0195 679
## avg_veg_height-Sylvilagus_floridanus 0.4134 1.0459 556
## avg_veg_height-Meleagris_gallopavo 0.6148 1.0294 483
## avg_veg_height-Sciurus_carolinensis 0.9975 1.0487 703
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6169 0.1714 -2.9645 -2.6074 -2.2956 1.0022
## (Intercept)-Procyon_lotor -2.2700 0.1282 -2.5291 -2.2701 -2.0284 1.0049
## (Intercept)-Dasypus_novemcinctus -1.6145 0.1384 -1.9018 -1.6140 -1.3616 1.0011
## (Intercept)-Lynx_rufus -3.4927 0.3055 -4.0886 -3.4897 -2.9013 1.0798
## (Intercept)-Didelphis_virginiana -2.3544 0.2465 -2.8559 -2.3480 -1.9009 1.0033
## (Intercept)-Sylvilagus_floridanus -3.1425 0.2601 -3.6760 -3.1317 -2.6620 1.0015
## (Intercept)-Meleagris_gallopavo -3.3757 0.2992 -3.9896 -3.3643 -2.8360 1.0166
## (Intercept)-Sciurus_carolinensis -2.4753 0.2667 -3.0464 -2.4651 -1.9824 0.9999
## week-Canis_latrans 0.0670 0.1303 -0.1960 0.0710 0.3071 1.0012
## week-Procyon_lotor -0.0594 0.1203 -0.3097 -0.0547 0.1645 1.0154
## week-Dasypus_novemcinctus -0.1736 0.1384 -0.4568 -0.1716 0.0873 1.0016
## week-Lynx_rufus -0.0526 0.1916 -0.4453 -0.0483 0.3087 1.0343
## week-Didelphis_virginiana -0.2294 0.2175 -0.7009 -0.2193 0.1651 1.0009
## week-Sylvilagus_floridanus -0.1701 0.2001 -0.5825 -0.1632 0.2023 1.0027
## week-Meleagris_gallopavo -0.2795 0.2365 -0.7796 -0.2658 0.1412 1.0078
## week-Sciurus_carolinensis 0.1201 0.1769 -0.2264 0.1217 0.4638 1.0023
## ESS
## (Intercept)-Canis_latrans 918
## (Intercept)-Procyon_lotor 1607
## (Intercept)-Dasypus_novemcinctus 2366
## (Intercept)-Lynx_rufus 289
## (Intercept)-Didelphis_virginiana 1368
## (Intercept)-Sylvilagus_floridanus 557
## (Intercept)-Meleagris_gallopavo 503
## (Intercept)-Sciurus_carolinensis 1188
## week-Canis_latrans 1570
## week-Procyon_lotor 1667
## week-Dasypus_novemcinctus 2028
## week-Lynx_rufus 995
## week-Didelphis_virginiana 1515
## week-Sylvilagus_floridanus 1139
## week-Meleagris_gallopavo 848
## week-Sciurus_carolinensis 1871
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7715
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5144 0.3775 -1.2518 -0.5240 0.2499 1.0096 960
## Avg_Cogongrass_Cover 0.1513 0.3217 -0.4796 0.1512 0.7752 1.0007 855
## total_shrub_cover -0.4485 0.3367 -1.1403 -0.4419 0.1780 1.0053 1385
## avg_veg_height -0.0150 0.3121 -0.6236 -0.0232 0.6060 1.0018 673
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7174 0.8517 0.0656 0.4718 2.8197 1.0135 962
## Avg_Cogongrass_Cover 0.3602 0.4600 0.0384 0.2204 1.5386 1.0111 1111
## total_shrub_cover 0.6432 0.8185 0.0608 0.4188 2.5823 1.0081 1151
## avg_veg_height 0.2647 0.3001 0.0353 0.1691 1.0878 1.0043 1432
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8075 0.7805 0.0723 0.5831 2.7938 1.0039 261
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5832 0.2803 -3.1178 -2.5882 -2.0155 1.0079 2585
## week -0.1073 0.1438 -0.4140 -0.1051 0.1542 1.0014 1659
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6015 0.4942 0.1439 0.4629 1.9477 1.0120 1621
## week 0.1131 0.1102 0.0252 0.0839 0.3816 1.0021 1613
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0196 0.5549 -1.0583 -0.0416
## (Intercept)-Procyon_lotor 0.1890 0.6062 -0.9239 0.1463
## (Intercept)-Dasypus_novemcinctus -0.6861 0.4964 -1.7359 -0.6725
## (Intercept)-Lynx_rufus -0.3522 0.6194 -1.5818 -0.3788
## (Intercept)-Didelphis_virginiana -1.0931 0.5475 -2.2274 -1.0717
## (Intercept)-Sylvilagus_floridanus -0.4504 0.5613 -1.5440 -0.4679
## (Intercept)-Meleagris_gallopavo -0.7114 0.5677 -1.8686 -0.6982
## (Intercept)-Sciurus_carolinensis -1.1350 0.5859 -2.4250 -1.1061
## Avg_Cogongrass_Cover-Canis_latrans 0.4070 0.4338 -0.3842 0.3711
## Avg_Cogongrass_Cover-Procyon_lotor 0.0881 0.4220 -0.7337 0.0901
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2517 0.3905 -0.5062 0.2393
## Avg_Cogongrass_Cover-Lynx_rufus 0.4412 0.4659 -0.4231 0.4190
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3248 0.4208 -0.4710 0.3126
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2294 0.5016 -1.3464 -0.1920
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3045 0.5557 -1.5430 -0.2419
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2252 0.4193 -0.5729 0.2096
## total_shrub_cover-Canis_latrans 0.1257 0.4454 -0.6870 0.1088
## total_shrub_cover-Procyon_lotor -0.8802 0.4621 -1.8871 -0.8487
## total_shrub_cover-Dasypus_novemcinctus -0.0847 0.3830 -0.8049 -0.0910
## total_shrub_cover-Lynx_rufus -0.8585 0.5803 -2.1719 -0.7867
## total_shrub_cover-Didelphis_virginiana -0.2716 0.4194 -1.0738 -0.2667
## total_shrub_cover-Sylvilagus_floridanus -0.4063 0.5057 -1.4746 -0.3855
## total_shrub_cover-Meleagris_gallopavo -1.2424 0.6362 -2.6883 -1.1661
## total_shrub_cover-Sciurus_carolinensis -0.1180 0.4352 -1.0014 -0.1236
## avg_veg_height-Canis_latrans -0.0900 0.3987 -0.9062 -0.0808
## avg_veg_height-Procyon_lotor 0.0766 0.4151 -0.7067 0.0695
## avg_veg_height-Dasypus_novemcinctus 0.1794 0.4027 -0.5871 0.1636
## avg_veg_height-Lynx_rufus -0.0390 0.4742 -1.0162 -0.0453
## avg_veg_height-Didelphis_virginiana -0.0567 0.4127 -0.8784 -0.0463
## avg_veg_height-Sylvilagus_floridanus -0.1684 0.4245 -1.0306 -0.1564
## avg_veg_height-Meleagris_gallopavo -0.2789 0.5181 -1.4280 -0.2377
## avg_veg_height-Sciurus_carolinensis 0.2623 0.4351 -0.5401 0.2340
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1121 1.0151 780
## (Intercept)-Procyon_lotor 1.4825 1.0088 660
## (Intercept)-Dasypus_novemcinctus 0.2529 1.0041 1143
## (Intercept)-Lynx_rufus 0.9537 1.0052 809
## (Intercept)-Didelphis_virginiana -0.1036 1.0015 930
## (Intercept)-Sylvilagus_floridanus 0.7205 1.0019 798
## (Intercept)-Meleagris_gallopavo 0.4280 1.0078 1063
## (Intercept)-Sciurus_carolinensis -0.0980 0.9999 942
## Avg_Cogongrass_Cover-Canis_latrans 1.3579 1.0001 1095
## Avg_Cogongrass_Cover-Procyon_lotor 0.9401 1.0014 1469
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0437 1.0011 1215
## Avg_Cogongrass_Cover-Lynx_rufus 1.4438 0.9999 1053
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2098 1.0001 1185
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6528 1.0009 1112
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6491 1.0065 971
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0436 1.0001 1431
## total_shrub_cover-Canis_latrans 1.0515 1.0045 1705
## total_shrub_cover-Procyon_lotor -0.0649 1.0078 1307
## total_shrub_cover-Dasypus_novemcinctus 0.6678 1.0057 2277
## total_shrub_cover-Lynx_rufus 0.0932 1.0026 981
## total_shrub_cover-Didelphis_virginiana 0.5510 1.0087 2264
## total_shrub_cover-Sylvilagus_floridanus 0.5316 1.0017 1030
## total_shrub_cover-Meleagris_gallopavo -0.1971 1.0006 827
## total_shrub_cover-Sciurus_carolinensis 0.7706 1.0005 2020
## avg_veg_height-Canis_latrans 0.6934 1.0012 1135
## avg_veg_height-Procyon_lotor 0.9317 1.0020 1144
## avg_veg_height-Dasypus_novemcinctus 1.0046 1.0014 1341
## avg_veg_height-Lynx_rufus 0.9465 1.0030 1046
## avg_veg_height-Didelphis_virginiana 0.7570 1.0005 1267
## avg_veg_height-Sylvilagus_floridanus 0.6570 1.0021 1172
## avg_veg_height-Meleagris_gallopavo 0.6377 1.0013 865
## avg_veg_height-Sciurus_carolinensis 1.2003 1.0056 1277
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6219 0.1691 -2.9572 -2.6202 -2.3053 1.0044
## (Intercept)-Procyon_lotor -2.2782 0.1285 -2.5376 -2.2770 -2.0281 1.0152
## (Intercept)-Dasypus_novemcinctus -1.6221 0.1366 -1.8932 -1.6229 -1.3520 1.0049
## (Intercept)-Lynx_rufus -3.4056 0.2959 -3.9952 -3.4031 -2.8444 1.0029
## (Intercept)-Didelphis_virginiana -2.3812 0.2518 -2.9081 -2.3692 -1.9062 1.0088
## (Intercept)-Sylvilagus_floridanus -3.1667 0.2900 -3.7860 -3.1482 -2.6289 1.0012
## (Intercept)-Meleagris_gallopavo -3.2587 0.3014 -3.8819 -3.2470 -2.7013 1.0052
## (Intercept)-Sciurus_carolinensis -2.4968 0.2634 -3.0594 -2.4801 -2.0054 1.0077
## week-Canis_latrans 0.0566 0.1325 -0.2074 0.0583 0.3101 1.0099
## week-Procyon_lotor -0.0624 0.1220 -0.3143 -0.0581 0.1694 1.0089
## week-Dasypus_novemcinctus -0.1769 0.1406 -0.4744 -0.1722 0.0788 1.0028
## week-Lynx_rufus -0.0595 0.1939 -0.4710 -0.0500 0.3017 1.0045
## week-Didelphis_virginiana -0.2389 0.2194 -0.7117 -0.2261 0.1595 1.0011
## week-Sylvilagus_floridanus -0.1690 0.2078 -0.6063 -0.1585 0.2162 1.0027
## week-Meleagris_gallopavo -0.3040 0.2439 -0.8581 -0.2825 0.1146 1.0150
## week-Sciurus_carolinensis 0.1198 0.1889 -0.2439 0.1200 0.4826 1.0002
## ESS
## (Intercept)-Canis_latrans 1110
## (Intercept)-Procyon_lotor 1501
## (Intercept)-Dasypus_novemcinctus 1892
## (Intercept)-Lynx_rufus 358
## (Intercept)-Didelphis_virginiana 1295
## (Intercept)-Sylvilagus_floridanus 484
## (Intercept)-Meleagris_gallopavo 496
## (Intercept)-Sciurus_carolinensis 1067
## week-Canis_latrans 1681
## week-Procyon_lotor 1621
## week-Dasypus_novemcinctus 1869
## week-Lynx_rufus 1042
## week-Didelphis_virginiana 1496
## week-Sylvilagus_floridanus 1028
## week-Meleagris_gallopavo 822
## week-Sciurus_carolinensis 1797
# Includes week covariate for detection and none for occupancy
ms_week_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.778
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3816 0.3345 -1.0477 -0.3803 0.2991 1.0069 1268
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6843 0.9586 0.1047 0.5005 2.2873 1.172 1508
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5765 0.2783 -3.1263 -2.5783 -2.0306 1.0072 1882
## week -0.1034 0.1428 -0.3933 -0.0995 0.1593 1.0012 1438
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5815 0.5455 0.1411 0.4361 1.8135 1.0161 1893
## week 0.1092 0.1462 0.0239 0.0795 0.3675 1.0682 2204
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1506 0.3776 -0.5498 0.1383 0.9295 1.0125
## (Intercept)-Procyon_lotor 0.4758 0.3832 -0.2375 0.4688 1.2764 1.0015
## (Intercept)-Dasypus_novemcinctus -0.5969 0.3334 -1.2458 -0.5961 0.0483 1.0065
## (Intercept)-Lynx_rufus -0.1309 0.4835 -0.9749 -0.1757 0.9413 1.0053
## (Intercept)-Didelphis_virginiana -1.1201 0.4020 -1.9688 -1.1017 -0.3875 1.0002
## (Intercept)-Sylvilagus_floridanus -0.3933 0.4674 -1.1677 -0.4139 0.4875 1.0318
## (Intercept)-Meleagris_gallopavo -0.4379 0.4546 -1.2675 -0.4551 0.5584 1.0018
## (Intercept)-Sciurus_carolinensis -1.1039 0.4121 -1.9788 -1.0817 -0.3630 1.0006
## ESS
## (Intercept)-Canis_latrans 1620
## (Intercept)-Procyon_lotor 1836
## (Intercept)-Dasypus_novemcinctus 2912
## (Intercept)-Lynx_rufus 841
## (Intercept)-Didelphis_virginiana 1886
## (Intercept)-Sylvilagus_floridanus 574
## (Intercept)-Meleagris_gallopavo 678
## (Intercept)-Sciurus_carolinensis 1989
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6057 0.1614 -2.9328 -2.6014 -2.2908 1.0130
## (Intercept)-Procyon_lotor -2.2744 0.1268 -2.5303 -2.2731 -2.0301 1.0026
## (Intercept)-Dasypus_novemcinctus -1.6233 0.1376 -1.8986 -1.6240 -1.3584 1.0068
## (Intercept)-Lynx_rufus -3.3619 0.3030 -3.9941 -3.3519 -2.8169 1.0034
## (Intercept)-Didelphis_virginiana -2.3862 0.2482 -2.9085 -2.3780 -1.9298 1.0057
## (Intercept)-Sylvilagus_floridanus -3.1128 0.2698 -3.6772 -3.0963 -2.6174 1.0221
## (Intercept)-Meleagris_gallopavo -3.3051 0.3195 -4.0022 -3.2896 -2.7253 1.0001
## (Intercept)-Sciurus_carolinensis -2.4865 0.2556 -3.0324 -2.4787 -2.0197 1.0019
## week-Canis_latrans 0.0602 0.1342 -0.2191 0.0629 0.3150 1.0015
## week-Procyon_lotor -0.0600 0.1190 -0.3038 -0.0558 0.1627 1.0015
## week-Dasypus_novemcinctus -0.1765 0.1386 -0.4730 -0.1699 0.0747 1.0020
## week-Lynx_rufus -0.0601 0.1939 -0.4708 -0.0511 0.3004 1.0084
## week-Didelphis_virginiana -0.2341 0.2148 -0.7134 -0.2165 0.1423 1.0043
## week-Sylvilagus_floridanus -0.1707 0.2013 -0.5955 -0.1608 0.2010 1.0007
## week-Meleagris_gallopavo -0.2926 0.2410 -0.8175 -0.2704 0.1258 1.0045
## week-Sciurus_carolinensis 0.1157 0.1804 -0.2452 0.1189 0.4607 1.0107
## ESS
## (Intercept)-Canis_latrans 1191
## (Intercept)-Procyon_lotor 1514
## (Intercept)-Dasypus_novemcinctus 2338
## (Intercept)-Lynx_rufus 375
## (Intercept)-Didelphis_virginiana 1232
## (Intercept)-Sylvilagus_floridanus 517
## (Intercept)-Meleagris_gallopavo 374
## (Intercept)-Sciurus_carolinensis 1170
## week-Canis_latrans 1671
## week-Procyon_lotor 1765
## week-Dasypus_novemcinctus 2053
## week-Lynx_rufus 1029
## week-Didelphis_virginiana 1481
## week-Sylvilagus_floridanus 1427
## week-Meleagris_gallopavo 681
## week-Sciurus_carolinensis 1786
#Includes week for detection and only foraging for occupancy
ms_week_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7407
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5324 0.3645 -1.2351 -0.5421 0.2034 1.0194 744
## Veg_shannon_index 0.3531 0.2723 -0.1792 0.3489 0.8935 1.0407 866
## Avg_Cogongrass_Cover 0.3372 0.2681 -0.1800 0.3336 0.8562 1.0121 981
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5824 0.6333 0.0559 0.3860 2.2482 1.0330 1001
## Veg_shannon_index 0.2499 0.2965 0.0349 0.1637 1.0044 1.0046 1428
## Avg_Cogongrass_Cover 0.3131 0.3903 0.0356 0.1966 1.3085 1.0098 1198
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.773 0.7093 0.0657 0.5641 2.5792 1.0284 274
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5793 0.2814 -3.1076 -2.5855 -1.9903 1.0048 2712
## week -0.1018 0.1351 -0.3912 -0.0999 0.1493 1.0018 1449
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6178 0.555 0.1496 0.4663 1.9131 1.0122 1936
## week 0.1056 0.099 0.0245 0.0793 0.3296 1.0068 1547
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1064 0.5347 -1.1148 -0.1269
## (Intercept)-Procyon_lotor 0.0638 0.5740 -1.0226 0.0467
## (Intercept)-Dasypus_novemcinctus -0.6765 0.4582 -1.6112 -0.6647
## (Intercept)-Lynx_rufus -0.4001 0.5461 -1.4403 -0.4152
## (Intercept)-Didelphis_virginiana -1.0706 0.5364 -2.2585 -1.0410
## (Intercept)-Sylvilagus_floridanus -0.5136 0.5392 -1.5529 -0.5321
## (Intercept)-Meleagris_gallopavo -0.5722 0.5588 -1.6646 -0.5732
## (Intercept)-Sciurus_carolinensis -1.0850 0.5314 -2.2189 -1.0571
## Veg_shannon_index-Canis_latrans 0.6247 0.3803 -0.0475 0.6033
## Veg_shannon_index-Procyon_lotor 0.4691 0.3676 -0.2157 0.4501
## Veg_shannon_index-Dasypus_novemcinctus 0.2068 0.3462 -0.4847 0.2143
## Veg_shannon_index-Lynx_rufus 0.2060 0.4458 -0.7525 0.2143
## Veg_shannon_index-Didelphis_virginiana 0.4582 0.3790 -0.2510 0.4360
## Veg_shannon_index-Sylvilagus_floridanus 0.4397 0.4055 -0.2898 0.4175
## Veg_shannon_index-Meleagris_gallopavo 0.4588 0.4271 -0.3489 0.4377
## Veg_shannon_index-Sciurus_carolinensis 0.0432 0.3803 -0.7644 0.0574
## Avg_Cogongrass_Cover-Canis_latrans 0.5354 0.3803 -0.1424 0.5080
## Avg_Cogongrass_Cover-Procyon_lotor 0.4086 0.3859 -0.2933 0.3879
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4462 0.3317 -0.1761 0.4356
## Avg_Cogongrass_Cover-Lynx_rufus 0.6075 0.4136 -0.1035 0.5731
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4676 0.3666 -0.2114 0.4569
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0570 0.4415 -1.0363 -0.0258
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0777 0.4824 -1.0944 -0.0432
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4238 0.3553 -0.2492 0.4224
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9727 1.0134 851
## (Intercept)-Procyon_lotor 1.2634 1.0242 551
## (Intercept)-Dasypus_novemcinctus 0.2149 1.0211 1228
## (Intercept)-Lynx_rufus 0.7204 1.0210 745
## (Intercept)-Didelphis_virginiana -0.1089 1.0455 1014
## (Intercept)-Sylvilagus_floridanus 0.5824 1.0205 858
## (Intercept)-Meleagris_gallopavo 0.5558 1.0183 667
## (Intercept)-Sciurus_carolinensis -0.0933 1.0485 931
## Veg_shannon_index-Canis_latrans 1.4327 1.0076 1497
## Veg_shannon_index-Procyon_lotor 1.2444 1.0097 1024
## Veg_shannon_index-Dasypus_novemcinctus 0.8798 1.0080 1772
## Veg_shannon_index-Lynx_rufus 1.0462 1.0079 1226
## Veg_shannon_index-Didelphis_virginiana 1.2458 1.0283 1304
## Veg_shannon_index-Sylvilagus_floridanus 1.2663 1.0135 1319
## Veg_shannon_index-Meleagris_gallopavo 1.3825 1.0143 1356
## Veg_shannon_index-Sciurus_carolinensis 0.7340 1.0208 1381
## Avg_Cogongrass_Cover-Canis_latrans 1.3699 1.0073 1692
## Avg_Cogongrass_Cover-Procyon_lotor 1.2736 1.0024 1583
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1088 1.0113 1886
## Avg_Cogongrass_Cover-Lynx_rufus 1.5130 1.0111 1438
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2055 1.0060 1722
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7111 1.0072 1230
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7840 1.0038 881
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1257 1.0127 1570
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6039 0.1711 -2.9450 -2.5967 -2.2833 1.0023
## (Intercept)-Procyon_lotor -2.2864 0.1294 -2.5416 -2.2846 -2.0381 0.9997
## (Intercept)-Dasypus_novemcinctus -1.6223 0.1377 -1.8998 -1.6200 -1.3567 1.0014
## (Intercept)-Lynx_rufus -3.3994 0.2836 -3.9619 -3.3965 -2.8720 1.0031
## (Intercept)-Didelphis_virginiana -2.3795 0.2495 -2.9046 -2.3665 -1.9229 1.0342
## (Intercept)-Sylvilagus_floridanus -3.1267 0.2776 -3.6857 -3.1156 -2.6273 1.0238
## (Intercept)-Meleagris_gallopavo -3.3491 0.3222 -4.0149 -3.3380 -2.7755 1.0169
## (Intercept)-Sciurus_carolinensis -2.4890 0.2654 -3.0459 -2.4810 -2.0123 1.0068
## week-Canis_latrans 0.0588 0.1319 -0.2000 0.0607 0.3109 1.0102
## week-Procyon_lotor -0.0578 0.1164 -0.2909 -0.0567 0.1644 1.0070
## week-Dasypus_novemcinctus -0.1721 0.1380 -0.4549 -0.1679 0.0929 1.0004
## week-Lynx_rufus -0.0524 0.1935 -0.4471 -0.0515 0.3221 1.0059
## week-Didelphis_virginiana -0.2366 0.2164 -0.7064 -0.2176 0.1529 1.0041
## week-Sylvilagus_floridanus -0.1652 0.2010 -0.5886 -0.1497 0.2020 1.0055
## week-Meleagris_gallopavo -0.2907 0.2451 -0.8299 -0.2686 0.1348 1.0053
## week-Sciurus_carolinensis 0.1182 0.1819 -0.2529 0.1237 0.4596 1.0042
## ESS
## (Intercept)-Canis_latrans 1052
## (Intercept)-Procyon_lotor 1371
## (Intercept)-Dasypus_novemcinctus 2036
## (Intercept)-Lynx_rufus 436
## (Intercept)-Didelphis_virginiana 1056
## (Intercept)-Sylvilagus_floridanus 608
## (Intercept)-Meleagris_gallopavo 437
## (Intercept)-Sciurus_carolinensis 1097
## week-Canis_latrans 1617
## week-Procyon_lotor 1391
## week-Dasypus_novemcinctus 1748
## week-Lynx_rufus 998
## week-Didelphis_virginiana 1495
## week-Sylvilagus_floridanus 1125
## week-Meleagris_gallopavo 748
## week-Sciurus_carolinensis 1804
# Includes movement covariates of occupancy and week for detection
ms_week_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7247
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5339 0.3955 -1.3126 -0.5375 0.2644 1.0010 803
## Cogon_Patch_Size -0.0292 0.3671 -0.8094 -0.0202 0.6892 1.0014 1207
## Avg_Cogongrass_Cover 0.1518 0.3108 -0.4431 0.1516 0.7508 1.0013 1119
## total_shrub_cover -0.4792 0.3432 -1.2272 -0.4705 0.1576 1.0113 1535
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8357 1.0584 0.0644 0.5225 3.4704 1.0064 622
## Cogon_Patch_Size 0.6580 0.8662 0.0585 0.3976 2.8948 1.0275 1006
## Avg_Cogongrass_Cover 0.3875 0.5407 0.0412 0.2253 1.7444 1.0159 873
## total_shrub_cover 0.5977 0.7808 0.0553 0.3716 2.4251 1.0359 840
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8572 0.7612 0.0834 0.6298 2.8877 1.019 149
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5768 0.2772 -3.1240 -2.5842 -1.9813 1.0031 2387
## week -0.0976 0.1368 -0.3735 -0.0956 0.1697 1.0016 1940
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5729 0.4845 0.1522 0.4416 1.7445 1.0202 1683
## week 0.1072 0.0954 0.0248 0.0789 0.3373 1.0224 1979
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0296 0.5669 -1.0519 0.0026
## (Intercept)-Procyon_lotor 0.2045 0.6379 -0.9795 0.1820
## (Intercept)-Dasypus_novemcinctus -0.6832 0.4841 -1.6905 -0.6589
## (Intercept)-Lynx_rufus -0.3706 0.6463 -1.5320 -0.4131
## (Intercept)-Didelphis_virginiana -1.1522 0.5990 -2.4801 -1.1244
## (Intercept)-Sylvilagus_floridanus -0.5286 0.5843 -1.6347 -0.5399
## (Intercept)-Meleagris_gallopavo -0.7256 0.5785 -1.9211 -0.7178
## (Intercept)-Sciurus_carolinensis -1.2082 0.6397 -2.5402 -1.1728
## Cogon_Patch_Size-Canis_latrans 0.6209 0.5761 -0.2877 0.5518
## Cogon_Patch_Size-Procyon_lotor -0.1424 0.4402 -1.0360 -0.1315
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0613 0.4005 -0.8901 -0.0508
## Cogon_Patch_Size-Lynx_rufus -0.1454 0.6294 -1.4148 -0.1541
## Cogon_Patch_Size-Didelphis_virginiana 0.6098 0.4664 -0.1827 0.5528
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6091 0.6982 -2.3573 -0.5220
## Cogon_Patch_Size-Meleagris_gallopavo -0.0008 0.5384 -1.0474 -0.0015
## Cogon_Patch_Size-Sciurus_carolinensis -0.4876 0.5698 -1.8469 -0.4180
## Avg_Cogongrass_Cover-Canis_latrans 0.2217 0.3830 -0.4993 0.2050
## Avg_Cogongrass_Cover-Procyon_lotor 0.1547 0.4254 -0.6547 0.1351
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3419 0.3711 -0.3720 0.3258
## Avg_Cogongrass_Cover-Lynx_rufus 0.4926 0.4848 -0.3268 0.4477
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1727 0.3937 -0.6276 0.1627
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1881 0.4805 -1.2265 -0.1629
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3762 0.5812 -1.6878 -0.3292
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4034 0.4007 -0.3362 0.3860
## total_shrub_cover-Canis_latrans 0.0315 0.4484 -0.7825 0.0017
## total_shrub_cover-Procyon_lotor -0.8799 0.4728 -1.9273 -0.8325
## total_shrub_cover-Dasypus_novemcinctus -0.1216 0.3779 -0.8267 -0.1310
## total_shrub_cover-Lynx_rufus -0.8255 0.6311 -2.2921 -0.7372
## total_shrub_cover-Didelphis_virginiana -0.3840 0.4403 -1.2774 -0.3802
## total_shrub_cover-Sylvilagus_floridanus -0.3741 0.5112 -1.3927 -0.3626
## total_shrub_cover-Meleagris_gallopavo -1.1889 0.6276 -2.6675 -1.1126
## total_shrub_cover-Sciurus_carolinensis -0.1554 0.4380 -0.9893 -0.1641
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1963 1.0003 409
## (Intercept)-Procyon_lotor 1.5123 1.0012 558
## (Intercept)-Dasypus_novemcinctus 0.2371 1.0049 748
## (Intercept)-Lynx_rufus 1.0386 1.0144 681
## (Intercept)-Didelphis_virginiana -0.0803 1.0020 768
## (Intercept)-Sylvilagus_floridanus 0.6458 1.0082 765
## (Intercept)-Meleagris_gallopavo 0.4234 1.0107 898
## (Intercept)-Sciurus_carolinensis -0.1183 1.0016 632
## Cogon_Patch_Size-Canis_latrans 1.9891 1.0020 1098
## Cogon_Patch_Size-Procyon_lotor 0.7086 1.0008 1828
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6825 1.0090 1851
## Cogon_Patch_Size-Lynx_rufus 1.1584 1.0031 906
## Cogon_Patch_Size-Didelphis_virginiana 1.6393 1.0011 1460
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4653 1.0056 798
## Cogon_Patch_Size-Meleagris_gallopavo 1.1351 1.0002 1451
## Cogon_Patch_Size-Sciurus_carolinensis 0.4253 1.0063 982
## Avg_Cogongrass_Cover-Canis_latrans 1.0040 1.0002 1780
## Avg_Cogongrass_Cover-Procyon_lotor 1.0455 1.0026 1498
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0849 1.0015 1668
## Avg_Cogongrass_Cover-Lynx_rufus 1.5956 1.0042 1292
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9531 1.0049 1718
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7011 1.0034 1177
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5921 1.0054 649
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2504 1.0032 1638
## total_shrub_cover-Canis_latrans 0.9749 1.0031 1493
## total_shrub_cover-Procyon_lotor -0.0933 1.0109 1333
## total_shrub_cover-Dasypus_novemcinctus 0.6500 1.0091 2145
## total_shrub_cover-Lynx_rufus 0.1534 1.0377 706
## total_shrub_cover-Didelphis_virginiana 0.4852 1.0062 2084
## total_shrub_cover-Sylvilagus_floridanus 0.5843 1.0060 1101
## total_shrub_cover-Meleagris_gallopavo -0.2086 1.0166 661
## total_shrub_cover-Sciurus_carolinensis 0.7555 1.0032 2095
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6034 0.1662 -2.9536 -2.5965 -2.3038 1.0289
## (Intercept)-Procyon_lotor -2.2811 0.1286 -2.5328 -2.2798 -2.0396 1.0083
## (Intercept)-Dasypus_novemcinctus -1.6221 0.1357 -1.8901 -1.6202 -1.3613 1.0050
## (Intercept)-Lynx_rufus -3.3950 0.2694 -3.9298 -3.3885 -2.9004 1.0047
## (Intercept)-Didelphis_virginiana -2.3674 0.2517 -2.9040 -2.3588 -1.9006 1.0035
## (Intercept)-Sylvilagus_floridanus -3.1427 0.2783 -3.7024 -3.1303 -2.6320 1.0060
## (Intercept)-Meleagris_gallopavo -3.2557 0.2985 -3.8712 -3.2401 -2.7066 1.0005
## (Intercept)-Sciurus_carolinensis -2.4851 0.2661 -3.0511 -2.4712 -2.0165 1.0061
## week-Canis_latrans 0.0612 0.1329 -0.2108 0.0647 0.3113 1.0016
## week-Procyon_lotor -0.0558 0.1169 -0.2889 -0.0537 0.1648 1.0054
## week-Dasypus_novemcinctus -0.1780 0.1378 -0.4682 -0.1729 0.0745 1.0073
## week-Lynx_rufus -0.0489 0.1896 -0.4251 -0.0390 0.3192 1.0193
## week-Didelphis_virginiana -0.2347 0.2155 -0.6881 -0.2195 0.1473 1.0039
## week-Sylvilagus_floridanus -0.1627 0.2002 -0.5847 -0.1555 0.2138 1.0058
## week-Meleagris_gallopavo -0.2946 0.2389 -0.8107 -0.2713 0.1207 1.0081
## week-Sciurus_carolinensis 0.1204 0.1811 -0.2346 0.1211 0.4715 1.0035
## ESS
## (Intercept)-Canis_latrans 1070
## (Intercept)-Procyon_lotor 1461
## (Intercept)-Dasypus_novemcinctus 2252
## (Intercept)-Lynx_rufus 506
## (Intercept)-Didelphis_virginiana 1348
## (Intercept)-Sylvilagus_floridanus 559
## (Intercept)-Meleagris_gallopavo 532
## (Intercept)-Sciurus_carolinensis 1021
## week-Canis_latrans 1912
## week-Procyon_lotor 1863
## week-Dasypus_novemcinctus 1825
## week-Lynx_rufus 1033
## week-Didelphis_virginiana 1621
## week-Sylvilagus_floridanus 1150
## week-Meleagris_gallopavo 781
## week-Sciurus_carolinensis 1876
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7413
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6139 0.4564 -1.4829 -0.6237 0.3146 1.0033 711
## Tree_Density -0.7668 0.4446 -1.7072 -0.7401 0.0354 1.0044 925
## Avg_Canopy_Cover 0.9738 0.3698 0.2767 0.9523 1.7658 1.0155 1335
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2203 1.4757 0.0985 0.7732 4.9307 1.0432 370
## Tree_Density 0.9275 1.5469 0.0521 0.4553 4.3134 1.0666 422
## Avg_Canopy_Cover 0.7633 0.9715 0.0646 0.4673 3.4437 1.0313 625
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5707 0.5676 0.0557 0.3864 2.1504 1.0197 191
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5929 0.3016 -3.1610 -2.6032 -1.9638 1.0065 2476
## week -0.1050 0.1401 -0.3952 -0.0979 0.1523 1.0092 1274
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6470 0.5242 0.1608 0.5052 1.9967 1.0146 1521
## week 0.1069 0.1026 0.0247 0.0788 0.3557 1.0221 1724
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0152 0.5883 -1.0862 -0.0451 1.1529
## (Intercept)-Procyon_lotor 0.3045 0.6077 -0.8808 0.3184 1.5042
## (Intercept)-Dasypus_novemcinctus -0.9350 0.5344 -2.0769 -0.9026 0.0405
## (Intercept)-Lynx_rufus -0.1093 0.9547 -1.4995 -0.2326 2.3321
## (Intercept)-Didelphis_virginiana -1.5080 0.6445 -2.9449 -1.4539 -0.3699
## (Intercept)-Sylvilagus_floridanus -0.7258 0.6418 -1.9915 -0.7290 0.5660
## (Intercept)-Meleagris_gallopavo -0.6291 0.6236 -1.8394 -0.6396 0.6287
## (Intercept)-Sciurus_carolinensis -1.5452 0.6614 -2.9937 -1.4913 -0.3943
## Tree_Density-Canis_latrans -0.9480 0.5852 -2.2607 -0.8861 0.0382
## Tree_Density-Procyon_lotor -0.4766 0.4207 -1.3246 -0.4686 0.3727
## Tree_Density-Dasypus_novemcinctus -1.3925 0.8875 -3.6477 -1.2063 -0.1814
## Tree_Density-Lynx_rufus 0.1887 0.7401 -0.9669 0.0922 2.0052
## Tree_Density-Didelphis_virginiana -1.0122 0.7558 -2.9453 -0.8863 0.1077
## Tree_Density-Sylvilagus_floridanus -1.0937 0.7594 -2.9442 -0.9755 0.1057
## Tree_Density-Meleagris_gallopavo -0.9058 0.7263 -2.5959 -0.8380 0.3268
## Tree_Density-Sciurus_carolinensis -0.8856 0.7183 -2.6201 -0.7842 0.2443
## Avg_Canopy_Cover-Canis_latrans 0.0527 0.4702 -0.8886 0.0562 0.9136
## Avg_Canopy_Cover-Procyon_lotor 0.9699 0.4478 0.1675 0.9430 1.9227
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9990 0.4355 0.1847 0.9648 1.9483
## Avg_Canopy_Cover-Lynx_rufus 0.6435 0.6386 -0.5400 0.6135 2.0137
## Avg_Canopy_Cover-Didelphis_virginiana 1.1983 0.4930 0.3352 1.1556 2.2791
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7006 0.7787 0.5498 1.5643 3.5639
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3700 0.6884 0.2806 1.2762 2.9682
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1854 0.4907 0.3580 1.1456 2.2645
## Rhat ESS
## (Intercept)-Canis_latrans 1.0127 732
## (Intercept)-Procyon_lotor 1.0175 566
## (Intercept)-Dasypus_novemcinctus 1.0187 1086
## (Intercept)-Lynx_rufus 1.0813 203
## (Intercept)-Didelphis_virginiana 1.0016 809
## (Intercept)-Sylvilagus_floridanus 1.0053 901
## (Intercept)-Meleagris_gallopavo 1.0146 959
## (Intercept)-Sciurus_carolinensis 1.0052 808
## Tree_Density-Canis_latrans 1.0124 1071
## Tree_Density-Procyon_lotor 1.0004 2054
## Tree_Density-Dasypus_novemcinctus 1.0138 490
## Tree_Density-Lynx_rufus 1.0105 469
## Tree_Density-Didelphis_virginiana 1.0018 710
## Tree_Density-Sylvilagus_floridanus 1.0017 706
## Tree_Density-Meleagris_gallopavo 1.0156 908
## Tree_Density-Sciurus_carolinensis 1.0011 1071
## Avg_Canopy_Cover-Canis_latrans 1.0079 1331
## Avg_Canopy_Cover-Procyon_lotor 1.0064 1821
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0171 2018
## Avg_Canopy_Cover-Lynx_rufus 1.0070 786
## Avg_Canopy_Cover-Didelphis_virginiana 1.0078 1380
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0414 653
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0311 654
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0143 1773
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6292 0.1694 -2.9665 -2.6246 -2.3173 1.0223
## (Intercept)-Procyon_lotor -2.2703 0.1285 -2.5241 -2.2674 -2.0264 1.0126
## (Intercept)-Dasypus_novemcinctus -1.6203 0.1350 -1.8883 -1.6198 -1.3593 1.0034
## (Intercept)-Lynx_rufus -3.4849 0.3097 -4.1085 -3.4803 -2.9006 1.0091
## (Intercept)-Didelphis_virginiana -2.3730 0.2449 -2.8782 -2.3609 -1.9285 1.0102
## (Intercept)-Sylvilagus_floridanus -3.1064 0.2679 -3.6731 -3.0950 -2.6245 1.0018
## (Intercept)-Meleagris_gallopavo -3.3705 0.3299 -4.0765 -3.3435 -2.7908 1.0080
## (Intercept)-Sciurus_carolinensis -2.4915 0.2685 -3.0752 -2.4841 -2.0023 1.0038
## week-Canis_latrans 0.0576 0.1353 -0.2162 0.0623 0.3156 1.0198
## week-Procyon_lotor -0.0590 0.1170 -0.2985 -0.0545 0.1579 1.0032
## week-Dasypus_novemcinctus -0.1719 0.1367 -0.4552 -0.1684 0.0889 1.0154
## week-Lynx_rufus -0.0626 0.1914 -0.4665 -0.0541 0.2888 1.0157
## week-Didelphis_virginiana -0.2370 0.2197 -0.7134 -0.2239 0.1582 1.0016
## week-Sylvilagus_floridanus -0.1713 0.2017 -0.5919 -0.1575 0.1904 1.0061
## week-Meleagris_gallopavo -0.2812 0.2414 -0.8094 -0.2619 0.1420 1.0044
## week-Sciurus_carolinensis 0.1169 0.1815 -0.2428 0.1178 0.4624 1.0070
## ESS
## (Intercept)-Canis_latrans 1044
## (Intercept)-Procyon_lotor 1541
## (Intercept)-Dasypus_novemcinctus 2046
## (Intercept)-Lynx_rufus 332
## (Intercept)-Didelphis_virginiana 1348
## (Intercept)-Sylvilagus_floridanus 620
## (Intercept)-Meleagris_gallopavo 457
## (Intercept)-Sciurus_carolinensis 1130
## week-Canis_latrans 1366
## week-Procyon_lotor 1611
## week-Dasypus_novemcinctus 2051
## week-Lynx_rufus 1016
## week-Didelphis_virginiana 1360
## week-Sylvilagus_floridanus 1092
## week-Meleagris_gallopavo 820
## week-Sciurus_carolinensis 1768
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7513
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1523 0.4118 -1.9722 -1.1415 -0.3640 1.0032 559
## Avg_Cogongrass_Cover -0.5688 0.3694 -1.3031 -0.5639 0.1399 1.0002 651
## I(Avg_Cogongrass_Cover^2) 0.7874 0.3906 0.0936 0.7557 1.6363 1.0134 562
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6220 0.7423 0.0546 0.4083 2.4454 1.0174 1238
## Avg_Cogongrass_Cover 0.3981 0.5413 0.0414 0.2284 1.7151 1.0157 1179
## I(Avg_Cogongrass_Cover^2) 0.6508 1.3706 0.0399 0.2678 3.3640 1.1471 336
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.465 0.4462 0.0436 0.3148 1.6764 1.0019 228
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5843 0.2785 -3.0998 -2.5893 -2.0070 1.0015 2482
## week -0.0964 0.1410 -0.3874 -0.0925 0.1697 1.0208 1402
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5668 0.5751 0.1445 0.4355 1.7375 1.0655 2185
## week 0.1065 0.1104 0.0250 0.0790 0.3408 1.0758 1869
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8005 0.5638 -1.8816 -0.8157
## (Intercept)-Procyon_lotor -0.5771 0.5807 -1.6483 -0.5981
## (Intercept)-Dasypus_novemcinctus -1.2788 0.4990 -2.2988 -1.2761
## (Intercept)-Lynx_rufus -1.2534 0.5959 -2.4993 -1.2442
## (Intercept)-Didelphis_virginiana -1.6003 0.5649 -2.8056 -1.5770
## (Intercept)-Sylvilagus_floridanus -1.1396 0.5595 -2.2431 -1.1353
## (Intercept)-Meleagris_gallopavo -0.9827 0.6059 -2.1129 -1.0087
## (Intercept)-Sciurus_carolinensis -1.8167 0.6336 -3.1836 -1.7673
## Avg_Cogongrass_Cover-Canis_latrans -0.3988 0.4889 -1.3306 -0.4136
## Avg_Cogongrass_Cover-Procyon_lotor -0.5423 0.5034 -1.4876 -0.5595
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4285 0.4555 -1.3077 -0.4461
## Avg_Cogongrass_Cover-Lynx_rufus -0.4895 0.5305 -1.5142 -0.4895
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2922 0.5133 -1.2119 -0.3224
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.0101 0.5738 -2.2770 -0.9643
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8732 0.5793 -2.1064 -0.8318
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5817 0.5096 -1.6210 -0.5740
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3625 0.8861 0.2700 1.1150
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2434 0.8016 0.2386 1.0452
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6461 0.3402 -0.0280 0.6434
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1293 0.5516 0.3036 1.0501
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4349 0.3829 -0.3528 0.4399
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6979 0.4828 -0.1464 0.6606
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2095 0.6006 -1.0834 0.2428
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7845 0.3723 0.1128 0.7649
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3426 1.0109 655
## (Intercept)-Procyon_lotor 0.6433 1.0078 548
## (Intercept)-Dasypus_novemcinctus -0.3512 1.0113 950
## (Intercept)-Lynx_rufus -0.0973 1.0033 603
## (Intercept)-Didelphis_virginiana -0.5716 1.0046 872
## (Intercept)-Sylvilagus_floridanus -0.0714 1.0078 759
## (Intercept)-Meleagris_gallopavo 0.3276 1.0110 715
## (Intercept)-Sciurus_carolinensis -0.7093 1.0013 885
## Avg_Cogongrass_Cover-Canis_latrans 0.6529 1.0053 1003
## Avg_Cogongrass_Cover-Procyon_lotor 0.5348 1.0121 984
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5216 1.0040 954
## Avg_Cogongrass_Cover-Lynx_rufus 0.5990 1.0057 910
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7783 1.0045 936
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0037 1.0032 754
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1569 1.0012 853
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3747 1.0102 1020
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.6362 1.0958 260
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.3196 1.0544 247
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3257 1.0152 850
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4478 1.0732 363
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1762 1.0085 919
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8491 1.0027 583
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.3088 1.0101 411
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5447 1.0070 985
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6323 0.1667 -2.9798 -2.6271 -2.3108 1.0096
## (Intercept)-Procyon_lotor -2.2824 0.1257 -2.5324 -2.2814 -2.0405 1.0045
## (Intercept)-Dasypus_novemcinctus -1.6246 0.1372 -1.9044 -1.6219 -1.3583 1.0013
## (Intercept)-Lynx_rufus -3.3136 0.2838 -3.9446 -3.3010 -2.8022 1.0408
## (Intercept)-Didelphis_virginiana -2.3833 0.2524 -2.9286 -2.3697 -1.9372 1.0013
## (Intercept)-Sylvilagus_floridanus -3.1321 0.2796 -3.6979 -3.1196 -2.6280 1.0043
## (Intercept)-Meleagris_gallopavo -3.3194 0.3245 -3.9853 -3.3011 -2.7320 1.0040
## (Intercept)-Sciurus_carolinensis -2.4849 0.2527 -3.0117 -2.4734 -2.0029 1.0220
## week-Canis_latrans 0.0612 0.1310 -0.2080 0.0661 0.2979 1.0041
## week-Procyon_lotor -0.0616 0.1210 -0.3033 -0.0593 0.1636 1.0017
## week-Dasypus_novemcinctus -0.1721 0.1372 -0.4485 -0.1674 0.0833 1.0012
## week-Lynx_rufus -0.0547 0.1899 -0.4354 -0.0502 0.2968 1.0092
## week-Didelphis_virginiana -0.2335 0.2145 -0.6913 -0.2201 0.1482 1.0103
## week-Sylvilagus_floridanus -0.1617 0.2012 -0.5847 -0.1533 0.2153 1.0210
## week-Meleagris_gallopavo -0.2911 0.2478 -0.8512 -0.2642 0.1249 1.0061
## week-Sciurus_carolinensis 0.1196 0.1848 -0.2349 0.1198 0.4883 1.0171
## ESS
## (Intercept)-Canis_latrans 1062
## (Intercept)-Procyon_lotor 1585
## (Intercept)-Dasypus_novemcinctus 2137
## (Intercept)-Lynx_rufus 480
## (Intercept)-Didelphis_virginiana 1072
## (Intercept)-Sylvilagus_floridanus 538
## (Intercept)-Meleagris_gallopavo 400
## (Intercept)-Sciurus_carolinensis 1233
## week-Canis_latrans 1507
## week-Procyon_lotor 1578
## week-Dasypus_novemcinctus 1935
## week-Lynx_rufus 1088
## week-Didelphis_virginiana 1573
## week-Sylvilagus_floridanus 1021
## week-Meleagris_gallopavo 744
## week-Sciurus_carolinensis 1928
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8005
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0218 0.6977 -3.4249 -2.0063 -0.6842 1.0826 396
## Cogon_Patch_Size 0.0085 0.6435 -1.3251 0.0169 1.2066 1.0267 641
## Veg_shannon_index 0.9044 0.4470 0.1506 0.8722 1.8314 1.0784 280
## total_shrub_cover -0.7121 0.5645 -1.9362 -0.6677 0.3414 1.0121 1062
## Avg_Cogongrass_Cover 0.2562 0.8954 -1.4040 0.2499 2.1342 1.0311 168
## Tree_Density -2.1521 0.7714 -3.7775 -2.1458 -0.6909 1.0611 242
## Avg_Canopy_Cover 1.6388 0.6322 0.4698 1.6076 3.0146 1.0444 524
## I(Avg_Cogongrass_Cover^2) 1.2267 0.6626 -0.0232 1.1906 2.5879 1.0359 270
## avg_veg_height -0.1265 0.4985 -1.0965 -0.1297 0.8490 1.0082 351
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7598 2.5571 0.0743 1.0309 8.1728 1.0522 728
## Cogon_Patch_Size 2.4733 3.4955 0.1118 1.3575 11.9137 1.1430 220
## Veg_shannon_index 0.5349 0.9195 0.0447 0.2712 2.5458 1.1726 474
## total_shrub_cover 2.0009 2.7067 0.1034 1.1796 9.1660 1.0626 343
## Avg_Cogongrass_Cover 1.1625 2.1516 0.0502 0.4915 6.2051 1.0162 530
## Tree_Density 2.3215 5.1850 0.0656 0.8258 13.4464 1.1738 152
## Avg_Canopy_Cover 2.4367 4.2394 0.0845 1.1444 12.9836 1.2615 123
## I(Avg_Cogongrass_Cover^2) 2.5176 4.1491 0.1035 1.2277 12.8455 1.0415 185
## avg_veg_height 0.5084 0.7828 0.0426 0.2758 2.4605 1.0209 811
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7944 2.2338 0.0686 1.0389 8.0895 1.116 80
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5800 0.2959 -3.1275 -2.5866 -1.9866 1.0029 2485
## week -0.1036 0.1404 -0.3967 -0.0989 0.1678 1.0050 1328
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.67 0.6228 0.1702 0.5250 1.9869 1.0392 2211
## week 0.11 0.0968 0.0253 0.0808 0.3627 1.0063 1502
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5823 0.9861 -3.5513 -1.5866
## (Intercept)-Procyon_lotor -1.2407 1.0140 -3.1829 -1.2354
## (Intercept)-Dasypus_novemcinctus -2.3590 0.8724 -4.3577 -2.3033
## (Intercept)-Lynx_rufus -1.7319 1.1234 -3.8857 -1.7628
## (Intercept)-Didelphis_virginiana -2.9098 0.9991 -5.1213 -2.8239
## (Intercept)-Sylvilagus_floridanus -2.0820 0.9653 -4.0645 -2.0596
## (Intercept)-Meleagris_gallopavo -2.0112 0.9832 -3.9961 -2.0087
## (Intercept)-Sciurus_carolinensis -3.3317 1.1554 -5.9022 -3.2000
## Cogon_Patch_Size-Canis_latrans 1.2660 1.0760 -0.3851 1.0899
## Cogon_Patch_Size-Procyon_lotor -0.4911 0.8129 -2.2025 -0.4646
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2073 0.6658 -1.6264 -0.1877
## Cogon_Patch_Size-Lynx_rufus -0.2397 1.3186 -2.6680 -0.2754
## Cogon_Patch_Size-Didelphis_virginiana 1.4247 1.0884 -0.1216 1.2599
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1399 1.2939 -4.2613 -0.9538
## Cogon_Patch_Size-Meleagris_gallopavo 0.1931 1.0245 -1.6343 0.1191
## Cogon_Patch_Size-Sciurus_carolinensis -0.8133 0.9993 -3.2331 -0.6791
## Veg_shannon_index-Canis_latrans 1.2202 0.6725 0.1985 1.1371
## Veg_shannon_index-Procyon_lotor 1.0906 0.5829 0.0815 1.0500
## Veg_shannon_index-Dasypus_novemcinctus 0.6633 0.5096 -0.3210 0.6648
## Veg_shannon_index-Lynx_rufus 0.9022 0.7322 -0.4003 0.8625
## Veg_shannon_index-Didelphis_virginiana 0.9616 0.6369 -0.1745 0.9171
## Veg_shannon_index-Sylvilagus_floridanus 0.9623 0.7035 -0.1659 0.9117
## Veg_shannon_index-Meleagris_gallopavo 1.1350 0.7020 -0.0149 1.0585
## Veg_shannon_index-Sciurus_carolinensis 0.4390 0.6328 -0.9458 0.4752
## total_shrub_cover-Canis_latrans 0.0298 0.6546 -1.1390 -0.0052
## total_shrub_cover-Procyon_lotor -1.1643 0.6584 -2.6206 -1.0975
## total_shrub_cover-Dasypus_novemcinctus 0.0701 0.5882 -1.0267 0.0391
## total_shrub_cover-Lynx_rufus -1.5175 1.1344 -4.3847 -1.3103
## total_shrub_cover-Didelphis_virginiana -0.7084 0.7624 -2.3193 -0.6350
## total_shrub_cover-Sylvilagus_floridanus -0.4213 0.9135 -2.2952 -0.4176
## total_shrub_cover-Meleagris_gallopavo -2.4031 1.3027 -5.3663 -2.2402
## total_shrub_cover-Sciurus_carolinensis -0.0427 0.7408 -1.3942 -0.0880
## Avg_Cogongrass_Cover-Canis_latrans 0.1495 1.1112 -2.1306 0.1845
## Avg_Cogongrass_Cover-Procyon_lotor 0.2527 1.1292 -1.9656 0.2827
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7586 1.1371 -1.2445 0.6962
## Avg_Cogongrass_Cover-Lynx_rufus 0.3317 1.2051 -2.1341 0.3422
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.5792 1.1471 -1.5284 0.5452
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2969 1.3410 -3.3296 -0.1924
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.1344 1.2491 -2.8399 -0.0554
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4317 1.1547 -1.6526 0.3792
## Tree_Density-Canis_latrans -2.8428 1.3270 -6.0479 -2.6324
## Tree_Density-Procyon_lotor -2.1438 1.0644 -4.3272 -2.0750
## Tree_Density-Dasypus_novemcinctus -3.3592 1.5410 -7.3102 -3.0407
## Tree_Density-Lynx_rufus -1.1840 1.3439 -3.4643 -1.3246
## Tree_Density-Didelphis_virginiana -2.2062 1.0772 -4.4785 -2.1416
## Tree_Density-Sylvilagus_floridanus -2.5347 1.2638 -5.5452 -2.3889
## Tree_Density-Meleagris_gallopavo -2.2118 1.1969 -4.8758 -2.1673
## Tree_Density-Sciurus_carolinensis -2.4062 1.1107 -4.9215 -2.3118
## Avg_Canopy_Cover-Canis_latrans 0.2877 0.7155 -1.1758 0.2806
## Avg_Canopy_Cover-Procyon_lotor 1.6338 0.7443 0.3697 1.5727
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8996 0.7366 0.7083 1.8171
## Avg_Canopy_Cover-Lynx_rufus 1.0101 1.0581 -1.1112 1.0366
## Avg_Canopy_Cover-Didelphis_virginiana 2.3882 0.9455 0.9788 2.2483
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.1786 1.8124 1.0118 2.7087
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1801 1.2494 0.6008 1.9556
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0607 0.8698 0.7195 1.9283
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3627 1.3250 0.5982 2.0749
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2688 1.2511 0.4845 2.0383
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2384 0.7180 -0.0269 1.1802
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4517 1.2996 0.6021 2.2067
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6360 0.6887 -0.7320 0.6340
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.9555 0.8614 -0.4947 0.8711
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.2446 1.3690 -3.5105 -0.0743
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2987 0.7448 0.0287 1.2285
## avg_veg_height-Canis_latrans -0.3864 0.6236 -1.6966 -0.3571
## avg_veg_height-Procyon_lotor 0.0777 0.6284 -1.0872 0.0590
## avg_veg_height-Dasypus_novemcinctus 0.1413 0.5977 -0.9322 0.1215
## avg_veg_height-Lynx_rufus -0.3005 0.7924 -2.1399 -0.2520
## avg_veg_height-Didelphis_virginiana -0.2146 0.6498 -1.4913 -0.2144
## avg_veg_height-Sylvilagus_floridanus -0.2870 0.6871 -1.7383 -0.2565
## avg_veg_height-Meleagris_gallopavo -0.2384 0.7613 -1.7365 -0.2145
## avg_veg_height-Sciurus_carolinensis 0.1361 0.6549 -1.0582 0.0950
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3103 1.0237 346
## (Intercept)-Procyon_lotor 0.7241 1.0252 256
## (Intercept)-Dasypus_novemcinctus -0.7775 1.0627 455
## (Intercept)-Lynx_rufus 0.6411 1.0238 365
## (Intercept)-Didelphis_virginiana -1.2728 1.0469 568
## (Intercept)-Sylvilagus_floridanus -0.2636 1.0848 437
## (Intercept)-Meleagris_gallopavo -0.0860 1.0463 481
## (Intercept)-Sciurus_carolinensis -1.5040 1.0872 475
## Cogon_Patch_Size-Canis_latrans 3.9297 1.0047 653
## Cogon_Patch_Size-Procyon_lotor 1.0012 1.0731 254
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0447 1.0290 509
## Cogon_Patch_Size-Lynx_rufus 2.5326 1.0118 350
## Cogon_Patch_Size-Didelphis_virginiana 3.8126 1.0954 283
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8749 1.0770 224
## Cogon_Patch_Size-Meleagris_gallopavo 2.5317 1.0230 577
## Cogon_Patch_Size-Sciurus_carolinensis 0.7753 1.0777 360
## Veg_shannon_index-Canis_latrans 2.6327 1.0784 332
## Veg_shannon_index-Procyon_lotor 2.3498 1.1058 246
## Veg_shannon_index-Dasypus_novemcinctus 1.6702 1.0209 798
## Veg_shannon_index-Lynx_rufus 2.4294 1.0746 307
## Veg_shannon_index-Didelphis_virginiana 2.3407 1.0458 430
## Veg_shannon_index-Sylvilagus_floridanus 2.3781 1.1111 258
## Veg_shannon_index-Meleagris_gallopavo 2.7669 1.0512 605
## Veg_shannon_index-Sciurus_carolinensis 1.5740 1.0089 815
## total_shrub_cover-Canis_latrans 1.4314 1.0024 915
## total_shrub_cover-Procyon_lotor -0.0452 1.0178 793
## total_shrub_cover-Dasypus_novemcinctus 1.2759 1.0205 1127
## total_shrub_cover-Lynx_rufus 0.1386 1.0413 272
## total_shrub_cover-Didelphis_virginiana 0.6433 1.0416 451
## total_shrub_cover-Sylvilagus_floridanus 1.3518 1.0046 711
## total_shrub_cover-Meleagris_gallopavo -0.4394 1.0730 224
## total_shrub_cover-Sciurus_carolinensis 1.5350 1.0052 980
## Avg_Cogongrass_Cover-Canis_latrans 2.2888 1.0182 259
## Avg_Cogongrass_Cover-Procyon_lotor 2.4654 1.0400 261
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.1725 1.0299 257
## Avg_Cogongrass_Cover-Lynx_rufus 2.7879 1.0153 274
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.9609 1.0086 239
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0092 1.0144 299
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1707 1.0300 268
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.8306 1.0165 294
## Tree_Density-Canis_latrans -0.8346 1.0831 158
## Tree_Density-Procyon_lotor -0.2138 1.0604 254
## Tree_Density-Dasypus_novemcinctus -1.2576 1.0923 212
## Tree_Density-Lynx_rufus 1.9238 1.0100 225
## Tree_Density-Didelphis_virginiana -0.2614 1.0553 388
## Tree_Density-Sylvilagus_floridanus -0.4094 1.1086 406
## Tree_Density-Meleagris_gallopavo 0.0712 1.0663 600
## Tree_Density-Sciurus_carolinensis -0.5871 1.0932 344
## Avg_Canopy_Cover-Canis_latrans 1.6902 1.0060 516
## Avg_Canopy_Cover-Procyon_lotor 3.2327 1.0406 304
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.5957 1.0675 243
## Avg_Canopy_Cover-Lynx_rufus 3.1217 1.0304 390
## Avg_Canopy_Cover-Didelphis_virginiana 4.6209 1.1177 136
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.1713 1.1428 117
## Avg_Canopy_Cover-Meleagris_gallopavo 5.3553 1.1071 225
## Avg_Canopy_Cover-Sciurus_carolinensis 4.0851 1.0568 318
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.7259 1.0879 208
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.4706 1.0107 237
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8309 1.0167 476
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.6569 1.0623 171
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0594 1.0222 429
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9313 1.0123 305
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.0511 1.0193 176
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.0032 1.0794 313
## avg_veg_height-Canis_latrans 0.7785 1.0204 445
## avg_veg_height-Procyon_lotor 1.3776 1.0061 564
## avg_veg_height-Dasypus_novemcinctus 1.3410 1.0028 525
## avg_veg_height-Lynx_rufus 1.1228 1.0237 460
## avg_veg_height-Didelphis_virginiana 1.0809 1.0073 700
## avg_veg_height-Sylvilagus_floridanus 0.9947 1.0097 568
## avg_veg_height-Meleagris_gallopavo 1.2533 1.0070 431
## avg_veg_height-Sciurus_carolinensis 1.5516 1.0009 723
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6117 0.1647 -2.9497 -2.6044 -2.3043 1.0052
## (Intercept)-Procyon_lotor -2.2759 0.1306 -2.5446 -2.2707 -2.0324 1.0031
## (Intercept)-Dasypus_novemcinctus -1.6201 0.1359 -1.8950 -1.6177 -1.3581 1.0014
## (Intercept)-Lynx_rufus -3.5366 0.2747 -4.0823 -3.5340 -3.0029 1.0017
## (Intercept)-Didelphis_virginiana -2.3557 0.2500 -2.8820 -2.3470 -1.8833 0.9998
## (Intercept)-Sylvilagus_floridanus -3.1816 0.2668 -3.7207 -3.1752 -2.6634 1.0170
## (Intercept)-Meleagris_gallopavo -3.3011 0.2953 -3.8995 -3.2914 -2.7532 1.0087
## (Intercept)-Sciurus_carolinensis -2.4833 0.2643 -3.0624 -2.4661 -2.0054 1.0026
## week-Canis_latrans 0.0593 0.1340 -0.2135 0.0635 0.3142 1.0088
## week-Procyon_lotor -0.0635 0.1188 -0.3070 -0.0613 0.1587 1.0017
## week-Dasypus_novemcinctus -0.1705 0.1394 -0.4655 -0.1675 0.0899 0.9998
## week-Lynx_rufus -0.0579 0.1938 -0.4555 -0.0520 0.2954 1.0023
## week-Didelphis_virginiana -0.2351 0.2203 -0.7284 -0.2172 0.1589 1.0023
## week-Sylvilagus_floridanus -0.1737 0.2086 -0.6225 -0.1615 0.1965 1.0032
## week-Meleagris_gallopavo -0.2966 0.2471 -0.8606 -0.2806 0.1208 1.0008
## week-Sciurus_carolinensis 0.1273 0.1871 -0.2422 0.1304 0.4886 1.0017
## ESS
## (Intercept)-Canis_latrans 963
## (Intercept)-Procyon_lotor 1375
## (Intercept)-Dasypus_novemcinctus 2265
## (Intercept)-Lynx_rufus 375
## (Intercept)-Didelphis_virginiana 1445
## (Intercept)-Sylvilagus_floridanus 497
## (Intercept)-Meleagris_gallopavo 356
## (Intercept)-Sciurus_carolinensis 1010
## week-Canis_latrans 1548
## week-Procyon_lotor 1675
## week-Dasypus_novemcinctus 1857
## week-Lynx_rufus 708
## week-Didelphis_virginiana 1355
## week-Sylvilagus_floridanus 1058
## week-Meleagris_gallopavo 1000
## week-Sciurus_carolinensis 1692
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6292
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3288 0.3654 -1.0199 -0.3290 0.3944 1.0149 549
## Avg_Cogongrass_Cover 0.2206 0.2752 -0.3293 0.2229 0.7697 1.0080 1088
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5798 0.8127 0.0512 0.360 2.4143 1.0356 940
## Avg_Cogongrass_Cover 0.3346 0.4610 0.0384 0.199 1.3982 1.0538 1209
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7063 0.6932 0.0641 0.5072 2.5817 1.017 237
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7262 0.3193 -3.3412 -2.7313 -2.0563 1.0016 1402
## shrub_cover 0.2738 0.3136 -0.3298 0.2778 0.9064 1.0040 1650
## veg_height 0.0581 0.2028 -0.3612 0.0644 0.4432 1.0065 1724
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7376 0.6488 0.1698 0.5652 2.3825 1.0221 964
## shrub_cover 0.6946 0.6673 0.1344 0.5174 2.2799 1.0079 1040
## veg_height 0.2604 0.2351 0.0528 0.1970 0.8532 1.0098 1520
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0648 0.5072 -0.8736 0.0370
## (Intercept)-Procyon_lotor 0.1905 0.5505 -0.9037 0.1633
## (Intercept)-Dasypus_novemcinctus -0.5094 0.4618 -1.4422 -0.4933
## (Intercept)-Lynx_rufus -0.2185 0.5960 -1.3206 -0.2472
## (Intercept)-Didelphis_virginiana -0.8354 0.5332 -1.9275 -0.8145
## (Intercept)-Sylvilagus_floridanus -0.3723 0.5132 -1.3964 -0.3760
## (Intercept)-Meleagris_gallopavo -0.1521 0.6258 -1.2943 -0.1762
## (Intercept)-Sciurus_carolinensis -0.8545 0.5481 -2.0384 -0.8013
## Avg_Cogongrass_Cover-Canis_latrans 0.4238 0.3672 -0.2270 0.3998
## Avg_Cogongrass_Cover-Procyon_lotor 0.2263 0.3455 -0.4433 0.2223
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3605 0.3370 -0.2757 0.3504
## Avg_Cogongrass_Cover-Lynx_rufus 0.4537 0.4061 -0.2745 0.4260
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3247 0.3739 -0.3886 0.3035
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1868 0.4395 -1.1841 -0.1418
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2200 0.5862 -1.5355 -0.1636
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3565 0.3591 -0.3387 0.3413
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1337 1.0095 566
## (Intercept)-Procyon_lotor 1.3851 1.0059 668
## (Intercept)-Dasypus_novemcinctus 0.3599 0.9996 1427
## (Intercept)-Lynx_rufus 1.0459 1.0491 475
## (Intercept)-Didelphis_virginiana 0.1459 1.0105 853
## (Intercept)-Sylvilagus_floridanus 0.6565 1.0114 745
## (Intercept)-Meleagris_gallopavo 1.1829 1.0130 433
## (Intercept)-Sciurus_carolinensis 0.1071 1.0050 941
## Avg_Cogongrass_Cover-Canis_latrans 1.2217 1.0005 1661
## Avg_Cogongrass_Cover-Procyon_lotor 0.9335 1.0006 2330
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0125 0.9999 2225
## Avg_Cogongrass_Cover-Lynx_rufus 1.3713 0.9996 1249
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1140 1.0111 1591
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5721 1.0137 988
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7764 1.0128 673
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0966 1.0001 2021
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7413 0.1873 -3.1324 -2.7338 -2.3913 1.0101
## (Intercept)-Procyon_lotor -2.2924 0.1409 -2.5770 -2.2868 -2.0275 1.0198
## (Intercept)-Dasypus_novemcinctus -1.7684 0.1592 -2.1050 -1.7611 -1.4707 1.0052
## (Intercept)-Lynx_rufus -3.5255 0.3454 -4.1918 -3.5215 -2.8733 1.0257
## (Intercept)-Didelphis_virginiana -2.6050 0.2851 -3.1971 -2.5993 -2.0687 1.0015
## (Intercept)-Sylvilagus_floridanus -3.1211 0.2766 -3.7244 -3.0968 -2.6340 1.0181
## (Intercept)-Meleagris_gallopavo -3.8115 0.4566 -4.6958 -3.8143 -2.9179 1.0144
## (Intercept)-Sciurus_carolinensis -2.6579 0.3140 -3.2979 -2.6490 -2.0818 1.0180
## shrub_cover-Canis_latrans -0.2834 0.2242 -0.7165 -0.2839 0.1533 1.0197
## shrub_cover-Procyon_lotor 0.2541 0.1666 -0.0781 0.2563 0.5752 1.0058
## shrub_cover-Dasypus_novemcinctus 0.8777 0.3082 0.3187 0.8698 1.4898 1.0186
## shrub_cover-Lynx_rufus -0.2042 0.3647 -0.9565 -0.2015 0.4995 1.0297
## shrub_cover-Didelphis_virginiana 1.0088 0.3822 0.3021 0.9993 1.8012 1.0104
## shrub_cover-Sylvilagus_floridanus 0.3289 0.4252 -0.4770 0.3187 1.1759 1.0136
## shrub_cover-Meleagris_gallopavo -0.6278 0.4034 -1.4219 -0.6321 0.1968 1.0198
## shrub_cover-Sciurus_carolinensis 0.8872 0.3995 0.1474 0.8816 1.6963 1.0048
## veg_height-Canis_latrans -0.5906 0.1875 -0.9627 -0.5877 -0.2365 1.0046
## veg_height-Procyon_lotor 0.3365 0.1217 0.1048 0.3358 0.5744 1.0002
## veg_height-Dasypus_novemcinctus 0.2588 0.1350 -0.0049 0.2576 0.5326 1.0051
## veg_height-Lynx_rufus 0.0155 0.2460 -0.5042 0.0226 0.4809 1.0039
## veg_height-Didelphis_virginiana 0.4482 0.2481 -0.0205 0.4450 0.9500 1.0041
## veg_height-Sylvilagus_floridanus 0.1424 0.2521 -0.3547 0.1421 0.6402 1.0151
## veg_height-Meleagris_gallopavo -0.1702 0.4085 -0.9834 -0.1745 0.6465 1.0108
## veg_height-Sciurus_carolinensis 0.0914 0.2182 -0.3265 0.0887 0.5393 1.0018
## ESS
## (Intercept)-Canis_latrans 785
## (Intercept)-Procyon_lotor 1002
## (Intercept)-Dasypus_novemcinctus 1435
## (Intercept)-Lynx_rufus 352
## (Intercept)-Didelphis_virginiana 648
## (Intercept)-Sylvilagus_floridanus 633
## (Intercept)-Meleagris_gallopavo 230
## (Intercept)-Sciurus_carolinensis 831
## shrub_cover-Canis_latrans 813
## shrub_cover-Procyon_lotor 1400
## shrub_cover-Dasypus_novemcinctus 1326
## shrub_cover-Lynx_rufus 374
## shrub_cover-Didelphis_virginiana 675
## shrub_cover-Sylvilagus_floridanus 490
## shrub_cover-Meleagris_gallopavo 233
## shrub_cover-Sciurus_carolinensis 766
## veg_height-Canis_latrans 738
## veg_height-Procyon_lotor 1725
## veg_height-Dasypus_novemcinctus 1744
## veg_height-Lynx_rufus 734
## veg_height-Didelphis_virginiana 1182
## veg_height-Sylvilagus_floridanus 788
## veg_height-Meleagris_gallopavo 478
## veg_height-Sciurus_carolinensis 1002
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6418
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7345 0.6961 -2.0611 -0.7488 0.7001 1.0158 297
## Cogon_Patch_Size -0.3753 0.6821 -1.7958 -0.3795 0.9434 1.0095 562
## Veg_shannon_index 1.0063 0.4838 0.1138 0.9877 2.0358 1.0897 272
## total_shrub_cover -0.9164 0.6554 -2.2759 -0.8873 0.3507 1.0581 275
## Avg_Cogongrass_Cover 1.9081 0.7744 0.4086 1.9097 3.4099 1.0791 183
## Tree_Density -1.9003 0.8310 -3.4732 -1.8712 -0.2759 1.0565 425
## Avg_Canopy_Cover 1.8934 0.7785 0.4018 1.8424 3.5413 1.0433 651
## avg_veg_height -0.3606 0.5418 -1.3746 -0.3646 0.7384 1.0125 284
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5880 3.8772 0.0744 1.3876 12.1635 1.0327 225
## Cogon_Patch_Size 2.3158 3.5846 0.0858 1.2707 11.0611 1.0504 361
## Veg_shannon_index 0.6422 1.0667 0.0466 0.3485 2.7394 1.0822 876
## total_shrub_cover 2.3276 3.7484 0.0931 1.2413 10.3131 1.0764 311
## Avg_Cogongrass_Cover 1.2231 2.1863 0.0448 0.4960 6.6530 1.0033 313
## Tree_Density 4.4040 8.6888 0.0895 1.8975 24.2238 1.4191 179
## Avg_Canopy_Cover 4.4478 6.3727 0.2196 2.5072 20.3271 1.0417 319
## avg_veg_height 0.5763 0.9221 0.0448 0.2770 3.0868 1.0466 533
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.1168 3.2524 0.105 2.3044 11.6864 1.051 151
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7539 0.3016 -3.3131 -2.7638 -2.1360 1.0037 1964
## shrub_cover 0.3994 0.3301 -0.2573 0.4044 1.0509 1.0011 945
## veg_height 0.0987 0.2076 -0.3159 0.1035 0.4823 1.0075 1398
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6778 0.6009 0.1574 0.5152 2.0733 1.0211 1415
## shrub_cover 0.6934 0.6184 0.1298 0.5213 2.2683 1.0040 1504
## veg_height 0.2648 0.2398 0.0588 0.2017 0.8260 1.0118 1720
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1592 1.0027 -1.6231 0.1168
## (Intercept)-Procyon_lotor 0.1739 1.0051 -1.6534 0.1368
## (Intercept)-Dasypus_novemcinctus -1.1955 0.9035 -3.2395 -1.0997
## (Intercept)-Lynx_rufus -0.0409 1.4778 -2.3620 -0.2568
## (Intercept)-Didelphis_virginiana -1.8900 1.2323 -4.5499 -1.7759
## (Intercept)-Sylvilagus_floridanus -0.9339 1.0772 -3.1551 -0.9245
## (Intercept)-Meleagris_gallopavo -0.8801 1.2039 -3.3347 -0.8819
## (Intercept)-Sciurus_carolinensis -1.8544 1.2207 -4.6109 -1.6943
## Cogon_Patch_Size-Canis_latrans 0.5976 1.0809 -1.0076 0.4436
## Cogon_Patch_Size-Procyon_lotor -0.9810 0.7537 -2.4898 -0.9544
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3964 0.8334 -2.0335 -0.4270
## Cogon_Patch_Size-Lynx_rufus -0.7316 1.2656 -3.1176 -0.7320
## Cogon_Patch_Size-Didelphis_virginiana 0.8771 0.9684 -0.6648 0.7677
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4682 1.3821 -4.7386 -1.2750
## Cogon_Patch_Size-Meleagris_gallopavo -0.1877 1.1499 -2.2166 -0.2802
## Cogon_Patch_Size-Sciurus_carolinensis -1.2011 1.1978 -4.0186 -1.0744
## Veg_shannon_index-Canis_latrans 1.3169 0.6508 0.1845 1.2607
## Veg_shannon_index-Procyon_lotor 1.3202 0.6313 0.2065 1.2797
## Veg_shannon_index-Dasypus_novemcinctus 0.6986 0.5853 -0.4811 0.7126
## Veg_shannon_index-Lynx_rufus 0.9425 0.7653 -0.6129 0.9372
## Veg_shannon_index-Didelphis_virginiana 1.1693 0.6649 -0.0284 1.1252
## Veg_shannon_index-Sylvilagus_floridanus 1.1091 0.6752 -0.1152 1.0681
## Veg_shannon_index-Meleagris_gallopavo 1.2783 0.7480 -0.0318 1.2115
## Veg_shannon_index-Sciurus_carolinensis 0.4221 0.7657 -1.2577 0.4730
## total_shrub_cover-Canis_latrans 0.5071 0.9834 -1.0343 0.3650
## total_shrub_cover-Procyon_lotor -1.1765 0.6648 -2.6575 -1.1225
## total_shrub_cover-Dasypus_novemcinctus -0.4024 0.8417 -2.2691 -0.3460
## total_shrub_cover-Lynx_rufus -1.6333 1.3786 -4.9306 -1.4254
## total_shrub_cover-Didelphis_virginiana -1.1529 0.9974 -3.5067 -1.0448
## total_shrub_cover-Sylvilagus_floridanus -0.9836 1.3201 -3.8241 -0.8926
## total_shrub_cover-Meleagris_gallopavo -2.2233 1.4002 -5.5893 -1.9767
## total_shrub_cover-Sciurus_carolinensis -1.0923 1.2072 -4.0022 -0.9592
## Avg_Cogongrass_Cover-Canis_latrans 2.2479 0.9586 0.4799 2.1854
## Avg_Cogongrass_Cover-Procyon_lotor 2.0465 0.9257 0.3230 2.0234
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.5076 1.0461 0.6966 2.4318
## Avg_Cogongrass_Cover-Lynx_rufus 2.2837 1.0220 0.5252 2.2019
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.9480 0.9312 0.1080 1.9464
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3133 1.1766 -1.2732 1.3894
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4943 1.3411 -1.7860 1.6759
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.1857 1.0239 0.3314 2.1273
## Tree_Density-Canis_latrans -2.8601 1.4659 -6.3957 -2.6254
## Tree_Density-Procyon_lotor -1.5045 0.8072 -3.0882 -1.5080
## Tree_Density-Dasypus_novemcinctus -3.7969 2.0939 -8.7776 -3.2880
## Tree_Density-Lynx_rufus -0.1756 1.6229 -2.6410 -0.3778
## Tree_Density-Didelphis_virginiana -2.1395 1.2862 -4.8211 -2.0815
## Tree_Density-Sylvilagus_floridanus -2.6913 1.7618 -6.9673 -2.4074
## Tree_Density-Meleagris_gallopavo -2.2708 1.4971 -5.5584 -2.1891
## Tree_Density-Sciurus_carolinensis -2.3732 1.6408 -6.3805 -2.1470
## Avg_Canopy_Cover-Canis_latrans 0.1331 0.6866 -1.2320 0.1365
## Avg_Canopy_Cover-Procyon_lotor 1.7799 0.7764 0.4084 1.7322
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3472 0.9247 0.9152 2.2370
## Avg_Canopy_Cover-Lynx_rufus 0.7351 1.2418 -1.5188 0.6771
## Avg_Canopy_Cover-Didelphis_virginiana 3.3029 1.5012 1.2671 3.0148
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9298 1.9347 1.2734 3.5173
## Avg_Canopy_Cover-Meleagris_gallopavo 2.7073 1.4211 0.5091 2.4812
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1630 1.3928 1.1442 2.9181
## avg_veg_height-Canis_latrans -0.4523 0.6323 -1.7216 -0.4606
## avg_veg_height-Procyon_lotor -0.3541 0.6264 -1.5936 -0.3606
## avg_veg_height-Dasypus_novemcinctus -0.1180 0.6342 -1.2715 -0.1454
## avg_veg_height-Lynx_rufus -0.5089 0.8470 -2.2873 -0.4808
## avg_veg_height-Didelphis_virginiana -0.5484 0.7367 -2.1057 -0.5323
## avg_veg_height-Sylvilagus_floridanus -0.5992 0.7403 -2.1971 -0.5768
## avg_veg_height-Meleagris_gallopavo -0.3889 0.8904 -2.1703 -0.3967
## avg_veg_height-Sciurus_carolinensis 0.0350 0.7588 -1.3011 -0.0174
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2996 1.0321 231
## (Intercept)-Procyon_lotor 2.2070 1.0489 288
## (Intercept)-Dasypus_novemcinctus 0.3824 1.0323 404
## (Intercept)-Lynx_rufus 3.7686 1.1051 136
## (Intercept)-Didelphis_virginiana 0.1551 1.0164 284
## (Intercept)-Sylvilagus_floridanus 1.2166 1.0247 374
## (Intercept)-Meleagris_gallopavo 1.6498 1.0409 356
## (Intercept)-Sciurus_carolinensis 0.1329 1.0112 369
## Cogon_Patch_Size-Canis_latrans 3.1547 1.0137 579
## Cogon_Patch_Size-Procyon_lotor 0.4077 1.0502 307
## Cogon_Patch_Size-Dasypus_novemcinctus 1.3540 1.0035 608
## Cogon_Patch_Size-Lynx_rufus 1.7814 1.0090 227
## Cogon_Patch_Size-Didelphis_virginiana 3.1521 1.0166 399
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6987 1.0131 313
## Cogon_Patch_Size-Meleagris_gallopavo 2.3229 1.0478 475
## Cogon_Patch_Size-Sciurus_carolinensis 0.7910 1.0198 337
## Veg_shannon_index-Canis_latrans 2.7367 1.0656 348
## Veg_shannon_index-Procyon_lotor 2.6944 1.1054 262
## Veg_shannon_index-Dasypus_novemcinctus 1.8640 1.0506 529
## Veg_shannon_index-Lynx_rufus 2.4345 1.0312 523
## Veg_shannon_index-Didelphis_virginiana 2.6279 1.0310 508
## Veg_shannon_index-Sylvilagus_floridanus 2.5245 1.0303 460
## Veg_shannon_index-Meleagris_gallopavo 2.9376 1.0567 525
## Veg_shannon_index-Sciurus_carolinensis 1.7842 1.0148 498
## total_shrub_cover-Canis_latrans 2.9361 1.0140 337
## total_shrub_cover-Procyon_lotor -0.0126 1.0125 982
## total_shrub_cover-Dasypus_novemcinctus 1.1348 1.0209 528
## total_shrub_cover-Lynx_rufus 0.5551 1.0769 206
## total_shrub_cover-Didelphis_virginiana 0.5015 1.0489 247
## total_shrub_cover-Sylvilagus_floridanus 1.2757 1.0793 206
## total_shrub_cover-Meleagris_gallopavo -0.0873 1.0576 195
## total_shrub_cover-Sciurus_carolinensis 0.8933 1.0512 239
## Avg_Cogongrass_Cover-Canis_latrans 4.3115 1.0971 261
## Avg_Cogongrass_Cover-Procyon_lotor 3.9380 1.0951 241
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.7439 1.0834 270
## Avg_Cogongrass_Cover-Lynx_rufus 4.4788 1.0696 320
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7671 1.0489 338
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3477 1.0382 186
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.6231 1.0409 241
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.3085 1.0682 268
## Tree_Density-Canis_latrans -0.6546 1.1686 266
## Tree_Density-Procyon_lotor 0.1063 1.0511 588
## Tree_Density-Dasypus_novemcinctus -1.2667 1.2211 144
## Tree_Density-Lynx_rufus 3.6896 1.0651 205
## Tree_Density-Didelphis_virginiana 0.4741 1.0477 525
## Tree_Density-Sylvilagus_floridanus 0.0954 1.2300 248
## Tree_Density-Meleagris_gallopavo 0.6251 1.0666 367
## Tree_Density-Sciurus_carolinensis 0.3936 1.1086 298
## Avg_Canopy_Cover-Canis_latrans 1.5152 1.0113 585
## Avg_Canopy_Cover-Procyon_lotor 3.4309 1.0227 643
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.6396 1.0878 234
## Avg_Canopy_Cover-Lynx_rufus 3.2673 1.0034 300
## Avg_Canopy_Cover-Didelphis_virginiana 6.9624 1.0774 267
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.6929 1.0593 239
## Avg_Canopy_Cover-Meleagris_gallopavo 6.1720 1.0572 265
## Avg_Canopy_Cover-Sciurus_carolinensis 6.5479 1.0241 205
## avg_veg_height-Canis_latrans 0.7738 1.0096 399
## avg_veg_height-Procyon_lotor 0.8801 1.0099 437
## avg_veg_height-Dasypus_novemcinctus 1.2056 1.0092 454
## avg_veg_height-Lynx_rufus 1.0352 1.0154 378
## avg_veg_height-Didelphis_virginiana 0.8076 1.0224 454
## avg_veg_height-Sylvilagus_floridanus 0.8032 1.0237 420
## avg_veg_height-Meleagris_gallopavo 1.3044 1.0078 421
## avg_veg_height-Sciurus_carolinensis 1.6740 1.0078 453
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7458 0.1815 -3.1033 -2.7383 -2.3922 1.0012
## (Intercept)-Procyon_lotor -2.3035 0.1382 -2.6003 -2.2968 -2.0437 1.0005
## (Intercept)-Dasypus_novemcinctus -1.8083 0.1689 -2.1517 -1.7996 -1.4905 1.0043
## (Intercept)-Lynx_rufus -3.5648 0.3469 -4.2687 -3.5442 -2.9262 1.0225
## (Intercept)-Didelphis_virginiana -2.6281 0.2892 -3.2257 -2.6119 -2.0991 1.0168
## (Intercept)-Sylvilagus_floridanus -3.1786 0.2629 -3.7263 -3.1644 -2.7171 1.0050
## (Intercept)-Meleagris_gallopavo -3.6559 0.4149 -4.4606 -3.6636 -2.8161 1.0025
## (Intercept)-Sciurus_carolinensis -2.8263 0.3192 -3.4751 -2.8254 -2.2092 1.0008
## shrub_cover-Canis_latrans -0.3356 0.2281 -0.7623 -0.3445 0.1132 1.0032
## shrub_cover-Procyon_lotor 0.2816 0.1642 -0.0351 0.2840 0.5970 1.0029
## shrub_cover-Dasypus_novemcinctus 0.9954 0.3210 0.3698 0.9965 1.6362 1.0189
## shrub_cover-Lynx_rufus 0.0625 0.3890 -0.7416 0.0820 0.7725 1.0075
## shrub_cover-Didelphis_virginiana 1.0398 0.3837 0.3686 1.0105 1.8684 1.0018
## shrub_cover-Sylvilagus_floridanus 0.5568 0.4346 -0.3344 0.5722 1.3685 1.0091
## shrub_cover-Meleagris_gallopavo -0.4095 0.4081 -1.1875 -0.4169 0.4254 1.0170
## shrub_cover-Sciurus_carolinensis 1.1040 0.4027 0.3060 1.1056 1.8916 1.0072
## veg_height-Canis_latrans -0.5826 0.1887 -0.9581 -0.5831 -0.2120 1.0020
## veg_height-Procyon_lotor 0.3575 0.1270 0.1062 0.3572 0.6019 1.0080
## veg_height-Dasypus_novemcinctus 0.2768 0.1390 0.0140 0.2750 0.5610 1.0032
## veg_height-Lynx_rufus 0.0835 0.2492 -0.4466 0.0951 0.5469 1.0401
## veg_height-Didelphis_virginiana 0.4986 0.2438 0.0394 0.4888 0.9965 1.0191
## veg_height-Sylvilagus_floridanus 0.1707 0.2540 -0.3324 0.1727 0.6585 1.0202
## veg_height-Meleagris_gallopavo -0.2012 0.3748 -0.9669 -0.1909 0.5065 1.0057
## veg_height-Sciurus_carolinensis 0.1711 0.2198 -0.2676 0.1695 0.5958 1.0012
## ESS
## (Intercept)-Canis_latrans 829
## (Intercept)-Procyon_lotor 1361
## (Intercept)-Dasypus_novemcinctus 846
## (Intercept)-Lynx_rufus 251
## (Intercept)-Didelphis_virginiana 474
## (Intercept)-Sylvilagus_floridanus 523
## (Intercept)-Meleagris_gallopavo 318
## (Intercept)-Sciurus_carolinensis 295
## shrub_cover-Canis_latrans 631
## shrub_cover-Procyon_lotor 1464
## shrub_cover-Dasypus_novemcinctus 507
## shrub_cover-Lynx_rufus 299
## shrub_cover-Didelphis_virginiana 407
## shrub_cover-Sylvilagus_floridanus 312
## shrub_cover-Meleagris_gallopavo 436
## shrub_cover-Sciurus_carolinensis 387
## veg_height-Canis_latrans 610
## veg_height-Procyon_lotor 1362
## veg_height-Dasypus_novemcinctus 1575
## veg_height-Lynx_rufus 589
## veg_height-Didelphis_virginiana 1089
## veg_height-Sylvilagus_floridanus 776
## veg_height-Meleagris_gallopavo 469
## veg_height-Sciurus_carolinensis 850
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6497
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1397 0.4433 -0.9928 -0.1505 0.7816 1.0592 332
## Avg_Cogongrass_Cover 0.0227 0.3898 -0.7682 0.0292 0.7806 1.0073 622
## total_shrub_cover -0.9424 0.4924 -2.0241 -0.9065 -0.0828 1.0628 328
## avg_veg_height 0.1154 0.4017 -0.6399 0.1056 0.9053 1.0098 386
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5857 0.9035 0.0483 0.3299 2.4799 1.0274 568
## Avg_Cogongrass_Cover 0.5112 0.6885 0.0449 0.2980 2.1556 1.0039 912
## total_shrub_cover 1.0678 1.4187 0.0775 0.6381 4.5795 1.0452 477
## avg_veg_height 0.4560 0.6862 0.0381 0.2425 2.2657 1.0243 790
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.0568 0.9688 0.096 0.7691 3.5758 1.0162 234
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7577 0.2821 -3.3207 -2.7555 -2.1903 1.0023 1157
## shrub_cover 0.5378 0.3219 -0.0800 0.5328 1.1921 1.0151 1126
## veg_height 0.0885 0.2028 -0.3329 0.0908 0.4745 1.0123 1021
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5611 0.5529 0.1283 0.4064 1.8632 1.0077 1058
## shrub_cover 0.7571 0.6465 0.1391 0.5727 2.5255 1.0084 821
## veg_height 0.2570 0.2257 0.0567 0.1957 0.8315 1.0032 1476
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2003 0.5574 -0.8153 0.1749
## (Intercept)-Procyon_lotor 0.3934 0.6159 -0.7294 0.3583
## (Intercept)-Dasypus_novemcinctus -0.2430 0.5888 -1.3735 -0.2564
## (Intercept)-Lynx_rufus -0.1137 0.5987 -1.2485 -0.1259
## (Intercept)-Didelphis_virginiana -0.5328 0.6279 -1.8138 -0.5177
## (Intercept)-Sylvilagus_floridanus 0.0125 0.6746 -1.1711 -0.0122
## (Intercept)-Meleagris_gallopavo -0.3489 0.6713 -1.7435 -0.3409
## (Intercept)-Sciurus_carolinensis -0.4971 0.6644 -1.8437 -0.4854
## Avg_Cogongrass_Cover-Canis_latrans 0.3500 0.5188 -0.6321 0.3166
## Avg_Cogongrass_Cover-Procyon_lotor -0.0900 0.4885 -1.0913 -0.0718
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1534 0.4732 -0.7991 0.1526
## Avg_Cogongrass_Cover-Lynx_rufus 0.4155 0.5875 -0.6172 0.3617
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1725 0.5328 -0.8955 0.1677
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4385 0.5971 -1.7553 -0.3793
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4934 0.7060 -2.0590 -0.4111
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0207 0.5267 -1.0832 0.0478
## total_shrub_cover-Canis_latrans 0.1425 0.6412 -0.9400 0.0920
## total_shrub_cover-Procyon_lotor -1.2643 0.6095 -2.7116 -1.1927
## total_shrub_cover-Dasypus_novemcinctus -0.5904 0.6745 -2.1928 -0.4932
## total_shrub_cover-Lynx_rufus -1.3553 0.8077 -3.1905 -1.2867
## total_shrub_cover-Didelphis_virginiana -0.9502 0.6940 -2.5903 -0.8725
## total_shrub_cover-Sylvilagus_floridanus -1.3288 0.8979 -3.3957 -1.1888
## total_shrub_cover-Meleagris_gallopavo -1.5732 0.8096 -3.5002 -1.4685
## total_shrub_cover-Sciurus_carolinensis -1.0291 0.7593 -2.7111 -0.9467
## avg_veg_height-Canis_latrans 0.1084 0.5026 -0.8618 0.1082
## avg_veg_height-Procyon_lotor 0.1505 0.4949 -0.7810 0.1392
## avg_veg_height-Dasypus_novemcinctus 0.3389 0.4946 -0.5223 0.3042
## avg_veg_height-Lynx_rufus 0.0063 0.6233 -1.3253 0.0165
## avg_veg_height-Didelphis_virginiana 0.0010 0.5424 -1.1160 0.0038
## avg_veg_height-Sylvilagus_floridanus 0.0678 0.5491 -0.9685 0.0588
## avg_veg_height-Meleagris_gallopavo -0.2746 0.8008 -2.1022 -0.2071
## avg_veg_height-Sciurus_carolinensis 0.5202 0.5695 -0.4236 0.4615
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3850 1.0274 656
## (Intercept)-Procyon_lotor 1.7061 1.0282 619
## (Intercept)-Dasypus_novemcinctus 1.0056 1.0379 396
## (Intercept)-Lynx_rufus 1.1547 1.0141 509
## (Intercept)-Didelphis_virginiana 0.6556 1.0170 395
## (Intercept)-Sylvilagus_floridanus 1.4777 1.0516 393
## (Intercept)-Meleagris_gallopavo 0.9459 1.0122 393
## (Intercept)-Sciurus_carolinensis 0.7703 1.0527 357
## Avg_Cogongrass_Cover-Canis_latrans 1.4731 1.0032 1058
## Avg_Cogongrass_Cover-Procyon_lotor 0.8115 1.0055 796
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1151 1.0020 1159
## Avg_Cogongrass_Cover-Lynx_rufus 1.7265 1.0019 977
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2852 1.0011 787
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6085 1.0053 959
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6777 1.0179 559
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0086 1.0033 1020
## total_shrub_cover-Canis_latrans 1.6106 1.0156 363
## total_shrub_cover-Procyon_lotor -0.2670 1.0394 511
## total_shrub_cover-Dasypus_novemcinctus 0.5079 1.0655 331
## total_shrub_cover-Lynx_rufus 0.0784 1.0730 375
## total_shrub_cover-Didelphis_virginiana 0.1836 1.0886 235
## total_shrub_cover-Sylvilagus_floridanus 0.0487 1.0503 279
## total_shrub_cover-Meleagris_gallopavo -0.2712 1.0203 400
## total_shrub_cover-Sciurus_carolinensis 0.2472 1.0554 351
## avg_veg_height-Canis_latrans 1.1041 1.0061 707
## avg_veg_height-Procyon_lotor 1.2119 1.0039 909
## avg_veg_height-Dasypus_novemcinctus 1.4231 1.0169 551
## avg_veg_height-Lynx_rufus 1.2321 1.0141 564
## avg_veg_height-Didelphis_virginiana 1.0952 1.0035 569
## avg_veg_height-Sylvilagus_floridanus 1.2297 1.0263 678
## avg_veg_height-Meleagris_gallopavo 1.1430 1.0044 372
## avg_veg_height-Sciurus_carolinensis 1.7782 1.0148 645
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7529 0.1890 -3.1358 -2.7492 -2.3976 1.0227
## (Intercept)-Procyon_lotor -2.3062 0.1408 -2.5994 -2.3011 -2.0446 1.0181
## (Intercept)-Dasypus_novemcinctus -1.8723 0.1853 -2.2437 -1.8682 -1.5176 1.0407
## (Intercept)-Lynx_rufus -3.4165 0.3116 -4.0766 -3.3984 -2.8493 1.0055
## (Intercept)-Didelphis_virginiana -2.7562 0.2947 -3.3421 -2.7460 -2.2063 1.0170
## (Intercept)-Sylvilagus_floridanus -3.2025 0.2557 -3.7139 -3.2018 -2.6967 1.0099
## (Intercept)-Meleagris_gallopavo -3.4589 0.5154 -4.5656 -3.4357 -2.5074 1.0042
## (Intercept)-Sciurus_carolinensis -2.8591 0.3239 -3.5223 -2.8521 -2.2599 1.0082
## shrub_cover-Canis_latrans -0.2722 0.2433 -0.7581 -0.2734 0.1904 1.0108
## shrub_cover-Procyon_lotor 0.3238 0.1589 0.0171 0.3228 0.6326 1.0163
## shrub_cover-Dasypus_novemcinctus 1.1345 0.3589 0.4423 1.1354 1.8311 1.0647
## shrub_cover-Lynx_rufus 0.1963 0.3721 -0.5571 0.2166 0.8633 1.0134
## shrub_cover-Didelphis_virginiana 1.2804 0.4136 0.5368 1.2594 2.1458 1.0404
## shrub_cover-Sylvilagus_floridanus 0.7601 0.4106 -0.1025 0.7746 1.5350 1.0680
## shrub_cover-Meleagris_gallopavo -0.2506 0.4700 -1.1887 -0.2253 0.6018 1.0053
## shrub_cover-Sciurus_carolinensis 1.2634 0.4324 0.4193 1.2662 2.1336 1.0128
## veg_height-Canis_latrans -0.5749 0.1871 -0.9547 -0.5696 -0.2231 1.0317
## veg_height-Procyon_lotor 0.3473 0.1248 0.0998 0.3467 0.5897 1.0083
## veg_height-Dasypus_novemcinctus 0.2864 0.1420 0.0186 0.2829 0.5669 1.0018
## veg_height-Lynx_rufus 0.0650 0.2482 -0.4357 0.0702 0.5492 1.0017
## veg_height-Didelphis_virginiana 0.4521 0.2538 -0.0127 0.4385 0.9816 1.0012
## veg_height-Sylvilagus_floridanus 0.0853 0.2483 -0.4039 0.0853 0.5757 1.0157
## veg_height-Meleagris_gallopavo -0.0175 0.4819 -0.9614 -0.0086 0.8957 1.0093
## veg_height-Sciurus_carolinensis 0.1169 0.2221 -0.3052 0.1139 0.5649 1.0145
## ESS
## (Intercept)-Canis_latrans 589
## (Intercept)-Procyon_lotor 1309
## (Intercept)-Dasypus_novemcinctus 562
## (Intercept)-Lynx_rufus 502
## (Intercept)-Didelphis_virginiana 344
## (Intercept)-Sylvilagus_floridanus 508
## (Intercept)-Meleagris_gallopavo 240
## (Intercept)-Sciurus_carolinensis 302
## shrub_cover-Canis_latrans 537
## shrub_cover-Procyon_lotor 1347
## shrub_cover-Dasypus_novemcinctus 380
## shrub_cover-Lynx_rufus 452
## shrub_cover-Didelphis_virginiana 318
## shrub_cover-Sylvilagus_floridanus 295
## shrub_cover-Meleagris_gallopavo 368
## shrub_cover-Sciurus_carolinensis 316
## veg_height-Canis_latrans 644
## veg_height-Procyon_lotor 1365
## veg_height-Dasypus_novemcinctus 1407
## veg_height-Lynx_rufus 686
## veg_height-Didelphis_virginiana 747
## veg_height-Sylvilagus_floridanus 538
## veg_height-Meleagris_gallopavo 256
## veg_height-Sciurus_carolinensis 892
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.584
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2087 0.3391 -0.8491 -0.2166 0.4832 1.0119 1278
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7029 0.7625 0.1027 0.489 2.6827 1.0061 1196
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7198 0.3300 -3.3434 -2.7249 -2.0370 1.0092 2267
## shrub_cover 0.2362 0.3148 -0.4380 0.2340 0.8635 1.0128 1318
## veg_height 0.0662 0.2003 -0.3284 0.0656 0.4653 1.0084 1193
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7622 0.6868 0.1600 0.5750 2.3720 1.0208 838
## shrub_cover 0.7051 0.6156 0.1333 0.5358 2.3725 1.0056 1315
## veg_height 0.2659 0.2384 0.0598 0.2023 0.8462 1.0080 1427
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2543 0.3749 -0.4293 0.2327 1.0444 1.0032
## (Intercept)-Procyon_lotor 0.5399 0.3870 -0.1608 0.5149 1.3394 1.0009
## (Intercept)-Dasypus_novemcinctus -0.4911 0.3524 -1.1929 -0.4899 0.1815 1.0116
## (Intercept)-Lynx_rufus 0.0618 0.5261 -0.8098 0.0077 1.2540 1.0238
## (Intercept)-Didelphis_virginiana -0.9297 0.4292 -1.8030 -0.9130 -0.1139 1.0015
## (Intercept)-Sylvilagus_floridanus -0.3239 0.4185 -1.1057 -0.3379 0.5534 1.0049
## (Intercept)-Meleagris_gallopavo 0.1013 0.6714 -0.9862 0.0227 1.6673 1.0256
## (Intercept)-Sciurus_carolinensis -0.9173 0.4345 -1.8291 -0.8915 -0.1276 1.0012
## ESS
## (Intercept)-Canis_latrans 1844
## (Intercept)-Procyon_lotor 1871
## (Intercept)-Dasypus_novemcinctus 2603
## (Intercept)-Lynx_rufus 431
## (Intercept)-Didelphis_virginiana 1433
## (Intercept)-Sylvilagus_floridanus 1144
## (Intercept)-Meleagris_gallopavo 307
## (Intercept)-Sciurus_carolinensis 1578
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7256 0.1764 -3.0789 -2.7260 -2.3934 1.0048
## (Intercept)-Procyon_lotor -2.2953 0.1413 -2.5801 -2.2925 -2.0276 1.0030
## (Intercept)-Dasypus_novemcinctus -1.7621 0.1624 -2.0983 -1.7617 -1.4528 1.0068
## (Intercept)-Lynx_rufus -3.5389 0.3468 -4.2563 -3.5339 -2.8945 1.0148
## (Intercept)-Didelphis_virginiana -2.6194 0.2907 -3.2189 -2.5948 -2.0901 1.0134
## (Intercept)-Sylvilagus_floridanus -3.1307 0.2864 -3.7464 -3.1164 -2.6118 1.0143
## (Intercept)-Meleagris_gallopavo -3.8406 0.4769 -4.7129 -3.8599 -2.8540 1.0688
## (Intercept)-Sciurus_carolinensis -2.6652 0.3183 -3.3187 -2.6468 -2.0888 1.0020
## shrub_cover-Canis_latrans -0.2953 0.2182 -0.7100 -0.2943 0.1198 1.0110
## shrub_cover-Procyon_lotor 0.2588 0.1640 -0.0781 0.2623 0.5631 1.0008
## shrub_cover-Dasypus_novemcinctus 0.8657 0.2906 0.3291 0.8537 1.4548 0.9999
## shrub_cover-Lynx_rufus -0.2617 0.3816 -1.0713 -0.2507 0.4735 1.0039
## shrub_cover-Didelphis_virginiana 0.9853 0.3705 0.2942 0.9702 1.7479 1.0033
## shrub_cover-Sylvilagus_floridanus 0.2336 0.4169 -0.5446 0.2261 1.0650 1.0121
## shrub_cover-Meleagris_gallopavo -0.6374 0.3986 -1.4000 -0.6480 0.1952 1.0690
## shrub_cover-Sciurus_carolinensis 0.8805 0.4078 0.0875 0.8759 1.6966 1.0021
## veg_height-Canis_latrans -0.5904 0.1842 -0.9647 -0.5856 -0.2440 1.0029
## veg_height-Procyon_lotor 0.3454 0.1211 0.1063 0.3462 0.5870 1.0025
## veg_height-Dasypus_novemcinctus 0.2629 0.1378 0.0055 0.2619 0.5454 1.0164
## veg_height-Lynx_rufus 0.0409 0.2460 -0.4564 0.0464 0.5108 1.0101
## veg_height-Didelphis_virginiana 0.4870 0.2534 0.0091 0.4769 1.0141 1.0129
## veg_height-Sylvilagus_floridanus 0.1337 0.2519 -0.3480 0.1353 0.6385 1.0041
## veg_height-Meleagris_gallopavo -0.2321 0.3967 -1.0319 -0.2386 0.5631 1.0604
## veg_height-Sciurus_carolinensis 0.0882 0.2156 -0.3201 0.0883 0.5283 1.0027
## ESS
## (Intercept)-Canis_latrans 770
## (Intercept)-Procyon_lotor 1268
## (Intercept)-Dasypus_novemcinctus 1412
## (Intercept)-Lynx_rufus 282
## (Intercept)-Didelphis_virginiana 746
## (Intercept)-Sylvilagus_floridanus 558
## (Intercept)-Meleagris_gallopavo 216
## (Intercept)-Sciurus_carolinensis 816
## shrub_cover-Canis_latrans 946
## shrub_cover-Procyon_lotor 1489
## shrub_cover-Dasypus_novemcinctus 1306
## shrub_cover-Lynx_rufus 424
## shrub_cover-Didelphis_virginiana 609
## shrub_cover-Sylvilagus_floridanus 530
## shrub_cover-Meleagris_gallopavo 221
## shrub_cover-Sciurus_carolinensis 782
## veg_height-Canis_latrans 612
## veg_height-Procyon_lotor 1523
## veg_height-Dasypus_novemcinctus 1933
## veg_height-Lynx_rufus 777
## veg_height-Didelphis_virginiana 1101
## veg_height-Sylvilagus_floridanus 685
## veg_height-Meleagris_gallopavo 396
## veg_height-Sciurus_carolinensis 1139
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3562 0.3962 -1.1192 -0.3665 0.4679 1.0122 555
## Veg_shannon_index 0.3596 0.2742 -0.1783 0.3502 0.9396 1.0172 930
## Avg_Cogongrass_Cover 0.3231 0.2924 -0.2763 0.3218 0.8873 1.0000 1127
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6367 0.7526 0.0550 0.4016 2.6482 1.1125 316
## Veg_shannon_index 0.2797 0.3392 0.0365 0.1777 1.1530 1.0065 999
## Avg_Cogongrass_Cover 0.3670 0.4577 0.0388 0.2238 1.5457 1.0075 986
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7514 0.721 0.0581 0.5348 2.7526 1.0163 235
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7299 0.3179 -3.3337 -2.7396 -2.0954 1.0033 1961
## shrub_cover 0.2579 0.3191 -0.4093 0.2613 0.8699 1.0012 1232
## veg_height 0.0739 0.1959 -0.3130 0.0731 0.4587 1.0083 1388
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7624 0.6817 0.1773 0.5863 2.4730 1.0115 1080
## shrub_cover 0.7026 0.6465 0.1324 0.5165 2.2934 1.0036 923
## veg_height 0.2497 0.2333 0.0561 0.1844 0.7830 1.0208 1617
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0432 0.5372 -0.9288 0.0130
## (Intercept)-Procyon_lotor 0.1564 0.5524 -0.8376 0.1412
## (Intercept)-Dasypus_novemcinctus -0.5617 0.4710 -1.5598 -0.5470
## (Intercept)-Lynx_rufus -0.1842 0.6216 -1.2675 -0.2286
## (Intercept)-Didelphis_virginiana -0.8955 0.5468 -2.0660 -0.8578
## (Intercept)-Sylvilagus_floridanus -0.4201 0.5287 -1.4504 -0.4365
## (Intercept)-Meleagris_gallopavo -0.1390 0.7389 -1.3662 -0.2135
## (Intercept)-Sciurus_carolinensis -0.9047 0.5588 -2.0673 -0.8721
## Veg_shannon_index-Canis_latrans 0.6379 0.4046 -0.0740 0.6157
## Veg_shannon_index-Procyon_lotor 0.4475 0.3647 -0.2280 0.4325
## Veg_shannon_index-Dasypus_novemcinctus 0.1983 0.3420 -0.5160 0.2117
## Veg_shannon_index-Lynx_rufus 0.2310 0.4662 -0.7348 0.2338
## Veg_shannon_index-Didelphis_virginiana 0.4698 0.3734 -0.2233 0.4480
## Veg_shannon_index-Sylvilagus_floridanus 0.4320 0.4194 -0.3311 0.4090
## Veg_shannon_index-Meleagris_gallopavo 0.5042 0.4772 -0.3652 0.4660
## Veg_shannon_index-Sciurus_carolinensis -0.0146 0.4026 -0.8644 0.0060
## Avg_Cogongrass_Cover-Canis_latrans 0.5936 0.4017 -0.0910 0.5540
## Avg_Cogongrass_Cover-Procyon_lotor 0.3594 0.3772 -0.3653 0.3504
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4465 0.3385 -0.2071 0.4457
## Avg_Cogongrass_Cover-Lynx_rufus 0.5888 0.4418 -0.1884 0.5553
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4459 0.3888 -0.2799 0.4256
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1037 0.4810 -1.1170 -0.0676
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0938 0.6166 -1.4567 -0.0304
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4300 0.3766 -0.2830 0.4254
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1533 1.0242 567
## (Intercept)-Procyon_lotor 1.2658 1.0191 677
## (Intercept)-Dasypus_novemcinctus 0.3312 1.0226 950
## (Intercept)-Lynx_rufus 1.1611 1.0200 431
## (Intercept)-Didelphis_virginiana 0.0857 1.0553 606
## (Intercept)-Sylvilagus_floridanus 0.6935 1.0038 941
## (Intercept)-Meleagris_gallopavo 1.5173 1.0723 235
## (Intercept)-Sciurus_carolinensis 0.1325 1.0580 725
## Veg_shannon_index-Canis_latrans 1.5107 1.0018 1445
## Veg_shannon_index-Procyon_lotor 1.1890 1.0047 1252
## Veg_shannon_index-Dasypus_novemcinctus 0.8371 1.0042 1820
## Veg_shannon_index-Lynx_rufus 1.1211 1.0052 1098
## Veg_shannon_index-Didelphis_virginiana 1.2403 1.0107 1546
## Veg_shannon_index-Sylvilagus_floridanus 1.3524 1.0105 1315
## Veg_shannon_index-Meleagris_gallopavo 1.5410 1.0017 1118
## Veg_shannon_index-Sciurus_carolinensis 0.7041 1.0057 1350
## Avg_Cogongrass_Cover-Canis_latrans 1.4843 1.0098 1388
## Avg_Cogongrass_Cover-Procyon_lotor 1.1570 1.0031 1829
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1588 1.0001 2039
## Avg_Cogongrass_Cover-Lynx_rufus 1.5718 1.0033 1361
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2433 1.0032 1656
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7395 1.0110 892
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9754 1.0032 586
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2065 1.0038 1676
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7250 0.1839 -3.1002 -2.7239 -2.3665 1.0018
## (Intercept)-Procyon_lotor -2.2998 0.1440 -2.5899 -2.2980 -2.0256 1.0008
## (Intercept)-Dasypus_novemcinctus -1.7724 0.1621 -2.0872 -1.7658 -1.4615 1.0015
## (Intercept)-Lynx_rufus -3.5343 0.3276 -4.1741 -3.5263 -2.9315 1.0120
## (Intercept)-Didelphis_virginiana -2.5859 0.2907 -3.1736 -2.5789 -2.0328 1.0031
## (Intercept)-Sylvilagus_floridanus -3.1596 0.2809 -3.7664 -3.1493 -2.6329 1.0020
## (Intercept)-Meleagris_gallopavo -3.8579 0.4944 -4.8551 -3.8805 -2.8868 1.0296
## (Intercept)-Sciurus_carolinensis -2.6436 0.3061 -3.3156 -2.6298 -2.0901 1.0158
## shrub_cover-Canis_latrans -0.2733 0.2127 -0.6901 -0.2706 0.1309 1.0026
## shrub_cover-Procyon_lotor 0.2525 0.1693 -0.0904 0.2576 0.5693 1.0070
## shrub_cover-Dasypus_novemcinctus 0.8668 0.3008 0.2972 0.8591 1.4714 1.0021
## shrub_cover-Lynx_rufus -0.2240 0.3622 -0.9544 -0.2246 0.4896 1.0056
## shrub_cover-Didelphis_virginiana 0.9877 0.3787 0.3054 0.9689 1.7521 1.0032
## shrub_cover-Sylvilagus_floridanus 0.2686 0.4158 -0.5282 0.2624 1.0935 1.0107
## shrub_cover-Meleagris_gallopavo -0.6657 0.4352 -1.5245 -0.6494 0.1737 1.0227
## shrub_cover-Sciurus_carolinensis 0.8850 0.4010 0.0968 0.8860 1.6970 1.0153
## veg_height-Canis_latrans -0.5682 0.1866 -0.9400 -0.5645 -0.2206 1.0047
## veg_height-Procyon_lotor 0.3390 0.1243 0.1000 0.3368 0.5791 1.0017
## veg_height-Dasypus_novemcinctus 0.2563 0.1364 -0.0068 0.2504 0.5277 1.0020
## veg_height-Lynx_rufus 0.0142 0.2505 -0.4995 0.0187 0.4798 1.0110
## veg_height-Didelphis_virginiana 0.4408 0.2449 -0.0203 0.4383 0.9265 1.0051
## veg_height-Sylvilagus_floridanus 0.1649 0.2483 -0.3277 0.1659 0.6638 1.0109
## veg_height-Meleagris_gallopavo -0.1643 0.3943 -0.9441 -0.1617 0.5633 1.0035
## veg_height-Sciurus_carolinensis 0.0815 0.2137 -0.3337 0.0752 0.5086 1.0013
## ESS
## (Intercept)-Canis_latrans 732
## (Intercept)-Procyon_lotor 1267
## (Intercept)-Dasypus_novemcinctus 1488
## (Intercept)-Lynx_rufus 341
## (Intercept)-Didelphis_virginiana 776
## (Intercept)-Sylvilagus_floridanus 547
## (Intercept)-Meleagris_gallopavo 204
## (Intercept)-Sciurus_carolinensis 938
## shrub_cover-Canis_latrans 783
## shrub_cover-Procyon_lotor 1267
## shrub_cover-Dasypus_novemcinctus 1196
## shrub_cover-Lynx_rufus 426
## shrub_cover-Didelphis_virginiana 642
## shrub_cover-Sylvilagus_floridanus 503
## shrub_cover-Meleagris_gallopavo 203
## shrub_cover-Sciurus_carolinensis 630
## veg_height-Canis_latrans 592
## veg_height-Procyon_lotor 1473
## veg_height-Dasypus_novemcinctus 1813
## veg_height-Lynx_rufus 739
## veg_height-Didelphis_virginiana 1055
## veg_height-Sylvilagus_floridanus 850
## veg_height-Meleagris_gallopavo 358
## veg_height-Sciurus_carolinensis 1164
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.689
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2743 0.4352 -1.1417 -0.2679 0.5562 1.0037 478
## Cogon_Patch_Size 0.0022 0.3922 -0.7799 0.0039 0.7608 1.0114 815
## Avg_Cogongrass_Cover 0.1128 0.3509 -0.5977 0.1146 0.7954 1.0121 856
## total_shrub_cover -0.8763 0.4587 -1.8700 -0.8458 -0.0479 1.0163 404
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7490 1.2743 0.0541 0.4089 3.5414 1.0474 351
## Cogon_Patch_Size 0.7717 1.4156 0.0494 0.4061 3.5592 1.2239 612
## Avg_Cogongrass_Cover 0.4894 0.7370 0.0471 0.2757 2.2250 1.0288 994
## total_shrub_cover 0.8589 1.4033 0.0601 0.4802 3.8198 1.0249 569
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.268 1.2017 0.0949 0.9275 4.4108 1.073 144
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7465 0.2837 -3.3167 -2.7433 -2.1980 1.0076 1549
## shrub_cover 0.5086 0.3237 -0.1240 0.5027 1.1727 1.0011 933
## veg_height 0.0900 0.2030 -0.3270 0.0931 0.4689 1.0088 1610
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5586 0.5383 0.1205 0.4212 1.8624 1.0318 1167
## shrub_cover 0.7052 0.6629 0.1205 0.5165 2.3804 1.0036 1075
## veg_height 0.2532 0.2192 0.0578 0.1930 0.8160 1.0211 1096
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2246 0.6650 -0.8962 0.1749
## (Intercept)-Procyon_lotor 0.3074 0.6574 -0.8894 0.2700
## (Intercept)-Dasypus_novemcinctus -0.3998 0.5814 -1.5932 -0.3854
## (Intercept)-Lynx_rufus -0.2601 0.6469 -1.5413 -0.2578
## (Intercept)-Didelphis_virginiana -0.7044 0.6436 -2.0801 -0.6619
## (Intercept)-Sylvilagus_floridanus -0.1870 0.6627 -1.4553 -0.2040
## (Intercept)-Meleagris_gallopavo -0.4649 0.6923 -1.9042 -0.4430
## (Intercept)-Sciurus_carolinensis -0.6750 0.6650 -2.0885 -0.6382
## Cogon_Patch_Size-Canis_latrans 0.6604 0.6630 -0.3332 0.5550
## Cogon_Patch_Size-Procyon_lotor -0.1360 0.4580 -1.0851 -0.1212
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0227 0.4548 -0.8644 0.0103
## Cogon_Patch_Size-Lynx_rufus -0.0400 0.7237 -1.3496 -0.0878
## Cogon_Patch_Size-Didelphis_virginiana 0.5722 0.5019 -0.3027 0.5333
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6294 0.8023 -2.6917 -0.4841
## Cogon_Patch_Size-Meleagris_gallopavo 0.0580 0.6040 -1.0821 0.0467
## Cogon_Patch_Size-Sciurus_carolinensis -0.4720 0.6538 -2.0433 -0.3760
## Avg_Cogongrass_Cover-Canis_latrans 0.2996 0.4573 -0.5523 0.2698
## Avg_Cogongrass_Cover-Procyon_lotor 0.0566 0.4544 -0.8391 0.0519
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2999 0.4205 -0.4807 0.2839
## Avg_Cogongrass_Cover-Lynx_rufus 0.4913 0.5381 -0.3909 0.4287
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0770 0.4713 -0.9193 0.0889
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2593 0.5475 -1.4485 -0.2277
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4345 0.7125 -1.9158 -0.3584
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3199 0.4697 -0.5854 0.3056
## total_shrub_cover-Canis_latrans 0.0003 0.6138 -1.0944 -0.0379
## total_shrub_cover-Procyon_lotor -1.1805 0.5470 -2.3809 -1.1199
## total_shrub_cover-Dasypus_novemcinctus -0.5194 0.5795 -1.8410 -0.4718
## total_shrub_cover-Lynx_rufus -1.2438 0.7904 -3.0904 -1.1494
## total_shrub_cover-Didelphis_virginiana -0.9079 0.6272 -2.3180 -0.8551
## total_shrub_cover-Sylvilagus_floridanus -1.1099 0.7860 -2.9628 -1.0211
## total_shrub_cover-Meleagris_gallopavo -1.4559 0.7710 -3.2287 -1.3635
## total_shrub_cover-Sciurus_carolinensis -0.8697 0.6834 -2.4204 -0.8133
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6210 1.0131 414
## (Intercept)-Procyon_lotor 1.7842 1.0073 395
## (Intercept)-Dasypus_novemcinctus 0.7203 1.0060 521
## (Intercept)-Lynx_rufus 1.0326 1.0116 484
## (Intercept)-Didelphis_virginiana 0.4518 1.0056 418
## (Intercept)-Sylvilagus_floridanus 1.1485 1.0180 521
## (Intercept)-Meleagris_gallopavo 0.8917 1.0187 425
## (Intercept)-Sciurus_carolinensis 0.5126 1.0139 437
## Cogon_Patch_Size-Canis_latrans 2.2781 1.0293 778
## Cogon_Patch_Size-Procyon_lotor 0.7433 1.0066 1277
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9338 1.0026 1528
## Cogon_Patch_Size-Lynx_rufus 1.5028 1.0151 669
## Cogon_Patch_Size-Didelphis_virginiana 1.6933 1.0196 981
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5317 1.0444 525
## Cogon_Patch_Size-Meleagris_gallopavo 1.3003 1.0112 899
## Cogon_Patch_Size-Sciurus_carolinensis 0.5702 1.0779 782
## Avg_Cogongrass_Cover-Canis_latrans 1.2966 1.0055 1310
## Avg_Cogongrass_Cover-Procyon_lotor 0.9528 1.0164 1176
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1768 1.0008 1618
## Avg_Cogongrass_Cover-Lynx_rufus 1.7309 1.0029 1155
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0294 1.0176 1100
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7359 1.0333 971
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8315 1.0160 499
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2744 1.0034 987
## total_shrub_cover-Canis_latrans 1.4440 1.0126 477
## total_shrub_cover-Procyon_lotor -0.2464 1.0191 802
## total_shrub_cover-Dasypus_novemcinctus 0.4592 1.0106 534
## total_shrub_cover-Lynx_rufus 0.0550 1.0260 355
## total_shrub_cover-Didelphis_virginiana 0.1864 1.0095 451
## total_shrub_cover-Sylvilagus_floridanus 0.1680 1.0381 313
## total_shrub_cover-Meleagris_gallopavo -0.1810 1.0140 380
## total_shrub_cover-Sciurus_carolinensis 0.3048 1.0212 388
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7490 0.1870 -3.1304 -2.7441 -2.4066 1.0129
## (Intercept)-Procyon_lotor -2.3052 0.1393 -2.5916 -2.3020 -2.0398 1.0049
## (Intercept)-Dasypus_novemcinctus -1.8518 0.1827 -2.2263 -1.8457 -1.5113 1.0054
## (Intercept)-Lynx_rufus -3.3853 0.2953 -3.9761 -3.3767 -2.8209 1.0110
## (Intercept)-Didelphis_virginiana -2.6958 0.2936 -3.2668 -2.6945 -2.1424 1.0075
## (Intercept)-Sylvilagus_floridanus -3.1972 0.2594 -3.7178 -3.1922 -2.7082 1.0076
## (Intercept)-Meleagris_gallopavo -3.4566 0.5027 -4.5277 -3.4249 -2.5496 1.0905
## (Intercept)-Sciurus_carolinensis -2.8471 0.3377 -3.5427 -2.8381 -2.2251 1.0118
## shrub_cover-Canis_latrans -0.2613 0.2304 -0.7175 -0.2600 0.1933 1.0065
## shrub_cover-Procyon_lotor 0.3229 0.1602 -0.0029 0.3241 0.6228 1.0054
## shrub_cover-Dasypus_novemcinctus 1.0780 0.3538 0.4260 1.0716 1.7670 1.0081
## shrub_cover-Lynx_rufus 0.1918 0.3516 -0.5238 0.2095 0.8268 1.0072
## shrub_cover-Didelphis_virginiana 1.1910 0.4150 0.4413 1.1664 2.0396 1.0036
## shrub_cover-Sylvilagus_floridanus 0.7360 0.4164 -0.1343 0.7477 1.5371 0.9999
## shrub_cover-Meleagris_gallopavo -0.2485 0.4682 -1.2490 -0.2171 0.6201 1.0259
## shrub_cover-Sciurus_carolinensis 1.1634 0.4106 0.3636 1.1600 1.9919 1.0051
## veg_height-Canis_latrans -0.5800 0.1889 -0.9573 -0.5769 -0.2270 1.0119
## veg_height-Procyon_lotor 0.3507 0.1232 0.1130 0.3496 0.5988 1.0019
## veg_height-Dasypus_novemcinctus 0.2864 0.1435 0.0141 0.2833 0.5663 1.0037
## veg_height-Lynx_rufus 0.0640 0.2354 -0.4130 0.0647 0.5157 1.0038
## veg_height-Didelphis_virginiana 0.4527 0.2441 -0.0181 0.4509 0.9458 1.0010
## veg_height-Sylvilagus_floridanus 0.0871 0.2420 -0.3728 0.0848 0.5709 1.0100
## veg_height-Meleagris_gallopavo -0.1068 0.4413 -0.9990 -0.1107 0.7398 1.0429
## veg_height-Sciurus_carolinensis 0.1434 0.2377 -0.3246 0.1431 0.6262 1.0199
## ESS
## (Intercept)-Canis_latrans 753
## (Intercept)-Procyon_lotor 1168
## (Intercept)-Dasypus_novemcinctus 585
## (Intercept)-Lynx_rufus 606
## (Intercept)-Didelphis_virginiana 397
## (Intercept)-Sylvilagus_floridanus 547
## (Intercept)-Meleagris_gallopavo 293
## (Intercept)-Sciurus_carolinensis 381
## shrub_cover-Canis_latrans 667
## shrub_cover-Procyon_lotor 1218
## shrub_cover-Dasypus_novemcinctus 445
## shrub_cover-Lynx_rufus 532
## shrub_cover-Didelphis_virginiana 339
## shrub_cover-Sylvilagus_floridanus 370
## shrub_cover-Meleagris_gallopavo 316
## shrub_cover-Sciurus_carolinensis 362
## veg_height-Canis_latrans 634
## veg_height-Procyon_lotor 1650
## veg_height-Dasypus_novemcinctus 1540
## veg_height-Lynx_rufus 764
## veg_height-Didelphis_virginiana 1038
## veg_height-Sylvilagus_floridanus 694
## veg_height-Meleagris_gallopavo 406
## veg_height-Sciurus_carolinensis 646
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6393
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4230 0.4687 -1.3979 -0.4165 0.4779 1.0019 648
## Tree_Density -0.7804 0.4567 -1.7525 -0.7428 0.0432 1.0116 880
## Avg_Canopy_Cover 1.1357 0.4785 0.2296 1.1119 2.1441 1.0021 891
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1604 1.4927 0.0756 0.7205 4.6514 1.0370 524
## Tree_Density 1.0340 1.5355 0.0578 0.5396 4.7613 1.0882 577
## Avg_Canopy_Cover 1.2531 1.4880 0.1096 0.7976 5.1199 1.0074 771
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5956 0.7624 0.0493 0.3473 2.4242 1.0448 188
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7507 0.3109 -3.3508 -2.7622 -2.1020 1.0027 1965
## shrub_cover 0.3030 0.3136 -0.3104 0.2994 0.9159 1.0055 1957
## veg_height 0.1047 0.1887 -0.2703 0.1057 0.4838 1.0011 1615
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7487 0.5987 0.1856 0.5839 2.3685 1.0078 1681
## shrub_cover 0.7170 0.6275 0.1360 0.5419 2.2947 1.0090 1136
## veg_height 0.2549 0.2219 0.0573 0.1986 0.7880 1.0025 1466
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1014 0.5751 -1.0152 0.0972 1.2400
## (Intercept)-Procyon_lotor 0.3763 0.6385 -0.9412 0.3822 1.6283
## (Intercept)-Dasypus_novemcinctus -0.8046 0.5538 -2.0061 -0.7807 0.2553
## (Intercept)-Lynx_rufus 0.1245 0.9164 -1.2794 -0.0236 2.4787
## (Intercept)-Didelphis_virginiana -1.2624 0.6881 -2.6851 -1.2210 -0.0159
## (Intercept)-Sylvilagus_floridanus -0.6033 0.6018 -1.8443 -0.5836 0.5073
## (Intercept)-Meleagris_gallopavo -0.2044 0.7781 -1.6033 -0.2346 1.4936
## (Intercept)-Sciurus_carolinensis -1.2730 0.6963 -2.7293 -1.2291 -0.0359
## Tree_Density-Canis_latrans -1.0156 0.6258 -2.4566 -0.9203 -0.0070
## Tree_Density-Procyon_lotor -0.4873 0.4203 -1.3160 -0.4811 0.3259
## Tree_Density-Dasypus_novemcinctus -1.4372 0.8727 -3.6058 -1.2514 -0.2250
## Tree_Density-Lynx_rufus 0.2455 0.7966 -0.9826 0.1418 2.1764
## Tree_Density-Didelphis_virginiana -0.9770 0.7420 -2.7732 -0.8736 0.2065
## Tree_Density-Sylvilagus_floridanus -1.0844 0.7834 -2.9896 -0.9896 0.2134
## Tree_Density-Meleagris_gallopavo -1.0669 0.8294 -3.1486 -0.9443 0.2703
## Tree_Density-Sciurus_carolinensis -0.8580 0.7632 -2.6267 -0.7686 0.4225
## Avg_Canopy_Cover-Canis_latrans -0.0792 0.4851 -1.0663 -0.0770 0.8659
## Avg_Canopy_Cover-Procyon_lotor 1.0310 0.4938 0.1388 1.0051 2.0664
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1126 0.4998 0.2319 1.0751 2.1713
## Avg_Canopy_Cover-Lynx_rufus 0.7319 0.7481 -0.6189 0.6941 2.4044
## Avg_Canopy_Cover-Didelphis_virginiana 1.6011 0.7171 0.4621 1.4864 3.2921
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.1426 0.9902 0.7134 1.9645 4.5843
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5555 0.8044 0.2669 1.4443 3.4712
## Avg_Canopy_Cover-Sciurus_carolinensis 1.5202 0.6689 0.4150 1.4312 3.0413
## Rhat ESS
## (Intercept)-Canis_latrans 1.0030 681
## (Intercept)-Procyon_lotor 1.0024 332
## (Intercept)-Dasypus_novemcinctus 1.0027 711
## (Intercept)-Lynx_rufus 1.0465 229
## (Intercept)-Didelphis_virginiana 1.0033 638
## (Intercept)-Sylvilagus_floridanus 1.0010 864
## (Intercept)-Meleagris_gallopavo 1.0063 381
## (Intercept)-Sciurus_carolinensis 1.0044 568
## Tree_Density-Canis_latrans 1.0269 1043
## Tree_Density-Procyon_lotor 1.0151 1930
## Tree_Density-Dasypus_novemcinctus 1.0234 473
## Tree_Density-Lynx_rufus 1.0202 387
## Tree_Density-Didelphis_virginiana 1.0066 866
## Tree_Density-Sylvilagus_floridanus 1.0180 593
## Tree_Density-Meleagris_gallopavo 1.0112 654
## Tree_Density-Sciurus_carolinensis 1.0207 802
## Avg_Canopy_Cover-Canis_latrans 1.0047 1116
## Avg_Canopy_Cover-Procyon_lotor 1.0010 1742
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0126 1485
## Avg_Canopy_Cover-Lynx_rufus 1.0102 570
## Avg_Canopy_Cover-Didelphis_virginiana 1.0049 821
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0016 484
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0015 564
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0070 875
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7385 0.1804 -3.1037 -2.7318 -2.4121 0.9996
## (Intercept)-Procyon_lotor -2.3023 0.1428 -2.6047 -2.2965 -2.0403 1.0053
## (Intercept)-Dasypus_novemcinctus -1.7831 0.1619 -2.1062 -1.7802 -1.4725 1.0011
## (Intercept)-Lynx_rufus -3.6459 0.3563 -4.3672 -3.6507 -2.9664 1.0094
## (Intercept)-Didelphis_virginiana -2.6548 0.2971 -3.2664 -2.6480 -2.0881 1.0030
## (Intercept)-Sylvilagus_floridanus -3.1013 0.2475 -3.6079 -3.0938 -2.6315 1.0144
## (Intercept)-Meleagris_gallopavo -3.8070 0.4278 -4.6787 -3.8006 -2.9809 1.0243
## (Intercept)-Sciurus_carolinensis -2.7377 0.3142 -3.3845 -2.7221 -2.1433 1.0066
## shrub_cover-Canis_latrans -0.2842 0.2219 -0.7201 -0.2799 0.1417 1.0060
## shrub_cover-Procyon_lotor 0.2646 0.1610 -0.0686 0.2651 0.5668 1.0177
## shrub_cover-Dasypus_novemcinctus 0.9092 0.2982 0.3255 0.9081 1.4987 1.0015
## shrub_cover-Lynx_rufus -0.2394 0.3399 -0.9168 -0.2339 0.4110 1.0095
## shrub_cover-Didelphis_virginiana 1.0345 0.3721 0.3609 1.0157 1.7955 1.0112
## shrub_cover-Sylvilagus_floridanus 0.4431 0.3708 -0.2974 0.4459 1.1661 1.0071
## shrub_cover-Meleagris_gallopavo -0.6123 0.3863 -1.3716 -0.6074 0.1259 1.0109
## shrub_cover-Sciurus_carolinensis 0.9619 0.4005 0.1846 0.9456 1.7727 1.0240
## veg_height-Canis_latrans -0.5723 0.1866 -0.9422 -0.5720 -0.2068 1.0035
## veg_height-Procyon_lotor 0.3550 0.1222 0.1082 0.3581 0.5938 1.0027
## veg_height-Dasypus_novemcinctus 0.2730 0.1393 0.0052 0.2720 0.5527 1.0028
## veg_height-Lynx_rufus 0.1002 0.2406 -0.4173 0.1097 0.5540 1.0077
## veg_height-Didelphis_virginiana 0.5195 0.2431 0.0683 0.5036 1.0254 1.0006
## veg_height-Sylvilagus_floridanus 0.1791 0.2398 -0.2769 0.1801 0.6429 1.0033
## veg_height-Meleagris_gallopavo -0.1581 0.3501 -0.8761 -0.1415 0.5041 1.0103
## veg_height-Sciurus_carolinensis 0.1462 0.2226 -0.2960 0.1435 0.6062 1.0050
## ESS
## (Intercept)-Canis_latrans 807
## (Intercept)-Procyon_lotor 1349
## (Intercept)-Dasypus_novemcinctus 1288
## (Intercept)-Lynx_rufus 269
## (Intercept)-Didelphis_virginiana 585
## (Intercept)-Sylvilagus_floridanus 853
## (Intercept)-Meleagris_gallopavo 273
## (Intercept)-Sciurus_carolinensis 527
## shrub_cover-Canis_latrans 872
## shrub_cover-Procyon_lotor 1550
## shrub_cover-Dasypus_novemcinctus 1132
## shrub_cover-Lynx_rufus 467
## shrub_cover-Didelphis_virginiana 484
## shrub_cover-Sylvilagus_floridanus 741
## shrub_cover-Meleagris_gallopavo 349
## shrub_cover-Sciurus_carolinensis 645
## veg_height-Canis_latrans 801
## veg_height-Procyon_lotor 1668
## veg_height-Dasypus_novemcinctus 1854
## veg_height-Lynx_rufus 756
## veg_height-Didelphis_virginiana 937
## veg_height-Sylvilagus_floridanus 1071
## veg_height-Meleagris_gallopavo 634
## veg_height-Sciurus_carolinensis 852
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6258
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0198 0.4385 -1.9117 -1.0100 -0.1566 1.0109 620
## Avg_Cogongrass_Cover -0.6245 0.3984 -1.3978 -0.6122 0.1279 1.0304 525
## I(Avg_Cogongrass_Cover^2) 0.8312 0.4033 0.1568 0.7841 1.7954 1.0312 421
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7018 0.9313 0.0583 0.4391 2.7867 1.0084 1138
## Avg_Cogongrass_Cover 0.4872 0.6767 0.0441 0.2728 2.1893 1.0204 826
## I(Avg_Cogongrass_Cover^2) 0.6090 1.1398 0.0417 0.2596 3.5765 1.0359 288
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5192 0.5384 0.0554 0.3399 1.9661 1.0223 252
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7164 0.3231 -3.3403 -2.7220 -2.0384 1.0084 1949
## shrub_cover 0.2526 0.3104 -0.3661 0.2534 0.8696 1.0082 1128
## veg_height 0.0930 0.2052 -0.3114 0.0967 0.4853 1.0034 1531
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7444 0.6707 0.1590 0.5499 2.4706 1.0620 679
## shrub_cover 0.6543 0.6650 0.1089 0.4797 2.2182 1.0201 1015
## veg_height 0.2390 0.2215 0.0560 0.1806 0.7689 1.0026 1747
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6939 0.6080 -1.8890 -0.6939
## (Intercept)-Procyon_lotor -0.4875 0.6014 -1.6724 -0.5007
## (Intercept)-Dasypus_novemcinctus -1.1863 0.5296 -2.2773 -1.1704
## (Intercept)-Lynx_rufus -1.0938 0.6561 -2.3940 -1.0874
## (Intercept)-Didelphis_virginiana -1.4881 0.6044 -2.7966 -1.4531
## (Intercept)-Sylvilagus_floridanus -1.0530 0.5713 -2.1993 -1.0464
## (Intercept)-Meleagris_gallopavo -0.6252 0.7188 -1.8892 -0.6766
## (Intercept)-Sciurus_carolinensis -1.7416 0.6592 -3.1444 -1.6998
## Avg_Cogongrass_Cover-Canis_latrans -0.3107 0.5399 -1.3087 -0.3243
## Avg_Cogongrass_Cover-Procyon_lotor -0.6581 0.5085 -1.6805 -0.6606
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4329 0.4907 -1.4149 -0.4427
## Avg_Cogongrass_Cover-Lynx_rufus -0.5755 0.5643 -1.7266 -0.5655
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3531 0.5350 -1.3538 -0.3707
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1330 0.6531 -2.5760 -1.0700
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.9790 0.7146 -2.5438 -0.9315
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6284 0.5400 -1.7095 -0.6276
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3377 0.8418 0.2690 1.1445
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2088 0.8224 0.2890 1.0193
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6795 0.3695 -0.0063 0.6676
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1638 0.5569 0.3371 1.0722
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4859 0.4152 -0.2977 0.4671
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7149 0.5325 -0.0886 0.6611
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4448 0.7898 -0.9661 0.4062
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8513 0.3930 0.1534 0.8199
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5549 1.0165 406
## (Intercept)-Procyon_lotor 0.7152 1.0003 669
## (Intercept)-Dasypus_novemcinctus -0.1834 1.0112 1050
## (Intercept)-Lynx_rufus 0.2359 1.0455 624
## (Intercept)-Didelphis_virginiana -0.3942 1.0152 745
## (Intercept)-Sylvilagus_floridanus 0.0574 1.0064 862
## (Intercept)-Meleagris_gallopavo 0.9837 1.0177 499
## (Intercept)-Sciurus_carolinensis -0.6020 1.0236 759
## Avg_Cogongrass_Cover-Canis_latrans 0.8220 1.0204 1014
## Avg_Cogongrass_Cover-Procyon_lotor 0.3567 1.0055 1251
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5449 1.0279 1118
## Avg_Cogongrass_Cover-Lynx_rufus 0.5532 1.0234 645
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.7396 1.0163 1225
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0404 1.0036 510
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2967 1.0196 470
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4262 1.0306 923
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.6691 1.0409 216
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.6975 1.0298 199
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4403 1.0034 1038
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4534 1.0083 441
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3733 1.0384 811
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.8146 1.0130 471
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.1841 1.0359 225
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.6983 1.0254 747
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7319 0.1768 -3.0955 -2.7263 -2.4069 1.0167
## (Intercept)-Procyon_lotor -2.3171 0.1461 -2.6223 -2.3100 -2.0509 1.0032
## (Intercept)-Dasypus_novemcinctus -1.7622 0.1610 -2.0975 -1.7583 -1.4611 1.0072
## (Intercept)-Lynx_rufus -3.4628 0.3490 -4.2060 -3.4460 -2.8332 1.0139
## (Intercept)-Didelphis_virginiana -2.5778 0.2778 -3.1483 -2.5727 -2.0561 1.0147
## (Intercept)-Sylvilagus_floridanus -3.1451 0.2779 -3.7373 -3.1367 -2.6267 1.0099
## (Intercept)-Meleagris_gallopavo -3.8057 0.5172 -4.7794 -3.8290 -2.7840 1.1434
## (Intercept)-Sciurus_carolinensis -2.6403 0.3057 -3.3018 -2.6235 -2.0841 1.0006
## shrub_cover-Canis_latrans -0.2524 0.2200 -0.6873 -0.2503 0.1654 1.0049
## shrub_cover-Procyon_lotor 0.2257 0.1681 -0.1068 0.2306 0.5427 1.0064
## shrub_cover-Dasypus_novemcinctus 0.8517 0.2954 0.2859 0.8440 1.4555 1.0061
## shrub_cover-Lynx_rufus -0.1700 0.3699 -0.9240 -0.1559 0.5373 1.0039
## shrub_cover-Didelphis_virginiana 0.9570 0.3698 0.2860 0.9275 1.7510 1.0216
## shrub_cover-Sylvilagus_floridanus 0.2372 0.4105 -0.5499 0.2203 1.0864 1.0122
## shrub_cover-Meleagris_gallopavo -0.6057 0.4494 -1.4991 -0.6015 0.2646 1.0881
## shrub_cover-Sciurus_carolinensis 0.8325 0.4130 0.0609 0.8195 1.6814 1.0325
## veg_height-Canis_latrans -0.5567 0.1846 -0.9303 -0.5505 -0.2157 1.0099
## veg_height-Procyon_lotor 0.3490 0.1233 0.1135 0.3475 0.5920 1.0121
## veg_height-Dasypus_novemcinctus 0.2595 0.1362 -0.0094 0.2595 0.5267 1.0030
## veg_height-Lynx_rufus 0.0858 0.2391 -0.4027 0.0948 0.5293 1.0009
## veg_height-Didelphis_virginiana 0.4354 0.2438 -0.0261 0.4339 0.9449 1.0010
## veg_height-Sylvilagus_floridanus 0.1724 0.2450 -0.3120 0.1712 0.6457 1.0000
## veg_height-Meleagris_gallopavo -0.0854 0.4011 -0.8753 -0.0893 0.7085 1.0056
## veg_height-Sciurus_carolinensis 0.1096 0.2141 -0.3039 0.1034 0.5361 1.0065
## ESS
## (Intercept)-Canis_latrans 886
## (Intercept)-Procyon_lotor 989
## (Intercept)-Dasypus_novemcinctus 1679
## (Intercept)-Lynx_rufus 306
## (Intercept)-Didelphis_virginiana 714
## (Intercept)-Sylvilagus_floridanus 523
## (Intercept)-Meleagris_gallopavo 151
## (Intercept)-Sciurus_carolinensis 625
## shrub_cover-Canis_latrans 851
## shrub_cover-Procyon_lotor 1136
## shrub_cover-Dasypus_novemcinctus 1348
## shrub_cover-Lynx_rufus 378
## shrub_cover-Didelphis_virginiana 639
## shrub_cover-Sylvilagus_floridanus 559
## shrub_cover-Meleagris_gallopavo 214
## shrub_cover-Sciurus_carolinensis 767
## veg_height-Canis_latrans 760
## veg_height-Procyon_lotor 1652
## veg_height-Dasypus_novemcinctus 1892
## veg_height-Lynx_rufus 840
## veg_height-Didelphis_virginiana 1124
## veg_height-Sylvilagus_floridanus 716
## veg_height-Meleagris_gallopavo 274
## veg_height-Sciurus_carolinensis 903
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.6863
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8131 0.7670 -3.3345 -1.8009 -0.3684 1.0498 253
## Cogon_Patch_Size 0.1758 0.7037 -1.2006 0.1702 1.5934 1.0343 468
## Veg_shannon_index 0.9686 0.5222 -0.0496 0.9652 2.0465 1.0206 430
## total_shrub_cover -1.1495 0.7129 -2.6496 -1.1168 0.1831 1.0179 444
## Avg_Cogongrass_Cover -0.1182 1.0521 -2.4586 -0.0631 1.8107 1.0228 148
## Tree_Density -2.0971 0.8720 -3.7480 -2.1160 -0.2976 1.0630 393
## Avg_Canopy_Cover 1.8802 0.7748 0.4224 1.8498 3.5140 1.0495 438
## I(Avg_Cogongrass_Cover^2) 1.4195 0.6959 0.0935 1.3924 2.8884 1.0757 222
## avg_veg_height -0.0606 0.5797 -1.1708 -0.0701 1.1334 1.0185 203
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1945 5.8116 0.0712 1.0036 10.7044 1.0860 422
## Cogon_Patch_Size 2.8108 4.1526 0.0932 1.4698 13.5380 1.0178 416
## Veg_shannon_index 0.8187 1.2926 0.0502 0.4107 4.2225 1.0745 281
## total_shrub_cover 3.0678 4.8642 0.0911 1.6196 15.6439 1.1265 121
## Avg_Cogongrass_Cover 1.4473 3.3487 0.0535 0.5631 7.5143 1.0695 532
## Tree_Density 4.0272 7.2101 0.0706 1.7445 22.5919 1.0364 244
## Avg_Canopy_Cover 4.5897 7.1843 0.2105 2.5812 20.5509 1.0567 577
## I(Avg_Cogongrass_Cover^2) 2.1735 3.9350 0.0698 0.8954 11.2233 1.1171 192
## avg_veg_height 0.6959 1.1084 0.0429 0.3530 3.2536 1.0308 540
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6536 1.9197 0.0817 0.9876 7.0958 1.0099 132
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.7564 0.2949 -3.3487 -2.7607 -2.1440 1.0047 1332
## shrub_cover 0.4410 0.3238 -0.1997 0.4481 1.0846 1.0038 1163
## veg_height 0.1305 0.1969 -0.2834 0.1314 0.5106 1.0083 1006
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6473 0.6785 0.1496 0.4751 2.1244 1.0728 746
## shrub_cover 0.7153 0.6022 0.1323 0.5503 2.3411 1.0134 699
## veg_height 0.2433 0.2063 0.0535 0.1862 0.7726 1.0022 1567
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3907 1.0091 -3.4028 -1.4138
## (Intercept)-Procyon_lotor -1.0251 1.0199 -2.9414 -1.0478
## (Intercept)-Dasypus_novemcinctus -2.1830 0.9673 -4.2730 -2.1097
## (Intercept)-Lynx_rufus -1.4700 1.2252 -3.6910 -1.5388
## (Intercept)-Didelphis_virginiana -2.7938 1.2756 -5.7394 -2.6543
## (Intercept)-Sylvilagus_floridanus -1.9401 1.0482 -4.1280 -1.9037
## (Intercept)-Meleagris_gallopavo -1.8271 1.2402 -4.2711 -1.8443
## (Intercept)-Sciurus_carolinensis -3.0017 1.3985 -6.2168 -2.7999
## Cogon_Patch_Size-Canis_latrans 1.4644 1.2236 -0.3407 1.2477
## Cogon_Patch_Size-Procyon_lotor -0.4155 0.8428 -2.1267 -0.3823
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1601 0.8461 -1.4322 0.1311
## Cogon_Patch_Size-Lynx_rufus -0.0875 1.3813 -2.7974 -0.0555
## Cogon_Patch_Size-Didelphis_virginiana 1.4527 1.0357 -0.2589 1.3304
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0037 1.5486 -4.8017 -0.7570
## Cogon_Patch_Size-Meleagris_gallopavo 0.4502 1.2574 -1.7785 0.3512
## Cogon_Patch_Size-Sciurus_carolinensis -0.6315 1.2357 -3.6569 -0.4763
## Veg_shannon_index-Canis_latrans 1.3087 0.6884 0.1088 1.2476
## Veg_shannon_index-Procyon_lotor 1.2111 0.6255 0.0656 1.1727
## Veg_shannon_index-Dasypus_novemcinctus 0.6110 0.6214 -0.6350 0.6321
## Veg_shannon_index-Lynx_rufus 0.9637 0.9282 -1.0781 0.9729
## Veg_shannon_index-Didelphis_virginiana 1.1996 0.7513 -0.1563 1.1393
## Veg_shannon_index-Sylvilagus_floridanus 1.0517 0.7740 -0.3881 1.0134
## Veg_shannon_index-Meleagris_gallopavo 1.3217 0.8352 -0.0937 1.2601
## Veg_shannon_index-Sciurus_carolinensis 0.3548 0.8438 -1.5407 0.4428
## total_shrub_cover-Canis_latrans 0.2755 0.9124 -1.2170 0.1670
## total_shrub_cover-Procyon_lotor -1.5621 0.7620 -3.2226 -1.5146
## total_shrub_cover-Dasypus_novemcinctus -0.5164 0.9011 -2.5488 -0.4451
## total_shrub_cover-Lynx_rufus -1.9748 1.4131 -5.1395 -1.7542
## total_shrub_cover-Didelphis_virginiana -1.6407 1.4260 -5.6232 -1.3672
## total_shrub_cover-Sylvilagus_floridanus -1.2758 1.3508 -4.2415 -1.1779
## total_shrub_cover-Meleagris_gallopavo -2.7783 1.5319 -6.4632 -2.5627
## total_shrub_cover-Sciurus_carolinensis -1.1633 1.2758 -3.9146 -1.0348
## Avg_Cogongrass_Cover-Canis_latrans 0.0123 1.2726 -2.6919 0.0216
## Avg_Cogongrass_Cover-Procyon_lotor -0.2332 1.2772 -2.9969 -0.1550
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4937 1.3521 -2.1172 0.4210
## Avg_Cogongrass_Cover-Lynx_rufus -0.0895 1.3300 -2.8693 -0.0680
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0631 1.3642 -2.7634 0.0546
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7401 1.4213 -4.0270 -0.6040
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4689 1.5688 -4.0568 -0.3180
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0033 1.3264 -2.7238 0.0192
## Tree_Density-Canis_latrans -3.0636 1.4360 -6.5918 -2.7980
## Tree_Density-Procyon_lotor -2.1962 1.0490 -4.4420 -2.1463
## Tree_Density-Dasypus_novemcinctus -3.9817 1.9587 -9.1150 -3.5578
## Tree_Density-Lynx_rufus -0.7570 1.6761 -3.3584 -1.0005
## Tree_Density-Didelphis_virginiana -2.1296 1.3433 -4.9115 -2.1025
## Tree_Density-Sylvilagus_floridanus -2.6789 1.5138 -6.0689 -2.5396
## Tree_Density-Meleagris_gallopavo -2.3951 1.5669 -5.8068 -2.3113
## Tree_Density-Sciurus_carolinensis -2.2485 1.4655 -5.3307 -2.1980
## Avg_Canopy_Cover-Canis_latrans 0.0863 0.6863 -1.2267 0.0774
## Avg_Canopy_Cover-Procyon_lotor 1.6106 0.8101 0.0798 1.5737
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3012 0.9189 0.8472 2.1763
## Avg_Canopy_Cover-Lynx_rufus 1.0168 1.4009 -1.5250 0.9720
## Avg_Canopy_Cover-Didelphis_virginiana 3.2952 1.5379 1.1528 2.9471
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0449 1.9043 1.3085 3.7625
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5552 1.4386 0.5159 2.2782
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1450 1.5227 1.0302 2.8155
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2992 1.1684 0.5928 2.0673
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2569 1.1016 0.5679 2.0682
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3906 0.7754 0.0364 1.3189
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5717 1.3617 0.6873 2.3175
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8949 0.8276 -0.6678 0.8619
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1936 0.9295 -0.3673 1.0933
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.2910 1.5420 -3.3553 0.5198
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5130 0.8495 0.0509 1.4454
## avg_veg_height-Canis_latrans -0.2211 0.6883 -1.5918 -0.2137
## avg_veg_height-Procyon_lotor 0.0752 0.6825 -1.2406 0.0562
## avg_veg_height-Dasypus_novemcinctus 0.3049 0.6997 -0.9761 0.2473
## avg_veg_height-Lynx_rufus -0.4006 0.9477 -2.5770 -0.3329
## avg_veg_height-Didelphis_virginiana -0.2416 0.7952 -1.9884 -0.2092
## avg_veg_height-Sylvilagus_floridanus -0.1944 0.8251 -1.9267 -0.1809
## avg_veg_height-Meleagris_gallopavo -0.1557 0.9857 -2.3303 -0.1345
## avg_veg_height-Sciurus_carolinensis 0.3486 0.8221 -1.1023 0.2754
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6823 1.0881 295
## (Intercept)-Procyon_lotor 1.0545 1.0529 253
## (Intercept)-Dasypus_novemcinctus -0.4641 1.0793 305
## (Intercept)-Lynx_rufus 1.2379 1.0164 272
## (Intercept)-Didelphis_virginiana -0.7708 1.0273 252
## (Intercept)-Sylvilagus_floridanus 0.1155 1.0224 375
## (Intercept)-Meleagris_gallopavo 0.8140 1.0244 206
## (Intercept)-Sciurus_carolinensis -0.8847 1.0189 226
## Cogon_Patch_Size-Canis_latrans 4.6095 1.0275 343
## Cogon_Patch_Size-Procyon_lotor 1.1841 1.0068 379
## Cogon_Patch_Size-Dasypus_novemcinctus 1.9706 1.0026 413
## Cogon_Patch_Size-Lynx_rufus 2.7306 1.0619 257
## Cogon_Patch_Size-Didelphis_virginiana 3.9101 1.0142 294
## Cogon_Patch_Size-Sylvilagus_floridanus 1.3117 1.0380 378
## Cogon_Patch_Size-Meleagris_gallopavo 3.3427 1.0313 364
## Cogon_Patch_Size-Sciurus_carolinensis 1.4810 1.0187 379
## Veg_shannon_index-Canis_latrans 2.8252 1.0038 516
## Veg_shannon_index-Procyon_lotor 2.5513 1.0109 409
## Veg_shannon_index-Dasypus_novemcinctus 1.7784 1.0164 661
## Veg_shannon_index-Lynx_rufus 2.8502 1.0270 335
## Veg_shannon_index-Didelphis_virginiana 2.8111 1.0273 638
## Veg_shannon_index-Sylvilagus_floridanus 2.6840 1.0355 545
## Veg_shannon_index-Meleagris_gallopavo 3.1620 1.0086 544
## Veg_shannon_index-Sciurus_carolinensis 1.8187 1.0330 408
## total_shrub_cover-Canis_latrans 2.4249 1.0687 417
## total_shrub_cover-Procyon_lotor -0.2295 1.0115 499
## total_shrub_cover-Dasypus_novemcinctus 1.0587 1.0072 438
## total_shrub_cover-Lynx_rufus 0.1996 1.1179 234
## total_shrub_cover-Didelphis_virginiana 0.2926 1.1270 109
## total_shrub_cover-Sylvilagus_floridanus 1.0083 1.0166 342
## total_shrub_cover-Meleagris_gallopavo -0.4719 1.0494 227
## total_shrub_cover-Sciurus_carolinensis 0.9003 1.0699 204
## Avg_Cogongrass_Cover-Canis_latrans 2.5048 1.0372 224
## Avg_Cogongrass_Cover-Procyon_lotor 2.1286 1.0242 215
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3902 1.0234 274
## Avg_Cogongrass_Cover-Lynx_rufus 2.6075 1.0115 228
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7761 1.0188 200
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.7035 1.0028 271
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.1766 1.0077 158
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.7537 1.0231 262
## Tree_Density-Canis_latrans -0.9063 1.0030 260
## Tree_Density-Procyon_lotor -0.2606 1.0429 433
## Tree_Density-Dasypus_novemcinctus -1.3789 1.0217 146
## Tree_Density-Lynx_rufus 3.2792 1.0539 201
## Tree_Density-Didelphis_virginiana 0.8270 1.0351 472
## Tree_Density-Sylvilagus_floridanus -0.0230 1.0015 385
## Tree_Density-Meleagris_gallopavo 0.4920 1.0185 336
## Tree_Density-Sciurus_carolinensis 0.5388 1.0920 276
## Avg_Canopy_Cover-Canis_latrans 1.4726 1.0089 522
## Avg_Canopy_Cover-Procyon_lotor 3.3224 1.0019 600
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.4209 1.0398 348
## Avg_Canopy_Cover-Lynx_rufus 3.9759 1.0066 254
## Avg_Canopy_Cover-Didelphis_virginiana 7.0456 1.0780 208
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.5180 1.0882 276
## Avg_Canopy_Cover-Meleagris_gallopavo 6.0514 1.1504 246
## Avg_Canopy_Cover-Sciurus_carolinensis 6.8776 1.0779 316
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.0666 1.1373 154
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.8401 1.0957 205
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1228 1.0681 257
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.9972 1.1904 148
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.6698 1.0242 168
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2936 1.1311 291
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.8296 1.0261 86
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3785 1.0547 331
## avg_veg_height-Canis_latrans 1.1738 1.0065 326
## avg_veg_height-Procyon_lotor 1.4601 1.0130 365
## avg_veg_height-Dasypus_novemcinctus 1.8272 1.0140 333
## avg_veg_height-Lynx_rufus 1.3024 1.0283 217
## avg_veg_height-Didelphis_virginiana 1.2704 1.0030 407
## avg_veg_height-Sylvilagus_floridanus 1.4964 1.0227 258
## avg_veg_height-Meleagris_gallopavo 1.8493 1.0068 251
## avg_veg_height-Sciurus_carolinensis 2.2994 1.0070 454
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7058 0.1854 -3.0790 -2.6984 -2.3568 1.0211
## (Intercept)-Procyon_lotor -2.3098 0.1409 -2.6011 -2.3052 -2.0488 1.0005
## (Intercept)-Dasypus_novemcinctus -1.8304 0.1715 -2.1707 -1.8278 -1.5042 1.0103
## (Intercept)-Lynx_rufus -3.5891 0.3152 -4.2227 -3.5862 -2.9923 1.0231
## (Intercept)-Didelphis_virginiana -2.6921 0.2976 -3.2888 -2.6913 -2.1087 1.0002
## (Intercept)-Sylvilagus_floridanus -3.1740 0.2531 -3.6893 -3.1674 -2.6982 1.0030
## (Intercept)-Meleagris_gallopavo -3.5857 0.4911 -4.5916 -3.5770 -2.6437 1.0148
## (Intercept)-Sciurus_carolinensis -2.8207 0.3206 -3.4848 -2.8111 -2.2299 1.0455
## shrub_cover-Canis_latrans -0.2912 0.2318 -0.7409 -0.2950 0.1795 1.0149
## shrub_cover-Procyon_lotor 0.2858 0.1633 -0.0537 0.2914 0.5891 1.0029
## shrub_cover-Dasypus_novemcinctus 1.0293 0.3188 0.4202 1.0299 1.6567 1.0207
## shrub_cover-Lynx_rufus 0.0512 0.3705 -0.6955 0.0562 0.7525 1.0769
## shrub_cover-Didelphis_virginiana 1.1654 0.4025 0.4325 1.1552 1.9822 1.0120
## shrub_cover-Sylvilagus_floridanus 0.6271 0.3850 -0.1661 0.6349 1.3616 1.0105
## shrub_cover-Meleagris_gallopavo -0.3498 0.4398 -1.2191 -0.3411 0.4751 1.0054
## shrub_cover-Sciurus_carolinensis 1.1082 0.4031 0.3186 1.1044 1.9007 1.0293
## veg_height-Canis_latrans -0.5309 0.1864 -0.9155 -0.5233 -0.1740 1.0024
## veg_height-Procyon_lotor 0.3655 0.1218 0.1277 0.3637 0.5976 1.0078
## veg_height-Dasypus_novemcinctus 0.2957 0.1407 0.0321 0.2942 0.5801 1.0153
## veg_height-Lynx_rufus 0.1684 0.2385 -0.2996 0.1650 0.6407 1.0303
## veg_height-Didelphis_virginiana 0.4862 0.2515 0.0172 0.4751 0.9942 0.9999
## veg_height-Sylvilagus_floridanus 0.1503 0.2391 -0.3116 0.1477 0.6273 1.0236
## veg_height-Meleagris_gallopavo -0.0855 0.3942 -0.8598 -0.0837 0.6962 1.0015
## veg_height-Sciurus_carolinensis 0.1742 0.2211 -0.2477 0.1713 0.6051 1.0284
## ESS
## (Intercept)-Canis_latrans 758
## (Intercept)-Procyon_lotor 1053
## (Intercept)-Dasypus_novemcinctus 637
## (Intercept)-Lynx_rufus 291
## (Intercept)-Didelphis_virginiana 442
## (Intercept)-Sylvilagus_floridanus 607
## (Intercept)-Meleagris_gallopavo 173
## (Intercept)-Sciurus_carolinensis 367
## shrub_cover-Canis_latrans 561
## shrub_cover-Procyon_lotor 966
## shrub_cover-Dasypus_novemcinctus 423
## shrub_cover-Lynx_rufus 280
## shrub_cover-Didelphis_virginiana 274
## shrub_cover-Sylvilagus_floridanus 505
## shrub_cover-Meleagris_gallopavo 207
## shrub_cover-Sciurus_carolinensis 299
## veg_height-Canis_latrans 682
## veg_height-Procyon_lotor 1593
## veg_height-Dasypus_novemcinctus 1492
## veg_height-Lynx_rufus 518
## veg_height-Didelphis_virginiana 906
## veg_height-Sylvilagus_floridanus 767
## veg_height-Meleagris_gallopavo 254
## veg_height-Sciurus_carolinensis 564
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T10<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8168
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3741 0.3369 -1.0341 -0.373 0.3033 1.0052 1676
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6895 0.6715 0.1155 0.5038 2.4717 1.0346 1192
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4352 0.2899 -3.0096 -2.4380 -1.8419 1.0029 2247
## week 0.1796 0.2123 -0.2490 0.1824 0.5988 1.0060 1284
## I(week^2) -0.2132 0.1195 -0.4710 -0.2074 0.0077 1.0124 975
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6101 0.6067 0.1525 0.4660 1.8810 1.0257 1592
## week 0.2007 0.2683 0.0299 0.1373 0.7528 1.0653 1290
## I(week^2) 0.0770 0.0776 0.0186 0.0562 0.2556 1.0505 647
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.1466 0.3754 -0.5720 0.1307 0.9327 1.0034
## (Intercept)-Procyon_lotor 0.4773 0.3772 -0.2412 0.4665 1.2280 1.0029
## (Intercept)-Dasypus_novemcinctus -0.5957 0.3299 -1.2669 -0.5872 0.0405 1.0019
## (Intercept)-Lynx_rufus -0.0714 0.5648 -0.9778 -0.1382 1.1482 1.0697
## (Intercept)-Didelphis_virginiana -1.1368 0.4082 -1.9987 -1.1139 -0.4167 1.0038
## (Intercept)-Sylvilagus_floridanus -0.3950 0.4311 -1.2158 -0.4129 0.5234 1.0009
## (Intercept)-Meleagris_gallopavo -0.4181 0.4509 -1.2609 -0.4316 0.4781 1.0141
## (Intercept)-Sciurus_carolinensis -1.1043 0.4124 -1.9755 -1.0908 -0.3506 1.0002
## ESS
## (Intercept)-Canis_latrans 1941
## (Intercept)-Procyon_lotor 1898
## (Intercept)-Dasypus_novemcinctus 3000
## (Intercept)-Lynx_rufus 405
## (Intercept)-Didelphis_virginiana 2210
## (Intercept)-Sylvilagus_floridanus 1100
## (Intercept)-Meleagris_gallopavo 1123
## (Intercept)-Sciurus_carolinensis 2006
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4466 0.1808 -2.8147 -2.4407 -2.1076 1.0028
## (Intercept)-Procyon_lotor -2.1743 0.1439 -2.4647 -2.1720 -1.9055 1.0004
## (Intercept)-Dasypus_novemcinctus -1.4945 0.1574 -1.8170 -1.4906 -1.1930 1.0020
## (Intercept)-Lynx_rufus -3.2529 0.3282 -3.9239 -3.2388 -2.6629 1.0359
## (Intercept)-Didelphis_virginiana -2.1699 0.2736 -2.7474 -2.1502 -1.6818 1.0026
## (Intercept)-Sylvilagus_floridanus -2.9804 0.3021 -3.6199 -2.9609 -2.4426 1.0028
## (Intercept)-Meleagris_gallopavo -3.1553 0.3406 -3.8556 -3.1394 -2.5387 1.0257
## (Intercept)-Sciurus_carolinensis -2.3326 0.2799 -2.9168 -2.3197 -1.8136 1.0087
## week-Canis_latrans 0.4427 0.2466 0.0012 0.4276 0.9608 1.0006
## week-Procyon_lotor 0.1547 0.1970 -0.2240 0.1520 0.5412 1.0037
## week-Dasypus_novemcinctus 0.0607 0.2088 -0.3404 0.0628 0.4714 1.0025
## week-Lynx_rufus 0.2689 0.3007 -0.3026 0.2642 0.9021 1.0014
## week-Didelphis_virginiana 0.0467 0.3035 -0.5778 0.0532 0.6270 1.0055
## week-Sylvilagus_floridanus 0.0612 0.2923 -0.5275 0.0627 0.6333 1.0449
## week-Meleagris_gallopavo -0.0960 0.3457 -0.8273 -0.0774 0.5194 1.0053
## week-Sciurus_carolinensis 0.5323 0.3361 -0.0461 0.4999 1.2584 1.0033
## I(week^2)-Canis_latrans -0.1945 0.1039 -0.4064 -0.1941 0.0011 1.0021
## I(week^2)-Procyon_lotor -0.1121 0.0863 -0.2814 -0.1136 0.0551 1.0050
## I(week^2)-Dasypus_novemcinctus -0.1533 0.1004 -0.3607 -0.1507 0.0350 1.0032
## I(week^2)-Lynx_rufus -0.2110 0.1492 -0.5325 -0.2022 0.0632 1.0000
## I(week^2)-Didelphis_virginiana -0.3448 0.2031 -0.8275 -0.3170 -0.0155 1.0068
## I(week^2)-Sylvilagus_floridanus -0.1643 0.1461 -0.4641 -0.1627 0.1138 1.0243
## I(week^2)-Meleagris_gallopavo -0.3489 0.2363 -0.9162 -0.3182 0.0150 1.0412
## I(week^2)-Sciurus_carolinensis -0.1884 0.1408 -0.4868 -0.1838 0.0750 1.0016
## ESS
## (Intercept)-Canis_latrans 1444
## (Intercept)-Procyon_lotor 1705
## (Intercept)-Dasypus_novemcinctus 2350
## (Intercept)-Lynx_rufus 388
## (Intercept)-Didelphis_virginiana 1425
## (Intercept)-Sylvilagus_floridanus 596
## (Intercept)-Meleagris_gallopavo 501
## (Intercept)-Sciurus_carolinensis 1092
## week-Canis_latrans 929
## week-Procyon_lotor 1769
## week-Dasypus_novemcinctus 2049
## week-Lynx_rufus 1036
## week-Didelphis_virginiana 1421
## week-Sylvilagus_floridanus 1151
## week-Meleagris_gallopavo 599
## week-Sciurus_carolinensis 1192
## I(week^2)-Canis_latrans 1200
## I(week^2)-Procyon_lotor 1655
## I(week^2)-Dasypus_novemcinctus 1668
## I(week^2)-Lynx_rufus 657
## I(week^2)-Didelphis_virginiana 599
## I(week^2)-Sylvilagus_floridanus 982
## I(week^2)-Meleagris_gallopavo 273
## I(week^2)-Sciurus_carolinensis 1360
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.807
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.0065 0.6540 -2.3170 -1.0076 0.3500 1.0056 393
## Cogon_Patch_Size -0.5629 0.6095 -1.7831 -0.5632 0.6755 1.0008 879
## Veg_shannon_index 0.9423 0.4037 0.1689 0.9435 1.7606 1.0089 367
## total_shrub_cover -0.5576 0.4996 -1.5935 -0.5361 0.4126 1.0025 1252
## Avg_Cogongrass_Cover 2.0699 0.6318 0.8218 2.0745 3.2921 1.0089 268
## Tree_Density -1.8762 0.6637 -3.2456 -1.8812 -0.4929 1.0047 701
## Avg_Canopy_Cover 1.6560 0.5899 0.5475 1.6435 2.8309 1.0173 967
## avg_veg_height -0.5071 0.4447 -1.3492 -0.5201 0.3978 1.0074 390
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3095 2.9783 0.1038 1.5349 9.4562 1.0111 647
## Cogon_Patch_Size 2.2809 3.0218 0.1354 1.3517 10.5104 1.0020 470
## Veg_shannon_index 0.4684 0.6457 0.0406 0.2620 2.1180 1.0104 545
## total_shrub_cover 1.5779 2.1657 0.0830 0.8738 6.8394 1.0029 316
## Avg_Cogongrass_Cover 0.7530 1.2186 0.0434 0.3487 3.8610 1.0188 546
## Tree_Density 2.0244 2.8179 0.0708 1.0647 9.2092 1.0408 452
## Avg_Canopy_Cover 2.0554 3.0371 0.1109 1.1445 9.4104 1.0158 467
## avg_veg_height 0.3822 0.5223 0.0433 0.2286 1.7059 1.0129 1116
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0981 2.184 0.1126 1.4091 8.2542 1.0157 110
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4566 0.2867 -3.0248 -2.4665 -1.8735 1.0009 2497
## week 0.1798 0.2039 -0.2210 0.1770 0.5717 1.0023 894
## I(week^2) -0.2189 0.1208 -0.4723 -0.2129 0.0019 1.0078 928
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6522 0.5736 0.1637 0.4992 2.0085 1.0120 1582
## week 0.1950 0.1986 0.0332 0.1339 0.7021 1.0023 922
## I(week^2) 0.0774 0.0782 0.0185 0.0577 0.2505 1.0324 1508
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0980 0.9862 -1.9847 -0.1060
## (Intercept)-Procyon_lotor 0.0568 0.9946 -1.8694 0.0853
## (Intercept)-Dasypus_novemcinctus -1.4258 0.7914 -3.0875 -1.3937
## (Intercept)-Lynx_rufus -0.3029 1.3013 -2.4104 -0.4700
## (Intercept)-Didelphis_virginiana -2.2312 0.9438 -4.2486 -2.1517
## (Intercept)-Sylvilagus_floridanus -1.3009 0.9816 -3.3457 -1.2837
## (Intercept)-Meleagris_gallopavo -1.3152 0.9752 -3.2745 -1.3051
## (Intercept)-Sciurus_carolinensis -2.3131 0.9620 -4.3843 -2.2386
## Cogon_Patch_Size-Canis_latrans 0.5450 1.0076 -0.9864 0.4011
## Cogon_Patch_Size-Procyon_lotor -0.9708 0.6865 -2.3646 -0.9408
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7298 0.5939 -1.9172 -0.7210
## Cogon_Patch_Size-Lynx_rufus -0.8003 1.2031 -3.1224 -0.8580
## Cogon_Patch_Size-Didelphis_virginiana 0.8106 0.8643 -0.6813 0.7226
## Cogon_Patch_Size-Sylvilagus_floridanus -1.8092 1.3360 -5.1122 -1.5901
## Cogon_Patch_Size-Meleagris_gallopavo -0.4384 0.9916 -2.2298 -0.5083
## Cogon_Patch_Size-Sciurus_carolinensis -1.4572 0.9814 -3.8332 -1.3301
## Veg_shannon_index-Canis_latrans 1.2013 0.5530 0.2067 1.1651
## Veg_shannon_index-Procyon_lotor 1.1978 0.5433 0.2205 1.1608
## Veg_shannon_index-Dasypus_novemcinctus 0.7201 0.4744 -0.2021 0.7264
## Veg_shannon_index-Lynx_rufus 0.8932 0.6788 -0.4076 0.8812
## Veg_shannon_index-Didelphis_virginiana 1.0251 0.5714 0.0017 1.0006
## Veg_shannon_index-Sylvilagus_floridanus 1.0361 0.5881 -0.0016 0.9893
## Veg_shannon_index-Meleagris_gallopavo 1.1791 0.6500 0.0370 1.1286
## Veg_shannon_index-Sciurus_carolinensis 0.4513 0.5929 -0.8194 0.4929
## total_shrub_cover-Canis_latrans 0.1816 0.6614 -0.9406 0.1065
## total_shrub_cover-Procyon_lotor -0.9752 0.5879 -2.2174 -0.9227
## total_shrub_cover-Dasypus_novemcinctus 0.0790 0.5500 -0.9726 0.0641
## total_shrub_cover-Lynx_rufus -1.1892 1.0072 -3.7741 -1.0231
## total_shrub_cover-Didelphis_virginiana -0.6026 0.6968 -2.1072 -0.5571
## total_shrub_cover-Sylvilagus_floridanus -0.2157 0.7754 -1.7252 -0.2330
## total_shrub_cover-Meleagris_gallopavo -2.0346 1.2201 -4.8833 -1.8577
## total_shrub_cover-Sciurus_carolinensis -0.0674 0.6628 -1.2838 -0.1023
## Avg_Cogongrass_Cover-Canis_latrans 2.2391 0.7766 0.8149 2.2103
## Avg_Cogongrass_Cover-Procyon_lotor 2.2011 0.7830 0.7114 2.1677
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.4907 0.8017 1.1087 2.4493
## Avg_Cogongrass_Cover-Lynx_rufus 2.3873 0.8771 0.8628 2.3090
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.1328 0.7777 0.6945 2.1151
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6093 0.8822 -0.3213 1.6776
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.6856 1.0390 -0.7413 1.8109
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.3469 0.7803 0.9276 2.3052
## Tree_Density-Canis_latrans -2.3931 1.0396 -4.8150 -2.2636
## Tree_Density-Procyon_lotor -1.5472 0.7129 -2.8922 -1.5694
## Tree_Density-Dasypus_novemcinctus -3.0755 1.2526 -6.1234 -2.8371
## Tree_Density-Lynx_rufus -0.5245 1.2040 -2.5160 -0.6763
## Tree_Density-Didelphis_virginiana -2.1819 0.9242 -4.3077 -2.0887
## Tree_Density-Sylvilagus_floridanus -2.2892 1.0985 -4.7569 -2.1767
## Tree_Density-Meleagris_gallopavo -2.1226 1.0907 -4.5959 -2.0612
## Tree_Density-Sciurus_carolinensis -2.2023 1.0280 -4.5924 -2.0821
## Avg_Canopy_Cover-Canis_latrans 0.3465 0.6567 -0.9635 0.3583
## Avg_Canopy_Cover-Procyon_lotor 1.6513 0.6486 0.4602 1.6135
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9068 0.6517 0.8195 1.8487
## Avg_Canopy_Cover-Lynx_rufus 0.8450 1.0288 -1.2303 0.8432
## Avg_Canopy_Cover-Didelphis_virginiana 2.4440 0.8285 1.1007 2.3664
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.0158 1.4017 1.0899 2.7350
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1696 0.9696 0.6677 2.0083
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0951 0.7718 0.8530 2.0209
## avg_veg_height-Canis_latrans -0.6947 0.5519 -1.8391 -0.6925
## avg_veg_height-Procyon_lotor -0.4016 0.5334 -1.4405 -0.4171
## avg_veg_height-Dasypus_novemcinctus -0.2998 0.5518 -1.3483 -0.3160
## avg_veg_height-Lynx_rufus -0.6038 0.6514 -1.9011 -0.6072
## avg_veg_height-Didelphis_virginiana -0.6083 0.6111 -1.8354 -0.5909
## avg_veg_height-Sylvilagus_floridanus -0.7083 0.5966 -1.9048 -0.7071
## avg_veg_height-Meleagris_gallopavo -0.6326 0.6499 -1.9399 -0.6285
## avg_veg_height-Sciurus_carolinensis -0.2135 0.6104 -1.3275 -0.2481
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.9410 1.0163 267
## (Intercept)-Procyon_lotor 1.9168 1.0016 167
## (Intercept)-Dasypus_novemcinctus -0.0227 1.0090 609
## (Intercept)-Lynx_rufus 2.6727 1.0101 187
## (Intercept)-Didelphis_virginiana -0.5331 1.0055 434
## (Intercept)-Sylvilagus_floridanus 0.6050 1.0162 522
## (Intercept)-Meleagris_gallopavo 0.6105 1.0218 403
## (Intercept)-Sciurus_carolinensis -0.6906 1.0090 430
## Cogon_Patch_Size-Canis_latrans 3.0388 1.0054 750
## Cogon_Patch_Size-Procyon_lotor 0.3402 1.0079 353
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4526 1.0020 1190
## Cogon_Patch_Size-Lynx_rufus 1.8261 1.0116 405
## Cogon_Patch_Size-Didelphis_virginiana 2.6710 1.0078 680
## Cogon_Patch_Size-Sylvilagus_floridanus 0.1734 1.0104 380
## Cogon_Patch_Size-Meleagris_gallopavo 1.7520 1.0047 520
## Cogon_Patch_Size-Sciurus_carolinensis 0.0791 1.0057 701
## Veg_shannon_index-Canis_latrans 2.4072 1.0063 470
## Veg_shannon_index-Procyon_lotor 2.3519 1.0147 319
## Veg_shannon_index-Dasypus_novemcinctus 1.6219 1.0174 769
## Veg_shannon_index-Lynx_rufus 2.3125 1.0010 587
## Veg_shannon_index-Didelphis_virginiana 2.2557 1.0096 590
## Veg_shannon_index-Sylvilagus_floridanus 2.2889 1.0116 524
## Veg_shannon_index-Meleagris_gallopavo 2.5998 1.0012 542
## Veg_shannon_index-Sciurus_carolinensis 1.4843 1.0051 775
## total_shrub_cover-Canis_latrans 1.6883 1.0105 830
## total_shrub_cover-Procyon_lotor 0.0609 0.9999 1204
## total_shrub_cover-Dasypus_novemcinctus 1.2168 1.0063 1268
## total_shrub_cover-Lynx_rufus 0.3372 1.0365 342
## total_shrub_cover-Didelphis_virginiana 0.6753 1.0004 860
## total_shrub_cover-Sylvilagus_floridanus 1.3727 1.0038 929
## total_shrub_cover-Meleagris_gallopavo -0.2845 1.0116 327
## total_shrub_cover-Sciurus_carolinensis 1.3123 1.0017 1260
## Avg_Cogongrass_Cover-Canis_latrans 3.9137 1.0128 441
## Avg_Cogongrass_Cover-Procyon_lotor 3.8315 1.0055 402
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.2559 1.0011 411
## Avg_Cogongrass_Cover-Lynx_rufus 4.3777 1.0006 463
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.7825 1.0017 519
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2427 1.0055 459
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.4334 1.0011 316
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.0285 1.0073 441
## Tree_Density-Canis_latrans -0.6905 1.0042 546
## Tree_Density-Procyon_lotor -0.0642 1.0062 665
## Tree_Density-Dasypus_novemcinctus -1.2620 1.0054 338
## Tree_Density-Lynx_rufus 2.2277 1.0136 338
## Tree_Density-Didelphis_virginiana -0.5508 1.0033 524
## Tree_Density-Sylvilagus_floridanus -0.3581 1.0039 587
## Tree_Density-Meleagris_gallopavo -0.0416 1.0058 602
## Tree_Density-Sciurus_carolinensis -0.5395 1.0154 735
## Avg_Canopy_Cover-Canis_latrans 1.5832 1.0045 776
## Avg_Canopy_Cover-Procyon_lotor 3.0073 1.0004 674
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.4007 1.0205 530
## Avg_Canopy_Cover-Lynx_rufus 2.8577 1.0038 422
## Avg_Canopy_Cover-Didelphis_virginiana 4.3149 1.0030 494
## Avg_Canopy_Cover-Sylvilagus_floridanus 6.4579 1.0441 200
## Avg_Canopy_Cover-Meleagris_gallopavo 4.4804 1.0033 436
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8239 1.0292 745
## avg_veg_height-Canis_latrans 0.3910 1.0009 542
## avg_veg_height-Procyon_lotor 0.6918 1.0018 613
## avg_veg_height-Dasypus_novemcinctus 0.8363 1.0064 648
## avg_veg_height-Lynx_rufus 0.6524 1.0038 601
## avg_veg_height-Didelphis_virginiana 0.6237 1.0058 661
## avg_veg_height-Sylvilagus_floridanus 0.4289 1.0018 573
## avg_veg_height-Meleagris_gallopavo 0.6259 1.0020 584
## avg_veg_height-Sciurus_carolinensis 1.0804 1.0055 651
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4678 0.1833 -2.8411 -2.4635 -2.1200 1.0007
## (Intercept)-Procyon_lotor -2.1645 0.1436 -2.4509 -2.1652 -1.9021 1.0023
## (Intercept)-Dasypus_novemcinctus -1.4927 0.1578 -1.8113 -1.4924 -1.1854 1.0083
## (Intercept)-Lynx_rufus -3.3517 0.3168 -4.0188 -3.3407 -2.7740 1.0086
## (Intercept)-Didelphis_virginiana -2.1505 0.2550 -2.6758 -2.1443 -1.6753 1.0045
## (Intercept)-Sylvilagus_floridanus -3.0095 0.2764 -3.5840 -2.9996 -2.4806 1.0028
## (Intercept)-Meleagris_gallopavo -3.1947 0.3154 -3.8326 -3.1939 -2.6000 1.0082
## (Intercept)-Sciurus_carolinensis -2.3283 0.2740 -2.9058 -2.3168 -1.8337 1.0012
## week-Canis_latrans 0.4401 0.2470 -0.0246 0.4284 0.9618 1.0014
## week-Procyon_lotor 0.1643 0.1985 -0.2166 0.1611 0.5679 1.0016
## week-Dasypus_novemcinctus 0.0638 0.2076 -0.3448 0.0614 0.4836 1.0017
## week-Lynx_rufus 0.2729 0.2991 -0.2936 0.2636 0.9057 1.0016
## week-Didelphis_virginiana 0.0327 0.3168 -0.6311 0.0396 0.6483 1.0008
## week-Sylvilagus_floridanus 0.0543 0.2904 -0.5377 0.0583 0.6254 1.0057
## week-Meleagris_gallopavo -0.0925 0.3420 -0.8318 -0.0638 0.5315 1.0100
## week-Sciurus_carolinensis 0.5333 0.3401 -0.0620 0.5085 1.2971 1.0225
## I(week^2)-Canis_latrans -0.1951 0.1051 -0.4089 -0.1939 0.0084 1.0003
## I(week^2)-Procyon_lotor -0.1156 0.0870 -0.2915 -0.1154 0.0528 1.0012
## I(week^2)-Dasypus_novemcinctus -0.1525 0.0982 -0.3487 -0.1512 0.0346 1.0019
## I(week^2)-Lynx_rufus -0.2057 0.1484 -0.5123 -0.1956 0.0612 1.0021
## I(week^2)-Didelphis_virginiana -0.3682 0.2068 -0.8458 -0.3436 -0.0267 1.0332
## I(week^2)-Sylvilagus_floridanus -0.1614 0.1498 -0.4750 -0.1543 0.1117 1.0038
## I(week^2)-Meleagris_gallopavo -0.3533 0.2282 -0.8733 -0.3277 0.0175 1.0843
## I(week^2)-Sciurus_carolinensis -0.1914 0.1419 -0.5022 -0.1805 0.0715 1.0207
## ESS
## (Intercept)-Canis_latrans 1116
## (Intercept)-Procyon_lotor 1594
## (Intercept)-Dasypus_novemcinctus 2624
## (Intercept)-Lynx_rufus 367
## (Intercept)-Didelphis_virginiana 1793
## (Intercept)-Sylvilagus_floridanus 731
## (Intercept)-Meleagris_gallopavo 609
## (Intercept)-Sciurus_carolinensis 1493
## week-Canis_latrans 1215
## week-Procyon_lotor 1589
## week-Dasypus_novemcinctus 1999
## week-Lynx_rufus 1066
## week-Didelphis_virginiana 1561
## week-Sylvilagus_floridanus 1129
## week-Meleagris_gallopavo 678
## week-Sciurus_carolinensis 942
## I(week^2)-Canis_latrans 1203
## I(week^2)-Procyon_lotor 1548
## I(week^2)-Dasypus_novemcinctus 1944
## I(week^2)-Lynx_rufus 655
## I(week^2)-Didelphis_virginiana 579
## I(week^2)-Sylvilagus_floridanus 761
## I(week^2)-Meleagris_gallopavo 289
## I(week^2)-Sciurus_carolinensis 1534
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8287
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5018 0.3980 -1.2646 -0.5101 0.3139 1.0019 922
## Avg_Cogongrass_Cover 0.1612 0.3380 -0.5120 0.1593 0.8309 1.0301 752
## total_shrub_cover -0.4721 0.3443 -1.2028 -0.4621 0.1593 1.0009 1217
## avg_veg_height -0.0123 0.3222 -0.6343 -0.0042 0.6110 1.0225 588
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7785 0.9541 0.0618 0.5221 2.9461 1.0196 747
## Avg_Cogongrass_Cover 0.3956 0.4848 0.0444 0.2388 1.6722 1.0120 1207
## total_shrub_cover 0.6731 0.8812 0.0664 0.4108 2.8351 1.0123 937
## avg_veg_height 0.2766 0.4239 0.0355 0.1663 1.1551 1.0522 1375
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.795 0.6877 0.0773 0.5989 2.6285 1.0179 278
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4360 0.2888 -2.9597 -2.4457 -1.8328 1.0002 2214
## week 0.1917 0.2119 -0.2241 0.1925 0.5974 1.0032 1207
## I(week^2) -0.2282 0.1245 -0.4873 -0.2237 0.0021 1.0053 726
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6091 0.6079 0.1540 0.4599 1.9200 1.0546 1786
## week 0.2068 0.2123 0.0326 0.1438 0.7508 1.0066 1232
## I(week^2) 0.0826 0.0851 0.0191 0.0591 0.2886 1.0227 493
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0048 0.5551 -1.0513 -0.0113
## (Intercept)-Procyon_lotor 0.2231 0.6434 -0.9943 0.2013
## (Intercept)-Dasypus_novemcinctus -0.6650 0.5009 -1.6957 -0.6491
## (Intercept)-Lynx_rufus -0.3860 0.6294 -1.5256 -0.4224
## (Intercept)-Didelphis_virginiana -1.1200 0.5699 -2.3124 -1.0703
## (Intercept)-Sylvilagus_floridanus -0.4202 0.6092 -1.5064 -0.4475
## (Intercept)-Meleagris_gallopavo -0.7052 0.6023 -1.9512 -0.6869
## (Intercept)-Sciurus_carolinensis -1.1651 0.5660 -2.3706 -1.1145
## Avg_Cogongrass_Cover-Canis_latrans 0.4251 0.4556 -0.4287 0.3893
## Avg_Cogongrass_Cover-Procyon_lotor 0.0772 0.4393 -0.8288 0.0825
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2650 0.3982 -0.4987 0.2546
## Avg_Cogongrass_Cover-Lynx_rufus 0.4878 0.4970 -0.3995 0.4530
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3449 0.4259 -0.4325 0.3238
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2478 0.5330 -1.4283 -0.2071
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3380 0.5725 -1.6159 -0.2824
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.2499 0.4358 -0.5835 0.2465
## total_shrub_cover-Canis_latrans 0.1097 0.4574 -0.7165 0.0750
## total_shrub_cover-Procyon_lotor -0.8917 0.4789 -2.0119 -0.8418
## total_shrub_cover-Dasypus_novemcinctus -0.0863 0.3737 -0.7891 -0.0982
## total_shrub_cover-Lynx_rufus -0.9058 0.6221 -2.3485 -0.8241
## total_shrub_cover-Didelphis_virginiana -0.2831 0.4231 -1.1032 -0.2890
## total_shrub_cover-Sylvilagus_floridanus -0.4337 0.5240 -1.5246 -0.4138
## total_shrub_cover-Meleagris_gallopavo -1.2763 0.6764 -2.8760 -1.1755
## total_shrub_cover-Sciurus_carolinensis -0.1292 0.4304 -0.9787 -0.1350
## avg_veg_height-Canis_latrans -0.0984 0.4061 -0.9182 -0.0835
## avg_veg_height-Procyon_lotor 0.0774 0.4115 -0.7228 0.0724
## avg_veg_height-Dasypus_novemcinctus 0.1758 0.3911 -0.5755 0.1763
## avg_veg_height-Lynx_rufus -0.0563 0.5046 -1.1359 -0.0351
## avg_veg_height-Didelphis_virginiana -0.0649 0.4291 -0.9282 -0.0583
## avg_veg_height-Sylvilagus_floridanus -0.1625 0.4366 -1.0645 -0.1446
## avg_veg_height-Meleagris_gallopavo -0.2601 0.5405 -1.5003 -0.2100
## avg_veg_height-Sciurus_carolinensis 0.2495 0.4373 -0.5473 0.2429
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1638 1.0281 695
## (Intercept)-Procyon_lotor 1.5669 1.0070 578
## (Intercept)-Dasypus_novemcinctus 0.2855 1.0018 1019
## (Intercept)-Lynx_rufus 1.0121 1.0247 680
## (Intercept)-Didelphis_virginiana -0.1309 1.0015 963
## (Intercept)-Sylvilagus_floridanus 0.9318 1.0042 707
## (Intercept)-Meleagris_gallopavo 0.4888 1.0131 855
## (Intercept)-Sciurus_carolinensis -0.1531 1.0007 992
## Avg_Cogongrass_Cover-Canis_latrans 1.4082 1.0063 1041
## Avg_Cogongrass_Cover-Procyon_lotor 0.9399 1.0323 1187
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0598 1.0210 1422
## Avg_Cogongrass_Cover-Lynx_rufus 1.5753 1.0006 1022
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2135 1.0129 1083
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6902 1.0178 916
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.6376 1.0128 827
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1228 1.0140 942
## total_shrub_cover-Canis_latrans 1.0723 1.0015 1252
## total_shrub_cover-Procyon_lotor -0.0823 1.0154 1162
## total_shrub_cover-Dasypus_novemcinctus 0.6812 1.0035 2057
## total_shrub_cover-Lynx_rufus 0.0761 1.0181 770
## total_shrub_cover-Didelphis_virginiana 0.5897 1.0011 2420
## total_shrub_cover-Sylvilagus_floridanus 0.5469 1.0079 1052
## total_shrub_cover-Meleagris_gallopavo -0.2370 1.0090 800
## total_shrub_cover-Sciurus_carolinensis 0.7307 1.0107 2094
## avg_veg_height-Canis_latrans 0.6611 1.0085 954
## avg_veg_height-Procyon_lotor 0.9141 1.0150 1251
## avg_veg_height-Dasypus_novemcinctus 0.9652 1.0178 1329
## avg_veg_height-Lynx_rufus 0.8912 1.0088 979
## avg_veg_height-Didelphis_virginiana 0.7603 1.0050 1127
## avg_veg_height-Sylvilagus_floridanus 0.6468 1.0124 959
## avg_veg_height-Meleagris_gallopavo 0.6732 1.0102 768
## avg_veg_height-Sciurus_carolinensis 1.1555 1.0130 1073
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4590 0.1876 -2.8478 -2.4537 -2.1058 1.0057
## (Intercept)-Procyon_lotor -2.1804 0.1474 -2.4798 -2.1754 -1.9069 1.0104
## (Intercept)-Dasypus_novemcinctus -1.4938 0.1597 -1.8082 -1.4911 -1.2034 1.0148
## (Intercept)-Lynx_rufus -3.2391 0.2941 -3.8530 -3.2342 -2.6728 1.0146
## (Intercept)-Didelphis_virginiana -2.1666 0.2672 -2.7146 -2.1560 -1.6586 1.0024
## (Intercept)-Sylvilagus_floridanus -3.0538 0.3200 -3.7670 -3.0433 -2.4697 1.0240
## (Intercept)-Meleagris_gallopavo -3.0901 0.3079 -3.7142 -3.0763 -2.5190 1.0554
## (Intercept)-Sciurus_carolinensis -2.3527 0.2796 -2.9311 -2.3413 -1.8328 1.0023
## week-Canis_latrans 0.4687 0.2479 0.0066 0.4590 0.9827 1.0052
## week-Procyon_lotor 0.1696 0.2005 -0.2270 0.1727 0.5771 1.0017
## week-Dasypus_novemcinctus 0.0612 0.2145 -0.3631 0.0602 0.4884 1.0031
## week-Lynx_rufus 0.2928 0.3103 -0.2907 0.2855 0.9391 1.0144
## week-Didelphis_virginiana 0.0452 0.3167 -0.5991 0.0562 0.6353 1.0020
## week-Sylvilagus_floridanus 0.0607 0.3033 -0.5449 0.0693 0.6616 1.0108
## week-Meleagris_gallopavo -0.0994 0.3527 -0.8639 -0.0794 0.5393 1.0107
## week-Sciurus_carolinensis 0.5502 0.3411 -0.0360 0.5224 1.2857 1.0196
## I(week^2)-Canis_latrans -0.2011 0.1058 -0.4158 -0.1995 0.0005 1.0076
## I(week^2)-Procyon_lotor -0.1166 0.0870 -0.2855 -0.1171 0.0502 1.0031
## I(week^2)-Dasypus_novemcinctus -0.1513 0.1032 -0.3588 -0.1487 0.0428 1.0031
## I(week^2)-Lynx_rufus -0.2183 0.1506 -0.5371 -0.2137 0.0660 1.0028
## I(week^2)-Didelphis_virginiana -0.3629 0.2156 -0.8921 -0.3330 -0.0289 1.0569
## I(week^2)-Sylvilagus_floridanus -0.1728 0.1533 -0.4939 -0.1668 0.1228 1.0017
## I(week^2)-Meleagris_gallopavo -0.3932 0.2602 -0.9925 -0.3572 0.0025 1.0283
## I(week^2)-Sciurus_carolinensis -0.1984 0.1425 -0.5018 -0.1925 0.0693 1.0036
## ESS
## (Intercept)-Canis_latrans 1034
## (Intercept)-Procyon_lotor 1546
## (Intercept)-Dasypus_novemcinctus 2182
## (Intercept)-Lynx_rufus 485
## (Intercept)-Didelphis_virginiana 1337
## (Intercept)-Sylvilagus_floridanus 437
## (Intercept)-Meleagris_gallopavo 669
## (Intercept)-Sciurus_carolinensis 1390
## week-Canis_latrans 1065
## week-Procyon_lotor 1672
## week-Dasypus_novemcinctus 1882
## week-Lynx_rufus 881
## week-Didelphis_virginiana 1383
## week-Sylvilagus_floridanus 1077
## week-Meleagris_gallopavo 733
## week-Sciurus_carolinensis 1181
## I(week^2)-Canis_latrans 1131
## I(week^2)-Procyon_lotor 1583
## I(week^2)-Dasypus_novemcinctus 1648
## I(week^2)-Lynx_rufus 664
## I(week^2)-Didelphis_virginiana 450
## I(week^2)-Sylvilagus_floridanus 831
## I(week^2)-Meleagris_gallopavo 226
## I(week^2)-Sciurus_carolinensis 1515
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T10 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7887
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6612 0.4303 -1.4977 -0.6592 0.2273 1.0093 1222
## Tree_Density -0.7489 0.4511 -1.8119 -0.7030 0.0249 1.0023 1044
## Avg_Canopy_Cover 0.9826 0.3930 0.2620 0.9585 1.8436 1.0042 1280
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1009 1.3579 0.0867 0.7654 4.2197 1.0879 1249
## Tree_Density 0.9880 1.6558 0.0535 0.4682 5.0167 1.0128 472
## Avg_Canopy_Cover 0.7671 0.9660 0.0695 0.4731 3.2189 1.0184 740
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6073 0.5908 0.0618 0.4237 2.1634 1.0249 237
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4436 0.2801 -2.9992 -2.4507 -1.8379 0.9997 2579
## week 0.1758 0.2066 -0.2390 0.1755 0.5798 1.0023 1177
## I(week^2) -0.2246 0.1298 -0.5005 -0.2182 0.0146 1.0152 669
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6031 0.5079 0.1567 0.4671 1.8201 1.0103 1765
## week 0.2037 0.2100 0.0319 0.1405 0.7334 1.0373 931
## I(week^2) 0.0893 0.1187 0.0190 0.0598 0.3334 1.1208 235
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans -0.0590 0.5530 -1.0902 -0.0840 1.0724
## (Intercept)-Procyon_lotor 0.2504 0.6269 -0.9871 0.2632 1.4440
## (Intercept)-Dasypus_novemcinctus -0.9665 0.5414 -2.1352 -0.9389 0.0428
## (Intercept)-Lynx_rufus -0.2789 0.7450 -1.6013 -0.3335 1.3675
## (Intercept)-Didelphis_virginiana -1.5233 0.6508 -2.9411 -1.4825 -0.3636
## (Intercept)-Sylvilagus_floridanus -0.7667 0.6022 -1.9453 -0.7634 0.4077
## (Intercept)-Meleagris_gallopavo -0.6301 0.6126 -1.8481 -0.6368 0.6087
## (Intercept)-Sciurus_carolinensis -1.5577 0.6675 -2.9581 -1.5104 -0.3830
## Tree_Density-Canis_latrans -0.8843 0.5554 -2.1209 -0.8197 0.0060
## Tree_Density-Procyon_lotor -0.4715 0.4068 -1.2925 -0.4667 0.3209
## Tree_Density-Dasypus_novemcinctus -1.3985 0.9262 -3.7585 -1.1740 -0.1909
## Tree_Density-Lynx_rufus 0.2330 0.7204 -0.8742 0.1303 2.0100
## Tree_Density-Didelphis_virginiana -0.9928 0.7576 -2.9097 -0.8697 0.1277
## Tree_Density-Sylvilagus_floridanus -1.0769 0.7930 -2.9992 -0.9413 0.1441
## Tree_Density-Meleagris_gallopavo -0.9147 0.7362 -2.6582 -0.8238 0.2431
## Tree_Density-Sciurus_carolinensis -0.8670 0.7401 -2.6429 -0.7634 0.2707
## Avg_Canopy_Cover-Canis_latrans 0.0333 0.4575 -0.8804 0.0399 0.9096
## Avg_Canopy_Cover-Procyon_lotor 0.9754 0.4471 0.1652 0.9509 1.9373
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0079 0.4500 0.1983 0.9781 1.9511
## Avg_Canopy_Cover-Lynx_rufus 0.6196 0.6086 -0.5555 0.6030 1.9068
## Avg_Canopy_Cover-Didelphis_virginiana 1.2221 0.4877 0.3731 1.1850 2.3282
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.7145 0.8212 0.5664 1.5533 3.9134
## Avg_Canopy_Cover-Meleagris_gallopavo 1.3897 0.6823 0.2710 1.3120 2.9759
## Avg_Canopy_Cover-Sciurus_carolinensis 1.1801 0.4873 0.3232 1.1457 2.2376
## Rhat ESS
## (Intercept)-Canis_latrans 1.0057 1015
## (Intercept)-Procyon_lotor 1.0033 610
## (Intercept)-Dasypus_novemcinctus 1.0005 1107
## (Intercept)-Lynx_rufus 1.0053 459
## (Intercept)-Didelphis_virginiana 1.0178 758
## (Intercept)-Sylvilagus_floridanus 1.0229 946
## (Intercept)-Meleagris_gallopavo 1.0055 1100
## (Intercept)-Sciurus_carolinensis 1.0033 842
## Tree_Density-Canis_latrans 1.0015 1202
## Tree_Density-Procyon_lotor 1.0039 2187
## Tree_Density-Dasypus_novemcinctus 1.0061 499
## Tree_Density-Lynx_rufus 1.0006 426
## Tree_Density-Didelphis_virginiana 1.0161 726
## Tree_Density-Sylvilagus_floridanus 1.0025 631
## Tree_Density-Meleagris_gallopavo 1.0007 859
## Tree_Density-Sciurus_carolinensis 1.0013 959
## Avg_Canopy_Cover-Canis_latrans 1.0029 1249
## Avg_Canopy_Cover-Procyon_lotor 1.0071 2091
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0020 1850
## Avg_Canopy_Cover-Lynx_rufus 1.0191 971
## Avg_Canopy_Cover-Didelphis_virginiana 1.0011 1136
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0141 565
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0163 676
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0085 1256
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4624 0.1865 -2.8292 -2.4583 -2.1094 1.0002
## (Intercept)-Procyon_lotor -2.1803 0.1465 -2.4812 -2.1798 -1.9016 1.0019
## (Intercept)-Dasypus_novemcinctus -1.4945 0.1576 -1.8073 -1.4926 -1.2042 1.0000
## (Intercept)-Lynx_rufus -3.2852 0.3196 -3.9299 -3.2809 -2.6888 1.0096
## (Intercept)-Didelphis_virginiana -2.1657 0.2607 -2.7003 -2.1592 -1.6776 1.0005
## (Intercept)-Sylvilagus_floridanus -2.9631 0.2715 -3.5118 -2.9520 -2.4535 1.0059
## (Intercept)-Meleagris_gallopavo -3.1484 0.3076 -3.7875 -3.1374 -2.5909 1.0266
## (Intercept)-Sciurus_carolinensis -2.3336 0.2734 -2.8778 -2.3213 -1.8201 1.0078
## week-Canis_latrans 0.4440 0.2500 -0.0249 0.4352 0.9655 1.0008
## week-Procyon_lotor 0.1518 0.1985 -0.2186 0.1512 0.5476 1.0050
## week-Dasypus_novemcinctus 0.0567 0.2085 -0.3589 0.0621 0.4666 1.0004
## week-Lynx_rufus 0.2751 0.2905 -0.3072 0.2764 0.8570 1.0002
## week-Didelphis_virginiana 0.0296 0.3185 -0.6261 0.0421 0.6210 1.0110
## week-Sylvilagus_floridanus 0.0395 0.2979 -0.5509 0.0428 0.6151 1.0029
## week-Meleagris_gallopavo -0.1190 0.3597 -0.9266 -0.0847 0.4923 1.0169
## week-Sciurus_carolinensis 0.5323 0.3386 -0.0867 0.5141 1.2606 1.0157
## I(week^2)-Canis_latrans -0.1916 0.1068 -0.4167 -0.1904 0.0088 1.0038
## I(week^2)-Procyon_lotor -0.1088 0.0885 -0.2852 -0.1072 0.0597 1.0088
## I(week^2)-Dasypus_novemcinctus -0.1521 0.1030 -0.3572 -0.1508 0.0515 1.0008
## I(week^2)-Lynx_rufus -0.2107 0.1499 -0.5209 -0.2022 0.0672 1.0259
## I(week^2)-Didelphis_virginiana -0.3656 0.2267 -0.8947 -0.3334 -0.0172 1.0184
## I(week^2)-Sylvilagus_floridanus -0.1591 0.1487 -0.4744 -0.1521 0.1161 1.0082
## I(week^2)-Meleagris_gallopavo -0.4150 0.2937 -1.1447 -0.3614 -0.0083 1.0576
## I(week^2)-Sciurus_carolinensis -0.1940 0.1432 -0.5054 -0.1835 0.0675 1.0087
## ESS
## (Intercept)-Canis_latrans 1221
## (Intercept)-Procyon_lotor 1642
## (Intercept)-Dasypus_novemcinctus 2579
## (Intercept)-Lynx_rufus 430
## (Intercept)-Didelphis_virginiana 1642
## (Intercept)-Sylvilagus_floridanus 697
## (Intercept)-Meleagris_gallopavo 621
## (Intercept)-Sciurus_carolinensis 1291
## week-Canis_latrans 1087
## week-Procyon_lotor 1715
## week-Dasypus_novemcinctus 2265
## week-Lynx_rufus 1029
## week-Didelphis_virginiana 1204
## week-Sylvilagus_floridanus 1161
## week-Meleagris_gallopavo 559
## week-Sciurus_carolinensis 945
## I(week^2)-Canis_latrans 1104
## I(week^2)-Procyon_lotor 1555
## I(week^2)-Dasypus_novemcinctus 2075
## I(week^2)-Lynx_rufus 627
## I(week^2)-Didelphis_virginiana 434
## I(week^2)-Sylvilagus_floridanus 718
## I(week^2)-Meleagris_gallopavo 143
## I(week^2)-Sciurus_carolinensis 1365
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7315
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5234 0.3918 -1.3452 -0.5197 0.2496 1.0200 653
## Cogon_Patch_Size -0.0387 0.3779 -0.8156 -0.0313 0.7128 1.0072 1160
## Avg_Cogongrass_Cover 0.1600 0.3031 -0.4276 0.1535 0.7387 1.0056 1160
## total_shrub_cover -0.4564 0.3423 -1.2013 -0.4320 0.1711 1.0016 1359
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7528 0.9099 0.0632 0.5022 3.1399 1.0027 824
## Cogon_Patch_Size 0.7066 0.8856 0.0609 0.4352 3.0454 1.0412 808
## Avg_Cogongrass_Cover 0.3981 0.5109 0.0400 0.2361 1.7523 1.0020 1035
## total_shrub_cover 0.6190 0.9498 0.0522 0.3503 2.8227 1.0528 1180
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8125 0.761 0.0623 0.6085 2.8092 1.0114 225
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4404 0.2838 -3.0023 -2.4471 -1.8571 1.0059 2640
## week 0.1816 0.1983 -0.2083 0.1820 0.5659 1.0047 1193
## I(week^2) -0.2115 0.1148 -0.4430 -0.2085 0.0153 1.0095 1180
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5989 0.4894 0.1464 0.4650 1.8775 1.0088 2088
## week 0.1943 0.2084 0.0321 0.1341 0.7228 1.0046 1220
## I(week^2) 0.0750 0.0755 0.0189 0.0549 0.2538 1.0229 1569
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0208 0.5589 -1.0633 0.0034
## (Intercept)-Procyon_lotor 0.1818 0.6355 -1.0349 0.1646
## (Intercept)-Dasypus_novemcinctus -0.6842 0.4909 -1.6720 -0.6729
## (Intercept)-Lynx_rufus -0.3603 0.6342 -1.5525 -0.3943
## (Intercept)-Didelphis_virginiana -1.1097 0.5671 -2.2863 -1.0879
## (Intercept)-Sylvilagus_floridanus -0.5176 0.5825 -1.6118 -0.5261
## (Intercept)-Meleagris_gallopavo -0.6705 0.5823 -1.8121 -0.6590
## (Intercept)-Sciurus_carolinensis -1.1672 0.5893 -2.3991 -1.1404
## Cogon_Patch_Size-Canis_latrans 0.6588 0.6200 -0.2741 0.5671
## Cogon_Patch_Size-Procyon_lotor -0.1465 0.4365 -1.0068 -0.1388
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0808 0.3996 -0.9159 -0.0752
## Cogon_Patch_Size-Lynx_rufus -0.1292 0.6924 -1.4743 -0.1704
## Cogon_Patch_Size-Didelphis_virginiana 0.6042 0.4838 -0.2047 0.5654
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6506 0.7322 -2.4904 -0.5421
## Cogon_Patch_Size-Meleagris_gallopavo -0.0187 0.5694 -1.1828 -0.0212
## Cogon_Patch_Size-Sciurus_carolinensis -0.5424 0.5909 -1.9853 -0.4658
## Avg_Cogongrass_Cover-Canis_latrans 0.2361 0.3863 -0.4907 0.2236
## Avg_Cogongrass_Cover-Procyon_lotor 0.1733 0.4035 -0.6011 0.1531
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3434 0.3762 -0.3699 0.3297
## Avg_Cogongrass_Cover-Lynx_rufus 0.5034 0.4881 -0.3289 0.4447
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1925 0.4013 -0.5931 0.1899
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1894 0.4669 -1.2313 -0.1502
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.3842 0.5846 -1.7413 -0.3151
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4079 0.3993 -0.3137 0.3882
## total_shrub_cover-Canis_latrans 0.0261 0.4527 -0.7622 -0.0047
## total_shrub_cover-Procyon_lotor -0.8391 0.4654 -1.9034 -0.7723
## total_shrub_cover-Dasypus_novemcinctus -0.1136 0.3725 -0.8303 -0.1285
## total_shrub_cover-Lynx_rufus -0.7911 0.6121 -2.2141 -0.7163
## total_shrub_cover-Didelphis_virginiana -0.3615 0.4165 -1.2132 -0.3574
## total_shrub_cover-Sylvilagus_floridanus -0.3468 0.4964 -1.4271 -0.3385
## total_shrub_cover-Meleagris_gallopavo -1.1908 0.6845 -2.7910 -1.0807
## total_shrub_cover-Sciurus_carolinensis -0.1444 0.4299 -0.9971 -0.1567
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1896 1.0093 632
## (Intercept)-Procyon_lotor 1.4623 1.0027 407
## (Intercept)-Dasypus_novemcinctus 0.2753 1.0094 1089
## (Intercept)-Lynx_rufus 0.9788 1.0157 490
## (Intercept)-Didelphis_virginiana -0.0429 1.0093 939
## (Intercept)-Sylvilagus_floridanus 0.6636 1.0183 800
## (Intercept)-Meleagris_gallopavo 0.4656 1.0115 833
## (Intercept)-Sciurus_carolinensis -0.0791 1.0067 801
## Cogon_Patch_Size-Canis_latrans 2.1309 1.0023 912
## Cogon_Patch_Size-Procyon_lotor 0.7273 1.0009 1553
## Cogon_Patch_Size-Dasypus_novemcinctus 0.6652 1.0040 1680
## Cogon_Patch_Size-Lynx_rufus 1.3595 1.0184 559
## Cogon_Patch_Size-Didelphis_virginiana 1.6939 1.0096 1106
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4680 1.0075 682
## Cogon_Patch_Size-Meleagris_gallopavo 1.1116 1.0182 1388
## Cogon_Patch_Size-Sciurus_carolinensis 0.3872 1.0009 846
## Avg_Cogongrass_Cover-Canis_latrans 1.0442 1.0041 1488
## Avg_Cogongrass_Cover-Procyon_lotor 1.0050 1.0024 1884
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0988 1.0048 1704
## Avg_Cogongrass_Cover-Lynx_rufus 1.6104 1.0127 1175
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9996 1.0013 1566
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6753 0.9998 1454
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5706 1.0040 943
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.2426 1.0061 1335
## total_shrub_cover-Canis_latrans 1.0173 1.0136 1384
## total_shrub_cover-Procyon_lotor -0.0907 1.0025 1186
## total_shrub_cover-Dasypus_novemcinctus 0.6536 1.0037 2197
## total_shrub_cover-Lynx_rufus 0.1951 1.0157 688
## total_shrub_cover-Didelphis_virginiana 0.4308 1.0057 2254
## total_shrub_cover-Sylvilagus_floridanus 0.5957 1.0035 1194
## total_shrub_cover-Meleagris_gallopavo -0.1850 1.0247 625
## total_shrub_cover-Sciurus_carolinensis 0.6970 1.0028 1781
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4427 0.1815 -2.8200 -2.4399 -2.1045 1.0088
## (Intercept)-Procyon_lotor -2.1874 0.1506 -2.4961 -2.1810 -1.9086 1.0272
## (Intercept)-Dasypus_novemcinctus -1.4971 0.1596 -1.8236 -1.4943 -1.1973 1.0056
## (Intercept)-Lynx_rufus -3.2622 0.3077 -3.8876 -3.2509 -2.6818 1.0157
## (Intercept)-Didelphis_virginiana -2.1710 0.2605 -2.6973 -2.1643 -1.6823 1.0023
## (Intercept)-Sylvilagus_floridanus -3.0519 0.3067 -3.6805 -3.0419 -2.4897 1.0442
## (Intercept)-Meleagris_gallopavo -3.1116 0.3247 -3.7777 -3.0959 -2.5148 1.0259
## (Intercept)-Sciurus_carolinensis -2.3598 0.2798 -2.9326 -2.3483 -1.8560 1.0000
## week-Canis_latrans 0.4407 0.2426 0.0046 0.4295 0.9467 1.0025
## week-Procyon_lotor 0.1565 0.1971 -0.2314 0.1586 0.5447 1.0044
## week-Dasypus_novemcinctus 0.0597 0.2137 -0.3632 0.0619 0.4712 1.0002
## week-Lynx_rufus 0.2718 0.3016 -0.2986 0.2604 0.8911 1.0085
## week-Didelphis_virginiana 0.0471 0.3094 -0.5998 0.0564 0.6444 1.0015
## week-Sylvilagus_floridanus 0.0483 0.2842 -0.5389 0.0539 0.5895 1.0111
## week-Meleagris_gallopavo -0.0928 0.3478 -0.8533 -0.0694 0.5282 1.0107
## week-Sciurus_carolinensis 0.5139 0.3268 -0.0710 0.4949 1.1955 1.0053
## I(week^2)-Canis_latrans -0.1911 0.1011 -0.3936 -0.1886 -0.0029 1.0034
## I(week^2)-Procyon_lotor -0.1106 0.0888 -0.2839 -0.1109 0.0653 1.0005
## I(week^2)-Dasypus_novemcinctus -0.1505 0.1010 -0.3523 -0.1491 0.0407 1.0013
## I(week^2)-Lynx_rufus -0.2026 0.1455 -0.4992 -0.1981 0.0657 1.0107
## I(week^2)-Didelphis_virginiana -0.3477 0.1907 -0.7882 -0.3291 -0.0235 1.0248
## I(week^2)-Sylvilagus_floridanus -0.1615 0.1422 -0.4551 -0.1559 0.1084 1.0030
## I(week^2)-Meleagris_gallopavo -0.3531 0.2200 -0.8630 -0.3295 0.0155 1.0150
## I(week^2)-Sciurus_carolinensis -0.1844 0.1348 -0.4567 -0.1807 0.0700 1.0019
## ESS
## (Intercept)-Canis_latrans 1189
## (Intercept)-Procyon_lotor 1413
## (Intercept)-Dasypus_novemcinctus 2447
## (Intercept)-Lynx_rufus 509
## (Intercept)-Didelphis_virginiana 1432
## (Intercept)-Sylvilagus_floridanus 565
## (Intercept)-Meleagris_gallopavo 611
## (Intercept)-Sciurus_carolinensis 1278
## week-Canis_latrans 1143
## week-Procyon_lotor 1357
## week-Dasypus_novemcinctus 2117
## week-Lynx_rufus 1056
## week-Didelphis_virginiana 1061
## week-Sylvilagus_floridanus 1141
## week-Meleagris_gallopavo 764
## week-Sciurus_carolinensis 1243
## I(week^2)-Canis_latrans 1255
## I(week^2)-Procyon_lotor 1627
## I(week^2)-Dasypus_novemcinctus 1755
## I(week^2)-Lynx_rufus 813
## I(week^2)-Didelphis_virginiana 746
## I(week^2)-Sylvilagus_floridanus 831
## I(week^2)-Meleagris_gallopavo 380
## I(week^2)-Sciurus_carolinensis 1699
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.723
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5437 0.3714 -1.3041 -0.5346 0.1792 1.0022 708
## Veg_shannon_index 0.3729 0.2637 -0.1274 0.3638 0.9195 1.0175 1044
## Avg_Cogongrass_Cover 0.3396 0.2680 -0.1852 0.3423 0.8704 1.0357 1193
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6233 0.7856 0.0565 0.3959 2.4957 1.0076 915
## Veg_shannon_index 0.2672 0.3283 0.0358 0.1678 1.0449 1.0247 1444
## Avg_Cogongrass_Cover 0.3175 0.3854 0.0382 0.2001 1.3819 1.0021 1212
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7466 0.687 0.0691 0.5641 2.465 1.0195 253
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4414 0.2816 -2.9813 -2.4490 -1.8490 1.0024 2370
## week 0.1761 0.2103 -0.2393 0.1774 0.5762 1.0016 1141
## I(week^2) -0.2289 0.1325 -0.4903 -0.2216 0.0075 1.0397 562
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6100 0.6271 0.1478 0.4552 1.9529 1.0313 1833
## week 0.2151 0.2667 0.0337 0.1431 0.8498 1.0086 1059
## I(week^2) 0.0968 0.1313 0.0212 0.0623 0.3932 1.2155 234
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.1046 0.5356 -1.1400 -0.1163
## (Intercept)-Procyon_lotor 0.0510 0.5786 -1.0506 0.0308
## (Intercept)-Dasypus_novemcinctus -0.6916 0.4774 -1.6859 -0.6693
## (Intercept)-Lynx_rufus -0.4242 0.5684 -1.5267 -0.4461
## (Intercept)-Didelphis_virginiana -1.1121 0.5589 -2.3015 -1.0818
## (Intercept)-Sylvilagus_floridanus -0.4956 0.5605 -1.5050 -0.5291
## (Intercept)-Meleagris_gallopavo -0.5873 0.5618 -1.7092 -0.6011
## (Intercept)-Sciurus_carolinensis -1.1088 0.5378 -2.2553 -1.0685
## Veg_shannon_index-Canis_latrans 0.6366 0.3641 -0.0374 0.6139
## Veg_shannon_index-Procyon_lotor 0.4901 0.3764 -0.2220 0.4806
## Veg_shannon_index-Dasypus_novemcinctus 0.2251 0.3367 -0.4563 0.2354
## Veg_shannon_index-Lynx_rufus 0.2191 0.4395 -0.7135 0.2332
## Veg_shannon_index-Didelphis_virginiana 0.4914 0.3679 -0.1888 0.4759
## Veg_shannon_index-Sylvilagus_floridanus 0.4718 0.4136 -0.3069 0.4473
## Veg_shannon_index-Meleagris_gallopavo 0.4814 0.4214 -0.2720 0.4575
## Veg_shannon_index-Sciurus_carolinensis 0.0372 0.3826 -0.7996 0.0594
## Avg_Cogongrass_Cover-Canis_latrans 0.5397 0.3744 -0.1268 0.5207
## Avg_Cogongrass_Cover-Procyon_lotor 0.4235 0.3740 -0.2564 0.4020
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4420 0.3377 -0.2140 0.4375
## Avg_Cogongrass_Cover-Lynx_rufus 0.5965 0.4020 -0.1072 0.5610
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4681 0.3593 -0.2252 0.4584
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0511 0.4456 -1.0294 -0.0097
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0892 0.5104 -1.2421 -0.0429
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4270 0.3609 -0.2729 0.4230
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9792 1.0048 809
## (Intercept)-Procyon_lotor 1.1800 1.0003 552
## (Intercept)-Dasypus_novemcinctus 0.2006 1.0021 1308
## (Intercept)-Lynx_rufus 0.7608 1.0013 693
## (Intercept)-Didelphis_virginiana -0.0974 1.0103 914
## (Intercept)-Sylvilagus_floridanus 0.6520 1.0076 700
## (Intercept)-Meleagris_gallopavo 0.5104 1.0043 760
## (Intercept)-Sciurus_carolinensis -0.1586 1.0021 1092
## Veg_shannon_index-Canis_latrans 1.4073 1.0047 1604
## Veg_shannon_index-Procyon_lotor 1.2846 1.0084 1233
## Veg_shannon_index-Dasypus_novemcinctus 0.8710 1.0053 1995
## Veg_shannon_index-Lynx_rufus 1.0612 1.0193 1161
## Veg_shannon_index-Didelphis_virginiana 1.2725 1.0033 1741
## Veg_shannon_index-Sylvilagus_floridanus 1.3271 1.0057 1618
## Veg_shannon_index-Meleagris_gallopavo 1.3852 1.0146 1472
## Veg_shannon_index-Sciurus_carolinensis 0.7289 1.0074 1575
## Avg_Cogongrass_Cover-Canis_latrans 1.3799 1.0032 1775
## Avg_Cogongrass_Cover-Procyon_lotor 1.2290 1.0100 1588
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1376 1.0091 2084
## Avg_Cogongrass_Cover-Lynx_rufus 1.4741 1.0091 1618
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1596 1.0249 2182
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7203 1.0371 1231
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.8075 1.0360 870
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1475 1.0186 2228
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4522 0.1776 -2.8143 -2.4491 -2.1157 1.0054
## (Intercept)-Procyon_lotor -2.1852 0.1484 -2.4872 -2.1781 -1.9027 1.0049
## (Intercept)-Dasypus_novemcinctus -1.4974 0.1600 -1.8169 -1.4965 -1.1916 1.0048
## (Intercept)-Lynx_rufus -3.2363 0.3126 -3.8470 -3.2300 -2.6468 1.0048
## (Intercept)-Didelphis_virginiana -2.1699 0.2652 -2.7108 -2.1631 -1.6805 1.0051
## (Intercept)-Sylvilagus_floridanus -3.0117 0.3062 -3.6735 -2.9962 -2.4495 1.0420
## (Intercept)-Meleagris_gallopavo -3.1423 0.3343 -3.8386 -3.1276 -2.5297 1.0059
## (Intercept)-Sciurus_carolinensis -2.3425 0.2885 -2.9244 -2.3296 -1.8171 1.0006
## week-Canis_latrans 0.4564 0.2438 0.0156 0.4432 0.9728 1.0049
## week-Procyon_lotor 0.1585 0.1965 -0.2233 0.1572 0.5385 1.0045
## week-Dasypus_novemcinctus 0.0570 0.2141 -0.3762 0.0648 0.4521 1.0006
## week-Lynx_rufus 0.2732 0.3006 -0.3093 0.2628 0.8940 0.9995
## week-Didelphis_virginiana 0.0301 0.3213 -0.6396 0.0441 0.6323 1.0037
## week-Sylvilagus_floridanus 0.0446 0.2982 -0.5545 0.0505 0.5968 1.0005
## week-Meleagris_gallopavo -0.1264 0.3626 -0.9045 -0.1093 0.5023 1.0239
## week-Sciurus_carolinensis 0.5539 0.3513 -0.0380 0.5266 1.3290 1.0000
## I(week^2)-Canis_latrans -0.1971 0.1036 -0.4130 -0.1916 -0.0055 1.0040
## I(week^2)-Procyon_lotor -0.1089 0.0885 -0.2852 -0.1060 0.0577 1.0007
## I(week^2)-Dasypus_novemcinctus -0.1483 0.1029 -0.3633 -0.1453 0.0508 1.0079
## I(week^2)-Lynx_rufus -0.2057 0.1489 -0.5162 -0.1967 0.0669 1.0009
## I(week^2)-Didelphis_virginiana -0.3868 0.2399 -0.9514 -0.3547 -0.0253 1.0212
## I(week^2)-Sylvilagus_floridanus -0.1770 0.1490 -0.4930 -0.1713 0.1011 1.0017
## I(week^2)-Meleagris_gallopavo -0.4300 0.3092 -1.1623 -0.3827 0.0033 1.1087
## I(week^2)-Sciurus_carolinensis -0.1997 0.1443 -0.5097 -0.1950 0.0707 1.0025
## ESS
## (Intercept)-Canis_latrans 1353
## (Intercept)-Procyon_lotor 1716
## (Intercept)-Dasypus_novemcinctus 2292
## (Intercept)-Lynx_rufus 487
## (Intercept)-Didelphis_virginiana 1657
## (Intercept)-Sylvilagus_floridanus 526
## (Intercept)-Meleagris_gallopavo 563
## (Intercept)-Sciurus_carolinensis 1312
## week-Canis_latrans 1237
## week-Procyon_lotor 1858
## week-Dasypus_novemcinctus 1863
## week-Lynx_rufus 1132
## week-Didelphis_virginiana 1121
## week-Sylvilagus_floridanus 1225
## week-Meleagris_gallopavo 580
## week-Sciurus_carolinensis 985
## I(week^2)-Canis_latrans 1371
## I(week^2)-Procyon_lotor 1676
## I(week^2)-Dasypus_novemcinctus 1519
## I(week^2)-Lynx_rufus 841
## I(week^2)-Didelphis_virginiana 345
## I(week^2)-Sylvilagus_floridanus 671
## I(week^2)-Meleagris_gallopavo 133
## I(week^2)-Sciurus_carolinensis 1472
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.724
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5215 0.3454 -1.2134 -0.5178 0.1546 1.0265 905
## Avg_Cogongrass_Cover 0.2105 0.2573 -0.3247 0.2107 0.7066 1.0112 1398
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5545 0.6246 0.053 0.3851 2.3198 1.0085 1403
## Avg_Cogongrass_Cover 0.3008 0.3608 0.036 0.1905 1.3452 1.0156 1288
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6238 0.5389 0.0587 0.4663 2.0368 1.0325 246
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4319 0.2892 -2.9625 -2.4429 -1.8473 0.9998 2393
## week 0.1843 0.2052 -0.2248 0.1872 0.5710 1.0152 1394
## I(week^2) -0.2159 0.1161 -0.4570 -0.2117 0.0090 1.0241 1085
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5974 0.6226 0.1452 0.4528 1.8804 1.0774 2164
## week 0.2007 0.2082 0.0335 0.1373 0.7473 1.0033 1331
## I(week^2) 0.0768 0.0681 0.0186 0.0576 0.2598 1.0098 922
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0659 0.5001 -1.0066 -0.0770
## (Intercept)-Procyon_lotor 0.0939 0.5225 -0.9106 0.0812
## (Intercept)-Dasypus_novemcinctus -0.6491 0.4361 -1.5406 -0.6421
## (Intercept)-Lynx_rufus -0.4405 0.5337 -1.5286 -0.4590
## (Intercept)-Didelphis_virginiana -1.0308 0.5016 -2.1117 -0.9953
## (Intercept)-Sylvilagus_floridanus -0.5103 0.4757 -1.4710 -0.5230
## (Intercept)-Meleagris_gallopavo -0.5624 0.5211 -1.6082 -0.5657
## (Intercept)-Sciurus_carolinensis -1.0700 0.5223 -2.1651 -1.0367
## Avg_Cogongrass_Cover-Canis_latrans 0.3555 0.3438 -0.2658 0.3325
## Avg_Cogongrass_Cover-Procyon_lotor 0.2545 0.3516 -0.3831 0.2380
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3402 0.3214 -0.2614 0.3351
## Avg_Cogongrass_Cover-Lynx_rufus 0.4631 0.3865 -0.2382 0.4420
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3477 0.3509 -0.3333 0.3298
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1827 0.4207 -1.1315 -0.1428
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2122 0.4615 -1.2925 -0.1669
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3413 0.3467 -0.3235 0.3250
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.9797 1.0094 953
## (Intercept)-Procyon_lotor 1.1053 1.0063 700
## (Intercept)-Dasypus_novemcinctus 0.1753 1.0071 1446
## (Intercept)-Lynx_rufus 0.6009 1.0063 716
## (Intercept)-Didelphis_virginiana -0.1092 1.0137 1141
## (Intercept)-Sylvilagus_floridanus 0.4449 1.0059 1274
## (Intercept)-Meleagris_gallopavo 0.5194 1.0064 719
## (Intercept)-Sciurus_carolinensis -0.1333 1.0153 1038
## Avg_Cogongrass_Cover-Canis_latrans 1.1320 1.0008 2191
## Avg_Cogongrass_Cover-Procyon_lotor 0.9919 1.0024 2118
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0158 1.0074 2145
## Avg_Cogongrass_Cover-Lynx_rufus 1.3220 1.0063 1720
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.0788 1.0058 1568
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5473 1.0142 1261
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5695 1.0290 1030
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0652 1.0045 2153
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4453 0.1780 -2.8134 -2.4364 -2.1106 1.0015
## (Intercept)-Procyon_lotor -2.1751 0.1502 -2.4854 -2.1775 -1.8906 1.0040
## (Intercept)-Dasypus_novemcinctus -1.4939 0.1575 -1.8108 -1.4898 -1.1986 1.0022
## (Intercept)-Lynx_rufus -3.2108 0.2972 -3.8285 -3.2004 -2.6597 1.0023
## (Intercept)-Didelphis_virginiana -2.1781 0.2682 -2.7229 -2.1712 -1.6654 1.0024
## (Intercept)-Sylvilagus_floridanus -3.0038 0.2952 -3.6346 -2.9848 -2.4745 1.0088
## (Intercept)-Meleagris_gallopavo -3.1331 0.3251 -3.7952 -3.1293 -2.5280 1.0040
## (Intercept)-Sciurus_carolinensis -2.3397 0.2817 -2.8976 -2.3328 -1.8147 1.0026
## week-Canis_latrans 0.4348 0.2396 -0.0234 0.4261 0.9227 1.0044
## week-Procyon_lotor 0.1622 0.1943 -0.2163 0.1577 0.5493 1.0010
## week-Dasypus_novemcinctus 0.0592 0.2096 -0.3474 0.0566 0.4709 0.9998
## week-Lynx_rufus 0.2842 0.3031 -0.3222 0.2847 0.8973 1.0179
## week-Didelphis_virginiana 0.0324 0.3163 -0.6186 0.0437 0.6233 1.0021
## week-Sylvilagus_floridanus 0.0554 0.2933 -0.5355 0.0684 0.6086 1.0010
## week-Meleagris_gallopavo -0.0884 0.3461 -0.8869 -0.0561 0.5211 1.0075
## week-Sciurus_carolinensis 0.5294 0.3319 -0.0472 0.5072 1.2576 1.0186
## I(week^2)-Canis_latrans -0.1895 0.1035 -0.3919 -0.1871 0.0094 1.0047
## I(week^2)-Procyon_lotor -0.1134 0.0861 -0.2856 -0.1118 0.0500 1.0018
## I(week^2)-Dasypus_novemcinctus -0.1518 0.0976 -0.3529 -0.1486 0.0356 1.0036
## I(week^2)-Lynx_rufus -0.2012 0.1447 -0.4948 -0.1990 0.0696 1.0272
## I(week^2)-Didelphis_virginiana -0.3597 0.2126 -0.8350 -0.3341 -0.0117 1.0431
## I(week^2)-Sylvilagus_floridanus -0.1611 0.1512 -0.4764 -0.1567 0.1172 1.0013
## I(week^2)-Meleagris_gallopavo -0.3586 0.2223 -0.8842 -0.3304 0.0095 1.0195
## I(week^2)-Sciurus_carolinensis -0.1892 0.1396 -0.4721 -0.1872 0.0733 1.0099
## ESS
## (Intercept)-Canis_latrans 1274
## (Intercept)-Procyon_lotor 1575
## (Intercept)-Dasypus_novemcinctus 2298
## (Intercept)-Lynx_rufus 581
## (Intercept)-Didelphis_virginiana 1342
## (Intercept)-Sylvilagus_floridanus 669
## (Intercept)-Meleagris_gallopavo 496
## (Intercept)-Sciurus_carolinensis 1621
## week-Canis_latrans 1341
## week-Procyon_lotor 1680
## week-Dasypus_novemcinctus 2230
## week-Lynx_rufus 960
## week-Didelphis_virginiana 1256
## week-Sylvilagus_floridanus 1191
## week-Meleagris_gallopavo 814
## week-Sciurus_carolinensis 1285
## I(week^2)-Canis_latrans 1377
## I(week^2)-Procyon_lotor 1298
## I(week^2)-Dasypus_novemcinctus 1779
## I(week^2)-Lynx_rufus 813
## I(week^2)-Didelphis_virginiana 500
## I(week^2)-Sylvilagus_floridanus 773
## I(week^2)-Meleagris_gallopavo 307
## I(week^2)-Sciurus_carolinensis 1262
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.731
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1353 0.3976 -1.9534 -1.1342 -0.3364 1.0055 679
## Avg_Cogongrass_Cover -0.5611 0.3595 -1.2932 -0.5506 0.1303 1.0175 572
## I(Avg_Cogongrass_Cover^2) 0.7804 0.3790 0.1599 0.7425 1.6297 1.0382 434
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5807 0.6706 0.0583 0.3778 2.3180 1.0029 1194
## Avg_Cogongrass_Cover 0.3655 0.4656 0.0388 0.2137 1.6090 1.0082 1022
## I(Avg_Cogongrass_Cover^2) 0.6591 1.4043 0.0435 0.2788 3.7432 1.1010 298
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4758 0.4971 0.0506 0.3169 1.7091 1.0081 280
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4259 0.2771 -2.9563 -2.4371 -1.8389 0.9997 2122
## week 0.1898 0.2043 -0.2225 0.1869 0.6030 1.0189 1188
## I(week^2) -0.2192 0.1196 -0.4899 -0.2131 -0.0059 1.0029 957
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5587 0.4680 0.1407 0.4322 1.7252 1.0088 2057
## week 0.2029 0.2163 0.0336 0.1373 0.7636 1.0185 798
## I(week^2) 0.0772 0.0696 0.0192 0.0576 0.2632 1.0304 1065
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7967 0.5504 -1.8603 -0.7996
## (Intercept)-Procyon_lotor -0.5896 0.5669 -1.6603 -0.6187
## (Intercept)-Dasypus_novemcinctus -1.2640 0.4962 -2.3174 -1.2473
## (Intercept)-Lynx_rufus -1.2398 0.5653 -2.3886 -1.2351
## (Intercept)-Didelphis_virginiana -1.5721 0.5525 -2.7727 -1.5283
## (Intercept)-Sylvilagus_floridanus -1.1123 0.5413 -2.1868 -1.1139
## (Intercept)-Meleagris_gallopavo -0.9835 0.5704 -2.0999 -0.9869
## (Intercept)-Sciurus_carolinensis -1.7950 0.6019 -3.1183 -1.7400
## Avg_Cogongrass_Cover-Canis_latrans -0.4014 0.4914 -1.3047 -0.4228
## Avg_Cogongrass_Cover-Procyon_lotor -0.5446 0.4863 -1.5216 -0.5365
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4199 0.4519 -1.2477 -0.4289
## Avg_Cogongrass_Cover-Lynx_rufus -0.4879 0.5265 -1.5087 -0.4915
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.2852 0.5137 -1.2196 -0.3160
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.9842 0.5643 -2.2688 -0.9279
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.8397 0.5559 -2.0498 -0.8113
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.5672 0.4914 -1.5740 -0.5707
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3567 0.9174 0.2473 1.1024
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.2358 0.8216 0.2602 1.0350
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6300 0.3315 -0.0088 0.6305
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.1129 0.5317 0.2934 1.0341
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4294 0.3732 -0.3148 0.4341
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.6789 0.4707 -0.1186 0.6258
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.1855 0.5907 -1.0845 0.2115
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.7754 0.3651 0.1114 0.7650
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.3131 1.0150 781
## (Intercept)-Procyon_lotor 0.6024 1.0119 706
## (Intercept)-Dasypus_novemcinctus -0.3213 1.0079 1052
## (Intercept)-Lynx_rufus -0.1291 1.0055 809
## (Intercept)-Didelphis_virginiana -0.5760 1.0049 903
## (Intercept)-Sylvilagus_floridanus -0.0130 1.0006 899
## (Intercept)-Meleagris_gallopavo 0.1838 1.0141 813
## (Intercept)-Sciurus_carolinensis -0.7848 1.0055 795
## Avg_Cogongrass_Cover-Canis_latrans 0.6338 1.0095 745
## Avg_Cogongrass_Cover-Procyon_lotor 0.4691 1.0005 1177
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5095 1.0038 1025
## Avg_Cogongrass_Cover-Lynx_rufus 0.5649 1.0111 989
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8367 1.0031 861
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0272 1.0050 988
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.1807 1.0037 874
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3977 1.0061 864
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.7360 1.0813 218
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.6317 1.0443 253
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2911 1.0005 1232
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.3636 1.0204 391
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.1564 1.0028 858
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7425 1.0102 618
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.2658 1.0236 424
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.5327 1.0034 1118
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4636 0.1829 -2.8313 -2.4574 -2.1185 1.0139
## (Intercept)-Procyon_lotor -2.1877 0.1476 -2.4857 -2.1854 -1.9146 1.0033
## (Intercept)-Dasypus_novemcinctus -1.4978 0.1565 -1.8154 -1.4958 -1.2003 1.0039
## (Intercept)-Lynx_rufus -3.1385 0.2954 -3.7689 -3.1224 -2.6048 1.0164
## (Intercept)-Didelphis_virginiana -2.1876 0.2690 -2.7505 -2.1809 -1.6826 1.0007
## (Intercept)-Sylvilagus_floridanus -2.9956 0.2960 -3.6179 -2.9809 -2.4644 1.0183
## (Intercept)-Meleagris_gallopavo -3.1343 0.3301 -3.8128 -3.1182 -2.5365 1.0103
## (Intercept)-Sciurus_carolinensis -2.3423 0.2845 -2.9415 -2.3291 -1.8315 1.0023
## week-Canis_latrans 0.4677 0.2471 0.0180 0.4591 0.9943 1.0037
## week-Procyon_lotor 0.1629 0.1988 -0.2262 0.1646 0.5714 1.0163
## week-Dasypus_novemcinctus 0.0646 0.2053 -0.3454 0.0635 0.4691 1.0069
## week-Lynx_rufus 0.2967 0.3011 -0.2782 0.2896 0.9175 0.9999
## week-Didelphis_virginiana 0.0541 0.3099 -0.5740 0.0612 0.6487 1.0077
## week-Sylvilagus_floridanus 0.0545 0.2863 -0.5494 0.0611 0.5938 1.0081
## week-Meleagris_gallopavo -0.0931 0.3565 -0.8657 -0.0603 0.5336 1.0418
## week-Sciurus_carolinensis 0.5371 0.3351 -0.0616 0.5148 1.2596 1.0131
## I(week^2)-Canis_latrans -0.2007 0.1044 -0.4118 -0.1962 -0.0069 1.0027
## I(week^2)-Procyon_lotor -0.1124 0.0872 -0.2897 -0.1108 0.0560 1.0204
## I(week^2)-Dasypus_novemcinctus -0.1514 0.0985 -0.3490 -0.1498 0.0399 1.0019
## I(week^2)-Lynx_rufus -0.2011 0.1376 -0.4741 -0.1962 0.0572 1.0007
## I(week^2)-Didelphis_virginiana -0.3551 0.2096 -0.8493 -0.3326 -0.0060 1.0142
## I(week^2)-Sylvilagus_floridanus -0.1648 0.1434 -0.4599 -0.1581 0.1064 1.0048
## I(week^2)-Meleagris_gallopavo -0.3697 0.2333 -0.9178 -0.3390 -0.0001 1.0858
## I(week^2)-Sciurus_carolinensis -0.1963 0.1389 -0.4881 -0.1922 0.0659 1.0062
## ESS
## (Intercept)-Canis_latrans 1309
## (Intercept)-Procyon_lotor 1269
## (Intercept)-Dasypus_novemcinctus 2671
## (Intercept)-Lynx_rufus 596
## (Intercept)-Didelphis_virginiana 1419
## (Intercept)-Sylvilagus_floridanus 621
## (Intercept)-Meleagris_gallopavo 522
## (Intercept)-Sciurus_carolinensis 1245
## week-Canis_latrans 1270
## week-Procyon_lotor 1774
## week-Dasypus_novemcinctus 2312
## week-Lynx_rufus 1067
## week-Didelphis_virginiana 1016
## week-Sylvilagus_floridanus 1273
## week-Meleagris_gallopavo 536
## week-Sciurus_carolinensis 1180
## I(week^2)-Canis_latrans 1432
## I(week^2)-Procyon_lotor 1662
## I(week^2)-Dasypus_novemcinctus 1931
## I(week^2)-Lynx_rufus 992
## I(week^2)-Didelphis_virginiana 542
## I(week^2)-Sylvilagus_floridanus 909
## I(week^2)-Meleagris_gallopavo 237
## I(week^2)-Sciurus_carolinensis 1553
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.786
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0148 0.7335 -3.5631 -1.9953 -0.6432 1.0177 322
## Cogon_Patch_Size -0.0174 0.6647 -1.3445 -0.0204 1.3435 1.0009 641
## Veg_shannon_index 0.8884 0.4411 0.0527 0.8833 1.8063 1.0263 515
## total_shrub_cover -0.7159 0.5809 -1.9720 -0.6951 0.3806 1.0079 1033
## Avg_Cogongrass_Cover 0.3475 0.8961 -1.4644 0.3406 2.0984 1.0390 184
## Tree_Density -2.2155 0.8376 -3.9739 -2.1728 -0.6312 1.0974 228
## Avg_Canopy_Cover 1.6714 0.6261 0.4562 1.6472 2.9311 1.0642 393
## I(Avg_Cogongrass_Cover^2) 1.2578 0.6902 -0.0488 1.2409 2.7319 1.0167 377
## avg_veg_height -0.1714 0.4908 -1.1694 -0.1648 0.7622 1.0314 310
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8548 2.9082 0.0709 1.0459 8.3760 1.0330 691
## Cogon_Patch_Size 2.6375 4.2846 0.1313 1.4704 13.1916 1.0140 389
## Veg_shannon_index 0.5643 0.7835 0.0458 0.3064 2.7138 1.0086 607
## total_shrub_cover 2.2602 3.0638 0.1481 1.3155 10.1862 1.0533 355
## Avg_Cogongrass_Cover 1.0687 1.6981 0.0543 0.4897 5.8675 1.0159 443
## Tree_Density 2.7415 10.1398 0.0578 0.8480 13.8885 1.4744 212
## Avg_Canopy_Cover 2.4647 5.9787 0.0978 1.1824 11.1784 1.3601 156
## I(Avg_Cogongrass_Cover^2) 2.8151 5.3370 0.0800 1.1811 16.2525 1.0882 144
## avg_veg_height 0.4722 0.6881 0.0391 0.2682 2.0933 1.0699 844
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6797 2.0364 0.0715 1.0791 6.7541 1.0913 98
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.4542 0.2954 -3.0440 -2.4567 -1.8489 1.0060 2559
## week 0.1821 0.2056 -0.2308 0.1823 0.5867 1.0040 1357
## I(week^2) -0.2195 0.1147 -0.4557 -0.2174 -0.0039 1.0062 1120
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6456 0.5233 0.1700 0.5012 2.0640 1.0019 1885
## week 0.2004 0.2033 0.0345 0.1398 0.7419 1.0427 1066
## I(week^2) 0.0752 0.0732 0.0187 0.0574 0.2340 1.0430 1680
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.5664 1.0227 -3.6445 -1.5882
## (Intercept)-Procyon_lotor -1.2451 1.0307 -3.3289 -1.2396
## (Intercept)-Dasypus_novemcinctus -2.3911 0.8970 -4.3895 -2.3199
## (Intercept)-Lynx_rufus -1.6755 1.1928 -3.9630 -1.7144
## (Intercept)-Didelphis_virginiana -2.9991 1.0941 -5.4904 -2.8576
## (Intercept)-Sylvilagus_floridanus -2.1844 0.9662 -4.2794 -2.1473
## (Intercept)-Meleagris_gallopavo -2.0037 1.0744 -4.2359 -1.9973
## (Intercept)-Sciurus_carolinensis -3.3220 1.1487 -5.8903 -3.1815
## Cogon_Patch_Size-Canis_latrans 1.2916 1.2055 -0.4733 1.0787
## Cogon_Patch_Size-Procyon_lotor -0.4667 0.9379 -2.2008 -0.4806
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2323 0.6669 -1.5651 -0.2285
## Cogon_Patch_Size-Lynx_rufus -0.2294 1.3664 -3.0519 -0.2411
## Cogon_Patch_Size-Didelphis_virginiana 1.3576 0.9693 -0.2474 1.2486
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2227 1.3806 -4.2993 -1.0303
## Cogon_Patch_Size-Meleagris_gallopavo 0.0883 1.0537 -1.8073 0.0374
## Cogon_Patch_Size-Sciurus_carolinensis -0.9240 1.0940 -3.4112 -0.7658
## Veg_shannon_index-Canis_latrans 1.2383 0.6500 0.1096 1.1844
## Veg_shannon_index-Procyon_lotor 1.0937 0.5942 0.0516 1.0444
## Veg_shannon_index-Dasypus_novemcinctus 0.6637 0.5346 -0.4158 0.6595
## Veg_shannon_index-Lynx_rufus 0.8931 0.7391 -0.5067 0.8707
## Veg_shannon_index-Didelphis_virginiana 0.9673 0.6027 -0.1652 0.9322
## Veg_shannon_index-Sylvilagus_floridanus 0.9482 0.6648 -0.3325 0.9307
## Veg_shannon_index-Meleagris_gallopavo 1.1580 0.7000 -0.0818 1.0925
## Veg_shannon_index-Sciurus_carolinensis 0.3788 0.6807 -1.1017 0.4293
## total_shrub_cover-Canis_latrans 0.0560 0.7146 -1.2772 0.0032
## total_shrub_cover-Procyon_lotor -1.1995 0.6701 -2.6465 -1.1614
## total_shrub_cover-Dasypus_novemcinctus 0.0838 0.5760 -1.0149 0.0803
## total_shrub_cover-Lynx_rufus -1.5740 1.1608 -4.2740 -1.3920
## total_shrub_cover-Didelphis_virginiana -0.6786 0.7432 -2.1828 -0.6510
## total_shrub_cover-Sylvilagus_floridanus -0.4788 0.9390 -2.5595 -0.4143
## total_shrub_cover-Meleagris_gallopavo -2.5700 1.4522 -6.0508 -2.3480
## total_shrub_cover-Sciurus_carolinensis -0.0273 0.7108 -1.4377 -0.0439
## Avg_Cogongrass_Cover-Canis_latrans 0.2519 1.1420 -1.9729 0.2711
## Avg_Cogongrass_Cover-Procyon_lotor 0.3676 1.1260 -1.8843 0.3595
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8671 1.1578 -1.1825 0.8057
## Avg_Cogongrass_Cover-Lynx_rufus 0.4447 1.1899 -1.8717 0.4162
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.6347 1.1467 -1.5497 0.5770
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1572 1.2310 -2.8196 -0.0833
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.0084 1.2631 -2.7193 0.0649
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.5218 1.1553 -1.6235 0.4728
## Tree_Density-Canis_latrans -2.9393 1.5071 -6.1005 -2.7187
## Tree_Density-Procyon_lotor -2.2140 1.1148 -4.5201 -2.1411
## Tree_Density-Dasypus_novemcinctus -3.4395 1.6103 -7.3950 -3.1111
## Tree_Density-Lynx_rufus -1.2479 1.3921 -3.8109 -1.3823
## Tree_Density-Didelphis_virginiana -2.3314 1.0876 -4.7883 -2.2108
## Tree_Density-Sylvilagus_floridanus -2.7499 1.6437 -5.8665 -2.5263
## Tree_Density-Meleagris_gallopavo -2.1825 1.2705 -4.8341 -2.1320
## Tree_Density-Sciurus_carolinensis -2.4562 1.2344 -5.1118 -2.3354
## Avg_Canopy_Cover-Canis_latrans 0.3072 0.7534 -1.1930 0.3115
## Avg_Canopy_Cover-Procyon_lotor 1.6045 0.7205 0.3216 1.5761
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9012 0.7253 0.6958 1.8151
## Avg_Canopy_Cover-Lynx_rufus 1.1446 1.1348 -1.1189 1.1555
## Avg_Canopy_Cover-Didelphis_virginiana 2.4024 0.9162 1.0452 2.2766
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.2042 1.9394 1.0502 2.8246
## Avg_Canopy_Cover-Meleagris_gallopavo 2.1413 1.0754 0.5465 1.9705
## Avg_Canopy_Cover-Sciurus_carolinensis 2.0314 0.7935 0.7259 1.9244
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4402 1.5864 0.5692 2.0923
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3286 1.4554 0.4472 2.0124
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2553 0.7200 0.0073 1.2076
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5074 1.4039 0.4940 2.2424
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.6603 0.6914 -0.6596 0.6514
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0166 0.9602 -0.4645 0.8999
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo -0.0568 1.4032 -3.3761 0.0863
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.2520 0.7061 -0.0120 1.2070
## avg_veg_height-Canis_latrans -0.4396 0.6245 -1.7266 -0.4146
## avg_veg_height-Procyon_lotor 0.0301 0.6032 -1.0852 -0.0031
## avg_veg_height-Dasypus_novemcinctus 0.0928 0.5979 -1.0524 0.0747
## avg_veg_height-Lynx_rufus -0.3581 0.7535 -1.9589 -0.2984
## avg_veg_height-Didelphis_virginiana -0.2617 0.6518 -1.5985 -0.2420
## avg_veg_height-Sylvilagus_floridanus -0.3113 0.6682 -1.7115 -0.2832
## avg_veg_height-Meleagris_gallopavo -0.2610 0.7623 -1.9019 -0.2262
## avg_veg_height-Sciurus_carolinensis 0.1046 0.6498 -1.1197 0.0790
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.4479 1.0599 262
## (Intercept)-Procyon_lotor 0.7424 1.0538 186
## (Intercept)-Dasypus_novemcinctus -0.7584 1.0106 435
## (Intercept)-Lynx_rufus 0.8932 1.0831 302
## (Intercept)-Didelphis_virginiana -1.2163 1.0081 430
## (Intercept)-Sylvilagus_floridanus -0.3750 1.0098 375
## (Intercept)-Meleagris_gallopavo 0.0572 1.0099 372
## (Intercept)-Sciurus_carolinensis -1.4563 1.0334 408
## Cogon_Patch_Size-Canis_latrans 4.2777 1.0000 396
## Cogon_Patch_Size-Procyon_lotor 1.1814 1.0339 309
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0033 1.0044 810
## Cogon_Patch_Size-Lynx_rufus 2.5984 1.0260 369
## Cogon_Patch_Size-Didelphis_virginiana 3.4822 1.0026 386
## Cogon_Patch_Size-Sylvilagus_floridanus 1.0340 1.0132 421
## Cogon_Patch_Size-Meleagris_gallopavo 2.3411 1.0070 577
## Cogon_Patch_Size-Sciurus_carolinensis 0.7374 1.0094 352
## Veg_shannon_index-Canis_latrans 2.7005 1.0145 596
## Veg_shannon_index-Procyon_lotor 2.4230 1.0162 425
## Veg_shannon_index-Dasypus_novemcinctus 1.6956 1.0167 1095
## Veg_shannon_index-Lynx_rufus 2.4642 1.0242 496
## Veg_shannon_index-Didelphis_virginiana 2.2110 1.0062 812
## Veg_shannon_index-Sylvilagus_floridanus 2.3441 1.0030 725
## Veg_shannon_index-Meleagris_gallopavo 2.6926 1.0094 467
## Veg_shannon_index-Sciurus_carolinensis 1.6033 1.0015 674
## total_shrub_cover-Canis_latrans 1.5910 1.0155 719
## total_shrub_cover-Procyon_lotor 0.0050 1.0044 1082
## total_shrub_cover-Dasypus_novemcinctus 1.2381 1.0097 1561
## total_shrub_cover-Lynx_rufus 0.1632 1.0292 273
## total_shrub_cover-Didelphis_virginiana 0.7664 1.0145 933
## total_shrub_cover-Sylvilagus_floridanus 1.2965 1.0013 582
## total_shrub_cover-Meleagris_gallopavo -0.4485 1.0381 290
## total_shrub_cover-Sciurus_carolinensis 1.4161 0.9999 1279
## Avg_Cogongrass_Cover-Canis_latrans 2.4557 1.0292 276
## Avg_Cogongrass_Cover-Procyon_lotor 2.5587 1.0242 229
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.5037 1.0201 268
## Avg_Cogongrass_Cover-Lynx_rufus 2.7899 1.0320 294
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.0346 1.0099 311
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.0057 1.0200 357
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.2983 1.0182 259
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.9670 1.0083 283
## Tree_Density-Canis_latrans -0.9030 1.0439 154
## Tree_Density-Procyon_lotor -0.2317 1.0287 201
## Tree_Density-Dasypus_novemcinctus -1.3682 1.0278 144
## Tree_Density-Lynx_rufus 1.7815 1.0822 194
## Tree_Density-Didelphis_virginiana -0.5109 1.0371 173
## Tree_Density-Sylvilagus_floridanus -0.6357 1.0786 174
## Tree_Density-Meleagris_gallopavo 0.2745 1.0693 309
## Tree_Density-Sciurus_carolinensis -0.4119 1.0181 340
## Avg_Canopy_Cover-Canis_latrans 1.7770 1.0234 423
## Avg_Canopy_Cover-Procyon_lotor 3.1462 1.0193 601
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.5538 1.0094 339
## Avg_Canopy_Cover-Lynx_rufus 3.4241 1.0960 346
## Avg_Canopy_Cover-Didelphis_virginiana 4.5741 1.0158 186
## Avg_Canopy_Cover-Sylvilagus_floridanus 7.3441 1.1039 112
## Avg_Canopy_Cover-Meleagris_gallopavo 4.8160 1.0143 355
## Avg_Canopy_Cover-Sciurus_carolinensis 3.8657 1.0202 340
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.5842 1.0475 117
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2714 1.0532 110
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8693 1.0191 473
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 6.0300 1.1048 150
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.0616 1.0041 367
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3584 1.0064 298
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.3653 1.0119 151
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 2.8405 1.0038 532
## avg_veg_height-Canis_latrans 0.6963 1.0044 495
## avg_veg_height-Procyon_lotor 1.2385 1.0064 490
## avg_veg_height-Dasypus_novemcinctus 1.3199 1.0083 535
## avg_veg_height-Lynx_rufus 0.9857 1.0376 463
## avg_veg_height-Didelphis_virginiana 0.9208 1.0054 384
## avg_veg_height-Sylvilagus_floridanus 0.9197 1.0258 449
## avg_veg_height-Meleagris_gallopavo 1.1740 1.0232 449
## avg_veg_height-Sciurus_carolinensis 1.4853 1.0138 591
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4564 0.1812 -2.8106 -2.4542 -2.1082 1.0043
## (Intercept)-Procyon_lotor -2.1769 0.1506 -2.4744 -2.1734 -1.8921 1.0022
## (Intercept)-Dasypus_novemcinctus -1.4930 0.1564 -1.8070 -1.4897 -1.1966 1.0025
## (Intercept)-Lynx_rufus -3.3831 0.2937 -3.9644 -3.3766 -2.8119 1.0405
## (Intercept)-Didelphis_virginiana -2.1522 0.2590 -2.6723 -2.1465 -1.6634 1.0034
## (Intercept)-Sylvilagus_floridanus -3.0593 0.2896 -3.6564 -3.0457 -2.5234 1.0221
## (Intercept)-Meleagris_gallopavo -3.1373 0.3204 -3.7727 -3.1319 -2.5368 1.0061
## (Intercept)-Sciurus_carolinensis -2.3409 0.2774 -2.9176 -2.3347 -1.8298 1.0029
## week-Canis_latrans 0.4572 0.2473 0.0123 0.4510 0.9526 1.0034
## week-Procyon_lotor 0.1606 0.1954 -0.2180 0.1601 0.5492 1.0001
## week-Dasypus_novemcinctus 0.0572 0.2115 -0.3738 0.0594 0.4715 1.0064
## week-Lynx_rufus 0.2942 0.2920 -0.2892 0.2878 0.8912 1.0044
## week-Didelphis_virginiana 0.0253 0.3132 -0.6209 0.0339 0.6226 1.0141
## week-Sylvilagus_floridanus 0.0698 0.2921 -0.5280 0.0808 0.6315 1.0008
## week-Meleagris_gallopavo -0.1039 0.3486 -0.8791 -0.0824 0.5194 1.0524
## week-Sciurus_carolinensis 0.5301 0.3291 -0.0643 0.5092 1.2674 1.0070
## I(week^2)-Canis_latrans -0.1973 0.1023 -0.3991 -0.1978 -0.0075 1.0088
## I(week^2)-Procyon_lotor -0.1119 0.0877 -0.2816 -0.1123 0.0593 1.0034
## I(week^2)-Dasypus_novemcinctus -0.1503 0.0996 -0.3565 -0.1477 0.0364 1.0059
## I(week^2)-Lynx_rufus -0.2056 0.1441 -0.4985 -0.1999 0.0656 1.0156
## I(week^2)-Didelphis_virginiana -0.3671 0.2066 -0.8333 -0.3470 -0.0283 1.0115
## I(week^2)-Sylvilagus_floridanus -0.1716 0.1490 -0.4709 -0.1665 0.1174 1.0099
## I(week^2)-Meleagris_gallopavo -0.3517 0.2171 -0.8578 -0.3303 0.0142 1.0426
## I(week^2)-Sciurus_carolinensis -0.1924 0.1339 -0.4675 -0.1882 0.0568 1.0003
## ESS
## (Intercept)-Canis_latrans 1239
## (Intercept)-Procyon_lotor 1693
## (Intercept)-Dasypus_novemcinctus 2595
## (Intercept)-Lynx_rufus 454
## (Intercept)-Didelphis_virginiana 1660
## (Intercept)-Sylvilagus_floridanus 755
## (Intercept)-Meleagris_gallopavo 534
## (Intercept)-Sciurus_carolinensis 1184
## week-Canis_latrans 1153
## week-Procyon_lotor 1750
## week-Dasypus_novemcinctus 2049
## week-Lynx_rufus 1113
## week-Didelphis_virginiana 1191
## week-Sylvilagus_floridanus 1200
## week-Meleagris_gallopavo 787
## week-Sciurus_carolinensis 1207
## I(week^2)-Canis_latrans 1387
## I(week^2)-Procyon_lotor 1478
## I(week^2)-Dasypus_novemcinctus 1702
## I(week^2)-Lynx_rufus 826
## I(week^2)-Didelphis_virginiana 676
## I(week^2)-Sylvilagus_floridanus 716
## I(week^2)-Meleagris_gallopavo 348
## I(week^2)-Sciurus_carolinensis 1462
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T10 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_null_T10)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8445
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2149 0.3388 -0.8623 -0.2193 0.4759 1.0085 1092
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6575 0.6498 0.0986 0.4743 2.3327 1.0006 1453
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6091 0.3195 -3.2350 -2.6151 -1.9141 1.0005 1869
## shrub_cover 0.2526 0.3146 -0.3663 0.2548 0.8743 1.0072 1967
## veg_height 0.0746 0.2025 -0.3322 0.0756 0.4552 1.0009 1993
## week 0.1761 0.2053 -0.2260 0.1747 0.5828 1.0024 947
## I(week^2) -0.2257 0.1255 -0.4883 -0.2207 0.0120 1.0080 714
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7412 0.6169 0.1635 0.5645 2.4130 1.0076 1018
## shrub_cover 0.7058 0.6399 0.1313 0.5302 2.2048 1.0228 982
## veg_height 0.2595 0.2293 0.0617 0.1929 0.8575 1.0071 1294
## week 0.2049 0.2098 0.0346 0.1422 0.8241 1.0140 1058
## I(week^2) 0.0893 0.0957 0.0205 0.0636 0.3156 1.0116 645
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2505 0.3837 -0.4807 0.2351 1.0378 1.0049
## (Intercept)-Procyon_lotor 0.5194 0.3809 -0.1882 0.5049 1.2943 1.0116
## (Intercept)-Dasypus_novemcinctus -0.4867 0.3480 -1.1809 -0.4801 0.1840 1.0019
## (Intercept)-Lynx_rufus 0.0692 0.5235 -0.8391 0.0219 1.1897 1.0035
## (Intercept)-Didelphis_virginiana -0.9228 0.4406 -1.8266 -0.8973 -0.1205 1.0010
## (Intercept)-Sylvilagus_floridanus -0.3147 0.4219 -1.1286 -0.3185 0.5544 1.0097
## (Intercept)-Meleagris_gallopavo 0.0305 0.6140 -0.9721 -0.0347 1.4414 1.0355
## (Intercept)-Sciurus_carolinensis -0.9183 0.4399 -1.8353 -0.9127 -0.0914 1.0055
## ESS
## (Intercept)-Canis_latrans 1864
## (Intercept)-Procyon_lotor 1578
## (Intercept)-Dasypus_novemcinctus 2555
## (Intercept)-Lynx_rufus 737
## (Intercept)-Didelphis_virginiana 1658
## (Intercept)-Sylvilagus_floridanus 1336
## (Intercept)-Meleagris_gallopavo 365
## (Intercept)-Sciurus_carolinensis 1523
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5802 0.1957 -2.9803 -2.5724 -2.2127 1.0007
## (Intercept)-Procyon_lotor -2.1939 0.1554 -2.5019 -2.1901 -1.8959 1.0002
## (Intercept)-Dasypus_novemcinctus -1.6472 0.1751 -1.9884 -1.6474 -1.3058 1.0008
## (Intercept)-Lynx_rufus -3.4112 0.3469 -4.1136 -3.4072 -2.7606 1.0056
## (Intercept)-Didelphis_virginiana -2.4367 0.2986 -3.0356 -2.4364 -1.8599 1.0029
## (Intercept)-Sylvilagus_floridanus -3.0408 0.3174 -3.7103 -3.0237 -2.4710 1.0244
## (Intercept)-Meleagris_gallopavo -3.6679 0.4798 -4.5869 -3.6701 -2.7153 1.0288
## (Intercept)-Sciurus_carolinensis -2.5251 0.3317 -3.1891 -2.5218 -1.9061 1.0060
## shrub_cover-Canis_latrans -0.3134 0.2126 -0.7330 -0.3128 0.0886 1.0046
## shrub_cover-Procyon_lotor 0.2551 0.1630 -0.0669 0.2557 0.5665 1.0123
## shrub_cover-Dasypus_novemcinctus 0.8666 0.2993 0.2933 0.8654 1.4647 1.0053
## shrub_cover-Lynx_rufus -0.2542 0.3570 -0.9402 -0.2505 0.4538 1.0143
## shrub_cover-Didelphis_virginiana 1.0224 0.3897 0.3094 1.0057 1.8623 1.0077
## shrub_cover-Sylvilagus_floridanus 0.2780 0.4168 -0.5033 0.2719 1.1205 1.0009
## shrub_cover-Meleagris_gallopavo -0.6173 0.4090 -1.4190 -0.6151 0.2052 1.0268
## shrub_cover-Sciurus_carolinensis 0.8674 0.4145 0.0645 0.8646 1.6946 1.0105
## veg_height-Canis_latrans -0.5895 0.1875 -0.9703 -0.5872 -0.2241 1.0041
## veg_height-Procyon_lotor 0.3464 0.1222 0.1137 0.3433 0.5890 1.0080
## veg_height-Dasypus_novemcinctus 0.2557 0.1357 -0.0081 0.2578 0.5261 1.0057
## veg_height-Lynx_rufus 0.0471 0.2404 -0.4351 0.0507 0.5202 1.0028
## veg_height-Didelphis_virginiana 0.4773 0.2454 0.0072 0.4708 0.9583 1.0047
## veg_height-Sylvilagus_floridanus 0.1425 0.2511 -0.3580 0.1426 0.6396 1.0025
## veg_height-Meleagris_gallopavo -0.2041 0.3865 -0.9675 -0.2117 0.6052 1.0197
## veg_height-Sciurus_carolinensis 0.0954 0.2146 -0.3259 0.0940 0.5303 1.0098
## week-Canis_latrans 0.4507 0.2480 -0.0106 0.4444 0.9614 1.0013
## week-Procyon_lotor 0.1654 0.1956 -0.2317 0.1659 0.5503 1.0091
## week-Dasypus_novemcinctus 0.0630 0.2079 -0.3577 0.0592 0.4580 1.0014
## week-Lynx_rufus 0.2615 0.3021 -0.2879 0.2517 0.8995 1.0009
## week-Didelphis_virginiana 0.0280 0.3253 -0.6530 0.0412 0.6276 1.0002
## week-Sylvilagus_floridanus 0.0413 0.2951 -0.5512 0.0404 0.6032 1.0008
## week-Meleagris_gallopavo -0.1007 0.3494 -0.8769 -0.0788 0.5119 1.0340
## week-Sciurus_carolinensis 0.5408 0.3411 -0.0692 0.5249 1.2737 1.0012
## I(week^2)-Canis_latrans -0.1939 0.1076 -0.4152 -0.1916 0.0092 1.0051
## I(week^2)-Procyon_lotor -0.1138 0.0871 -0.2882 -0.1115 0.0547 1.0023
## I(week^2)-Dasypus_novemcinctus -0.1524 0.0982 -0.3461 -0.1507 0.0384 0.9997
## I(week^2)-Lynx_rufus -0.2079 0.1472 -0.5213 -0.2008 0.0585 1.0191
## I(week^2)-Didelphis_virginiana -0.3997 0.2291 -0.9590 -0.3727 -0.0316 1.0024
## I(week^2)-Sylvilagus_floridanus -0.1613 0.1557 -0.4961 -0.1517 0.1236 1.0016
## I(week^2)-Meleagris_gallopavo -0.3857 0.2461 -0.9758 -0.3590 0.0124 1.0419
## I(week^2)-Sciurus_carolinensis -0.1932 0.1428 -0.4888 -0.1913 0.0845 1.0010
## ESS
## (Intercept)-Canis_latrans 897
## (Intercept)-Procyon_lotor 1481
## (Intercept)-Dasypus_novemcinctus 1674
## (Intercept)-Lynx_rufus 410
## (Intercept)-Didelphis_virginiana 921
## (Intercept)-Sylvilagus_floridanus 466
## (Intercept)-Meleagris_gallopavo 218
## (Intercept)-Sciurus_carolinensis 976
## shrub_cover-Canis_latrans 800
## shrub_cover-Procyon_lotor 1529
## shrub_cover-Dasypus_novemcinctus 1374
## shrub_cover-Lynx_rufus 424
## shrub_cover-Didelphis_virginiana 613
## shrub_cover-Sylvilagus_floridanus 548
## shrub_cover-Meleagris_gallopavo 253
## shrub_cover-Sciurus_carolinensis 848
## veg_height-Canis_latrans 723
## veg_height-Procyon_lotor 1616
## veg_height-Dasypus_novemcinctus 2022
## veg_height-Lynx_rufus 808
## veg_height-Didelphis_virginiana 1142
## veg_height-Sylvilagus_floridanus 782
## veg_height-Meleagris_gallopavo 470
## veg_height-Sciurus_carolinensis 1227
## week-Canis_latrans 1320
## week-Procyon_lotor 1606
## week-Dasypus_novemcinctus 1640
## week-Lynx_rufus 918
## week-Didelphis_virginiana 1128
## week-Sylvilagus_floridanus 968
## week-Meleagris_gallopavo 468
## week-Sciurus_carolinensis 1024
## I(week^2)-Canis_latrans 1396
## I(week^2)-Procyon_lotor 1637
## I(week^2)-Dasypus_novemcinctus 2092
## I(week^2)-Lynx_rufus 620
## I(week^2)-Didelphis_virginiana 370
## I(week^2)-Sylvilagus_floridanus 686
## I(week^2)-Meleagris_gallopavo 244
## I(week^2)-Sciurus_carolinensis 1189
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T10 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_full_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9275
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7002 0.6956 -2.0365 -0.7143 0.7318 1.0153 255
## Cogon_Patch_Size -0.3894 0.6581 -1.7076 -0.3964 0.9099 1.0361 390
## Veg_shannon_index 0.9667 0.4816 0.0906 0.9398 2.0381 1.0714 240
## total_shrub_cover -0.8647 0.6418 -2.2193 -0.8352 0.3193 1.0069 322
## Avg_Cogongrass_Cover 1.8138 0.7493 0.5134 1.7665 3.3882 1.0474 168
## Tree_Density -1.8023 0.8033 -3.4669 -1.7844 -0.2375 1.0020 360
## Avg_Canopy_Cover 1.8284 0.7210 0.4268 1.8065 3.2882 1.0277 594
## avg_veg_height -0.3182 0.5065 -1.3282 -0.3238 0.6628 1.0161 292
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2440 3.0252 0.0917 1.2796 10.4735 1.0921 326
## Cogon_Patch_Size 1.9809 2.8419 0.0786 1.0843 9.3207 1.0169 543
## Veg_shannon_index 0.6195 0.9463 0.0427 0.3327 2.8574 1.0748 575
## total_shrub_cover 2.1574 4.3196 0.0871 1.1235 9.9388 1.3375 260
## Avg_Cogongrass_Cover 0.9913 1.7774 0.0491 0.4445 5.0178 1.0872 269
## Tree_Density 3.3312 4.9612 0.0963 1.7317 15.8459 1.0537 380
## Avg_Canopy_Cover 3.8762 5.8834 0.1951 2.1796 17.2514 1.0534 235
## avg_veg_height 0.5428 0.9374 0.0382 0.2692 2.7525 1.0553 321
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7038 2.929 0.0914 1.8347 10.8061 1.1762 70
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6207 0.3119 -3.2368 -2.6225 -1.9787 1.0118 1696
## shrub_cover 0.4093 0.3317 -0.2477 0.4113 1.0806 1.0134 891
## veg_height 0.0933 0.2033 -0.3131 0.0950 0.4965 1.0238 1619
## week 0.1841 0.2016 -0.2219 0.1871 0.5750 1.0126 1180
## I(week^2) -0.2233 0.1213 -0.4747 -0.2180 0.0028 1.0009 878
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6824 0.6281 0.1517 0.5033 2.4485 1.0348 963
## shrub_cover 0.7343 0.6697 0.1400 0.5521 2.4113 1.0123 677
## veg_height 0.2816 0.2682 0.0605 0.2103 0.9649 1.0099 1238
## week 0.2043 0.2234 0.0341 0.1396 0.7681 1.0068 1323
## I(week^2) 0.0823 0.0758 0.0199 0.0605 0.2875 1.0036 699
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1898 0.9942 -1.6304 0.1534
## (Intercept)-Procyon_lotor 0.1625 1.0027 -1.7989 0.1731
## (Intercept)-Dasypus_novemcinctus -1.0688 0.8578 -2.8447 -1.0502
## (Intercept)-Lynx_rufus -0.1031 1.3376 -2.4417 -0.2373
## (Intercept)-Didelphis_virginiana -1.7588 1.0756 -4.0415 -1.7247
## (Intercept)-Sylvilagus_floridanus -0.8757 0.9739 -2.7055 -0.8978
## (Intercept)-Meleagris_gallopavo -0.7854 1.2735 -3.1429 -0.8282
## (Intercept)-Sciurus_carolinensis -1.7668 1.1583 -4.3796 -1.6761
## Cogon_Patch_Size-Canis_latrans 0.5131 1.0329 -1.0617 0.3651
## Cogon_Patch_Size-Procyon_lotor -0.9011 0.7398 -2.4403 -0.8838
## Cogon_Patch_Size-Dasypus_novemcinctus -0.3928 0.7797 -1.8505 -0.4241
## Cogon_Patch_Size-Lynx_rufus -0.6448 1.2403 -3.1545 -0.6426
## Cogon_Patch_Size-Didelphis_virginiana 0.7610 0.9651 -0.8529 0.6478
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4040 1.3300 -4.4142 -1.2056
## Cogon_Patch_Size-Meleagris_gallopavo -0.2164 1.0913 -2.2634 -0.2988
## Cogon_Patch_Size-Sciurus_carolinensis -1.1719 1.1140 -3.7124 -1.0175
## Veg_shannon_index-Canis_latrans 1.2621 0.6419 0.1264 1.2096
## Veg_shannon_index-Procyon_lotor 1.2530 0.6134 0.1733 1.2013
## Veg_shannon_index-Dasypus_novemcinctus 0.6628 0.5744 -0.4908 0.6709
## Veg_shannon_index-Lynx_rufus 0.8836 0.7784 -0.7226 0.8807
## Veg_shannon_index-Didelphis_virginiana 1.1608 0.6819 -0.0227 1.1212
## Veg_shannon_index-Sylvilagus_floridanus 1.0882 0.7113 -0.1763 1.0465
## Veg_shannon_index-Meleagris_gallopavo 1.2236 0.7842 -0.1202 1.1669
## Veg_shannon_index-Sciurus_carolinensis 0.4182 0.7365 -1.1406 0.4769
## total_shrub_cover-Canis_latrans 0.4587 0.8727 -0.9214 0.3149
## total_shrub_cover-Procyon_lotor -1.1710 0.6592 -2.5793 -1.1208
## total_shrub_cover-Dasypus_novemcinctus -0.3812 0.7752 -2.0682 -0.3345
## total_shrub_cover-Lynx_rufus -1.5264 1.3242 -4.8526 -1.3091
## total_shrub_cover-Didelphis_virginiana -1.1928 1.0076 -3.6449 -1.0644
## total_shrub_cover-Sylvilagus_floridanus -0.8621 1.0451 -3.1155 -0.7889
## total_shrub_cover-Meleagris_gallopavo -2.0372 1.5469 -5.2294 -1.8039
## total_shrub_cover-Sciurus_carolinensis -1.0169 1.1617 -3.8422 -0.8377
## Avg_Cogongrass_Cover-Canis_latrans 2.1312 0.9214 0.5506 2.0665
## Avg_Cogongrass_Cover-Procyon_lotor 1.9149 0.8844 0.3555 1.8592
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.3239 0.9748 0.7339 2.2187
## Avg_Cogongrass_Cover-Lynx_rufus 2.1609 1.0071 0.4994 2.0503
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.8644 0.9332 0.1233 1.8209
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.2658 1.0117 -0.8593 1.2886
## Avg_Cogongrass_Cover-Meleagris_gallopavo 1.4593 1.2538 -1.3064 1.5135
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.0547 0.9574 0.3599 1.9939
## Tree_Density-Canis_latrans -2.6601 1.3121 -5.7538 -2.4210
## Tree_Density-Procyon_lotor -1.4184 0.7708 -2.8722 -1.4390
## Tree_Density-Dasypus_novemcinctus -3.5188 1.7318 -7.7793 -3.1787
## Tree_Density-Lynx_rufus -0.2826 1.3505 -2.4153 -0.4328
## Tree_Density-Didelphis_virginiana -1.9898 1.2608 -4.8072 -1.9401
## Tree_Density-Sylvilagus_floridanus -2.2748 1.4430 -5.4809 -2.1625
## Tree_Density-Meleagris_gallopavo -2.2079 1.4401 -5.2830 -2.0862
## Tree_Density-Sciurus_carolinensis -1.9604 1.4762 -5.1742 -1.9212
## Avg_Canopy_Cover-Canis_latrans 0.1758 0.6482 -1.0860 0.1714
## Avg_Canopy_Cover-Procyon_lotor 1.6915 0.7454 0.3512 1.6304
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2140 0.8259 0.8613 2.1076
## Avg_Canopy_Cover-Lynx_rufus 0.7878 1.2137 -1.5016 0.7480
## Avg_Canopy_Cover-Didelphis_virginiana 3.1081 1.3143 1.2093 2.8775
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.7993 1.8570 1.3025 3.4682
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5318 1.4398 0.5262 2.2601
## Avg_Canopy_Cover-Sciurus_carolinensis 2.9265 1.3437 1.0192 2.6771
## avg_veg_height-Canis_latrans -0.3984 0.6137 -1.6125 -0.4056
## avg_veg_height-Procyon_lotor -0.2971 0.5963 -1.4685 -0.2851
## avg_veg_height-Dasypus_novemcinctus -0.0884 0.5946 -1.1732 -0.1093
## avg_veg_height-Lynx_rufus -0.4732 0.8085 -2.2631 -0.4433
## avg_veg_height-Didelphis_virginiana -0.5133 0.6933 -2.0467 -0.4790
## avg_veg_height-Sylvilagus_floridanus -0.5489 0.7207 -2.1410 -0.5139
## avg_veg_height-Meleagris_gallopavo -0.3725 0.8892 -2.2880 -0.3439
## avg_veg_height-Sciurus_carolinensis 0.0425 0.7053 -1.1825 0.0107
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3066 1.0289 279
## (Intercept)-Procyon_lotor 2.1333 1.0396 257
## (Intercept)-Dasypus_novemcinctus 0.6484 1.0027 457
## (Intercept)-Lynx_rufus 2.9712 1.0796 137
## (Intercept)-Didelphis_virginiana 0.1598 1.0449 351
## (Intercept)-Sylvilagus_floridanus 1.1902 1.0140 470
## (Intercept)-Meleagris_gallopavo 1.9041 1.0773 222
## (Intercept)-Sciurus_carolinensis 0.2317 1.0124 245
## Cogon_Patch_Size-Canis_latrans 2.8738 1.0005 505
## Cogon_Patch_Size-Procyon_lotor 0.4473 1.0311 330
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2345 1.0102 803
## Cogon_Patch_Size-Lynx_rufus 1.8786 1.0615 280
## Cogon_Patch_Size-Didelphis_virginiana 3.0009 1.0037 390
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7161 1.0442 338
## Cogon_Patch_Size-Meleagris_gallopavo 2.2661 1.0098 497
## Cogon_Patch_Size-Sciurus_carolinensis 0.6262 1.0327 489
## Veg_shannon_index-Canis_latrans 2.6956 1.0727 352
## Veg_shannon_index-Procyon_lotor 2.6248 1.1028 266
## Veg_shannon_index-Dasypus_novemcinctus 1.7578 1.0061 525
## Veg_shannon_index-Lynx_rufus 2.4953 1.0648 390
## Veg_shannon_index-Didelphis_virginiana 2.6063 1.0570 428
## Veg_shannon_index-Sylvilagus_floridanus 2.6689 1.0485 493
## Veg_shannon_index-Meleagris_gallopavo 3.0471 1.0130 478
## Veg_shannon_index-Sciurus_carolinensis 1.7508 1.0216 544
## total_shrub_cover-Canis_latrans 2.5056 1.0153 396
## total_shrub_cover-Procyon_lotor -0.0226 1.0135 593
## total_shrub_cover-Dasypus_novemcinctus 1.0082 1.0046 631
## total_shrub_cover-Lynx_rufus 0.5432 1.0120 183
## total_shrub_cover-Didelphis_virginiana 0.4099 1.0068 320
## total_shrub_cover-Sylvilagus_floridanus 1.1412 1.0011 457
## total_shrub_cover-Meleagris_gallopavo 0.0191 1.0916 219
## total_shrub_cover-Sciurus_carolinensis 0.8321 1.1057 190
## Avg_Cogongrass_Cover-Canis_latrans 4.1907 1.0334 263
## Avg_Cogongrass_Cover-Procyon_lotor 3.8770 1.0233 262
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5766 1.0408 277
## Avg_Cogongrass_Cover-Lynx_rufus 4.4060 1.0075 237
## Avg_Cogongrass_Cover-Didelphis_virginiana 3.8290 1.0298 269
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.2268 1.0567 356
## Avg_Cogongrass_Cover-Meleagris_gallopavo 3.7536 1.0480 251
## Avg_Cogongrass_Cover-Sciurus_carolinensis 4.1690 1.0149 273
## Tree_Density-Canis_latrans -0.6721 1.0308 298
## Tree_Density-Procyon_lotor 0.1289 1.0083 608
## Tree_Density-Dasypus_novemcinctus -1.1789 1.1064 254
## Tree_Density-Lynx_rufus 2.6261 1.0347 210
## Tree_Density-Didelphis_virginiana 0.5771 1.0307 477
## Tree_Density-Sylvilagus_floridanus 0.4012 1.0208 323
## Tree_Density-Meleagris_gallopavo 0.4002 1.0164 372
## Tree_Density-Sciurus_carolinensis 0.9522 1.0068 344
## Avg_Canopy_Cover-Canis_latrans 1.4966 1.0094 812
## Avg_Canopy_Cover-Procyon_lotor 3.3844 1.0325 364
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.1733 1.0892 352
## Avg_Canopy_Cover-Lynx_rufus 3.3523 1.0269 279
## Avg_Canopy_Cover-Didelphis_virginiana 6.3717 1.0687 245
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.0802 1.0313 165
## Avg_Canopy_Cover-Meleagris_gallopavo 5.9997 1.0384 277
## Avg_Canopy_Cover-Sciurus_carolinensis 6.1993 1.0929 219
## avg_veg_height-Canis_latrans 0.8030 1.0063 458
## avg_veg_height-Procyon_lotor 0.9211 1.0167 515
## avg_veg_height-Dasypus_novemcinctus 1.1556 1.0052 472
## avg_veg_height-Lynx_rufus 1.0237 1.0208 310
## avg_veg_height-Didelphis_virginiana 0.7562 1.0010 496
## avg_veg_height-Sylvilagus_floridanus 0.7793 1.0071 529
## avg_veg_height-Meleagris_gallopavo 1.2110 1.0315 305
## avg_veg_height-Sciurus_carolinensis 1.6391 1.0027 400
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5932 0.1982 -2.9937 -2.5876 -2.2039 1.0059
## (Intercept)-Procyon_lotor -2.2070 0.1591 -2.5230 -2.2050 -1.9055 1.0003
## (Intercept)-Dasypus_novemcinctus -1.6900 0.1824 -2.0546 -1.6888 -1.3471 1.0056
## (Intercept)-Lynx_rufus -3.4215 0.3831 -4.2277 -3.3999 -2.7544 1.0722
## (Intercept)-Didelphis_virginiana -2.4819 0.3082 -3.1123 -2.4641 -1.9241 1.0160
## (Intercept)-Sylvilagus_floridanus -3.0713 0.2767 -3.6287 -3.0598 -2.5505 1.0043
## (Intercept)-Meleagris_gallopavo -3.5167 0.4716 -4.4781 -3.5151 -2.6076 1.0307
## (Intercept)-Sciurus_carolinensis -2.6765 0.3266 -3.3320 -2.6757 -2.0341 1.0482
## shrub_cover-Canis_latrans -0.3493 0.2226 -0.7612 -0.3567 0.1031 1.0102
## shrub_cover-Procyon_lotor 0.2800 0.1604 -0.0423 0.2806 0.5914 1.0033
## shrub_cover-Dasypus_novemcinctus 0.9837 0.3171 0.3464 0.9847 1.6063 1.0020
## shrub_cover-Lynx_rufus 0.0633 0.3903 -0.7451 0.0755 0.7841 1.0518
## shrub_cover-Didelphis_virginiana 1.1264 0.4011 0.4096 1.1019 1.9574 1.0168
## shrub_cover-Sylvilagus_floridanus 0.5746 0.4109 -0.2034 0.5674 1.3920 1.0020
## shrub_cover-Meleagris_gallopavo -0.4413 0.4353 -1.2631 -0.4371 0.4108 1.0256
## shrub_cover-Sciurus_carolinensis 1.1160 0.4289 0.2704 1.1210 1.9408 1.0325
## veg_height-Canis_latrans -0.5952 0.1897 -0.9784 -0.5928 -0.2313 1.0026
## veg_height-Procyon_lotor 0.3610 0.1221 0.1246 0.3608 0.6060 1.0081
## veg_height-Dasypus_novemcinctus 0.2782 0.1409 -0.0002 0.2755 0.5516 1.0048
## veg_height-Lynx_rufus 0.1099 0.2412 -0.3699 0.1138 0.5740 1.0220
## veg_height-Didelphis_virginiana 0.5074 0.2458 0.0528 0.5013 0.9969 1.0348
## veg_height-Sylvilagus_floridanus 0.1783 0.2485 -0.3074 0.1815 0.6712 1.0150
## veg_height-Meleagris_gallopavo -0.2142 0.4205 -1.0853 -0.2074 0.5675 1.0362
## veg_height-Sciurus_carolinensis 0.1541 0.2217 -0.2733 0.1529 0.5911 1.0427
## week-Canis_latrans 0.4644 0.2490 -0.0069 0.4597 0.9658 1.0060
## week-Procyon_lotor 0.1568 0.1925 -0.2132 0.1593 0.5368 1.0020
## week-Dasypus_novemcinctus 0.0683 0.2140 -0.3415 0.0665 0.4998 1.0023
## week-Lynx_rufus 0.2717 0.2877 -0.2837 0.2671 0.8447 1.0007
## week-Didelphis_virginiana 0.0437 0.3183 -0.6354 0.0629 0.6112 1.0074
## week-Sylvilagus_floridanus 0.0481 0.3008 -0.5638 0.0517 0.6189 1.0043
## week-Meleagris_gallopavo -0.0957 0.3528 -0.8513 -0.0635 0.5083 1.0055
## week-Sciurus_carolinensis 0.5535 0.3310 -0.0224 0.5277 1.2824 1.0047
## I(week^2)-Canis_latrans -0.1973 0.1062 -0.4096 -0.1967 0.0045 0.9997
## I(week^2)-Procyon_lotor -0.1131 0.0865 -0.2867 -0.1129 0.0534 1.0014
## I(week^2)-Dasypus_novemcinctus -0.1559 0.1012 -0.3588 -0.1552 0.0322 1.0018
## I(week^2)-Lynx_rufus -0.2053 0.1489 -0.5271 -0.2020 0.0790 1.0091
## I(week^2)-Didelphis_virginiana -0.3830 0.2197 -0.9001 -0.3528 -0.0295 1.0191
## I(week^2)-Sylvilagus_floridanus -0.1645 0.1497 -0.4706 -0.1612 0.1170 1.0121
## I(week^2)-Meleagris_gallopavo -0.3936 0.2390 -0.9562 -0.3627 -0.0105 1.0186
## I(week^2)-Sciurus_carolinensis -0.2050 0.1391 -0.4894 -0.1999 0.0547 1.0105
## ESS
## (Intercept)-Canis_latrans 950
## (Intercept)-Procyon_lotor 1536
## (Intercept)-Dasypus_novemcinctus 1237
## (Intercept)-Lynx_rufus 234
## (Intercept)-Didelphis_virginiana 515
## (Intercept)-Sylvilagus_floridanus 721
## (Intercept)-Meleagris_gallopavo 269
## (Intercept)-Sciurus_carolinensis 486
## shrub_cover-Canis_latrans 558
## shrub_cover-Procyon_lotor 1584
## shrub_cover-Dasypus_novemcinctus 839
## shrub_cover-Lynx_rufus 301
## shrub_cover-Didelphis_virginiana 366
## shrub_cover-Sylvilagus_floridanus 374
## shrub_cover-Meleagris_gallopavo 303
## shrub_cover-Sciurus_carolinensis 307
## veg_height-Canis_latrans 583
## veg_height-Procyon_lotor 1508
## veg_height-Dasypus_novemcinctus 1693
## veg_height-Lynx_rufus 461
## veg_height-Didelphis_virginiana 1178
## veg_height-Sylvilagus_floridanus 822
## veg_height-Meleagris_gallopavo 303
## veg_height-Sciurus_carolinensis 833
## week-Canis_latrans 1319
## week-Procyon_lotor 1794
## week-Dasypus_novemcinctus 2066
## week-Lynx_rufus 1245
## week-Didelphis_virginiana 1046
## week-Sylvilagus_floridanus 772
## week-Meleagris_gallopavo 671
## week-Sciurus_carolinensis 1208
## I(week^2)-Canis_latrans 1279
## I(week^2)-Procyon_lotor 1692
## I(week^2)-Dasypus_novemcinctus 1827
## I(week^2)-Lynx_rufus 696
## I(week^2)-Didelphis_virginiana 380
## I(week^2)-Sylvilagus_floridanus 787
## I(week^2)-Meleagris_gallopavo 222
## I(week^2)-Sciurus_carolinensis 1340
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T10 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cover_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9407
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1554 0.4382 -1.0341 -0.1735 0.7176 1.0174 281
## Avg_Cogongrass_Cover -0.0472 0.3818 -0.8177 -0.0413 0.7134 1.0259 878
## total_shrub_cover -0.9760 0.4999 -2.0389 -0.9368 -0.0887 1.0010 375
## avg_veg_height 0.1756 0.3945 -0.6067 0.1793 0.9498 1.0461 468
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5860 0.8264 0.0506 0.3332 2.6366 1.0133 759
## Avg_Cogongrass_Cover 0.5287 0.7697 0.0454 0.3020 2.5372 1.0207 460
## total_shrub_cover 1.1544 1.8991 0.0795 0.6436 5.1353 1.0894 500
## avg_veg_height 0.4080 0.5488 0.0391 0.2305 1.7873 1.0225 560
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.1652 1.1698 0.0936 0.8312 4.0292 1.0307 205
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6415 0.2968 -3.2076 -2.6414 -2.0357 1.0053 1036
## shrub_cover 0.5501 0.3425 -0.1401 0.5518 1.2578 1.0116 1066
## veg_height 0.0844 0.2084 -0.3403 0.0866 0.4986 1.0354 1042
## week 0.1917 0.2123 -0.2350 0.1946 0.6026 1.0216 698
## I(week^2) -0.2291 0.1263 -0.4877 -0.2262 0.0045 1.0181 953
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5788 0.5676 0.1162 0.4277 1.9819 1.0068 706
## shrub_cover 0.8041 0.7868 0.1535 0.5963 2.6035 1.0023 779
## veg_height 0.2644 0.2516 0.0606 0.1918 0.8917 1.0343 1682
## week 0.2050 0.2083 0.0340 0.1387 0.7931 1.0441 616
## I(week^2) 0.0858 0.1084 0.0198 0.0607 0.2870 1.3674 427
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1844 0.5946 -0.9198 0.1558
## (Intercept)-Procyon_lotor 0.3484 0.6105 -0.7980 0.3257
## (Intercept)-Dasypus_novemcinctus -0.2559 0.5881 -1.4064 -0.2734
## (Intercept)-Lynx_rufus -0.0864 0.6288 -1.2834 -0.1230
## (Intercept)-Didelphis_virginiana -0.5181 0.6373 -1.8902 -0.4938
## (Intercept)-Sylvilagus_floridanus -0.0189 0.6361 -1.2109 -0.0423
## (Intercept)-Meleagris_gallopavo -0.3437 0.6595 -1.7063 -0.3462
## (Intercept)-Sciurus_carolinensis -0.5055 0.6720 -1.9214 -0.4632
## Avg_Cogongrass_Cover-Canis_latrans 0.3121 0.5300 -0.6508 0.2781
## Avg_Cogongrass_Cover-Procyon_lotor -0.1346 0.4858 -1.1154 -0.1247
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.0800 0.4693 -0.8826 0.0840
## Avg_Cogongrass_Cover-Lynx_rufus 0.3357 0.5845 -0.6900 0.2921
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0993 0.5185 -0.9028 0.0920
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5159 0.6007 -1.8568 -0.4551
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.5352 0.6863 -2.1209 -0.4624
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.0357 0.5005 -1.0402 -0.0325
## total_shrub_cover-Canis_latrans 0.1435 0.6409 -0.9882 0.0930
## total_shrub_cover-Procyon_lotor -1.2700 0.5938 -2.6799 -1.2017
## total_shrub_cover-Dasypus_novemcinctus -0.6010 0.7047 -2.3379 -0.5082
## total_shrub_cover-Lynx_rufus -1.3909 0.8414 -3.3228 -1.2808
## total_shrub_cover-Didelphis_virginiana -0.9770 0.7057 -2.5918 -0.9085
## total_shrub_cover-Sylvilagus_floridanus -1.4021 0.9569 -3.7670 -1.2463
## total_shrub_cover-Meleagris_gallopavo -1.6107 0.8518 -3.5625 -1.4942
## total_shrub_cover-Sciurus_carolinensis -1.0694 0.8093 -2.8915 -0.9660
## avg_veg_height-Canis_latrans 0.1444 0.4836 -0.7846 0.1322
## avg_veg_height-Procyon_lotor 0.1923 0.4903 -0.7433 0.1757
## avg_veg_height-Dasypus_novemcinctus 0.4234 0.5049 -0.4548 0.3893
## avg_veg_height-Lynx_rufus 0.0998 0.6300 -1.2425 0.1094
## avg_veg_height-Didelphis_virginiana 0.0533 0.5105 -0.9715 0.0651
## avg_veg_height-Sylvilagus_floridanus 0.1092 0.5258 -0.9301 0.1112
## avg_veg_height-Meleagris_gallopavo -0.1579 0.7343 -1.8164 -0.0951
## avg_veg_height-Sciurus_carolinensis 0.5537 0.5574 -0.4052 0.5129
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4747 1.0548 400
## (Intercept)-Procyon_lotor 1.5966 1.0297 566
## (Intercept)-Dasypus_novemcinctus 0.9461 1.0106 407
## (Intercept)-Lynx_rufus 1.2506 1.0074 716
## (Intercept)-Didelphis_virginiana 0.7193 1.0040 467
## (Intercept)-Sylvilagus_floridanus 1.3238 1.0222 416
## (Intercept)-Meleagris_gallopavo 0.9378 1.0084 505
## (Intercept)-Sciurus_carolinensis 0.7377 1.0003 376
## Avg_Cogongrass_Cover-Canis_latrans 1.4786 1.0113 1137
## Avg_Cogongrass_Cover-Procyon_lotor 0.7964 1.0265 1205
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0291 1.0063 1280
## Avg_Cogongrass_Cover-Lynx_rufus 1.6412 1.0225 1033
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1751 1.0120 1007
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5017 1.0092 709
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.5842 1.0309 562
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.9205 1.0089 1109
## total_shrub_cover-Canis_latrans 1.6029 1.0302 548
## total_shrub_cover-Procyon_lotor -0.3200 1.0181 596
## total_shrub_cover-Dasypus_novemcinctus 0.4828 1.0064 357
## total_shrub_cover-Lynx_rufus -0.0117 1.0146 245
## total_shrub_cover-Didelphis_virginiana 0.1767 1.0008 350
## total_shrub_cover-Sylvilagus_floridanus 0.0091 1.0565 182
## total_shrub_cover-Meleagris_gallopavo -0.1790 1.0122 366
## total_shrub_cover-Sciurus_carolinensis 0.2490 1.0057 334
## avg_veg_height-Canis_latrans 1.0811 1.0217 762
## avg_veg_height-Procyon_lotor 1.2193 1.0071 1089
## avg_veg_height-Dasypus_novemcinctus 1.5231 1.0367 601
## avg_veg_height-Lynx_rufus 1.3876 1.0178 738
## avg_veg_height-Didelphis_virginiana 1.0443 1.0193 666
## avg_veg_height-Sylvilagus_floridanus 1.1895 1.0500 574
## avg_veg_height-Meleagris_gallopavo 1.1445 1.0289 352
## avg_veg_height-Sciurus_carolinensis 1.7915 1.0139 764
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6111 0.2060 -3.0098 -2.6081 -2.2224 1.0036
## (Intercept)-Procyon_lotor -2.2123 0.1573 -2.5381 -2.2051 -1.9137 1.0082
## (Intercept)-Dasypus_novemcinctus -1.7671 0.2053 -2.1664 -1.7616 -1.3730 1.0471
## (Intercept)-Lynx_rufus -3.2813 0.3529 -4.0451 -3.2661 -2.6514 1.0098
## (Intercept)-Didelphis_virginiana -2.6045 0.3135 -3.2564 -2.5940 -2.0094 1.0103
## (Intercept)-Sylvilagus_floridanus -3.1339 0.2933 -3.7265 -3.1299 -2.5759 1.0459
## (Intercept)-Meleagris_gallopavo -3.3290 0.5181 -4.3840 -3.3112 -2.3733 1.0548
## (Intercept)-Sciurus_carolinensis -2.7377 0.3376 -3.4064 -2.7265 -2.0916 1.0052
## shrub_cover-Canis_latrans -0.2787 0.2412 -0.7581 -0.2759 0.1767 1.0060
## shrub_cover-Procyon_lotor 0.3279 0.1634 0.0052 0.3315 0.6329 1.0005
## shrub_cover-Dasypus_novemcinctus 1.1751 0.3665 0.4925 1.1668 1.8916 1.0396
## shrub_cover-Lynx_rufus 0.1760 0.3736 -0.6189 0.1955 0.8442 1.0224
## shrub_cover-Didelphis_virginiana 1.3328 0.4101 0.5860 1.3281 2.1926 1.0098
## shrub_cover-Sylvilagus_floridanus 0.8034 0.4353 -0.0973 0.8269 1.6438 1.0763
## shrub_cover-Meleagris_gallopavo -0.2586 0.4708 -1.2258 -0.2492 0.6167 1.0656
## shrub_cover-Sciurus_carolinensis 1.2760 0.4045 0.4369 1.2909 2.0191 1.0244
## veg_height-Canis_latrans -0.5907 0.1912 -0.9822 -0.5821 -0.2423 1.0055
## veg_height-Procyon_lotor 0.3472 0.1264 0.1070 0.3468 0.5840 1.0001
## veg_height-Dasypus_novemcinctus 0.2867 0.1455 0.0052 0.2836 0.5792 1.0000
## veg_height-Lynx_rufus 0.0458 0.2441 -0.4570 0.0586 0.5262 1.0046
## veg_height-Didelphis_virginiana 0.4540 0.2562 -0.0308 0.4462 0.9699 1.0071
## veg_height-Sylvilagus_floridanus 0.0840 0.2445 -0.3965 0.0796 0.5670 1.0480
## veg_height-Meleagris_gallopavo -0.0618 0.4610 -0.9426 -0.0731 0.8521 1.1124
## veg_height-Sciurus_carolinensis 0.1309 0.2352 -0.3251 0.1295 0.5767 1.0034
## week-Canis_latrans 0.4609 0.2511 -0.0120 0.4510 0.9865 1.0066
## week-Procyon_lotor 0.1572 0.1989 -0.2094 0.1539 0.5641 1.0026
## week-Dasypus_novemcinctus 0.0661 0.2143 -0.3694 0.0700 0.4902 1.0137
## week-Lynx_rufus 0.2934 0.3116 -0.3004 0.2881 0.9109 1.0136
## week-Didelphis_virginiana 0.0425 0.3260 -0.6452 0.0589 0.6445 1.0226
## week-Sylvilagus_floridanus 0.0549 0.2925 -0.5358 0.0611 0.6037 1.0232
## week-Meleagris_gallopavo -0.0875 0.3588 -0.8781 -0.0583 0.5696 1.0743
## week-Sciurus_carolinensis 0.5678 0.3403 -0.0125 0.5338 1.3313 1.0295
## I(week^2)-Canis_latrans -0.2000 0.1061 -0.4177 -0.1963 0.0072 1.0019
## I(week^2)-Procyon_lotor -0.1119 0.0874 -0.2868 -0.1102 0.0559 1.0030
## I(week^2)-Dasypus_novemcinctus -0.1524 0.1033 -0.3610 -0.1511 0.0494 1.0053
## I(week^2)-Lynx_rufus -0.2071 0.1493 -0.5080 -0.2003 0.0718 1.0082
## I(week^2)-Didelphis_virginiana -0.3818 0.2120 -0.8464 -0.3616 -0.0322 1.0095
## I(week^2)-Sylvilagus_floridanus -0.1644 0.1504 -0.4793 -0.1602 0.1097 1.0243
## I(week^2)-Meleagris_gallopavo -0.3982 0.2873 -1.1319 -0.3542 0.0168 1.3639
## I(week^2)-Sciurus_carolinensis -0.2073 0.1429 -0.5220 -0.1986 0.0533 1.0133
## ESS
## (Intercept)-Canis_latrans 725
## (Intercept)-Procyon_lotor 1459
## (Intercept)-Dasypus_novemcinctus 605
## (Intercept)-Lynx_rufus 471
## (Intercept)-Didelphis_virginiana 437
## (Intercept)-Sylvilagus_floridanus 494
## (Intercept)-Meleagris_gallopavo 312
## (Intercept)-Sciurus_carolinensis 581
## shrub_cover-Canis_latrans 659
## shrub_cover-Procyon_lotor 1457
## shrub_cover-Dasypus_novemcinctus 382
## shrub_cover-Lynx_rufus 446
## shrub_cover-Didelphis_virginiana 306
## shrub_cover-Sylvilagus_floridanus 336
## shrub_cover-Meleagris_gallopavo 401
## shrub_cover-Sciurus_carolinensis 383
## veg_height-Canis_latrans 711
## veg_height-Procyon_lotor 1593
## veg_height-Dasypus_novemcinctus 1408
## veg_height-Lynx_rufus 738
## veg_height-Didelphis_virginiana 789
## veg_height-Sylvilagus_floridanus 658
## veg_height-Meleagris_gallopavo 319
## veg_height-Sciurus_carolinensis 827
## week-Canis_latrans 1243
## week-Procyon_lotor 1750
## week-Dasypus_novemcinctus 1833
## week-Lynx_rufus 903
## week-Didelphis_virginiana 999
## week-Sylvilagus_floridanus 945
## week-Meleagris_gallopavo 376
## week-Sciurus_carolinensis 850
## I(week^2)-Canis_latrans 1347
## I(week^2)-Procyon_lotor 1623
## I(week^2)-Dasypus_novemcinctus 1723
## I(week^2)-Lynx_rufus 675
## I(week^2)-Didelphis_virginiana 365
## I(week^2)-Sylvilagus_floridanus 674
## I(week^2)-Meleagris_gallopavo 156
## I(week^2)-Sciurus_carolinensis 875
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T10<- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_canopy_T10)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8932
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4302 0.4720 -1.3539 -0.4321 0.5541 1.0051 675
## Tree_Density -0.7993 0.4726 -1.8131 -0.7722 0.0432 1.0119 954
## Avg_Canopy_Cover 1.1283 0.4717 0.2860 1.0974 2.1695 1.0114 973
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1284 1.4005 0.0901 0.7645 4.5304 1.0607 459
## Tree_Density 1.0978 1.6139 0.0601 0.5665 5.5944 1.0849 396
## Avg_Canopy_Cover 1.2424 1.4777 0.0997 0.7790 5.3775 1.0474 585
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6829 0.8576 0.0463 0.3823 3.0144 1.1479 152
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6163 0.3260 -3.2452 -2.6226 -1.9399 1.0036 2135
## shrub_cover 0.2890 0.3240 -0.3432 0.2858 0.9634 1.0134 1627
## veg_height 0.0955 0.1951 -0.3125 0.0981 0.4873 1.0042 1783
## week 0.1937 0.2112 -0.2253 0.1973 0.6285 1.0012 1215
## I(week^2) -0.2272 0.1222 -0.4941 -0.2211 0.0015 1.0049 808
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7610 0.6761 0.1810 0.5768 2.4581 1.0106 1648
## shrub_cover 0.7463 0.7141 0.1426 0.5614 2.4290 1.0125 983
## veg_height 0.2570 0.2203 0.0577 0.1942 0.8459 1.0059 1713
## week 0.2039 0.2204 0.0328 0.1380 0.7595 1.0175 1417
## I(week^2) 0.0803 0.0856 0.0191 0.0576 0.2750 1.0831 548
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1028 0.5847 -1.0375 0.0954 1.2646
## (Intercept)-Procyon_lotor 0.3568 0.6474 -0.9683 0.3614 1.6131
## (Intercept)-Dasypus_novemcinctus -0.8380 0.6016 -2.0800 -0.8074 0.2353
## (Intercept)-Lynx_rufus 0.0678 0.9256 -1.4469 -0.0450 2.1368
## (Intercept)-Didelphis_virginiana -1.2576 0.6650 -2.7547 -1.2272 -0.0209
## (Intercept)-Sylvilagus_floridanus -0.5888 0.6379 -1.8164 -0.5931 0.7008
## (Intercept)-Meleagris_gallopavo -0.1985 0.7472 -1.6255 -0.2201 1.4171
## (Intercept)-Sciurus_carolinensis -1.3145 0.6998 -2.7575 -1.2665 -0.0566
## Tree_Density-Canis_latrans -0.9967 0.6361 -2.4755 -0.9206 0.0349
## Tree_Density-Procyon_lotor -0.4761 0.4278 -1.3496 -0.4710 0.3490
## Tree_Density-Dasypus_novemcinctus -1.4788 0.9506 -3.8937 -1.2739 -0.2070
## Tree_Density-Lynx_rufus 0.2568 0.7843 -0.9004 0.1357 2.1224
## Tree_Density-Didelphis_virginiana -1.0052 0.7697 -2.7756 -0.9014 0.2525
## Tree_Density-Sylvilagus_floridanus -1.0928 0.8095 -3.0399 -0.9782 0.1643
## Tree_Density-Meleagris_gallopavo -1.0853 0.8958 -3.1447 -0.9507 0.3344
## Tree_Density-Sciurus_carolinensis -0.8575 0.7546 -2.5860 -0.7715 0.4326
## Avg_Canopy_Cover-Canis_latrans -0.0792 0.4951 -1.1158 -0.0650 0.8445
## Avg_Canopy_Cover-Procyon_lotor 1.0275 0.4908 0.1535 0.9961 2.0548
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1068 0.4803 0.2174 1.0696 2.1197
## Avg_Canopy_Cover-Lynx_rufus 0.7077 0.7227 -0.6375 0.6729 2.2663
## Avg_Canopy_Cover-Didelphis_virginiana 1.5829 0.7018 0.4683 1.4864 3.2274
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.1888 1.0332 0.6888 1.9850 4.7566
## Avg_Canopy_Cover-Meleagris_gallopavo 1.5914 0.8139 0.3010 1.4783 3.4451
## Avg_Canopy_Cover-Sciurus_carolinensis 1.4942 0.6891 0.4200 1.3980 3.1452
## Rhat ESS
## (Intercept)-Canis_latrans 1.0043 807
## (Intercept)-Procyon_lotor 1.0312 530
## (Intercept)-Dasypus_novemcinctus 1.0141 626
## (Intercept)-Lynx_rufus 1.0703 249
## (Intercept)-Didelphis_virginiana 1.0345 625
## (Intercept)-Sylvilagus_floridanus 1.0094 904
## (Intercept)-Meleagris_gallopavo 1.0092 588
## (Intercept)-Sciurus_carolinensis 1.0105 594
## Tree_Density-Canis_latrans 1.0110 1236
## Tree_Density-Procyon_lotor 1.0150 1609
## Tree_Density-Dasypus_novemcinctus 1.0202 463
## Tree_Density-Lynx_rufus 1.0235 516
## Tree_Density-Didelphis_virginiana 1.0261 789
## Tree_Density-Sylvilagus_floridanus 1.0048 788
## Tree_Density-Meleagris_gallopavo 1.0247 568
## Tree_Density-Sciurus_carolinensis 1.0149 1019
## Avg_Canopy_Cover-Canis_latrans 1.0214 1138
## Avg_Canopy_Cover-Procyon_lotor 1.0085 1641
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0006 1806
## Avg_Canopy_Cover-Lynx_rufus 1.0468 633
## Avg_Canopy_Cover-Didelphis_virginiana 1.0044 736
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0452 323
## Avg_Canopy_Cover-Meleagris_gallopavo 1.0200 479
## Avg_Canopy_Cover-Sciurus_carolinensis 1.0328 715
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5928 0.1996 -3.0015 -2.5855 -2.2109 1.0081
## (Intercept)-Procyon_lotor -2.2019 0.1661 -2.5305 -2.1961 -1.8877 1.0065
## (Intercept)-Dasypus_novemcinctus -1.6686 0.1866 -2.0432 -1.6650 -1.3107 1.0023
## (Intercept)-Lynx_rufus -3.4970 0.3501 -4.2213 -3.4934 -2.8167 1.0258
## (Intercept)-Didelphis_virginiana -2.4623 0.2947 -3.0606 -2.4611 -1.9069 1.0096
## (Intercept)-Sylvilagus_floridanus -3.0211 0.2796 -3.6073 -3.0124 -2.4927 1.0106
## (Intercept)-Meleagris_gallopavo -3.6803 0.4690 -4.6389 -3.6584 -2.7870 1.0217
## (Intercept)-Sciurus_carolinensis -2.5794 0.3312 -3.2937 -2.5695 -1.9727 1.0124
## shrub_cover-Canis_latrans -0.3016 0.2223 -0.7280 -0.3000 0.1308 1.0014
## shrub_cover-Procyon_lotor 0.2629 0.1616 -0.0673 0.2653 0.5764 1.0045
## shrub_cover-Dasypus_novemcinctus 0.9150 0.3067 0.3510 0.9122 1.5349 0.9998
## shrub_cover-Lynx_rufus -0.2675 0.3395 -0.9358 -0.2696 0.4004 1.0315
## shrub_cover-Didelphis_virginiana 1.0327 0.3575 0.3675 1.0175 1.7841 1.0013
## shrub_cover-Sylvilagus_floridanus 0.4320 0.4056 -0.3342 0.4352 1.2189 1.0460
## shrub_cover-Meleagris_gallopavo -0.6481 0.4108 -1.4849 -0.6339 0.1201 1.0339
## shrub_cover-Sciurus_carolinensis 0.9383 0.4107 0.1627 0.9359 1.7646 1.0332
## veg_height-Canis_latrans -0.5733 0.1908 -0.9548 -0.5700 -0.1982 1.0027
## veg_height-Procyon_lotor 0.3468 0.1222 0.1152 0.3450 0.5892 1.0020
## veg_height-Dasypus_novemcinctus 0.2714 0.1384 -0.0026 0.2698 0.5509 1.0059
## veg_height-Lynx_rufus 0.0789 0.2460 -0.4249 0.0884 0.5484 1.0061
## veg_height-Didelphis_virginiana 0.5153 0.2526 0.0507 0.5021 1.0380 1.0054
## veg_height-Sylvilagus_floridanus 0.1726 0.2441 -0.3174 0.1707 0.6448 1.0051
## veg_height-Meleagris_gallopavo -0.1728 0.3498 -0.9001 -0.1626 0.4986 1.0214
## veg_height-Sciurus_carolinensis 0.1381 0.2144 -0.2688 0.1389 0.5813 1.0023
## week-Canis_latrans 0.4554 0.2524 -0.0189 0.4407 0.9863 1.0204
## week-Procyon_lotor 0.1611 0.1952 -0.2165 0.1614 0.5487 1.0018
## week-Dasypus_novemcinctus 0.0689 0.2108 -0.3517 0.0724 0.4796 1.0003
## week-Lynx_rufus 0.2646 0.2994 -0.3130 0.2620 0.8680 1.0045
## week-Didelphis_virginiana 0.0443 0.3177 -0.6092 0.0644 0.6501 1.0044
## week-Sylvilagus_floridanus 0.0530 0.3013 -0.5870 0.0538 0.6307 1.0026
## week-Meleagris_gallopavo -0.0683 0.3522 -0.8566 -0.0342 0.5489 1.0122
## week-Sciurus_carolinensis 0.5511 0.3296 -0.0309 0.5317 1.2700 1.0136
## I(week^2)-Canis_latrans -0.1945 0.1078 -0.4225 -0.1929 0.0076 1.0088
## I(week^2)-Procyon_lotor -0.1141 0.0900 -0.2962 -0.1130 0.0574 1.0055
## I(week^2)-Dasypus_novemcinctus -0.1545 0.1021 -0.3593 -0.1524 0.0380 1.0002
## I(week^2)-Lynx_rufus -0.2105 0.1480 -0.5160 -0.2049 0.0633 1.0051
## I(week^2)-Didelphis_virginiana -0.3824 0.2182 -0.9092 -0.3567 -0.0323 1.0403
## I(week^2)-Sylvilagus_floridanus -0.1703 0.1529 -0.5000 -0.1616 0.1098 1.0100
## I(week^2)-Meleagris_gallopavo -0.3836 0.2377 -0.9555 -0.3520 -0.0111 1.0202
## I(week^2)-Sciurus_carolinensis -0.1956 0.1375 -0.4833 -0.1910 0.0578 1.0086
## ESS
## (Intercept)-Canis_latrans 862
## (Intercept)-Procyon_lotor 1375
## (Intercept)-Dasypus_novemcinctus 1642
## (Intercept)-Lynx_rufus 293
## (Intercept)-Didelphis_virginiana 778
## (Intercept)-Sylvilagus_floridanus 849
## (Intercept)-Meleagris_gallopavo 293
## (Intercept)-Sciurus_carolinensis 675
## shrub_cover-Canis_latrans 836
## shrub_cover-Procyon_lotor 1431
## shrub_cover-Dasypus_novemcinctus 1137
## shrub_cover-Lynx_rufus 450
## shrub_cover-Didelphis_virginiana 655
## shrub_cover-Sylvilagus_floridanus 612
## shrub_cover-Meleagris_gallopavo 316
## shrub_cover-Sciurus_carolinensis 516
## veg_height-Canis_latrans 745
## veg_height-Procyon_lotor 1634
## veg_height-Dasypus_novemcinctus 1608
## veg_height-Lynx_rufus 690
## veg_height-Didelphis_virginiana 994
## veg_height-Sylvilagus_floridanus 979
## veg_height-Meleagris_gallopavo 562
## veg_height-Sciurus_carolinensis 1129
## week-Canis_latrans 935
## week-Procyon_lotor 1758
## week-Dasypus_novemcinctus 1770
## week-Lynx_rufus 1047
## week-Didelphis_virginiana 1140
## week-Sylvilagus_floridanus 1263
## week-Meleagris_gallopavo 634
## week-Sciurus_carolinensis 1156
## I(week^2)-Canis_latrans 1057
## I(week^2)-Procyon_lotor 1680
## I(week^2)-Dasypus_novemcinctus 1815
## I(week^2)-Lynx_rufus 663
## I(week^2)-Didelphis_virginiana 470
## I(week^2)-Sylvilagus_floridanus 766
## I(week^2)-Meleagris_gallopavo 263
## I(week^2)-Sciurus_carolinensis 1255
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T10 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_move_T10)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.9305
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2101 0.4458 -1.0563 -0.2306 0.7028 1.0121 359
## Cogon_Patch_Size 0.0156 0.4041 -0.8034 0.0165 0.8425 1.0065 752
## Avg_Cogongrass_Cover 0.0960 0.3491 -0.5683 0.0866 0.7993 1.0027 679
## total_shrub_cover -0.9152 0.4537 -1.9450 -0.8833 -0.1110 1.0022 309
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6111 0.7993 0.0465 0.3674 2.7697 1.0264 811
## Cogon_Patch_Size 0.8509 1.8238 0.0480 0.4036 4.3945 1.1297 434
## Avg_Cogongrass_Cover 0.4427 0.6010 0.0414 0.2564 1.9937 1.0555 809
## total_shrub_cover 0.8467 1.1104 0.0595 0.4737 3.9176 1.0167 438
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3812 1.3405 0.1024 0.9729 4.7864 1.0063 207
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6200 0.2850 -3.1709 -2.6216 -2.0310 1.0071 1676
## shrub_cover 0.5200 0.3233 -0.1289 0.5262 1.1554 1.0024 1283
## veg_height 0.0858 0.2019 -0.3203 0.0890 0.4961 1.0133 1415
## week 0.1944 0.2070 -0.2002 0.1911 0.6223 1.0002 1092
## I(week^2) -0.2274 0.1218 -0.4901 -0.2222 -0.0009 1.0096 969
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5459 0.4978 0.1196 0.4073 1.7547 1.0188 1120
## shrub_cover 0.7186 0.6613 0.1328 0.5323 2.4060 1.0077 1034
## veg_height 0.2529 0.2368 0.0571 0.1901 0.8014 1.0278 1683
## week 0.2137 0.2495 0.0331 0.1441 0.7625 1.0133 1259
## I(week^2) 0.0811 0.0767 0.0199 0.0599 0.2602 1.0302 774
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1928 0.5935 -0.8680 0.1474
## (Intercept)-Procyon_lotor 0.3068 0.6413 -0.8364 0.2628
## (Intercept)-Dasypus_novemcinctus -0.3112 0.5778 -1.4633 -0.3212
## (Intercept)-Lynx_rufus -0.1767 0.6514 -1.4281 -0.2025
## (Intercept)-Didelphis_virginiana -0.6040 0.6569 -1.9436 -0.5977
## (Intercept)-Sylvilagus_floridanus -0.1128 0.6517 -1.3273 -0.1492
## (Intercept)-Meleagris_gallopavo -0.3868 0.6832 -1.7178 -0.3791
## (Intercept)-Sciurus_carolinensis -0.6361 0.6831 -2.0842 -0.6006
## Cogon_Patch_Size-Canis_latrans 0.6876 0.7106 -0.3059 0.5608
## Cogon_Patch_Size-Procyon_lotor -0.1317 0.4482 -1.0248 -0.1352
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0418 0.4478 -0.8378 0.0469
## Cogon_Patch_Size-Lynx_rufus -0.0286 0.7033 -1.3989 -0.0620
## Cogon_Patch_Size-Didelphis_virginiana 0.5790 0.5054 -0.3053 0.5338
## Cogon_Patch_Size-Sylvilagus_floridanus -0.6418 0.9257 -2.9460 -0.4521
## Cogon_Patch_Size-Meleagris_gallopavo 0.0866 0.6926 -1.2119 0.0569
## Cogon_Patch_Size-Sciurus_carolinensis -0.4810 0.7051 -2.0996 -0.3800
## Avg_Cogongrass_Cover-Canis_latrans 0.2705 0.4345 -0.5106 0.2389
## Avg_Cogongrass_Cover-Procyon_lotor 0.0562 0.4510 -0.8053 0.0475
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3081 0.4358 -0.4779 0.2874
## Avg_Cogongrass_Cover-Lynx_rufus 0.4573 0.5550 -0.4648 0.3968
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.0683 0.4780 -0.9528 0.0815
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2440 0.5620 -1.4704 -0.2139
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4319 0.6883 -2.0140 -0.3763
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3163 0.4763 -0.5705 0.2966
## total_shrub_cover-Canis_latrans -0.0155 0.5982 -1.0418 -0.0667
## total_shrub_cover-Procyon_lotor -1.1999 0.5835 -2.5475 -1.1361
## total_shrub_cover-Dasypus_novemcinctus -0.5863 0.6224 -2.0868 -0.5054
## total_shrub_cover-Lynx_rufus -1.3184 0.8128 -3.2524 -1.2049
## total_shrub_cover-Didelphis_virginiana -0.9285 0.6280 -2.3410 -0.8719
## total_shrub_cover-Sylvilagus_floridanus -1.1872 0.8361 -3.2665 -1.0836
## total_shrub_cover-Meleagris_gallopavo -1.4437 0.7839 -3.2724 -1.3391
## total_shrub_cover-Sciurus_carolinensis -0.8801 0.7009 -2.4873 -0.8165
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5273 1.0272 577
## (Intercept)-Procyon_lotor 1.6956 1.0165 488
## (Intercept)-Dasypus_novemcinctus 0.8741 1.0075 503
## (Intercept)-Lynx_rufus 1.1496 1.0060 569
## (Intercept)-Didelphis_virginiana 0.6906 1.0031 379
## (Intercept)-Sylvilagus_floridanus 1.3147 1.0073 525
## (Intercept)-Meleagris_gallopavo 1.0346 1.0058 407
## (Intercept)-Sciurus_carolinensis 0.6659 1.0056 431
## Cogon_Patch_Size-Canis_latrans 2.3891 1.0070 484
## Cogon_Patch_Size-Procyon_lotor 0.7533 1.0016 1362
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9511 1.0058 1064
## Cogon_Patch_Size-Lynx_rufus 1.4577 1.0073 640
## Cogon_Patch_Size-Didelphis_virginiana 1.6732 1.0020 925
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5992 1.0130 429
## Cogon_Patch_Size-Meleagris_gallopavo 1.5476 1.0118 725
## Cogon_Patch_Size-Sciurus_carolinensis 0.5903 1.0065 514
## Avg_Cogongrass_Cover-Canis_latrans 1.2167 1.0016 1052
## Avg_Cogongrass_Cover-Procyon_lotor 1.0030 1.0011 1010
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2349 1.0004 1192
## Avg_Cogongrass_Cover-Lynx_rufus 1.7478 1.0131 909
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.9911 1.0030 767
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8127 1.0124 685
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7408 1.0146 424
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.3270 1.0066 965
## total_shrub_cover-Canis_latrans 1.3338 1.0189 613
## total_shrub_cover-Procyon_lotor -0.2429 1.0020 557
## total_shrub_cover-Dasypus_novemcinctus 0.4379 1.0048 384
## total_shrub_cover-Lynx_rufus -0.0177 1.0251 386
## total_shrub_cover-Didelphis_virginiana 0.1615 1.0016 532
## total_shrub_cover-Sylvilagus_floridanus 0.1634 1.0114 260
## total_shrub_cover-Meleagris_gallopavo -0.2051 1.0027 420
## total_shrub_cover-Sciurus_carolinensis 0.2924 1.0086 438
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5750 0.1944 -2.9634 -2.5668 -2.2069 1.0188
## (Intercept)-Procyon_lotor -2.2170 0.1529 -2.5206 -2.2156 -1.9278 0.9999
## (Intercept)-Dasypus_novemcinctus -1.7486 0.2035 -2.1570 -1.7438 -1.3660 1.0158
## (Intercept)-Lynx_rufus -3.2568 0.3217 -3.9216 -3.2452 -2.6680 1.0021
## (Intercept)-Didelphis_virginiana -2.5223 0.3131 -3.1530 -2.5212 -1.9285 1.0210
## (Intercept)-Sylvilagus_floridanus -3.1074 0.2867 -3.6940 -3.0946 -2.5842 1.0021
## (Intercept)-Meleagris_gallopavo -3.3148 0.5059 -4.3462 -3.2800 -2.3833 1.0025
## (Intercept)-Sciurus_carolinensis -2.7070 0.3419 -3.3905 -2.7011 -2.0737 1.0186
## shrub_cover-Canis_latrans -0.2659 0.2337 -0.7298 -0.2690 0.1994 1.0076
## shrub_cover-Procyon_lotor 0.3250 0.1637 0.0033 0.3240 0.6410 1.0010
## shrub_cover-Dasypus_novemcinctus 1.1141 0.3666 0.4331 1.1042 1.8229 1.0229
## shrub_cover-Lynx_rufus 0.2030 0.3577 -0.5383 0.2153 0.8482 1.0030
## shrub_cover-Didelphis_virginiana 1.2033 0.3891 0.4770 1.1983 1.9669 1.0100
## shrub_cover-Sylvilagus_floridanus 0.7814 0.4034 -0.0569 0.7886 1.5414 1.0174
## shrub_cover-Meleagris_gallopavo -0.2353 0.4580 -1.1799 -0.2109 0.6133 1.0115
## shrub_cover-Sciurus_carolinensis 1.1856 0.4255 0.3170 1.1845 2.0152 1.0149
## veg_height-Canis_latrans -0.5754 0.1835 -0.9501 -0.5697 -0.2263 1.0155
## veg_height-Procyon_lotor 0.3488 0.1253 0.1068 0.3468 0.5922 1.0058
## veg_height-Dasypus_novemcinctus 0.2873 0.1397 0.0206 0.2814 0.5666 1.0003
## veg_height-Lynx_rufus 0.0495 0.2372 -0.4407 0.0620 0.4932 1.0133
## veg_height-Didelphis_virginiana 0.4598 0.2489 -0.0145 0.4508 0.9540 1.0087
## veg_height-Sylvilagus_floridanus 0.0814 0.2375 -0.3801 0.0799 0.5534 1.0134
## veg_height-Meleagris_gallopavo -0.0898 0.4167 -0.9021 -0.0975 0.7376 1.0067
## veg_height-Sciurus_carolinensis 0.1350 0.2278 -0.2951 0.1317 0.5802 1.0120
## week-Canis_latrans 0.4644 0.2486 0.0112 0.4552 0.9938 1.0066
## week-Procyon_lotor 0.1633 0.2031 -0.2351 0.1685 0.5497 1.0026
## week-Dasypus_novemcinctus 0.0681 0.2111 -0.3373 0.0661 0.4878 0.9997
## week-Lynx_rufus 0.2867 0.3059 -0.2972 0.2643 0.9195 1.0004
## week-Didelphis_virginiana 0.0475 0.3207 -0.6223 0.0593 0.6558 1.0062
## week-Sylvilagus_floridanus 0.0775 0.2948 -0.5294 0.0835 0.6429 1.0013
## week-Meleagris_gallopavo -0.0924 0.3406 -0.8032 -0.0745 0.5171 1.0121
## week-Sciurus_carolinensis 0.5688 0.3397 -0.0238 0.5419 1.3305 1.0002
## I(week^2)-Canis_latrans -0.2019 0.1052 -0.4137 -0.1988 -0.0012 1.0016
## I(week^2)-Procyon_lotor -0.1142 0.0885 -0.2840 -0.1147 0.0649 1.0095
## I(week^2)-Dasypus_novemcinctus -0.1550 0.1046 -0.3756 -0.1523 0.0445 1.0012
## I(week^2)-Lynx_rufus -0.2117 0.1453 -0.5015 -0.2071 0.0687 1.0037
## I(week^2)-Didelphis_virginiana -0.3855 0.2113 -0.8509 -0.3652 -0.0309 1.0063
## I(week^2)-Sylvilagus_floridanus -0.1792 0.1510 -0.4938 -0.1741 0.0952 1.0134
## I(week^2)-Meleagris_gallopavo -0.3692 0.2397 -0.9509 -0.3366 0.0186 1.0477
## I(week^2)-Sciurus_carolinensis -0.2089 0.1378 -0.4942 -0.2044 0.0487 1.0006
## ESS
## (Intercept)-Canis_latrans 818
## (Intercept)-Procyon_lotor 1614
## (Intercept)-Dasypus_novemcinctus 733
## (Intercept)-Lynx_rufus 546
## (Intercept)-Didelphis_virginiana 660
## (Intercept)-Sylvilagus_floridanus 544
## (Intercept)-Meleagris_gallopavo 243
## (Intercept)-Sciurus_carolinensis 437
## shrub_cover-Canis_latrans 666
## shrub_cover-Procyon_lotor 1332
## shrub_cover-Dasypus_novemcinctus 374
## shrub_cover-Lynx_rufus 568
## shrub_cover-Didelphis_virginiana 548
## shrub_cover-Sylvilagus_floridanus 352
## shrub_cover-Meleagris_gallopavo 291
## shrub_cover-Sciurus_carolinensis 378
## veg_height-Canis_latrans 750
## veg_height-Procyon_lotor 1559
## veg_height-Dasypus_novemcinctus 1568
## veg_height-Lynx_rufus 813
## veg_height-Didelphis_virginiana 990
## veg_height-Sylvilagus_floridanus 633
## veg_height-Meleagris_gallopavo 479
## veg_height-Sciurus_carolinensis 835
## week-Canis_latrans 1097
## week-Procyon_lotor 1594
## week-Dasypus_novemcinctus 1774
## week-Lynx_rufus 1050
## week-Didelphis_virginiana 1058
## week-Sylvilagus_floridanus 1061
## week-Meleagris_gallopavo 656
## week-Sciurus_carolinensis 956
## I(week^2)-Canis_latrans 1321
## I(week^2)-Procyon_lotor 1634
## I(week^2)-Dasypus_novemcinctus 1522
## I(week^2)-Lynx_rufus 805
## I(week^2)-Didelphis_virginiana 428
## I(week^2)-Sylvilagus_floridanus 643
## I(week^2)-Meleagris_gallopavo 326
## I(week^2)-Sciurus_carolinensis 1123
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T10 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_forage_T10)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8975
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3913 0.3719 -1.1024 -0.3973 0.3539 1.0091 697
## Veg_shannon_index 0.3699 0.2758 -0.1446 0.3619 0.9325 1.0075 1012
## Avg_Cogongrass_Cover 0.3326 0.2892 -0.2580 0.3372 0.8802 1.0050 1074
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5936 0.7333 0.0533 0.3740 2.3355 1.0894 888
## Veg_shannon_index 0.2982 0.3805 0.0364 0.1852 1.3612 1.0091 1113
## Avg_Cogongrass_Cover 0.3606 0.4700 0.0392 0.2196 1.6357 1.0551 928
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7356 0.6971 0.0578 0.5277 2.5368 1.0416 231
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5909 0.3232 -3.2292 -2.5977 -1.9531 1.0073 1639
## shrub_cover 0.2558 0.3127 -0.3376 0.2515 0.8848 0.9999 1645
## veg_height 0.0720 0.2004 -0.3358 0.0721 0.4597 1.0076 1492
## week 0.1915 0.2052 -0.2159 0.1936 0.5858 1.0166 1423
## I(week^2) -0.2243 0.1228 -0.4789 -0.2192 0.0002 1.0079 893
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7397 0.6330 0.1603 0.5666 2.3970 1.0113 1506
## shrub_cover 0.6978 0.6348 0.1371 0.5246 2.2666 1.0321 1124
## veg_height 0.2513 0.2304 0.0572 0.1888 0.8218 1.0278 1256
## week 0.2036 0.2114 0.0326 0.1441 0.7319 1.0027 844
## I(week^2) 0.0807 0.0760 0.0190 0.0588 0.2719 1.0053 693
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.0059 0.5325 -1.0190 -0.0236
## (Intercept)-Procyon_lotor 0.1141 0.5568 -0.9406 0.1160
## (Intercept)-Dasypus_novemcinctus -0.5659 0.4719 -1.5336 -0.5451
## (Intercept)-Lynx_rufus -0.2627 0.5850 -1.3729 -0.2916
## (Intercept)-Didelphis_virginiana -0.9178 0.5377 -2.0510 -0.8853
## (Intercept)-Sylvilagus_floridanus -0.4540 0.5157 -1.4507 -0.4407
## (Intercept)-Meleagris_gallopavo -0.1887 0.6865 -1.3502 -0.2577
## (Intercept)-Sciurus_carolinensis -0.9079 0.5351 -2.0414 -0.8701
## Veg_shannon_index-Canis_latrans 0.6548 0.3985 -0.0565 0.6307
## Veg_shannon_index-Procyon_lotor 0.4443 0.3595 -0.2085 0.4313
## Veg_shannon_index-Dasypus_novemcinctus 0.1965 0.3519 -0.5380 0.1999
## Veg_shannon_index-Lynx_rufus 0.2227 0.4765 -0.7741 0.2352
## Veg_shannon_index-Didelphis_virginiana 0.4863 0.3857 -0.2356 0.4706
## Veg_shannon_index-Sylvilagus_floridanus 0.4264 0.4183 -0.3494 0.4058
## Veg_shannon_index-Meleagris_gallopavo 0.5473 0.5235 -0.3481 0.4996
## Veg_shannon_index-Sciurus_carolinensis -0.0130 0.3926 -0.8499 0.0107
## Avg_Cogongrass_Cover-Canis_latrans 0.6061 0.3973 -0.0968 0.5736
## Avg_Cogongrass_Cover-Procyon_lotor 0.3511 0.3690 -0.3612 0.3432
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4500 0.3376 -0.2085 0.4480
## Avg_Cogongrass_Cover-Lynx_rufus 0.6023 0.4303 -0.1435 0.5597
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.4532 0.3787 -0.2766 0.4476
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0918 0.4709 -1.1172 -0.0549
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.0852 0.6167 -1.4629 -0.0397
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4189 0.3640 -0.2942 0.4158
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0767 1.0165 585
## (Intercept)-Procyon_lotor 1.2233 1.0374 379
## (Intercept)-Dasypus_novemcinctus 0.3370 1.0063 1164
## (Intercept)-Lynx_rufus 1.0133 1.0390 674
## (Intercept)-Didelphis_virginiana 0.0361 1.0232 1079
## (Intercept)-Sylvilagus_floridanus 0.5642 1.0156 990
## (Intercept)-Meleagris_gallopavo 1.3931 1.0241 452
## (Intercept)-Sciurus_carolinensis 0.0324 1.0363 1035
## Veg_shannon_index-Canis_latrans 1.5363 1.0079 1203
## Veg_shannon_index-Procyon_lotor 1.1951 1.0011 1537
## Veg_shannon_index-Dasypus_novemcinctus 0.8664 1.0019 1596
## Veg_shannon_index-Lynx_rufus 1.1293 1.0038 1091
## Veg_shannon_index-Didelphis_virginiana 1.3134 1.0026 1823
## Veg_shannon_index-Sylvilagus_floridanus 1.3302 1.0085 1172
## Veg_shannon_index-Meleagris_gallopavo 1.7652 1.0083 907
## Veg_shannon_index-Sciurus_carolinensis 0.6851 1.0014 1477
## Avg_Cogongrass_Cover-Canis_latrans 1.4913 1.0013 1570
## Avg_Cogongrass_Cover-Procyon_lotor 1.1110 1.0035 2091
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1071 1.0028 2019
## Avg_Cogongrass_Cover-Lynx_rufus 1.5585 1.0086 1435
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.2339 1.0009 1649
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7451 1.0214 1171
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.9849 1.0499 558
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.1813 1.0005 1789
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5594 0.1963 -2.9551 -2.5569 -2.1831 1.0007
## (Intercept)-Procyon_lotor -2.2034 0.1615 -2.5342 -2.1945 -1.9073 1.0044
## (Intercept)-Dasypus_novemcinctus -1.6527 0.1820 -2.0247 -1.6466 -1.3038 1.0086
## (Intercept)-Lynx_rufus -3.3771 0.3507 -4.0891 -3.3742 -2.7001 1.0329
## (Intercept)-Didelphis_virginiana -2.4311 0.3061 -3.0637 -2.4233 -1.8484 1.0159
## (Intercept)-Sylvilagus_floridanus -3.0356 0.2955 -3.6469 -3.0193 -2.4884 1.0115
## (Intercept)-Meleagris_gallopavo -3.6661 0.4781 -4.5842 -3.6749 -2.6627 1.0027
## (Intercept)-Sciurus_carolinensis -2.5233 0.3201 -3.2053 -2.5111 -1.9399 1.0097
## shrub_cover-Canis_latrans -0.2936 0.2151 -0.7202 -0.2938 0.1326 1.0004
## shrub_cover-Procyon_lotor 0.2472 0.1670 -0.0978 0.2537 0.5596 1.0064
## shrub_cover-Dasypus_novemcinctus 0.8791 0.3057 0.2921 0.8741 1.4991 1.0091
## shrub_cover-Lynx_rufus -0.2077 0.3774 -0.9738 -0.2090 0.5071 1.0103
## shrub_cover-Didelphis_virginiana 1.0088 0.3727 0.3386 0.9952 1.7833 1.0051
## shrub_cover-Sylvilagus_floridanus 0.2537 0.4209 -0.5398 0.2445 1.0788 1.0061
## shrub_cover-Meleagris_gallopavo -0.6324 0.3999 -1.3845 -0.6366 0.1921 1.0071
## shrub_cover-Sciurus_carolinensis 0.8789 0.4122 0.0640 0.8762 1.6960 1.0299
## veg_height-Canis_latrans -0.5782 0.1797 -0.9472 -0.5703 -0.2477 1.0028
## veg_height-Procyon_lotor 0.3395 0.1207 0.1110 0.3390 0.5829 1.0076
## veg_height-Dasypus_novemcinctus 0.2558 0.1373 -0.0170 0.2532 0.5290 1.0001
## veg_height-Lynx_rufus 0.0008 0.2491 -0.4895 0.0014 0.4703 1.0053
## veg_height-Didelphis_virginiana 0.4666 0.2448 0.0131 0.4611 0.9664 1.0013
## veg_height-Sylvilagus_floridanus 0.1550 0.2388 -0.3213 0.1575 0.6180 1.0044
## veg_height-Meleagris_gallopavo -0.1581 0.3985 -0.9661 -0.1493 0.6133 1.0744
## veg_height-Sciurus_carolinensis 0.0781 0.2226 -0.3479 0.0769 0.5055 1.0229
## week-Canis_latrans 0.4604 0.2412 0.0052 0.4494 0.9527 1.0183
## week-Procyon_lotor 0.1684 0.1982 -0.2207 0.1676 0.5564 1.0022
## week-Dasypus_novemcinctus 0.0763 0.2093 -0.3234 0.0740 0.5060 1.0063
## week-Lynx_rufus 0.2850 0.2950 -0.2861 0.2771 0.9090 1.0094
## week-Didelphis_virginiana 0.0466 0.3222 -0.6221 0.0644 0.6505 1.0295
## week-Sylvilagus_floridanus 0.0420 0.3005 -0.5633 0.0537 0.6230 1.0022
## week-Meleagris_gallopavo -0.0800 0.3518 -0.8399 -0.0561 0.5503 1.0146
## week-Sciurus_carolinensis 0.5540 0.3298 -0.0291 0.5348 1.2778 1.0107
## I(week^2)-Canis_latrans -0.1985 0.1039 -0.4059 -0.1973 -0.0006 1.0074
## I(week^2)-Procyon_lotor -0.1149 0.0872 -0.2896 -0.1140 0.0543 1.0070
## I(week^2)-Dasypus_novemcinctus -0.1574 0.1033 -0.3617 -0.1577 0.0442 1.0033
## I(week^2)-Lynx_rufus -0.2083 0.1461 -0.5246 -0.2001 0.0620 1.0238
## I(week^2)-Didelphis_virginiana -0.3703 0.2088 -0.8507 -0.3473 -0.0364 1.0424
## I(week^2)-Sylvilagus_floridanus -0.1665 0.1453 -0.4653 -0.1604 0.1115 1.0107
## I(week^2)-Meleagris_gallopavo -0.3871 0.2446 -0.9577 -0.3530 0.0005 1.0521
## I(week^2)-Sciurus_carolinensis -0.1976 0.1383 -0.4870 -0.1930 0.0662 1.0177
## ESS
## (Intercept)-Canis_latrans 826
## (Intercept)-Procyon_lotor 1393
## (Intercept)-Dasypus_novemcinctus 1621
## (Intercept)-Lynx_rufus 390
## (Intercept)-Didelphis_virginiana 748
## (Intercept)-Sylvilagus_floridanus 696
## (Intercept)-Meleagris_gallopavo 233
## (Intercept)-Sciurus_carolinensis 916
## shrub_cover-Canis_latrans 940
## shrub_cover-Procyon_lotor 1284
## shrub_cover-Dasypus_novemcinctus 1267
## shrub_cover-Lynx_rufus 396
## shrub_cover-Didelphis_virginiana 744
## shrub_cover-Sylvilagus_floridanus 510
## shrub_cover-Meleagris_gallopavo 297
## shrub_cover-Sciurus_carolinensis 689
## veg_height-Canis_latrans 652
## veg_height-Procyon_lotor 1760
## veg_height-Dasypus_novemcinctus 1704
## veg_height-Lynx_rufus 988
## veg_height-Didelphis_virginiana 1143
## veg_height-Sylvilagus_floridanus 953
## veg_height-Meleagris_gallopavo 499
## veg_height-Sciurus_carolinensis 1111
## week-Canis_latrans 1221
## week-Procyon_lotor 1832
## week-Dasypus_novemcinctus 2265
## week-Lynx_rufus 1042
## week-Didelphis_virginiana 1113
## week-Sylvilagus_floridanus 1185
## week-Meleagris_gallopavo 526
## week-Sciurus_carolinensis 1240
## I(week^2)-Canis_latrans 1156
## I(week^2)-Procyon_lotor 1701
## I(week^2)-Dasypus_novemcinctus 1816
## I(week^2)-Lynx_rufus 805
## I(week^2)-Didelphis_virginiana 513
## I(week^2)-Sylvilagus_floridanus 731
## I(week^2)-Meleagris_gallopavo 213
## I(week^2)-Sciurus_carolinensis 1352
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T10 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogon_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8445
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.3321 0.3767 -1.1019 -0.3363 0.4065 1.0063 430
## Avg_Cogongrass_Cover 0.2156 0.2659 -0.3098 0.2177 0.7371 1.0009 1298
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.5920 0.7791 0.0521 0.3710 2.4125 1.0497 515
## Avg_Cogongrass_Cover 0.3209 0.3832 0.0409 0.2024 1.2816 1.0107 1066
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6703 0.6665 0.0567 0.472 2.4286 1.0262 172
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6007 0.3141 -3.2281 -2.6015 -1.9389 1.0010 1905
## shrub_cover 0.2683 0.3038 -0.3274 0.2687 0.8952 1.0051 1722
## veg_height 0.0664 0.1975 -0.3306 0.0723 0.4545 1.0048 1450
## week 0.1843 0.2077 -0.2245 0.1859 0.5935 1.0109 1029
## I(week^2) -0.2210 0.1241 -0.4774 -0.2156 0.0037 1.0087 989
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7285 0.7680 0.1487 0.5287 2.5317 1.0507 782
## shrub_cover 0.6677 0.6301 0.1291 0.4955 2.1190 1.0122 872
## veg_height 0.2702 0.3047 0.0597 0.1986 0.8726 1.0320 883
## week 0.1973 0.1879 0.0331 0.1405 0.7143 1.0302 1229
## I(week^2) 0.0802 0.0708 0.0202 0.0596 0.2556 1.0221 1106
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.0771 0.5124 -0.9097 0.0579
## (Intercept)-Procyon_lotor 0.1975 0.5486 -0.8415 0.1766
## (Intercept)-Dasypus_novemcinctus -0.5080 0.4498 -1.4431 -0.4919
## (Intercept)-Lynx_rufus -0.2723 0.6162 -1.3749 -0.3072
## (Intercept)-Didelphis_virginiana -0.8268 0.5330 -1.9378 -0.8060
## (Intercept)-Sylvilagus_floridanus -0.3974 0.5175 -1.4309 -0.3902
## (Intercept)-Meleagris_gallopavo -0.1215 0.6904 -1.2766 -0.1829
## (Intercept)-Sciurus_carolinensis -0.8553 0.5377 -2.0129 -0.8291
## Avg_Cogongrass_Cover-Canis_latrans 0.4139 0.3714 -0.2504 0.3926
## Avg_Cogongrass_Cover-Procyon_lotor 0.2217 0.3491 -0.4504 0.2218
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3660 0.3244 -0.2716 0.3587
## Avg_Cogongrass_Cover-Lynx_rufus 0.4637 0.4070 -0.2408 0.4211
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.3278 0.3695 -0.3635 0.3164
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2052 0.4276 -1.1199 -0.1733
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.2057 0.5569 -1.3845 -0.1612
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.3568 0.3506 -0.3109 0.3433
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1604 1.0044 610
## (Intercept)-Procyon_lotor 1.3194 1.0061 580
## (Intercept)-Dasypus_novemcinctus 0.3382 1.0048 746
## (Intercept)-Lynx_rufus 1.0231 1.0278 363
## (Intercept)-Didelphis_virginiana 0.1439 1.0063 942
## (Intercept)-Sylvilagus_floridanus 0.6300 1.0039 943
## (Intercept)-Meleagris_gallopavo 1.4313 1.0077 297
## (Intercept)-Sciurus_carolinensis 0.1334 1.0131 1017
## Avg_Cogongrass_Cover-Canis_latrans 1.2320 1.0004 1901
## Avg_Cogongrass_Cover-Procyon_lotor 0.9158 1.0021 2160
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0412 1.0003 2259
## Avg_Cogongrass_Cover-Lynx_rufus 1.3886 1.0046 1302
## Avg_Cogongrass_Cover-Didelphis_virginiana 1.1071 1.0048 1350
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5448 1.0013 1087
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.7639 1.0019 699
## Avg_Cogongrass_Cover-Sciurus_carolinensis 1.0666 1.0050 1949
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5820 0.2056 -3.0043 -2.5815 -2.1778 1.0012
## (Intercept)-Procyon_lotor -2.2000 0.1640 -2.5359 -2.1933 -1.8928 1.0021
## (Intercept)-Dasypus_novemcinctus -1.6564 0.1802 -2.0226 -1.6512 -1.3156 1.0019
## (Intercept)-Lynx_rufus -3.3419 0.3466 -4.0604 -3.3263 -2.7270 1.0085
## (Intercept)-Didelphis_virginiana -2.4198 0.2946 -3.0150 -2.4083 -1.8701 1.0142
## (Intercept)-Sylvilagus_floridanus -3.0155 0.3065 -3.6531 -2.9987 -2.4593 1.0101
## (Intercept)-Meleagris_gallopavo -3.6641 0.5070 -4.7131 -3.6461 -2.7174 1.0086
## (Intercept)-Sciurus_carolinensis -2.5280 0.3271 -3.2431 -2.5168 -1.9218 1.0032
## shrub_cover-Canis_latrans -0.2727 0.2212 -0.7215 -0.2705 0.1696 1.0043
## shrub_cover-Procyon_lotor 0.2588 0.1624 -0.0722 0.2643 0.5739 1.0015
## shrub_cover-Dasypus_novemcinctus 0.8798 0.2974 0.3025 0.8766 1.4732 1.0097
## shrub_cover-Lynx_rufus -0.1756 0.3475 -0.8571 -0.1768 0.4976 1.0008
## shrub_cover-Didelphis_virginiana 0.9925 0.3796 0.2971 0.9697 1.7567 1.0063
## shrub_cover-Sylvilagus_floridanus 0.2719 0.4070 -0.5033 0.2674 1.0621 1.0043
## shrub_cover-Meleagris_gallopavo -0.6132 0.4374 -1.5265 -0.6012 0.2416 1.0048
## shrub_cover-Sciurus_carolinensis 0.8756 0.4182 0.0762 0.8635 1.7564 1.0035
## veg_height-Canis_latrans -0.5910 0.1923 -0.9906 -0.5803 -0.2279 1.0094
## veg_height-Procyon_lotor 0.3460 0.1232 0.1137 0.3458 0.5853 1.0056
## veg_height-Dasypus_novemcinctus 0.2594 0.1359 0.0024 0.2536 0.5294 1.0017
## veg_height-Lynx_rufus 0.0016 0.2475 -0.5392 0.0189 0.4620 1.0121
## veg_height-Didelphis_virginiana 0.4580 0.2470 -0.0117 0.4497 0.9474 1.0176
## veg_height-Sylvilagus_floridanus 0.1580 0.2463 -0.3225 0.1602 0.6418 1.0276
## veg_height-Meleagris_gallopavo -0.1721 0.4161 -1.0281 -0.1713 0.6419 1.0075
## veg_height-Sciurus_carolinensis 0.0912 0.2203 -0.3328 0.0870 0.5305 1.0024
## week-Canis_latrans 0.4427 0.2419 -0.0185 0.4336 0.9213 1.0228
## week-Procyon_lotor 0.1575 0.2040 -0.2393 0.1576 0.5684 1.0016
## week-Dasypus_novemcinctus 0.0667 0.2128 -0.3567 0.0712 0.4757 1.0068
## week-Lynx_rufus 0.2671 0.3064 -0.2956 0.2522 0.9135 1.0082
## week-Didelphis_virginiana 0.0339 0.3167 -0.6186 0.0486 0.6153 1.0157
## week-Sylvilagus_floridanus 0.0329 0.2928 -0.5577 0.0426 0.5902 1.0027
## week-Meleagris_gallopavo -0.0707 0.3567 -0.8346 -0.0462 0.5636 1.0442
## week-Sciurus_carolinensis 0.5409 0.3322 -0.0416 0.5158 1.2576 1.0107
## I(week^2)-Canis_latrans -0.1922 0.1046 -0.3951 -0.1913 0.0060 1.0127
## I(week^2)-Procyon_lotor -0.1118 0.0924 -0.2949 -0.1098 0.0689 1.0095
## I(week^2)-Dasypus_novemcinctus -0.1571 0.1026 -0.3595 -0.1579 0.0394 1.0016
## I(week^2)-Lynx_rufus -0.1973 0.1481 -0.5053 -0.1938 0.0864 1.0016
## I(week^2)-Didelphis_virginiana -0.3846 0.2272 -0.9092 -0.3545 -0.0183 1.0105
## I(week^2)-Sylvilagus_floridanus -0.1641 0.1527 -0.4870 -0.1569 0.1126 1.0069
## I(week^2)-Meleagris_gallopavo -0.3792 0.2301 -0.9034 -0.3554 0.0073 1.0361
## I(week^2)-Sciurus_carolinensis -0.1928 0.1369 -0.4762 -0.1893 0.0621 1.0088
## ESS
## (Intercept)-Canis_latrans 814
## (Intercept)-Procyon_lotor 1271
## (Intercept)-Dasypus_novemcinctus 1603
## (Intercept)-Lynx_rufus 410
## (Intercept)-Didelphis_virginiana 1013
## (Intercept)-Sylvilagus_floridanus 645
## (Intercept)-Meleagris_gallopavo 215
## (Intercept)-Sciurus_carolinensis 850
## shrub_cover-Canis_latrans 830
## shrub_cover-Procyon_lotor 1378
## shrub_cover-Dasypus_novemcinctus 1245
## shrub_cover-Lynx_rufus 506
## shrub_cover-Didelphis_virginiana 665
## shrub_cover-Sylvilagus_floridanus 601
## shrub_cover-Meleagris_gallopavo 186
## shrub_cover-Sciurus_carolinensis 647
## veg_height-Canis_latrans 682
## veg_height-Procyon_lotor 1682
## veg_height-Dasypus_novemcinctus 2060
## veg_height-Lynx_rufus 721
## veg_height-Didelphis_virginiana 1194
## veg_height-Sylvilagus_floridanus 778
## veg_height-Meleagris_gallopavo 345
## veg_height-Sciurus_carolinensis 1082
## week-Canis_latrans 1222
## week-Procyon_lotor 1256
## week-Dasypus_novemcinctus 2024
## week-Lynx_rufus 1099
## week-Didelphis_virginiana 1114
## week-Sylvilagus_floridanus 1157
## week-Meleagris_gallopavo 655
## week-Sciurus_carolinensis 1222
## I(week^2)-Canis_latrans 1360
## I(week^2)-Procyon_lotor 1214
## I(week^2)-Dasypus_novemcinctus 1772
## I(week^2)-Lynx_rufus 918
## I(week^2)-Didelphis_virginiana 454
## I(week^2)-Sylvilagus_floridanus 751
## I(week^2)-Meleagris_gallopavo 339
## I(week^2)-Sciurus_carolinensis 1438
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T10 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogonQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.7875
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9845 0.4329 -1.8104 -0.9966 -0.0808 1.0030 594
## Avg_Cogongrass_Cover -0.6603 0.4154 -1.5111 -0.6358 0.1397 1.0066 524
## I(Avg_Cogongrass_Cover^2) 0.8490 0.4109 0.1258 0.8195 1.6902 1.0117 404
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6972 0.8219 0.0572 0.4306 3.0681 1.0113 778
## Avg_Cogongrass_Cover 0.5052 0.6307 0.0468 0.2976 2.1413 1.0133 862
## I(Avg_Cogongrass_Cover^2) 0.5896 1.3078 0.0412 0.2704 2.9149 1.1662 532
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.455 0.6241 0.0469 0.2773 1.7246 1.1024 240
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.5899 0.3230 -3.1824 -2.6024 -1.9167 1.0021 1730
## shrub_cover 0.2716 0.3217 -0.3858 0.2659 0.9331 1.0088 1528
## veg_height 0.0989 0.1967 -0.2976 0.0999 0.4806 1.0144 1043
## week 0.1843 0.1994 -0.2050 0.1809 0.5928 1.0037 1436
## I(week^2) -0.2336 0.1267 -0.5024 -0.2293 0.0054 1.0034 932
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7415 0.6858 0.1488 0.5449 2.4842 1.0304 542
## shrub_cover 0.7318 0.6832 0.1284 0.5473 2.4623 1.0068 1086
## veg_height 0.2491 0.2481 0.0567 0.1860 0.8242 1.0252 1471
## week 0.2077 0.2598 0.0324 0.1431 0.8157 1.0567 1469
## I(week^2) 0.0875 0.0863 0.0211 0.0615 0.2971 1.0055 776
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6816 0.5814 -1.7860 -0.6901
## (Intercept)-Procyon_lotor -0.4654 0.5854 -1.5604 -0.4893
## (Intercept)-Dasypus_novemcinctus -1.1773 0.5177 -2.2491 -1.1593
## (Intercept)-Lynx_rufus -1.0761 0.6482 -2.3748 -1.0847
## (Intercept)-Didelphis_virginiana -1.4636 0.5758 -2.6985 -1.4358
## (Intercept)-Sylvilagus_floridanus -1.0541 0.5657 -2.1544 -1.0579
## (Intercept)-Meleagris_gallopavo -0.5851 0.7448 -1.8730 -0.6601
## (Intercept)-Sciurus_carolinensis -1.7138 0.6573 -3.1246 -1.6618
## Avg_Cogongrass_Cover-Canis_latrans -0.3324 0.5476 -1.3287 -0.3611
## Avg_Cogongrass_Cover-Procyon_lotor -0.6847 0.5229 -1.7397 -0.6864
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.4541 0.4946 -1.4260 -0.4592
## Avg_Cogongrass_Cover-Lynx_rufus -0.6169 0.6099 -1.8468 -0.6005
## Avg_Cogongrass_Cover-Didelphis_virginiana -0.3446 0.5633 -1.4295 -0.3610
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1701 0.6612 -2.6649 -1.1006
## Avg_Cogongrass_Cover-Meleagris_gallopavo -1.0903 0.7768 -2.9028 -0.9970
## Avg_Cogongrass_Cover-Sciurus_carolinensis -0.6416 0.5621 -1.7856 -0.6329
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.3650 0.8327 0.2575 1.1823
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.1816 0.7107 0.2203 1.0600
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6952 0.3840 -0.0114 0.6822
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 1.2045 0.6055 0.2787 1.1221
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.4971 0.4175 -0.3068 0.4876
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7231 0.4632 -0.1055 0.7036
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.4369 0.7765 -1.0614 0.4178
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 0.8519 0.4141 0.1154 0.8237
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5143 1.0079 649
## (Intercept)-Procyon_lotor 0.7387 1.0001 686
## (Intercept)-Dasypus_novemcinctus -0.2069 1.0004 1076
## (Intercept)-Lynx_rufus 0.2526 1.0006 392
## (Intercept)-Didelphis_virginiana -0.4209 1.0042 851
## (Intercept)-Sylvilagus_floridanus 0.0772 1.0057 756
## (Intercept)-Meleagris_gallopavo 1.1579 1.0195 512
## (Intercept)-Sciurus_carolinensis -0.5192 1.0060 623
## Avg_Cogongrass_Cover-Canis_latrans 0.8116 1.0009 877
## Avg_Cogongrass_Cover-Procyon_lotor 0.3265 1.0008 1025
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5268 0.9999 911
## Avg_Cogongrass_Cover-Lynx_rufus 0.5690 1.0022 799
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.8076 1.0130 783
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.0254 1.0086 636
## Avg_Cogongrass_Cover-Meleagris_gallopavo 0.2256 1.0226 480
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.4468 1.0040 776
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.5640 1.0103 323
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.9366 1.0383 323
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4789 1.0113 880
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.5384 1.0247 468
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 1.3459 1.0058 686
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.7602 1.0008 676
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 1.9566 1.0105 266
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.7182 1.0036 807
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5747 0.1953 -2.9600 -2.5756 -2.1972 1.0102
## (Intercept)-Procyon_lotor -2.2184 0.1622 -2.5519 -2.2122 -1.9139 1.0043
## (Intercept)-Dasypus_novemcinctus -1.6543 0.1822 -2.0243 -1.6515 -1.3074 1.0024
## (Intercept)-Lynx_rufus -3.3483 0.3726 -4.1021 -3.3430 -2.6463 1.0009
## (Intercept)-Didelphis_virginiana -2.4252 0.2982 -3.0222 -2.4190 -1.8542 1.0098
## (Intercept)-Sylvilagus_floridanus -3.0424 0.3071 -3.6996 -3.0239 -2.4977 1.0054
## (Intercept)-Meleagris_gallopavo -3.6585 0.4998 -4.6441 -3.6706 -2.6671 1.0314
## (Intercept)-Sciurus_carolinensis -2.5323 0.3230 -3.1968 -2.5259 -1.9231 1.0112
## shrub_cover-Canis_latrans -0.2534 0.2223 -0.6883 -0.2505 0.1755 1.0019
## shrub_cover-Procyon_lotor 0.2366 0.1690 -0.1096 0.2416 0.5523 1.0007
## shrub_cover-Dasypus_novemcinctus 0.8821 0.2991 0.3187 0.8760 1.4794 1.0036
## shrub_cover-Lynx_rufus -0.1765 0.3809 -0.9931 -0.1603 0.5239 1.0191
## shrub_cover-Didelphis_virginiana 1.0236 0.3859 0.3267 1.0108 1.8400 1.0177
## shrub_cover-Sylvilagus_floridanus 0.2295 0.4157 -0.5571 0.2197 1.0732 1.0194
## shrub_cover-Meleagris_gallopavo -0.6451 0.4249 -1.4413 -0.6451 0.2435 1.0482
## shrub_cover-Sciurus_carolinensis 0.8802 0.4063 0.1023 0.8631 1.6919 1.0046
## veg_height-Canis_latrans -0.5668 0.1858 -0.9273 -0.5655 -0.2117 1.0057
## veg_height-Procyon_lotor 0.3537 0.1231 0.1148 0.3498 0.6007 1.0029
## veg_height-Dasypus_novemcinctus 0.2622 0.1353 0.0033 0.2655 0.5284 1.0025
## veg_height-Lynx_rufus 0.0976 0.2467 -0.4012 0.1047 0.5743 1.0224
## veg_height-Didelphis_virginiana 0.4412 0.2524 -0.0449 0.4409 0.9530 1.0093
## veg_height-Sylvilagus_floridanus 0.1730 0.2558 -0.3241 0.1755 0.6855 1.0042
## veg_height-Meleagris_gallopavo -0.0790 0.4035 -0.9002 -0.0778 0.7094 1.0227
## veg_height-Sciurus_carolinensis 0.1210 0.2258 -0.3320 0.1229 0.5681 1.0134
## week-Canis_latrans 0.4575 0.2522 -0.0187 0.4472 0.9713 1.0004
## week-Procyon_lotor 0.1661 0.1968 -0.2260 0.1660 0.5475 1.0035
## week-Dasypus_novemcinctus 0.0683 0.2130 -0.3612 0.0678 0.4860 1.0014
## week-Lynx_rufus 0.2716 0.3016 -0.3058 0.2611 0.9058 1.0038
## week-Didelphis_virginiana 0.0378 0.3260 -0.6480 0.0503 0.6414 1.0018
## week-Sylvilagus_floridanus 0.0497 0.2960 -0.5507 0.0596 0.5967 1.0040
## week-Meleagris_gallopavo -0.0972 0.3348 -0.8054 -0.0802 0.5153 1.0021
## week-Sciurus_carolinensis 0.5397 0.3471 -0.0610 0.5115 1.2759 1.0087
## I(week^2)-Canis_latrans -0.2005 0.1083 -0.4149 -0.1980 0.0065 1.0045
## I(week^2)-Procyon_lotor -0.1151 0.0874 -0.2881 -0.1142 0.0542 1.0053
## I(week^2)-Dasypus_novemcinctus -0.1554 0.1030 -0.3687 -0.1548 0.0377 1.0027
## I(week^2)-Lynx_rufus -0.2113 0.1479 -0.5180 -0.2023 0.0551 1.0130
## I(week^2)-Didelphis_virginiana -0.3878 0.2101 -0.8667 -0.3637 -0.0441 1.0052
## I(week^2)-Sylvilagus_floridanus -0.1690 0.1531 -0.4832 -0.1632 0.1136 1.0065
## I(week^2)-Meleagris_gallopavo -0.4154 0.2573 -1.0342 -0.3770 -0.0195 1.0055
## I(week^2)-Sciurus_carolinensis -0.1960 0.1413 -0.4995 -0.1912 0.0521 1.0059
## ESS
## (Intercept)-Canis_latrans 978
## (Intercept)-Procyon_lotor 1338
## (Intercept)-Dasypus_novemcinctus 1726
## (Intercept)-Lynx_rufus 321
## (Intercept)-Didelphis_virginiana 839
## (Intercept)-Sylvilagus_floridanus 602
## (Intercept)-Meleagris_gallopavo 196
## (Intercept)-Sciurus_carolinensis 938
## shrub_cover-Canis_latrans 949
## shrub_cover-Procyon_lotor 1328
## shrub_cover-Dasypus_novemcinctus 1308
## shrub_cover-Lynx_rufus 290
## shrub_cover-Didelphis_virginiana 546
## shrub_cover-Sylvilagus_floridanus 530
## shrub_cover-Meleagris_gallopavo 215
## shrub_cover-Sciurus_carolinensis 788
## veg_height-Canis_latrans 774
## veg_height-Procyon_lotor 1437
## veg_height-Dasypus_novemcinctus 2151
## veg_height-Lynx_rufus 778
## veg_height-Didelphis_virginiana 872
## veg_height-Sylvilagus_floridanus 738
## veg_height-Meleagris_gallopavo 252
## veg_height-Sciurus_carolinensis 1099
## week-Canis_latrans 1121
## week-Procyon_lotor 1635
## week-Dasypus_novemcinctus 1982
## week-Lynx_rufus 992
## week-Didelphis_virginiana 1042
## week-Sylvilagus_floridanus 1134
## week-Meleagris_gallopavo 663
## week-Sciurus_carolinensis 1031
## I(week^2)-Canis_latrans 1335
## I(week^2)-Procyon_lotor 1585
## I(week^2)-Dasypus_novemcinctus 1832
## I(week^2)-Lynx_rufus 772
## I(week^2)-Didelphis_virginiana 499
## I(week^2)-Sylvilagus_floridanus 776
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1201
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T10 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 8 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_fullQ_T10)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8125
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7806 0.7183 -3.1451 -1.7836 -0.4109 1.0226 305
## Cogon_Patch_Size 0.1598 0.7018 -1.2422 0.1821 1.5394 1.0241 449
## Veg_shannon_index 0.9399 0.5400 -0.0817 0.9239 2.0248 1.0153 294
## total_shrub_cover -1.1468 0.6671 -2.4977 -1.1408 0.1504 1.0149 441
## Avg_Cogongrass_Cover -0.0852 0.9667 -1.9177 -0.0785 1.8253 1.0639 152
## Tree_Density -2.1582 0.8660 -3.8350 -2.1785 -0.4999 1.0129 169
## Avg_Canopy_Cover 1.8408 0.7780 0.3556 1.8185 3.5001 1.0038 545
## I(Avg_Cogongrass_Cover^2) 1.3730 0.6385 0.1411 1.3451 2.7315 1.0303 299
## avg_veg_height -0.0363 0.5557 -1.1559 -0.0228 1.0220 1.0127 264
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8236 3.2209 0.0606 0.8209 9.6893 1.1086 168
## Cogon_Patch_Size 2.6418 4.5740 0.0831 1.3660 12.6192 1.0527 290
## Veg_shannon_index 0.7455 1.2667 0.0509 0.3757 3.6803 1.0674 460
## total_shrub_cover 2.5483 4.3620 0.1129 1.4321 11.7866 1.1590 271
## Avg_Cogongrass_Cover 1.4750 2.5817 0.0608 0.6096 8.7252 1.0773 378
## Tree_Density 3.4618 8.0074 0.0653 1.1751 20.5321 1.1781 126
## Avg_Canopy_Cover 4.8387 8.7553 0.2050 2.5976 21.6318 1.1073 176
## I(Avg_Cogongrass_Cover^2) 1.9063 3.2678 0.0661 0.9125 9.8966 1.2358 234
## avg_veg_height 0.6900 1.1148 0.0431 0.3205 3.5939 1.0408 593
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0143 2.9829 0.0557 0.91 9.8452 1.0518 54
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6143 0.2950 -3.1799 -2.6229 -1.9825 1.0019 2182
## shrub_cover 0.4449 0.3121 -0.1646 0.4470 1.0645 1.0068 1188
## veg_height 0.1309 0.2046 -0.2816 0.1352 0.5502 1.0016 1244
## week 0.1792 0.2099 -0.2102 0.1766 0.6065 1.0049 1028
## I(week^2) -0.2267 0.1276 -0.5024 -0.2197 0.0069 1.0225 974
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6252 0.5808 0.1417 0.4659 1.9656 1.0396 1175
## shrub_cover 0.6821 0.5787 0.1337 0.5242 2.1675 1.0067 820
## veg_height 0.2596 0.2442 0.0580 0.1904 0.8360 1.0099 1868
## week 0.2040 0.2294 0.0351 0.1425 0.7137 1.0075 941
## I(week^2) 0.0871 0.0935 0.0207 0.0619 0.3126 1.0927 533
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3868 1.0174 -3.2733 -1.4343
## (Intercept)-Procyon_lotor -1.1021 1.0140 -2.9892 -1.1195
## (Intercept)-Dasypus_novemcinctus -2.1048 0.9657 -4.1838 -2.0399
## (Intercept)-Lynx_rufus -1.5126 1.1760 -3.6499 -1.5728
## (Intercept)-Didelphis_virginiana -2.6720 1.1523 -5.3441 -2.5295
## (Intercept)-Sylvilagus_floridanus -1.8421 1.0534 -3.9400 -1.8673
## (Intercept)-Meleagris_gallopavo -1.8958 1.0745 -4.0999 -1.8848
## (Intercept)-Sciurus_carolinensis -2.8301 1.3035 -5.9459 -2.6710
## Cogon_Patch_Size-Canis_latrans 1.4220 1.3916 -0.3654 1.1624
## Cogon_Patch_Size-Procyon_lotor -0.4368 0.8503 -2.2558 -0.3697
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1280 0.8441 -1.4807 0.1211
## Cogon_Patch_Size-Lynx_rufus -0.0372 1.4226 -2.6588 -0.0167
## Cogon_Patch_Size-Didelphis_virginiana 1.4073 1.0617 -0.2299 1.2487
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8662 1.3595 -4.1227 -0.6690
## Cogon_Patch_Size-Meleagris_gallopavo 0.4590 1.2879 -1.6153 0.3259
## Cogon_Patch_Size-Sciurus_carolinensis -0.6282 1.2481 -3.6059 -0.4668
## Veg_shannon_index-Canis_latrans 1.2859 0.7315 0.0308 1.2209
## Veg_shannon_index-Procyon_lotor 1.1804 0.6689 0.0341 1.1151
## Veg_shannon_index-Dasypus_novemcinctus 0.6219 0.6301 -0.6330 0.6255
## Veg_shannon_index-Lynx_rufus 0.9226 0.9447 -0.9148 0.9189
## Veg_shannon_index-Didelphis_virginiana 1.1355 0.7113 -0.1380 1.0740
## Veg_shannon_index-Sylvilagus_floridanus 1.0266 0.7483 -0.3509 0.9808
## Veg_shannon_index-Meleagris_gallopavo 1.2246 0.7982 -0.1889 1.1493
## Veg_shannon_index-Sciurus_carolinensis 0.3856 0.8345 -1.4147 0.4585
## total_shrub_cover-Canis_latrans 0.2434 0.9562 -1.3976 0.1368
## total_shrub_cover-Procyon_lotor -1.5448 0.7116 -3.0738 -1.4824
## total_shrub_cover-Dasypus_novemcinctus -0.5529 0.8699 -2.3172 -0.4913
## total_shrub_cover-Lynx_rufus -1.8851 1.2944 -4.8729 -1.7348
## total_shrub_cover-Didelphis_virginiana -1.5096 1.0706 -3.9130 -1.4024
## total_shrub_cover-Sylvilagus_floridanus -1.2507 1.1458 -3.8957 -1.1415
## total_shrub_cover-Meleagris_gallopavo -2.6508 1.4384 -5.8819 -2.4422
## total_shrub_cover-Sciurus_carolinensis -1.2319 1.2517 -4.1484 -1.1074
## Avg_Cogongrass_Cover-Canis_latrans 0.0224 1.2242 -2.3866 0.0159
## Avg_Cogongrass_Cover-Procyon_lotor -0.1705 1.2195 -2.5956 -0.1730
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5475 1.3139 -1.7231 0.4590
## Avg_Cogongrass_Cover-Lynx_rufus 0.0216 1.2638 -2.3819 -0.0034
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1160 1.2996 -2.3289 0.0937
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7387 1.3398 -3.5943 -0.6346
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4672 1.4278 -3.5797 -0.3926
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0307 1.2993 -2.4511 -0.0162
## Tree_Density-Canis_latrans -3.0229 1.5162 -6.6493 -2.7909
## Tree_Density-Procyon_lotor -2.2024 1.0337 -4.2641 -2.1803
## Tree_Density-Dasypus_novemcinctus -3.7497 1.9723 -8.5776 -3.3286
## Tree_Density-Lynx_rufus -1.0130 1.4764 -3.4842 -1.1329
## Tree_Density-Didelphis_virginiana -2.2030 1.3476 -5.0540 -2.1635
## Tree_Density-Sylvilagus_floridanus -2.6577 1.4673 -6.3308 -2.4842
## Tree_Density-Meleagris_gallopavo -2.3475 1.4202 -5.6157 -2.2396
## Tree_Density-Sciurus_carolinensis -2.3307 1.5150 -5.6209 -2.2613
## Avg_Canopy_Cover-Canis_latrans 0.0397 0.7280 -1.4752 0.0610
## Avg_Canopy_Cover-Procyon_lotor 1.6085 0.8416 0.1696 1.5204
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2799 0.9874 0.7708 2.1324
## Avg_Canopy_Cover-Lynx_rufus 0.8924 1.3305 -1.5134 0.8067
## Avg_Canopy_Cover-Didelphis_virginiana 3.2053 1.4729 1.1073 2.9177
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0632 2.1129 1.1349 3.7362
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5187 1.4463 0.4065 2.2760
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1719 1.6470 0.9435 2.8516
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1509 0.9795 0.6536 2.0021
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1868 1.0156 0.6680 2.0172
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3326 0.7302 0.0302 1.2777
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4091 1.2639 0.6416 2.1669
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8233 0.7605 -0.6194 0.8253
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1087 0.8255 -0.4420 1.0702
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3206 1.3015 -2.6248 0.4374
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.4805 0.8095 0.0964 1.4283
## avg_veg_height-Canis_latrans -0.1934 0.6532 -1.4890 -0.1777
## avg_veg_height-Procyon_lotor 0.0732 0.6666 -1.1977 0.0626
## avg_veg_height-Dasypus_novemcinctus 0.2850 0.6811 -0.9452 0.2467
## avg_veg_height-Lynx_rufus -0.3553 0.9390 -2.5088 -0.2815
## avg_veg_height-Didelphis_virginiana -0.2090 0.7612 -1.8324 -0.1793
## avg_veg_height-Sylvilagus_floridanus -0.1894 0.7740 -1.8338 -0.1437
## avg_veg_height-Meleagris_gallopavo -0.1410 0.9631 -2.2144 -0.1070
## avg_veg_height-Sciurus_carolinensis 0.3675 0.7917 -1.0075 0.2868
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7320 1.0194 391
## (Intercept)-Procyon_lotor 0.9726 1.0130 203
## (Intercept)-Dasypus_novemcinctus -0.3941 1.0355 322
## (Intercept)-Lynx_rufus 1.1618 1.0023 310
## (Intercept)-Didelphis_virginiana -0.8796 1.0154 347
## (Intercept)-Sylvilagus_floridanus 0.3485 1.0533 411
## (Intercept)-Meleagris_gallopavo 0.2195 1.0106 227
## (Intercept)-Sciurus_carolinensis -0.8030 1.0389 230
## Cogon_Patch_Size-Canis_latrans 4.8066 1.0506 283
## Cogon_Patch_Size-Procyon_lotor 1.1130 1.0173 320
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8564 1.0200 580
## Cogon_Patch_Size-Lynx_rufus 2.7590 1.0447 226
## Cogon_Patch_Size-Didelphis_virginiana 3.9182 1.0188 282
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2463 1.0247 324
## Cogon_Patch_Size-Meleagris_gallopavo 3.4059 1.0326 284
## Cogon_Patch_Size-Sciurus_carolinensis 1.3596 1.0234 321
## Veg_shannon_index-Canis_latrans 2.9477 1.0062 433
## Veg_shannon_index-Procyon_lotor 2.7096 1.0081 207
## Veg_shannon_index-Dasypus_novemcinctus 1.8464 1.0166 387
## Veg_shannon_index-Lynx_rufus 2.8485 1.0371 328
## Veg_shannon_index-Didelphis_virginiana 2.6679 1.0082 449
## Veg_shannon_index-Sylvilagus_floridanus 2.6642 1.0231 427
## Veg_shannon_index-Meleagris_gallopavo 3.0784 1.0036 470
## Veg_shannon_index-Sciurus_carolinensis 1.8942 1.0281 564
## total_shrub_cover-Canis_latrans 2.4265 1.0171 344
## total_shrub_cover-Procyon_lotor -0.3427 1.0185 611
## total_shrub_cover-Dasypus_novemcinctus 1.0180 1.0105 373
## total_shrub_cover-Lynx_rufus 0.3465 1.0148 250
## total_shrub_cover-Didelphis_virginiana 0.2181 1.0441 404
## total_shrub_cover-Sylvilagus_floridanus 0.6744 1.0378 446
## total_shrub_cover-Meleagris_gallopavo -0.4859 1.0296 200
## total_shrub_cover-Sciurus_carolinensis 0.8322 1.0434 276
## Avg_Cogongrass_Cover-Canis_latrans 2.4125 1.0317 259
## Avg_Cogongrass_Cover-Procyon_lotor 2.2315 1.0578 201
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3808 1.0242 239
## Avg_Cogongrass_Cover-Lynx_rufus 2.5088 1.0341 314
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7816 1.0452 263
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6620 1.0301 280
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0593 1.0491 250
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6106 1.0220 304
## Tree_Density-Canis_latrans -0.7515 1.0571 202
## Tree_Density-Procyon_lotor -0.2569 1.0126 238
## Tree_Density-Dasypus_novemcinctus -1.2873 1.0533 117
## Tree_Density-Lynx_rufus 2.2291 1.0077 187
## Tree_Density-Didelphis_virginiana 0.3545 1.0091 339
## Tree_Density-Sylvilagus_floridanus -0.2294 1.0181 237
## Tree_Density-Meleagris_gallopavo 0.0993 1.0192 317
## Tree_Density-Sciurus_carolinensis 0.5931 1.0360 182
## Avg_Canopy_Cover-Canis_latrans 1.4225 1.0238 364
## Avg_Canopy_Cover-Procyon_lotor 3.4975 1.0055 326
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7105 1.0297 163
## Avg_Canopy_Cover-Lynx_rufus 3.7124 1.0033 230
## Avg_Canopy_Cover-Didelphis_virginiana 6.8028 1.0221 258
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.3762 1.0204 175
## Avg_Canopy_Cover-Meleagris_gallopavo 6.1474 1.0117 158
## Avg_Canopy_Cover-Sciurus_carolinensis 7.2430 1.0122 156
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4492 1.1190 246
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6361 1.1052 296
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9245 1.0415 361
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.5115 1.1043 189
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.2770 1.0020 255
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9128 1.0667 352
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5894 1.0867 165
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3395 1.0336 370
## avg_veg_height-Canis_latrans 1.0337 1.0112 437
## avg_veg_height-Procyon_lotor 1.4151 1.0194 534
## avg_veg_height-Dasypus_novemcinctus 1.7957 1.0200 486
## avg_veg_height-Lynx_rufus 1.3029 1.0113 327
## avg_veg_height-Didelphis_virginiana 1.1777 1.0118 532
## avg_veg_height-Sylvilagus_floridanus 1.2570 1.0158 427
## avg_veg_height-Meleagris_gallopavo 1.5835 1.0148 322
## avg_veg_height-Sciurus_carolinensis 2.1839 1.0105 526
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5649 0.1912 -2.9541 -2.5589 -2.2067 1.0111
## (Intercept)-Procyon_lotor -2.2157 0.1567 -2.5236 -2.2159 -1.9063 1.0034
## (Intercept)-Dasypus_novemcinctus -1.7153 0.1871 -2.0801 -1.7164 -1.3534 1.0040
## (Intercept)-Lynx_rufus -3.4324 0.3463 -4.1667 -3.4236 -2.7899 1.0104
## (Intercept)-Didelphis_virginiana -2.4946 0.3004 -3.1210 -2.4841 -1.9305 1.0006
## (Intercept)-Sylvilagus_floridanus -3.0780 0.2826 -3.6798 -3.0662 -2.5612 1.0074
## (Intercept)-Meleagris_gallopavo -3.4001 0.4625 -4.3155 -3.3898 -2.5033 1.0447
## (Intercept)-Sciurus_carolinensis -2.7077 0.3256 -3.3556 -2.7001 -2.0725 1.0399
## shrub_cover-Canis_latrans -0.2838 0.2248 -0.7052 -0.2871 0.1623 1.0086
## shrub_cover-Procyon_lotor 0.2976 0.1580 -0.0155 0.3030 0.6040 1.0039
## shrub_cover-Dasypus_novemcinctus 1.0467 0.3283 0.4235 1.0496 1.6721 1.0083
## shrub_cover-Lynx_rufus 0.0886 0.3675 -0.6604 0.0986 0.7569 1.0018
## shrub_cover-Didelphis_virginiana 1.1481 0.3813 0.4299 1.1405 1.9261 1.0201
## shrub_cover-Sylvilagus_floridanus 0.6131 0.3853 -0.1453 0.6130 1.3656 1.0305
## shrub_cover-Meleagris_gallopavo -0.3360 0.4246 -1.1868 -0.3369 0.4867 1.0276
## shrub_cover-Sciurus_carolinensis 1.1286 0.4059 0.2846 1.1378 1.9399 1.0452
## veg_height-Canis_latrans -0.5451 0.1838 -0.9000 -0.5458 -0.1952 1.0063
## veg_height-Procyon_lotor 0.3703 0.1227 0.1387 0.3660 0.6148 1.0045
## veg_height-Dasypus_novemcinctus 0.2984 0.1421 0.0204 0.2959 0.5750 1.0039
## veg_height-Lynx_rufus 0.1612 0.2355 -0.3056 0.1659 0.6062 1.0075
## veg_height-Didelphis_virginiana 0.5100 0.2471 0.0460 0.5005 1.0256 1.0026
## veg_height-Sylvilagus_floridanus 0.1689 0.2562 -0.3244 0.1677 0.6550 1.0104
## veg_height-Meleagris_gallopavo -0.0855 0.4037 -0.8848 -0.0884 0.7269 1.0407
## veg_height-Sciurus_carolinensis 0.1855 0.2218 -0.2394 0.1850 0.6330 1.0090
## week-Canis_latrans 0.4515 0.2485 -0.0185 0.4438 0.9653 1.0135
## week-Procyon_lotor 0.1559 0.1997 -0.2260 0.1526 0.5596 1.0015
## week-Dasypus_novemcinctus 0.0649 0.2108 -0.3553 0.0655 0.4822 1.0017
## week-Lynx_rufus 0.2559 0.3102 -0.3379 0.2529 0.8830 1.0121
## week-Didelphis_virginiana 0.0399 0.3134 -0.5833 0.0459 0.6180 1.0029
## week-Sylvilagus_floridanus 0.0443 0.2991 -0.5654 0.0551 0.6114 1.0144
## week-Meleagris_gallopavo -0.1126 0.3600 -0.8699 -0.0924 0.5429 1.0098
## week-Sciurus_carolinensis 0.5337 0.3338 -0.0642 0.5094 1.2788 1.0037
## I(week^2)-Canis_latrans -0.1958 0.1051 -0.4042 -0.1956 0.0080 1.0175
## I(week^2)-Procyon_lotor -0.1098 0.0882 -0.2831 -0.1098 0.0576 1.0008
## I(week^2)-Dasypus_novemcinctus -0.1545 0.1011 -0.3568 -0.1533 0.0430 1.0000
## I(week^2)-Lynx_rufus -0.2117 0.1528 -0.5259 -0.2047 0.0664 1.0003
## I(week^2)-Didelphis_virginiana -0.3807 0.2151 -0.8973 -0.3546 -0.0350 1.0153
## I(week^2)-Sylvilagus_floridanus -0.1640 0.1570 -0.4966 -0.1582 0.1266 1.0028
## I(week^2)-Meleagris_gallopavo -0.4046 0.2841 -1.1030 -0.3555 0.0001 1.1379
## I(week^2)-Sciurus_carolinensis -0.1999 0.1428 -0.5040 -0.1947 0.0566 1.0112
## ESS
## (Intercept)-Canis_latrans 1014
## (Intercept)-Procyon_lotor 1430
## (Intercept)-Dasypus_novemcinctus 864
## (Intercept)-Lynx_rufus 277
## (Intercept)-Didelphis_virginiana 647
## (Intercept)-Sylvilagus_floridanus 786
## (Intercept)-Meleagris_gallopavo 284
## (Intercept)-Sciurus_carolinensis 477
## shrub_cover-Canis_latrans 630
## shrub_cover-Procyon_lotor 1360
## shrub_cover-Dasypus_novemcinctus 571
## shrub_cover-Lynx_rufus 391
## shrub_cover-Didelphis_virginiana 481
## shrub_cover-Sylvilagus_floridanus 511
## shrub_cover-Meleagris_gallopavo 404
## shrub_cover-Sciurus_carolinensis 295
## veg_height-Canis_latrans 772
## veg_height-Procyon_lotor 1543
## veg_height-Dasypus_novemcinctus 1434
## veg_height-Lynx_rufus 578
## veg_height-Didelphis_virginiana 944
## veg_height-Sylvilagus_floridanus 426
## veg_height-Meleagris_gallopavo 353
## veg_height-Sciurus_carolinensis 928
## week-Canis_latrans 975
## week-Procyon_lotor 1637
## week-Dasypus_novemcinctus 1800
## week-Lynx_rufus 800
## week-Didelphis_virginiana 1089
## week-Sylvilagus_floridanus 1200
## week-Meleagris_gallopavo 529
## week-Sciurus_carolinensis 983
## I(week^2)-Canis_latrans 1201
## I(week^2)-Procyon_lotor 1607
## I(week^2)-Dasypus_novemcinctus 1677
## I(week^2)-Lynx_rufus 548
## I(week^2)-Didelphis_virginiana 408
## I(week^2)-Sylvilagus_floridanus 625
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1135
waicOcc(ms_full_full_T10, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -963.89994 99.03378 2125.86742
waicOcc(ms_full_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1004.66407 92.45651 2194.24116
waicOcc(ms_full_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -996.66849 80.48293 2154.30284
waicOcc(ms_full_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1002.81979 90.65599 2186.95155
waicOcc(ms_full_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1011.74490 83.30806 2190.10592
waicOcc(ms_full_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1016.26917 77.44687 2187.43208
waicOcc(ms_full_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1026.7787 67.2023 2187.9620
waicOcc(ms_full_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1010.73039 80.82337 2183.10752
waicOcc(ms_full_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -962.41577 98.11527 2121.06207
waicOcc(ms_null_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1066.37739 27.98093 2188.71664
waicOcc(ms_null_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1002.96093 62.95962 2131.84111
waicOcc(ms_null_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1045.5100 48.9457 2188.9115
waicOcc(ms_null_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1037.05786 42.98138 2160.07848
waicOcc(ms_null_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1041.68330 49.63395 2182.63449
waicOcc(ms_null_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1049.69490 45.02176 2189.43332
waicOcc(ms_null_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1054.71907 39.08442 2187.60699
waicOcc(ms_null_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1048.33424 43.50972 2183.68793
waicOcc(ms_null_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -997.99512 61.70495 2119.40013
waicOcc(ms_week_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -1000.88457 67.71381 2137.19675
waicOcc(ms_week_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1040.96894 55.96466 2193.86720
waicOcc(ms_week_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1063.12083 33.95723 2194.15611
waicOcc(ms_week_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1046.62958 50.79117 2194.84150
waicOcc(ms_week_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1038.40271 55.35577 2187.51695
waicOcc(ms_week_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1033.73134 48.92845 2165.31957
waicOcc(ms_week_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1050.25663 46.41316 2193.33958
waicOcc(ms_week_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1044.78487 48.96469 2187.49912
waicOcc(ms_week_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -994.65111 69.03782 2127.37787
waicOcc(ms_cover_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -967.97090 94.04037 2124.02254
waicOcc(ms_cover_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1007.93939 86.19682 2188.27242
waicOcc(ms_cover_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1031.24170 58.22793 2178.93927
waicOcc(ms_cover_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1015.85800 76.29017 2184.29634
waicOcc(ms_cover_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1005.97360 86.60892 2185.16504
waicOcc(ms_cover_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1001.12069 72.73075 2147.70289
waicOcc(ms_cover_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1019.50390 71.94967 2182.90714
waicOcc(ms_cover_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1014.24303 74.87495 2178.23597
waicOcc(ms_cover_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -965.57704 92.17486 2115.50380
waicOcc(ms_weekQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -992.87934 75.32672 2136.41211
waicOcc(ms_weekQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -1034.66249 64.30429 2197.93356
waicOcc(ms_weekQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1056.30108 42.26298 2197.12812
waicOcc(ms_weekQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1039.93620 58.79875 2197.46992
waicOcc(ms_weekQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -1032.13154 63.28617 2190.83542
waicOcc(ms_weekQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -1026.92527 56.76395 2167.37843
waicOcc(ms_weekQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1044.1004 54.0697 2196.3402
waicOcc(ms_weekQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1038.41024 56.23327 2189.28701
waicOcc(ms_weekQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -988.12960 76.43129 2129.12177
waicOcc(ms_fullQ_full_T10, by.sp = FALSE)
## elpd pD WAIC
## -958.8968 106.4877 2130.7690
waicOcc(ms_fullQ_cover_T10, by.sp = FALSE)
## elpd pD WAIC
## -996.9555 102.0199 2197.9508
waicOcc(ms_fullQ_null_T10, by.sp = FALSE)
## elpd pD WAIC
## -1020.83616 73.59704 2188.86640
waicOcc(ms_fullQ_forage_T10, by.sp = FALSE)
## elpd pD WAIC
## -1005.74290 89.98906 2191.46391
waicOcc(ms_fullQ_move_T10, by.sp = FALSE)
## elpd pD WAIC
## -995.2768 101.1192 2192.7919
waicOcc(ms_fullQ_canopy_T10, by.sp = FALSE)
## elpd pD WAIC
## -989.89700 88.70237 2157.19872
waicOcc(ms_fullQ_cogon_T10, by.sp = FALSE)
## elpd pD WAIC
## -1009.56691 85.07945 2189.29272
waicOcc(ms_fullQ_cogonQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -1004.03323 89.35453 2186.77552
waicOcc(ms_fullQ_fullQ_T10, by.sp = FALSE)
## elpd pD WAIC
## -955.3461 107.2100 2125.1122
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T10 <- ppcOcc(ms_fullQ_fullQ_T10, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 8
## Currently on species 2 out of 8
## Currently on species 3 out of 8
## Currently on species 4 out of 8
## Currently on species 5 out of 8
## Currently on species 6 out of 8
## Currently on species 7 out of 8
## Currently on species 8 out of 8
summary(ppc.ms_fullQ_fullQ_T10)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T10, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.3365
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.6327
## Procyon_lotor Bayesian p-value: 0.0733
## Dasypus_novemcinctus Bayesian p-value: 3e-04
## Lynx_rufus Bayesian p-value: 0.354
## Didelphis_virginiana Bayesian p-value: 0.3717
## Sylvilagus_floridanus Bayesian p-value: 0.403
## Meleagris_gallopavo Bayesian p-value: 0.575
## Sciurus_carolinensis Bayesian p-value: 0.282
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T10) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8125
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7806 0.7183 -3.1451 -1.7836 -0.4109 1.0226 305
## Cogon_Patch_Size 0.1598 0.7018 -1.2422 0.1821 1.5394 1.0241 449
## Veg_shannon_index 0.9399 0.5400 -0.0817 0.9239 2.0248 1.0153 294
## total_shrub_cover -1.1468 0.6671 -2.4977 -1.1408 0.1504 1.0149 441
## Avg_Cogongrass_Cover -0.0852 0.9667 -1.9177 -0.0785 1.8253 1.0639 152
## Tree_Density -2.1582 0.8660 -3.8350 -2.1785 -0.4999 1.0129 169
## Avg_Canopy_Cover 1.8408 0.7780 0.3556 1.8185 3.5001 1.0038 545
## I(Avg_Cogongrass_Cover^2) 1.3730 0.6385 0.1411 1.3451 2.7315 1.0303 299
## avg_veg_height -0.0363 0.5557 -1.1559 -0.0228 1.0220 1.0127 264
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8236 3.2209 0.0606 0.8209 9.6893 1.1086 168
## Cogon_Patch_Size 2.6418 4.5740 0.0831 1.3660 12.6192 1.0527 290
## Veg_shannon_index 0.7455 1.2667 0.0509 0.3757 3.6803 1.0674 460
## total_shrub_cover 2.5483 4.3620 0.1129 1.4321 11.7866 1.1590 271
## Avg_Cogongrass_Cover 1.4750 2.5817 0.0608 0.6096 8.7252 1.0773 378
## Tree_Density 3.4618 8.0074 0.0653 1.1751 20.5321 1.1781 126
## Avg_Canopy_Cover 4.8387 8.7553 0.2050 2.5976 21.6318 1.1073 176
## I(Avg_Cogongrass_Cover^2) 1.9063 3.2678 0.0661 0.9125 9.8966 1.2358 234
## avg_veg_height 0.6900 1.1148 0.0431 0.3205 3.5939 1.0408 593
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0143 2.9829 0.0557 0.91 9.8452 1.0518 54
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6143 0.2950 -3.1799 -2.6229 -1.9825 1.0019 2182
## shrub_cover 0.4449 0.3121 -0.1646 0.4470 1.0645 1.0068 1188
## veg_height 0.1309 0.2046 -0.2816 0.1352 0.5502 1.0016 1244
## week 0.1792 0.2099 -0.2102 0.1766 0.6065 1.0049 1028
## I(week^2) -0.2267 0.1276 -0.5024 -0.2197 0.0069 1.0225 974
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6252 0.5808 0.1417 0.4659 1.9656 1.0396 1175
## shrub_cover 0.6821 0.5787 0.1337 0.5242 2.1675 1.0067 820
## veg_height 0.2596 0.2442 0.0580 0.1904 0.8360 1.0099 1868
## week 0.2040 0.2294 0.0351 0.1425 0.7137 1.0075 941
## I(week^2) 0.0871 0.0935 0.0207 0.0619 0.3126 1.0927 533
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3868 1.0174 -3.2733 -1.4343
## (Intercept)-Procyon_lotor -1.1021 1.0140 -2.9892 -1.1195
## (Intercept)-Dasypus_novemcinctus -2.1048 0.9657 -4.1838 -2.0399
## (Intercept)-Lynx_rufus -1.5126 1.1760 -3.6499 -1.5728
## (Intercept)-Didelphis_virginiana -2.6720 1.1523 -5.3441 -2.5295
## (Intercept)-Sylvilagus_floridanus -1.8421 1.0534 -3.9400 -1.8673
## (Intercept)-Meleagris_gallopavo -1.8958 1.0745 -4.0999 -1.8848
## (Intercept)-Sciurus_carolinensis -2.8301 1.3035 -5.9459 -2.6710
## Cogon_Patch_Size-Canis_latrans 1.4220 1.3916 -0.3654 1.1624
## Cogon_Patch_Size-Procyon_lotor -0.4368 0.8503 -2.2558 -0.3697
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1280 0.8441 -1.4807 0.1211
## Cogon_Patch_Size-Lynx_rufus -0.0372 1.4226 -2.6588 -0.0167
## Cogon_Patch_Size-Didelphis_virginiana 1.4073 1.0617 -0.2299 1.2487
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8662 1.3595 -4.1227 -0.6690
## Cogon_Patch_Size-Meleagris_gallopavo 0.4590 1.2879 -1.6153 0.3259
## Cogon_Patch_Size-Sciurus_carolinensis -0.6282 1.2481 -3.6059 -0.4668
## Veg_shannon_index-Canis_latrans 1.2859 0.7315 0.0308 1.2209
## Veg_shannon_index-Procyon_lotor 1.1804 0.6689 0.0341 1.1151
## Veg_shannon_index-Dasypus_novemcinctus 0.6219 0.6301 -0.6330 0.6255
## Veg_shannon_index-Lynx_rufus 0.9226 0.9447 -0.9148 0.9189
## Veg_shannon_index-Didelphis_virginiana 1.1355 0.7113 -0.1380 1.0740
## Veg_shannon_index-Sylvilagus_floridanus 1.0266 0.7483 -0.3509 0.9808
## Veg_shannon_index-Meleagris_gallopavo 1.2246 0.7982 -0.1889 1.1493
## Veg_shannon_index-Sciurus_carolinensis 0.3856 0.8345 -1.4147 0.4585
## total_shrub_cover-Canis_latrans 0.2434 0.9562 -1.3976 0.1368
## total_shrub_cover-Procyon_lotor -1.5448 0.7116 -3.0738 -1.4824
## total_shrub_cover-Dasypus_novemcinctus -0.5529 0.8699 -2.3172 -0.4913
## total_shrub_cover-Lynx_rufus -1.8851 1.2944 -4.8729 -1.7348
## total_shrub_cover-Didelphis_virginiana -1.5096 1.0706 -3.9130 -1.4024
## total_shrub_cover-Sylvilagus_floridanus -1.2507 1.1458 -3.8957 -1.1415
## total_shrub_cover-Meleagris_gallopavo -2.6508 1.4384 -5.8819 -2.4422
## total_shrub_cover-Sciurus_carolinensis -1.2319 1.2517 -4.1484 -1.1074
## Avg_Cogongrass_Cover-Canis_latrans 0.0224 1.2242 -2.3866 0.0159
## Avg_Cogongrass_Cover-Procyon_lotor -0.1705 1.2195 -2.5956 -0.1730
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5475 1.3139 -1.7231 0.4590
## Avg_Cogongrass_Cover-Lynx_rufus 0.0216 1.2638 -2.3819 -0.0034
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1160 1.2996 -2.3289 0.0937
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7387 1.3398 -3.5943 -0.6346
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4672 1.4278 -3.5797 -0.3926
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0307 1.2993 -2.4511 -0.0162
## Tree_Density-Canis_latrans -3.0229 1.5162 -6.6493 -2.7909
## Tree_Density-Procyon_lotor -2.2024 1.0337 -4.2641 -2.1803
## Tree_Density-Dasypus_novemcinctus -3.7497 1.9723 -8.5776 -3.3286
## Tree_Density-Lynx_rufus -1.0130 1.4764 -3.4842 -1.1329
## Tree_Density-Didelphis_virginiana -2.2030 1.3476 -5.0540 -2.1635
## Tree_Density-Sylvilagus_floridanus -2.6577 1.4673 -6.3308 -2.4842
## Tree_Density-Meleagris_gallopavo -2.3475 1.4202 -5.6157 -2.2396
## Tree_Density-Sciurus_carolinensis -2.3307 1.5150 -5.6209 -2.2613
## Avg_Canopy_Cover-Canis_latrans 0.0397 0.7280 -1.4752 0.0610
## Avg_Canopy_Cover-Procyon_lotor 1.6085 0.8416 0.1696 1.5204
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2799 0.9874 0.7708 2.1324
## Avg_Canopy_Cover-Lynx_rufus 0.8924 1.3305 -1.5134 0.8067
## Avg_Canopy_Cover-Didelphis_virginiana 3.2053 1.4729 1.1073 2.9177
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0632 2.1129 1.1349 3.7362
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5187 1.4463 0.4065 2.2760
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1719 1.6470 0.9435 2.8516
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1509 0.9795 0.6536 2.0021
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1868 1.0156 0.6680 2.0172
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3326 0.7302 0.0302 1.2777
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4091 1.2639 0.6416 2.1669
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8233 0.7605 -0.6194 0.8253
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1087 0.8255 -0.4420 1.0702
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3206 1.3015 -2.6248 0.4374
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.4805 0.8095 0.0964 1.4283
## avg_veg_height-Canis_latrans -0.1934 0.6532 -1.4890 -0.1777
## avg_veg_height-Procyon_lotor 0.0732 0.6666 -1.1977 0.0626
## avg_veg_height-Dasypus_novemcinctus 0.2850 0.6811 -0.9452 0.2467
## avg_veg_height-Lynx_rufus -0.3553 0.9390 -2.5088 -0.2815
## avg_veg_height-Didelphis_virginiana -0.2090 0.7612 -1.8324 -0.1793
## avg_veg_height-Sylvilagus_floridanus -0.1894 0.7740 -1.8338 -0.1437
## avg_veg_height-Meleagris_gallopavo -0.1410 0.9631 -2.2144 -0.1070
## avg_veg_height-Sciurus_carolinensis 0.3675 0.7917 -1.0075 0.2868
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7320 1.0194 391
## (Intercept)-Procyon_lotor 0.9726 1.0130 203
## (Intercept)-Dasypus_novemcinctus -0.3941 1.0355 322
## (Intercept)-Lynx_rufus 1.1618 1.0023 310
## (Intercept)-Didelphis_virginiana -0.8796 1.0154 347
## (Intercept)-Sylvilagus_floridanus 0.3485 1.0533 411
## (Intercept)-Meleagris_gallopavo 0.2195 1.0106 227
## (Intercept)-Sciurus_carolinensis -0.8030 1.0389 230
## Cogon_Patch_Size-Canis_latrans 4.8066 1.0506 283
## Cogon_Patch_Size-Procyon_lotor 1.1130 1.0173 320
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8564 1.0200 580
## Cogon_Patch_Size-Lynx_rufus 2.7590 1.0447 226
## Cogon_Patch_Size-Didelphis_virginiana 3.9182 1.0188 282
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2463 1.0247 324
## Cogon_Patch_Size-Meleagris_gallopavo 3.4059 1.0326 284
## Cogon_Patch_Size-Sciurus_carolinensis 1.3596 1.0234 321
## Veg_shannon_index-Canis_latrans 2.9477 1.0062 433
## Veg_shannon_index-Procyon_lotor 2.7096 1.0081 207
## Veg_shannon_index-Dasypus_novemcinctus 1.8464 1.0166 387
## Veg_shannon_index-Lynx_rufus 2.8485 1.0371 328
## Veg_shannon_index-Didelphis_virginiana 2.6679 1.0082 449
## Veg_shannon_index-Sylvilagus_floridanus 2.6642 1.0231 427
## Veg_shannon_index-Meleagris_gallopavo 3.0784 1.0036 470
## Veg_shannon_index-Sciurus_carolinensis 1.8942 1.0281 564
## total_shrub_cover-Canis_latrans 2.4265 1.0171 344
## total_shrub_cover-Procyon_lotor -0.3427 1.0185 611
## total_shrub_cover-Dasypus_novemcinctus 1.0180 1.0105 373
## total_shrub_cover-Lynx_rufus 0.3465 1.0148 250
## total_shrub_cover-Didelphis_virginiana 0.2181 1.0441 404
## total_shrub_cover-Sylvilagus_floridanus 0.6744 1.0378 446
## total_shrub_cover-Meleagris_gallopavo -0.4859 1.0296 200
## total_shrub_cover-Sciurus_carolinensis 0.8322 1.0434 276
## Avg_Cogongrass_Cover-Canis_latrans 2.4125 1.0317 259
## Avg_Cogongrass_Cover-Procyon_lotor 2.2315 1.0578 201
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3808 1.0242 239
## Avg_Cogongrass_Cover-Lynx_rufus 2.5088 1.0341 314
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7816 1.0452 263
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6620 1.0301 280
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0593 1.0491 250
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6106 1.0220 304
## Tree_Density-Canis_latrans -0.7515 1.0571 202
## Tree_Density-Procyon_lotor -0.2569 1.0126 238
## Tree_Density-Dasypus_novemcinctus -1.2873 1.0533 117
## Tree_Density-Lynx_rufus 2.2291 1.0077 187
## Tree_Density-Didelphis_virginiana 0.3545 1.0091 339
## Tree_Density-Sylvilagus_floridanus -0.2294 1.0181 237
## Tree_Density-Meleagris_gallopavo 0.0993 1.0192 317
## Tree_Density-Sciurus_carolinensis 0.5931 1.0360 182
## Avg_Canopy_Cover-Canis_latrans 1.4225 1.0238 364
## Avg_Canopy_Cover-Procyon_lotor 3.4975 1.0055 326
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7105 1.0297 163
## Avg_Canopy_Cover-Lynx_rufus 3.7124 1.0033 230
## Avg_Canopy_Cover-Didelphis_virginiana 6.8028 1.0221 258
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.3762 1.0204 175
## Avg_Canopy_Cover-Meleagris_gallopavo 6.1474 1.0117 158
## Avg_Canopy_Cover-Sciurus_carolinensis 7.2430 1.0122 156
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4492 1.1190 246
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6361 1.1052 296
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9245 1.0415 361
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.5115 1.1043 189
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.2770 1.0020 255
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9128 1.0667 352
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5894 1.0867 165
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3395 1.0336 370
## avg_veg_height-Canis_latrans 1.0337 1.0112 437
## avg_veg_height-Procyon_lotor 1.4151 1.0194 534
## avg_veg_height-Dasypus_novemcinctus 1.7957 1.0200 486
## avg_veg_height-Lynx_rufus 1.3029 1.0113 327
## avg_veg_height-Didelphis_virginiana 1.1777 1.0118 532
## avg_veg_height-Sylvilagus_floridanus 1.2570 1.0158 427
## avg_veg_height-Meleagris_gallopavo 1.5835 1.0148 322
## avg_veg_height-Sciurus_carolinensis 2.1839 1.0105 526
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5649 0.1912 -2.9541 -2.5589 -2.2067 1.0111
## (Intercept)-Procyon_lotor -2.2157 0.1567 -2.5236 -2.2159 -1.9063 1.0034
## (Intercept)-Dasypus_novemcinctus -1.7153 0.1871 -2.0801 -1.7164 -1.3534 1.0040
## (Intercept)-Lynx_rufus -3.4324 0.3463 -4.1667 -3.4236 -2.7899 1.0104
## (Intercept)-Didelphis_virginiana -2.4946 0.3004 -3.1210 -2.4841 -1.9305 1.0006
## (Intercept)-Sylvilagus_floridanus -3.0780 0.2826 -3.6798 -3.0662 -2.5612 1.0074
## (Intercept)-Meleagris_gallopavo -3.4001 0.4625 -4.3155 -3.3898 -2.5033 1.0447
## (Intercept)-Sciurus_carolinensis -2.7077 0.3256 -3.3556 -2.7001 -2.0725 1.0399
## shrub_cover-Canis_latrans -0.2838 0.2248 -0.7052 -0.2871 0.1623 1.0086
## shrub_cover-Procyon_lotor 0.2976 0.1580 -0.0155 0.3030 0.6040 1.0039
## shrub_cover-Dasypus_novemcinctus 1.0467 0.3283 0.4235 1.0496 1.6721 1.0083
## shrub_cover-Lynx_rufus 0.0886 0.3675 -0.6604 0.0986 0.7569 1.0018
## shrub_cover-Didelphis_virginiana 1.1481 0.3813 0.4299 1.1405 1.9261 1.0201
## shrub_cover-Sylvilagus_floridanus 0.6131 0.3853 -0.1453 0.6130 1.3656 1.0305
## shrub_cover-Meleagris_gallopavo -0.3360 0.4246 -1.1868 -0.3369 0.4867 1.0276
## shrub_cover-Sciurus_carolinensis 1.1286 0.4059 0.2846 1.1378 1.9399 1.0452
## veg_height-Canis_latrans -0.5451 0.1838 -0.9000 -0.5458 -0.1952 1.0063
## veg_height-Procyon_lotor 0.3703 0.1227 0.1387 0.3660 0.6148 1.0045
## veg_height-Dasypus_novemcinctus 0.2984 0.1421 0.0204 0.2959 0.5750 1.0039
## veg_height-Lynx_rufus 0.1612 0.2355 -0.3056 0.1659 0.6062 1.0075
## veg_height-Didelphis_virginiana 0.5100 0.2471 0.0460 0.5005 1.0256 1.0026
## veg_height-Sylvilagus_floridanus 0.1689 0.2562 -0.3244 0.1677 0.6550 1.0104
## veg_height-Meleagris_gallopavo -0.0855 0.4037 -0.8848 -0.0884 0.7269 1.0407
## veg_height-Sciurus_carolinensis 0.1855 0.2218 -0.2394 0.1850 0.6330 1.0090
## week-Canis_latrans 0.4515 0.2485 -0.0185 0.4438 0.9653 1.0135
## week-Procyon_lotor 0.1559 0.1997 -0.2260 0.1526 0.5596 1.0015
## week-Dasypus_novemcinctus 0.0649 0.2108 -0.3553 0.0655 0.4822 1.0017
## week-Lynx_rufus 0.2559 0.3102 -0.3379 0.2529 0.8830 1.0121
## week-Didelphis_virginiana 0.0399 0.3134 -0.5833 0.0459 0.6180 1.0029
## week-Sylvilagus_floridanus 0.0443 0.2991 -0.5654 0.0551 0.6114 1.0144
## week-Meleagris_gallopavo -0.1126 0.3600 -0.8699 -0.0924 0.5429 1.0098
## week-Sciurus_carolinensis 0.5337 0.3338 -0.0642 0.5094 1.2788 1.0037
## I(week^2)-Canis_latrans -0.1958 0.1051 -0.4042 -0.1956 0.0080 1.0175
## I(week^2)-Procyon_lotor -0.1098 0.0882 -0.2831 -0.1098 0.0576 1.0008
## I(week^2)-Dasypus_novemcinctus -0.1545 0.1011 -0.3568 -0.1533 0.0430 1.0000
## I(week^2)-Lynx_rufus -0.2117 0.1528 -0.5259 -0.2047 0.0664 1.0003
## I(week^2)-Didelphis_virginiana -0.3807 0.2151 -0.8973 -0.3546 -0.0350 1.0153
## I(week^2)-Sylvilagus_floridanus -0.1640 0.1570 -0.4966 -0.1582 0.1266 1.0028
## I(week^2)-Meleagris_gallopavo -0.4046 0.2841 -1.1030 -0.3555 0.0001 1.1379
## I(week^2)-Sciurus_carolinensis -0.1999 0.1428 -0.5040 -0.1947 0.0566 1.0112
## ESS
## (Intercept)-Canis_latrans 1014
## (Intercept)-Procyon_lotor 1430
## (Intercept)-Dasypus_novemcinctus 864
## (Intercept)-Lynx_rufus 277
## (Intercept)-Didelphis_virginiana 647
## (Intercept)-Sylvilagus_floridanus 786
## (Intercept)-Meleagris_gallopavo 284
## (Intercept)-Sciurus_carolinensis 477
## shrub_cover-Canis_latrans 630
## shrub_cover-Procyon_lotor 1360
## shrub_cover-Dasypus_novemcinctus 571
## shrub_cover-Lynx_rufus 391
## shrub_cover-Didelphis_virginiana 481
## shrub_cover-Sylvilagus_floridanus 511
## shrub_cover-Meleagris_gallopavo 404
## shrub_cover-Sciurus_carolinensis 295
## veg_height-Canis_latrans 772
## veg_height-Procyon_lotor 1543
## veg_height-Dasypus_novemcinctus 1434
## veg_height-Lynx_rufus 578
## veg_height-Didelphis_virginiana 944
## veg_height-Sylvilagus_floridanus 426
## veg_height-Meleagris_gallopavo 353
## veg_height-Sciurus_carolinensis 928
## week-Canis_latrans 975
## week-Procyon_lotor 1637
## week-Dasypus_novemcinctus 1800
## week-Lynx_rufus 800
## week-Didelphis_virginiana 1089
## week-Sylvilagus_floridanus 1200
## week-Meleagris_gallopavo 529
## week-Sciurus_carolinensis 983
## I(week^2)-Canis_latrans 1201
## I(week^2)-Procyon_lotor 1607
## I(week^2)-Dasypus_novemcinctus 1677
## I(week^2)-Lynx_rufus 548
## I(week^2)-Didelphis_virginiana 408
## I(week^2)-Sylvilagus_floridanus 625
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1135
names(ms_fullQ_fullQ_T10)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T10$beta.samples)
## 'mcmc' num [1:3000, 1:72] -3.03 -2.82 -3.17 -2.37 -3.15 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "(Intercept)-Lynx_rufus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.002333333
MCMCplot(ms_fullQ_fullQ_T10$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T10$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T10, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:3000, 1:8, 1:100] 0.494601 0.008641 0.000744 0.003169 0.014643 ...
## $ z.0.samples : int [1:3000, 1:8, 1:100] 0 0 0 0 0 0 0 1 0 1 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T10, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:8, 1:100] 0.494601 0.008641 0.000744 0.003169 0.014643 ...
psi.0.quants <- apply(out.pred$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] 0.00146 0.12267 0.87581 0.00136 0.12369 ...
## - 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 0.123 0.124 0.125 0.124 0.127 ...
## $ psi.low : num 0.00146 0.00136 0.00156 0.00171 0.00162 ...
## $ psi.high : num 0.876 0.867 0.871 0.866 0.862 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T10) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.8125
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7806 0.7183 -3.1451 -1.7836 -0.4109 1.0226 305
## Cogon_Patch_Size 0.1598 0.7018 -1.2422 0.1821 1.5394 1.0241 449
## Veg_shannon_index 0.9399 0.5400 -0.0817 0.9239 2.0248 1.0153 294
## total_shrub_cover -1.1468 0.6671 -2.4977 -1.1408 0.1504 1.0149 441
## Avg_Cogongrass_Cover -0.0852 0.9667 -1.9177 -0.0785 1.8253 1.0639 152
## Tree_Density -2.1582 0.8660 -3.8350 -2.1785 -0.4999 1.0129 169
## Avg_Canopy_Cover 1.8408 0.7780 0.3556 1.8185 3.5001 1.0038 545
## I(Avg_Cogongrass_Cover^2) 1.3730 0.6385 0.1411 1.3451 2.7315 1.0303 299
## avg_veg_height -0.0363 0.5557 -1.1559 -0.0228 1.0220 1.0127 264
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8236 3.2209 0.0606 0.8209 9.6893 1.1086 168
## Cogon_Patch_Size 2.6418 4.5740 0.0831 1.3660 12.6192 1.0527 290
## Veg_shannon_index 0.7455 1.2667 0.0509 0.3757 3.6803 1.0674 460
## total_shrub_cover 2.5483 4.3620 0.1129 1.4321 11.7866 1.1590 271
## Avg_Cogongrass_Cover 1.4750 2.5817 0.0608 0.6096 8.7252 1.0773 378
## Tree_Density 3.4618 8.0074 0.0653 1.1751 20.5321 1.1781 126
## Avg_Canopy_Cover 4.8387 8.7553 0.2050 2.5976 21.6318 1.1073 176
## I(Avg_Cogongrass_Cover^2) 1.9063 3.2678 0.0661 0.9125 9.8966 1.2358 234
## avg_veg_height 0.6900 1.1148 0.0431 0.3205 3.5939 1.0408 593
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0143 2.9829 0.0557 0.91 9.8452 1.0518 54
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.6143 0.2950 -3.1799 -2.6229 -1.9825 1.0019 2182
## shrub_cover 0.4449 0.3121 -0.1646 0.4470 1.0645 1.0068 1188
## veg_height 0.1309 0.2046 -0.2816 0.1352 0.5502 1.0016 1244
## week 0.1792 0.2099 -0.2102 0.1766 0.6065 1.0049 1028
## I(week^2) -0.2267 0.1276 -0.5024 -0.2197 0.0069 1.0225 974
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6252 0.5808 0.1417 0.4659 1.9656 1.0396 1175
## shrub_cover 0.6821 0.5787 0.1337 0.5242 2.1675 1.0067 820
## veg_height 0.2596 0.2442 0.0580 0.1904 0.8360 1.0099 1868
## week 0.2040 0.2294 0.0351 0.1425 0.7137 1.0075 941
## I(week^2) 0.0871 0.0935 0.0207 0.0619 0.3126 1.0927 533
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3868 1.0174 -3.2733 -1.4343
## (Intercept)-Procyon_lotor -1.1021 1.0140 -2.9892 -1.1195
## (Intercept)-Dasypus_novemcinctus -2.1048 0.9657 -4.1838 -2.0399
## (Intercept)-Lynx_rufus -1.5126 1.1760 -3.6499 -1.5728
## (Intercept)-Didelphis_virginiana -2.6720 1.1523 -5.3441 -2.5295
## (Intercept)-Sylvilagus_floridanus -1.8421 1.0534 -3.9400 -1.8673
## (Intercept)-Meleagris_gallopavo -1.8958 1.0745 -4.0999 -1.8848
## (Intercept)-Sciurus_carolinensis -2.8301 1.3035 -5.9459 -2.6710
## Cogon_Patch_Size-Canis_latrans 1.4220 1.3916 -0.3654 1.1624
## Cogon_Patch_Size-Procyon_lotor -0.4368 0.8503 -2.2558 -0.3697
## Cogon_Patch_Size-Dasypus_novemcinctus 0.1280 0.8441 -1.4807 0.1211
## Cogon_Patch_Size-Lynx_rufus -0.0372 1.4226 -2.6588 -0.0167
## Cogon_Patch_Size-Didelphis_virginiana 1.4073 1.0617 -0.2299 1.2487
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8662 1.3595 -4.1227 -0.6690
## Cogon_Patch_Size-Meleagris_gallopavo 0.4590 1.2879 -1.6153 0.3259
## Cogon_Patch_Size-Sciurus_carolinensis -0.6282 1.2481 -3.6059 -0.4668
## Veg_shannon_index-Canis_latrans 1.2859 0.7315 0.0308 1.2209
## Veg_shannon_index-Procyon_lotor 1.1804 0.6689 0.0341 1.1151
## Veg_shannon_index-Dasypus_novemcinctus 0.6219 0.6301 -0.6330 0.6255
## Veg_shannon_index-Lynx_rufus 0.9226 0.9447 -0.9148 0.9189
## Veg_shannon_index-Didelphis_virginiana 1.1355 0.7113 -0.1380 1.0740
## Veg_shannon_index-Sylvilagus_floridanus 1.0266 0.7483 -0.3509 0.9808
## Veg_shannon_index-Meleagris_gallopavo 1.2246 0.7982 -0.1889 1.1493
## Veg_shannon_index-Sciurus_carolinensis 0.3856 0.8345 -1.4147 0.4585
## total_shrub_cover-Canis_latrans 0.2434 0.9562 -1.3976 0.1368
## total_shrub_cover-Procyon_lotor -1.5448 0.7116 -3.0738 -1.4824
## total_shrub_cover-Dasypus_novemcinctus -0.5529 0.8699 -2.3172 -0.4913
## total_shrub_cover-Lynx_rufus -1.8851 1.2944 -4.8729 -1.7348
## total_shrub_cover-Didelphis_virginiana -1.5096 1.0706 -3.9130 -1.4024
## total_shrub_cover-Sylvilagus_floridanus -1.2507 1.1458 -3.8957 -1.1415
## total_shrub_cover-Meleagris_gallopavo -2.6508 1.4384 -5.8819 -2.4422
## total_shrub_cover-Sciurus_carolinensis -1.2319 1.2517 -4.1484 -1.1074
## Avg_Cogongrass_Cover-Canis_latrans 0.0224 1.2242 -2.3866 0.0159
## Avg_Cogongrass_Cover-Procyon_lotor -0.1705 1.2195 -2.5956 -0.1730
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5475 1.3139 -1.7231 0.4590
## Avg_Cogongrass_Cover-Lynx_rufus 0.0216 1.2638 -2.3819 -0.0034
## Avg_Cogongrass_Cover-Didelphis_virginiana 0.1160 1.2996 -2.3289 0.0937
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7387 1.3398 -3.5943 -0.6346
## Avg_Cogongrass_Cover-Meleagris_gallopavo -0.4672 1.4278 -3.5797 -0.3926
## Avg_Cogongrass_Cover-Sciurus_carolinensis 0.0307 1.2993 -2.4511 -0.0162
## Tree_Density-Canis_latrans -3.0229 1.5162 -6.6493 -2.7909
## Tree_Density-Procyon_lotor -2.2024 1.0337 -4.2641 -2.1803
## Tree_Density-Dasypus_novemcinctus -3.7497 1.9723 -8.5776 -3.3286
## Tree_Density-Lynx_rufus -1.0130 1.4764 -3.4842 -1.1329
## Tree_Density-Didelphis_virginiana -2.2030 1.3476 -5.0540 -2.1635
## Tree_Density-Sylvilagus_floridanus -2.6577 1.4673 -6.3308 -2.4842
## Tree_Density-Meleagris_gallopavo -2.3475 1.4202 -5.6157 -2.2396
## Tree_Density-Sciurus_carolinensis -2.3307 1.5150 -5.6209 -2.2613
## Avg_Canopy_Cover-Canis_latrans 0.0397 0.7280 -1.4752 0.0610
## Avg_Canopy_Cover-Procyon_lotor 1.6085 0.8416 0.1696 1.5204
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.2799 0.9874 0.7708 2.1324
## Avg_Canopy_Cover-Lynx_rufus 0.8924 1.3305 -1.5134 0.8067
## Avg_Canopy_Cover-Didelphis_virginiana 3.2053 1.4729 1.1073 2.9177
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0632 2.1129 1.1349 3.7362
## Avg_Canopy_Cover-Meleagris_gallopavo 2.5187 1.4463 0.4065 2.2760
## Avg_Canopy_Cover-Sciurus_carolinensis 3.1719 1.6470 0.9435 2.8516
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1509 0.9795 0.6536 2.0021
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1868 1.0156 0.6680 2.0172
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3326 0.7302 0.0302 1.2777
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 2.4091 1.2639 0.6416 2.1669
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 0.8233 0.7605 -0.6194 0.8253
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1087 0.8255 -0.4420 1.0702
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 0.3206 1.3015 -2.6248 0.4374
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 1.4805 0.8095 0.0964 1.4283
## avg_veg_height-Canis_latrans -0.1934 0.6532 -1.4890 -0.1777
## avg_veg_height-Procyon_lotor 0.0732 0.6666 -1.1977 0.0626
## avg_veg_height-Dasypus_novemcinctus 0.2850 0.6811 -0.9452 0.2467
## avg_veg_height-Lynx_rufus -0.3553 0.9390 -2.5088 -0.2815
## avg_veg_height-Didelphis_virginiana -0.2090 0.7612 -1.8324 -0.1793
## avg_veg_height-Sylvilagus_floridanus -0.1894 0.7740 -1.8338 -0.1437
## avg_veg_height-Meleagris_gallopavo -0.1410 0.9631 -2.2144 -0.1070
## avg_veg_height-Sciurus_carolinensis 0.3675 0.7917 -1.0075 0.2868
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7320 1.0194 391
## (Intercept)-Procyon_lotor 0.9726 1.0130 203
## (Intercept)-Dasypus_novemcinctus -0.3941 1.0355 322
## (Intercept)-Lynx_rufus 1.1618 1.0023 310
## (Intercept)-Didelphis_virginiana -0.8796 1.0154 347
## (Intercept)-Sylvilagus_floridanus 0.3485 1.0533 411
## (Intercept)-Meleagris_gallopavo 0.2195 1.0106 227
## (Intercept)-Sciurus_carolinensis -0.8030 1.0389 230
## Cogon_Patch_Size-Canis_latrans 4.8066 1.0506 283
## Cogon_Patch_Size-Procyon_lotor 1.1130 1.0173 320
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8564 1.0200 580
## Cogon_Patch_Size-Lynx_rufus 2.7590 1.0447 226
## Cogon_Patch_Size-Didelphis_virginiana 3.9182 1.0188 282
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2463 1.0247 324
## Cogon_Patch_Size-Meleagris_gallopavo 3.4059 1.0326 284
## Cogon_Patch_Size-Sciurus_carolinensis 1.3596 1.0234 321
## Veg_shannon_index-Canis_latrans 2.9477 1.0062 433
## Veg_shannon_index-Procyon_lotor 2.7096 1.0081 207
## Veg_shannon_index-Dasypus_novemcinctus 1.8464 1.0166 387
## Veg_shannon_index-Lynx_rufus 2.8485 1.0371 328
## Veg_shannon_index-Didelphis_virginiana 2.6679 1.0082 449
## Veg_shannon_index-Sylvilagus_floridanus 2.6642 1.0231 427
## Veg_shannon_index-Meleagris_gallopavo 3.0784 1.0036 470
## Veg_shannon_index-Sciurus_carolinensis 1.8942 1.0281 564
## total_shrub_cover-Canis_latrans 2.4265 1.0171 344
## total_shrub_cover-Procyon_lotor -0.3427 1.0185 611
## total_shrub_cover-Dasypus_novemcinctus 1.0180 1.0105 373
## total_shrub_cover-Lynx_rufus 0.3465 1.0148 250
## total_shrub_cover-Didelphis_virginiana 0.2181 1.0441 404
## total_shrub_cover-Sylvilagus_floridanus 0.6744 1.0378 446
## total_shrub_cover-Meleagris_gallopavo -0.4859 1.0296 200
## total_shrub_cover-Sciurus_carolinensis 0.8322 1.0434 276
## Avg_Cogongrass_Cover-Canis_latrans 2.4125 1.0317 259
## Avg_Cogongrass_Cover-Procyon_lotor 2.2315 1.0578 201
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.3808 1.0242 239
## Avg_Cogongrass_Cover-Lynx_rufus 2.5088 1.0341 314
## Avg_Cogongrass_Cover-Didelphis_virginiana 2.7816 1.0452 263
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.6620 1.0301 280
## Avg_Cogongrass_Cover-Meleagris_gallopavo 2.0593 1.0491 250
## Avg_Cogongrass_Cover-Sciurus_carolinensis 2.6106 1.0220 304
## Tree_Density-Canis_latrans -0.7515 1.0571 202
## Tree_Density-Procyon_lotor -0.2569 1.0126 238
## Tree_Density-Dasypus_novemcinctus -1.2873 1.0533 117
## Tree_Density-Lynx_rufus 2.2291 1.0077 187
## Tree_Density-Didelphis_virginiana 0.3545 1.0091 339
## Tree_Density-Sylvilagus_floridanus -0.2294 1.0181 237
## Tree_Density-Meleagris_gallopavo 0.0993 1.0192 317
## Tree_Density-Sciurus_carolinensis 0.5931 1.0360 182
## Avg_Canopy_Cover-Canis_latrans 1.4225 1.0238 364
## Avg_Canopy_Cover-Procyon_lotor 3.4975 1.0055 326
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7105 1.0297 163
## Avg_Canopy_Cover-Lynx_rufus 3.7124 1.0033 230
## Avg_Canopy_Cover-Didelphis_virginiana 6.8028 1.0221 258
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.3762 1.0204 175
## Avg_Canopy_Cover-Meleagris_gallopavo 6.1474 1.0117 158
## Avg_Canopy_Cover-Sciurus_carolinensis 7.2430 1.0122 156
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.4492 1.1190 246
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6361 1.1052 296
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.9245 1.0415 361
## I(Avg_Cogongrass_Cover^2)-Lynx_rufus 5.5115 1.1043 189
## I(Avg_Cogongrass_Cover^2)-Didelphis_virginiana 2.2770 1.0020 255
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9128 1.0667 352
## I(Avg_Cogongrass_Cover^2)-Meleagris_gallopavo 2.5894 1.0867 165
## I(Avg_Cogongrass_Cover^2)-Sciurus_carolinensis 3.3395 1.0336 370
## avg_veg_height-Canis_latrans 1.0337 1.0112 437
## avg_veg_height-Procyon_lotor 1.4151 1.0194 534
## avg_veg_height-Dasypus_novemcinctus 1.7957 1.0200 486
## avg_veg_height-Lynx_rufus 1.3029 1.0113 327
## avg_veg_height-Didelphis_virginiana 1.1777 1.0118 532
## avg_veg_height-Sylvilagus_floridanus 1.2570 1.0158 427
## avg_veg_height-Meleagris_gallopavo 1.5835 1.0148 322
## avg_veg_height-Sciurus_carolinensis 2.1839 1.0105 526
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5649 0.1912 -2.9541 -2.5589 -2.2067 1.0111
## (Intercept)-Procyon_lotor -2.2157 0.1567 -2.5236 -2.2159 -1.9063 1.0034
## (Intercept)-Dasypus_novemcinctus -1.7153 0.1871 -2.0801 -1.7164 -1.3534 1.0040
## (Intercept)-Lynx_rufus -3.4324 0.3463 -4.1667 -3.4236 -2.7899 1.0104
## (Intercept)-Didelphis_virginiana -2.4946 0.3004 -3.1210 -2.4841 -1.9305 1.0006
## (Intercept)-Sylvilagus_floridanus -3.0780 0.2826 -3.6798 -3.0662 -2.5612 1.0074
## (Intercept)-Meleagris_gallopavo -3.4001 0.4625 -4.3155 -3.3898 -2.5033 1.0447
## (Intercept)-Sciurus_carolinensis -2.7077 0.3256 -3.3556 -2.7001 -2.0725 1.0399
## shrub_cover-Canis_latrans -0.2838 0.2248 -0.7052 -0.2871 0.1623 1.0086
## shrub_cover-Procyon_lotor 0.2976 0.1580 -0.0155 0.3030 0.6040 1.0039
## shrub_cover-Dasypus_novemcinctus 1.0467 0.3283 0.4235 1.0496 1.6721 1.0083
## shrub_cover-Lynx_rufus 0.0886 0.3675 -0.6604 0.0986 0.7569 1.0018
## shrub_cover-Didelphis_virginiana 1.1481 0.3813 0.4299 1.1405 1.9261 1.0201
## shrub_cover-Sylvilagus_floridanus 0.6131 0.3853 -0.1453 0.6130 1.3656 1.0305
## shrub_cover-Meleagris_gallopavo -0.3360 0.4246 -1.1868 -0.3369 0.4867 1.0276
## shrub_cover-Sciurus_carolinensis 1.1286 0.4059 0.2846 1.1378 1.9399 1.0452
## veg_height-Canis_latrans -0.5451 0.1838 -0.9000 -0.5458 -0.1952 1.0063
## veg_height-Procyon_lotor 0.3703 0.1227 0.1387 0.3660 0.6148 1.0045
## veg_height-Dasypus_novemcinctus 0.2984 0.1421 0.0204 0.2959 0.5750 1.0039
## veg_height-Lynx_rufus 0.1612 0.2355 -0.3056 0.1659 0.6062 1.0075
## veg_height-Didelphis_virginiana 0.5100 0.2471 0.0460 0.5005 1.0256 1.0026
## veg_height-Sylvilagus_floridanus 0.1689 0.2562 -0.3244 0.1677 0.6550 1.0104
## veg_height-Meleagris_gallopavo -0.0855 0.4037 -0.8848 -0.0884 0.7269 1.0407
## veg_height-Sciurus_carolinensis 0.1855 0.2218 -0.2394 0.1850 0.6330 1.0090
## week-Canis_latrans 0.4515 0.2485 -0.0185 0.4438 0.9653 1.0135
## week-Procyon_lotor 0.1559 0.1997 -0.2260 0.1526 0.5596 1.0015
## week-Dasypus_novemcinctus 0.0649 0.2108 -0.3553 0.0655 0.4822 1.0017
## week-Lynx_rufus 0.2559 0.3102 -0.3379 0.2529 0.8830 1.0121
## week-Didelphis_virginiana 0.0399 0.3134 -0.5833 0.0459 0.6180 1.0029
## week-Sylvilagus_floridanus 0.0443 0.2991 -0.5654 0.0551 0.6114 1.0144
## week-Meleagris_gallopavo -0.1126 0.3600 -0.8699 -0.0924 0.5429 1.0098
## week-Sciurus_carolinensis 0.5337 0.3338 -0.0642 0.5094 1.2788 1.0037
## I(week^2)-Canis_latrans -0.1958 0.1051 -0.4042 -0.1956 0.0080 1.0175
## I(week^2)-Procyon_lotor -0.1098 0.0882 -0.2831 -0.1098 0.0576 1.0008
## I(week^2)-Dasypus_novemcinctus -0.1545 0.1011 -0.3568 -0.1533 0.0430 1.0000
## I(week^2)-Lynx_rufus -0.2117 0.1528 -0.5259 -0.2047 0.0664 1.0003
## I(week^2)-Didelphis_virginiana -0.3807 0.2151 -0.8973 -0.3546 -0.0350 1.0153
## I(week^2)-Sylvilagus_floridanus -0.1640 0.1570 -0.4966 -0.1582 0.1266 1.0028
## I(week^2)-Meleagris_gallopavo -0.4046 0.2841 -1.1030 -0.3555 0.0001 1.1379
## I(week^2)-Sciurus_carolinensis -0.1999 0.1428 -0.5040 -0.1947 0.0566 1.0112
## ESS
## (Intercept)-Canis_latrans 1014
## (Intercept)-Procyon_lotor 1430
## (Intercept)-Dasypus_novemcinctus 864
## (Intercept)-Lynx_rufus 277
## (Intercept)-Didelphis_virginiana 647
## (Intercept)-Sylvilagus_floridanus 786
## (Intercept)-Meleagris_gallopavo 284
## (Intercept)-Sciurus_carolinensis 477
## shrub_cover-Canis_latrans 630
## shrub_cover-Procyon_lotor 1360
## shrub_cover-Dasypus_novemcinctus 571
## shrub_cover-Lynx_rufus 391
## shrub_cover-Didelphis_virginiana 481
## shrub_cover-Sylvilagus_floridanus 511
## shrub_cover-Meleagris_gallopavo 404
## shrub_cover-Sciurus_carolinensis 295
## veg_height-Canis_latrans 772
## veg_height-Procyon_lotor 1543
## veg_height-Dasypus_novemcinctus 1434
## veg_height-Lynx_rufus 578
## veg_height-Didelphis_virginiana 944
## veg_height-Sylvilagus_floridanus 426
## veg_height-Meleagris_gallopavo 353
## veg_height-Sciurus_carolinensis 928
## week-Canis_latrans 975
## week-Procyon_lotor 1637
## week-Dasypus_novemcinctus 1800
## week-Lynx_rufus 800
## week-Didelphis_virginiana 1089
## week-Sylvilagus_floridanus 1200
## week-Meleagris_gallopavo 529
## week-Sciurus_carolinensis 983
## I(week^2)-Canis_latrans 1201
## I(week^2)-Procyon_lotor 1607
## I(week^2)-Dasypus_novemcinctus 1677
## I(week^2)-Lynx_rufus 548
## I(week^2)-Didelphis_virginiana 408
## I(week^2)-Sylvilagus_floridanus 625
## I(week^2)-Meleagris_gallopavo 181
## I(week^2)-Sciurus_carolinensis 1135
names(ms_fullQ_fullQ_T10)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T10$beta.samples)
## 'mcmc' num [1:3000, 1:72] -3.03 -2.82 -3.17 -2.37 -3.15 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:72] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "(Intercept)-Lynx_rufus" ...
mean(ms_fullQ_fullQ_T10$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.002333333
MCMCplot(ms_fullQ_fullQ_T10$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T10$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T10$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T10$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 206 × 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 196 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] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter((Class == "Mammalia" | Name == "Meleagris_gallopavo") & Name != "Odocoileus_virginianus") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 25
species_subset <- species_counts %>%
filter(count > 25) %>%
pull(Name)
# Filter CameraData to only include species with count > 25
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 4
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$week)
week_matrix <- matrix(rep(week_vals, each = num_sites), nrow = num_sites, ncol = num_weeks, byrow = FALSE)
# Create detection covariate list
det.covs <- list(
shrub_cover = covariates_matrix[, "total_shrub_cover"],
veg_height = covariates_matrix[, "avg_veg_height"],
week = week_matrix
)
# Remove dash and convert to numeric
week_numeric <- as.numeric(gsub("-", "", det.covs$week))
## Scale and center week_numeric
week_numeric <- scale(week_numeric)
# Reshape into the original 32x36 matrix
det.covs$week <- matrix(week_numeric, nrow = 32, ncol = 36)
str(det.covs)
## List of 3
## $ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## $ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
This requires combining the presence data and the site covariate data into a single list. This also means that the presence data is in a 3-d format.
# Combine into a named list
data_list <- list(
y = detection_array,
occ.covs = covariates_matrix,
det.covs = det.covs
)
str(data_list)
## List of 3
## $ y : num [1:32, 1:36, 1:4] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## .. ..$ : chr [1:4] "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" "Sylvilagus_floridanus"
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:4, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:4] "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus" "Sylvilagus_floridanus"
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
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## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
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## Sampled: 3000 of 5000, 60.00%
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## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
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## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3003
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0276 0.461 -0.8554 0.0178 0.9451 1.0029 2478
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8448 1.7483 0.0619 0.4141 4.0829 1.0197 2099
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.161 0.5296 -3.0184 -2.2246 -0.8691 1.0074 2526
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.229 3.3802 0.1268 0.573 6.0062 1.0994 2393
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2313 0.3617 -0.4386 0.2220 0.9751 1.0015
## (Intercept)-Procyon_lotor 0.5391 0.3734 -0.1675 0.5264 1.3412 1.0029
## (Intercept)-Dasypus_novemcinctus -0.4768 0.3470 -1.1988 -0.4685 0.1683 1.0019
## (Intercept)-Sylvilagus_floridanus -0.2613 0.4383 -1.0765 -0.2765 0.6516 1.0060
## ESS
## (Intercept)-Canis_latrans 2420
## (Intercept)-Procyon_lotor 2323
## (Intercept)-Dasypus_novemcinctus 2464
## (Intercept)-Sylvilagus_floridanus 977
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5804 0.1657 -2.9174 -2.5767 -2.2660 1.0026
## (Intercept)-Procyon_lotor -2.2650 0.1254 -2.5138 -2.2640 -2.0308 1.0041
## (Intercept)-Dasypus_novemcinctus -1.5900 0.1343 -1.8634 -1.5890 -1.3407 1.0012
## (Intercept)-Sylvilagus_floridanus -3.0953 0.2989 -3.7244 -3.0719 -2.5501 1.0420
## ESS
## (Intercept)-Canis_latrans 1096
## (Intercept)-Procyon_lotor 1701
## (Intercept)-Dasypus_novemcinctus 2289
## (Intercept)-Sylvilagus_floridanus 526
# Includes all covariates of detection and occupancy
ms_full_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4342
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1147 0.8149 -1.8079 -0.0977 1.5065 1.0156 692
## Cogon_Patch_Size -0.5569 0.8300 -2.2170 -0.5573 1.1783 1.0239 716
## Veg_shannon_index 1.0799 0.6495 -0.1875 1.0615 2.4580 1.0440 500
## total_shrub_cover -0.2884 0.7799 -1.8609 -0.2696 1.2912 1.0288 786
## Avg_Cogongrass_Cover 1.6992 0.9870 -0.4308 1.7614 3.5000 1.0180 540
## Tree_Density -2.0122 1.1459 -4.1399 -2.0778 0.7364 1.0006 1068
## Avg_Canopy_Cover 1.3110 1.0285 -1.1075 1.3528 3.2455 1.0018 2466
## avg_veg_height -0.2954 0.6238 -1.4897 -0.2984 0.9627 1.0635 585
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6765 5.9297 0.0695 1.0421 15.3420 1.0564 889
## Cogon_Patch_Size 3.6016 11.7564 0.0673 0.9650 22.7080 1.0225 769
## Veg_shannon_index 1.1400 5.4819 0.0432 0.3739 6.6026 1.2388 2480
## total_shrub_cover 3.2437 8.5420 0.0716 1.0823 20.5614 1.0187 579
## Avg_Cogongrass_Cover 3.9819 11.6415 0.0625 1.1496 22.1787 1.0194 992
## Tree_Density 10.1652 34.4351 0.0794 1.7721 77.9580 1.0306 367
## Avg_Canopy_Cover 11.1641 31.6963 0.1928 3.6539 65.8587 1.0160 997
## avg_veg_height 0.8227 1.7662 0.0410 0.3185 4.5608 1.0310 1229
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0673 3.3 0.0728 0.9393 11.8853 1.0921 104
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3133 0.5064 -3.1423 -2.3578 -1.1548 1.0028 2477
## shrub_cover 0.3304 0.4633 -0.5896 0.3262 1.2353 1.0221 1674
## veg_height 0.0379 0.3864 -0.7586 0.0486 0.8315 1.0002 2579
## week -0.0824 0.2510 -0.5912 -0.0815 0.4246 1.0035 2573
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0959 2.9358 0.1061 0.5207 5.3596 1.0470 1824
## shrub_cover 0.9961 2.7041 0.0865 0.4782 4.6158 1.0643 1799
## veg_height 0.6857 1.5145 0.0706 0.3360 3.3306 1.0126 2757
## week 0.2361 0.6461 0.0301 0.1167 1.1042 1.0625 2577
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5080 0.9319 -1.2159 0.4498
## (Intercept)-Procyon_lotor 0.5430 0.9480 -1.4120 0.5404
## (Intercept)-Dasypus_novemcinctus -0.9029 0.9326 -2.8598 -0.8333
## (Intercept)-Sylvilagus_floridanus -0.6455 1.1545 -3.1466 -0.5783
## Cogon_Patch_Size-Canis_latrans 0.4211 1.2512 -1.4807 0.2455
## Cogon_Patch_Size-Procyon_lotor -1.0027 0.7665 -2.6802 -0.9577
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6128 0.8241 -2.1999 -0.6369
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4313 1.4680 -5.3060 -1.2212
## Veg_shannon_index-Canis_latrans 1.4484 0.8042 0.0851 1.3662
## Veg_shannon_index-Procyon_lotor 1.3156 0.6631 0.1562 1.2591
## Veg_shannon_index-Dasypus_novemcinctus 0.7666 0.6401 -0.5550 0.7653
## Veg_shannon_index-Sylvilagus_floridanus 1.1822 0.7885 -0.2222 1.1353
## total_shrub_cover-Canis_latrans 0.7377 1.0612 -0.7892 0.5411
## total_shrub_cover-Procyon_lotor -1.0449 0.7194 -2.7042 -0.9839
## total_shrub_cover-Dasypus_novemcinctus -0.2272 0.8471 -2.2768 -0.1440
## total_shrub_cover-Sylvilagus_floridanus -0.8162 1.5023 -4.5263 -0.6003
## Avg_Cogongrass_Cover-Canis_latrans 2.5629 1.3061 0.4781 2.3977
## Avg_Cogongrass_Cover-Procyon_lotor 2.1116 1.0507 0.1001 2.0715
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7864 1.2731 0.6784 2.6304
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9741 1.3171 -1.8274 1.0682
## Tree_Density-Canis_latrans -3.2643 1.7563 -7.7650 -2.8946
## Tree_Density-Procyon_lotor -1.5767 0.9127 -3.3642 -1.5844
## Tree_Density-Dasypus_novemcinctus -3.9345 2.0097 -9.4008 -3.4336
## Tree_Density-Sylvilagus_floridanus -2.6815 1.8297 -7.2454 -2.4894
## Avg_Canopy_Cover-Canis_latrans 0.0684 0.7126 -1.3865 0.0732
## Avg_Canopy_Cover-Procyon_lotor 1.6698 0.8263 0.2609 1.5967
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3133 0.9405 0.8453 2.1986
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.3429 2.4758 0.9571 3.8393
## avg_veg_height-Canis_latrans -0.3827 0.7194 -1.7471 -0.3912
## avg_veg_height-Procyon_lotor -0.2772 0.6522 -1.5634 -0.2934
## avg_veg_height-Dasypus_novemcinctus -0.1150 0.6826 -1.3790 -0.1437
## avg_veg_height-Sylvilagus_floridanus -0.4883 0.8488 -2.1942 -0.4752
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5883 1.0419 452
## (Intercept)-Procyon_lotor 2.4132 1.0367 321
## (Intercept)-Dasypus_novemcinctus 0.7877 1.0332 437
## (Intercept)-Sylvilagus_floridanus 1.4894 1.0696 406
## Cogon_Patch_Size-Canis_latrans 3.5427 1.0336 564
## Cogon_Patch_Size-Procyon_lotor 0.4522 1.0450 392
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1385 1.0133 522
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7810 1.0158 336
## Veg_shannon_index-Canis_latrans 3.2194 1.0308 456
## Veg_shannon_index-Procyon_lotor 2.8106 1.0585 225
## Veg_shannon_index-Dasypus_novemcinctus 2.0682 1.0207 514
## Veg_shannon_index-Sylvilagus_floridanus 2.9216 1.0173 337
## total_shrub_cover-Canis_latrans 3.3461 1.0113 202
## total_shrub_cover-Procyon_lotor 0.2034 1.0063 504
## total_shrub_cover-Dasypus_novemcinctus 1.1992 1.0235 396
## total_shrub_cover-Sylvilagus_floridanus 1.3726 1.0980 174
## Avg_Cogongrass_Cover-Canis_latrans 5.5951 1.0190 408
## Avg_Cogongrass_Cover-Procyon_lotor 4.2885 1.0584 426
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.7177 1.0211 294
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.3599 1.0243 470
## Tree_Density-Canis_latrans -0.8717 1.0196 251
## Tree_Density-Procyon_lotor 0.2665 1.0134 678
## Tree_Density-Dasypus_novemcinctus -1.3599 1.0148 223
## Tree_Density-Sylvilagus_floridanus 0.4368 1.0741 287
## Avg_Canopy_Cover-Canis_latrans 1.4105 1.0107 517
## Avg_Canopy_Cover-Procyon_lotor 3.5204 1.0447 373
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.5656 1.0219 278
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.4233 1.0101 182
## avg_veg_height-Canis_latrans 1.0685 1.0352 651
## avg_veg_height-Procyon_lotor 1.0167 1.0584 710
## avg_veg_height-Dasypus_novemcinctus 1.3596 1.0491 634
## avg_veg_height-Sylvilagus_floridanus 1.1125 1.0342 680
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7546 0.1797 -3.1177 -2.7480 -2.4279 1.0110
## (Intercept)-Procyon_lotor -2.2948 0.1386 -2.5712 -2.2931 -2.0297 1.0074
## (Intercept)-Dasypus_novemcinctus -1.8113 0.1724 -2.1549 -1.8081 -1.4773 1.0183
## (Intercept)-Sylvilagus_floridanus -3.1609 0.2781 -3.7499 -3.1545 -2.6620 1.0225
## shrub_cover-Canis_latrans -0.3770 0.2285 -0.8147 -0.3791 0.0699 1.0092
## shrub_cover-Procyon_lotor 0.2926 0.1578 -0.0296 0.2932 0.5927 1.0023
## shrub_cover-Dasypus_novemcinctus 0.9685 0.3316 0.3550 0.9630 1.6424 1.0210
## shrub_cover-Sylvilagus_floridanus 0.5014 0.4105 -0.3025 0.4915 1.3224 1.0704
## veg_height-Canis_latrans -0.6296 0.1874 -1.0117 -0.6285 -0.2761 1.0136
## veg_height-Procyon_lotor 0.3615 0.1278 0.1173 0.3621 0.6054 1.0021
## veg_height-Dasypus_novemcinctus 0.2736 0.1433 -0.0071 0.2696 0.5586 1.0037
## veg_height-Sylvilagus_floridanus 0.1867 0.2691 -0.3590 0.1905 0.6942 1.0195
## week-Canis_latrans 0.0734 0.1377 -0.2025 0.0740 0.3388 1.0016
## week-Procyon_lotor -0.0517 0.1220 -0.3023 -0.0448 0.1728 1.0037
## week-Dasypus_novemcinctus -0.1839 0.1470 -0.4934 -0.1766 0.0877 1.0051
## week-Sylvilagus_floridanus -0.1762 0.2237 -0.6602 -0.1622 0.2285 1.0175
## ESS
## (Intercept)-Canis_latrans 775
## (Intercept)-Procyon_lotor 1248
## (Intercept)-Dasypus_novemcinctus 810
## (Intercept)-Sylvilagus_floridanus 536
## shrub_cover-Canis_latrans 528
## shrub_cover-Procyon_lotor 1458
## shrub_cover-Dasypus_novemcinctus 486
## shrub_cover-Sylvilagus_floridanus 307
## veg_height-Canis_latrans 637
## veg_height-Procyon_lotor 1431
## veg_height-Dasypus_novemcinctus 1742
## veg_height-Sylvilagus_floridanus 555
## week-Canis_latrans 1431
## week-Procyon_lotor 1528
## week-Dasypus_novemcinctus 1964
## week-Sylvilagus_floridanus 831
#Includes all covariates of detection and only null for occupancy
ms_full_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4072
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0524 0.4762 -0.9168 0.0542 1.0244 1.0023 2314
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9364 3.0885 0.058 0.4093 4.8528 1.1731 2757
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2920 0.5086 -3.0837 -2.3548 -1.0134 1.0047 2308
## shrub_cover 0.2574 0.4218 -0.5751 0.2605 1.1080 1.0080 2061
## veg_height 0.0326 0.3760 -0.7371 0.0304 0.7656 1.0033 2615
## week -0.0832 0.2620 -0.5523 -0.0868 0.4093 1.0024 2441
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1398 3.1926 0.0981 0.4997 5.6815 1.0010 2313
## shrub_cover 0.7590 1.7893 0.0705 0.3765 3.6256 1.0996 2796
## veg_height 0.6029 0.9574 0.0727 0.3274 2.8230 1.0191 2678
## week 0.2424 0.4779 0.0273 0.1179 1.1760 1.0125 2311
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3208 0.3824 -0.3942 0.3020 1.1154 1.0071
## (Intercept)-Procyon_lotor 0.5665 0.3773 -0.1094 0.5482 1.3671 1.0011
## (Intercept)-Dasypus_novemcinctus -0.3975 0.3701 -1.1046 -0.3946 0.3111 1.0070
## (Intercept)-Sylvilagus_floridanus -0.2454 0.4234 -1.0960 -0.2423 0.5765 1.0006
## ESS
## (Intercept)-Canis_latrans 2254
## (Intercept)-Procyon_lotor 2037
## (Intercept)-Dasypus_novemcinctus 2122
## (Intercept)-Sylvilagus_floridanus 1330
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7385 0.1871 -3.1188 -2.7298 -2.4005 1.0014
## (Intercept)-Procyon_lotor -2.2906 0.1412 -2.5828 -2.2864 -2.0235 1.0039
## (Intercept)-Dasypus_novemcinctus -1.7729 0.1672 -2.1079 -1.7674 -1.4687 1.0049
## (Intercept)-Sylvilagus_floridanus -3.1074 0.2901 -3.7017 -3.0935 -2.5610 1.0063
## shrub_cover-Canis_latrans -0.2760 0.2213 -0.7214 -0.2735 0.1589 1.0104
## shrub_cover-Procyon_lotor 0.2632 0.1624 -0.0702 0.2673 0.5613 1.0013
## shrub_cover-Dasypus_novemcinctus 0.8336 0.3136 0.2611 0.8236 1.4849 1.0236
## shrub_cover-Sylvilagus_floridanus 0.2804 0.4066 -0.4742 0.2715 1.1115 1.0052
## veg_height-Canis_latrans -0.6196 0.1873 -0.9961 -0.6115 -0.2714 1.0055
## veg_height-Procyon_lotor 0.3463 0.1255 0.1048 0.3460 0.5940 1.0022
## veg_height-Dasypus_novemcinctus 0.2553 0.1392 -0.0082 0.2540 0.5310 1.0004
## veg_height-Sylvilagus_floridanus 0.1442 0.2510 -0.3510 0.1455 0.6384 1.0187
## week-Canis_latrans 0.0721 0.1335 -0.1995 0.0766 0.3199 1.0015
## week-Procyon_lotor -0.0601 0.1223 -0.3101 -0.0571 0.1673 1.0090
## week-Dasypus_novemcinctus -0.1827 0.1468 -0.4881 -0.1780 0.0960 1.0014
## week-Sylvilagus_floridanus -0.1784 0.2242 -0.6654 -0.1651 0.2365 1.0096
## ESS
## (Intercept)-Canis_latrans 718
## (Intercept)-Procyon_lotor 1333
## (Intercept)-Dasypus_novemcinctus 1632
## (Intercept)-Sylvilagus_floridanus 581
## shrub_cover-Canis_latrans 868
## shrub_cover-Procyon_lotor 1467
## shrub_cover-Dasypus_novemcinctus 1057
## shrub_cover-Sylvilagus_floridanus 539
## veg_height-Canis_latrans 782
## veg_height-Procyon_lotor 1330
## veg_height-Dasypus_novemcinctus 1791
## veg_height-Sylvilagus_floridanus 752
## week-Canis_latrans 1705
## week-Procyon_lotor 1697
## week-Dasypus_novemcinctus 1889
## week-Sylvilagus_floridanus 855
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4562
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4962 0.6621 -0.7868 0.4497 1.9172 1.0450 182
## Avg_Cogongrass_Cover -0.0981 0.5761 -1.3161 -0.0734 1.0104 1.0297 890
## total_shrub_cover -0.5783 0.8187 -2.3209 -0.5383 1.0167 1.0180 503
## avg_veg_height 0.3621 0.5856 -0.7565 0.3447 1.5988 1.0888 303
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0451 2.4070 0.0526 0.4246 6.1318 1.0177 2024
## Avg_Cogongrass_Cover 1.1996 3.6205 0.0528 0.4521 6.4276 1.0588 1967
## total_shrub_cover 3.7662 7.9688 0.0970 1.3877 23.1501 1.1412 436
## avg_veg_height 0.7174 1.9622 0.0437 0.2915 3.9866 1.1129 1709
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8556 1.0909 0.0531 0.514 3.7603 1.0062 308
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3422 0.5578 -3.1944 -2.4189 -0.9117 1.0005 2451
## shrub_cover 0.4128 0.5082 -0.6127 0.4151 1.4141 1.0115 1046
## veg_height 0.0057 0.3866 -0.7788 0.0015 0.7850 1.0031 2766
## week -0.0836 0.2417 -0.5676 -0.0847 0.4049 1.0023 1820
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2447 2.8403 0.1065 0.5560 6.1380 1.0146 2400
## shrub_cover 1.2169 2.5838 0.1104 0.6295 5.9601 1.0571 1928
## veg_height 0.6861 1.7965 0.0768 0.3366 3.3781 1.0570 2386
## week 0.2563 0.9083 0.0280 0.1194 1.0811 1.1661 2700
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6818 0.6546 -0.4290 0.6239
## (Intercept)-Procyon_lotor 0.8517 0.6614 -0.3272 0.8040
## (Intercept)-Dasypus_novemcinctus 0.1150 0.8094 -1.3129 0.0423
## (Intercept)-Sylvilagus_floridanus 0.4761 0.8670 -1.0538 0.3817
## Avg_Cogongrass_Cover-Canis_latrans 0.3615 0.6714 -0.7871 0.2899
## Avg_Cogongrass_Cover-Procyon_lotor -0.2169 0.5691 -1.4501 -0.1743
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1128 0.5663 -1.0422 0.1125
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6826 0.8134 -2.5529 -0.5480
## total_shrub_cover-Canis_latrans 0.5803 0.8189 -0.7061 0.4411
## total_shrub_cover-Procyon_lotor -1.3621 0.7387 -3.0337 -1.2662
## total_shrub_cover-Dasypus_novemcinctus -0.7135 1.0989 -3.4993 -0.3979
## total_shrub_cover-Sylvilagus_floridanus -1.6111 1.4888 -5.3969 -1.3140
## avg_veg_height-Canis_latrans 0.3076 0.6082 -0.8442 0.2833
## avg_veg_height-Procyon_lotor 0.3143 0.5631 -0.7382 0.2931
## avg_veg_height-Dasypus_novemcinctus 0.6233 0.6640 -0.4575 0.5446
## avg_veg_height-Sylvilagus_floridanus 0.2639 0.6994 -1.0665 0.2348
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1488 1.0113 380
## (Intercept)-Procyon_lotor 2.3024 1.0121 429
## (Intercept)-Dasypus_novemcinctus 1.9485 1.0705 106
## (Intercept)-Sylvilagus_floridanus 2.3243 1.0988 186
## Avg_Cogongrass_Cover-Canis_latrans 1.8347 1.0593 912
## Avg_Cogongrass_Cover-Procyon_lotor 0.8220 1.0287 594
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2831 1.0067 1303
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5629 1.0211 532
## total_shrub_cover-Canis_latrans 2.5713 1.0267 291
## total_shrub_cover-Procyon_lotor -0.1865 1.0260 341
## total_shrub_cover-Dasypus_novemcinctus 0.6519 1.0994 77
## total_shrub_cover-Sylvilagus_floridanus 0.6315 1.1796 130
## avg_veg_height-Canis_latrans 1.6168 1.0855 576
## avg_veg_height-Procyon_lotor 1.5283 1.0477 663
## avg_veg_height-Dasypus_novemcinctus 2.1713 1.0658 156
## avg_veg_height-Sylvilagus_floridanus 1.7146 1.1187 201
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7923 0.1956 -3.1949 -2.7853 -2.4287 1.0146
## (Intercept)-Procyon_lotor -2.3044 0.1419 -2.6006 -2.2982 -2.0515 1.0056
## (Intercept)-Dasypus_novemcinctus -1.8697 0.2011 -2.2748 -1.8630 -1.5081 1.0433
## (Intercept)-Sylvilagus_floridanus -3.2594 0.2837 -3.8274 -3.2525 -2.7176 1.0152
## shrub_cover-Canis_latrans -0.3580 0.2450 -0.8359 -0.3574 0.1204 1.0035
## shrub_cover-Procyon_lotor 0.3229 0.1599 0.0021 0.3274 0.6251 1.0081
## shrub_cover-Dasypus_novemcinctus 1.1401 0.4256 0.3879 1.1211 1.9318 1.0637
## shrub_cover-Sylvilagus_floridanus 0.7156 0.4960 -0.3607 0.7559 1.6368 1.0490
## veg_height-Canis_latrans -0.6466 0.1948 -1.0400 -0.6446 -0.2831 1.0025
## veg_height-Procyon_lotor 0.3444 0.1265 0.1026 0.3418 0.5961 1.0103
## veg_height-Dasypus_novemcinctus 0.2702 0.1493 -0.0111 0.2674 0.5808 1.0020
## veg_height-Sylvilagus_floridanus 0.0647 0.2724 -0.4549 0.0572 0.6168 1.0491
## week-Canis_latrans 0.0716 0.1375 -0.2062 0.0760 0.3361 1.0008
## week-Procyon_lotor -0.0555 0.1221 -0.3049 -0.0515 0.1727 1.0048
## week-Dasypus_novemcinctus -0.1793 0.1433 -0.4680 -0.1749 0.0861 1.0023
## week-Sylvilagus_floridanus -0.1789 0.2282 -0.6645 -0.1655 0.2376 1.0084
## ESS
## (Intercept)-Canis_latrans 584
## (Intercept)-Procyon_lotor 1348
## (Intercept)-Dasypus_novemcinctus 218
## (Intercept)-Sylvilagus_floridanus 422
## shrub_cover-Canis_latrans 411
## shrub_cover-Procyon_lotor 1520
## shrub_cover-Dasypus_novemcinctus 141
## shrub_cover-Sylvilagus_floridanus 166
## veg_height-Canis_latrans 699
## veg_height-Procyon_lotor 1531
## veg_height-Dasypus_novemcinctus 1575
## veg_height-Sylvilagus_floridanus 367
## week-Canis_latrans 1462
## week-Procyon_lotor 1526
## week-Dasypus_novemcinctus 1688
## week-Sylvilagus_floridanus 752
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.439
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0525 0.5743 -1.1910 -0.0478 1.1252 1.0031 1366
## Tree_Density -0.9648 0.6387 -2.3637 -0.9288 0.2090 1.0082 1046
## Avg_Canopy_Cover 0.8279 0.7806 -0.8535 0.8222 2.3916 1.0046 2359
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.390 3.8617 0.0597 0.5938 7.0111 1.1516 2200
## Tree_Density 1.483 5.9055 0.0468 0.4277 8.9641 1.1860 2262
## Avg_Canopy_Cover 4.230 10.7099 0.1364 1.5899 24.2435 1.0893 1125
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5302 0.7746 0.0456 0.2845 2.6273 1.0145 323
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2998 0.5094 -3.1018 -2.3631 -1.1216 1.0060 2368
## shrub_cover 0.3211 0.4183 -0.5410 0.3201 1.1897 1.0034 1880
## veg_height 0.0527 0.3765 -0.7474 0.0531 0.8331 0.9999 2436
## week -0.0847 0.2501 -0.6196 -0.0790 0.4128 1.0029 2343
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1292 3.2107 0.1070 0.4971 5.0896 1.1301 2220
## shrub_cover 0.7685 1.4884 0.0755 0.3940 3.6334 1.0683 2451
## veg_height 0.6407 1.2272 0.0717 0.3270 3.1342 1.1020 2430
## week 0.2426 0.6572 0.0281 0.1183 1.1360 1.1237 2693
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2375 0.5354 -0.7864 0.2184 1.3459
## (Intercept)-Procyon_lotor 0.5159 0.5955 -0.6157 0.4929 1.7076
## (Intercept)-Dasypus_novemcinctus -0.6518 0.6289 -1.9828 -0.6274 0.4813
## (Intercept)-Sylvilagus_floridanus -0.4128 0.6533 -1.8192 -0.3786 0.7816
## Tree_Density-Canis_latrans -1.0809 0.6617 -2.6112 -0.9899 -0.0113
## Tree_Density-Procyon_lotor -0.5648 0.4540 -1.5221 -0.5499 0.2829
## Tree_Density-Dasypus_novemcinctus -1.5036 1.0042 -4.1591 -1.2741 -0.2048
## Tree_Density-Sylvilagus_floridanus -1.1841 0.8778 -3.2998 -1.0773 0.2355
## Avg_Canopy_Cover-Canis_latrans -0.2625 0.4945 -1.2695 -0.2566 0.7044
## Avg_Canopy_Cover-Procyon_lotor 1.0037 0.5131 0.0785 0.9752 2.0925
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0737 0.5300 0.1594 1.0186 2.2819
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.5541 1.4175 0.6005 2.2752 6.1751
## Rhat ESS
## (Intercept)-Canis_latrans 1.0062 1158
## (Intercept)-Procyon_lotor 1.0031 785
## (Intercept)-Dasypus_novemcinctus 1.0107 840
## (Intercept)-Sylvilagus_floridanus 1.0029 760
## Tree_Density-Canis_latrans 1.0099 1103
## Tree_Density-Procyon_lotor 1.0007 1961
## Tree_Density-Dasypus_novemcinctus 1.0414 520
## Tree_Density-Sylvilagus_floridanus 1.0316 535
## Avg_Canopy_Cover-Canis_latrans 1.0064 1470
## Avg_Canopy_Cover-Procyon_lotor 1.0046 1672
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0029 1528
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0056 168
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7454 0.1832 -3.1230 -2.7415 -2.4066 1.0091
## (Intercept)-Procyon_lotor -2.2962 0.1424 -2.5832 -2.2936 -2.0283 1.0019
## (Intercept)-Dasypus_novemcinctus -1.7839 0.1703 -2.1306 -1.7751 -1.4704 1.0038
## (Intercept)-Sylvilagus_floridanus -3.0935 0.2577 -3.6202 -3.0807 -2.6120 1.0027
## shrub_cover-Canis_latrans -0.2917 0.2318 -0.7380 -0.2951 0.1607 1.0061
## shrub_cover-Procyon_lotor 0.2748 0.1566 -0.0404 0.2759 0.5732 1.0027
## shrub_cover-Dasypus_novemcinctus 0.8803 0.3128 0.3075 0.8710 1.5193 1.0023
## shrub_cover-Sylvilagus_floridanus 0.4938 0.3610 -0.2011 0.4804 1.2449 1.0089
## veg_height-Canis_latrans -0.6102 0.1885 -0.9933 -0.6065 -0.2509 1.0067
## veg_height-Procyon_lotor 0.3561 0.1261 0.1055 0.3563 0.5995 1.0001
## veg_height-Dasypus_novemcinctus 0.2645 0.1435 -0.0148 0.2630 0.5521 0.9996
## veg_height-Sylvilagus_floridanus 0.1603 0.2560 -0.3701 0.1715 0.6547 1.0068
## week-Canis_latrans 0.0706 0.1345 -0.2001 0.0754 0.3161 1.0123
## week-Procyon_lotor -0.0592 0.1245 -0.3134 -0.0577 0.1718 1.0051
## week-Dasypus_novemcinctus -0.1802 0.1448 -0.4804 -0.1725 0.0820 1.0006
## week-Sylvilagus_floridanus -0.1806 0.2307 -0.6706 -0.1706 0.2365 1.0018
## ESS
## (Intercept)-Canis_latrans 642
## (Intercept)-Procyon_lotor 1359
## (Intercept)-Dasypus_novemcinctus 1212
## (Intercept)-Sylvilagus_floridanus 775
## shrub_cover-Canis_latrans 722
## shrub_cover-Procyon_lotor 1549
## shrub_cover-Dasypus_novemcinctus 1067
## shrub_cover-Sylvilagus_floridanus 613
## veg_height-Canis_latrans 773
## veg_height-Procyon_lotor 1588
## veg_height-Dasypus_novemcinctus 1809
## veg_height-Sylvilagus_floridanus 893
## week-Canis_latrans 1648
## week-Procyon_lotor 1673
## week-Dasypus_novemcinctus 1889
## week-Sylvilagus_floridanus 857
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4532
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2919 0.6228 -0.9177 0.2636 1.6022 1.0018 460
## Cogon_Patch_Size -0.0485 0.6357 -1.4444 -0.0355 1.1987 1.0017 1345
## Avg_Cogongrass_Cover 0.1447 0.5019 -0.8286 0.1385 1.1545 1.0026 977
## total_shrub_cover -0.5043 0.6795 -1.9782 -0.4730 0.8222 1.0037 944
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0411 2.4370 0.0505 0.4426 5.9124 1.0783 2061
## Cogon_Patch_Size 2.1020 6.4366 0.0568 0.6293 13.3820 1.0200 728
## Avg_Cogongrass_Cover 0.7682 2.2564 0.0404 0.3070 4.2884 1.1069 2187
## total_shrub_cover 2.1797 5.3184 0.0668 0.7629 13.1104 1.1480 733
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8411 1.448 0.0483 0.4327 3.9907 1.0411 329
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3191 0.5398 -3.1408 -2.3908 -0.9585 1.0038 2600
## shrub_cover 0.3806 0.4650 -0.5793 0.3798 1.2760 1.0087 2043
## veg_height 0.0163 0.3899 -0.7270 0.0248 0.7634 1.0035 2586
## week -0.0884 0.2407 -0.5748 -0.0863 0.3824 1.0014 2038
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2502 2.6137 0.1119 0.5602 7.3013 1.0026 2001
## shrub_cover 1.0143 2.2711 0.0949 0.4922 4.9659 1.0118 2683
## veg_height 1.7194 56.3372 0.0711 0.3209 3.0436 1.3191 3000
## week 0.2362 0.5987 0.0283 0.1161 1.1443 1.0790 2605
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.5637 0.6377 -0.6287 0.5387
## (Intercept)-Procyon_lotor 0.6706 0.6380 -0.5165 0.6464
## (Intercept)-Dasypus_novemcinctus -0.1576 0.6707 -1.3652 -0.1938
## (Intercept)-Sylvilagus_floridanus 0.1936 0.7902 -1.2252 0.1262
## Cogon_Patch_Size-Canis_latrans 0.7948 0.8473 -0.4298 0.6480
## Cogon_Patch_Size-Procyon_lotor -0.1488 0.4893 -1.1423 -0.1455
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0371 0.5001 -0.9921 -0.0397
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8919 1.1684 -4.0599 -0.6439
## Avg_Cogongrass_Cover-Canis_latrans 0.3473 0.5168 -0.5447 0.3145
## Avg_Cogongrass_Cover-Procyon_lotor 0.1097 0.4982 -0.8640 0.0990
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3638 0.4814 -0.5043 0.3497
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1990 0.6966 -1.6744 -0.1753
## total_shrub_cover-Canis_latrans 0.2518 0.6806 -0.9041 0.1851
## total_shrub_cover-Procyon_lotor -1.1069 0.6445 -2.5757 -1.0262
## total_shrub_cover-Dasypus_novemcinctus -0.4463 0.8186 -2.7875 -0.3006
## total_shrub_cover-Sylvilagus_floridanus -1.1827 1.1275 -3.9329 -1.0041
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8831 1.0055 611
## (Intercept)-Procyon_lotor 2.0338 1.0006 701
## (Intercept)-Dasypus_novemcinctus 1.4152 1.0117 290
## (Intercept)-Sylvilagus_floridanus 1.9332 1.0074 370
## Cogon_Patch_Size-Canis_latrans 2.7999 1.0418 667
## Cogon_Patch_Size-Procyon_lotor 0.8230 1.0018 1363
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9936 1.0018 1281
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5841 1.0433 343
## Avg_Cogongrass_Cover-Canis_latrans 1.5604 1.0051 905
## Avg_Cogongrass_Cover-Procyon_lotor 1.1420 1.0032 1357
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3634 1.0044 1357
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1246 1.0109 613
## total_shrub_cover-Canis_latrans 1.9378 1.0504 607
## total_shrub_cover-Procyon_lotor -0.0749 1.0155 722
## total_shrub_cover-Dasypus_novemcinctus 0.6443 1.0654 179
## total_shrub_cover-Sylvilagus_floridanus 0.5005 1.0401 245
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7628 0.1900 -3.1635 -2.7570 -2.4099 1.0224
## (Intercept)-Procyon_lotor -2.2942 0.1358 -2.5675 -2.2895 -2.0384 1.0004
## (Intercept)-Dasypus_novemcinctus -1.8245 0.1858 -2.2134 -1.8137 -1.4857 1.0027
## (Intercept)-Sylvilagus_floridanus -3.2634 0.2833 -3.8364 -3.2621 -2.7036 1.0115
## shrub_cover-Canis_latrans -0.3121 0.2411 -0.7732 -0.3132 0.1552 1.0495
## shrub_cover-Procyon_lotor 0.3191 0.1614 -0.0074 0.3239 0.6337 1.0021
## shrub_cover-Dasypus_novemcinctus 1.0164 0.3798 0.3308 0.9891 1.7978 1.0077
## shrub_cover-Sylvilagus_floridanus 0.6878 0.4556 -0.2981 0.7224 1.5056 1.0142
## veg_height-Canis_latrans -0.6234 0.1950 -1.0044 -0.6165 -0.2537 1.0199
## veg_height-Procyon_lotor 0.3478 0.1257 0.1013 0.3480 0.5903 1.0018
## veg_height-Dasypus_novemcinctus 0.2673 0.1389 0.0001 0.2650 0.5365 1.0055
## veg_height-Sylvilagus_floridanus 0.0653 0.2644 -0.4546 0.0617 0.5854 1.0342
## week-Canis_latrans 0.0716 0.1363 -0.2141 0.0766 0.3270 1.0067
## week-Procyon_lotor -0.0537 0.1229 -0.3000 -0.0514 0.1757 1.0035
## week-Dasypus_novemcinctus -0.1795 0.1466 -0.4839 -0.1769 0.0927 1.0024
## week-Sylvilagus_floridanus -0.1802 0.2262 -0.6594 -0.1654 0.2173 1.0014
## ESS
## (Intercept)-Canis_latrans 548
## (Intercept)-Procyon_lotor 1315
## (Intercept)-Dasypus_novemcinctus 392
## (Intercept)-Sylvilagus_floridanus 414
## shrub_cover-Canis_latrans 587
## shrub_cover-Procyon_lotor 1434
## shrub_cover-Dasypus_novemcinctus 344
## shrub_cover-Sylvilagus_floridanus 216
## veg_height-Canis_latrans 771
## veg_height-Procyon_lotor 1525
## veg_height-Dasypus_novemcinctus 1747
## veg_height-Sylvilagus_floridanus 584
## week-Canis_latrans 1498
## week-Procyon_lotor 1573
## week-Dasypus_novemcinctus 1894
## week-Sylvilagus_floridanus 740
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4207
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0094 0.5209 -0.9995 0.0121 1.0755 1.0276 1037
## Veg_shannon_index 0.4698 0.4349 -0.3456 0.4675 1.3361 1.0022 1525
## Avg_Cogongrass_Cover 0.3331 0.4891 -0.6652 0.3481 1.2925 1.0047 1844
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1189 5.1805 0.0511 0.3960 5.0505 1.0999 2496
## Veg_shannon_index 0.5909 1.5791 0.0397 0.2424 3.2648 1.0317 2106
## Avg_Cogongrass_Cover 0.8739 1.8291 0.0449 0.3723 4.9267 1.0060 2239
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5425 0.6429 0.0453 0.3287 2.3209 1.0119 440
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2937 0.5227 -3.1541 -2.3483 -0.9976 1.0036 2566
## shrub_cover 0.2479 0.4245 -0.6192 0.2439 1.1102 1.0125 2256
## veg_height 0.0224 0.3681 -0.7206 0.0261 0.7348 1.0005 2674
## week -0.0778 0.2527 -0.5799 -0.0712 0.3973 1.0074 2640
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1355 2.6063 0.1064 0.5391 5.6180 1.0043 2358
## shrub_cover 0.7670 1.7085 0.0668 0.3752 3.7629 1.0431 2556
## veg_height 0.5967 1.0503 0.0718 0.3221 2.7238 1.0877 2725
## week 0.2854 2.3321 0.0277 0.1145 1.1866 1.2897 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2869 0.5306 -0.7200 0.2817
## (Intercept)-Procyon_lotor 0.4011 0.5312 -0.6242 0.3934
## (Intercept)-Dasypus_novemcinctus -0.3807 0.4945 -1.3908 -0.3658
## (Intercept)-Sylvilagus_floridanus -0.2516 0.5693 -1.3606 -0.2356
## Veg_shannon_index-Canis_latrans 0.7646 0.4487 -0.0308 0.7302
## Veg_shannon_index-Procyon_lotor 0.4969 0.4061 -0.2642 0.4846
## Veg_shannon_index-Dasypus_novemcinctus 0.2258 0.3858 -0.5566 0.2301
## Veg_shannon_index-Sylvilagus_floridanus 0.5003 0.4652 -0.3500 0.4837
## Avg_Cogongrass_Cover-Canis_latrans 0.7503 0.5123 -0.1000 0.6945
## Avg_Cogongrass_Cover-Procyon_lotor 0.4031 0.4268 -0.3865 0.3854
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4792 0.3895 -0.2512 0.4694
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1973 0.5276 -1.3312 -0.1617
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3791 1.0225 802
## (Intercept)-Procyon_lotor 1.5487 1.0091 753
## (Intercept)-Dasypus_novemcinctus 0.5776 1.0279 772
## (Intercept)-Sylvilagus_floridanus 0.8675 1.0638 705
## Veg_shannon_index-Canis_latrans 1.7457 1.0018 1512
## Veg_shannon_index-Procyon_lotor 1.3331 1.0007 1696
## Veg_shannon_index-Dasypus_novemcinctus 0.9889 1.0050 1918
## Veg_shannon_index-Sylvilagus_floridanus 1.4707 1.0009 1238
## Avg_Cogongrass_Cover-Canis_latrans 1.9536 1.0021 1230
## Avg_Cogongrass_Cover-Procyon_lotor 1.2916 0.9998 2124
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2991 1.0046 2311
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7526 1.0037 1075
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7327 0.1884 -3.1131 -2.7282 -2.3888 1.0151
## (Intercept)-Procyon_lotor -2.2978 0.1425 -2.5865 -2.2965 -2.0312 1.0012
## (Intercept)-Dasypus_novemcinctus -1.7660 0.1581 -2.0901 -1.7631 -1.4683 1.0039
## (Intercept)-Sylvilagus_floridanus -3.1215 0.3017 -3.7594 -3.1009 -2.5844 1.0467
## shrub_cover-Canis_latrans -0.2769 0.2209 -0.7185 -0.2743 0.1439 1.0137
## shrub_cover-Procyon_lotor 0.2477 0.1714 -0.1018 0.2539 0.5752 1.0103
## shrub_cover-Dasypus_novemcinctus 0.8270 0.3087 0.2525 0.8188 1.4548 1.0015
## shrub_cover-Sylvilagus_floridanus 0.2332 0.3961 -0.5106 0.2256 1.0700 1.0101
## veg_height-Canis_latrans -0.6270 0.1977 -1.0192 -0.6267 -0.2459 1.0041
## veg_height-Procyon_lotor 0.3428 0.1233 0.0990 0.3437 0.5927 1.0039
## veg_height-Dasypus_novemcinctus 0.2519 0.1337 -0.0089 0.2497 0.5160 1.0076
## veg_height-Sylvilagus_floridanus 0.1672 0.2594 -0.3631 0.1694 0.6700 1.0061
## week-Canis_latrans 0.0703 0.1351 -0.1997 0.0718 0.3284 1.0017
## week-Procyon_lotor -0.0499 0.1191 -0.2954 -0.0453 0.1694 1.0030
## week-Dasypus_novemcinctus -0.1746 0.1429 -0.4664 -0.1701 0.0965 1.0059
## week-Sylvilagus_floridanus -0.1727 0.2152 -0.6540 -0.1603 0.2091 1.0274
## ESS
## (Intercept)-Canis_latrans 703
## (Intercept)-Procyon_lotor 1300
## (Intercept)-Dasypus_novemcinctus 1345
## (Intercept)-Sylvilagus_floridanus 441
## shrub_cover-Canis_latrans 827
## shrub_cover-Procyon_lotor 1173
## shrub_cover-Dasypus_novemcinctus 1160
## shrub_cover-Sylvilagus_floridanus 467
## veg_height-Canis_latrans 667
## veg_height-Procyon_lotor 1642
## veg_height-Dasypus_novemcinctus 1972
## veg_height-Sylvilagus_floridanus 884
## week-Canis_latrans 1489
## week-Procyon_lotor 1666
## week-Dasypus_novemcinctus 2030
## week-Sylvilagus_floridanus 967
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4208
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6692 0.5683 -1.7749 -0.6874 0.4683 1.0081 866
## Avg_Cogongrass_Cover -0.5336 0.6594 -1.8253 -0.5373 0.7370 1.0248 1414
## I(Avg_Cogongrass_Cover^2) 0.9665 0.6363 -0.1899 0.9032 2.3829 1.0142 768
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9989 4.3096 0.0462 0.3453 5.2025 1.1946 3000
## Avg_Cogongrass_Cover 1.5651 5.9726 0.0502 0.5207 8.9945 1.2156 2089
## I(Avg_Cogongrass_Cover^2) 1.7206 6.7365 0.0480 0.4487 10.8831 1.1294 1019
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4706 0.6262 0.045 0.2635 2.095 1.0807 428
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2952 0.5306 -3.1370 -2.3622 -1.0533 1.0049 2480
## shrub_cover 0.2355 0.4147 -0.6333 0.2415 1.0213 1.0000 1504
## veg_height 0.0590 0.3859 -0.7308 0.0572 0.8508 1.0020 2320
## week -0.0910 0.2617 -0.6453 -0.0855 0.3988 1.0047 2372
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1660 4.2240 0.1027 0.5064 5.9354 1.2020 2486
## shrub_cover 0.7535 1.6610 0.0672 0.3625 4.0283 1.0648 1829
## veg_height 0.7183 2.5487 0.0750 0.3448 3.2937 1.1530 3000
## week 0.2530 0.5553 0.0285 0.1188 1.2882 1.1239 2593
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5320 0.6251 -1.7258 -0.5418
## (Intercept)-Procyon_lotor -0.3694 0.5930 -1.5585 -0.3764
## (Intercept)-Dasypus_novemcinctus -0.9982 0.5757 -2.2438 -0.9872
## (Intercept)-Sylvilagus_floridanus -0.9366 0.6187 -2.2052 -0.9262
## Avg_Cogongrass_Cover-Canis_latrans -0.1164 0.6935 -1.3211 -0.1655
## Avg_Cogongrass_Cover-Procyon_lotor -0.6108 0.6450 -1.8732 -0.6188
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3214 0.5887 -1.4752 -0.3256
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.3141 0.9353 -3.5478 -1.1860
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.5807 1.0103 0.2216 1.3784
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.4553 1.0952 0.2348 1.1762
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6349 0.4341 -0.1721 0.6266
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7417 0.5763 -0.2573 0.6885
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6684 1.0162 759
## (Intercept)-Procyon_lotor 0.8073 1.0140 785
## (Intercept)-Dasypus_novemcinctus 0.0806 1.0138 928
## (Intercept)-Sylvilagus_floridanus 0.2332 1.0091 807
## Avg_Cogongrass_Cover-Canis_latrans 1.3641 1.0182 963
## Avg_Cogongrass_Cover-Procyon_lotor 0.6855 1.0076 1209
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8477 1.0118 1946
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1399 1.0737 480
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.1963 1.0039 314
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6408 1.0264 192
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5534 1.0158 1046
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.0419 1.0380 576
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7482 0.1755 -3.1024 -2.7490 -2.4095 1.0028
## (Intercept)-Procyon_lotor -2.3269 0.1548 -2.6429 -2.3226 -2.0419 1.0023
## (Intercept)-Dasypus_novemcinctus -1.7753 0.1651 -2.1201 -1.7695 -1.4631 1.0032
## (Intercept)-Sylvilagus_floridanus -3.1333 0.2920 -3.7495 -3.1217 -2.5961 1.0065
## shrub_cover-Canis_latrans -0.2502 0.2273 -0.7028 -0.2473 0.1935 1.0052
## shrub_cover-Procyon_lotor 0.2232 0.1735 -0.1284 0.2287 0.5585 1.0069
## shrub_cover-Dasypus_novemcinctus 0.8361 0.3183 0.2585 0.8204 1.4938 1.0001
## shrub_cover-Sylvilagus_floridanus 0.2307 0.4032 -0.5231 0.2205 1.0887 1.0108
## veg_height-Canis_latrans -0.6133 0.1925 -0.9938 -0.6055 -0.2465 0.9997
## veg_height-Procyon_lotor 0.3558 0.1265 0.1055 0.3563 0.6034 1.0048
## veg_height-Dasypus_novemcinctus 0.2602 0.1362 0.0030 0.2570 0.5354 1.0000
## veg_height-Sylvilagus_floridanus 0.2165 0.2773 -0.3270 0.2175 0.7351 1.0126
## week-Canis_latrans 0.0723 0.1382 -0.2065 0.0737 0.3418 1.0021
## week-Procyon_lotor -0.0498 0.1236 -0.2981 -0.0475 0.1854 1.0004
## week-Dasypus_novemcinctus -0.1881 0.1490 -0.5000 -0.1832 0.0886 1.0053
## week-Sylvilagus_floridanus -0.1900 0.2234 -0.6854 -0.1746 0.2063 1.0010
## ESS
## (Intercept)-Canis_latrans 835
## (Intercept)-Procyon_lotor 966
## (Intercept)-Dasypus_novemcinctus 1158
## (Intercept)-Sylvilagus_floridanus 539
## shrub_cover-Canis_latrans 937
## shrub_cover-Procyon_lotor 898
## shrub_cover-Dasypus_novemcinctus 930
## shrub_cover-Sylvilagus_floridanus 444
## veg_height-Canis_latrans 750
## veg_height-Procyon_lotor 1392
## veg_height-Dasypus_novemcinctus 1824
## veg_height-Sylvilagus_floridanus 500
## week-Canis_latrans 1409
## week-Procyon_lotor 1626
## week-Dasypus_novemcinctus 1980
## week-Sylvilagus_floridanus 869
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4192
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2304 0.9153 -3.0650 -1.2012 0.5604 1.0554 461
## Cogon_Patch_Size -0.1175 0.8801 -1.9222 -0.1211 1.6909 1.0002 1002
## Veg_shannon_index 0.9903 0.6516 -0.3660 0.9932 2.2550 1.0205 646
## total_shrub_cover -0.4354 0.7660 -1.9522 -0.4447 1.1634 1.0064 813
## Avg_Cogongrass_Cover 0.3002 1.1942 -2.0432 0.3085 2.6428 1.0325 375
## Tree_Density -2.3018 1.3193 -4.6304 -2.4141 0.6282 1.0100 731
## Avg_Canopy_Cover 1.2775 1.1066 -1.1710 1.3316 3.3570 1.0069 2409
## I(Avg_Cogongrass_Cover^2) 1.5093 0.9125 -0.3924 1.5067 3.2288 1.0240 364
## avg_veg_height -0.0104 0.7173 -1.4369 -0.0053 1.3886 1.0042 514
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.4955 7.0150 0.0540 0.7960 14.0326 1.0932 820
## Cogon_Patch_Size 4.9637 14.7654 0.0633 1.3117 32.2294 1.0785 842
## Veg_shannon_index 1.3191 3.7178 0.0471 0.4453 8.1204 1.0656 1303
## total_shrub_cover 2.9278 7.3618 0.0678 0.9911 17.8062 1.0495 893
## Avg_Cogongrass_Cover 5.0782 16.6909 0.0622 1.3161 31.5913 1.1403 700
## Tree_Density 17.3142 82.4140 0.0687 2.2423 119.8673 1.1364 475
## Avg_Canopy_Cover 13.8720 33.6132 0.2264 4.5712 85.9117 1.0103 563
## I(Avg_Cogongrass_Cover^2) 7.6103 48.9069 0.0557 0.7246 46.3079 1.6834 141
## avg_veg_height 1.0928 2.7076 0.0467 0.3881 6.4201 1.0559 1599
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.5467 2.4289 0.0562 0.6585 7.9277 1.1879 215
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3303 0.4802 -3.1008 -2.3832 -1.2250 1.0035 2615
## shrub_cover 0.3403 0.4633 -0.5692 0.3365 1.2841 1.0005 1910
## veg_height 0.0428 0.3782 -0.7079 0.0466 0.7670 1.0035 2615
## week -0.0865 0.2718 -0.6063 -0.0840 0.4202 1.0195 2491
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0314 2.1370 0.1101 0.4941 5.1670 1.0081 2181
## shrub_cover 0.9428 2.0030 0.0897 0.4830 4.4112 1.0790 2575
## veg_height 0.6526 1.4567 0.0705 0.3247 3.0370 1.0442 2660
## week 0.2528 0.6478 0.0291 0.1178 1.3100 1.0105 2481
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.1298 1.1202 -3.2411 -1.1261
## (Intercept)-Procyon_lotor -0.8300 1.0617 -2.8374 -0.8257
## (Intercept)-Dasypus_novemcinctus -1.9829 1.1907 -4.7523 -1.8771
## (Intercept)-Sylvilagus_floridanus -1.8147 1.2095 -4.4772 -1.7076
## Cogon_Patch_Size-Canis_latrans 1.3159 1.6142 -0.9156 0.9872
## Cogon_Patch_Size-Procyon_lotor -0.5872 0.8642 -2.3620 -0.5566
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1353 0.9274 -1.9953 -0.1263
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1739 1.6495 -5.5614 -0.8783
## Veg_shannon_index-Canis_latrans 1.4322 0.8460 0.0350 1.3254
## Veg_shannon_index-Procyon_lotor 1.2238 0.6902 -0.0351 1.1680
## Veg_shannon_index-Dasypus_novemcinctus 0.6678 0.6814 -0.7301 0.6885
## Veg_shannon_index-Sylvilagus_floridanus 1.1042 0.8750 -0.4828 1.0619
## total_shrub_cover-Canis_latrans 0.4168 0.9691 -1.1049 0.2752
## total_shrub_cover-Procyon_lotor -1.2685 0.8121 -3.1148 -1.1812
## total_shrub_cover-Dasypus_novemcinctus -0.3404 0.8591 -2.2627 -0.2731
## total_shrub_cover-Sylvilagus_floridanus -0.8768 1.2589 -3.6695 -0.7602
## Avg_Cogongrass_Cover-Canis_latrans 0.6644 1.5948 -2.2333 0.5968
## Avg_Cogongrass_Cover-Procyon_lotor 0.1491 1.4344 -2.7116 0.1875
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4677 1.8540 -1.5876 1.1953
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7603 1.8161 -4.9894 -0.5607
## Tree_Density-Canis_latrans -3.8435 1.9599 -9.0046 -3.5013
## Tree_Density-Procyon_lotor -2.4278 1.3017 -5.0710 -2.3709
## Tree_Density-Dasypus_novemcinctus -4.9402 2.9601 -12.9900 -4.1219
## Tree_Density-Sylvilagus_floridanus -3.2651 2.1423 -8.5691 -2.9606
## Avg_Canopy_Cover-Canis_latrans 0.0052 0.7548 -1.6411 0.0315
## Avg_Canopy_Cover-Procyon_lotor 1.6435 1.0445 0.0587 1.5573
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5552 1.2215 0.7814 2.3457
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.7849 2.7923 0.9053 4.2915
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4450 1.5103 0.4823 2.1710
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6954 3.0489 0.4792 2.1690
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3679 0.9394 -0.3357 1.2999
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.2164 0.9910 -0.6466 1.1695
## avg_veg_height-Canis_latrans -0.2113 0.7854 -1.7693 -0.2062
## avg_veg_height-Procyon_lotor 0.1185 0.7773 -1.3924 0.1026
## avg_veg_height-Dasypus_novemcinctus 0.2588 0.8018 -1.2489 0.2377
## avg_veg_height-Sylvilagus_floridanus -0.1344 0.9404 -2.0064 -0.1161
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2299 1.0355 356
## (Intercept)-Procyon_lotor 1.1561 1.0456 476
## (Intercept)-Dasypus_novemcinctus -0.0908 1.0486 272
## (Intercept)-Sylvilagus_floridanus 0.2247 1.0545 335
## Cogon_Patch_Size-Canis_latrans 5.5742 1.1027 391
## Cogon_Patch_Size-Procyon_lotor 0.9981 1.0083 531
## Cogon_Patch_Size-Dasypus_novemcinctus 1.7795 1.0056 655
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2105 1.0824 324
## Veg_shannon_index-Canis_latrans 3.4835 1.0424 463
## Veg_shannon_index-Procyon_lotor 2.7279 1.0927 582
## Veg_shannon_index-Dasypus_novemcinctus 2.0210 1.0098 848
## Veg_shannon_index-Sylvilagus_floridanus 2.9280 1.0213 581
## total_shrub_cover-Canis_latrans 2.6341 1.0518 355
## total_shrub_cover-Procyon_lotor 0.0765 1.0129 338
## total_shrub_cover-Dasypus_novemcinctus 1.1938 1.0306 442
## total_shrub_cover-Sylvilagus_floridanus 1.2458 1.0388 321
## Avg_Cogongrass_Cover-Canis_latrans 4.0786 1.0772 392
## Avg_Cogongrass_Cover-Procyon_lotor 2.8809 1.0315 395
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.8583 1.0130 274
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.3809 1.0097 352
## Tree_Density-Canis_latrans -1.0448 1.0040 233
## Tree_Density-Procyon_lotor -0.0477 1.0278 419
## Tree_Density-Dasypus_novemcinctus -1.5492 1.0520 107
## Tree_Density-Sylvilagus_floridanus 0.0793 1.0136 229
## Avg_Canopy_Cover-Canis_latrans 1.4951 1.0067 815
## Avg_Canopy_Cover-Procyon_lotor 3.6559 1.1402 305
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.6610 1.0315 226
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.5954 1.0354 148
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.0323 1.0430 157
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 7.2239 1.8175 78
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.4346 1.0363 373
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2414 1.0326 313
## avg_veg_height-Canis_latrans 1.3433 1.0017 554
## avg_veg_height-Procyon_lotor 1.6974 1.0192 606
## avg_veg_height-Dasypus_novemcinctus 1.8950 1.0026 504
## avg_veg_height-Sylvilagus_floridanus 1.6809 1.0036 484
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7278 0.1807 -3.0930 -2.7232 -2.3789 1.0229
## (Intercept)-Procyon_lotor -2.3163 0.1490 -2.6221 -2.3120 -2.0390 1.0258
## (Intercept)-Dasypus_novemcinctus -1.8121 0.1717 -2.1603 -1.8064 -1.4923 1.0205
## (Intercept)-Sylvilagus_floridanus -3.1678 0.2631 -3.7235 -3.1638 -2.6731 1.0353
## shrub_cover-Canis_latrans -0.3225 0.2332 -0.7600 -0.3228 0.1348 1.0067
## shrub_cover-Procyon_lotor 0.2726 0.1637 -0.0721 0.2782 0.5748 1.0743
## shrub_cover-Dasypus_novemcinctus 0.9842 0.3318 0.3700 0.9801 1.6483 1.0483
## shrub_cover-Sylvilagus_floridanus 0.5537 0.3905 -0.2440 0.5547 1.3227 1.0008
## veg_height-Canis_latrans -0.5929 0.1917 -0.9796 -0.5861 -0.2366 1.0197
## veg_height-Procyon_lotor 0.3661 0.1262 0.1176 0.3680 0.6112 1.0013
## veg_height-Dasypus_novemcinctus 0.2706 0.1393 -0.0034 0.2711 0.5449 1.0009
## veg_height-Sylvilagus_floridanus 0.1548 0.2740 -0.3987 0.1539 0.7075 1.0039
## week-Canis_latrans 0.0760 0.1366 -0.2006 0.0803 0.3380 1.0028
## week-Procyon_lotor -0.0556 0.1221 -0.3032 -0.0522 0.1696 1.0249
## week-Dasypus_novemcinctus -0.1802 0.1397 -0.4786 -0.1745 0.0781 1.0053
## week-Sylvilagus_floridanus -0.1847 0.2317 -0.6810 -0.1723 0.2294 1.0092
## ESS
## (Intercept)-Canis_latrans 767
## (Intercept)-Procyon_lotor 980
## (Intercept)-Dasypus_novemcinctus 798
## (Intercept)-Sylvilagus_floridanus 550
## shrub_cover-Canis_latrans 614
## shrub_cover-Procyon_lotor 572
## shrub_cover-Dasypus_novemcinctus 580
## shrub_cover-Sylvilagus_floridanus 442
## veg_height-Canis_latrans 610
## veg_height-Procyon_lotor 1298
## veg_height-Dasypus_novemcinctus 1679
## veg_height-Sylvilagus_floridanus 602
## week-Canis_latrans 1617
## week-Procyon_lotor 1509
## week-Dasypus_novemcinctus 2101
## week-Sylvilagus_floridanus 844
# Includes all covariates of occupancy and null for detection
ms_null_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.334
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2689 0.7218 -1.6939 -0.2846 1.2674 1.0021 878
## Cogon_Patch_Size -0.6463 0.7828 -2.2327 -0.6504 1.0037 1.0150 1083
## Veg_shannon_index 1.0554 0.5922 -0.1136 1.0562 2.2053 1.0188 626
## total_shrub_cover -0.1050 0.5950 -1.2895 -0.1074 1.0860 1.0113 1198
## Avg_Cogongrass_Cover 1.9020 0.9513 0.0162 1.8949 3.7882 1.0048 469
## Tree_Density -2.0285 0.9591 -3.7809 -2.0658 0.1027 1.0153 778
## Avg_Canopy_Cover 1.3195 0.9988 -0.9247 1.3582 3.2106 1.0035 2384
## avg_veg_height -0.4244 0.6176 -1.6076 -0.4378 0.8061 1.0197 746
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6801 10.1702 0.0798 0.9941 15.2392 1.1319 1024
## Cogon_Patch_Size 3.3980 13.6809 0.0612 0.8678 19.3407 1.1762 927
## Veg_shannon_index 0.9865 3.8864 0.0447 0.3255 5.4712 1.1718 1498
## total_shrub_cover 1.2368 3.2896 0.0513 0.5033 6.4928 1.1195 1966
## Avg_Cogongrass_Cover 2.7341 17.0128 0.0539 0.7395 14.4686 1.2006 3000
## Tree_Density 5.1701 22.3196 0.0570 0.9041 37.7823 1.0987 1226
## Avg_Canopy_Cover 9.5047 32.2621 0.1588 2.8002 55.9468 1.1142 513
## avg_veg_height 0.8857 2.6889 0.0454 0.3463 5.0462 1.1371 2306
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3534 2.3048 0.0757 0.7197 6.3736 1.074 227
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1914 0.5314 -3.0731 -2.255 -0.8481 1.0078 2542
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3001 3.5448 0.134 0.5936 6.2969 1.1308 2271
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2868 0.8094 -1.1712 0.2305
## (Intercept)-Procyon_lotor 0.4268 0.7876 -1.0689 0.4098
## (Intercept)-Dasypus_novemcinctus -1.0852 0.8288 -3.0303 -1.0147
## (Intercept)-Sylvilagus_floridanus -0.8395 0.9763 -2.9151 -0.8078
## Cogon_Patch_Size-Canis_latrans 0.3230 1.1568 -1.3650 0.1062
## Cogon_Patch_Size-Procyon_lotor -0.9724 0.6949 -2.3848 -0.9613
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8404 0.6496 -2.1824 -0.8087
## Cogon_Patch_Size-Sylvilagus_floridanus -1.5121 1.2520 -4.6145 -1.3355
## Veg_shannon_index-Canis_latrans 1.3629 0.7028 0.2354 1.3069
## Veg_shannon_index-Procyon_lotor 1.2084 0.5751 0.1878 1.1811
## Veg_shannon_index-Dasypus_novemcinctus 0.7829 0.5446 -0.3199 0.7915
## Veg_shannon_index-Sylvilagus_floridanus 1.2243 0.8996 -0.2105 1.1461
## total_shrub_cover-Canis_latrans 0.2121 0.6303 -0.8690 0.1626
## total_shrub_cover-Procyon_lotor -0.7194 0.6022 -2.0284 -0.6772
## total_shrub_cover-Dasypus_novemcinctus 0.1609 0.5372 -0.8233 0.1391
## total_shrub_cover-Sylvilagus_floridanus -0.0369 0.8543 -1.7086 -0.0746
## Avg_Cogongrass_Cover-Canis_latrans 2.4209 1.0210 0.6720 2.3160
## Avg_Cogongrass_Cover-Procyon_lotor 2.2798 0.9993 0.5397 2.2022
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7371 1.1239 0.8884 2.5980
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3736 1.1796 -1.0419 1.4067
## Tree_Density-Canis_latrans -2.6383 1.3276 -5.8371 -2.4302
## Tree_Density-Procyon_lotor -1.6153 0.8198 -3.2366 -1.6358
## Tree_Density-Dasypus_novemcinctus -3.3204 1.6722 -7.7177 -2.9373
## Tree_Density-Sylvilagus_floridanus -2.6778 1.5886 -6.4071 -2.4107
## Avg_Canopy_Cover-Canis_latrans 0.1479 0.6825 -1.2297 0.1800
## Avg_Canopy_Cover-Procyon_lotor 1.6313 0.7246 0.3167 1.5874
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0208 0.7724 0.7988 1.9263
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.0194 2.4839 1.0265 3.3993
## avg_veg_height-Canis_latrans -0.6899 0.6737 -2.0432 -0.6696
## avg_veg_height-Procyon_lotor -0.3085 0.6235 -1.5286 -0.3200
## avg_veg_height-Dasypus_novemcinctus -0.2271 0.6310 -1.4637 -0.2419
## avg_veg_height-Sylvilagus_floridanus -0.6061 0.7915 -2.1996 -0.5947
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0189 1.0015 377
## (Intercept)-Procyon_lotor 1.9811 1.0036 402
## (Intercept)-Dasypus_novemcinctus 0.3285 1.0042 437
## (Intercept)-Sylvilagus_floridanus 1.0224 1.0104 402
## Cogon_Patch_Size-Canis_latrans 3.3344 1.0502 542
## Cogon_Patch_Size-Procyon_lotor 0.3556 1.0119 745
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3712 1.0078 678
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4090 1.0101 438
## Veg_shannon_index-Canis_latrans 2.9064 1.0373 327
## Veg_shannon_index-Procyon_lotor 2.4257 1.0265 507
## Veg_shannon_index-Dasypus_novemcinctus 1.8264 1.0260 947
## Veg_shannon_index-Sylvilagus_floridanus 2.9097 1.0419 423
## total_shrub_cover-Canis_latrans 1.5748 1.0089 1031
## total_shrub_cover-Procyon_lotor 0.3565 1.0229 1211
## total_shrub_cover-Dasypus_novemcinctus 1.2473 1.0029 1262
## total_shrub_cover-Sylvilagus_floridanus 1.7873 1.0331 751
## Avg_Cogongrass_Cover-Canis_latrans 4.6066 1.0283 345
## Avg_Cogongrass_Cover-Procyon_lotor 4.3581 1.0333 444
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2565 1.0196 421
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5773 1.0072 470
## Tree_Density-Canis_latrans -0.7294 1.0264 303
## Tree_Density-Procyon_lotor 0.0277 1.0386 549
## Tree_Density-Dasypus_novemcinctus -1.2669 1.0295 172
## Tree_Density-Sylvilagus_floridanus -0.5408 1.0487 180
## Avg_Canopy_Cover-Canis_latrans 1.4314 1.0049 715
## Avg_Canopy_Cover-Procyon_lotor 3.2263 1.0079 762
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.8252 1.0035 427
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.2399 1.0365 182
## avg_veg_height-Canis_latrans 0.5887 1.0057 757
## avg_veg_height-Procyon_lotor 0.9373 1.0289 929
## avg_veg_height-Dasypus_novemcinctus 1.0576 1.0109 841
## avg_veg_height-Sylvilagus_floridanus 0.9006 1.0053 629
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6212 0.1713 -2.9774 -2.6155 -2.3032 1.0043
## (Intercept)-Procyon_lotor -2.2532 0.1278 -2.5177 -2.2468 -2.0158 1.0024
## (Intercept)-Dasypus_novemcinctus -1.5903 0.1373 -1.8621 -1.5883 -1.3338 1.0066
## (Intercept)-Sylvilagus_floridanus -3.1141 0.2680 -3.6720 -3.0980 -2.6279 1.0165
## ESS
## (Intercept)-Canis_latrans 894
## (Intercept)-Procyon_lotor 1539
## (Intercept)-Dasypus_novemcinctus 2078
## (Intercept)-Sylvilagus_floridanus 540
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3518
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0390 0.5366 -1.0398 0.0450 1.0704 1.0063 1139
## Avg_Cogongrass_Cover 0.0563 0.5011 -0.9916 0.0654 0.9986 1.0031 1290
## total_shrub_cover -0.2280 0.5089 -1.2260 -0.2312 0.7765 1.0012 1906
## avg_veg_height 0.0934 0.4711 -0.8418 0.0918 1.0182 1.0088 1349
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0560 2.7241 0.0533 0.4312 5.9248 1.0566 2259
## Avg_Cogongrass_Cover 0.7158 1.3634 0.0441 0.3109 4.0347 1.0148 1461
## total_shrub_cover 0.9842 2.3124 0.0540 0.4190 5.6604 1.0801 2337
## avg_veg_height 0.5589 1.1626 0.0391 0.2417 3.1207 1.0153 2081
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5593 0.6614 0.048 0.3325 2.4877 1.1107 430
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1988 0.5393 -3.1279 -2.2601 -0.9981 1.0004 2395
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3822 3.1436 0.1378 0.6575 7.0314 1.0347 2199
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2551 0.5289 -0.7441 0.2390
## (Intercept)-Procyon_lotor 0.5155 0.5682 -0.5507 0.4904
## (Intercept)-Dasypus_novemcinctus -0.4553 0.5049 -1.5239 -0.4384
## (Intercept)-Sylvilagus_floridanus -0.1370 0.6349 -1.3405 -0.1414
## Avg_Cogongrass_Cover-Canis_latrans 0.4015 0.5249 -0.5707 0.3709
## Avg_Cogongrass_Cover-Procyon_lotor 0.0028 0.5144 -1.0130 -0.0018
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1969 0.4480 -0.6749 0.2050
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3682 0.5909 -1.6327 -0.3269
## total_shrub_cover-Canis_latrans 0.2047 0.4867 -0.6444 0.1718
## total_shrub_cover-Procyon_lotor -0.8502 0.5423 -2.0800 -0.8069
## total_shrub_cover-Dasypus_novemcinctus -0.0058 0.3788 -0.7277 -0.0153
## total_shrub_cover-Sylvilagus_floridanus -0.3433 0.6029 -1.5838 -0.3141
## avg_veg_height-Canis_latrans -0.0311 0.4813 -1.0027 -0.0257
## avg_veg_height-Procyon_lotor 0.1871 0.4841 -0.7089 0.1715
## avg_veg_height-Dasypus_novemcinctus 0.2874 0.4533 -0.5755 0.2873
## avg_veg_height-Sylvilagus_floridanus -0.0723 0.5236 -1.1152 -0.0779
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3819 1.0173 831
## (Intercept)-Procyon_lotor 1.7350 1.0239 834
## (Intercept)-Dasypus_novemcinctus 0.4756 1.0060 1065
## (Intercept)-Sylvilagus_floridanus 1.1114 1.0078 641
## Avg_Cogongrass_Cover-Canis_latrans 1.5292 1.0007 1487
## Avg_Cogongrass_Cover-Procyon_lotor 1.0240 1.0011 1268
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1085 1.0048 1403
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6701 1.0080 1133
## total_shrub_cover-Canis_latrans 1.2015 1.0062 1276
## total_shrub_cover-Procyon_lotor 0.1002 1.0044 1386
## total_shrub_cover-Dasypus_novemcinctus 0.7557 1.0051 2112
## total_shrub_cover-Sylvilagus_floridanus 0.7815 1.0023 1062
## avg_veg_height-Canis_latrans 0.8950 1.0056 1404
## avg_veg_height-Procyon_lotor 1.2215 1.0033 1314
## avg_veg_height-Dasypus_novemcinctus 1.2458 1.0108 1524
## avg_veg_height-Sylvilagus_floridanus 0.9479 1.0011 1124
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6017 0.1728 -2.9435 -2.6013 -2.2723 1.0122
## (Intercept)-Procyon_lotor -2.2731 0.1289 -2.5209 -2.2721 -2.0231 1.0146
## (Intercept)-Dasypus_novemcinctus -1.5866 0.1383 -1.8649 -1.5859 -1.3206 1.0043
## (Intercept)-Sylvilagus_floridanus -3.1901 0.3129 -3.8534 -3.1747 -2.6201 1.0030
## ESS
## (Intercept)-Canis_latrans 970
## (Intercept)-Procyon_lotor 1397
## (Intercept)-Dasypus_novemcinctus 2218
## (Intercept)-Sylvilagus_floridanus 408
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3745
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1819 0.5738 -1.3506 -0.1739 0.9642 1.0130 1499
## Tree_Density -0.9813 0.6046 -2.2478 -0.9488 0.1501 1.0004 1411
## Avg_Canopy_Cover 0.8353 0.7174 -0.6819 0.8374 2.2942 1.0093 2601
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3766 2.8979 0.0646 0.5758 7.9369 1.0094 1776
## Tree_Density 1.4761 5.9093 0.0469 0.4303 9.5613 1.1053 1418
## Avg_Canopy_Cover 3.2363 8.9342 0.1105 1.2162 18.1278 1.0296 1393
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5379 0.7558 0.0453 0.2983 2.5408 1.0116 376
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1586 0.5451 -2.999 -2.224 -0.8025 1.0043 2558
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2411 2.4787 0.1237 0.5773 6.3341 1.0245 2260
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1005 0.5355 -0.9096 0.0739 1.1760
## (Intercept)-Procyon_lotor 0.4042 0.5924 -0.7413 0.3904 1.6086
## (Intercept)-Dasypus_novemcinctus -0.7697 0.5691 -1.9762 -0.7401 0.2873
## (Intercept)-Sylvilagus_floridanus -0.5266 0.6370 -1.8019 -0.5139 0.7090
## Tree_Density-Canis_latrans -1.0624 0.6191 -2.4830 -0.9835 -0.0727
## Tree_Density-Procyon_lotor -0.5657 0.4541 -1.4799 -0.5513 0.2894
## Tree_Density-Dasypus_novemcinctus -1.4961 0.9372 -3.8763 -1.3032 -0.2231
## Tree_Density-Sylvilagus_floridanus -1.2589 0.8356 -3.3163 -1.1235 0.0079
## Avg_Canopy_Cover-Canis_latrans -0.1612 0.4824 -1.1358 -0.1629 0.7661
## Avg_Canopy_Cover-Procyon_lotor 0.9976 0.5173 0.0646 0.9654 2.1426
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0002 0.4789 0.1571 0.9682 2.0324
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.2438 1.2478 0.5887 1.9976 5.4176
## Rhat ESS
## (Intercept)-Canis_latrans 1.0077 1029
## (Intercept)-Procyon_lotor 1.0068 789
## (Intercept)-Dasypus_novemcinctus 1.0373 914
## (Intercept)-Sylvilagus_floridanus 1.0016 841
## Tree_Density-Canis_latrans 1.0075 1258
## Tree_Density-Procyon_lotor 1.0058 1593
## Tree_Density-Dasypus_novemcinctus 1.0298 501
## Tree_Density-Sylvilagus_floridanus 1.0163 612
## Avg_Canopy_Cover-Canis_latrans 1.0020 1284
## Avg_Canopy_Cover-Procyon_lotor 1.0006 1522
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0050 2013
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0349 385
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6018 0.1738 -2.9593 -2.5939 -2.2742 1.0006
## (Intercept)-Procyon_lotor -2.2614 0.1289 -2.5246 -2.2581 -2.0163 1.0098
## (Intercept)-Dasypus_novemcinctus -1.5905 0.1340 -1.8520 -1.5884 -1.3292 1.0024
## (Intercept)-Sylvilagus_floridanus -3.0703 0.2719 -3.6363 -3.0601 -2.5769 1.0101
## ESS
## (Intercept)-Canis_latrans 966
## (Intercept)-Procyon_lotor 1579
## (Intercept)-Dasypus_novemcinctus 2360
## (Intercept)-Sylvilagus_floridanus 594
# Includes movement covariates of occupancy and null for detection
ms_null_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.369
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0501 0.5289 -0.9877 0.0463 1.1123 1.0047 1226
## Cogon_Patch_Size -0.0512 0.6510 -1.4751 -0.0427 1.2388 1.0006 1813
## Avg_Cogongrass_Cover 0.1347 0.4483 -0.7832 0.1409 0.9939 1.0004 1443
## total_shrub_cover -0.2394 0.4857 -1.2225 -0.2389 0.7364 1.0041 1525
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0909 3.3730 0.0550 0.4560 5.7139 1.0525 2704
## Cogon_Patch_Size 2.1675 5.4949 0.0639 0.7121 14.3942 1.0691 766
## Avg_Cogongrass_Cover 0.6558 2.3746 0.0423 0.2672 3.4691 1.1092 2587
## total_shrub_cover 0.8745 1.9142 0.0527 0.3556 4.7498 1.0165 1933
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5186 0.6203 0.0437 0.3088 2.273 1.0604 455
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2027 0.5523 -3.0717 -2.2752 -0.823 0.9997 2380
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3478 2.8 0.1346 0.6338 7.2817 1.0028 2242
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3268 0.5299 -0.6585 0.3062
## (Intercept)-Procyon_lotor 0.4972 0.5504 -0.5226 0.4634
## (Intercept)-Dasypus_novemcinctus -0.4357 0.4897 -1.4254 -0.4266
## (Intercept)-Sylvilagus_floridanus -0.2249 0.6501 -1.4886 -0.2478
## Cogon_Patch_Size-Canis_latrans 0.9109 0.9531 -0.3173 0.7097
## Cogon_Patch_Size-Procyon_lotor -0.1387 0.5090 -1.0553 -0.1562
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1023 0.4219 -0.9834 -0.1021
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8645 0.9767 -3.3233 -0.6927
## Avg_Cogongrass_Cover-Canis_latrans 0.2252 0.4384 -0.6324 0.2189
## Avg_Cogongrass_Cover-Procyon_lotor 0.1839 0.4597 -0.6645 0.1742
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3683 0.4072 -0.3958 0.3511
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1743 0.5558 -1.4305 -0.1368
## total_shrub_cover-Canis_latrans 0.1075 0.4721 -0.7409 0.0746
## total_shrub_cover-Procyon_lotor -0.8068 0.5211 -1.9409 -0.7434
## total_shrub_cover-Dasypus_novemcinctus -0.0480 0.3802 -0.7657 -0.0489
## total_shrub_cover-Sylvilagus_floridanus -0.2834 0.5742 -1.5013 -0.2667
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4347 1.0322 1100
## (Intercept)-Procyon_lotor 1.6755 1.0145 906
## (Intercept)-Dasypus_novemcinctus 0.5074 1.0060 1326
## (Intercept)-Sylvilagus_floridanus 1.1163 1.0125 442
## Cogon_Patch_Size-Canis_latrans 3.3793 1.0212 612
## Cogon_Patch_Size-Procyon_lotor 0.8655 1.0039 1475
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7058 1.0088 2325
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5785 1.0393 556
## Avg_Cogongrass_Cover-Canis_latrans 1.1089 1.0071 1558
## Avg_Cogongrass_Cover-Procyon_lotor 1.1371 1.0011 1587
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2326 1.0032 1776
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7959 1.0036 826
## total_shrub_cover-Canis_latrans 1.0836 1.0019 1511
## total_shrub_cover-Procyon_lotor 0.0528 1.0071 1466
## total_shrub_cover-Dasypus_novemcinctus 0.7064 1.0108 1690
## total_shrub_cover-Sylvilagus_floridanus 0.8333 1.0022 910
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5900 0.1681 -2.9420 -2.5845 -2.2784 1.0088
## (Intercept)-Procyon_lotor -2.2685 0.1261 -2.5223 -2.2664 -2.0338 1.0046
## (Intercept)-Dasypus_novemcinctus -1.5938 0.1365 -1.8736 -1.5896 -1.3341 1.0019
## (Intercept)-Sylvilagus_floridanus -3.1952 0.3102 -3.8545 -3.1849 -2.6286 1.0132
## ESS
## (Intercept)-Canis_latrans 944
## (Intercept)-Procyon_lotor 1504
## (Intercept)-Dasypus_novemcinctus 2004
## (Intercept)-Sylvilagus_floridanus 374
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3515
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0222 0.5009 -1.0177 -0.0257 0.9334 1.0027 1217
## Veg_shannon_index 0.5083 0.4202 -0.3600 0.5100 1.3261 1.0004 1604
## Avg_Cogongrass_Cover 0.3379 0.4654 -0.5408 0.3287 1.2453 1.0067 1555
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9264 2.2332 0.0544 0.3759 4.7464 1.0227 1646
## Veg_shannon_index 0.5475 1.4844 0.0394 0.2363 2.9930 1.1170 1522
## Avg_Cogongrass_Cover 0.7835 4.3084 0.0413 0.2957 4.1275 1.2477 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5661 0.7529 0.0453 0.3154 2.6713 1.0463 252
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.192 0.5544 -3.0727 -2.245 -0.8737 1.0038 2549
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3158 3.3163 0.1235 0.5932 6.5229 1.0389 2678
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1673 0.4930 -0.7919 0.1630
## (Intercept)-Procyon_lotor 0.4025 0.5253 -0.6088 0.3980
## (Intercept)-Dasypus_novemcinctus -0.4446 0.4666 -1.4025 -0.4121
## (Intercept)-Sylvilagus_floridanus -0.2391 0.6072 -1.3913 -0.2421
## Veg_shannon_index-Canis_latrans 0.7649 0.4331 -0.0112 0.7327
## Veg_shannon_index-Procyon_lotor 0.5669 0.4271 -0.2301 0.5477
## Veg_shannon_index-Dasypus_novemcinctus 0.2764 0.3746 -0.4545 0.2844
## Veg_shannon_index-Sylvilagus_floridanus 0.5718 0.5168 -0.3522 0.5538
## Avg_Cogongrass_Cover-Canis_latrans 0.6262 0.4562 -0.1418 0.5846
## Avg_Cogongrass_Cover-Procyon_lotor 0.4683 0.4576 -0.3325 0.4388
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4666 0.3662 -0.2375 0.4586
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1226 0.5215 -1.2743 -0.0948
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1793 1.0334 1023
## (Intercept)-Procyon_lotor 1.4316 1.0050 915
## (Intercept)-Dasypus_novemcinctus 0.4261 1.0137 985
## (Intercept)-Sylvilagus_floridanus 1.0155 1.0162 741
## Veg_shannon_index-Canis_latrans 1.6858 1.0010 1366
## Veg_shannon_index-Procyon_lotor 1.4526 1.0097 1012
## Veg_shannon_index-Dasypus_novemcinctus 1.0093 1.0005 1906
## Veg_shannon_index-Sylvilagus_floridanus 1.6919 1.0033 1142
## Avg_Cogongrass_Cover-Canis_latrans 1.6665 1.0071 1287
## Avg_Cogongrass_Cover-Procyon_lotor 1.4807 1.0147 1334
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2269 1.0058 1980
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8512 1.0017 1185
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5856 0.1720 -2.9390 -2.5808 -2.2666 1.0078
## (Intercept)-Procyon_lotor -2.2717 0.1270 -2.5359 -2.2696 -2.0328 1.0089
## (Intercept)-Dasypus_novemcinctus -1.5914 0.1361 -1.8609 -1.5917 -1.3308 1.0010
## (Intercept)-Sylvilagus_floridanus -3.1414 0.3049 -3.7701 -3.1350 -2.5662 1.0278
## ESS
## (Intercept)-Canis_latrans 984
## (Intercept)-Procyon_lotor 1436
## (Intercept)-Dasypus_novemcinctus 2152
## (Intercept)-Sylvilagus_floridanus 379
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3587
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7281 0.5497 -1.8046 -0.7264 0.3334 1.0041 465
## Avg_Cogongrass_Cover -0.5050 0.6087 -1.7155 -0.5078 0.6901 1.0067 1225
## I(Avg_Cogongrass_Cover^2) 0.9892 0.7077 -0.2489 0.8863 2.5973 1.0106 452
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1248 12.6161 0.0438 0.3226 4.5016 1.2772 3000
## Avg_Cogongrass_Cover 1.2841 4.0760 0.0478 0.4243 7.9093 1.0535 1909
## I(Avg_Cogongrass_Cover^2) 2.3419 7.8257 0.0449 0.5450 14.9507 1.0727 686
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4258 0.5252 0.0399 0.2516 1.9177 1.0532 387
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1729 0.5511 -3.0269 -2.2463 -0.8168 1.0032 2504
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3343 3.0801 0.1255 0.62 7.0515 1.0293 2280
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6542 0.6035 -1.8728 -0.6419
## (Intercept)-Procyon_lotor -0.4724 0.5922 -1.6509 -0.4607
## (Intercept)-Dasypus_novemcinctus -1.0573 0.5346 -2.1848 -1.0372
## (Intercept)-Sylvilagus_floridanus -0.9497 0.6418 -2.3038 -0.9200
## Avg_Cogongrass_Cover-Canis_latrans -0.2401 0.6150 -1.3832 -0.2747
## Avg_Cogongrass_Cover-Procyon_lotor -0.4333 0.6237 -1.6483 -0.4450
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3099 0.5542 -1.3775 -0.3082
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1528 0.8437 -3.0310 -1.0461
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.7263 1.2444 0.1717 1.3657
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.6455 1.2401 0.2474 1.2554
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5676 0.3862 -0.1808 0.5622
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8796 0.8472 -0.2189 0.7223
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5064 1.0194 398
## (Intercept)-Procyon_lotor 0.6615 1.0038 498
## (Intercept)-Dasypus_novemcinctus -0.0696 1.0082 932
## (Intercept)-Sylvilagus_floridanus 0.2012 0.9996 741
## Avg_Cogongrass_Cover-Canis_latrans 1.0653 1.0108 1017
## Avg_Cogongrass_Cover-Procyon_lotor 0.8655 1.0132 1047
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8102 1.0276 1336
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1908 1.0211 663
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.7939 1.0704 189
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.9703 1.0248 167
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3471 1.0302 1189
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.9655 1.0383 241
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6210 0.1706 -2.9568 -2.6207 -2.2907 1.0044
## (Intercept)-Procyon_lotor -2.2776 0.1305 -2.5413 -2.2756 -2.0269 1.0085
## (Intercept)-Dasypus_novemcinctus -1.5931 0.1351 -1.8530 -1.5917 -1.3317 1.0017
## (Intercept)-Sylvilagus_floridanus -3.1419 0.3131 -3.8038 -3.1194 -2.5879 1.0360
## ESS
## (Intercept)-Canis_latrans 935
## (Intercept)-Procyon_lotor 1373
## (Intercept)-Dasypus_novemcinctus 2434
## (Intercept)-Sylvilagus_floridanus 381
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3588
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3843 0.8571 -3.0900 -1.4061 0.3574 1.0316 543
## Cogon_Patch_Size -0.1747 0.8683 -1.9090 -0.1712 1.5691 1.0061 976
## Veg_shannon_index 0.9893 0.6090 -0.1689 0.9703 2.2357 1.0065 572
## total_shrub_cover -0.1499 0.6215 -1.3420 -0.1734 1.1191 1.0169 1098
## Avg_Cogongrass_Cover 0.2732 1.0755 -1.8712 0.2655 2.3357 1.0095 414
## Tree_Density -2.3615 1.1119 -4.3354 -2.4334 0.1213 1.0099 633
## Avg_Canopy_Cover 1.2861 0.9833 -0.8292 1.3336 3.1173 1.0017 2089
## I(Avg_Cogongrass_Cover^2) 1.5358 0.8567 -0.2021 1.5298 3.3191 1.0265 718
## avg_veg_height -0.0872 0.6637 -1.4343 -0.0614 1.1951 1.0076 634
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5670 7.0458 0.0594 0.7781 15.7007 1.0179 1507
## Cogon_Patch_Size 4.5387 9.5035 0.0836 1.6211 25.9567 1.0664 1089
## Veg_shannon_index 1.0702 3.2530 0.0443 0.3567 5.8294 1.0194 2236
## total_shrub_cover 1.4974 5.2173 0.0545 0.5584 7.9537 1.0575 1627
## Avg_Cogongrass_Cover 3.5013 8.7660 0.0643 1.1171 21.7521 1.0355 950
## Tree_Density 6.0358 18.5876 0.0554 1.1171 46.6837 1.0273 549
## Avg_Canopy_Cover 9.8454 30.9583 0.1642 3.3074 59.0545 1.0297 698
## I(Avg_Cogongrass_Cover^2) 4.0351 15.8299 0.0574 0.8216 28.4477 1.5394 377
## avg_veg_height 1.3967 8.1642 0.0474 0.4456 6.8875 1.1170 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4023 2.1492 0.054 0.652 7.1083 1.0567 220
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1971 0.5191 -3.0368 -2.2532 -0.8954 1.0129 2601
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2746 3.1319 0.1316 0.6165 6.7043 1.0113 2024
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3480 1.0056 -3.2635 -1.3421
## (Intercept)-Procyon_lotor -0.9423 0.9520 -2.8227 -0.9694
## (Intercept)-Dasypus_novemcinctus -2.1808 1.0020 -4.5482 -2.0727
## (Intercept)-Sylvilagus_floridanus -2.0489 1.1582 -4.7572 -1.9082
## Cogon_Patch_Size-Canis_latrans 1.4729 1.5474 -0.6612 1.2031
## Cogon_Patch_Size-Procyon_lotor -0.5761 0.8786 -2.3407 -0.5570
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4030 0.7455 -1.9610 -0.3660
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2753 1.5531 -5.3190 -0.9955
## Veg_shannon_index-Canis_latrans 1.3423 0.7511 0.1329 1.2342
## Veg_shannon_index-Procyon_lotor 1.1158 0.6360 -0.0219 1.0712
## Veg_shannon_index-Dasypus_novemcinctus 0.6842 0.5845 -0.4582 0.6810
## Veg_shannon_index-Sylvilagus_floridanus 1.0866 0.8028 -0.2577 1.0155
## total_shrub_cover-Canis_latrans 0.1035 0.6637 -1.0625 0.0495
## total_shrub_cover-Procyon_lotor -0.8381 0.6832 -2.3220 -0.7981
## total_shrub_cover-Dasypus_novemcinctus 0.1696 0.5426 -0.8438 0.1579
## total_shrub_cover-Sylvilagus_floridanus -0.1060 0.8998 -1.8550 -0.1386
## Avg_Cogongrass_Cover-Canis_latrans 0.2110 1.3575 -2.4982 0.2180
## Avg_Cogongrass_Cover-Procyon_lotor 0.4413 1.3234 -2.1751 0.3864
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1828 1.4879 -1.2837 1.0427
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5950 1.5589 -4.0362 -0.4675
## Tree_Density-Canis_latrans -3.2531 1.5476 -7.2278 -3.0101
## Tree_Density-Procyon_lotor -2.2799 1.1156 -4.4784 -2.2912
## Tree_Density-Dasypus_novemcinctus -3.8834 1.8390 -8.7647 -3.4734
## Tree_Density-Sylvilagus_floridanus -2.9538 1.4924 -6.5858 -2.7996
## Avg_Canopy_Cover-Canis_latrans 0.0040 0.7485 -1.5445 0.0203
## Avg_Canopy_Cover-Procyon_lotor 1.6510 0.8004 0.2994 1.5880
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0638 0.8456 0.6959 1.9715
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.1834 2.3041 1.0556 3.7585
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4721 1.4232 0.6895 2.1939
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4819 1.3892 0.6534 2.2212
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3409 0.8210 -0.1844 1.3032
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1655 0.9267 -0.5930 1.1407
## avg_veg_height-Canis_latrans -0.4769 0.7423 -1.9832 -0.4371
## avg_veg_height-Procyon_lotor 0.2119 0.7211 -1.1243 0.1784
## avg_veg_height-Dasypus_novemcinctus 0.1752 0.6982 -1.1714 0.1764
## avg_veg_height-Sylvilagus_floridanus -0.2663 0.8526 -2.0660 -0.2210
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6722 1.0765 442
## (Intercept)-Procyon_lotor 1.0099 1.0447 386
## (Intercept)-Dasypus_novemcinctus -0.5384 1.0390 444
## (Intercept)-Sylvilagus_floridanus -0.0697 1.0238 498
## Cogon_Patch_Size-Canis_latrans 5.2336 1.0638 465
## Cogon_Patch_Size-Procyon_lotor 1.1317 1.0069 736
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9467 1.0021 617
## Cogon_Patch_Size-Sylvilagus_floridanus 1.0273 1.0409 429
## Veg_shannon_index-Canis_latrans 3.1081 1.0076 515
## Veg_shannon_index-Procyon_lotor 2.4665 1.0028 508
## Veg_shannon_index-Dasypus_novemcinctus 1.8425 1.0008 975
## Veg_shannon_index-Sylvilagus_floridanus 2.9802 1.0059 446
## total_shrub_cover-Canis_latrans 1.5406 1.0110 935
## total_shrub_cover-Procyon_lotor 0.3955 1.0149 913
## total_shrub_cover-Dasypus_novemcinctus 1.3066 1.0142 1196
## total_shrub_cover-Sylvilagus_floridanus 1.8746 1.0108 597
## Avg_Cogongrass_Cover-Canis_latrans 2.8452 1.0131 605
## Avg_Cogongrass_Cover-Procyon_lotor 3.1902 1.0097 379
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.5947 1.0005 397
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.1610 1.0094 486
## Tree_Density-Canis_latrans -0.8924 1.0574 242
## Tree_Density-Procyon_lotor -0.0456 1.0444 532
## Tree_Density-Dasypus_novemcinctus -1.4351 1.0369 261
## Tree_Density-Sylvilagus_floridanus -0.6582 1.0406 290
## Avg_Canopy_Cover-Canis_latrans 1.4300 1.0148 653
## Avg_Canopy_Cover-Procyon_lotor 3.5254 1.0312 645
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9531 1.0146 480
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.6657 1.0104 159
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.2852 1.4048 151
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.3564 1.3643 148
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0966 1.0265 545
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1114 1.0160 416
## avg_veg_height-Canis_latrans 0.8600 1.0096 641
## avg_veg_height-Procyon_lotor 1.7145 1.0146 608
## avg_veg_height-Dasypus_novemcinctus 1.5647 1.0023 613
## avg_veg_height-Sylvilagus_floridanus 1.2648 1.0137 774
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5841 0.1660 -2.9228 -2.5774 -2.2753 1.0109
## (Intercept)-Procyon_lotor -2.2684 0.1316 -2.5300 -2.2651 -2.0267 1.0247
## (Intercept)-Dasypus_novemcinctus -1.5898 0.1346 -1.8599 -1.5894 -1.3349 1.0002
## (Intercept)-Sylvilagus_floridanus -3.1444 0.2744 -3.7092 -3.1402 -2.6216 1.0316
## ESS
## (Intercept)-Canis_latrans 915
## (Intercept)-Procyon_lotor 1500
## (Intercept)-Dasypus_novemcinctus 2193
## (Intercept)-Sylvilagus_floridanus 573
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4667
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0468 0.5072 -0.9019 0.0399 1.0773 1.0062 836
## Avg_Cogongrass_Cover 0.1854 0.4528 -0.7381 0.1784 1.0758 1.0013 1769
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8431 2.5300 0.0485 0.3682 4.1481 1.0859 2409
## Avg_Cogongrass_Cover 0.7046 1.3873 0.0455 0.3193 3.9039 1.0065 1843
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5642 0.8171 0.0473 0.3169 2.4667 1.1015 432
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2877 0.5077 -3.1348 -2.3383 -1.1020 1.0010 2338
## shrub_cover 0.2350 0.4289 -0.6346 0.2233 1.1100 1.0009 1682
## veg_height 0.0291 0.3753 -0.7199 0.0393 0.7648 1.0017 2399
## week -0.0910 0.2347 -0.5732 -0.0866 0.3523 1.0002 2113
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1400 2.7609 0.1087 0.5488 5.6641 1.0030 2057
## shrub_cover 0.7454 1.4581 0.0695 0.3809 3.6500 1.0325 2133
## veg_height 0.6483 1.1748 0.0729 0.3300 3.3702 1.0215 2810
## week 0.2207 0.4396 0.0291 0.1188 0.9911 1.0437 2104
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3295 0.5358 -0.6560 0.3018
## (Intercept)-Procyon_lotor 0.4014 0.5114 -0.6108 0.3875
## (Intercept)-Dasypus_novemcinctus -0.3383 0.5072 -1.4091 -0.3223
## (Intercept)-Sylvilagus_floridanus -0.1893 0.5564 -1.2937 -0.1876
## Avg_Cogongrass_Cover-Canis_latrans 0.4884 0.4521 -0.2651 0.4367
## Avg_Cogongrass_Cover-Procyon_lotor 0.2204 0.3880 -0.5060 0.2108
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3723 0.3623 -0.3019 0.3565
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3085 0.5166 -1.4417 -0.2668
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4529 1.0240 740
## (Intercept)-Procyon_lotor 1.4371 1.0045 643
## (Intercept)-Dasypus_novemcinctus 0.5970 1.0143 1025
## (Intercept)-Sylvilagus_floridanus 0.9411 1.0126 748
## Avg_Cogongrass_Cover-Canis_latrans 1.5005 1.0077 1366
## Avg_Cogongrass_Cover-Procyon_lotor 1.0362 1.0070 1907
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1108 1.0004 2140
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6066 1.0034 976
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7499 0.1945 -3.1610 -2.7438 -2.3912 1.0062
## (Intercept)-Procyon_lotor -2.2984 0.1470 -2.6036 -2.2934 -2.0235 1.0058
## (Intercept)-Dasypus_novemcinctus -1.7728 0.1666 -2.1072 -1.7705 -1.4543 1.0120
## (Intercept)-Sylvilagus_floridanus -3.1363 0.3082 -3.7566 -3.1133 -2.5652 1.0126
## shrub_cover-Canis_latrans -0.2691 0.2221 -0.7056 -0.2672 0.1594 1.0082
## shrub_cover-Procyon_lotor 0.2499 0.1721 -0.1092 0.2560 0.5638 1.0081
## shrub_cover-Dasypus_novemcinctus 0.8342 0.3172 0.2111 0.8303 1.4592 1.0025
## shrub_cover-Sylvilagus_floridanus 0.2361 0.4269 -0.5964 0.2401 1.0818 1.0091
## veg_height-Canis_latrans -0.6281 0.1957 -1.0202 -0.6205 -0.2455 1.0279
## veg_height-Procyon_lotor 0.3420 0.1267 0.1033 0.3408 0.5849 1.0046
## veg_height-Dasypus_novemcinctus 0.2520 0.1383 -0.0204 0.2538 0.5290 1.0050
## veg_height-Sylvilagus_floridanus 0.1849 0.2676 -0.3320 0.1838 0.7121 1.0063
## week-Canis_latrans 0.0711 0.1363 -0.2022 0.0717 0.3344 1.0033
## week-Procyon_lotor -0.0557 0.1217 -0.3114 -0.0498 0.1708 1.0044
## week-Dasypus_novemcinctus -0.1797 0.1468 -0.4790 -0.1727 0.0868 1.0008
## week-Sylvilagus_floridanus -0.1887 0.2269 -0.6613 -0.1797 0.2213 1.0067
## ESS
## (Intercept)-Canis_latrans 704
## (Intercept)-Procyon_lotor 1246
## (Intercept)-Dasypus_novemcinctus 1375
## (Intercept)-Sylvilagus_floridanus 464
## shrub_cover-Canis_latrans 859
## shrub_cover-Procyon_lotor 1331
## shrub_cover-Dasypus_novemcinctus 1036
## shrub_cover-Sylvilagus_floridanus 395
## veg_height-Canis_latrans 689
## veg_height-Procyon_lotor 1552
## veg_height-Dasypus_novemcinctus 2018
## veg_height-Sylvilagus_floridanus 714
## week-Canis_latrans 1535
## week-Procyon_lotor 1565
## week-Dasypus_novemcinctus 1949
## week-Sylvilagus_floridanus 856
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3138
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0207 0.5291 -1.0577 -0.0135 1.0389 1.0089 1376
## Avg_Cogongrass_Cover 0.1564 0.4067 -0.6918 0.1615 0.9656 1.0010 1651
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9752 2.0181 0.0514 0.4106 5.8597 1.0526 1753
## Avg_Cogongrass_Cover 0.6358 1.5800 0.0422 0.2796 3.3471 1.0723 2448
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5491 0.6808 0.0505 0.3317 2.3331 1.0684 430
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.184 0.5214 -3.0259 -2.2305 -0.8898 1.0003 2796
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2349 2.4211 0.1283 0.5982 6.3664 1.0018 2550
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1912 0.4869 -0.7765 0.1881
## (Intercept)-Procyon_lotor 0.3711 0.5214 -0.6859 0.3747
## (Intercept)-Dasypus_novemcinctus -0.4444 0.4794 -1.4643 -0.4239
## (Intercept)-Sylvilagus_floridanus -0.2401 0.6017 -1.3582 -0.2358
## Avg_Cogongrass_Cover-Canis_latrans 0.3592 0.3921 -0.3566 0.3425
## Avg_Cogongrass_Cover-Procyon_lotor 0.2371 0.3785 -0.4847 0.2171
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3456 0.3561 -0.3420 0.3370
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2747 0.4778 -1.3011 -0.2305
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1852 1.0112 1081
## (Intercept)-Procyon_lotor 1.3989 1.0025 952
## (Intercept)-Dasypus_novemcinctus 0.4375 1.0033 1066
## (Intercept)-Sylvilagus_floridanus 0.9437 1.0306 400
## Avg_Cogongrass_Cover-Canis_latrans 1.2119 1.0051 1994
## Avg_Cogongrass_Cover-Procyon_lotor 1.0514 1.0084 2185
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0492 1.0102 2015
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5605 1.0052 937
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5905 0.1691 -2.9381 -2.5823 -2.2770 1.0070
## (Intercept)-Procyon_lotor -2.2665 0.1281 -2.5199 -2.2613 -2.0221 1.0063
## (Intercept)-Dasypus_novemcinctus -1.5949 0.1363 -1.8597 -1.5978 -1.3268 1.0013
## (Intercept)-Sylvilagus_floridanus -3.1067 0.3072 -3.7594 -3.0936 -2.5632 1.0059
## ESS
## (Intercept)-Canis_latrans 1039
## (Intercept)-Procyon_lotor 1487
## (Intercept)-Dasypus_novemcinctus 2353
## (Intercept)-Sylvilagus_floridanus 291
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.429
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0030 0.5088 -1.0222 -0.0187 1.0715 1.0046 1725
## Avg_Cogongrass_Cover 0.1571 0.4140 -0.6867 0.1718 0.9257 1.0032 2101
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9245 2.1925 0.0490 0.3865 5.4594 1.0021 2487
## Avg_Cogongrass_Cover 0.6174 1.7160 0.0421 0.2648 3.1091 1.0622 2587
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5087 0.5647 0.0479 0.319 2.0375 1.0016 416
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2068 0.5350 -3.0699 -2.2647 -0.9429 1.0080 2531
## week -0.0889 0.2496 -0.5773 -0.0865 0.3946 1.0006 2816
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5019 15.3026 0.1244 0.5828 6.5694 1.3365 3000
## week 0.2205 0.3391 0.0287 0.1174 1.1064 1.0036 2235
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2149 0.4828 -0.6960 0.1992
## (Intercept)-Procyon_lotor 0.3851 0.4850 -0.5276 0.3665
## (Intercept)-Dasypus_novemcinctus -0.4358 0.4845 -1.4129 -0.4138
## (Intercept)-Sylvilagus_floridanus -0.2334 0.5380 -1.3001 -0.2295
## Avg_Cogongrass_Cover-Canis_latrans 0.3806 0.3939 -0.3338 0.3565
## Avg_Cogongrass_Cover-Procyon_lotor 0.2503 0.3911 -0.4552 0.2336
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3373 0.3523 -0.3485 0.3297
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2608 0.4664 -1.2822 -0.2288
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2183 1.0006 1128
## (Intercept)-Procyon_lotor 1.4128 1.0008 1130
## (Intercept)-Dasypus_novemcinctus 0.4522 1.0186 1274
## (Intercept)-Sylvilagus_floridanus 0.7832 1.0276 809
## Avg_Cogongrass_Cover-Canis_latrans 1.2461 1.0053 2068
## Avg_Cogongrass_Cover-Procyon_lotor 1.0978 1.0012 2010
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0669 1.0060 2222
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5734 1.0030 1913
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5978 0.1689 -2.9477 -2.5892 -2.2890 1.0045
## (Intercept)-Procyon_lotor -2.2709 0.1285 -2.5305 -2.2696 -2.0252 1.0301
## (Intercept)-Dasypus_novemcinctus -1.6109 0.1379 -1.8903 -1.6079 -1.3524 1.0015
## (Intercept)-Sylvilagus_floridanus -3.1378 0.3045 -3.7609 -3.1220 -2.5960 1.0049
## week-Canis_latrans 0.0670 0.1340 -0.2116 0.0707 0.3153 1.0023
## week-Procyon_lotor -0.0573 0.1216 -0.2971 -0.0557 0.1700 1.0099
## week-Dasypus_novemcinctus -0.1780 0.1431 -0.4703 -0.1695 0.0828 1.0021
## week-Sylvilagus_floridanus -0.1892 0.2300 -0.6756 -0.1672 0.2318 1.0009
## ESS
## (Intercept)-Canis_latrans 1089
## (Intercept)-Procyon_lotor 1550
## (Intercept)-Dasypus_novemcinctus 2535
## (Intercept)-Sylvilagus_floridanus 436
## week-Canis_latrans 1592
## week-Procyon_lotor 1646
## week-Dasypus_novemcinctus 1829
## week-Sylvilagus_floridanus 931
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4368
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2846 0.7597 -1.7978 -0.2752 1.2460 1.0067 937
## Cogon_Patch_Size -0.6307 0.7940 -2.1259 -0.6535 1.0485 1.0091 1102
## Veg_shannon_index 1.0374 0.5718 -0.0890 1.0290 2.1691 1.0110 762
## total_shrub_cover -0.0529 0.6021 -1.2333 -0.0543 1.1905 1.0068 1323
## Avg_Cogongrass_Cover 1.8971 0.9103 0.0313 1.9359 3.6009 1.0029 518
## Tree_Density -1.9763 0.9604 -3.7299 -2.0115 0.1218 1.0030 1027
## Avg_Canopy_Cover 1.3018 0.9553 -0.9158 1.3435 3.1477 1.0020 1818
## avg_veg_height -0.4496 0.5910 -1.5936 -0.4711 0.7375 1.0121 777
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5090 5.3354 0.0748 1.0363 14.3645 1.0009 1226
## Cogon_Patch_Size 3.2747 9.2496 0.0683 0.9743 19.8731 1.0312 968
## Veg_shannon_index 0.8693 2.9403 0.0444 0.3186 4.8752 1.1251 2125
## total_shrub_cover 1.2870 3.2716 0.0540 0.4918 6.9312 1.0442 1745
## Avg_Cogongrass_Cover 2.4411 6.5232 0.0545 0.7707 14.3685 1.0730 1352
## Tree_Density 4.8093 13.9463 0.0635 1.0677 35.7294 1.0888 548
## Avg_Canopy_Cover 7.9785 33.0639 0.1398 2.3816 47.7589 1.2353 1308
## avg_veg_height 0.9324 5.9183 0.0447 0.3157 4.8516 1.3105 2810
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.4537 2.2946 0.0589 0.828 7.1343 1.0382 269
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2049 0.5361 -3.0663 -2.2648 -0.9066 1.0060 2717
## week -0.0787 0.2456 -0.5855 -0.0743 0.3960 1.0118 2239
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3295 3.0395 0.1288 0.6112 6.8873 1.0104 2565
## week 0.2323 0.5652 0.0283 0.1162 1.0768 1.0463 2221
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2705 0.8546 -1.3747 0.2398
## (Intercept)-Procyon_lotor 0.3995 0.8509 -1.3276 0.4072
## (Intercept)-Dasypus_novemcinctus -1.1091 0.8481 -3.0015 -1.0455
## (Intercept)-Sylvilagus_floridanus -0.8634 0.9594 -2.9201 -0.7962
## Cogon_Patch_Size-Canis_latrans 0.3818 1.0804 -1.2372 0.2072
## Cogon_Patch_Size-Procyon_lotor -0.9977 0.6864 -2.4001 -0.9883
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8488 0.6600 -2.2204 -0.8154
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6514 1.4059 -5.0589 -1.3968
## Veg_shannon_index-Canis_latrans 1.3276 0.6526 0.2001 1.2783
## Veg_shannon_index-Procyon_lotor 1.2086 0.5738 0.1609 1.1911
## Veg_shannon_index-Dasypus_novemcinctus 0.7689 0.5331 -0.2570 0.7568
## Veg_shannon_index-Sylvilagus_floridanus 1.1431 0.7120 -0.1660 1.0959
## total_shrub_cover-Canis_latrans 0.2470 0.6462 -0.8223 0.1843
## total_shrub_cover-Procyon_lotor -0.7246 0.6123 -2.0957 -0.6849
## total_shrub_cover-Dasypus_novemcinctus 0.1657 0.5284 -0.8678 0.1590
## total_shrub_cover-Sylvilagus_floridanus 0.0170 0.8029 -1.5026 -0.0216
## Avg_Cogongrass_Cover-Canis_latrans 2.4098 0.9914 0.5974 2.3371
## Avg_Cogongrass_Cover-Procyon_lotor 2.2720 0.9964 0.4854 2.2293
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7247 1.0929 0.8337 2.6308
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3985 1.0928 -0.7898 1.4419
## Tree_Density-Canis_latrans -2.6219 1.1798 -5.4058 -2.4852
## Tree_Density-Procyon_lotor -1.5830 0.8224 -3.1594 -1.5848
## Tree_Density-Dasypus_novemcinctus -3.3404 1.6764 -7.8228 -2.9563
## Tree_Density-Sylvilagus_floridanus -2.5124 1.4053 -5.9962 -2.3435
## Avg_Canopy_Cover-Canis_latrans 0.1844 0.6888 -1.2174 0.2013
## Avg_Canopy_Cover-Procyon_lotor 1.6238 0.7069 0.4119 1.5647
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9888 0.7387 0.7534 1.9140
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.6935 2.0842 1.0104 3.2390
## avg_veg_height-Canis_latrans -0.7222 0.6558 -2.0821 -0.7186
## avg_veg_height-Procyon_lotor -0.3159 0.6156 -1.4444 -0.3416
## avg_veg_height-Dasypus_novemcinctus -0.2453 0.6453 -1.4954 -0.2588
## avg_veg_height-Sylvilagus_floridanus -0.6523 0.7543 -2.2150 -0.6387
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1139 1.0032 532
## (Intercept)-Procyon_lotor 2.1027 1.0253 316
## (Intercept)-Dasypus_novemcinctus 0.3492 1.0200 509
## (Intercept)-Sylvilagus_floridanus 0.9312 1.0199 555
## Cogon_Patch_Size-Canis_latrans 3.0174 1.0144 602
## Cogon_Patch_Size-Procyon_lotor 0.2735 1.0122 523
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4148 1.0207 741
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2938 1.0132 358
## Veg_shannon_index-Canis_latrans 2.7816 1.0234 631
## Veg_shannon_index-Procyon_lotor 2.3947 1.0204 586
## Veg_shannon_index-Dasypus_novemcinctus 1.8509 1.0088 692
## Veg_shannon_index-Sylvilagus_floridanus 2.6428 1.0145 704
## total_shrub_cover-Canis_latrans 1.7138 1.0103 850
## total_shrub_cover-Procyon_lotor 0.3400 1.0114 1279
## total_shrub_cover-Dasypus_novemcinctus 1.2468 1.0067 1305
## total_shrub_cover-Sylvilagus_floridanus 1.6714 1.0026 939
## Avg_Cogongrass_Cover-Canis_latrans 4.5754 1.0154 533
## Avg_Cogongrass_Cover-Procyon_lotor 4.4029 1.0070 462
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.1389 1.0356 388
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.4720 1.0028 588
## Tree_Density-Canis_latrans -0.7735 1.0305 547
## Tree_Density-Procyon_lotor 0.0733 1.0009 584
## Tree_Density-Dasypus_novemcinctus -1.1922 1.0316 276
## Tree_Density-Sylvilagus_floridanus -0.1054 1.0436 335
## Avg_Canopy_Cover-Canis_latrans 1.4928 1.0132 679
## Avg_Canopy_Cover-Procyon_lotor 3.1838 1.0101 766
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.6288 1.0251 590
## Avg_Canopy_Cover-Sylvilagus_floridanus 8.9428 1.0355 239
## avg_veg_height-Canis_latrans 0.5308 1.0007 919
## avg_veg_height-Procyon_lotor 0.9658 1.0053 832
## avg_veg_height-Dasypus_novemcinctus 1.0668 1.0112 834
## avg_veg_height-Sylvilagus_floridanus 0.8237 1.0149 830
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6269 0.1776 -2.9802 -2.6200 -2.2897 1.0129
## (Intercept)-Procyon_lotor -2.2669 0.1291 -2.5337 -2.2606 -2.0199 1.0164
## (Intercept)-Dasypus_novemcinctus -1.6058 0.1376 -1.8883 -1.6042 -1.3436 1.0045
## (Intercept)-Sylvilagus_floridanus -3.1520 0.2751 -3.7173 -3.1486 -2.6439 1.0069
## week-Canis_latrans 0.0736 0.1356 -0.2003 0.0763 0.3349 1.0026
## week-Procyon_lotor -0.0496 0.1225 -0.2916 -0.0452 0.1852 1.0006
## week-Dasypus_novemcinctus -0.1784 0.1478 -0.4919 -0.1733 0.0827 1.0003
## week-Sylvilagus_floridanus -0.1692 0.2271 -0.6715 -0.1536 0.2343 1.0286
## ESS
## (Intercept)-Canis_latrans 877
## (Intercept)-Procyon_lotor 1346
## (Intercept)-Dasypus_novemcinctus 2335
## (Intercept)-Sylvilagus_floridanus 439
## week-Canis_latrans 1592
## week-Procyon_lotor 1726
## week-Dasypus_novemcinctus 1953
## week-Sylvilagus_floridanus 859
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.432
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0119 0.5370 -1.1185 0.0138 1.1112 1.0062 1405
## Avg_Cogongrass_Cover 0.0556 0.5023 -0.9406 0.0661 1.0093 1.0013 1362
## total_shrub_cover -0.2132 0.5330 -1.3290 -0.2251 0.8843 1.0094 1844
## avg_veg_height 0.0766 0.4758 -0.8312 0.0681 1.0407 1.0015 1372
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0126 2.0234 0.0550 0.4457 5.6394 1.0368 1561
## Avg_Cogongrass_Cover 0.8172 2.4354 0.0437 0.3213 4.1824 1.0484 1879
## total_shrub_cover 1.0531 2.0004 0.0574 0.4576 5.9702 1.0140 1993
## avg_veg_height 0.6746 2.8229 0.0410 0.2553 3.3087 1.1037 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5899 0.7511 0.0517 0.3546 2.4137 1.0091 492
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2134 0.5497 -3.1091 -2.2733 -0.9019 1.0098 2840
## week -0.0887 0.2456 -0.6059 -0.0830 0.3768 1.0057 2107
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3388 3.4499 0.1331 0.6206 6.4726 1.0244 2477
## week 0.2454 0.7962 0.0270 0.1136 1.2299 1.1543 2708
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2390 0.5120 -0.7200 0.2198
## (Intercept)-Procyon_lotor 0.4914 0.5719 -0.5801 0.4549
## (Intercept)-Dasypus_novemcinctus -0.4523 0.5116 -1.5013 -0.4489
## (Intercept)-Sylvilagus_floridanus -0.1756 0.6312 -1.3517 -0.2110
## Avg_Cogongrass_Cover-Canis_latrans 0.4117 0.5218 -0.5251 0.3798
## Avg_Cogongrass_Cover-Procyon_lotor 0.0190 0.5008 -0.9838 0.0285
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.1844 0.4458 -0.6860 0.1889
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3688 0.5955 -1.6538 -0.3235
## total_shrub_cover-Canis_latrans 0.2031 0.4657 -0.6417 0.1706
## total_shrub_cover-Procyon_lotor -0.8660 0.5536 -2.1041 -0.8024
## total_shrub_cover-Dasypus_novemcinctus 0.0027 0.3899 -0.7579 0.0010
## total_shrub_cover-Sylvilagus_floridanus -0.3146 0.5855 -1.5322 -0.2999
## avg_veg_height-Canis_latrans -0.0594 0.4716 -0.9865 -0.0629
## avg_veg_height-Procyon_lotor 0.1676 0.4966 -0.7869 0.1471
## avg_veg_height-Dasypus_novemcinctus 0.2981 0.4588 -0.5492 0.2858
## avg_veg_height-Sylvilagus_floridanus -0.0945 0.5374 -1.2013 -0.0870
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2956 1.0232 962
## (Intercept)-Procyon_lotor 1.7209 1.0115 921
## (Intercept)-Dasypus_novemcinctus 0.5383 1.0171 1035
## (Intercept)-Sylvilagus_floridanus 1.1978 1.0079 603
## Avg_Cogongrass_Cover-Canis_latrans 1.5774 1.0007 1184
## Avg_Cogongrass_Cover-Procyon_lotor 0.9749 1.0023 1441
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0735 1.0017 1729
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6799 1.0118 1142
## total_shrub_cover-Canis_latrans 1.2357 1.0167 1636
## total_shrub_cover-Procyon_lotor 0.0656 1.0113 1139
## total_shrub_cover-Dasypus_novemcinctus 0.7653 1.0024 2311
## total_shrub_cover-Sylvilagus_floridanus 0.7586 1.0083 677
## avg_veg_height-Canis_latrans 0.8578 1.0045 1469
## avg_veg_height-Procyon_lotor 1.1527 1.0049 1522
## avg_veg_height-Dasypus_novemcinctus 1.2357 1.0030 1463
## avg_veg_height-Sylvilagus_floridanus 0.9260 1.0001 1262
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6188 0.1670 -2.9678 -2.6122 -2.3199 1.0034
## (Intercept)-Procyon_lotor -2.2727 0.1263 -2.5255 -2.2680 -2.0295 1.0047
## (Intercept)-Dasypus_novemcinctus -1.6048 0.1348 -1.8779 -1.6052 -1.3490 1.0063
## (Intercept)-Sylvilagus_floridanus -3.1794 0.3222 -3.8920 -3.1575 -2.6173 1.0006
## week-Canis_latrans 0.0629 0.1331 -0.2093 0.0643 0.3157 1.0007
## week-Procyon_lotor -0.0541 0.1204 -0.3008 -0.0508 0.1706 0.9997
## week-Dasypus_novemcinctus -0.1855 0.1467 -0.4871 -0.1801 0.0858 1.0025
## week-Sylvilagus_floridanus -0.1623 0.2255 -0.6524 -0.1459 0.2305 1.0134
## ESS
## (Intercept)-Canis_latrans 993
## (Intercept)-Procyon_lotor 1612
## (Intercept)-Dasypus_novemcinctus 2204
## (Intercept)-Sylvilagus_floridanus 365
## week-Canis_latrans 1473
## week-Procyon_lotor 1630
## week-Dasypus_novemcinctus 2056
## week-Sylvilagus_floridanus 1041
# Includes week covariate for detection and none for occupancy
ms_week_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.422
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0285 0.452 -0.9016 0.036 0.9453 1.0038 2061
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9022 2.6897 0.0609 0.4076 4.5216 1.1192 2124
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1850 0.5391 -3.0856 -2.2488 -0.8943 1.0167 2340
## week -0.0815 0.2500 -0.5732 -0.0786 0.4053 1.0097 2223
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2328 2.2571 0.1278 0.6044 6.8947 1.007 1961
## week 0.2608 1.2811 0.0281 0.1198 1.0927 1.297 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2286 0.3666 -0.4436 0.2109 0.9892 1.0011
## (Intercept)-Procyon_lotor 0.5461 0.3810 -0.1260 0.5266 1.3669 1.0017
## (Intercept)-Dasypus_novemcinctus -0.4684 0.3486 -1.1717 -0.4547 0.1653 1.0158
## (Intercept)-Sylvilagus_floridanus -0.2368 0.4510 -1.0634 -0.2523 0.6976 1.0058
## ESS
## (Intercept)-Canis_latrans 2106
## (Intercept)-Procyon_lotor 2276
## (Intercept)-Dasypus_novemcinctus 2377
## (Intercept)-Sylvilagus_floridanus 919
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5965 0.1698 -2.9370 -2.5940 -2.2801 1.0108
## (Intercept)-Procyon_lotor -2.2589 0.1263 -2.5104 -2.2589 -2.0089 0.9999
## (Intercept)-Dasypus_novemcinctus -1.6054 0.1351 -1.8845 -1.6022 -1.3515 1.0013
## (Intercept)-Sylvilagus_floridanus -3.1359 0.3076 -3.8033 -3.1124 -2.6131 1.0229
## week-Canis_latrans 0.0673 0.1374 -0.2172 0.0724 0.3238 1.0047
## week-Procyon_lotor -0.0542 0.1205 -0.3025 -0.0548 0.1746 1.0000
## week-Dasypus_novemcinctus -0.1817 0.1436 -0.4789 -0.1751 0.0846 1.0051
## week-Sylvilagus_floridanus -0.1802 0.2211 -0.6465 -0.1646 0.2161 1.0212
## ESS
## (Intercept)-Canis_latrans 1284
## (Intercept)-Procyon_lotor 1649
## (Intercept)-Dasypus_novemcinctus 2005
## (Intercept)-Sylvilagus_floridanus 459
## week-Canis_latrans 1518
## week-Procyon_lotor 1741
## week-Dasypus_novemcinctus 2019
## week-Sylvilagus_floridanus 934
#Includes week for detection and only foraging for occupancy
ms_week_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4378
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0390 0.5167 -1.0580 -0.0403 1.0059 1.0060 923
## Veg_shannon_index 0.5081 0.4330 -0.2835 0.5017 1.3654 1.0013 1453
## Avg_Cogongrass_Cover 0.3567 0.4681 -0.5883 0.3527 1.2997 1.0031 1430
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8396 1.6013 0.0534 0.3756 4.7440 1.0412 2114
## Veg_shannon_index 0.5597 1.3014 0.0399 0.2420 3.0657 1.0244 2195
## Avg_Cogongrass_Cover 0.7489 2.4807 0.0444 0.3028 3.9619 1.1890 2763
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6178 0.7434 0.0477 0.3776 2.7133 1.0014 436
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2050 0.5578 -3.1160 -2.2705 -0.8258 1.0014 2647
## week -0.0809 0.2496 -0.5524 -0.0809 0.4190 1.0031 2691
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2985 3.5816 0.1281 0.6043 6.6721 1.0328 2522
## week 0.2309 0.3968 0.0288 0.1193 1.1563 1.0248 2359
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1486 0.5115 -0.8459 0.1326
## (Intercept)-Procyon_lotor 0.3694 0.5346 -0.6498 0.3542
## (Intercept)-Dasypus_novemcinctus -0.4392 0.4894 -1.4544 -0.4305
## (Intercept)-Sylvilagus_floridanus -0.2309 0.6107 -1.3724 -0.2516
## Veg_shannon_index-Canis_latrans 0.7723 0.4346 0.0050 0.7452
## Veg_shannon_index-Procyon_lotor 0.5674 0.4544 -0.2171 0.5426
## Veg_shannon_index-Dasypus_novemcinctus 0.2581 0.3727 -0.5155 0.2623
## Veg_shannon_index-Sylvilagus_floridanus 0.5665 0.5037 -0.3441 0.5211
## Avg_Cogongrass_Cover-Canis_latrans 0.6311 0.4543 -0.1237 0.5823
## Avg_Cogongrass_Cover-Procyon_lotor 0.5017 0.4620 -0.3201 0.4622
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4776 0.3722 -0.2695 0.4773
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1111 0.5483 -1.2656 -0.0951
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1949 1.0004 912
## (Intercept)-Procyon_lotor 1.4794 1.0004 918
## (Intercept)-Dasypus_novemcinctus 0.4953 1.0020 1138
## (Intercept)-Sylvilagus_floridanus 1.0243 1.0166 551
## Veg_shannon_index-Canis_latrans 1.7302 1.0093 1634
## Veg_shannon_index-Procyon_lotor 1.5505 1.0015 1064
## Veg_shannon_index-Dasypus_novemcinctus 0.9509 1.0013 2210
## Veg_shannon_index-Sylvilagus_floridanus 1.6385 1.0146 720
## Avg_Cogongrass_Cover-Canis_latrans 1.6391 1.0069 1428
## Avg_Cogongrass_Cover-Procyon_lotor 1.5595 1.0060 1419
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2264 1.0031 2191
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.9277 1.0117 1190
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5867 0.1663 -2.9204 -2.5810 -2.2756 0.9997
## (Intercept)-Procyon_lotor -2.2766 0.1290 -2.5438 -2.2749 -2.0380 1.0016
## (Intercept)-Dasypus_novemcinctus -1.6055 0.1395 -1.8878 -1.6003 -1.3489 1.0021
## (Intercept)-Sylvilagus_floridanus -3.1627 0.3104 -3.8132 -3.1522 -2.5884 1.0186
## week-Canis_latrans 0.0696 0.1382 -0.2141 0.0757 0.3255 1.0029
## week-Procyon_lotor -0.0581 0.1232 -0.3116 -0.0562 0.1751 1.0125
## week-Dasypus_novemcinctus -0.1823 0.1455 -0.4813 -0.1777 0.0871 1.0016
## week-Sylvilagus_floridanus -0.1846 0.2216 -0.6588 -0.1676 0.1999 1.0081
## ESS
## (Intercept)-Canis_latrans 1125
## (Intercept)-Procyon_lotor 1404
## (Intercept)-Dasypus_novemcinctus 2196
## (Intercept)-Sylvilagus_floridanus 373
## week-Canis_latrans 1524
## week-Procyon_lotor 1434
## week-Dasypus_novemcinctus 2042
## week-Sylvilagus_floridanus 940
# Includes movement covariates of occupancy and week for detection
ms_week_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4457
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0449 0.5605 -1.0506 0.0414 1.1131 1.0032 817
## Cogon_Patch_Size -0.0691 0.6462 -1.4412 -0.0510 1.2610 1.0177 1418
## Avg_Cogongrass_Cover 0.1433 0.4545 -0.7304 0.1346 1.0646 1.0039 1171
## total_shrub_cover -0.2336 0.5023 -1.2134 -0.2395 0.8186 1.0103 1446
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0871 2.2693 0.0536 0.4944 5.6246 1.0356 1711
## Cogon_Patch_Size 2.3756 5.9517 0.0613 0.7689 15.0497 1.0367 732
## Avg_Cogongrass_Cover 0.5963 1.5735 0.0429 0.2630 3.1908 1.1625 2059
## total_shrub_cover 0.9626 3.8121 0.0505 0.3865 5.1455 1.1982 2570
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5633 0.7639 0.0448 0.3118 2.4878 1.0254 437
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2118 0.5523 -3.1307 -2.2668 -0.8953 1.0014 2500
## week -0.0713 0.2424 -0.5227 -0.0794 0.4325 1.0025 2157
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3794 3.5709 0.1303 0.6514 6.6111 1.0224 2626
## week 0.2334 0.5824 0.0281 0.1159 1.1535 1.0322 2738
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3447 0.5446 -0.6701 0.3163
## (Intercept)-Procyon_lotor 0.5270 0.5650 -0.6162 0.5190
## (Intercept)-Dasypus_novemcinctus -0.4479 0.5069 -1.4990 -0.4437
## (Intercept)-Sylvilagus_floridanus -0.2319 0.6711 -1.5422 -0.2328
## Cogon_Patch_Size-Canis_latrans 0.9212 0.8912 -0.3216 0.7473
## Cogon_Patch_Size-Procyon_lotor -0.1402 0.5011 -1.1197 -0.1348
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1050 0.4464 -1.0416 -0.1003
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0201 1.2132 -4.0508 -0.7538
## Avg_Cogongrass_Cover-Canis_latrans 0.2083 0.4340 -0.6233 0.1935
## Avg_Cogongrass_Cover-Procyon_lotor 0.1776 0.4656 -0.6720 0.1544
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3545 0.4050 -0.3913 0.3430
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1780 0.5438 -1.3549 -0.1621
## total_shrub_cover-Canis_latrans 0.1136 0.4791 -0.7513 0.0842
## total_shrub_cover-Procyon_lotor -0.8194 0.5247 -1.9547 -0.7546
## total_shrub_cover-Dasypus_novemcinctus -0.0520 0.3915 -0.8248 -0.0555
## total_shrub_cover-Sylvilagus_floridanus -0.2562 0.6296 -1.5548 -0.2473
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4592 1.0027 936
## (Intercept)-Procyon_lotor 1.6675 1.0046 755
## (Intercept)-Dasypus_novemcinctus 0.5251 1.0029 896
## (Intercept)-Sylvilagus_floridanus 1.1433 1.0144 520
## Cogon_Patch_Size-Canis_latrans 3.1807 1.0121 735
## Cogon_Patch_Size-Procyon_lotor 0.8573 1.0048 1664
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7586 1.0036 2392
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4811 1.0933 364
## Avg_Cogongrass_Cover-Canis_latrans 1.1234 1.0142 1381
## Avg_Cogongrass_Cover-Procyon_lotor 1.2058 1.0030 1442
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1883 1.0105 1582
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8453 1.0051 1162
## total_shrub_cover-Canis_latrans 1.1702 1.0111 1362
## total_shrub_cover-Procyon_lotor 0.0635 1.0070 1247
## total_shrub_cover-Dasypus_novemcinctus 0.7530 1.0084 1984
## total_shrub_cover-Sylvilagus_floridanus 0.9814 1.0169 655
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6045 0.1589 -2.9239 -2.5984 -2.2968 1.0087
## (Intercept)-Procyon_lotor -2.2710 0.1279 -2.5309 -2.2655 -2.0250 1.0014
## (Intercept)-Dasypus_novemcinctus -1.6080 0.1383 -1.8909 -1.6074 -1.3385 1.0006
## (Intercept)-Sylvilagus_floridanus -3.2311 0.3272 -3.9035 -3.2115 -2.6388 1.0581
## week-Canis_latrans 0.0672 0.1344 -0.2082 0.0697 0.3299 1.0163
## week-Procyon_lotor -0.0519 0.1192 -0.2987 -0.0489 0.1767 1.0030
## week-Dasypus_novemcinctus -0.1762 0.1416 -0.4708 -0.1699 0.0706 1.0004
## week-Sylvilagus_floridanus -0.1734 0.2233 -0.6556 -0.1586 0.2192 1.0145
## ESS
## (Intercept)-Canis_latrans 1112
## (Intercept)-Procyon_lotor 1489
## (Intercept)-Dasypus_novemcinctus 2463
## (Intercept)-Sylvilagus_floridanus 345
## week-Canis_latrans 1629
## week-Procyon_lotor 1636
## week-Dasypus_novemcinctus 1967
## week-Sylvilagus_floridanus 857
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4362
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1688 0.5963 -1.3622 -0.1717 1.0172 1.0015 1364
## Tree_Density -1.0010 0.6108 -2.3299 -0.9687 0.1456 1.0019 1038
## Avg_Canopy_Cover 0.8499 0.6934 -0.6094 0.8516 2.2677 1.0040 2197
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3641 2.8788 0.0659 0.6138 7.0752 1.0074 1798
## Tree_Density 1.5344 5.4255 0.0477 0.4585 9.8288 1.1072 1477
## Avg_Canopy_Cover 3.1019 9.6024 0.0997 1.1872 16.5184 1.0306 1630
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4617 0.5637 0.0432 0.2747 1.974 1.0202 503
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2067 0.5318 -3.0671 -2.2742 -0.9414 1.0037 2561
## week -0.0888 0.2420 -0.5876 -0.0874 0.3854 1.0045 2632
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2701 3.4463 0.1233 0.5744 6.2021 1.0350 2628
## week 0.2244 0.3770 0.0287 0.1175 1.0759 1.0034 2217
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1055 0.5406 -0.9287 0.0938 1.1993
## (Intercept)-Procyon_lotor 0.4199 0.5737 -0.6670 0.4079 1.5861
## (Intercept)-Dasypus_novemcinctus -0.8004 0.5998 -2.1358 -0.7610 0.2548
## (Intercept)-Sylvilagus_floridanus -0.5354 0.6311 -1.8199 -0.5191 0.6576
## Tree_Density-Canis_latrans -1.0788 0.6233 -2.4968 -1.0193 -0.0148
## Tree_Density-Procyon_lotor -0.5524 0.4616 -1.4970 -0.5446 0.3198
## Tree_Density-Dasypus_novemcinctus -1.5343 0.9964 -4.1242 -1.3188 -0.2236
## Tree_Density-Sylvilagus_floridanus -1.2676 0.8970 -3.5342 -1.1443 0.1084
## Avg_Canopy_Cover-Canis_latrans -0.1413 0.4828 -1.1231 -0.1371 0.7703
## Avg_Canopy_Cover-Procyon_lotor 0.9716 0.4995 0.0810 0.9502 2.0126
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0019 0.4736 0.1874 0.9839 1.9983
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.2237 1.2568 0.5726 1.9505 5.4188
## Rhat ESS
## (Intercept)-Canis_latrans 1.0073 1017
## (Intercept)-Procyon_lotor 1.0051 1041
## (Intercept)-Dasypus_novemcinctus 1.0093 729
## (Intercept)-Sylvilagus_floridanus 1.0104 681
## Tree_Density-Canis_latrans 1.0026 1198
## Tree_Density-Procyon_lotor 1.0041 1620
## Tree_Density-Dasypus_novemcinctus 1.0031 530
## Tree_Density-Sylvilagus_floridanus 1.0120 520
## Avg_Canopy_Cover-Canis_latrans 1.0028 1447
## Avg_Canopy_Cover-Procyon_lotor 1.0085 1726
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0006 2184
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0252 373
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6188 0.1812 -2.9898 -2.6136 -2.2808 1.0070
## (Intercept)-Procyon_lotor -2.2646 0.1264 -2.5184 -2.2606 -2.0273 1.0001
## (Intercept)-Dasypus_novemcinctus -1.6092 0.1368 -1.9014 -1.6058 -1.3451 1.0010
## (Intercept)-Sylvilagus_floridanus -3.1001 0.2742 -3.6494 -3.0889 -2.6067 1.0140
## week-Canis_latrans 0.0713 0.1345 -0.1945 0.0716 0.3307 1.0033
## week-Procyon_lotor -0.0525 0.1209 -0.2900 -0.0518 0.1811 1.0012
## week-Dasypus_novemcinctus -0.1798 0.1424 -0.4807 -0.1734 0.0862 0.9997
## week-Sylvilagus_floridanus -0.1838 0.2242 -0.6635 -0.1643 0.2105 1.0008
## ESS
## (Intercept)-Canis_latrans 881
## (Intercept)-Procyon_lotor 1490
## (Intercept)-Dasypus_novemcinctus 2249
## (Intercept)-Sylvilagus_floridanus 514
## week-Canis_latrans 1501
## week-Procyon_lotor 1651
## week-Dasypus_novemcinctus 2149
## week-Sylvilagus_floridanus 943
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4525
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7290 0.5576 -1.8065 -0.7466 0.4526 1.0086 813
## Avg_Cogongrass_Cover -0.5005 0.6131 -1.6867 -0.5030 0.7539 1.0131 875
## I(Avg_Cogongrass_Cover^2) 0.9945 0.7192 -0.2882 0.9309 2.5499 1.0194 639
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8188 2.8646 0.0440 0.3271 4.2770 1.1324 2751
## Avg_Cogongrass_Cover 1.4294 9.2821 0.0521 0.4255 7.3802 1.1486 3000
## I(Avg_Cogongrass_Cover^2) 2.3567 7.3772 0.0527 0.5678 16.0106 1.1349 830
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4176 0.5218 0.0418 0.2594 1.7586 1.0324 458
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2166 0.5447 -3.1167 -2.2797 -0.8907 1.0056 2636
## week -0.0934 0.2438 -0.5972 -0.0882 0.3993 1.0039 2325
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3159 2.6160 0.1281 0.6293 7.4807 1.0115 2495
## week 0.2418 0.5935 0.0284 0.1166 1.1923 1.0662 2276
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6622 0.6328 -1.9559 -0.6479
## (Intercept)-Procyon_lotor -0.4680 0.6192 -1.6894 -0.4691
## (Intercept)-Dasypus_novemcinctus -1.0648 0.5476 -2.1710 -1.0489
## (Intercept)-Sylvilagus_floridanus -0.9314 0.6240 -2.2345 -0.9319
## Avg_Cogongrass_Cover-Canis_latrans -0.2118 0.6455 -1.3962 -0.2427
## Avg_Cogongrass_Cover-Procyon_lotor -0.4309 0.6607 -1.6160 -0.4509
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3300 0.5590 -1.4479 -0.3348
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1892 0.8705 -3.3467 -1.0837
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8037 1.2840 0.1437 1.4814
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.6650 1.1845 0.2272 1.3629
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5923 0.4096 -0.1778 0.5883
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7994 0.6717 -0.2219 0.7094
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5489 1.0204 598
## (Intercept)-Procyon_lotor 0.7703 1.0124 616
## (Intercept)-Dasypus_novemcinctus -0.0122 1.0110 886
## (Intercept)-Sylvilagus_floridanus 0.3467 1.0043 764
## Avg_Cogongrass_Cover-Canis_latrans 1.1092 1.0017 890
## Avg_Cogongrass_Cover-Procyon_lotor 0.9323 1.0150 887
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.7960 1.0113 966
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.1650 1.0155 472
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.1479 1.1074 195
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.9921 1.0504 179
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4224 1.0041 1094
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.4745 1.0113 286
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6340 0.1705 -2.9804 -2.6265 -2.3161 1.0105
## (Intercept)-Procyon_lotor -2.2859 0.1319 -2.5558 -2.2828 -2.0309 1.0029
## (Intercept)-Dasypus_novemcinctus -1.6086 0.1352 -1.8850 -1.6024 -1.3561 1.0033
## (Intercept)-Sylvilagus_floridanus -3.1794 0.3263 -3.8712 -3.1614 -2.5978 1.0147
## week-Canis_latrans 0.0614 0.1378 -0.2228 0.0659 0.3234 1.0005
## week-Procyon_lotor -0.0592 0.1178 -0.2896 -0.0602 0.1675 1.0019
## week-Dasypus_novemcinctus -0.1764 0.1438 -0.4736 -0.1708 0.0982 1.0080
## week-Sylvilagus_floridanus -0.1858 0.2269 -0.6813 -0.1726 0.2236 1.0053
## ESS
## (Intercept)-Canis_latrans 785
## (Intercept)-Procyon_lotor 1394
## (Intercept)-Dasypus_novemcinctus 2030
## (Intercept)-Sylvilagus_floridanus 321
## week-Canis_latrans 1617
## week-Procyon_lotor 1448
## week-Dasypus_novemcinctus 2009
## week-Sylvilagus_floridanus 865
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4548
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.4867 0.8569 -3.1298 -1.5216 0.3118 1.0150 602
## Cogon_Patch_Size -0.1706 0.8642 -1.8479 -0.1695 1.5596 1.0072 1162
## Veg_shannon_index 1.0094 0.6236 -0.2111 1.0043 2.2270 1.0171 845
## total_shrub_cover -0.1474 0.6067 -1.3905 -0.1458 1.0289 1.0154 1183
## Avg_Cogongrass_Cover 0.2852 1.1524 -1.8255 0.2466 2.6105 1.0138 370
## Tree_Density -2.4175 1.1418 -4.4464 -2.4708 0.2875 1.0062 833
## Avg_Canopy_Cover 1.2854 1.0613 -1.0782 1.3450 3.3231 1.0043 1894
## I(Avg_Cogongrass_Cover^2) 1.5481 0.8465 -0.1483 1.5344 3.2122 1.0114 513
## avg_veg_height -0.0428 0.6901 -1.3904 -0.0473 1.2832 1.0153 690
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5434 10.4608 0.0580 0.8127 14.2161 1.0845 2024
## Cogon_Patch_Size 5.2070 14.9924 0.0859 1.4571 33.4630 1.0109 444
## Veg_shannon_index 1.0275 2.7754 0.0461 0.3774 6.3484 1.0761 1621
## total_shrub_cover 1.5041 5.5896 0.0566 0.5312 7.9370 1.1448 2017
## Avg_Cogongrass_Cover 3.9249 10.7835 0.0638 1.1738 25.4132 1.0533 767
## Tree_Density 7.9097 23.9950 0.0558 1.4278 61.9357 1.0968 560
## Avg_Canopy_Cover 11.7877 35.5952 0.1923 4.2913 69.6833 1.2254 642
## I(Avg_Cogongrass_Cover^2) 4.0834 15.7047 0.0652 0.8182 28.4739 1.3676 213
## avg_veg_height 1.1920 2.7672 0.0520 0.4523 7.7267 1.0346 1748
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6604 2.597 0.057 0.8594 7.7878 1.0319 255
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1882 0.5536 -3.1179 -2.2473 -0.8820 1.0017 2413
## week -0.0863 0.2476 -0.5949 -0.0858 0.4169 1.0054 2583
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5008 5.9337 0.1397 0.6280 6.8856 1.0868 2493
## week 0.2223 0.3944 0.0280 0.1165 1.0463 1.0000 2506
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4155 1.0246 -3.4354 -1.4315
## (Intercept)-Procyon_lotor -1.0500 0.9585 -2.8856 -1.0938
## (Intercept)-Dasypus_novemcinctus -2.3160 1.0702 -4.9086 -2.1860
## (Intercept)-Sylvilagus_floridanus -2.0408 1.1715 -4.7660 -1.9243
## Cogon_Patch_Size-Canis_latrans 1.4555 1.6461 -0.7035 1.1047
## Cogon_Patch_Size-Procyon_lotor -0.5767 0.9131 -2.3815 -0.5925
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4712 0.8160 -2.2357 -0.4039
## Cogon_Patch_Size-Sylvilagus_floridanus -1.2879 1.5566 -5.0789 -1.0453
## Veg_shannon_index-Canis_latrans 1.3744 0.7572 0.1021 1.3051
## Veg_shannon_index-Procyon_lotor 1.1892 0.6580 0.0286 1.1392
## Veg_shannon_index-Dasypus_novemcinctus 0.7233 0.6054 -0.4146 0.7356
## Veg_shannon_index-Sylvilagus_floridanus 1.1662 0.8217 -0.3034 1.0841
## total_shrub_cover-Canis_latrans 0.1050 0.6648 -1.1219 0.0755
## total_shrub_cover-Procyon_lotor -0.8060 0.6709 -2.2644 -0.7507
## total_shrub_cover-Dasypus_novemcinctus 0.1751 0.5523 -0.8640 0.1519
## total_shrub_cover-Sylvilagus_floridanus -0.0612 0.9002 -1.8764 -0.0797
## Avg_Cogongrass_Cover-Canis_latrans 0.2079 1.5085 -2.7216 0.1795
## Avg_Cogongrass_Cover-Procyon_lotor 0.4361 1.3970 -2.2084 0.4345
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2678 1.5528 -1.3537 1.1227
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.5340 1.6825 -4.3081 -0.4192
## Tree_Density-Canis_latrans -3.3677 1.6032 -7.5237 -3.1302
## Tree_Density-Procyon_lotor -2.2752 1.1741 -4.5379 -2.3061
## Tree_Density-Dasypus_novemcinctus -4.1918 1.9796 -9.4130 -3.7272
## Tree_Density-Sylvilagus_floridanus -3.1329 1.6495 -6.9809 -2.9168
## Avg_Canopy_Cover-Canis_latrans -0.0166 0.7765 -1.5884 0.0022
## Avg_Canopy_Cover-Procyon_lotor 1.7085 0.8396 0.2541 1.6255
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1490 0.8768 0.7385 2.0392
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.6077 2.5749 1.0256 4.1326
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.6126 1.8235 0.6659 2.2053
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.4424 1.3758 0.5858 2.1791
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3769 0.8194 -0.0685 1.3270
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1921 0.9650 -0.5371 1.1209
## avg_veg_height-Canis_latrans -0.4494 0.7702 -2.0573 -0.4176
## avg_veg_height-Procyon_lotor 0.2707 0.7519 -1.1570 0.2745
## avg_veg_height-Dasypus_novemcinctus 0.1973 0.7185 -1.1823 0.1920
## avg_veg_height-Sylvilagus_floridanus -0.2303 0.9038 -2.0581 -0.1939
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6546 1.0071 539
## (Intercept)-Procyon_lotor 0.9601 1.0365 400
## (Intercept)-Dasypus_novemcinctus -0.5591 1.0352 327
## (Intercept)-Sylvilagus_floridanus 0.0532 1.0324 429
## Cogon_Patch_Size-Canis_latrans 5.7949 1.0154 389
## Cogon_Patch_Size-Procyon_lotor 1.1668 1.0046 568
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9477 1.0033 541
## Cogon_Patch_Size-Sylvilagus_floridanus 0.9474 1.0281 275
## Veg_shannon_index-Canis_latrans 3.0948 1.0194 487
## Veg_shannon_index-Procyon_lotor 2.5822 1.0107 469
## Veg_shannon_index-Dasypus_novemcinctus 1.9389 1.0259 989
## Veg_shannon_index-Sylvilagus_floridanus 3.0897 1.0149 667
## total_shrub_cover-Canis_latrans 1.5744 1.0111 960
## total_shrub_cover-Procyon_lotor 0.4174 1.0045 835
## total_shrub_cover-Dasypus_novemcinctus 1.2899 1.0140 1463
## total_shrub_cover-Sylvilagus_floridanus 1.8830 1.0181 671
## Avg_Cogongrass_Cover-Canis_latrans 3.1911 1.0096 453
## Avg_Cogongrass_Cover-Procyon_lotor 3.2728 1.0054 461
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6216 1.0030 407
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4312 1.0047 389
## Tree_Density-Canis_latrans -1.0264 1.0452 354
## Tree_Density-Procyon_lotor 0.0443 1.0057 570
## Tree_Density-Dasypus_novemcinctus -1.5834 1.0439 216
## Tree_Density-Sylvilagus_floridanus -0.4624 1.0765 301
## Avg_Canopy_Cover-Canis_latrans 1.4710 1.0049 611
## Avg_Canopy_Cover-Procyon_lotor 3.5544 1.0058 686
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.1593 1.0020 429
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.8004 1.0786 176
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 7.7597 1.2165 74
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2365 1.0362 226
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.1204 1.0191 382
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.3283 1.0171 399
## avg_veg_height-Canis_latrans 0.9851 1.0051 611
## avg_veg_height-Procyon_lotor 1.8039 1.0152 773
## avg_veg_height-Dasypus_novemcinctus 1.6333 1.0126 712
## avg_veg_height-Sylvilagus_floridanus 1.4363 1.0175 622
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6080 0.1704 -2.9499 -2.5998 -2.2866 1.0118
## (Intercept)-Procyon_lotor -2.2763 0.1319 -2.5465 -2.2743 -2.0273 1.0032
## (Intercept)-Dasypus_novemcinctus -1.6018 0.1408 -1.8793 -1.5993 -1.3260 1.0002
## (Intercept)-Sylvilagus_floridanus -3.1695 0.2794 -3.7339 -3.1593 -2.6460 1.0327
## week-Canis_latrans 0.0719 0.1328 -0.1903 0.0749 0.3284 1.0010
## week-Procyon_lotor -0.0491 0.1198 -0.2885 -0.0467 0.1829 0.9998
## week-Dasypus_novemcinctus -0.1830 0.1420 -0.4701 -0.1780 0.0820 1.0028
## week-Sylvilagus_floridanus -0.1719 0.2222 -0.6459 -0.1576 0.2209 1.0055
## ESS
## (Intercept)-Canis_latrans 740
## (Intercept)-Procyon_lotor 1245
## (Intercept)-Dasypus_novemcinctus 2237
## (Intercept)-Sylvilagus_floridanus 465
## week-Canis_latrans 1492
## week-Procyon_lotor 1631
## week-Dasypus_novemcinctus 2030
## week-Sylvilagus_floridanus 888
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3352
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0587 0.4965 -0.8990 0.0582 1.0524 1.0018 1443
## Avg_Cogongrass_Cover 0.1791 0.4721 -0.7879 0.1841 1.1513 1.0011 2260
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8649 2.9176 0.0505 0.3873 4.1451 1.2056 2746
## Avg_Cogongrass_Cover 0.9127 4.1129 0.0457 0.3313 4.6655 1.1993 2403
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4751 0.578 0.0427 0.2778 1.9865 1.0047 423
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2615 0.5412 -3.0653 -2.3395 -0.9223 1.0003 2333
## shrub_cover 0.2523 0.4057 -0.5972 0.2548 1.0745 1.0060 2504
## veg_height 0.0426 0.3678 -0.6902 0.0458 0.7826 0.9997 2268
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2121 3.4334 0.1061 0.5200 6.0445 1.0164 2343
## shrub_cover 0.7433 1.6071 0.0743 0.3733 3.5296 1.0663 2588
## veg_height 0.6337 1.2726 0.0686 0.3350 3.1505 1.0296 2729
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3129 0.4958 -0.6246 0.2956
## (Intercept)-Procyon_lotor 0.4280 0.5032 -0.5115 0.4065
## (Intercept)-Dasypus_novemcinctus -0.3481 0.4952 -1.3587 -0.3304
## (Intercept)-Sylvilagus_floridanus -0.2063 0.5323 -1.2651 -0.1974
## Avg_Cogongrass_Cover-Canis_latrans 0.4912 0.4585 -0.2963 0.4449
## Avg_Cogongrass_Cover-Procyon_lotor 0.2205 0.3909 -0.4937 0.2075
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3678 0.3717 -0.3098 0.3569
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3326 0.5073 -1.4462 -0.3005
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3563 1.0110 1143
## (Intercept)-Procyon_lotor 1.4889 1.0045 1132
## (Intercept)-Dasypus_novemcinctus 0.5603 1.0103 1111
## (Intercept)-Sylvilagus_floridanus 0.8488 1.0167 892
## Avg_Cogongrass_Cover-Canis_latrans 1.5167 1.0092 1672
## Avg_Cogongrass_Cover-Procyon_lotor 1.0556 1.0015 2264
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1201 1.0038 2483
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5752 1.0189 970
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7426 0.1876 -3.1279 -2.7354 -2.3980 1.0006
## (Intercept)-Procyon_lotor -2.2819 0.1350 -2.5644 -2.2821 -2.0299 1.0074
## (Intercept)-Dasypus_novemcinctus -1.7565 0.1651 -2.0809 -1.7508 -1.4408 1.0072
## (Intercept)-Sylvilagus_floridanus -3.1039 0.2955 -3.7288 -3.0872 -2.5706 1.0095
## shrub_cover-Canis_latrans -0.2691 0.2234 -0.7131 -0.2661 0.1481 1.0006
## shrub_cover-Procyon_lotor 0.2608 0.1599 -0.0582 0.2646 0.5683 1.0046
## shrub_cover-Dasypus_novemcinctus 0.8479 0.3132 0.2786 0.8421 1.4834 1.0090
## shrub_cover-Sylvilagus_floridanus 0.2659 0.4027 -0.4863 0.2485 1.0884 1.0043
## veg_height-Canis_latrans -0.6271 0.2021 -1.0357 -0.6157 -0.2470 0.9998
## veg_height-Procyon_lotor 0.3446 0.1228 0.1053 0.3442 0.5772 1.0079
## veg_height-Dasypus_novemcinctus 0.2570 0.1390 -0.0115 0.2546 0.5315 1.0003
## veg_height-Sylvilagus_floridanus 0.1801 0.2656 -0.3469 0.1773 0.6900 1.0148
## ESS
## (Intercept)-Canis_latrans 637
## (Intercept)-Procyon_lotor 1358
## (Intercept)-Dasypus_novemcinctus 1307
## (Intercept)-Sylvilagus_floridanus 516
## shrub_cover-Canis_latrans 835
## shrub_cover-Procyon_lotor 1445
## shrub_cover-Dasypus_novemcinctus 1140
## shrub_cover-Sylvilagus_floridanus 550
## veg_height-Canis_latrans 479
## veg_height-Procyon_lotor 1621
## veg_height-Dasypus_novemcinctus 1995
## veg_height-Sylvilagus_floridanus 712
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3892
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1741 0.8371 -1.8714 -0.1595 1.4810 1.0563 410
## Cogon_Patch_Size -0.6152 0.8864 -2.2408 -0.6519 1.2717 1.0257 812
## Veg_shannon_index 1.1878 0.6716 -0.1098 1.1749 2.5897 1.0326 468
## total_shrub_cover -0.2559 0.7943 -1.8874 -0.2508 1.3618 1.0060 784
## Avg_Cogongrass_Cover 1.8563 1.0646 -0.3922 1.8973 3.8792 1.0058 658
## Tree_Density -2.0697 1.1643 -4.1188 -2.1752 0.6121 1.0008 847
## Avg_Canopy_Cover 1.3405 1.1271 -1.0877 1.3845 3.5289 1.0081 2345
## avg_veg_height -0.3531 0.6759 -1.7140 -0.3548 1.0509 1.0267 514
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5658 5.7646 0.0651 0.9780 15.0524 1.0065 758
## Cogon_Patch_Size 4.2346 13.2795 0.0711 1.0653 27.1350 1.0439 462
## Veg_shannon_index 1.3270 4.3695 0.0499 0.4107 6.9947 1.0125 2071
## total_shrub_cover 3.2441 9.0744 0.0706 1.0514 19.8226 1.0697 781
## Avg_Cogongrass_Cover 4.4084 11.0985 0.0580 1.1798 30.0287 1.0827 366
## Tree_Density 14.4183 49.7877 0.0761 2.2877 108.0597 1.0526 256
## Avg_Canopy_Cover 14.3622 39.7568 0.2577 4.9434 85.5038 1.0910 312
## avg_veg_height 0.9795 6.5988 0.0454 0.3367 5.0028 1.2537 2836
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.3199 5.8814 0.08 1.4344 17.8962 1.0407 99
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2997 0.5055 -3.1070 -2.3679 -1.0532 1.0084 2481
## shrub_cover 0.3270 0.4602 -0.6564 0.3323 1.2517 1.0075 2216
## veg_height 0.0397 0.3754 -0.7223 0.0401 0.7969 1.0032 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1619 3.7505 0.1096 0.5194 5.5570 1.0767 2748
## shrub_cover 0.9370 2.5653 0.0960 0.4720 4.3902 1.1921 2190
## veg_height 0.6696 2.0603 0.0732 0.3363 3.1290 1.1412 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4269 1.0586 -1.5284 0.3796
## (Intercept)-Procyon_lotor 0.3328 1.0690 -1.9163 0.3847
## (Intercept)-Dasypus_novemcinctus -0.9447 1.0393 -3.1889 -0.8517
## (Intercept)-Sylvilagus_floridanus -0.6574 1.1727 -3.1062 -0.6132
## Cogon_Patch_Size-Canis_latrans 0.4716 1.3938 -1.5320 0.2112
## Cogon_Patch_Size-Procyon_lotor -1.1512 0.8257 -2.9416 -1.0941
## Cogon_Patch_Size-Dasypus_novemcinctus -0.6880 0.8846 -2.3898 -0.7207
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6882 1.6590 -6.1072 -1.3797
## Veg_shannon_index-Canis_latrans 1.6051 0.8041 0.2189 1.5292
## Veg_shannon_index-Procyon_lotor 1.4708 0.7157 0.2562 1.4105
## Veg_shannon_index-Dasypus_novemcinctus 0.8316 0.7007 -0.5527 0.8361
## Veg_shannon_index-Sylvilagus_floridanus 1.3022 0.8379 -0.2566 1.2594
## total_shrub_cover-Canis_latrans 0.7452 1.0477 -0.8325 0.5550
## total_shrub_cover-Procyon_lotor -0.9661 0.7198 -2.5303 -0.9055
## total_shrub_cover-Dasypus_novemcinctus -0.2204 0.8967 -2.2837 -0.1467
## total_shrub_cover-Sylvilagus_floridanus -0.7726 1.4462 -4.1180 -0.5632
## Avg_Cogongrass_Cover-Canis_latrans 2.7661 1.2990 0.6800 2.6079
## Avg_Cogongrass_Cover-Procyon_lotor 2.3581 1.1404 0.3579 2.3031
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 3.1128 1.5769 0.8907 2.8505
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.1564 1.4154 -1.7590 1.2743
## Tree_Density-Canis_latrans -3.4294 1.8481 -8.3123 -3.0666
## Tree_Density-Procyon_lotor -1.7179 0.9262 -3.5554 -1.7041
## Tree_Density-Dasypus_novemcinctus -4.6534 3.5484 -12.5164 -3.7113
## Tree_Density-Sylvilagus_floridanus -2.8820 1.9206 -7.5464 -2.6459
## Avg_Canopy_Cover-Canis_latrans 0.0224 0.7061 -1.4164 0.0312
## Avg_Canopy_Cover-Procyon_lotor 1.8193 0.8928 0.3534 1.7290
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5699 1.1604 0.8723 2.3817
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.9558 2.8071 1.0837 4.4289
## avg_veg_height-Canis_latrans -0.4640 0.7403 -1.9032 -0.4607
## avg_veg_height-Procyon_lotor -0.3181 0.7142 -1.7132 -0.3223
## avg_veg_height-Dasypus_novemcinctus -0.1845 0.7495 -1.6280 -0.2116
## avg_veg_height-Sylvilagus_floridanus -0.5518 0.8831 -2.3593 -0.5124
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.7338 1.0214 364
## (Intercept)-Procyon_lotor 2.2440 1.1965 182
## (Intercept)-Dasypus_novemcinctus 0.8583 1.0335 344
## (Intercept)-Sylvilagus_floridanus 1.5014 1.0398 329
## Cogon_Patch_Size-Canis_latrans 4.0823 1.0376 263
## Cogon_Patch_Size-Procyon_lotor 0.3407 1.0299 331
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1592 1.0372 588
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5968 1.0109 312
## Veg_shannon_index-Canis_latrans 3.4567 1.0335 372
## Veg_shannon_index-Procyon_lotor 3.0545 1.0297 260
## Veg_shannon_index-Dasypus_novemcinctus 2.2628 1.0401 464
## Veg_shannon_index-Sylvilagus_floridanus 3.0496 1.0089 479
## total_shrub_cover-Canis_latrans 3.3888 1.0783 310
## total_shrub_cover-Procyon_lotor 0.2356 1.0071 736
## total_shrub_cover-Dasypus_novemcinctus 1.2776 1.0529 266
## total_shrub_cover-Sylvilagus_floridanus 1.7312 1.0143 227
## Avg_Cogongrass_Cover-Canis_latrans 5.8217 1.0138 328
## Avg_Cogongrass_Cover-Procyon_lotor 4.8954 1.0153 474
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 6.9149 1.0545 203
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.6895 1.0036 441
## Tree_Density-Canis_latrans -0.7531 1.0211 192
## Tree_Density-Procyon_lotor 0.0945 1.0071 736
## Tree_Density-Dasypus_novemcinctus -1.4188 1.0439 83
## Tree_Density-Sylvilagus_floridanus 0.2986 1.0117 293
## Avg_Canopy_Cover-Canis_latrans 1.4005 1.0041 982
## Avg_Canopy_Cover-Procyon_lotor 3.7758 1.0377 424
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.4133 1.0350 176
## Avg_Canopy_Cover-Sylvilagus_floridanus 11.9155 1.0323 143
## avg_veg_height-Canis_latrans 1.0364 1.0241 456
## avg_veg_height-Procyon_lotor 1.0906 1.0234 693
## avg_veg_height-Dasypus_novemcinctus 1.3441 1.0283 615
## avg_veg_height-Sylvilagus_floridanus 1.1217 1.0176 477
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7545 0.1898 -3.1481 -2.7436 -2.4034 1.0384
## (Intercept)-Procyon_lotor -2.2900 0.1398 -2.5699 -2.2883 -2.0224 1.0096
## (Intercept)-Dasypus_novemcinctus -1.7822 0.1666 -2.1136 -1.7797 -1.4703 1.0104
## (Intercept)-Sylvilagus_floridanus -3.1400 0.2611 -3.6650 -3.1330 -2.6565 1.0626
## shrub_cover-Canis_latrans -0.3620 0.2309 -0.8031 -0.3632 0.1118 1.0046
## shrub_cover-Procyon_lotor 0.2710 0.1564 -0.0349 0.2731 0.5654 1.0165
## shrub_cover-Dasypus_novemcinctus 0.9428 0.3336 0.2991 0.9345 1.6023 1.0058
## shrub_cover-Sylvilagus_floridanus 0.5210 0.4122 -0.2988 0.5196 1.3007 1.0237
## veg_height-Canis_latrans -0.6300 0.1920 -1.0190 -0.6294 -0.2589 1.0231
## veg_height-Procyon_lotor 0.3544 0.1235 0.1093 0.3570 0.5836 1.0103
## veg_height-Dasypus_novemcinctus 0.2676 0.1398 -0.0046 0.2649 0.5490 1.0069
## veg_height-Sylvilagus_floridanus 0.1899 0.2648 -0.3413 0.1879 0.7198 1.0068
## ESS
## (Intercept)-Canis_latrans 687
## (Intercept)-Procyon_lotor 1165
## (Intercept)-Dasypus_novemcinctus 693
## (Intercept)-Sylvilagus_floridanus 478
## shrub_cover-Canis_latrans 457
## shrub_cover-Procyon_lotor 1289
## shrub_cover-Dasypus_novemcinctus 607
## shrub_cover-Sylvilagus_floridanus 301
## veg_height-Canis_latrans 639
## veg_height-Procyon_lotor 1536
## veg_height-Dasypus_novemcinctus 1602
## veg_height-Sylvilagus_floridanus 485
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.431
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.6623 0.7384 -0.7348 0.6062 2.2133 1.1423 163
## Avg_Cogongrass_Cover -0.1423 0.6181 -1.3808 -0.1443 1.0863 1.0353 916
## total_shrub_cover -0.7044 0.9032 -2.5457 -0.6862 1.1331 1.0493 542
## avg_veg_height 0.4845 0.5970 -0.6143 0.4613 1.7554 1.1292 315
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0990 2.6444 0.0472 0.4176 6.3856 1.0032 1063
## Avg_Cogongrass_Cover 1.3410 3.4130 0.0529 0.4918 7.4873 1.0059 1392
## total_shrub_cover 5.8563 26.2372 0.1110 1.9135 32.5767 1.1090 1946
## avg_veg_height 0.7330 3.1990 0.0442 0.2786 3.5509 1.2632 2622
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.9811 1.3489 0.0512 0.5021 4.5911 1.0244 270
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3630 0.5664 -3.2455 -2.4393 -0.9349 1.0120 2117
## shrub_cover 0.4796 0.5159 -0.5511 0.4748 1.5321 1.0067 1511
## veg_height -0.0057 0.3889 -0.8045 0.0015 0.7810 1.0028 2566
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2929 2.9960 0.1148 0.5598 7.2106 1.0105 1831
## shrub_cover 1.2562 2.2827 0.1134 0.6663 5.8311 1.0656 1036
## veg_height 0.6523 1.1277 0.0764 0.3425 3.2390 1.0049 2121
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.8531 0.7498 -0.4693 0.7836
## (Intercept)-Procyon_lotor 0.9808 0.7348 -0.3548 0.9233
## (Intercept)-Dasypus_novemcinctus 0.3046 0.9013 -1.2746 0.2081
## (Intercept)-Sylvilagus_floridanus 0.7815 0.9949 -0.8476 0.6711
## Avg_Cogongrass_Cover-Canis_latrans 0.3381 0.7074 -0.9067 0.2895
## Avg_Cogongrass_Cover-Procyon_lotor -0.2884 0.6038 -1.5508 -0.2544
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.0732 0.6031 -1.1238 0.0634
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.7733 0.8901 -2.8753 -0.6421
## total_shrub_cover-Canis_latrans 0.6450 0.9874 -1.0056 0.5057
## total_shrub_cover-Procyon_lotor -1.5030 0.7924 -3.3447 -1.3854
## total_shrub_cover-Dasypus_novemcinctus -0.9565 1.2650 -4.1407 -0.6164
## total_shrub_cover-Sylvilagus_floridanus -2.0893 1.5094 -5.6118 -1.8260
## avg_veg_height-Canis_latrans 0.4274 0.6438 -0.7972 0.3934
## avg_veg_height-Procyon_lotor 0.4222 0.5988 -0.6578 0.3894
## avg_veg_height-Dasypus_novemcinctus 0.7434 0.6843 -0.3516 0.6578
## avg_veg_height-Sylvilagus_floridanus 0.4519 0.7619 -0.9576 0.3918
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5281 1.1467 193
## (Intercept)-Procyon_lotor 2.5487 1.0626 284
## (Intercept)-Dasypus_novemcinctus 2.2624 1.1821 125
## (Intercept)-Sylvilagus_floridanus 3.0931 1.1492 132
## Avg_Cogongrass_Cover-Canis_latrans 1.9652 1.0364 884
## Avg_Cogongrass_Cover-Procyon_lotor 0.8338 1.0594 470
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3533 1.0527 1107
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6322 1.0472 564
## total_shrub_cover-Canis_latrans 3.0945 1.0330 203
## total_shrub_cover-Procyon_lotor -0.2778 1.0595 280
## total_shrub_cover-Dasypus_novemcinctus 0.6561 1.1656 94
## total_shrub_cover-Sylvilagus_floridanus 0.1300 1.1293 113
## avg_veg_height-Canis_latrans 1.7808 1.0800 479
## avg_veg_height-Procyon_lotor 1.6692 1.1074 523
## avg_veg_height-Dasypus_novemcinctus 2.3523 1.1801 175
## avg_veg_height-Sylvilagus_floridanus 2.0740 1.1005 215
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.8203 0.2036 -3.2523 -2.8100 -2.4445 1.1107
## (Intercept)-Procyon_lotor -2.2993 0.1384 -2.5714 -2.2944 -2.0414 1.0036
## (Intercept)-Dasypus_novemcinctus -1.8846 0.2014 -2.2725 -1.8816 -1.5043 1.1131
## (Intercept)-Sylvilagus_floridanus -3.2955 0.2813 -3.8725 -3.2813 -2.7766 1.0873
## shrub_cover-Canis_latrans -0.3501 0.2678 -0.8409 -0.3624 0.1934 1.0098
## shrub_cover-Procyon_lotor 0.3395 0.1609 0.0147 0.3430 0.6432 1.0085
## shrub_cover-Dasypus_novemcinctus 1.2284 0.4334 0.4061 1.2471 1.9785 1.1957
## shrub_cover-Sylvilagus_floridanus 0.8445 0.4199 -0.0559 0.8656 1.6271 1.0860
## veg_height-Canis_latrans -0.6707 0.1998 -1.0704 -0.6622 -0.3041 1.0578
## veg_height-Procyon_lotor 0.3435 0.1229 0.1063 0.3411 0.5884 1.0017
## veg_height-Dasypus_novemcinctus 0.2801 0.1477 0.0023 0.2765 0.5851 1.0329
## veg_height-Sylvilagus_floridanus 0.0296 0.2517 -0.4303 0.0207 0.5504 1.0197
## ESS
## (Intercept)-Canis_latrans 432
## (Intercept)-Procyon_lotor 1249
## (Intercept)-Dasypus_novemcinctus 289
## (Intercept)-Sylvilagus_floridanus 339
## shrub_cover-Canis_latrans 418
## shrub_cover-Procyon_lotor 1465
## shrub_cover-Dasypus_novemcinctus 161
## shrub_cover-Sylvilagus_floridanus 205
## veg_height-Canis_latrans 497
## veg_height-Procyon_lotor 1485
## veg_height-Dasypus_novemcinctus 1073
## veg_height-Sylvilagus_floridanus 487
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # thinning parameter for the MCMC chain
n.burn = 3000, # burn-in period for the sampler
n.chains = 3, # number of MCMC chains
n.report = 500, # reporting interval for the sampler
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3455
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0467 0.4536 -0.8901 0.0464 0.9524 1.0012 2562
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8658 1.9383 0.0576 0.417 4.2711 1.017 2361
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2944 0.4990 -3.1723 -2.3331 -1.1434 1.0017 2527
## shrub_cover 0.2480 0.4186 -0.5497 0.2460 1.1050 1.0009 1884
## veg_height 0.0341 0.3877 -0.7691 0.0370 0.8337 1.0029 2812
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0795 3.7033 0.1053 0.4755 5.0676 1.0258 2780
## shrub_cover 0.7561 1.5728 0.0717 0.3702 3.8522 1.0259 2358
## veg_height 0.6380 1.1409 0.0733 0.3330 3.1918 1.0013 2283
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3104 0.3793 -0.3862 0.2895 1.1184 1.0003
## (Intercept)-Procyon_lotor 0.5697 0.3790 -0.1182 0.5515 1.3750 0.9998
## (Intercept)-Dasypus_novemcinctus -0.3935 0.3615 -1.1493 -0.3808 0.2855 1.0004
## (Intercept)-Sylvilagus_floridanus -0.2385 0.4186 -1.0761 -0.2305 0.5588 1.0079
## ESS
## (Intercept)-Canis_latrans 1858
## (Intercept)-Procyon_lotor 1951
## (Intercept)-Dasypus_novemcinctus 1576
## (Intercept)-Sylvilagus_floridanus 1495
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7285 0.1831 -3.1143 -2.7196 -2.3836 1.0176
## (Intercept)-Procyon_lotor -2.2803 0.1404 -2.5875 -2.2732 -2.0224 1.0026
## (Intercept)-Dasypus_novemcinctus -1.7540 0.1605 -2.0819 -1.7512 -1.4511 1.0048
## (Intercept)-Sylvilagus_floridanus -3.0804 0.2891 -3.6720 -3.0686 -2.5358 1.0154
## shrub_cover-Canis_latrans -0.2851 0.2243 -0.7292 -0.2834 0.1402 1.0014
## shrub_cover-Procyon_lotor 0.2600 0.1629 -0.0676 0.2614 0.5694 1.0061
## shrub_cover-Dasypus_novemcinctus 0.8316 0.3065 0.2391 0.8202 1.4372 1.0007
## shrub_cover-Sylvilagus_floridanus 0.2473 0.4090 -0.5496 0.2383 1.0712 1.0191
## veg_height-Canis_latrans -0.6141 0.1897 -1.0138 -0.6079 -0.2617 1.0303
## veg_height-Procyon_lotor 0.3483 0.1275 0.0922 0.3487 0.5940 1.0010
## veg_height-Dasypus_novemcinctus 0.2601 0.1356 -0.0023 0.2602 0.5309 1.0005
## veg_height-Sylvilagus_floridanus 0.1523 0.2535 -0.3613 0.1582 0.6374 0.9996
## ESS
## (Intercept)-Canis_latrans 813
## (Intercept)-Procyon_lotor 1204
## (Intercept)-Dasypus_novemcinctus 1152
## (Intercept)-Sylvilagus_floridanus 601
## shrub_cover-Canis_latrans 855
## shrub_cover-Procyon_lotor 1371
## shrub_cover-Dasypus_novemcinctus 970
## shrub_cover-Sylvilagus_floridanus 498
## veg_height-Canis_latrans 776
## veg_height-Procyon_lotor 1443
## veg_height-Dasypus_novemcinctus 2059
## veg_height-Sylvilagus_floridanus 803
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.35
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0139 0.5119 -1.0458 -0.0106 0.9402 1.0062 1107
## Veg_shannon_index 0.4801 0.4482 -0.4165 0.4903 1.3325 1.0028 1700
## Avg_Cogongrass_Cover 0.3289 0.4790 -0.6025 0.3272 1.2955 1.0070 1779
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1567 14.4914 0.0494 0.3856 4.8663 1.3123 3000
## Veg_shannon_index 0.6143 2.9100 0.0388 0.2562 2.8345 1.2064 2795
## Avg_Cogongrass_Cover 0.8417 2.1319 0.0450 0.3393 4.2160 1.0553 2446
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6495 1.1251 0.05 0.3654 2.8313 1.1208 347
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2759 0.5336 -3.1133 -2.3415 -0.9517 1.0041 2553
## shrub_cover 0.2372 0.4024 -0.5795 0.2337 1.0802 1.0033 1805
## veg_height 0.0414 0.4008 -0.7398 0.0425 0.8496 1.0084 2348
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2496 4.4373 0.1050 0.5034 5.9843 1.0384 2309
## shrub_cover 0.7281 1.6241 0.0652 0.3545 3.8821 1.0681 2595
## veg_height 0.6367 1.1747 0.0738 0.3334 3.0523 1.0888 2103
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2570 0.5551 -0.8459 0.2427
## (Intercept)-Procyon_lotor 0.3649 0.5327 -0.7255 0.3646
## (Intercept)-Dasypus_novemcinctus -0.4049 0.5014 -1.4737 -0.3716
## (Intercept)-Sylvilagus_floridanus -0.2635 0.5484 -1.3521 -0.2492
## Veg_shannon_index-Canis_latrans 0.7719 0.4582 -0.0516 0.7435
## Veg_shannon_index-Procyon_lotor 0.5254 0.4273 -0.2524 0.4964
## Veg_shannon_index-Dasypus_novemcinctus 0.2426 0.3941 -0.5163 0.2427
## Veg_shannon_index-Sylvilagus_floridanus 0.5189 0.4653 -0.3609 0.4942
## Avg_Cogongrass_Cover-Canis_latrans 0.7259 0.5046 -0.1400 0.6716
## Avg_Cogongrass_Cover-Procyon_lotor 0.4181 0.4530 -0.3677 0.3823
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4799 0.3923 -0.2385 0.4676
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2091 0.5386 -1.4262 -0.1544
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4045 1.0323 871
## (Intercept)-Procyon_lotor 1.4233 1.0212 835
## (Intercept)-Dasypus_novemcinctus 0.5316 1.0022 871
## (Intercept)-Sylvilagus_floridanus 0.7883 1.0010 885
## Veg_shannon_index-Canis_latrans 1.7484 1.0108 1355
## Veg_shannon_index-Procyon_lotor 1.4201 1.0218 979
## Veg_shannon_index-Dasypus_novemcinctus 0.9954 1.0080 1579
## Veg_shannon_index-Sylvilagus_floridanus 1.5126 1.0108 1354
## Avg_Cogongrass_Cover-Canis_latrans 1.8763 1.0028 1095
## Avg_Cogongrass_Cover-Procyon_lotor 1.4101 1.0108 1337
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3085 1.0014 1813
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7388 1.0005 1037
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7214 0.1860 -3.1067 -2.7138 -2.3784 1.0217
## (Intercept)-Procyon_lotor -2.2923 0.1415 -2.5812 -2.2895 -2.0282 1.0014
## (Intercept)-Dasypus_novemcinctus -1.7510 0.1677 -2.0882 -1.7423 -1.4307 1.0104
## (Intercept)-Sylvilagus_floridanus -3.0986 0.3001 -3.7345 -3.0711 -2.5518 1.0032
## shrub_cover-Canis_latrans -0.2653 0.2222 -0.6976 -0.2667 0.1751 1.0174
## shrub_cover-Procyon_lotor 0.2370 0.1725 -0.1209 0.2417 0.5569 1.0227
## shrub_cover-Dasypus_novemcinctus 0.8255 0.3197 0.2375 0.8083 1.4823 1.0131
## shrub_cover-Sylvilagus_floridanus 0.2000 0.4081 -0.5730 0.1869 1.0152 1.0273
## veg_height-Canis_latrans -0.6118 0.1878 -1.0036 -0.6048 -0.2815 1.0104
## veg_height-Procyon_lotor 0.3412 0.1286 0.0852 0.3422 0.5908 1.0026
## veg_height-Dasypus_novemcinctus 0.2505 0.1369 -0.0144 0.2468 0.5193 1.0058
## veg_height-Sylvilagus_floridanus 0.1719 0.2521 -0.3374 0.1750 0.6428 1.0023
## ESS
## (Intercept)-Canis_latrans 736
## (Intercept)-Procyon_lotor 1082
## (Intercept)-Dasypus_novemcinctus 1259
## (Intercept)-Sylvilagus_floridanus 446
## shrub_cover-Canis_latrans 863
## shrub_cover-Procyon_lotor 1235
## shrub_cover-Dasypus_novemcinctus 1021
## shrub_cover-Sylvilagus_floridanus 547
## veg_height-Canis_latrans 746
## veg_height-Procyon_lotor 1316
## veg_height-Dasypus_novemcinctus 1913
## veg_height-Sylvilagus_floridanus 797
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3783
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3782 0.6302 -0.8134 0.3585 1.7127 1.0219 319
## Cogon_Patch_Size -0.0280 0.6645 -1.4923 -0.0127 1.3294 1.0078 1180
## Avg_Cogongrass_Cover 0.1223 0.5223 -0.9378 0.1282 1.1188 1.0127 1210
## total_shrub_cover -0.5124 0.7505 -2.0752 -0.4884 0.9395 1.0222 663
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1672 2.7401 0.0551 0.4700 6.4878 1.0382 1830
## Cogon_Patch_Size 2.2968 7.6160 0.0533 0.6792 13.3316 1.0700 588
## Avg_Cogongrass_Cover 0.8754 2.2525 0.0414 0.2958 5.2016 1.0455 1652
## total_shrub_cover 2.6160 8.2512 0.0721 0.9984 14.1889 1.1781 1725
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8022 1.067 0.0504 0.4508 3.5561 1.0266 348
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3117 0.5564 -3.1778 -2.3920 -0.8576 1.0030 2332
## shrub_cover 0.3921 0.4978 -0.6286 0.3894 1.4096 1.0191 886
## veg_height 0.0122 0.3624 -0.7218 0.0064 0.7754 1.0025 2731
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4398 10.6625 0.1148 0.5769 6.6633 1.3058 1820
## shrub_cover 1.1087 3.0896 0.0928 0.5383 4.9601 1.0960 2386
## veg_height 0.6495 1.5063 0.0726 0.3369 2.8114 1.0942 2720
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6324 0.6582 -0.5471 0.5888
## (Intercept)-Procyon_lotor 0.7610 0.6584 -0.4377 0.7273
## (Intercept)-Dasypus_novemcinctus -0.0656 0.6925 -1.3168 -0.1154
## (Intercept)-Sylvilagus_floridanus 0.2949 0.8257 -1.1733 0.2492
## Cogon_Patch_Size-Canis_latrans 0.8679 0.9054 -0.3968 0.6818
## Cogon_Patch_Size-Procyon_lotor -0.1209 0.5251 -1.1565 -0.1269
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0134 0.5038 -0.9860 -0.0267
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8803 1.1640 -3.5451 -0.6477
## Avg_Cogongrass_Cover-Canis_latrans 0.3607 0.5101 -0.5274 0.3312
## Avg_Cogongrass_Cover-Procyon_lotor 0.0763 0.5057 -0.9079 0.0792
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3688 0.4841 -0.5042 0.3403
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2411 0.7158 -1.8095 -0.1941
## total_shrub_cover-Canis_latrans 0.3454 0.7293 -0.8599 0.2548
## total_shrub_cover-Procyon_lotor -1.2226 0.6932 -2.8104 -1.1517
## total_shrub_cover-Dasypus_novemcinctus -0.5087 0.8494 -2.8531 -0.3467
## total_shrub_cover-Sylvilagus_floridanus -1.1510 1.3058 -4.1454 -1.0169
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0419 1.0136 539
## (Intercept)-Procyon_lotor 2.1793 1.0332 571
## (Intercept)-Dasypus_novemcinctus 1.4268 1.0152 194
## (Intercept)-Sylvilagus_floridanus 2.0530 1.0262 268
## Cogon_Patch_Size-Canis_latrans 3.1207 1.0121 570
## Cogon_Patch_Size-Procyon_lotor 0.9060 1.0018 1117
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9938 1.0159 971
## Cogon_Patch_Size-Sylvilagus_floridanus 0.6728 1.0354 410
## Avg_Cogongrass_Cover-Canis_latrans 1.5007 1.0179 911
## Avg_Cogongrass_Cover-Procyon_lotor 1.0318 1.0158 958
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4418 1.0195 1309
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0638 1.0146 669
## total_shrub_cover-Canis_latrans 2.0651 1.0052 514
## total_shrub_cover-Procyon_lotor -0.1094 1.0397 530
## total_shrub_cover-Dasypus_novemcinctus 0.6337 1.0322 162
## total_shrub_cover-Sylvilagus_floridanus 1.3456 1.0634 163
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7518 0.1926 -3.1622 -2.7447 -2.3935 1.0076
## (Intercept)-Procyon_lotor -2.2971 0.1358 -2.5712 -2.2935 -2.0398 1.0236
## (Intercept)-Dasypus_novemcinctus -1.8263 0.1905 -2.2273 -1.8141 -1.4871 1.0064
## (Intercept)-Sylvilagus_floridanus -3.2591 0.2844 -3.8283 -3.2552 -2.7073 1.0005
## shrub_cover-Canis_latrans -0.3263 0.2384 -0.7850 -0.3293 0.1284 1.0059
## shrub_cover-Procyon_lotor 0.3199 0.1609 -0.0013 0.3256 0.6247 1.0207
## shrub_cover-Dasypus_novemcinctus 1.0526 0.3960 0.3254 1.0316 1.8262 1.0030
## shrub_cover-Sylvilagus_floridanus 0.6572 0.5112 -0.4606 0.7099 1.5613 1.0292
## veg_height-Canis_latrans -0.6311 0.1878 -1.0122 -0.6252 -0.2698 1.0033
## veg_height-Procyon_lotor 0.3469 0.1223 0.1002 0.3503 0.5861 1.0210
## veg_height-Dasypus_novemcinctus 0.2699 0.1486 -0.0083 0.2632 0.5575 1.0041
## veg_height-Sylvilagus_floridanus 0.0896 0.2777 -0.4457 0.0863 0.6243 1.0037
## ESS
## (Intercept)-Canis_latrans 692
## (Intercept)-Procyon_lotor 1390
## (Intercept)-Dasypus_novemcinctus 353
## (Intercept)-Sylvilagus_floridanus 407
## shrub_cover-Canis_latrans 590
## shrub_cover-Procyon_lotor 1269
## shrub_cover-Dasypus_novemcinctus 348
## shrub_cover-Sylvilagus_floridanus 133
## veg_height-Canis_latrans 809
## veg_height-Procyon_lotor 1553
## veg_height-Dasypus_novemcinctus 1594
## veg_height-Sylvilagus_floridanus 459
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.352
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0785 0.5707 -1.1944 -0.0696 1.0565 1.0002 1346
## Tree_Density -0.9922 0.6197 -2.2865 -0.9547 0.1350 1.0003 1151
## Avg_Canopy_Cover 0.8889 0.8008 -0.8456 0.8955 2.4520 1.0053 2537
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2940 3.1171 0.0579 0.5315 6.9944 1.0261 2273
## Tree_Density 1.5413 5.0176 0.0470 0.4573 9.4463 1.0050 1674
## Avg_Canopy_Cover 4.3243 9.6081 0.1622 1.8514 24.0633 1.0345 1023
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5737 0.8129 0.0425 0.3094 2.7269 1.0094 384
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2849 0.4977 -3.1023 -2.3410 -0.9736 1.0053 2653
## shrub_cover 0.2910 0.4103 -0.5127 0.2935 1.1270 1.0009 2167
## veg_height 0.0406 0.3827 -0.7677 0.0427 0.7846 1.0043 2676
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1290 3.8864 0.1025 0.4939 5.5671 1.1370 2774
## shrub_cover 0.7933 2.3347 0.0698 0.3945 3.5156 1.1224 2812
## veg_height 0.6939 1.5711 0.0754 0.3545 3.4788 1.0358 2173
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2241 0.5691 -0.8430 0.2013 1.4313
## (Intercept)-Procyon_lotor 0.4688 0.5949 -0.6753 0.4482 1.7022
## (Intercept)-Dasypus_novemcinctus -0.6377 0.6227 -1.9434 -0.6099 0.5304
## (Intercept)-Sylvilagus_floridanus -0.3884 0.6321 -1.6867 -0.3723 0.8033
## Tree_Density-Canis_latrans -1.0852 0.6720 -2.5377 -1.0131 -0.0290
## Tree_Density-Procyon_lotor -0.5518 0.4626 -1.4754 -0.5371 0.3195
## Tree_Density-Dasypus_novemcinctus -1.5681 1.0620 -4.5394 -1.3220 -0.2448
## Tree_Density-Sylvilagus_floridanus -1.2497 0.8833 -3.3768 -1.1259 0.1349
## Avg_Canopy_Cover-Canis_latrans -0.2641 0.4974 -1.2362 -0.2685 0.6890
## Avg_Canopy_Cover-Procyon_lotor 1.0177 0.5554 0.0261 0.9737 2.2400
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.1089 0.5395 0.1744 1.0618 2.2716
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.7509 1.4817 0.7325 2.4589 6.3860
## Rhat ESS
## (Intercept)-Canis_latrans 1.0031 866
## (Intercept)-Procyon_lotor 1.0025 752
## (Intercept)-Dasypus_novemcinctus 1.0054 751
## (Intercept)-Sylvilagus_floridanus 1.0001 992
## Tree_Density-Canis_latrans 1.0062 896
## Tree_Density-Procyon_lotor 1.0059 1399
## Tree_Density-Dasypus_novemcinctus 1.0108 491
## Tree_Density-Sylvilagus_floridanus 1.0079 654
## Avg_Canopy_Cover-Canis_latrans 1.0019 1312
## Avg_Canopy_Cover-Procyon_lotor 1.0011 1490
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0032 1629
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.0156 322
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7486 0.1926 -3.1544 -2.7394 -2.3815 1.0118
## (Intercept)-Procyon_lotor -2.2872 0.1404 -2.5733 -2.2790 -2.0280 1.0012
## (Intercept)-Dasypus_novemcinctus -1.7736 0.1672 -2.1068 -1.7740 -1.4566 1.0052
## (Intercept)-Sylvilagus_floridanus -3.0596 0.2572 -3.5846 -3.0482 -2.5737 1.0052
## shrub_cover-Canis_latrans -0.2876 0.2273 -0.7297 -0.2868 0.1645 1.0096
## shrub_cover-Procyon_lotor 0.2611 0.1587 -0.0573 0.2637 0.5574 1.0036
## shrub_cover-Dasypus_novemcinctus 0.8673 0.3085 0.2760 0.8603 1.4836 1.0027
## shrub_cover-Sylvilagus_floridanus 0.4441 0.3688 -0.2742 0.4368 1.1656 1.0045
## veg_height-Canis_latrans -0.6324 0.1944 -1.0242 -0.6302 -0.2587 1.0012
## veg_height-Procyon_lotor 0.3484 0.1266 0.1024 0.3465 0.5996 1.0019
## veg_height-Dasypus_novemcinctus 0.2681 0.1387 0.0001 0.2674 0.5419 1.0053
## veg_height-Sylvilagus_floridanus 0.1700 0.2478 -0.3218 0.1710 0.6521 1.0120
## ESS
## (Intercept)-Canis_latrans 671
## (Intercept)-Procyon_lotor 1248
## (Intercept)-Dasypus_novemcinctus 1311
## (Intercept)-Sylvilagus_floridanus 789
## shrub_cover-Canis_latrans 804
## shrub_cover-Procyon_lotor 1571
## shrub_cover-Dasypus_novemcinctus 1031
## shrub_cover-Sylvilagus_floridanus 576
## veg_height-Canis_latrans 629
## veg_height-Procyon_lotor 1570
## veg_height-Dasypus_novemcinctus 1456
## veg_height-Sylvilagus_floridanus 1005
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3727
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6625 0.5599 -1.7533 -0.6669 0.4024 1.0168 686
## Avg_Cogongrass_Cover -0.5120 0.6125 -1.7159 -0.5134 0.6785 1.0033 1227
## I(Avg_Cogongrass_Cover^2) 0.9380 0.6652 -0.3410 0.8919 2.4163 1.0203 791
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8166 1.9106 0.0455 0.3397 4.1829 1.0720 1991
## Avg_Cogongrass_Cover 1.4168 4.6112 0.0538 0.5063 7.2972 1.1180 1176
## I(Avg_Cogongrass_Cover^2) 2.2407 10.7342 0.0486 0.4935 12.6456 1.2111 786
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4281 0.5374 0.0394 0.2569 1.9074 1.0668 364
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2884 0.5203 -3.1202 -2.3455 -0.9692 1.0033 2482
## shrub_cover 0.2504 0.4045 -0.5930 0.2514 1.0412 1.0069 1969
## veg_height 0.0349 0.3730 -0.7316 0.0414 0.7401 0.9996 2257
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0699 2.2569 0.1035 0.5171 5.3150 1.0075 2305
## shrub_cover 0.7283 1.5033 0.0621 0.3485 3.8360 1.0236 2461
## veg_height 0.6435 1.3037 0.0695 0.3339 3.0653 1.0471 2681
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5336 0.6448 -1.7820 -0.5474
## (Intercept)-Procyon_lotor -0.3814 0.5824 -1.4946 -0.3993
## (Intercept)-Dasypus_novemcinctus -0.9707 0.5528 -2.1174 -0.9506
## (Intercept)-Sylvilagus_floridanus -0.9055 0.6239 -2.2467 -0.8800
## Avg_Cogongrass_Cover-Canis_latrans -0.1105 0.6476 -1.2834 -0.1380
## Avg_Cogongrass_Cover-Procyon_lotor -0.5666 0.6308 -1.8160 -0.5688
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3034 0.5655 -1.3776 -0.3045
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2433 0.8747 -3.1334 -1.1482
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6072 1.0381 0.1354 1.3956
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.4966 1.1520 0.1964 1.2132
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6080 0.4175 -0.1580 0.5989
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7427 0.6890 -0.2303 0.6639
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8241 1.0558 733
## (Intercept)-Procyon_lotor 0.7632 1.0168 784
## (Intercept)-Dasypus_novemcinctus 0.0693 1.0169 632
## (Intercept)-Sylvilagus_floridanus 0.2548 1.0226 689
## Avg_Cogongrass_Cover-Canis_latrans 1.2943 1.0019 1023
## Avg_Cogongrass_Cover-Procyon_lotor 0.7413 1.0100 928
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8175 1.0041 1475
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0564 1.0131 501
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.1893 1.0557 307
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 4.6707 1.0761 165
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4637 1.0059 1333
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.1829 1.0796 260
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7480 0.1892 -3.1233 -2.7410 -2.4007 1.0033
## (Intercept)-Procyon_lotor -2.3191 0.1490 -2.6148 -2.3130 -2.0396 1.0295
## (Intercept)-Dasypus_novemcinctus -1.7551 0.1662 -2.0923 -1.7515 -1.4438 1.0100
## (Intercept)-Sylvilagus_floridanus -3.1144 0.2987 -3.7334 -3.0937 -2.5599 1.0088
## shrub_cover-Canis_latrans -0.2381 0.2155 -0.6713 -0.2341 0.1694 1.0053
## shrub_cover-Procyon_lotor 0.2268 0.1722 -0.1095 0.2281 0.5523 1.0160
## shrub_cover-Dasypus_novemcinctus 0.8238 0.3174 0.2312 0.8119 1.4626 1.0020
## shrub_cover-Sylvilagus_floridanus 0.2429 0.4008 -0.4833 0.2259 1.0566 1.0035
## veg_height-Canis_latrans -0.6149 0.1934 -1.0248 -0.6035 -0.2534 1.0057
## veg_height-Procyon_lotor 0.3562 0.1280 0.1148 0.3587 0.6049 1.0007
## veg_height-Dasypus_novemcinctus 0.2528 0.1390 -0.0093 0.2478 0.5324 1.0015
## veg_height-Sylvilagus_floridanus 0.1888 0.2636 -0.3021 0.1847 0.7169 1.0025
## ESS
## (Intercept)-Canis_latrans 722
## (Intercept)-Procyon_lotor 846
## (Intercept)-Dasypus_novemcinctus 1309
## (Intercept)-Sylvilagus_floridanus 462
## shrub_cover-Canis_latrans 885
## shrub_cover-Procyon_lotor 995
## shrub_cover-Dasypus_novemcinctus 1061
## shrub_cover-Sylvilagus_floridanus 428
## veg_height-Canis_latrans 765
## veg_height-Procyon_lotor 1430
## veg_height-Dasypus_novemcinctus 1652
## veg_height-Sylvilagus_floridanus 461
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3655
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2253 0.8855 -2.9595 -1.2352 0.5597 1.0264 572
## Cogon_Patch_Size -0.1219 0.8984 -1.8889 -0.1441 1.7962 1.0174 893
## Veg_shannon_index 1.0249 0.6555 -0.2722 1.0155 2.3050 1.0014 461
## total_shrub_cover -0.4004 0.8128 -2.0275 -0.4060 1.1918 1.0525 544
## Avg_Cogongrass_Cover 0.2649 1.1742 -2.0210 0.2558 2.7357 1.0040 411
## Tree_Density -2.2730 1.2729 -4.4249 -2.3903 0.7145 1.0037 542
## Avg_Canopy_Cover 1.2718 1.0940 -1.0978 1.3079 3.3868 1.0044 2542
## I(Avg_Cogongrass_Cover^2) 1.5183 0.8403 -0.1619 1.5222 3.2324 1.0114 428
## avg_veg_height 0.0341 0.7008 -1.2636 0.0147 1.4354 1.0044 564
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6732 8.7735 0.0608 0.7518 17.6185 1.2017 206
## Cogon_Patch_Size 5.0182 15.3326 0.0711 1.2644 32.9845 1.1724 276
## Veg_shannon_index 1.2509 4.7477 0.0440 0.4167 6.7485 1.0139 1522
## total_shrub_cover 3.4188 11.2577 0.0726 1.0555 19.4746 1.1787 349
## Avg_Cogongrass_Cover 6.2159 20.0725 0.0731 1.3704 43.3364 1.0816 172
## Tree_Density 17.4968 62.2578 0.0645 2.3315 134.9707 1.1698 163
## Avg_Canopy_Cover 16.3830 44.5121 0.2242 5.2146 103.2563 1.0146 292
## I(Avg_Cogongrass_Cover^2) 3.5930 16.4558 0.0662 0.8114 20.7825 1.1365 1123
## avg_veg_height 1.1562 5.0803 0.0507 0.3626 6.0980 1.1204 2552
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9959 4.9246 0.061 0.6813 11.4 1.346 97
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.3081 0.4937 -3.1377 -2.3546 -1.1521 1.0036 2293
## shrub_cover 0.3375 0.4427 -0.5760 0.3401 1.2197 1.0005 2150
## veg_height 0.0527 0.3645 -0.6758 0.0565 0.7895 1.0015 2408
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0057 2.0560 0.1056 0.5066 4.8139 1.0629 2497
## shrub_cover 0.9506 2.0210 0.0861 0.4596 4.4423 1.0232 2325
## veg_height 0.6325 1.7632 0.0681 0.3172 2.9936 1.1614 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.0775 1.0979 -3.2145 -1.0862
## (Intercept)-Procyon_lotor -0.8057 1.0195 -2.8238 -0.8125
## (Intercept)-Dasypus_novemcinctus -2.0599 1.1898 -4.9262 -1.9092
## (Intercept)-Sylvilagus_floridanus -1.8095 1.2157 -4.4280 -1.6943
## Cogon_Patch_Size-Canis_latrans 1.2919 1.8329 -0.9795 0.9171
## Cogon_Patch_Size-Procyon_lotor -0.6372 0.9061 -2.4984 -0.6009
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1483 0.9654 -1.9373 -0.1857
## Cogon_Patch_Size-Sylvilagus_floridanus -1.1664 1.6413 -5.2423 -0.9042
## Veg_shannon_index-Canis_latrans 1.4113 0.8182 -0.0234 1.3321
## Veg_shannon_index-Procyon_lotor 1.2331 0.7076 0.0630 1.1604
## Veg_shannon_index-Dasypus_novemcinctus 0.6719 0.6718 -0.6272 0.6722
## Veg_shannon_index-Sylvilagus_floridanus 1.1838 0.8891 -0.3346 1.1066
## total_shrub_cover-Canis_latrans 0.5682 1.1769 -1.0325 0.3449
## total_shrub_cover-Procyon_lotor -1.2712 0.8198 -3.1432 -1.1936
## total_shrub_cover-Dasypus_novemcinctus -0.3387 1.0096 -2.8252 -0.2171
## total_shrub_cover-Sylvilagus_floridanus -0.8266 1.2711 -3.9397 -0.6649
## Avg_Cogongrass_Cover-Canis_latrans 0.6103 1.6739 -2.3391 0.4921
## Avg_Cogongrass_Cover-Procyon_lotor 0.0947 1.5133 -3.0073 0.1213
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4798 1.9691 -1.4001 1.2146
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.8074 1.8947 -5.1563 -0.5757
## Tree_Density-Canis_latrans -3.8785 2.2207 -9.8826 -3.3789
## Tree_Density-Procyon_lotor -2.4267 1.2031 -4.9095 -2.3870
## Tree_Density-Dasypus_novemcinctus -5.0580 3.2795 -14.2555 -4.1269
## Tree_Density-Sylvilagus_floridanus -3.1618 2.4007 -8.7440 -2.8927
## Avg_Canopy_Cover-Canis_latrans -0.0608 0.7521 -1.6230 -0.0519
## Avg_Canopy_Cover-Procyon_lotor 1.6160 0.9458 0.0324 1.5308
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5990 1.3104 0.7850 2.3473
## Avg_Canopy_Cover-Sylvilagus_floridanus 5.1613 3.3149 1.1060 4.4191
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4135 1.5230 0.4995 2.1011
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3392 1.2502 0.4973 2.1413
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3578 0.8592 -0.1379 1.3043
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1609 1.0195 -0.6410 1.1057
## avg_veg_height-Canis_latrans -0.1822 0.7755 -1.7286 -0.1669
## avg_veg_height-Procyon_lotor 0.1332 0.7606 -1.3418 0.1144
## avg_veg_height-Dasypus_novemcinctus 0.2769 0.8217 -1.1772 0.2269
## avg_veg_height-Sylvilagus_floridanus -0.1023 0.9276 -2.0125 -0.1169
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1638 1.0384 450
## (Intercept)-Procyon_lotor 1.1711 1.0323 434
## (Intercept)-Dasypus_novemcinctus -0.2306 1.0236 230
## (Intercept)-Sylvilagus_floridanus 0.2618 1.0188 231
## Cogon_Patch_Size-Canis_latrans 5.7515 1.2131 207
## Cogon_Patch_Size-Procyon_lotor 1.1163 1.0263 472
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8564 1.0897 437
## Cogon_Patch_Size-Sylvilagus_floridanus 1.2759 1.0481 274
## Veg_shannon_index-Canis_latrans 3.2251 1.0131 505
## Veg_shannon_index-Procyon_lotor 2.7737 1.0046 327
## Veg_shannon_index-Dasypus_novemcinctus 2.0274 1.0027 528
## Veg_shannon_index-Sylvilagus_floridanus 3.1054 1.0308 282
## total_shrub_cover-Canis_latrans 3.6789 1.1274 209
## total_shrub_cover-Procyon_lotor 0.1619 1.0174 440
## total_shrub_cover-Dasypus_novemcinctus 1.2662 1.1523 229
## total_shrub_cover-Sylvilagus_floridanus 1.2681 1.0134 376
## Avg_Cogongrass_Cover-Canis_latrans 4.2212 1.0340 340
## Avg_Cogongrass_Cover-Procyon_lotor 3.0294 1.0038 424
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 6.2897 1.0315 195
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.2779 1.0037 348
## Tree_Density-Canis_latrans -1.0719 1.0134 168
## Tree_Density-Procyon_lotor -0.1831 1.0037 492
## Tree_Density-Dasypus_novemcinctus -1.6131 1.0334 79
## Tree_Density-Sylvilagus_floridanus 0.4090 1.1637 128
## Avg_Canopy_Cover-Canis_latrans 1.4185 1.0165 520
## Avg_Canopy_Cover-Procyon_lotor 3.7284 1.0034 434
## Avg_Canopy_Cover-Dasypus_novemcinctus 6.0104 1.0421 109
## Avg_Canopy_Cover-Sylvilagus_floridanus 12.7918 1.0148 99
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.3513 1.1267 214
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.3157 1.0069 253
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.2786 1.0077 410
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.4212 1.0639 346
## avg_veg_height-Canis_latrans 1.3328 1.0125 547
## avg_veg_height-Procyon_lotor 1.6541 1.0210 665
## avg_veg_height-Dasypus_novemcinctus 1.9907 1.0130 500
## avg_veg_height-Sylvilagus_floridanus 1.7446 1.0109 702
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7208 0.1814 -3.0882 -2.7164 -2.3736 1.0106
## (Intercept)-Procyon_lotor -2.3046 0.1428 -2.5880 -2.3050 -2.0362 1.0052
## (Intercept)-Dasypus_novemcinctus -1.7971 0.1676 -2.1327 -1.7936 -1.4763 1.0325
## (Intercept)-Sylvilagus_floridanus -3.1223 0.2693 -3.7091 -3.1090 -2.6276 1.0194
## shrub_cover-Canis_latrans -0.3266 0.2424 -0.7781 -0.3393 0.1546 1.0334
## shrub_cover-Procyon_lotor 0.2782 0.1622 -0.0472 0.2809 0.5895 1.0087
## shrub_cover-Dasypus_novemcinctus 0.9732 0.3341 0.3375 0.9713 1.6193 1.0550
## shrub_cover-Sylvilagus_floridanus 0.5102 0.3779 -0.2203 0.5062 1.2691 1.0001
## veg_height-Canis_latrans -0.5897 0.1886 -0.9651 -0.5856 -0.2228 1.0070
## veg_height-Procyon_lotor 0.3644 0.1219 0.1273 0.3665 0.6009 1.0011
## veg_height-Dasypus_novemcinctus 0.2706 0.1403 -0.0018 0.2717 0.5426 1.0149
## veg_height-Sylvilagus_floridanus 0.1752 0.2639 -0.3322 0.1717 0.7005 1.0022
## ESS
## (Intercept)-Canis_latrans 884
## (Intercept)-Procyon_lotor 1155
## (Intercept)-Dasypus_novemcinctus 805
## (Intercept)-Sylvilagus_floridanus 589
## shrub_cover-Canis_latrans 523
## shrub_cover-Procyon_lotor 1467
## shrub_cover-Dasypus_novemcinctus 440
## shrub_cover-Sylvilagus_floridanus 523
## veg_height-Canis_latrans 804
## veg_height-Procyon_lotor 1458
## veg_height-Dasypus_novemcinctus 819
## veg_height-Sylvilagus_floridanus 617
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T25<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4253
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0268 0.4464 -0.8632 0.0206 0.9235 1.005 1609
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8955 2.6778 0.058 0.4051 3.9701 1.0576 1532
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1019 0.5312 -3.0344 -2.1481 -0.8728 1.0040 2319
## week 0.1686 0.3074 -0.4309 0.1697 0.8080 1.0000 1891
## I(week^2) -0.1456 0.2317 -0.6094 -0.1391 0.2973 1.0039 2117
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1406 1.9856 0.1101 0.5697 6.1279 1.0006 1699
## week 0.3901 1.8768 0.0331 0.1701 1.8499 1.2010 3000
## I(week^2) 0.1971 0.5960 0.0247 0.0969 0.9160 1.0713 2544
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2444 0.3627 -0.4367 0.2275 1.0035 1.0001
## (Intercept)-Procyon_lotor 0.5355 0.3756 -0.1307 0.5115 1.3339 0.9999
## (Intercept)-Dasypus_novemcinctus -0.4668 0.3597 -1.1874 -0.4614 0.1897 1.0021
## (Intercept)-Sylvilagus_floridanus -0.2410 0.4681 -1.0953 -0.2583 0.7123 1.0127
## ESS
## (Intercept)-Canis_latrans 2056
## (Intercept)-Procyon_lotor 2226
## (Intercept)-Dasypus_novemcinctus 2260
## (Intercept)-Sylvilagus_floridanus 738
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4376 0.1879 -2.8318 -2.4316 -2.0861 1.0003
## (Intercept)-Procyon_lotor -2.1703 0.1524 -2.4668 -2.1705 -1.8777 1.0067
## (Intercept)-Dasypus_novemcinctus -1.4914 0.1638 -1.8235 -1.4855 -1.1810 1.0056
## (Intercept)-Sylvilagus_floridanus -3.0178 0.3418 -3.7509 -2.9960 -2.4126 1.0225
## week-Canis_latrans 0.4573 0.2716 -0.0303 0.4416 1.0398 1.0101
## week-Procyon_lotor 0.1470 0.2109 -0.2674 0.1480 0.5556 1.0021
## week-Dasypus_novemcinctus 0.0430 0.2186 -0.3936 0.0439 0.4659 1.0004
## week-Sylvilagus_floridanus 0.0274 0.3152 -0.5919 0.0354 0.6303 1.0090
## I(week^2)-Canis_latrans -0.1959 0.1126 -0.4271 -0.1939 0.0142 1.0006
## I(week^2)-Procyon_lotor -0.1056 0.0908 -0.2869 -0.1047 0.0690 1.0040
## I(week^2)-Dasypus_novemcinctus -0.1422 0.1058 -0.3563 -0.1399 0.0622 1.0017
## I(week^2)-Sylvilagus_floridanus -0.1444 0.1598 -0.4771 -0.1393 0.1643 1.0022
## ESS
## (Intercept)-Canis_latrans 1330
## (Intercept)-Procyon_lotor 1586
## (Intercept)-Dasypus_novemcinctus 2078
## (Intercept)-Sylvilagus_floridanus 435
## week-Canis_latrans 1152
## week-Procyon_lotor 1620
## week-Dasypus_novemcinctus 1819
## week-Sylvilagus_floridanus 1197
## I(week^2)-Canis_latrans 1144
## I(week^2)-Procyon_lotor 1602
## I(week^2)-Dasypus_novemcinctus 1666
## I(week^2)-Sylvilagus_floridanus 681
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4333
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.2844 0.8054 -1.9292 -0.2686 1.3587 1.0066 656
## Cogon_Patch_Size -0.6804 0.7685 -2.2203 -0.6945 0.9762 1.0129 867
## Veg_shannon_index 1.1180 0.6082 -0.0349 1.1187 2.3578 1.0060 365
## total_shrub_cover -0.0565 0.6301 -1.2944 -0.0562 1.2587 1.0148 1300
## Avg_Cogongrass_Cover 1.9266 0.9819 -0.0664 1.9633 3.7949 1.0231 676
## Tree_Density -2.0234 1.0009 -3.8826 -2.0754 0.2815 1.0351 956
## Avg_Canopy_Cover 1.3377 0.9867 -0.8467 1.3707 3.2753 1.0014 2481
## avg_veg_height -0.4278 0.6281 -1.6134 -0.4382 0.8550 1.0045 768
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5560 8.3931 0.0702 0.9553 14.2237 1.1504 1979
## Cogon_Patch_Size 3.3682 13.1197 0.0637 0.9299 18.4436 1.0004 1202
## Veg_shannon_index 1.0746 4.1907 0.0456 0.3432 6.4568 1.1022 1383
## total_shrub_cover 1.4328 3.3184 0.0539 0.5512 8.4631 1.0193 989
## Avg_Cogongrass_Cover 2.9811 8.1335 0.0584 0.8576 18.1980 1.0414 1006
## Tree_Density 6.0720 24.2904 0.0697 1.0393 43.9426 1.2466 487
## Avg_Canopy_Cover 8.7337 23.6779 0.1474 2.6586 54.1213 1.0812 439
## avg_veg_height 0.9539 2.8555 0.0488 0.3410 5.2971 1.1786 2262
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.7537 2.4782 0.0568 0.9148 8.1607 1.0107 204
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1082 0.5225 -2.9748 -2.1652 -0.8248 1.0066 2665
## week 0.1535 0.3062 -0.4955 0.1589 0.7438 1.0068 1900
## I(week^2) -0.1452 0.2150 -0.5689 -0.1430 0.2529 1.0027 2266
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2115 2.8074 0.1263 0.6263 5.7676 1.0603 2582
## week 0.3584 0.7799 0.0356 0.1687 1.9729 1.0113 1812
## I(week^2) 0.1826 0.3731 0.0254 0.0949 0.8678 1.0755 1851
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2328 0.9121 -1.5140 0.1960
## (Intercept)-Procyon_lotor 0.3914 0.8870 -1.4679 0.3877
## (Intercept)-Dasypus_novemcinctus -1.0716 0.8631 -3.0304 -1.0001
## (Intercept)-Sylvilagus_floridanus -0.7920 0.9807 -2.8934 -0.7309
## Cogon_Patch_Size-Canis_latrans 0.3300 1.1230 -1.3129 0.1433
## Cogon_Patch_Size-Procyon_lotor -1.0724 0.7117 -2.6336 -1.0279
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8966 0.6570 -2.2770 -0.8554
## Cogon_Patch_Size-Sylvilagus_floridanus -1.6234 1.3358 -5.0036 -1.3994
## Veg_shannon_index-Canis_latrans 1.4325 0.7377 0.2519 1.3538
## Veg_shannon_index-Procyon_lotor 1.3010 0.6432 0.1528 1.2703
## Veg_shannon_index-Dasypus_novemcinctus 0.8062 0.5633 -0.2784 0.7886
## Veg_shannon_index-Sylvilagus_floridanus 1.2220 0.7857 -0.1992 1.1769
## total_shrub_cover-Canis_latrans 0.2829 0.6808 -0.8700 0.2194
## total_shrub_cover-Procyon_lotor -0.7362 0.6335 -2.1372 -0.7009
## total_shrub_cover-Dasypus_novemcinctus 0.1851 0.5338 -0.8105 0.1636
## total_shrub_cover-Sylvilagus_floridanus 0.0175 0.9203 -1.8065 -0.0132
## Avg_Cogongrass_Cover-Canis_latrans 2.5251 1.0859 0.6869 2.4110
## Avg_Cogongrass_Cover-Procyon_lotor 2.3686 1.0035 0.5645 2.3281
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7987 1.1279 0.9600 2.6730
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.3509 1.1899 -1.1819 1.3989
## Tree_Density-Canis_latrans -2.8383 1.4689 -6.6131 -2.5731
## Tree_Density-Procyon_lotor -1.6520 0.8654 -3.3117 -1.6674
## Tree_Density-Dasypus_novemcinctus -3.4289 1.7321 -8.0445 -3.0192
## Tree_Density-Sylvilagus_floridanus -2.5401 1.5042 -5.9366 -2.3514
## Avg_Canopy_Cover-Canis_latrans 0.1551 0.7136 -1.2937 0.1668
## Avg_Canopy_Cover-Procyon_lotor 1.7029 0.7667 0.3891 1.6304
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0615 0.7673 0.7969 1.9848
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9927 2.4722 0.9969 3.3892
## avg_veg_height-Canis_latrans -0.7175 0.6637 -2.0864 -0.7012
## avg_veg_height-Procyon_lotor -0.2892 0.6240 -1.4663 -0.3086
## avg_veg_height-Dasypus_novemcinctus -0.2269 0.6387 -1.4640 -0.2331
## avg_veg_height-Sylvilagus_floridanus -0.6321 0.7857 -2.3327 -0.5912
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1833 1.0323 403
## (Intercept)-Procyon_lotor 2.1241 1.0164 317
## (Intercept)-Dasypus_novemcinctus 0.4433 1.0431 417
## (Intercept)-Sylvilagus_floridanus 1.0969 1.0094 386
## Cogon_Patch_Size-Canis_latrans 3.1641 1.0011 533
## Cogon_Patch_Size-Procyon_lotor 0.2190 1.0279 309
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3175 1.0006 691
## Cogon_Patch_Size-Sylvilagus_floridanus 0.2854 1.0137 355
## Veg_shannon_index-Canis_latrans 3.1660 1.0131 363
## Veg_shannon_index-Procyon_lotor 2.6698 1.0084 270
## Veg_shannon_index-Dasypus_novemcinctus 1.9383 1.0028 696
## Veg_shannon_index-Sylvilagus_floridanus 2.9723 1.0209 418
## total_shrub_cover-Canis_latrans 1.9140 1.0074 727
## total_shrub_cover-Procyon_lotor 0.3821 1.0100 933
## total_shrub_cover-Dasypus_novemcinctus 1.3196 1.0016 826
## total_shrub_cover-Sylvilagus_floridanus 1.9775 1.0100 482
## Avg_Cogongrass_Cover-Canis_latrans 4.9246 1.0098 394
## Avg_Cogongrass_Cover-Procyon_lotor 4.5310 1.0186 596
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.4060 1.0090 411
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5915 1.0259 578
## Tree_Density-Canis_latrans -0.8843 1.0277 266
## Tree_Density-Procyon_lotor 0.0488 1.0165 713
## Tree_Density-Dasypus_novemcinctus -1.3013 1.0292 248
## Tree_Density-Sylvilagus_floridanus -0.1496 1.0442 287
## Avg_Canopy_Cover-Canis_latrans 1.5186 1.0040 797
## Avg_Canopy_Cover-Procyon_lotor 3.3769 1.0023 644
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.8037 1.0018 322
## Avg_Canopy_Cover-Sylvilagus_floridanus 10.2093 1.0275 177
## avg_veg_height-Canis_latrans 0.5676 1.0032 741
## avg_veg_height-Procyon_lotor 0.9653 1.0061 946
## avg_veg_height-Dasypus_novemcinctus 1.0746 1.0020 963
## avg_veg_height-Sylvilagus_floridanus 0.7779 1.0091 797
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4702 0.1877 -2.8477 -2.4713 -2.1049 1.0053
## (Intercept)-Procyon_lotor -2.1692 0.1498 -2.4734 -2.1646 -1.8791 1.0037
## (Intercept)-Dasypus_novemcinctus -1.4860 0.1616 -1.8079 -1.4840 -1.1687 1.0001
## (Intercept)-Sylvilagus_floridanus -3.0655 0.3124 -3.7180 -3.0580 -2.4863 1.0017
## week-Canis_latrans 0.4404 0.2573 -0.0435 0.4318 0.9793 1.0117
## week-Procyon_lotor 0.1472 0.2030 -0.2351 0.1450 0.5475 1.0000
## week-Dasypus_novemcinctus 0.0439 0.2147 -0.3826 0.0462 0.4680 1.0016
## week-Sylvilagus_floridanus 0.0070 0.3156 -0.6071 0.0060 0.6341 1.0102
## I(week^2)-Canis_latrans -0.1937 0.1101 -0.4138 -0.1899 0.0083 1.0054
## I(week^2)-Procyon_lotor -0.1050 0.0896 -0.2796 -0.1036 0.0657 1.0009
## I(week^2)-Dasypus_novemcinctus -0.1414 0.1054 -0.3534 -0.1394 0.0599 1.0006
## I(week^2)-Sylvilagus_floridanus -0.1423 0.1601 -0.4663 -0.1362 0.1529 1.0149
## ESS
## (Intercept)-Canis_latrans 956
## (Intercept)-Procyon_lotor 1355
## (Intercept)-Dasypus_novemcinctus 2348
## (Intercept)-Sylvilagus_floridanus 515
## week-Canis_latrans 1112
## week-Procyon_lotor 1870
## week-Dasypus_novemcinctus 2309
## week-Sylvilagus_floridanus 1134
## I(week^2)-Canis_latrans 1664
## I(week^2)-Procyon_lotor 1747
## I(week^2)-Dasypus_novemcinctus 1873
## I(week^2)-Sylvilagus_floridanus 660
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4313
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0088 0.5346 -1.0370 -0.0015 1.1299 1.0043 889
## Avg_Cogongrass_Cover 0.0693 0.5222 -0.9769 0.0790 1.0453 1.0027 1410
## total_shrub_cover -0.1967 0.4887 -1.1699 -0.2087 0.7805 1.0063 2178
## avg_veg_height 0.0470 0.4605 -0.8405 0.0596 0.9284 1.0035 1295
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9617 1.7857 0.0554 0.4491 5.2258 1.0073 1735
## Avg_Cogongrass_Cover 0.8620 3.2320 0.0461 0.3235 4.3293 1.1679 2663
## total_shrub_cover 1.0448 3.4485 0.0505 0.4015 5.8867 1.0484 2708
## avg_veg_height 0.5545 1.4396 0.0381 0.2424 2.8716 1.1155 1894
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5752 0.7425 0.0497 0.3249 2.7665 1.0044 403
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0932 0.5562 -3.0390 -2.1600 -0.7317 1.0090 2519
## week 0.1514 0.3213 -0.4846 0.1567 0.7687 1.0014 1686
## I(week^2) -0.1364 0.2156 -0.5395 -0.1377 0.2921 1.0026 2695
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3689 3.7666 0.1262 0.6424 6.9449 1.1815 2516
## week 0.3668 0.9520 0.0371 0.1739 1.7132 1.0366 2545
## I(week^2) 0.1818 0.4483 0.0227 0.0931 0.8150 1.0014 2500
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.2418 0.5319 -0.8021 0.2191
## (Intercept)-Procyon_lotor 0.4610 0.5706 -0.6573 0.4525
## (Intercept)-Dasypus_novemcinctus -0.4669 0.4939 -1.4829 -0.4496
## (Intercept)-Sylvilagus_floridanus -0.2069 0.6184 -1.4038 -0.2240
## Avg_Cogongrass_Cover-Canis_latrans 0.4229 0.5186 -0.4907 0.3912
## Avg_Cogongrass_Cover-Procyon_lotor 0.0451 0.5137 -0.9789 0.0550
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2214 0.4444 -0.6245 0.2162
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3487 0.5954 -1.6500 -0.3086
## total_shrub_cover-Canis_latrans 0.2119 0.4700 -0.5806 0.1823
## total_shrub_cover-Procyon_lotor -0.8129 0.5241 -2.0083 -0.7589
## total_shrub_cover-Dasypus_novemcinctus -0.0008 0.3832 -0.7309 -0.0036
## total_shrub_cover-Sylvilagus_floridanus -0.2802 0.5291 -1.3340 -0.2716
## avg_veg_height-Canis_latrans -0.0526 0.4709 -1.0192 -0.0425
## avg_veg_height-Procyon_lotor 0.1617 0.4864 -0.7773 0.1525
## avg_veg_height-Dasypus_novemcinctus 0.2521 0.4472 -0.5882 0.2295
## avg_veg_height-Sylvilagus_floridanus -0.0979 0.5036 -1.1455 -0.0777
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3447 0.9998 810
## (Intercept)-Procyon_lotor 1.6248 0.9999 657
## (Intercept)-Dasypus_novemcinctus 0.4531 1.0187 995
## (Intercept)-Sylvilagus_floridanus 1.0889 1.0059 587
## Avg_Cogongrass_Cover-Canis_latrans 1.5313 1.0033 1416
## Avg_Cogongrass_Cover-Procyon_lotor 1.0715 1.0016 1335
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0947 1.0014 1502
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6924 1.0066 1053
## total_shrub_cover-Canis_latrans 1.2658 1.0088 1443
## total_shrub_cover-Procyon_lotor 0.0775 1.0100 1079
## total_shrub_cover-Dasypus_novemcinctus 0.7687 1.0053 2152
## total_shrub_cover-Sylvilagus_floridanus 0.7865 1.0031 1207
## avg_veg_height-Canis_latrans 0.8659 1.0055 1238
## avg_veg_height-Procyon_lotor 1.1445 1.0037 1167
## avg_veg_height-Dasypus_novemcinctus 1.1661 1.0051 1461
## avg_veg_height-Sylvilagus_floridanus 0.8399 1.0056 1346
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4560 0.1914 -2.8523 -2.4524 -2.0939 1.0010
## (Intercept)-Procyon_lotor -2.1816 0.1527 -2.4780 -2.1825 -1.8917 1.0219
## (Intercept)-Dasypus_novemcinctus -1.4814 0.1589 -1.7911 -1.4774 -1.1678 1.0015
## (Intercept)-Sylvilagus_floridanus -3.0995 0.3534 -3.8303 -3.0745 -2.4675 1.0157
## week-Canis_latrans 0.4508 0.2593 -0.0232 0.4361 0.9873 1.0038
## week-Procyon_lotor 0.1351 0.2002 -0.2485 0.1366 0.5320 1.0035
## week-Dasypus_novemcinctus 0.0453 0.2216 -0.4013 0.0487 0.4755 1.0047
## week-Sylvilagus_floridanus 0.0123 0.3277 -0.6597 0.0242 0.6263 1.0140
## I(week^2)-Canis_latrans -0.1939 0.1098 -0.4104 -0.1941 0.0153 1.0076
## I(week^2)-Procyon_lotor -0.1017 0.0919 -0.2889 -0.1005 0.0723 1.0114
## I(week^2)-Dasypus_novemcinctus -0.1411 0.1009 -0.3449 -0.1430 0.0489 1.0013
## I(week^2)-Sylvilagus_floridanus -0.1390 0.1567 -0.4663 -0.1334 0.1550 1.0097
## ESS
## (Intercept)-Canis_latrans 1021
## (Intercept)-Procyon_lotor 1416
## (Intercept)-Dasypus_novemcinctus 2133
## (Intercept)-Sylvilagus_floridanus 410
## week-Canis_latrans 1204
## week-Procyon_lotor 1600
## week-Dasypus_novemcinctus 2164
## week-Sylvilagus_floridanus 1036
## I(week^2)-Canis_latrans 1164
## I(week^2)-Procyon_lotor 1525
## I(week^2)-Dasypus_novemcinctus 1862
## I(week^2)-Sylvilagus_floridanus 862
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4122
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1835 0.5468 -1.2824 -0.1882 0.8825 1.0008 1329
## Tree_Density -0.9696 0.6065 -2.2509 -0.9283 0.1304 1.0149 1024
## Avg_Canopy_Cover 0.7999 0.6784 -0.6738 0.7943 2.2195 1.0061 2663
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4263 10.1678 0.0585 0.5322 6.7151 1.2882 3000
## Tree_Density 1.3529 4.5154 0.0469 0.4388 8.4407 1.0777 1858
## Avg_Canopy_Cover 3.0208 9.8024 0.1004 1.1193 16.8794 1.0357 2005
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5091 0.6716 0.0442 0.2945 2.375 1.0154 491
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0960 0.4974 -2.9697 -2.1408 -0.9972 1.0013 2840
## week 0.1625 0.3136 -0.4724 0.1657 0.7895 1.0093 1985
## I(week^2) -0.1384 0.2198 -0.5896 -0.1367 0.2899 1.0018 2317
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1331 2.2122 0.1147 0.5715 5.7558 1.0155 2527
## week 0.3542 0.9224 0.0345 0.1678 1.7461 1.0149 2620
## I(week^2) 0.1887 0.5389 0.0235 0.0929 0.9067 1.0088 2644
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1003 0.5422 -0.9186 0.0820 1.2028
## (Intercept)-Procyon_lotor 0.3848 0.5576 -0.6858 0.3814 1.4940
## (Intercept)-Dasypus_novemcinctus -0.7394 0.5573 -1.8953 -0.7040 0.2732
## (Intercept)-Sylvilagus_floridanus -0.5147 0.6409 -1.8323 -0.4806 0.6789
## Tree_Density-Canis_latrans -1.0308 0.5803 -2.3102 -0.9855 -0.0444
## Tree_Density-Procyon_lotor -0.5500 0.4606 -1.4723 -0.5401 0.3501
## Tree_Density-Dasypus_novemcinctus -1.4690 0.8928 -3.7383 -1.3007 -0.2196
## Tree_Density-Sylvilagus_floridanus -1.2368 0.8003 -3.1478 -1.1140 -0.0717
## Avg_Canopy_Cover-Canis_latrans -0.1401 0.4767 -1.1288 -0.1377 0.7737
## Avg_Canopy_Cover-Procyon_lotor 0.9488 0.5029 0.0543 0.9037 2.0562
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9653 0.4667 0.1107 0.9321 1.9751
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.0965 1.1582 0.5273 1.8511 5.0282
## Rhat ESS
## (Intercept)-Canis_latrans 1.0045 1072
## (Intercept)-Procyon_lotor 1.0021 862
## (Intercept)-Dasypus_novemcinctus 1.0047 849
## (Intercept)-Sylvilagus_floridanus 1.0032 849
## Tree_Density-Canis_latrans 1.0056 1295
## Tree_Density-Procyon_lotor 1.0252 1502
## Tree_Density-Dasypus_novemcinctus 1.0023 589
## Tree_Density-Sylvilagus_floridanus 1.0059 809
## Avg_Canopy_Cover-Canis_latrans 1.0004 1308
## Avg_Canopy_Cover-Procyon_lotor 1.0015 1435
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9999 2103
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1720 420
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4502 0.1888 -2.8314 -2.4438 -2.0897 1.0072
## (Intercept)-Procyon_lotor -2.1706 0.1467 -2.4564 -2.1694 -1.8912 1.0133
## (Intercept)-Dasypus_novemcinctus -1.4872 0.1642 -1.8256 -1.4829 -1.1735 1.0008
## (Intercept)-Sylvilagus_floridanus -2.9987 0.3002 -3.6136 -2.9881 -2.4335 1.0153
## week-Canis_latrans 0.4428 0.2594 -0.0450 0.4396 0.9554 1.0003
## week-Procyon_lotor 0.1395 0.2079 -0.2513 0.1329 0.5541 1.0058
## week-Dasypus_novemcinctus 0.0434 0.2230 -0.3970 0.0434 0.4884 1.0039
## week-Sylvilagus_floridanus 0.0255 0.3219 -0.6223 0.0340 0.6362 1.0147
## I(week^2)-Canis_latrans -0.1911 0.1100 -0.4131 -0.1913 0.0199 1.0015
## I(week^2)-Procyon_lotor -0.1056 0.0920 -0.2881 -0.1061 0.0721 0.9999
## I(week^2)-Dasypus_novemcinctus -0.1421 0.1057 -0.3512 -0.1427 0.0612 1.0021
## I(week^2)-Sylvilagus_floridanus -0.1350 0.1587 -0.4640 -0.1285 0.1681 1.0008
## ESS
## (Intercept)-Canis_latrans 1027
## (Intercept)-Procyon_lotor 1640
## (Intercept)-Dasypus_novemcinctus 2215
## (Intercept)-Sylvilagus_floridanus 623
## week-Canis_latrans 1292
## week-Procyon_lotor 1661
## week-Dasypus_novemcinctus 1820
## week-Sylvilagus_floridanus 1149
## I(week^2)-Canis_latrans 1290
## I(week^2)-Procyon_lotor 1499
## I(week^2)-Dasypus_novemcinctus 1457
## I(week^2)-Sylvilagus_floridanus 746
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4352
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0201 0.5813 -1.1560 0.0239 1.1654 1.0053 1122
## Cogon_Patch_Size -0.0564 0.6710 -1.4296 -0.0463 1.3051 1.0053 1824
## Avg_Cogongrass_Cover 0.1573 0.4498 -0.7473 0.1574 1.0396 1.0054 1425
## total_shrub_cover -0.2477 0.5267 -1.3495 -0.2469 0.8177 1.0115 1392
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3170 2.6044 0.0538 0.5530 8.0881 1.0136 1249
## Cogon_Patch_Size 3.0480 12.7335 0.0658 0.7879 18.0575 1.1253 334
## Avg_Cogongrass_Cover 0.7082 2.2558 0.0440 0.2637 3.6629 1.0765 1800
## total_shrub_cover 1.1664 3.6756 0.0512 0.4287 7.0389 1.0993 758
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6275 0.8241 0.0475 0.3549 2.7446 1.0142 500
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0859 0.5466 -3.0113 -2.1429 -0.7451 1.0053 2535
## week 0.1711 0.3141 -0.4418 0.1727 0.8082 1.0091 1995
## I(week^2) -0.1473 0.2165 -0.5658 -0.1461 0.2677 1.0065 2661
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3705 2.9458 0.1247 0.6584 7.2404 1.0495 2057
## week 0.3405 0.6679 0.0341 0.1699 1.6627 1.0597 2369
## I(week^2) 0.1944 1.1513 0.0240 0.0916 0.8246 1.3143 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3598 0.5692 -0.6796 0.3242
## (Intercept)-Procyon_lotor 0.5186 0.5697 -0.5848 0.5087
## (Intercept)-Dasypus_novemcinctus -0.4787 0.5147 -1.5647 -0.4575
## (Intercept)-Sylvilagus_floridanus -0.2546 0.7620 -1.7034 -0.2693
## Cogon_Patch_Size-Canis_latrans 0.9458 0.9189 -0.2518 0.7566
## Cogon_Patch_Size-Procyon_lotor -0.1596 0.4855 -1.1101 -0.1580
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1144 0.4310 -0.9883 -0.1110
## Cogon_Patch_Size-Sylvilagus_floridanus -1.0780 1.5264 -4.5092 -0.7401
## Avg_Cogongrass_Cover-Canis_latrans 0.2393 0.4360 -0.5965 0.2363
## Avg_Cogongrass_Cover-Procyon_lotor 0.1896 0.4741 -0.6941 0.1706
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3764 0.4056 -0.3849 0.3681
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1626 0.5611 -1.3970 -0.1394
## total_shrub_cover-Canis_latrans 0.1382 0.4987 -0.7486 0.1144
## total_shrub_cover-Procyon_lotor -0.8436 0.5473 -2.1139 -0.7824
## total_shrub_cover-Dasypus_novemcinctus -0.0228 0.3820 -0.7510 -0.0257
## total_shrub_cover-Sylvilagus_floridanus -0.3435 0.8135 -2.0874 -0.2639
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.5766 1.0139 946
## (Intercept)-Procyon_lotor 1.6664 1.0028 1011
## (Intercept)-Dasypus_novemcinctus 0.5160 1.0037 1129
## (Intercept)-Sylvilagus_floridanus 1.1673 1.0817 315
## Cogon_Patch_Size-Canis_latrans 3.2974 1.0138 662
## Cogon_Patch_Size-Procyon_lotor 0.8182 1.0013 1548
## Cogon_Patch_Size-Dasypus_novemcinctus 0.7500 1.0023 2319
## Cogon_Patch_Size-Sylvilagus_floridanus 0.4661 1.2248 122
## Avg_Cogongrass_Cover-Canis_latrans 1.1635 1.0008 1559
## Avg_Cogongrass_Cover-Procyon_lotor 1.1605 1.0030 1363
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2122 1.0063 1634
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8653 1.0088 1066
## total_shrub_cover-Canis_latrans 1.1836 1.0205 1276
## total_shrub_cover-Procyon_lotor 0.0438 1.0038 930
## total_shrub_cover-Dasypus_novemcinctus 0.7450 1.0019 2187
## total_shrub_cover-Sylvilagus_floridanus 0.8323 1.1946 223
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4469 0.1858 -2.8178 -2.4448 -2.0936 1.0108
## (Intercept)-Procyon_lotor -2.1834 0.1469 -2.4753 -2.1816 -1.9064 1.0040
## (Intercept)-Dasypus_novemcinctus -1.4859 0.1608 -1.8121 -1.4810 -1.1831 1.0009
## (Intercept)-Sylvilagus_floridanus -3.1114 0.3499 -3.8383 -3.0960 -2.4826 1.0235
## week-Canis_latrans 0.4576 0.2642 -0.0390 0.4470 0.9918 1.0171
## week-Procyon_lotor 0.1486 0.2019 -0.2403 0.1435 0.5560 1.0003
## week-Dasypus_novemcinctus 0.0481 0.2123 -0.3812 0.0501 0.4534 1.0064
## week-Sylvilagus_floridanus 0.0188 0.3214 -0.6328 0.0233 0.6320 1.0197
## I(week^2)-Canis_latrans -0.1977 0.1100 -0.4127 -0.1959 0.0065 1.0175
## I(week^2)-Procyon_lotor -0.1039 0.0895 -0.2809 -0.1043 0.0677 1.0003
## I(week^2)-Dasypus_novemcinctus -0.1435 0.1012 -0.3451 -0.1420 0.0516 1.0169
## I(week^2)-Sylvilagus_floridanus -0.1460 0.1591 -0.4786 -0.1382 0.1428 1.0270
## ESS
## (Intercept)-Canis_latrans 941
## (Intercept)-Procyon_lotor 1620
## (Intercept)-Dasypus_novemcinctus 2258
## (Intercept)-Sylvilagus_floridanus 280
## week-Canis_latrans 1083
## week-Procyon_lotor 1550
## week-Dasypus_novemcinctus 2157
## week-Sylvilagus_floridanus 1167
## I(week^2)-Canis_latrans 1187
## I(week^2)-Procyon_lotor 1514
## I(week^2)-Dasypus_novemcinctus 1844
## I(week^2)-Sylvilagus_floridanus 763
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.431
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0050 0.5236 -1.0279 0.0026 1.0533 1.0022 1171
## Veg_shannon_index 0.5171 0.4435 -0.3855 0.5178 1.4306 1.0076 1408
## Avg_Cogongrass_Cover 0.3468 0.4720 -0.5901 0.3380 1.3217 1.0160 1366
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9123 1.9703 0.0514 0.3827 5.0233 1.0201 2335
## Veg_shannon_index 0.6319 1.4991 0.0398 0.2502 3.8832 1.0199 2305
## Avg_Cogongrass_Cover 0.7218 1.3647 0.0403 0.3064 4.0348 1.0092 2040
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6496 0.8222 0.0531 0.3946 2.8282 1.1369 412
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1076 0.5184 -3.0167 -2.1566 -0.8577 1.0040 2899
## week 0.1637 0.3113 -0.4514 0.1700 0.7552 1.0049 1543
## I(week^2) -0.1488 0.2071 -0.5841 -0.1515 0.2829 1.0023 2297
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2721 4.0713 0.1201 0.6178 6.1599 1.0812 2843
## week 0.3425 0.9373 0.0346 0.1705 1.6260 1.2179 2491
## I(week^2) 0.1803 0.3218 0.0240 0.0932 0.8740 1.0369 2064
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1804 0.5172 -0.8397 0.1701
## (Intercept)-Procyon_lotor 0.3834 0.5406 -0.6692 0.3891
## (Intercept)-Dasypus_novemcinctus -0.4188 0.4994 -1.4240 -0.4042
## (Intercept)-Sylvilagus_floridanus -0.1703 0.6091 -1.2799 -0.1904
## Veg_shannon_index-Canis_latrans 0.7831 0.4350 0.0088 0.7538
## Veg_shannon_index-Procyon_lotor 0.5824 0.4454 -0.1903 0.5557
## Veg_shannon_index-Dasypus_novemcinctus 0.2547 0.3815 -0.5098 0.2610
## Veg_shannon_index-Sylvilagus_floridanus 0.5838 0.5380 -0.3637 0.5450
## Avg_Cogongrass_Cover-Canis_latrans 0.6433 0.4554 -0.1383 0.6037
## Avg_Cogongrass_Cover-Procyon_lotor 0.4826 0.4616 -0.3544 0.4412
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4698 0.3749 -0.2473 0.4677
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1391 0.5442 -1.2838 -0.1043
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.2190 1.0025 1014
## (Intercept)-Procyon_lotor 1.5028 1.0025 848
## (Intercept)-Dasypus_novemcinctus 0.5686 1.0062 1281
## (Intercept)-Sylvilagus_floridanus 1.0786 1.0377 573
## Veg_shannon_index-Canis_latrans 1.7513 1.0277 1407
## Veg_shannon_index-Procyon_lotor 1.5318 1.0269 1068
## Veg_shannon_index-Dasypus_novemcinctus 0.9974 1.0011 2082
## Veg_shannon_index-Sylvilagus_floridanus 1.7111 1.0249 844
## Avg_Cogongrass_Cover-Canis_latrans 1.6994 1.0094 1299
## Avg_Cogongrass_Cover-Procyon_lotor 1.5496 1.0209 1382
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2393 1.0043 1802
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.8363 1.0162 1375
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4397 0.1838 -2.8107 -2.4318 -2.0966 1.0057
## (Intercept)-Procyon_lotor -2.1862 0.1479 -2.4775 -2.1790 -1.8966 1.0008
## (Intercept)-Dasypus_novemcinctus -1.4878 0.1583 -1.7923 -1.4902 -1.1990 1.0103
## (Intercept)-Sylvilagus_floridanus -3.0858 0.3330 -3.7797 -3.0705 -2.4910 1.0210
## week-Canis_latrans 0.4578 0.2597 -0.0182 0.4375 1.0014 1.0130
## week-Procyon_lotor 0.1477 0.2051 -0.2459 0.1491 0.5437 1.0095
## week-Dasypus_novemcinctus 0.0479 0.2189 -0.3998 0.0472 0.4666 1.0050
## week-Sylvilagus_floridanus 0.0174 0.3209 -0.6186 0.0216 0.6293 1.0057
## I(week^2)-Canis_latrans -0.1954 0.1089 -0.4181 -0.1925 0.0005 1.0163
## I(week^2)-Procyon_lotor -0.1029 0.0891 -0.2761 -0.1029 0.0689 1.0101
## I(week^2)-Dasypus_novemcinctus -0.1431 0.1039 -0.3546 -0.1400 0.0561 1.0088
## I(week^2)-Sylvilagus_floridanus -0.1551 0.1599 -0.4851 -0.1513 0.1519 1.0151
## ESS
## (Intercept)-Canis_latrans 1340
## (Intercept)-Procyon_lotor 1693
## (Intercept)-Dasypus_novemcinctus 2404
## (Intercept)-Sylvilagus_floridanus 426
## week-Canis_latrans 1197
## week-Procyon_lotor 1835
## week-Dasypus_novemcinctus 1902
## week-Sylvilagus_floridanus 1090
## I(week^2)-Canis_latrans 1264
## I(week^2)-Procyon_lotor 1574
## I(week^2)-Dasypus_novemcinctus 1812
## I(week^2)-Sylvilagus_floridanus 616
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4305
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0385 0.5063 -1.0342 -0.0354 0.9558 1.0083 1225
## Avg_Cogongrass_Cover 0.1752 0.4120 -0.6801 0.1820 0.9583 1.0016 2271
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9503 2.5784 0.0484 0.3870 5.4271 1.0224 2191
## Avg_Cogongrass_Cover 0.6035 1.4808 0.0408 0.2487 3.0321 1.0247 2264
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4631 0.5466 0.0451 0.2705 1.9865 1.0121 433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0904 0.5392 -2.9720 -2.1504 -0.8104 1.0136 2701
## week 0.1504 0.3077 -0.4594 0.1481 0.7888 1.0010 2179
## I(week^2) -0.1374 0.2300 -0.5978 -0.1307 0.3070 1.0031 2506
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3110 3.5941 0.1262 0.6056 6.6288 1.0870 2770
## week 0.3532 0.8569 0.0359 0.1688 1.6192 1.0525 2725
## I(week^2) 0.2161 0.7402 0.0243 0.0962 1.0839 1.1084 1429
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.1830 0.4693 -0.7020 0.1686
## (Intercept)-Procyon_lotor 0.3857 0.5086 -0.5834 0.3747
## (Intercept)-Dasypus_novemcinctus -0.4333 0.4708 -1.3914 -0.4204
## (Intercept)-Sylvilagus_floridanus -0.2617 0.5459 -1.3496 -0.2514
## Avg_Cogongrass_Cover-Canis_latrans 0.3772 0.3827 -0.3155 0.3538
## Avg_Cogongrass_Cover-Procyon_lotor 0.2505 0.3899 -0.4179 0.2261
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3486 0.3355 -0.2784 0.3408
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2411 0.4799 -1.3226 -0.1923
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1266 1.0138 1231
## (Intercept)-Procyon_lotor 1.4140 1.0020 856
## (Intercept)-Dasypus_novemcinctus 0.4325 1.0070 1205
## (Intercept)-Sylvilagus_floridanus 0.8163 1.0136 835
## Avg_Cogongrass_Cover-Canis_latrans 1.2477 1.0083 2080
## Avg_Cogongrass_Cover-Procyon_lotor 1.0689 1.0018 2102
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0501 1.0171 2467
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6105 1.0043 1372
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4545 0.1938 -2.8502 -2.4445 -2.1021 1.0140
## (Intercept)-Procyon_lotor -2.1783 0.1528 -2.4934 -2.1759 -1.8924 0.9999
## (Intercept)-Dasypus_novemcinctus -1.4887 0.1628 -1.8215 -1.4893 -1.1700 1.0035
## (Intercept)-Sylvilagus_floridanus -3.0418 0.3388 -3.7623 -3.0196 -2.4396 1.0250
## week-Canis_latrans 0.4436 0.2686 -0.0485 0.4331 0.9973 1.0031
## week-Procyon_lotor 0.1448 0.2080 -0.2555 0.1453 0.5516 1.0025
## week-Dasypus_novemcinctus 0.0387 0.2230 -0.4036 0.0415 0.4740 1.0004
## week-Sylvilagus_floridanus -0.0020 0.3147 -0.6209 -0.0004 0.5889 1.0024
## I(week^2)-Canis_latrans -0.1907 0.1104 -0.4095 -0.1900 0.0196 1.0036
## I(week^2)-Procyon_lotor -0.1034 0.0916 -0.2885 -0.1029 0.0731 1.0025
## I(week^2)-Dasypus_novemcinctus -0.1386 0.1056 -0.3457 -0.1380 0.0636 1.0016
## I(week^2)-Sylvilagus_floridanus -0.1407 0.1630 -0.4807 -0.1357 0.1616 1.0030
## ESS
## (Intercept)-Canis_latrans 1119
## (Intercept)-Procyon_lotor 1559
## (Intercept)-Dasypus_novemcinctus 2284
## (Intercept)-Sylvilagus_floridanus 429
## week-Canis_latrans 1059
## week-Procyon_lotor 1386
## week-Dasypus_novemcinctus 2070
## week-Sylvilagus_floridanus 1174
## I(week^2)-Canis_latrans 1359
## I(week^2)-Procyon_lotor 1290
## I(week^2)-Dasypus_novemcinctus 1622
## I(week^2)-Sylvilagus_floridanus 755
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4253
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.7328 0.5616 -1.8210 -0.7325 0.4263 1.0051 683
## Avg_Cogongrass_Cover -0.4771 0.5969 -1.6869 -0.4646 0.6963 1.0019 1237
## I(Avg_Cogongrass_Cover^2) 1.0361 0.7248 -0.3118 0.9805 2.6558 1.0256 667
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.7563 1.5684 0.0465 0.3180 4.2178 1.0572 1604
## Avg_Cogongrass_Cover 1.2900 3.6161 0.0494 0.4644 8.0173 1.0817 1056
## I(Avg_Cogongrass_Cover^2) 2.9957 22.9783 0.0510 0.6160 14.4096 1.2511 2025
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4177 0.6129 0.0427 0.2445 1.7738 1.0699 288
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1122 0.5462 -3.0556 -2.1607 -0.8328 1.0032 2521
## week 0.1694 0.3133 -0.4505 0.1713 0.7914 1.0007 1907
## I(week^2) -0.1477 0.2135 -0.6056 -0.1436 0.2759 1.0043 2426
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3703 3.7329 0.1300 0.6335 6.8746 1.0569 2166
## week 0.3529 0.7893 0.0338 0.1707 1.8005 1.0444 2360
## I(week^2) 0.1874 0.3904 0.0243 0.0925 0.9139 1.0233 2636
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6651 0.6159 -1.8983 -0.6510
## (Intercept)-Procyon_lotor -0.4670 0.6191 -1.6761 -0.4743
## (Intercept)-Dasypus_novemcinctus -1.0325 0.5637 -2.1555 -1.0163
## (Intercept)-Sylvilagus_floridanus -0.9232 0.6402 -2.1980 -0.9110
## Avg_Cogongrass_Cover-Canis_latrans -0.2106 0.6474 -1.4169 -0.2486
## Avg_Cogongrass_Cover-Procyon_lotor -0.4405 0.6390 -1.6315 -0.4602
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3041 0.5586 -1.4158 -0.3108
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.1598 0.8531 -3.0678 -1.0735
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.8410 1.2726 0.2466 1.5382
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.7122 1.2614 0.2185 1.3548
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.5820 0.4112 -0.2144 0.5781
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.8353 0.7167 -0.2232 0.7242
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5286 1.0012 627
## (Intercept)-Procyon_lotor 0.8265 1.0089 535
## (Intercept)-Dasypus_novemcinctus 0.0592 1.0057 1034
## (Intercept)-Sylvilagus_floridanus 0.2955 1.0055 781
## Avg_Cogongrass_Cover-Canis_latrans 1.1577 1.0045 919
## Avg_Cogongrass_Cover-Procyon_lotor 0.8919 1.0125 968
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8089 1.0007 1194
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.2032 1.0303 655
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.0437 1.0258 206
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 5.0166 1.0511 210
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4186 1.0011 1173
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 2.7299 1.0457 439
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4821 0.1899 -2.8679 -2.4738 -2.1277 1.0113
## (Intercept)-Procyon_lotor -2.1899 0.1519 -2.4963 -2.1858 -1.9029 1.0050
## (Intercept)-Dasypus_novemcinctus -1.4839 0.1635 -1.8104 -1.4777 -1.1692 1.0031
## (Intercept)-Sylvilagus_floridanus -3.0866 0.3396 -3.8020 -3.0729 -2.4474 1.0382
## week-Canis_latrans 0.4542 0.2641 -0.0418 0.4454 1.0005 1.0007
## week-Procyon_lotor 0.1481 0.2074 -0.2700 0.1512 0.5607 1.0001
## week-Dasypus_novemcinctus 0.0467 0.2188 -0.3718 0.0422 0.4765 1.0002
## week-Sylvilagus_floridanus 0.0189 0.3120 -0.6113 0.0328 0.5990 1.0065
## I(week^2)-Canis_latrans -0.1961 0.1100 -0.4168 -0.1937 0.0100 1.0036
## I(week^2)-Procyon_lotor -0.1099 0.0907 -0.2887 -0.1094 0.0649 1.0017
## I(week^2)-Dasypus_novemcinctus -0.1410 0.1027 -0.3432 -0.1410 0.0610 1.0011
## I(week^2)-Sylvilagus_floridanus -0.1428 0.1606 -0.4803 -0.1371 0.1539 1.0022
## ESS
## (Intercept)-Canis_latrans 1173
## (Intercept)-Procyon_lotor 1294
## (Intercept)-Dasypus_novemcinctus 2305
## (Intercept)-Sylvilagus_floridanus 480
## week-Canis_latrans 1226
## week-Procyon_lotor 1670
## week-Dasypus_novemcinctus 1950
## week-Sylvilagus_floridanus 1321
## I(week^2)-Canis_latrans 1224
## I(week^2)-Procyon_lotor 1592
## I(week^2)-Dasypus_novemcinctus 1786
## I(week^2)-Sylvilagus_floridanus 770
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4392
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.3999 0.8101 -2.9467 -1.4234 0.2867 1.0049 762
## Cogon_Patch_Size -0.2081 0.8827 -1.9899 -0.2226 1.5781 1.0034 1279
## Veg_shannon_index 0.9855 0.6215 -0.2575 0.9710 2.2245 1.0360 585
## total_shrub_cover -0.1783 0.5955 -1.3718 -0.1782 1.0177 1.0050 1108
## Avg_Cogongrass_Cover 0.3742 1.0936 -1.8334 0.3710 2.4499 1.0488 484
## Tree_Density -2.3167 1.1469 -4.3718 -2.3673 0.3789 1.0074 541
## Avg_Canopy_Cover 1.2632 0.9979 -0.9361 1.3118 3.1273 1.0024 2017
## I(Avg_Cogongrass_Cover^2) 1.4694 0.8531 -0.2603 1.4635 3.1418 1.0047 776
## avg_veg_height -0.0881 0.6512 -1.3418 -0.0890 1.1962 1.0110 669
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3735 7.5896 0.0530 0.7180 13.8467 1.0204 1106
## Cogon_Patch_Size 5.8431 19.4789 0.0794 1.5024 35.9514 1.0184 792
## Veg_shannon_index 1.0464 3.2568 0.0456 0.3659 6.1028 1.0768 2059
## total_shrub_cover 1.4211 4.3245 0.0521 0.5484 7.9839 1.0525 1914
## Avg_Cogongrass_Cover 3.3514 8.9420 0.0602 0.9376 22.1058 1.0669 778
## Tree_Density 6.0884 18.7089 0.0640 1.1616 46.0587 1.0380 607
## Avg_Canopy_Cover 8.2341 17.4990 0.1640 2.9717 45.8770 1.0083 628
## I(Avg_Cogongrass_Cover^2) 3.6578 22.0519 0.0610 0.8101 20.1558 1.1644 1278
## avg_veg_height 1.2022 5.0517 0.0475 0.4280 6.7073 1.2199 1913
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.2395 1.6979 0.06 0.6772 5.2567 1.1865 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0884 0.5224 -2.9541 -2.1527 -0.8128 0.9998 2728
## week 0.1508 0.3169 -0.4819 0.1564 0.7748 1.0109 1756
## I(week^2) -0.1428 0.2181 -0.5572 -0.1455 0.3110 0.9996 2636
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3501 3.4870 0.1289 0.6047 6.9274 1.0266 2475
## week 0.3849 0.9744 0.0358 0.1730 1.9895 1.0392 2122
## I(week^2) 0.1913 0.5683 0.0244 0.0941 0.9267 1.1789 2832
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3051 0.9593 -3.1723 -1.3118
## (Intercept)-Procyon_lotor -0.9747 0.9191 -2.7250 -1.0016
## (Intercept)-Dasypus_novemcinctus -2.1562 0.9388 -4.3261 -2.0583
## (Intercept)-Sylvilagus_floridanus -1.9613 1.0825 -4.4891 -1.8663
## Cogon_Patch_Size-Canis_latrans 1.4020 1.6801 -0.7687 1.0414
## Cogon_Patch_Size-Procyon_lotor -0.6296 0.8026 -2.2990 -0.6226
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4540 0.7673 -2.0696 -0.4125
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3826 1.6008 -5.6087 -1.0743
## Veg_shannon_index-Canis_latrans 1.3709 0.7412 0.1262 1.2969
## Veg_shannon_index-Procyon_lotor 1.1161 0.6333 -0.0640 1.0824
## Veg_shannon_index-Dasypus_novemcinctus 0.7088 0.5833 -0.4350 0.7211
## Veg_shannon_index-Sylvilagus_floridanus 1.0642 0.7534 -0.3308 1.0017
## total_shrub_cover-Canis_latrans 0.0916 0.6690 -1.1036 0.0305
## total_shrub_cover-Procyon_lotor -0.8604 0.6660 -2.3290 -0.7833
## total_shrub_cover-Dasypus_novemcinctus 0.1574 0.5578 -0.8812 0.1334
## total_shrub_cover-Sylvilagus_floridanus -0.1516 0.8497 -1.8901 -0.1664
## Avg_Cogongrass_Cover-Canis_latrans 0.3107 1.4037 -2.5901 0.3104
## Avg_Cogongrass_Cover-Procyon_lotor 0.4646 1.2927 -2.0848 0.4777
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2826 1.4793 -1.2660 1.1252
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3357 1.5644 -3.8680 -0.1926
## Tree_Density-Canis_latrans -3.2537 1.4676 -6.8479 -3.0239
## Tree_Density-Procyon_lotor -2.2278 1.1143 -4.5003 -2.2137
## Tree_Density-Dasypus_novemcinctus -3.8658 1.7247 -8.2828 -3.5240
## Tree_Density-Sylvilagus_floridanus -2.8094 1.4346 -6.1461 -2.6720
## Avg_Canopy_Cover-Canis_latrans 0.0643 0.6949 -1.3051 0.0690
## Avg_Canopy_Cover-Procyon_lotor 1.6129 0.8068 0.2270 1.5540
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0544 0.8482 0.6816 1.9617
## Avg_Canopy_Cover-Sylvilagus_floridanus 3.9809 2.2540 0.9420 3.5027
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3673 1.2712 0.5736 2.1338
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3287 1.4384 0.5407 2.0614
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2791 0.7741 -0.1049 1.2425
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.1127 0.9069 -0.4563 1.0442
## avg_veg_height-Canis_latrans -0.4496 0.7196 -1.9825 -0.4160
## avg_veg_height-Procyon_lotor 0.2172 0.7312 -1.0936 0.1893
## avg_veg_height-Dasypus_novemcinctus 0.1646 0.6957 -1.1675 0.1523
## avg_veg_height-Sylvilagus_floridanus -0.2751 0.8475 -1.9313 -0.2721
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6279 1.0086 538
## (Intercept)-Procyon_lotor 0.8993 1.0205 462
## (Intercept)-Dasypus_novemcinctus -0.5358 1.0135 455
## (Intercept)-Sylvilagus_floridanus -0.1072 1.0137 334
## Cogon_Patch_Size-Canis_latrans 5.8019 1.0420 252
## Cogon_Patch_Size-Procyon_lotor 0.8889 1.0309 687
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9665 1.0260 547
## Cogon_Patch_Size-Sylvilagus_floridanus 0.8505 1.0374 350
## Veg_shannon_index-Canis_latrans 3.0714 1.0556 630
## Veg_shannon_index-Procyon_lotor 2.4638 1.0529 409
## Veg_shannon_index-Dasypus_novemcinctus 1.8280 1.0346 832
## Veg_shannon_index-Sylvilagus_floridanus 2.6569 1.0150 541
## total_shrub_cover-Canis_latrans 1.5551 1.0199 854
## total_shrub_cover-Procyon_lotor 0.2611 1.0031 882
## total_shrub_cover-Dasypus_novemcinctus 1.3311 1.0066 1580
## total_shrub_cover-Sylvilagus_floridanus 1.5734 1.0072 734
## Avg_Cogongrass_Cover-Canis_latrans 3.0553 1.0600 531
## Avg_Cogongrass_Cover-Procyon_lotor 2.9539 1.0650 504
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6806 1.0349 355
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.3290 1.0293 467
## Tree_Density-Canis_latrans -1.0328 1.0753 280
## Tree_Density-Procyon_lotor -0.0665 1.0266 469
## Tree_Density-Dasypus_novemcinctus -1.5292 1.0615 211
## Tree_Density-Sylvilagus_floridanus -0.3601 1.0024 394
## Avg_Canopy_Cover-Canis_latrans 1.4764 1.0100 876
## Avg_Canopy_Cover-Procyon_lotor 3.3217 1.0428 625
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9078 1.0620 414
## Avg_Canopy_Cover-Sylvilagus_floridanus 9.6367 1.0081 160
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.6170 1.0946 208
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.0853 1.0580 176
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 2.8933 1.0156 627
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.1329 1.0318 358
## avg_veg_height-Canis_latrans 0.8714 1.0085 761
## avg_veg_height-Procyon_lotor 1.7912 1.0151 731
## avg_veg_height-Dasypus_novemcinctus 1.5566 1.0033 755
## avg_veg_height-Sylvilagus_floridanus 1.4051 1.0110 749
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4502 0.1890 -2.8316 -2.4469 -2.0925 1.0244
## (Intercept)-Procyon_lotor -2.1784 0.1512 -2.4897 -2.1754 -1.8944 1.0006
## (Intercept)-Dasypus_novemcinctus -1.4914 0.1627 -1.8250 -1.4879 -1.1822 1.0006
## (Intercept)-Sylvilagus_floridanus -3.0495 0.3007 -3.7057 -3.0258 -2.5002 1.0137
## week-Canis_latrans 0.4445 0.2603 -0.0422 0.4327 0.9983 1.0013
## week-Procyon_lotor 0.1402 0.1968 -0.2439 0.1381 0.5374 1.0030
## week-Dasypus_novemcinctus 0.0330 0.2206 -0.4108 0.0357 0.4623 1.0090
## week-Sylvilagus_floridanus 0.0148 0.3190 -0.6356 0.0215 0.6112 1.0005
## I(week^2)-Canis_latrans -0.1912 0.1095 -0.4127 -0.1874 0.0068 1.0014
## I(week^2)-Procyon_lotor -0.1031 0.0902 -0.2835 -0.1033 0.0714 1.0001
## I(week^2)-Dasypus_novemcinctus -0.1386 0.1068 -0.3554 -0.1348 0.0616 1.0071
## I(week^2)-Sylvilagus_floridanus -0.1582 0.1598 -0.4807 -0.1523 0.1486 1.0129
## ESS
## (Intercept)-Canis_latrans 1101
## (Intercept)-Procyon_lotor 1647
## (Intercept)-Dasypus_novemcinctus 2277
## (Intercept)-Sylvilagus_floridanus 519
## week-Canis_latrans 1082
## week-Procyon_lotor 1892
## week-Dasypus_novemcinctus 1979
## week-Sylvilagus_floridanus 1104
## I(week^2)-Canis_latrans 1257
## I(week^2)-Procyon_lotor 1555
## I(week^2)-Dasypus_novemcinctus 1563
## I(week^2)-Sylvilagus_floridanus 752
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T25 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_null_T25)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4485
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.063 0.4651 -0.8338 0.0472 1.0175 1.0053 2377
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9159 2.827 0.0592 0.4117 4.6517 1.0941 2749
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1787 0.5108 -3.0372 -2.2364 -0.9091 1.0033 2421
## shrub_cover 0.2632 0.4243 -0.5829 0.2566 1.1452 1.0020 2585
## veg_height 0.0286 0.3622 -0.6853 0.0285 0.7484 1.0046 2510
## week 0.1524 0.3146 -0.5087 0.1578 0.7733 1.0073 1790
## I(week^2) -0.1397 0.2148 -0.5714 -0.1436 0.2991 1.0030 1970
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1506 2.5628 0.1022 0.5287 6.5415 1.0009 2358
## shrub_cover 0.7833 2.1093 0.0671 0.3787 3.7204 1.0323 2561
## veg_height 0.6202 1.3281 0.0670 0.3121 2.9607 1.0676 2059
## week 0.3776 0.8767 0.0357 0.1814 1.9721 1.0375 2139
## I(week^2) 0.1714 0.3307 0.0248 0.0925 0.7646 1.0152 2606
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3161 0.3745 -0.3676 0.2951 1.0798 1.0019
## (Intercept)-Procyon_lotor 0.5523 0.3684 -0.1291 0.5338 1.3465 1.0008
## (Intercept)-Dasypus_novemcinctus -0.4060 0.3655 -1.1489 -0.3979 0.3006 1.0027
## (Intercept)-Sylvilagus_floridanus -0.2529 0.4226 -1.0816 -0.2467 0.5673 1.0051
## ESS
## (Intercept)-Canis_latrans 2328
## (Intercept)-Procyon_lotor 2292
## (Intercept)-Dasypus_novemcinctus 2165
## (Intercept)-Sylvilagus_floridanus 1318
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5689 0.2012 -2.9983 -2.5690 -2.1992 1.0220
## (Intercept)-Procyon_lotor -2.1947 0.1606 -2.5187 -2.1894 -1.8941 1.0464
## (Intercept)-Dasypus_novemcinctus -1.6489 0.1845 -2.0102 -1.6487 -1.2930 1.0112
## (Intercept)-Sylvilagus_floridanus -3.0090 0.3219 -3.7067 -2.9984 -2.4368 1.0207
## shrub_cover-Canis_latrans -0.2834 0.2229 -0.7322 -0.2828 0.1436 1.0162
## shrub_cover-Procyon_lotor 0.2613 0.1605 -0.0623 0.2671 0.5696 1.0045
## shrub_cover-Dasypus_novemcinctus 0.8315 0.3101 0.2441 0.8240 1.4454 1.0036
## shrub_cover-Sylvilagus_floridanus 0.2790 0.4185 -0.5169 0.2606 1.1293 1.0249
## veg_height-Canis_latrans -0.6081 0.1828 -0.9674 -0.6074 -0.2693 1.0166
## veg_height-Procyon_lotor 0.3448 0.1257 0.1001 0.3434 0.5955 1.0246
## veg_height-Dasypus_novemcinctus 0.2554 0.1374 -0.0173 0.2557 0.5262 1.0066
## veg_height-Sylvilagus_floridanus 0.1351 0.2514 -0.3437 0.1302 0.6243 1.0059
## week-Canis_latrans 0.4616 0.2725 -0.0379 0.4539 1.0305 1.0246
## week-Procyon_lotor 0.1340 0.2102 -0.2693 0.1323 0.5384 1.0086
## week-Dasypus_novemcinctus 0.0419 0.2259 -0.4028 0.0392 0.4858 1.0017
## week-Sylvilagus_floridanus -0.0061 0.3231 -0.6341 0.0027 0.6094 1.0006
## I(week^2)-Canis_latrans -0.1943 0.1130 -0.4188 -0.1926 0.0241 1.0355
## I(week^2)-Procyon_lotor -0.1046 0.0915 -0.2862 -0.1027 0.0761 1.0083
## I(week^2)-Dasypus_novemcinctus -0.1435 0.1041 -0.3470 -0.1444 0.0592 1.0022
## I(week^2)-Sylvilagus_floridanus -0.1399 0.1689 -0.4973 -0.1347 0.1782 1.0081
## ESS
## (Intercept)-Canis_latrans 921
## (Intercept)-Procyon_lotor 742
## (Intercept)-Dasypus_novemcinctus 1525
## (Intercept)-Sylvilagus_floridanus 615
## shrub_cover-Canis_latrans 857
## shrub_cover-Procyon_lotor 1412
## shrub_cover-Dasypus_novemcinctus 1185
## shrub_cover-Sylvilagus_floridanus 502
## veg_height-Canis_latrans 796
## veg_height-Procyon_lotor 1647
## veg_height-Dasypus_novemcinctus 1860
## veg_height-Sylvilagus_floridanus 833
## week-Canis_latrans 989
## week-Procyon_lotor 1627
## week-Dasypus_novemcinctus 2145
## week-Sylvilagus_floridanus 1060
## I(week^2)-Canis_latrans 1030
## I(week^2)-Procyon_lotor 1575
## I(week^2)-Dasypus_novemcinctus 1740
## I(week^2)-Sylvilagus_floridanus 515
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T25 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_full_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4805
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.1250 0.8001 -1.7685 -0.1121 1.4154 1.0523 612
## Cogon_Patch_Size -0.5576 0.8262 -2.1790 -0.5698 1.1719 1.0317 798
## Veg_shannon_index 1.1422 0.6815 -0.1083 1.1029 2.6230 1.1341 342
## total_shrub_cover -0.1936 0.7608 -1.8030 -0.1888 1.3691 1.0076 756
## Avg_Cogongrass_Cover 1.7144 1.0654 -0.5785 1.7532 3.6757 1.0170 620
## Tree_Density -1.9108 1.1481 -4.0568 -1.9728 0.7087 1.0287 962
## Avg_Canopy_Cover 1.3046 1.0649 -1.1553 1.3618 3.2800 1.0047 1997
## avg_veg_height -0.3207 0.6647 -1.6181 -0.3076 0.9280 1.0096 558
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.1801 23.4955 0.0696 0.9811 15.9978 1.2705 2491
## Cogon_Patch_Size 3.4544 11.6723 0.0634 0.8531 23.7002 1.0907 225
## Veg_shannon_index 1.1443 3.8795 0.0468 0.4004 6.1086 1.0252 925
## total_shrub_cover 2.5925 6.1968 0.0664 0.8686 15.6508 1.1947 595
## Avg_Cogongrass_Cover 5.2365 16.7470 0.0574 1.1585 36.5106 1.2179 307
## Tree_Density 17.5074 79.1628 0.0867 2.3407 122.0761 1.4019 226
## Avg_Canopy_Cover 15.3628 45.5327 0.2002 4.1436 99.3416 1.2224 178
## avg_veg_height 0.8089 2.1780 0.0418 0.3272 4.2054 1.0262 2050
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8821 5.8077 0.0789 1.1095 16.3843 2.0386 43
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1904 0.5118 -3.0152 -2.2556 -0.9765 1.0042 2278
## shrub_cover 0.3007 0.4523 -0.6486 0.3017 1.2085 1.0029 2254
## veg_height 0.0454 0.3969 -0.7805 0.0502 0.8244 1.0059 3000
## week 0.1708 0.3181 -0.4757 0.1724 0.8290 1.0078 2244
## I(week^2) -0.1453 0.2055 -0.5528 -0.1467 0.2543 1.0037 2228
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1151 2.7876 0.1060 0.5239 5.3467 1.0039 2337
## shrub_cover 0.9875 2.4914 0.0913 0.4807 4.7632 1.1053 3000
## veg_height 0.6985 1.3366 0.0780 0.3564 3.3788 1.0316 2256
## week 0.3828 1.0852 0.0351 0.1674 1.9385 1.0209 2243
## I(week^2) 0.1756 0.4058 0.0249 0.0911 0.7588 1.0684 2140
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4381 0.9688 -1.3963 0.3795
## (Intercept)-Procyon_lotor 0.4585 0.9425 -1.5285 0.4468
## (Intercept)-Dasypus_novemcinctus -0.9354 1.0910 -3.5505 -0.7959
## (Intercept)-Sylvilagus_floridanus -0.6683 1.2245 -3.2002 -0.5690
## Cogon_Patch_Size-Canis_latrans 0.3793 1.2374 -1.4197 0.1527
## Cogon_Patch_Size-Procyon_lotor -1.0311 0.7954 -2.7813 -0.9674
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5976 0.8803 -2.3770 -0.6091
## Cogon_Patch_Size-Sylvilagus_floridanus -1.4436 1.6966 -5.1793 -1.1787
## Veg_shannon_index-Canis_latrans 1.5186 0.8346 0.2001 1.4164
## Veg_shannon_index-Procyon_lotor 1.3681 0.6777 0.2172 1.3018
## Veg_shannon_index-Dasypus_novemcinctus 0.7829 0.6966 -0.5620 0.7612
## Veg_shannon_index-Sylvilagus_floridanus 1.2723 0.8509 -0.2212 1.1913
## total_shrub_cover-Canis_latrans 0.7173 1.0584 -0.7932 0.5121
## total_shrub_cover-Procyon_lotor -0.9239 0.7145 -2.5596 -0.8484
## total_shrub_cover-Dasypus_novemcinctus -0.1818 0.8171 -2.0542 -0.1353
## total_shrub_cover-Sylvilagus_floridanus -0.5421 1.2586 -3.4765 -0.4452
## Avg_Cogongrass_Cover-Canis_latrans 2.6039 1.3849 0.4692 2.4315
## Avg_Cogongrass_Cover-Procyon_lotor 2.1738 1.1097 0.2589 2.0738
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.9920 1.6467 0.6645 2.7194
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0136 1.3915 -1.9509 1.1149
## Tree_Density-Canis_latrans -3.3546 1.9454 -8.3367 -2.9274
## Tree_Density-Procyon_lotor -1.5574 0.8961 -3.3201 -1.5596
## Tree_Density-Dasypus_novemcinctus -4.5051 3.0916 -13.3306 -3.5925
## Tree_Density-Sylvilagus_floridanus -2.9268 2.0644 -8.2123 -2.5267
## Avg_Canopy_Cover-Canis_latrans 0.0412 0.7641 -1.4215 0.0647
## Avg_Canopy_Cover-Procyon_lotor 1.7274 0.8605 0.2787 1.6250
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.4954 1.1962 0.8640 2.2790
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.8660 3.3264 1.1374 3.9739
## avg_veg_height-Canis_latrans -0.4098 0.7705 -1.9449 -0.4095
## avg_veg_height-Procyon_lotor -0.2808 0.6975 -1.6844 -0.2746
## avg_veg_height-Dasypus_novemcinctus -0.1398 0.7304 -1.5166 -0.1692
## avg_veg_height-Sylvilagus_floridanus -0.5313 0.8659 -2.3910 -0.5102
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4823 1.0393 467
## (Intercept)-Procyon_lotor 2.4075 1.1162 319
## (Intercept)-Dasypus_novemcinctus 0.7948 1.1392 263
## (Intercept)-Sylvilagus_floridanus 1.3469 1.1361 196
## Cogon_Patch_Size-Canis_latrans 3.2148 1.0207 529
## Cogon_Patch_Size-Procyon_lotor 0.3390 1.1020 326
## Cogon_Patch_Size-Dasypus_novemcinctus 1.1556 1.0506 631
## Cogon_Patch_Size-Sylvilagus_floridanus 0.7250 1.1934 119
## Veg_shannon_index-Canis_latrans 3.3323 1.1601 255
## Veg_shannon_index-Procyon_lotor 2.8516 1.1650 196
## Veg_shannon_index-Dasypus_novemcinctus 2.3151 1.1192 596
## Veg_shannon_index-Sylvilagus_floridanus 3.2525 1.1493 383
## total_shrub_cover-Canis_latrans 3.4385 1.2087 210
## total_shrub_cover-Procyon_lotor 0.3069 1.0109 786
## total_shrub_cover-Dasypus_novemcinctus 1.2530 1.0162 416
## total_shrub_cover-Sylvilagus_floridanus 1.7961 1.0101 308
## Avg_Cogongrass_Cover-Canis_latrans 6.0436 1.1674 321
## Avg_Cogongrass_Cover-Procyon_lotor 4.6615 1.0804 462
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 7.3695 1.3135 131
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 3.5915 1.0148 423
## Tree_Density-Canis_latrans -0.8081 1.3434 122
## Tree_Density-Procyon_lotor 0.1860 1.0366 819
## Tree_Density-Dasypus_novemcinctus -1.3051 1.5587 72
## Tree_Density-Sylvilagus_floridanus 0.0214 1.2262 118
## Avg_Canopy_Cover-Canis_latrans 1.4493 1.0545 407
## Avg_Canopy_Cover-Procyon_lotor 3.7211 1.1052 369
## Avg_Canopy_Cover-Dasypus_novemcinctus 5.5592 1.3381 77
## Avg_Canopy_Cover-Sylvilagus_floridanus 14.0684 1.4544 70
## avg_veg_height-Canis_latrans 1.0534 1.0118 554
## avg_veg_height-Procyon_lotor 1.1001 1.0118 653
## avg_veg_height-Dasypus_novemcinctus 1.3313 1.0184 567
## avg_veg_height-Sylvilagus_floridanus 1.0646 1.0087 653
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5991 0.1952 -2.9954 -2.5918 -2.2286 1.0168
## (Intercept)-Procyon_lotor -2.1959 0.1568 -2.5139 -2.1964 -1.9048 1.0118
## (Intercept)-Dasypus_novemcinctus -1.6811 0.1830 -2.0539 -1.6813 -1.3307 1.0050
## (Intercept)-Sylvilagus_floridanus -3.0444 0.2900 -3.6617 -3.0337 -2.4999 1.0160
## shrub_cover-Canis_latrans -0.3725 0.2270 -0.8169 -0.3746 0.0787 1.0022
## shrub_cover-Procyon_lotor 0.2778 0.1617 -0.0458 0.2822 0.5880 1.0091
## shrub_cover-Dasypus_novemcinctus 0.9510 0.3217 0.3549 0.9456 1.6014 1.0143
## shrub_cover-Sylvilagus_floridanus 0.4728 0.4231 -0.3610 0.4802 1.2766 1.0192
## veg_height-Canis_latrans -0.6359 0.1864 -1.0326 -0.6348 -0.2839 0.9998
## veg_height-Procyon_lotor 0.3592 0.1217 0.1198 0.3573 0.5915 1.0061
## veg_height-Dasypus_novemcinctus 0.2751 0.1389 0.0016 0.2761 0.5401 1.0043
## veg_height-Sylvilagus_floridanus 0.1853 0.2607 -0.3095 0.1832 0.6979 1.0053
## week-Canis_latrans 0.4653 0.2632 -0.0257 0.4547 1.0077 1.0184
## week-Procyon_lotor 0.1565 0.2051 -0.2482 0.1576 0.5491 1.0055
## week-Dasypus_novemcinctus 0.0461 0.2175 -0.3998 0.0478 0.4609 1.0056
## week-Sylvilagus_floridanus 0.0192 0.3204 -0.6559 0.0292 0.6316 1.0112
## I(week^2)-Canis_latrans -0.1973 0.1107 -0.4283 -0.1954 0.0113 1.0242
## I(week^2)-Procyon_lotor -0.1092 0.0931 -0.2867 -0.1118 0.0758 1.0137
## I(week^2)-Dasypus_novemcinctus -0.1448 0.1031 -0.3528 -0.1417 0.0525 1.0010
## I(week^2)-Sylvilagus_floridanus -0.1403 0.1624 -0.4891 -0.1329 0.1488 1.0052
## ESS
## (Intercept)-Canis_latrans 871
## (Intercept)-Procyon_lotor 1544
## (Intercept)-Dasypus_novemcinctus 1310
## (Intercept)-Sylvilagus_floridanus 594
## shrub_cover-Canis_latrans 661
## shrub_cover-Procyon_lotor 1561
## shrub_cover-Dasypus_novemcinctus 649
## shrub_cover-Sylvilagus_floridanus 270
## veg_height-Canis_latrans 758
## veg_height-Procyon_lotor 1468
## veg_height-Dasypus_novemcinctus 1938
## veg_height-Sylvilagus_floridanus 792
## week-Canis_latrans 1241
## week-Procyon_lotor 1707
## week-Dasypus_novemcinctus 2189
## week-Sylvilagus_floridanus 1010
## I(week^2)-Canis_latrans 1311
## I(week^2)-Procyon_lotor 1525
## I(week^2)-Dasypus_novemcinctus 1797
## I(week^2)-Sylvilagus_floridanus 699
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T25 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cover_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.5153
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4369 0.6415 -0.7522 0.4220 1.8337 1.0323 251
## Avg_Cogongrass_Cover -0.0719 0.5891 -1.2625 -0.0661 1.0836 1.0085 1146
## total_shrub_cover -0.5712 0.7478 -2.1748 -0.5589 0.9443 1.0122 461
## avg_veg_height 0.3303 0.5437 -0.6785 0.3135 1.4354 1.0176 395
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1320 4.4783 0.0530 0.4393 5.6227 1.2175 1911
## Avg_Cogongrass_Cover 1.1453 2.7852 0.0488 0.4204 6.2582 1.0600 1560
## total_shrub_cover 3.3370 9.2494 0.0843 1.1614 19.2980 1.0599 594
## avg_veg_height 0.7036 2.9182 0.0419 0.2631 3.5731 1.1745 2136
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8279 1.4511 0.0469 0.4188 4.1434 1.1084 183
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2301 0.5181 -3.1031 -2.2953 -0.9768 1.0023 2314
## shrub_cover 0.4093 0.4969 -0.5706 0.4143 1.3753 1.0160 1724
## veg_height 0.0052 0.4087 -0.8061 0.0138 0.7842 1.0082 2441
## week 0.1665 0.3067 -0.4229 0.1724 0.7542 1.0031 2217
## I(week^2) -0.1520 0.2309 -0.6033 -0.1522 0.3058 1.0071 2176
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1952 2.5151 0.1076 0.5626 6.4597 1.0396 2110
## shrub_cover 1.1786 2.8538 0.1010 0.5801 5.6960 1.0672 1388
## veg_height 0.6719 1.8963 0.0812 0.3403 2.8528 1.1917 2017
## week 0.3702 1.0808 0.0354 0.1730 2.0588 1.1402 2531
## I(week^2) 0.1930 0.5278 0.0242 0.0937 0.9502 1.1355 2121
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6689 0.6493 -0.4613 0.5936
## (Intercept)-Procyon_lotor 0.8010 0.6420 -0.3741 0.7737
## (Intercept)-Dasypus_novemcinctus 0.0138 0.7587 -1.3210 -0.0475
## (Intercept)-Sylvilagus_floridanus 0.4128 0.8144 -1.0110 0.3501
## Avg_Cogongrass_Cover-Canis_latrans 0.3531 0.6422 -0.8166 0.3160
## Avg_Cogongrass_Cover-Procyon_lotor -0.1965 0.5601 -1.3619 -0.1744
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.0880 0.5665 -0.9901 0.0785
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.6694 0.7725 -2.3595 -0.5780
## total_shrub_cover-Canis_latrans 0.4978 0.8346 -0.8834 0.3646
## total_shrub_cover-Procyon_lotor -1.2923 0.7094 -2.9447 -1.1854
## total_shrub_cover-Dasypus_novemcinctus -0.5586 0.9688 -3.1004 -0.3331
## total_shrub_cover-Sylvilagus_floridanus -1.4312 1.2252 -4.2697 -1.2383
## avg_veg_height-Canis_latrans 0.2975 0.5899 -0.8491 0.2773
## avg_veg_height-Procyon_lotor 0.3112 0.5442 -0.7210 0.2863
## avg_veg_height-Dasypus_novemcinctus 0.5434 0.6116 -0.4783 0.4938
## avg_veg_height-Sylvilagus_floridanus 0.2321 0.6477 -0.9878 0.2156
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1379 1.0153 252
## (Intercept)-Procyon_lotor 2.2068 1.0396 364
## (Intercept)-Dasypus_novemcinctus 1.6810 1.0615 267
## (Intercept)-Sylvilagus_floridanus 2.2503 1.0592 135
## Avg_Cogongrass_Cover-Canis_latrans 1.7631 1.0013 886
## Avg_Cogongrass_Cover-Procyon_lotor 0.8467 1.0180 820
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2185 1.0173 947
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.6120 1.0530 683
## total_shrub_cover-Canis_latrans 2.5472 1.0305 307
## total_shrub_cover-Procyon_lotor -0.1926 1.0317 334
## total_shrub_cover-Dasypus_novemcinctus 0.6631 1.1631 125
## total_shrub_cover-Sylvilagus_floridanus 0.4982 1.0403 144
## avg_veg_height-Canis_latrans 1.5294 1.0100 522
## avg_veg_height-Procyon_lotor 1.4310 1.0033 637
## avg_veg_height-Dasypus_novemcinctus 1.8741 1.0452 340
## avg_veg_height-Sylvilagus_floridanus 1.5689 1.0144 324
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6398 0.2056 -3.0601 -2.6300 -2.2509 1.0309
## (Intercept)-Procyon_lotor -2.2089 0.1551 -2.5199 -2.2066 -1.9129 1.0021
## (Intercept)-Dasypus_novemcinctus -1.7293 0.2103 -2.1631 -1.7218 -1.3338 1.0419
## (Intercept)-Sylvilagus_floridanus -3.1512 0.3144 -3.7943 -3.1418 -2.5508 1.0290
## shrub_cover-Canis_latrans -0.3443 0.2510 -0.8259 -0.3458 0.1508 1.0008
## shrub_cover-Procyon_lotor 0.3277 0.1592 0.0007 0.3293 0.6394 1.0029
## shrub_cover-Dasypus_novemcinctus 1.1039 0.4148 0.3720 1.0739 1.9061 1.0896
## shrub_cover-Sylvilagus_floridanus 0.7235 0.4707 -0.3241 0.7673 1.5365 1.0090
## veg_height-Canis_latrans -0.6577 0.2000 -1.0592 -0.6531 -0.2747 1.0416
## veg_height-Procyon_lotor 0.3515 0.1261 0.1079 0.3490 0.5984 1.0027
## veg_height-Dasypus_novemcinctus 0.2742 0.1460 -0.0003 0.2701 0.5680 1.0074
## veg_height-Sylvilagus_floridanus 0.0667 0.2736 -0.4635 0.0630 0.6001 1.0120
## week-Canis_latrans 0.4689 0.2713 -0.0253 0.4534 1.0295 1.0039
## week-Procyon_lotor 0.1438 0.2082 -0.2571 0.1401 0.5566 1.0111
## week-Dasypus_novemcinctus 0.0512 0.2258 -0.4003 0.0511 0.4983 1.0032
## week-Sylvilagus_floridanus 0.0194 0.3265 -0.6585 0.0304 0.6537 1.0018
## I(week^2)-Canis_latrans -0.2027 0.1118 -0.4293 -0.1983 0.0121 1.0056
## I(week^2)-Procyon_lotor -0.1079 0.0932 -0.2970 -0.1070 0.0695 1.0161
## I(week^2)-Dasypus_novemcinctus -0.1496 0.1074 -0.3658 -0.1477 0.0608 1.0014
## I(week^2)-Sylvilagus_floridanus -0.1551 0.1666 -0.4883 -0.1465 0.1525 1.0032
## ESS
## (Intercept)-Canis_latrans 722
## (Intercept)-Procyon_lotor 1650
## (Intercept)-Dasypus_novemcinctus 435
## (Intercept)-Sylvilagus_floridanus 416
## shrub_cover-Canis_latrans 477
## shrub_cover-Procyon_lotor 1461
## shrub_cover-Dasypus_novemcinctus 175
## shrub_cover-Sylvilagus_floridanus 193
## veg_height-Canis_latrans 675
## veg_height-Procyon_lotor 1376
## veg_height-Dasypus_novemcinctus 1285
## veg_height-Sylvilagus_floridanus 371
## week-Canis_latrans 1194
## week-Procyon_lotor 1563
## week-Dasypus_novemcinctus 1936
## week-Sylvilagus_floridanus 1001
## I(week^2)-Canis_latrans 1344
## I(week^2)-Procyon_lotor 1413
## I(week^2)-Dasypus_novemcinctus 1480
## I(week^2)-Sylvilagus_floridanus 568
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T25 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_canopy_T25)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.469
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0744 0.5734 -1.3005 -0.0672 1.0518 1.0037 1321
## Tree_Density -1.0227 0.6283 -2.3671 -1.0037 0.2018 1.0162 1156
## Avg_Canopy_Cover 0.8392 0.7946 -0.9148 0.8536 2.4124 1.0026 2401
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3076 2.6230 0.0570 0.5828 7.6825 1.0058 1782
## Tree_Density 1.5632 4.6693 0.0526 0.4689 8.8267 1.0756 1132
## Avg_Canopy_Cover 4.6519 11.7860 0.1329 1.8444 27.3049 1.1292 1046
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5183 0.8369 0.0454 0.2774 2.3659 1.1295 360
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1924 0.5034 -3.0515 -2.2422 -0.9637 1.0054 2589
## shrub_cover 0.3068 0.4139 -0.5271 0.2960 1.1855 1.0013 2243
## veg_height 0.0313 0.3836 -0.7589 0.0355 0.7903 1.0069 2527
## week 0.1573 0.3179 -0.4675 0.1599 0.7940 1.0059 1849
## I(week^2) -0.1487 0.2151 -0.5843 -0.1476 0.2648 1.0046 2444
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0109 2.2881 0.1048 0.4782 5.2714 1.1272 2574
## shrub_cover 0.8473 2.1218 0.0753 0.4105 3.8911 1.0086 2674
## veg_height 0.6957 2.6364 0.0708 0.3253 3.2616 1.0948 2525
## week 0.3891 1.0875 0.0358 0.1790 1.9343 1.0753 2050
## I(week^2) 0.1791 0.3208 0.0245 0.0917 0.9118 1.0235 2405
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2356 0.5487 -0.7976 0.2250 1.3508
## (Intercept)-Procyon_lotor 0.4947 0.5707 -0.6046 0.4769 1.6415
## (Intercept)-Dasypus_novemcinctus -0.6418 0.5924 -1.9000 -0.6081 0.3812
## (Intercept)-Sylvilagus_floridanus -0.4097 0.6924 -1.8446 -0.3861 0.8897
## Tree_Density-Canis_latrans -1.1583 0.6833 -2.6518 -1.0786 -0.0461
## Tree_Density-Procyon_lotor -0.5584 0.4601 -1.4990 -0.5523 0.3723
## Tree_Density-Dasypus_novemcinctus -1.5422 0.9613 -3.9862 -1.3507 -0.2163
## Tree_Density-Sylvilagus_floridanus -1.3059 0.9359 -3.6637 -1.1568 0.0647
## Avg_Canopy_Cover-Canis_latrans -0.2713 0.4796 -1.2637 -0.2524 0.6221
## Avg_Canopy_Cover-Procyon_lotor 0.9870 0.5260 0.0698 0.9490 2.0719
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0760 0.5325 0.1434 1.0353 2.1952
## Avg_Canopy_Cover-Sylvilagus_floridanus 2.7872 1.6019 0.6378 2.4542 6.9619
## Rhat ESS
## (Intercept)-Canis_latrans 1.0041 1079
## (Intercept)-Procyon_lotor 1.0117 872
## (Intercept)-Dasypus_novemcinctus 1.0109 723
## (Intercept)-Sylvilagus_floridanus 1.0176 645
## Tree_Density-Canis_latrans 1.0066 930
## Tree_Density-Procyon_lotor 1.0005 1853
## Tree_Density-Dasypus_novemcinctus 1.0261 517
## Tree_Density-Sylvilagus_floridanus 1.0228 581
## Avg_Canopy_Cover-Canis_latrans 1.0100 1541
## Avg_Canopy_Cover-Procyon_lotor 1.0177 1517
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0042 1788
## Avg_Canopy_Cover-Sylvilagus_floridanus 1.1109 253
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5929 0.2061 -3.0173 -2.5854 -2.1962 1.0269
## (Intercept)-Procyon_lotor -2.2063 0.1574 -2.5253 -2.2030 -1.9066 1.0015
## (Intercept)-Dasypus_novemcinctus -1.6608 0.1852 -2.0283 -1.6594 -1.3077 1.0035
## (Intercept)-Sylvilagus_floridanus -2.9942 0.2934 -3.5880 -2.9911 -2.4298 1.0344
## shrub_cover-Canis_latrans -0.3046 0.2210 -0.7433 -0.3030 0.1117 1.0073
## shrub_cover-Procyon_lotor 0.2661 0.1621 -0.0530 0.2672 0.5745 1.0129
## shrub_cover-Dasypus_novemcinctus 0.8818 0.3106 0.2938 0.8749 1.5137 1.0006
## shrub_cover-Sylvilagus_floridanus 0.4534 0.3931 -0.3255 0.4480 1.2311 1.0057
## veg_height-Canis_latrans -0.6245 0.1894 -1.0085 -0.6227 -0.2691 1.0118
## veg_height-Procyon_lotor 0.3511 0.1205 0.1140 0.3515 0.5844 1.0032
## veg_height-Dasypus_novemcinctus 0.2644 0.1399 -0.0030 0.2606 0.5365 1.0146
## veg_height-Sylvilagus_floridanus 0.1697 0.2515 -0.3433 0.1738 0.6486 1.0052
## week-Canis_latrans 0.4627 0.2680 -0.0179 0.4428 1.0031 1.0102
## week-Procyon_lotor 0.1407 0.2121 -0.2696 0.1410 0.5587 1.0027
## week-Dasypus_novemcinctus 0.0446 0.2219 -0.3655 0.0413 0.4770 1.0006
## week-Sylvilagus_floridanus 0.0163 0.3295 -0.6552 0.0235 0.6421 1.0109
## I(week^2)-Canis_latrans -0.1976 0.1119 -0.4209 -0.1967 0.0140 1.0183
## I(week^2)-Procyon_lotor -0.1017 0.0922 -0.2873 -0.1009 0.0751 1.0007
## I(week^2)-Dasypus_novemcinctus -0.1449 0.1054 -0.3591 -0.1445 0.0489 1.0011
## I(week^2)-Sylvilagus_floridanus -0.1493 0.1680 -0.5066 -0.1457 0.1632 1.0043
## ESS
## (Intercept)-Canis_latrans 922
## (Intercept)-Procyon_lotor 1468
## (Intercept)-Dasypus_novemcinctus 1549
## (Intercept)-Sylvilagus_floridanus 683
## shrub_cover-Canis_latrans 761
## shrub_cover-Procyon_lotor 1445
## shrub_cover-Dasypus_novemcinctus 1036
## shrub_cover-Sylvilagus_floridanus 550
## veg_height-Canis_latrans 754
## veg_height-Procyon_lotor 1682
## veg_height-Dasypus_novemcinctus 1788
## veg_height-Sylvilagus_floridanus 815
## week-Canis_latrans 1179
## week-Procyon_lotor 1943
## week-Dasypus_novemcinctus 2170
## week-Sylvilagus_floridanus 1034
## I(week^2)-Canis_latrans 1260
## I(week^2)-Procyon_lotor 1591
## I(week^2)-Dasypus_novemcinctus 1822
## I(week^2)-Sylvilagus_floridanus 735
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T25 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_move_T25)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4997
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3451 0.6231 -0.9058 0.3316 1.6328 1.0687 540
## Cogon_Patch_Size -0.0313 0.6547 -1.4263 -0.0242 1.2598 1.0004 1787
## Avg_Cogongrass_Cover 0.1313 0.4988 -0.8281 0.1267 1.1484 1.0113 1058
## total_shrub_cover -0.5408 0.6974 -2.0389 -0.5103 0.8252 1.0035 693
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2495 3.3460 0.0499 0.4854 6.3571 1.0285 1793
## Cogon_Patch_Size 2.4382 20.2833 0.0596 0.6878 12.6419 1.3071 2467
## Avg_Cogongrass_Cover 0.7296 1.5431 0.0456 0.3068 3.7724 1.0402 1519
## total_shrub_cover 2.4460 10.8232 0.0733 0.7770 13.4051 1.3492 922
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6864 1.0851 0.0457 0.3401 3.3216 1.1427 214
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2383 0.5356 -3.1227 -2.2995 -1.0240 1.0016 2357
## shrub_cover 0.3749 0.4966 -0.6030 0.3828 1.3632 1.0055 1433
## veg_height 0.0149 0.3678 -0.7347 0.0195 0.7703 1.0029 3000
## week 0.1535 0.3210 -0.5041 0.1594 0.7422 1.0069 1746
## I(week^2) -0.1484 0.2207 -0.5929 -0.1488 0.2645 1.0022 2196
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2466 4.0258 0.1041 0.5301 6.4527 1.0296 2726
## shrub_cover 1.0990 2.6498 0.0892 0.5412 5.4702 1.0334 1951
## veg_height 0.6240 1.2968 0.0697 0.3131 3.2179 1.1453 3000
## week 0.3795 1.4242 0.0332 0.1777 1.8167 1.2345 2676
## I(week^2) 0.1874 0.4038 0.0237 0.0938 0.8831 1.0080 2741
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6413 0.6444 -0.4649 0.5864
## (Intercept)-Procyon_lotor 0.7807 0.6076 -0.2806 0.7389
## (Intercept)-Dasypus_novemcinctus -0.1430 0.6783 -1.4032 -0.1693
## (Intercept)-Sylvilagus_floridanus 0.1927 0.7982 -1.2853 0.1588
## Cogon_Patch_Size-Canis_latrans 0.8469 0.9106 -0.3981 0.6674
## Cogon_Patch_Size-Procyon_lotor -0.1451 0.5045 -1.1893 -0.1297
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0172 0.4842 -0.9570 -0.0347
## Cogon_Patch_Size-Sylvilagus_floridanus -0.8630 1.0392 -3.3683 -0.6427
## Avg_Cogongrass_Cover-Canis_latrans 0.3540 0.5191 -0.5565 0.3099
## Avg_Cogongrass_Cover-Procyon_lotor 0.0865 0.5190 -0.9264 0.0993
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3721 0.4859 -0.4955 0.3474
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.2095 0.6678 -1.6814 -0.1765
## total_shrub_cover-Canis_latrans 0.3077 0.7560 -0.8915 0.1995
## total_shrub_cover-Procyon_lotor -1.1905 0.6982 -2.8864 -1.0679
## total_shrub_cover-Dasypus_novemcinctus -0.4586 0.8054 -2.6908 -0.3198
## total_shrub_cover-Sylvilagus_floridanus -1.1968 1.2444 -4.2987 -0.9850
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0630 1.0425 477
## (Intercept)-Procyon_lotor 2.1196 1.0538 820
## (Intercept)-Dasypus_novemcinctus 1.3597 1.0298 238
## (Intercept)-Sylvilagus_floridanus 1.8675 1.0740 262
## Cogon_Patch_Size-Canis_latrans 3.1963 1.0260 568
## Cogon_Patch_Size-Procyon_lotor 0.8382 1.0016 1578
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9745 1.0018 1380
## Cogon_Patch_Size-Sylvilagus_floridanus 0.5339 1.0044 504
## Avg_Cogongrass_Cover-Canis_latrans 1.5288 1.0040 1147
## Avg_Cogongrass_Cover-Procyon_lotor 1.1462 1.0089 1101
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4511 1.0023 1400
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 1.0375 1.0013 711
## total_shrub_cover-Canis_latrans 2.1090 1.0458 443
## total_shrub_cover-Procyon_lotor -0.1090 1.0485 404
## total_shrub_cover-Dasypus_novemcinctus 0.6233 1.0138 180
## total_shrub_cover-Sylvilagus_floridanus 0.6278 1.0801 206
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5998 0.2085 -3.0183 -2.5966 -2.2150 1.0037
## (Intercept)-Procyon_lotor -2.2157 0.1570 -2.5303 -2.2122 -1.9174 1.0038
## (Intercept)-Dasypus_novemcinctus -1.7167 0.2066 -2.1489 -1.7108 -1.3398 1.0007
## (Intercept)-Sylvilagus_floridanus -3.1365 0.3104 -3.7874 -3.1208 -2.5765 1.0414
## shrub_cover-Canis_latrans -0.3324 0.2463 -0.7954 -0.3304 0.1455 1.0411
## shrub_cover-Procyon_lotor 0.3175 0.1600 -0.0033 0.3204 0.6214 1.0021
## shrub_cover-Dasypus_novemcinctus 1.0325 0.3898 0.3424 1.0126 1.8488 1.0046
## shrub_cover-Sylvilagus_floridanus 0.6745 0.4713 -0.2978 0.6962 1.5752 1.0375
## veg_height-Canis_latrans -0.6247 0.1886 -1.0146 -0.6232 -0.2790 1.0129
## veg_height-Procyon_lotor 0.3540 0.1279 0.0988 0.3547 0.6043 1.0118
## veg_height-Dasypus_novemcinctus 0.2727 0.1508 -0.0193 0.2730 0.5717 1.0000
## veg_height-Sylvilagus_floridanus 0.0558 0.2611 -0.4284 0.0482 0.5832 1.0203
## week-Canis_latrans 0.4584 0.2692 -0.0381 0.4451 1.0304 1.0041
## week-Procyon_lotor 0.1375 0.2048 -0.2556 0.1360 0.5447 0.9999
## week-Dasypus_novemcinctus 0.0393 0.2236 -0.3941 0.0351 0.4867 1.0001
## week-Sylvilagus_floridanus 0.0212 0.3164 -0.6074 0.0320 0.6546 1.0024
## I(week^2)-Canis_latrans -0.1978 0.1123 -0.4260 -0.1937 0.0148 1.0046
## I(week^2)-Procyon_lotor -0.1031 0.0925 -0.2883 -0.1044 0.0838 1.0004
## I(week^2)-Dasypus_novemcinctus -0.1446 0.1077 -0.3654 -0.1417 0.0552 1.0025
## I(week^2)-Sylvilagus_floridanus -0.1502 0.1601 -0.4889 -0.1432 0.1369 1.0260
## ESS
## (Intercept)-Canis_latrans 724
## (Intercept)-Procyon_lotor 1145
## (Intercept)-Dasypus_novemcinctus 533
## (Intercept)-Sylvilagus_floridanus 462
## shrub_cover-Canis_latrans 449
## shrub_cover-Procyon_lotor 1464
## shrub_cover-Dasypus_novemcinctus 256
## shrub_cover-Sylvilagus_floridanus 240
## veg_height-Canis_latrans 607
## veg_height-Procyon_lotor 1529
## veg_height-Dasypus_novemcinctus 1351
## veg_height-Sylvilagus_floridanus 570
## week-Canis_latrans 1074
## week-Procyon_lotor 1654
## week-Dasypus_novemcinctus 2075
## week-Sylvilagus_floridanus 1019
## I(week^2)-Canis_latrans 1026
## I(week^2)-Procyon_lotor 1622
## I(week^2)-Dasypus_novemcinctus 1668
## I(week^2)-Sylvilagus_floridanus 816
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T25 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_forage_T25)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4703
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0463 0.5150 -0.9541 0.0485 1.0811 1.0048 1494
## Veg_shannon_index 0.4714 0.4371 -0.3807 0.4735 1.3450 1.0105 1621
## Avg_Cogongrass_Cover 0.3582 0.4636 -0.5427 0.3564 1.3054 1.0071 1738
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8790 2.0106 0.0457 0.4082 4.6461 1.0765 2106
## Veg_shannon_index 0.6212 1.7946 0.0382 0.2549 3.5659 1.0055 2481
## Avg_Cogongrass_Cover 0.8834 2.4731 0.0469 0.3490 4.7617 1.0592 2241
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5733 0.6255 0.0475 0.3721 2.2564 1.0015 487
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1910 0.4941 -2.9906 -2.2523 -1.0646 1.0011 2406
## shrub_cover 0.2541 0.4235 -0.6230 0.2633 1.1339 1.0044 2252
## veg_height 0.0574 0.3851 -0.6936 0.0467 0.8874 1.0002 2701
## week 0.1678 0.3220 -0.4874 0.1691 0.8194 1.0045 1854
## I(week^2) -0.1443 0.2170 -0.5654 -0.1423 0.2617 1.0031 2200
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.9946 1.7501 0.1035 0.5011 5.5227 1.0307 2078
## shrub_cover 0.7368 1.2712 0.0669 0.3769 3.9209 1.0300 2308
## veg_height 0.6465 1.2609 0.0714 0.3317 2.9849 1.0490 2411
## week 0.3499 0.6241 0.0352 0.1776 1.7881 1.0036 2501
## I(week^2) 0.1904 0.5223 0.0228 0.0921 0.8968 1.1676 2101
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3006 0.5339 -0.6689 0.2651
## (Intercept)-Procyon_lotor 0.4296 0.5282 -0.5382 0.3986
## (Intercept)-Dasypus_novemcinctus -0.3532 0.5088 -1.4256 -0.3286
## (Intercept)-Sylvilagus_floridanus -0.2005 0.5501 -1.3179 -0.1991
## Veg_shannon_index-Canis_latrans 0.7610 0.4487 -0.0234 0.7277
## Veg_shannon_index-Procyon_lotor 0.5249 0.4142 -0.2345 0.5104
## Veg_shannon_index-Dasypus_novemcinctus 0.2142 0.3898 -0.5808 0.2296
## Veg_shannon_index-Sylvilagus_floridanus 0.5015 0.4681 -0.3644 0.4851
## Avg_Cogongrass_Cover-Canis_latrans 0.7359 0.5168 -0.1132 0.6718
## Avg_Cogongrass_Cover-Procyon_lotor 0.4212 0.4451 -0.3857 0.3873
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4758 0.3880 -0.2370 0.4586
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.1705 0.5340 -1.3345 -0.1346
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.4852 1.0053 972
## (Intercept)-Procyon_lotor 1.5817 1.0046 927
## (Intercept)-Dasypus_novemcinctus 0.5667 1.0023 1083
## (Intercept)-Sylvilagus_floridanus 0.8988 1.0016 895
## Veg_shannon_index-Canis_latrans 1.7243 1.0049 1372
## Veg_shannon_index-Procyon_lotor 1.3672 1.0036 1341
## Veg_shannon_index-Dasypus_novemcinctus 0.9475 1.0016 1764
## Veg_shannon_index-Sylvilagus_floridanus 1.5210 1.0115 819
## Avg_Cogongrass_Cover-Canis_latrans 1.9745 1.0016 1315
## Avg_Cogongrass_Cover-Procyon_lotor 1.3864 1.0011 1664
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2652 1.0025 2082
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.7891 1.0083 1227
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5755 0.1943 -2.9703 -2.5652 -2.2209 1.0008
## (Intercept)-Procyon_lotor -2.2080 0.1592 -2.5338 -2.2054 -1.9049 1.0184
## (Intercept)-Dasypus_novemcinctus -1.6514 0.1833 -2.0164 -1.6494 -1.3069 1.0031
## (Intercept)-Sylvilagus_floridanus -3.0309 0.3260 -3.7189 -3.0141 -2.4561 1.0033
## shrub_cover-Canis_latrans -0.2785 0.2217 -0.7139 -0.2750 0.1473 1.0006
## shrub_cover-Procyon_lotor 0.2535 0.1699 -0.0972 0.2582 0.5774 1.0001
## shrub_cover-Dasypus_novemcinctus 0.8520 0.3247 0.2457 0.8423 1.5043 1.0002
## shrub_cover-Sylvilagus_floridanus 0.2586 0.4030 -0.4961 0.2426 1.0525 1.0111
## veg_height-Canis_latrans -0.6239 0.1913 -1.0015 -0.6220 -0.2559 1.0039
## veg_height-Procyon_lotor 0.3491 0.1264 0.1027 0.3455 0.5944 1.0121
## veg_height-Dasypus_novemcinctus 0.2548 0.1370 -0.0120 0.2532 0.5276 1.0012
## veg_height-Sylvilagus_floridanus 0.1703 0.2678 -0.3664 0.1747 0.6901 1.0174
## week-Canis_latrans 0.4698 0.2691 -0.0308 0.4600 1.0290 1.0236
## week-Procyon_lotor 0.1481 0.2067 -0.2576 0.1449 0.5427 1.0043
## week-Dasypus_novemcinctus 0.0439 0.2232 -0.3975 0.0468 0.4727 1.0089
## week-Sylvilagus_floridanus 0.0154 0.3247 -0.6367 0.0208 0.6343 1.0014
## I(week^2)-Canis_latrans -0.2005 0.1130 -0.4262 -0.1994 0.0096 1.0204
## I(week^2)-Procyon_lotor -0.1060 0.0911 -0.2866 -0.1059 0.0716 1.0024
## I(week^2)-Dasypus_novemcinctus -0.1457 0.1031 -0.3591 -0.1422 0.0481 1.0105
## I(week^2)-Sylvilagus_floridanus -0.1416 0.1640 -0.4861 -0.1310 0.1530 1.0009
## ESS
## (Intercept)-Canis_latrans 857
## (Intercept)-Procyon_lotor 1255
## (Intercept)-Dasypus_novemcinctus 1134
## (Intercept)-Sylvilagus_floridanus 441
## shrub_cover-Canis_latrans 726
## shrub_cover-Procyon_lotor 1187
## shrub_cover-Dasypus_novemcinctus 974
## shrub_cover-Sylvilagus_floridanus 476
## veg_height-Canis_latrans 724
## veg_height-Procyon_lotor 1269
## veg_height-Dasypus_novemcinctus 1392
## veg_height-Sylvilagus_floridanus 566
## week-Canis_latrans 1277
## week-Procyon_lotor 1696
## week-Dasypus_novemcinctus 2166
## week-Sylvilagus_floridanus 1095
## I(week^2)-Canis_latrans 1367
## I(week^2)-Procyon_lotor 1576
## I(week^2)-Dasypus_novemcinctus 1890
## I(week^2)-Sylvilagus_floridanus 847
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T25 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogon_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4657
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0381 0.4882 -0.9345 0.0359 1.0394 1.0012 1265
## Avg_Cogongrass_Cover 0.1822 0.4480 -0.7115 0.1801 1.0396 1.0076 2182
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8867 1.9732 0.0518 0.3813 4.6648 1.0185 1872
## Avg_Cogongrass_Cover 0.7544 1.6968 0.0447 0.3299 3.9825 1.0410 1895
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5343 0.7739 0.0472 0.3153 2.2932 1.0404 492
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1886 0.5269 -3.0248 -2.2429 -0.9262 1.0034 2723
## shrub_cover 0.2783 0.4190 -0.5435 0.2754 1.1074 1.0040 2128
## veg_height 0.0401 0.3756 -0.7192 0.0366 0.8298 1.0027 2833
## week 0.1722 0.3251 -0.4815 0.1754 0.7933 1.0016 1972
## I(week^2) -0.1489 0.2303 -0.6082 -0.1445 0.3018 1.0052 2513
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1479 3.1513 0.1034 0.5191 5.8570 1.0281 2747
## shrub_cover 0.8594 4.7977 0.0657 0.3722 3.6367 1.2709 3000
## veg_height 0.6557 1.6842 0.0730 0.3305 3.0319 1.0957 2449
## week 0.3350 0.6817 0.0365 0.1680 1.5880 1.0275 2170
## I(week^2) 0.1974 0.5183 0.0249 0.0977 0.8792 1.0102 2664
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3164 0.5151 -0.6178 0.2926
## (Intercept)-Procyon_lotor 0.4014 0.5002 -0.5811 0.3835
## (Intercept)-Dasypus_novemcinctus -0.3375 0.4738 -1.2935 -0.3213
## (Intercept)-Sylvilagus_floridanus -0.2268 0.5309 -1.2674 -0.2154
## Avg_Cogongrass_Cover-Canis_latrans 0.4923 0.4447 -0.2569 0.4514
## Avg_Cogongrass_Cover-Procyon_lotor 0.2162 0.3829 -0.4960 0.1987
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3727 0.3669 -0.3103 0.3601
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.3229 0.5058 -1.4320 -0.2801
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.3876 1.0079 839
## (Intercept)-Procyon_lotor 1.4169 1.0277 985
## (Intercept)-Dasypus_novemcinctus 0.5638 1.0022 1231
## (Intercept)-Sylvilagus_floridanus 0.8498 1.0007 836
## Avg_Cogongrass_Cover-Canis_latrans 1.4710 1.0067 1663
## Avg_Cogongrass_Cover-Procyon_lotor 1.0279 1.0125 2472
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1566 1.0020 2207
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.5447 1.0238 1121
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5960 0.1992 -2.9931 -2.5933 -2.2040 1.0008
## (Intercept)-Procyon_lotor -2.2026 0.1599 -2.5345 -2.2012 -1.9008 1.0042
## (Intercept)-Dasypus_novemcinctus -1.6493 0.1855 -2.0221 -1.6456 -1.3005 1.0017
## (Intercept)-Sylvilagus_floridanus -3.0097 0.3206 -3.7011 -2.9865 -2.4366 1.0015
## shrub_cover-Canis_latrans -0.2565 0.2254 -0.6949 -0.2527 0.1756 1.0014
## shrub_cover-Procyon_lotor 0.2589 0.1680 -0.0730 0.2629 0.5809 1.0006
## shrub_cover-Dasypus_novemcinctus 0.8454 0.3165 0.2436 0.8375 1.4787 1.0003
## shrub_cover-Sylvilagus_floridanus 0.2721 0.4143 -0.5238 0.2613 1.0947 1.0087
## veg_height-Canis_latrans -0.6312 0.1950 -1.0264 -0.6268 -0.2599 1.0002
## veg_height-Procyon_lotor 0.3444 0.1276 0.0925 0.3438 0.5928 1.0037
## veg_height-Dasypus_novemcinctus 0.2571 0.1340 0.0015 0.2564 0.5215 1.0028
## veg_height-Sylvilagus_floridanus 0.1661 0.2608 -0.3517 0.1728 0.6610 1.0167
## week-Canis_latrans 0.4631 0.2661 -0.0385 0.4537 1.0022 1.0064
## week-Procyon_lotor 0.1470 0.2008 -0.2435 0.1490 0.5367 1.0002
## week-Dasypus_novemcinctus 0.0462 0.2219 -0.3957 0.0505 0.4572 1.0019
## week-Sylvilagus_floridanus 0.0398 0.3160 -0.5912 0.0430 0.6705 1.0013
## I(week^2)-Canis_latrans -0.2020 0.1089 -0.4178 -0.1989 0.0026 1.0057
## I(week^2)-Procyon_lotor -0.1026 0.0887 -0.2742 -0.1019 0.0677 1.0016
## I(week^2)-Dasypus_novemcinctus -0.1452 0.1057 -0.3542 -0.1434 0.0570 0.9995
## I(week^2)-Sylvilagus_floridanus -0.1569 0.1690 -0.5069 -0.1495 0.1592 1.0222
## ESS
## (Intercept)-Canis_latrans 872
## (Intercept)-Procyon_lotor 1497
## (Intercept)-Dasypus_novemcinctus 1714
## (Intercept)-Sylvilagus_floridanus 581
## shrub_cover-Canis_latrans 850
## shrub_cover-Procyon_lotor 1374
## shrub_cover-Dasypus_novemcinctus 1065
## shrub_cover-Sylvilagus_floridanus 513
## veg_height-Canis_latrans 759
## veg_height-Procyon_lotor 1837
## veg_height-Dasypus_novemcinctus 1988
## veg_height-Sylvilagus_floridanus 598
## week-Canis_latrans 1172
## week-Procyon_lotor 1665
## week-Dasypus_novemcinctus 2175
## week-Sylvilagus_floridanus 1241
## I(week^2)-Canis_latrans 1132
## I(week^2)-Procyon_lotor 1566
## I(week^2)-Dasypus_novemcinctus 1854
## I(week^2)-Sylvilagus_floridanus 791
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T25 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogonQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4703
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6516 0.5650 -1.8070 -0.6347 0.4582 1.0162 633
## Avg_Cogongrass_Cover -0.5054 0.6326 -1.7621 -0.5042 0.7338 1.0050 1163
## I(Avg_Cogongrass_Cover^2) 0.9765 0.6441 -0.1826 0.9265 2.3787 1.0347 632
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.8674 1.9694 0.0460 0.3467 4.6262 1.0086 2278
## Avg_Cogongrass_Cover 1.4922 3.9650 0.0573 0.5533 8.6268 1.0459 1883
## I(Avg_Cogongrass_Cover^2) 1.6560 5.1814 0.0439 0.4577 9.8371 1.0130 740
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4307 0.5568 0.0423 0.2453 1.9133 1.0712 427
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.1883 0.5138 -3.0400 -2.2427 -0.9567 1.0005 3000
## shrub_cover 0.2400 0.4096 -0.5774 0.2394 1.0320 1.0097 2265
## veg_height 0.0425 0.3774 -0.7605 0.0484 0.8206 1.0066 2729
## week 0.1679 0.3215 -0.4853 0.1661 0.8359 1.0227 2086
## I(week^2) -0.1454 0.2122 -0.5504 -0.1451 0.2698 1.0082 2155
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.1512 2.3618 0.1128 0.5245 6.7908 1.0086 2220
## shrub_cover 0.7586 2.4672 0.0608 0.3633 3.7712 1.1796 2432
## veg_height 0.6366 1.1589 0.0823 0.3441 3.1368 1.0251 2667
## week 0.3681 0.8767 0.0360 0.1805 1.7763 1.0057 2565
## I(week^2) 0.1790 0.5623 0.0226 0.0909 0.8335 1.2043 2645
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5423 0.6235 -1.8269 -0.5331
## (Intercept)-Procyon_lotor -0.3582 0.5807 -1.4474 -0.3684
## (Intercept)-Dasypus_novemcinctus -0.9960 0.5687 -2.1675 -0.9737
## (Intercept)-Sylvilagus_floridanus -0.8936 0.6248 -2.1976 -0.8781
## Avg_Cogongrass_Cover-Canis_latrans -0.0943 0.6842 -1.3617 -0.1453
## Avg_Cogongrass_Cover-Procyon_lotor -0.5976 0.6494 -1.8408 -0.6028
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.3053 0.5875 -1.4144 -0.3051
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -1.2904 0.8490 -3.1691 -1.2000
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.6232 1.0593 0.1914 1.3676
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.3877 0.9729 0.1877 1.1643
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.6276 0.4513 -0.1744 0.6079
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 0.7155 0.5404 -0.2435 0.6768
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7141 1.0154 734
## (Intercept)-Procyon_lotor 0.8147 1.0154 787
## (Intercept)-Dasypus_novemcinctus 0.0650 1.0090 805
## (Intercept)-Sylvilagus_floridanus 0.2565 1.0102 783
## Avg_Cogongrass_Cover-Canis_latrans 1.4262 1.0059 1010
## Avg_Cogongrass_Cover-Procyon_lotor 0.7261 1.0023 1090
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.8825 1.0139 988
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 0.0810 1.0219 537
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.3060 1.0546 266
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.9100 1.1112 230
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5725 1.0128 934
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.9203 1.0471 616
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5968 0.2004 -3.0193 -2.5865 -2.2194 1.0128
## (Intercept)-Procyon_lotor -2.2250 0.1620 -2.5539 -2.2205 -1.9203 1.0047
## (Intercept)-Dasypus_novemcinctus -1.6511 0.1851 -2.0305 -1.6482 -1.2953 1.0073
## (Intercept)-Sylvilagus_floridanus -3.0371 0.3089 -3.6832 -3.0302 -2.4652 1.0402
## shrub_cover-Canis_latrans -0.2375 0.2189 -0.6649 -0.2421 0.1956 1.0049
## shrub_cover-Procyon_lotor 0.2321 0.1691 -0.1077 0.2334 0.5586 1.0081
## shrub_cover-Dasypus_novemcinctus 0.8418 0.3099 0.2671 0.8276 1.4582 1.0158
## shrub_cover-Sylvilagus_floridanus 0.2194 0.3986 -0.5150 0.2000 1.0508 1.0342
## veg_height-Canis_latrans -0.6350 0.1922 -1.0308 -0.6294 -0.2690 1.0450
## veg_height-Procyon_lotor 0.3617 0.1280 0.1072 0.3610 0.6099 1.0015
## veg_height-Dasypus_novemcinctus 0.2605 0.1392 -0.0098 0.2615 0.5365 1.0026
## veg_height-Sylvilagus_floridanus 0.2100 0.2698 -0.3078 0.2132 0.7437 1.0132
## week-Canis_latrans 0.4690 0.2621 -0.0130 0.4601 1.0082 1.0328
## week-Procyon_lotor 0.1491 0.2037 -0.2528 0.1487 0.5529 1.0059
## week-Dasypus_novemcinctus 0.0448 0.2207 -0.3744 0.0426 0.4745 1.0140
## week-Sylvilagus_floridanus 0.0068 0.3256 -0.6529 0.0133 0.6435 1.0109
## I(week^2)-Canis_latrans -0.1991 0.1096 -0.4227 -0.1979 0.0118 1.0304
## I(week^2)-Procyon_lotor -0.1050 0.0894 -0.2833 -0.1025 0.0711 0.9998
## I(week^2)-Dasypus_novemcinctus -0.1469 0.1068 -0.3682 -0.1460 0.0593 1.0026
## I(week^2)-Sylvilagus_floridanus -0.1411 0.1623 -0.4820 -0.1353 0.1609 1.0244
## ESS
## (Intercept)-Canis_latrans 890
## (Intercept)-Procyon_lotor 1306
## (Intercept)-Dasypus_novemcinctus 1718
## (Intercept)-Sylvilagus_floridanus 635
## shrub_cover-Canis_latrans 940
## shrub_cover-Procyon_lotor 1271
## shrub_cover-Dasypus_novemcinctus 1026
## shrub_cover-Sylvilagus_floridanus 474
## veg_height-Canis_latrans 793
## veg_height-Procyon_lotor 1454
## veg_height-Dasypus_novemcinctus 1905
## veg_height-Sylvilagus_floridanus 595
## week-Canis_latrans 1261
## week-Procyon_lotor 1488
## week-Dasypus_novemcinctus 1912
## week-Sylvilagus_floridanus 1051
## I(week^2)-Canis_latrans 1179
## I(week^2)-Procyon_lotor 1539
## I(week^2)-Dasypus_novemcinctus 1555
## I(week^2)-Sylvilagus_floridanus 677
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T25 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
n.burn = 3000,
n.chains = 3,
n.report = 500,
)
## ----------------------------------------
## Preparing to run the model
## ----------------------------------------
## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded
## No prior specified for beta.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for alpha.comm.normal.
## Setting prior mean to 0 and prior variance to 2.72
## No prior specified for tau.sq.beta.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for tau.sq.alpha.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## No prior specified for sigma.sq.psi.ig.
## Setting prior shape to 0.1 and prior scale to 0.1
## z is not specified in initial values.
## Setting initial values based on observed data
## beta.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## alpha.comm is not specified in initial values.
## Setting initial values to random values from the prior distribution
## tau.sq.beta is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## tau.sq.alpha is not specified in initial values.
## Setting to initial values to random values between 0.5 and 10
## beta is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## alpha is not specified in initial values.
## Setting initial values to random values from the community-level normal distribution
## sigma.sq.psi is not specified in initial values.
## Setting initial values to random values between 0.5 and 10
## ----------------------------------------
## Model description
## ----------------------------------------
## Multi-species Occupancy Model with Polya-Gamma latent
## variable fit with 32 sites and 4 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_fullQ_T25)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4868
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1870 0.8669 -2.8467 -1.1990 0.5629 1.0107 611
## Cogon_Patch_Size -0.2082 0.9105 -1.9574 -0.2136 1.6424 1.0113 834
## Veg_shannon_index 1.0000 0.6496 -0.2704 0.9806 2.3065 1.0257 682
## total_shrub_cover -0.3570 0.7501 -1.8295 -0.3703 1.3123 1.0058 702
## Avg_Cogongrass_Cover 0.3992 1.1618 -1.8823 0.4155 2.6211 1.0608 220
## Tree_Density -2.3611 1.2211 -4.5622 -2.4346 0.5113 1.0030 658
## Avg_Canopy_Cover 1.2838 1.0937 -1.1944 1.3307 3.3449 1.0025 2010
## I(Avg_Cogongrass_Cover^2) 1.4239 0.8641 -0.3518 1.4259 3.1592 1.0495 461
## avg_veg_height -0.0610 0.6954 -1.4128 -0.0657 1.3370 1.0369 617
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8555 10.2688 0.0600 0.7846 18.8225 1.1291 562
## Cogon_Patch_Size 5.3739 16.7028 0.0737 1.4446 34.1414 1.0058 785
## Veg_shannon_index 1.2340 5.1755 0.0490 0.4051 6.7433 1.1523 2647
## total_shrub_cover 2.3651 5.4591 0.0730 0.8618 14.6900 1.0279 418
## Avg_Cogongrass_Cover 3.8854 11.9125 0.0677 1.0146 25.7143 1.0779 1393
## Tree_Density 13.2648 74.3443 0.0707 1.7689 93.2405 1.0785 863
## Avg_Canopy_Cover 13.7002 44.4064 0.1744 3.7180 86.6986 1.0359 489
## I(Avg_Cogongrass_Cover^2) 3.4691 13.3914 0.0573 0.7462 21.8971 1.1890 327
## avg_veg_height 1.1225 2.7811 0.0462 0.3935 6.6714 1.0151 717
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3942 2.4718 0.0499 0.6074 7.7168 1.1194 82
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2112 0.4774 -3.0377 -2.2565 -1.0825 1.0010 2590
## shrub_cover 0.3210 0.4535 -0.5815 0.3244 1.2039 1.0118 1576
## veg_height 0.0658 0.3877 -0.7322 0.0599 0.8618 1.0026 3000
## week 0.1551 0.3157 -0.4560 0.1523 0.7594 1.0031 2051
## I(week^2) -0.1385 0.2131 -0.5519 -0.1392 0.2738 1.0014 2649
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0036 1.9846 0.0937 0.4919 5.3294 1.0285 2398
## shrub_cover 0.8689 1.8971 0.0867 0.4604 3.7644 1.0462 2401
## veg_height 0.6550 1.8323 0.0749 0.3200 3.2212 1.1470 2089
## week 0.3809 0.9482 0.0368 0.1723 1.8903 1.0443 2258
## I(week^2) 0.1793 0.3504 0.0241 0.0932 0.8727 1.0390 2732
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.0060 1.0528 -3.0594 -1.0300
## (Intercept)-Procyon_lotor -0.7364 0.9631 -2.5853 -0.7641
## (Intercept)-Dasypus_novemcinctus -2.0525 1.2126 -4.8945 -1.8824
## (Intercept)-Sylvilagus_floridanus -1.7773 1.2340 -4.4922 -1.6760
## Cogon_Patch_Size-Canis_latrans 1.2819 1.6317 -1.0116 0.9433
## Cogon_Patch_Size-Procyon_lotor -0.6953 0.8207 -2.3744 -0.6658
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2625 0.9234 -2.1276 -0.2747
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3286 1.6893 -5.5367 -1.0400
## Veg_shannon_index-Canis_latrans 1.4151 0.7810 0.0784 1.3324
## Veg_shannon_index-Procyon_lotor 1.2037 0.6860 0.0079 1.1406
## Veg_shannon_index-Dasypus_novemcinctus 0.6476 0.6641 -0.7143 0.6617
## Veg_shannon_index-Sylvilagus_floridanus 1.0780 0.7988 -0.4143 1.0318
## total_shrub_cover-Canis_latrans 0.3872 0.9243 -1.0594 0.2433
## total_shrub_cover-Procyon_lotor -1.1767 0.7687 -2.9022 -1.1071
## total_shrub_cover-Dasypus_novemcinctus -0.2313 0.7911 -1.9600 -0.1935
## total_shrub_cover-Sylvilagus_floridanus -0.6074 1.3916 -3.5512 -0.5792
## Avg_Cogongrass_Cover-Canis_latrans 0.6382 1.5008 -2.0973 0.5937
## Avg_Cogongrass_Cover-Procyon_lotor 0.3824 1.3976 -2.3812 0.3938
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3712 1.6364 -1.4640 1.2417
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4309 1.6360 -3.8691 -0.3380
## Tree_Density-Canis_latrans -3.6195 1.8587 -8.0791 -3.3138
## Tree_Density-Procyon_lotor -2.4203 1.1763 -4.8593 -2.3885
## Tree_Density-Dasypus_novemcinctus -4.4882 2.4629 -10.9805 -3.8378
## Tree_Density-Sylvilagus_floridanus -3.1210 1.9055 -7.9737 -2.8509
## Avg_Canopy_Cover-Canis_latrans 0.0330 0.7518 -1.4679 0.0328
## Avg_Canopy_Cover-Procyon_lotor 1.6236 0.8696 0.0889 1.5619
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3469 1.0187 0.7667 2.1985
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.6537 3.0653 1.0287 3.9287
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1561 1.2374 0.4581 1.9439
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3556 1.7080 0.4481 2.0572
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2671 0.8260 -0.2483 1.2202
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0861 1.0005 -0.8027 1.0635
## avg_veg_height-Canis_latrans -0.2669 0.7367 -1.7651 -0.2535
## avg_veg_height-Procyon_lotor 0.0563 0.7606 -1.3889 0.0316
## avg_veg_height-Dasypus_novemcinctus 0.1992 0.7463 -1.2868 0.1881
## avg_veg_height-Sylvilagus_floridanus -0.2768 0.9667 -2.2922 -0.2279
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1593 1.0044 420
## (Intercept)-Procyon_lotor 1.1727 1.0136 524
## (Intercept)-Dasypus_novemcinctus -0.2582 1.0405 289
## (Intercept)-Sylvilagus_floridanus 0.2645 1.0309 386
## Cogon_Patch_Size-Canis_latrans 5.4632 1.0171 402
## Cogon_Patch_Size-Procyon_lotor 0.8938 1.0051 471
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6518 1.0043 534
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1296 1.0076 296
## Veg_shannon_index-Canis_latrans 3.1087 1.0109 770
## Veg_shannon_index-Procyon_lotor 2.6266 1.0158 411
## Veg_shannon_index-Dasypus_novemcinctus 1.9538 1.0166 867
## Veg_shannon_index-Sylvilagus_floridanus 2.8136 1.0271 663
## total_shrub_cover-Canis_latrans 2.5251 1.0087 418
## total_shrub_cover-Procyon_lotor 0.1468 1.0023 709
## total_shrub_cover-Dasypus_novemcinctus 1.2030 1.0008 484
## total_shrub_cover-Sylvilagus_floridanus 1.9893 1.0505 207
## Avg_Cogongrass_Cover-Canis_latrans 3.8002 1.0620 378
## Avg_Cogongrass_Cover-Procyon_lotor 3.1916 1.0603 360
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2204 1.0585 273
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4611 1.0408 468
## Tree_Density-Canis_latrans -1.0475 1.0670 136
## Tree_Density-Procyon_lotor -0.2177 1.0097 530
## Tree_Density-Dasypus_novemcinctus -1.4728 1.0073 207
## Tree_Density-Sylvilagus_floridanus -0.2621 1.0691 232
## Avg_Canopy_Cover-Canis_latrans 1.4189 1.0106 833
## Avg_Canopy_Cover-Procyon_lotor 3.5117 1.0200 414
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7117 1.0051 319
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.2757 1.0583 127
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.9852 1.0175 197
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2517 1.1236 156
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0861 1.0319 506
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2478 1.0693 322
## avg_veg_height-Canis_latrans 1.1347 1.0243 639
## avg_veg_height-Procyon_lotor 1.6108 1.0115 602
## avg_veg_height-Dasypus_novemcinctus 1.7068 1.0201 576
## avg_veg_height-Sylvilagus_floridanus 1.4961 1.0661 272
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5605 0.2016 -2.9832 -2.5525 -2.1921 1.0057
## (Intercept)-Procyon_lotor -2.2106 0.1607 -2.5373 -2.2062 -1.9053 1.0026
## (Intercept)-Dasypus_novemcinctus -1.6852 0.1892 -2.0558 -1.6792 -1.3188 1.0105
## (Intercept)-Sylvilagus_floridanus -3.0380 0.2820 -3.6360 -3.0285 -2.5121 1.0043
## shrub_cover-Canis_latrans -0.3146 0.2304 -0.7673 -0.3151 0.1381 1.0182
## shrub_cover-Procyon_lotor 0.2757 0.1632 -0.0565 0.2799 0.5920 1.0096
## shrub_cover-Dasypus_novemcinctus 0.9471 0.3297 0.3260 0.9420 1.5991 1.0047
## shrub_cover-Sylvilagus_floridanus 0.4924 0.4146 -0.3660 0.5012 1.2845 1.0798
## veg_height-Canis_latrans -0.5873 0.1887 -0.9837 -0.5823 -0.2266 1.0194
## veg_height-Procyon_lotor 0.3656 0.1245 0.1250 0.3664 0.6159 1.0000
## veg_height-Dasypus_novemcinctus 0.2809 0.1455 -0.0041 0.2807 0.5667 1.0063
## veg_height-Sylvilagus_floridanus 0.1805 0.2610 -0.3015 0.1738 0.7068 1.0306
## week-Canis_latrans 0.4463 0.2567 -0.0391 0.4380 0.9685 1.0041
## week-Procyon_lotor 0.1489 0.2115 -0.2714 0.1489 0.5648 1.0044
## week-Dasypus_novemcinctus 0.0415 0.2239 -0.3973 0.0449 0.4896 1.0034
## week-Sylvilagus_floridanus 0.0275 0.3190 -0.6110 0.0358 0.6441 1.0017
## I(week^2)-Canis_latrans -0.1925 0.1074 -0.4058 -0.1929 0.0152 1.0076
## I(week^2)-Procyon_lotor -0.1062 0.0919 -0.2885 -0.1047 0.0697 1.0017
## I(week^2)-Dasypus_novemcinctus -0.1433 0.1036 -0.3502 -0.1449 0.0581 1.0172
## I(week^2)-Sylvilagus_floridanus -0.1478 0.1530 -0.4629 -0.1444 0.1418 1.0174
## ESS
## (Intercept)-Canis_latrans 823
## (Intercept)-Procyon_lotor 1153
## (Intercept)-Dasypus_novemcinctus 977
## (Intercept)-Sylvilagus_floridanus 848
## shrub_cover-Canis_latrans 631
## shrub_cover-Procyon_lotor 1017
## shrub_cover-Dasypus_novemcinctus 528
## shrub_cover-Sylvilagus_floridanus 340
## veg_height-Canis_latrans 768
## veg_height-Procyon_lotor 1371
## veg_height-Dasypus_novemcinctus 1434
## veg_height-Sylvilagus_floridanus 446
## week-Canis_latrans 1372
## week-Procyon_lotor 1916
## week-Dasypus_novemcinctus 1767
## week-Sylvilagus_floridanus 1312
## I(week^2)-Canis_latrans 1297
## I(week^2)-Procyon_lotor 1671
## I(week^2)-Dasypus_novemcinctus 1594
## I(week^2)-Sylvilagus_floridanus 850
waicOcc(ms_full_full_T25, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -677.62724 76.64742 1508.54932
waicOcc(ms_full_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -698.21970 81.15793 1558.75526
waicOcc(ms_full_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -695.01375 63.27174 1516.57099
waicOcc(ms_full_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -699.43106 73.15657 1545.17526
waicOcc(ms_full_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -704.58303 63.42103 1536.00811
waicOcc(ms_full_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -706.36250 63.62543 1539.97586
waicOcc(ms_full_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -711.8277 54.4257 1532.5068
waicOcc(ms_full_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -702.75215 66.40564 1538.31558
waicOcc(ms_full_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -674.78945 77.29848 1504.17585
waicOcc(ms_null_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -738.34253 18.25109 1513.18724
waicOcc(ms_null_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -706.01904 40.27857 1492.59521
waicOcc(ms_null_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -729.78659 32.89159 1525.35636
waicOcc(ms_null_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -722.2511 27.9396 1500.3815
waicOcc(ms_null_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -728.14868 31.22727 1518.75189
waicOcc(ms_null_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -730.28496 28.98454 1518.53900
waicOcc(ms_null_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -733.03758 26.05518 1518.18552
waicOcc(ms_null_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -729.51927 28.31401 1515.66657
waicOcc(ms_null_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -701.58462 40.99968 1485.16859
waicOcc(ms_week_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -704.35652 45.20424 1499.12151
waicOcc(ms_week_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -728.19050 36.91331 1530.20763
waicOcc(ms_week_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -736.77034 22.95174 1519.44416
waicOcc(ms_week_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -728.48142 34.17299 1525.30883
waicOcc(ms_week_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -726.77703 36.17203 1525.89811
waicOcc(ms_week_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -720.59069 32.56809 1506.31756
waicOcc(ms_week_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -731.60135 30.41984 1524.04239
waicOcc(ms_week_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -727.93719 33.05436 1521.98309
waicOcc(ms_week_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -699.16188 46.53025 1491.38426
waicOcc(ms_cover_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -678.07670 72.58341 1501.32023
waicOcc(ms_cover_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -699.47285 76.94271 1552.83112
waicOcc(ms_cover_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -714.32960 48.28544 1525.23008
waicOcc(ms_cover_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -705.91270 61.79339 1535.41219
waicOcc(ms_cover_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -700.56073 72.39237 1545.90620
waicOcc(ms_cover_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -696.70334 57.97499 1509.35667
waicOcc(ms_cover_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -708.88691 56.22771 1530.22924
waicOcc(ms_cover_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -704.8836 61.4527 1532.6726
waicOcc(ms_cover_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -676.38901 72.21742 1497.21285
waicOcc(ms_weekQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -700.24335 51.20617 1502.89903
waicOcc(ms_weekQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -725.15149 42.10233 1534.50765
waicOcc(ms_weekQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -733.2230 29.0197 1524.4854
waicOcc(ms_weekQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -725.19250 39.31009 1529.00517
waicOcc(ms_weekQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -723.46186 41.59783 1530.11939
waicOcc(ms_weekQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -717.58436 37.17315 1509.51502
waicOcc(ms_weekQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -728.68463 35.64453 1528.65831
waicOcc(ms_weekQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -724.52590 38.27187 1525.59553
waicOcc(ms_weekQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -697.23299 50.77615 1496.01827
waicOcc(ms_fullQ_full_T25, by.sp = FALSE)
## elpd pD WAIC
## -674.0500 80.0373 1508.1745
waicOcc(ms_fullQ_cover_T25, by.sp = FALSE)
## elpd pD WAIC
## -695.3870 84.1366 1559.0472
waicOcc(ms_fullQ_null_T25, by.sp = FALSE)
## elpd pD WAIC
## -708.83385 59.23617 1536.14005
waicOcc(ms_fullQ_forage_T25, by.sp = FALSE)
## elpd pD WAIC
## -700.66558 71.75045 1544.83206
waicOcc(ms_fullQ_move_T25, by.sp = FALSE)
## elpd pD WAIC
## -695.82436 81.08074 1553.81021
waicOcc(ms_fullQ_canopy_T25, by.sp = FALSE)
## elpd pD WAIC
## -691.88188 68.08308 1519.92991
waicOcc(ms_fullQ_cogon_T25, by.sp = FALSE)
## elpd pD WAIC
## -703.47021 67.73584 1542.41208
waicOcc(ms_fullQ_cogonQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -699.60951 70.96358 1541.14618
waicOcc(ms_fullQ_fullQ_T25, by.sp = FALSE)
## elpd pD WAIC
## -672.13096 82.19036 1508.64265
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T25 <- ppcOcc(ms_fullQ_fullQ_T25, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 4
## Currently on species 2 out of 4
## Currently on species 3 out of 4
## Currently on species 4 out of 4
summary(ppc.ms_fullQ_fullQ_T25)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T25, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.2677
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.6493
## Procyon_lotor Bayesian p-value: 0.0683
## Dasypus_novemcinctus Bayesian p-value: 3e-04
## Sylvilagus_floridanus Bayesian p-value: 0.3527
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T25) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4868
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1870 0.8669 -2.8467 -1.1990 0.5629 1.0107 611
## Cogon_Patch_Size -0.2082 0.9105 -1.9574 -0.2136 1.6424 1.0113 834
## Veg_shannon_index 1.0000 0.6496 -0.2704 0.9806 2.3065 1.0257 682
## total_shrub_cover -0.3570 0.7501 -1.8295 -0.3703 1.3123 1.0058 702
## Avg_Cogongrass_Cover 0.3992 1.1618 -1.8823 0.4155 2.6211 1.0608 220
## Tree_Density -2.3611 1.2211 -4.5622 -2.4346 0.5113 1.0030 658
## Avg_Canopy_Cover 1.2838 1.0937 -1.1944 1.3307 3.3449 1.0025 2010
## I(Avg_Cogongrass_Cover^2) 1.4239 0.8641 -0.3518 1.4259 3.1592 1.0495 461
## avg_veg_height -0.0610 0.6954 -1.4128 -0.0657 1.3370 1.0369 617
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8555 10.2688 0.0600 0.7846 18.8225 1.1291 562
## Cogon_Patch_Size 5.3739 16.7028 0.0737 1.4446 34.1414 1.0058 785
## Veg_shannon_index 1.2340 5.1755 0.0490 0.4051 6.7433 1.1523 2647
## total_shrub_cover 2.3651 5.4591 0.0730 0.8618 14.6900 1.0279 418
## Avg_Cogongrass_Cover 3.8854 11.9125 0.0677 1.0146 25.7143 1.0779 1393
## Tree_Density 13.2648 74.3443 0.0707 1.7689 93.2405 1.0785 863
## Avg_Canopy_Cover 13.7002 44.4064 0.1744 3.7180 86.6986 1.0359 489
## I(Avg_Cogongrass_Cover^2) 3.4691 13.3914 0.0573 0.7462 21.8971 1.1890 327
## avg_veg_height 1.1225 2.7811 0.0462 0.3935 6.6714 1.0151 717
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3942 2.4718 0.0499 0.6074 7.7168 1.1194 82
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2112 0.4774 -3.0377 -2.2565 -1.0825 1.0010 2590
## shrub_cover 0.3210 0.4535 -0.5815 0.3244 1.2039 1.0118 1576
## veg_height 0.0658 0.3877 -0.7322 0.0599 0.8618 1.0026 3000
## week 0.1551 0.3157 -0.4560 0.1523 0.7594 1.0031 2051
## I(week^2) -0.1385 0.2131 -0.5519 -0.1392 0.2738 1.0014 2649
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0036 1.9846 0.0937 0.4919 5.3294 1.0285 2398
## shrub_cover 0.8689 1.8971 0.0867 0.4604 3.7644 1.0462 2401
## veg_height 0.6550 1.8323 0.0749 0.3200 3.2212 1.1470 2089
## week 0.3809 0.9482 0.0368 0.1723 1.8903 1.0443 2258
## I(week^2) 0.1793 0.3504 0.0241 0.0932 0.8727 1.0390 2732
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.0060 1.0528 -3.0594 -1.0300
## (Intercept)-Procyon_lotor -0.7364 0.9631 -2.5853 -0.7641
## (Intercept)-Dasypus_novemcinctus -2.0525 1.2126 -4.8945 -1.8824
## (Intercept)-Sylvilagus_floridanus -1.7773 1.2340 -4.4922 -1.6760
## Cogon_Patch_Size-Canis_latrans 1.2819 1.6317 -1.0116 0.9433
## Cogon_Patch_Size-Procyon_lotor -0.6953 0.8207 -2.3744 -0.6658
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2625 0.9234 -2.1276 -0.2747
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3286 1.6893 -5.5367 -1.0400
## Veg_shannon_index-Canis_latrans 1.4151 0.7810 0.0784 1.3324
## Veg_shannon_index-Procyon_lotor 1.2037 0.6860 0.0079 1.1406
## Veg_shannon_index-Dasypus_novemcinctus 0.6476 0.6641 -0.7143 0.6617
## Veg_shannon_index-Sylvilagus_floridanus 1.0780 0.7988 -0.4143 1.0318
## total_shrub_cover-Canis_latrans 0.3872 0.9243 -1.0594 0.2433
## total_shrub_cover-Procyon_lotor -1.1767 0.7687 -2.9022 -1.1071
## total_shrub_cover-Dasypus_novemcinctus -0.2313 0.7911 -1.9600 -0.1935
## total_shrub_cover-Sylvilagus_floridanus -0.6074 1.3916 -3.5512 -0.5792
## Avg_Cogongrass_Cover-Canis_latrans 0.6382 1.5008 -2.0973 0.5937
## Avg_Cogongrass_Cover-Procyon_lotor 0.3824 1.3976 -2.3812 0.3938
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3712 1.6364 -1.4640 1.2417
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4309 1.6360 -3.8691 -0.3380
## Tree_Density-Canis_latrans -3.6195 1.8587 -8.0791 -3.3138
## Tree_Density-Procyon_lotor -2.4203 1.1763 -4.8593 -2.3885
## Tree_Density-Dasypus_novemcinctus -4.4882 2.4629 -10.9805 -3.8378
## Tree_Density-Sylvilagus_floridanus -3.1210 1.9055 -7.9737 -2.8509
## Avg_Canopy_Cover-Canis_latrans 0.0330 0.7518 -1.4679 0.0328
## Avg_Canopy_Cover-Procyon_lotor 1.6236 0.8696 0.0889 1.5619
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3469 1.0187 0.7667 2.1985
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.6537 3.0653 1.0287 3.9287
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1561 1.2374 0.4581 1.9439
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3556 1.7080 0.4481 2.0572
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2671 0.8260 -0.2483 1.2202
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0861 1.0005 -0.8027 1.0635
## avg_veg_height-Canis_latrans -0.2669 0.7367 -1.7651 -0.2535
## avg_veg_height-Procyon_lotor 0.0563 0.7606 -1.3889 0.0316
## avg_veg_height-Dasypus_novemcinctus 0.1992 0.7463 -1.2868 0.1881
## avg_veg_height-Sylvilagus_floridanus -0.2768 0.9667 -2.2922 -0.2279
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1593 1.0044 420
## (Intercept)-Procyon_lotor 1.1727 1.0136 524
## (Intercept)-Dasypus_novemcinctus -0.2582 1.0405 289
## (Intercept)-Sylvilagus_floridanus 0.2645 1.0309 386
## Cogon_Patch_Size-Canis_latrans 5.4632 1.0171 402
## Cogon_Patch_Size-Procyon_lotor 0.8938 1.0051 471
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6518 1.0043 534
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1296 1.0076 296
## Veg_shannon_index-Canis_latrans 3.1087 1.0109 770
## Veg_shannon_index-Procyon_lotor 2.6266 1.0158 411
## Veg_shannon_index-Dasypus_novemcinctus 1.9538 1.0166 867
## Veg_shannon_index-Sylvilagus_floridanus 2.8136 1.0271 663
## total_shrub_cover-Canis_latrans 2.5251 1.0087 418
## total_shrub_cover-Procyon_lotor 0.1468 1.0023 709
## total_shrub_cover-Dasypus_novemcinctus 1.2030 1.0008 484
## total_shrub_cover-Sylvilagus_floridanus 1.9893 1.0505 207
## Avg_Cogongrass_Cover-Canis_latrans 3.8002 1.0620 378
## Avg_Cogongrass_Cover-Procyon_lotor 3.1916 1.0603 360
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2204 1.0585 273
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4611 1.0408 468
## Tree_Density-Canis_latrans -1.0475 1.0670 136
## Tree_Density-Procyon_lotor -0.2177 1.0097 530
## Tree_Density-Dasypus_novemcinctus -1.4728 1.0073 207
## Tree_Density-Sylvilagus_floridanus -0.2621 1.0691 232
## Avg_Canopy_Cover-Canis_latrans 1.4189 1.0106 833
## Avg_Canopy_Cover-Procyon_lotor 3.5117 1.0200 414
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7117 1.0051 319
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.2757 1.0583 127
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.9852 1.0175 197
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2517 1.1236 156
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0861 1.0319 506
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2478 1.0693 322
## avg_veg_height-Canis_latrans 1.1347 1.0243 639
## avg_veg_height-Procyon_lotor 1.6108 1.0115 602
## avg_veg_height-Dasypus_novemcinctus 1.7068 1.0201 576
## avg_veg_height-Sylvilagus_floridanus 1.4961 1.0661 272
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5605 0.2016 -2.9832 -2.5525 -2.1921 1.0057
## (Intercept)-Procyon_lotor -2.2106 0.1607 -2.5373 -2.2062 -1.9053 1.0026
## (Intercept)-Dasypus_novemcinctus -1.6852 0.1892 -2.0558 -1.6792 -1.3188 1.0105
## (Intercept)-Sylvilagus_floridanus -3.0380 0.2820 -3.6360 -3.0285 -2.5121 1.0043
## shrub_cover-Canis_latrans -0.3146 0.2304 -0.7673 -0.3151 0.1381 1.0182
## shrub_cover-Procyon_lotor 0.2757 0.1632 -0.0565 0.2799 0.5920 1.0096
## shrub_cover-Dasypus_novemcinctus 0.9471 0.3297 0.3260 0.9420 1.5991 1.0047
## shrub_cover-Sylvilagus_floridanus 0.4924 0.4146 -0.3660 0.5012 1.2845 1.0798
## veg_height-Canis_latrans -0.5873 0.1887 -0.9837 -0.5823 -0.2266 1.0194
## veg_height-Procyon_lotor 0.3656 0.1245 0.1250 0.3664 0.6159 1.0000
## veg_height-Dasypus_novemcinctus 0.2809 0.1455 -0.0041 0.2807 0.5667 1.0063
## veg_height-Sylvilagus_floridanus 0.1805 0.2610 -0.3015 0.1738 0.7068 1.0306
## week-Canis_latrans 0.4463 0.2567 -0.0391 0.4380 0.9685 1.0041
## week-Procyon_lotor 0.1489 0.2115 -0.2714 0.1489 0.5648 1.0044
## week-Dasypus_novemcinctus 0.0415 0.2239 -0.3973 0.0449 0.4896 1.0034
## week-Sylvilagus_floridanus 0.0275 0.3190 -0.6110 0.0358 0.6441 1.0017
## I(week^2)-Canis_latrans -0.1925 0.1074 -0.4058 -0.1929 0.0152 1.0076
## I(week^2)-Procyon_lotor -0.1062 0.0919 -0.2885 -0.1047 0.0697 1.0017
## I(week^2)-Dasypus_novemcinctus -0.1433 0.1036 -0.3502 -0.1449 0.0581 1.0172
## I(week^2)-Sylvilagus_floridanus -0.1478 0.1530 -0.4629 -0.1444 0.1418 1.0174
## ESS
## (Intercept)-Canis_latrans 823
## (Intercept)-Procyon_lotor 1153
## (Intercept)-Dasypus_novemcinctus 977
## (Intercept)-Sylvilagus_floridanus 848
## shrub_cover-Canis_latrans 631
## shrub_cover-Procyon_lotor 1017
## shrub_cover-Dasypus_novemcinctus 528
## shrub_cover-Sylvilagus_floridanus 340
## veg_height-Canis_latrans 768
## veg_height-Procyon_lotor 1371
## veg_height-Dasypus_novemcinctus 1434
## veg_height-Sylvilagus_floridanus 446
## week-Canis_latrans 1372
## week-Procyon_lotor 1916
## week-Dasypus_novemcinctus 1767
## week-Sylvilagus_floridanus 1312
## I(week^2)-Canis_latrans 1297
## I(week^2)-Procyon_lotor 1671
## I(week^2)-Dasypus_novemcinctus 1594
## I(week^2)-Sylvilagus_floridanus 850
names(ms_fullQ_fullQ_T25)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T25$beta.samples)
## 'mcmc' num [1:3000, 1:36] -1.23 -1.74 -1.38 -2.24 -2.19 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:36] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T25$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.8066667
MCMCplot(ms_fullQ_fullQ_T25$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T25$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T25, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:3000, 1:4, 1:100] 0.2752 0.0943 0.0617 0.0104 0.2188 ...
## $ z.0.samples : int [1:3000, 1:4, 1:100] 1 0 0 0 1 0 0 0 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T25, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:4, 1:100] 0.2752 0.0943 0.0617 0.0104 0.2188 ...
psi.0.quants <- apply(out.pred$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] 0.00567 0.2388 0.92816 0.00655 0.2303 ...
## - 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 0.239 0.23 0.238 0.232 0.229 ...
## $ psi.low : num 0.00567 0.00655 0.00593 0.00639 0.00651 ...
## $ psi.high : num 0.928 0.91 0.903 0.91 0.91 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T25) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.4868
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1870 0.8669 -2.8467 -1.1990 0.5629 1.0107 611
## Cogon_Patch_Size -0.2082 0.9105 -1.9574 -0.2136 1.6424 1.0113 834
## Veg_shannon_index 1.0000 0.6496 -0.2704 0.9806 2.3065 1.0257 682
## total_shrub_cover -0.3570 0.7501 -1.8295 -0.3703 1.3123 1.0058 702
## Avg_Cogongrass_Cover 0.3992 1.1618 -1.8823 0.4155 2.6211 1.0608 220
## Tree_Density -2.3611 1.2211 -4.5622 -2.4346 0.5113 1.0030 658
## Avg_Canopy_Cover 1.2838 1.0937 -1.1944 1.3307 3.3449 1.0025 2010
## I(Avg_Cogongrass_Cover^2) 1.4239 0.8641 -0.3518 1.4259 3.1592 1.0495 461
## avg_veg_height -0.0610 0.6954 -1.4128 -0.0657 1.3370 1.0369 617
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8555 10.2688 0.0600 0.7846 18.8225 1.1291 562
## Cogon_Patch_Size 5.3739 16.7028 0.0737 1.4446 34.1414 1.0058 785
## Veg_shannon_index 1.2340 5.1755 0.0490 0.4051 6.7433 1.1523 2647
## total_shrub_cover 2.3651 5.4591 0.0730 0.8618 14.6900 1.0279 418
## Avg_Cogongrass_Cover 3.8854 11.9125 0.0677 1.0146 25.7143 1.0779 1393
## Tree_Density 13.2648 74.3443 0.0707 1.7689 93.2405 1.0785 863
## Avg_Canopy_Cover 13.7002 44.4064 0.1744 3.7180 86.6986 1.0359 489
## I(Avg_Cogongrass_Cover^2) 3.4691 13.3914 0.0573 0.7462 21.8971 1.1890 327
## avg_veg_height 1.1225 2.7811 0.0462 0.3935 6.6714 1.0151 717
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.3942 2.4718 0.0499 0.6074 7.7168 1.1194 82
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.2112 0.4774 -3.0377 -2.2565 -1.0825 1.0010 2590
## shrub_cover 0.3210 0.4535 -0.5815 0.3244 1.2039 1.0118 1576
## veg_height 0.0658 0.3877 -0.7322 0.0599 0.8618 1.0026 3000
## week 0.1551 0.3157 -0.4560 0.1523 0.7594 1.0031 2051
## I(week^2) -0.1385 0.2131 -0.5519 -0.1392 0.2738 1.0014 2649
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.0036 1.9846 0.0937 0.4919 5.3294 1.0285 2398
## shrub_cover 0.8689 1.8971 0.0867 0.4604 3.7644 1.0462 2401
## veg_height 0.6550 1.8323 0.0749 0.3200 3.2212 1.1470 2089
## week 0.3809 0.9482 0.0368 0.1723 1.8903 1.0443 2258
## I(week^2) 0.1793 0.3504 0.0241 0.0932 0.8727 1.0390 2732
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.0060 1.0528 -3.0594 -1.0300
## (Intercept)-Procyon_lotor -0.7364 0.9631 -2.5853 -0.7641
## (Intercept)-Dasypus_novemcinctus -2.0525 1.2126 -4.8945 -1.8824
## (Intercept)-Sylvilagus_floridanus -1.7773 1.2340 -4.4922 -1.6760
## Cogon_Patch_Size-Canis_latrans 1.2819 1.6317 -1.0116 0.9433
## Cogon_Patch_Size-Procyon_lotor -0.6953 0.8207 -2.3744 -0.6658
## Cogon_Patch_Size-Dasypus_novemcinctus -0.2625 0.9234 -2.1276 -0.2747
## Cogon_Patch_Size-Sylvilagus_floridanus -1.3286 1.6893 -5.5367 -1.0400
## Veg_shannon_index-Canis_latrans 1.4151 0.7810 0.0784 1.3324
## Veg_shannon_index-Procyon_lotor 1.2037 0.6860 0.0079 1.1406
## Veg_shannon_index-Dasypus_novemcinctus 0.6476 0.6641 -0.7143 0.6617
## Veg_shannon_index-Sylvilagus_floridanus 1.0780 0.7988 -0.4143 1.0318
## total_shrub_cover-Canis_latrans 0.3872 0.9243 -1.0594 0.2433
## total_shrub_cover-Procyon_lotor -1.1767 0.7687 -2.9022 -1.1071
## total_shrub_cover-Dasypus_novemcinctus -0.2313 0.7911 -1.9600 -0.1935
## total_shrub_cover-Sylvilagus_floridanus -0.6074 1.3916 -3.5512 -0.5792
## Avg_Cogongrass_Cover-Canis_latrans 0.6382 1.5008 -2.0973 0.5937
## Avg_Cogongrass_Cover-Procyon_lotor 0.3824 1.3976 -2.3812 0.3938
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3712 1.6364 -1.4640 1.2417
## Avg_Cogongrass_Cover-Sylvilagus_floridanus -0.4309 1.6360 -3.8691 -0.3380
## Tree_Density-Canis_latrans -3.6195 1.8587 -8.0791 -3.3138
## Tree_Density-Procyon_lotor -2.4203 1.1763 -4.8593 -2.3885
## Tree_Density-Dasypus_novemcinctus -4.4882 2.4629 -10.9805 -3.8378
## Tree_Density-Sylvilagus_floridanus -3.1210 1.9055 -7.9737 -2.8509
## Avg_Canopy_Cover-Canis_latrans 0.0330 0.7518 -1.4679 0.0328
## Avg_Canopy_Cover-Procyon_lotor 1.6236 0.8696 0.0889 1.5619
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3469 1.0187 0.7667 2.1985
## Avg_Canopy_Cover-Sylvilagus_floridanus 4.6537 3.0653 1.0287 3.9287
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1561 1.2374 0.4581 1.9439
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3556 1.7080 0.4481 2.0572
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2671 0.8260 -0.2483 1.2202
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 1.0861 1.0005 -0.8027 1.0635
## avg_veg_height-Canis_latrans -0.2669 0.7367 -1.7651 -0.2535
## avg_veg_height-Procyon_lotor 0.0563 0.7606 -1.3889 0.0316
## avg_veg_height-Dasypus_novemcinctus 0.1992 0.7463 -1.2868 0.1881
## avg_veg_height-Sylvilagus_floridanus -0.2768 0.9667 -2.2922 -0.2279
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.1593 1.0044 420
## (Intercept)-Procyon_lotor 1.1727 1.0136 524
## (Intercept)-Dasypus_novemcinctus -0.2582 1.0405 289
## (Intercept)-Sylvilagus_floridanus 0.2645 1.0309 386
## Cogon_Patch_Size-Canis_latrans 5.4632 1.0171 402
## Cogon_Patch_Size-Procyon_lotor 0.8938 1.0051 471
## Cogon_Patch_Size-Dasypus_novemcinctus 1.6518 1.0043 534
## Cogon_Patch_Size-Sylvilagus_floridanus 1.1296 1.0076 296
## Veg_shannon_index-Canis_latrans 3.1087 1.0109 770
## Veg_shannon_index-Procyon_lotor 2.6266 1.0158 411
## Veg_shannon_index-Dasypus_novemcinctus 1.9538 1.0166 867
## Veg_shannon_index-Sylvilagus_floridanus 2.8136 1.0271 663
## total_shrub_cover-Canis_latrans 2.5251 1.0087 418
## total_shrub_cover-Procyon_lotor 0.1468 1.0023 709
## total_shrub_cover-Dasypus_novemcinctus 1.2030 1.0008 484
## total_shrub_cover-Sylvilagus_floridanus 1.9893 1.0505 207
## Avg_Cogongrass_Cover-Canis_latrans 3.8002 1.0620 378
## Avg_Cogongrass_Cover-Procyon_lotor 3.1916 1.0603 360
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2204 1.0585 273
## Avg_Cogongrass_Cover-Sylvilagus_floridanus 2.4611 1.0408 468
## Tree_Density-Canis_latrans -1.0475 1.0670 136
## Tree_Density-Procyon_lotor -0.2177 1.0097 530
## Tree_Density-Dasypus_novemcinctus -1.4728 1.0073 207
## Tree_Density-Sylvilagus_floridanus -0.2621 1.0691 232
## Avg_Canopy_Cover-Canis_latrans 1.4189 1.0106 833
## Avg_Canopy_Cover-Procyon_lotor 3.5117 1.0200 414
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7117 1.0051 319
## Avg_Canopy_Cover-Sylvilagus_floridanus 13.2757 1.0583 127
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 4.9852 1.0175 197
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2517 1.1236 156
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.0861 1.0319 506
## I(Avg_Cogongrass_Cover^2)-Sylvilagus_floridanus 3.2478 1.0693 322
## avg_veg_height-Canis_latrans 1.1347 1.0243 639
## avg_veg_height-Procyon_lotor 1.6108 1.0115 602
## avg_veg_height-Dasypus_novemcinctus 1.7068 1.0201 576
## avg_veg_height-Sylvilagus_floridanus 1.4961 1.0661 272
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5605 0.2016 -2.9832 -2.5525 -2.1921 1.0057
## (Intercept)-Procyon_lotor -2.2106 0.1607 -2.5373 -2.2062 -1.9053 1.0026
## (Intercept)-Dasypus_novemcinctus -1.6852 0.1892 -2.0558 -1.6792 -1.3188 1.0105
## (Intercept)-Sylvilagus_floridanus -3.0380 0.2820 -3.6360 -3.0285 -2.5121 1.0043
## shrub_cover-Canis_latrans -0.3146 0.2304 -0.7673 -0.3151 0.1381 1.0182
## shrub_cover-Procyon_lotor 0.2757 0.1632 -0.0565 0.2799 0.5920 1.0096
## shrub_cover-Dasypus_novemcinctus 0.9471 0.3297 0.3260 0.9420 1.5991 1.0047
## shrub_cover-Sylvilagus_floridanus 0.4924 0.4146 -0.3660 0.5012 1.2845 1.0798
## veg_height-Canis_latrans -0.5873 0.1887 -0.9837 -0.5823 -0.2266 1.0194
## veg_height-Procyon_lotor 0.3656 0.1245 0.1250 0.3664 0.6159 1.0000
## veg_height-Dasypus_novemcinctus 0.2809 0.1455 -0.0041 0.2807 0.5667 1.0063
## veg_height-Sylvilagus_floridanus 0.1805 0.2610 -0.3015 0.1738 0.7068 1.0306
## week-Canis_latrans 0.4463 0.2567 -0.0391 0.4380 0.9685 1.0041
## week-Procyon_lotor 0.1489 0.2115 -0.2714 0.1489 0.5648 1.0044
## week-Dasypus_novemcinctus 0.0415 0.2239 -0.3973 0.0449 0.4896 1.0034
## week-Sylvilagus_floridanus 0.0275 0.3190 -0.6110 0.0358 0.6441 1.0017
## I(week^2)-Canis_latrans -0.1925 0.1074 -0.4058 -0.1929 0.0152 1.0076
## I(week^2)-Procyon_lotor -0.1062 0.0919 -0.2885 -0.1047 0.0697 1.0017
## I(week^2)-Dasypus_novemcinctus -0.1433 0.1036 -0.3502 -0.1449 0.0581 1.0172
## I(week^2)-Sylvilagus_floridanus -0.1478 0.1530 -0.4629 -0.1444 0.1418 1.0174
## ESS
## (Intercept)-Canis_latrans 823
## (Intercept)-Procyon_lotor 1153
## (Intercept)-Dasypus_novemcinctus 977
## (Intercept)-Sylvilagus_floridanus 848
## shrub_cover-Canis_latrans 631
## shrub_cover-Procyon_lotor 1017
## shrub_cover-Dasypus_novemcinctus 528
## shrub_cover-Sylvilagus_floridanus 340
## veg_height-Canis_latrans 768
## veg_height-Procyon_lotor 1371
## veg_height-Dasypus_novemcinctus 1434
## veg_height-Sylvilagus_floridanus 446
## week-Canis_latrans 1372
## week-Procyon_lotor 1916
## week-Dasypus_novemcinctus 1767
## week-Sylvilagus_floridanus 1312
## I(week^2)-Canis_latrans 1297
## I(week^2)-Procyon_lotor 1671
## I(week^2)-Dasypus_novemcinctus 1594
## I(week^2)-Sylvilagus_floridanus 850
names(ms_fullQ_fullQ_T25)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T25$beta.samples)
## 'mcmc' num [1:3000, 1:36] -1.23 -1.74 -1.38 -2.24 -2.19 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:36] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "(Intercept)-Sylvilagus_floridanus" ...
mean(ms_fullQ_fullQ_T25$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.8066667
MCMCplot(ms_fullQ_fullQ_T25$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T25$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T25$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T25$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}
Install necessary packages and import appropriate data
rm(list = ls())
pacman::p_load(tidyverse, readxl, raster, vegan, tigris, sf, sjPlot, sp, spOccupancy, ggrepel, lme4, lmerTest, MuMIn, brms, MCMCvis)
# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Veg_Cover")
# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Shrub_Cover")
# Site Data
CameraData <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraData.xlsx")
CameraLoc <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "CameraLocations")
# Add effort data
effort_matrix <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/04_Wildlife/02_Data/CameraLoc.xlsx",
sheet = "Effort_Matrix_Full") %>%
pivot_longer(cols = matches("^202[4-5]-"), names_to = "week", values_to = "days") %>%
filter(days == "7") %>%
dplyr::select(Plot, week)
I moved this from a later section because the filtering process removed quadrats that did not capture any species. Rows labeled as “None” were removed, suggesting that the number of quadrats sampled per plot is not consistent across all plots.
# Count the total number of quadrats per plot
quadrat_count <- Veg_Cover %>%
group_by(Plot) %>%
summarize(total_quadrats = n_distinct(Quadrat), .groups = "drop")
#Filter tree data to only include trees with "tree" in the growth column
tree_data <- dplyr::filter(tree_data, Growth == "Tree")
#Filter Veg Cover to exclude Shrubs and Trees
Veg_Cover <- dplyr::filter(Veg_Cover, Growth != "Shrub" & Growth != "Tree")
#Filter Shrub Cover to only include Shrubs and Trees
shrub_data <- dplyr::filter(shrub_data, Growth == "Shrub" | Growth == "Tree")
This is not needed for non-ordination analysis. Moving the threshold down to 0% to keep the option, but to ensure it has no effect for now.
# Calculate the total number of sites
total_sites <- nrow(CameraLoc)
# Function to filter data by frequency
filter_by_frequency <- function(df) {
# Group data by species and calculate the frequency
freq <- df %>%
group_by(Species) %>%
summarise(Frequency = n_distinct(Plot) / nrow(CameraLoc) * 100) %>%
filter(Frequency >= 0)
# Filter the original data to include only species with frequency >= 3%
filtered_df <- df %>%
filter(Species %in% freq$Species)
return(filtered_df)
}
# Filter tree data by frequency
tree_data <- filter_by_frequency(tree_data)
# Filter Veg Cover data by frequency
Veg_Cover <- filter_by_frequency(Veg_Cover)
# Filter Shrub Cover data by frequency
shrub_data <- filter_by_frequency(shrub_data)
# Total length of Shrub cover at a site
shrub_cover <- shrub_data %>%
mutate(Cover = Line_End - Line_Start) %>%
group_by(Species_Name, Plot) %>%
summarise(Shrub_Total_Cover = sum(Cover, na.rm = TRUE), .groups = "drop") %>%
mutate(Shrub_Percent_Cover = Shrub_Total_Cover / 3000 * 100)
# Summed length of shrub over at a site
shrub_cover_summed <- shrub_cover %>%
group_by(Plot) %>%
summarize(total_shrub_cover = sum(Shrub_Total_Cover, na.rm = TRUE), .groups = "drop")
# Combine Plot and Quadrat columns
Veg_Cover <- Veg_Cover %>%
mutate(Plot_Quadrat = paste(Plot, Quadrat, sep = '_'))
# Join with CogonSites to get site information
Veg_Cover <- Veg_Cover %>%
left_join(CameraLoc, by = "Plot")
# Sum species cover across quadrats for each species at each plot
veg_cover_summed <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE), .groups = "drop")
# Calculate average herbaceous species cover
avg_species_cover <- veg_cover_summed %>%
left_join(quadrat_count, by = "Plot") %>%
mutate(avg_cover = total_cover / total_quadrats)
This species matrix includes herbaceous and shrub species
# Merge shrub cover with herbaceous average cover
combined_cover <- avg_species_cover %>%
full_join(
shrub_cover %>%
dplyr::select(Plot, Species_Name, Shrub_Percent_Cover),
by = c("Plot", "Species_Name")
) %>%
mutate(
overlap_flag = ifelse(!is.na(avg_cover) & !is.na(Shrub_Percent_Cover), TRUE, FALSE), # Flag overlaps
final_cover = case_when(
!is.na(avg_cover) & is.na(Shrub_Percent_Cover) ~ avg_cover, # Use herbaceous cover if no shrub data
is.na(avg_cover) & !is.na(Shrub_Percent_Cover) ~ Shrub_Percent_Cover, # Use shrub cover if no herbaceous data
TRUE ~ NA_real_ # Leave as NA where overlaps exist
)
)
# Species Matrix
species_matrix <- combined_cover %>%
dplyr::select(Plot, Species_Name, final_cover) %>%
pivot_wider(
names_from = Species_Name,
values_from = final_cover,
values_fill = 0
)
avg_cogongrass_cover <- species_matrix %>%
group_by(Plot) %>%
summarize(Avg_Cogongrass_Cover = sum(Imperata_cylindrica, na.rm = TRUE) / n(), .groups = "drop")
# Summarize species cover by site
site_species_cover <- Veg_Cover %>%
group_by(Plot, Species_Name) %>%
summarize(total_cover = sum(Cover_Per, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'Plot'. You can override using the
## `.groups` argument.
## Remove all Imperata_cylindrica_Live and Imperata_cylindrica from species
site_species_cover <- site_species_cover %>%
filter(Species_Name != "Imperata_cylindrica_Live" & Species_Name != "Imperata_cylindrica")
# Calculate Shannon diversity per site
Veg_shannon_diversity <- site_species_cover %>%
group_by(Plot) %>%
mutate(proportion = total_cover / sum(total_cover)) %>%
summarize(Veg_shannon_index = -sum(proportion * log(proportion), na.rm = TRUE))
print(Veg_shannon_diversity)
## # A tibble: 206 × 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 196 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] 0.2742307
CameraLoc <- CameraLoc %>%
left_join(Veg_shannon_diversity, by = "Plot") %>%
left_join(avg_cogongrass_cover, by = "Plot") %>%
left_join(shrub_cover_summed %>% dplyr::select(Plot, total_shrub_cover), by = "Plot") %>%
left_join(veg_height, by = "Plot") %>%
left_join(tree_density_data %>% dplyr::select(Plot, Tree_Density), by = "Plot") %>%
left_join(tree_canopy_data %>% dplyr::select(Plot, Avg_Canopy_Cover), by = "Plot") %>%
dplyr::select(-Authority)
# Group by Name and count the number of observations
species_counts <- CameraData %>%
filter((Class == "Mammalia" | Name == "Meleagris_gallopavo") & Name != "Odocoileus_virginianus") %>%
group_by(Name) %>%
summarize(count = n(), .groups = "drop")
# Filter for species with count greater than 25
species_subset <- species_counts %>%
filter(count > 50) %>%
pull(Name)
# Filter CameraData to only include species with count > 25
CameraData <- CameraData %>%
filter(Name %in% species_subset)
# Format Data Weekly
observations_weekly <- CameraData %>%
group_by(Plot, week = format(as.Date(Date), "%Y-%U"), Name) %>%
summarise(observations = n(), .groups = 'drop')
# Merge with Effort Matrix to include only valid weeks
observations_weekly <- effort_matrix %>%
left_join(observations_weekly, by = c("Plot" = "Plot", "week")) %>%
replace_na(list(observations = 0))
# Convert to wide format
observations_species <- observations_weekly %>%
pivot_wider(names_from = Name, values_from = observations, values_fill = list(observations = 0)) %>%
dplyr::select(-"NA")
# Create detection array
site_names <- sort(unique(observations_species$Plot))
species_names <- setdiff(colnames(observations_species), c("Plot", "week"))
num_sites <- length(site_names)
num_weeks <- length(unique(observations_species$week))
num_species <- length(species_names)
detection_array <- array(0, dim = c(num_sites, num_weeks, num_species))
dimnames(detection_array) <- list(site_names, unique(observations_species$week), species_names)
for (s in seq_along(species_names)) {
species_col <- species_names[s]
for (i in seq_along(site_names)) {
site <- site_names[i]
for (j in seq_along(unique(observations_species$week))) {
week <- unique(observations_species$week)[j]
detection_array[i, j, s] <- ifelse(
any(observations_species$Plot == site & observations_species$week == week & observations_species[[species_col]] > 0),
1, 0
)
}
}
}
dim(detection_array) # Should be num_sites x num_weeks x num_species
## [1] 32 36 3
# Duplicate CameraLoc to be used in Objective 2
CameraLoc_O2 <- CameraLoc
# Standardize the covariates
CameraLoc <- CameraLoc %>%
dplyr::select(-Plot, -Camera, -Lat, -Long, -Status, - Start_Date)
covariates_matrix <- as.matrix(CameraLoc)
rownames(covariates_matrix) <- site_names
# Standardizing covariates
covariates_matrix <- scale(covariates_matrix)
# Create week matrix for covariate structure [site x week]
week_vals <- unique(observations_species$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, 1:3] 0 0 0 0 0 0 0 0 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 [1:3] "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus"
## $ occ.covs: num [1:32, 1:10] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ : chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:center")= Named num [1:10] 458.388 21.875 0.898 2.844 2.411 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## ..- attr(*, "scaled:scale")= Named num [1:10] 1027.633 6.871 1.232 0.808 0.429 ...
## .. ..- attr(*, "names")= chr [1:10] "Cogon_Patch_Size" "VegetationDiversity" "PostTreatmentDensities" "Auth" ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
I am unsure why I only had an issue with total shrub cover, but this should fix the “cannot find” issue.
# Convert occupancy and detection covariates to a dataframe
data_list[["occ.covs"]] <- as.data.frame(data_list[["occ.covs"]])
data_list[["occ.covs"]]$total_shrub_cover <- as.numeric(data_list[["occ.covs"]]$total_shrub_cover)
#data_list[["det.covs"]] <- as.data.frame(data_list[["det.covs"]])
#data_list[["det.covs"]]$total_shrub_cover <- as.numeric(data_list[["det.covs"]]$total_shrub_cover)
# Make species the first dimension
data_list$y <- aperm(data_list$y, c(3, 1, 2))
dimnames(data_list$y) <- list(species = dimnames(data_list$y)[[1]],
site = dimnames(data_list$y)[[2]],
week = dimnames(data_list$y)[[3]])
str(data_list)
## List of 3
## $ y : num [1:3, 1:32, 1:36] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 3
## .. ..$ species: chr [1:3] "Canis_latrans" "Procyon_lotor" "Dasypus_novemcinctus"
## .. ..$ site : chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## .. ..$ week : chr [1:36] "2024-29" "2024-30" "2024-31" "2024-32" ...
## $ occ.covs:'data.frame': 32 obs. of 10 variables:
## ..$ Cogon_Patch_Size : num [1:32] -0.237 -0.446 -0.337 -0.308 0.039 ...
## ..$ VegetationDiversity : num [1:32] -0.273 0.455 1.619 -0.273 2.929 ...
## ..$ PostTreatmentDensities: num [1:32] 0.432 -0.729 0.432 2.169 1.13 ...
## ..$ Auth : num [1:32] -2.28 -2.28 -1.04 -1.04 -1.04 ...
## ..$ Veg_shannon_index : num [1:32] 0.6829 0.0427 0.7279 -0.5991 1.1371 ...
## ..$ Avg_Cogongrass_Cover : num [1:32] -0.154 -0.708 0.308 2.045 1.121 ...
## ..$ total_shrub_cover : num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## ..$ avg_veg_height : num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## ..$ Tree_Density : num [1:32] -0.3629 -0.3564 -0.5111 3.5896 0.0958 ...
## ..$ Avg_Canopy_Cover : num [1:32] 0.1362 -0.0252 -0.9132 0.782 -1.9627 ...
## $ det.covs:List of 3
## ..$ shrub_cover: Named num [1:32] 1.526 0.5 -0.153 -1.003 -0.811 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ veg_height : Named num [1:32] 0.466 -1.044 1.181 0.879 1.684 ...
## .. ..- attr(*, "names")= chr [1:32] "BI201" "BN211" "EI100" "EI102" ...
## ..$ week : num [1:32, 1:36] -0.613 -0.613 -0.613 -0.613 -0.613 ...
# Define detection formulas
det.null <- ~ 1
det.full <- ~ shrub_cover + veg_height + week
det.cover <- ~ shrub_cover + veg_height
det.week <- ~ week
det.week.quad <- ~ week + I(week^2)
det.full.quad <- ~ shrub_cover + veg_height + week + I(week^2)
# Define occupancy formulas
occ.null <- ~ 1
occ.full <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + avg_veg_height + (1 | Auth)
occ.full.quad <- ~ Cogon_Patch_Size + Veg_shannon_index + total_shrub_cover + Avg_Cogongrass_Cover +
Tree_Density + Avg_Canopy_Cover + I(Avg_Cogongrass_Cover^2) + avg_veg_height + (1 | Auth)
occ.cover <- ~ Avg_Cogongrass_Cover + total_shrub_cover + avg_veg_height + (1 | Auth)
occ.canopy <- ~ Tree_Density + Avg_Canopy_Cover + (1 | Auth)
occ.move <- ~ Cogon_Patch_Size + Avg_Cogongrass_Cover + total_shrub_cover + (1 | Auth)
occ.forage <- ~ Veg_shannon_index + Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon <- ~ Avg_Cogongrass_Cover + (1 | Auth)
occ.cogon.quad <- ~ Avg_Cogongrass_Cover + I(Avg_Cogongrass_Cover^2) + (1 | Auth)
ms_null_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
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## Sampled: 2000 of 5000, 40.00%
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## Sampled: 2500 of 5000, 50.00%
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## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2763
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1174 0.5524 -0.9854 0.1077 1.259 1.0071 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6613 6.764 0.064 0.5447 8.8077 1.0508 2700
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.932 0.5858 -2.8476 -2.0251 -0.4629 1.0089 2426
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4874 6.1119 0.0775 0.4402 8.8876 1.0281 2653
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2737 0.3704 -0.4073 0.2640 1.0370 1.0120
## (Intercept)-Procyon_lotor 0.5979 0.3784 -0.1172 0.5786 1.4043 1.0001
## (Intercept)-Dasypus_novemcinctus -0.4683 0.3697 -1.2239 -0.4528 0.2400 0.9999
## ESS
## (Intercept)-Canis_latrans 2314
## (Intercept)-Procyon_lotor 2088
## (Intercept)-Dasypus_novemcinctus 2398
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5665 0.1686 -2.9057 -2.5632 -2.2584 1.0103
## (Intercept)-Procyon_lotor -2.2531 0.1238 -2.4951 -2.2502 -2.0126 1.0020
## (Intercept)-Dasypus_novemcinctus -1.5924 0.1316 -1.8498 -1.5917 -1.3366 1.0054
## ESS
## (Intercept)-Canis_latrans 1019
## (Intercept)-Procyon_lotor 1692
## (Intercept)-Dasypus_novemcinctus 2289
# Includes all covariates of detection and occupancy
ms_full_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3787
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0721 0.8948 -1.7876 0.0894 1.8372 1.0067 865
## Cogon_Patch_Size -0.2627 0.8623 -1.9117 -0.3167 1.6554 1.0030 880
## Veg_shannon_index 0.9780 0.7566 -0.6016 1.0087 2.3438 1.0107 1036
## total_shrub_cover -0.0825 0.8745 -1.7777 -0.0883 1.7352 1.0063 1606
## Avg_Cogongrass_Cover 2.0155 1.0900 -0.3065 2.0794 3.9478 1.0137 745
## Tree_Density -1.7210 1.2239 -3.8448 -1.8626 1.1173 1.0030 1192
## Avg_Canopy_Cover 0.9895 0.8753 -0.9644 1.0280 2.6225 1.0010 2255
## avg_veg_height -0.2476 0.7076 -1.5849 -0.2555 1.1974 1.0077 837
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.4132 23.3622 0.0768 1.2323 25.9415 1.2115 3000
## Cogon_Patch_Size 3.7804 15.7398 0.0582 0.8891 23.1397 1.1043 2052
## Veg_shannon_index 2.0422 6.9160 0.0523 0.5347 13.0050 1.0254 1536
## total_shrub_cover 4.6106 18.0763 0.0811 1.3094 24.9700 1.0472 1874
## Avg_Cogongrass_Cover 5.2911 33.0850 0.0560 0.7229 37.0468 1.0740 2584
## Tree_Density 17.6757 59.5547 0.0881 3.0034 130.3772 1.0504 857
## Avg_Canopy_Cover 4.4752 24.9374 0.0913 1.3585 24.2331 1.3001 2326
## avg_veg_height 1.4733 10.8651 0.0438 0.3841 8.6160 1.2477 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.253 3.8532 0.0558 1.0084 11.7732 1.0954 195
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0545 0.6192 -2.9805 -2.1543 -0.5038 1.0053 2468
## shrub_cover 0.2443 0.6134 -1.0880 0.2498 1.4481 1.0024 2501
## veg_height -0.0004 0.5286 -1.1055 0.0076 1.0521 1.0036 3000
## week -0.0515 0.3448 -0.7652 -0.0570 0.6618 1.0078 2806
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4753 5.6277 0.0755 0.4550 8.7173 1.1516 2543
## shrub_cover 2.0030 7.6219 0.1127 0.7062 10.4334 1.0257 3000
## veg_height 1.4309 5.7860 0.0910 0.4967 7.9117 1.0325 3000
## week 0.4834 1.7491 0.0325 0.1662 2.6826 1.0937 2747
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6882 1.0025 -1.1266 0.6250
## (Intercept)-Procyon_lotor 0.5931 0.9008 -1.2836 0.6043
## (Intercept)-Dasypus_novemcinctus -0.9411 1.0613 -3.2043 -0.8624
## Cogon_Patch_Size-Canis_latrans 0.5347 1.2479 -1.2855 0.3193
## Cogon_Patch_Size-Procyon_lotor -0.9015 0.7970 -2.5529 -0.8898
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5267 0.8203 -2.0766 -0.5665
## Veg_shannon_index-Canis_latrans 1.4733 0.7857 0.0490 1.4245
## Veg_shannon_index-Procyon_lotor 1.3016 0.7114 0.0223 1.2626
## Veg_shannon_index-Dasypus_novemcinctus 0.6482 0.6631 -0.7018 0.6640
## total_shrub_cover-Canis_latrans 0.9387 1.0633 -0.6946 0.7664
## total_shrub_cover-Procyon_lotor -1.0221 0.7622 -2.7211 -0.9678
## total_shrub_cover-Dasypus_novemcinctus -0.1618 0.8569 -1.9328 -0.0759
## Avg_Cogongrass_Cover-Canis_latrans 2.6962 1.2331 0.6472 2.6045
## Avg_Cogongrass_Cover-Procyon_lotor 2.1606 1.0423 0.2006 2.1400
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7580 1.1799 0.7362 2.6576
## Tree_Density-Canis_latrans -3.3636 1.8333 -8.1062 -2.9597
## Tree_Density-Procyon_lotor -1.4867 0.9110 -3.2478 -1.5212
## Tree_Density-Dasypus_novemcinctus -4.1088 2.2014 -9.8326 -3.5565
## Avg_Canopy_Cover-Canis_latrans 0.1452 0.6702 -1.2012 0.1607
## Avg_Canopy_Cover-Procyon_lotor 1.5101 0.7475 0.2228 1.4668
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0737 0.8690 0.6267 1.9760
## avg_veg_height-Canis_latrans -0.3853 0.7492 -1.8603 -0.3797
## avg_veg_height-Procyon_lotor -0.2871 0.6772 -1.5985 -0.3019
## avg_veg_height-Dasypus_novemcinctus -0.1213 0.6972 -1.4177 -0.1552
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.9905 1.0185 373
## (Intercept)-Procyon_lotor 2.3932 1.0505 377
## (Intercept)-Dasypus_novemcinctus 0.8843 1.0793 348
## Cogon_Patch_Size-Canis_latrans 3.5328 1.0032 561
## Cogon_Patch_Size-Procyon_lotor 0.6511 1.0100 548
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2751 1.0363 798
## Veg_shannon_index-Canis_latrans 3.1861 1.0022 515
## Veg_shannon_index-Procyon_lotor 2.7705 1.0208 336
## Veg_shannon_index-Dasypus_novemcinctus 1.9466 1.0047 907
## total_shrub_cover-Canis_latrans 3.5707 1.0092 363
## total_shrub_cover-Procyon_lotor 0.3353 1.0269 887
## total_shrub_cover-Dasypus_novemcinctus 1.1713 1.0276 434
## Avg_Cogongrass_Cover-Canis_latrans 5.6332 1.0239 540
## Avg_Cogongrass_Cover-Procyon_lotor 4.3829 1.0185 537
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.3997 1.0151 564
## Tree_Density-Canis_latrans -0.8263 1.0523 359
## Tree_Density-Procyon_lotor 0.4059 1.0115 656
## Tree_Density-Dasypus_novemcinctus -1.3840 1.0260 297
## Avg_Canopy_Cover-Canis_latrans 1.4044 1.0045 904
## Avg_Canopy_Cover-Procyon_lotor 3.1796 1.0045 715
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.0791 1.0020 771
## avg_veg_height-Canis_latrans 1.0818 1.0095 762
## avg_veg_height-Procyon_lotor 1.0290 1.0128 852
## avg_veg_height-Dasypus_novemcinctus 1.2780 1.0031 780
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7452 0.1811 -3.1138 -2.7405 -2.4068 1.0322
## (Intercept)-Procyon_lotor -2.2902 0.1425 -2.5886 -2.2831 -2.0283 1.0163
## (Intercept)-Dasypus_novemcinctus -1.7951 0.1686 -2.1312 -1.7945 -1.4757 1.0061
## shrub_cover-Canis_latrans -0.4157 0.2306 -0.8765 -0.4154 0.0343 1.0090
## shrub_cover-Procyon_lotor 0.2768 0.1638 -0.0519 0.2813 0.5820 1.0042
## shrub_cover-Dasypus_novemcinctus 0.9535 0.3352 0.3231 0.9469 1.6182 1.0174
## veg_height-Canis_latrans -0.6523 0.1835 -1.0246 -0.6489 -0.3065 1.0149
## veg_height-Procyon_lotor 0.3547 0.1229 0.1204 0.3537 0.5949 1.0060
## veg_height-Dasypus_novemcinctus 0.2716 0.1406 0.0015 0.2692 0.5520 1.0060
## week-Canis_latrans 0.0761 0.1404 -0.2048 0.0814 0.3442 1.0028
## week-Procyon_lotor -0.0497 0.1234 -0.3072 -0.0481 0.1819 1.0173
## week-Dasypus_novemcinctus -0.1818 0.1462 -0.4737 -0.1740 0.0868 1.0032
## ESS
## (Intercept)-Canis_latrans 685
## (Intercept)-Procyon_lotor 1210
## (Intercept)-Dasypus_novemcinctus 917
## shrub_cover-Canis_latrans 718
## shrub_cover-Procyon_lotor 893
## shrub_cover-Dasypus_novemcinctus 609
## veg_height-Canis_latrans 722
## veg_height-Procyon_lotor 1813
## veg_height-Dasypus_novemcinctus 2018
## week-Canis_latrans 1425
## week-Procyon_lotor 1675
## week-Dasypus_novemcinctus 1979
#Includes all covariates of detection and only null for occupancy
ms_full_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3555
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1714 0.5923 -1.0431 0.1792 1.3678 1.0003 2716
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4272 4.1854 0.0669 0.5219 8.3898 1.1009 2493
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0333 0.6089 -2.9650 -2.1321 -0.4664 1.0062 2063
## shrub_cover 0.2376 0.5331 -0.8959 0.2415 1.2854 1.0064 2742
## veg_height -0.0087 0.5237 -1.1312 -0.0121 1.1096 1.0036 2813
## week -0.0396 0.3692 -0.7319 -0.0387 0.6663 1.0052 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3795 4.2181 0.0697 0.4654 8.4512 1.0294 2514
## shrub_cover 1.4377 4.9763 0.0870 0.5192 7.1689 1.0329 2519
## veg_height 1.2970 4.0638 0.0913 0.4927 7.4678 1.0757 2262
## week 0.5208 2.2540 0.0319 0.1598 2.9196 1.0770 1551
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3515 0.3856 -0.3590 0.3408 1.1777 1.0009
## (Intercept)-Procyon_lotor 0.6270 0.3871 -0.0810 0.6034 1.4171 1.0027
## (Intercept)-Dasypus_novemcinctus -0.3902 0.3851 -1.1802 -0.3747 0.3264 1.0014
## ESS
## (Intercept)-Canis_latrans 1898
## (Intercept)-Procyon_lotor 1877
## (Intercept)-Dasypus_novemcinctus 2265
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7339 0.1924 -3.1190 -2.7238 -2.3764 1.0065
## (Intercept)-Procyon_lotor -2.2901 0.1433 -2.5794 -2.2884 -2.0203 1.0076
## (Intercept)-Dasypus_novemcinctus -1.7692 0.1663 -2.1199 -1.7642 -1.4547 1.0086
## shrub_cover-Canis_latrans -0.3093 0.2243 -0.7433 -0.3026 0.1275 1.0121
## shrub_cover-Procyon_lotor 0.2588 0.1665 -0.0705 0.2603 0.5706 1.0041
## shrub_cover-Dasypus_novemcinctus 0.8544 0.3155 0.2474 0.8495 1.5076 1.0070
## veg_height-Canis_latrans -0.6408 0.1902 -1.0135 -0.6365 -0.2756 1.0050
## veg_height-Procyon_lotor 0.3508 0.1234 0.1158 0.3527 0.5896 1.0323
## veg_height-Dasypus_novemcinctus 0.2581 0.1408 -0.0188 0.2583 0.5420 1.0034
## week-Canis_latrans 0.0810 0.1402 -0.1954 0.0822 0.3546 1.0003
## week-Procyon_lotor -0.0457 0.1242 -0.2986 -0.0391 0.1882 1.0074
## week-Dasypus_novemcinctus -0.1836 0.1473 -0.4867 -0.1803 0.0938 1.0031
## ESS
## (Intercept)-Canis_latrans 688
## (Intercept)-Procyon_lotor 1134
## (Intercept)-Dasypus_novemcinctus 1163
## shrub_cover-Canis_latrans 798
## shrub_cover-Procyon_lotor 1442
## shrub_cover-Dasypus_novemcinctus 1162
## veg_height-Canis_latrans 757
## veg_height-Procyon_lotor 1602
## veg_height-Dasypus_novemcinctus 1748
## week-Canis_latrans 1544
## week-Procyon_lotor 1496
## week-Dasypus_novemcinctus 1947
#Includes all covariates of detection and only cover for occupancy
ms_full_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3677
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4493 0.7210 -1.0068 0.4492 1.8705 1.0096 895
## Avg_Cogongrass_Cover 0.2061 0.6536 -1.0820 0.2032 1.5564 1.0067 1313
## total_shrub_cover -0.2755 0.8324 -1.9218 -0.2918 1.4515 1.0065 1498
## avg_veg_height 0.2602 0.6683 -1.0541 0.2554 1.5647 1.0075 966
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9556 6.3782 0.0541 0.5883 12.9431 1.0713 2202
## Avg_Cogongrass_Cover 1.1814 3.6388 0.0508 0.3724 6.9296 1.0119 1457
## total_shrub_cover 4.4235 23.1557 0.0859 1.2636 23.3337 1.1488 2113
## avg_veg_height 1.2277 5.7534 0.0437 0.3408 7.2570 1.2398 2709
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6663 0.9881 0.0497 0.3487 3.3792 1.0232 518
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0765 0.6241 -3.0065 -2.1694 -0.4343 1.0060 2295
## shrub_cover 0.3241 0.6339 -0.9754 0.3111 1.6352 1.0062 2352
## veg_height -0.0104 0.5278 -1.0481 -0.0055 1.0278 1.0016 2780
## week -0.0396 0.3546 -0.7408 -0.0403 0.6700 1.0039 2720
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5573 7.8179 0.0722 0.4333 9.1051 1.1913 2798
## shrub_cover 1.9820 5.2656 0.1014 0.7819 11.0312 1.0236 2767
## veg_height 1.2933 5.4889 0.0915 0.4853 6.6693 1.2522 3000
## week 0.5393 3.1338 0.0323 0.1638 2.3661 1.0819 2772
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7350 0.7149 -0.4003 0.6746
## (Intercept)-Procyon_lotor 0.8636 0.6490 -0.3554 0.8310
## (Intercept)-Dasypus_novemcinctus -0.0225 0.7278 -1.3247 -0.0808
## Avg_Cogongrass_Cover-Canis_latrans 0.5254 0.7356 -0.6941 0.4618
## Avg_Cogongrass_Cover-Procyon_lotor -0.0422 0.5928 -1.2577 -0.0251
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2172 0.5567 -0.8708 0.2220
## total_shrub_cover-Canis_latrans 0.6347 0.8494 -0.6218 0.4973
## total_shrub_cover-Procyon_lotor -1.2317 0.6767 -2.7614 -1.1478
## total_shrub_cover-Dasypus_novemcinctus -0.5144 0.9109 -3.0056 -0.3242
## avg_veg_height-Canis_latrans 0.2099 0.6367 -0.9760 0.1926
## avg_veg_height-Procyon_lotor 0.2118 0.5757 -0.9013 0.2060
## avg_veg_height-Dasypus_novemcinctus 0.5107 0.6422 -0.5886 0.4585
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3918 1.0200 523
## (Intercept)-Procyon_lotor 2.2018 1.0154 575
## (Intercept)-Dasypus_novemcinctus 1.5632 1.0479 220
## Avg_Cogongrass_Cover-Canis_latrans 2.2617 1.0261 598
## Avg_Cogongrass_Cover-Procyon_lotor 1.0722 1.0001 927
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3084 1.0038 1161
## total_shrub_cover-Canis_latrans 2.7272 1.0216 385
## total_shrub_cover-Procyon_lotor -0.1154 1.0386 787
## total_shrub_cover-Dasypus_novemcinctus 0.6759 1.0832 164
## avg_veg_height-Canis_latrans 1.5379 1.0026 665
## avg_veg_height-Procyon_lotor 1.3785 1.0012 1110
## avg_veg_height-Dasypus_novemcinctus 1.9482 1.0034 498
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7851 0.2041 -3.2056 -2.7813 -2.4022 1.0082
## (Intercept)-Procyon_lotor -2.3015 0.1360 -2.5810 -2.2992 -2.0429 1.0029
## (Intercept)-Dasypus_novemcinctus -1.8522 0.1971 -2.2498 -1.8429 -1.4951 1.0753
## shrub_cover-Canis_latrans -0.3859 0.2396 -0.8448 -0.3866 0.0824 0.9998
## shrub_cover-Procyon_lotor 0.3163 0.1592 -0.0080 0.3189 0.6133 1.0005
## shrub_cover-Dasypus_novemcinctus 1.1078 0.4206 0.3637 1.0916 1.9330 1.1002
## veg_height-Canis_latrans -0.6663 0.2012 -1.0724 -0.6625 -0.2774 1.0009
## veg_height-Procyon_lotor 0.3511 0.1250 0.1023 0.3523 0.5995 1.0010
## veg_height-Dasypus_novemcinctus 0.2735 0.1454 -0.0081 0.2743 0.5588 1.0048
## week-Canis_latrans 0.0830 0.1396 -0.1955 0.0891 0.3472 1.0013
## week-Procyon_lotor -0.0499 0.1257 -0.3075 -0.0457 0.1919 1.0063
## week-Dasypus_novemcinctus -0.1780 0.1496 -0.4745 -0.1716 0.1037 1.0102
## ESS
## (Intercept)-Canis_latrans 458
## (Intercept)-Procyon_lotor 1322
## (Intercept)-Dasypus_novemcinctus 397
## shrub_cover-Canis_latrans 475
## shrub_cover-Procyon_lotor 1425
## shrub_cover-Dasypus_novemcinctus 291
## veg_height-Canis_latrans 645
## veg_height-Procyon_lotor 1497
## veg_height-Dasypus_novemcinctus 1500
## week-Canis_latrans 1397
## week-Procyon_lotor 1474
## week-Dasypus_novemcinctus 1756
#Includes all covariates of detection and only canopy for occupancy
ms_full_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3607
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0561 0.6893 -1.3119 0.0646 1.5298 1.0038 1705
## Tree_Density -0.8800 0.7258 -2.3155 -0.8639 0.6608 1.0035 1637
## Avg_Canopy_Cover 0.4245 0.6830 -1.0469 0.4521 1.7758 1.0016 2610
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.8148 26.6945 0.0604 0.6885 13.2925 1.2731 3000
## Tree_Density 2.6571 11.6937 0.0471 0.5854 15.9679 1.0182 1728
## Avg_Canopy_Cover 2.1761 7.0671 0.0759 0.7091 12.9091 1.0438 2465
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6136 0.8991 0.0437 0.3213 3.0057 1.0064 456
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0292 0.6085 -2.9753 -2.1167 -0.4421 1.0021 2777
## shrub_cover 0.2597 0.5707 -0.9874 0.2587 1.4928 1.0021 2869
## veg_height 0.0115 0.5233 -1.0419 0.0135 1.0701 1.0018 3000
## week -0.0508 0.3294 -0.6984 -0.0480 0.6346 1.0015 3084
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6379 6.8278 0.0751 0.4622 9.6846 1.0716 2751
## shrub_cover 1.4838 5.5780 0.0827 0.5422 7.7865 1.0581 2664
## veg_height 1.8121 26.9329 0.0873 0.4875 6.7624 1.3304 3000
## week 0.4514 1.9251 0.0302 0.1573 2.5833 1.0746 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2498 0.5696 -0.8575 0.2431 1.4409
## (Intercept)-Procyon_lotor 0.5618 0.5924 -0.6025 0.5439 1.7702
## (Intercept)-Dasypus_novemcinctus -0.6497 0.6654 -2.0820 -0.6095 0.5071
## Tree_Density-Canis_latrans -1.0903 0.6899 -2.6473 -1.0019 0.0314
## Tree_Density-Procyon_lotor -0.5258 0.4775 -1.5278 -0.5103 0.3904
## Tree_Density-Dasypus_novemcinctus -1.5493 1.0727 -4.3114 -1.3543 -0.1392
## Avg_Canopy_Cover-Canis_latrans -0.2338 0.4608 -1.1563 -0.2250 0.6045
## Avg_Canopy_Cover-Procyon_lotor 0.8789 0.5143 -0.0323 0.8368 2.0232
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9243 0.5123 0.0368 0.8776 2.0048
## Rhat ESS
## (Intercept)-Canis_latrans 1.0025 1041
## (Intercept)-Procyon_lotor 0.9998 848
## (Intercept)-Dasypus_novemcinctus 1.0051 585
## Tree_Density-Canis_latrans 1.0058 1061
## Tree_Density-Procyon_lotor 1.0060 1882
## Tree_Density-Dasypus_novemcinctus 1.0160 478
## Avg_Canopy_Cover-Canis_latrans 1.0033 1774
## Avg_Canopy_Cover-Procyon_lotor 1.0020 1563
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0022 1581
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7248 0.1946 -3.1251 -2.7223 -2.3635 1.0141
## (Intercept)-Procyon_lotor -2.2909 0.1431 -2.5869 -2.2835 -2.0298 1.0001
## (Intercept)-Dasypus_novemcinctus -1.7796 0.1634 -2.1154 -1.7780 -1.4692 1.0090
## shrub_cover-Canis_latrans -0.3211 0.2182 -0.7549 -0.3203 0.1144 1.0394
## shrub_cover-Procyon_lotor 0.2682 0.1606 -0.0602 0.2705 0.5775 1.0044
## shrub_cover-Dasypus_novemcinctus 0.8816 0.3148 0.2925 0.8692 1.5164 1.0148
## veg_height-Canis_latrans -0.6358 0.1934 -1.0295 -0.6320 -0.2735 1.0000
## veg_height-Procyon_lotor 0.3606 0.1275 0.1160 0.3604 0.6155 1.0066
## veg_height-Dasypus_novemcinctus 0.2655 0.1392 0.0053 0.2620 0.5401 1.0105
## week-Canis_latrans 0.0755 0.1374 -0.2006 0.0790 0.3441 1.0011
## week-Procyon_lotor -0.0513 0.1201 -0.2942 -0.0490 0.1694 1.0009
## week-Dasypus_novemcinctus -0.1814 0.1458 -0.4964 -0.1759 0.0896 1.0035
## ESS
## (Intercept)-Canis_latrans 599
## (Intercept)-Procyon_lotor 1237
## (Intercept)-Dasypus_novemcinctus 1570
## shrub_cover-Canis_latrans 836
## shrub_cover-Procyon_lotor 1423
## shrub_cover-Dasypus_novemcinctus 1082
## veg_height-Canis_latrans 715
## veg_height-Procyon_lotor 1569
## veg_height-Dasypus_novemcinctus 1799
## week-Canis_latrans 1381
## week-Procyon_lotor 1719
## week-Dasypus_novemcinctus 2020
#Includes all covariates of detection and only movement for occupancy
ms_full_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3703
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3861 0.7312 -1.0331 0.3619 1.9080 1.0084 873
## Cogon_Patch_Size 0.2384 0.7050 -1.1512 0.2106 1.7381 1.0070 1961
## Avg_Cogongrass_Cover 0.2989 0.5730 -0.8420 0.2903 1.4481 1.0029 1646
## total_shrub_cover -0.2824 0.7689 -1.8528 -0.2917 1.3377 1.0061 2041
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2011 15.2339 0.0560 0.6202 11.6560 1.2269 3000
## Cogon_Patch_Size 2.2483 10.9506 0.0513 0.5172 14.9474 1.0947 2226
## Avg_Cogongrass_Cover 1.0744 4.3603 0.0416 0.3083 5.7822 1.0906 2748
## total_shrub_cover 3.1838 10.0937 0.0713 0.9202 17.9800 1.0976 1662
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6919 0.9733 0.046 0.383 3.1693 1.1118 410
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0597 0.6138 -2.9423 -2.1569 -0.5286 1.0022 2385
## shrub_cover 0.2924 0.6188 -0.9585 0.2983 1.5850 1.0141 2728
## veg_height -0.0162 0.5146 -1.0625 -0.0198 0.9940 1.0021 3000
## week -0.0399 0.3564 -0.7146 -0.0420 0.6633 1.0054 2743
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4657 6.0048 0.0698 0.4413 9.8615 1.1379 2508
## shrub_cover 2.0746 7.1318 0.0919 0.6939 11.2841 1.0455 2722
## veg_height 1.3064 4.7128 0.0875 0.4890 6.4377 1.0364 3000
## week 0.5422 3.4294 0.0339 0.1624 2.7205 1.0644 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6966 0.6771 -0.4482 0.6321
## (Intercept)-Procyon_lotor 0.8061 0.6510 -0.3822 0.7648
## (Intercept)-Dasypus_novemcinctus -0.1207 0.7008 -1.3704 -0.1634
## Cogon_Patch_Size-Canis_latrans 0.9430 1.0618 -0.3691 0.7018
## Cogon_Patch_Size-Procyon_lotor -0.0855 0.5196 -1.1470 -0.0807
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0286 0.4957 -0.9703 0.0216
## Avg_Cogongrass_Cover-Canis_latrans 0.4061 0.5362 -0.5310 0.3604
## Avg_Cogongrass_Cover-Procyon_lotor 0.1647 0.5206 -0.8319 0.1536
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4092 0.4727 -0.4935 0.3855
## total_shrub_cover-Canis_latrans 0.4100 0.7580 -0.7648 0.3124
## total_shrub_cover-Procyon_lotor -1.1312 0.6808 -2.7191 -1.0350
## total_shrub_cover-Dasypus_novemcinctus -0.4196 0.8651 -2.9267 -0.2583
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2391 1.0015 639
## (Intercept)-Procyon_lotor 2.2532 1.0016 608
## (Intercept)-Dasypus_novemcinctus 1.4913 1.0880 256
## Cogon_Patch_Size-Canis_latrans 3.7948 1.0095 456
## Cogon_Patch_Size-Procyon_lotor 0.9170 1.0128 1389
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0681 1.0128 1269
## Avg_Cogongrass_Cover-Canis_latrans 1.6058 1.0013 1208
## Avg_Cogongrass_Cover-Procyon_lotor 1.1988 1.0094 1417
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3746 1.0031 1191
## total_shrub_cover-Canis_latrans 2.1962 1.0053 529
## total_shrub_cover-Procyon_lotor -0.0650 1.0073 509
## total_shrub_cover-Dasypus_novemcinctus 0.6960 1.1881 168
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7499 0.1958 -3.1460 -2.7490 -2.3895 1.0118
## (Intercept)-Procyon_lotor -2.2910 0.1370 -2.5704 -2.2904 -2.0284 1.0041
## (Intercept)-Dasypus_novemcinctus -1.8296 0.1892 -2.2061 -1.8246 -1.4705 1.1205
## shrub_cover-Canis_latrans -0.3531 0.2422 -0.8399 -0.3496 0.1111 1.0023
## shrub_cover-Procyon_lotor 0.3181 0.1603 -0.0022 0.3206 0.6257 0.9997
## shrub_cover-Dasypus_novemcinctus 1.0260 0.3866 0.3320 1.0015 1.8215 1.1137
## veg_height-Canis_latrans -0.6522 0.1891 -1.0365 -0.6478 -0.2854 1.0322
## veg_height-Procyon_lotor 0.3483 0.1279 0.0911 0.3460 0.6027 1.0020
## veg_height-Dasypus_novemcinctus 0.2767 0.1508 -0.0186 0.2763 0.5828 1.0269
## week-Canis_latrans 0.0861 0.1378 -0.1902 0.0887 0.3497 1.0039
## week-Procyon_lotor -0.0486 0.1246 -0.3027 -0.0437 0.1812 1.0018
## week-Dasypus_novemcinctus -0.1775 0.1492 -0.4842 -0.1719 0.0959 1.0025
## ESS
## (Intercept)-Canis_latrans 673
## (Intercept)-Procyon_lotor 1342
## (Intercept)-Dasypus_novemcinctus 359
## shrub_cover-Canis_latrans 629
## shrub_cover-Procyon_lotor 1529
## shrub_cover-Dasypus_novemcinctus 310
## veg_height-Canis_latrans 730
## veg_height-Procyon_lotor 1545
## veg_height-Dasypus_novemcinctus 1103
## week-Canis_latrans 1569
## week-Procyon_lotor 1620
## week-Dasypus_novemcinctus 1828
#Includes all covariates of detection and only foraging for occupancy
ms_full_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3615
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2197 0.6629 -1.1050 0.2206 1.6389 1.0047 1446
## Veg_shannon_index 0.5241 0.5890 -0.6537 0.5273 1.6850 1.0058 1729
## Avg_Cogongrass_Cover 0.6423 0.5588 -0.4655 0.6404 1.7484 1.0044 1293
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7460 5.8413 0.0509 0.5493 10.4498 1.0423 2499
## Veg_shannon_index 1.4330 8.2468 0.0463 0.3710 8.0855 1.0963 2415
## Avg_Cogongrass_Cover 1.1105 8.6964 0.0385 0.2814 5.6673 1.1871 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7283 1.1751 0.0453 0.3668 3.7174 1.127 292
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0265 0.6352 -2.9608 -2.1258 -0.4273 1.0115 2465
## shrub_cover 0.2409 0.5681 -0.9691 0.2326 1.4044 1.0019 3000
## veg_height -0.0167 0.5251 -1.0611 -0.0288 1.1025 1.0039 2769
## week -0.0489 0.3552 -0.7519 -0.0521 0.6794 1.0138 2533
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5384 5.0974 0.0713 0.4329 10.3027 1.0537 1715
## shrub_cover 1.4413 4.3902 0.0828 0.5447 7.6814 1.0499 2713
## veg_height 1.3932 9.9882 0.0857 0.4843 6.9977 1.2768 3000
## week 0.5039 2.0186 0.0328 0.1641 3.1081 1.0536 2628
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4431 0.6032 -0.6384 0.4100 1.7829
## (Intercept)-Procyon_lotor 0.5741 0.5760 -0.5114 0.5570 1.7524
## (Intercept)-Dasypus_novemcinctus -0.3364 0.5962 -1.5683 -0.3222 0.7274
## Veg_shannon_index-Canis_latrans 0.8809 0.5098 -0.0031 0.8479 2.0229
## Veg_shannon_index-Procyon_lotor 0.6229 0.5066 -0.2806 0.5978 1.7311
## Veg_shannon_index-Dasypus_novemcinctus 0.2261 0.4234 -0.6407 0.2341 1.0253
## Avg_Cogongrass_Cover-Canis_latrans 0.9257 0.5574 0.0103 0.8581 2.2211
## Avg_Cogongrass_Cover-Procyon_lotor 0.6036 0.5029 -0.2861 0.5632 1.7284
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6034 0.4132 -0.1641 0.5879 1.4674
## Rhat ESS
## (Intercept)-Canis_latrans 1.0203 778
## (Intercept)-Procyon_lotor 1.0061 958
## (Intercept)-Dasypus_novemcinctus 1.0077 636
## Veg_shannon_index-Canis_latrans 1.0040 1061
## Veg_shannon_index-Procyon_lotor 1.0375 661
## Veg_shannon_index-Dasypus_novemcinctus 1.0013 1890
## Avg_Cogongrass_Cover-Canis_latrans 1.0030 806
## Avg_Cogongrass_Cover-Procyon_lotor 1.0250 1049
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0010 1378
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7243 0.1859 -3.0930 -2.7148 -2.3778 1.0055
## (Intercept)-Procyon_lotor -2.3090 0.1509 -2.6136 -2.3042 -2.0277 1.0216
## (Intercept)-Dasypus_novemcinctus -1.7827 0.1662 -2.1126 -1.7817 -1.4688 1.0001
## shrub_cover-Canis_latrans -0.2976 0.2157 -0.7285 -0.2938 0.1189 1.0087
## shrub_cover-Procyon_lotor 0.2279 0.1769 -0.1417 0.2359 0.5604 1.0111
## shrub_cover-Dasypus_novemcinctus 0.8910 0.3320 0.2857 0.8795 1.5713 1.0049
## veg_height-Canis_latrans -0.6481 0.1932 -1.0497 -0.6408 -0.2846 1.0016
## veg_height-Procyon_lotor 0.3485 0.1234 0.1132 0.3472 0.5895 1.0016
## veg_height-Dasypus_novemcinctus 0.2582 0.1427 -0.0319 0.2601 0.5455 1.0018
## week-Canis_latrans 0.0783 0.1356 -0.1984 0.0827 0.3393 1.0072
## week-Procyon_lotor -0.0520 0.1272 -0.3139 -0.0497 0.1769 1.0028
## week-Dasypus_novemcinctus -0.1869 0.1510 -0.5025 -0.1796 0.0931 1.0037
## ESS
## (Intercept)-Canis_latrans 733
## (Intercept)-Procyon_lotor 1010
## (Intercept)-Dasypus_novemcinctus 1231
## shrub_cover-Canis_latrans 903
## shrub_cover-Procyon_lotor 1115
## shrub_cover-Dasypus_novemcinctus 927
## veg_height-Canis_latrans 685
## veg_height-Procyon_lotor 1597
## veg_height-Dasypus_novemcinctus 1865
## week-Canis_latrans 1496
## week-Procyon_lotor 1658
## week-Dasypus_novemcinctus 1955
#Includes all covariates of detection and only quadratic cogongrass cover for occupancy
ms_full_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3617
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.4703 0.6763 -1.7400 -0.4848 0.9381 1.0040 708
## Avg_Cogongrass_Cover -0.0413 0.6750 -1.3113 -0.0615 1.3948 1.0299 920
## I(Avg_Cogongrass_Cover^2) 0.9647 0.9087 -0.8945 0.9293 2.8896 1.0216 660
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4159 5.2275 0.0502 0.3999 8.6958 1.1575 2661
## Avg_Cogongrass_Cover 1.3554 5.1650 0.0439 0.3892 8.7307 1.1857 2657
## I(Avg_Cogongrass_Cover^2) 6.8474 40.5865 0.0565 0.9959 48.3598 1.2673 289
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4724 0.8413 0.0407 0.2386 2.2304 1.0541 547
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0360 0.6563 -3.0024 -2.1499 -0.3036 1.0152 2167
## shrub_cover 0.2551 0.5517 -0.9005 0.2535 1.3895 1.0032 2610
## veg_height -0.0006 0.5325 -1.0713 -0.0062 1.1160 1.0078 2787
## week -0.0405 0.3586 -0.7058 -0.0389 0.6729 1.0007 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6666 6.9258 0.0746 0.4667 10.4555 1.0457 2311
## shrub_cover 1.4996 8.7409 0.0711 0.5062 8.3060 1.2446 2834
## veg_height 1.2495 3.2034 0.0918 0.4936 6.7635 1.0186 2631
## week 0.4296 1.1242 0.0324 0.1647 2.6210 1.0076 2539
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.4823 0.6965 -1.9153 -0.4809
## (Intercept)-Procyon_lotor -0.2634 0.6719 -1.5613 -0.2721
## (Intercept)-Dasypus_novemcinctus -0.8449 0.5867 -2.0358 -0.8141
## Avg_Cogongrass_Cover-Canis_latrans 0.1635 0.6939 -1.0644 0.1102
## Avg_Cogongrass_Cover-Procyon_lotor -0.2619 0.6964 -1.6330 -0.2593
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.0580 0.5787 -1.1761 -0.0622
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 1.9612 1.3859 0.1098 1.6638
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 1.8589 1.7504 0.0663 1.2943
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4915 0.4572 -0.3486 0.4619
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8894 1.0245 530
## (Intercept)-Procyon_lotor 1.0376 1.0328 437
## (Intercept)-Dasypus_novemcinctus 0.2230 1.0015 1089
## Avg_Cogongrass_Cover-Canis_latrans 1.6750 1.0155 758
## Avg_Cogongrass_Cover-Procyon_lotor 1.1601 1.0915 469
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0873 1.0097 1268
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4625 1.0693 165
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.7839 1.3696 89
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4902 1.0042 1212
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7455 0.1876 -3.1242 -2.7383 -2.4022 1.0006
## (Intercept)-Procyon_lotor -2.3338 0.1485 -2.6304 -2.3303 -2.0582 1.0653
## (Intercept)-Dasypus_novemcinctus -1.7683 0.1645 -2.1045 -1.7625 -1.4656 1.0077
## shrub_cover-Canis_latrans -0.2516 0.2193 -0.6839 -0.2529 0.1697 1.0082
## shrub_cover-Procyon_lotor 0.2173 0.1732 -0.1195 0.2181 0.5473 1.0428
## shrub_cover-Dasypus_novemcinctus 0.8596 0.3207 0.2412 0.8544 1.5079 1.0212
## veg_height-Canis_latrans -0.6385 0.1887 -1.0251 -0.6359 -0.2854 1.0000
## veg_height-Procyon_lotor 0.3562 0.1270 0.1084 0.3571 0.6140 1.0115
## veg_height-Dasypus_novemcinctus 0.2595 0.1424 -0.0136 0.2570 0.5460 1.0024
## week-Canis_latrans 0.0855 0.1351 -0.1827 0.0914 0.3382 1.0060
## week-Procyon_lotor -0.0445 0.1245 -0.2977 -0.0370 0.1913 1.0013
## week-Dasypus_novemcinctus -0.1717 0.1499 -0.4782 -0.1669 0.1090 0.9999
## ESS
## (Intercept)-Canis_latrans 688
## (Intercept)-Procyon_lotor 751
## (Intercept)-Dasypus_novemcinctus 1072
## shrub_cover-Canis_latrans 962
## shrub_cover-Procyon_lotor 934
## shrub_cover-Dasypus_novemcinctus 1076
## veg_height-Canis_latrans 868
## veg_height-Procyon_lotor 1719
## veg_height-Dasypus_novemcinctus 1803
## week-Canis_latrans 1584
## week-Procyon_lotor 1790
## week-Dasypus_novemcinctus 2026
## Includes all covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_full_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3737
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9957 1.0736 -3.0923 -1.0228 1.1826 1.0051 705
## Cogon_Patch_Size 0.0994 1.0187 -1.9444 0.0962 2.1871 1.0015 787
## Veg_shannon_index 0.9467 0.8565 -0.9182 0.9839 2.5609 1.0060 1389
## total_shrub_cover -0.2212 0.9462 -2.1201 -0.2285 1.7576 1.0033 588
## Avg_Cogongrass_Cover 0.8952 1.3053 -1.6770 0.9270 3.4388 1.0110 408
## Tree_Density -1.8120 1.5521 -4.5431 -2.0047 1.6125 1.0283 552
## Avg_Canopy_Cover 0.9572 0.9892 -1.3189 0.9886 2.8632 1.0166 1979
## I(Avg_Cogongrass_Cover^2) 1.5964 1.2088 -1.1359 1.6330 3.9156 1.0145 712
## avg_veg_height -0.0288 0.8107 -1.6272 -0.0139 1.5458 1.0091 516
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.1765 62.2150 0.0587 1.0166 43.1335 1.1361 2474
## Cogon_Patch_Size 6.8425 19.8683 0.0745 1.6263 48.9074 1.0122 1147
## Veg_shannon_index 4.1692 24.6232 0.0594 0.8061 25.5635 1.0492 2712
## total_shrub_cover 6.7504 44.5282 0.0770 1.4574 40.2372 1.0434 2669
## Avg_Cogongrass_Cover 6.4822 27.5654 0.0567 0.9990 42.7115 1.0214 538
## Tree_Density 56.2471 236.4790 0.0825 8.9192 375.9125 1.1714 563
## Avg_Canopy_Cover 8.2532 33.9636 0.0896 1.9793 48.7835 1.0605 922
## I(Avg_Cogongrass_Cover^2) 14.8050 90.5822 0.0641 1.4158 100.1331 1.1116 394
## avg_veg_height 1.7633 8.1473 0.0470 0.4131 10.4392 1.0663 2816
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 3.3234 6.6116 0.0697 1.1183 21.4868 1.0545 123
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0562 0.6040 -2.9241 -2.1555 -0.5213 1.0036 1966
## shrub_cover 0.2511 0.5956 -1.0040 0.2595 1.4827 1.0068 2463
## veg_height 0.0004 0.5326 -1.1627 0.0028 1.0552 0.9999 3000
## week -0.0480 0.3646 -0.7276 -0.0514 0.6744 1.0032 3372
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4730 6.0736 0.0707 0.4318 8.9042 1.1329 2298
## shrub_cover 1.7010 4.0794 0.1032 0.6926 9.7390 1.0341 2669
## veg_height 1.4064 4.4374 0.0883 0.4871 7.9971 1.0494 2207
## week 0.6995 4.9273 0.0310 0.1618 3.1559 1.1959 2435
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.0597 1.4087 -3.8790 -1.0556
## (Intercept)-Procyon_lotor -0.8357 1.1565 -3.3441 -0.7896
## (Intercept)-Dasypus_novemcinctus -2.1363 1.5260 -5.7526 -1.9035
## Cogon_Patch_Size-Canis_latrans 1.6186 1.8964 -1.0202 1.2373
## Cogon_Patch_Size-Procyon_lotor -0.6256 1.0986 -2.7481 -0.6028
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1664 1.0772 -2.2224 -0.1943
## Veg_shannon_index-Canis_latrans 1.7000 1.1405 -0.1293 1.5516
## Veg_shannon_index-Procyon_lotor 1.4017 0.9255 -0.0538 1.3022
## Veg_shannon_index-Dasypus_novemcinctus 0.5450 0.7920 -1.0593 0.5681
## total_shrub_cover-Canis_latrans 0.9095 1.4860 -1.2242 0.6102
## total_shrub_cover-Procyon_lotor -1.2429 0.9486 -3.3533 -1.1561
## total_shrub_cover-Dasypus_novemcinctus -0.4099 1.1782 -3.2391 -0.2150
## Avg_Cogongrass_Cover-Canis_latrans 1.1720 1.8219 -2.1113 1.0709
## Avg_Cogongrass_Cover-Procyon_lotor 0.5789 1.7440 -2.9049 0.6675
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.8499 1.9734 -1.2362 1.6020
## Tree_Density-Canis_latrans -4.9997 3.2075 -13.6076 -4.0741
## Tree_Density-Procyon_lotor -2.4657 1.4666 -5.6276 -2.3901
## Tree_Density-Dasypus_novemcinctus -6.3478 4.1459 -17.1633 -5.0110
## Avg_Canopy_Cover-Canis_latrans 0.0194 0.8264 -1.7207 0.0636
## Avg_Canopy_Cover-Procyon_lotor 1.5677 0.9224 -0.0048 1.4761
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.5657 1.4177 0.6354 2.2842
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0644 2.1002 0.5434 2.5917
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.1684 2.4673 0.5323 2.4805
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5254 1.2154 -0.3337 1.3623
## avg_veg_height-Canis_latrans -0.2711 0.8901 -2.1216 -0.2504
## avg_veg_height-Procyon_lotor 0.0309 0.8731 -1.7027 0.0325
## avg_veg_height-Dasypus_novemcinctus 0.1367 0.8556 -1.5524 0.1395
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.6745 1.0479 334
## (Intercept)-Procyon_lotor 1.3662 1.0273 346
## (Intercept)-Dasypus_novemcinctus 0.1654 1.1364 246
## Cogon_Patch_Size-Canis_latrans 6.5646 1.0062 350
## Cogon_Patch_Size-Procyon_lotor 1.4779 1.0226 370
## Cogon_Patch_Size-Dasypus_novemcinctus 2.0714 1.0141 491
## Veg_shannon_index-Canis_latrans 4.4366 1.0037 310
## Veg_shannon_index-Procyon_lotor 3.4797 1.0142 167
## Veg_shannon_index-Dasypus_novemcinctus 2.0660 1.0126 883
## total_shrub_cover-Canis_latrans 4.5447 1.0272 140
## total_shrub_cover-Procyon_lotor 0.4028 1.0065 267
## total_shrub_cover-Dasypus_novemcinctus 1.2542 1.0896 244
## Avg_Cogongrass_Cover-Canis_latrans 5.2352 1.0220 378
## Avg_Cogongrass_Cover-Procyon_lotor 3.6746 1.0134 340
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 6.3707 1.0329 226
## Tree_Density-Canis_latrans -1.0124 1.1633 110
## Tree_Density-Procyon_lotor 0.2198 1.0134 434
## Tree_Density-Dasypus_novemcinctus -1.7234 1.2149 105
## Avg_Canopy_Cover-Canis_latrans 1.5396 1.0107 607
## Avg_Canopy_Cover-Procyon_lotor 3.5412 1.0489 423
## Avg_Canopy_Cover-Dasypus_novemcinctus 6.1214 1.1222 139
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.7246 1.2353 103
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 10.5437 1.2537 60
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 4.4423 1.0585 215
## avg_veg_height-Canis_latrans 1.3910 1.0130 340
## avg_veg_height-Procyon_lotor 1.8218 1.0154 488
## avg_veg_height-Dasypus_novemcinctus 1.8403 1.0333 341
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7374 0.1899 -3.1239 -2.7298 -2.3755 1.0058
## (Intercept)-Procyon_lotor -2.3201 0.1477 -2.6264 -2.3158 -2.0373 1.0155
## (Intercept)-Dasypus_novemcinctus -1.8175 0.1716 -2.1585 -1.8151 -1.5006 1.0152
## shrub_cover-Canis_latrans -0.3672 0.2379 -0.8204 -0.3680 0.1053 1.0052
## shrub_cover-Procyon_lotor 0.2518 0.1742 -0.1124 0.2543 0.5833 1.0017
## shrub_cover-Dasypus_novemcinctus 1.0054 0.3446 0.3524 0.9981 1.6692 1.0387
## veg_height-Canis_latrans -0.6293 0.1870 -1.0058 -0.6314 -0.2650 1.0037
## veg_height-Procyon_lotor 0.3658 0.1272 0.1140 0.3658 0.6124 1.0015
## veg_height-Dasypus_novemcinctus 0.2786 0.1416 0.0035 0.2770 0.5567 1.0050
## week-Canis_latrans 0.0780 0.1378 -0.1999 0.0800 0.3360 1.0115
## week-Procyon_lotor -0.0476 0.1224 -0.3005 -0.0404 0.1778 1.0038
## week-Dasypus_novemcinctus -0.1773 0.1490 -0.4858 -0.1710 0.0980 1.0037
## ESS
## (Intercept)-Canis_latrans 786
## (Intercept)-Procyon_lotor 435
## (Intercept)-Dasypus_novemcinctus 834
## shrub_cover-Canis_latrans 529
## shrub_cover-Procyon_lotor 244
## shrub_cover-Dasypus_novemcinctus 437
## veg_height-Canis_latrans 697
## veg_height-Procyon_lotor 1263
## veg_height-Dasypus_novemcinctus 1464
## week-Canis_latrans 1522
## week-Procyon_lotor 1640
## week-Dasypus_novemcinctus 1805
# Includes all covariates of occupancy and null for detection
ms_null_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2737
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0284 0.8710 -1.8257 -0.0300 1.7731 1.0124 936
## Cogon_Patch_Size -0.3258 0.7904 -1.7735 -0.3589 1.4238 1.0060 1504
## Veg_shannon_index 0.9749 0.7150 -0.5041 0.9888 2.3007 1.0238 973
## total_shrub_cover -0.0950 0.6943 -1.4352 -0.1099 1.3121 1.0068 1549
## Avg_Cogongrass_Cover 2.0741 1.0076 -0.0606 2.1323 3.8992 1.0072 679
## Tree_Density -1.7144 1.1127 -3.7259 -1.8025 0.8635 1.0139 1362
## Avg_Canopy_Cover 0.9543 0.8283 -0.9208 0.9746 2.5367 1.0017 2131
## avg_veg_height -0.3789 0.6764 -1.6767 -0.3802 0.9978 1.0095 898
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.9278 18.0563 0.0735 1.0779 20.8411 1.1874 2810
## Cogon_Patch_Size 2.8266 8.2734 0.0571 0.7972 18.3158 1.0441 1371
## Veg_shannon_index 1.6541 5.3918 0.0526 0.4679 10.0943 1.0257 1294
## total_shrub_cover 1.9881 5.0292 0.0634 0.6719 12.7667 1.0214 2072
## Avg_Cogongrass_Cover 4.3461 54.8123 0.0489 0.5105 23.3191 1.2926 3000
## Tree_Density 9.4969 52.0953 0.0651 1.5766 65.0710 1.3726 531
## Avg_Canopy_Cover 3.3701 9.5431 0.0844 1.0552 20.6794 1.0163 2104
## avg_veg_height 1.1554 3.8406 0.0446 0.3592 7.4982 1.0913 2336
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.6905 2.8299 0.0567 0.8074 8.8955 1.1842 195
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.909 0.6234 -2.8953 -2.0001 -0.3358 1.0066 2588
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9171 13.4559 0.0897 0.5141 9.8623 1.1193 2206
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3616 0.8836 -1.3573 0.3154
## (Intercept)-Procyon_lotor 0.5110 0.8110 -1.2300 0.5384
## (Intercept)-Dasypus_novemcinctus -1.0143 0.8822 -2.9598 -0.9574
## Cogon_Patch_Size-Canis_latrans 0.4933 1.1113 -1.1488 0.3244
## Cogon_Patch_Size-Procyon_lotor -0.8816 0.7327 -2.4042 -0.8641
## Cogon_Patch_Size-Dasypus_novemcinctus -0.7312 0.6397 -2.0559 -0.7137
## Veg_shannon_index-Canis_latrans 1.4283 0.8418 0.1239 1.3369
## Veg_shannon_index-Procyon_lotor 1.2309 0.6400 0.1024 1.1954
## Veg_shannon_index-Dasypus_novemcinctus 0.6832 0.5618 -0.4317 0.6896
## total_shrub_cover-Canis_latrans 0.3046 0.6963 -0.8940 0.2310
## total_shrub_cover-Procyon_lotor -0.8150 0.6547 -2.2414 -0.7599
## total_shrub_cover-Dasypus_novemcinctus 0.1730 0.5300 -0.8318 0.1616
## Avg_Cogongrass_Cover-Canis_latrans 2.5161 1.1561 0.6240 2.4221
## Avg_Cogongrass_Cover-Procyon_lotor 2.2451 0.9701 0.4026 2.2156
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.6537 1.0752 0.8113 2.5621
## Tree_Density-Canis_latrans -2.6849 1.4420 -6.3372 -2.4414
## Tree_Density-Procyon_lotor -1.4915 0.8527 -3.1715 -1.5035
## Tree_Density-Dasypus_novemcinctus -3.3885 1.7363 -8.0357 -2.9811
## Avg_Canopy_Cover-Canis_latrans 0.2226 0.6598 -1.1183 0.2313
## Avg_Canopy_Cover-Procyon_lotor 1.4895 0.6950 0.3071 1.4105
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8069 0.7070 0.6320 1.7456
## avg_veg_height-Canis_latrans -0.6791 0.7054 -2.0595 -0.6804
## avg_veg_height-Procyon_lotor -0.3187 0.6451 -1.5613 -0.3409
## avg_veg_height-Dasypus_novemcinctus -0.2435 0.6444 -1.5378 -0.2508
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.1821 1.0511 393
## (Intercept)-Procyon_lotor 2.0347 1.0135 419
## (Intercept)-Dasypus_novemcinctus 0.5112 1.0567 433
## Cogon_Patch_Size-Canis_latrans 3.1783 1.0044 481
## Cogon_Patch_Size-Procyon_lotor 0.4478 1.0154 624
## Cogon_Patch_Size-Dasypus_novemcinctus 0.4956 1.0071 821
## Veg_shannon_index-Canis_latrans 3.4184 1.0310 355
## Veg_shannon_index-Procyon_lotor 2.6801 1.0407 505
## Veg_shannon_index-Dasypus_novemcinctus 1.7815 1.0115 1160
## total_shrub_cover-Canis_latrans 1.9394 1.0008 789
## total_shrub_cover-Procyon_lotor 0.3425 1.0093 809
## total_shrub_cover-Dasypus_novemcinctus 1.2463 1.0266 1532
## Avg_Cogongrass_Cover-Canis_latrans 5.2178 1.0339 427
## Avg_Cogongrass_Cover-Procyon_lotor 4.2499 1.0013 519
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0961 1.0090 420
## Tree_Density-Canis_latrans -0.6620 1.0224 323
## Tree_Density-Procyon_lotor 0.2133 1.0028 946
## Tree_Density-Dasypus_novemcinctus -1.1754 1.0706 284
## Avg_Canopy_Cover-Canis_latrans 1.4866 1.0050 971
## Avg_Canopy_Cover-Procyon_lotor 3.0374 1.0026 893
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.3719 1.0175 673
## avg_veg_height-Canis_latrans 0.7107 1.0074 957
## avg_veg_height-Procyon_lotor 0.9945 1.0055 982
## avg_veg_height-Dasypus_novemcinctus 1.0613 0.9998 883
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6006 0.1710 -2.9495 -2.5967 -2.2762 1.0084
## (Intercept)-Procyon_lotor -2.2476 0.1234 -2.4908 -2.2450 -2.0116 0.9998
## (Intercept)-Dasypus_novemcinctus -1.5863 0.1371 -1.8576 -1.5821 -1.3249 1.0070
## ESS
## (Intercept)-Canis_latrans 895
## (Intercept)-Procyon_lotor 1537
## (Intercept)-Dasypus_novemcinctus 2287
# Includes cover covariates of occupancy and null for detection
ms_null_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.269
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1593 0.6585 -1.1742 0.1514 1.5418 1.0051 1537
## Avg_Cogongrass_Cover 0.2827 0.5881 -0.8540 0.2823 1.4588 1.0044 1738
## total_shrub_cover -0.1898 0.6369 -1.5536 -0.1896 1.0860 1.0014 2512
## avg_veg_height 0.1469 0.5932 -1.0556 0.1546 1.2466 1.0062 1452
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7692 6.0573 0.0595 0.6009 10.0030 1.1692 2569
## Avg_Cogongrass_Cover 1.0037 3.2613 0.0429 0.3164 5.9840 1.0675 2594
## total_shrub_cover 2.0335 6.9397 0.0632 0.6502 11.5119 1.0120 2422
## avg_veg_height 0.9493 2.7097 0.0402 0.3029 5.9264 1.0562 1969
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6105 0.8722 0.0418 0.3267 2.9917 1.0329 398
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9336 0.5525 -2.7997 -2.015 -0.5565 1.0005 2673
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3221 4.7337 0.082 0.4484 7.6256 1.07 2617
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3546 0.5617 -0.6377 0.3199 1.5878
## (Intercept)-Procyon_lotor 0.6104 0.6024 -0.4720 0.5749 1.9534
## (Intercept)-Dasypus_novemcinctus -0.4442 0.5411 -1.5482 -0.4253 0.5775
## Avg_Cogongrass_Cover-Canis_latrans 0.5395 0.5793 -0.4958 0.4890 1.8163
## Avg_Cogongrass_Cover-Procyon_lotor 0.0919 0.5223 -0.9591 0.0986 1.1034
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2721 0.4580 -0.6239 0.2726 1.1623
## total_shrub_cover-Canis_latrans 0.2675 0.5297 -0.6365 0.2228 1.4433
## total_shrub_cover-Procyon_lotor -0.9110 0.5859 -2.2544 -0.8601 0.0659
## total_shrub_cover-Dasypus_novemcinctus 0.0460 0.4089 -0.7407 0.0402 0.8803
## avg_veg_height-Canis_latrans -0.0724 0.5160 -1.1158 -0.0500 0.9163
## avg_veg_height-Procyon_lotor 0.2010 0.5341 -0.7808 0.1753 1.2967
## avg_veg_height-Dasypus_novemcinctus 0.2931 0.4822 -0.6249 0.2808 1.2797
## Rhat ESS
## (Intercept)-Canis_latrans 1.0047 1002
## (Intercept)-Procyon_lotor 1.0088 901
## (Intercept)-Dasypus_novemcinctus 1.0104 1009
## Avg_Cogongrass_Cover-Canis_latrans 1.0038 1006
## Avg_Cogongrass_Cover-Procyon_lotor 1.0149 1364
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0028 1684
## total_shrub_cover-Canis_latrans 1.0014 1347
## total_shrub_cover-Procyon_lotor 1.0112 1123
## total_shrub_cover-Dasypus_novemcinctus 1.0006 2397
## avg_veg_height-Canis_latrans 1.0017 1256
## avg_veg_height-Procyon_lotor 1.0026 1484
## avg_veg_height-Dasypus_novemcinctus 1.0028 1553
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5832 0.1832 -2.9653 -2.5726 -2.2469 1.0072
## (Intercept)-Procyon_lotor -2.2543 0.1255 -2.5036 -2.2547 -2.0121 1.0036
## (Intercept)-Dasypus_novemcinctus -1.5911 0.1360 -1.8524 -1.5913 -1.3266 1.0007
## ESS
## (Intercept)-Canis_latrans 799
## (Intercept)-Procyon_lotor 1460
## (Intercept)-Dasypus_novemcinctus 2385
# Includes canopy covariates of occupancy and null for detection
ms_null_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2883
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0595 0.7223 -1.5757 -0.0473 1.4095 1.0056 1547
## Tree_Density -0.8652 0.7195 -2.3716 -0.8441 0.5886 1.0056 1779
## Avg_Canopy_Cover 0.4760 0.6372 -0.9125 0.4946 1.6959 1.0020 2482
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6357 23.2266 0.0679 0.7386 12.9231 1.2530 3000
## Tree_Density 2.2573 6.9288 0.0558 0.5826 15.5385 1.0275 1982
## Avg_Canopy_Cover 2.1291 16.7665 0.0678 0.6243 11.1011 1.3143 2154
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6467 1.0072 0.0441 0.3399 3.1577 1.0704 515
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9234 0.6029 -2.8875 -2.0081 -0.3552 1.0017 2544
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4397 4.6382 0.0865 0.4604 8.6216 1.0029 2590
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1612 0.5825 -0.9701 0.1546 1.3833
## (Intercept)-Procyon_lotor 0.4571 0.5989 -0.7895 0.4708 1.5990
## (Intercept)-Dasypus_novemcinctus -0.7962 0.6553 -2.2165 -0.7345 0.3215
## Tree_Density-Canis_latrans -0.9915 0.6400 -2.4285 -0.9050 0.0407
## Tree_Density-Procyon_lotor -0.5008 0.4772 -1.4522 -0.4954 0.3947
## Tree_Density-Dasypus_novemcinctus -1.5532 1.0598 -4.3148 -1.3247 -0.1321
## Avg_Canopy_Cover-Canis_latrans -0.1377 0.4694 -1.0872 -0.1255 0.7672
## Avg_Canopy_Cover-Procyon_lotor 0.8623 0.4932 0.0105 0.8312 1.9329
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8785 0.4580 0.0741 0.8494 1.8878
## Rhat ESS
## (Intercept)-Canis_latrans 1.0149 982
## (Intercept)-Procyon_lotor 1.0148 882
## (Intercept)-Dasypus_novemcinctus 1.0244 811
## Tree_Density-Canis_latrans 1.0153 1201
## Tree_Density-Procyon_lotor 1.0019 1705
## Tree_Density-Dasypus_novemcinctus 1.0495 614
## Avg_Canopy_Cover-Canis_latrans 1.0076 1575
## Avg_Canopy_Cover-Procyon_lotor 1.0054 1654
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0026 1819
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5845 0.1709 -2.9421 -2.5738 -2.2741 1.0123
## (Intercept)-Procyon_lotor -2.2440 0.1295 -2.5061 -2.2432 -2.0007 1.0069
## (Intercept)-Dasypus_novemcinctus -1.5900 0.1349 -1.8606 -1.5853 -1.3353 1.0017
## ESS
## (Intercept)-Canis_latrans 998
## (Intercept)-Procyon_lotor 1470
## (Intercept)-Dasypus_novemcinctus 2287
# Includes movement covariates of occupancy and null for detection
ms_null_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.268
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1575 0.6833 -1.2404 0.1372 1.6254 1.0069 1549
## Cogon_Patch_Size 0.2130 0.6682 -1.1315 0.2037 1.6127 1.0019 1833
## Avg_Cogongrass_Cover 0.3236 0.5191 -0.6573 0.3031 1.4158 1.0034 1362
## total_shrub_cover -0.2098 0.6473 -1.4791 -0.2195 1.1177 1.0021 2322
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8996 7.5191 0.0603 0.6096 11.7623 1.1120 2831
## Cogon_Patch_Size 2.4548 26.6297 0.0524 0.5850 11.9769 1.3121 2235
## Avg_Cogongrass_Cover 0.8816 3.3312 0.0384 0.2645 5.5803 1.0855 3000
## total_shrub_cover 1.8819 6.7459 0.0600 0.6087 10.1218 1.0394 2759
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5766 0.7634 0.0412 0.3267 2.6018 1.0286 545
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9112 0.6269 -2.8571 -2.012 -0.302 1.0056 2397
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7783 14.823 0.0801 0.4587 10.2304 1.2475 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4270 0.6040 -0.6538 0.3738 1.7694
## (Intercept)-Procyon_lotor 0.5881 0.5867 -0.4934 0.5619 1.8039
## (Intercept)-Dasypus_novemcinctus -0.4517 0.5442 -1.5308 -0.4258 0.5641
## Cogon_Patch_Size-Canis_latrans 0.9554 0.9743 -0.3061 0.7762 3.3473
## Cogon_Patch_Size-Procyon_lotor -0.0840 0.5128 -1.1150 -0.0849 0.9264
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0590 0.4477 -0.9553 -0.0466 0.7950
## Avg_Cogongrass_Cover-Canis_latrans 0.3224 0.4917 -0.5741 0.2923 1.3228
## Avg_Cogongrass_Cover-Procyon_lotor 0.2446 0.5009 -0.7022 0.2242 1.3063
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4320 0.4276 -0.3695 0.3998 1.3309
## total_shrub_cover-Canis_latrans 0.1890 0.5122 -0.7644 0.1579 1.2825
## total_shrub_cover-Procyon_lotor -0.8902 0.5734 -2.2099 -0.8199 0.0543
## total_shrub_cover-Dasypus_novemcinctus -0.0052 0.4058 -0.8329 -0.0121 0.7999
## Rhat ESS
## (Intercept)-Canis_latrans 1.0132 965
## (Intercept)-Procyon_lotor 1.0040 917
## (Intercept)-Dasypus_novemcinctus 1.0057 918
## Cogon_Patch_Size-Canis_latrans 1.0338 584
## Cogon_Patch_Size-Procyon_lotor 1.0078 1721
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0008 2012
## Avg_Cogongrass_Cover-Canis_latrans 1.0118 1231
## Avg_Cogongrass_Cover-Procyon_lotor 1.0024 1437
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0020 1553
## total_shrub_cover-Canis_latrans 1.0032 1221
## total_shrub_cover-Procyon_lotor 1.0059 1306
## total_shrub_cover-Dasypus_novemcinctus 1.0080 2350
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5614 0.1687 -2.9122 -2.5535 -2.2507 1.0007
## (Intercept)-Procyon_lotor -2.2561 0.1314 -2.5230 -2.2515 -2.0063 0.9998
## (Intercept)-Dasypus_novemcinctus -1.5937 0.1373 -1.8696 -1.5906 -1.3224 1.0003
## ESS
## (Intercept)-Canis_latrans 1087
## (Intercept)-Procyon_lotor 1511
## (Intercept)-Dasypus_novemcinctus 2248
# Includes foraging covariates of occupancy and null for detection
ms_null_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2665
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0626 0.6941 -1.3447 0.0684 1.4706 1.0080 1582
## Veg_shannon_index 0.5835 0.5352 -0.4377 0.5709 1.7107 1.0054 1674
## Avg_Cogongrass_Cover 0.6326 0.5358 -0.4276 0.6331 1.7072 1.0034 1292
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9397 14.5467 0.0584 0.5592 10.4633 1.2575 3000
## Veg_shannon_index 1.0226 2.9254 0.0449 0.3664 6.0049 1.0194 1785
## Avg_Cogongrass_Cover 0.7871 2.2491 0.0407 0.2622 4.9343 1.0107 2168
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8605 1.1507 0.0515 0.4705 3.9195 1.0706 487
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9114 0.6166 -2.8427 -2.0023 -0.3826 1.0035 2733
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.491 4.2702 0.0804 0.4621 10.1707 1.0606 2331
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2389 0.5981 -0.9340 0.2421 1.4683
## (Intercept)-Procyon_lotor 0.4578 0.6190 -0.7455 0.4621 1.7114
## (Intercept)-Dasypus_novemcinctus -0.4750 0.5751 -1.7102 -0.4508 0.5850
## Veg_shannon_index-Canis_latrans 0.8862 0.4829 0.0256 0.8501 1.9531
## Veg_shannon_index-Procyon_lotor 0.6897 0.5043 -0.1889 0.6514 1.8187
## Veg_shannon_index-Dasypus_novemcinctus 0.2708 0.4011 -0.5123 0.2723 1.0362
## Avg_Cogongrass_Cover-Canis_latrans 0.7888 0.4846 -0.0278 0.7420 1.8661
## Avg_Cogongrass_Cover-Procyon_lotor 0.6748 0.5100 -0.2082 0.6271 1.8599
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5826 0.3809 -0.1536 0.5681 1.3363
## Rhat ESS
## (Intercept)-Canis_latrans 1.0297 842
## (Intercept)-Procyon_lotor 1.0203 753
## (Intercept)-Dasypus_novemcinctus 1.0125 890
## Veg_shannon_index-Canis_latrans 1.0036 1587
## Veg_shannon_index-Procyon_lotor 1.0013 908
## Veg_shannon_index-Dasypus_novemcinctus 1.0002 1677
## Avg_Cogongrass_Cover-Canis_latrans 0.9999 1145
## Avg_Cogongrass_Cover-Procyon_lotor 1.0002 987
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0073 1838
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5651 0.1685 -2.9121 -2.5609 -2.2532 1.0018
## (Intercept)-Procyon_lotor -2.2689 0.1296 -2.5389 -2.2639 -2.0207 1.0002
## (Intercept)-Dasypus_novemcinctus -1.5902 0.1354 -1.8546 -1.5910 -1.3298 1.0018
## ESS
## (Intercept)-Canis_latrans 1029
## (Intercept)-Procyon_lotor 1397
## (Intercept)-Dasypus_novemcinctus 2397
# Includes null covariate for detection and quadratic cogongrass cover for occupancy
ms_null_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2725
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5966 0.6739 -1.9066 -0.6040 0.7735 1.0046 850
## Avg_Cogongrass_Cover -0.0246 0.6760 -1.3117 -0.0382 1.4129 1.0252 899
## I(Avg_Cogongrass_Cover^2) 1.0385 0.9707 -1.0295 1.0215 3.0641 1.0059 1239
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4462 5.6770 0.0494 0.4115 8.5002 1.0994 2563
## Avg_Cogongrass_Cover 1.2452 4.9364 0.0459 0.3564 7.5437 1.0760 2667
## I(Avg_Cogongrass_Cover^2) 6.5810 20.5277 0.0713 1.5495 43.1280 1.1571 812
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4236 0.5247 0.0428 0.2554 1.7185 1.0488 734
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.937 0.5934 -2.8823 -2.0201 -0.3871 1.0032 2529
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5618 6.7958 0.091 0.4921 8.6402 1.038 2618
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6778 0.6806 -2.1157 -0.6502
## (Intercept)-Procyon_lotor -0.4128 0.6415 -1.7244 -0.4031
## (Intercept)-Dasypus_novemcinctus -0.9623 0.5821 -2.1605 -0.9386
## Avg_Cogongrass_Cover-Canis_latrans 0.1007 0.6921 -1.1343 0.0601
## Avg_Cogongrass_Cover-Procyon_lotor -0.0602 0.7543 -1.3787 -0.1208
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.0730 0.5824 -1.2331 -0.0645
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3229 1.6457 0.1647 2.0006
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.0801 1.6405 0.1436 1.5875
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4555 0.4246 -0.3318 0.4441
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5741 1.0180 524
## (Intercept)-Procyon_lotor 0.8378 1.0101 457
## (Intercept)-Dasypus_novemcinctus 0.1211 1.0022 1244
## Avg_Cogongrass_Cover-Canis_latrans 1.5784 1.0324 840
## Avg_Cogongrass_Cover-Procyon_lotor 1.6782 1.0578 397
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0521 1.0103 1184
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.4479 1.0764 181
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.5645 1.1293 128
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3184 1.0038 1378
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6017 0.1680 -2.9449 -2.5942 -2.2883 1.0077
## (Intercept)-Procyon_lotor -2.2737 0.1292 -2.5419 -2.2717 -2.0263 1.0014
## (Intercept)-Dasypus_novemcinctus -1.5873 0.1338 -1.8543 -1.5831 -1.3285 1.0064
## ESS
## (Intercept)-Canis_latrans 1009
## (Intercept)-Procyon_lotor 1551
## (Intercept)-Dasypus_novemcinctus 2279
# Includes null covariates of detection and all covariates and quadratic cogongrass cover for occupancy
ms_null_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.null,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.null,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.274
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.1131 0.9900 -3.0362 -1.1192 0.9157 1.0064 783
## Cogon_Patch_Size 0.0698 1.0057 -1.8055 0.0195 2.2749 1.0076 1221
## Veg_shannon_index 0.9505 0.7317 -0.5425 0.9559 2.4306 1.0057 980
## total_shrub_cover -0.1437 0.7404 -1.6129 -0.1440 1.4193 1.0084 1371
## Avg_Cogongrass_Cover 0.7054 1.1959 -1.6870 0.7303 3.0607 1.0018 460
## Tree_Density -1.9648 1.4142 -4.3568 -2.1172 1.2931 1.0078 637
## Avg_Canopy_Cover 0.9404 0.8604 -0.9566 0.9858 2.5408 1.0042 1713
## I(Avg_Cogongrass_Cover^2) 1.6253 1.0484 -0.6944 1.6491 3.5885 1.0195 865
## avg_veg_height -0.1193 0.7799 -1.6672 -0.1240 1.4422 1.0202 767
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.2402 22.3870 0.0618 1.0782 35.2672 1.0022 811
## Cogon_Patch_Size 12.0329 102.9676 0.0801 1.7690 79.0663 1.2183 1954
## Veg_shannon_index 2.4839 15.9758 0.0502 0.5069 11.4681 1.1002 2835
## total_shrub_cover 4.4818 118.4842 0.0592 0.6993 14.3834 1.3225 3000
## Avg_Cogongrass_Cover 4.9429 25.6761 0.0582 0.8720 31.2991 1.0573 2097
## Tree_Density 21.6285 83.8289 0.0735 2.9720 150.1065 1.0202 524
## Avg_Canopy_Cover 4.0192 11.6594 0.0770 1.2426 24.8310 1.0155 2429
## I(Avg_Cogongrass_Cover^2) 9.4647 43.0574 0.0584 1.1521 67.1375 1.0647 223
## avg_veg_height 1.8951 7.0822 0.0540 0.5361 10.7578 1.0812 1924
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1022 3.5169 0.0621 0.9285 11.6685 1.0297 208
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9068 0.629 -2.8749 -2.0045 -0.3141 1.0143 2563
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7262 5.8679 0.0842 0.5019 10.2673 1.0455 2566
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.3060 1.3848 -4.3228 -1.2133
## (Intercept)-Procyon_lotor -0.8225 1.0324 -2.9068 -0.8157
## (Intercept)-Dasypus_novemcinctus -2.2721 1.3807 -5.5504 -2.1257
## Cogon_Patch_Size-Canis_latrans 1.8901 2.5781 -0.6676 1.2731
## Cogon_Patch_Size-Procyon_lotor -0.5263 1.1396 -2.5276 -0.5926
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5242 0.8326 -2.3469 -0.4616
## Veg_shannon_index-Canis_latrans 1.4748 0.9085 0.0448 1.3438
## Veg_shannon_index-Procyon_lotor 1.2256 0.7230 -0.0193 1.1652
## Veg_shannon_index-Dasypus_novemcinctus 0.6383 0.6154 -0.5787 0.6439
## total_shrub_cover-Canis_latrans 0.1342 0.7289 -1.1964 0.0914
## total_shrub_cover-Procyon_lotor -0.8576 0.7592 -2.5474 -0.7876
## total_shrub_cover-Dasypus_novemcinctus 0.1688 0.5792 -0.9417 0.1463
## Avg_Cogongrass_Cover-Canis_latrans 0.5424 1.5897 -2.7450 0.5726
## Avg_Cogongrass_Cover-Procyon_lotor 0.7911 1.5007 -2.0392 0.7843
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4108 1.5498 -1.3288 1.2902
## Tree_Density-Canis_latrans -3.8759 2.3080 -10.4419 -3.3878
## Tree_Density-Procyon_lotor -2.1601 1.2251 -4.5806 -2.1622
## Tree_Density-Dasypus_novemcinctus -4.5065 2.4633 -11.1755 -3.9022
## Avg_Canopy_Cover-Canis_latrans 0.1872 0.7752 -1.4462 0.2076
## Avg_Canopy_Cover-Procyon_lotor 1.4952 0.7866 0.1592 1.4364
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9390 0.8636 0.5917 1.8106
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.9436 1.9855 0.7319 2.4737
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.7273 1.8242 0.5125 2.3454
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3833 0.9677 -0.2372 1.2939
## avg_veg_height-Canis_latrans -0.6443 0.8804 -2.5949 -0.5808
## avg_veg_height-Procyon_lotor 0.1513 0.7909 -1.2841 0.1218
## avg_veg_height-Dasypus_novemcinctus 0.0909 0.7438 -1.3703 0.0788
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.0683 1.0394 229
## (Intercept)-Procyon_lotor 1.1076 1.0083 494
## (Intercept)-Dasypus_novemcinctus -0.1041 1.0304 280
## Cogon_Patch_Size-Canis_latrans 8.1659 1.1694 149
## Cogon_Patch_Size-Procyon_lotor 1.7990 1.0219 464
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9734 1.0075 626
## Veg_shannon_index-Canis_latrans 3.7056 1.0051 529
## Veg_shannon_index-Procyon_lotor 2.8361 1.0038 383
## Veg_shannon_index-Dasypus_novemcinctus 1.8806 1.0051 1051
## total_shrub_cover-Canis_latrans 1.7293 1.0412 739
## total_shrub_cover-Procyon_lotor 0.4574 1.0069 708
## total_shrub_cover-Dasypus_novemcinctus 1.3465 1.0214 1219
## Avg_Cogongrass_Cover-Canis_latrans 3.5172 1.0079 449
## Avg_Cogongrass_Cover-Procyon_lotor 3.8252 1.0097 454
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.9597 1.0070 468
## Tree_Density-Canis_latrans -0.8247 1.0093 154
## Tree_Density-Procyon_lotor 0.1801 1.0116 589
## Tree_Density-Dasypus_novemcinctus -1.5197 1.0144 194
## Avg_Canopy_Cover-Canis_latrans 1.6739 1.0011 735
## Avg_Canopy_Cover-Procyon_lotor 3.2336 1.0029 563
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9256 1.0035 426
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.9499 1.0349 123
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 7.2620 1.1702 81
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.5094 1.0082 468
## avg_veg_height-Canis_latrans 0.8641 1.0291 523
## avg_veg_height-Procyon_lotor 1.8269 1.0135 787
## avg_veg_height-Dasypus_novemcinctus 1.5248 1.0154 884
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5936 0.1712 -2.9465 -2.5865 -2.2800 1.0020
## (Intercept)-Procyon_lotor -2.2721 0.1298 -2.5364 -2.2690 -2.0228 1.0052
## (Intercept)-Dasypus_novemcinctus -1.5887 0.1351 -1.8649 -1.5827 -1.3280 0.9999
## ESS
## (Intercept)-Canis_latrans 858
## (Intercept)-Procyon_lotor 1200
## (Intercept)-Dasypus_novemcinctus 2287
# Includes all covariates of detection and cogongrass cover occupancy
ms_full_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_full_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3638
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1749 0.6334 -1.1435 0.1870 1.4258 1.0029 1741
## Avg_Cogongrass_Cover 0.4403 0.5190 -0.6077 0.4444 1.4777 1.0026 2029
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4986 5.1293 0.0557 0.511 8.9815 1.0643 2816
## Avg_Cogongrass_Cover 0.8571 3.4775 0.0390 0.251 4.2733 1.0155 2394
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5731 0.8992 0.0424 0.3065 2.6921 1.045 535
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0419 0.6047 -2.9819 -2.1298 -0.4764 1.0040 2218
## shrub_cover 0.2529 0.5547 -0.9582 0.2622 1.3891 1.0016 2505
## veg_height -0.0231 0.5527 -1.1754 -0.0187 1.1144 1.0034 2429
## week -0.0338 0.3531 -0.7006 -0.0446 0.6794 1.0032 2913
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3820 4.1645 0.0764 0.4406 9.1554 1.0718 2294
## shrub_cover 1.6565 7.2295 0.0838 0.5590 9.5752 1.1993 2198
## veg_height 1.5728 13.6329 0.0905 0.4782 7.6051 1.2786 3000
## week 0.5110 3.1738 0.0309 0.1566 2.4281 1.1314 1919
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4064 0.5595 -0.6770 0.3900 1.5604
## (Intercept)-Procyon_lotor 0.4961 0.5296 -0.5800 0.4920 1.5548
## (Intercept)-Dasypus_novemcinctus -0.3151 0.5230 -1.3977 -0.2984 0.6678
## Avg_Cogongrass_Cover-Canis_latrans 0.6054 0.4571 -0.1719 0.5696 1.6185
## Avg_Cogongrass_Cover-Procyon_lotor 0.3492 0.4089 -0.4293 0.3304 1.1929
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4765 0.3705 -0.2162 0.4653 1.2607
## Rhat ESS
## (Intercept)-Canis_latrans 1.0068 880
## (Intercept)-Procyon_lotor 1.0052 870
## (Intercept)-Dasypus_novemcinctus 1.0096 1257
## Avg_Cogongrass_Cover-Canis_latrans 1.0192 1491
## Avg_Cogongrass_Cover-Procyon_lotor 1.0056 1639
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0012 1906
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7457 0.1975 -3.1448 -2.7419 -2.3753 1.0022
## (Intercept)-Procyon_lotor -2.2871 0.1416 -2.5718 -2.2839 -2.0169 1.0040
## (Intercept)-Dasypus_novemcinctus -1.7709 0.1653 -2.1072 -1.7639 -1.4576 1.0132
## shrub_cover-Canis_latrans -0.2935 0.2251 -0.7566 -0.2931 0.1413 1.0178
## shrub_cover-Procyon_lotor 0.2538 0.1663 -0.0792 0.2544 0.5774 1.0026
## shrub_cover-Dasypus_novemcinctus 0.8717 0.3116 0.3087 0.8593 1.5164 1.0108
## veg_height-Canis_latrans -0.6525 0.1930 -1.0413 -0.6543 -0.2834 1.0108
## veg_height-Procyon_lotor 0.3454 0.1239 0.1061 0.3419 0.5865 1.0071
## veg_height-Dasypus_novemcinctus 0.2576 0.1380 -0.0046 0.2553 0.5316 1.0025
## week-Canis_latrans 0.0766 0.1358 -0.2044 0.0776 0.3289 1.0006
## week-Procyon_lotor -0.0529 0.1227 -0.3036 -0.0494 0.1750 1.0018
## week-Dasypus_novemcinctus -0.1793 0.1437 -0.4777 -0.1731 0.0921 1.0029
## ESS
## (Intercept)-Canis_latrans 664
## (Intercept)-Procyon_lotor 1268
## (Intercept)-Dasypus_novemcinctus 1418
## shrub_cover-Canis_latrans 867
## shrub_cover-Procyon_lotor 1274
## shrub_cover-Dasypus_novemcinctus 1122
## veg_height-Canis_latrans 711
## veg_height-Procyon_lotor 1476
## veg_height-Dasypus_novemcinctus 1869
## week-Canis_latrans 1616
## week-Procyon_lotor 1685
## week-Dasypus_novemcinctus 2187
# Includes no covariates of detection and cogongrass cover for occupancy
ms_null_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.null,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_null_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.null, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2632
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0889 0.6074 -1.1612 0.0951 1.3039 1.0035 1599
## Avg_Cogongrass_Cover 0.4041 0.4710 -0.5991 0.4050 1.3516 1.0027 2115
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5219 4.7723 0.0586 0.5203 8.7782 1.0720 2745
## Avg_Cogongrass_Cover 0.8008 4.8707 0.0389 0.2324 3.8535 1.1688 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5473 0.7555 0.046 0.3077 2.5044 1.0088 587
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9375 0.5928 -2.8251 -2.0171 -0.5703 1.0049 2555
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3882 41.0537 0.0799 0.454 8.6447 1.31 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2630 0.5172 -0.7535 0.2523 1.3270
## (Intercept)-Procyon_lotor 0.4827 0.5319 -0.5634 0.4852 1.5451
## (Intercept)-Dasypus_novemcinctus -0.4198 0.5081 -1.4661 -0.3932 0.5408
## Avg_Cogongrass_Cover-Canis_latrans 0.4819 0.3916 -0.2342 0.4583 1.2764
## Avg_Cogongrass_Cover-Procyon_lotor 0.3572 0.4175 -0.4141 0.3339 1.2577
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4398 0.3536 -0.2214 0.4331 1.1473
## Rhat ESS
## (Intercept)-Canis_latrans 1.0141 914
## (Intercept)-Procyon_lotor 1.0050 1083
## (Intercept)-Dasypus_novemcinctus 1.0019 1242
## Avg_Cogongrass_Cover-Canis_latrans 1.0062 1947
## Avg_Cogongrass_Cover-Procyon_lotor 1.0014 1848
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0056 2387
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5690 0.1688 -2.9164 -2.5644 -2.2553 1.0010
## (Intercept)-Procyon_lotor -2.2536 0.1258 -2.5067 -2.2528 -2.0190 1.0002
## (Intercept)-Dasypus_novemcinctus -1.5918 0.1357 -1.8641 -1.5930 -1.3303 1.0015
## ESS
## (Intercept)-Canis_latrans 1018
## (Intercept)-Procyon_lotor 1440
## (Intercept)-Dasypus_novemcinctus 2016
# Includes week covariate for detection and cogongrass cover for occupancy
ms_week_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3367
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0946 0.6243 -1.1219 0.0870 1.3814 1.0084 1525
## Avg_Cogongrass_Cover 0.3906 0.4807 -0.6051 0.3905 1.3367 0.9999 1665
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6545 10.6991 0.0533 0.4964 8.5045 1.0902 2572
## Avg_Cogongrass_Cover 0.6865 2.2280 0.0359 0.2282 4.0429 1.0339 2215
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5276 0.7024 0.0422 0.3 2.391 1.0163 611
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9155 0.6209 -2.8809 -2.0125 -0.3496 1.0039 2520
## week -0.0381 0.3512 -0.7291 -0.0457 0.7036 1.0016 2714
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5258 4.9176 0.0821 0.4823 9.2619 1.0025 2602
## week 0.4809 1.8704 0.0310 0.1598 2.7908 1.0640 2769
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2668 0.5182 -0.6806 0.2444 1.3479
## (Intercept)-Procyon_lotor 0.4582 0.5107 -0.5208 0.4459 1.4947
## (Intercept)-Dasypus_novemcinctus -0.4223 0.5203 -1.4846 -0.4076 0.5507
## Avg_Cogongrass_Cover-Canis_latrans 0.4816 0.3901 -0.2112 0.4666 1.2864
## Avg_Cogongrass_Cover-Procyon_lotor 0.3520 0.3935 -0.3819 0.3406 1.1654
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4254 0.3451 -0.2277 0.4193 1.1390
## Rhat ESS
## (Intercept)-Canis_latrans 1.0031 1007
## (Intercept)-Procyon_lotor 1.0193 1069
## (Intercept)-Dasypus_novemcinctus 1.0031 1163
## Avg_Cogongrass_Cover-Canis_latrans 0.9997 1891
## Avg_Cogongrass_Cover-Procyon_lotor 1.0042 1827
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0028 2079
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5881 0.1703 -2.9517 -2.5803 -2.2727 1.0053
## (Intercept)-Procyon_lotor -2.2624 0.1298 -2.5214 -2.2583 -2.0164 1.0168
## (Intercept)-Dasypus_novemcinctus -1.6031 0.1416 -1.8855 -1.6037 -1.3373 1.0062
## week-Canis_latrans 0.0744 0.1379 -0.1985 0.0781 0.3390 1.0005
## week-Procyon_lotor -0.0429 0.1244 -0.2985 -0.0367 0.1929 1.0044
## week-Dasypus_novemcinctus -0.1774 0.1436 -0.4737 -0.1710 0.0888 1.0029
## ESS
## (Intercept)-Canis_latrans 1035
## (Intercept)-Procyon_lotor 1471
## (Intercept)-Dasypus_novemcinctus 2069
## week-Canis_latrans 1321
## week-Procyon_lotor 1570
## week-Dasypus_novemcinctus 2024
# Includes week covariate for detection and all covariates for occupancy
ms_week_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3377
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0431 0.8937 -1.8303 -0.0508 1.7683 1.0062 886
## Cogon_Patch_Size -0.4200 0.8151 -1.9759 -0.4396 1.3193 1.0074 1091
## Veg_shannon_index 1.0515 0.7293 -0.4677 1.0394 2.4947 1.0084 584
## total_shrub_cover -0.0550 0.7577 -1.5391 -0.0761 1.5963 1.0016 1525
## Avg_Cogongrass_Cover 2.1962 1.0652 -0.1328 2.2372 4.1560 1.0109 846
## Tree_Density -1.7595 1.1833 -3.8433 -1.8444 0.9560 1.0013 1474
## Avg_Canopy_Cover 0.9466 0.8580 -1.0721 1.0077 2.4841 1.0028 2138
## avg_veg_height -0.3866 0.6905 -1.7094 -0.4030 1.0834 1.0100 912
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.5883 17.1659 0.0715 1.1787 25.8004 1.0365 1750
## Cogon_Patch_Size 3.4286 21.0699 0.0626 0.8732 18.3387 1.2565 2567
## Veg_shannon_index 1.7937 7.3588 0.0518 0.4454 11.3423 1.1204 1209
## total_shrub_cover 2.2736 5.3248 0.0711 0.8006 13.4821 1.0445 1498
## Avg_Cogongrass_Cover 4.2255 21.1915 0.0510 0.6044 28.4479 1.2658 424
## Tree_Density 12.6015 41.8384 0.0782 2.0188 98.8320 1.0603 491
## Avg_Canopy_Cover 4.7440 20.5513 0.0798 1.1586 25.9526 1.0594 2150
## avg_veg_height 1.2889 8.0288 0.0453 0.3733 6.8147 1.2394 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.8566 5.2301 0.0701 1.1626 15.8563 1.0637 127
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9404 0.6149 -2.8928 -2.0247 -0.3922 1.0131 2020
## week -0.0646 0.3590 -0.8252 -0.0592 0.6156 1.0016 2838
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6710 6.9730 0.0859 0.4732 9.5864 1.0341 1872
## week 0.9836 16.9572 0.0322 0.1645 3.0700 1.2152 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.4329 0.9820 -1.3526 0.3631
## (Intercept)-Procyon_lotor 0.4569 0.8913 -1.3126 0.4697
## (Intercept)-Dasypus_novemcinctus -1.0390 1.0256 -3.2996 -0.9758
## Cogon_Patch_Size-Canis_latrans 0.4588 1.2043 -1.3074 0.2687
## Cogon_Patch_Size-Procyon_lotor -0.9970 0.7706 -2.6254 -0.9756
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8384 0.7023 -2.3414 -0.7955
## Veg_shannon_index-Canis_latrans 1.4879 0.7975 0.1836 1.3972
## Veg_shannon_index-Procyon_lotor 1.3284 0.7013 0.0976 1.2696
## Veg_shannon_index-Dasypus_novemcinctus 0.7617 0.6126 -0.4142 0.7565
## total_shrub_cover-Canis_latrans 0.4511 0.8945 -0.8393 0.2955
## total_shrub_cover-Procyon_lotor -0.8099 0.6602 -2.3051 -0.7619
## total_shrub_cover-Dasypus_novemcinctus 0.1865 0.5695 -0.8825 0.1718
## Avg_Cogongrass_Cover-Canis_latrans 2.7378 1.1830 0.8367 2.6064
## Avg_Cogongrass_Cover-Procyon_lotor 2.3979 1.0670 0.4084 2.3435
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.8617 1.1864 0.9371 2.7326
## Tree_Density-Canis_latrans -2.9790 1.7046 -6.9244 -2.6574
## Tree_Density-Procyon_lotor -1.5449 0.8823 -3.3017 -1.5436
## Tree_Density-Dasypus_novemcinctus -3.8266 2.2346 -9.8879 -3.2519
## Avg_Canopy_Cover-Canis_latrans 0.1971 0.7264 -1.4095 0.2241
## Avg_Canopy_Cover-Procyon_lotor 1.5605 0.7623 0.3181 1.4683
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9292 0.8371 0.6876 1.8051
## avg_veg_height-Canis_latrans -0.7663 0.7260 -2.3065 -0.7280
## avg_veg_height-Procyon_lotor -0.3159 0.6524 -1.5540 -0.3338
## avg_veg_height-Dasypus_novemcinctus -0.2655 0.6579 -1.4904 -0.2820
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.5990 1.0053 495
## (Intercept)-Procyon_lotor 2.1690 1.0190 446
## (Intercept)-Dasypus_novemcinctus 0.8354 1.0060 445
## Cogon_Patch_Size-Canis_latrans 3.4240 1.0049 659
## Cogon_Patch_Size-Procyon_lotor 0.4443 1.0233 448
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3943 1.0198 475
## Veg_shannon_index-Canis_latrans 3.3179 1.0051 359
## Veg_shannon_index-Procyon_lotor 2.8726 1.0129 406
## Veg_shannon_index-Dasypus_novemcinctus 2.0350 1.0189 461
## total_shrub_cover-Canis_latrans 2.6158 1.0165 352
## total_shrub_cover-Procyon_lotor 0.3444 1.0082 1257
## total_shrub_cover-Dasypus_novemcinctus 1.3886 1.0027 1284
## Avg_Cogongrass_Cover-Canis_latrans 5.5453 1.0331 387
## Avg_Cogongrass_Cover-Procyon_lotor 4.6390 1.0180 599
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.6338 1.0617 379
## Tree_Density-Canis_latrans -0.6752 1.0307 227
## Tree_Density-Procyon_lotor 0.2055 1.0034 884
## Tree_Density-Dasypus_novemcinctus -1.2066 1.0450 143
## Avg_Canopy_Cover-Canis_latrans 1.5069 1.0051 733
## Avg_Canopy_Cover-Procyon_lotor 3.3281 1.0099 398
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9871 1.0301 179
## avg_veg_height-Canis_latrans 0.6000 1.0006 826
## avg_veg_height-Procyon_lotor 1.0757 1.0081 1016
## avg_veg_height-Dasypus_novemcinctus 1.1245 1.0086 969
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6195 0.1784 -2.9804 -2.6098 -2.2908 1.0116
## (Intercept)-Procyon_lotor -2.2631 0.1299 -2.5194 -2.2602 -2.0125 1.0161
## (Intercept)-Dasypus_novemcinctus -1.6002 0.1327 -1.8666 -1.5999 -1.3517 1.0042
## week-Canis_latrans 0.0701 0.1388 -0.2214 0.0777 0.3235 1.0018
## week-Procyon_lotor -0.0530 0.1251 -0.3036 -0.0498 0.1732 1.0037
## week-Dasypus_novemcinctus -0.1775 0.1452 -0.4757 -0.1699 0.0941 1.0018
## ESS
## (Intercept)-Canis_latrans 625
## (Intercept)-Procyon_lotor 1467
## (Intercept)-Dasypus_novemcinctus 2231
## week-Canis_latrans 1522
## week-Procyon_lotor 1660
## week-Dasypus_novemcinctus 2158
# Includes week covariate for detection and only cover for occupancy
ms_week_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3355
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1297 0.6635 -1.2784 0.1289 1.4211 1.0046 1763
## Avg_Cogongrass_Cover 0.3001 0.5831 -0.7933 0.2993 1.4748 1.0059 1546
## total_shrub_cover -0.1847 0.6627 -1.5097 -0.1674 1.1703 1.0149 2569
## avg_veg_height 0.0893 0.5489 -1.0237 0.0964 1.1854 1.0025 1243
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0258 6.6779 0.0566 0.6547 12.7392 1.0773 2435
## Avg_Cogongrass_Cover 1.0567 5.0107 0.0414 0.2917 5.9188 1.2329 2827
## total_shrub_cover 2.1251 7.9374 0.0642 0.6560 12.5608 1.0614 2587
## avg_veg_height 0.8163 2.1405 0.0426 0.2786 5.0575 1.0198 2334
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6464 0.9508 0.0494 0.3524 2.9513 1.0318 624
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9057 0.6321 -2.8783 -2.0055 -0.2956 1.0106 2574
## week -0.0466 0.3595 -0.8210 -0.0422 0.6566 1.0021 2812
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0404 12.8932 0.0858 0.4992 9.0404 1.0627 2602
## week 0.4773 1.5594 0.0315 0.1646 2.7930 1.0413 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3079 0.6015 -0.8245 0.2781 1.5668
## (Intercept)-Procyon_lotor 0.6343 0.6226 -0.5104 0.6115 1.9534
## (Intercept)-Dasypus_novemcinctus -0.4663 0.5520 -1.6234 -0.4572 0.5709
## Avg_Cogongrass_Cover-Canis_latrans 0.5462 0.5644 -0.4198 0.5005 1.7953
## Avg_Cogongrass_Cover-Procyon_lotor 0.1400 0.5471 -0.9142 0.1343 1.2932
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.3109 0.4648 -0.6029 0.3106 1.2491
## total_shrub_cover-Canis_latrans 0.2741 0.5339 -0.6538 0.2362 1.4620
## total_shrub_cover-Procyon_lotor -0.9178 0.5838 -2.2302 -0.8683 0.1180
## total_shrub_cover-Dasypus_novemcinctus 0.0445 0.3910 -0.7139 0.0383 0.8211
## avg_veg_height-Canis_latrans -0.0805 0.5176 -1.1224 -0.0722 0.9359
## avg_veg_height-Procyon_lotor 0.1566 0.5251 -0.8500 0.1378 1.2488
## avg_veg_height-Dasypus_novemcinctus 0.2581 0.4735 -0.6539 0.2436 1.2427
## Rhat ESS
## (Intercept)-Canis_latrans 1.0233 892
## (Intercept)-Procyon_lotor 1.0290 873
## (Intercept)-Dasypus_novemcinctus 1.0025 1141
## Avg_Cogongrass_Cover-Canis_latrans 1.0002 1238
## Avg_Cogongrass_Cover-Procyon_lotor 1.0052 1185
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0024 1591
## total_shrub_cover-Canis_latrans 1.0145 1233
## total_shrub_cover-Procyon_lotor 1.0092 1162
## total_shrub_cover-Dasypus_novemcinctus 1.0027 2403
## avg_veg_height-Canis_latrans 0.9998 1131
## avg_veg_height-Procyon_lotor 1.0014 1275
## avg_veg_height-Dasypus_novemcinctus 1.0048 1475
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6061 0.1790 -2.9755 -2.5970 -2.2730 1.0147
## (Intercept)-Procyon_lotor -2.2709 0.1307 -2.5348 -2.2687 -2.0203 1.0053
## (Intercept)-Dasypus_novemcinctus -1.6092 0.1371 -1.8880 -1.6060 -1.3511 1.0133
## week-Canis_latrans 0.0803 0.1346 -0.1951 0.0841 0.3357 1.0015
## week-Procyon_lotor -0.0442 0.1242 -0.3037 -0.0377 0.1823 1.0050
## week-Dasypus_novemcinctus -0.1777 0.1460 -0.4716 -0.1729 0.0893 1.0074
## ESS
## (Intercept)-Canis_latrans 835
## (Intercept)-Procyon_lotor 1269
## (Intercept)-Dasypus_novemcinctus 2098
## week-Canis_latrans 1594
## week-Procyon_lotor 1628
## week-Dasypus_novemcinctus 1890
# Includes week covariate for detection and none for occupancy
ms_week_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.week,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3257
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.125 0.5786 -1.0391 0.1211 1.3035 1.0042 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6982 11.0715 0.0653 0.547 7.6896 1.1698 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9314 0.5890 -2.8959 -2.0084 -0.3997 1.0027 2633
## week -0.0391 0.3455 -0.7123 -0.0457 0.6900 1.0071 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5314 6.0181 0.0807 0.4471 9.1467 1.0014 2744
## week 0.4866 1.7596 0.0330 0.1642 2.6778 1.0715 2646
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2663 0.3706 -0.4337 0.2514 1.0541 1.0003
## (Intercept)-Procyon_lotor 0.5907 0.3837 -0.0928 0.5660 1.4246 1.0027
## (Intercept)-Dasypus_novemcinctus -0.4722 0.3658 -1.2009 -0.4578 0.2332 1.0069
## ESS
## (Intercept)-Canis_latrans 2293
## (Intercept)-Procyon_lotor 2530
## (Intercept)-Dasypus_novemcinctus 2287
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5807 0.1699 -2.9364 -2.5748 -2.2634 1.0029
## (Intercept)-Procyon_lotor -2.2501 0.1258 -2.5053 -2.2469 -2.0102 1.0013
## (Intercept)-Dasypus_novemcinctus -1.6065 0.1354 -1.8804 -1.6035 -1.3471 1.0093
## week-Canis_latrans 0.0747 0.1360 -0.2057 0.0812 0.3260 1.0082
## week-Procyon_lotor -0.0502 0.1220 -0.2986 -0.0460 0.1702 1.0017
## week-Dasypus_novemcinctus -0.1812 0.1477 -0.4897 -0.1713 0.0914 1.0012
## ESS
## (Intercept)-Canis_latrans 995
## (Intercept)-Procyon_lotor 1493
## (Intercept)-Dasypus_novemcinctus 2187
## week-Canis_latrans 1508
## week-Procyon_lotor 1679
## week-Dasypus_novemcinctus 2028
#Includes week for detection and only foraging for occupancy
ms_week_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3365
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0935 0.6505 -1.2326 0.0955 1.4282 1.0085 1190
## Veg_shannon_index 0.5534 0.5954 -0.6792 0.5633 1.7783 1.0051 1913
## Avg_Cogongrass_Cover 0.6319 0.5507 -0.4557 0.6250 1.7325 1.0052 1361
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5214 4.0356 0.0498 0.5218 9.2436 1.0172 2312
## Veg_shannon_index 1.4120 6.5824 0.0493 0.3797 8.7792 1.1772 2835
## Avg_Cogongrass_Cover 0.8477 2.3473 0.0435 0.2770 5.4806 1.0041 1993
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.8067 1.2255 0.0472 0.4238 4.0317 1.013 381
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9294 0.5948 -2.8467 -2.0147 -0.3758 1.0069 2434
## week -0.0466 0.3365 -0.6986 -0.0541 0.6157 1.0086 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4478 5.6736 0.0846 0.4671 9.4008 1.1335 2822
## week 0.5014 2.2478 0.0319 0.1556 2.6038 1.1201 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2663 0.5759 -0.8367 0.2366 1.5008
## (Intercept)-Procyon_lotor 0.4540 0.5900 -0.6785 0.4526 1.6635
## (Intercept)-Dasypus_novemcinctus -0.4592 0.5869 -1.7130 -0.4269 0.5832
## Veg_shannon_index-Canis_latrans 0.8816 0.4916 0.0146 0.8484 1.9441
## Veg_shannon_index-Procyon_lotor 0.6904 0.5145 -0.2216 0.6435 1.8755
## Veg_shannon_index-Dasypus_novemcinctus 0.2527 0.3954 -0.5324 0.2542 1.0054
## Avg_Cogongrass_Cover-Canis_latrans 0.8076 0.4948 -0.0461 0.7601 1.8649
## Avg_Cogongrass_Cover-Procyon_lotor 0.6650 0.5233 -0.2234 0.6200 1.8231
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5829 0.3891 -0.1745 0.5829 1.3689
## Rhat ESS
## (Intercept)-Canis_latrans 1.0146 951
## (Intercept)-Procyon_lotor 1.0009 849
## (Intercept)-Dasypus_novemcinctus 1.0079 819
## Veg_shannon_index-Canis_latrans 1.0108 1074
## Veg_shannon_index-Procyon_lotor 1.0093 632
## Veg_shannon_index-Dasypus_novemcinctus 1.0029 2378
## Avg_Cogongrass_Cover-Canis_latrans 1.0118 1217
## Avg_Cogongrass_Cover-Procyon_lotor 1.0087 709
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0013 1367
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5857 0.1654 -2.9201 -2.5828 -2.2730 1.0030
## (Intercept)-Procyon_lotor -2.2714 0.1270 -2.5266 -2.2672 -2.0339 1.0038
## (Intercept)-Dasypus_novemcinctus -1.6040 0.1365 -1.8815 -1.6025 -1.3412 1.0042
## week-Canis_latrans 0.0806 0.1334 -0.1869 0.0840 0.3292 1.0058
## week-Procyon_lotor -0.0479 0.1214 -0.2952 -0.0445 0.1833 1.0035
## week-Dasypus_novemcinctus -0.1810 0.1470 -0.4845 -0.1748 0.0916 1.0022
## ESS
## (Intercept)-Canis_latrans 1130
## (Intercept)-Procyon_lotor 1461
## (Intercept)-Dasypus_novemcinctus 2313
## week-Canis_latrans 1691
## week-Procyon_lotor 1607
## week-Dasypus_novemcinctus 2041
# Includes movement covariates of occupancy and week for detection
ms_week_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.331
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1778 0.7125 -1.3125 0.1816 1.6159 1.0021 1522
## Cogon_Patch_Size 0.2316 0.6852 -1.1516 0.2354 1.5950 1.0092 1993
## Avg_Cogongrass_Cover 0.2802 0.5280 -0.8125 0.2882 1.3119 1.0015 1668
## total_shrub_cover -0.2244 0.6571 -1.5521 -0.2178 1.1323 1.0067 2358
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1403 7.3131 0.0634 0.7091 11.8928 1.0773 2704
## Cogon_Patch_Size 2.0986 7.7083 0.0569 0.5927 12.6271 1.1038 1647
## Avg_Cogongrass_Cover 0.8576 2.9101 0.0407 0.2728 5.5501 1.0446 2638
## total_shrub_cover 1.7921 4.9587 0.0565 0.6016 11.3033 1.0388 2563
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.607 0.941 0.0425 0.3177 2.7958 1.0212 449
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9230 0.6191 -2.8455 -2.0197 -0.3713 1.0003 2446
## week -0.0614 0.3469 -0.7659 -0.0561 0.6216 1.0003 2782
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6125 8.8418 0.0878 0.4699 9.5113 1.1115 2769
## week 0.4378 1.1225 0.0297 0.1632 2.7548 1.0126 2488
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4378 0.5888 -0.6701 0.4062 1.7188
## (Intercept)-Procyon_lotor 0.6274 0.6510 -0.6500 0.6036 1.9978
## (Intercept)-Dasypus_novemcinctus -0.4642 0.5460 -1.5648 -0.4531 0.5856
## Cogon_Patch_Size-Canis_latrans 0.9678 0.9159 -0.2708 0.7880 3.3202
## Cogon_Patch_Size-Procyon_lotor -0.0463 0.5220 -1.0504 -0.0511 0.9695
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0594 0.4503 -0.9886 -0.0452 0.8202
## Avg_Cogongrass_Cover-Canis_latrans 0.3116 0.4454 -0.5256 0.2992 1.2884
## Avg_Cogongrass_Cover-Procyon_lotor 0.2033 0.4832 -0.7239 0.1970 1.2001
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4101 0.4138 -0.3969 0.4042 1.2709
## total_shrub_cover-Canis_latrans 0.1688 0.5087 -0.7307 0.1380 1.2553
## total_shrub_cover-Procyon_lotor -0.9296 0.5759 -2.2438 -0.8641 0.0113
## total_shrub_cover-Dasypus_novemcinctus -0.0207 0.4110 -0.8131 -0.0345 0.8028
## Rhat ESS
## (Intercept)-Canis_latrans 1.0033 1006
## (Intercept)-Procyon_lotor 1.0070 811
## (Intercept)-Dasypus_novemcinctus 1.0009 1024
## Cogon_Patch_Size-Canis_latrans 1.0345 754
## Cogon_Patch_Size-Procyon_lotor 1.0084 1566
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0028 2048
## Avg_Cogongrass_Cover-Canis_latrans 1.0010 1333
## Avg_Cogongrass_Cover-Procyon_lotor 1.0013 1581
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0001 1824
## total_shrub_cover-Canis_latrans 1.0327 1302
## total_shrub_cover-Procyon_lotor 1.0000 1139
## total_shrub_cover-Dasypus_novemcinctus 1.0071 2458
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5778 0.1703 -2.9241 -2.5751 -2.2634 1.0146
## (Intercept)-Procyon_lotor -2.2720 0.1315 -2.5410 -2.2716 -2.0278 1.0050
## (Intercept)-Dasypus_novemcinctus -1.6028 0.1365 -1.8750 -1.6011 -1.3404 1.0025
## week-Canis_latrans 0.0737 0.1354 -0.2014 0.0760 0.3292 1.0023
## week-Procyon_lotor -0.0519 0.1228 -0.3049 -0.0467 0.1763 1.0087
## week-Dasypus_novemcinctus -0.1793 0.1415 -0.4806 -0.1710 0.0787 1.0047
## ESS
## (Intercept)-Canis_latrans 1046
## (Intercept)-Procyon_lotor 1408
## (Intercept)-Dasypus_novemcinctus 2253
## week-Canis_latrans 1793
## week-Procyon_lotor 1734
## week-Dasypus_novemcinctus 2013
#Includes week covariate of detection and only canopy for occupancy
ms_week_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3295
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0307 0.7151 -1.4887 -0.0216 1.4034 1.0021 1782
## Tree_Density -0.8408 0.7405 -2.3366 -0.8302 0.6738 1.0014 1776
## Avg_Canopy_Cover 0.4548 0.6460 -0.9535 0.4622 1.7114 1.0025 2269
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1971 8.1049 0.0667 0.7829 13.6290 1.1344 2336
## Tree_Density 2.3430 7.4322 0.0513 0.5913 16.1137 1.0314 1666
## Avg_Canopy_Cover 1.8504 5.2207 0.0675 0.6309 11.3675 1.0212 2722
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6423 0.9613 0.0414 0.3445 3.0214 1.0164 507
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9278 0.6032 -2.8407 -2.0146 -0.4181 1.0008 2294
## week -0.0545 0.3427 -0.7170 -0.0464 0.6150 1.0015 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6310 10.2610 0.0807 0.4722 8.2587 1.2462 969
## week 0.4486 1.4213 0.0320 0.1619 2.3516 1.0022 2656
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1331 0.5669 -0.9710 0.1267 1.3054
## (Intercept)-Procyon_lotor 0.4884 0.5998 -0.7325 0.5007 1.6874
## (Intercept)-Dasypus_novemcinctus -0.7718 0.6876 -2.2395 -0.7175 0.4262
## Tree_Density-Canis_latrans -0.9871 0.6338 -2.3816 -0.9226 0.0604
## Tree_Density-Procyon_lotor -0.4968 0.4571 -1.4480 -0.4865 0.4097
## Tree_Density-Dasypus_novemcinctus -1.5506 1.0755 -4.3569 -1.2954 -0.1612
## Avg_Canopy_Cover-Canis_latrans -0.1586 0.4666 -1.1511 -0.1483 0.7034
## Avg_Canopy_Cover-Procyon_lotor 0.8468 0.4905 -0.0065 0.8157 1.8721
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8763 0.4590 0.0813 0.8386 1.8542
## Rhat ESS
## (Intercept)-Canis_latrans 1.0279 974
## (Intercept)-Procyon_lotor 1.0064 946
## (Intercept)-Dasypus_novemcinctus 1.0223 667
## Tree_Density-Canis_latrans 1.0076 1380
## Tree_Density-Procyon_lotor 1.0095 1806
## Tree_Density-Dasypus_novemcinctus 1.0195 516
## Avg_Canopy_Cover-Canis_latrans 1.0039 1722
## Avg_Canopy_Cover-Procyon_lotor 1.0033 1739
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0116 1571
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5933 0.1721 -2.9552 -2.5859 -2.2710 1.0077
## (Intercept)-Procyon_lotor -2.2590 0.1282 -2.5073 -2.2589 -2.0098 1.0016
## (Intercept)-Dasypus_novemcinctus -1.6066 0.1365 -1.8752 -1.6034 -1.3475 1.0053
## week-Canis_latrans 0.0768 0.1387 -0.1952 0.0791 0.3469 1.0043
## week-Procyon_lotor -0.0472 0.1233 -0.2937 -0.0444 0.1830 1.0069
## week-Dasypus_novemcinctus -0.1781 0.1484 -0.4858 -0.1720 0.1055 1.0012
## ESS
## (Intercept)-Canis_latrans 1071
## (Intercept)-Procyon_lotor 1523
## (Intercept)-Dasypus_novemcinctus 2192
## week-Canis_latrans 1379
## week-Procyon_lotor 1826
## week-Dasypus_novemcinctus 1970
# Includes week covaritate of detection and quadratic cogongrass cover for occupancy
ms_week_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3798
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6196 0.6750 -1.8907 -0.6379 0.7857 1.0007 884
## Avg_Cogongrass_Cover 0.0132 0.6573 -1.2801 -0.0036 1.3044 1.0048 1014
## I(Avg_Cogongrass_Cover^2) 1.0783 0.9922 -0.9515 1.0494 3.1389 1.0040 1380
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4243 6.0134 0.0475 0.4249 8.5753 1.1253 2838
## Avg_Cogongrass_Cover 1.1385 3.7279 0.0436 0.3565 6.5177 1.0744 2212
## I(Avg_Cogongrass_Cover^2) 9.7829 80.8437 0.0699 1.7311 55.8583 1.2393 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4895 0.6568 0.0427 0.2841 2.1696 1.0598 733
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9395 0.6075 -2.9234 -2.0236 -0.3767 1.0094 2358
## week -0.0553 0.3388 -0.7684 -0.0493 0.6323 1.0042 2829
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2006 24.2893 0.0830 0.4817 8.5582 1.3421 3000
## week 0.4786 2.2192 0.0322 0.1648 2.4667 1.0869 2693
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.6837 0.6772 -2.0418 -0.6678
## (Intercept)-Procyon_lotor -0.4333 0.6631 -1.7497 -0.4358
## (Intercept)-Dasypus_novemcinctus -0.9566 0.5577 -2.0894 -0.9392
## Avg_Cogongrass_Cover-Canis_latrans 0.1252 0.6948 -1.0922 0.0917
## Avg_Cogongrass_Cover-Procyon_lotor -0.0470 0.7165 -1.4005 -0.0799
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.0323 0.5582 -1.1541 -0.0278
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.3671 1.6210 0.2320 2.0597
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1890 1.6369 0.1660 1.8170
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4242 0.4061 -0.3440 0.4092
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6004 1.0030 539
## (Intercept)-Procyon_lotor 0.9076 1.0031 479
## (Intercept)-Dasypus_novemcinctus 0.1068 1.0056 1284
## Avg_Cogongrass_Cover-Canis_latrans 1.6114 1.0078 554
## Avg_Cogongrass_Cover-Procyon_lotor 1.4578 1.0144 411
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0072 1.0018 1280
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.1208 1.0009 217
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.2143 1.0095 177
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2700 1.0060 1420
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6154 0.1677 -2.9551 -2.6120 -2.3090 1.0111
## (Intercept)-Procyon_lotor -2.2813 0.1314 -2.5469 -2.2824 -2.0218 1.0053
## (Intercept)-Dasypus_novemcinctus -1.6052 0.1390 -1.8819 -1.6003 -1.3369 1.0014
## week-Canis_latrans 0.0781 0.1365 -0.1932 0.0817 0.3385 1.0080
## week-Procyon_lotor -0.0491 0.1206 -0.3019 -0.0450 0.1695 1.0022
## week-Dasypus_novemcinctus -0.1819 0.1474 -0.4874 -0.1772 0.0891 1.0036
## ESS
## (Intercept)-Canis_latrans 979
## (Intercept)-Procyon_lotor 1339
## (Intercept)-Dasypus_novemcinctus 2093
## week-Canis_latrans 1971
## week-Procyon_lotor 1610
## week-Dasypus_novemcinctus 1911
# Includes week covaritate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_week_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_week_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3542
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2431 1.0235 -3.1971 -1.2611 0.8516 1.0140 602
## Cogon_Patch_Size 0.0852 1.0036 -1.9608 0.0653 2.1662 1.0037 1154
## Veg_shannon_index 1.0371 0.8143 -0.5891 1.0456 2.6590 1.0247 1025
## total_shrub_cover -0.0879 0.7307 -1.5502 -0.0909 1.4240 1.0045 960
## Avg_Cogongrass_Cover 0.7756 1.2409 -1.6294 0.7563 3.3961 1.0226 364
## Tree_Density -1.9820 1.4456 -4.5111 -2.1481 1.2437 1.0560 624
## Avg_Canopy_Cover 0.9978 0.8796 -0.9643 1.0263 2.6360 1.0213 1505
## I(Avg_Cogongrass_Cover^2) 1.6026 1.1423 -0.9214 1.6456 3.7491 1.0082 764
## avg_veg_height -0.0446 0.7523 -1.5798 -0.0498 1.4644 1.0192 825
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.7750 17.1331 0.0624 0.9709 33.9401 1.0776 775
## Cogon_Patch_Size 13.7008 218.7797 0.0786 1.8153 55.3296 1.2899 3000
## Veg_shannon_index 2.6166 13.6097 0.0525 0.6314 16.4038 1.0977 2324
## total_shrub_cover 2.2550 11.9595 0.0525 0.6585 12.0447 1.1110 2168
## Avg_Cogongrass_Cover 4.3407 18.3130 0.0564 0.7619 29.2089 1.0821 1442
## Tree_Density 22.4968 80.7961 0.0780 3.3254 159.8906 1.0954 287
## Avg_Canopy_Cover 4.5251 20.7989 0.0904 1.2678 24.7247 1.1012 2068
## I(Avg_Cogongrass_Cover^2) 15.1136 106.4645 0.0628 1.5522 102.6370 1.2469 975
## avg_veg_height 2.0580 9.1353 0.0502 0.5623 11.3757 1.1233 964
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.7386 5.7458 0.0609 1.0689 15.5568 1.2756 121
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.945 0.6059 -2.8972 -2.0314 -0.4257 1.0062 2594
## week -0.043 0.3550 -0.7685 -0.0498 0.6846 1.0052 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7139 7.1993 0.0808 0.4686 10.4624 1.0983 2813
## week 0.4152 1.0787 0.0326 0.1624 2.3365 1.1194 2266
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4777 1.2696 -4.1577 -1.3855
## (Intercept)-Procyon_lotor -1.0088 1.1451 -3.2828 -0.9856
## (Intercept)-Dasypus_novemcinctus -2.3460 1.3008 -5.4940 -2.1717
## Cogon_Patch_Size-Canis_latrans 1.7732 1.9347 -0.7118 1.3526
## Cogon_Patch_Size-Procyon_lotor -0.4670 1.2195 -2.6038 -0.5269
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5081 0.8298 -2.3366 -0.4597
## Veg_shannon_index-Canis_latrans 1.6193 0.9955 0.1028 1.4697
## Veg_shannon_index-Procyon_lotor 1.3609 0.8719 -0.0181 1.2537
## Veg_shannon_index-Dasypus_novemcinctus 0.6840 0.6695 -0.5856 0.6686
## total_shrub_cover-Canis_latrans 0.2367 0.7768 -1.0845 0.1624
## total_shrub_cover-Procyon_lotor -0.7646 0.7632 -2.4014 -0.7067
## total_shrub_cover-Dasypus_novemcinctus 0.2262 0.5780 -0.8464 0.1984
## Avg_Cogongrass_Cover-Canis_latrans 0.6700 1.5298 -2.3814 0.6710
## Avg_Cogongrass_Cover-Procyon_lotor 0.8394 1.4701 -1.9862 0.7999
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4523 1.5322 -1.1643 1.3126
## Tree_Density-Canis_latrans -3.7317 2.1825 -9.1225 -3.3490
## Tree_Density-Procyon_lotor -2.1508 1.3096 -4.7874 -2.1664
## Tree_Density-Dasypus_novemcinctus -4.8752 2.8050 -12.6888 -4.1337
## Avg_Canopy_Cover-Canis_latrans 0.1967 0.7931 -1.4171 0.2086
## Avg_Canopy_Cover-Procyon_lotor 1.5684 0.8613 0.1318 1.5007
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0662 0.9427 0.6481 1.9144
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.1936 2.2266 0.7572 2.5920
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0264 2.1513 0.4831 2.4539
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4671 1.0283 -0.1995 1.3319
## avg_veg_height-Canis_latrans -0.5873 0.8821 -2.4797 -0.5187
## avg_veg_height-Procyon_lotor 0.2213 0.8147 -1.3336 0.1793
## avg_veg_height-Dasypus_novemcinctus 0.1392 0.7302 -1.3003 0.1386
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.7343 1.0485 286
## (Intercept)-Procyon_lotor 1.1221 1.0474 317
## (Intercept)-Dasypus_novemcinctus -0.3075 1.0126 307
## Cogon_Patch_Size-Canis_latrans 7.2367 1.0266 340
## Cogon_Patch_Size-Procyon_lotor 2.3289 1.0181 334
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0493 1.0146 714
## Veg_shannon_index-Canis_latrans 4.0325 1.0129 311
## Veg_shannon_index-Procyon_lotor 3.3974 1.0956 276
## Veg_shannon_index-Dasypus_novemcinctus 2.0308 1.0283 452
## total_shrub_cover-Canis_latrans 2.0906 1.0028 512
## total_shrub_cover-Procyon_lotor 0.6123 1.0321 615
## total_shrub_cover-Dasypus_novemcinctus 1.4101 1.0127 1241
## Avg_Cogongrass_Cover-Canis_latrans 3.7303 1.0112 371
## Avg_Cogongrass_Cover-Procyon_lotor 3.9684 1.0445 395
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.0072 1.0305 331
## Tree_Density-Canis_latrans -0.5197 1.0666 246
## Tree_Density-Procyon_lotor 0.4012 1.0417 459
## Tree_Density-Dasypus_novemcinctus -1.7180 1.0962 136
## Avg_Canopy_Cover-Canis_latrans 1.7709 1.0175 638
## Avg_Canopy_Cover-Procyon_lotor 3.4625 1.0357 467
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.4301 1.0682 183
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 9.3552 1.0988 102
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 9.3386 1.1990 128
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.8826 1.0364 367
## avg_veg_height-Canis_latrans 0.9332 1.0472 589
## avg_veg_height-Procyon_lotor 2.0084 1.0307 583
## avg_veg_height-Dasypus_novemcinctus 1.5578 1.0162 707
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6035 0.1710 -2.9551 -2.5980 -2.2753 1.0095
## (Intercept)-Procyon_lotor -2.2855 0.1301 -2.5479 -2.2842 -2.0355 1.0259
## (Intercept)-Dasypus_novemcinctus -1.6112 0.1385 -1.8896 -1.6108 -1.3453 1.0042
## week-Canis_latrans 0.0741 0.1370 -0.1983 0.0775 0.3366 1.0009
## week-Procyon_lotor -0.0482 0.1199 -0.2965 -0.0443 0.1761 1.0003
## week-Dasypus_novemcinctus -0.1812 0.1482 -0.4853 -0.1765 0.0930 1.0010
## ESS
## (Intercept)-Canis_latrans 813
## (Intercept)-Procyon_lotor 1348
## (Intercept)-Dasypus_novemcinctus 2242
## week-Canis_latrans 1622
## week-Procyon_lotor 1655
## week-Dasypus_novemcinctus 2064
# Includes cover covariate for detection and cogongrass cover for occupancy
ms_cover_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2735
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1936 0.6181 -1.0604 0.1892 1.4250 1.0048 1463
## Avg_Cogongrass_Cover 0.4498 0.4953 -0.5740 0.4448 1.4764 1.0195 1975
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6576 7.0155 0.0580 0.4999 9.5275 1.1719 2792
## Avg_Cogongrass_Cover 0.8736 3.0066 0.0424 0.2660 4.7092 1.0400 2546
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4935 0.7187 0.043 0.2772 2.3084 1.018 642
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0143 0.5965 -2.9411 -2.1090 -0.4781 1.0053 2499
## shrub_cover 0.2474 0.5377 -0.8329 0.2476 1.3231 1.0057 2828
## veg_height -0.0218 0.5259 -1.1441 -0.0229 1.1105 1.0016 2768
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6126 9.5937 0.0767 0.4397 7.9805 1.1545 2806
## shrub_cover 1.4442 4.4824 0.0809 0.5315 7.8443 1.0104 3000
## veg_height 1.3231 4.1197 0.0867 0.4956 7.3111 1.0320 2731
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4454 0.5398 -0.5321 0.4242 1.5995
## (Intercept)-Procyon_lotor 0.5347 0.5070 -0.4639 0.5309 1.5426
## (Intercept)-Dasypus_novemcinctus -0.3333 0.5151 -1.3976 -0.3158 0.6385
## Avg_Cogongrass_Cover-Canis_latrans 0.6163 0.4757 -0.1819 0.5730 1.6601
## Avg_Cogongrass_Cover-Procyon_lotor 0.3370 0.3971 -0.4210 0.3230 1.1433
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4661 0.3831 -0.2421 0.4528 1.2511
## Rhat ESS
## (Intercept)-Canis_latrans 1.0079 1128
## (Intercept)-Procyon_lotor 1.0037 1209
## (Intercept)-Dasypus_novemcinctus 1.0060 1298
## Avg_Cogongrass_Cover-Canis_latrans 1.0074 1062
## Avg_Cogongrass_Cover-Procyon_lotor 1.0088 1984
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0006 1822
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7321 0.1944 -3.1299 -2.7312 -2.3573 1.0026
## (Intercept)-Procyon_lotor -2.2750 0.1407 -2.5742 -2.2685 -2.0118 1.0107
## (Intercept)-Dasypus_novemcinctus -1.7529 0.1644 -2.0687 -1.7539 -1.4380 1.0034
## shrub_cover-Canis_latrans -0.2939 0.2224 -0.7290 -0.2923 0.1333 1.0027
## shrub_cover-Procyon_lotor 0.2628 0.1661 -0.0661 0.2610 0.5812 0.9999
## shrub_cover-Dasypus_novemcinctus 0.8651 0.3173 0.2819 0.8466 1.5390 1.0005
## veg_height-Canis_latrans -0.6503 0.1908 -1.0381 -0.6434 -0.2862 1.0019
## veg_height-Procyon_lotor 0.3430 0.1278 0.1001 0.3396 0.5994 1.0008
## veg_height-Dasypus_novemcinctus 0.2563 0.1399 -0.0077 0.2547 0.5382 1.0028
## ESS
## (Intercept)-Canis_latrans 600
## (Intercept)-Procyon_lotor 1264
## (Intercept)-Dasypus_novemcinctus 1476
## shrub_cover-Canis_latrans 783
## shrub_cover-Procyon_lotor 1577
## shrub_cover-Dasypus_novemcinctus 1104
## veg_height-Canis_latrans 704
## veg_height-Procyon_lotor 1443
## veg_height-Dasypus_novemcinctus 1939
# Includes cover covariate for detection and all covariates for occupancy
ms_cover_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2828
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0822 0.8967 -1.8549 0.1285 1.8100 1.0477 945
## Cogon_Patch_Size -0.2785 0.8929 -2.0139 -0.3135 1.6106 1.0107 814
## Veg_shannon_index 0.9633 0.7822 -0.6892 0.9562 2.4788 1.0063 487
## total_shrub_cover -0.0813 0.8571 -1.8284 -0.0882 1.7350 1.0022 1348
## Avg_Cogongrass_Cover 2.0450 1.1159 -0.3762 2.0590 4.0858 1.0096 603
## Tree_Density -1.6880 1.2658 -3.8331 -1.8459 1.3690 1.0092 1201
## Avg_Canopy_Cover 0.9487 0.8637 -0.9893 0.9872 2.5283 1.0131 2153
## avg_veg_height -0.2334 0.7233 -1.6387 -0.2502 1.2349 1.0222 754
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2973 25.9760 0.0737 1.2319 22.6047 1.2387 2621
## Cogon_Patch_Size 3.1038 8.1309 0.0615 0.8697 19.2363 1.0215 1496
## Veg_shannon_index 2.1203 9.4712 0.0496 0.5017 14.2454 1.0684 2527
## total_shrub_cover 4.7524 17.4190 0.0827 1.2195 28.9276 1.0931 785
## Avg_Cogongrass_Cover 5.4139 45.7028 0.0546 0.7037 31.4868 1.2070 3000
## Tree_Density 28.9433 184.9121 0.0932 3.1136 176.6769 1.2366 1725
## Avg_Canopy_Cover 4.9506 34.9144 0.0893 1.3937 27.3686 1.2782 3000
## avg_veg_height 1.6153 12.2356 0.0431 0.3751 8.0566 1.0941 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.3721 3.8875 0.0624 0.9232 13.9689 1.0847 133
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0366 0.5930 -2.9229 -2.1246 -0.5033 1.0051 2340
## shrub_cover 0.2530 0.5893 -0.9822 0.2550 1.4068 1.0178 2824
## veg_height -0.0216 0.5254 -1.0472 -0.0172 1.0470 1.0044 2748
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4336 4.3798 0.0795 0.4697 8.5108 1.0070 2494
## shrub_cover 1.8091 4.9737 0.1095 0.6925 9.9262 1.0417 2203
## veg_height 1.2133 2.9324 0.0978 0.4840 6.5868 1.0032 2731
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.6540 0.9655 -1.0594 0.6094
## (Intercept)-Procyon_lotor 0.6318 0.9431 -1.3886 0.6339
## (Intercept)-Dasypus_novemcinctus -0.9204 1.0611 -3.4675 -0.8174
## Cogon_Patch_Size-Canis_latrans 0.5018 1.2360 -1.3825 0.3021
## Cogon_Patch_Size-Procyon_lotor -0.9256 0.8371 -2.7064 -0.8729
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5394 0.8886 -2.3323 -0.5557
## Veg_shannon_index-Canis_latrans 1.4146 0.8504 -0.0829 1.3216
## Veg_shannon_index-Procyon_lotor 1.2851 0.7285 0.0305 1.2097
## Veg_shannon_index-Dasypus_novemcinctus 0.6633 0.6830 -0.7169 0.6563
## total_shrub_cover-Canis_latrans 0.9317 1.1060 -0.6478 0.6997
## total_shrub_cover-Procyon_lotor -1.0437 0.7860 -2.8320 -0.9755
## total_shrub_cover-Dasypus_novemcinctus -0.2407 0.9800 -2.7245 -0.1043
## Avg_Cogongrass_Cover-Canis_latrans 2.7273 1.2962 0.6349 2.5578
## Avg_Cogongrass_Cover-Procyon_lotor 2.1695 1.0987 0.1253 2.1178
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.8050 1.3443 0.6390 2.6339
## Tree_Density-Canis_latrans -3.6421 2.2086 -9.4643 -3.1057
## Tree_Density-Procyon_lotor -1.5007 0.9032 -3.3180 -1.5008
## Tree_Density-Dasypus_novemcinctus -4.3837 2.7020 -11.9234 -3.5937
## Avg_Canopy_Cover-Canis_latrans 0.1625 0.6658 -1.1465 0.1659
## Avg_Canopy_Cover-Procyon_lotor 1.5331 0.7811 0.2141 1.4705
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1257 0.9943 0.6864 1.9550
## avg_veg_height-Canis_latrans -0.3899 0.7756 -1.8957 -0.3911
## avg_veg_height-Procyon_lotor -0.2969 0.6959 -1.6483 -0.3033
## avg_veg_height-Dasypus_novemcinctus -0.1116 0.7183 -1.4920 -0.1310
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.8038 1.0700 526
## (Intercept)-Procyon_lotor 2.4896 1.0694 390
## (Intercept)-Dasypus_novemcinctus 0.8772 1.0092 405
## Cogon_Patch_Size-Canis_latrans 3.6453 1.0114 540
## Cogon_Patch_Size-Procyon_lotor 0.6230 1.0377 335
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2609 1.0384 531
## Veg_shannon_index-Canis_latrans 3.2685 1.0172 459
## Veg_shannon_index-Procyon_lotor 2.8851 1.0116 247
## Veg_shannon_index-Dasypus_novemcinctus 2.0416 1.0286 410
## total_shrub_cover-Canis_latrans 3.7230 1.0186 201
## total_shrub_cover-Procyon_lotor 0.2237 1.0111 585
## total_shrub_cover-Dasypus_novemcinctus 1.2120 1.0738 201
## Avg_Cogongrass_Cover-Canis_latrans 5.8224 1.0116 341
## Avg_Cogongrass_Cover-Procyon_lotor 4.4847 1.0292 556
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.8937 1.0658 292
## Tree_Density-Canis_latrans -0.8081 1.0073 128
## Tree_Density-Procyon_lotor 0.2552 1.0251 925
## Tree_Density-Dasypus_novemcinctus -1.3100 1.0061 180
## Avg_Canopy_Cover-Canis_latrans 1.4625 1.0003 1126
## Avg_Canopy_Cover-Procyon_lotor 3.3010 1.0156 369
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.7475 1.0232 258
## avg_veg_height-Canis_latrans 1.1590 1.0163 705
## avg_veg_height-Procyon_lotor 1.1067 1.0250 866
## avg_veg_height-Dasypus_novemcinctus 1.3881 1.0202 495
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7458 0.1866 -3.1240 -2.7421 -2.3924 1.0089
## (Intercept)-Procyon_lotor -2.2842 0.1397 -2.5639 -2.2815 -2.0200 1.0052
## (Intercept)-Dasypus_novemcinctus -1.7814 0.1697 -2.1297 -1.7745 -1.4613 1.0119
## shrub_cover-Canis_latrans -0.4194 0.2241 -0.8485 -0.4248 0.0399 1.0072
## shrub_cover-Procyon_lotor 0.2823 0.1640 -0.0434 0.2842 0.5952 1.0143
## shrub_cover-Dasypus_novemcinctus 0.9626 0.3393 0.3065 0.9702 1.6408 1.0270
## veg_height-Canis_latrans -0.6658 0.1925 -1.0519 -0.6633 -0.3046 1.0031
## veg_height-Procyon_lotor 0.3559 0.1228 0.1164 0.3558 0.5955 1.0008
## veg_height-Dasypus_novemcinctus 0.2657 0.1413 -0.0028 0.2635 0.5519 1.0014
## ESS
## (Intercept)-Canis_latrans 605
## (Intercept)-Procyon_lotor 1422
## (Intercept)-Dasypus_novemcinctus 790
## shrub_cover-Canis_latrans 765
## shrub_cover-Procyon_lotor 1309
## shrub_cover-Dasypus_novemcinctus 571
## veg_height-Canis_latrans 577
## veg_height-Procyon_lotor 1621
## veg_height-Dasypus_novemcinctus 1827
# Includes cover covariate for detection and only cover for occupancy
ms_cover_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2905
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4752 0.7287 -0.9292 0.4610 2.0032 1.0208 481
## Avg_Cogongrass_Cover 0.2094 0.6518 -1.0517 0.2178 1.4824 1.0018 1142
## total_shrub_cover -0.3495 0.8471 -2.0903 -0.3434 1.3698 1.0122 1166
## avg_veg_height 0.3054 0.6437 -0.9584 0.2923 1.6685 1.0062 914
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7594 6.4793 0.0569 0.5740 10.3426 1.1737 2089
## Avg_Cogongrass_Cover 1.2625 4.1738 0.0440 0.3616 7.9465 1.0054 2163
## total_shrub_cover 4.0093 11.7771 0.0830 1.1982 25.2355 1.0554 882
## avg_veg_height 1.1663 7.5525 0.0421 0.3200 6.4022 1.2662 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6866 1.028 0.0449 0.3346 3.4591 1.0045 413
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0799 0.6162 -3.0087 -2.1789 -0.5019 1.0061 2263
## shrub_cover 0.3028 0.6407 -1.0027 0.3112 1.5822 1.0046 2448
## veg_height -0.0148 0.5475 -1.1729 -0.0178 1.1463 1.0060 3239
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3281 3.3738 0.0695 0.4192 8.7456 1.0149 2195
## shrub_cover 2.0107 5.1861 0.1100 0.7671 11.5230 1.0667 2269
## veg_height 1.4735 10.7815 0.0874 0.5009 7.4103 1.2720 2263
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7312 0.7087 -0.4706 0.6591
## (Intercept)-Procyon_lotor 0.8422 0.6680 -0.3904 0.8141
## (Intercept)-Dasypus_novemcinctus 0.0267 0.8225 -1.2926 -0.0777
## Avg_Cogongrass_Cover-Canis_latrans 0.4895 0.6560 -0.6729 0.4544
## Avg_Cogongrass_Cover-Procyon_lotor -0.0233 0.5977 -1.3087 -0.0057
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2307 0.5629 -0.8653 0.2323
## total_shrub_cover-Canis_latrans 0.5807 0.8381 -0.6681 0.4381
## total_shrub_cover-Procyon_lotor -1.2398 0.7159 -2.9231 -1.1339
## total_shrub_cover-Dasypus_novemcinctus -0.5624 0.9911 -3.1857 -0.3211
## avg_veg_height-Canis_latrans 0.2394 0.6243 -0.8943 0.2151
## avg_veg_height-Procyon_lotor 0.2545 0.6117 -0.9298 0.2412
## avg_veg_height-Dasypus_novemcinctus 0.5497 0.6697 -0.5272 0.4813
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.3292 1.0117 480
## (Intercept)-Procyon_lotor 2.2950 1.0231 675
## (Intercept)-Dasypus_novemcinctus 1.9489 1.0804 157
## Avg_Cogongrass_Cover-Canis_latrans 1.8904 1.0127 998
## Avg_Cogongrass_Cover-Procyon_lotor 1.1175 1.0105 973
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3412 1.0014 1173
## total_shrub_cover-Canis_latrans 2.5970 1.0107 340
## total_shrub_cover-Procyon_lotor -0.0977 1.0114 636
## total_shrub_cover-Dasypus_novemcinctus 0.6958 1.1004 151
## avg_veg_height-Canis_latrans 1.5644 1.0094 763
## avg_veg_height-Procyon_lotor 1.5128 1.0093 979
## avg_veg_height-Dasypus_novemcinctus 2.1117 1.0388 440
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7701 0.2037 -3.1886 -2.7628 -2.4088 1.0110
## (Intercept)-Procyon_lotor -2.2864 0.1346 -2.5546 -2.2863 -2.0257 1.0084
## (Intercept)-Dasypus_novemcinctus -1.8374 0.1957 -2.2345 -1.8297 -1.4794 1.0907
## shrub_cover-Canis_latrans -0.3721 0.2469 -0.8484 -0.3718 0.1130 1.0290
## shrub_cover-Procyon_lotor 0.3211 0.1610 -0.0029 0.3226 0.6334 1.0008
## shrub_cover-Dasypus_novemcinctus 1.1093 0.4143 0.3443 1.0958 1.8963 1.0994
## veg_height-Canis_latrans -0.6639 0.2023 -1.0823 -0.6523 -0.3049 1.0101
## veg_height-Procyon_lotor 0.3432 0.1267 0.0950 0.3445 0.6013 1.0045
## veg_height-Dasypus_novemcinctus 0.2714 0.1471 -0.0100 0.2683 0.5711 1.0135
## ESS
## (Intercept)-Canis_latrans 510
## (Intercept)-Procyon_lotor 1254
## (Intercept)-Dasypus_novemcinctus 298
## shrub_cover-Canis_latrans 395
## shrub_cover-Procyon_lotor 1282
## shrub_cover-Dasypus_novemcinctus 258
## veg_height-Canis_latrans 622
## veg_height-Procyon_lotor 1527
## veg_height-Dasypus_novemcinctus 1043
# Includes cover covariate for detection and none for occupancy
ms_cover_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.cover,
data = data_list,
n.samples = 5000, # number of MCMC samples
n.thin = 2, # 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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2843
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1838 0.5876 -1.0339 0.1981 1.3379 1.0011 2654
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8005 9.4731 0.0636 0.527 10.1318 1.0962 2803
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0131 0.6076 -2.9067 -2.1055 -0.4511 1.0071 2259
## shrub_cover 0.2374 0.5526 -0.9460 0.2337 1.3951 1.0037 2664
## veg_height -0.0067 0.5290 -1.1213 -0.0023 1.0664 1.0005 2843
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4039 5.1945 0.0707 0.4336 8.5263 1.0954 2506
## shrub_cover 1.3803 3.6542 0.0801 0.5284 7.8603 1.0475 2635
## veg_height 1.3445 5.1050 0.0885 0.4798 7.2603 1.0193 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3770 0.3767 -0.3284 0.3645 1.1719 1.0028
## (Intercept)-Procyon_lotor 0.6412 0.3792 -0.0371 0.6187 1.4339 1.0020
## (Intercept)-Dasypus_novemcinctus -0.3800 0.3901 -1.1753 -0.3822 0.3670 1.0051
## ESS
## (Intercept)-Canis_latrans 1937
## (Intercept)-Procyon_lotor 2138
## (Intercept)-Dasypus_novemcinctus 2278
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7087 0.1885 -3.0806 -2.7034 -2.3473 1.0004
## (Intercept)-Procyon_lotor -2.2767 0.1409 -2.5710 -2.2714 -2.0262 1.0037
## (Intercept)-Dasypus_novemcinctus -1.7526 0.1616 -2.0812 -1.7464 -1.4553 1.0126
## shrub_cover-Canis_latrans -0.3038 0.2226 -0.7383 -0.3038 0.1229 1.0177
## shrub_cover-Procyon_lotor 0.2662 0.1635 -0.0615 0.2659 0.5850 1.0128
## shrub_cover-Dasypus_novemcinctus 0.8576 0.3100 0.2672 0.8414 1.4800 1.0129
## veg_height-Canis_latrans -0.6319 0.1953 -1.0174 -0.6227 -0.2633 1.0105
## veg_height-Procyon_lotor 0.3518 0.1256 0.1077 0.3516 0.5946 1.0065
## veg_height-Dasypus_novemcinctus 0.2603 0.1367 -0.0063 0.2594 0.5278 1.0144
## ESS
## (Intercept)-Canis_latrans 661
## (Intercept)-Procyon_lotor 1157
## (Intercept)-Dasypus_novemcinctus 1666
## shrub_cover-Canis_latrans 923
## shrub_cover-Procyon_lotor 1434
## shrub_cover-Dasypus_novemcinctus 1296
## veg_height-Canis_latrans 612
## veg_height-Procyon_lotor 1477
## veg_height-Dasypus_novemcinctus 2155
#Includes cover for detection and only foraging for occupancy
ms_cover_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2937
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1756 0.6637 -1.1505 0.1840 1.5449 1.0044 1719
## Veg_shannon_index 0.5129 0.5753 -0.6632 0.5071 1.6803 1.0018 1833
## Avg_Cogongrass_Cover 0.6469 0.5532 -0.5251 0.6608 1.7233 1.0040 1508
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9154 8.2798 0.0553 0.5384 10.7142 1.0755 2343
## Veg_shannon_index 1.5664 10.7505 0.0481 0.3804 9.1081 1.0867 3000
## Avg_Cogongrass_Cover 1.2220 5.3734 0.0465 0.3016 6.7143 1.0314 1725
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7732 1.3016 0.0496 0.3683 3.8782 1.0415 349
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0229 0.6058 -2.9122 -2.1184 -0.4511 1.0079 2363
## shrub_cover 0.2398 0.5722 -1.0027 0.2316 1.3991 1.0040 2358
## veg_height -0.0232 0.5362 -1.1149 -0.0168 1.0984 0.9998 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4423 4.4716 0.0688 0.4209 9.0098 1.0306 2202
## shrub_cover 1.5530 6.1434 0.0815 0.5703 7.6347 1.0881 2753
## veg_height 1.2925 5.7959 0.0908 0.4740 6.6232 1.2306 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4356 0.5836 -0.6706 0.4170 1.6909
## (Intercept)-Procyon_lotor 0.5216 0.5970 -0.6550 0.5152 1.7426
## (Intercept)-Dasypus_novemcinctus -0.3586 0.5701 -1.5256 -0.3353 0.6520
## Veg_shannon_index-Canis_latrans 0.8928 0.5337 -0.0396 0.8470 2.0123
## Veg_shannon_index-Procyon_lotor 0.6397 0.5245 -0.2393 0.5859 1.7509
## Veg_shannon_index-Dasypus_novemcinctus 0.2375 0.4200 -0.6408 0.2608 1.0132
## Avg_Cogongrass_Cover-Canis_latrans 0.9391 0.5749 0.0391 0.8764 2.2875
## Avg_Cogongrass_Cover-Procyon_lotor 0.6040 0.5118 -0.2975 0.5694 1.7247
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6209 0.4042 -0.1243 0.6049 1.4609
## Rhat ESS
## (Intercept)-Canis_latrans 1.0032 873
## (Intercept)-Procyon_lotor 1.0084 767
## (Intercept)-Dasypus_novemcinctus 1.0083 1052
## Veg_shannon_index-Canis_latrans 1.0061 774
## Veg_shannon_index-Procyon_lotor 1.0158 494
## Veg_shannon_index-Dasypus_novemcinctus 1.0070 1897
## Avg_Cogongrass_Cover-Canis_latrans 1.0201 596
## Avg_Cogongrass_Cover-Procyon_lotor 1.0144 802
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0071 1521
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7074 0.1842 -3.0927 -2.6963 -2.3819 1.0044
## (Intercept)-Procyon_lotor -2.2892 0.1449 -2.5837 -2.2859 -2.0285 1.0092
## (Intercept)-Dasypus_novemcinctus -1.7634 0.1691 -2.0992 -1.7598 -1.4509 1.0015
## shrub_cover-Canis_latrans -0.3041 0.2194 -0.7487 -0.2999 0.1134 1.0020
## shrub_cover-Procyon_lotor 0.2376 0.1791 -0.1437 0.2492 0.5649 1.0002
## shrub_cover-Dasypus_novemcinctus 0.8962 0.3261 0.3058 0.8881 1.5760 1.0105
## veg_height-Canis_latrans -0.6363 0.1888 -1.0198 -0.6298 -0.2795 1.0105
## veg_height-Procyon_lotor 0.3431 0.1295 0.0896 0.3402 0.5998 1.0005
## veg_height-Dasypus_novemcinctus 0.2553 0.1419 -0.0196 0.2528 0.5331 1.0037
## ESS
## (Intercept)-Canis_latrans 667
## (Intercept)-Procyon_lotor 1056
## (Intercept)-Dasypus_novemcinctus 1294
## shrub_cover-Canis_latrans 914
## shrub_cover-Procyon_lotor 1114
## shrub_cover-Dasypus_novemcinctus 967
## veg_height-Canis_latrans 641
## veg_height-Procyon_lotor 1496
## veg_height-Dasypus_novemcinctus 1854
# Includes movement covariates of occupancy and cover for detection
ms_cover_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3143
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.3953 0.7247 -1.0566 0.3769 1.9339 1.0091 804
## Cogon_Patch_Size 0.2370 0.6633 -1.0594 0.2209 1.6993 1.0065 1846
## Avg_Cogongrass_Cover 0.3144 0.5777 -0.8180 0.3111 1.5165 1.0005 1418
## total_shrub_cover -0.2833 0.7569 -1.9466 -0.2761 1.2988 1.0151 1638
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0492 7.4207 0.0596 0.6444 13.4324 1.1466 2360
## Cogon_Patch_Size 2.2640 11.0591 0.0520 0.4934 13.4412 1.0432 2596
## Avg_Cogongrass_Cover 1.0050 3.2898 0.0430 0.3019 6.2189 1.0108 2768
## total_shrub_cover 3.8681 25.9800 0.0754 0.8929 20.9347 1.0463 2785
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6462 0.9976 0.0432 0.3219 3.3203 1.0588 433
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0457 0.6186 -2.9383 -2.1503 -0.4132 1.0005 2161
## shrub_cover 0.2830 0.6078 -0.9394 0.2957 1.5201 1.0015 2716
## veg_height -0.0048 0.5126 -1.0670 0.0026 1.0054 1.0016 2658
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5991 6.0365 0.0700 0.4194 9.6145 1.0077 1985
## shrub_cover 2.0729 10.6337 0.0999 0.7140 10.3593 1.0823 3000
## veg_height 1.8747 24.6976 0.0854 0.4853 6.4451 1.2637 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7152 0.6719 -0.4546 0.6641
## (Intercept)-Procyon_lotor 0.8017 0.6410 -0.3831 0.7710
## (Intercept)-Dasypus_novemcinctus -0.1553 0.6942 -1.3508 -0.2080
## Cogon_Patch_Size-Canis_latrans 0.8778 0.9362 -0.3485 0.7012
## Cogon_Patch_Size-Procyon_lotor -0.0766 0.5113 -1.0882 -0.0783
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0257 0.4987 -0.9665 0.0359
## Avg_Cogongrass_Cover-Canis_latrans 0.4365 0.5349 -0.4804 0.3978
## Avg_Cogongrass_Cover-Procyon_lotor 0.1791 0.5327 -0.8292 0.1681
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4302 0.4823 -0.4546 0.4088
## total_shrub_cover-Canis_latrans 0.3973 0.7096 -0.7319 0.2968
## total_shrub_cover-Procyon_lotor -1.1185 0.6870 -2.7355 -1.0296
## total_shrub_cover-Dasypus_novemcinctus -0.3968 0.8037 -2.7368 -0.2402
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2383 1.0075 491
## (Intercept)-Procyon_lotor 2.1609 1.0103 751
## (Intercept)-Dasypus_novemcinctus 1.4176 1.0122 315
## Cogon_Patch_Size-Canis_latrans 3.2507 1.0120 542
## Cogon_Patch_Size-Procyon_lotor 0.9596 1.0083 1436
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0492 1.0126 1340
## Avg_Cogongrass_Cover-Canis_latrans 1.5693 1.0122 876
## Avg_Cogongrass_Cover-Procyon_lotor 1.2594 1.0018 1211
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4812 1.0101 1084
## total_shrub_cover-Canis_latrans 2.1627 1.0281 641
## total_shrub_cover-Procyon_lotor -0.0218 1.0277 630
## total_shrub_cover-Dasypus_novemcinctus 0.6427 1.0409 223
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7356 0.2002 -3.1532 -2.7285 -2.3647 1.0088
## (Intercept)-Procyon_lotor -2.2837 0.1362 -2.5536 -2.2807 -2.0188 1.0007
## (Intercept)-Dasypus_novemcinctus -1.8142 0.1867 -2.1982 -1.8087 -1.4650 1.0126
## shrub_cover-Canis_latrans -0.3623 0.2381 -0.8277 -0.3645 0.1128 1.0117
## shrub_cover-Procyon_lotor 0.3075 0.1610 -0.0157 0.3090 0.6130 1.0020
## shrub_cover-Dasypus_novemcinctus 1.0180 0.3834 0.3191 0.9926 1.8170 1.0164
## veg_height-Canis_latrans -0.6447 0.1944 -1.0494 -0.6339 -0.2783 1.0118
## veg_height-Procyon_lotor 0.3512 0.1230 0.1139 0.3536 0.5880 1.0042
## veg_height-Dasypus_novemcinctus 0.2709 0.1409 -0.0002 0.2679 0.5480 1.0045
## ESS
## (Intercept)-Canis_latrans 515
## (Intercept)-Procyon_lotor 1314
## (Intercept)-Dasypus_novemcinctus 525
## shrub_cover-Canis_latrans 650
## shrub_cover-Procyon_lotor 1471
## shrub_cover-Dasypus_novemcinctus 324
## veg_height-Canis_latrans 689
## veg_height-Procyon_lotor 1456
## veg_height-Dasypus_novemcinctus 1781
#Includes cover covariate of detection and only canopy for occupancy
ms_cover_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.cover, data = data_list,
## n.samples = 5000, n.report = 500, n.burn = 3000, n.thin = 2,
## n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2787
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0151 0.6640 -1.4308 0.0397 1.2901 1.0036 1568
## Tree_Density -0.8653 0.7233 -2.3629 -0.8503 0.6428 1.0033 1904
## Avg_Canopy_Cover 0.4461 0.6399 -0.9365 0.4472 1.7347 1.0055 2707
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.6453 26.8060 0.0639 0.6731 12.7812 1.2989 3000
## Tree_Density 2.9311 14.1649 0.0548 0.5996 17.8576 1.0642 2339
## Avg_Canopy_Cover 2.1082 6.1255 0.0649 0.6655 13.4554 1.0086 2512
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5805 0.8527 0.0471 0.306 2.7143 1.0491 646
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0448 0.5799 -2.9586 -2.1332 -0.5222 1.0023 2236
## shrub_cover 0.2472 0.5626 -0.9548 0.2587 1.3588 1.0018 3000
## veg_height -0.0003 0.5115 -1.0839 0.0022 1.0253 1.0030 2442
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3377 4.9177 0.0711 0.4262 8.2072 1.0813 2549
## shrub_cover 1.3706 2.7545 0.0855 0.5809 7.8446 1.0200 2736
## veg_height 1.4722 12.9760 0.0905 0.4651 6.6849 1.2802 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2617 0.5628 -0.7796 0.2413 1.3901
## (Intercept)-Procyon_lotor 0.5059 0.6044 -0.6770 0.4961 1.7153
## (Intercept)-Dasypus_novemcinctus -0.6603 0.6625 -2.0490 -0.6067 0.4885
## Tree_Density-Canis_latrans -1.0821 0.6767 -2.7105 -0.9783 -0.0023
## Tree_Density-Procyon_lotor -0.4912 0.4702 -1.4290 -0.4882 0.4267
## Tree_Density-Dasypus_novemcinctus -1.5623 1.0765 -4.3550 -1.3152 -0.2012
## Avg_Canopy_Cover-Canis_latrans -0.2150 0.4730 -1.2040 -0.1904 0.6421
## Avg_Canopy_Cover-Procyon_lotor 0.8374 0.4952 0.0009 0.7945 1.8996
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9383 0.5078 0.0646 0.8958 2.1055
## Rhat ESS
## (Intercept)-Canis_latrans 1.0005 1157
## (Intercept)-Procyon_lotor 1.0023 961
## (Intercept)-Dasypus_novemcinctus 1.0068 846
## Tree_Density-Canis_latrans 1.0116 1135
## Tree_Density-Procyon_lotor 1.0091 1638
## Tree_Density-Dasypus_novemcinctus 1.0094 726
## Avg_Canopy_Cover-Canis_latrans 1.0007 1636
## Avg_Canopy_Cover-Procyon_lotor 1.0004 1789
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0050 1392
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7179 0.1941 -3.1238 -2.7078 -2.3595 1.0468
## (Intercept)-Procyon_lotor -2.2836 0.1434 -2.5798 -2.2762 -2.0170 1.0027
## (Intercept)-Dasypus_novemcinctus -1.7672 0.1664 -2.1094 -1.7622 -1.4485 1.0123
## shrub_cover-Canis_latrans -0.3113 0.2222 -0.7423 -0.3124 0.1157 1.0220
## shrub_cover-Procyon_lotor 0.2669 0.1619 -0.0632 0.2712 0.5746 1.0055
## shrub_cover-Dasypus_novemcinctus 0.9034 0.3172 0.2852 0.9029 1.5297 1.0114
## veg_height-Canis_latrans -0.6291 0.1925 -1.0221 -0.6229 -0.2678 1.0541
## veg_height-Procyon_lotor 0.3563 0.1246 0.1172 0.3550 0.6049 1.0116
## veg_height-Dasypus_novemcinctus 0.2658 0.1413 -0.0035 0.2601 0.5449 1.0015
## ESS
## (Intercept)-Canis_latrans 703
## (Intercept)-Procyon_lotor 1247
## (Intercept)-Dasypus_novemcinctus 1442
## shrub_cover-Canis_latrans 876
## shrub_cover-Procyon_lotor 1660
## shrub_cover-Dasypus_novemcinctus 1138
## veg_height-Canis_latrans 737
## veg_height-Procyon_lotor 1584
## veg_height-Dasypus_novemcinctus 1807
# Includes cover covariate of detection and quadratic cogongrass cover for occupancy
ms_cover_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2877
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5384 0.6962 -1.8378 -0.5508 0.8637 1.0002 937
## Avg_Cogongrass_Cover 0.0071 0.6885 -1.2674 -0.0115 1.4291 1.0071 1068
## I(Avg_Cogongrass_Cover^2) 1.0551 1.0027 -0.9888 1.0005 3.2358 1.0029 1103
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4389 9.0252 0.0482 0.3974 8.3532 1.2547 2780
## Avg_Cogongrass_Cover 1.5561 6.0103 0.0478 0.3914 10.0821 1.0029 2521
## I(Avg_Cogongrass_Cover^2) 9.3437 48.9134 0.0641 1.4370 62.3703 1.0747 1130
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4757 0.9435 0.0421 0.243 2.0117 1.2021 397
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0218 0.6513 -3.0098 -2.1248 -0.3714 1.0054 1971
## shrub_cover 0.2453 0.5483 -0.8400 0.2496 1.3425 1.0089 2721
## veg_height 0.0037 0.5328 -1.0650 -0.0004 1.0846 1.0059 2745
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.5707 52.1233 0.0825 0.4815 8.4901 1.3050 3000
## shrub_cover 1.6463 6.4456 0.0825 0.5096 9.1887 1.0653 2763
## veg_height 1.2716 3.6842 0.0918 0.5012 7.2876 1.0127 2807
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5415 0.7369 -2.0459 -0.5316
## (Intercept)-Procyon_lotor -0.3393 0.6749 -1.6847 -0.3230
## (Intercept)-Dasypus_novemcinctus -0.8736 0.6139 -2.1655 -0.8396
## Avg_Cogongrass_Cover-Canis_latrans 0.2285 0.7651 -1.0575 0.1596
## Avg_Cogongrass_Cover-Procyon_lotor -0.1830 0.7581 -1.5921 -0.2073
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.0200 0.6067 -1.1710 -0.0258
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.2109 1.5214 0.1613 1.9255
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.1930 1.9560 0.0694 1.4909
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4797 0.4857 -0.3793 0.4473
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8834 1.0085 566
## (Intercept)-Procyon_lotor 0.9445 1.0047 426
## (Intercept)-Dasypus_novemcinctus 0.2639 1.0000 930
## Avg_Cogongrass_Cover-Canis_latrans 1.8916 1.0071 624
## Avg_Cogongrass_Cover-Procyon_lotor 1.3956 1.0033 698
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.1674 1.0048 1283
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.7417 1.0363 234
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 7.3850 1.0094 128
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.5448 1.0022 1190
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7464 0.1874 -3.1362 -2.7422 -2.3878 1.0067
## (Intercept)-Procyon_lotor -2.3390 0.1558 -2.6640 -2.3347 -2.0542 1.0133
## (Intercept)-Dasypus_novemcinctus -1.7571 0.1625 -2.0951 -1.7521 -1.4624 1.0047
## shrub_cover-Canis_latrans -0.2609 0.2252 -0.7136 -0.2615 0.1775 1.0245
## shrub_cover-Procyon_lotor 0.1902 0.1735 -0.1530 0.1939 0.5262 1.0058
## shrub_cover-Dasypus_novemcinctus 0.8634 0.3160 0.2439 0.8513 1.5006 1.0012
## veg_height-Canis_latrans -0.6532 0.1930 -1.0500 -0.6426 -0.2966 1.0009
## veg_height-Procyon_lotor 0.3572 0.1307 0.0936 0.3593 0.6124 1.0162
## veg_height-Dasypus_novemcinctus 0.2592 0.1399 -0.0020 0.2564 0.5476 1.0045
## ESS
## (Intercept)-Canis_latrans 670
## (Intercept)-Procyon_lotor 671
## (Intercept)-Dasypus_novemcinctus 1370
## shrub_cover-Canis_latrans 836
## shrub_cover-Procyon_lotor 826
## shrub_cover-Dasypus_novemcinctus 1060
## veg_height-Canis_latrans 671
## veg_height-Procyon_lotor 1376
## veg_height-Dasypus_novemcinctus 1800
# Includes cover covariate of detection and all covariates and quadratic cogongrass cover for occupancy
ms_cover_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.cover,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_cover_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.cover,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.2868
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9773 0.9815 -2.9553 -0.9975 1.0737 1.0516 728
## Cogon_Patch_Size 0.1355 0.9809 -1.7683 0.0947 2.1946 1.0111 1125
## Veg_shannon_index 0.9227 0.7866 -0.7342 0.9400 2.4444 1.0062 1010
## total_shrub_cover -0.2766 0.8908 -2.0169 -0.2924 1.6118 1.0188 874
## Avg_Cogongrass_Cover 0.7530 1.2284 -1.7453 0.7382 3.2038 1.0126 360
## Tree_Density -1.9495 1.4792 -4.3884 -2.1373 1.4230 1.0111 1038
## Avg_Canopy_Cover 0.9324 0.8906 -1.0661 0.9769 2.5943 1.0048 2021
## I(Avg_Cogongrass_Cover^2) 1.5916 1.0305 -0.6733 1.6150 3.5851 1.0265 667
## avg_veg_height -0.0236 0.7597 -1.5309 -0.0284 1.4728 1.0137 776
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.1184 15.3370 0.0633 0.9547 25.7623 1.0342 1897
## Cogon_Patch_Size 6.9843 29.0714 0.0673 1.4763 46.7212 1.0554 846
## Veg_shannon_index 2.5632 16.1960 0.0522 0.5934 13.8378 1.0996 2842
## total_shrub_cover 4.9926 21.7673 0.0777 1.2194 28.7736 1.0721 1529
## Avg_Cogongrass_Cover 4.3874 19.1223 0.0569 0.8830 26.8942 1.1196 2131
## Tree_Density 29.1543 100.1478 0.0776 4.3631 219.6669 1.0279 781
## Avg_Canopy_Cover 5.7380 29.7713 0.0911 1.5625 30.9009 1.1899 2787
## I(Avg_Cogongrass_Cover^2) 8.1947 47.3342 0.0581 0.8928 60.3296 1.5186 324
## avg_veg_height 1.5532 5.7161 0.0461 0.4455 9.9816 1.0732 2457
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.9982 3.3307 0.0567 0.7676 11.2445 1.0704 85
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.0553 0.5959 -2.9826 -2.1391 -0.5688 1.0101 2407
## shrub_cover 0.2655 0.5982 -0.9390 0.2596 1.4811 1.0081 2701
## veg_height 0.0010 0.5202 -1.0771 0.0130 1.0021 1.0272 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3664 4.3696 0.0666 0.4064 8.4078 1.0758 2266
## shrub_cover 1.9931 8.6109 0.1055 0.6841 10.1290 1.1833 3000
## veg_height 1.3096 3.7135 0.0898 0.4922 7.2102 1.0300 2563
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8869 1.2780 -3.2489 -0.9290
## (Intercept)-Procyon_lotor -0.7636 1.0417 -2.9317 -0.7407
## (Intercept)-Dasypus_novemcinctus -1.9776 1.2550 -4.7698 -1.8422
## Cogon_Patch_Size-Canis_latrans 1.5582 1.8805 -0.8537 1.1432
## Cogon_Patch_Size-Procyon_lotor -0.6920 0.9425 -2.6531 -0.6271
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1707 0.9447 -2.0168 -0.1965
## Veg_shannon_index-Canis_latrans 1.5039 1.0025 -0.1104 1.3849
## Veg_shannon_index-Procyon_lotor 1.2661 0.7707 -0.0709 1.2098
## Veg_shannon_index-Dasypus_novemcinctus 0.5483 0.7168 -0.9331 0.5727
## total_shrub_cover-Canis_latrans 0.6336 1.1771 -1.1975 0.4460
## total_shrub_cover-Procyon_lotor -1.2435 0.8184 -2.9860 -1.1801
## total_shrub_cover-Dasypus_novemcinctus -0.3427 0.9435 -2.4624 -0.2415
## Avg_Cogongrass_Cover-Canis_latrans 0.9198 1.5503 -2.0447 0.8720
## Avg_Cogongrass_Cover-Procyon_lotor 0.4721 1.5380 -2.7192 0.4942
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.5089 1.6689 -1.3064 1.3455
## Tree_Density-Canis_latrans -4.3035 2.5827 -11.0656 -3.7452
## Tree_Density-Procyon_lotor -2.4197 1.4072 -5.2781 -2.3418
## Tree_Density-Dasypus_novemcinctus -5.1110 2.8200 -12.5747 -4.3942
## Avg_Canopy_Cover-Canis_latrans 0.0552 0.8256 -1.7185 0.1133
## Avg_Canopy_Cover-Procyon_lotor 1.4937 0.9119 -0.0073 1.3877
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.1964 1.0231 0.6403 2.0403
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4926 1.4630 0.4929 2.2283
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.6366 1.9538 0.4689 2.1947
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4000 0.9810 -0.3116 1.3253
## avg_veg_height-Canis_latrans -0.2746 0.7927 -1.9326 -0.2509
## avg_veg_height-Procyon_lotor 0.0644 0.8072 -1.5086 0.0646
## avg_veg_height-Dasypus_novemcinctus 0.1584 0.7884 -1.3272 0.1617
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8988 1.1180 348
## (Intercept)-Procyon_lotor 1.2456 1.1079 492
## (Intercept)-Dasypus_novemcinctus 0.2146 1.0416 345
## Cogon_Patch_Size-Canis_latrans 6.3016 1.0082 296
## Cogon_Patch_Size-Procyon_lotor 0.9734 1.0127 517
## Cogon_Patch_Size-Dasypus_novemcinctus 1.7793 1.0044 598
## Veg_shannon_index-Canis_latrans 4.0064 1.0686 394
## Veg_shannon_index-Procyon_lotor 3.0574 1.0176 380
## Veg_shannon_index-Dasypus_novemcinctus 1.8832 1.0067 988
## total_shrub_cover-Canis_latrans 3.4939 1.1140 289
## total_shrub_cover-Procyon_lotor 0.1884 1.0084 606
## total_shrub_cover-Dasypus_novemcinctus 1.1303 1.0703 330
## Avg_Cogongrass_Cover-Canis_latrans 4.1321 1.0376 371
## Avg_Cogongrass_Cover-Procyon_lotor 3.3932 1.0084 356
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.3811 1.0267 319
## Tree_Density-Canis_latrans -0.9685 1.1539 166
## Tree_Density-Procyon_lotor 0.0367 1.0112 368
## Tree_Density-Dasypus_novemcinctus -1.5460 1.0460 185
## Avg_Canopy_Cover-Canis_latrans 1.4609 1.0495 381
## Avg_Canopy_Cover-Procyon_lotor 3.6618 1.0110 366
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.5671 1.0237 391
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.4732 1.1131 160
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 8.6771 1.5536 63
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.4625 1.0289 354
## avg_veg_height-Canis_latrans 1.2416 1.0249 638
## avg_veg_height-Procyon_lotor 1.6743 1.0001 655
## avg_veg_height-Dasypus_novemcinctus 1.7241 1.0146 581
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.7043 0.1919 -3.0925 -2.6923 -2.3456 1.0778
## (Intercept)-Procyon_lotor -2.2999 0.1433 -2.5987 -2.2971 -2.0278 1.0018
## (Intercept)-Dasypus_novemcinctus -1.7959 0.1722 -2.1373 -1.7940 -1.4740 1.0199
## shrub_cover-Canis_latrans -0.3745 0.2363 -0.8258 -0.3759 0.0871 1.0542
## shrub_cover-Procyon_lotor 0.2776 0.1688 -0.0650 0.2811 0.6054 1.0072
## shrub_cover-Dasypus_novemcinctus 0.9947 0.3419 0.3348 0.9950 1.6496 1.0331
## veg_height-Canis_latrans -0.6127 0.1938 -1.0025 -0.6109 -0.2571 1.0356
## veg_height-Procyon_lotor 0.3727 0.1268 0.1298 0.3747 0.6161 1.0039
## veg_height-Dasypus_novemcinctus 0.2789 0.1457 0.0004 0.2782 0.5747 1.0119
## ESS
## (Intercept)-Canis_latrans 508
## (Intercept)-Procyon_lotor 1068
## (Intercept)-Dasypus_novemcinctus 742
## shrub_cover-Canis_latrans 460
## shrub_cover-Procyon_lotor 912
## shrub_cover-Dasypus_novemcinctus 488
## veg_height-Canis_latrans 623
## veg_height-Procyon_lotor 1504
## veg_height-Dasypus_novemcinctus 1440
#Includes quadratic week covariate of detection and only null for occupancy
ms_weekQ_null_T50<- msPGOcc(
occ.formula = occ.null,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.334
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0963 0.5791 -1.1126 0.1095 1.2471 1.0031 3000
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5777 7.5216 0.0679 0.5421 8.5392 1.2112 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8031 0.5963 -2.7449 -1.8843 -0.2103 1.0020 2668
## week 0.2235 0.4219 -0.6204 0.2364 1.1393 1.0000 2695
## I(week^2) -0.1622 0.3383 -0.8047 -0.1665 0.4810 1.0006 2702
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5650 6.1886 0.0729 0.4367 9.7468 1.0519 2490
## week 0.6635 2.3142 0.0405 0.2392 3.9178 1.1738 3000
## I(week^2) 0.4210 1.6956 0.0273 0.1346 2.2779 1.0330 2660
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.2573 0.3678 -0.4242 0.2431 1.0265 1.0000
## (Intercept)-Procyon_lotor 0.5948 0.3725 -0.1075 0.5775 1.3591 1.0006
## (Intercept)-Dasypus_novemcinctus -0.4810 0.3674 -1.2438 -0.4719 0.2037 1.0003
## ESS
## (Intercept)-Canis_latrans 2553
## (Intercept)-Procyon_lotor 2228
## (Intercept)-Dasypus_novemcinctus 2513
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.3967 0.1855 -2.7687 -2.3899 -2.0409 1.0033
## (Intercept)-Procyon_lotor -2.1597 0.1475 -2.4544 -2.1554 -1.8886 1.0021
## (Intercept)-Dasypus_novemcinctus -1.4828 0.1650 -1.8126 -1.4814 -1.1668 1.0010
## week-Canis_latrans 0.5082 0.2669 0.0041 0.4943 1.0634 1.0020
## week-Procyon_lotor 0.1708 0.2081 -0.2294 0.1670 0.5810 1.0014
## week-Dasypus_novemcinctus 0.0554 0.2263 -0.3813 0.0573 0.5001 0.9996
## I(week^2)-Canis_latrans -0.2185 0.1116 -0.4443 -0.2166 -0.0085 1.0040
## I(week^2)-Procyon_lotor -0.1121 0.0931 -0.3006 -0.1116 0.0676 1.0017
## I(week^2)-Dasypus_novemcinctus -0.1470 0.1069 -0.3633 -0.1465 0.0553 1.0003
## ESS
## (Intercept)-Canis_latrans 1317
## (Intercept)-Procyon_lotor 1702
## (Intercept)-Dasypus_novemcinctus 2152
## week-Canis_latrans 1338
## week-Procyon_lotor 1677
## week-Dasypus_novemcinctus 2110
## I(week^2)-Canis_latrans 1313
## I(week^2)-Procyon_lotor 1595
## I(week^2)-Dasypus_novemcinctus 1762
#Includes quadratic week covariate of detection and full for occupancy
ms_weekQ_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3495
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0611 0.8759 -1.7938 -0.0592 1.7012 1.0009 963
## Cogon_Patch_Size -0.3300 0.8567 -2.0918 -0.3537 1.5052 1.0046 1129
## Veg_shannon_index 0.9773 0.7083 -0.5893 0.9844 2.3470 1.0026 611
## total_shrub_cover -0.1001 0.6925 -1.4475 -0.1156 1.4011 1.0020 1739
## Avg_Cogongrass_Cover 2.0950 1.0538 -0.0614 2.1379 4.0472 1.0098 822
## Tree_Density -1.7068 1.1468 -3.7149 -1.7725 0.8750 1.0256 1286
## Avg_Canopy_Cover 0.9499 0.8105 -0.8878 0.9935 2.4245 1.0053 2162
## avg_veg_height -0.4252 0.6742 -1.7316 -0.4330 0.9617 1.0304 1012
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 3.6775 11.4809 0.0773 1.1124 21.9517 1.0117 1862
## Cogon_Patch_Size 4.0155 16.7228 0.0598 0.9721 24.6717 1.1223 2308
## Veg_shannon_index 1.8688 7.9255 0.0482 0.4639 11.7261 1.1185 2054
## total_shrub_cover 2.1939 7.4641 0.0586 0.6214 14.6935 1.0264 2525
## Avg_Cogongrass_Cover 4.2424 32.8050 0.0551 0.6248 29.8189 1.1935 1609
## Tree_Density 10.6372 42.9591 0.0692 1.7451 72.3237 1.1206 1764
## Avg_Canopy_Cover 3.5578 19.2589 0.0764 1.0582 17.5840 1.1121 3000
## avg_veg_height 1.4368 7.1154 0.0428 0.3721 8.5315 1.1563 2523
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.1091 4.4636 0.0799 0.95 11.1255 1.1732 281
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8167 0.6026 -2.7678 -1.8981 -0.3385 1.0089 2538
## week 0.2394 0.4317 -0.6582 0.2436 1.1779 1.0018 2351
## I(week^2) -0.1526 0.3177 -0.7997 -0.1570 0.5091 0.9997 2872
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6603 14.7365 0.0793 0.4371 9.3682 1.2843 3000
## week 0.6823 2.0155 0.0395 0.2388 4.1347 1.0184 2633
## I(week^2) 0.4639 3.4219 0.0275 0.1394 2.5603 1.2458 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.3640 0.8550 -1.2334 0.3186
## (Intercept)-Procyon_lotor 0.4760 0.8593 -1.3125 0.4923
## (Intercept)-Dasypus_novemcinctus -1.0468 0.9499 -3.2323 -0.9465
## Cogon_Patch_Size-Canis_latrans 0.5442 1.2331 -1.2184 0.3194
## Cogon_Patch_Size-Procyon_lotor -0.9727 0.7645 -2.5603 -0.9325
## Cogon_Patch_Size-Dasypus_novemcinctus -0.8126 0.6834 -2.3109 -0.7713
## Veg_shannon_index-Canis_latrans 1.4079 0.7982 0.1345 1.3048
## Veg_shannon_index-Procyon_lotor 1.2683 0.6751 0.0587 1.2270
## Veg_shannon_index-Dasypus_novemcinctus 0.7193 0.5719 -0.4082 0.7195
## total_shrub_cover-Canis_latrans 0.2797 0.6976 -0.8922 0.2210
## total_shrub_cover-Procyon_lotor -0.7942 0.6651 -2.2137 -0.7621
## total_shrub_cover-Dasypus_novemcinctus 0.1612 0.5418 -0.8610 0.1507
## Avg_Cogongrass_Cover-Canis_latrans 2.5969 1.1547 0.6523 2.5058
## Avg_Cogongrass_Cover-Procyon_lotor 2.3628 1.1043 0.4090 2.2928
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7615 1.1678 0.8841 2.6451
## Tree_Density-Canis_latrans -2.7767 1.4997 -6.4646 -2.4914
## Tree_Density-Procyon_lotor -1.5309 0.8993 -3.3314 -1.5104
## Tree_Density-Dasypus_novemcinctus -3.5056 1.8523 -8.4905 -3.0817
## Avg_Canopy_Cover-Canis_latrans 0.2193 0.6946 -1.2741 0.2511
## Avg_Canopy_Cover-Procyon_lotor 1.4968 0.7077 0.3065 1.4163
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.8377 0.7777 0.6315 1.7292
## avg_veg_height-Canis_latrans -0.7664 0.7098 -2.2604 -0.7260
## avg_veg_height-Procyon_lotor -0.3287 0.6789 -1.6043 -0.3601
## avg_veg_height-Dasypus_novemcinctus -0.3036 0.6435 -1.5006 -0.3229
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.2059 1.0073 496
## (Intercept)-Procyon_lotor 2.1712 1.0042 413
## (Intercept)-Dasypus_novemcinctus 0.5222 1.0163 429
## Cogon_Patch_Size-Canis_latrans 3.7068 1.0150 637
## Cogon_Patch_Size-Procyon_lotor 0.3923 1.0169 344
## Cogon_Patch_Size-Dasypus_novemcinctus 0.3810 1.0305 485
## Veg_shannon_index-Canis_latrans 3.2634 1.0628 398
## Veg_shannon_index-Procyon_lotor 2.7091 1.0098 524
## Veg_shannon_index-Dasypus_novemcinctus 1.8110 1.0100 637
## total_shrub_cover-Canis_latrans 1.8996 1.0073 815
## total_shrub_cover-Procyon_lotor 0.4011 1.0144 1160
## total_shrub_cover-Dasypus_novemcinctus 1.2225 1.0022 1572
## Avg_Cogongrass_Cover-Canis_latrans 5.2587 1.0897 272
## Avg_Cogongrass_Cover-Procyon_lotor 4.6200 1.0553 289
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.4242 1.0524 269
## Tree_Density-Canis_latrans -0.7025 1.1454 231
## Tree_Density-Procyon_lotor 0.2307 1.0450 601
## Tree_Density-Dasypus_novemcinctus -1.0851 1.0929 282
## Avg_Canopy_Cover-Canis_latrans 1.4856 1.0258 773
## Avg_Canopy_Cover-Procyon_lotor 3.0699 1.0229 361
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.7061 1.0444 437
## avg_veg_height-Canis_latrans 0.5511 1.0351 715
## avg_veg_height-Procyon_lotor 1.0584 1.0308 876
## avg_veg_height-Dasypus_novemcinctus 1.0179 1.0271 916
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4275 0.1894 -2.8183 -2.4237 -2.0687 1.0106
## (Intercept)-Procyon_lotor -2.1549 0.1492 -2.4591 -2.1508 -1.8767 1.0012
## (Intercept)-Dasypus_novemcinctus -1.4831 0.1578 -1.8020 -1.4783 -1.1861 1.0001
## week-Canis_latrans 0.5190 0.2797 -0.0053 0.5168 1.0910 1.0003
## week-Procyon_lotor 0.1661 0.2123 -0.2498 0.1628 0.5899 1.0023
## week-Dasypus_novemcinctus 0.0613 0.2253 -0.3673 0.0625 0.5034 0.9999
## I(week^2)-Canis_latrans -0.2213 0.1143 -0.4430 -0.2216 -0.0026 1.0049
## I(week^2)-Procyon_lotor -0.1134 0.0924 -0.2955 -0.1142 0.0690 1.0055
## I(week^2)-Dasypus_novemcinctus -0.1482 0.1049 -0.3452 -0.1494 0.0634 1.0022
## ESS
## (Intercept)-Canis_latrans 831
## (Intercept)-Procyon_lotor 1547
## (Intercept)-Dasypus_novemcinctus 2264
## week-Canis_latrans 1371
## week-Procyon_lotor 1723
## week-Dasypus_novemcinctus 2251
## I(week^2)-Canis_latrans 1330
## I(week^2)-Procyon_lotor 1509
## I(week^2)-Dasypus_novemcinctus 1951
#Includes quadratic week covariate of detection and only cover for occupancy
ms_weekQ_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.343
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1298 0.6597 -1.2041 0.1310 1.4296 1.0010 1570
## Avg_Cogongrass_Cover 0.2785 0.5649 -0.8691 0.2785 1.3837 1.0121 1357
## total_shrub_cover -0.1538 0.6444 -1.4352 -0.1554 1.1912 1.0003 2678
## avg_veg_height 0.1162 0.5431 -1.0001 0.1205 1.1842 1.0006 1422
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 7.3069 308.4640 0.0596 0.6171 9.5717 1.3231 3000
## Avg_Cogongrass_Cover 1.1845 5.8806 0.0411 0.3082 6.2537 1.1724 3000
## total_shrub_cover 2.1231 8.5564 0.0650 0.6728 11.3410 1.0256 2620
## avg_veg_height 0.8597 3.4233 0.0422 0.2915 4.5462 1.1950 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6157 0.8796 0.0465 0.3464 2.8197 1.0382 456
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8287 0.5592 -2.7049 -1.9148 -0.3432 1.0028 2607
## week 0.2156 0.4402 -0.7713 0.2286 1.0442 1.0013 2451
## I(week^2) -0.1512 0.3212 -0.7792 -0.1503 0.5071 1.0002 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3540 4.6617 0.0757 0.4359 7.6782 1.0276 2604
## week 0.7665 3.1663 0.0397 0.2523 4.5309 1.1114 2648
## I(week^2) 0.4040 1.5728 0.0305 0.1368 2.1433 1.0733 2673
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.3042 0.5290 -0.7099 0.2840 1.3915
## (Intercept)-Procyon_lotor 0.6049 0.6027 -0.4683 0.5725 1.8924
## (Intercept)-Dasypus_novemcinctus -0.4304 0.5394 -1.5144 -0.4303 0.6156
## Avg_Cogongrass_Cover-Canis_latrans 0.5290 0.5422 -0.4111 0.4930 1.6833
## Avg_Cogongrass_Cover-Procyon_lotor 0.1159 0.5109 -0.9671 0.1174 1.1043
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2909 0.4636 -0.5921 0.2822 1.2303
## total_shrub_cover-Canis_latrans 0.2908 0.5261 -0.6154 0.2488 1.4453
## total_shrub_cover-Procyon_lotor -0.8943 0.5682 -2.1806 -0.8367 0.0581
## total_shrub_cover-Dasypus_novemcinctus 0.0545 0.4013 -0.7226 0.0485 0.8335
## avg_veg_height-Canis_latrans -0.0648 0.5166 -1.1015 -0.0597 0.9207
## avg_veg_height-Procyon_lotor 0.1657 0.5090 -0.7896 0.1553 1.2074
## avg_veg_height-Dasypus_novemcinctus 0.2694 0.4709 -0.6028 0.2636 1.2446
## Rhat ESS
## (Intercept)-Canis_latrans 1.0070 978
## (Intercept)-Procyon_lotor 1.0019 915
## (Intercept)-Dasypus_novemcinctus 1.0027 806
## Avg_Cogongrass_Cover-Canis_latrans 1.0035 1383
## Avg_Cogongrass_Cover-Procyon_lotor 1.0001 1458
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0082 1437
## total_shrub_cover-Canis_latrans 1.0014 1442
## total_shrub_cover-Procyon_lotor 1.0017 1326
## total_shrub_cover-Dasypus_novemcinctus 1.0011 2466
## avg_veg_height-Canis_latrans 1.0003 1346
## avg_veg_height-Procyon_lotor 1.0009 1517
## avg_veg_height-Dasypus_novemcinctus 1.0007 1533
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4281 0.1907 -2.8097 -2.4286 -2.0624 1.0003
## (Intercept)-Procyon_lotor -2.1691 0.1499 -2.4655 -2.1669 -1.8889 1.0032
## (Intercept)-Dasypus_novemcinctus -1.4795 0.1640 -1.7914 -1.4848 -1.1556 1.0022
## week-Canis_latrans 0.5035 0.2793 -0.0143 0.4943 1.0761 1.0028
## week-Procyon_lotor 0.1648 0.2135 -0.2550 0.1631 0.5874 1.0102
## week-Dasypus_novemcinctus 0.0513 0.2235 -0.3881 0.0524 0.4912 1.0012
## I(week^2)-Canis_latrans -0.2119 0.1134 -0.4336 -0.2111 0.0055 1.0035
## I(week^2)-Procyon_lotor -0.1086 0.0921 -0.2843 -0.1084 0.0767 1.0023
## I(week^2)-Dasypus_novemcinctus -0.1474 0.1069 -0.3661 -0.1451 0.0537 1.0115
## ESS
## (Intercept)-Canis_latrans 1214
## (Intercept)-Procyon_lotor 1572
## (Intercept)-Dasypus_novemcinctus 2822
## week-Canis_latrans 1147
## week-Procyon_lotor 1579
## week-Dasypus_novemcinctus 2257
## I(week^2)-Canis_latrans 1276
## I(week^2)-Procyon_lotor 1523
## I(week^2)-Dasypus_novemcinctus 1702
#Includes quadratic week covariate of detection and only canopy for occupancy
ms_weekQ_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3435
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.0484 0.6936 -1.4803 -0.0438 1.3339 1.0007 2194
## Tree_Density -0.8876 0.7184 -2.3490 -0.8682 0.6636 1.0026 1817
## Avg_Canopy_Cover 0.4650 0.6232 -0.8967 0.4746 1.7024 1.0026 2148
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1657 9.1119 0.0662 0.6988 12.5936 1.0727 2669
## Tree_Density 3.3181 41.7248 0.0496 0.5419 13.4793 1.2286 3000
## Avg_Canopy_Cover 1.9696 10.5077 0.0637 0.6037 9.7316 1.0967 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6639 1.0024 0.0422 0.3199 3.7116 1.021 407
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.7920 0.6286 -2.7363 -1.8954 -0.1654 1.0094 2572
## week 0.2237 0.4326 -0.6589 0.2231 1.1251 1.0058 2400
## I(week^2) -0.1415 0.3244 -0.7695 -0.1435 0.5577 1.0111 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.9741 16.3837 0.0725 0.4531 10.1169 1.2547 3000
## week 0.8105 5.4283 0.0382 0.2377 4.4692 1.2006 2799
## I(week^2) 0.4342 2.7971 0.0278 0.1424 2.2085 1.2219 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.1272 0.5644 -0.9610 0.1235 1.2785
## (Intercept)-Procyon_lotor 0.4519 0.5887 -0.7180 0.4519 1.6491
## (Intercept)-Dasypus_novemcinctus -0.7635 0.6730 -2.3127 -0.7066 0.4417
## Tree_Density-Canis_latrans -1.0069 0.6069 -2.4206 -0.9531 0.0329
## Tree_Density-Procyon_lotor -0.5217 0.4650 -1.4401 -0.5102 0.3629
## Tree_Density-Dasypus_novemcinctus -1.5305 1.0459 -4.4818 -1.3177 -0.1705
## Avg_Canopy_Cover-Canis_latrans -0.1358 0.4582 -1.1157 -0.1122 0.6868
## Avg_Canopy_Cover-Procyon_lotor 0.8491 0.4922 -0.0180 0.8038 1.9623
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.8648 0.4621 0.0443 0.8355 1.8674
## Rhat ESS
## (Intercept)-Canis_latrans 1.0156 942
## (Intercept)-Procyon_lotor 1.0020 793
## (Intercept)-Dasypus_novemcinctus 1.0060 762
## Tree_Density-Canis_latrans 1.0026 1585
## Tree_Density-Procyon_lotor 1.0135 1823
## Tree_Density-Dasypus_novemcinctus 1.0089 609
## Avg_Canopy_Cover-Canis_latrans 1.0027 1530
## Avg_Canopy_Cover-Procyon_lotor 1.0049 1686
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0030 1789
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4198 0.1952 -2.8327 -2.4156 -2.0462 1.0013
## (Intercept)-Procyon_lotor -2.1567 0.1502 -2.4595 -2.1506 -1.8743 1.0019
## (Intercept)-Dasypus_novemcinctus -1.4802 0.1603 -1.8086 -1.4762 -1.1701 1.0018
## week-Canis_latrans 0.5142 0.2803 -0.0041 0.5007 1.1085 0.9998
## week-Procyon_lotor 0.1580 0.2110 -0.2591 0.1569 0.5806 1.0139
## week-Dasypus_novemcinctus 0.0498 0.2252 -0.4060 0.0557 0.4868 1.0032
## I(week^2)-Canis_latrans -0.2174 0.1145 -0.4571 -0.2119 -0.0029 1.0017
## I(week^2)-Procyon_lotor -0.1102 0.0939 -0.3020 -0.1081 0.0669 1.0059
## I(week^2)-Dasypus_novemcinctus -0.1403 0.1084 -0.3615 -0.1372 0.0709 1.0118
## ESS
## (Intercept)-Canis_latrans 1097
## (Intercept)-Procyon_lotor 1608
## (Intercept)-Dasypus_novemcinctus 2372
## week-Canis_latrans 1093
## week-Procyon_lotor 1706
## week-Dasypus_novemcinctus 2111
## I(week^2)-Canis_latrans 1154
## I(week^2)-Procyon_lotor 1680
## I(week^2)-Dasypus_novemcinctus 1813
#Includes quadratic week covariate of detection and only movement for occupancy
ms_weekQ_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3397
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2017 0.6715 -1.1361 0.1847 1.5790 1.0077 1880
## Cogon_Patch_Size 0.2446 0.6890 -1.1768 0.2278 1.7161 1.0021 2182
## Avg_Cogongrass_Cover 0.2839 0.5211 -0.7129 0.2859 1.3069 1.0035 1770
## total_shrub_cover -0.2185 0.6235 -1.4729 -0.2360 1.0951 1.0025 2476
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.1589 10.7694 0.0587 0.6313 12.0082 1.1371 3000
## Cogon_Patch_Size 2.5003 8.8783 0.0537 0.5852 15.8128 1.0525 2223
## Avg_Cogongrass_Cover 0.8408 2.8749 0.0397 0.2669 4.9692 1.0601 2310
## total_shrub_cover 1.7135 5.7151 0.0577 0.5482 9.9779 1.0695 2695
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5518 0.7093 0.0449 0.3162 2.4207 1.0012 627
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8203 0.5685 -2.7062 -1.9041 -0.3288 1.0033 2233
## week 0.2251 0.4236 -0.6314 0.2233 1.0543 1.0008 2229
## I(week^2) -0.1480 0.3160 -0.7961 -0.1493 0.4633 1.0026 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2913 4.2460 0.0763 0.4172 7.8171 1.0508 2446
## week 0.7848 3.2216 0.0414 0.2376 3.9311 1.0621 3000
## I(week^2) 0.3938 1.2741 0.0279 0.1362 2.4955 1.1359 2687
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4403 0.5477 -0.5759 0.4154 1.5685
## (Intercept)-Procyon_lotor 0.6551 0.5770 -0.4584 0.6329 1.8590
## (Intercept)-Dasypus_novemcinctus -0.4305 0.5229 -1.5253 -0.4225 0.5713
## Cogon_Patch_Size-Canis_latrans 0.9829 0.9094 -0.2352 0.7843 3.2946
## Cogon_Patch_Size-Procyon_lotor -0.0545 0.5129 -1.0712 -0.0635 0.9479
## Cogon_Patch_Size-Dasypus_novemcinctus -0.0432 0.4332 -0.9400 -0.0435 0.7915
## Avg_Cogongrass_Cover-Canis_latrans 0.2950 0.4527 -0.5732 0.2842 1.2356
## Avg_Cogongrass_Cover-Procyon_lotor 0.2240 0.4694 -0.6705 0.2176 1.1567
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4041 0.4084 -0.3625 0.3953 1.2393
## total_shrub_cover-Canis_latrans 0.1766 0.5127 -0.7154 0.1416 1.3108
## total_shrub_cover-Procyon_lotor -0.9017 0.5557 -2.1275 -0.8447 0.0455
## total_shrub_cover-Dasypus_novemcinctus -0.0250 0.3925 -0.7891 -0.0292 0.7703
## Rhat ESS
## (Intercept)-Canis_latrans 1.0163 1201
## (Intercept)-Procyon_lotor 1.0102 1200
## (Intercept)-Dasypus_novemcinctus 1.0013 1386
## Cogon_Patch_Size-Canis_latrans 1.0158 705
## Cogon_Patch_Size-Procyon_lotor 1.0059 1223
## Cogon_Patch_Size-Dasypus_novemcinctus 0.9997 2135
## Avg_Cogongrass_Cover-Canis_latrans 1.0023 1553
## Avg_Cogongrass_Cover-Procyon_lotor 1.0072 1768
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0033 1856
## total_shrub_cover-Canis_latrans 1.0095 1397
## total_shrub_cover-Procyon_lotor 1.0043 1359
## total_shrub_cover-Dasypus_novemcinctus 1.0004 2095
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4051 0.1897 -2.7963 -2.3950 -2.0485 1.0097
## (Intercept)-Procyon_lotor -2.1618 0.1480 -2.4501 -2.1579 -1.8813 1.0018
## (Intercept)-Dasypus_novemcinctus -1.4872 0.1593 -1.8006 -1.4833 -1.1717 1.0009
## week-Canis_latrans 0.5103 0.2765 0.0112 0.4946 1.0775 1.0034
## week-Procyon_lotor 0.1627 0.2099 -0.2406 0.1619 0.5947 1.0035
## week-Dasypus_novemcinctus 0.0503 0.2306 -0.3950 0.0481 0.5066 1.0002
## I(week^2)-Canis_latrans -0.2179 0.1158 -0.4640 -0.2135 -0.0084 1.0002
## I(week^2)-Procyon_lotor -0.1145 0.0922 -0.2993 -0.1150 0.0570 1.0014
## I(week^2)-Dasypus_novemcinctus -0.1453 0.1081 -0.3685 -0.1429 0.0619 1.0048
## ESS
## (Intercept)-Canis_latrans 1285
## (Intercept)-Procyon_lotor 1568
## (Intercept)-Dasypus_novemcinctus 2224
## week-Canis_latrans 1203
## week-Procyon_lotor 1594
## week-Dasypus_novemcinctus 2185
## I(week^2)-Canis_latrans 1148
## I(week^2)-Procyon_lotor 1646
## I(week^2)-Dasypus_novemcinctus 1898
#Includes quadratic week covariate of detection and only foraging for occupancy
ms_weekQ_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3428
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0811 0.6286 -1.1988 0.0941 1.3313 1.0075 1496
## Veg_shannon_index 0.5470 0.5442 -0.5600 0.5471 1.6533 1.0001 1982
## Avg_Cogongrass_Cover 0.6269 0.5476 -0.4401 0.6271 1.7397 1.0047 1821
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4234 3.9559 0.0546 0.5061 8.0721 1.0569 2451
## Veg_shannon_index 1.0936 5.4297 0.0453 0.3613 6.4070 1.1842 3000
## Avg_Cogongrass_Cover 0.9137 3.5818 0.0384 0.2720 4.9190 1.1026 2728
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7571 1.0306 0.05 0.4415 3.4071 1.0291 695
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8447 0.5684 -2.7433 -1.9201 -0.3024 1.0002 2576
## week 0.2270 0.4197 -0.7150 0.2360 1.0328 1.0022 2384
## I(week^2) -0.1435 0.3373 -0.7796 -0.1519 0.5576 1.0011 2810
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3948 9.9113 0.0707 0.4136 6.7194 1.0618 3000
## week 0.8415 4.6295 0.0401 0.2383 4.6492 1.1801 3000
## I(week^2) 0.3858 1.2268 0.0291 0.1426 2.3383 1.1183 2615
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2499 0.5537 -0.8493 0.2412 1.3900
## (Intercept)-Procyon_lotor 0.4746 0.5649 -0.6163 0.4719 1.6061
## (Intercept)-Dasypus_novemcinctus -0.4357 0.5578 -1.5980 -0.4172 0.6086
## Veg_shannon_index-Canis_latrans 0.8602 0.4798 0.0151 0.8229 1.9302
## Veg_shannon_index-Procyon_lotor 0.6712 0.5070 -0.2251 0.6338 1.8227
## Veg_shannon_index-Dasypus_novemcinctus 0.2578 0.4030 -0.5573 0.2662 1.0494
## Avg_Cogongrass_Cover-Canis_latrans 0.7733 0.4643 -0.0294 0.7373 1.8176
## Avg_Cogongrass_Cover-Procyon_lotor 0.6707 0.5173 -0.1868 0.6332 1.8196
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.5716 0.3847 -0.1485 0.5659 1.3693
## Rhat ESS
## (Intercept)-Canis_latrans 1.0072 954
## (Intercept)-Procyon_lotor 1.0228 805
## (Intercept)-Dasypus_novemcinctus 1.0217 824
## Veg_shannon_index-Canis_latrans 1.0147 1367
## Veg_shannon_index-Procyon_lotor 1.0051 939
## Veg_shannon_index-Dasypus_novemcinctus 1.0024 2155
## Avg_Cogongrass_Cover-Canis_latrans 1.0098 1294
## Avg_Cogongrass_Cover-Procyon_lotor 1.0037 1022
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0054 1700
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4037 0.1858 -2.7819 -2.3961 -2.0615 1.0064
## (Intercept)-Procyon_lotor -2.1749 0.1507 -2.4866 -2.1692 -1.8955 1.0018
## (Intercept)-Dasypus_novemcinctus -1.4858 0.1637 -1.8125 -1.4853 -1.1690 1.0000
## week-Canis_latrans 0.5246 0.2732 0.0246 0.5131 1.1127 1.0089
## week-Procyon_lotor 0.1648 0.2083 -0.2288 0.1670 0.5574 1.0079
## week-Dasypus_novemcinctus 0.0492 0.2322 -0.4042 0.0537 0.5118 1.0088
## I(week^2)-Canis_latrans -0.2211 0.1155 -0.4616 -0.2169 -0.0028 1.0007
## I(week^2)-Procyon_lotor -0.1143 0.0911 -0.2927 -0.1133 0.0594 1.0035
## I(week^2)-Dasypus_novemcinctus -0.1416 0.1100 -0.3605 -0.1407 0.0702 1.0048
## ESS
## (Intercept)-Canis_latrans 1274
## (Intercept)-Procyon_lotor 1513
## (Intercept)-Dasypus_novemcinctus 2228
## week-Canis_latrans 1122
## week-Procyon_lotor 1678
## week-Dasypus_novemcinctus 1956
## I(week^2)-Canis_latrans 1290
## I(week^2)-Procyon_lotor 1536
## I(week^2)-Dasypus_novemcinctus 1758
#Includes quadratic week covariate of detection and only cogon for occupancy
ms_weekQ_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.341
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0728 0.6328 -1.1598 0.0663 1.3717 1.0010 1902
## Avg_Cogongrass_Cover 0.3883 0.5235 -0.6852 0.3865 1.3830 1.0057 2147
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.5311 4.7198 0.0594 0.5382 10.0224 1.1219 2694
## Avg_Cogongrass_Cover 0.9051 4.1078 0.0389 0.2437 5.1996 1.1381 2273
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5453 0.7617 0.0461 0.3072 2.4872 1.0051 705
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8094 0.5968 -2.7575 -1.8836 -0.3136 1.0041 2282
## week 0.2426 0.4364 -0.6548 0.2409 1.1164 1.0009 2250
## I(week^2) -0.1546 0.3495 -0.8097 -0.1689 0.5535 1.0092 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6509 10.7196 0.0738 0.4174 8.8353 1.1113 2473
## week 0.7897 5.9638 0.0367 0.2425 4.2208 1.2551 3000
## I(week^2) 0.4310 2.1318 0.0291 0.1363 2.6147 1.2080 2818
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2370 0.4985 -0.7073 0.2237 1.2738
## (Intercept)-Procyon_lotor 0.4513 0.5330 -0.6247 0.4400 1.5467
## (Intercept)-Dasypus_novemcinctus -0.4384 0.5016 -1.4675 -0.4333 0.4989
## Avg_Cogongrass_Cover-Canis_latrans 0.4810 0.4016 -0.2510 0.4579 1.3314
## Avg_Cogongrass_Cover-Procyon_lotor 0.3701 0.4166 -0.3710 0.3510 1.2580
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4347 0.3519 -0.2378 0.4198 1.1665
## Rhat ESS
## (Intercept)-Canis_latrans 1.0017 1228
## (Intercept)-Procyon_lotor 1.0054 862
## (Intercept)-Dasypus_novemcinctus 1.0133 1123
## Avg_Cogongrass_Cover-Canis_latrans 1.0044 1691
## Avg_Cogongrass_Cover-Procyon_lotor 1.0016 1863
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0035 2057
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.3991 0.1861 -2.7827 -2.3957 -2.0454 1.0020
## (Intercept)-Procyon_lotor -2.1545 0.1448 -2.4569 -2.1485 -1.8864 1.0086
## (Intercept)-Dasypus_novemcinctus -1.4874 0.1630 -1.8059 -1.4855 -1.1709 1.0028
## week-Canis_latrans 0.5225 0.2760 -0.0005 0.5130 1.0766 1.0156
## week-Procyon_lotor 0.1728 0.2100 -0.2559 0.1703 0.5758 1.0009
## week-Dasypus_novemcinctus 0.0418 0.2300 -0.4228 0.0482 0.4805 0.9998
## I(week^2)-Canis_latrans -0.2231 0.1156 -0.4576 -0.2204 -0.0104 1.0093
## I(week^2)-Procyon_lotor -0.1158 0.0917 -0.2952 -0.1148 0.0598 1.0065
## I(week^2)-Dasypus_novemcinctus -0.1466 0.1076 -0.3596 -0.1460 0.0529 1.0006
## ESS
## (Intercept)-Canis_latrans 1270
## (Intercept)-Procyon_lotor 1702
## (Intercept)-Dasypus_novemcinctus 2168
## week-Canis_latrans 1213
## week-Procyon_lotor 1800
## week-Dasypus_novemcinctus 2231
## I(week^2)-Canis_latrans 1145
## I(week^2)-Procyon_lotor 1543
## I(week^2)-Dasypus_novemcinctus 1976
# Includes quadratic week covariate of detection and quadratic cogon for occupancy
ms_weekQ_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3427
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.6523 0.6995 -1.9996 -0.6647 0.8106 1.0213 499
## Avg_Cogongrass_Cover -0.0057 0.6763 -1.2951 -0.0292 1.4109 1.0213 780
## I(Avg_Cogongrass_Cover^2) 1.0732 1.0300 -1.0231 1.0224 3.1907 1.0112 1060
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.2500 3.4981 0.0495 0.4242 6.7557 1.0319 2619
## Avg_Cogongrass_Cover 1.1086 3.8428 0.0424 0.3547 6.8021 1.0216 2807
## I(Avg_Cogongrass_Cover^2) 11.9689 107.9167 0.0673 1.9112 65.7741 1.3615 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4897 0.632 0.0431 0.2773 2.3188 1.0118 680
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8297 0.5730 -2.7579 -1.9030 -0.3296 1.0001 2598
## week 0.2334 0.4302 -0.6709 0.2366 1.0724 1.0050 2272
## I(week^2) -0.1521 0.3265 -0.8136 -0.1536 0.5116 1.0015 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3972 5.9166 0.0796 0.4628 8.1884 1.1355 3000
## week 0.7010 3.0072 0.0392 0.2276 3.7672 1.2280 2817
## I(week^2) 0.3985 1.3793 0.0279 0.1375 2.4552 1.0239 2840
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.7283 0.7081 -2.1340 -0.6960
## (Intercept)-Procyon_lotor -0.4998 0.7116 -1.9163 -0.4928
## (Intercept)-Dasypus_novemcinctus -0.9879 0.5812 -2.1587 -0.9601
## Avg_Cogongrass_Cover-Canis_latrans 0.1286 0.7114 -1.1663 0.0841
## Avg_Cogongrass_Cover-Procyon_lotor -0.0510 0.7252 -1.3796 -0.0935
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.0602 0.5757 -1.1712 -0.0602
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.4819 1.7458 0.1459 2.1156
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3501 1.8239 0.1497 1.7997
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4439 0.4171 -0.3483 0.4270
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.5935 1.0153 272
## (Intercept)-Procyon_lotor 0.9211 1.0169 354
## (Intercept)-Dasypus_novemcinctus 0.0742 1.0055 1082
## Avg_Cogongrass_Cover-Canis_latrans 1.7105 1.0491 295
## Avg_Cogongrass_Cover-Procyon_lotor 1.5075 1.0169 373
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0712 1.0074 977
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.4251 1.0970 146
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.8443 1.0270 135
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.2785 1.0104 1368
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4411 0.1860 -2.8165 -2.4372 -2.0824 1.0033
## (Intercept)-Procyon_lotor -2.1847 0.1472 -2.4825 -2.1818 -1.9048 1.0211
## (Intercept)-Dasypus_novemcinctus -1.4840 0.1651 -1.8117 -1.4781 -1.1629 0.9999
## week-Canis_latrans 0.5195 0.2735 -0.0002 0.5154 1.0599 1.0226
## week-Procyon_lotor 0.1568 0.2065 -0.2385 0.1521 0.5686 1.0035
## week-Dasypus_novemcinctus 0.0505 0.2271 -0.3966 0.0539 0.4768 1.0000
## I(week^2)-Canis_latrans -0.2203 0.1150 -0.4524 -0.2177 0.0004 1.0244
## I(week^2)-Procyon_lotor -0.1126 0.0915 -0.2972 -0.1105 0.0603 1.0060
## I(week^2)-Dasypus_novemcinctus -0.1463 0.1075 -0.3663 -0.1427 0.0529 1.0009
## ESS
## (Intercept)-Canis_latrans 1256
## (Intercept)-Procyon_lotor 1296
## (Intercept)-Dasypus_novemcinctus 2321
## week-Canis_latrans 1202
## week-Procyon_lotor 1749
## week-Dasypus_novemcinctus 2257
## I(week^2)-Canis_latrans 1254
## I(week^2)-Procyon_lotor 1559
## I(week^2)-Dasypus_novemcinctus 1882
# Includes quadratic week covariate of detection and all covariates and quadratic cogon for occupancy
ms_weekQ_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.week.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_weekQ_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.week.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3485
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.2501 0.9751 -3.1603 -1.2504 0.7571 1.0138 754
## Cogon_Patch_Size 0.1288 0.9622 -1.7760 0.0839 2.1386 1.0165 1155
## Veg_shannon_index 0.9432 0.7385 -0.6116 0.9350 2.5004 1.0063 1224
## total_shrub_cover -0.1762 0.6988 -1.5744 -0.1600 1.2952 1.0038 1328
## Avg_Cogongrass_Cover 0.7181 1.2442 -1.6144 0.6605 3.3592 1.0662 322
## Tree_Density -1.9990 1.3925 -4.4903 -2.1318 1.1668 1.0022 799
## Avg_Canopy_Cover 0.9257 0.8746 -0.9009 0.9720 2.5401 1.0125 1908
## I(Avg_Cogongrass_Cover^2) 1.6017 1.1127 -1.0204 1.6654 3.7205 1.0161 965
## avg_veg_height -0.0402 0.7780 -1.5641 -0.0323 1.5397 1.0332 682
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.0003 16.3309 0.0595 0.9249 24.4245 1.0658 1641
## Cogon_Patch_Size 7.8547 58.6521 0.0715 1.5486 40.5738 1.2328 3000
## Veg_shannon_index 2.2079 10.8040 0.0487 0.5485 13.2829 1.1210 1646
## total_shrub_cover 2.4997 12.3256 0.0616 0.6973 12.5203 1.0643 3000
## Avg_Cogongrass_Cover 5.7906 112.5513 0.0542 0.7385 26.6137 1.2980 3000
## Tree_Density 27.0054 422.0077 0.0721 2.5481 152.5444 1.3139 3000
## Avg_Canopy_Cover 4.5779 17.0446 0.0791 1.2517 30.4550 1.0102 2168
## I(Avg_Cogongrass_Cover^2) 17.2864 174.1312 0.0674 1.2579 113.5811 1.2736 1659
## avg_veg_height 1.9625 9.0185 0.0468 0.4958 11.4235 1.0536 2057
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.995 4.7596 0.0543 0.721 11.4611 1.0908 178
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.8313 0.5717 -2.7738 -1.8988 -0.4301 1.0009 2715
## week 0.2240 0.4382 -0.6702 0.2377 1.0713 1.0013 2674
## I(week^2) -0.1486 0.3340 -0.7702 -0.1537 0.5259 1.0029 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3261 4.5342 0.0757 0.4434 8.9647 1.0653 2205
## week 0.8243 4.8593 0.0379 0.2298 5.1371 1.2102 3000
## I(week^2) 0.4441 1.7939 0.0305 0.1389 2.1850 1.0311 1848
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -1.4002 1.0576 -3.6066 -1.3752
## (Intercept)-Procyon_lotor -1.0278 1.0279 -3.1931 -0.9926
## (Intercept)-Dasypus_novemcinctus -2.2194 1.1371 -4.8271 -2.0982
## Cogon_Patch_Size-Canis_latrans 1.5635 1.6592 -0.5703 1.2290
## Cogon_Patch_Size-Procyon_lotor -0.4787 0.9797 -2.3676 -0.4754
## Cogon_Patch_Size-Dasypus_novemcinctus -0.4360 0.8104 -2.1802 -0.3920
## Veg_shannon_index-Canis_latrans 1.4868 0.9700 0.0739 1.3415
## Veg_shannon_index-Procyon_lotor 1.1979 0.7599 -0.0411 1.1129
## Veg_shannon_index-Dasypus_novemcinctus 0.5967 0.6146 -0.6730 0.5872
## total_shrub_cover-Canis_latrans 0.1346 0.7329 -1.1182 0.0869
## total_shrub_cover-Procyon_lotor -0.8832 0.7189 -2.4690 -0.8375
## total_shrub_cover-Dasypus_novemcinctus 0.1545 0.5475 -0.9002 0.1546
## Avg_Cogongrass_Cover-Canis_latrans 0.5616 1.5759 -2.6736 0.5485
## Avg_Cogongrass_Cover-Procyon_lotor 0.7032 1.4905 -2.1058 0.6250
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.3340 1.5171 -1.2449 1.1843
## Tree_Density-Canis_latrans -3.6121 2.0532 -9.4419 -3.2382
## Tree_Density-Procyon_lotor -2.2158 1.2551 -4.7758 -2.2035
## Tree_Density-Dasypus_novemcinctus -4.3947 2.3212 -10.3441 -3.8304
## Avg_Canopy_Cover-Canis_latrans 0.1329 0.7838 -1.6137 0.1885
## Avg_Canopy_Cover-Procyon_lotor 1.4994 0.7792 0.1732 1.4292
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.9154 0.8582 0.5907 1.7969
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 3.0742 2.1257 0.8120 2.5087
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 3.0016 2.2969 0.5810 2.4020
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.3850 0.9343 -0.2895 1.3195
## avg_veg_height-Canis_latrans -0.5360 0.8366 -2.3741 -0.4889
## avg_veg_height-Procyon_lotor 0.2137 0.8015 -1.3193 0.1948
## avg_veg_height-Dasypus_novemcinctus 0.1404 0.7418 -1.3367 0.1252
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.6009 1.0152 497
## (Intercept)-Procyon_lotor 0.9277 1.0430 238
## (Intercept)-Dasypus_novemcinctus -0.2936 1.0232 380
## Cogon_Patch_Size-Canis_latrans 5.7948 1.0034 321
## Cogon_Patch_Size-Procyon_lotor 1.5409 1.0246 566
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0985 1.0355 453
## Veg_shannon_index-Canis_latrans 3.7994 1.0274 287
## Veg_shannon_index-Procyon_lotor 2.8679 1.0153 299
## Veg_shannon_index-Dasypus_novemcinctus 1.7677 1.0064 1211
## total_shrub_cover-Canis_latrans 1.7408 1.0085 871
## total_shrub_cover-Procyon_lotor 0.3583 1.0041 845
## total_shrub_cover-Dasypus_novemcinctus 1.2270 1.0201 1438
## Avg_Cogongrass_Cover-Canis_latrans 3.6036 1.0476 428
## Avg_Cogongrass_Cover-Procyon_lotor 3.8482 1.0531 365
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 4.6918 1.0417 375
## Tree_Density-Canis_latrans -0.7779 1.0789 201
## Tree_Density-Procyon_lotor 0.2028 1.0166 600
## Tree_Density-Dasypus_novemcinctus -1.5131 1.0908 118
## Avg_Canopy_Cover-Canis_latrans 1.5338 1.0086 410
## Avg_Canopy_Cover-Procyon_lotor 3.3130 1.0070 630
## Avg_Canopy_Cover-Dasypus_novemcinctus 3.9373 1.0287 251
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 8.7574 1.3538 65
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 9.9320 1.3895 80
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.3971 1.0351 545
## avg_veg_height-Canis_latrans 1.0336 1.0344 523
## avg_veg_height-Procyon_lotor 1.8178 1.0355 516
## avg_veg_height-Dasypus_novemcinctus 1.6347 1.0235 636
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.4198 0.1893 -2.8006 -2.4116 -2.0667 1.0006
## (Intercept)-Procyon_lotor -2.1662 0.1477 -2.4588 -2.1624 -1.8785 1.0044
## (Intercept)-Dasypus_novemcinctus -1.4855 0.1577 -1.7954 -1.4813 -1.1735 1.0069
## week-Canis_latrans 0.5096 0.2691 0.0175 0.4976 1.0639 1.0195
## week-Procyon_lotor 0.1663 0.2111 -0.2510 0.1660 0.5853 1.0017
## week-Dasypus_novemcinctus 0.0581 0.2293 -0.3957 0.0550 0.5056 1.0014
## I(week^2)-Canis_latrans -0.2148 0.1149 -0.4491 -0.2110 -0.0013 1.0150
## I(week^2)-Procyon_lotor -0.1145 0.0917 -0.2941 -0.1148 0.0652 1.0010
## I(week^2)-Dasypus_novemcinctus -0.1480 0.1067 -0.3610 -0.1461 0.0561 1.0050
## ESS
## (Intercept)-Canis_latrans 847
## (Intercept)-Procyon_lotor 1633
## (Intercept)-Dasypus_novemcinctus 2262
## week-Canis_latrans 1219
## week-Procyon_lotor 1676
## week-Dasypus_novemcinctus 1937
## I(week^2)-Canis_latrans 1249
## I(week^2)-Procyon_lotor 1643
## I(week^2)-Dasypus_novemcinctus 1844
#Includes quadratic week and full covariates of detection and only null for occupancy
ms_fullQ_null_T50 <- msPGOcc(
occ.formula = occ.null,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_null_T50)
##
## Call:
## msPGOcc(occ.formula = occ.null, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3667
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1751 0.5706 -1.0186 0.183 1.3394 1.0051 2493
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6107 7.1774 0.0643 0.5069 7.9472 1.0587 3000
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9285 0.5708 -2.8019 -2.0122 -0.4887 1.0031 2046
## shrub_cover 0.2491 0.5572 -0.8750 0.2553 1.3938 1.0053 2688
## veg_height -0.0102 0.5239 -1.1095 0.0056 1.0729 1.0020 3000
## week 0.2220 0.4368 -0.6490 0.2212 1.1159 1.0045 2254
## I(week^2) -0.1437 0.3323 -0.7951 -0.1493 0.5548 1.0018 2661
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.7131 20.1032 0.0615 0.4033 7.7535 1.2819 3000
## shrub_cover 1.5111 5.1073 0.0767 0.5370 8.2294 1.0855 2841
## veg_height 1.3864 5.4198 0.0901 0.4991 8.3555 1.1453 2832
## week 0.6918 1.8301 0.0413 0.2487 4.0645 1.0426 2230
## I(week^2) 0.4138 1.3152 0.0283 0.1436 2.4228 1.0314 2705
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans 0.3619 0.4049 -0.3752 0.3419 1.2067 1.0002
## (Intercept)-Procyon_lotor 0.6286 0.3869 -0.0766 0.6098 1.4314 1.0006
## (Intercept)-Dasypus_novemcinctus -0.3837 0.3789 -1.1366 -0.3765 0.3485 1.0127
## ESS
## (Intercept)-Canis_latrans 2000
## (Intercept)-Procyon_lotor 1899
## (Intercept)-Dasypus_novemcinctus 2235
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5424 0.2070 -2.9674 -2.5335 -2.1610 1.0169
## (Intercept)-Procyon_lotor -2.1807 0.1612 -2.4968 -2.1813 -1.8797 1.0044
## (Intercept)-Dasypus_novemcinctus -1.6525 0.1845 -2.0151 -1.6492 -1.2982 1.0039
## shrub_cover-Canis_latrans -0.3006 0.2191 -0.7476 -0.2971 0.1209 1.0039
## shrub_cover-Procyon_lotor 0.2680 0.1640 -0.0649 0.2678 0.5856 1.0208
## shrub_cover-Dasypus_novemcinctus 0.8712 0.3192 0.2647 0.8652 1.5027 1.0040
## veg_height-Canis_latrans -0.6323 0.1872 -1.0137 -0.6238 -0.2805 1.0402
## veg_height-Procyon_lotor 0.3460 0.1267 0.1057 0.3440 0.5968 1.0017
## veg_height-Dasypus_novemcinctus 0.2618 0.1408 -0.0007 0.2555 0.5413 1.0198
## week-Canis_latrans 0.5296 0.2776 0.0137 0.5203 1.1005 1.0129
## week-Procyon_lotor 0.1635 0.2096 -0.2346 0.1636 0.5771 1.0000
## week-Dasypus_novemcinctus 0.0508 0.2338 -0.4083 0.0473 0.5089 1.0036
## I(week^2)-Canis_latrans -0.2242 0.1152 -0.4602 -0.2192 0.0031 1.0017
## I(week^2)-Procyon_lotor -0.1110 0.0917 -0.2919 -0.1095 0.0660 1.0046
## I(week^2)-Dasypus_novemcinctus -0.1474 0.1100 -0.3729 -0.1446 0.0615 0.9997
## ESS
## (Intercept)-Canis_latrans 813
## (Intercept)-Procyon_lotor 1178
## (Intercept)-Dasypus_novemcinctus 1734
## shrub_cover-Canis_latrans 885
## shrub_cover-Procyon_lotor 1430
## shrub_cover-Dasypus_novemcinctus 1155
## veg_height-Canis_latrans 745
## veg_height-Procyon_lotor 1360
## veg_height-Dasypus_novemcinctus 1949
## week-Canis_latrans 1191
## week-Procyon_lotor 1765
## week-Dasypus_novemcinctus 2021
## I(week^2)-Canis_latrans 1247
## I(week^2)-Procyon_lotor 1630
## I(week^2)-Dasypus_novemcinctus 1668
#Includes quadratic week and full covariates of detection and full for occupancy
ms_fullQ_full_T50 <- msPGOcc(
occ.formula = occ.full,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_full_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3857
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1310 0.9162 -1.8038 0.1498 1.9713 1.0109 789
## Cogon_Patch_Size -0.2709 0.8809 -1.9914 -0.3043 1.6243 1.0024 941
## Veg_shannon_index 0.9408 0.7857 -0.7287 0.9471 2.4257 1.0159 742
## total_shrub_cover -0.1252 0.8724 -1.7863 -0.1404 1.7030 1.0073 1229
## Avg_Cogongrass_Cover 2.0010 1.0867 -0.2316 1.9994 4.1546 1.0159 429
## Tree_Density -1.6656 1.2672 -3.8284 -1.8163 1.3712 1.0000 1271
## Avg_Canopy_Cover 0.9049 0.8805 -1.0192 0.9516 2.5382 1.0020 2361
## avg_veg_height -0.2749 0.6979 -1.6485 -0.2685 1.0833 1.0019 631
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.6684 16.8717 0.0729 1.3289 28.2762 1.2164 1941
## Cogon_Patch_Size 4.1400 40.9411 0.0627 0.8909 19.4305 1.2344 2648
## Veg_shannon_index 2.0052 6.7906 0.0530 0.5492 13.2175 1.0463 2120
## total_shrub_cover 4.2163 14.6450 0.0769 1.2184 25.4285 1.0018 780
## Avg_Cogongrass_Cover 4.8327 36.1537 0.0500 0.6539 32.7612 1.2411 2502
## Tree_Density 18.4589 53.6323 0.0770 2.7991 149.2614 1.0301 410
## Avg_Canopy_Cover 4.4583 15.9618 0.1059 1.4363 26.4939 1.1121 1803
## avg_veg_height 1.4560 11.2801 0.0431 0.3412 7.7558 1.1056 3000
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 2.0227 3.4251 0.0612 0.9226 10.7172 1.0717 152
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9257 0.5882 -2.8324 -2.0192 -0.4327 1.0044 2368
## shrub_cover 0.2456 0.6025 -1.0052 0.2442 1.5050 1.0042 2697
## veg_height -0.0361 0.5503 -1.1956 -0.0221 1.1332 1.0051 3000
## week 0.2249 0.4246 -0.6567 0.2312 1.0489 1.0030 2392
## I(week^2) -0.1524 0.3493 -0.8416 -0.1515 0.5099 1.0067 2716
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3729 6.2449 0.0687 0.4128 8.2179 1.2094 2142
## shrub_cover 1.8441 4.6556 0.1110 0.7023 10.8641 1.0384 2756
## veg_height 1.3015 3.1215 0.0937 0.5095 7.5616 1.0219 2530
## week 0.8343 9.1136 0.0417 0.2350 4.0052 1.3064 3000
## I(week^2) 0.4518 2.0756 0.0300 0.1389 2.5065 1.1648 2405
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7062 0.9618 -0.9771 0.6229
## (Intercept)-Procyon_lotor 0.6866 0.9235 -1.1010 0.6928
## (Intercept)-Dasypus_novemcinctus -0.9556 1.0770 -3.5438 -0.8403
## Cogon_Patch_Size-Canis_latrans 0.5961 1.3301 -1.2922 0.3613
## Cogon_Patch_Size-Procyon_lotor -0.9257 0.8193 -2.7765 -0.8837
## Cogon_Patch_Size-Dasypus_novemcinctus -0.5183 0.8572 -2.3403 -0.5241
## Veg_shannon_index-Canis_latrans 1.4820 0.8604 0.0557 1.3897
## Veg_shannon_index-Procyon_lotor 1.2751 0.7336 -0.0856 1.2333
## Veg_shannon_index-Dasypus_novemcinctus 0.5771 0.6848 -0.8268 0.5998
## total_shrub_cover-Canis_latrans 0.8021 1.1376 -0.8217 0.5735
## total_shrub_cover-Procyon_lotor -1.0578 0.7593 -2.7081 -0.9863
## total_shrub_cover-Dasypus_novemcinctus -0.2162 0.8244 -2.0081 -0.1525
## Avg_Cogongrass_Cover-Canis_latrans 2.6503 1.3156 0.5162 2.4798
## Avg_Cogongrass_Cover-Procyon_lotor 2.1440 1.0842 0.2248 2.0822
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 2.7378 1.3634 0.5597 2.5347
## Tree_Density-Canis_latrans -3.3389 2.1320 -8.7747 -2.8800
## Tree_Density-Procyon_lotor -1.4956 0.9136 -3.3949 -1.5005
## Tree_Density-Dasypus_novemcinctus -4.2491 2.7587 -11.5432 -3.4563
## Avg_Canopy_Cover-Canis_latrans 0.1221 0.6782 -1.2938 0.1338
## Avg_Canopy_Cover-Procyon_lotor 1.5133 0.7878 0.2269 1.4215
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.0435 0.9346 0.6670 1.9146
## avg_veg_height-Canis_latrans -0.4063 0.7246 -1.8076 -0.4177
## avg_veg_height-Procyon_lotor -0.3044 0.6733 -1.6459 -0.2911
## avg_veg_height-Dasypus_novemcinctus -0.1696 0.6893 -1.5341 -0.1754
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.8805 1.0397 393
## (Intercept)-Procyon_lotor 2.4946 1.0537 307
## (Intercept)-Dasypus_novemcinctus 0.7915 1.0153 373
## Cogon_Patch_Size-Canis_latrans 3.9758 1.0234 484
## Cogon_Patch_Size-Procyon_lotor 0.5497 1.0153 265
## Cogon_Patch_Size-Dasypus_novemcinctus 1.2130 1.0076 558
## Veg_shannon_index-Canis_latrans 3.3503 1.0110 337
## Veg_shannon_index-Procyon_lotor 2.8528 1.0312 381
## Veg_shannon_index-Dasypus_novemcinctus 1.8861 1.0184 657
## total_shrub_cover-Canis_latrans 3.7144 1.0068 261
## total_shrub_cover-Procyon_lotor 0.2409 1.0037 826
## total_shrub_cover-Dasypus_novemcinctus 1.1993 1.0056 431
## Avg_Cogongrass_Cover-Canis_latrans 5.7148 1.0083 304
## Avg_Cogongrass_Cover-Procyon_lotor 4.5910 1.0058 399
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 6.0457 1.0018 254
## Tree_Density-Canis_latrans -0.6636 1.0098 173
## Tree_Density-Procyon_lotor 0.2376 1.0056 579
## Tree_Density-Dasypus_novemcinctus -1.1992 1.0136 168
## Avg_Canopy_Cover-Canis_latrans 1.4050 1.0044 847
## Avg_Canopy_Cover-Procyon_lotor 3.3066 1.0171 380
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.3076 1.0049 381
## avg_veg_height-Canis_latrans 0.9916 1.0060 644
## avg_veg_height-Procyon_lotor 0.9500 1.0015 648
## avg_veg_height-Dasypus_novemcinctus 1.2098 1.0025 600
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5655 0.1948 -2.9556 -2.5622 -2.1983 1.0004
## (Intercept)-Procyon_lotor -2.1914 0.1614 -2.5263 -2.1914 -1.8880 1.0007
## (Intercept)-Dasypus_novemcinctus -1.6752 0.1844 -2.0425 -1.6728 -1.3204 1.0032
## shrub_cover-Canis_latrans -0.4013 0.2332 -0.8472 -0.4086 0.0659 1.0160
## shrub_cover-Procyon_lotor 0.2782 0.1656 -0.0484 0.2805 0.5954 1.0102
## shrub_cover-Dasypus_novemcinctus 0.9685 0.3273 0.3277 0.9636 1.6178 1.0038
## veg_height-Canis_latrans -0.6542 0.1889 -1.0393 -0.6496 -0.2958 1.0024
## veg_height-Procyon_lotor 0.3492 0.1255 0.1077 0.3479 0.5975 1.0223
## veg_height-Dasypus_novemcinctus 0.2695 0.1388 -0.0045 0.2694 0.5528 1.0037
## week-Canis_latrans 0.5178 0.2689 0.0395 0.5043 1.0643 1.0021
## week-Procyon_lotor 0.1600 0.2108 -0.2473 0.1582 0.5766 1.0064
## week-Dasypus_novemcinctus 0.0551 0.2297 -0.3943 0.0521 0.4985 1.0026
## I(week^2)-Canis_latrans -0.2202 0.1154 -0.4617 -0.2154 -0.0043 1.0006
## I(week^2)-Procyon_lotor -0.1095 0.0918 -0.2880 -0.1078 0.0677 1.0051
## I(week^2)-Dasypus_novemcinctus -0.1496 0.1080 -0.3689 -0.1455 0.0587 1.0027
## ESS
## (Intercept)-Canis_latrans 820
## (Intercept)-Procyon_lotor 1366
## (Intercept)-Dasypus_novemcinctus 1354
## shrub_cover-Canis_latrans 608
## shrub_cover-Procyon_lotor 1405
## shrub_cover-Dasypus_novemcinctus 653
## veg_height-Canis_latrans 676
## veg_height-Procyon_lotor 1632
## veg_height-Dasypus_novemcinctus 1626
## week-Canis_latrans 1070
## week-Procyon_lotor 1541
## week-Dasypus_novemcinctus 1799
## I(week^2)-Canis_latrans 1234
## I(week^2)-Procyon_lotor 1449
## I(week^2)-Dasypus_novemcinctus 1702
#Includes quadratic week and full covariates of detection and only cover for occupancy
ms_fullQ_cover_T50 <- msPGOcc(
occ.formula = occ.cover,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cover_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cover, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3912
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4690 0.7634 -1.0799 0.4323 2.0590 1.0465 629
## Avg_Cogongrass_Cover 0.1917 0.6116 -1.0110 0.1869 1.4065 1.0107 1253
## total_shrub_cover -0.3043 0.8443 -2.0076 -0.2994 1.4001 1.0065 1156
## avg_veg_height 0.3223 0.6502 -0.9796 0.3134 1.6505 1.0167 1060
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.2509 9.0986 0.0545 0.6004 13.2754 1.0090 3000
## Avg_Cogongrass_Cover 1.1400 3.7297 0.0428 0.3473 6.8165 1.0343 2099
## total_shrub_cover 6.3878 113.6836 0.0890 1.2891 26.0171 1.3028 3000
## avg_veg_height 1.4684 12.7099 0.0442 0.3419 7.7141 1.3262 970
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.7382 1.0824 0.0497 0.4117 3.3547 1.045 655
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9930 0.5772 -2.8553 -2.0743 -0.4641 1.0170 2149
## shrub_cover 0.3201 0.6579 -1.1250 0.3205 1.6627 1.0035 2496
## veg_height 0.0080 0.5468 -1.1002 -0.0030 1.1494 1.0044 2836
## week 0.2407 0.4152 -0.5707 0.2464 1.0437 1.0032 2244
## I(week^2) -0.1573 0.3191 -0.7748 -0.1547 0.4651 1.0074 3394
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3519 4.9420 0.0614 0.3913 8.1780 1.0247 2240
## shrub_cover 2.1949 6.9009 0.1130 0.8229 11.5860 1.0785 2242
## veg_height 1.3278 4.2307 0.0959 0.5207 7.2871 1.0915 2780
## week 0.6555 2.1442 0.0407 0.2287 4.0420 1.0657 3000
## I(week^2) 0.4558 1.9813 0.0288 0.1368 2.4836 1.0481 2590
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7713 0.7381 -0.4840 0.7035
## (Intercept)-Procyon_lotor 0.8845 0.6718 -0.2967 0.8436
## (Intercept)-Dasypus_novemcinctus 0.0185 0.8130 -1.4213 -0.0522
## Avg_Cogongrass_Cover-Canis_latrans 0.5088 0.6745 -0.6430 0.4572
## Avg_Cogongrass_Cover-Procyon_lotor -0.0326 0.5623 -1.1771 -0.0168
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.2139 0.5411 -0.8603 0.2183
## total_shrub_cover-Canis_latrans 0.6620 0.8822 -0.7035 0.5190
## total_shrub_cover-Procyon_lotor -1.2404 0.7271 -2.8705 -1.1508
## total_shrub_cover-Dasypus_novemcinctus -0.5644 0.9618 -3.0946 -0.3339
## avg_veg_height-Canis_latrans 0.2512 0.6064 -0.9125 0.2462
## avg_veg_height-Procyon_lotor 0.2431 0.5857 -0.9037 0.2323
## avg_veg_height-Dasypus_novemcinctus 0.5614 0.6595 -0.5100 0.4883
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.4416 1.0491 487
## (Intercept)-Procyon_lotor 2.3431 1.0422 566
## (Intercept)-Dasypus_novemcinctus 1.8420 1.0719 183
## Avg_Cogongrass_Cover-Canis_latrans 2.0047 1.0013 1020
## Avg_Cogongrass_Cover-Procyon_lotor 1.0502 1.0027 1228
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2801 1.0160 1297
## total_shrub_cover-Canis_latrans 2.7396 1.0419 380
## total_shrub_cover-Procyon_lotor -0.0911 1.0250 701
## total_shrub_cover-Dasypus_novemcinctus 0.7321 1.0841 142
## avg_veg_height-Canis_latrans 1.4425 1.0188 973
## avg_veg_height-Procyon_lotor 1.3846 1.0092 1145
## avg_veg_height-Dasypus_novemcinctus 2.1727 1.0644 285
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.6174 0.2127 -3.0428 -2.6123 -2.2130 1.0048
## (Intercept)-Procyon_lotor -2.1991 0.1575 -2.5156 -2.1920 -1.9060 1.0057
## (Intercept)-Dasypus_novemcinctus -1.7426 0.2136 -2.1592 -1.7396 -1.3313 1.0234
## shrub_cover-Canis_latrans -0.3964 0.2564 -0.8847 -0.3999 0.1258 1.0218
## shrub_cover-Procyon_lotor 0.3233 0.1629 -0.0040 0.3251 0.6347 1.0048
## shrub_cover-Dasypus_novemcinctus 1.1478 0.4280 0.3662 1.1469 1.9499 1.0552
## veg_height-Canis_latrans -0.6754 0.1981 -1.0757 -0.6710 -0.2955 1.0020
## veg_height-Procyon_lotor 0.3511 0.1262 0.1052 0.3525 0.5985 1.0007
## veg_height-Dasypus_novemcinctus 0.2780 0.1452 0.0056 0.2739 0.5748 1.0107
## week-Canis_latrans 0.5220 0.2720 0.0083 0.5154 1.0640 1.0018
## week-Procyon_lotor 0.1644 0.2125 -0.2608 0.1666 0.5729 1.0073
## week-Dasypus_novemcinctus 0.0555 0.2286 -0.3872 0.0605 0.4820 1.0001
## I(week^2)-Canis_latrans -0.2210 0.1124 -0.4513 -0.2174 -0.0127 1.0043
## I(week^2)-Procyon_lotor -0.1110 0.0939 -0.2943 -0.1144 0.0765 1.0055
## I(week^2)-Dasypus_novemcinctus -0.1451 0.1042 -0.3531 -0.1442 0.0545 1.0013
## ESS
## (Intercept)-Canis_latrans 683
## (Intercept)-Procyon_lotor 1164
## (Intercept)-Dasypus_novemcinctus 337
## shrub_cover-Canis_latrans 429
## shrub_cover-Procyon_lotor 1546
## shrub_cover-Dasypus_novemcinctus 170
## veg_height-Canis_latrans 634
## veg_height-Procyon_lotor 1737
## veg_height-Dasypus_novemcinctus 1512
## week-Canis_latrans 1141
## week-Procyon_lotor 1631
## week-Dasypus_novemcinctus 1900
## I(week^2)-Canis_latrans 1101
## I(week^2)-Procyon_lotor 1528
## I(week^2)-Dasypus_novemcinctus 1754
#Includes quadratic week and full covariates of detection and only canopy for occupancy
ms_fullQ_canopy_T50 <- msPGOcc(
occ.formula = occ.canopy,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_canopy_T50)
##
## Call:
## msPGOcc(occ.formula = occ.canopy, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3715
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.0383 0.7132 -1.4066 0.0356 1.5113 1.0003 1500
## Tree_Density -0.8472 0.7210 -2.3884 -0.8406 0.6098 1.0021 1646
## Avg_Canopy_Cover 0.4432 0.6800 -0.9852 0.4680 1.8251 1.0017 2653
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.0923 6.4165 0.0633 0.7157 12.3146 1.0809 2431
## Tree_Density 2.3563 7.8548 0.0507 0.5582 15.2810 1.0354 2089
## Avg_Canopy_Cover 2.0765 7.8505 0.0765 0.7203 10.7742 1.0892 2325
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.5969 1.0418 0.0438 0.2955 2.8693 1.0298 383
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9287 0.5792 -2.8081 -2.0099 -0.5257 1.0066 2364
## shrub_cover 0.2559 0.5566 -0.9355 0.2569 1.3972 1.0097 3000
## veg_height 0.0134 0.5127 -1.0230 0.0118 1.1232 1.0009 3000
## week 0.2300 0.4126 -0.6097 0.2263 1.0365 1.0063 2628
## I(week^2) -0.1528 0.3567 -0.8681 -0.1529 0.5413 1.0062 2784
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3789 4.2165 0.0644 0.4130 9.8289 1.0055 2070
## shrub_cover 2.1845 27.7680 0.0856 0.5839 9.0846 1.3375 3000
## veg_height 1.2521 3.7450 0.0924 0.4743 6.9593 1.0687 2835
## week 0.8207 4.9851 0.0403 0.2332 4.6532 1.1548 3000
## I(week^2) 0.4524 1.6741 0.0273 0.1401 2.9337 1.1234 2271
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.2726 0.5737 -0.8149 0.2505 1.4825
## (Intercept)-Procyon_lotor 0.5636 0.6046 -0.5474 0.5359 1.8343
## (Intercept)-Dasypus_novemcinctus -0.6628 0.6520 -2.0438 -0.6290 0.5154
## Tree_Density-Canis_latrans -1.0614 0.6722 -2.6777 -0.9733 0.0424
## Tree_Density-Procyon_lotor -0.5049 0.4724 -1.4579 -0.5003 0.3795
## Tree_Density-Dasypus_novemcinctus -1.5273 1.0288 -4.1194 -1.2934 -0.1690
## Avg_Canopy_Cover-Canis_latrans -0.2405 0.4689 -1.2486 -0.2244 0.6067
## Avg_Canopy_Cover-Procyon_lotor 0.8465 0.5006 -0.0372 0.8077 1.9293
## Avg_Canopy_Cover-Dasypus_novemcinctus 0.9330 0.5019 0.0634 0.9013 2.0116
## Rhat ESS
## (Intercept)-Canis_latrans 1.0154 919
## (Intercept)-Procyon_lotor 1.0013 892
## (Intercept)-Dasypus_novemcinctus 1.0164 764
## Tree_Density-Canis_latrans 1.0007 944
## Tree_Density-Procyon_lotor 1.0026 1442
## Tree_Density-Dasypus_novemcinctus 1.0047 523
## Avg_Canopy_Cover-Canis_latrans 1.0029 1661
## Avg_Canopy_Cover-Procyon_lotor 1.0002 1674
## Avg_Canopy_Cover-Dasypus_novemcinctus 1.0015 1575
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5548 0.2042 -2.9728 -2.5480 -2.1711 1.0048
## (Intercept)-Procyon_lotor -2.1857 0.1597 -2.5062 -2.1824 -1.8725 1.0008
## (Intercept)-Dasypus_novemcinctus -1.6532 0.1888 -2.0289 -1.6546 -1.2928 1.0148
## shrub_cover-Canis_latrans -0.3225 0.2322 -0.7891 -0.3165 0.1390 1.0119
## shrub_cover-Procyon_lotor 0.2618 0.1663 -0.0828 0.2623 0.5807 1.0020
## shrub_cover-Dasypus_novemcinctus 0.8853 0.3185 0.2685 0.8866 1.5195 1.0102
## veg_height-Canis_latrans -0.6415 0.1918 -1.0292 -0.6391 -0.2830 1.0011
## veg_height-Procyon_lotor 0.3496 0.1253 0.1111 0.3511 0.5946 1.0002
## veg_height-Dasypus_novemcinctus 0.2747 0.1405 0.0092 0.2694 0.5574 0.9996
## week-Canis_latrans 0.5174 0.2633 0.0185 0.5056 1.0613 1.0063
## week-Procyon_lotor 0.1628 0.2158 -0.2582 0.1644 0.5859 1.0025
## week-Dasypus_novemcinctus 0.0558 0.2345 -0.4028 0.0528 0.5110 1.0039
## I(week^2)-Canis_latrans -0.2210 0.1121 -0.4427 -0.2173 -0.0074 1.0053
## I(week^2)-Procyon_lotor -0.1108 0.0937 -0.2980 -0.1097 0.0706 1.0024
## I(week^2)-Dasypus_novemcinctus -0.1526 0.1102 -0.3780 -0.1487 0.0568 1.0013
## ESS
## (Intercept)-Canis_latrans 606
## (Intercept)-Procyon_lotor 1298
## (Intercept)-Dasypus_novemcinctus 1552
## shrub_cover-Canis_latrans 746
## shrub_cover-Procyon_lotor 1478
## shrub_cover-Dasypus_novemcinctus 1081
## veg_height-Canis_latrans 734
## veg_height-Procyon_lotor 1629
## veg_height-Dasypus_novemcinctus 1821
## week-Canis_latrans 1265
## week-Procyon_lotor 1593
## week-Dasypus_novemcinctus 1936
## I(week^2)-Canis_latrans 1290
## I(week^2)-Procyon_lotor 1560
## I(week^2)-Dasypus_novemcinctus 1650
#Includes quadratic week and full covariates of detection and only movement for occupancy
ms_fullQ_move_T50 <- msPGOcc(
occ.formula = occ.move,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_move_T50)
##
## Call:
## msPGOcc(occ.formula = occ.move, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3857
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.4185 0.6986 -0.9791 0.4244 1.8274 1.0010 1090
## Cogon_Patch_Size 0.2212 0.6735 -1.0542 0.2055 1.6231 1.0008 1699
## Avg_Cogongrass_Cover 0.3061 0.5512 -0.8327 0.3149 1.3751 1.0048 1565
## total_shrub_cover -0.3344 0.7502 -1.8635 -0.3510 1.2515 1.0015 1983
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 2.3218 15.0023 0.0570 0.6310 12.2168 1.2134 3000
## Cogon_Patch_Size 1.9662 5.2457 0.0523 0.5537 14.3490 1.0164 1742
## Avg_Cogongrass_Cover 0.9940 3.5839 0.0433 0.2769 6.0067 1.0431 2518
## total_shrub_cover 4.2281 32.2172 0.0719 0.9210 19.6700 1.0174 2650
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6395 1.2332 0.0433 0.3096 3.1378 1.0262 595
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9482 0.5954 -2.8504 -2.0361 -0.4303 1.0022 2147
## shrub_cover 0.2898 0.6198 -1.0333 0.2946 1.5514 1.0020 2745
## veg_height -0.0029 0.5589 -1.1348 -0.0126 1.1552 1.0057 2386
## week 0.2333 0.4160 -0.6308 0.2360 1.1006 1.0082 2438
## I(week^2) -0.1610 0.3196 -0.8345 -0.1604 0.4719 1.0050 2210
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3520 4.7457 0.0577 0.3767 8.3873 1.0289 2192
## shrub_cover 1.9623 6.0310 0.1074 0.7688 10.4798 1.1561 2831
## veg_height 1.5760 9.0009 0.0958 0.5146 7.7405 1.1363 3000
## week 0.8260 8.1703 0.0388 0.2378 3.8353 1.2438 3000
## I(week^2) 0.4340 2.1880 0.0277 0.1423 2.2652 1.2288 2698
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans 0.7002 0.6387 -0.4116 0.6469
## (Intercept)-Procyon_lotor 0.8334 0.6183 -0.2875 0.8128
## (Intercept)-Dasypus_novemcinctus -0.1452 0.7053 -1.4430 -0.1901
## Cogon_Patch_Size-Canis_latrans 0.9312 0.9337 -0.3517 0.7670
## Cogon_Patch_Size-Procyon_lotor -0.0749 0.5144 -1.0717 -0.0853
## Cogon_Patch_Size-Dasypus_novemcinctus 0.0390 0.5155 -0.9673 0.0211
## Avg_Cogongrass_Cover-Canis_latrans 0.3903 0.5168 -0.4919 0.3572
## Avg_Cogongrass_Cover-Procyon_lotor 0.1469 0.5269 -0.9313 0.1545
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4279 0.4834 -0.4832 0.4048
## total_shrub_cover-Canis_latrans 0.4323 0.7740 -0.7630 0.3421
## total_shrub_cover-Procyon_lotor -1.1371 0.6741 -2.6997 -1.0485
## total_shrub_cover-Dasypus_novemcinctus -0.4660 0.8687 -2.7150 -0.3020
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 2.0925 1.0052 630
## (Intercept)-Procyon_lotor 2.1487 1.0017 916
## (Intercept)-Dasypus_novemcinctus 1.3412 1.0234 406
## Cogon_Patch_Size-Canis_latrans 3.2873 1.0118 746
## Cogon_Patch_Size-Procyon_lotor 0.9948 1.0054 931
## Cogon_Patch_Size-Dasypus_novemcinctus 1.0870 1.0081 1220
## Avg_Cogongrass_Cover-Canis_latrans 1.4719 1.0141 1071
## Avg_Cogongrass_Cover-Procyon_lotor 1.1898 1.0007 1346
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.4546 1.0013 1471
## total_shrub_cover-Canis_latrans 2.3855 1.0147 556
## total_shrub_cover-Procyon_lotor -0.0705 1.0015 882
## total_shrub_cover-Dasypus_novemcinctus 0.6403 1.0490 180
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5607 0.2066 -2.9910 -2.5472 -2.1838 1.0002
## (Intercept)-Procyon_lotor -2.1940 0.1542 -2.4976 -2.1922 -1.8890 1.0064
## (Intercept)-Dasypus_novemcinctus -1.7101 0.2059 -2.1328 -1.7009 -1.3122 1.0071
## shrub_cover-Canis_latrans -0.3759 0.2463 -0.8415 -0.3793 0.1028 1.0196
## shrub_cover-Procyon_lotor 0.3187 0.1615 -0.0060 0.3199 0.6321 1.0033
## shrub_cover-Dasypus_novemcinctus 1.0694 0.3899 0.3715 1.0483 1.8704 1.0060
## veg_height-Canis_latrans -0.6486 0.1860 -1.0257 -0.6490 -0.2930 1.0029
## veg_height-Procyon_lotor 0.3518 0.1261 0.1016 0.3553 0.5947 1.0165
## veg_height-Dasypus_novemcinctus 0.2770 0.1452 -0.0067 0.2751 0.5721 1.0055
## week-Canis_latrans 0.5120 0.2673 0.0266 0.5000 1.0545 1.0288
## week-Procyon_lotor 0.1675 0.2162 -0.2372 0.1649 0.5984 1.0111
## week-Dasypus_novemcinctus 0.0533 0.2281 -0.3900 0.0533 0.5093 1.0013
## I(week^2)-Canis_latrans -0.2199 0.1142 -0.4438 -0.2204 -0.0011 1.0412
## I(week^2)-Procyon_lotor -0.1142 0.0961 -0.3078 -0.1109 0.0706 1.0029
## I(week^2)-Dasypus_novemcinctus -0.1529 0.1095 -0.3693 -0.1514 0.0559 1.0013
## ESS
## (Intercept)-Canis_latrans 757
## (Intercept)-Procyon_lotor 1564
## (Intercept)-Dasypus_novemcinctus 569
## shrub_cover-Canis_latrans 524
## shrub_cover-Procyon_lotor 1523
## shrub_cover-Dasypus_novemcinctus 352
## veg_height-Canis_latrans 757
## veg_height-Procyon_lotor 1589
## veg_height-Dasypus_novemcinctus 1293
## week-Canis_latrans 1201
## week-Procyon_lotor 1645
## week-Dasypus_novemcinctus 2034
## I(week^2)-Canis_latrans 1204
## I(week^2)-Procyon_lotor 1370
## I(week^2)-Dasypus_novemcinctus 1499
#Includes quadratic week and full covariates of detection and only foraging for occupancy
ms_fullQ_forage_T50 <- msPGOcc(
occ.formula = occ.forage,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_forage_T50)
##
## Call:
## msPGOcc(occ.formula = occ.forage, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3763
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.2078 0.6560 -1.1341 0.2146 1.5070 1.0019 1471
## Veg_shannon_index 0.5056 0.5832 -0.7339 0.5131 1.6244 1.0020 2146
## Avg_Cogongrass_Cover 0.6458 0.5510 -0.4539 0.6453 1.8052 1.0022 1600
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.8138 7.2642 0.0540 0.5522 10.4203 1.0104 2631
## Veg_shannon_index 1.4489 8.2122 0.0438 0.3785 7.4414 1.1629 2249
## Avg_Cogongrass_Cover 1.0695 4.5037 0.0422 0.3072 5.9119 1.0888 2691
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.6535 1.0301 0.0413 0.3466 2.972 1.0506 609
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9328 0.6066 -2.8901 -2.0342 -0.3075 1.0034 2302
## shrub_cover 0.2470 0.5596 -0.8622 0.2505 1.4381 1.0068 2769
## veg_height -0.0168 0.5333 -1.1087 -0.0116 1.1141 1.0061 3000
## week 0.2476 0.4166 -0.5877 0.2464 1.0428 1.0028 2485
## I(week^2) -0.1590 0.3231 -0.7842 -0.1612 0.5221 1.0122 2842
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4443 5.9819 0.0674 0.4163 8.0105 1.0513 2638
## shrub_cover 1.5054 6.5464 0.0794 0.5611 7.9182 1.2385 3000
## veg_height 1.5734 12.8112 0.0923 0.5253 7.5315 1.2256 2844
## week 0.6289 1.8476 0.0388 0.2292 3.6373 1.0168 2295
## I(week^2) 0.4344 1.5400 0.0282 0.1375 2.6672 1.0313 2461
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4475 0.5647 -0.6260 0.4173 1.6398
## (Intercept)-Procyon_lotor 0.5437 0.5673 -0.5847 0.5410 1.6570
## (Intercept)-Dasypus_novemcinctus -0.3411 0.5566 -1.4607 -0.3286 0.7492
## Veg_shannon_index-Canis_latrans 0.8982 0.5208 -0.0301 0.8626 2.0378
## Veg_shannon_index-Procyon_lotor 0.6120 0.4924 -0.2909 0.5889 1.6985
## Veg_shannon_index-Dasypus_novemcinctus 0.2269 0.4093 -0.5773 0.2326 1.0229
## Avg_Cogongrass_Cover-Canis_latrans 0.9357 0.5641 -0.0056 0.8700 2.2515
## Avg_Cogongrass_Cover-Procyon_lotor 0.5849 0.4928 -0.3001 0.5553 1.6468
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.6010 0.4029 -0.1388 0.5910 1.4469
## Rhat ESS
## (Intercept)-Canis_latrans 1.0016 966
## (Intercept)-Procyon_lotor 1.0213 895
## (Intercept)-Dasypus_novemcinctus 0.9997 976
## Veg_shannon_index-Canis_latrans 1.0019 1335
## Veg_shannon_index-Procyon_lotor 1.0237 1067
## Veg_shannon_index-Dasypus_novemcinctus 1.0112 1956
## Avg_Cogongrass_Cover-Canis_latrans 1.0030 1028
## Avg_Cogongrass_Cover-Procyon_lotor 1.0041 1467
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0006 1591
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5568 0.2055 -2.9713 -2.5518 -2.1660 1.0023
## (Intercept)-Procyon_lotor -2.1956 0.1656 -2.5296 -2.1902 -1.8862 1.0121
## (Intercept)-Dasypus_novemcinctus -1.6458 0.1877 -2.0198 -1.6389 -1.2972 1.0037
## shrub_cover-Canis_latrans -0.2895 0.2238 -0.7455 -0.2853 0.1312 1.0010
## shrub_cover-Procyon_lotor 0.2428 0.1714 -0.1158 0.2483 0.5672 1.0064
## shrub_cover-Dasypus_novemcinctus 0.8828 0.3257 0.2797 0.8678 1.5455 1.0014
## veg_height-Canis_latrans -0.6573 0.1875 -1.0295 -0.6496 -0.3067 1.0046
## veg_height-Procyon_lotor 0.3465 0.1225 0.1113 0.3461 0.5853 1.0028
## veg_height-Dasypus_novemcinctus 0.2608 0.1370 -0.0124 0.2584 0.5327 1.0010
## week-Canis_latrans 0.5254 0.2737 0.0156 0.5084 1.1020 1.0047
## week-Procyon_lotor 0.1688 0.2167 -0.2369 0.1638 0.6169 1.0067
## week-Dasypus_novemcinctus 0.0679 0.2353 -0.3988 0.0673 0.5387 1.0021
## I(week^2)-Canis_latrans -0.2227 0.1141 -0.4517 -0.2184 0.0012 1.0078
## I(week^2)-Procyon_lotor -0.1130 0.0952 -0.2966 -0.1109 0.0711 1.0037
## I(week^2)-Dasypus_novemcinctus -0.1526 0.1076 -0.3682 -0.1511 0.0548 1.0010
## ESS
## (Intercept)-Canis_latrans 990
## (Intercept)-Procyon_lotor 1363
## (Intercept)-Dasypus_novemcinctus 1597
## shrub_cover-Canis_latrans 889
## shrub_cover-Procyon_lotor 1168
## shrub_cover-Dasypus_novemcinctus 1015
## veg_height-Canis_latrans 738
## veg_height-Procyon_lotor 1500
## veg_height-Dasypus_novemcinctus 2041
## week-Canis_latrans 1096
## week-Procyon_lotor 1652
## week-Dasypus_novemcinctus 2012
## I(week^2)-Canis_latrans 1196
## I(week^2)-Procyon_lotor 1559
## I(week^2)-Dasypus_novemcinctus 1919
#Includes quadratic week and full covariates of detection and only cogon for occupancy
ms_fullQ_cogon_T50 <- msPGOcc(
occ.formula = occ.cogon,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogon_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3812
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 0.1748 0.6263 -1.0791 0.1718 1.4767 1.0099 1705
## Avg_Cogongrass_Cover 0.4173 0.5119 -0.6232 0.4208 1.4444 1.0029 1860
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.4129 4.2508 0.0570 0.4855 8.6426 1.004 2621
## Avg_Cogongrass_Cover 0.8276 2.7754 0.0397 0.2591 4.8722 1.059 2703
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.508 0.7393 0.043 0.2784 2.6298 1.052 488
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9526 0.5637 -2.8476 -2.0212 -0.5810 1.0051 2103
## shrub_cover 0.2442 0.5503 -0.9228 0.2397 1.4096 1.0050 3000
## veg_height -0.0105 0.5444 -1.1972 -0.0034 1.1024 1.0008 3124
## week 0.2315 0.4248 -0.6336 0.2350 1.0878 1.0059 2623
## I(week^2) -0.1501 0.3300 -0.7927 -0.1543 0.5012 1.0056 3000
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6900 16.9250 0.0638 0.3956 7.5630 1.3040 1853
## shrub_cover 1.4287 4.9688 0.0791 0.5300 7.5057 1.1253 3000
## veg_height 1.2714 3.4485 0.0966 0.4799 7.1227 1.0754 2290
## week 0.8707 9.6286 0.0419 0.2192 4.2191 1.2799 3000
## I(week^2) 0.3821 1.2170 0.0278 0.1408 1.9953 1.0560 2223
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5%
## (Intercept)-Canis_latrans 0.4023 0.5384 -0.5983 0.3805 1.5615
## (Intercept)-Procyon_lotor 0.5030 0.5206 -0.4946 0.4947 1.5452
## (Intercept)-Dasypus_novemcinctus -0.2977 0.5360 -1.3695 -0.2803 0.7806
## Avg_Cogongrass_Cover-Canis_latrans 0.5844 0.4658 -0.2028 0.5427 1.6029
## Avg_Cogongrass_Cover-Procyon_lotor 0.3320 0.3989 -0.4387 0.3262 1.1203
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 0.4578 0.3718 -0.2286 0.4420 1.2524
## Rhat ESS
## (Intercept)-Canis_latrans 1.0087 920
## (Intercept)-Procyon_lotor 1.0051 921
## (Intercept)-Dasypus_novemcinctus 1.0072 937
## Avg_Cogongrass_Cover-Canis_latrans 1.0022 1370
## Avg_Cogongrass_Cover-Procyon_lotor 1.0110 1924
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.0047 2289
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5594 0.1995 -2.9744 -2.5530 -2.1944 1.0033
## (Intercept)-Procyon_lotor -2.1894 0.1653 -2.5279 -2.1846 -1.8688 1.0117
## (Intercept)-Dasypus_novemcinctus -1.6549 0.1816 -2.0249 -1.6536 -1.3156 1.0018
## shrub_cover-Canis_latrans -0.2913 0.2260 -0.7245 -0.2903 0.1530 1.0099
## shrub_cover-Procyon_lotor 0.2630 0.1689 -0.0799 0.2664 0.5799 1.0044
## shrub_cover-Dasypus_novemcinctus 0.8785 0.3217 0.2500 0.8773 1.5134 1.0037
## veg_height-Canis_latrans -0.6436 0.1857 -1.0235 -0.6418 -0.2850 1.0021
## veg_height-Procyon_lotor 0.3526 0.1248 0.1174 0.3487 0.6017 1.0071
## veg_height-Dasypus_novemcinctus 0.2546 0.1390 -0.0120 0.2504 0.5349 1.0055
## week-Canis_latrans 0.5261 0.2723 0.0101 0.5138 1.0968 1.0022
## week-Procyon_lotor 0.1718 0.2127 -0.2411 0.1715 0.6028 1.0028
## week-Dasypus_novemcinctus 0.0573 0.2206 -0.3751 0.0564 0.4816 1.0108
## I(week^2)-Canis_latrans -0.2227 0.1117 -0.4512 -0.2194 -0.0098 1.0024
## I(week^2)-Procyon_lotor -0.1141 0.0942 -0.2960 -0.1160 0.0684 1.0010
## I(week^2)-Dasypus_novemcinctus -0.1462 0.1049 -0.3608 -0.1445 0.0516 1.0041
## ESS
## (Intercept)-Canis_latrans 777
## (Intercept)-Procyon_lotor 1457
## (Intercept)-Dasypus_novemcinctus 1665
## shrub_cover-Canis_latrans 826
## shrub_cover-Procyon_lotor 1391
## shrub_cover-Dasypus_novemcinctus 1105
## veg_height-Canis_latrans 752
## veg_height-Procyon_lotor 1406
## veg_height-Dasypus_novemcinctus 1862
## week-Canis_latrans 1064
## week-Procyon_lotor 1673
## week-Dasypus_novemcinctus 1928
## I(week^2)-Canis_latrans 1107
## I(week^2)-Procyon_lotor 1663
## I(week^2)-Dasypus_novemcinctus 1702
# Includes quadratic week and full covariates of detection and quadratic cogon for occupancy
ms_fullQ_cogonQ_T50 <- msPGOcc(
occ.formula = occ.cogon.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_cogonQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.cogon.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.382
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.5503 0.6681 -1.8430 -0.5578 0.8157 1.0087 898
## Avg_Cogongrass_Cover 0.0175 0.6961 -1.3506 -0.0118 1.4726 1.0180 1064
## I(Avg_Cogongrass_Cover^2) 1.0270 1.0066 -1.0153 0.9878 3.1721 1.0082 1139
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3302 4.6578 0.0467 0.4298 8.1918 1.1605 2408
## Avg_Cogongrass_Cover 1.3031 3.6604 0.0495 0.3873 8.2742 1.0325 2297
## I(Avg_Cogongrass_Cover^2) 8.7807 42.9476 0.0636 1.3901 68.5247 1.2408 439
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 0.4798 0.6809 0.0448 0.2674 2.2159 1.0023 626
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9334 0.5950 -2.8331 -2.0251 -0.4194 1.0010 2432
## shrub_cover 0.2179 0.5655 -0.9498 0.2193 1.3583 1.0011 2523
## veg_height -0.0196 0.5326 -1.1116 -0.0055 1.1073 1.0019 3000
## week 0.2446 0.4119 -0.6007 0.2465 1.0491 1.0048 2688
## I(week^2) -0.1551 0.3473 -0.8524 -0.1592 0.5305 1.0060 3090
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.6284 6.9181 0.0664 0.4485 8.4841 1.0516 2181
## shrub_cover 1.4574 4.5724 0.0781 0.5383 7.7440 1.0125 3000
## veg_height 1.1911 3.0210 0.0814 0.4576 6.9448 1.0228 3000
## week 0.6995 3.0976 0.0380 0.2319 4.0806 1.2077 2712
## I(week^2) 0.4267 1.5421 0.0284 0.1435 2.3003 1.0081 2540
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.5524 0.7257 -1.9816 -0.5432
## (Intercept)-Procyon_lotor -0.3426 0.6832 -1.6883 -0.3345
## (Intercept)-Dasypus_novemcinctus -0.8803 0.5995 -2.0968 -0.8614
## Avg_Cogongrass_Cover-Canis_latrans 0.1996 0.7031 -1.0701 0.1473
## Avg_Cogongrass_Cover-Procyon_lotor -0.1559 0.7574 -1.5344 -0.1869
## Avg_Cogongrass_Cover-Dasypus_novemcinctus -0.0139 0.6122 -1.1618 -0.0125
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.1407 1.4378 0.1489 1.8906
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.2627 2.1864 0.1229 1.4735
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 0.4749 0.4744 -0.3889 0.4484
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 0.8566 1.0251 415
## (Intercept)-Procyon_lotor 1.0193 1.0201 483
## (Intercept)-Dasypus_novemcinctus 0.2610 1.0077 1068
## Avg_Cogongrass_Cover-Canis_latrans 1.7094 1.0210 755
## Avg_Cogongrass_Cover-Procyon_lotor 1.4125 1.0190 488
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.2127 1.0168 1043
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 5.4329 1.0386 204
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 7.4772 1.1957 72
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4809 1.0046 1209
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5681 0.2075 -2.9854 -2.5605 -2.1914 1.0064
## (Intercept)-Procyon_lotor -2.2371 0.1693 -2.5795 -2.2339 -1.9111 1.0207
## (Intercept)-Dasypus_novemcinctus -1.6440 0.1902 -2.0318 -1.6384 -1.2873 1.0013
## shrub_cover-Canis_latrans -0.2738 0.2175 -0.6941 -0.2730 0.1584 1.0172
## shrub_cover-Procyon_lotor 0.1987 0.1761 -0.1587 0.1974 0.5427 1.0156
## shrub_cover-Dasypus_novemcinctus 0.8651 0.3144 0.2850 0.8586 1.5076 1.0079
## veg_height-Canis_latrans -0.6349 0.2008 -1.0420 -0.6320 -0.2551 1.0038
## veg_height-Procyon_lotor 0.3488 0.1281 0.0972 0.3484 0.5965 1.0072
## veg_height-Dasypus_novemcinctus 0.2553 0.1396 -0.0219 0.2542 0.5185 1.0009
## week-Canis_latrans 0.5223 0.2765 0.0066 0.5129 1.0902 1.0046
## week-Procyon_lotor 0.1690 0.2074 -0.2382 0.1653 0.5827 1.0056
## week-Dasypus_novemcinctus 0.0678 0.2245 -0.3780 0.0630 0.5147 1.0022
## I(week^2)-Canis_latrans -0.2212 0.1146 -0.4513 -0.2222 0.0014 1.0064
## I(week^2)-Procyon_lotor -0.1149 0.0901 -0.2929 -0.1151 0.0589 1.0136
## I(week^2)-Dasypus_novemcinctus -0.1516 0.1107 -0.3657 -0.1502 0.0619 1.0019
## ESS
## (Intercept)-Canis_latrans 983
## (Intercept)-Procyon_lotor 842
## (Intercept)-Dasypus_novemcinctus 1697
## shrub_cover-Canis_latrans 917
## shrub_cover-Procyon_lotor 461
## shrub_cover-Dasypus_novemcinctus 975
## veg_height-Canis_latrans 665
## veg_height-Procyon_lotor 1580
## veg_height-Dasypus_novemcinctus 2386
## week-Canis_latrans 1196
## week-Procyon_lotor 1588
## week-Dasypus_novemcinctus 2271
## I(week^2)-Canis_latrans 1362
## I(week^2)-Procyon_lotor 1605
## I(week^2)-Dasypus_novemcinctus 1674
# Includes quadratic week and full covariates of detection and all covariates and quadratic cogon for occupancy
ms_fullQ_fullQ_T50 <- msPGOcc(
occ.formula = occ.full.quad,
det.formula = det.full.quad,
data = data_list,
n.samples = 5000,
n.thin = 2,
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 3 species.
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
##
## Source compiled with OpenMP support and model fit using 1 thread(s).
##
## ----------------------------------------
## Chain 1
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 2
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
## ----------------------------------------
## Chain 3
## ----------------------------------------
## Sampling ...
## Sampled: 500 of 5000, 10.00%
## -------------------------------------------------
## Sampled: 1000 of 5000, 20.00%
## -------------------------------------------------
## Sampled: 1500 of 5000, 30.00%
## -------------------------------------------------
## Sampled: 2000 of 5000, 40.00%
## -------------------------------------------------
## Sampled: 2500 of 5000, 50.00%
## -------------------------------------------------
## Sampled: 3000 of 5000, 60.00%
## -------------------------------------------------
## Sampled: 3500 of 5000, 70.00%
## -------------------------------------------------
## Sampled: 4000 of 5000, 80.00%
## -------------------------------------------------
## Sampled: 4500 of 5000, 90.00%
## -------------------------------------------------
## Sampled: 5000 of 5000, 100.00%
summary(ms_fullQ_fullQ_T50)
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3887
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9609 1.0371 -2.8872 -0.9990 1.3357 1.0248 705
## Cogon_Patch_Size 0.1301 1.0323 -1.8846 0.1103 2.2875 1.0043 1150
## Veg_shannon_index 0.9651 0.7825 -0.5764 0.9557 2.5328 1.0297 914
## total_shrub_cover -0.2203 0.8943 -1.9826 -0.2397 1.6946 1.0140 822
## Avg_Cogongrass_Cover 0.7205 1.1972 -1.7232 0.7351 2.9930 1.0038 529
## Tree_Density -2.0041 1.5226 -4.6260 -2.1816 1.5991 1.0133 897
## Avg_Canopy_Cover 0.9651 0.9265 -1.0167 1.0102 2.7089 1.0115 1912
## I(Avg_Cogongrass_Cover^2) 1.6071 0.9952 -0.5895 1.6328 3.4269 1.0140 660
## avg_veg_height -0.0028 0.7949 -1.6122 -0.0106 1.4895 1.0079 816
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.1801 39.8489 0.0624 1.1175 31.7502 1.2860 3000
## Cogon_Patch_Size 7.7269 23.5996 0.0686 1.6872 52.9931 1.0368 855
## Veg_shannon_index 2.4048 8.3815 0.0526 0.5838 15.7909 1.0220 2378
## total_shrub_cover 4.3914 18.8308 0.0758 1.3371 25.0951 1.0794 1571
## Avg_Cogongrass_Cover 5.5607 25.6280 0.0599 0.8824 33.7230 1.0087 1408
## Tree_Density 34.8132 169.8778 0.0914 5.6019 214.4754 1.1579 1336
## Avg_Canopy_Cover 5.8946 21.6111 0.1012 1.7266 34.2468 1.0355 1876
## I(Avg_Cogongrass_Cover^2) 5.8390 25.7422 0.0627 0.8706 44.3410 1.2140 773
## avg_veg_height 1.8706 6.9309 0.0490 0.4769 12.5857 1.0371 1793
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.98 3.47 0.0612 0.8365 10.5556 1.1877 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9408 0.5754 -2.7745 -2.0305 -0.3331 1.0015 2214
## shrub_cover 0.2554 0.6077 -1.0209 0.2777 1.4439 1.0062 2898
## veg_height -0.0080 0.5318 -1.0925 -0.0067 1.1359 1.0074 2888
## week 0.2381 0.4557 -0.6585 0.2436 1.1447 1.0013 2540
## I(week^2) -0.1495 0.3319 -0.8225 -0.1566 0.5283 1.0013 3223
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3192 6.2565 0.0625 0.3786 7.6263 1.1436 2756
## shrub_cover 1.6696 4.5541 0.1033 0.6547 10.1531 1.0279 2609
## veg_height 1.4075 7.2904 0.0925 0.4799 8.4367 1.2304 3000
## week 1.0122 6.4813 0.0428 0.2522 4.9206 1.0666 3000
## I(week^2) 0.4526 1.7045 0.0275 0.1453 2.5110 1.0344 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8918 1.2087 -3.0696 -0.9379
## (Intercept)-Procyon_lotor -0.6586 1.1580 -2.6659 -0.7033
## (Intercept)-Dasypus_novemcinctus -2.1621 1.2615 -5.1892 -2.0161
## Cogon_Patch_Size-Canis_latrans 1.7953 2.2210 -0.7944 1.2323
## Cogon_Patch_Size-Procyon_lotor -0.6770 1.0009 -2.7113 -0.6674
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1942 0.9893 -2.1636 -0.2269
## Veg_shannon_index-Canis_latrans 1.5150 0.9848 -0.1206 1.3982
## Veg_shannon_index-Procyon_lotor 1.2929 0.7615 -0.0092 1.2225
## Veg_shannon_index-Dasypus_novemcinctus 0.5779 0.7462 -0.9100 0.5939
## total_shrub_cover-Canis_latrans 0.6817 1.1229 -1.0487 0.5033
## total_shrub_cover-Procyon_lotor -1.2373 0.8482 -3.0840 -1.1422
## total_shrub_cover-Dasypus_novemcinctus -0.3181 0.9780 -2.6227 -0.2150
## Avg_Cogongrass_Cover-Canis_latrans 0.9109 1.6007 -1.9488 0.8650
## Avg_Cogongrass_Cover-Procyon_lotor 0.4459 1.4662 -2.5509 0.4782
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.5176 1.6570 -1.3085 1.3688
## Tree_Density-Canis_latrans -4.2782 2.3175 -9.7402 -3.7947
## Tree_Density-Procyon_lotor -2.3369 1.3504 -4.9431 -2.3191
## Tree_Density-Dasypus_novemcinctus -5.4117 2.8424 -12.6630 -4.7220
## Avg_Canopy_Cover-Canis_latrans 0.1046 0.7623 -1.4543 0.1106
## Avg_Canopy_Cover-Procyon_lotor 1.5227 0.8586 0.0438 1.4566
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3055 1.1129 0.6316 2.1535
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5252 1.4742 0.5787 2.2131
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3955 1.3301 0.5099 2.1589
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4145 0.9389 -0.2790 1.3536
## avg_veg_height-Canis_latrans -0.2712 0.8405 -1.9729 -0.2680
## avg_veg_height-Procyon_lotor 0.0307 0.8148 -1.5949 0.0567
## avg_veg_height-Dasypus_novemcinctus 0.2085 0.8195 -1.3553 0.1819
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8072 1.0148 405
## (Intercept)-Procyon_lotor 1.7590 1.0515 285
## (Intercept)-Dasypus_novemcinctus -0.1342 1.0538 327
## Cogon_Patch_Size-Canis_latrans 7.6590 1.0565 257
## Cogon_Patch_Size-Procyon_lotor 1.1562 1.0127 455
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8772 1.0169 600
## Veg_shannon_index-Canis_latrans 3.7962 1.0435 469
## Veg_shannon_index-Procyon_lotor 3.0442 1.0434 507
## Veg_shannon_index-Dasypus_novemcinctus 2.0355 1.0205 675
## total_shrub_cover-Canis_latrans 3.4750 1.0072 285
## total_shrub_cover-Procyon_lotor 0.2541 1.0109 470
## total_shrub_cover-Dasypus_novemcinctus 1.2112 1.0539 387
## Avg_Cogongrass_Cover-Canis_latrans 4.4156 1.0016 500
## Avg_Cogongrass_Cover-Procyon_lotor 3.2064 1.0015 528
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2850 1.0395 397
## Tree_Density-Canis_latrans -1.0832 1.0147 233
## Tree_Density-Procyon_lotor 0.2557 1.0058 338
## Tree_Density-Dasypus_novemcinctus -1.7751 1.1472 197
## Avg_Canopy_Cover-Canis_latrans 1.6212 1.0029 946
## Avg_Canopy_Cover-Procyon_lotor 3.4621 1.0162 619
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.9962 1.0614 359
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.5478 1.0503 179
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.0387 1.0861 215
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.4021 1.0113 541
## avg_veg_height-Canis_latrans 1.3579 1.0114 645
## avg_veg_height-Procyon_lotor 1.6180 1.0059 782
## avg_veg_height-Dasypus_novemcinctus 1.8972 1.0066 600
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5433 0.2010 -2.9647 -2.5369 -2.1746 1.0236
## (Intercept)-Procyon_lotor -2.2120 0.1657 -2.5500 -2.2049 -1.8952 1.0071
## (Intercept)-Dasypus_novemcinctus -1.6810 0.1924 -2.0625 -1.6792 -1.3116 1.0131
## shrub_cover-Canis_latrans -0.3625 0.2281 -0.7996 -0.3615 0.0747 1.0148
## shrub_cover-Procyon_lotor 0.2615 0.1718 -0.1115 0.2682 0.5806 1.0042
## shrub_cover-Dasypus_novemcinctus 0.9900 0.3337 0.3486 0.9930 1.6669 1.0168
## veg_height-Canis_latrans -0.6254 0.1897 -1.0164 -0.6196 -0.2799 1.0289
## veg_height-Procyon_lotor 0.3657 0.1275 0.1166 0.3699 0.6190 1.0035
## veg_height-Dasypus_novemcinctus 0.2775 0.1426 0.0098 0.2748 0.5660 1.0180
## week-Canis_latrans 0.5371 0.2820 0.0186 0.5256 1.1176 1.0033
## week-Procyon_lotor 0.1716 0.2122 -0.2398 0.1711 0.5918 0.9999
## week-Dasypus_novemcinctus 0.0568 0.2283 -0.3977 0.0569 0.5104 1.0125
## I(week^2)-Canis_latrans -0.2276 0.1144 -0.4604 -0.2276 -0.0032 1.0131
## I(week^2)-Procyon_lotor -0.1147 0.0920 -0.2983 -0.1124 0.0611 1.0058
## I(week^2)-Dasypus_novemcinctus -0.1506 0.1071 -0.3618 -0.1468 0.0521 1.0206
## ESS
## (Intercept)-Canis_latrans 926
## (Intercept)-Procyon_lotor 782
## (Intercept)-Dasypus_novemcinctus 890
## shrub_cover-Canis_latrans 558
## shrub_cover-Procyon_lotor 440
## shrub_cover-Dasypus_novemcinctus 557
## veg_height-Canis_latrans 485
## veg_height-Procyon_lotor 1355
## veg_height-Dasypus_novemcinctus 1601
## week-Canis_latrans 1189
## week-Procyon_lotor 1714
## week-Dasypus_novemcinctus 1980
## I(week^2)-Canis_latrans 1280
## I(week^2)-Procyon_lotor 1462
## I(week^2)-Dasypus_novemcinctus 1677
waicOcc(ms_full_full_T50, by.sp = FALSE) # Best Model
## elpd pD WAIC
## -594.98193 69.27366 1328.51119
waicOcc(ms_full_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -610.97609 70.37533 1362.70284
waicOcc(ms_full_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -611.26302 56.53255 1335.59113
waicOcc(ms_full_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -611.38303 67.15202 1357.07010
waicOcc(ms_full_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -613.58998 59.84872 1346.87740
waicOcc(ms_full_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -617.46102 53.54198 1342.00601
waicOcc(ms_full_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -620.53009 49.69983 1340.45985
waicOcc(ms_full_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -613.99714 59.07234 1346.13897
waicOcc(ms_full_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -591.17593 74.46469 1331.28124
waicOcc(ms_null_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -646.23143 15.93654 1324.33593
waicOcc(ms_null_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -621.94119 36.10651 1316.09541
waicOcc(ms_null_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -639.05799 28.09503 1334.30605
waicOcc(ms_null_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -636.45965 25.08449 1323.08829
waicOcc(ms_null_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -638.37887 27.88632 1332.53039
waicOcc(ms_null_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -638.71380 25.53131 1328.49021
waicOcc(ms_null_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -642.54223 21.80997 1328.70441
waicOcc(ms_null_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -639.86533 23.59754 1326.92576
waicOcc(ms_null_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -617.86787 37.67305 1311.08184
waicOcc(ms_week_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -619.7516 40.4409 1320.3851
waicOcc(ms_week_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -637.63382 32.06954 1339.40672
waicOcc(ms_week_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -644.12908 20.05883 1328.37582
waicOcc(ms_week_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -637.57339 28.90772 1332.96221
waicOcc(ms_week_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -637.15381 31.17396 1336.65555
waicOcc(ms_week_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -634.97111 28.63693 1327.21609
waicOcc(ms_week_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -640.67541 26.22498 1333.80079
waicOcc(ms_week_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -637.99382 28.09762 1332.18288
waicOcc(ms_week_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -615.99079 42.67961 1317.34079
waicOcc(ms_cover_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -596.6131 65.2306 1323.6875
waicOcc(ms_cover_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -612.4713 67.0612 1359.0650
waicOcc(ms_cover_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -622.15725 44.15541 1332.62532
waicOcc(ms_cover_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -614.91539 55.81914 1341.46905
waicOcc(ms_cover_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -613.09181 61.43883 1349.06128
waicOcc(ms_cover_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -612.56282 53.06892 1331.26348
waicOcc(ms_cover_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -618.83656 49.79356 1337.26023
waicOcc(ms_cover_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -615.45475 54.45002 1339.80954
waicOcc(ms_cover_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -593.96167 67.88811 1323.69958
waicOcc(ms_weekQ_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -616.20278 44.57281 1321.55118
waicOcc(ms_weekQ_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -634.07023 36.26585 1340.67215
waicOcc(ms_weekQ_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -640.93760 24.74732 1331.36984
waicOcc(ms_weekQ_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -634.29298 33.20983 1335.00563
waicOcc(ms_weekQ_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -633.63035 34.81493 1336.89056
waicOcc(ms_weekQ_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -631.29994 32.53407 1327.66801
waicOcc(ms_weekQ_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -637.5221 29.8979 1334.8400
waicOcc(ms_weekQ_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -634.24820 32.43044 1333.35729
waicOcc(ms_weekQ_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -613.39092 44.73541 1316.25267
waicOcc(ms_fullQ_full_T50, by.sp = FALSE)
## elpd pD WAIC
## -591.48750 72.92445 1328.82391
waicOcc(ms_fullQ_cover_T50, by.sp = FALSE)
## elpd pD WAIC
## -606.96755 76.28785 1366.51081
waicOcc(ms_fullQ_null_T50, by.sp = FALSE)
## elpd pD WAIC
## -617.00110 54.24901 1342.50021
waicOcc(ms_fullQ_forage_T50, by.sp = FALSE)
## elpd pD WAIC
## -610.55533 62.93742 1346.98550
waicOcc(ms_fullQ_move_T50, by.sp = FALSE)
## elpd pD WAIC
## -607.81818 70.97856 1357.59348
waicOcc(ms_fullQ_canopy_T50, by.sp = FALSE)
## elpd pD WAIC
## -607.25252 61.58801 1337.68107
waicOcc(ms_fullQ_cogon_T50, by.sp = FALSE)
## elpd pD WAIC
## -613.40868 59.02875 1344.87487
waicOcc(ms_fullQ_cogonQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -610.72286 61.67654 1344.79880
waicOcc(ms_fullQ_fullQ_T50, by.sp = FALSE)
## elpd pD WAIC
## -588.56980 75.77336 1328.68633
This test explains how well the model fits that data at the community and species level. I believe 0.5 is the target p-value, though how far from this number is considered adequate, I do not know yet. I believe this is a good place to check when thinking about which species we include in the model (currently set at mammals with > 2 occurences).
ppc.ms_fullQ_fullQ_T50 <- ppcOcc(ms_fullQ_fullQ_T50, fit.stat = "freeman-tukey", group = 1)
## Currently on species 1 out of 3
## Currently on species 2 out of 3
## Currently on species 3 out of 3
summary(ppc.ms_fullQ_fullQ_T50)
##
## Call:
## ppcOcc(object = ms_fullQ_fullQ_T50, fit.stat = "freeman-tukey",
## group = 1)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Bayesian p-value: 0.2181
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Canis_latrans Bayesian p-value: 0.5957
## Procyon_lotor Bayesian p-value: 0.0587
## Dasypus_novemcinctus Bayesian p-value: 0
## Fit statistic: freeman-tukey
summary(ms_fullQ_fullQ_T50) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3887
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9609 1.0371 -2.8872 -0.9990 1.3357 1.0248 705
## Cogon_Patch_Size 0.1301 1.0323 -1.8846 0.1103 2.2875 1.0043 1150
## Veg_shannon_index 0.9651 0.7825 -0.5764 0.9557 2.5328 1.0297 914
## total_shrub_cover -0.2203 0.8943 -1.9826 -0.2397 1.6946 1.0140 822
## Avg_Cogongrass_Cover 0.7205 1.1972 -1.7232 0.7351 2.9930 1.0038 529
## Tree_Density -2.0041 1.5226 -4.6260 -2.1816 1.5991 1.0133 897
## Avg_Canopy_Cover 0.9651 0.9265 -1.0167 1.0102 2.7089 1.0115 1912
## I(Avg_Cogongrass_Cover^2) 1.6071 0.9952 -0.5895 1.6328 3.4269 1.0140 660
## avg_veg_height -0.0028 0.7949 -1.6122 -0.0106 1.4895 1.0079 816
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.1801 39.8489 0.0624 1.1175 31.7502 1.2860 3000
## Cogon_Patch_Size 7.7269 23.5996 0.0686 1.6872 52.9931 1.0368 855
## Veg_shannon_index 2.4048 8.3815 0.0526 0.5838 15.7909 1.0220 2378
## total_shrub_cover 4.3914 18.8308 0.0758 1.3371 25.0951 1.0794 1571
## Avg_Cogongrass_Cover 5.5607 25.6280 0.0599 0.8824 33.7230 1.0087 1408
## Tree_Density 34.8132 169.8778 0.0914 5.6019 214.4754 1.1579 1336
## Avg_Canopy_Cover 5.8946 21.6111 0.1012 1.7266 34.2468 1.0355 1876
## I(Avg_Cogongrass_Cover^2) 5.8390 25.7422 0.0627 0.8706 44.3410 1.2140 773
## avg_veg_height 1.8706 6.9309 0.0490 0.4769 12.5857 1.0371 1793
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.98 3.47 0.0612 0.8365 10.5556 1.1877 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9408 0.5754 -2.7745 -2.0305 -0.3331 1.0015 2214
## shrub_cover 0.2554 0.6077 -1.0209 0.2777 1.4439 1.0062 2898
## veg_height -0.0080 0.5318 -1.0925 -0.0067 1.1359 1.0074 2888
## week 0.2381 0.4557 -0.6585 0.2436 1.1447 1.0013 2540
## I(week^2) -0.1495 0.3319 -0.8225 -0.1566 0.5283 1.0013 3223
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3192 6.2565 0.0625 0.3786 7.6263 1.1436 2756
## shrub_cover 1.6696 4.5541 0.1033 0.6547 10.1531 1.0279 2609
## veg_height 1.4075 7.2904 0.0925 0.4799 8.4367 1.2304 3000
## week 1.0122 6.4813 0.0428 0.2522 4.9206 1.0666 3000
## I(week^2) 0.4526 1.7045 0.0275 0.1453 2.5110 1.0344 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8918 1.2087 -3.0696 -0.9379
## (Intercept)-Procyon_lotor -0.6586 1.1580 -2.6659 -0.7033
## (Intercept)-Dasypus_novemcinctus -2.1621 1.2615 -5.1892 -2.0161
## Cogon_Patch_Size-Canis_latrans 1.7953 2.2210 -0.7944 1.2323
## Cogon_Patch_Size-Procyon_lotor -0.6770 1.0009 -2.7113 -0.6674
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1942 0.9893 -2.1636 -0.2269
## Veg_shannon_index-Canis_latrans 1.5150 0.9848 -0.1206 1.3982
## Veg_shannon_index-Procyon_lotor 1.2929 0.7615 -0.0092 1.2225
## Veg_shannon_index-Dasypus_novemcinctus 0.5779 0.7462 -0.9100 0.5939
## total_shrub_cover-Canis_latrans 0.6817 1.1229 -1.0487 0.5033
## total_shrub_cover-Procyon_lotor -1.2373 0.8482 -3.0840 -1.1422
## total_shrub_cover-Dasypus_novemcinctus -0.3181 0.9780 -2.6227 -0.2150
## Avg_Cogongrass_Cover-Canis_latrans 0.9109 1.6007 -1.9488 0.8650
## Avg_Cogongrass_Cover-Procyon_lotor 0.4459 1.4662 -2.5509 0.4782
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.5176 1.6570 -1.3085 1.3688
## Tree_Density-Canis_latrans -4.2782 2.3175 -9.7402 -3.7947
## Tree_Density-Procyon_lotor -2.3369 1.3504 -4.9431 -2.3191
## Tree_Density-Dasypus_novemcinctus -5.4117 2.8424 -12.6630 -4.7220
## Avg_Canopy_Cover-Canis_latrans 0.1046 0.7623 -1.4543 0.1106
## Avg_Canopy_Cover-Procyon_lotor 1.5227 0.8586 0.0438 1.4566
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3055 1.1129 0.6316 2.1535
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5252 1.4742 0.5787 2.2131
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3955 1.3301 0.5099 2.1589
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4145 0.9389 -0.2790 1.3536
## avg_veg_height-Canis_latrans -0.2712 0.8405 -1.9729 -0.2680
## avg_veg_height-Procyon_lotor 0.0307 0.8148 -1.5949 0.0567
## avg_veg_height-Dasypus_novemcinctus 0.2085 0.8195 -1.3553 0.1819
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8072 1.0148 405
## (Intercept)-Procyon_lotor 1.7590 1.0515 285
## (Intercept)-Dasypus_novemcinctus -0.1342 1.0538 327
## Cogon_Patch_Size-Canis_latrans 7.6590 1.0565 257
## Cogon_Patch_Size-Procyon_lotor 1.1562 1.0127 455
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8772 1.0169 600
## Veg_shannon_index-Canis_latrans 3.7962 1.0435 469
## Veg_shannon_index-Procyon_lotor 3.0442 1.0434 507
## Veg_shannon_index-Dasypus_novemcinctus 2.0355 1.0205 675
## total_shrub_cover-Canis_latrans 3.4750 1.0072 285
## total_shrub_cover-Procyon_lotor 0.2541 1.0109 470
## total_shrub_cover-Dasypus_novemcinctus 1.2112 1.0539 387
## Avg_Cogongrass_Cover-Canis_latrans 4.4156 1.0016 500
## Avg_Cogongrass_Cover-Procyon_lotor 3.2064 1.0015 528
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2850 1.0395 397
## Tree_Density-Canis_latrans -1.0832 1.0147 233
## Tree_Density-Procyon_lotor 0.2557 1.0058 338
## Tree_Density-Dasypus_novemcinctus -1.7751 1.1472 197
## Avg_Canopy_Cover-Canis_latrans 1.6212 1.0029 946
## Avg_Canopy_Cover-Procyon_lotor 3.4621 1.0162 619
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.9962 1.0614 359
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.5478 1.0503 179
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.0387 1.0861 215
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.4021 1.0113 541
## avg_veg_height-Canis_latrans 1.3579 1.0114 645
## avg_veg_height-Procyon_lotor 1.6180 1.0059 782
## avg_veg_height-Dasypus_novemcinctus 1.8972 1.0066 600
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5433 0.2010 -2.9647 -2.5369 -2.1746 1.0236
## (Intercept)-Procyon_lotor -2.2120 0.1657 -2.5500 -2.2049 -1.8952 1.0071
## (Intercept)-Dasypus_novemcinctus -1.6810 0.1924 -2.0625 -1.6792 -1.3116 1.0131
## shrub_cover-Canis_latrans -0.3625 0.2281 -0.7996 -0.3615 0.0747 1.0148
## shrub_cover-Procyon_lotor 0.2615 0.1718 -0.1115 0.2682 0.5806 1.0042
## shrub_cover-Dasypus_novemcinctus 0.9900 0.3337 0.3486 0.9930 1.6669 1.0168
## veg_height-Canis_latrans -0.6254 0.1897 -1.0164 -0.6196 -0.2799 1.0289
## veg_height-Procyon_lotor 0.3657 0.1275 0.1166 0.3699 0.6190 1.0035
## veg_height-Dasypus_novemcinctus 0.2775 0.1426 0.0098 0.2748 0.5660 1.0180
## week-Canis_latrans 0.5371 0.2820 0.0186 0.5256 1.1176 1.0033
## week-Procyon_lotor 0.1716 0.2122 -0.2398 0.1711 0.5918 0.9999
## week-Dasypus_novemcinctus 0.0568 0.2283 -0.3977 0.0569 0.5104 1.0125
## I(week^2)-Canis_latrans -0.2276 0.1144 -0.4604 -0.2276 -0.0032 1.0131
## I(week^2)-Procyon_lotor -0.1147 0.0920 -0.2983 -0.1124 0.0611 1.0058
## I(week^2)-Dasypus_novemcinctus -0.1506 0.1071 -0.3618 -0.1468 0.0521 1.0206
## ESS
## (Intercept)-Canis_latrans 926
## (Intercept)-Procyon_lotor 782
## (Intercept)-Dasypus_novemcinctus 890
## shrub_cover-Canis_latrans 558
## shrub_cover-Procyon_lotor 440
## shrub_cover-Dasypus_novemcinctus 557
## veg_height-Canis_latrans 485
## veg_height-Procyon_lotor 1355
## veg_height-Dasypus_novemcinctus 1601
## week-Canis_latrans 1189
## week-Procyon_lotor 1714
## week-Dasypus_novemcinctus 1980
## I(week^2)-Canis_latrans 1280
## I(week^2)-Procyon_lotor 1462
## I(week^2)-Dasypus_novemcinctus 1677
names(ms_fullQ_fullQ_T50)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T50$beta.samples)
## 'mcmc' num [1:3000, 1:27] -0.653 -0.662 -1.229 -0.303 -1.549 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:27] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "Cogon_Patch_Size-Canis_latrans" ...
mean(ms_fullQ_fullQ_T50$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.2433333
MCMCplot(ms_fullQ_fullQ_T50$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T50$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
# Create a set of values across the range of observed cogongrass values
cogon.pred.vals <- seq(min(data_list$occ.covs$Avg_Cogongrass_Cover),
max(data_list$occ.covs$Avg_Cogongrass_Cover),
length.out = 100)
# Scale predicted values by mean and standard deviation used to fit the model
cogon.pred.vals.scale <- (cogon.pred.vals - mean(data_list$occ.covs$Avg_Cogongrass_Cover)) /
sd(data_list$occ.covs$Avg_Cogongrass_Cover)
# Predict occupancy across cogongrass cover values at mean values of all other variables
pred.df <- as.matrix(data.frame(intercept = 1, Avg_Cogongrass_Cover =
cogon.pred.vals.scale, 'I(Avg_Cogongrass_Cover^2)' = 0,
Cogon_Patch_Size = 0, Veg_shannon_index = 0,
total_shrub_cover = 0, Tree_Density = 0,
Avg_Canopy_Cover = 0, avg_veg_height = 0, Auth = 0))
out.pred <- predict(ms_fullQ_fullQ_T50, pred.df)
str(out.pred)
## List of 3
## $ psi.0.samples: num [1:3000, 1:3, 1:100] 0.627 0.696 0.112 0.52 0.107 ...
## $ z.0.samples : int [1:3000, 1:3, 1:100] 1 1 0 1 0 0 0 1 0 0 ...
## $ call : language predict.msPGOcc(object = ms_fullQ_fullQ_T50, X.0 = pred.df)
## - attr(*, "class")= chr "predict.msPGOcc"
str(out.pred$psi.0.samples)
## num [1:3000, 1:3, 1:100] 0.627 0.696 0.112 0.52 0.107 ...
psi.0.quants <- apply(out.pred$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] 0.00211 0.20122 0.93832 0.00241 0.20113 ...
## - 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 0.201 0.201 0.198 0.208 0.205 ...
## $ psi.low : num 0.00211 0.00241 0.00288 0.0028 0.00287 ...
## $ psi.high : num 0.938 0.941 0.942 0.933 0.932 ...
## $ Avg_Cogongrass_Cover: num -0.708 -0.675 -0.641 -0.608 -0.575 ...
ggplot(psi.plot.dat, aes(x = Avg_Cogongrass_Cover, y = psi.med)) +
geom_ribbon(aes(ymin = psi.low, ymax = psi.high), fill = "grey70") +
geom_line() +
theme_bw() +
scale_y_continuous(limits = c(0, 1)) +
labs(x = "Average Cogongrass Cover", y = "Occupancy Probability")
summary(ms_fullQ_fullQ_T50) # Summary of parameter estimates
##
## Call:
## msPGOcc(occ.formula = occ.full.quad, det.formula = det.full.quad,
## data = data_list, n.samples = 5000, n.report = 500, n.burn = 3000,
## n.thin = 2, n.chains = 3)
##
## Samples per Chain: 5000
## Burn-in: 3000
## Thinning Rate: 2
## Number of Chains: 3
## Total Posterior Samples: 3000
## Run Time (min): 0.3887
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -0.9609 1.0371 -2.8872 -0.9990 1.3357 1.0248 705
## Cogon_Patch_Size 0.1301 1.0323 -1.8846 0.1103 2.2875 1.0043 1150
## Veg_shannon_index 0.9651 0.7825 -0.5764 0.9557 2.5328 1.0297 914
## total_shrub_cover -0.2203 0.8943 -1.9826 -0.2397 1.6946 1.0140 822
## Avg_Cogongrass_Cover 0.7205 1.1972 -1.7232 0.7351 2.9930 1.0038 529
## Tree_Density -2.0041 1.5226 -4.6260 -2.1816 1.5991 1.0133 897
## Avg_Canopy_Cover 0.9651 0.9265 -1.0167 1.0102 2.7089 1.0115 1912
## I(Avg_Cogongrass_Cover^2) 1.6071 0.9952 -0.5895 1.6328 3.4269 1.0140 660
## avg_veg_height -0.0028 0.7949 -1.6122 -0.0106 1.4895 1.0079 816
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 5.1801 39.8489 0.0624 1.1175 31.7502 1.2860 3000
## Cogon_Patch_Size 7.7269 23.5996 0.0686 1.6872 52.9931 1.0368 855
## Veg_shannon_index 2.4048 8.3815 0.0526 0.5838 15.7909 1.0220 2378
## total_shrub_cover 4.3914 18.8308 0.0758 1.3371 25.0951 1.0794 1571
## Avg_Cogongrass_Cover 5.5607 25.6280 0.0599 0.8824 33.7230 1.0087 1408
## Tree_Density 34.8132 169.8778 0.0914 5.6019 214.4754 1.1579 1336
## Avg_Canopy_Cover 5.8946 21.6111 0.1012 1.7266 34.2468 1.0355 1876
## I(Avg_Cogongrass_Cover^2) 5.8390 25.7422 0.0627 0.8706 44.3410 1.2140 773
## avg_veg_height 1.8706 6.9309 0.0490 0.4769 12.5857 1.0371 1793
##
## Occurrence Random Effect Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## Auth 1.98 3.47 0.0612 0.8365 10.5556 1.1877 206
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.9408 0.5754 -2.7745 -2.0305 -0.3331 1.0015 2214
## shrub_cover 0.2554 0.6077 -1.0209 0.2777 1.4439 1.0062 2898
## veg_height -0.0080 0.5318 -1.0925 -0.0067 1.1359 1.0074 2888
## week 0.2381 0.4557 -0.6585 0.2436 1.1447 1.0013 2540
## I(week^2) -0.1495 0.3319 -0.8225 -0.1566 0.5283 1.0013 3223
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 1.3192 6.2565 0.0625 0.3786 7.6263 1.1436 2756
## shrub_cover 1.6696 4.5541 0.1033 0.6547 10.1531 1.0279 2609
## veg_height 1.4075 7.2904 0.0925 0.4799 8.4367 1.2304 3000
## week 1.0122 6.4813 0.0428 0.2522 4.9206 1.0666 3000
## I(week^2) 0.4526 1.7045 0.0275 0.1453 2.5110 1.0344 3000
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50%
## (Intercept)-Canis_latrans -0.8918 1.2087 -3.0696 -0.9379
## (Intercept)-Procyon_lotor -0.6586 1.1580 -2.6659 -0.7033
## (Intercept)-Dasypus_novemcinctus -2.1621 1.2615 -5.1892 -2.0161
## Cogon_Patch_Size-Canis_latrans 1.7953 2.2210 -0.7944 1.2323
## Cogon_Patch_Size-Procyon_lotor -0.6770 1.0009 -2.7113 -0.6674
## Cogon_Patch_Size-Dasypus_novemcinctus -0.1942 0.9893 -2.1636 -0.2269
## Veg_shannon_index-Canis_latrans 1.5150 0.9848 -0.1206 1.3982
## Veg_shannon_index-Procyon_lotor 1.2929 0.7615 -0.0092 1.2225
## Veg_shannon_index-Dasypus_novemcinctus 0.5779 0.7462 -0.9100 0.5939
## total_shrub_cover-Canis_latrans 0.6817 1.1229 -1.0487 0.5033
## total_shrub_cover-Procyon_lotor -1.2373 0.8482 -3.0840 -1.1422
## total_shrub_cover-Dasypus_novemcinctus -0.3181 0.9780 -2.6227 -0.2150
## Avg_Cogongrass_Cover-Canis_latrans 0.9109 1.6007 -1.9488 0.8650
## Avg_Cogongrass_Cover-Procyon_lotor 0.4459 1.4662 -2.5509 0.4782
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 1.5176 1.6570 -1.3085 1.3688
## Tree_Density-Canis_latrans -4.2782 2.3175 -9.7402 -3.7947
## Tree_Density-Procyon_lotor -2.3369 1.3504 -4.9431 -2.3191
## Tree_Density-Dasypus_novemcinctus -5.4117 2.8424 -12.6630 -4.7220
## Avg_Canopy_Cover-Canis_latrans 0.1046 0.7623 -1.4543 0.1106
## Avg_Canopy_Cover-Procyon_lotor 1.5227 0.8586 0.0438 1.4566
## Avg_Canopy_Cover-Dasypus_novemcinctus 2.3055 1.1129 0.6316 2.1535
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 2.5252 1.4742 0.5787 2.2131
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 2.3955 1.3301 0.5099 2.1589
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 1.4145 0.9389 -0.2790 1.3536
## avg_veg_height-Canis_latrans -0.2712 0.8405 -1.9729 -0.2680
## avg_veg_height-Procyon_lotor 0.0307 0.8148 -1.5949 0.0567
## avg_veg_height-Dasypus_novemcinctus 0.2085 0.8195 -1.3553 0.1819
## 97.5% Rhat ESS
## (Intercept)-Canis_latrans 1.8072 1.0148 405
## (Intercept)-Procyon_lotor 1.7590 1.0515 285
## (Intercept)-Dasypus_novemcinctus -0.1342 1.0538 327
## Cogon_Patch_Size-Canis_latrans 7.6590 1.0565 257
## Cogon_Patch_Size-Procyon_lotor 1.1562 1.0127 455
## Cogon_Patch_Size-Dasypus_novemcinctus 1.8772 1.0169 600
## Veg_shannon_index-Canis_latrans 3.7962 1.0435 469
## Veg_shannon_index-Procyon_lotor 3.0442 1.0434 507
## Veg_shannon_index-Dasypus_novemcinctus 2.0355 1.0205 675
## total_shrub_cover-Canis_latrans 3.4750 1.0072 285
## total_shrub_cover-Procyon_lotor 0.2541 1.0109 470
## total_shrub_cover-Dasypus_novemcinctus 1.2112 1.0539 387
## Avg_Cogongrass_Cover-Canis_latrans 4.4156 1.0016 500
## Avg_Cogongrass_Cover-Procyon_lotor 3.2064 1.0015 528
## Avg_Cogongrass_Cover-Dasypus_novemcinctus 5.2850 1.0395 397
## Tree_Density-Canis_latrans -1.0832 1.0147 233
## Tree_Density-Procyon_lotor 0.2557 1.0058 338
## Tree_Density-Dasypus_novemcinctus -1.7751 1.1472 197
## Avg_Canopy_Cover-Canis_latrans 1.6212 1.0029 946
## Avg_Canopy_Cover-Procyon_lotor 3.4621 1.0162 619
## Avg_Canopy_Cover-Dasypus_novemcinctus 4.9962 1.0614 359
## I(Avg_Cogongrass_Cover^2)-Canis_latrans 6.5478 1.0503 179
## I(Avg_Cogongrass_Cover^2)-Procyon_lotor 6.0387 1.0861 215
## I(Avg_Cogongrass_Cover^2)-Dasypus_novemcinctus 3.4021 1.0113 541
## avg_veg_height-Canis_latrans 1.3579 1.0114 645
## avg_veg_height-Procyon_lotor 1.6180 1.0059 782
## avg_veg_height-Dasypus_novemcinctus 1.8972 1.0066 600
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat
## (Intercept)-Canis_latrans -2.5433 0.2010 -2.9647 -2.5369 -2.1746 1.0236
## (Intercept)-Procyon_lotor -2.2120 0.1657 -2.5500 -2.2049 -1.8952 1.0071
## (Intercept)-Dasypus_novemcinctus -1.6810 0.1924 -2.0625 -1.6792 -1.3116 1.0131
## shrub_cover-Canis_latrans -0.3625 0.2281 -0.7996 -0.3615 0.0747 1.0148
## shrub_cover-Procyon_lotor 0.2615 0.1718 -0.1115 0.2682 0.5806 1.0042
## shrub_cover-Dasypus_novemcinctus 0.9900 0.3337 0.3486 0.9930 1.6669 1.0168
## veg_height-Canis_latrans -0.6254 0.1897 -1.0164 -0.6196 -0.2799 1.0289
## veg_height-Procyon_lotor 0.3657 0.1275 0.1166 0.3699 0.6190 1.0035
## veg_height-Dasypus_novemcinctus 0.2775 0.1426 0.0098 0.2748 0.5660 1.0180
## week-Canis_latrans 0.5371 0.2820 0.0186 0.5256 1.1176 1.0033
## week-Procyon_lotor 0.1716 0.2122 -0.2398 0.1711 0.5918 0.9999
## week-Dasypus_novemcinctus 0.0568 0.2283 -0.3977 0.0569 0.5104 1.0125
## I(week^2)-Canis_latrans -0.2276 0.1144 -0.4604 -0.2276 -0.0032 1.0131
## I(week^2)-Procyon_lotor -0.1147 0.0920 -0.2983 -0.1124 0.0611 1.0058
## I(week^2)-Dasypus_novemcinctus -0.1506 0.1071 -0.3618 -0.1468 0.0521 1.0206
## ESS
## (Intercept)-Canis_latrans 926
## (Intercept)-Procyon_lotor 782
## (Intercept)-Dasypus_novemcinctus 890
## shrub_cover-Canis_latrans 558
## shrub_cover-Procyon_lotor 440
## shrub_cover-Dasypus_novemcinctus 557
## veg_height-Canis_latrans 485
## veg_height-Procyon_lotor 1355
## veg_height-Dasypus_novemcinctus 1601
## week-Canis_latrans 1189
## week-Procyon_lotor 1714
## week-Dasypus_novemcinctus 1980
## I(week^2)-Canis_latrans 1280
## I(week^2)-Procyon_lotor 1462
## I(week^2)-Dasypus_novemcinctus 1677
names(ms_fullQ_fullQ_T50)
## [1] "rhat" "beta.comm.samples" "alpha.comm.samples"
## [4] "tau.sq.beta.samples" "tau.sq.alpha.samples" "beta.samples"
## [7] "alpha.samples" "z.samples" "psi.samples"
## [10] "like.samples" "sigma.sq.psi.samples" "beta.star.samples"
## [13] "re.level.names" "ESS" "X"
## [16] "X.p" "X.p.re" "X.re"
## [19] "y" "call" "n.samples"
## [22] "x.names" "sp.names" "x.p.names"
## [25] "n.post" "n.thin" "n.burn"
## [28] "n.chains" "pRE" "psiRE"
## [31] "run.time"
str(ms_fullQ_fullQ_T50$beta.samples)
## 'mcmc' num [1:3000, 1:27] -0.653 -0.662 -1.229 -0.303 -1.549 ...
## - attr(*, "mcpar")= num [1:3] 1 3000 1
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:27] "(Intercept)-Canis_latrans" "(Intercept)-Procyon_lotor" "(Intercept)-Dasypus_novemcinctus" "Cogon_Patch_Size-Canis_latrans" ...
mean(ms_fullQ_fullQ_T50$beta.samples[, 5] > 0) # effect of ? on occupancy
## [1] 0.2433333
MCMCplot(ms_fullQ_fullQ_T50$beta.samples, ref_ovl = TRUE, ci = c(50, 95)) # Occupancy
MCMCplot(ms_fullQ_fullQ_T50$alpha.samples, ref_ovl = TRUE, ci = c(50, 95)) # Detection
## Occupancy
# Total number of parameters
n_params <- ncol(ms_fullQ_fullQ_T50$beta.samples)
# Choose how many parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$beta.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Occupancy_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$beta.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Occupancy Parameters: ", i, " to ", end))
#
# dev.off()
#}
## Detection
# Number of parameters
n_params <- ncol(ms_fullQ_fullQ_T50$alpha.samples)
# Number of parameters to plot at a time
chunk_size <- 10
# Split and plot
#for (i in seq(1, n_params, by = chunk_size)) {
# end <- min(i + chunk_size - 1, n_params)
# param_names <- colnames(ms_fullQ_fullQ$alpha.samples)[i:end]
#
# # Set filename
# file_name <- paste0("MCMCplot_Detection_Params_", i, "_to_", end, ".png")
#
# # Save plot to PNG
# png(filename = file_name, width = 1200, height = 800, res = 150)
#
# MCMCplot(ms_fullQ_fullQ$alpha.samples[, param_names],
# ref_ovl = TRUE,
# ci = c(50, 95),
# main = paste0("Detection Parameters: ", i, " to ", end))
#
# dev.off()
#}